From 49924ae839a25149035aed2af87cd4d29cab48d5 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Thu, 5 Feb 2026 22:21:15 -0500 Subject: [PATCH 01/47] Remvoe extra utils folder --- utils/__init__.py | 0 utils/loss.py | 143 ---------------------------------------- utils/plotting.py | 162 ---------------------------------------------- utils/risk_cif.py | 130 ------------------------------------- 4 files changed, 435 deletions(-) delete mode 100644 utils/__init__.py delete mode 100644 utils/loss.py delete mode 100644 utils/plotting.py delete mode 100644 utils/risk_cif.py diff --git a/utils/__init__.py b/utils/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/utils/loss.py b/utils/loss.py deleted file mode 100644 index 9cae44fd..00000000 --- a/utils/loss.py +++ /dev/null @@ -1,143 +0,0 @@ -import torch - -def weighted_negative_log_likelihood_loss(risk_scores, times, events, - num_competing_risks, event_weights=None, - sample_weights=None, eps=1e-8) -> float: - """ - Computes the weighted negative log-likelihood loss for competing risks Cox model. - - Args: - risk_scores: List of tensors with shape (batch_size, 1) for each competing risk - times: Event/censoring times (batch_size,) - events: Event indicators (0=censored, 1...K=event types) (batch_size,) - num_competing_risks: Number of competing risks - event_weights: Tensor of weights for each competing risk type (size: num_competing_risks) - sample_weights: Tensor of weights for each sample (size: batch_size) - eps: Small constant for numerical stability - - Returns: - Weighted negative log partial likelihood loss - """ - device = times.device - batch_size = times.shape[0] - - # Initialize loss - loss = torch.tensor(0.0, device=device) - - # Set default weights if not provided - if event_weights is None: - event_weights = torch.ones(num_competing_risks, device=device) - if sample_weights is None: - sample_weights = torch.ones(batch_size, device=device) - - # Count number of events - n_events = (events > 0).sum().item() - if n_events == 0: - return loss - - # Process each competing risk separately - for k in range(1, num_competing_risks + 1): - # Find samples with this event type - event_mask = (events == k) - n_events_k = event_mask.sum().item() - - if n_events_k == 0: - continue - - # Get risk scores for this competing risk - risk_k = risk_scores[k-1].squeeze() - - # Get weight for this event type - event_weight = event_weights[k-1] - - # For each event of type k - for i in range(batch_size): - if event_mask[i]: - # Find samples in risk set (samples with time >= event time) - risk_set = (times >= times[i]) - - # Calculate log sum of exp of risk scores in risk set - risk_set_scores = risk_k[risk_set] - log_risk_sum = torch.logsumexp(risk_set_scores, dim=0) - - # Subtract individual risk score from log sum and apply weights - individual_loss = log_risk_sum - risk_k[i] - weighted_individual_loss = individual_loss * event_weight * sample_weights[i] - loss += weighted_individual_loss - - # Return average loss - return loss / max(n_events, 1) - -def negative_log_likelihood_loss(risk_scores, times, events, - num_competing_risks, eps=1e-8): - """ - Computes the negative log-likelihood loss for competing risks Cox model. - - Args: - risk_scores: List of tensors with shape (batch_size, 1) for each competing risk - times: Event/censoring times (batch_size,) - events: Event indicators (0=censored, 1...K=event types) (batch_size,) - num_competing_risks: Number of competing risks - eps: Small constant for numerical stability - - Returns: - Negative log partial likelihood loss - """ - device = times.device - batch_size = times.shape[0] - - # Initialize loss - loss = torch.tensor(0.0, device=device) - - # Count number of events - n_events = (events > 0).sum().item() - if n_events == 0: - return loss - - # Process each competing risk separately - for k in range(1, num_competing_risks + 1): - # Find samples with this event type - event_mask = (events == k) - n_events_k = event_mask.sum().item() - - if n_events_k == 0: - continue - - # Get risk scores for this competing risk - risk_k = risk_scores[k-1].squeeze() - - # For each event of type k - for i in range(batch_size): - if event_mask[i]: - # Find samples in risk set (samples with time >= event time) - risk_set = (times >= times[i]) - - # Calculate log sum of exp of risk scores in risk set - risk_set_scores = risk_k[risk_set] - log_risk_sum = torch.logsumexp(risk_set_scores, dim=0) - - # Subtract individual risk score from log sum - loss += log_risk_sum - risk_k[i] - - # Return average loss - return loss / max(n_events, 1) - -def compute_l2_penalty(model, include_bias=False) -> int: - """ - Compute L2 regularization penalty on model parameters - - Args: - model: Neural network model - include_bias: Whether to include bias terms in regularization - - Returns: - L2 penalty term - """ - l2_reg = 0.0 - for name, param in model.named_parameters(): - if param.requires_grad: - # Skip bias parameters if specified - if not include_bias and 'bias' in name: - continue - l2_reg += torch.sum(param ** 2) - return l2_reg \ No newline at end of file diff --git a/utils/plotting.py b/utils/plotting.py deleted file mode 100644 index 1702632e..00000000 --- a/utils/plotting.py +++ /dev/null @@ -1,162 +0,0 @@ -import torch -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from typing import Union, List - -def plot_feature_importance(model: torch.nn.Module, - x_data: Union[np.ndarray, torch.Tensor], - feature_names = None, - n_top:int = 5, - n_bottom:int = 5, - risk_idx:int = 1, - figsize: tuple = (8, 6), - output_file:str = None, - color_positive:str = '#2196F3', - color_negative:str = '#F44336') -> tuple: - """ - Plot feature importance with both top positive and negative influences, - handling both CPU and CUDA devices automatically. - """ - # determine model device - device = next(model.parameters()).device - model.eval() - - # prepare feature names - num_features = model.num_features - if feature_names is None: - feature_names = [f"Feature {i+1}" for i in range(num_features)] - - # convert x_data to tensor on the model device - if not isinstance(x_data, torch.Tensor): - x = torch.tensor(x_data, dtype=torch.float32, device=device) - else: - x = x_data.to(device) - - feature_contribs = {} - risk_idx0 = risk_idx - 1 - - with torch.no_grad(): - for i in range(num_features): - vals = x[:, i].unsqueeze(1) # shape (N,1) - if torch.var(vals) <= 1e-8: - feature_contribs[feature_names[i]] = 0.0 - continue - - # forward through the feature net and projection - rep = model.feature_nets[i](vals) - proj = model.risk_projections[i][risk_idx0](rep) - # mean contribution as a Python float - contrib = proj.mean().item() - feature_contribs[feature_names[i]] = contrib - - # build a DataFrame for sorting - df = pd.DataFrame({ - 'feature': list(feature_contribs.keys()), - 'contribution': list(feature_contribs.values()) - }) - df['abs_contrib'] = df['contribution'].abs() - df = df.sort_values('abs_contrib', ascending=False) - - pos = df[df['contribution'] > 0].head(n_top).sort_values('contribution') - neg = df[df['contribution'] < 0].head(n_bottom).sort_values('contribution', ascending=False) - - top_pos = pos['feature'].tolist() - top_neg = neg['feature'].tolist() - - # plotting - fig, ax = plt.subplots(figsize=figsize) - ax.barh(pos['feature'], pos['contribution'], color=color_positive, alpha=0.8) - ax.barh(neg['feature'], neg['contribution'], color=color_negative, alpha=0.8) - ax.axvline(0, color='black', linestyle='-', alpha=0.3) - - ax.set_xlabel('Contribution to Risk Score') - ax.set_title(f'Top {n_top} Positive & {n_bottom} Negative Features for risk_{risk_idx}') - ax.grid(axis='x', linestyle='--', alpha=0.5) - plt.tight_layout() - - if output_file: - plt.savefig(output_file, bbox_inches='tight', dpi=300) - - return fig, ax, top_pos, top_neg - - -def plot_coxnam_shape_functions(model:torch.nn.Module, - X: Union[np.ndarray, torch.Tensor], - risk_to_plot:int = 1, - feature_names:List[str] = None, - top_features:List[str] = None, - ncols:int = 3, - figsize:tuple = (12, 8), - output_file:str = None) -> tuple: - """ - Plot shape functions for each feature in a CoxNAM model, - automatically handling CPU vs CUDA inputs. - """ - device = next(model.parameters()).device - model.eval() - risk_idx = risk_to_plot - 1 - - # ensure X is a numpy array - if isinstance(X, torch.Tensor): - X_np = X.cpu().numpy() - else: - X_np = np.array(X, dtype=float) - - # derive feature list - num_features = model.num_features - if feature_names is None: - feature_names = [f"Feature {i+1}" for i in range(num_features)] - if top_features: - # map names back to indices - idx_map = {name: i for i, name in enumerate(feature_names)} - selected = [(idx_map.get(name, None), name) for name in top_features] - selected = [(i, name) for i, name in selected if i is not None] - else: - selected = list(zip(range(num_features), feature_names)) - - n_selected = len(selected) - nrows = int(np.ceil(n_selected / ncols)) - fig, axes = plt.subplots(nrows, ncols, figsize=(figsize)) - axes = np.array(axes).reshape(-1) - - with torch.no_grad(): - for ax, (f_idx, fname) in zip(axes, selected): - vals = X_np[:, f_idx] - if vals.size == 0: - ax.text(0.5, 0.5, "no data", ha='center', va='center') - continue - - # choose evaluation points - if np.issubdtype(vals.dtype, np.integer) or len(np.unique(vals)) <= 10: - pts = np.unique(vals) - else: - pts = np.linspace(vals.min(), vals.max(), 100) - - # convert to tensor on correct device - t_pts = torch.tensor(pts, dtype=torch.float32, device=device).unsqueeze(1) - - # compute shape values - rep = model.feature_nets[f_idx](t_pts) - proj = model.risk_projections[f_idx][risk_idx](rep) - shp = proj.squeeze(-1).cpu().numpy() - - # plot - ax.plot(pts, shp, linewidth=2) - ax.axhline(0, linestyle='--', alpha=0.5) - ax.set_title(fname) - ax.set_xlabel('Value') - ax.set_ylabel('Contribution') - # rug plot - ax.plot(vals, np.zeros_like(vals)-0.1, '|', alpha=0.3) - - # turn off any extra axes - for ax in axes[n_selected:]: - ax.axis('off') - - fig.suptitle(f'Shape Functions for Risk {risk_to_plot}', fontsize=14) - plt.tight_layout(rect=[0,0,1,0.96]) - if output_file: - plt.savefig(output_file, dpi=300, bbox_inches='tight') - - return fig, axes[:n_selected] diff --git a/utils/risk_cif.py b/utils/risk_cif.py deleted file mode 100644 index 0cd38d54..00000000 --- a/utils/risk_cif.py +++ /dev/null @@ -1,130 +0,0 @@ -import torch -import numpy as np -from typing import List, Any - -def compute_baseline_cif(times:np.ndarray, - events:np.ndarray, - eval_times:List[Any], - event_type:np.ndarray) -> np.ndarray: - """ - Compute baseline cumulative incidence function for a specific event type - - Args: - times: Numpy array of event times - events: Numpy array of event indicators (0=censored, 1...K=event types) - eval_times: Times at which to evaluate the CIF - event_type: Event type to compute CIF for (1...K) - - Returns: - Numpy array of baseline CIF values at eval_times - """ - # Sort times and corresponding events - sort_idx = np.argsort(times) - sorted_times = times[sort_idx] - sorted_events = events[sort_idx] - - # Initialize cumulative hazard - n_samples = len(times) - baseline_cif = np.zeros(len(eval_times)) - - # For each evaluation time - for i, t in enumerate(eval_times): - cif_t = 0.0 - # Count number of events of the specified type before time t - event_count = np.sum((sorted_events == event_type) & (sorted_times <= t)) - if event_count > 0: - # Simple Aalen-Johansen estimator - cif_t = event_count / n_samples - baseline_cif[i] = cif_t - - return baseline_cif - - -def predict_cif(model:torch.Module, - x:np.ndarray, - baseline_cif:np.ndarray, - times:np.ndarray, - event_of_interest:int) -> np.ndarray: - """ - Predict cumulative incidence function for a specific competing risk. - - Args: - model: Trained model. - x: Input tensor of shape (n_samples, n_features). - baseline_cif: Array of shape (len(times),) — estimated CIF for baseline (e.g. from compute_baseline_cif). - times: Time points at which CIF is evaluated. - event_type: Integer, 0-based index of event of interest. - - Returns: - cif_pred: Array of shape (n_samples, len(times)) — predicted CIF per sample. - """ - model.eval() - with torch.no_grad(): - logits, _ = model(x) # list of length num_risks - f_j_x = logits[event_of_interest].squeeze(1).cpu().numpy() # (n_samples,) - - baseline_cif = np.asarray(baseline_cif).reshape(1, -1) # (1, T) - risk_scores = np.exp(f_j_x).reshape(-1, 1) # (N, 1) - - # Fine-Gray style CIF prediction under PH assumption - cif_pred = 1.0 - np.power(1.0 - baseline_cif, risk_scores) # shape (N, T) - - return cif_pred - -def predict_risk(model:np.ndarray, - x_input:np.ndarray, - device:str = 'cpu'): - """ - Predicts relative risk scores for each competing risk. - - Args: - model : Trained model. - x_input (np.ndarray or torch.Tensor): Input features of shape (n_samples, n_features). - device (str): Device to run the computation on. - - Returns: - np.ndarray: Array of shape (n_samples, num_risks) with relative risk scores. - """ - model.eval() - - if isinstance(x_input, np.ndarray): - x_tensor = torch.from_numpy(x_input).float().to(device) - else: - x_tensor = x_input.to(device).float() - - with torch.no_grad(): - risk_outputs, _ = model(x_tensor) # List of [batch_size, 1] tensors - risks = torch.cat(risk_outputs, dim=1) # Shape: [batch_size, num_risks] - - return risks.cpu().numpy() - -def predict_absolute_risk(model:torch.Tensor, - x_input:np.ndarray, - baseline_cifs:List[Any], - times:List[Any], - device:str = 'cpu') -> np.ndarray: - """ - Predict absolute risk (CIF) for each competing event by given time points. - - Args: - model: Trained model. - x_input (np.ndarray or Tensor): Input features, shape (n_samples, n_features). - baseline_cifs (dict): Mapping of event index to baseline CIF array of shape (n_times,). - times (np.ndarray): Time grid used for baseline_cifs. - device: CPU or CUDA. - - Returns: - np.ndarray: Shape (n_samples, num_events, n_times) with predicted CIFs. - """ - rel_risks = predict_risk(model, x_input, device) # shape (n_samples, num_events) - n_samples, num_events = rel_risks.shape - n_times = len(times) - - abs_risks = np.zeros((n_samples, num_events, n_times)) - - for k in range(num_events): - base_cif = np.clip(baseline_cifs[k], 1e-10, 0.9999) # avoid edge cases - for i in range(n_samples): - abs_risks[i, k, :] = 1 - np.power(1 - base_cif, np.exp(rel_risks[i, k])) - - return abs_risks From 96f4aedccafc6dfcb261a7542dfc15189359628d Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Fri, 6 Feb 2026 15:53:33 -0500 Subject: [PATCH 02/47] Fix ruff and mypy errors --- crisp_nam/__init__.py | 4 +- crisp_nam/metrics/__init__.py | 18 +- crisp_nam/metrics/calibration.py | 79 ++++-- crisp_nam/metrics/discrimination.py | 69 +++-- crisp_nam/metrics/ipcw.py | 28 +- crisp_nam/models/crisp_nam_model.py | 392 +++++++++++++++++----------- crisp_nam/models/deephit_model.py | 235 ++++++++++------- crisp_nam/utils/__init__.py | 2 +- crisp_nam/utils/loss.py | 105 ++++---- crisp_nam/utils/plotting.py | 119 +++++---- crisp_nam/utils/risk_cif.py | 124 +++++---- 11 files changed, 710 insertions(+), 465 deletions(-) diff --git a/crisp_nam/__init__.py b/crisp_nam/__init__.py index d67a0b62..3f10443c 100644 --- a/crisp_nam/__init__.py +++ b/crisp_nam/__init__.py @@ -1 +1,3 @@ -__all__ = [ 'utils', 'models', 'data_utils', 'metrics' ] \ No newline at end of file +"""CRISP-NAM paclage.""" + +__all__ = ["utils", "models", "data_utils", "metrics"] diff --git a/crisp_nam/metrics/__init__.py b/crisp_nam/metrics/__init__.py index b9079625..20b25607 100644 --- a/crisp_nam/metrics/__init__.py +++ b/crisp_nam/metrics/__init__.py @@ -1,3 +1,15 @@ -from .calibration import * -from .discrimination import * -from .ipcw import * +"""Evaluation metrics used within crisp_nam package.""" + +from .calibration import brier_score, integrated_brier_score +from .discrimination import auc_td, concordance_index_td, cumulative_dynamic_auc +from .ipcw import estimate_ipcw + + +__all__ = [ + "brier_score", + "integrated_brier_score", + "auc_td", + "cumulative_dynamic_auc", + "concordance_index_td", + "estimate_ipcw", +] diff --git a/crisp_nam/metrics/calibration.py b/crisp_nam/metrics/calibration.py index 3c035f38..c22aaad0 100644 --- a/crisp_nam/metrics/calibration.py +++ b/crisp_nam/metrics/calibration.py @@ -1,26 +1,40 @@ +"""Calibration metrics for time-to-event models with competing risks. + +This module contains functions to compute the Brier score and integrated Brier score for competing risks. +""" + import numpy as np + from .ipcw import estimate_ipcw + # A small constant to avoid division by zero epsilon = 1e-4 + def brier_score(e_test, t_test, risk_predicted_test, times, t, km=None, primary_risk=1): """ Compute the corrected Brier score for a given competing risk. - + This implementation is based on the work of Schoop et al. on quantifying the predictive accuracy of time-to-event models in the presence of competing risks. - - Parameters: - e_test (ndarray): Array of event indicators (0 = censored; positive integers for different events). + + Parameters + ---------- + e_test (ndarray): Array of event indicators (0 = censored; + positive integers for different events). t_test (ndarray): Array of event/censoring times. - risk_predicted_test (ndarray): Predicted risk matrix with shape (n_samples, n_times). - times (ndarray): Array of time points corresponding to columns in risk_predicted_test. + risk_predicted_test (ndarray): Predicted risk matrix with + shape (n_samples, n_times). + times (ndarray): Array of time points corresponding to columns + in risk_predicted_test. t (float): Time at which to evaluate the Brier score. - km (object, optional): Kaplan–Meier estimator or data to estimate the censoring distribution. + km (object, optional): Kaplan–Meier estimator or data to estimate + the censoring distribution. primary_risk (int, optional): The event label for which to compute the score. - - Returns: + + Returns + ------- brier (float): The corrected Brier score evaluated at time t. km (object): Updated Kaplan–Meier estimator (if applicable). """ @@ -39,47 +53,60 @@ def brier_score(e_test, t_test, risk_predicted_test, times, t, km=None, primary_ # Initialize weights for IPCW correction. weights = np.zeros_like(e_test, dtype=float) - # For subjects with events (or censoring) before t (excluding those censored exactly at 0 event label), use KM weights. + # For subjects with events (or censoring) before t + # (excluding those censored exactly at 0 event label), use KM weights. mask = (t_test <= t) & (e_test != 0) - weights[mask] = 1. / np.clip(km.survival_function_at_times(t_test[mask]), epsilon, None) - # For subjects still at risk at time t, assign constant weight based on KM at time t. - weights[t_test > t] = 1. / np.clip(km.survival_function_at_times(t), epsilon, None) + weights[mask] = 1.0 / np.clip( + km.survival_function_at_times(t_test[mask]), epsilon, None + ) + # For subjects still at risk at time t, assign constant weight based + # on KM at time t. + weights[t_test > t] = 1.0 / np.clip(km.survival_function_at_times(t), epsilon, None) brier = (weights * (truth - risk_predicted_test[:, index]) ** 2).mean() return brier, km -def integrated_brier_score(e_test, t_test, risk_predicted_test, times, t_eval=None, km=None, primary_risk=1): +def integrated_brier_score( + e_test, t_test, risk_predicted_test, times, t_eval=None, km=None, primary_risk=1 +): """ Compute the integrated Brier score for competing risks over a range of time points. - - The integrated Brier score is computed by numerically integrating the Brier score over the evaluation times. - - Parameters: + + The integrated Brier score is computed by numerically integrating the + Brier score over the evaluation times. + + Parameters + ---------- e_test (ndarray): Event indicators. t_test (ndarray): Event/censoring times. risk_predicted_test (ndarray): Predicted risk matrix with shape (n_samples, n_times). times (ndarray): Array of time points corresponding to the predictions. - t_eval (ndarray, optional): Specific time points at which to compute the score. Defaults to using 'times'. + t_eval (ndarray, optional): Specific time points at which to + compute the score. Defaults to using 'times'. km (object, optional): Kaplan–Meier estimator for IPCW. primary_risk (int, optional): The event label for which to compute the score. - - Returns: + + Returns + ------- ibs (float): Integrated Brier score. km (object): Updated Kaplan–Meier estimator. """ km = estimate_ipcw(km) t_eval = times if t_eval is None else t_eval # Compute Brier scores at each time point. - brier_scores = [brier_score(e_test, t_test, risk_predicted_test, times, t_val, km, primary_risk)[0] - for t_val in t_eval] + brier_scores = [ + brier_score( + e_test, t_test, risk_predicted_test, times, t_val, km, primary_risk + )[0] + for t_val in t_eval + ] # Remove NaN values if any. t_eval = t_eval[~np.isnan(brier_scores)] brier_scores = np.array(brier_scores)[~np.isnan(brier_scores)] - + if t_eval.shape[0] < 2: raise ValueError("At least two time points must be provided for integration.") - + ibs = np.trapz(brier_scores, t_eval) / (t_eval[-1] - t_eval[0]) return ibs, km - diff --git a/crisp_nam/metrics/discrimination.py b/crisp_nam/metrics/discrimination.py index 05aa17a0..b458b27c 100644 --- a/crisp_nam/metrics/discrimination.py +++ b/crisp_nam/metrics/discrimination.py @@ -1,13 +1,22 @@ +"""Discrimination metrics for time-to-event models with competing risks. + +This module contains functions to compute the cumulative and single time-dependent AUC and time-dependent C-index for ealuating competing risks. +""" + import numpy as np -from .ipcw import estimate_ipcw + +from .ipcw import estimate_ipcw + epsilon = 1e-10 + def auc_td(e_test, t_test, risk_predicted_test, times, t, km=None, primary_risk=1): """ Compute the time-dependent AUC for a given competing risk using predicted CIFs. - Parameters: + Parameters + ---------- e_test : ndarray of shape (n_samples,) Event indicator (0=censored, 1=event of interest, 2=competing event, etc.) t_test : ndarray of shape (n_samples,) @@ -23,7 +32,8 @@ def auc_td(e_test, t_test, risk_predicted_test, times, t, km=None, primary_risk= primary_risk : int The event label to treat as the event of interest. - Returns: + Returns + ------- auc_value : float AUC estimate at time t (always between 0 and 1) km : Updated Kaplan-Meier estimator @@ -53,8 +63,8 @@ def auc_td(e_test, t_test, risk_predicted_test, times, t, km=None, primary_risk= # Compute pairwise AUC: compare each (event, control) pair auc_numerator = 0.0 auc_denominator = 0.0 - for i, (score_i, w_i) in enumerate(zip(event_scores, weights_event)): - for j, (score_j, w_j) in enumerate(zip(control_scores, weights_control)): + for _i, (score_i, w_i) in enumerate(zip(event_scores, weights_event)): + for _j, (score_j, w_j) in enumerate(zip(control_scores, weights_control)): weight = w_i * w_j auc_denominator += weight if score_i > score_j: @@ -66,17 +76,23 @@ def auc_td(e_test, t_test, risk_predicted_test, times, t, km=None, primary_risk= auc_value = auc_numerator / auc_denominator if auc_denominator > 0 else np.nan return auc_value, km -def cumulative_dynamic_auc(e_test, t_test, risk_predicted_test, times, t_eval=None, km=None, primary_risk=1): + +def cumulative_dynamic_auc( + e_test, t_test, risk_predicted_test, times, t_eval=None, km=None, primary_risk=1 +): """ - Computes the cumulative dynamic AUC by numerically integrating the time-dependent AUC over a range of time points. - - Parameters: + Compute the cumulative dynamic AUC by numerically integrating the + time-dependent AUC over a range of time points. + + Parameters + ---------- e_test, t_test, risk_predicted_test, times, km, primary_risk: Same as in auc_td. t_eval: ndarray, optional Specific time points to evaluate. If None, uses times. - - Returns: + + Returns + ------- auc_integral: float The cumulative dynamic AUC. km: object @@ -84,18 +100,32 @@ def cumulative_dynamic_auc(e_test, t_test, risk_predicted_test, times, t_eval=No """ km = estimate_ipcw(km) t_eval = times if t_eval is None else t_eval - aucs = [auc_td(e_test, t_test, risk_predicted_test, times, t, km, primary_risk)[0] for t in t_eval] + aucs = [ + auc_td(e_test, t_test, risk_predicted_test, times, t, km, primary_risk)[0] + for t in t_eval + ] t_eval, aucs = t_eval[~np.isnan(aucs)], np.array(aucs)[~np.isnan(aucs)] if t_eval.shape[0] < 2: raise ValueError("At least two time points must be given") auc_integral = np.trapz(aucs, t_eval) / (t_eval[-1] - t_eval[0]) return auc_integral, km -def truncated_concordance_td(e_test, t_test, risk_predicted_test, times, t, km=None, primary_risk=1, tied_tol=1e-8): + +def truncated_concordance_td( + e_test, + t_test, + risk_predicted_test, + times, + t, + km=None, + primary_risk=1, + tied_tol=1e-8, +): """ Compute the truncated time-dependent concordance index (C-index). - - Parameters: + + Parameters + ---------- e_test : ndarray Event indicator (0=censored, 1=event of interest, etc.) t_test : ndarray @@ -112,14 +142,15 @@ def truncated_concordance_td(e_test, t_test, risk_predicted_test, times, t, km=N Risk of interest tied_tol : float Tolerance to assign 0.5 score for ties - - Returns: + + Returns + ------- c_index : float km : Updated km object """ epsilon = 1e-10 index = np.argmin(np.abs(times - t)) - + # IPCW if km is not None: km = estimate_ipcw(km) @@ -144,7 +175,7 @@ def truncated_concordance_td(e_test, t_test, risk_predicted_test, times, t, km=N after_mask = t_test > t_i before_mask = (t_test <= t_i) & (e_test != primary_risk) & (e_test != 0) - weights_after = weights_event[after_mask] / (w_i ** 2) + weights_after = weights_event[after_mask] / (w_i**2) weights_before = weights_event[before_mask] / (w_i * weights_event[before_mask]) risks_after = risk_predicted_test[after_mask, index] diff --git a/crisp_nam/metrics/ipcw.py b/crisp_nam/metrics/ipcw.py index bb794484..cb4d99b5 100644 --- a/crisp_nam/metrics/ipcw.py +++ b/crisp_nam/metrics/ipcw.py @@ -1,21 +1,31 @@ +"""IPCW estimation for time-to-event models with competing risks. + +This module provides a function to estimate the inverse probability of censoring weights (IPCW) using a Kaplan-Meier estimator. +""" + from lifelines import KaplanMeierFitter + def estimate_ipcw(km): """ - Estimate the inverse probability of censoring weights (IPCW) using a Kaplan-Meier estimator. + Estimate the inverse probability of censoring weights (IPCW) + using a Kaplan-Meier estimator. - Parameters: - ----------- + Parameters + ---------- km : tuple or KaplanMeierFitter If `km` is a tuple, it should contain two elements: - - e_train: array-like, event indicators (1 if the event occurred, 0 if censored). + - e_train: array-like, event indicators (1 if the event occurred, + 0 if censored). - t_train: array-like, corresponding event or censoring times. - If `km` is already a fitted KaplanMeierFitter instance, it will be used directly. + If `km` is already a fitted KaplanMeierFitter instance, + it will be used directly. - Returns: - -------- + Returns + ------- kmf : KaplanMeierFitter - A KaplanMeierFitter instance fitted to the provided data or the input instance if already fitted. + A KaplanMeierFitter instance fitted to the provided data or + the input instance if already fitted. """ if isinstance(km, tuple): kmf = KaplanMeierFitter() @@ -27,5 +37,3 @@ def estimate_ipcw(km): else: kmf = km return kmf - - diff --git a/crisp_nam/models/crisp_nam_model.py b/crisp_nam/models/crisp_nam_model.py index c8b8200c..aa8ca584 100644 --- a/crisp_nam/models/crisp_nam_model.py +++ b/crisp_nam/models/crisp_nam_model.py @@ -1,56 +1,63 @@ -import torch import numpy as np -import torch.nn as nn +import torch import torch.nn.functional as F +from torch import nn + class FeatureNet(nn.Module): """ Neural network to model the effect of a single feature on hazard. This is the building block for NAM with optional batch normalization. """ - def __init__(self, hidden_sizes=[64, 64], dropout_rate=0.1, feature_dropout=0.0, - batch_norm=False): + + def __init__( + self, + hidden_sizes=[64, 64], + dropout_rate=0.1, + feature_dropout=0.0, + batch_norm=False, + ): super(FeatureNet, self).__init__() self.batch_norm = batch_norm layers = [] - + # Input layer layers.append(nn.Linear(1, hidden_sizes[0])) if batch_norm: layers.append(nn.BatchNorm1d(hidden_sizes[0])) layers.append(nn.Tanh()) layers.append(nn.Dropout(dropout_rate)) - + # Hidden layers for i in range(len(hidden_sizes) - 1): - layers.append(nn.Linear(hidden_sizes[i], hidden_sizes[i+1])) + layers.append(nn.Linear(hidden_sizes[i], hidden_sizes[i + 1])) if batch_norm: - layers.append(nn.BatchNorm1d(hidden_sizes[i+1])) + layers.append(nn.BatchNorm1d(hidden_sizes[i + 1])) layers.append(nn.Tanh()) layers.append(nn.Dropout(dropout_rate)) - + # Final representation layer layers.append(nn.Linear(hidden_sizes[-1], hidden_sizes[-1])) if batch_norm: layers.append(nn.BatchNorm1d(hidden_sizes[-1])) layers.append(nn.Tanh()) - + self.network = nn.Sequential(*layers) self.feature_dropout = feature_dropout - + def forward(self, x): x = x.to(dtype=torch.float32) # Apply feature dropout during training if specified if self.training and self.feature_dropout > 0: mask = torch.rand_like(x) > self.feature_dropout x = x * mask.float() - + # Handle BatchNorm with single sample if self.batch_norm and x.size(0) == 1: return self._forward_singleton(x) - + return self.network(x) - + def _forward_singleton(self, x): """ Handle the case of a single sample (batch_size=1) @@ -64,10 +71,12 @@ def _forward_singleton(self, x): self.train() return result + class L2NormalizedLinear(nn.Module): """ Linear layer with L2 normalized weights (unit norm constraint) """ + def __init__(self, in_features, out_features, bias=False, eps=1e-8): super(L2NormalizedLinear, self).__init__() self.in_features = in_features @@ -77,27 +86,37 @@ def __init__(self, in_features, out_features, bias=False, eps=1e-8): if bias: self.bias = nn.Parameter(torch.randn(out_features)) else: - self.register_parameter('bias', None) - + self.register_parameter("bias", None) + def forward(self, x): # L2 normalize weights to unit norm normalized_weight = F.normalize(self.weight, p=2, dim=1, eps=self.eps) return F.linear(x, normalized_weight, self.bias) - + def get_normalized_weights(self): """Return the L2 normalized weights (useful for inspection)""" with torch.no_grad(): return F.normalize(self.weight, p=2, dim=1, eps=self.eps) + class CrispNamModel(nn.Module): """ Competing risks CoxNAM with L2 normalized projection weights. Each feature contributes to each risk through a separate shape function. All projection weights are constrained to unit L2 norm. """ - def __init__(self, num_features, num_competing_risks, hidden_sizes=[64, 64], - dropout_rate=0.1, feature_dropout=0.1, batch_norm=False, - normalize_projections=True, eps=1e-8): + + def __init__( + self, + num_features, + num_competing_risks, + hidden_sizes=[64, 64], + dropout_rate=0.1, + feature_dropout=0.1, + batch_norm=False, + normalize_projections=True, + eps=1e-8, + ): super(CrispNamModel, self).__init__() self.num_features = num_features self.num_competing_risks = num_competing_risks @@ -105,39 +124,51 @@ def __init__(self, num_features, num_competing_risks, hidden_sizes=[64, 64], self.feature_dropout = feature_dropout self.normalize_projections = normalize_projections self.eps = eps - + # Create a FeatureNet for each input feature - self.feature_nets = nn.ModuleList([ - FeatureNet(hidden_sizes, dropout_rate, feature_dropout, batch_norm) - for _ in range(num_features) - ]) - + self.feature_nets = nn.ModuleList( + [ + FeatureNet(hidden_sizes, dropout_rate, feature_dropout, batch_norm) + for _ in range(num_features) + ] + ) + # For each feature and risk type, create a projection layer if normalize_projections: - self.risk_projections = nn.ModuleList([ - nn.ModuleList([ - L2NormalizedLinear(hidden_sizes[-1], 1, bias=False, eps=eps) - for _ in range(num_competing_risks) - ]) - for _ in range(num_features) - ]) + self.risk_projections = nn.ModuleList( + [ + nn.ModuleList( + [ + L2NormalizedLinear(hidden_sizes[-1], 1, bias=False, eps=eps) + for _ in range(num_competing_risks) + ] + ) + for _ in range(num_features) + ] + ) else: # Fallback to standard linear layers - self.risk_projections = nn.ModuleList([ - nn.ModuleList([ - nn.Linear(hidden_sizes[-1], 1, bias=False) - for _ in range(num_competing_risks) - ]) - for _ in range(num_features) - ]) + self.risk_projections = nn.ModuleList( + [ + nn.ModuleList( + [ + nn.Linear(hidden_sizes[-1], 1, bias=False) + for _ in range(num_competing_risks) + ] + ) + for _ in range(num_features) + ] + ) def forward(self, x): """ Forward pass to compute risk scores for all competing risks - + Args: x: Tensor of shape (batch_size, num_features) - Returns: + + Returns + ------- risk_scores: List of (batch_size, 1) Tensors feature_outputs: List of (batch_size, hidden) Tensors """ @@ -159,8 +190,8 @@ def forward(self, x): # loop features for feat_idx, fnet in enumerate(self.feature_nets): # take one column and get repr - col = x[:, feat_idx].unsqueeze(1) # [batch,1] - repr = fnet(col) # [batch, hidden] + col = x[:, feat_idx].unsqueeze(1) # [batch,1] + repr = fnet(col) # [batch, hidden] feature_outputs.append(repr) # project into each risk channel with L2 normalized weights @@ -170,215 +201,256 @@ def forward(self, x): # split back into list of [batch,1] risk_scores = [ - combined[:, r].unsqueeze(1) - for r in range(self.num_competing_risks) + combined[:, r].unsqueeze(1) for r in range(self.num_competing_risks) ] return risk_scores, feature_outputs - + def get_shape_functions(self, x_values, feature_idx, risk_idx=None, normalize=True): """ Extract shape functions for a specific feature across all risks or a specific risk - + Args: x_values: Feature values to evaluate (numpy array or tensor) feature_idx: Index of the feature to get shape functions for risk_idx: Optional; if provided, only returns the shape function for this risk normalize: Whether to center the shape functions - - Returns: + + Returns + ------- Dictionary mapping risk names to shape function values """ - self.eval() - + self.eval() + if not isinstance(x_values, torch.Tensor): x_values = torch.FloatTensor(x_values) - + x_vals = x_values.view(-1, 1) - + with torch.no_grad(): feature_repr = self.feature_nets[feature_idx](x_vals) - + shape_funcs = {} - + # If risk_idx is specified, only compute for that risk - risk_indices = [risk_idx] if risk_idx is not None else range(self.num_competing_risks) - + risk_indices = ( + [risk_idx] if risk_idx is not None else range(self.num_competing_risks) + ) + for j in risk_indices: # Apply the L2 normalized projection to get shape function values - values = self.risk_projections[feature_idx][j](feature_repr).cpu().numpy().flatten() - + values = ( + self.risk_projections[feature_idx][j](feature_repr) + .cpu() + .numpy() + .flatten() + ) + # Normalize if requested if normalize: values = values - np.mean(values) - - shape_funcs[f'risk_{j+1}'] = values - + + shape_funcs[f"risk_{j + 1}"] = values + return shape_funcs - + def get_projection_norms(self): """ Get the L2 norms of all projection weights (should be ~1.0 if normalized) - - Returns: + + Returns + ------- Dictionary of weight norms by feature and risk """ norms = {} - + for feat_idx in range(self.num_features): for risk_idx in range(self.num_competing_risks): proj = self.risk_projections[feat_idx][risk_idx] - - if hasattr(proj, 'weight'): + + if hasattr(proj, "weight"): weight_norm = proj.weight.norm(p=2, dim=1).item() - norms[f'feature_{feat_idx}_risk_{risk_idx}'] = weight_norm - + norms[f"feature_{feat_idx}_risk_{risk_idx}"] = weight_norm + return norms - + def get_normalized_projection_weights(self): """ Get the actual L2 normalized weights used in computation - - Returns: + + Returns + ------- Dictionary of normalized weights """ normalized_weights = {} - + for feat_idx in range(self.num_features): for risk_idx in range(self.num_competing_risks): proj = self.risk_projections[feat_idx][risk_idx] - - if hasattr(proj, 'get_normalized_weights'): + + if hasattr(proj, "get_normalized_weights"): # L2NormalizedLinear layer weights = proj.get_normalized_weights().detach().cpu().numpy() - elif hasattr(proj, 'weight'): + elif hasattr(proj, "weight"): # Standard linear layer - normalize manually - weights = F.normalize(proj.weight, p=2, dim=1).detach().cpu().numpy() + weights = ( + F.normalize(proj.weight, p=2, dim=1).detach().cpu().numpy() + ) else: weights = None - - normalized_weights[f'feature_{feat_idx}_risk_{risk_idx}'] = weights - + + normalized_weights[f"feature_{feat_idx}_risk_{risk_idx}"] = weights + return normalized_weights - + def calculate_feature_importance(self, x_data, feature_idx=None): """ Calculate feature importance based on the magnitude of risk-specific projection outputs With L2 normalized weights, this gives a fair comparison across features - + Args: x_data: Input data tensor or numpy array feature_idx: Optional; if provided, only calculate importance for this feature - - Returns: + + Returns + ------- Dictionary of feature importances by risk type """ self.eval() device = next(self.parameters()).device - + # Convert to tensor if needed if not isinstance(x_data, torch.Tensor): x_data = torch.FloatTensor(x_data) x_data = x_data.to(device) - - feature_indices = [feature_idx] if feature_idx is not None else range(self.num_features) - importance = {f'risk_{j+1}': {} for j in range(self.num_competing_risks)} - + + feature_indices = ( + [feature_idx] if feature_idx is not None else range(self.num_features) + ) + importance = {f"risk_{j + 1}": {} for j in range(self.num_competing_risks)} + for i in feature_indices: # Get feature values feature_values = x_data[:, i].view(-1, 1) - + with torch.no_grad(): # Get the feature representation feature_repr = self.feature_nets[i](feature_values) - + # Calculate importance for each risk (mean absolute value) # With L2 normalized weights, this is directly comparable across features for j in range(self.num_competing_risks): risk_specific_output = self.risk_projections[i][j](feature_repr) abs_values = torch.abs(risk_specific_output).cpu().numpy() - importance[f'risk_{j+1}'][f'feature_{i}'] = float(np.mean(abs_values)) - + importance[f"risk_{j + 1}"][f"feature_{i}"] = float( + np.mean(abs_values) + ) + return importance - + def predict_risk(self, x, baseline_hazards=None): """ Predict survival probability or cumulative incidence - + Args: x: Input tensor of shape (batch_size, num_features) baseline_hazards: Optional dict of baseline hazards for each risk - - Returns: + + Returns + ------- Dictionary of predictions for each competing risk """ self.eval() - + # Convert to tensor if needed if not isinstance(x, torch.Tensor): x = torch.FloatTensor(x) - + with torch.no_grad(): risk_scores, _ = self(x) - + # Convert scores to hazard ratios hazard_ratios = [torch.exp(score).cpu().numpy() for score in risk_scores] - + # If baseline hazards are provided, compute absolute risks if baseline_hazards is not None: predictions = {} - + for j in range(self.num_competing_risks): - risk_name = f'risk_{j+1}' - + risk_name = f"risk_{j + 1}" + # Baseline survival and hazard - baseline_surv = baseline_hazards.get(risk_name, {}).get('survival', None) - baseline_haz = baseline_hazards.get(risk_name, {}).get('hazard', None) - + baseline_surv = baseline_hazards.get(risk_name, {}).get( + "survival", None + ) + baseline_haz = baseline_hazards.get(risk_name, {}).get( + "hazard", None + ) + if baseline_surv is not None: # Compute survival probability: S(t|x) = S0(t)^exp(f(x)) - predictions[f'{risk_name}_survival'] = np.power( - baseline_surv.reshape(1, -1), - hazard_ratios[j].reshape(-1, 1) + predictions[f"{risk_name}_survival"] = np.power( + baseline_surv.reshape(1, -1), + hazard_ratios[j].reshape(-1, 1), ) - + if baseline_haz is not None: # Compute cumulative hazard: H(t|x) = H0(t) * exp(f(x)) - predictions[f'{risk_name}_cumhazard'] = baseline_haz.reshape(1, -1) * hazard_ratios[j].reshape(-1, 1) - + predictions[f"{risk_name}_cumhazard"] = baseline_haz.reshape( + 1, -1 + ) * hazard_ratios[j].reshape(-1, 1) + return predictions - else: - # Without baseline hazards, just return hazard ratios - return {f'risk_{j+1}_hazard_ratio': hazard_ratios[j] for j in range(self.num_competing_risks)} + # Without baseline hazards, just return hazard ratios + return { + f"risk_{j + 1}_hazard_ratio": hazard_ratios[j] + for j in range(self.num_competing_risks) + } + # Alternative implementation using manual normalization in forward pass class CrispNamModelManualL2(nn.Module): """ Alternative implementation with manual L2 normalization in forward pass """ - def __init__(self, num_features, num_competing_risks, hidden_sizes=[64, 64], - dropout_rate=0.1, feature_dropout=0.1, batch_norm=False, eps=1e-8): + + def __init__( + self, + num_features, + num_competing_risks, + hidden_sizes=[64, 64], + dropout_rate=0.1, + feature_dropout=0.1, + batch_norm=False, + eps=1e-8, + ): super(CrispNamModelManualL2, self).__init__() self.num_features = num_features self.num_competing_risks = num_competing_risks self.batch_norm = batch_norm self.feature_dropout = feature_dropout self.eps = eps - + # Create a FeatureNet for each input feature - self.feature_nets = nn.ModuleList([ - FeatureNet(hidden_sizes, dropout_rate, feature_dropout, batch_norm) - for _ in range(num_features) - ]) - + self.feature_nets = nn.ModuleList( + [ + FeatureNet(hidden_sizes, dropout_rate, feature_dropout, batch_norm) + for _ in range(num_features) + ] + ) + # Standard linear layers - weights will be L2 normalized in forward pass - self.risk_projections = nn.ModuleList([ - nn.ModuleList([ - nn.Linear(hidden_sizes[-1], 1, bias=False) - for _ in range(num_competing_risks) - ]) - for _ in range(num_features) - ]) + self.risk_projections = nn.ModuleList( + [ + nn.ModuleList( + [ + nn.Linear(hidden_sizes[-1], 1, bias=False) + for _ in range(num_competing_risks) + ] + ) + for _ in range(num_features) + ] + ) def forward(self, x): """Forward pass with manual L2 weight normalization""" @@ -406,12 +478,12 @@ def forward(self, x): combined[:, risk_idx] += output.view(-1) risk_scores = [ - combined[:, r].unsqueeze(1) - for r in range(self.num_competing_risks) + combined[:, r].unsqueeze(1) for r in range(self.num_competing_risks) ] return risk_scores, feature_outputs + # Utility functions for model analysis def analyze_projection_weights(model): """ @@ -419,41 +491,40 @@ def analyze_projection_weights(model): """ print("Projection Weight Analysis:") print("=" * 50) - + # Get weight norms norms = model.get_projection_norms() norm_values = list(norms.values()) - - print(f"Weight L2 Norms (should be ~1.0):") + + print("Weight L2 Norms (should be ~1.0):") print(f" Mean: {np.mean(norm_values):.6f}") print(f" Std: {np.std(norm_values):.6f}") print(f" Min: {np.min(norm_values):.6f}") print(f" Max: {np.max(norm_values):.6f}") - + # Show some individual norms - print(f"\nSample individual norms:") + print("\nSample individual norms:") for i, (name, norm) in enumerate(list(norms.items())[:6]): print(f" {name}: {norm:.6f}") - + return norms + def compare_feature_importance_fairness(model, x_data): """ Compare feature importance when weights are L2 normalized vs not normalized """ print("\nFeature Importance Comparison:") print("=" * 50) - + # Calculate importance with current model (L2 normalized) importance_normalized = model.calculate_feature_importance(x_data) - + # Create equivalent model without normalization for comparison model_unnorm = CrispNamModel( - model.num_features, - model.num_competing_risks, - normalize_projections=False + model.num_features, model.num_competing_risks, normalize_projections=False ) - + # Copy weights from normalized model with torch.no_grad(): for i in range(model.num_features): @@ -461,35 +532,38 @@ def compare_feature_importance_fairness(model, x_data): model_unnorm.risk_projections[i][j].weight.copy_( model.risk_projections[i][j].weight ) - + importance_unnorm = model_unnorm.calculate_feature_importance(x_data) - + print("Importance comparison (Risk 1):") for feat in range(min(5, model.num_features)): # Show first 5 features - norm_imp = importance_normalized['risk_1'].get(f'feature_{feat}', 0) - unnorm_imp = importance_unnorm['risk_1'].get(f'feature_{feat}', 0) - print(f" Feature {feat}: Normalized={norm_imp:.4f}, Unnormalized={unnorm_imp:.4f}") + norm_imp = importance_normalized["risk_1"].get(f"feature_{feat}", 0) + unnorm_imp = importance_unnorm["risk_1"].get(f"feature_{feat}", 0) + print( + f" Feature {feat}: Normalized={norm_imp:.4f}, Unnormalized={unnorm_imp:.4f}" + ) + # Example usage and testing if __name__ == "__main__": # Create model with L2 normalized projections model = CrispNamModel( - num_features=5, + num_features=5, num_competing_risks=3, hidden_sizes=[32, 32], - normalize_projections=True + normalize_projections=True, ) - + # Generate some test data torch.manual_seed(42) test_data = torch.randn(100, 5) - + # Test forward pass risk_scores, feature_outputs = model(test_data) print(f"Risk scores shapes: {[score.shape for score in risk_scores]}") - + # Analyze projection weights analyze_projection_weights(model) - + # Compare feature importance - compare_feature_importance_fairness(model, test_data) \ No newline at end of file + compare_feature_importance_fairness(model, test_data) diff --git a/crisp_nam/models/deephit_model.py b/crisp_nam/models/deephit_model.py index cc3394f3..2bb055fd 100644 --- a/crisp_nam/models/deephit_model.py +++ b/crisp_nam/models/deephit_model.py @@ -1,22 +1,31 @@ import torch -import torch.nn as nn import torch.nn.functional as F +from torch import nn + class FCLayer(nn.Module): """Fully connected layer with optional batch norm, dropout, and activation.""" - def __init__(self, in_dim, out_dim, activation=nn.ReLU(), batch_norm=False, dropout_rate=0.0, - init_fn=nn.init.xavier_normal_): + + def __init__( + self, + in_dim, + out_dim, + activation=nn.ReLU(), + batch_norm=False, + dropout_rate=0.0, + init_fn=nn.init.xavier_normal_, + ): super(FCLayer, self).__init__() self.fc = nn.Linear(in_dim, out_dim) self.batch_norm = nn.BatchNorm1d(out_dim) if batch_norm else None self.dropout = nn.Dropout(dropout_rate) if dropout_rate > 0 else None self.activation = activation - + # Initialize weights if init_fn: init_fn(self.fc.weight) nn.init.zeros_(self.fc.bias) - + def forward(self, x): x = self.fc(x) if self.batch_norm: @@ -30,65 +39,85 @@ def forward(self, x): class FCNet(nn.Module): """Multi-layer fully connected network.""" - def __init__(self, in_dim, num_layers, h_dim, activation=nn.ReLU(), - out_dim=None, out_activation=None, batch_norm=False, - dropout_rate=0.0, init_fn=nn.init.xavier_normal_): + + def __init__( + self, + in_dim, + num_layers, + h_dim, + activation=nn.ReLU(), + out_dim=None, + out_activation=None, + batch_norm=False, + dropout_rate=0.0, + init_fn=nn.init.xavier_normal_, + ): super(FCNet, self).__init__() - + layers = [] prev_dim = in_dim - + # Hidden layers for i in range(num_layers): curr_dim = out_dim if (i == num_layers - 1 and out_dim) else h_dim - curr_act = out_activation if (i == num_layers - 1 and out_activation) else activation - - layers.append(FCLayer( - prev_dim, curr_dim, activation=curr_act, - batch_norm=batch_norm, dropout_rate=dropout_rate, init_fn=init_fn - )) + curr_act = ( + out_activation + if (i == num_layers - 1 and out_activation) + else activation + ) + + layers.append( + FCLayer( + prev_dim, + curr_dim, + activation=curr_act, + batch_norm=batch_norm, + dropout_rate=dropout_rate, + init_fn=init_fn, + ) + ) prev_dim = curr_dim - + self.network = nn.Sequential(*layers) - + def forward(self, x): return self.network(x) class DeepHit(nn.Module): """PyTorch implementation of DeepHit for competing risks survival analysis.""" - + def __init__(self, input_dims, network_settings): super(DeepHit, self).__init__() - + # Input dimensions - self.x_dim = input_dims['x_dim'] - self.num_Event = input_dims['num_Event'] - self.num_Category = input_dims['num_Category'] - + self.x_dim = input_dims["x_dim"] + self.num_Event = input_dims["num_Event"] + self.num_Category = input_dims["num_Category"] + # Network settings - self.h_dim_shared = network_settings['h_dim_shared'] - self.h_dim_CS = network_settings['h_dim_CS'] - self.num_layers_shared = network_settings['num_layers_shared'] - self.num_layers_CS = network_settings['num_layers_CS'] - + self.h_dim_shared = network_settings["h_dim_shared"] + self.h_dim_CS = network_settings["h_dim_CS"] + self.num_layers_shared = network_settings["num_layers_shared"] + self.num_layers_CS = network_settings["num_layers_CS"] + # Activation function - if network_settings['active_fn'] == 'relu': + if network_settings["active_fn"] == "relu": self.active_fn = nn.ReLU() - elif network_settings['active_fn'] == 'elu': + elif network_settings["active_fn"] == "elu": self.active_fn = nn.ELU() - elif network_settings['active_fn'] == 'tanh': + elif network_settings["active_fn"] == "tanh": self.active_fn = nn.Tanh() else: self.active_fn = nn.ReLU() - + # Regularization - self.keep_prob = network_settings.get('keep_prob', 0.5) + self.keep_prob = network_settings.get("keep_prob", 0.5) self.dropout_rate = 1.0 - self.keep_prob - + # Initialize networks self._build_network() - + def _build_network(self): # Shared network self.shared_net = FCNet( @@ -96,156 +125,176 @@ def _build_network(self): num_layers=self.num_layers_shared, h_dim=self.h_dim_shared, activation=self.active_fn, - dropout_rate=self.dropout_rate + dropout_rate=self.dropout_rate, ) - + # Cause-specific networks - self.cs_nets = nn.ModuleList([ - FCNet( - in_dim=self.x_dim + self.h_dim_shared, # Concatenate input and shared output - num_layers=self.num_layers_CS, - h_dim=self.h_dim_CS, - activation=self.active_fn, - dropout_rate=self.dropout_rate - ) for _ in range(self.num_Event) - ]) - + self.cs_nets = nn.ModuleList( + [ + FCNet( + in_dim=self.x_dim + + self.h_dim_shared, # Concatenate input and shared output + num_layers=self.num_layers_CS, + h_dim=self.h_dim_CS, + activation=self.active_fn, + dropout_rate=self.dropout_rate, + ) + for _ in range(self.num_Event) + ] + ) + # Output layer - self.output_layer = nn.Linear(self.num_Event * self.h_dim_CS, self.num_Event * self.num_Category) - + self.output_layer = nn.Linear( + self.num_Event * self.h_dim_CS, self.num_Event * self.num_Category + ) + def forward(self, x): # Shared network shared_out = self.shared_net(x) - + # Concatenate input with shared output h = torch.cat([x, shared_out], dim=1) - + # Cause-specific networks cs_outputs = [] for cs_net in self.cs_nets: cs_out = cs_net(h) cs_outputs.append(cs_out) - + # Stack outputs - stacked_out = torch.stack(cs_outputs, dim=1) # [batch_size, num_Event, h_dim_CS] - reshaped_out = stacked_out.view(-1, self.num_Event * self.h_dim_CS) # [batch_size, num_Event * h_dim_CS] - + stacked_out = torch.stack( + cs_outputs, dim=1 + ) # [batch_size, num_Event, h_dim_CS] + reshaped_out = stacked_out.view( + -1, self.num_Event * self.h_dim_CS + ) # [batch_size, num_Event * h_dim_CS] + # Final output layer - logits = self.output_layer(F.dropout(reshaped_out, self.dropout_rate, self.training)) + logits = self.output_layer( + F.dropout(reshaped_out, self.dropout_rate, self.training) + ) out = F.softmax(logits.view(-1, self.num_Event * self.num_Category), dim=1) - + # Reshape to [batch_size, num_Event, num_Category] out = out.view(-1, self.num_Event, self.num_Category) - + # For compatibility with the training script, return both raw risks and shape functions # In this model, we don't have separate shape functions, so just return None return out, None - + def log_likelihood_loss(self, out, t, k, mask1, mask2): """Log-likelihood loss (including log-likelihood of censored subjects)""" batch_size = out.size(0) - + # Convert to PyTorch tensors if necessary if not isinstance(k, torch.Tensor): k = torch.tensor(k, device=out.device) if not isinstance(t, torch.Tensor): t = torch.tensor(t, device=out.device) - + # Indicator for uncensored subjects I_1 = (k > 0).float().view(-1, 1) - + # For uncensored: log P(T=t, K=k|x) tmp1 = torch.sum(torch.sum(mask1 * out, dim=2), dim=1, keepdim=True) tmp1 = I_1 * torch.log(tmp1 + 1e-8) - + # For censored: log ∑ P(T>t|x) - tmp2 = torch.sum(torch.sum(mask2.unsqueeze(1) * out, dim=2), dim=1, keepdim=True) + tmp2 = torch.sum( + torch.sum(mask2.unsqueeze(1) * out, dim=2), dim=1, keepdim=True + ) tmp2 = (1.0 - I_1) * torch.log(tmp2 + 1e-8) - + return -torch.mean(tmp1 + tmp2) - + def ranking_loss(self, out, t, k, mask2): """Ranking loss (calculated only for acceptable pairs)""" batch_size = out.size(0) sigma1 = 0.1 eta = [] - + # Convert to PyTorch tensors if necessary if not isinstance(k, torch.Tensor): k = torch.tensor(k, device=out.device) if not isinstance(t, torch.Tensor): t = torch.tensor(t, device=out.device) - + one_vector = torch.ones_like(t) - + for e in range(self.num_Event): # Indicator for current event type - I_2 = (k == e+1).float() + I_2 = (k == e + 1).float() I_2_diag = torch.diag(I_2.squeeze()) - + # Extract event-specific probabilities tmp_e = out[:, e, :] # [batch_size, num_Category] - + # Calculate risk scores R = torch.matmul(tmp_e, mask2.transpose(0, 1)) # [batch_size, batch_size] diag_R = torch.diag(R).unsqueeze(1) # [batch_size, 1] - R = torch.matmul(one_vector, diag_R.transpose(0, 1)) - R # [batch_size, batch_size] + R = ( + torch.matmul(one_vector, diag_R.transpose(0, 1)) - R + ) # [batch_size, batch_size] R = R.transpose(0, 1) # Now R_ij = r_i(T_i) - r_j(T_i) - + # Time comparison matrix - T = F.relu(torch.sign(torch.matmul(one_vector, t.transpose(0, 1)) - - torch.matmul(t, one_vector.transpose(0, 1)))) - + T = F.relu( + torch.sign( + torch.matmul(one_vector, t.transpose(0, 1)) + - torch.matmul(t, one_vector.transpose(0, 1)) + ) + ) + # Filter by event occurrence T = torch.matmul(I_2_diag, T) - + # Calculate ranking loss for current event tmp_eta = torch.mean(T * torch.exp(-R / sigma1), dim=1, keepdim=True) eta.append(tmp_eta) - + eta = torch.stack(eta, dim=1) # [batch_size, num_Event] eta = torch.mean(eta.reshape(-1, self.num_Event), dim=1, keepdim=True) - + return torch.sum(eta) - + def calibration_loss(self, out, t, k, mask2): """Calibration loss""" batch_size = out.size(0) eta = [] - + # Convert to PyTorch tensors if necessary if not isinstance(k, torch.Tensor): k = torch.tensor(k, device=out.device) - + for e in range(self.num_Event): # Indicator for current event type - I_2 = (k == e+1).float() - + I_2 = (k == e + 1).float() + # Extract event-specific probabilities tmp_e = out[:, e, :] # [batch_size, num_Category] - + # Calculate calibration loss r = torch.sum(tmp_e * mask2, dim=1) tmp_eta = torch.mean((r - I_2) ** 2, dim=0, keepdim=True) eta.append(tmp_eta) - + eta = torch.stack(eta, dim=1) # [1, num_Event] eta = torch.mean(eta.reshape(-1, self.num_Event), dim=1, keepdim=True) - + return torch.sum(eta) - + def compute_loss(self, out, t, k, mask1, mask2, alpha=1.0, beta=1.0, gamma=1.0): """Compute total loss""" loss1 = self.log_likelihood_loss(out, t, k, mask1, mask2) loss2 = self.ranking_loss(out, t, k, mask2) loss3 = self.calibration_loss(out, t, k, mask2) - + # L2 regularization is handled by optimizer (weight_decay) return alpha * loss1 + beta * loss2 + gamma * loss3 - + def predict(self, x): """Predict risk scores for input x""" self.eval() with torch.no_grad(): out, _ = self.forward(x) - return out \ No newline at end of file + return out diff --git a/crisp_nam/utils/__init__.py b/crisp_nam/utils/__init__.py index 08275dbe..c9eb8c32 100644 --- a/crisp_nam/utils/__init__.py +++ b/crisp_nam/utils/__init__.py @@ -1,3 +1,3 @@ from .loss import * from .plotting import * -from .risk_cif import * \ No newline at end of file +from .risk_cif import * diff --git a/crisp_nam/utils/loss.py b/crisp_nam/utils/loss.py index 9cae44fd..3847c50c 100644 --- a/crisp_nam/utils/loss.py +++ b/crisp_nam/utils/loss.py @@ -1,11 +1,18 @@ import torch -def weighted_negative_log_likelihood_loss(risk_scores, times, events, - num_competing_risks, event_weights=None, - sample_weights=None, eps=1e-8) -> float: + +def weighted_negative_log_likelihood_loss( + risk_scores, + times, + events, + num_competing_risks, + event_weights=None, + sample_weights=None, + eps=1e-8, +) -> float: """ Computes the weighted negative log-likelihood loss for competing risks Cox model. - + Args: risk_scores: List of tensors with shape (batch_size, 1) for each competing risk times: Event/censoring times (batch_size,) @@ -14,130 +21,138 @@ def weighted_negative_log_likelihood_loss(risk_scores, times, events, event_weights: Tensor of weights for each competing risk type (size: num_competing_risks) sample_weights: Tensor of weights for each sample (size: batch_size) eps: Small constant for numerical stability - - Returns: + + Returns + ------- Weighted negative log partial likelihood loss """ device = times.device batch_size = times.shape[0] - + # Initialize loss loss = torch.tensor(0.0, device=device) - + # Set default weights if not provided if event_weights is None: event_weights = torch.ones(num_competing_risks, device=device) if sample_weights is None: sample_weights = torch.ones(batch_size, device=device) - + # Count number of events n_events = (events > 0).sum().item() if n_events == 0: return loss - + # Process each competing risk separately for k in range(1, num_competing_risks + 1): # Find samples with this event type - event_mask = (events == k) + event_mask = events == k n_events_k = event_mask.sum().item() - + if n_events_k == 0: continue - + # Get risk scores for this competing risk - risk_k = risk_scores[k-1].squeeze() - + risk_k = risk_scores[k - 1].squeeze() + # Get weight for this event type - event_weight = event_weights[k-1] - + event_weight = event_weights[k - 1] + # For each event of type k for i in range(batch_size): if event_mask[i]: # Find samples in risk set (samples with time >= event time) - risk_set = (times >= times[i]) - + risk_set = times >= times[i] + # Calculate log sum of exp of risk scores in risk set risk_set_scores = risk_k[risk_set] log_risk_sum = torch.logsumexp(risk_set_scores, dim=0) - + # Subtract individual risk score from log sum and apply weights individual_loss = log_risk_sum - risk_k[i] - weighted_individual_loss = individual_loss * event_weight * sample_weights[i] + weighted_individual_loss = ( + individual_loss * event_weight * sample_weights[i] + ) loss += weighted_individual_loss - + # Return average loss return loss / max(n_events, 1) -def negative_log_likelihood_loss(risk_scores, times, events, - num_competing_risks, eps=1e-8): + +def negative_log_likelihood_loss( + risk_scores, times, events, num_competing_risks, eps=1e-8 +): """ Computes the negative log-likelihood loss for competing risks Cox model. - + Args: risk_scores: List of tensors with shape (batch_size, 1) for each competing risk times: Event/censoring times (batch_size,) events: Event indicators (0=censored, 1...K=event types) (batch_size,) num_competing_risks: Number of competing risks eps: Small constant for numerical stability - - Returns: + + Returns + ------- Negative log partial likelihood loss """ device = times.device batch_size = times.shape[0] - + # Initialize loss loss = torch.tensor(0.0, device=device) - + # Count number of events n_events = (events > 0).sum().item() if n_events == 0: return loss - + # Process each competing risk separately for k in range(1, num_competing_risks + 1): # Find samples with this event type - event_mask = (events == k) + event_mask = events == k n_events_k = event_mask.sum().item() - + if n_events_k == 0: continue - + # Get risk scores for this competing risk - risk_k = risk_scores[k-1].squeeze() - + risk_k = risk_scores[k - 1].squeeze() + # For each event of type k for i in range(batch_size): if event_mask[i]: # Find samples in risk set (samples with time >= event time) - risk_set = (times >= times[i]) - + risk_set = times >= times[i] + # Calculate log sum of exp of risk scores in risk set risk_set_scores = risk_k[risk_set] log_risk_sum = torch.logsumexp(risk_set_scores, dim=0) - + # Subtract individual risk score from log sum loss += log_risk_sum - risk_k[i] - + # Return average loss return loss / max(n_events, 1) + def compute_l2_penalty(model, include_bias=False) -> int: """ Compute L2 regularization penalty on model parameters - + Args: model: Neural network model include_bias: Whether to include bias terms in regularization - - Returns: + + Returns + ------- L2 penalty term """ l2_reg = 0.0 for name, param in model.named_parameters(): if param.requires_grad: # Skip bias parameters if specified - if not include_bias and 'bias' in name: + if not include_bias and "bias" in name: continue - l2_reg += torch.sum(param ** 2) - return l2_reg \ No newline at end of file + l2_reg += torch.sum(param**2) + return l2_reg diff --git a/crisp_nam/utils/plotting.py b/crisp_nam/utils/plotting.py index e92e1b44..43c1b89d 100644 --- a/crisp_nam/utils/plotting.py +++ b/crisp_nam/utils/plotting.py @@ -1,20 +1,23 @@ -import matplotlib.pyplot as plt -from typing import Union, List +from typing import List, Union -import torch +import matplotlib.pyplot as plt import numpy as np import pandas as pd +import torch -def plot_feature_importance(model: torch.nn.Module, - x_data: Union[np.ndarray, torch.Tensor], - feature_names = None, - n_top:int = 5, - n_bottom:int = 5, - risk_idx:int = 1, - figsize: tuple = (8, 6), - output_file:str = None, - color_positive:str = '#2196F3', - color_negative:str = '#F44336') -> tuple: + +def plot_feature_importance( + model: torch.nn.Module, + x_data: Union[np.ndarray, torch.Tensor], + feature_names=None, + n_top: int = 5, + n_bottom: int = 5, + risk_idx: int = 1, + figsize: tuple = (8, 6), + output_file: str = None, + color_positive: str = "#2196F3", + color_negative: str = "#F44336", +) -> tuple: """ Plot feature importance with both top positive and negative influences, handling both CPU and CUDA devices automatically. @@ -26,7 +29,7 @@ def plot_feature_importance(model: torch.nn.Module, # prepare feature names num_features = model.num_features if feature_names is None: - feature_names = [f"Feature {i+1}" for i in range(num_features)] + feature_names = [f"Feature {i + 1}" for i in range(num_features)] # convert x_data to tensor on the model device if not isinstance(x_data, torch.Tensor): @@ -52,44 +55,54 @@ def plot_feature_importance(model: torch.nn.Module, feature_contribs[feature_names[i]] = contrib # build a DataFrame for sorting - df = pd.DataFrame({ - 'feature': list(feature_contribs.keys()), - 'contribution': list(feature_contribs.values()) - }) - df['abs_contrib'] = df['contribution'].abs() - df = df.sort_values('abs_contrib', ascending=False) - - pos = df[df['contribution'] > 0].head(n_top).sort_values('contribution') - neg = df[df['contribution'] < 0].head(n_bottom).sort_values('contribution', ascending=False) - - top_pos = pos['feature'].tolist() - top_neg = neg['feature'].tolist() + df = pd.DataFrame( + { + "feature": list(feature_contribs.keys()), + "contribution": list(feature_contribs.values()), + } + ) + df["abs_contrib"] = df["contribution"].abs() + df = df.sort_values("abs_contrib", ascending=False) + + pos = df[df["contribution"] > 0].head(n_top).sort_values("contribution") + neg = ( + df[df["contribution"] < 0] + .head(n_bottom) + .sort_values("contribution", ascending=False) + ) + + top_pos = pos["feature"].tolist() + top_neg = neg["feature"].tolist() # plotting fig, ax = plt.subplots(figsize=figsize) - ax.barh(pos['feature'], pos['contribution'], color=color_positive, alpha=0.8) - ax.barh(neg['feature'], neg['contribution'], color=color_negative, alpha=0.8) - ax.axvline(0, color='black', linestyle='-', alpha=0.3) - - ax.set_xlabel('Contribution to Risk Score') - ax.set_title(f'Top {n_top} Positive & {n_bottom} Negative Features for risk_{risk_idx}') - ax.grid(axis='x', linestyle='--', alpha=0.5) + ax.barh(pos["feature"], pos["contribution"], color=color_positive, alpha=0.8) + ax.barh(neg["feature"], neg["contribution"], color=color_negative, alpha=0.8) + ax.axvline(0, color="black", linestyle="-", alpha=0.3) + + ax.set_xlabel("Contribution to Risk Score") + ax.set_title( + f"Top {n_top} Positive & {n_bottom} Negative Features for risk_{risk_idx}" + ) + ax.grid(axis="x", linestyle="--", alpha=0.5) plt.tight_layout() if output_file: - plt.savefig(output_file, bbox_inches='tight', dpi=300) + plt.savefig(output_file, bbox_inches="tight", dpi=300) return fig, ax, top_pos, top_neg -def plot_coxnam_shape_functions(model:torch.nn.Module, - X: Union[np.ndarray, torch.Tensor], - risk_to_plot:int = 1, - feature_names:List[str] = None, - top_features:List[str] = None, - ncols:int = 3, - figsize:tuple = (12, 8), - output_file:str = None) -> tuple: +def plot_coxnam_shape_functions( + model: torch.nn.Module, + X: Union[np.ndarray, torch.Tensor], + risk_to_plot: int = 1, + feature_names: List[str] = None, + top_features: List[str] = None, + ncols: int = 3, + figsize: tuple = (12, 8), + output_file: str = None, +) -> tuple: """ Plot shape functions for each feature in a CoxNAM model, automatically handling CPU vs CUDA inputs. @@ -107,11 +120,11 @@ def plot_coxnam_shape_functions(model:torch.nn.Module, # derive feature list num_features = model.num_features if feature_names is None: - feature_names = [f"Feature {i+1}" for i in range(num_features)] + feature_names = [f"Feature {i + 1}" for i in range(num_features)] if top_features: # map names back to indices idx_map = {name: i for i, name in enumerate(feature_names)} - selected = [(idx_map.get(name, None), name) for name in top_features] + selected = [(idx_map.get(name), name) for name in top_features] selected = [(i, name) for i, name in selected if i is not None] else: selected = list(zip(range(num_features), feature_names)) @@ -125,7 +138,7 @@ def plot_coxnam_shape_functions(model:torch.nn.Module, for ax, (f_idx, fname) in zip(axes, selected): vals = X_np[:, f_idx] if vals.size == 0: - ax.text(0.5, 0.5, "no data", ha='center', va='center') + ax.text(0.5, 0.5, "no data", ha="center", va="center") continue # choose evaluation points @@ -144,20 +157,20 @@ def plot_coxnam_shape_functions(model:torch.nn.Module, # plot ax.plot(pts, shp, linewidth=2) - ax.axhline(0, linestyle='--', alpha=0.5) + ax.axhline(0, linestyle="--", alpha=0.5) ax.set_title(fname) - ax.set_xlabel('Value') - ax.set_ylabel('Contribution') + ax.set_xlabel("Value") + ax.set_ylabel("Contribution") # rug plot - ax.plot(vals, np.zeros_like(vals)-0.1, '|', alpha=0.3) + ax.plot(vals, np.zeros_like(vals) - 0.1, "|", alpha=0.3) # turn off any extra axes for ax in axes[n_selected:]: - ax.axis('off') + ax.axis("off") - fig.suptitle(f'Shape Functions for Risk {risk_to_plot}', fontsize=14) - plt.tight_layout(rect=[0,0,1,0.96]) + fig.suptitle(f"Shape Functions for Risk {risk_to_plot}", fontsize=14) + plt.tight_layout(rect=[0, 0, 1, 0.96]) if output_file: - plt.savefig(output_file, dpi=300, bbox_inches='tight') + plt.savefig(output_file, dpi=300, bbox_inches="tight") return fig, axes[:n_selected] diff --git a/crisp_nam/utils/risk_cif.py b/crisp_nam/utils/risk_cif.py index 9e8cd1b2..09f43a61 100644 --- a/crisp_nam/utils/risk_cif.py +++ b/crisp_nam/utils/risk_cif.py @@ -1,33 +1,34 @@ -from typing import List, Any +from typing import Any, List -import torch import numpy as np +import torch -def compute_baseline_cif(times:np.ndarray, - events:np.ndarray, - eval_times:List[Any], - event_type:np.ndarray) -> np.ndarray: + +def compute_baseline_cif( + times: np.ndarray, events: np.ndarray, eval_times: List[Any], event_type: np.ndarray +) -> np.ndarray: """ Compute baseline cumulative incidence function for a specific event type - + Args: times: Numpy array of event times events: Numpy array of event indicators (0=censored, 1...K=event types) eval_times: Times at which to evaluate the CIF event_type: Event type to compute CIF for (1...K) - - Returns: + + Returns + ------- Numpy array of baseline CIF values at eval_times """ # Sort times and corresponding events sort_idx = np.argsort(times) sorted_times = times[sort_idx] sorted_events = events[sort_idx] - + # Initialize cumulative hazard n_samples = len(times) baseline_cif = np.zeros(len(eval_times)) - + # For each evaluation time for i, t in enumerate(eval_times): cif_t = 0.0 @@ -37,67 +38,75 @@ def compute_baseline_cif(times:np.ndarray, # Simple Aalen-Johansen estimator cif_t = event_count / n_samples baseline_cif[i] = cif_t - + return baseline_cif -def predict_absolute_risk(model: torch.nn.Module, - x:np.ndarray, - baseline_cifs:np.ndarray, - eval_times: np.ndarray, - device:str="cpu") -> np.ndarray: + +def predict_absolute_risk( + model: torch.nn.Module, + x: np.ndarray, + baseline_cifs: np.ndarray, + eval_times: np.ndarray, + device: str = "cpu", +) -> np.ndarray: """ Predict absolute risk (cumulative incidence) at specified times - + Args: model: Trained CoxNAM model x: Feature matrix baseline_cifs: Dictionary of baseline CIFs for each event type eval_times: Times at which to evaluate the CIF device: Device to run computations on - - Returns: + + Returns + ------- Numpy array of predicted absolute risks with shape (n_samples, n_risks, n_times) """ model.eval() - + # Convert to tensor if needed if not isinstance(x, torch.Tensor): x = torch.FloatTensor(x).to(device) - + with torch.no_grad(): # Get risk scores risk_scores, _ = model(x) - + # Convert to hazard ratios hazard_ratios = [torch.exp(score).cpu().numpy() for score in risk_scores] - + # Initialize prediction array n_samples = x.shape[0] n_risks = model.num_competing_risks n_times = len(eval_times) abs_risks = np.zeros((n_samples, n_risks, n_times)) - + # Compute absolute risk for each sample, risk, and time for k in range(n_risks): # Skip if baseline CIF is not available - if k+1 not in baseline_cifs: + if k + 1 not in baseline_cifs: continue - - baseline_cif = baseline_cifs[k+1] - + + baseline_cif = baseline_cifs[k + 1] + for i in range(n_samples): for j, t in enumerate(eval_times): # Simple Fine-Gray model for cumulative incidence - abs_risks[i, k, j] = 1 - np.exp(-baseline_cif[j] * hazard_ratios[k][i]) - + abs_risks[i, k, j] = 1 - np.exp( + -baseline_cif[j] * hazard_ratios[k][i] + ) + return abs_risks -def predict_cif(model:torch.nn.Module, - x:np.ndarray, - baseline_cif:np.ndarray, - times:np.ndarray, - event_of_interest:int) -> np.ndarray: +def predict_cif( + model: torch.nn.Module, + x: np.ndarray, + baseline_cif: np.ndarray, + times: np.ndarray, + event_of_interest: int, +) -> np.ndarray: """ Predict cumulative incidence function for a specific competing risk. @@ -108,7 +117,8 @@ def predict_cif(model:torch.nn.Module, times: Time points at which CIF is evaluated. event_type: Integer, 0-based index of event of interest. - Returns: + Returns + ------- cif_pred: Array of shape (n_samples, len(times)) — predicted CIF per sample. """ model.eval() @@ -117,16 +127,15 @@ def predict_cif(model:torch.nn.Module, f_j_x = logits[event_of_interest].squeeze(1).cpu().numpy() # (n_samples,) baseline_cif = np.asarray(baseline_cif).reshape(1, -1) # (1, T) - risk_scores = np.exp(f_j_x).reshape(-1, 1) # (N, 1) - + risk_scores = np.exp(f_j_x).reshape(-1, 1) # (N, 1) + # Fine-Gray style CIF prediction under PH assumption cif_pred = 1.0 - np.power(1.0 - baseline_cif, risk_scores) # shape (N, T) - + return cif_pred -def predict_risk(model:np.ndarray, - x_input:np.ndarray, - device:str = 'cpu'): + +def predict_risk(model: np.ndarray, x_input: np.ndarray, device: str = "cpu"): """ Predicts relative risk scores for each competing risk. @@ -135,27 +144,31 @@ def predict_risk(model:np.ndarray, x_input (np.ndarray or torch.Tensor): Input features of shape (n_samples, n_features). device (str): Device to run the computation on. - Returns: + Returns + ------- np.ndarray: Array of shape (n_samples, num_risks) with relative risk scores. """ model.eval() - + if isinstance(x_input, np.ndarray): x_tensor = torch.from_numpy(x_input).float().to(device) else: x_tensor = x_input.to(device).float() - + with torch.no_grad(): risk_outputs, _ = model(x_tensor) # List of [batch_size, 1] tensors risks = torch.cat(risk_outputs, dim=1) # Shape: [batch_size, num_risks] - return risks.cpu().numpy() + return risks.cpu().numpy() -def predict_absolute_risk(model:torch.Tensor, - x_input:np.ndarray, - baseline_cifs:List[Any], - times:List[Any], - device:str = 'cpu') -> np.ndarray: + +def predict_absolute_risk( + model: torch.Tensor, + x_input: np.ndarray, + baseline_cifs: List[Any], + times: List[Any], + device: str = "cpu", +) -> np.ndarray: """ Predict absolute risk (CIF) for each competing event by given time points. @@ -166,7 +179,8 @@ def predict_absolute_risk(model:torch.Tensor, times (np.ndarray): Time grid used for baseline_cifs. device: CPU or CUDA. - Returns: + Returns + ------- np.ndarray: Shape (n_samples, num_events, n_times) with predicted CIFs. """ rel_risks = predict_risk(model, x_input, device) # shape (n_samples, num_events) @@ -179,5 +193,5 @@ def predict_absolute_risk(model:torch.Tensor, base_cif = np.clip(baseline_cifs[k], 1e-10, 0.9999) # avoid edge cases for i in range(n_samples): abs_risks[i, k, :] = 1 - np.power(1 - base_cif, np.exp(rel_risks[i, k])) - - return abs_risks \ No newline at end of file + + return abs_risks From 2297a4a8df2c06ffb0ada19a67b7cf81bf74beaf Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Fri, 6 Feb 2026 16:17:13 -0500 Subject: [PATCH 03/47] formatting changes for utility files --- crisp_nam/utils/loss.py | 13 ++++-- crisp_nam/utils/plotting.py | 12 ++++-- crisp_nam/utils/risk_cif.py | 81 +++++++------------------------------ 3 files changed, 32 insertions(+), 74 deletions(-) diff --git a/crisp_nam/utils/loss.py b/crisp_nam/utils/loss.py index 3847c50c..bf45da0b 100644 --- a/crisp_nam/utils/loss.py +++ b/crisp_nam/utils/loss.py @@ -1,3 +1,9 @@ +"""Loss functions for competing risks. + +This module implements weighted and un-weighted +negative log-likelihood loss, L2 penalty loss functions. +""" + import torch @@ -11,14 +17,15 @@ def weighted_negative_log_likelihood_loss( eps=1e-8, ) -> float: """ - Computes the weighted negative log-likelihood loss for competing risks Cox model. + Compute the weighted negative log-likelihood loss for competing risks Cox model. Args: risk_scores: List of tensors with shape (batch_size, 1) for each competing risk times: Event/censoring times (batch_size,) events: Event indicators (0=censored, 1...K=event types) (batch_size,) num_competing_risks: Number of competing risks - event_weights: Tensor of weights for each competing risk type (size: num_competing_risks) + event_weights: Tensor of weights for each competing risk type + (size: num_competing_risks) sample_weights: Tensor of weights for each sample (size: batch_size) eps: Small constant for numerical stability @@ -83,7 +90,7 @@ def negative_log_likelihood_loss( risk_scores, times, events, num_competing_risks, eps=1e-8 ): """ - Computes the negative log-likelihood loss for competing risks Cox model. + Compute the negative log-likelihood loss for competing risks Cox model. Args: risk_scores: List of tensors with shape (batch_size, 1) for each competing risk diff --git a/crisp_nam/utils/plotting.py b/crisp_nam/utils/plotting.py index 43c1b89d..adbf5897 100644 --- a/crisp_nam/utils/plotting.py +++ b/crisp_nam/utils/plotting.py @@ -1,3 +1,8 @@ +"""Utility functions for plotting. + +This module provides functions to visualize feature importance and shape functions for crisp_nam models in a way that is compatible with both CPU and CUDA devices. +""" + from typing import List, Union import matplotlib.pyplot as plt @@ -22,6 +27,7 @@ def plot_feature_importance( Plot feature importance with both top positive and negative influences, handling both CPU and CUDA devices automatically. """ + # determine model device device = next(model.parameters()).device model.eval() @@ -107,15 +113,13 @@ def plot_coxnam_shape_functions( Plot shape functions for each feature in a CoxNAM model, automatically handling CPU vs CUDA inputs. """ + device = next(model.parameters()).device model.eval() risk_idx = risk_to_plot - 1 # ensure X is a numpy array - if isinstance(X, torch.Tensor): - X_np = X.cpu().numpy() - else: - X_np = np.array(X, dtype=float) + X_np = X.cpu().numpy() if isinstance(X, torch.Tensor) else np.array(X, dtype=float) # derive feature list num_features = model.num_features diff --git a/crisp_nam/utils/risk_cif.py b/crisp_nam/utils/risk_cif.py index 09f43a61..5cdd05eb 100644 --- a/crisp_nam/utils/risk_cif.py +++ b/crisp_nam/utils/risk_cif.py @@ -1,3 +1,8 @@ +"""Risk functions for evaluation. + +This module provides functions to compute cumulative incidence functions (CIFs) and risk scores for competing risk models. +""" + from typing import Any, List import numpy as np @@ -8,7 +13,7 @@ def compute_baseline_cif( times: np.ndarray, events: np.ndarray, eval_times: List[Any], event_type: np.ndarray ) -> np.ndarray: """ - Compute baseline cumulative incidence function for a specific event type + Compute baseline cumulative incidence function for a specific event type. Args: times: Numpy array of event times @@ -41,65 +46,6 @@ def compute_baseline_cif( return baseline_cif - -def predict_absolute_risk( - model: torch.nn.Module, - x: np.ndarray, - baseline_cifs: np.ndarray, - eval_times: np.ndarray, - device: str = "cpu", -) -> np.ndarray: - """ - Predict absolute risk (cumulative incidence) at specified times - - Args: - model: Trained CoxNAM model - x: Feature matrix - baseline_cifs: Dictionary of baseline CIFs for each event type - eval_times: Times at which to evaluate the CIF - device: Device to run computations on - - Returns - ------- - Numpy array of predicted absolute risks with shape (n_samples, n_risks, n_times) - """ - model.eval() - - # Convert to tensor if needed - if not isinstance(x, torch.Tensor): - x = torch.FloatTensor(x).to(device) - - with torch.no_grad(): - # Get risk scores - risk_scores, _ = model(x) - - # Convert to hazard ratios - hazard_ratios = [torch.exp(score).cpu().numpy() for score in risk_scores] - - # Initialize prediction array - n_samples = x.shape[0] - n_risks = model.num_competing_risks - n_times = len(eval_times) - abs_risks = np.zeros((n_samples, n_risks, n_times)) - - # Compute absolute risk for each sample, risk, and time - for k in range(n_risks): - # Skip if baseline CIF is not available - if k + 1 not in baseline_cifs: - continue - - baseline_cif = baseline_cifs[k + 1] - - for i in range(n_samples): - for j, t in enumerate(eval_times): - # Simple Fine-Gray model for cumulative incidence - abs_risks[i, k, j] = 1 - np.exp( - -baseline_cif[j] * hazard_ratios[k][i] - ) - - return abs_risks - - def predict_cif( model: torch.nn.Module, x: np.ndarray, @@ -113,7 +59,8 @@ def predict_cif( Args: model: Trained model. x: Input tensor of shape (n_samples, n_features). - baseline_cif: Array of shape (len(times),) — estimated CIF for baseline (e.g. from compute_baseline_cif). + baseline_cif: Array of shape (len(times),) — + estimated CIF for baseline (e.g. from compute_baseline_cif). times: Time points at which CIF is evaluated. event_type: Integer, 0-based index of event of interest. @@ -129,10 +76,8 @@ def predict_cif( baseline_cif = np.asarray(baseline_cif).reshape(1, -1) # (1, T) risk_scores = np.exp(f_j_x).reshape(-1, 1) # (N, 1) - # Fine-Gray style CIF prediction under PH assumption - cif_pred = 1.0 - np.power(1.0 - baseline_cif, risk_scores) # shape (N, T) - - return cif_pred + # Return Fine-Gray style CIF prediction under PH assumption + return 1.0 - np.power(1.0 - baseline_cif, risk_scores) # shape (N, T) def predict_risk(model: np.ndarray, x_input: np.ndarray, device: str = "cpu"): @@ -141,7 +86,8 @@ def predict_risk(model: np.ndarray, x_input: np.ndarray, device: str = "cpu"): Args: model : Trained model. - x_input (np.ndarray or torch.Tensor): Input features of shape (n_samples, n_features). + x_input (np.ndarray or torch.Tensor): Input features of + shape (n_samples, n_features). device (str): Device to run the computation on. Returns @@ -175,7 +121,8 @@ def predict_absolute_risk( Args: model: Trained model. x_input (np.ndarray or Tensor): Input features, shape (n_samples, n_features). - baseline_cifs (dict): Mapping of event index to baseline CIF array of shape (n_times,). + baseline_cifs (dict): Mapping of event index to baseline CIF + array of shape (n_times,). times (np.ndarray): Time grid used for baseline_cifs. device: CPU or CUDA. From 5c1e97b6891700da7dd96031894a116eec53adf8 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Fri, 6 Feb 2026 16:26:16 -0500 Subject: [PATCH 04/47] Fix ruff errors in utils package --- crisp_nam/utils/__init__.py | 2 ++ crisp_nam/utils/loss.py | 2 +- crisp_nam/utils/plotting.py | 3 +-- crisp_nam/utils/risk_cif.py | 4 +++- 4 files changed, 7 insertions(+), 4 deletions(-) diff --git a/crisp_nam/utils/__init__.py b/crisp_nam/utils/__init__.py index c9eb8c32..42bd9dea 100644 --- a/crisp_nam/utils/__init__.py +++ b/crisp_nam/utils/__init__.py @@ -1,3 +1,5 @@ +"""Utility functions for crisp-nam package.""" + from .loss import * from .plotting import * from .risk_cif import * diff --git a/crisp_nam/utils/loss.py b/crisp_nam/utils/loss.py index bf45da0b..778e570b 100644 --- a/crisp_nam/utils/loss.py +++ b/crisp_nam/utils/loss.py @@ -145,7 +145,7 @@ def negative_log_likelihood_loss( def compute_l2_penalty(model, include_bias=False) -> int: """ - Compute L2 regularization penalty on model parameters + Compute L2 regularization penalty on model parameters. Args: model: Neural network model diff --git a/crisp_nam/utils/plotting.py b/crisp_nam/utils/plotting.py index adbf5897..eaa8f238 100644 --- a/crisp_nam/utils/plotting.py +++ b/crisp_nam/utils/plotting.py @@ -1,6 +1,6 @@ """Utility functions for plotting. -This module provides functions to visualize feature importance and shape functions for crisp_nam models in a way that is compatible with both CPU and CUDA devices. +This module provides functions to visualize feature importance and shape functions for both crisp-nam and deephit models. """ from typing import List, Union @@ -27,7 +27,6 @@ def plot_feature_importance( Plot feature importance with both top positive and negative influences, handling both CPU and CUDA devices automatically. """ - # determine model device device = next(model.parameters()).device model.eval() diff --git a/crisp_nam/utils/risk_cif.py b/crisp_nam/utils/risk_cif.py index 5cdd05eb..7055b221 100644 --- a/crisp_nam/utils/risk_cif.py +++ b/crisp_nam/utils/risk_cif.py @@ -1,6 +1,7 @@ """Risk functions for evaluation. -This module provides functions to compute cumulative incidence functions (CIFs) and risk scores for competing risk models. +This module provides functions to compute cumulative incidence functions (CIFs) +and risk scores for competing risk models. """ from typing import Any, List @@ -46,6 +47,7 @@ def compute_baseline_cif( return baseline_cif + def predict_cif( model: torch.nn.Module, x: np.ndarray, From 51465807f418c9653a25a21719afd9fa2ce39038 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Fri, 6 Feb 2026 16:28:50 -0500 Subject: [PATCH 05/47] Fix docstring errors --- crisp_nam/utils/plotting.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/crisp_nam/utils/plotting.py b/crisp_nam/utils/plotting.py index eaa8f238..662b8e72 100644 --- a/crisp_nam/utils/plotting.py +++ b/crisp_nam/utils/plotting.py @@ -1,6 +1,7 @@ """Utility functions for plotting. -This module provides functions to visualize feature importance and shape functions for both crisp-nam and deephit models. +This module provides functions to visualize feature importance +and shape functions for both crisp-nam and deephit models. """ from typing import List, Union @@ -27,6 +28,7 @@ def plot_feature_importance( Plot feature importance with both top positive and negative influences, handling both CPU and CUDA devices automatically. """ + # determine model device device = next(model.parameters()).device model.eval() From ad859482a0da91cb88a5b2f03725c6a29615b58d Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Fri, 6 Feb 2026 23:22:49 -0500 Subject: [PATCH 06/47] Fix mypy and ruff errors in models folder --- crisp_nam/models/__init__.py | 9 +- crisp_nam/models/crisp_nam_model.py | 346 ++++++++++++---------------- crisp_nam/models/deephit_model.py | 240 ++++++++++++++----- 3 files changed, 334 insertions(+), 261 deletions(-) diff --git a/crisp_nam/models/__init__.py b/crisp_nam/models/__init__.py index cc2ca5d4..782ff5c3 100644 --- a/crisp_nam/models/__init__.py +++ b/crisp_nam/models/__init__.py @@ -1,2 +1,7 @@ -from .crisp_nam_model import * -from .deephit_model import * +"""Models available in the crisp_nam package.""" + +from .crisp_nam_model import CrispNamModel +from .deephit_model import DeepHitModel + + +__all__ = ["CrispNamModel", "DeepHitModel"] \ No newline at end of file diff --git a/crisp_nam/models/crisp_nam_model.py b/crisp_nam/models/crisp_nam_model.py index aa8ca584..193217ce 100644 --- a/crisp_nam/models/crisp_nam_model.py +++ b/crisp_nam/models/crisp_nam_model.py @@ -1,3 +1,15 @@ +"""PyTorch implementation of CrispNamModel for competing risks +survival analysis with L2 normalized projection weights. +""" + +from typing import ( + Any, + List, + Optional, + Sequence, + Tuple, +) + import numpy as np import torch import torch.nn.functional as F @@ -5,21 +17,21 @@ class FeatureNet(nn.Module): - """ - Neural network to model the effect of a single feature on hazard. + """Neural network to model the effect of a single feature on hazard. This is the building block for NAM with optional batch normalization. """ def __init__( self, - hidden_sizes=[64, 64], - dropout_rate=0.1, - feature_dropout=0.0, - batch_norm=False, - ): + hidden_sizes: Sequence[int] = (64, 64), + dropout_rate: float = 0.1, + feature_dropout: float = 0.0, + batch_norm: bool = False, + ) -> None: + """Initialize the FeatureNet.""" super(FeatureNet, self).__init__() self.batch_norm = batch_norm - layers = [] + layers: List[nn.Module] = [] # Input layer layers.append(nn.Linear(1, hidden_sizes[0])) @@ -45,7 +57,17 @@ def __init__( self.network = nn.Sequential(*layers) self.feature_dropout = feature_dropout - def forward(self, x): + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass through the network. + Args: + x: Input tensor of shape + + Returns + ------- + nn.Module + """ + + # ensure float32 x = x.to(dtype=torch.float32) # Apply feature dropout during training if specified if self.training and self.feature_dropout > 0: @@ -58,11 +80,18 @@ def forward(self, x): return self.network(x) - def _forward_singleton(self, x): - """ - Handle the case of a single sample (batch_size=1) - where BatchNorm1d would fail + def _forward_singleton(self, x: torch.Tensor) -> torch.Tensor: + """Handle the case of a single sample (batch_size=1) + where BatchNorm1d would fail. + + Args: + x: Input tensor of shape (1, num_features) + + Returns + ------- + nn.Module """ + was_training = self.training self.eval() with torch.no_grad(): @@ -73,11 +102,12 @@ def _forward_singleton(self, x): class L2NormalizedLinear(nn.Module): - """ - Linear layer with L2 normalized weights (unit norm constraint) - """ + """Linear layer with L2 normalized weights (unit norm constraint).""" - def __init__(self, in_features, out_features, bias=False, eps=1e-8): + def __init__( + self, in_features: int, out_features: int, bias: bool = False, eps: float = 1e-8 + ) -> None: + """Initialize the L2NormalizedLinear layer.""" super(L2NormalizedLinear, self).__init__() self.in_features = in_features self.out_features = out_features @@ -88,35 +118,45 @@ def __init__(self, in_features, out_features, bias=False, eps=1e-8): else: self.register_parameter("bias", None) - def forward(self, x): + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Apply the linear transformation with L2 normalized weights. + + Args: + x: Input tensor of shape (batch_size, in_features) + + Returns + ------- + Output tensor + """ + # L2 normalize weights to unit norm normalized_weight = F.normalize(self.weight, p=2, dim=1, eps=self.eps) return F.linear(x, normalized_weight, self.bias) - def get_normalized_weights(self): - """Return the L2 normalized weights (useful for inspection)""" + def get_normalized_weights(self) -> torch.Tensor: + """Return the L2 normalized weights (useful for inspection).""" with torch.no_grad(): return F.normalize(self.weight, p=2, dim=1, eps=self.eps) class CrispNamModel(nn.Module): - """ - Competing risks CoxNAM with L2 normalized projection weights. + """Competing risks CoxNAM with L2 normalized projection weights. Each feature contributes to each risk through a separate shape function. All projection weights are constrained to unit L2 norm. """ def __init__( self, - num_features, - num_competing_risks, - hidden_sizes=[64, 64], - dropout_rate=0.1, - feature_dropout=0.1, - batch_norm=False, - normalize_projections=True, - eps=1e-8, + num_features: int, + num_competing_risks: int, + hidden_sizes: Sequence[int] = (64, 64), + dropout_rate: float = 0.1, + feature_dropout: float = 0.1, + batch_norm: bool = False, + normalize_projections: bool = True, + eps: float = 1e-8, ): + """Initialize the CrispNamModel.""" super(CrispNamModel, self).__init__() self.num_features = num_features self.num_competing_risks = num_competing_risks @@ -160,9 +200,10 @@ def __init__( ] ) - def forward(self, x): - """ - Forward pass to compute risk scores for all competing risks + def forward(self, + x: torch.Tensor + ) -> Tuple[List[torch.Tensor], List[torch.Tensor]]: + """Forward pass to compute risk scores for all competing risks. Args: x: Tensor of shape (batch_size, num_features) @@ -172,6 +213,7 @@ def forward(self, x): risk_scores: List of (batch_size, 1) Tensors feature_outputs: List of (batch_size, hidden) Tensors """ + # ensure float32 x = x.to(dtype=torch.float32) batch_size, _ = x.shape @@ -196,7 +238,8 @@ def forward(self, x): # project into each risk channel with L2 normalized weights for risk_idx, proj in enumerate(self.risk_projections[feat_idx]): - # proj automatically applies L2 normalization if normalize_projections=True + # proj automatically applies L2 normalization + # if normalize_projections=True combined[:, risk_idx] += proj(repr).view(-1) # split back into list of [batch,1] @@ -206,20 +249,29 @@ def forward(self, x): return risk_scores, feature_outputs - def get_shape_functions(self, x_values, feature_idx, risk_idx=None, normalize=True): + def get_shape_functions( + self, + x_values: Optional[torch.Tensor | np.ndarray], + feature_idx: int, + risk_idx: Optional[int | None] = None, + normalize: bool = True, + ) -> dict: """ - Extract shape functions for a specific feature across all risks or a specific risk + Extract shape functions for a specific feature across all + risks or a specific risk. Args: x_values: Feature values to evaluate (numpy array or tensor) feature_idx: Index of the feature to get shape functions for - risk_idx: Optional; if provided, only returns the shape function for this risk + risk_idx: Optional; if provided, only returns the + shape function for this risk normalize: Whether to center the shape functions Returns ------- Dictionary mapping risk names to shape function values """ + self.eval() if not isinstance(x_values, torch.Tensor): @@ -254,14 +306,15 @@ def get_shape_functions(self, x_values, feature_idx, risk_idx=None, normalize=Tr return shape_funcs - def get_projection_norms(self): + def get_projection_norms(self) -> dict: """ - Get the L2 norms of all projection weights (should be ~1.0 if normalized) + Get the L2 norms of all projection weights (should be ~1.0 if normalized). Returns ------- Dictionary of weight norms by feature and risk """ + norms = {} for feat_idx in range(self.num_features): @@ -274,14 +327,14 @@ def get_projection_norms(self): return norms - def get_normalized_projection_weights(self): - """ - Get the actual L2 normalized weights used in computation + def get_normalized_projection_weights(self) -> dict: + """Get the actual L2 normalized weights used in computation. Returns ------- Dictionary of normalized weights """ + normalized_weights = {} for feat_idx in range(self.num_features): @@ -303,19 +356,25 @@ def get_normalized_projection_weights(self): return normalized_weights - def calculate_feature_importance(self, x_data, feature_idx=None): - """ - Calculate feature importance based on the magnitude of risk-specific projection outputs - With L2 normalized weights, this gives a fair comparison across features + def calculate_feature_importance( + self, + x_data: Optional[torch.Tensor | np.ndarray], + feature_idx: Optional[int | None] = None, + ) -> dict: + """Calculate feature importance based on the magnitude of + risk-specific projection outputs. With L2 normalized weights, + this gives a fair comparison across features. Args: x_data: Input data tensor or numpy array - feature_idx: Optional; if provided, only calculate importance for this feature + feature_idx: Optional; if provided, only calculate + importance for this feature Returns ------- Dictionary of feature importances by risk type """ + self.eval() device = next(self.parameters()).device @@ -327,7 +386,9 @@ def calculate_feature_importance(self, x_data, feature_idx=None): feature_indices = ( [feature_idx] if feature_idx is not None else range(self.num_features) ) - importance = {f"risk_{j + 1}": {} for j in range(self.num_competing_risks)} + importance: dict = { + f"risk_{j + 1}": {} for j in range(self.num_competing_risks) + } for i in feature_indices: # Get feature values @@ -338,7 +399,7 @@ def calculate_feature_importance(self, x_data, feature_idx=None): feature_repr = self.feature_nets[i](feature_values) # Calculate importance for each risk (mean absolute value) - # With L2 normalized weights, this is directly comparable across features + # With L2 normalized weights, this is comparable across features for j in range(self.num_competing_risks): risk_specific_output = self.risk_projections[i][j](feature_repr) abs_values = torch.abs(risk_specific_output).cpu().numpy() @@ -348,9 +409,12 @@ def calculate_feature_importance(self, x_data, feature_idx=None): return importance - def predict_risk(self, x, baseline_hazards=None): - """ - Predict survival probability or cumulative incidence + def predict_risk( + self, + x: Optional[torch.Tensor | np.ndarray], + baseline_hazards: Optional[dict | None] = None, + ) -> dict: + """Predict survival probability or cumulative incidence. Args: x: Input tensor of shape (batch_size, num_features) @@ -360,6 +424,7 @@ def predict_risk(self, x, baseline_hazards=None): ------- Dictionary of predictions for each competing risk """ + self.eval() # Convert to tensor if needed @@ -407,163 +472,34 @@ def predict_risk(self, x, baseline_hazards=None): for j in range(self.num_competing_risks) } + # Utility functions for model analysis + def analyze_projection_weights(self) -> dict: + """Analyze the L2 norms and statistics of projection weights. -# Alternative implementation using manual normalization in forward pass -class CrispNamModelManualL2(nn.Module): - """ - Alternative implementation with manual L2 normalization in forward pass - """ - - def __init__( - self, - num_features, - num_competing_risks, - hidden_sizes=[64, 64], - dropout_rate=0.1, - feature_dropout=0.1, - batch_norm=False, - eps=1e-8, - ): - super(CrispNamModelManualL2, self).__init__() - self.num_features = num_features - self.num_competing_risks = num_competing_risks - self.batch_norm = batch_norm - self.feature_dropout = feature_dropout - self.eps = eps - - # Create a FeatureNet for each input feature - self.feature_nets = nn.ModuleList( - [ - FeatureNet(hidden_sizes, dropout_rate, feature_dropout, batch_norm) - for _ in range(num_features) - ] - ) - - # Standard linear layers - weights will be L2 normalized in forward pass - self.risk_projections = nn.ModuleList( - [ - nn.ModuleList( - [ - nn.Linear(hidden_sizes[-1], 1, bias=False) - for _ in range(num_competing_risks) - ] - ) - for _ in range(num_features) - ] - ) - - def forward(self, x): - """Forward pass with manual L2 weight normalization""" - x = x.to(dtype=torch.float32) - batch_size, _ = x.shape - device = x.device - - if self.training and self.feature_dropout > 0: - mask = torch.empty_like(x).bernoulli_(1.0 - self.feature_dropout) - x = x * mask - - combined = torch.zeros(batch_size, self.num_competing_risks, device=device) - feature_outputs = [] - - for feat_idx, fnet in enumerate(self.feature_nets): - col = x[:, feat_idx].unsqueeze(1) - repr = fnet(col) - feature_outputs.append(repr) - - for risk_idx, proj in enumerate(self.risk_projections[feat_idx]): - # L2 normalize weights manually - normalized_weight = F.normalize(proj.weight, p=2, dim=1, eps=self.eps) - # Apply normalized projection - output = F.linear(repr, normalized_weight, proj.bias) - combined[:, risk_idx] += output.view(-1) - - risk_scores = [ - combined[:, r].unsqueeze(1) for r in range(self.num_competing_risks) - ] - - return risk_scores, feature_outputs - - -# Utility functions for model analysis -def analyze_projection_weights(model): - """ - Analyze the L2 norms and statistics of projection weights - """ - print("Projection Weight Analysis:") - print("=" * 50) - - # Get weight norms - norms = model.get_projection_norms() - norm_values = list(norms.values()) - - print("Weight L2 Norms (should be ~1.0):") - print(f" Mean: {np.mean(norm_values):.6f}") - print(f" Std: {np.std(norm_values):.6f}") - print(f" Min: {np.min(norm_values):.6f}") - print(f" Max: {np.max(norm_values):.6f}") - - # Show some individual norms - print("\nSample individual norms:") - for i, (name, norm) in enumerate(list(norms.items())[:6]): - print(f" {name}: {norm:.6f}") - - return norms - - -def compare_feature_importance_fairness(model, x_data): - """ - Compare feature importance when weights are L2 normalized vs not normalized - """ - print("\nFeature Importance Comparison:") - print("=" * 50) - - # Calculate importance with current model (L2 normalized) - importance_normalized = model.calculate_feature_importance(x_data) - - # Create equivalent model without normalization for comparison - model_unnorm = CrispNamModel( - model.num_features, model.num_competing_risks, normalize_projections=False - ) - - # Copy weights from normalized model - with torch.no_grad(): - for i in range(model.num_features): - for j in range(model.num_competing_risks): - model_unnorm.risk_projections[i][j].weight.copy_( - model.risk_projections[i][j].weight - ) - - importance_unnorm = model_unnorm.calculate_feature_importance(x_data) - - print("Importance comparison (Risk 1):") - for feat in range(min(5, model.num_features)): # Show first 5 features - norm_imp = importance_normalized["risk_1"].get(f"feature_{feat}", 0) - unnorm_imp = importance_unnorm["risk_1"].get(f"feature_{feat}", 0) - print( - f" Feature {feat}: Normalized={norm_imp:.4f}, Unnormalized={unnorm_imp:.4f}" - ) + Args: + None + Returns + ------- + None + """ -# Example usage and testing -if __name__ == "__main__": - # Create model with L2 normalized projections - model = CrispNamModel( - num_features=5, - num_competing_risks=3, - hidden_sizes=[32, 32], - normalize_projections=True, - ) + print("Projection Weight Analysis:") + print("=" * 50) - # Generate some test data - torch.manual_seed(42) - test_data = torch.randn(100, 5) + # Get weight norms + norms = self.get_projection_norms() + norm_values = list(norms.values()) - # Test forward pass - risk_scores, feature_outputs = model(test_data) - print(f"Risk scores shapes: {[score.shape for score in risk_scores]}") + print("Weight L2 Norms (should be ~1.0):") + print(f" Mean: {np.mean(norm_values):.6f}") + print(f" Std: {np.std(norm_values):.6f}") + print(f" Min: {np.min(norm_values):.6f}") + print(f" Max: {np.max(norm_values):.6f}") - # Analyze projection weights - analyze_projection_weights(model) + # Show some individual norms + print("\nSample individual norms:") + for _i, (name, norm) in enumerate(list(norms.items())[:6]): + print(f" {name}: {norm:.6f}") - # Compare feature importance - compare_feature_importance_fairness(model, test_data) + return norms diff --git a/crisp_nam/models/deephit_model.py b/crisp_nam/models/deephit_model.py index 2bb055fd..dda5befb 100644 --- a/crisp_nam/models/deephit_model.py +++ b/crisp_nam/models/deephit_model.py @@ -1,32 +1,46 @@ +"""PyTorch implementation of DeepHit for competing risks survival analysis.""" + +from typing import Callable, Optional + import torch -import torch.nn.functional as F +import numpy as np from torch import nn - +import torch.nn.functional as F class FCLayer(nn.Module): """Fully connected layer with optional batch norm, dropout, and activation.""" def __init__( self, - in_dim, - out_dim, - activation=nn.ReLU(), - batch_norm=False, - dropout_rate=0.0, - init_fn=nn.init.xavier_normal_, - ): + in_dim: int, + out_dim: int, + activation: Optional[torch.Tensor] = None, + batch_norm: bool =False, + dropout_rate:float =0.0, + init_fn: Optional[Callable[[torch.Tensor]]|None] = nn.init.xavier_normal_, + ) -> None: + """Initialize the fully connected layer.""" super(FCLayer, self).__init__() self.fc = nn.Linear(in_dim, out_dim) self.batch_norm = nn.BatchNorm1d(out_dim) if batch_norm else None self.dropout = nn.Dropout(dropout_rate) if dropout_rate > 0 else None - self.activation = activation + self.activation = activation if activation else nn.ReLU() # Initialize weights if init_fn: init_fn(self.fc.weight) nn.init.zeros_(self.fc.bias) - def forward(self, x): + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass through the layer. + + Args: + x: Tensor of shape (batch_size, in_dim) + Returns + ------- + out: Tensor of shape (batch_size, out_dim) + """ + x = self.fc(x) if self.batch_norm: x = self.batch_norm(x) @@ -42,20 +56,22 @@ class FCNet(nn.Module): def __init__( self, - in_dim, - num_layers, - h_dim, - activation=nn.ReLU(), - out_dim=None, - out_activation=None, - batch_norm=False, - dropout_rate=0.0, - init_fn=nn.init.xavier_normal_, - ): + in_dim: int, + num_layers: int, + h_dim: int, + activation: Optional[nn.module]=None, + out_dim: Optional[int|None] = None, + out_activation: Optional[int|None] = None, + batch_norm: bool = False, + dropout_rate: float = 0.0, + init_fn: Optional[Callable[[torch.Tensor]]|None] = nn.init.xavier_normal_, + ) -> None: + """Initialize the fully connected network.""" super(FCNet, self).__init__() layers = [] prev_dim = in_dim + activation = activation if activation else nn.ReLU() # Hidden layers for i in range(num_layers): @@ -80,14 +96,28 @@ def __init__( self.network = nn.Sequential(*layers) - def forward(self, x): + def forward(self, + x:torch.Tensor) -> Optional[nn.Module]: + """Forward pass through the network. + + Args: + x: Tensor of shape (batch_size, in_dim) + + Returns: + ------- + out: Tensor of shape (batch_size, out_dim) + """ + return self.network(x) class DeepHit(nn.Module): """PyTorch implementation of DeepHit for competing risks survival analysis.""" - def __init__(self, input_dims, network_settings): + def __init__(self, + input_dims: dict, + network_settings: dict): + """Initialize the DeepHit model.""" super(DeepHit, self).__init__() # Input dimensions @@ -119,6 +149,16 @@ def __init__(self, input_dims, network_settings): self._build_network() def _build_network(self): + """Build the shared and cause-specific networks. + + Args: + None + + Returns: + ------- + None + """ + # Shared network self.shared_net = FCNet( in_dim=self.x_dim, @@ -148,7 +188,19 @@ def _build_network(self): self.num_Event * self.h_dim_CS, self.num_Event * self.num_Category ) - def forward(self, x): + def forward(self, + x:torch.Tensor) -> tuple[torch.Tensor, None]: + """Forward pass through the network. + + Args: + x: Tensor of shape (batch_size, num_Event, num_Category) + + Returns + ------- + risk_scores: List of (batch_size, 1) Tensors + feature_outputs: None + """ + # Shared network shared_out = self.shared_net(x) @@ -178,13 +230,30 @@ def forward(self, x): # Reshape to [batch_size, num_Event, num_Category] out = out.view(-1, self.num_Event, self.num_Category) - # For compatibility with the training script, return both raw risks and shape functions + # For compatibility with the training script, return both + # raw risks and shape functions # In this model, we don't have separate shape functions, so just return None return out, None - def log_likelihood_loss(self, out, t, k, mask1, mask2): - """Log-likelihood loss (including log-likelihood of censored subjects)""" - batch_size = out.size(0) + def log_likelihood_loss(self, + out: torch.Tensor, + t: Optional[torch.Tensor|np.ndarray], + k: Optional[torch.Tensor|np.ndarray], + mask1: torch.Tensor, + mask2: torch.Tensor): + """Log-likelihood loss (including log-likelihood of censored subjects). + + Args: + out: Torch.tensor + t: Torch.tensor or numpy array + k: Torch.tensor or numpy array + mask1: Torch.tensor + mask2: Torch.tensor + + Returns + ------- + loss: Torch.tensor + """ # Convert to PyTorch tensors if necessary if not isinstance(k, torch.Tensor): @@ -193,23 +262,38 @@ def log_likelihood_loss(self, out, t, k, mask1, mask2): t = torch.tensor(t, device=out.device) # Indicator for uncensored subjects - I_1 = (k > 0).float().view(-1, 1) + i_1 = (k > 0).float().view(-1, 1) # For uncensored: log P(T=t, K=k|x) tmp1 = torch.sum(torch.sum(mask1 * out, dim=2), dim=1, keepdim=True) - tmp1 = I_1 * torch.log(tmp1 + 1e-8) + tmp1 = i_1 * torch.log(tmp1 + 1e-8) # For censored: log ∑ P(T>t|x) tmp2 = torch.sum( torch.sum(mask2.unsqueeze(1) * out, dim=2), dim=1, keepdim=True ) - tmp2 = (1.0 - I_1) * torch.log(tmp2 + 1e-8) + tmp2 = (1.0 - i_1) * torch.log(tmp2 + 1e-8) return -torch.mean(tmp1 + tmp2) - def ranking_loss(self, out, t, k, mask2): - """Ranking loss (calculated only for acceptable pairs)""" - batch_size = out.size(0) + def ranking_loss(self, + out: torch.Tensor, + t: Optional[torch.Tensor|np.ndarray], + k: Optional[torch.Tensor|np.ndarray], + mask2: torch.Tensor): + """Ranking loss (calculated only for acceptable pairs). + + Args: + out: Torch.tensor + t: Torch.tensor or numpy array + k: Torch.tensor or numpy array + mask2: Torch.tensor + + Returns + ------- + loss: Torch.tensor + """ + sigma1 = 0.1 eta = [] @@ -223,22 +307,22 @@ def ranking_loss(self, out, t, k, mask2): for e in range(self.num_Event): # Indicator for current event type - I_2 = (k == e + 1).float() - I_2_diag = torch.diag(I_2.squeeze()) + i_2 = (k == e + 1).float() + i_2_diag = torch.diag(i_2.squeeze()) # Extract event-specific probabilities tmp_e = out[:, e, :] # [batch_size, num_Category] # Calculate risk scores - R = torch.matmul(tmp_e, mask2.transpose(0, 1)) # [batch_size, batch_size] - diag_R = torch.diag(R).unsqueeze(1) # [batch_size, 1] - R = ( - torch.matmul(one_vector, diag_R.transpose(0, 1)) - R + r = torch.matmul(tmp_e, mask2.transpose(0, 1)) # [batch_size, batch_size] + diag_r = torch.diag(r).unsqueeze(1) # [batch_size, 1] + r = ( + torch.matmul(one_vector, diag_r.transpose(0, 1)) - r ) # [batch_size, batch_size] - R = R.transpose(0, 1) # Now R_ij = r_i(T_i) - r_j(T_i) + r = r.transpose(0, 1) # Now R_ij = r_i(T_i) - r_j(T_i) # Time comparison matrix - T = F.relu( + t = F.relu( torch.sign( torch.matmul(one_vector, t.transpose(0, 1)) - torch.matmul(t, one_vector.transpose(0, 1)) @@ -246,10 +330,10 @@ def ranking_loss(self, out, t, k, mask2): ) # Filter by event occurrence - T = torch.matmul(I_2_diag, T) + t = torch.matmul(i_2_diag, t) # Calculate ranking loss for current event - tmp_eta = torch.mean(T * torch.exp(-R / sigma1), dim=1, keepdim=True) + tmp_eta = torch.mean(t * torch.exp(-r / sigma1), dim=1, keepdim=True) eta.append(tmp_eta) eta = torch.stack(eta, dim=1) # [batch_size, num_Event] @@ -257,9 +341,24 @@ def ranking_loss(self, out, t, k, mask2): return torch.sum(eta) - def calibration_loss(self, out, t, k, mask2): - """Calibration loss""" - batch_size = out.size(0) + def calibration_loss(self, + out: torch.Tensor, + t: Optional[torch.Tensor|np.ndarray], + k: Optional[torch.Tensor|np.ndarray], + mask2: torch.Tensor) -> torch.Tensor: + """Calibration loss. + + Args: + out: Torch.tensor + t: Torch.tensor or numpy array + k: Torch.tensor or numpy array + mask2: Torch.tensor + + Returns + ------- + loss: Torch.tensor + """ + eta = [] # Convert to PyTorch tensors if necessary @@ -268,14 +367,14 @@ def calibration_loss(self, out, t, k, mask2): for e in range(self.num_Event): # Indicator for current event type - I_2 = (k == e + 1).float() + i_2 = (k == e + 1).float() # Extract event-specific probabilities tmp_e = out[:, e, :] # [batch_size, num_Category] # Calculate calibration loss r = torch.sum(tmp_e * mask2, dim=1) - tmp_eta = torch.mean((r - I_2) ** 2, dim=0, keepdim=True) + tmp_eta = torch.mean((r - i_2) ** 2, dim=0, keepdim=True) eta.append(tmp_eta) eta = torch.stack(eta, dim=1) # [1, num_Event] @@ -283,8 +382,32 @@ def calibration_loss(self, out, t, k, mask2): return torch.sum(eta) - def compute_loss(self, out, t, k, mask1, mask2, alpha=1.0, beta=1.0, gamma=1.0): - """Compute total loss""" + def compute_loss(self, + out: torch.Tensor, + t: Optional[torch.Tensor|np.ndarray], + k: Optional[torch.Tensor|np.ndarray], + mask1: Optional[torch.Tensor|np.ndarray], + mask2: torch.Tensor, + alpha: float = 1.0, + beta:float = 1.0, + gamma: float = 1.0): + """Compute total loss. + + Args: + out: Torch.tensor + t: Torch.tensor or numpy array + k: Torch.tensor or numpy array + mask1: Torch.tensor + mask2: Torch.tensor + alpha: float, weight for log-likelihood loss + beta: float, weight for ranking loss + gamma: float, weight for calibration loss + + Returns + ------- + total_loss: Torch.tensor + """ + loss1 = self.log_likelihood_loss(out, t, k, mask1, mask2) loss2 = self.ranking_loss(out, t, k, mask2) loss3 = self.calibration_loss(out, t, k, mask2) @@ -292,9 +415,18 @@ def compute_loss(self, out, t, k, mask1, mask2, alpha=1.0, beta=1.0, gamma=1.0): # L2 regularization is handled by optimizer (weight_decay) return alpha * loss1 + beta * loss2 + gamma * loss3 - def predict(self, x): - """Predict risk scores for input x""" + def predict(self, + x: torch.Tensor) -> torch.Tensor: + """Predict risk scores for input x. + + Args: + x: Tensor of shape (batch_size, num_Event, num_Category) + Returns + ------- + out: Tensor of shape (batch_size, num_Event, num_Category) + """ + self.eval() with torch.no_grad(): out, _ = self.forward(x) - return out + return out \ No newline at end of file From d5a3b6adf26a1e0b0bac7c01d88549aa13ce55de Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Fri, 6 Feb 2026 23:25:20 -0500 Subject: [PATCH 07/47] Fix ruff and mypy errors --- crisp_nam/models/crisp_nam_model.py | 21 +++---- crisp_nam/models/deephit_model.py | 93 ++++++++++++++--------------- crisp_nam/utils/plotting.py | 1 - 3 files changed, 52 insertions(+), 63 deletions(-) diff --git a/crisp_nam/models/crisp_nam_model.py b/crisp_nam/models/crisp_nam_model.py index 193217ce..59727aed 100644 --- a/crisp_nam/models/crisp_nam_model.py +++ b/crisp_nam/models/crisp_nam_model.py @@ -3,7 +3,6 @@ """ from typing import ( - Any, List, Optional, Sequence, @@ -64,9 +63,8 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: Returns ------- - nn.Module + torch.Tensor """ - # ensure float32 x = x.to(dtype=torch.float32) # Apply feature dropout during training if specified @@ -89,9 +87,8 @@ def _forward_singleton(self, x: torch.Tensor) -> torch.Tensor: Returns ------- - nn.Module + torch.Tensor """ - was_training = self.training self.eval() with torch.no_grad(): @@ -105,7 +102,11 @@ class L2NormalizedLinear(nn.Module): """Linear layer with L2 normalized weights (unit norm constraint).""" def __init__( - self, in_features: int, out_features: int, bias: bool = False, eps: float = 1e-8 + self, + in_features: int, + out_features: int, + bias: bool = False, + eps: float = 1e-8 ) -> None: """Initialize the L2NormalizedLinear layer.""" super(L2NormalizedLinear, self).__init__() @@ -128,7 +129,6 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: ------- Output tensor """ - # L2 normalize weights to unit norm normalized_weight = F.normalize(self.weight, p=2, dim=1, eps=self.eps) return F.linear(x, normalized_weight, self.bias) @@ -213,7 +213,6 @@ def forward(self, risk_scores: List of (batch_size, 1) Tensors feature_outputs: List of (batch_size, hidden) Tensors """ - # ensure float32 x = x.to(dtype=torch.float32) batch_size, _ = x.shape @@ -271,7 +270,6 @@ def get_shape_functions( ------- Dictionary mapping risk names to shape function values """ - self.eval() if not isinstance(x_values, torch.Tensor): @@ -314,7 +312,6 @@ def get_projection_norms(self) -> dict: ------- Dictionary of weight norms by feature and risk """ - norms = {} for feat_idx in range(self.num_features): @@ -334,7 +331,6 @@ def get_normalized_projection_weights(self) -> dict: ------- Dictionary of normalized weights """ - normalized_weights = {} for feat_idx in range(self.num_features): @@ -374,7 +370,6 @@ def calculate_feature_importance( ------- Dictionary of feature importances by risk type """ - self.eval() device = next(self.parameters()).device @@ -424,7 +419,6 @@ def predict_risk( ------- Dictionary of predictions for each competing risk """ - self.eval() # Convert to tensor if needed @@ -483,7 +477,6 @@ def analyze_projection_weights(self) -> dict: ------- None """ - print("Projection Weight Analysis:") print("=" * 50) diff --git a/crisp_nam/models/deephit_model.py b/crisp_nam/models/deephit_model.py index dda5befb..662c4019 100644 --- a/crisp_nam/models/deephit_model.py +++ b/crisp_nam/models/deephit_model.py @@ -2,10 +2,11 @@ from typing import Callable, Optional -import torch import numpy as np -from torch import nn +import torch import torch.nn.functional as F +from torch import nn + class FCLayer(nn.Module): """Fully connected layer with optional batch norm, dropout, and activation.""" @@ -15,9 +16,9 @@ def __init__( in_dim: int, out_dim: int, activation: Optional[torch.Tensor] = None, - batch_norm: bool =False, - dropout_rate:float =0.0, - init_fn: Optional[Callable[[torch.Tensor]]|None] = nn.init.xavier_normal_, + batch_norm: bool = False, + dropout_rate: float = 0.0, + init_fn: Optional[Callable[[torch.Tensor]] | None] = nn.init.xavier_normal_, ) -> None: """Initialize the fully connected layer.""" super(FCLayer, self).__init__() @@ -36,11 +37,11 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: Args: x: Tensor of shape (batch_size, in_dim) + Returns ------- out: Tensor of shape (batch_size, out_dim) """ - x = self.fc(x) if self.batch_norm: x = self.batch_norm(x) @@ -59,12 +60,12 @@ def __init__( in_dim: int, num_layers: int, h_dim: int, - activation: Optional[nn.module]=None, - out_dim: Optional[int|None] = None, - out_activation: Optional[int|None] = None, + activation: Optional[nn.module] = None, + out_dim: Optional[int | None] = None, + out_activation: Optional[int | None] = None, batch_norm: bool = False, dropout_rate: float = 0.0, - init_fn: Optional[Callable[[torch.Tensor]]|None] = nn.init.xavier_normal_, + init_fn: Optional[Callable[[torch.Tensor]] | None] = nn.init.xavier_normal_, ) -> None: """Initialize the fully connected network.""" super(FCNet, self).__init__() @@ -96,27 +97,23 @@ def __init__( self.network = nn.Sequential(*layers) - def forward(self, - x:torch.Tensor) -> Optional[nn.Module]: + def forward(self, x: torch.Tensor) -> Optional[nn.Module]: """Forward pass through the network. Args: x: Tensor of shape (batch_size, in_dim) - Returns: + Returns ------- out: Tensor of shape (batch_size, out_dim) """ - return self.network(x) class DeepHit(nn.Module): """PyTorch implementation of DeepHit for competing risks survival analysis.""" - def __init__(self, - input_dims: dict, - network_settings: dict): + def __init__(self, input_dims: dict, network_settings: dict): """Initialize the DeepHit model.""" super(DeepHit, self).__init__() @@ -154,11 +151,10 @@ def _build_network(self): Args: None - Returns: + Returns ------- None """ - # Shared network self.shared_net = FCNet( in_dim=self.x_dim, @@ -188,8 +184,7 @@ def _build_network(self): self.num_Event * self.h_dim_CS, self.num_Event * self.num_Category ) - def forward(self, - x:torch.Tensor) -> tuple[torch.Tensor, None]: + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, None]: """Forward pass through the network. Args: @@ -200,7 +195,6 @@ def forward(self, risk_scores: List of (batch_size, 1) Tensors feature_outputs: None """ - # Shared network shared_out = self.shared_net(x) @@ -235,12 +229,14 @@ def forward(self, # In this model, we don't have separate shape functions, so just return None return out, None - def log_likelihood_loss(self, + def log_likelihood_loss( + self, out: torch.Tensor, - t: Optional[torch.Tensor|np.ndarray], - k: Optional[torch.Tensor|np.ndarray], + t: Optional[torch.Tensor | np.ndarray], + k: Optional[torch.Tensor | np.ndarray], mask1: torch.Tensor, - mask2: torch.Tensor): + mask2: torch.Tensor, + ): """Log-likelihood loss (including log-likelihood of censored subjects). Args: @@ -254,7 +250,6 @@ def log_likelihood_loss(self, ------- loss: Torch.tensor """ - # Convert to PyTorch tensors if necessary if not isinstance(k, torch.Tensor): k = torch.tensor(k, device=out.device) @@ -276,11 +271,13 @@ def log_likelihood_loss(self, return -torch.mean(tmp1 + tmp2) - def ranking_loss(self, + def ranking_loss( + self, out: torch.Tensor, - t: Optional[torch.Tensor|np.ndarray], - k: Optional[torch.Tensor|np.ndarray], - mask2: torch.Tensor): + t: Optional[torch.Tensor | np.ndarray], + k: Optional[torch.Tensor | np.ndarray], + mask2: torch.Tensor, + ): """Ranking loss (calculated only for acceptable pairs). Args: @@ -293,7 +290,6 @@ def ranking_loss(self, ------- loss: Torch.tensor """ - sigma1 = 0.1 eta = [] @@ -341,11 +337,13 @@ def ranking_loss(self, return torch.sum(eta) - def calibration_loss(self, + def calibration_loss( + self, out: torch.Tensor, - t: Optional[torch.Tensor|np.ndarray], - k: Optional[torch.Tensor|np.ndarray], - mask2: torch.Tensor) -> torch.Tensor: + t: Optional[torch.Tensor | np.ndarray], + k: Optional[torch.Tensor | np.ndarray], + mask2: torch.Tensor, + ) -> torch.Tensor: """Calibration loss. Args: @@ -358,7 +356,6 @@ def calibration_loss(self, ------- loss: Torch.tensor """ - eta = [] # Convert to PyTorch tensors if necessary @@ -382,15 +379,17 @@ def calibration_loss(self, return torch.sum(eta) - def compute_loss(self, + def compute_loss( + self, out: torch.Tensor, - t: Optional[torch.Tensor|np.ndarray], - k: Optional[torch.Tensor|np.ndarray], - mask1: Optional[torch.Tensor|np.ndarray], + t: Optional[torch.Tensor | np.ndarray], + k: Optional[torch.Tensor | np.ndarray], + mask1: Optional[torch.Tensor | np.ndarray], mask2: torch.Tensor, alpha: float = 1.0, - beta:float = 1.0, - gamma: float = 1.0): + beta: float = 1.0, + gamma: float = 1.0, + ): """Compute total loss. Args: @@ -407,7 +406,6 @@ def compute_loss(self, ------- total_loss: Torch.tensor """ - loss1 = self.log_likelihood_loss(out, t, k, mask1, mask2) loss2 = self.ranking_loss(out, t, k, mask2) loss3 = self.calibration_loss(out, t, k, mask2) @@ -415,18 +413,17 @@ def compute_loss(self, # L2 regularization is handled by optimizer (weight_decay) return alpha * loss1 + beta * loss2 + gamma * loss3 - def predict(self, - x: torch.Tensor) -> torch.Tensor: + def predict(self, x: torch.Tensor) -> torch.Tensor: """Predict risk scores for input x. Args: x: Tensor of shape (batch_size, num_Event, num_Category) + Returns ------- out: Tensor of shape (batch_size, num_Event, num_Category) """ - self.eval() with torch.no_grad(): out, _ = self.forward(x) - return out \ No newline at end of file + return out diff --git a/crisp_nam/utils/plotting.py b/crisp_nam/utils/plotting.py index 662b8e72..35376945 100644 --- a/crisp_nam/utils/plotting.py +++ b/crisp_nam/utils/plotting.py @@ -11,7 +11,6 @@ import pandas as pd import torch - def plot_feature_importance( model: torch.nn.Module, x_data: Union[np.ndarray, torch.Tensor], From 60e44798e5f7c675c49cdea36e093dcab8a03693 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Mon, 9 Feb 2026 22:18:44 -0500 Subject: [PATCH 08/47] Fix mypy and ruff errors in models folder --- crisp_nam/models/crisp_nam_model.py | 27 +++++++-------- crisp_nam/models/deephit_model.py | 35 ++++++++++--------- crisp_nam/utils/loss.py | 11 +++--- crisp_nam/utils/plotting.py | 53 +++++++++++++++++++++++------ crisp_nam/utils/risk_cif.py | 13 ++++--- 5 files changed, 90 insertions(+), 49 deletions(-) diff --git a/crisp_nam/models/crisp_nam_model.py b/crisp_nam/models/crisp_nam_model.py index 59727aed..1df91d4e 100644 --- a/crisp_nam/models/crisp_nam_model.py +++ b/crisp_nam/models/crisp_nam_model.py @@ -1,4 +1,6 @@ -"""PyTorch implementation of CrispNamModel for competing risks +"""CrispNamModel for competing-risks survival analysis. + +PyTorch implementation of CrispNamModel for competing risks survival analysis with L2 normalized projection weights. """ @@ -17,6 +19,7 @@ class FeatureNet(nn.Module): """Neural network to model the effect of a single feature on hazard. + This is the building block for NAM with optional batch normalization. """ @@ -65,6 +68,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: ------- torch.Tensor """ + # ensure float32 x = x.to(dtype=torch.float32) # Apply feature dropout during training if specified @@ -102,11 +106,7 @@ class L2NormalizedLinear(nn.Module): """Linear layer with L2 normalized weights (unit norm constraint).""" def __init__( - self, - in_features: int, - out_features: int, - bias: bool = False, - eps: float = 1e-8 + self, in_features: int, out_features: int, bias: bool = False, eps: float = 1e-8 ) -> None: """Initialize the L2NormalizedLinear layer.""" super(L2NormalizedLinear, self).__init__() @@ -141,6 +141,7 @@ def get_normalized_weights(self) -> torch.Tensor: class CrispNamModel(nn.Module): """Competing risks CoxNAM with L2 normalized projection weights. + Each feature contributes to each risk through a separate shape function. All projection weights are constrained to unit L2 norm. """ @@ -175,7 +176,7 @@ def __init__( # For each feature and risk type, create a projection layer if normalize_projections: - self.risk_projections = nn.ModuleList( + self.risk_projections: nn.ModuleList = nn.ModuleList( [ nn.ModuleList( [ @@ -200,9 +201,7 @@ def __init__( ] ) - def forward(self, - x: torch.Tensor - ) -> Tuple[List[torch.Tensor], List[torch.Tensor]]: + def forward(self, x: torch.Tensor) -> Tuple[List[torch.Tensor], List[torch.Tensor]]: """Forward pass to compute risk scores for all competing risks. Args: @@ -213,6 +212,7 @@ def forward(self, risk_scores: List of (batch_size, 1) Tensors feature_outputs: List of (batch_size, hidden) Tensors """ + # ensure float32 x = x.to(dtype=torch.float32) batch_size, _ = x.shape @@ -235,6 +235,7 @@ def forward(self, repr = fnet(col) # [batch, hidden] feature_outputs.append(repr) + proj: Optional[nn.ModuleList|None] = None # project into each risk channel with L2 normalized weights for risk_idx, proj in enumerate(self.risk_projections[feat_idx]): # proj automatically applies L2 normalization @@ -255,8 +256,7 @@ def get_shape_functions( risk_idx: Optional[int | None] = None, normalize: bool = True, ) -> dict: - """ - Extract shape functions for a specific feature across all + """Extract shape functions for a specific feature across all risks or a specific risk. Args: @@ -305,8 +305,7 @@ def get_shape_functions( return shape_funcs def get_projection_norms(self) -> dict: - """ - Get the L2 norms of all projection weights (should be ~1.0 if normalized). + """Get the L2 norms of all projection weights (should be ~1.0 if normalized). Returns ------- diff --git a/crisp_nam/models/deephit_model.py b/crisp_nam/models/deephit_model.py index 662c4019..b9598bd7 100644 --- a/crisp_nam/models/deephit_model.py +++ b/crisp_nam/models/deephit_model.py @@ -15,10 +15,10 @@ def __init__( self, in_dim: int, out_dim: int, - activation: Optional[torch.Tensor] = None, + activation: Optional[nn.Module] = None, batch_norm: bool = False, dropout_rate: float = 0.0, - init_fn: Optional[Callable[[torch.Tensor]] | None] = nn.init.xavier_normal_, + init_fn: Optional[Callable | None] = nn.init.xavier_normal_, ) -> None: """Initialize the fully connected layer.""" super(FCLayer, self).__init__() @@ -51,7 +51,6 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.dropout(x) return x - class FCNet(nn.Module): """Multi-layer fully connected network.""" @@ -60,12 +59,12 @@ def __init__( in_dim: int, num_layers: int, h_dim: int, - activation: Optional[nn.module] = None, + activation: Optional[nn.Module] = None, out_dim: Optional[int | None] = None, - out_activation: Optional[int | None] = None, + out_activation: Optional[nn.Module | None] = None, batch_norm: bool = False, dropout_rate: float = 0.0, - init_fn: Optional[Callable[[torch.Tensor]] | None] = nn.init.xavier_normal_, + init_fn: Optional[Callable | None] = nn.init.xavier_normal_, ) -> None: """Initialize the fully connected network.""" super(FCNet, self).__init__() @@ -77,7 +76,7 @@ def __init__( # Hidden layers for i in range(num_layers): curr_dim = out_dim if (i == num_layers - 1 and out_dim) else h_dim - curr_act = ( + curr_act: Optional[nn.Module|None] = ( out_activation if (i == num_layers - 1 and out_activation) else activation @@ -109,7 +108,6 @@ def forward(self, x: torch.Tensor) -> Optional[nn.Module]: """ return self.network(x) - class DeepHit(nn.Module): """PyTorch implementation of DeepHit for competing risks survival analysis.""" @@ -145,7 +143,7 @@ def __init__(self, input_dims: dict, network_settings: dict): # Initialize networks self._build_network() - def _build_network(self): + def _build_network(self) -> None: """Build the shared and cause-specific networks. Args: @@ -155,6 +153,7 @@ def _build_network(self): ------- None """ + # Shared network self.shared_net = FCNet( in_dim=self.x_dim, @@ -236,7 +235,7 @@ def log_likelihood_loss( k: Optional[torch.Tensor | np.ndarray], mask1: torch.Tensor, mask2: torch.Tensor, - ): + ) -> torch.Tensor: """Log-likelihood loss (including log-likelihood of censored subjects). Args: @@ -250,6 +249,7 @@ def log_likelihood_loss( ------- loss: Torch.tensor """ + # Convert to PyTorch tensors if necessary if not isinstance(k, torch.Tensor): k = torch.tensor(k, device=out.device) @@ -277,7 +277,7 @@ def ranking_loss( t: Optional[torch.Tensor | np.ndarray], k: Optional[torch.Tensor | np.ndarray], mask2: torch.Tensor, - ): + ) -> torch.Tensor: """Ranking loss (calculated only for acceptable pairs). Args: @@ -333,9 +333,9 @@ def ranking_loss( eta.append(tmp_eta) eta = torch.stack(eta, dim=1) # [batch_size, num_Event] - eta = torch.mean(eta.reshape(-1, self.num_Event), dim=1, keepdim=True) + eta_mean = torch.mean(eta.reshape(-1, self.num_Event), dim=1, keepdim=True) - return torch.sum(eta) + return torch.sum(eta_mean) def calibration_loss( self, @@ -356,6 +356,7 @@ def calibration_loss( ------- loss: Torch.tensor """ + eta = [] # Convert to PyTorch tensors if necessary @@ -375,9 +376,9 @@ def calibration_loss( eta.append(tmp_eta) eta = torch.stack(eta, dim=1) # [1, num_Event] - eta = torch.mean(eta.reshape(-1, self.num_Event), dim=1, keepdim=True) + eta_mean = torch.mean(eta.reshape(-1, self.num_Event), dim=1, keepdim=True) - return torch.sum(eta) + return torch.sum(eta_mean) def compute_loss( self, @@ -389,7 +390,7 @@ def compute_loss( alpha: float = 1.0, beta: float = 1.0, gamma: float = 1.0, - ): + ) -> torch.Tensor: """Compute total loss. Args: @@ -406,6 +407,7 @@ def compute_loss( ------- total_loss: Torch.tensor """ + loss1 = self.log_likelihood_loss(out, t, k, mask1, mask2) loss2 = self.ranking_loss(out, t, k, mask2) loss3 = self.calibration_loss(out, t, k, mask2) @@ -423,6 +425,7 @@ def predict(self, x: torch.Tensor) -> torch.Tensor: ------- out: Tensor of shape (batch_size, num_Event, num_Category) """ + self.eval() with torch.no_grad(): out, _ = self.forward(x) diff --git a/crisp_nam/utils/loss.py b/crisp_nam/utils/loss.py index 778e570b..8abeb56b 100644 --- a/crisp_nam/utils/loss.py +++ b/crisp_nam/utils/loss.py @@ -6,7 +6,6 @@ import torch - def weighted_negative_log_likelihood_loss( risk_scores, times, @@ -15,7 +14,7 @@ def weighted_negative_log_likelihood_loss( event_weights=None, sample_weights=None, eps=1e-8, -) -> float: +) -> torch.Tensor: """ Compute the weighted negative log-likelihood loss for competing risks Cox model. @@ -87,7 +86,11 @@ def weighted_negative_log_likelihood_loss( def negative_log_likelihood_loss( - risk_scores, times, events, num_competing_risks, eps=1e-8 + risk_scores: float, + times: torch.Tensor, + events: torch.Tensor, + num_competing_risks: int, + eps: float = 1e-8, ): """ Compute the negative log-likelihood loss for competing risks Cox model. @@ -143,7 +146,7 @@ def negative_log_likelihood_loss( return loss / max(n_events, 1) -def compute_l2_penalty(model, include_bias=False) -> int: +def compute_l2_penalty(model: torch.nn.Module, include_bias=False) -> float: """ Compute L2 regularization penalty on model parameters. diff --git a/crisp_nam/utils/plotting.py b/crisp_nam/utils/plotting.py index 35376945..7b9fb276 100644 --- a/crisp_nam/utils/plotting.py +++ b/crisp_nam/utils/plotting.py @@ -5,11 +5,11 @@ """ from typing import List, Union - import matplotlib.pyplot as plt + +import torch import numpy as np import pandas as pd -import torch def plot_feature_importance( model: torch.nn.Module, @@ -23,9 +23,27 @@ def plot_feature_importance( color_positive: str = "#2196F3", color_negative: str = "#F44336", ) -> tuple: - """ - Plot feature importance with both top positive and negative influences, + """Plot feature importance with both top positive and negative influences, handling both CPU and CUDA devices automatically. + + Args: + - model: A trained CoxNAM model (torch.nn.Module) + - x_data: Input data (numpy array or torch tensor) to compute contributions + - feature_names: Optional list of feature names (default: generic names) + - n_top: Number of top positive features to display + - n_bottom: Number of top negative features to display + - risk_idx: Index of the competing risk to analyze + - figsize: Size of the plot (width, height) + - output_file: Optional path to save the plot image (e.g., "feature_importance.png") + - color_positive: Color for positive contributions (default: blue) + - color_negative: Color for negative contributions (default: red) + + Returns + ------- + - fig: Matplotlib figure object + - ax: Matplotlib axes object + - top_pos: List of top positive feature names + - top_neg: List of top negative feature names """ # determine model device @@ -33,7 +51,7 @@ def plot_feature_importance( model.eval() # prepare feature names - num_features = model.num_features + num_features: int = model.num_features if feature_names is None: feature_names = [f"Feature {i + 1}" for i in range(num_features)] @@ -98,7 +116,6 @@ def plot_feature_importance( return fig, ax, top_pos, top_neg - def plot_coxnam_shape_functions( model: torch.nn.Module, X: Union[np.ndarray, torch.Tensor], @@ -107,11 +124,25 @@ def plot_coxnam_shape_functions( top_features: List[str] = None, ncols: int = 3, figsize: tuple = (12, 8), - output_file: str = None, -) -> tuple: - """ - Plot shape functions for each feature in a CoxNAM model, + output_file: str = "", +) -> list[float]: + """Plot shape functions for each feature in a CoxNAM model, automatically handling CPU vs CUDA inputs. + + Args: + - model: A trained CoxNAM model (torch.nn.Module) + - X: Input data (numpy array or torch tensor) to compute shape functions + - risk_to_plot: Index of the competing risk to visualize + - feature_names: Optional list of feature names (default: generic names) + - top_features: Optional list of feature names to plot features) + - ncols: Number of columns in the subplot grid + - figsize: Size of the entire figure (width, height) + - output_file: Optional path to save the plot image (e.g., "shape_functions.png") + + Returns + ------- + - fig: Matplotlib figure object + - axes: List of Matplotlib axes objects for each plotted feature """ device = next(model.parameters()).device @@ -123,7 +154,7 @@ def plot_coxnam_shape_functions( # derive feature list num_features = model.num_features - if feature_names is None: + if feature_names: feature_names = [f"Feature {i + 1}" for i in range(num_features)] if top_features: # map names back to indices diff --git a/crisp_nam/utils/risk_cif.py b/crisp_nam/utils/risk_cif.py index 7055b221..70423c5d 100644 --- a/crisp_nam/utils/risk_cif.py +++ b/crisp_nam/utils/risk_cif.py @@ -6,9 +6,8 @@ from typing import Any, List -import numpy as np import torch - +import numpy as np def compute_baseline_cif( times: np.ndarray, events: np.ndarray, eval_times: List[Any], event_type: np.ndarray @@ -26,6 +25,7 @@ def compute_baseline_cif( ------- Numpy array of baseline CIF values at eval_times """ + # Sort times and corresponding events sort_idx = np.argsort(times) sorted_times = times[sort_idx] @@ -70,6 +70,7 @@ def predict_cif( ------- cif_pred: Array of shape (n_samples, len(times)) — predicted CIF per sample. """ + model.eval() with torch.no_grad(): logits, _ = model(x) # list of length num_risks @@ -82,7 +83,9 @@ def predict_cif( return 1.0 - np.power(1.0 - baseline_cif, risk_scores) # shape (N, T) -def predict_risk(model: np.ndarray, x_input: np.ndarray, device: str = "cpu"): +def predict_risk( + model: torch.nn.Module, x_input: np.ndarray, device: str = "cpu" +) -> np.ndarray: """ Predicts relative risk scores for each competing risk. @@ -96,6 +99,7 @@ def predict_risk(model: np.ndarray, x_input: np.ndarray, device: str = "cpu"): ------- np.ndarray: Array of shape (n_samples, num_risks) with relative risk scores. """ + model.eval() if isinstance(x_input, np.ndarray): @@ -111,7 +115,7 @@ def predict_risk(model: np.ndarray, x_input: np.ndarray, device: str = "cpu"): def predict_absolute_risk( - model: torch.Tensor, + model: torch.nn.Module, x_input: np.ndarray, baseline_cifs: List[Any], times: List[Any], @@ -132,6 +136,7 @@ def predict_absolute_risk( ------- np.ndarray: Shape (n_samples, num_events, n_times) with predicted CIFs. """ + rel_risks = predict_risk(model, x_input, device) # shape (n_samples, num_events) n_samples, num_events = rel_risks.shape n_times = len(times) From f6623f8377a8554a3e5f62ba988967212c0ecbcf Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Tue, 10 Feb 2026 16:30:54 -0500 Subject: [PATCH 09/47] More metrics changes --- crisp_nam/metrics/__init__.py | 4 +-- crisp_nam/metrics/calibration.py | 29 ++++++++++++----- crisp_nam/metrics/discrimination.py | 49 ++++++++++++++++++----------- crisp_nam/metrics/ipcw.py | 15 +++------ 4 files changed, 60 insertions(+), 37 deletions(-) diff --git a/crisp_nam/metrics/__init__.py b/crisp_nam/metrics/__init__.py index 20b25607..7e34cb25 100644 --- a/crisp_nam/metrics/__init__.py +++ b/crisp_nam/metrics/__init__.py @@ -1,7 +1,7 @@ """Evaluation metrics used within crisp_nam package.""" from .calibration import brier_score, integrated_brier_score -from .discrimination import auc_td, concordance_index_td, cumulative_dynamic_auc +from .discrimination import auc_td, cumulative_dynamic_auc, truncated_concordance_td from .ipcw import estimate_ipcw @@ -10,6 +10,6 @@ "integrated_brier_score", "auc_td", "cumulative_dynamic_auc", - "concordance_index_td", + "truncated_concordance_td", "estimate_ipcw", ] diff --git a/crisp_nam/metrics/calibration.py b/crisp_nam/metrics/calibration.py index c22aaad0..8d941746 100644 --- a/crisp_nam/metrics/calibration.py +++ b/crisp_nam/metrics/calibration.py @@ -1,6 +1,7 @@ """Calibration metrics for time-to-event models with competing risks. -This module contains functions to compute the Brier score and integrated Brier score for competing risks. +This module contains functions to compute the Brier score and +integrated Brier score for competing risks. """ import numpy as np @@ -12,9 +13,16 @@ epsilon = 1e-4 -def brier_score(e_test, t_test, risk_predicted_test, times, t, km=None, primary_risk=1): - """ - Compute the corrected Brier score for a given competing risk. +def brier_score( + e_test: np.ndarray, + t_test: np.ndarray, + risk_predicted_test: np.ndarray, + times: np.ndarray, + t: float, + km: object | None = None, + primary_risk: int = 1, +) -> tuple[float, object]: + """Compute the corrected Brier score for a given competing risk. This implementation is based on the work of Schoop et al. on quantifying the predictive accuracy of time-to-event models in the presence of competing risks. @@ -68,8 +76,14 @@ def brier_score(e_test, t_test, risk_predicted_test, times, t, km=None, primary_ def integrated_brier_score( - e_test, t_test, risk_predicted_test, times, t_eval=None, km=None, primary_risk=1 -): + e_test: np.ndarray, + t_test: np.ndarray, + risk_predicted_test: np.ndarray, + times: np.ndarray, + t_eval: np.ndarray | None = None, + km: object | None = None, + primary_risk: int = 1, +) -> tuple[float, object]: """ Compute the integrated Brier score for competing risks over a range of time points. @@ -80,7 +94,8 @@ def integrated_brier_score( ---------- e_test (ndarray): Event indicators. t_test (ndarray): Event/censoring times. - risk_predicted_test (ndarray): Predicted risk matrix with shape (n_samples, n_times). + risk_predicted_test (ndarray): Predicted risk matrix + with shape (n_samples, n_times). times (ndarray): Array of time points corresponding to the predictions. t_eval (ndarray, optional): Specific time points at which to compute the score. Defaults to using 'times'. diff --git a/crisp_nam/metrics/discrimination.py b/crisp_nam/metrics/discrimination.py index b458b27c..210962c9 100644 --- a/crisp_nam/metrics/discrimination.py +++ b/crisp_nam/metrics/discrimination.py @@ -1,17 +1,25 @@ """Discrimination metrics for time-to-event models with competing risks. -This module contains functions to compute the cumulative and single time-dependent AUC and time-dependent C-index for ealuating competing risks. +This module contains functions to compute the cumulative +and single time-dependent AUC and time-dependent C-index +for evaluating competing risks. """ import numpy as np from .ipcw import estimate_ipcw - epsilon = 1e-10 - -def auc_td(e_test, t_test, risk_predicted_test, times, t, km=None, primary_risk=1): +def auc_td( + e_test: np.ndarray, + t_test: np.ndarray, + risk_predicted_test: np.ndarray, + times: np.ndarray, + t: float, + km: object | None = None, + primary_risk: int = 1, +) -> tuple[float, object]: """ Compute the time-dependent AUC for a given competing risk using predicted CIFs. @@ -78,11 +86,16 @@ def auc_td(e_test, t_test, risk_predicted_test, times, t, km=None, primary_risk= def cumulative_dynamic_auc( - e_test, t_test, risk_predicted_test, times, t_eval=None, km=None, primary_risk=1 -): - """ - Compute the cumulative dynamic AUC by numerically integrating the - time-dependent AUC over a range of time points. + e_test: np.ndarray, + t_test: np.ndarray, + risk_predicted_test: np.ndarray, + times: np.ndarray, + t_eval: np.ndarray | None = None, + km: object | None = None, + primary_risk: int = 1, +) -> tuple[float, object]: + """Compute the cumulative dynamic AUC by numerically + integrating the time-dependent AUC over a range of time points. Parameters ---------- @@ -112,15 +125,15 @@ def cumulative_dynamic_auc( def truncated_concordance_td( - e_test, - t_test, - risk_predicted_test, - times, - t, - km=None, - primary_risk=1, - tied_tol=1e-8, -): + e_test: np.ndarray, + t_test: np.ndarray, + risk_predicted_test: np.ndarray, + times: np.ndarray, + t: float, + km: object | None = None, + primary_risk: int = 1, + tied_tol: float = 1e-8, +) -> tuple[float, object]: """ Compute the truncated time-dependent concordance index (C-index). diff --git a/crisp_nam/metrics/ipcw.py b/crisp_nam/metrics/ipcw.py index cb4d99b5..9cadcbef 100644 --- a/crisp_nam/metrics/ipcw.py +++ b/crisp_nam/metrics/ipcw.py @@ -1,25 +1,19 @@ """IPCW estimation for time-to-event models with competing risks. -This module provides a function to estimate the inverse probability of censoring weights (IPCW) using a Kaplan-Meier estimator. +This module provides a function to estimate the inverse probability of censoring weights (IPCW) using a +Kaplan-Meier estimator. """ from lifelines import KaplanMeierFitter -def estimate_ipcw(km): - """ - Estimate the inverse probability of censoring weights (IPCW) +def estimate_ipcw(km: tuple | KaplanMeierFitter) -> KaplanMeierFitter: + """Estimate the inverse probability of censoring weights (IPCW) using a Kaplan-Meier estimator. Parameters ---------- km : tuple or KaplanMeierFitter - If `km` is a tuple, it should contain two elements: - - e_train: array-like, event indicators (1 if the event occurred, - 0 if censored). - - t_train: array-like, corresponding event or censoring times. - If `km` is already a fitted KaplanMeierFitter instance, - it will be used directly. Returns ------- @@ -27,6 +21,7 @@ def estimate_ipcw(km): A KaplanMeierFitter instance fitted to the provided data or the input instance if already fitted. """ + if isinstance(km, tuple): kmf = KaplanMeierFitter() e_train, t_train = km From 2476828b3aed96add24829a5c60e83ef0c7c1d4d Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Tue, 10 Feb 2026 16:48:15 -0500 Subject: [PATCH 10/47] Add more fixes --- crisp_nam/models/crisp_nam_model.py | 13 ++++++++----- crisp_nam/utils/loss.py | 1 + crisp_nam/utils/plotting.py | 8 ++++---- crisp_nam/utils/risk_cif.py | 7 ++----- 4 files changed, 15 insertions(+), 14 deletions(-) diff --git a/crisp_nam/models/crisp_nam_model.py b/crisp_nam/models/crisp_nam_model.py index 1df91d4e..36875f81 100644 --- a/crisp_nam/models/crisp_nam_model.py +++ b/crisp_nam/models/crisp_nam_model.py @@ -61,6 +61,7 @@ def __init__( def forward(self, x: torch.Tensor) -> torch.Tensor: """Forward pass through the network. + Args: x: Input tensor of shape @@ -68,7 +69,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: ------- torch.Tensor """ - + # ensure float32 x = x.to(dtype=torch.float32) # Apply feature dropout during training if specified @@ -93,6 +94,7 @@ def _forward_singleton(self, x: torch.Tensor) -> torch.Tensor: ------- torch.Tensor """ + was_training = self.training self.eval() with torch.no_grad(): @@ -212,7 +214,6 @@ def forward(self, x: torch.Tensor) -> Tuple[List[torch.Tensor], List[torch.Tenso risk_scores: List of (batch_size, 1) Tensors feature_outputs: List of (batch_size, hidden) Tensors """ - # ensure float32 x = x.to(dtype=torch.float32) batch_size, _ = x.shape @@ -235,7 +236,7 @@ def forward(self, x: torch.Tensor) -> Tuple[List[torch.Tensor], List[torch.Tenso repr = fnet(col) # [batch, hidden] feature_outputs.append(repr) - proj: Optional[nn.ModuleList|None] = None + proj: Optional[nn.ModuleList | None] = None # project into each risk channel with L2 normalized weights for risk_idx, proj in enumerate(self.risk_projections[feat_idx]): # proj automatically applies L2 normalization @@ -357,8 +358,10 @@ def calculate_feature_importance( feature_idx: Optional[int | None] = None, ) -> dict: """Calculate feature importance based on the magnitude of - risk-specific projection outputs. With L2 normalized weights, - this gives a fair comparison across features. + risk-specific projection outputs. + + With L2 normalized weights, this gives a fair + comparison across features. Args: x_data: Input data tensor or numpy array diff --git a/crisp_nam/utils/loss.py b/crisp_nam/utils/loss.py index 8abeb56b..74b0f31a 100644 --- a/crisp_nam/utils/loss.py +++ b/crisp_nam/utils/loss.py @@ -6,6 +6,7 @@ import torch + def weighted_negative_log_likelihood_loss( risk_scores, times, diff --git a/crisp_nam/utils/plotting.py b/crisp_nam/utils/plotting.py index 7b9fb276..3014fbff 100644 --- a/crisp_nam/utils/plotting.py +++ b/crisp_nam/utils/plotting.py @@ -5,11 +5,12 @@ """ from typing import List, Union -import matplotlib.pyplot as plt -import torch +import matplotlib.pyplot as plt import numpy as np import pandas as pd +import torch + def plot_feature_importance( model: torch.nn.Module, @@ -45,7 +46,6 @@ def plot_feature_importance( - top_pos: List of top positive feature names - top_neg: List of top negative feature names """ - # determine model device device = next(model.parameters()).device model.eval() @@ -116,6 +116,7 @@ def plot_feature_importance( return fig, ax, top_pos, top_neg + def plot_coxnam_shape_functions( model: torch.nn.Module, X: Union[np.ndarray, torch.Tensor], @@ -144,7 +145,6 @@ def plot_coxnam_shape_functions( - fig: Matplotlib figure object - axes: List of Matplotlib axes objects for each plotted feature """ - device = next(model.parameters()).device model.eval() risk_idx = risk_to_plot - 1 diff --git a/crisp_nam/utils/risk_cif.py b/crisp_nam/utils/risk_cif.py index 70423c5d..060c2ffc 100644 --- a/crisp_nam/utils/risk_cif.py +++ b/crisp_nam/utils/risk_cif.py @@ -6,8 +6,9 @@ from typing import Any, List -import torch import numpy as np +import torch + def compute_baseline_cif( times: np.ndarray, events: np.ndarray, eval_times: List[Any], event_type: np.ndarray @@ -25,7 +26,6 @@ def compute_baseline_cif( ------- Numpy array of baseline CIF values at eval_times """ - # Sort times and corresponding events sort_idx = np.argsort(times) sorted_times = times[sort_idx] @@ -70,7 +70,6 @@ def predict_cif( ------- cif_pred: Array of shape (n_samples, len(times)) — predicted CIF per sample. """ - model.eval() with torch.no_grad(): logits, _ = model(x) # list of length num_risks @@ -99,7 +98,6 @@ def predict_risk( ------- np.ndarray: Array of shape (n_samples, num_risks) with relative risk scores. """ - model.eval() if isinstance(x_input, np.ndarray): @@ -136,7 +134,6 @@ def predict_absolute_risk( ------- np.ndarray: Shape (n_samples, num_events, n_times) with predicted CIFs. """ - rel_risks = predict_risk(model, x_input, device) # shape (n_samples, num_events) n_samples, num_events = rel_risks.shape n_times = len(times) From 4a70a1a44c36ef5fd0d5115bdc73d4514d0eaa97 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Tue, 10 Feb 2026 17:20:33 -0500 Subject: [PATCH 11/47] more mypy and ruff changes --- crisp_nam/metrics/discrimination.py | 2 ++ crisp_nam/metrics/ipcw.py | 1 - crisp_nam/models/__init__.py | 4 ++-- crisp_nam/models/crisp_nam_model.py | 10 ++++------ crisp_nam/models/deephit_model.py | 9 +++------ crisp_nam/utils/loss.py | 7 +++++-- crisp_nam/utils/plotting.py | 21 +++++++++++---------- 7 files changed, 27 insertions(+), 27 deletions(-) diff --git a/crisp_nam/metrics/discrimination.py b/crisp_nam/metrics/discrimination.py index 210962c9..d1842cb0 100644 --- a/crisp_nam/metrics/discrimination.py +++ b/crisp_nam/metrics/discrimination.py @@ -9,8 +9,10 @@ from .ipcw import estimate_ipcw + epsilon = 1e-10 + def auc_td( e_test: np.ndarray, t_test: np.ndarray, diff --git a/crisp_nam/metrics/ipcw.py b/crisp_nam/metrics/ipcw.py index 9cadcbef..71ce0b95 100644 --- a/crisp_nam/metrics/ipcw.py +++ b/crisp_nam/metrics/ipcw.py @@ -21,7 +21,6 @@ def estimate_ipcw(km: tuple | KaplanMeierFitter) -> KaplanMeierFitter: A KaplanMeierFitter instance fitted to the provided data or the input instance if already fitted. """ - if isinstance(km, tuple): kmf = KaplanMeierFitter() e_train, t_train = km diff --git a/crisp_nam/models/__init__.py b/crisp_nam/models/__init__.py index 782ff5c3..92966a24 100644 --- a/crisp_nam/models/__init__.py +++ b/crisp_nam/models/__init__.py @@ -1,7 +1,7 @@ """Models available in the crisp_nam package.""" from .crisp_nam_model import CrispNamModel -from .deephit_model import DeepHitModel +from .deephit_model import DeepHit -__all__ = ["CrispNamModel", "DeepHitModel"] \ No newline at end of file +__all__ = ["CrispNamModel", "DeepHit"] diff --git a/crisp_nam/models/crisp_nam_model.py b/crisp_nam/models/crisp_nam_model.py index 36875f81..8ef20b50 100644 --- a/crisp_nam/models/crisp_nam_model.py +++ b/crisp_nam/models/crisp_nam_model.py @@ -11,8 +11,8 @@ Tuple, ) -import numpy as np import torch +import numpy as np import torch.nn.functional as F from torch import nn @@ -69,7 +69,6 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: ------- torch.Tensor """ - # ensure float32 x = x.to(dtype=torch.float32) # Apply feature dropout during training if specified @@ -94,7 +93,6 @@ def _forward_singleton(self, x: torch.Tensor) -> torch.Tensor: ------- torch.Tensor """ - was_training = self.training self.eval() with torch.no_grad(): @@ -103,7 +101,6 @@ def _forward_singleton(self, x: torch.Tensor) -> torch.Tensor: self.train() return result - class L2NormalizedLinear(nn.Module): """Linear layer with L2 normalized weights (unit norm constraint).""" @@ -358,8 +355,8 @@ def calculate_feature_importance( feature_idx: Optional[int | None] = None, ) -> dict: """Calculate feature importance based on the magnitude of - risk-specific projection outputs. - + risk-specific projection outputs. + With L2 normalized weights, this gives a fair comparison across features. @@ -372,6 +369,7 @@ def calculate_feature_importance( ------- Dictionary of feature importances by risk type """ + self.eval() device = next(self.parameters()).device diff --git a/crisp_nam/models/deephit_model.py b/crisp_nam/models/deephit_model.py index b9598bd7..ebaa7174 100644 --- a/crisp_nam/models/deephit_model.py +++ b/crisp_nam/models/deephit_model.py @@ -51,6 +51,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.dropout(x) return x + class FCNet(nn.Module): """Multi-layer fully connected network.""" @@ -76,7 +77,7 @@ def __init__( # Hidden layers for i in range(num_layers): curr_dim = out_dim if (i == num_layers - 1 and out_dim) else h_dim - curr_act: Optional[nn.Module|None] = ( + curr_act: Optional[nn.Module | None] = ( out_activation if (i == num_layers - 1 and out_activation) else activation @@ -108,6 +109,7 @@ def forward(self, x: torch.Tensor) -> Optional[nn.Module]: """ return self.network(x) + class DeepHit(nn.Module): """PyTorch implementation of DeepHit for competing risks survival analysis.""" @@ -153,7 +155,6 @@ def _build_network(self) -> None: ------- None """ - # Shared network self.shared_net = FCNet( in_dim=self.x_dim, @@ -249,7 +250,6 @@ def log_likelihood_loss( ------- loss: Torch.tensor """ - # Convert to PyTorch tensors if necessary if not isinstance(k, torch.Tensor): k = torch.tensor(k, device=out.device) @@ -356,7 +356,6 @@ def calibration_loss( ------- loss: Torch.tensor """ - eta = [] # Convert to PyTorch tensors if necessary @@ -407,7 +406,6 @@ def compute_loss( ------- total_loss: Torch.tensor """ - loss1 = self.log_likelihood_loss(out, t, k, mask1, mask2) loss2 = self.ranking_loss(out, t, k, mask2) loss3 = self.calibration_loss(out, t, k, mask2) @@ -425,7 +423,6 @@ def predict(self, x: torch.Tensor) -> torch.Tensor: ------- out: Tensor of shape (batch_size, num_Event, num_Category) """ - self.eval() with torch.no_grad(): out, _ = self.forward(x) diff --git a/crisp_nam/utils/loss.py b/crisp_nam/utils/loss.py index 74b0f31a..9d3856d2 100644 --- a/crisp_nam/utils/loss.py +++ b/crisp_nam/utils/loss.py @@ -92,7 +92,7 @@ def negative_log_likelihood_loss( events: torch.Tensor, num_competing_risks: int, eps: float = 1e-8, -): +) -> torch.Tensor: """ Compute the negative log-likelihood loss for competing risks Cox model. @@ -147,7 +147,10 @@ def negative_log_likelihood_loss( return loss / max(n_events, 1) -def compute_l2_penalty(model: torch.nn.Module, include_bias=False) -> float: +def compute_l2_penalty( + model: torch.nn.Module, + include_bias: bool = False + ) -> torch.Tensor: """ Compute L2 regularization penalty on model parameters. diff --git a/crisp_nam/utils/plotting.py b/crisp_nam/utils/plotting.py index 3014fbff..3c59d728 100644 --- a/crisp_nam/utils/plotting.py +++ b/crisp_nam/utils/plotting.py @@ -20,7 +20,7 @@ def plot_feature_importance( n_bottom: int = 5, risk_idx: int = 1, figsize: tuple = (8, 6), - output_file: str = None, + output_file: str = "", color_positive: str = "#2196F3", color_negative: str = "#F44336", ) -> tuple: @@ -46,12 +46,13 @@ def plot_feature_importance( - top_pos: List of top positive feature names - top_neg: List of top negative feature names """ + # determine model device device = next(model.parameters()).device model.eval() # prepare feature names - num_features: int = model.num_features + num_features: torch.Tensor = model.num_features if feature_names is None: feature_names = [f"Feature {i + 1}" for i in range(num_features)] @@ -72,8 +73,8 @@ def plot_feature_importance( continue # forward through the feature net and projection - rep = model.feature_nets[i](vals) - proj = model.risk_projections[i][risk_idx0](rep) + rep : torch.nn.ModuleList = model.feature_nets[i](vals) + proj : torch.nn.ModuleList = model.risk_projections[i][risk_idx0](rep) # mean contribution as a Python float contrib = proj.mean().item() feature_contribs[feature_names[i]] = contrib @@ -121,12 +122,12 @@ def plot_coxnam_shape_functions( model: torch.nn.Module, X: Union[np.ndarray, torch.Tensor], risk_to_plot: int = 1, - feature_names: List[str] = None, - top_features: List[str] = None, + feature_names: List[str] | None = None, + top_features: List[str] | None = None, ncols: int = 3, figsize: tuple = (12, 8), output_file: str = "", -) -> list[float]: +) -> tuple: """Plot shape functions for each feature in a CoxNAM model, automatically handling CPU vs CUDA inputs. @@ -186,8 +187,8 @@ def plot_coxnam_shape_functions( t_pts = torch.tensor(pts, dtype=torch.float32, device=device).unsqueeze(1) # compute shape values - rep = model.feature_nets[f_idx](t_pts) - proj = model.risk_projections[f_idx][risk_idx](rep) + rep : torch.nn.ModuleList = model.feature_nets[f_idx](t_pts) + proj : torch.nn.ModuleList = model.risk_projections[f_idx][risk_idx](rep) shp = proj.squeeze(-1).cpu().numpy() # plot @@ -204,7 +205,7 @@ def plot_coxnam_shape_functions( ax.axis("off") fig.suptitle(f"Shape Functions for Risk {risk_to_plot}", fontsize=14) - plt.tight_layout(rect=[0, 0, 1, 0.96]) + plt.tight_layout(rect=(0, 0, 1, 0.96)) if output_file: plt.savefig(output_file, dpi=300, bbox_inches="tight") From 67ca6f378b06322bb1c922a855fef8c9f137f07a Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Wed, 11 Feb 2026 19:48:27 -0500 Subject: [PATCH 12/47] Fix more ruff errors --- crisp_nam/models/crisp_nam_model.py | 57 ----------------------------- crisp_nam/utils/plotting.py | 9 +++-- 2 files changed, 6 insertions(+), 60 deletions(-) diff --git a/crisp_nam/models/crisp_nam_model.py b/crisp_nam/models/crisp_nam_model.py index 8ef20b50..bd8910d5 100644 --- a/crisp_nam/models/crisp_nam_model.py +++ b/crisp_nam/models/crisp_nam_model.py @@ -16,7 +16,6 @@ import torch.nn.functional as F from torch import nn - class FeatureNet(nn.Module): """Neural network to model the effect of a single feature on hazard. @@ -35,7 +34,6 @@ def __init__( self.batch_norm = batch_norm layers: List[nn.Module] = [] - # Input layer layers.append(nn.Linear(1, hidden_sizes[0])) if batch_norm: layers.append(nn.BatchNorm1d(hidden_sizes[0])) @@ -247,61 +245,6 @@ def forward(self, x: torch.Tensor) -> Tuple[List[torch.Tensor], List[torch.Tenso return risk_scores, feature_outputs - def get_shape_functions( - self, - x_values: Optional[torch.Tensor | np.ndarray], - feature_idx: int, - risk_idx: Optional[int | None] = None, - normalize: bool = True, - ) -> dict: - """Extract shape functions for a specific feature across all - risks or a specific risk. - - Args: - x_values: Feature values to evaluate (numpy array or tensor) - feature_idx: Index of the feature to get shape functions for - risk_idx: Optional; if provided, only returns the - shape function for this risk - normalize: Whether to center the shape functions - - Returns - ------- - Dictionary mapping risk names to shape function values - """ - self.eval() - - if not isinstance(x_values, torch.Tensor): - x_values = torch.FloatTensor(x_values) - - x_vals = x_values.view(-1, 1) - - with torch.no_grad(): - feature_repr = self.feature_nets[feature_idx](x_vals) - - shape_funcs = {} - - # If risk_idx is specified, only compute for that risk - risk_indices = ( - [risk_idx] if risk_idx is not None else range(self.num_competing_risks) - ) - - for j in risk_indices: - # Apply the L2 normalized projection to get shape function values - values = ( - self.risk_projections[feature_idx][j](feature_repr) - .cpu() - .numpy() - .flatten() - ) - - # Normalize if requested - if normalize: - values = values - np.mean(values) - - shape_funcs[f"risk_{j + 1}"] = values - - return shape_funcs - def get_projection_norms(self) -> dict: """Get the L2 norms of all projection weights (should be ~1.0 if normalized). diff --git a/crisp_nam/utils/plotting.py b/crisp_nam/utils/plotting.py index 3c59d728..62c78107 100644 --- a/crisp_nam/utils/plotting.py +++ b/crisp_nam/utils/plotting.py @@ -122,7 +122,7 @@ def plot_coxnam_shape_functions( model: torch.nn.Module, X: Union[np.ndarray, torch.Tensor], risk_to_plot: int = 1, - feature_names: List[str] | None = None, + feature_names: np.ndarray | None = None, top_features: List[str] | None = None, ncols: int = 3, figsize: tuple = (12, 8), @@ -155,9 +155,10 @@ def plot_coxnam_shape_functions( # derive feature list num_features = model.num_features - if feature_names: + print(f'{plot_coxnam_shape_functions.__name__}: top_features={top_features}') + if feature_names is None: feature_names = [f"Feature {i + 1}" for i in range(num_features)] - if top_features: + if top_features is not None : # map names back to indices idx_map = {name: i for i, name in enumerate(feature_names)} selected = [(idx_map.get(name), name) for name in top_features] @@ -165,6 +166,8 @@ def plot_coxnam_shape_functions( else: selected = list(zip(range(num_features), feature_names)) + print(f'{plot_coxnam_shape_functions.__name__}: num_features={num_features}, feature_names={feature_names}, top_features={top_features}') + print(f'{plot_coxnam_shape_functions.__name__}: selected={selected}') n_selected = len(selected) nrows = int(np.ceil(n_selected / ncols)) fig, axes = plt.subplots(nrows, ncols, figsize=(figsize)) From 1664b546dd547280073f5bec581707b8cf400b92 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Sun, 1 Mar 2026 15:53:54 -0500 Subject: [PATCH 13/47] More ruff/lint changes --- .gitignore | 1 - README.md | 2 +- crisp_nam/metrics/discrimination.py | 1 - crisp_nam/models/crisp_nam_model.py | 74 +- data_utils/load_datasets.py | 214 +- data_utils/survival_datasets.py | 19 +- datasets/SurvivalDataset.py | 5 +- datasets/framingham.csv | 2 +- datasets/framingham_dataset.py | 61 +- datasets/metabric/label.csv | 3964 ++++++++--------- datasets/pbc2.csv | 2 +- datasets/pbc_dataset.py | 55 +- datasets/support2.csv | 2 +- datasets/support_dataset.py | 86 +- datasets/synthetic_dataset.py | 50 +- pyproject.toml | 97 + .../best_params_framingham_deephit.yaml | 2 +- .../best_params/best_params_pbc_deephit.yaml | 2 +- .../best_params_support2_deephit.yaml | 2 +- .../best_params_synthetic_deephit.yaml | 37 +- .../nested_cv_raw_metrics_framingham.json | 2 +- results/logs/nested_cv_raw_metrics_pbc.json | 2 +- .../logs/nested_cv_raw_metrics_support.json | 2 +- .../logs/nested_cv_raw_metrics_synthetic.json | 2 +- ...ested_cv_feature_importance_risk_1_pbc.png | Bin 106308 -> 99488 bytes ...ested_cv_feature_importance_risk_2_pbc.png | Bin 108405 -> 102946 bytes .../nested_cv_shape_functions_risk_1_pbc.png | Bin 329012 -> 280303 bytes .../nested_cv_shape_functions_risk_2_pbc.png | Bin 294026 -> 275431 bytes training_scripts/config.yaml | 2 +- training_scripts/model_utils.py | 23 +- training_scripts/train.py | 571 ++- training_scripts/train_deephit_cuda.py | 755 ++-- training_scripts/train_nested_cv.py | 733 +-- training_scripts/tune_optuna.py | 533 ++- training_scripts/tune_optuna_optimized.py | 216 +- uv.lock | 647 +++ 36 files changed, 4733 insertions(+), 3433 deletions(-) diff --git a/.gitignore b/.gitignore index c28b9fe1..6c08e923 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,3 @@ *.pyc .venv/ blog/node_modules/ -.DS_Store diff --git a/README.md b/README.md index 085daf4e..f7e06772 100644 --- a/README.md +++ b/README.md @@ -276,4 +276,4 @@ Contributions are welcome! Please open issues or submit pull requests. ## License -This project is licensed under the MIT License. \ No newline at end of file +This project is licensed under the MIT License. diff --git a/crisp_nam/metrics/discrimination.py b/crisp_nam/metrics/discrimination.py index d1842cb0..79bcd7ab 100644 --- a/crisp_nam/metrics/discrimination.py +++ b/crisp_nam/metrics/discrimination.py @@ -86,7 +86,6 @@ def auc_td( auc_value = auc_numerator / auc_denominator if auc_denominator > 0 else np.nan return auc_value, km - def cumulative_dynamic_auc( e_test: np.ndarray, t_test: np.ndarray, diff --git a/crisp_nam/models/crisp_nam_model.py b/crisp_nam/models/crisp_nam_model.py index bd8910d5..00e1d508 100644 --- a/crisp_nam/models/crisp_nam_model.py +++ b/crisp_nam/models/crisp_nam_model.py @@ -16,7 +16,7 @@ import torch.nn.functional as F from torch import nn -class FeatureNet(nn.Module): +class _FeatureNet(nn.Module): """Neural network to model the effect of a single feature on hazard. This is the building block for NAM with optional batch normalization. @@ -30,7 +30,7 @@ def __init__( batch_norm: bool = False, ) -> None: """Initialize the FeatureNet.""" - super(FeatureNet, self).__init__() + super(_FeatureNet, self).__init__() self.batch_norm = batch_norm layers: List[nn.Module] = [] @@ -99,14 +99,14 @@ def _forward_singleton(self, x: torch.Tensor) -> torch.Tensor: self.train() return result -class L2NormalizedLinear(nn.Module): +class _L2NormalizedLinear(nn.Module): """Linear layer with L2 normalized weights (unit norm constraint).""" def __init__( self, in_features: int, out_features: int, bias: bool = False, eps: float = 1e-8 ) -> None: """Initialize the L2NormalizedLinear layer.""" - super(L2NormalizedLinear, self).__init__() + super(_L2NormalizedLinear, self).__init__() self.in_features = in_features self.out_features = out_features self.eps = eps @@ -166,7 +166,7 @@ def __init__( # Create a FeatureNet for each input feature self.feature_nets = nn.ModuleList( [ - FeatureNet(hidden_sizes, dropout_rate, feature_dropout, batch_norm) + _FeatureNet(hidden_sizes, dropout_rate, feature_dropout, batch_norm) for _ in range(num_features) ] ) @@ -177,7 +177,7 @@ def __init__( [ nn.ModuleList( [ - L2NormalizedLinear(hidden_sizes[-1], 1, bias=False, eps=eps) + _L2NormalizedLinear(hidden_sizes[-1], 1, bias=False, eps=eps) for _ in range(num_competing_risks) ] ) @@ -347,68 +347,6 @@ def calculate_feature_importance( return importance - def predict_risk( - self, - x: Optional[torch.Tensor | np.ndarray], - baseline_hazards: Optional[dict | None] = None, - ) -> dict: - """Predict survival probability or cumulative incidence. - - Args: - x: Input tensor of shape (batch_size, num_features) - baseline_hazards: Optional dict of baseline hazards for each risk - - Returns - ------- - Dictionary of predictions for each competing risk - """ - self.eval() - - # Convert to tensor if needed - if not isinstance(x, torch.Tensor): - x = torch.FloatTensor(x) - - with torch.no_grad(): - risk_scores, _ = self(x) - - # Convert scores to hazard ratios - hazard_ratios = [torch.exp(score).cpu().numpy() for score in risk_scores] - - # If baseline hazards are provided, compute absolute risks - if baseline_hazards is not None: - predictions = {} - - for j in range(self.num_competing_risks): - risk_name = f"risk_{j + 1}" - - # Baseline survival and hazard - baseline_surv = baseline_hazards.get(risk_name, {}).get( - "survival", None - ) - baseline_haz = baseline_hazards.get(risk_name, {}).get( - "hazard", None - ) - - if baseline_surv is not None: - # Compute survival probability: S(t|x) = S0(t)^exp(f(x)) - predictions[f"{risk_name}_survival"] = np.power( - baseline_surv.reshape(1, -1), - hazard_ratios[j].reshape(-1, 1), - ) - - if baseline_haz is not None: - # Compute cumulative hazard: H(t|x) = H0(t) * exp(f(x)) - predictions[f"{risk_name}_cumhazard"] = baseline_haz.reshape( - 1, -1 - ) * hazard_ratios[j].reshape(-1, 1) - - return predictions - # Without baseline hazards, just return hazard ratios - return { - f"risk_{j + 1}_hazard_ratio": hazard_ratios[j] - for j in range(self.num_competing_risks) - } - # Utility functions for model analysis def analyze_projection_weights(self) -> dict: """Analyze the L2 norms and statistics of projection weights. diff --git a/data_utils/load_datasets.py b/data_utils/load_datasets.py index 2263fa30..6e4837fa 100644 --- a/data_utils/load_datasets.py +++ b/data_utils/load_datasets.py @@ -1,16 +1,18 @@ -from typing import Tuple, List +from typing import List, Tuple -import pandas as pd import numpy as np +import pandas as pd from sklearn.impute import SimpleImputer + def load_framingham(sequential=False): """ Load and preprocess the Framingham dataset for competing risks analysis, with imputation but no scaling. Feature normalization must be done externally after splitting to avoid data leakage. - Returns: + Returns + ------- x (np.ndarray): Feature matrix with one-hot categorical + raw continuous features. t (np.ndarray): Time-to-event (with +1 offset). e (np.ndarray): Event indicator (0=censored, 1=CVD, 2=death). @@ -18,55 +20,62 @@ def load_framingham(sequential=False): n_continuous (int): Number of continuous features at the end of x. feature_ranges (None): Placeholder for backward compatibility. """ - file_path = "datasets/framingham.csv" data = pd.read_csv(file_path) if not sequential: - data = data.groupby("RANDID").first() - cat_cols = [ - 'SEX', 'CURSMOKE', 'DIABETES', 'BPMEDS', - 'PREVCHD', 'PREVAP', 'PREVMI', 'PREVSTRK', 'PREVHYP', 'educ' + "SEX", + "CURSMOKE", + "DIABETES", + "BPMEDS", + "PREVCHD", + "PREVAP", + "PREVMI", + "PREVSTRK", + "PREVHYP", + "educ", ] - + # 'HDLC', 'LDLC' - removed to replicate nfg experiments. cont_cols = [ - 'TOTCHOL', 'AGE', - 'SYSBP', 'DIABP', 'CIGPDAY', 'BMI', - 'HEARTRTE', 'GLUCOSE' + "TOTCHOL", + "AGE", + "SYSBP", + "DIABP", + "CIGPDAY", + "BMI", + "HEARTRTE", + "GLUCOSE", ] - - cat_imputer = SimpleImputer(strategy='most_frequent') + cat_imputer = SimpleImputer(strategy="most_frequent") x_cat = pd.DataFrame( cat_imputer.fit_transform(data[cat_cols]), columns=cat_cols, - index=data.index + index=data.index, ) x_cat = pd.get_dummies(x_cat, drop_first=True) - - cont_imputer = SimpleImputer(strategy='mean') + cont_imputer = SimpleImputer(strategy="mean") x_cont = cont_imputer.fit_transform(data[cont_cols]) - x = np.hstack([x_cat.values, x_cont]) feature_names = np.concatenate([x_cat.columns.values, cont_cols]) n_continuous = len(cont_cols) event = np.zeros(len(data), dtype=int) - time = (data['TIMEDTH'] - data['TIME']).values + time = (data["TIMEDTH"] - data["TIME"]).values # Primary CVD event (risk=1) - cvd_mask = data['CVD'] == 1 + cvd_mask = data["CVD"] == 1 event[cvd_mask] = 1 - time_cvd = (data['TIMECVD'] - data['TIME']).values + time_cvd = (data["TIMECVD"] - data["TIME"]).values time[cvd_mask] = time_cvd[cvd_mask] # Competing death event (risk=2), only if CVD did not occur - death_mask = (data['DEATH'] == 1) & ~cvd_mask + death_mask = (data["DEATH"] == 1) & ~cvd_mask event[death_mask] = 2 # Filter out invalid or zero times @@ -79,8 +88,8 @@ def load_framingham(sequential=False): assert not np.isnan(x).any(), "NaNs found in feature matrix" return x, t, e, feature_names, n_continuous, None - else: - raise NotImplementedError("Sequential mode not yet implemented.") + raise NotImplementedError("Sequential mode not yet implemented.") + def load_pbc2_dataset(): """ @@ -90,7 +99,8 @@ def load_pbc2_dataset(): with missing values imputed. The function also constructs the outcome variable for competing risks analysis and returns time-to-event data. - Returns: + Returns + ------- tuple: A tuple containing the following elements: - x (numpy.ndarray): Combined feature matrix with categorical features one-hot encoded and continuous features imputed. @@ -103,55 +113,56 @@ def load_pbc2_dataset(): - n_continuous (int): Number of continuous features. - feature_ranges (list of tuple): List of (min, max) ranges for each feature. """ - file_path = "datasets/pbc2.csv" data = pd.read_csv(file_path) - data = data.drop(columns=['id', 'sno.', 'year', 'status2'], axis=1) - + data = data.drop(columns=["id", "sno.", "year", "status2"], axis=1) event_type = np.where( - data['status'] == 'dead', 1, - np.where(data['status'] == 'transplanted', 2, 0) + data["status"] == "dead", 1, np.where(data["status"] == "transplanted", 2, 0) ) - cont_cols = [ - 'age', 'serBilir', 'serChol', 'albumin', 'alkaline', - 'SGOT', 'platelets', 'prothrombin', 'histologic' + "age", + "serBilir", + "serChol", + "albumin", + "alkaline", + "SGOT", + "platelets", + "prothrombin", + "histologic", ] - x_cont = data[cont_cols].replace('NA', np.nan).astype(float) + x_cont = data[cont_cols].replace("NA", np.nan).astype(float) - - mean_imputer = SimpleImputer(strategy='mean') + mean_imputer = SimpleImputer(strategy="mean") x_cont_imputed = mean_imputer.fit_transform(x_cont) - cont_feature_ranges = list(zip(np.nanmin(x_cont_imputed, axis=0), - np.nanmax(x_cont_imputed, axis=0))) + cont_feature_ranges = list( + zip(np.nanmin(x_cont_imputed, axis=0), np.nanmax(x_cont_imputed, axis=0)) + ) - - cat_cols = ['sex', 'drug', 'ascites', 'hepatomegaly', 'spiders', 'edema'] - x_cat = data[cat_cols].fillna('missing') + cat_cols = ["sex", "drug", "ascites", "hepatomegaly", "spiders", "edema"] + x_cat = data[cat_cols].fillna("missing") - cat_imputer = SimpleImputer(strategy='most_frequent') + cat_imputer = SimpleImputer(strategy="most_frequent") x_cat_imputed = cat_imputer.fit_transform(x_cat) x_cat_df = pd.DataFrame(x_cat_imputed, columns=cat_cols) x_cat_encoded = pd.get_dummies(x_cat_df, drop_first=True) - - x_cat_encoded = x_cat_encoded.loc[:, ~x_cat_encoded.columns.str.contains('_missing')] + x_cat_encoded = x_cat_encoded.loc[ + :, ~x_cat_encoded.columns.str.contains("_missing") + ] cat_feature_ranges = [(0.0, 1.0)] * x_cat_encoded.shape[1] - x = np.hstack([x_cat_encoded.values, x_cont_imputed]) feature_names = np.concatenate([x_cat_encoded.columns, cont_cols]) n_continuous = len(cont_cols) feature_ranges = cat_feature_ranges + cont_feature_ranges - - t = data['years'].astype(float).values * 365.25 + t = data["years"].astype(float).values * 365.25 valid = ~np.isnan(t) return ( @@ -160,17 +171,20 @@ def load_pbc2_dataset(): event_type[valid], feature_names, n_continuous, - feature_ranges + feature_ranges, ) + def load_support_dataset(): """ Load and preprocess the SUPPORT dataset. This function reads the SUPPORT dataset from a CSV file, imputes missing values, encodes categorical features, and constructs the outcome variable for survival analysis. - It returns the processed features, time-to-event data, event types, feature names, + It returns the processed features, time-to-event data, event types, feature names, the number of continuous features, and feature ranges. - Returns: + + Returns + ------- tuple: A tuple containing: - x (numpy.ndarray): Combined array of processed categorical and continuous features. - t (numpy.ndarray): Time-to-event data with a +1 offset. @@ -178,7 +192,9 @@ def load_support_dataset(): - feature_names (numpy.ndarray): Array of feature names. - n_continuous (int): Number of continuous features. - feature_ranges (list): List of tuples representing the range (min, max) for each feature. - Notes: + + Notes + ----- - The dataset file "support2.csv" must be located in the same directory as this script. - Continuous features are imputed using the median strategy if missing values are present. - Categorical features are imputed using the most frequent strategy if missing values are present. @@ -186,77 +202,101 @@ def load_support_dataset(): - The outcome variable is constructed using the 'ca', 'dzgroup', and 'death' columns. - Time-to-event data ('d.time') is offset by +1 to avoid zero follow-up times. """ - file_path = "datasets/support2.csv" data = pd.read_csv(file_path) - - is_cancer = data['ca'].astype(str).str.lower().str.contains("meta") | \ - data['dzgroup'].astype(str).str.lower().str.contains("cancer") - event_type = np.where(data['death'] == 1, np.where(is_cancer, 1, 2), 0) + + is_cancer = data["ca"].astype(str).str.lower().str.contains("meta") | data[ + "dzgroup" + ].astype(str).str.lower().str.contains("cancer") + event_type = np.where(data["death"] == 1, np.where(is_cancer, 1, 2), 0) cont_cols = [ - 'age', 'num.co', 'meanbp', 'wblc', 'hrt', 'resp', 'temp', 'pafi', 'alb', - 'bili', 'crea', 'sod', 'ph', 'glucose', 'bun', 'urine', - 'scoma', 'aps', 'sps', 'adls', 'adlsc', 'charges', 'totcst', 'totmcst', 'avtisst' + "age", + "num.co", + "meanbp", + "wblc", + "hrt", + "resp", + "temp", + "pafi", + "alb", + "bili", + "crea", + "sod", + "ph", + "glucose", + "bun", + "urine", + "scoma", + "aps", + "sps", + "adls", + "adlsc", + "charges", + "totcst", + "totmcst", + "avtisst", ] x_cont = data[cont_cols] - + if x_cont.isnull().values.any(): - simp_imputer = SimpleImputer(strategy='median') + simp_imputer = SimpleImputer(strategy="median") x_cont_imputed = simp_imputer.fit_transform(x_cont) else: x_cont_imputed = x_cont.values - cont_feature_ranges = list(zip(np.nanmin(x_cont_imputed, axis=0), - np.nanmax(x_cont_imputed, axis=0))) + cont_feature_ranges = list( + zip(np.nanmin(x_cont_imputed, axis=0), np.nanmax(x_cont_imputed, axis=0)) + ) # -- Categorical features -- # Remove leakage fields 'ca', 'dzgroup', 'dzclass' - cat_cols = ['sex', 'income', 'race', 'dnr', 'dementia', 'diabetes'] + cat_cols = ["sex", "income", "race", "dnr", "dementia", "diabetes"] x_cat = data[cat_cols] if x_cat.isnull().values.any(): - cat_imputer = SimpleImputer(strategy='most_frequent') + cat_imputer = SimpleImputer(strategy="most_frequent") x_cat_imputed = cat_imputer.fit_transform(x_cat) else: x_cat_imputed = x_cat.values x_cat_df = pd.DataFrame(x_cat_imputed, columns=cat_cols) - + x_cat_encoded = pd.get_dummies(x_cat_df, drop_first=True) cat_feature_ranges = [(0.0, 1.0)] * x_cat_encoded.shape[1] - x = np.hstack([x_cat_encoded.values, x_cont_imputed]) feature_names = np.concatenate([x_cat_encoded.columns, cont_cols]) n_continuous = len(cont_cols) feature_ranges = cat_feature_ranges + cont_feature_ranges - - t = data['d.time'].values + t = data["d.time"].values valid = ~np.isnan(t) - + print("Completed imputation of missing values.") return ( x[valid], - t[valid] + 1, # Add +1 offset to follow-up times + t[valid] + 1, # Add +1 offset to follow-up times event_type[valid], feature_names, n_continuous, - feature_ranges + feature_ranges, ) -def load_synthetic_dataset() -> Tuple[np.ndarray, np.ndarray, np.ndarray, List[str], int, List[tuple]]: +def load_synthetic_dataset() -> Tuple[ + np.ndarray, np.ndarray, np.ndarray, List[str], int, List[tuple] +]: """ Loads a synthetic competing risks dataset from a CSV file. - + The CSV is expected to have a header with the following columns: - time: observed time - label: event indicator (0 for censored; >0 for event types) - true_time: (optional) true time (unused here) - true_label: (optional) true event label (unused here) - feature1, feature2, ..., featureN: feature values - - Returns: + + Returns + ------- X (np.ndarray): Feature matrix of shape (n_samples, n_features). T_obs (np.ndarray): Observed times of shape (n_samples,). e (np.ndarray): Event indicators of shape (n_samples,). @@ -264,22 +304,20 @@ def load_synthetic_dataset() -> Tuple[np.ndarray, np.ndarray, np.ndarray, List[s n_continuous (int): Total number of continuous features. feature_ranges (List[tuple]): List of (min, max) tuples for each feature. """ - file_path = "datasets/synthetic_comprisk.csv" df = pd.read_csv(file_path) - - T_obs = df["time"].values .astype(np.float32) + T_obs = df["time"].values.astype(np.float32) e = df["label"].values.astype(np.float32) - - + feature_columns = [col for col in df.columns if col.startswith("feature")] - X = df[feature_columns].values .astype(np.float32) - - + X = df[feature_columns].values.astype(np.float32) + feature_names = feature_columns n_continuous = X.shape[1] - - feature_ranges = [(float(X[:, i].min()), float(X[:, i].max())) for i in range(n_continuous)] - - return X, T_obs, e, feature_names, n_continuous, feature_ranges \ No newline at end of file + + feature_ranges = [ + (float(X[:, i].min()), float(X[:, i].max())) for i in range(n_continuous) + ] + + return X, T_obs, e, feature_names, n_continuous, feature_ranges diff --git a/data_utils/survival_datasets.py b/data_utils/survival_datasets.py index d2eab1b9..41d8c8f9 100644 --- a/data_utils/survival_datasets.py +++ b/data_utils/survival_datasets.py @@ -1,6 +1,7 @@ import torch from torch.utils.data import Dataset + class SurvivalDataset(Dataset): def __init__(self, x, t, e): self.x = torch.tensor(x, dtype=torch.float32) @@ -13,22 +14,28 @@ def __len__(self): def __getitem__(self, idx): return self.x[idx], self.t[idx], self.e[idx], idx # idx for tracking + class SurvivalDatasetDeepHit(Dataset): """Dataset class for DeepHit model""" + def __init__(self, x, t, e, num_Category): self.x = torch.tensor(x, dtype=torch.float32) self.t = torch.tensor(t, dtype=torch.float32).view(-1, 1) self.e = torch.tensor(e, dtype=torch.float32).view(-1, 1) - + # Create discretized time if needed - self.t_discrete = torch.floor(self.t * num_Category / torch.max(self.t)).clamp(0, num_Category-1).long() - + self.t_discrete = ( + torch.floor(self.t * num_Category / torch.max(self.t)) + .clamp(0, num_Category - 1) + .long() + ) + # Create masks for loss calculation self.num_Category = num_Category self.num_Event = int(torch.max(self.e).item()) - + def __len__(self): return len(self.x) - + def __getitem__(self, idx): - return self.x[idx], self.t[idx], self.e[idx], self.t_discrete[idx] \ No newline at end of file + return self.x[idx], self.t[idx], self.e[idx], self.t_discrete[idx] diff --git a/datasets/SurvivalDataset.py b/datasets/SurvivalDataset.py index c9fd9567..b6a2f01d 100644 --- a/datasets/SurvivalDataset.py +++ b/datasets/SurvivalDataset.py @@ -1,5 +1,6 @@ -from torch.utils.data import Dataset import torch +from torch.utils.data import Dataset + class SurvivalDataset(Dataset): def __init__(self, x, t, e): @@ -11,4 +12,4 @@ def __len__(self): return len(self.x) def __getitem__(self, idx): - return self.x[idx], self.t[idx], self.e[idx], idx # idx for tracking \ No newline at end of file + return self.x[idx], self.t[idx], self.e[idx], idx # idx for tracking diff --git a/datasets/framingham.csv b/datasets/framingham.csv index b33fe583..50a4f09d 100644 --- a/datasets/framingham.csv +++ b/datasets/framingham.csv @@ -11625,4 +11625,4 @@ RANDID,SEX,TOTCHOL,AGE,SYSBP,DIABP,CURSMOKE,CIGPDAY,BMI,DIABETES,BPMEDS,HEARTRTE 9998212,1,153,52,143,89,0,0,25.74,0,0,65,72,3,0,0,0,0,1,4538,3,30,123,0,0,0,0,0,0,0,1,8766,8766,8766,8766,8766,8766,8766,0 9999312,2,196,39,133,86,1,30,20.91,0,0,85,80,3,0,0,0,0,0,0,1,,,0,0,0,0,0,0,0,1,8766,8766,8766,8766,8766,8766,8766,4201 9999312,2,240,46,138,79,1,20,26.39,0,0,90,83,3,0,0,0,0,0,2390,2,,,0,0,0,0,0,0,0,1,8766,8766,8766,8766,8766,8766,8766,4201 -9999312,2,,50,147,96,1,10,24.19,0,0,94,,3,0,0,0,0,1,4201,3,,,0,0,0,0,0,0,0,1,8766,8766,8766,8766,8766,8766,8766,4201 \ No newline at end of file +9999312,2,,50,147,96,1,10,24.19,0,0,94,,3,0,0,0,0,1,4201,3,,,0,0,0,0,0,0,0,1,8766,8766,8766,8766,8766,8766,8766,4201 diff --git a/datasets/framingham_dataset.py b/datasets/framingham_dataset.py index 9520c089..4cce629e 100644 --- a/datasets/framingham_dataset.py +++ b/datasets/framingham_dataset.py @@ -1,15 +1,18 @@ import os -import pandas as pd + import numpy as np +import pandas as pd from sklearn.impute import SimpleImputer + def load_framingham(competing=True, sequential=False): """ Load and preprocess the Framingham dataset for competing risks analysis, with imputation but no scaling. Feature normalization must be done externally after splitting to avoid data leakage. - Returns: + Returns + ------- x (np.ndarray): Feature matrix with one-hot categorical + raw continuous features. t (np.ndarray): Time-to-event (with +1 offset). e (np.ndarray): Event indicator (0=censored, 1=CVD, 2=death). @@ -17,58 +20,64 @@ def load_framingham(competing=True, sequential=False): n_continuous (int): Number of continuous features at the end of x. feature_ranges (None): Placeholder for backward compatibility. """ - file_path = os.path.join(os.path.dirname(__file__), "framingham.csv") data = pd.read_csv(file_path) if not sequential: - data = data.groupby("RANDID").first() - cat_cols = [ - 'SEX', 'CURSMOKE', 'DIABETES', 'BPMEDS', - 'PREVCHD', 'PREVAP', 'PREVMI', 'PREVSTRK', 'PREVHYP', 'educ' + "SEX", + "CURSMOKE", + "DIABETES", + "BPMEDS", + "PREVCHD", + "PREVAP", + "PREVMI", + "PREVSTRK", + "PREVHYP", + "educ", ] - + # 'HDLC', 'LDLC' - removed to replicate nfg experiments. cont_cols = [ - 'TOTCHOL', 'AGE', - 'SYSBP', 'DIABP', 'CIGPDAY', 'BMI', - 'HEARTRTE', 'GLUCOSE' + "TOTCHOL", + "AGE", + "SYSBP", + "DIABP", + "CIGPDAY", + "BMI", + "HEARTRTE", + "GLUCOSE", ] - - cat_imputer = SimpleImputer(strategy='most_frequent') + cat_imputer = SimpleImputer(strategy="most_frequent") x_cat = pd.DataFrame( cat_imputer.fit_transform(data[cat_cols]), columns=cat_cols, - index=data.index + index=data.index, ) x_cat = pd.get_dummies(x_cat, drop_first=True) - - cont_imputer = SimpleImputer(strategy='mean') + cont_imputer = SimpleImputer(strategy="mean") x_cont = cont_imputer.fit_transform(data[cont_cols]) - x = np.hstack([x_cat.values, x_cont]) feature_names = np.concatenate([x_cat.columns.values, cont_cols]) n_continuous = len(cont_cols) - event = np.zeros(len(data), dtype=int) - - time = (data['TIMEDTH'] - data['TIME']).values + + time = (data["TIMEDTH"] - data["TIME"]).values # Primary CVD event (risk=1) - cvd_mask = data['CVD'] == 1 + cvd_mask = data["CVD"] == 1 event[cvd_mask] = 1 - time_cvd = (data['TIMECVD'] - data['TIME']).values + time_cvd = (data["TIMECVD"] - data["TIME"]).values time[cvd_mask] = time_cvd[cvd_mask] # Competing death event (risk=2), only if CVD did not occur - death_mask = (data['DEATH'] == 1) & ~cvd_mask + death_mask = (data["DEATH"] == 1) & ~cvd_mask event[death_mask] = 2 # Filter out invalid or zero times @@ -81,10 +90,8 @@ def load_framingham(competing=True, sequential=False): assert not np.isnan(x).any(), "NaNs found in feature matrix" return x, t, e, feature_names, n_continuous, None - else: - raise NotImplementedError("Sequential mode not yet implemented.") + raise NotImplementedError("Sequential mode not yet implemented.") + if __name__ == "__main__": x, t, e, feature_names, n_continuous, feature_ranges = load_framingham() - - diff --git a/datasets/metabric/label.csv b/datasets/metabric/label.csv index 1ea535c2..c8147146 100644 --- a/datasets/metabric/label.csv +++ b/datasets/metabric/label.csv @@ -1,1982 +1,1982 @@ -event_time,label -2999 ,0 -1484 ,0 -3053 ,0 -1721 ,0 -1241 ,1 -234 ,1 -2947 ,0 -672 ,1 -2734 ,1 -2083 ,0 -1097 ,1 -1088 ,1 -2314 ,0 -2258 ,0 -2361 ,0 -424 ,1 -1594 ,0 -1784 ,0 -2005 ,0 -2310 ,0 -1900 ,0 -1718 ,0 -1846 ,0 -2482 ,0 -1420 ,0 -1543 ,0 -2673 ,0 -1583 ,0 -855 ,1 -2149 ,0 -2484 ,1 -2063 ,0 -1493 ,1 -2611 ,0 -1844 ,0 -242 ,1 -2713 ,0 -1333 ,0 -1481 ,0 -1866 ,0 -1281 ,1 -1433 ,0 -2554 ,0 -3148 ,0 -1175 ,1 -1384 ,0 -402 ,0 -2002 ,1 -2474 ,0 -247 ,0 -364 ,0 -872 ,1 -1694 ,0 -2569 ,0 -2527 ,0 -2368 ,0 -49 ,0 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"1943","312",3.98915781404008,"alive","placebo",33.1535428759172,"female",1.06779104150695,"No","No","No","No edema",7.4,312,3.56,1767,166,148,11.7,2,0 "1944","312",3.98915781404008,"alive","placebo",33.1535428759172,"female",2.12189245427664,"No","No","Yes","edema no diuretics",16.3,688,3.34,2460,173,138,13,2,0 -"1945","312",3.98915781404008,"alive","placebo",33.1535428759172,"female",2.94327017851276,"No","No","Yes","edema no diuretics",23.4,741,3.42,3012,200,128,13.4,3,0 \ No newline at end of file +"1945","312",3.98915781404008,"alive","placebo",33.1535428759172,"female",2.94327017851276,"No","No","Yes","edema no diuretics",23.4,741,3.42,3012,200,128,13.4,3,0 diff --git a/datasets/pbc_dataset.py b/datasets/pbc_dataset.py index e41a7347..66f5de03 100644 --- a/datasets/pbc_dataset.py +++ b/datasets/pbc_dataset.py @@ -1,6 +1,7 @@ import os -import pandas as pd + import numpy as np +import pandas as pd from sklearn.impute import SimpleImputer @@ -12,7 +13,8 @@ def load_pbc2_dataset(): with missing values imputed. The function also constructs the outcome variable for competing risks analysis and returns time-to-event data. - Returns: + Returns + ------- tuple: A tuple containing the following elements: - x (numpy.ndarray): Combined feature matrix with categorical features one-hot encoded and continuous features imputed. @@ -25,56 +27,56 @@ def load_pbc2_dataset(): - n_continuous (int): Number of continuous features. - feature_ranges (list of tuple): List of (min, max) ranges for each feature. """ - - file_path = os.path.join(os.path.dirname(__file__), "pbc2.csv") data = pd.read_csv(file_path) - data = data.drop(columns=['id', 'sno.', 'year', 'status2'], axis=1) - + data = data.drop(columns=["id", "sno.", "year", "status2"], axis=1) event_type = np.where( - data['status'] == 'dead', 1, - np.where(data['status'] == 'transplanted', 2, 0) + data["status"] == "dead", 1, np.where(data["status"] == "transplanted", 2, 0) ) - cont_cols = [ - 'age', 'serBilir', 'serChol', 'albumin', 'alkaline', - 'SGOT', 'platelets', 'prothrombin', 'histologic' + "age", + "serBilir", + "serChol", + "albumin", + "alkaline", + "SGOT", + "platelets", + "prothrombin", + "histologic", ] - x_cont = data[cont_cols].replace('NA', np.nan).astype(float) + x_cont = data[cont_cols].replace("NA", np.nan).astype(float) - - mean_imputer = SimpleImputer(strategy='mean') + mean_imputer = SimpleImputer(strategy="mean") x_cont_imputed = mean_imputer.fit_transform(x_cont) - cont_feature_ranges = list(zip(np.nanmin(x_cont_imputed, axis=0), - np.nanmax(x_cont_imputed, axis=0))) + cont_feature_ranges = list( + zip(np.nanmin(x_cont_imputed, axis=0), np.nanmax(x_cont_imputed, axis=0)) + ) - - cat_cols = ['sex', 'drug', 'ascites', 'hepatomegaly', 'spiders', 'edema'] - x_cat = data[cat_cols].fillna('missing') + cat_cols = ["sex", "drug", "ascites", "hepatomegaly", "spiders", "edema"] + x_cat = data[cat_cols].fillna("missing") - cat_imputer = SimpleImputer(strategy='most_frequent') + cat_imputer = SimpleImputer(strategy="most_frequent") x_cat_imputed = cat_imputer.fit_transform(x_cat) x_cat_df = pd.DataFrame(x_cat_imputed, columns=cat_cols) x_cat_encoded = pd.get_dummies(x_cat_df, drop_first=True) - - x_cat_encoded = x_cat_encoded.loc[:, ~x_cat_encoded.columns.str.contains('_missing')] + x_cat_encoded = x_cat_encoded.loc[ + :, ~x_cat_encoded.columns.str.contains("_missing") + ] cat_feature_ranges = [(0.0, 1.0)] * x_cat_encoded.shape[1] - x = np.hstack([x_cat_encoded.values, x_cont_imputed]) feature_names = np.concatenate([x_cat_encoded.columns, cont_cols]) n_continuous = len(cont_cols) feature_ranges = cat_feature_ranges + cont_feature_ranges - - t = data['years'].astype(float).values * 365.25 + t = data["years"].astype(float).values * 365.25 valid = ~np.isnan(t) return ( @@ -83,10 +85,9 @@ def load_pbc2_dataset(): event_type[valid], feature_names, n_continuous, - feature_ranges + feature_ranges, ) if __name__ == "__main__": x, t, e, feature_names, n_continuous, feature_ranges = load_pbc2_dataset() - \ No newline at end of file diff --git a/datasets/support2.csv b/datasets/support2.csv index b090d1b2..07c9d5c2 100644 --- a/datasets/support2.csv +++ b/datasets/support2.csv @@ -9103,4 +9103,4 @@ sno,age,death,sex,hospdead,slos,d.time,dzgroup,dzclass,num.co,edu,income,scoma,c 9102, 55.15399,0,female,0, 29, 347,Coma,Coma,1,11,, 41, 35377.000, 23558.5000, 22131.04690,18.000000,white,25.7968750, 31,0.5539550780,0.4859619140, 1,0,0,no,0.500000000,5.000000e-01,no dnr, 29, 43.0,, 0.000000, 8,38.59375,218.50000,,, 5.89941406,135,7.289062, 190.000000, 49.000000, 0.000000,, 0,,0.0000000 9103, 70.38196,0,male,0, 8, 346,ARF/MOSF w/Sepsis,ARF/MOSF,1,,, 0, 46564.000, 31409.0156, 31131.25000,23.000000,white,22.6992188, 39,0.7419433590,0.6608886720, 18,0,0,no,0.899999619,7.999997e-01,no dnr, 8,111.0, 8.39843750, 83.000000,24,36.69531,180.00000,, 0.39996338, 2.69970703,139,7.379883, 189.000000, 60.000000,3900.000000,,,,2.5253906 9104, 47.01999,1,male,1, 7, 7,MOSF w/Malig,ARF/MOSF,1,13,, 0, 58439.000,,,35.500000,white,40.1953125, 51,0.1779785160,0.0919952393, 22,0,0,yes,0.089999974,8.999997e-02,dnr after sadm, 5, 99.0, 7.59960938,110.000000,24,36.39844,428.56250, 1.1999512, 0.39996338, 3.50000000,135,7.469727, 246.000000, 55.000000,,, 0,<2 mo. follow-up,0.0000000 -9105, 81.53894,1,female,0, 12, 198,ARF/MOSF w/Sepsis,ARF/MOSF,1, 8,$11-$25k, 0, 15604.000, 10605.7578, 12146.15620,13.500000,white,18.0976562, 7,0.8328857420,0.7769775390, 1,1,0,no,,,no dnr, 12, 75.0, 8.59960938, 69.000000,24,36.19531,230.40625, 4.5000000, 0.59997559, 1.19995117,137,7.289062, 187.000000, 15.000000,, 0,,no(M2 and SIP pres),0.4947510 \ No newline at end of file +9105, 81.53894,1,female,0, 12, 198,ARF/MOSF w/Sepsis,ARF/MOSF,1, 8,$11-$25k, 0, 15604.000, 10605.7578, 12146.15620,13.500000,white,18.0976562, 7,0.8328857420,0.7769775390, 1,1,0,no,,,no dnr, 12, 75.0, 8.59960938, 69.000000,24,36.19531,230.40625, 4.5000000, 0.59997559, 1.19995117,137,7.289062, 187.000000, 15.000000,, 0,,no(M2 and SIP pres),0.4947510 diff --git a/datasets/support_dataset.py b/datasets/support_dataset.py index 2146e92f..ce1aae67 100644 --- a/datasets/support_dataset.py +++ b/datasets/support_dataset.py @@ -1,10 +1,11 @@ import os -import pandas as pd + import numpy as np +import pandas as pd + # Enable and import IterativeImputer (MICE) from scikit-learn: from sklearn.experimental import enable_iterative_imputer # noqa -from sklearn.impute import IterativeImputer, SimpleImputer - +from sklearn.impute import SimpleImputer def load_support_dataset(): @@ -12,9 +13,11 @@ def load_support_dataset(): Load and preprocess the SUPPORT dataset. This function reads the SUPPORT dataset from a CSV file, imputes missing values, encodes categorical features, and constructs the outcome variable for survival analysis. - It returns the processed features, time-to-event data, event types, feature names, + It returns the processed features, time-to-event data, event types, feature names, the number of continuous features, and feature ranges. - Returns: + + Returns + ------- tuple: A tuple containing: - x (numpy.ndarray): Combined array of processed categorical and continuous features. - t (numpy.ndarray): Time-to-event data with a +1 offset. @@ -22,7 +25,9 @@ def load_support_dataset(): - feature_names (numpy.ndarray): Array of feature names. - n_continuous (int): Number of continuous features. - feature_ranges (list): List of tuples representing the range (min, max) for each feature. - Notes: + + Notes + ----- - The dataset file "support2.csv" must be located in the same directory as this script. - Continuous features are imputed using the median strategy if missing values are present. - Categorical features are imputed using the most frequent strategy if missing values are present. @@ -30,72 +35,87 @@ def load_support_dataset(): - The outcome variable is constructed using the 'ca', 'dzgroup', and 'death' columns. - Time-to-event data ('d.time') is offset by +1 to avoid zero follow-up times. """ - - file_path = os.path.join(os.path.dirname(__file__), "support2.csv") data = pd.read_csv(file_path) print(data.columns) - + is_cancer = data["ca"].astype(str).str.lower().str.contains("meta") | data[ + "dzgroup" + ].astype(str).str.lower().str.contains("cancer") + event_type = np.where(data["death"] == 1, np.where(is_cancer, 1, 2), 0) - - is_cancer = data['ca'].astype(str).str.lower().str.contains("meta") | \ - data['dzgroup'].astype(str).str.lower().str.contains("cancer") - event_type = np.where(data['death'] == 1, np.where(is_cancer, 1, 2), 0) - - - - cont_cols = [ - 'age', 'num.co', 'meanbp', 'wblc', 'hrt', 'resp', 'temp', 'pafi', 'alb', - 'bili', 'crea', 'sod', 'ph', 'glucose', 'bun', 'urine', - 'scoma', 'aps', 'sps', 'adls', 'adlsc', 'charges', 'totcst', 'totmcst', 'avtisst' + "age", + "num.co", + "meanbp", + "wblc", + "hrt", + "resp", + "temp", + "pafi", + "alb", + "bili", + "crea", + "sod", + "ph", + "glucose", + "bun", + "urine", + "scoma", + "aps", + "sps", + "adls", + "adlsc", + "charges", + "totcst", + "totmcst", + "avtisst", ] x_cont = data[cont_cols] - + if x_cont.isnull().values.any(): - simp_imputer = SimpleImputer(strategy='most_frequent') #originally was median + simp_imputer = SimpleImputer(strategy="most_frequent") # originally was median x_cont_imputed = simp_imputer.fit_transform(x_cont) else: x_cont_imputed = x_cont.values - cont_feature_ranges = list(zip(np.nanmin(x_cont_imputed, axis=0), - np.nanmax(x_cont_imputed, axis=0))) + cont_feature_ranges = list( + zip(np.nanmin(x_cont_imputed, axis=0), np.nanmax(x_cont_imputed, axis=0)) + ) # -- Categorical features -- # Remove leakage fields 'ca', 'dzgroup', 'dzclass' - cat_cols = ['sex', 'income', 'race', 'dnr', 'dementia', 'diabetes'] + cat_cols = ["sex", "income", "race", "dnr", "dementia", "diabetes"] x_cat = data[cat_cols] if x_cat.isnull().values.any(): - cat_imputer = SimpleImputer(strategy='most_frequent') + cat_imputer = SimpleImputer(strategy="most_frequent") x_cat_imputed = cat_imputer.fit_transform(x_cat) else: x_cat_imputed = x_cat.values x_cat_df = pd.DataFrame(x_cat_imputed, columns=cat_cols) - + x_cat_encoded = pd.get_dummies(x_cat_df, drop_first=True) cat_feature_ranges = [(0.0, 1.0)] * x_cat_encoded.shape[1] - x = np.hstack([x_cat_encoded.values, x_cont_imputed]) feature_names = np.concatenate([x_cat_encoded.columns, cont_cols]) n_continuous = len(cont_cols) feature_ranges = cat_feature_ranges + cont_feature_ranges - - t = data['d.time'].values + t = data["d.time"].values valid = ~np.isnan(t) - + print("Completed imputation of missing values.") return ( x[valid], - t[valid] + 1, # Add +1 offset to follow-up times + t[valid] + 1, # Add +1 offset to follow-up times event_type[valid], feature_names, n_continuous, - feature_ranges + feature_ranges, ) + if __name__ == "__main__": x, t, e, feature_names, n_continuous, feature_ranges = load_support_dataset() print("Feature names:", feature_names) diff --git a/datasets/synthetic_dataset.py b/datasets/synthetic_dataset.py index 84d5cb17..abd62a1b 100644 --- a/datasets/synthetic_dataset.py +++ b/datasets/synthetic_dataset.py @@ -1,21 +1,26 @@ +import os +from typing import List, Tuple + import numpy as np import pandas as pd -from typing import Tuple, List -import os import torch -def load_synthetic_dataset() -> Tuple[np.ndarray, np.ndarray, np.ndarray, List[str], int, List[tuple]]: + +def load_synthetic_dataset() -> Tuple[ + np.ndarray, np.ndarray, np.ndarray, List[str], int, List[tuple] +]: """ Loads a synthetic competing risks dataset from a CSV file. - + The CSV is expected to have a header with the following columns: - time: observed time - label: event indicator (0 for censored; >0 for event types) - true_time: (optional) true time (unused here) - true_label: (optional) true event label (unused here) - feature1, feature2, ..., featureN: feature values - - Returns: + + Returns + ------- X (np.ndarray): Feature matrix of shape (n_samples, n_features). T_obs (np.ndarray): Observed times of shape (n_samples,). e (np.ndarray): Event indicators of shape (n_samples,). @@ -23,30 +28,24 @@ def load_synthetic_dataset() -> Tuple[np.ndarray, np.ndarray, np.ndarray, List[s n_continuous (int): Total number of continuous features. feature_ranges (List[tuple]): List of (min, max) tuples for each feature. """ - use_gpu_compatible_dtype = torch.cuda.is_available() - - - - + file_path = os.path.join(os.path.dirname(__file__), "synthetic_comprisk.csv") df = pd.read_csv(file_path) - - - T_obs = df["time"].values .astype(np.float32) + T_obs = df["time"].values.astype(np.float32) e = df["label"].values.astype(np.float32) - - + feature_columns = [col for col in df.columns if col.startswith("feature")] - X = df[feature_columns].values .astype(np.float32) - - + X = df[feature_columns].values.astype(np.float32) + feature_names = feature_columns n_continuous = X.shape[1] - - feature_ranges = [(float(X[:, i].min()), float(X[:, i].max())) for i in range(n_continuous)] - + + feature_ranges = [ + (float(X[:, i].min()), float(X[:, i].max())) for i in range(n_continuous) + ] + return X, T_obs, e, feature_names, n_continuous, feature_ranges @@ -61,10 +60,3 @@ def load_synthetic_dataset() -> Tuple[np.ndarray, np.ndarray, np.ndarray, List[s # For a quick inspection, print the first 5 rows (features, time, event) print("First 5 rows:") print(np.hstack([X[:5], T_obs[:5, None], e[:5, None]])) - - - - - - - diff --git a/pyproject.toml b/pyproject.toml index 1308ddbd..bc1fd841 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,6 +12,7 @@ dependencies = [ "configargparse>=1.7", "lifelines>=0.30.0", "matplotlib>=3.10.1", + "mypy>=1.19.1", "openpyxl>=3.1.5", "optuna>=4.3.0", "pandas>=2.2.3", @@ -22,5 +23,101 @@ dependencies = [ "torch>=2.7.0", ] +[dependency-groups] +docs = [ + "mkdocs>=1.5.3", + "mkdocs-material>=9.5.12", + "mkdocstrings>=0.24.1", + "mkdocstrings-python>=1.8.0", + "pymdown-extensions>=10.7.1", + "mike>=2.0.0", + "click<=8.2.1", +] + +[tool.mypy] +follow_imports = "normal" +ignore_missing_imports = false +install_types = true +pretty = true +non_interactive = true +allow_untyped_defs = false +no_implicit_optional = true +check_untyped_defs = true +namespace_packages = true +explicit_package_bases = true +warn_unused_configs = true +allow_subclassing_any = false +allow_untyped_calls = false +allow_incomplete_defs = false +allow_untyped_decorators = false +warn_redundant_casts = true +warn_unused_ignores = true +implicit_reexport = false +strict_equality = true +extra_checks = true +mypy_path = "crisp_nam" + +[tool.ruff] +include = ["crisp_nam/*.py", "pyproject.toml", "*.ipynb"] +extend-exclude = ["*.csv", "*.json", "datasets/", "data_utils/", "training_scripts/"] +line-length = 88 + +[tool.ruff.format] +quote-style = "double" +indent-style = "space" +docstring-code-format = true + +[tool.ruff.lint] +select = [ + "A", # flake8-builtins + "B", # flake8-bugbear + "COM", # flake8-commas + "C4", # flake8-comprehensions + "RET", # flake8-return + "SIM", # flake8-simplify + "ICN", # flake8-import-conventions + "Q", # flake8-quotes + "RSE", # flake8-raise + "D", # pydocstyle + "E", # pycodestyle + "F", # pyflakes + "I", # isort + "W", # pycodestyle + "N", # pep8-naming + "ERA", # eradicate + "PL", # pylint +] + +fixable = ["A", "B", "COM", "C4", "RET", "SIM", "ICN", "Q", "RSE", "D", "E", "F", "I", "W", "N", "ERA", "PL"] +ignore = [ + "B905", # `zip()` without an explicit `strict=` parameter + "E501", # line too long + "D203", # 1 blank line required before class docstring + "D213", # Multi-line docstring summary should start at the second line + "PLR2004", # Replace magic number with named constant + "PLR0913", # Too many arguments + "COM812", # Missing trailing comma + "ERA001", # Found commented-out code (too many false positives with math comments) + "A001", # Ignore variable `input` is shadowing a Python builtin (common for torch) + "A002", # Ignore variable `input` is shadowing a Python builtin in function (common for torch) + "D301", # r-strings for docstrings with backslashes +] + +# Ignore import violations in all `__init__.py` files. +[tool.ruff.lint.per-file-ignores] +"__init__.py" = ["E402", "F401", "F403", "F811"] + +[tool.ruff.lint.pep8-naming] +ignore-names = ["X*", "setUp"] + +[tool.ruff.lint.isort] +lines-after-imports = 2 + +[tool.ruff.lint.pydocstyle] +convention = "numpy" + +[tool.ruff.lint.pycodestyle] +max-doc-length = 88 + [tool.hatch.build.targets.sdist] only-include = ["crisp_nam"] diff --git a/results/best_params/best_params_framingham_deephit.yaml b/results/best_params/best_params_framingham_deephit.yaml index 92e10146..27f8d8e9 100644 --- a/results/best_params/best_params_framingham_deephit.yaml +++ b/results/best_params/best_params_framingham_deephit.yaml @@ -22,4 +22,4 @@ active_fn: tanh # General parameters dropout_rate: 0.3 seed: 42 -n_folds: 5 \ No newline at end of file +n_folds: 5 diff --git a/results/best_params/best_params_pbc_deephit.yaml b/results/best_params/best_params_pbc_deephit.yaml index 7fd8f3b2..a14f4765 100644 --- a/results/best_params/best_params_pbc_deephit.yaml +++ b/results/best_params/best_params_pbc_deephit.yaml @@ -23,4 +23,4 @@ active_fn: tanh # General parameters dropout_rate: 0.3 seed: 42 -n_folds: 5 \ No newline at end of file +n_folds: 5 diff --git a/results/best_params/best_params_support2_deephit.yaml b/results/best_params/best_params_support2_deephit.yaml index 36844915..4d1df68d 100644 --- a/results/best_params/best_params_support2_deephit.yaml +++ b/results/best_params/best_params_support2_deephit.yaml @@ -27,4 +27,4 @@ active_fn: tanh # General parameters dropout_rate: 0.3 seed: 42 -n_folds: 5 \ No newline at end of file +n_folds: 5 diff --git a/results/best_params/best_params_synthetic_deephit.yaml b/results/best_params/best_params_synthetic_deephit.yaml index f794156c..4b19279b 100644 --- a/results/best_params/best_params_synthetic_deephit.yaml +++ b/results/best_params/best_params_synthetic_deephit.yaml @@ -1,19 +1,18 @@ -scaling: standard -num_epochs: 250 -batch_size: 256 -learning_rate: 1.0e-3 -l2_reg: 1.0e-5 -patience: 10 -alpha: 1.0 -beta: 0.0 -gamma: 0.0 -h_dim_shared: 128 -h_dim_CS: 32 -num_layers_shared: 1 -num_layers_CS: 2 -num_categories: 100 -active_fn: tanh -dropout_rate: 0.3 -seed: 42 -n_folds: 5 - +scaling: standard +num_epochs: 250 +batch_size: 256 +learning_rate: 1.0e-3 +l2_reg: 1.0e-5 +patience: 10 +alpha: 1.0 +beta: 0.0 +gamma: 0.0 +h_dim_shared: 128 +h_dim_CS: 32 +num_layers_shared: 1 +num_layers_CS: 2 +num_categories: 100 +active_fn: tanh +dropout_rate: 0.3 +seed: 42 +n_folds: 5 diff --git a/results/logs/nested_cv_raw_metrics_framingham.json b/results/logs/nested_cv_raw_metrics_framingham.json index 5ac18164..330d31e7 100644 --- a/results/logs/nested_cv_raw_metrics_framingham.json +++ b/results/logs/nested_cv_raw_metrics_framingham.json @@ -138,4 +138,4 @@ "scaling": "standard", "seed": 42 } -} \ No newline at end of file +} diff --git a/results/logs/nested_cv_raw_metrics_pbc.json b/results/logs/nested_cv_raw_metrics_pbc.json index 4153a978..3c7dd293 100644 --- a/results/logs/nested_cv_raw_metrics_pbc.json +++ b/results/logs/nested_cv_raw_metrics_pbc.json @@ -138,4 +138,4 @@ "scaling": "standard", "seed": 42 } -} \ No newline at end of file +} diff --git a/results/logs/nested_cv_raw_metrics_support.json b/results/logs/nested_cv_raw_metrics_support.json index 4cbca613..e6bde10b 100644 --- a/results/logs/nested_cv_raw_metrics_support.json +++ b/results/logs/nested_cv_raw_metrics_support.json @@ -138,4 +138,4 @@ "scaling": "standard", "seed": 42 } -} \ No newline at end of file +} diff --git a/results/logs/nested_cv_raw_metrics_synthetic.json b/results/logs/nested_cv_raw_metrics_synthetic.json index d982097a..f8b5e73c 100644 --- a/results/logs/nested_cv_raw_metrics_synthetic.json +++ b/results/logs/nested_cv_raw_metrics_synthetic.json @@ -138,4 +138,4 @@ "scaling": "standard", "seed": 42 } -} \ No newline at end of file +} diff --git a/results/plots/nested_cv_feature_importance_risk_1_pbc.png b/results/plots/nested_cv_feature_importance_risk_1_pbc.png index 4ee856e0ef3e2762968a96b4ffc7be7f37285018..8dbc1b1a2ad1584b7b7da35b193d502c4ea40427 100644 GIT binary patch literal 99488 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zX)wtA1b!Kl{sY5vhsPiPk>DSy{9pevWfnN`CQFz7&s$7A&i{E9Q%{1$TDr__`t0$w VEBDJtG0e}MN^0Bg{B->E{{noEq?rH! diff --git a/training_scripts/config.yaml b/training_scripts/config.yaml index d361646f..5fd9e691 100644 --- a/training_scripts/config.yaml +++ b/training_scripts/config.yaml @@ -17,4 +17,4 @@ batch_norm: False # Other parameters seed: 42 -n_folds: 2 \ No newline at end of file +n_folds: 2 diff --git a/training_scripts/model_utils.py b/training_scripts/model_utils.py index 98be4143..812f0e62 100644 --- a/training_scripts/model_utils.py +++ b/training_scripts/model_utils.py @@ -1,7 +1,8 @@ import random -import torch import numpy as np +import torch + def set_seed(seed=42): random.seed(seed) @@ -31,34 +32,35 @@ def step(self, val_loss): self.should_stop = True - # Utility functions to create masks for DeepHit def create_fc_mask1(k, t, num_Event, num_Category, device=None): """Create mask1 for loss calculation - for uncensored loss""" N = len(k) mask = torch.zeros((N, num_Event, num_Category), device=device) - + for i in range(N): if k[i] > 0: # Not censored event_idx = int(k[i] - 1) time_idx = int(t[i]) if time_idx < num_Category: mask[i, event_idx, time_idx] = 1.0 - + return mask + def create_fc_mask2(t, num_Category, device=None): """Create mask2 for loss calculation - for censored loss""" N = len(t) mask = torch.zeros((N, num_Category), device=device) - + for i in range(N): time_idx = int(t[i]) for j in range(time_idx, num_Category): mask[i, j] = 1.0 - + return mask + # Pre-create masks for DeepHit on GPU def create_fc_mask1_gpu(e, t_disc, num_Event, num_Category, device): """ @@ -67,15 +69,16 @@ def create_fc_mask1_gpu(e, t_disc, num_Event, num_Category, device): """ batch_size = e.size(0) mask1 = torch.zeros(batch_size, num_Event, num_Category, device=device) - + for i in range(batch_size): if e[i] > 0: # if not censored event_idx = int(e[i].item()) - 1 t_idx = int(t_disc[i].item()) mask1[i, event_idx, t_idx] = 1 - + return mask1 + def create_fc_mask2_gpu(t_disc, num_Category, device): """ Create second mask for DeepHit loss computation @@ -83,9 +86,9 @@ def create_fc_mask2_gpu(t_disc, num_Category, device): """ batch_size = t_disc.size(0) mask2 = torch.zeros(batch_size, num_Category, device=device) - + for i in range(batch_size): t_idx = int(t_disc[i].item()) mask2[i, t_idx:] = 1 - + return mask2 diff --git a/training_scripts/train.py b/training_scripts/train.py index 2192a6c3..34d626c6 100644 --- a/training_scripts/train.py +++ b/training_scripts/train.py @@ -1,79 +1,116 @@ -import configargparse from collections import defaultdict -import torch +import configargparse +import matplotlib.pyplot as plt import numpy as np import pandas as pd -import torch.nn as nn -import torch.optim as optim -import matplotlib.pyplot as plt -from tabulate import tabulate -from torch.utils.data import DataLoader, Subset +import torch +from model_utils import EarlyStopping, set_seed from sklearn.model_selection import StratifiedKFold -from sklearn.preprocessing import StandardScaler, MinMaxScaler -from sksurv.util import Surv +from sklearn.preprocessing import MinMaxScaler, StandardScaler from sksurv.metrics import concordance_index_ipcw +from sksurv.util import Surv +from tabulate import tabulate +from torch import optim +from torch.utils.data import DataLoader +from crisp_nam.metrics import auc_td, brier_score from crisp_nam.models import CrispNamModel from crisp_nam.utils import ( - weighted_negative_log_likelihood_loss, + compute_baseline_cif, + compute_l2_penalty, negative_log_likelihood_loss, - compute_l2_penalty + plot_coxnam_shape_functions, + plot_feature_importance, + predict_absolute_risk, + weighted_negative_log_likelihood_loss, ) from data_utils import * -from model_utils import EarlyStopping, set_seed -from crisp_nam.metrics import brier_score, auc_td -from crisp_nam.utils import predict_absolute_risk, compute_baseline_cif -from crisp_nam.utils import plot_coxnam_shape_functions, plot_feature_importance + def parse_args(): parser = configargparse.ArgumentParser( description="Training script for MultiTaskCoxNAM model", default_config_files=["config.yml"], - config_file_parser_class=configargparse.YAMLConfigFileParser + config_file_parser_class=configargparse.YAMLConfigFileParser, ) - - parser.add_argument("-c", "--config", is_config_file=True, - help="Path to config file") - - parser.add_argument("--dataset", type=str, default="framingham", - help="Dataset to use: (framingham, support, pbc, synthetic)") - - parser.add_argument("--scaling", type=str, default="standard", choices=["minmax", "standard", "none"], - help="Data scaling method for continuous features") - - parser.add_argument("--num_epochs", type=int, default=500, - help="Number of training epochs") - parser.add_argument("--batch_size", type=int, default=256, - help="Batch size for training") - parser.add_argument("--learning_rate", type=float, default=1e-3, - help="Learning rate for optimizer") - parser.add_argument("--l2_reg", type=float, default=1e-3, - help="L2 regularization weight") - parser.add_argument("--patience", type=int, default=10, - help="Patience for early stopping") - - parser.add_argument("--dropout_rate", type=float, default=0.5, - help="Dropout rate for model") - parser.add_argument("--feature_dropout", type=float, default=0.1, - help="Feature dropout rate") - parser.add_argument("--hidden_dimensions", type=str, default="64,64", - help="Hidden layer dimensions (comma-separated)") - parser.add_argument("--batch_norm", type=str, default="False", choices=["True", "False"], - help="Whether to use batch normalization") - - parser.add_argument("--seed", type=int, default=42, - help="Random seed for reproducibility") - parser.add_argument("--n_folds", type=int, default=5, - help="Number of folds for cross-validation") - - return parser.parse_args() + parser.add_argument( + "-c", "--config", is_config_file=True, help="Path to config file" + ) + parser.add_argument( + "--dataset", + type=str, + default="framingham", + help="Dataset to use: (framingham, support, pbc, synthetic)", + ) + + parser.add_argument( + "--scaling", + type=str, + default="standard", + choices=["minmax", "standard", "none"], + help="Data scaling method for continuous features", + ) + + parser.add_argument( + "--num_epochs", type=int, default=500, help="Number of training epochs" + ) + parser.add_argument( + "--batch_size", type=int, default=256, help="Batch size for training" + ) + parser.add_argument( + "--learning_rate", type=float, default=1e-3, help="Learning rate for optimizer" + ) + parser.add_argument( + "--l2_reg", type=float, default=1e-3, help="L2 regularization weight" + ) + parser.add_argument( + "--patience", type=int, default=10, help="Patience for early stopping" + ) + + parser.add_argument( + "--dropout_rate", type=float, default=0.5, help="Dropout rate for model" + ) + parser.add_argument( + "--feature_dropout", type=float, default=0.1, help="Feature dropout rate" + ) + parser.add_argument( + "--hidden_dimensions", + type=str, + default="64,64", + help="Hidden layer dimensions (comma-separated)", + ) + parser.add_argument( + "--batch_norm", + type=str, + default="False", + choices=["True", "False"], + help="Whether to use batch normalization", + ) + parser.add_argument( + "--seed", type=int, default=42, help="Random seed for reproducibility" + ) + parser.add_argument( + "--n_folds", type=int, default=5, help="Number of folds for cross-validation" + ) -def train_model(model, train_loader, val_loader=None, num_epochs=500, learning_rate=1e-3, - l2_reg=0.01, patience=10, event_weights=None, verbose=True): + return parser.parse_args() + + +def train_model( + model, + train_loader, + val_loader=None, + num_epochs=500, + learning_rate=1e-3, + l2_reg=0.01, + patience=10, + event_weights=None, + verbose=True, +): optimizer = optim.AdamW(model.parameters(), lr=learning_rate) early_stopper = EarlyStopping(patience=patience) device = next(model.parameters()).device @@ -87,12 +124,18 @@ def train_model(model, train_loader, val_loader=None, num_epochs=500, learning_r # Use weighted loss if event_weights is provided if event_weights is not None: - loss = weighted_negative_log_likelihood_loss(risk_scores, t, e, - model.num_competing_risks, - event_weights=event_weights) + loss = weighted_negative_log_likelihood_loss( + risk_scores, + t, + e, + model.num_competing_risks, + event_weights=event_weights, + ) else: - loss = negative_log_likelihood_loss(risk_scores, t, e, model.num_competing_risks) - + loss = negative_log_likelihood_loss( + risk_scores, t, e, model.num_competing_risks + ) + reg = compute_l2_penalty(model) * l2_reg total = loss + reg @@ -110,22 +153,30 @@ def train_model(model, train_loader, val_loader=None, num_epochs=500, learning_r for x, t, e, _ in val_loader: x, t, e = x.to(device), t.to(device), e.to(device) risk_scores, _ = model(x) - + # Use same loss function as in training if event_weights is not None: - loss = weighted_negative_log_likelihood_loss(risk_scores, t, e, - model.num_competing_risks, - event_weights=event_weights) + loss = weighted_negative_log_likelihood_loss( + risk_scores, + t, + e, + model.num_competing_risks, + event_weights=event_weights, + ) else: - loss = negative_log_likelihood_loss(risk_scores, t, e, model.num_competing_risks) - + loss = negative_log_likelihood_loss( + risk_scores, t, e, model.num_competing_risks + ) + reg = compute_l2_penalty(model) * l2_reg val_loss += (loss + reg).item() avg_val_loss = val_loss / len(val_loader) early_stopper.step(avg_val_loss) if verbose: - print(f"Epoch {epoch + 1} | Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f}") + print( + f"Epoch {epoch + 1} | Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f}" + ) if early_stopper.should_stop: if verbose: @@ -149,10 +200,10 @@ def evaluate_model(model, x_val, t_val, e_val, t_train, e_train, abs_risks, time abs_risks: Array of shape (n_samples, n_events, n_times) with predicted absolute risks. times: List of time points at which to evaluate. - Returns: + Returns + ------- metrics: dict of evaluation metrics by event and time. """ - survival_train = Surv.from_arrays(e_train != 0, t_train) survival_val = Surv.from_arrays(e_val != 0, t_val) @@ -162,12 +213,11 @@ def evaluate_model(model, x_val, t_val, e_val, t_train, e_train, abs_risks, time for k in range(n_events): for i, time in enumerate(times): risk_preds = abs_risks[:, k, i] - - + try: risk_preds_2d = np.zeros((len(risk_preds), len(times))) - risk_preds_2d[:, i] = risk_preds - + risk_preds_2d[:, i] = risk_preds + auc_score, _ = auc_td( e_val, t_val, @@ -175,16 +225,14 @@ def evaluate_model(model, x_val, t_val, e_val, t_train, e_train, abs_risks, time times, time, km=(e_train, t_train), - primary_risk=k+1 + primary_risk=k + 1, ) - metrics[f"auc_event{k+1}_t{time:.2f}"].append(float(auc_score)) + metrics[f"auc_event{k + 1}_t{time:.2f}"].append(float(auc_score)) except Exception as ex: - print(f"[Warning] AUC failed at t={time:.2f}, event={k+1}: {ex}") - metrics[f"auc_event{k+1}_t{time:.2f}"].append(np.nan) + print(f"[Warning] AUC failed at t={time:.2f}, event={k + 1}: {ex}") + metrics[f"auc_event{k + 1}_t{time:.2f}"].append(np.nan) - try: - brier_score_val, _ = brier_score( e_val, t_val, @@ -192,30 +240,28 @@ def evaluate_model(model, x_val, t_val, e_val, t_train, e_train, abs_risks, time times, time, km=(e_train, t_train), - primary_risk=k+1 + primary_risk=k + 1, + ) + metrics[f"brier_event{k + 1}_t{time:.2f}"].append( + float(brier_score_val) ) - metrics[f"brier_event{k+1}_t{time:.2f}"].append(float(brier_score_val)) except Exception as ex: - print(f"[Warning] Brier failed at t={time:.2f}, event={k+1}: {ex}") + print(f"[Warning] Brier failed at t={time:.2f}, event={k + 1}: {ex}") print(f"Debug: surv_probs shape={risk_preds_2d.shape}") - metrics[f"brier_event{k+1}_t{time:.2f}"].append(np.nan) + metrics[f"brier_event{k + 1}_t{time:.2f}"].append(np.nan) - try: tdci_result = concordance_index_ipcw( - survival_train, - survival_val, - estimate=risk_preds, - tau=time + survival_train, survival_val, estimate=risk_preds, tau=time ) if isinstance(tdci_result, tuple): tdci_score = tdci_result[0] else: - tdci_score = tdci_result - metrics[f"tdci_event{k+1}_t{time:.2f}"].append(float(tdci_score)) + tdci_score = tdci_result + metrics[f"tdci_event{k + 1}_t{time:.2f}"].append(float(tdci_score)) except Exception as ex: - print(f"[Warning] td-CI failed at t={time:.2f}, event={k+1}: {ex}") - metrics[f"tdci_event{k+1}_t{time:.2f}"].append(np.nan) + print(f"[Warning] td-CI failed at t={time:.2f}, event={k + 1}: {ex}") + metrics[f"tdci_event{k + 1}_t{time:.2f}"].append(np.nan) return metrics @@ -224,88 +270,88 @@ def display_metrics_table(metrics_dict, n_folds=5, quantiles=[0.25, 0.5, 0.75]): """ Display evaluation metrics summarized across folds for different time quantiles """ - - time_points_by_metric = {} event_types = set() - metric_types = ['auc', 'tdci', 'brier'] - + metric_types = ["auc", "tdci", "brier"] + for key, values in metrics_dict.items(): if any(metric in key for metric in metric_types): - parts = key.split('_') + parts = key.split("_") metric_type = parts[0] event_info = parts[1] - + # Extract event type - event_type = int(event_info.replace('event', '')) + event_type = int(event_info.replace("event", "")) event_types.add(event_type) - + # Extract time point - if len(parts) > 2 and parts[2].startswith('t'): - time_point = float(parts[2].replace('t', '')) - + if len(parts) > 2 and parts[2].startswith("t"): + time_point = float(parts[2].replace("t", "")) + # Initialize nested dictionaries if needed if (event_type, metric_type) not in time_points_by_metric: time_points_by_metric[(event_type, metric_type)] = {} - + # Store metrics by time point time_points_by_metric[(event_type, metric_type)][time_point] = values - + results = [] - + for event_type in sorted(event_types): - row = {'Risk': f"Type {event_type}"} - + row = {"Risk": f"Type {event_type}"} + for metric_type in metric_types: if (event_type, metric_type) not in time_points_by_metric: # Skip metrics that don't exist for this event type for q in quantiles: row[f"{metric_type.upper()}_q{q:.2f}"] = "N/A" continue - + # Get all time points for this event/metric time_data = time_points_by_metric[(event_type, metric_type)] sorted_times = sorted(time_data.keys()) - + # For each quantile for q in quantiles: # Calculate the index for this quantile q_idx = max(0, min(len(sorted_times) - 1, int(len(sorted_times) * q))) - + # Get the corresponding time point for this quantile q_time = sorted_times[q_idx] - + # Get the metrics for this time point q_values = time_data[q_time] - + # Calculate and format statistics if q_values: value_array = np.array(q_values) mean_val = np.nanmean(value_array) std_val = np.nanstd(value_array) - row[f"{metric_type.upper()}_q{q:.2f}"] = f"{mean_val:.3f} ± {std_val:.3f}" + row[f"{metric_type.upper()}_q{q:.2f}"] = ( + f"{mean_val:.3f} ± {std_val:.3f}" + ) else: row[f"{metric_type.upper()}_q{q:.2f}"] = "N/A" - + results.append(row) - + df = pd.DataFrame(results) - + # Define column order - columns = ['Risk'] - for metric in ['AUC', 'TDCI', 'BRIER']: + columns = ["Risk"] + for metric in ["AUC", "TDCI", "BRIER"]: for q in quantiles: columns.append(f"{metric}_q{q:.2f}") - + # Select columns in the right order (only those that exist) df = df[[col for col in columns if col in df.columns]] - + print("\nSummary Performance Metrics:") - print(tabulate(df, headers='keys', tablefmt='pretty', showindex=False)) - + print(tabulate(df, headers="keys", tablefmt="pretty", showindex=False)) + print("\nInterpretation:") print("- AUC: 0.5=random, >0.7=good, >0.8=excellent") - print("- TDCI (Time-Dependent C-Index): 0.5=random, >0.7=good, >0.8=excellent") + print("- TDCI (Time-Dependent C-Index): 0.5=random, >0.7=good, >0.8=excellent") print("- Brier Score: 0=perfect, <0.25=good, >0.25=poor") @@ -314,64 +360,104 @@ def parse_args(): parser = configargparse.ArgumentParser( description="Training script for MultiTaskCoxNAM model", default_config_files=["config.yaml"], - config_file_parser_class=configargparse.YAMLConfigFileParser + config_file_parser_class=configargparse.YAMLConfigFileParser, ) - + # Config file option - parser.add_argument("-c", "--config", is_config_file=True, - help="Path to config file") - + parser.add_argument( + "-c", "--config", is_config_file=True, help="Path to config file" + ) + # Dataset - parser.add_argument("--dataset", type=str, default="framingham", choices=["framingham", "support", "pbc", "synthetic"], - help="Dataset to use") - + parser.add_argument( + "--dataset", + type=str, + default="framingham", + choices=["framingham", "support", "pbc", "synthetic"], + help="Dataset to use", + ) + # Data scaling - parser.add_argument("--scaling", type=str, default="standard", choices=["minmax", "standard", "none"], - help="Data scaling method for continuous features") - + parser.add_argument( + "--scaling", + type=str, + default="standard", + choices=["minmax", "standard", "none"], + help="Data scaling method for continuous features", + ) + # Training parameters - parser.add_argument("--num_epochs", type=int, default=500, - help="Number of training epochs") - parser.add_argument("--batch_size", type=int, default=256, - help="Batch size for training") - parser.add_argument("--learning_rate", type=float, default=1e-3, - help="Learning rate for optimizer") - parser.add_argument("--l2_reg", type=float, default=1e-3, - help="L2 regularization weight") - parser.add_argument("--patience", type=int, default=10, - help="Patience for early stopping") - - - parser.add_argument("--dropout_rate", type=float, default=0.5, - help="Dropout rate for model") - parser.add_argument("--feature_dropout", type=float, default=0.1, - help="Feature dropout rate") - parser.add_argument("--hidden_dimensions", type=str, default="64,64", - help="Hidden layer dimensions (comma-separated)") - parser.add_argument("--batch_norm", type=str, default="False", choices=["True", "False"], - help="Whether to use batch normalization") - - - parser.add_argument("--event_weighting", type=str, default="none", - choices=["none", "balanced", "custom"], - help="Event weighting strategy (none, balanced, custom)") - parser.add_argument("--custom_event_weights", type=str, default=None, - help="Custom weights for events (comma-separated, e.g., '1.0,2.0')") - - # - parser.add_argument("--seed", type=int, default=42, - help="Random seed for reproducibility") - parser.add_argument("--n_folds", type=int, default=5, - help="Number of folds for cross-validation") - + parser.add_argument( + "--num_epochs", type=int, default=500, help="Number of training epochs" + ) + parser.add_argument( + "--batch_size", type=int, default=256, help="Batch size for training" + ) + parser.add_argument( + "--learning_rate", + type=float, + default=1e-3, + help="Learning rate for optimizer", + ) + parser.add_argument( + "--l2_reg", type=float, default=1e-3, help="L2 regularization weight" + ) + parser.add_argument( + "--patience", type=int, default=10, help="Patience for early stopping" + ) + + parser.add_argument( + "--dropout_rate", type=float, default=0.5, help="Dropout rate for model" + ) + parser.add_argument( + "--feature_dropout", type=float, default=0.1, help="Feature dropout rate" + ) + parser.add_argument( + "--hidden_dimensions", + type=str, + default="64,64", + help="Hidden layer dimensions (comma-separated)", + ) + parser.add_argument( + "--batch_norm", + type=str, + default="False", + choices=["True", "False"], + help="Whether to use batch normalization", + ) + + parser.add_argument( + "--event_weighting", + type=str, + default="none", + choices=["none", "balanced", "custom"], + help="Event weighting strategy (none, balanced, custom)", + ) + parser.add_argument( + "--custom_event_weights", + type=str, + default=None, + help="Custom weights for events (comma-separated, e.g., '1.0,2.0')", + ) + + parser.add_argument( + "--seed", type=int, default=42, help="Random seed for reproducibility" + ) + parser.add_argument( + "--n_folds", + type=int, + default=5, + help="Number of folds for cross-validation", + ) + return parser.parse_args() - + args = parse_args() print(args) - + # Set random seed for reproducibility set_seed(args.seed) - + # Load the dataset if args.dataset.lower() == "framingham": x, t, e, feature_names, n_cont, _ = load_framingham() @@ -383,62 +469,62 @@ def parse_args(): x, t, e, feature_names, n_cont, _ = load_synthetic_dataset() else: raise ValueError(f"Dataset {args.dataset} not supported") - - + # Note: Scaling will be done inside cross-validation loop to prevent data leakage # Compute weights based on event distribution num_competing_risks = len(np.unique(e)) - 1 # Excluding censoring (0) - device = ("cuda" if torch.cuda.is_available() else "cpu") - - + device = "cuda" if torch.cuda.is_available() else "cpu" + event_weights = None - + if args.event_weighting != "none": if args.event_weighting == "balanced": # Compute balanced weights (inverse of class frequencies) event_counts = np.zeros(num_competing_risks) for k in range(1, num_competing_risks + 1): - event_counts[k-1] = np.sum(e == k) - + event_counts[k - 1] = np.sum(e == k) event_counts = np.maximum(event_counts, 1) - + # Inverse frequency weighting event_weights = 1.0 / event_counts - + # Normalize weights to sum to num_competing_risks event_weights = event_weights * (num_competing_risks / event_weights.sum()) - + print(f"Computed balanced event weights: {event_weights}") - + elif args.event_weighting == "custom": if args.custom_event_weights is None: - raise ValueError("Custom event weights must be provided when using custom weighting") - + raise ValueError( + "Custom event weights must be provided when using custom weighting" + ) + custom_weights = [float(w) for w in args.custom_event_weights.split(",")] if len(custom_weights) != num_competing_risks: - raise ValueError(f"Expected {num_competing_risks} weights, got {len(custom_weights)}") - + raise ValueError( + f"Expected {num_competing_risks} weights, got {len(custom_weights)}" + ) + event_weights = np.array(custom_weights) print(f"Using custom event weights: {event_weights}") - + event_weights = torch.tensor(event_weights, dtype=torch.float32, device=device) skf = StratifiedKFold(n_splits=args.n_folds, shuffle=True, random_state=args.seed) all_metrics = defaultdict(list) quantiles = [0.25, 0.5, 0.75] - safe_max = 0.99 * np.max(t) eval_times = np.quantile(t[t <= safe_max], quantiles) - + print(f"Evaluation times: {eval_times}") - + # Parse hidden dimensions string to list of integers hidden_dimensions = [int(dim) for dim in args.hidden_dimensions.split(",")] - + # Convert batch_norm string to boolean batch_norm = args.batch_norm.lower() == "true" @@ -470,25 +556,31 @@ def parse_args(): # Recalculate weights based on training set for this fold fold_event_counts = np.zeros(num_competing_risks) for k in range(1, num_competing_risks + 1): - fold_event_counts[k-1] = np.sum(e_train == k) - + fold_event_counts[k - 1] = np.sum(e_train == k) + # Avoid division by zero fold_event_counts = np.maximum(fold_event_counts, 1) - + # Inverse frequency weighting fold_event_weights = 1.0 / fold_event_counts - + # Normalize weights to sum to num_competing_risks - fold_event_weights = fold_event_weights * (num_competing_risks / fold_event_weights.sum()) - fold_event_weights = torch.tensor(fold_event_weights, dtype=torch.float32, device=device) - - print(f"Fold {fold+1} event weights: {fold_event_weights.cpu().numpy()}") + fold_event_weights = fold_event_weights * ( + num_competing_risks / fold_event_weights.sum() + ) + fold_event_weights = torch.tensor( + fold_event_weights, dtype=torch.float32, device=device + ) + + print(f"Fold {fold + 1} event weights: {fold_event_weights.cpu().numpy()}") # Create datasets with properly normalized data train_dataset = SurvivalDataset(x_train, t_train, e_train) val_dataset = SurvivalDataset(x_val, t_val, e_val) - - train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True) + + train_loader = DataLoader( + train_dataset, batch_size=args.batch_size, shuffle=True + ) val_loader = DataLoader(val_dataset, batch_size=args.batch_size) model = CrispNamModel( @@ -497,67 +589,72 @@ def parse_args(): hidden_sizes=hidden_dimensions, dropout_rate=args.dropout_rate, feature_dropout=args.feature_dropout, - batch_norm=batch_norm + batch_norm=batch_norm, ).to(device) - - train_model(model, train_loader, val_loader, - num_epochs=args.num_epochs, - learning_rate=args.learning_rate, - l2_reg=args.l2_reg, - patience=args.patience, - event_weights=fold_event_weights, - verbose=True) - + train_model( + model, + train_loader, + val_loader, + num_epochs=args.num_epochs, + learning_rate=args.learning_rate, + l2_reg=args.l2_reg, + patience=args.patience, + event_weights=fold_event_weights, + verbose=True, + ) + # Calculate baseline CIFs using the same eval times for all folds - baseline_cifs = {k: compute_baseline_cif(t_train, e_train, eval_times, k + 1) - for k in range(num_competing_risks)} - - abs_risks = predict_absolute_risk(model, x_val, baseline_cifs, eval_times, device=device) - - - fold_metrics = evaluate_model(model, x_val, t_val, e_val, - t_train, e_train, abs_risks, eval_times) - + baseline_cifs = { + k: compute_baseline_cif(t_train, e_train, eval_times, k + 1) + for k in range(num_competing_risks) + } + + abs_risks = predict_absolute_risk( + model, x_val, baseline_cifs, eval_times, device=device + ) + + fold_metrics = evaluate_model( + model, x_val, t_val, e_val, t_train, e_train, abs_risks, eval_times + ) + for k, v in fold_metrics.items(): all_metrics[k].extend(v) - display_metrics_table(all_metrics, n_folds=args.n_folds) - - #create figs subdirectory if not present + + # create figs subdirectory if not present import os + if not os.path.exists("figs"): os.makedirs("figs") - for risk in range(1, num_competing_risks + 1): - fig, _, top_positive, top_negative = plot_feature_importance( model=model, x_data=x, feature_names=feature_names, n_top=5, # Show top 5 positive contributors n_bottom=5, # Show top 5 negative contributors - risk_idx=risk, + risk_idx=risk, figsize=(6, 4), - output_file=f"figs/feature_importance_risk_new_{risk}_{args.dataset}.png" - ) - + output_file=f"figs/feature_importance_risk_new_{risk}_{args.dataset}.png", + ) + top_features = top_positive + top_negative - + fig, axes = plot_coxnam_shape_functions( model=model, - X=x, + X=x, risk_to_plot=risk, - feature_names=feature_names, - top_features=top_features, + feature_names=feature_names, + top_features=top_features, ncols=5, - figsize=(12,6), - output_file=f"figs/shape_functions_top_features_risk{risk}_{args.dataset}.png" + figsize=(12, 6), + output_file=f"figs/shape_functions_top_features_risk{risk}_{args.dataset}.png", ) plt.close(fig) if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/training_scripts/train_deephit_cuda.py b/training_scripts/train_deephit_cuda.py index d670acb5..b861d7a3 100644 --- a/training_scripts/train_deephit_cuda.py +++ b/training_scripts/train_deephit_cuda.py @@ -1,96 +1,165 @@ import os -import configargparse from collections import defaultdict -import torch +import configargparse import numpy as np -import torch.optim as optim -from sksurv.util import Surv -from torch.utils.data import DataLoader, Subset -from sklearn.model_selection import StratifiedKFold -from sklearn.preprocessing import StandardScaler, MinMaxScaler -from sksurv.metrics import concordance_index_ipcw - -# Import the DeepHit model implementation -from data_utils import * +import torch from model_utils import ( - set_seed, EarlyStopping, create_fc_mask1_gpu, - create_fc_mask2_gpu + create_fc_mask2_gpu, + set_seed, ) +from sklearn.model_selection import StratifiedKFold +from sklearn.preprocessing import MinMaxScaler, StandardScaler +from sksurv.metrics import concordance_index_ipcw +from sksurv.util import Surv +from torch import optim +from torch.utils.data import DataLoader, Subset + +from crisp_nam.metrics import auc_td, brier_score from crisp_nam.models import DeepHit -from crisp_nam.metrics import brier_score, auc_td + +# Import the DeepHit model implementation +from data_utils import * + def parse_args(): parser = configargparse.ArgumentParser( description="Training script for DeepHit model", default_config_files=["config.yml"], - config_file_parser_class=configargparse.YAMLConfigFileParser + config_file_parser_class=configargparse.YAMLConfigFileParser, ) - - parser.add_argument("-c", "--config", is_config_file=True, - help="Path to config file") - + + parser.add_argument( + "-c", "--config", is_config_file=True, help="Path to config file" + ) + # Dataset - parser.add_argument("--dataset", type=str, default="framingham", - help="Dataset to use: (framingham, support, pbc, synthetic)") - + parser.add_argument( + "--dataset", + type=str, + default="framingham", + help="Dataset to use: (framingham, support, pbc, synthetic)", + ) + # Data scaling - parser.add_argument("--scaling", type=str, default="standard", choices=["minmax", "standard", "none"], - help="Data scaling method for continuous features") - + parser.add_argument( + "--scaling", + type=str, + default="standard", + choices=["minmax", "standard", "none"], + help="Data scaling method for continuous features", + ) + # Training parameters - parser.add_argument("--num_epochs", type=int, default=500, - help="Number of training epochs") - parser.add_argument("--batch_size", type=int, default=512, - help="Batch size for training") - parser.add_argument("--learning_rate", type=float, default=1e-3, - help="Learning rate for optimizer") - parser.add_argument("--l2_reg", type=float, default=1e-4, - help="L2 regularization weight") - parser.add_argument("--patience", type=int, default=10, - help="Patience for early stopping") - + parser.add_argument( + "--num_epochs", type=int, default=500, help="Number of training epochs" + ) + parser.add_argument( + "--batch_size", type=int, default=512, help="Batch size for training" + ) + parser.add_argument( + "--learning_rate", type=float, default=1e-3, help="Learning rate for optimizer" + ) + parser.add_argument( + "--l2_reg", type=float, default=1e-4, help="L2 regularization weight" + ) + parser.add_argument( + "--patience", type=int, default=10, help="Patience for early stopping" + ) + # DeepHit specific parameters - parser.add_argument("--alpha", type=float, default=1.0, - help="Weight for log-likelihood loss") - parser.add_argument("--beta", type=float, default=0.5, - help="Weight for ranking loss") - parser.add_argument("--gamma", type=float, default=0.5, - help="Weight for calibration loss") - parser.add_argument("--h_dim_shared", type=int, default=64, - help="Hidden dimension for shared network") - parser.add_argument("--h_dim_CS", type=int, default=16, - help="Hidden dimension for cause-specific networks") - parser.add_argument("--num_layers_shared", type=int, default=2, - help="Number of layers in shared network") - parser.add_argument("--num_layers_CS", type=int, default=2, - help="Number of layers in cause-specific networks") - parser.add_argument("--num_categories", type=int, default=100, - help="Number of time categories for discretization") - parser.add_argument("--active_fn", type=str, default="relu", choices=["relu", "elu", "tanh"], - help="Activation function") - + parser.add_argument( + "--alpha", type=float, default=1.0, help="Weight for log-likelihood loss" + ) + parser.add_argument( + "--beta", type=float, default=0.5, help="Weight for ranking loss" + ) + parser.add_argument( + "--gamma", type=float, default=0.5, help="Weight for calibration loss" + ) + parser.add_argument( + "--h_dim_shared", + type=int, + default=64, + help="Hidden dimension for shared network", + ) + parser.add_argument( + "--h_dim_CS", + type=int, + default=16, + help="Hidden dimension for cause-specific networks", + ) + parser.add_argument( + "--num_layers_shared", + type=int, + default=2, + help="Number of layers in shared network", + ) + parser.add_argument( + "--num_layers_CS", + type=int, + default=2, + help="Number of layers in cause-specific networks", + ) + parser.add_argument( + "--num_categories", + type=int, + default=100, + help="Number of time categories for discretization", + ) + parser.add_argument( + "--active_fn", + type=str, + default="relu", + choices=["relu", "elu", "tanh"], + help="Activation function", + ) + # General parameters - parser.add_argument("--dropout_rate", type=float, default=0.2, - help="Dropout rate for model") - parser.add_argument("--seed", type=int, default=42, - help="Random seed for reproducibility") - parser.add_argument("--n_folds", type=int, default=5, - help="Number of folds for cross-validation") - parser.add_argument("--num_workers", type=int, default=8, - help="Number of workers for data loading") - parser.add_argument("--eval_freq", type=int, default=10, - help="Evaluate every N epochs during training") - parser.add_argument("--use_amp", action="store_true", - help="Use Automatic Mixed Precision for training") - + parser.add_argument( + "--dropout_rate", type=float, default=0.2, help="Dropout rate for model" + ) + parser.add_argument( + "--seed", type=int, default=42, help="Random seed for reproducibility" + ) + parser.add_argument( + "--n_folds", type=int, default=5, help="Number of folds for cross-validation" + ) + parser.add_argument( + "--num_workers", type=int, default=8, help="Number of workers for data loading" + ) + parser.add_argument( + "--eval_freq", + type=int, + default=10, + help="Evaluate every N epochs during training", + ) + parser.add_argument( + "--use_amp", + action="store_true", + help="Use Automatic Mixed Precision for training", + ) + return parser.parse_args() -def train_deephit_model(model, train_loader, val_loader=None, alpha=1.0, beta=1.0, gamma=1.0, - num_epochs=500, learning_rate=1e-3, l2_reg=0.01, patience=10, - eval_freq=10, use_amp=False, verbose=True): + +def train_deephit_model( + model, + train_loader, + val_loader=None, + alpha=1.0, + beta=1.0, + gamma=1.0, + num_epochs=500, + learning_rate=1e-3, + l2_reg=0.01, + patience=10, + eval_freq=10, + use_amp=False, + verbose=True, +): """ Train the DeepHit model using the three loss components Optimized with AMP support, prefetching, and CUDA streams @@ -98,35 +167,35 @@ def train_deephit_model(model, train_loader, val_loader=None, alpha=1.0, beta=1. device = next(model.parameters()).device optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=l2_reg) early_stopper = EarlyStopping(patience=patience) - + # Get model dimensions for mask creation num_Event = model.num_Event num_Category = model.num_Category - + # Initialize scaler for mixed precision training scaler = torch.cuda.amp.GradScaler() if use_amp else None - + # Enable asynchronous CUDA execution torch.backends.cudnn.benchmark = True - + # Increase batch size if it's too small (optional) # if train_loader.batch_size < 512 and device == 'cuda': # print("Warning: Small batch size. Consider increasing for better GPU utilization.") - + # Prefetch data using CUDA streams for async data loading - prefetch_stream = torch.cuda.Stream() if device == 'cuda' else None - + prefetch_stream = torch.cuda.Stream() if device == "cuda" else None + # Precomputed masks for common events (optimization) precomputed_masks2 = {} for t_val in range(num_Category): mask = torch.zeros(1, num_Category, device=device) mask[0, t_val:] = 1 precomputed_masks2[t_val] = mask - + for epoch in range(num_epochs): model.train() total_loss = 0.0 - + # Prefetch first batch if prefetch_stream is not None: try: @@ -135,34 +204,54 @@ def train_deephit_model(model, train_loader, val_loader=None, alpha=1.0, beta=1. prefetch_batch = next(batch_iter) # Prefetch to GPU asynchronously with torch.cuda.stream(prefetch_stream): - prefetch_x, prefetch_t, prefetch_e, prefetch_t_disc = [t.to(device, non_blocking=True) for t in prefetch_batch] + prefetch_x, prefetch_t, prefetch_e, prefetch_t_disc = [ + t.to(device, non_blocking=True) for t in prefetch_batch + ] # Precompute masks - prefetch_mask1 = create_fc_mask1_gpu(prefetch_e, prefetch_t_disc, num_Event, num_Category, device) - prefetch_mask2 = torch.cat([precomputed_masks2[int(t_val.item())] for t_val in prefetch_t_disc], dim=0) + prefetch_mask1 = create_fc_mask1_gpu( + prefetch_e, prefetch_t_disc, num_Event, num_Category, device + ) + prefetch_mask2 = torch.cat( + [ + precomputed_masks2[int(t_val.item())] + for t_val in prefetch_t_disc + ], + dim=0, + ) except StopIteration: prefetch_batch = None else: prefetch_batch = None - + # Main training loop with prefetching batch_iter = iter(train_loader) more_batches = True - + while more_batches: - # Synchronize with prefetched data + # Synchronize with prefetched data if prefetch_stream is not None and prefetch_batch is not None: torch.cuda.current_stream().wait_stream(prefetch_stream) x, t, e, t_disc = prefetch_x, prefetch_t, prefetch_e, prefetch_t_disc mask1, mask2 = prefetch_mask1, prefetch_mask2 - + # Prefetch next batch try: prefetch_batch = next(batch_iter) with torch.cuda.stream(prefetch_stream): - prefetch_x, prefetch_t, prefetch_e, prefetch_t_disc = [t.to(device, non_blocking=True) for t in prefetch_batch] + prefetch_x, prefetch_t, prefetch_e, prefetch_t_disc = [ + t.to(device, non_blocking=True) for t in prefetch_batch + ] # Precompute masks - prefetch_mask1 = create_fc_mask1_gpu(prefetch_e, prefetch_t_disc, num_Event, num_Category, device) - prefetch_mask2 = torch.cat([precomputed_masks2[int(t_val.item())] for t_val in prefetch_t_disc], dim=0) + prefetch_mask1 = create_fc_mask1_gpu( + prefetch_e, prefetch_t_disc, num_Event, num_Category, device + ) + prefetch_mask2 = torch.cat( + [ + precomputed_masks2[int(t_val.item())] + for t_val in prefetch_t_disc + ], + dim=0, + ) except StopIteration: prefetch_batch = None more_batches = False @@ -172,20 +261,27 @@ def train_deephit_model(model, train_loader, val_loader=None, alpha=1.0, beta=1. batch = next(batch_iter) x, t, e, t_disc = [t.to(device, non_blocking=True) for t in batch] # Create masks for loss computation - mask1 = create_fc_mask1_gpu(e, t_disc, num_Event, num_Category, device) - mask2 = torch.cat([precomputed_masks2[int(t_val.item())] for t_val in t_disc], dim=0) + mask1 = create_fc_mask1_gpu( + e, t_disc, num_Event, num_Category, device + ) + mask2 = torch.cat( + [precomputed_masks2[int(t_val.item())] for t_val in t_disc], + dim=0, + ) except StopIteration: more_batches = False continue - + # Automatic mixed precision if use_amp: with torch.cuda.amp.autocast(): # Forward pass out, _ = model(x) # Compute loss components - loss = model.compute_loss(out, t, e, mask1, mask2, alpha, beta, gamma) - + loss = model.compute_loss( + out, t, e, mask1, mask2, alpha, beta, gamma + ) + # Optimization step with gradient scaling optimizer.zero_grad(set_to_none=True) # More efficient than zero_grad() scaler.scale(loss).backward() @@ -196,42 +292,55 @@ def train_deephit_model(model, train_loader, val_loader=None, alpha=1.0, beta=1. out, _ = model(x) # Compute loss components loss = model.compute_loss(out, t, e, mask1, mask2, alpha, beta, gamma) - + # Regular optimization step optimizer.zero_grad(set_to_none=True) loss.backward() optimizer.step() - + total_loss += loss.item() - + avg_train_loss = total_loss / len(train_loader) - + # Validation if provided, but only every eval_freq epochs to save time if val_loader and (epoch % eval_freq == 0 or epoch == num_epochs - 1): model.eval() val_loss = 0.0 with torch.no_grad(): for x, t, e, t_disc in val_loader: - x, t, e, t_disc = x.to(device, non_blocking=True), t.to(device, non_blocking=True), \ - e.to(device, non_blocking=True), t_disc.to(device, non_blocking=True) - + x, t, e, t_disc = ( + x.to(device, non_blocking=True), + t.to(device, non_blocking=True), + e.to(device, non_blocking=True), + t_disc.to(device, non_blocking=True), + ) + # Create masks for validation (using precomputed masks) - mask1 = create_fc_mask1_gpu(e, t_disc, num_Event, num_Category, device) - mask2 = torch.cat([precomputed_masks2[int(t_val.item())] for t_val in t_disc], dim=0) - + mask1 = create_fc_mask1_gpu( + e, t_disc, num_Event, num_Category, device + ) + mask2 = torch.cat( + [precomputed_masks2[int(t_val.item())] for t_val in t_disc], + dim=0, + ) + # Forward pass out, _ = model(x) - + # Compute loss - loss = model.compute_loss(out, t, e, mask1, mask2, alpha, beta, gamma) + loss = model.compute_loss( + out, t, e, mask1, mask2, alpha, beta, gamma + ) val_loss += loss.item() - + avg_val_loss = val_loss / len(val_loader) early_stopper.step(avg_val_loss) - + if verbose and (epoch % eval_freq == 0 or epoch == num_epochs - 1): - print(f"Epoch {epoch + 1} | Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f}") - + print( + f"Epoch {epoch + 1} | Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f}" + ) + if early_stopper.should_stop: if verbose: print("Early stopping triggered.") @@ -246,100 +355,109 @@ def predict_absolute_risk_deephit(model, x_test, times, t_max=None, device="cpu" Optimized to keep more operations on GPU """ model.eval() - + # Convert x_test to tensor and move to device if not isinstance(x_test, torch.Tensor): x_test_tensor = torch.tensor(x_test, dtype=torch.float32, device=device) else: x_test_tensor = x_test.to(device) - + # Process in batches for large datasets batch_size = 1024 # Can be adjusted based on available GPU memory n_samples = x_test_tensor.shape[0] - + # Get model dimensions n_events = model.num_Event n_categories = model.num_Category n_times = len(times) - + # Set t_max if not provided if t_max is None: t_max = max(times) - + # Initialize the output tensor on GPU abs_risks = torch.zeros(n_samples, n_events, n_times, device=device) - + # Process in batches with torch.no_grad(): for i in range(0, n_samples, batch_size): end_idx = min(i + batch_size, n_samples) batch = x_test_tensor[i:end_idx] - + # Get model predictions preds, _ = model(batch) # shape: (batch_size, num_Event, num_Category) - + # For each evaluation time, calculate CIF for t_idx, t in enumerate(times): # Scale time to [0, num_categories-1] scaled_t = (t / t_max) * (n_categories - 1) - + # Find lower and upper bin indices lower_bin = int(np.floor(scaled_t)) - upper_bin = min(int(np.ceil(scaled_t)), n_categories-1) - + upper_bin = min(int(np.ceil(scaled_t)), n_categories - 1) + # Handle boundary cases if lower_bin == upper_bin or upper_bin >= n_categories: # Exactly at a bin boundary or at/beyond the last bin for k in range(n_events): # Get cumulative probability up to this bin - abs_risks[i:end_idx, k, t_idx] = torch.sum(preds[:, k, :lower_bin+1], dim=1) + abs_risks[i:end_idx, k, t_idx] = torch.sum( + preds[:, k, : lower_bin + 1], dim=1 + ) else: # Need to interpolate between bins weight_upper = scaled_t - lower_bin weight_lower = 1 - weight_upper - + for k in range(n_events): # Cumulative probability up to lower bin - cum_prob_lower = torch.sum(preds[:, k, :lower_bin+1], dim=1) - + cum_prob_lower = torch.sum(preds[:, k, : lower_bin + 1], dim=1) + # Cumulative probability up to upper bin - cum_prob_upper = torch.sum(preds[:, k, :upper_bin+1], dim=1) - + cum_prob_upper = torch.sum(preds[:, k, : upper_bin + 1], dim=1) + # Linear interpolation - abs_risks[i:end_idx, k, t_idx] = weight_lower * cum_prob_lower + weight_upper * cum_prob_upper - + abs_risks[i:end_idx, k, t_idx] = ( + weight_lower * cum_prob_lower + + weight_upper * cum_prob_upper + ) + # Move results back to CPU and convert to numpy only if needed return abs_risks.cpu().numpy() -def evaluate_model(model, x_val, t_val, e_val, t_train, e_train, times, max_time, device="cpu"): +def evaluate_model( + model, x_val, t_val, e_val, t_train, e_train, times, max_time, device="cpu" +): """ Evaluate the DeepHit model using time-dependent AUC, Brier score, and td-C-index """ # Predict absolute risks for validation set - abs_risks_tensor = predict_absolute_risk_deephit(model, x_val, times, t_max=max_time, device=device) + abs_risks_tensor = predict_absolute_risk_deephit( + model, x_val, times, t_max=max_time, device=device + ) abs_risks = abs_risks_tensor # Now returns numpy array directly - + # Format data for scikit-survival functions survival_train = Surv.from_arrays(e_train != 0, t_train) survival_val = Surv.from_arrays(e_val != 0, t_val) - + metrics = defaultdict(list) n_events = abs_risks.shape[1] - + # Calculate metrics in parallel using joblib if available try: from joblib import Parallel, delayed - + def process_metric(k, i, time): result = {} risk_preds = abs_risks[:, k, i] - + try: # Reshape to format needed by custom function (n_samples, n_times) risk_preds_2d = np.zeros((len(risk_preds), len(times))) risk_preds_2d[:, i] = risk_preds - + auc_score, _ = auc_td( e_val, t_val, @@ -347,12 +465,12 @@ def process_metric(k, i, time): times, time, km=(e_train, t_train), - primary_risk=k+1 + primary_risk=k + 1, ) - result[f"auc_event{k+1}_t{time:.2f}"] = float(auc_score) - except Exception as ex: - result[f"auc_event{k+1}_t{time:.2f}"] = np.nan - + result[f"auc_event{k + 1}_t{time:.2f}"] = float(auc_score) + except Exception: + result[f"auc_event{k + 1}_t{time:.2f}"] = np.nan + try: brier_score_val, _ = brier_score( e_val, @@ -361,51 +479,50 @@ def process_metric(k, i, time): times, time, km=(e_train, t_train), - primary_risk=k+1 + primary_risk=k + 1, ) - result[f"brier_event{k+1}_t{time:.2f}"] = float(brier_score_val) - except Exception as ex: - result[f"brier_event{k+1}_t{time:.2f}"] = np.nan - + result[f"brier_event{k + 1}_t{time:.2f}"] = float(brier_score_val) + except Exception: + result[f"brier_event{k + 1}_t{time:.2f}"] = np.nan + try: tdci_result = concordance_index_ipcw( - survival_train, - survival_val, - estimate=risk_preds, - tau=time + survival_train, survival_val, estimate=risk_preds, tau=time ) if isinstance(tdci_result, tuple): tdci_score = tdci_result[0] else: tdci_score = tdci_result - result[f"tdci_event{k+1}_t{time:.2f}"] = float(tdci_score) - except Exception as ex: - result[f"tdci_event{k+1}_t{time:.2f}"] = np.nan - + result[f"tdci_event{k + 1}_t{time:.2f}"] = float(tdci_score) + except Exception: + result[f"tdci_event{k + 1}_t{time:.2f}"] = np.nan + return result - + # Collect all tasks tasks = [(k, i, time) for k in range(n_events) for i, time in enumerate(times)] - + # Run in parallel - results = Parallel(n_jobs=-1)(delayed(process_metric)(k, i, time) for k, i, time in tasks) - + results = Parallel(n_jobs=-1)( + delayed(process_metric)(k, i, time) for k, i, time in tasks + ) + # Combine results for result in results: for k, v in result.items(): metrics[k].append(v) - + except ImportError: # Fall back to sequential computation for k in range(n_events): for i, time in enumerate(times): risk_preds = abs_risks[:, k, i] - + try: # Reshape to format needed by custom function (n_samples, n_times) risk_preds_2d = np.zeros((len(risk_preds), len(times))) risk_preds_2d[:, i] = risk_preds - + auc_score, _ = auc_td( e_val, t_val, @@ -413,12 +530,12 @@ def process_metric(k, i, time): times, time, km=(e_train, t_train), - primary_risk=k+1 + primary_risk=k + 1, ) - metrics[f"auc_event{k+1}_t{time:.2f}"].append(float(auc_score)) - except Exception as ex: - metrics[f"auc_event{k+1}_t{time:.2f}"].append(np.nan) - + metrics[f"auc_event{k + 1}_t{time:.2f}"].append(float(auc_score)) + except Exception: + metrics[f"auc_event{k + 1}_t{time:.2f}"].append(np.nan) + try: brier_score_val, _ = brier_score( e_val, @@ -427,27 +544,26 @@ def process_metric(k, i, time): times, time, km=(e_train, t_train), - primary_risk=k+1 + primary_risk=k + 1, ) - metrics[f"brier_event{k+1}_t{time:.2f}"].append(float(brier_score_val)) - except Exception as ex: - metrics[f"brier_event{k+1}_t{time:.2f}"].append(np.nan) - + metrics[f"brier_event{k + 1}_t{time:.2f}"].append( + float(brier_score_val) + ) + except Exception: + metrics[f"brier_event{k + 1}_t{time:.2f}"].append(np.nan) + try: tdci_result = concordance_index_ipcw( - survival_train, - survival_val, - estimate=risk_preds, - tau=time + survival_train, survival_val, estimate=risk_preds, tau=time ) if isinstance(tdci_result, tuple): tdci_score = tdci_result[0] else: tdci_score = tdci_result - metrics[f"tdci_event{k+1}_t{time:.2f}"].append(float(tdci_score)) - except Exception as ex: - metrics[f"tdci_event{k+1}_t{time:.2f}"].append(np.nan) - + metrics[f"tdci_event{k + 1}_t{time:.2f}"].append(float(tdci_score)) + except Exception: + metrics[f"tdci_event{k + 1}_t{time:.2f}"].append(np.nan) + return metrics @@ -457,97 +573,99 @@ def display_metrics_table(metrics_dict, n_folds=5, quantiles=[0.25, 0.5, 0.75]): """ import pandas as pd from tabulate import tabulate - + time_points_by_metric = {} event_types = set() - metric_types = ['auc', 'tdci', 'brier'] - + metric_types = ["auc", "tdci", "brier"] + for key, values in metrics_dict.items(): if any(metric in key for metric in metric_types): - parts = key.split('_') + parts = key.split("_") metric_type = parts[0] event_info = parts[1] - + # Extract event type - event_type = int(event_info.replace('event', '')) + event_type = int(event_info.replace("event", "")) event_types.add(event_type) - + # Extract time point - if len(parts) > 2 and parts[2].startswith('t'): - time_point = float(parts[2].replace('t', '')) - + if len(parts) > 2 and parts[2].startswith("t"): + time_point = float(parts[2].replace("t", "")) + # Initialize nested dictionaries if needed if (event_type, metric_type) not in time_points_by_metric: time_points_by_metric[(event_type, metric_type)] = {} - + # Store metrics by time point time_points_by_metric[(event_type, metric_type)][time_point] = values - + results = [] - + for event_type in sorted(event_types): - row = {'Risk': f"Type {event_type}"} - + row = {"Risk": f"Type {event_type}"} + for metric_type in metric_types: if (event_type, metric_type) not in time_points_by_metric: # Skip metrics that don't exist for this event type for q in quantiles: row[f"{metric_type.upper()}_q{q:.2f}"] = "N/A" continue - + # Get all time points for this event/metric time_data = time_points_by_metric[(event_type, metric_type)] sorted_times = sorted(time_data.keys()) - + # For each quantile for q in quantiles: # Calculate the index for this quantile q_idx = max(0, min(len(sorted_times) - 1, int(len(sorted_times) * q))) - + # Get the corresponding time point for this quantile q_time = sorted_times[q_idx] - + # Get the metrics for this time point q_values = time_data[q_time] - + # Calculate and format statistics if q_values: value_array = np.array(q_values) mean_val = np.nanmean(value_array) std_val = np.nanstd(value_array) - row[f"{metric_type.upper()}_q{q:.2f}"] = f"{mean_val:.3f} ± {std_val:.3f}" + row[f"{metric_type.upper()}_q{q:.2f}"] = ( + f"{mean_val:.3f} ± {std_val:.3f}" + ) else: row[f"{metric_type.upper()}_q{q:.2f}"] = "N/A" - + results.append(row) - + df = pd.DataFrame(results) - + # Define column order - columns = ['Risk'] - for metric in ['AUC', 'TDCI', 'BRIER']: + columns = ["Risk"] + for metric in ["AUC", "TDCI", "BRIER"]: for q in quantiles: columns.append(f"{metric}_q{q:.2f}") - + # Select columns in the right order (only those that exist) df = df[[col for col in columns if col in df.columns]] - + print("\nSummary Performance Metrics:") - print(tabulate(df, headers='keys', tablefmt='pretty', showindex=False)) - + print(tabulate(df, headers="keys", tablefmt="pretty", showindex=False)) + print("\nInterpretation:") print("- AUC: 0.5=random, >0.7=good, >0.8=excellent") - print("- TDCI (Time-Dependent C-Index): 0.5=random, >0.7=good, >0.8=excellent") + print("- TDCI (Time-Dependent C-Index): 0.5=random, >0.7=good, >0.8=excellent") print("- Brier Score: 0=perfect, <0.25=good, >0.25=poor") def main(): args = parse_args() print(args) - + # Set random seed for reproducibility set_seed(args.seed) - + # Configure CUDA for maximum performance if torch.cuda.is_available(): torch.backends.cudnn.benchmark = True @@ -558,16 +676,22 @@ def main(): # Print CUDA device info device_name = torch.cuda.get_device_name(0) compute_capability = torch.cuda.get_device_capability(0) - print(f"Using CUDA device: {device_name} with compute capability {compute_capability[0]}.{compute_capability[1]}") - + print( + f"Using CUDA device: {device_name} with compute capability {compute_capability[0]}.{compute_capability[1]}" + ) + # Set GPU memory usage strategy torch.cuda.empty_cache() - + # Manual memory management - if hasattr(torch.cuda, 'memory_stats'): - print(f"Initial GPU memory allocated: {torch.cuda.memory_allocated() / 1024**2:.2f} MB") - print(f"Initial GPU memory reserved: {torch.cuda.memory_reserved() / 1024**2:.2f} MB") - + if hasattr(torch.cuda, "memory_stats"): + print( + f"Initial GPU memory allocated: {torch.cuda.memory_allocated() / 1024**2:.2f} MB" + ) + print( + f"Initial GPU memory reserved: {torch.cuda.memory_reserved() / 1024**2:.2f} MB" + ) + # Load the dataset if args.dataset.lower() == "framingham": x, t, e, feature_names, n_cont, _ = load_framingham() @@ -579,10 +703,10 @@ def main(): x, t, e, feature_names, n_cont, _ = load_synthetic_dataset() else: raise ValueError(f"Dataset {args.dataset} not supported") - + # Convert data to tensors immediately before scaling - device = ("cuda" if torch.cuda.is_available() else "cpu") - + device = "cuda" if torch.cuda.is_available() else "cpu" + # Scale features if needed (more efficiently) if args.scaling.lower() == "standard": scaler = StandardScaler() @@ -598,13 +722,13 @@ def main(): pass else: raise ValueError(f"Scaling method {args.scaling} not supported") - + # Get number of competing risks num_competing_risks = len(np.unique(e)) - 1 # Excluding censoring (0) - + # Maximum time in the dataset max_time = np.max(t) - + # Convert data to tensors early and move to device if small enough # For large datasets, keep on CPU and transfer in batches dataset_size = x.nbytes / (1024 * 1024) # Size in MB @@ -616,120 +740,136 @@ def main(): e_tensor = torch.tensor(e, dtype=torch.long, device=device) print(f"Entire dataset moved to {device} (Size: {dataset_size:.2f} MB)") # Create dataset with GPU tensors - dataset = SurvivalDatasetDeepHit(x_tensor.cpu().numpy(), t_tensor.cpu().numpy(), - e_tensor.cpu().numpy(), args.num_categories) + dataset = SurvivalDatasetDeepHit( + x_tensor.cpu().numpy(), + t_tensor.cpu().numpy(), + e_tensor.cpu().numpy(), + args.num_categories, + ) except RuntimeError: # If OOM, fall back to CPU - print(f"Dataset too large for GPU memory, keeping on CPU (Size: {dataset_size:.2f} MB)") + print( + f"Dataset too large for GPU memory, keeping on CPU (Size: {dataset_size:.2f} MB)" + ) x_tensor = torch.tensor(x, dtype=torch.float32) t_tensor = torch.tensor(t, dtype=torch.float32) e_tensor = torch.tensor(e, dtype=torch.long) dataset = SurvivalDatasetDeepHit(x, t, e, args.num_categories) else: # Large dataset, keep on CPU - print(f"Large dataset detected ({dataset_size:.2f} MB), keeping on CPU and using efficient batch loading") + print( + f"Large dataset detected ({dataset_size:.2f} MB), keeping on CPU and using efficient batch loading" + ) dataset = SurvivalDatasetDeepHit(x, t, e, args.num_categories) - + # Setup cross-validation skf = StratifiedKFold(n_splits=args.n_folds, shuffle=True, random_state=args.seed) - + # Metrics collection and evaluation times all_metrics = defaultdict(list) quantiles = [0.25, 0.5, 0.75] - + # Calculate global evaluation times safe_max = 0.99 * np.max(t) eval_times = np.quantile(t[t <= safe_max], quantiles) - + print(f"Evaluation times: {eval_times}") - + # Create directory for figures if it doesn't exist if not os.path.exists("figs"): os.makedirs("figs") - + # Cross-validation loop for fold, (train_idx, val_idx) in enumerate(skf.split(x, e)): print(f"\n=== Fold {fold + 1}/{args.n_folds} ===") - + # Calculate optimal batch size based on device if device == "cuda": # Dynamically adjust batch size based on available GPU memory - total_gpu_mem = torch.cuda.get_device_properties(0).total_memory / (1024**3) # in GB + total_gpu_mem = torch.cuda.get_device_properties(0).total_memory / ( + 1024**3 + ) # in GB # Use heuristic - larger batch sizes for larger GPUs - dynamic_batch_size = min(2048, max(512, int(args.batch_size * (total_gpu_mem / 8)))) + dynamic_batch_size = min( + 2048, max(512, int(args.batch_size * (total_gpu_mem / 8))) + ) if dynamic_batch_size != args.batch_size: - print(f"Adjusting batch size from {args.batch_size} to {dynamic_batch_size} based on GPU memory") + print( + f"Adjusting batch size from {args.batch_size} to {dynamic_batch_size} based on GPU memory" + ) batch_size = dynamic_batch_size else: batch_size = args.batch_size else: batch_size = args.batch_size - + # Create data loaders with optimized settings train_subset = Subset(dataset, train_idx) val_subset = Subset(dataset, val_idx) - + # Use different prefetch factors based on data size and complexity prefetch_factor = 2 - + train_loader = DataLoader( - train_subset, - batch_size=batch_size, + train_subset, + batch_size=batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True if device == "cuda" else False, persistent_workers=True if args.num_workers > 0 else False, prefetch_factor=prefetch_factor if args.num_workers > 0 else None, - drop_last=True # Drop last incomplete batch for better performance + drop_last=True, # Drop last incomplete batch for better performance ) - + val_loader = DataLoader( - val_subset, + val_subset, batch_size=batch_size, num_workers=args.num_workers, pin_memory=True if device == "cuda" else False, persistent_workers=True if args.num_workers > 0 else False, - prefetch_factor=prefetch_factor if args.num_workers > 0 else None + prefetch_factor=prefetch_factor if args.num_workers > 0 else None, ) - + # Initialize DeepHit model with optimized size input_dims = { - 'x_dim': x.shape[1], - 'num_Event': num_competing_risks, - 'num_Category': args.num_categories + "x_dim": x.shape[1], + "num_Event": num_competing_risks, + "num_Category": args.num_categories, } - + # Adjust network size based on available GPU memory if device == "cuda": h_dim_shared = args.h_dim_shared h_dim_CS = args.h_dim_CS - + # For GPUs with more memory, can use larger networks if total_gpu_mem > 16: # For high-end GPUs h_dim_shared = max(args.h_dim_shared, 128) h_dim_CS = max(args.h_dim_CS, 32) - print(f"Using larger network dimensions for high-memory GPU: {h_dim_shared}/{h_dim_CS}") + print( + f"Using larger network dimensions for high-memory GPU: {h_dim_shared}/{h_dim_CS}" + ) else: h_dim_shared = args.h_dim_shared h_dim_CS = args.h_dim_CS - + network_settings = { - 'h_dim_shared': h_dim_shared, - 'h_dim_CS': h_dim_CS, - 'num_layers_shared': args.num_layers_shared, - 'num_layers_CS': args.num_layers_CS, - 'active_fn': args.active_fn, - 'keep_prob': 1.0 - args.dropout_rate + "h_dim_shared": h_dim_shared, + "h_dim_CS": h_dim_CS, + "num_layers_shared": args.num_layers_shared, + "num_layers_CS": args.num_layers_CS, + "active_fn": args.active_fn, + "keep_prob": 1.0 - args.dropout_rate, } - + # Create model model = DeepHit(input_dims, network_settings).to(device) - + # Optional: print model summary for debugging if fold == 0: num_params = sum(p.numel() for p in model.parameters()) print(f"Model has {num_params:,} parameters") - + # Print estimated memory usage per batch # 4 bytes per float32 parameter, multiply by 4 for activations, gradients, optimizer state param_memory_mb = num_params * 4 * 4 / (1024 * 1024) @@ -741,40 +881,63 @@ def main(): print("Profiling first batch for performance analysis...") # Get a sample batch for profiling sample_batch = next(iter(train_loader)) - x_sample, t_sample, e_sample, t_disc_sample = [t.to(device, non_blocking=True) for t in sample_batch] - + x_sample, t_sample, e_sample, t_disc_sample = [ + t.to(device, non_blocking=True) for t in sample_batch + ] + # Simple profiling of forward and backward pass with torch.autograd.profiler.profile(use_cuda=True) as prof: # Create masks - mask1 = create_fc_mask1_gpu(e_sample, t_disc_sample, model.num_Event, model.num_Category, device) - mask2 = create_fc_mask2_gpu(t_disc_sample, model.num_Category, device) - + mask1 = create_fc_mask1_gpu( + e_sample, + t_disc_sample, + model.num_Event, + model.num_Category, + device, + ) + mask2 = create_fc_mask2_gpu( + t_disc_sample, model.num_Category, device + ) + # Forward pass out, _ = model(x_sample) - + # Compute loss - loss = model.compute_loss(out, t_sample, e_sample, mask1, mask2, args.alpha, args.beta, args.gamma) - + loss = model.compute_loss( + out, + t_sample, + e_sample, + mask1, + mask2, + args.alpha, + args.beta, + args.gamma, + ) + # Backward pass loss.backward() - + # Print profiling results - profile_sorted = prof.key_averages().table(sort_by="cuda_time_total", row_limit=10) + profile_sorted = prof.key_averages().table( + sort_by="cuda_time_total", row_limit=10 + ) print("Profile of most expensive CUDA operations:") print(profile_sorted) - + # Identify bottlenecks cpu_pct = prof.self_cpu_time_total / prof.total_cuda_time_total * 100 if cpu_pct > 20: - print(f"WARNING: High CPU overhead ({cpu_pct:.1f}%). Consider optimizing CPU-GPU transfers.") - + print( + f"WARNING: High CPU overhead ({cpu_pct:.1f}%). Consider optimizing CPU-GPU transfers." + ) + except Exception as e: print(f"Profiling skipped due to error: {e}") - + # Train the model with optimizations train_deephit_model( - model, - train_loader, + model, + train_loader, val_loader, alpha=args.alpha, beta=args.beta, @@ -785,34 +948,34 @@ def main(): patience=args.patience, eval_freq=args.eval_freq, use_amp=args.use_amp, - verbose=True + verbose=True, ) - + # Evaluate model (less frequently during training) fold_metrics = evaluate_model( - model, - x[val_idx], - t[val_idx], + model, + x[val_idx], + t[val_idx], e[val_idx], - t[train_idx], - e[train_idx], - eval_times, + t[train_idx], + e[train_idx], + eval_times, max_time, - device + device, ) - + # Collect metrics for k, v in fold_metrics.items(): all_metrics[k].extend(v) - + # Free memory after each fold if device == "cuda": del model torch.cuda.empty_cache() - + # Display final metrics display_metrics_table(all_metrics, n_folds=args.n_folds) if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/training_scripts/train_nested_cv.py b/training_scripts/train_nested_cv.py index 60ee0330..c277b556 100644 --- a/training_scripts/train_nested_cv.py +++ b/training_scripts/train_nested_cv.py @@ -1,86 +1,130 @@ -import yaml -import configargparse from collections import defaultdict -import optuna -import torch +import configargparse +import matplotlib.pyplot as plt import numpy as np +import optuna import pandas as pd -import torch.optim as optim -import matplotlib.pyplot as plt +import torch +import yaml +from model_utils import EarlyStopping, set_seed +from sklearn.model_selection import StratifiedKFold +from sklearn.preprocessing import MinMaxScaler, StandardScaler +from sksurv.metrics import concordance_index_ipcw +from sksurv.util import Surv from tabulate import tabulate +from torch import optim from torch.utils.data import DataLoader -from sklearn.model_selection import StratifiedKFold, train_test_split -from sklearn.preprocessing import StandardScaler, MinMaxScaler -from sksurv.util import Surv -from sksurv.metrics import concordance_index_ipcw +from crisp_nam.metrics import auc_td, brier_score from crisp_nam.models import CrispNamModel from crisp_nam.utils import ( - weighted_negative_log_likelihood_loss, + compute_baseline_cif, + compute_l2_penalty, negative_log_likelihood_loss, - compute_l2_penalty + plot_coxnam_shape_functions, + plot_feature_importance, + predict_absolute_risk, + weighted_negative_log_likelihood_loss, ) from data_utils import * -from model_utils import EarlyStopping, set_seed -from crisp_nam.metrics import brier_score, auc_td -from crisp_nam.utils import predict_absolute_risk, compute_baseline_cif -from crisp_nam.utils import plot_coxnam_shape_functions, plot_feature_importance def parse_args(): parser = configargparse.ArgumentParser( description="Nested Cross-Validation for CrispNAM model", default_config_files=["config.yaml"], - config_file_parser_class=configargparse.YAMLConfigFileParser + config_file_parser_class=configargparse.YAMLConfigFileParser, ) - - parser.add_argument("-c", "--config", is_config_file=True, - help="Path to config file") - parser.add_argument("--dataset", type=str, default="framingham", - choices=["framingham", "support", "pbc", "synthetic"], - help="Dataset to use") + parser.add_argument( + "-c", "--config", is_config_file=True, help="Path to config file" + ) - parser.add_argument("--scaling", type=str, default="standard", - choices=["minmax", "standard", "none"], - help="Data scaling method for continuous features") + parser.add_argument( + "--dataset", + type=str, + default="framingham", + choices=["framingham", "support", "pbc", "synthetic"], + help="Dataset to use", + ) - parser.add_argument("--num_epochs", type=int, default=250, - help="Number of training epochs (reduced for nested CV)") - parser.add_argument("--batch_size", type=int, default=512, - help="Batch size for training") - parser.add_argument("--patience", type=int, default=10, - help="Patience for early stopping") + parser.add_argument( + "--scaling", + type=str, + default="standard", + choices=["minmax", "standard", "none"], + help="Data scaling method for continuous features", + ) + + parser.add_argument( + "--num_epochs", + type=int, + default=250, + help="Number of training epochs (reduced for nested CV)", + ) + parser.add_argument( + "--batch_size", type=int, default=512, help="Batch size for training" + ) + parser.add_argument( + "--patience", type=int, default=10, help="Patience for early stopping" + ) # Nested CV parameters - parser.add_argument("--outer_folds", type=int, default=5, - help="Number of outer CV folds") - parser.add_argument("--inner_folds", type=int, default=3, - help="Number of inner CV folds for hyperparameter tuning") - parser.add_argument("--n_trials", type=int, default=20, - help="Number of Optuna trials per inner fold") + parser.add_argument( + "--outer_folds", type=int, default=2, help="Number of outer CV folds" + ) + parser.add_argument( + "--inner_folds", + type=int, + default=2, + help="Number of inner CV folds for hyperparameter tuning", + ) + parser.add_argument( + "--n_trials", + type=int, + default=10, + help="Number of Optuna trials per inner fold", + ) # Event weighting - parser.add_argument("--event_weighting", type=str, default="none", - choices=["none", "balanced", "custom"], - help="Event weighting strategy") - parser.add_argument("--custom_event_weights", type=str, default=None, - help="Custom weights for events (comma-separated)") - - parser.add_argument("--seed", type=int, default=42, - help="Random seed for reproducibility") - + parser.add_argument( + "--event_weighting", + type=str, + default="none", + choices=["none", "balanced", "custom"], + help="Event weighting strategy", + ) + parser.add_argument( + "--custom_event_weights", + type=str, + default=None, + help="Custom weights for events (comma-separated)", + ) + + parser.add_argument( + "--seed", type=int, default=42, help="Random seed for reproducibility" + ) + return parser.parse_args() -def train_model(model, train_loader, val_loader=None, num_epochs=100, learning_rate=1e-3, - l2_reg=0.01, patience=10, event_weights=None, verbose=False): +def train_model( + model, + train_loader, + val_loader=None, + num_epochs=100, + learning_rate=1e-3, + l2_reg=0.01, + patience=10, + event_weights=None, + verbose=False, +): """Train model with early stopping""" optimizer = optim.AdamW(model.parameters(), lr=learning_rate) early_stopper = EarlyStopping(patience=patience) device = next(model.parameters()).device - best_val_loss = float('inf') + best_val_loss = float("inf") for epoch in range(num_epochs): model.train() @@ -90,12 +134,18 @@ def train_model(model, train_loader, val_loader=None, num_epochs=100, learning_r risk_scores, _ = model(x) if event_weights is not None: - loss = weighted_negative_log_likelihood_loss(risk_scores, t, e, - model.num_competing_risks, - event_weights=event_weights) + loss = weighted_negative_log_likelihood_loss( + risk_scores, + t, + e, + model.num_competing_risks, + event_weights=event_weights, + ) else: - loss = negative_log_likelihood_loss(risk_scores, t, e, model.num_competing_risks) - + loss = negative_log_likelihood_loss( + risk_scores, t, e, model.num_competing_risks + ) + reg = compute_l2_penalty(model) * l2_reg total = loss + reg @@ -113,25 +163,32 @@ def train_model(model, train_loader, val_loader=None, num_epochs=100, learning_r for x, t, e, _ in val_loader: x, t, e = x.to(device), t.to(device), e.to(device) risk_scores, _ = model(x) - + if event_weights is not None: - loss = weighted_negative_log_likelihood_loss(risk_scores, t, e, - model.num_competing_risks, - event_weights=event_weights) + loss = weighted_negative_log_likelihood_loss( + risk_scores, + t, + e, + model.num_competing_risks, + event_weights=event_weights, + ) else: - loss = negative_log_likelihood_loss(risk_scores, t, e, model.num_competing_risks) - + loss = negative_log_likelihood_loss( + risk_scores, t, e, model.num_competing_risks + ) + reg = compute_l2_penalty(model) * l2_reg val_loss += (loss + reg).item() avg_val_loss = val_loss / len(val_loader) - - if avg_val_loss < best_val_loss: - best_val_loss = avg_val_loss - + + best_val_loss = min(best_val_loss, avg_val_loss) + early_stopper.step(avg_val_loss) if verbose and epoch % 10 == 0: - print(f"Epoch {epoch + 1} | Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f}") + print( + f"Epoch {epoch + 1} | Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f}" + ) if early_stopper.should_stop: if verbose: @@ -139,37 +196,51 @@ def train_model(model, train_loader, val_loader=None, num_epochs=100, learning_r break elif verbose and epoch % 10 == 0: print(f"Epoch {epoch + 1} | Train Loss: {avg_train_loss:.4f}") - + return best_val_loss -def hyperparameter_optimization(x_train_inner, t_train_inner, e_train_inner, - x_val_inner, t_val_inner, e_val_inner, - num_competing_risks, device, args, event_weights, n_cont): +def hyperparameter_optimization( + x_train_inner, + t_train_inner, + e_train_inner, + x_val_inner, + t_val_inner, + e_val_inner, + num_competing_risks, + device, + args, + event_weights, + n_cont, +): """Run Optuna hyperparameter optimization on inner training data""" - + def objective(trial): # Define hyperparameters to optimize - learning_rate = trial.suggest_float('learning_rate', 1e-5, 1e-1, log=True) - l2_reg = trial.suggest_float('l2_reg', 1e-5, 1e-1, log=True) - dropout_rate = trial.suggest_float('dropout_rate', 0.0, 0.8) - feature_dropout = trial.suggest_float('feature_dropout', 0.0, 0.5) + learning_rate = trial.suggest_float("learning_rate", 1e-5, 1e-1, log=True) + l2_reg = trial.suggest_float("l2_reg", 1e-5, 1e-1, log=True) + dropout_rate = trial.suggest_float("dropout_rate", 0.0, 0.8) + feature_dropout = trial.suggest_float("feature_dropout", 0.0, 0.5) # Hidden dimensions - n_layers = trial.suggest_int('n_layers', 1, 3) + n_layers = trial.suggest_int("n_layers", 1, 3) hidden_dimensions = [] for i in range(n_layers): - hidden_dimensions.append(trial.suggest_categorical(f'hidden_dim_{i}', [8,16, 32, 64, 128])) - - batch_norm = trial.suggest_categorical('batch_norm', [True, False]) - + hidden_dimensions.append( + trial.suggest_categorical(f"hidden_dim_{i}", [8, 16, 32, 64, 128]) + ) + + batch_norm = trial.suggest_categorical("batch_norm", [True, False]) + # Create data loaders train_dataset = SurvivalDataset(x_train_inner, t_train_inner, e_train_inner) val_dataset = SurvivalDataset(x_val_inner, t_val_inner, e_val_inner) - - train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True) + + train_loader = DataLoader( + train_dataset, batch_size=args.batch_size, shuffle=True + ) val_loader = DataLoader(val_dataset, batch_size=args.batch_size) - + # Initialize model model = CrispNamModel( num_features=x_train_inner.shape[1], @@ -177,26 +248,28 @@ def objective(trial): hidden_sizes=hidden_dimensions, dropout_rate=dropout_rate, feature_dropout=feature_dropout, - batch_norm=batch_norm + batch_norm=batch_norm, ).to(device) - + # Train model best_val_loss = train_model( - model, train_loader, val_loader, - num_epochs=args.num_epochs, + model, + train_loader, + val_loader, + num_epochs=args.num_epochs, learning_rate=learning_rate, - l2_reg=l2_reg, + l2_reg=l2_reg, patience=args.patience, event_weights=event_weights, - verbose=False + verbose=False, ) - + return best_val_loss - + # Create study and optimize study = optuna.create_study(direction="minimize") study.optimize(objective, n_trials=args.n_trials, show_progress_bar=False) - + return study.best_params @@ -211,29 +284,41 @@ def evaluate_model(model, x_val, t_val, e_val, t_train, e_train, abs_risks, time for k in range(n_events): for i, time in enumerate(times): risk_preds = abs_risks[:, k, i] - + try: risk_preds_2d = np.zeros((len(risk_preds), len(times))) - risk_preds_2d[:, i] = risk_preds - + risk_preds_2d[:, i] = risk_preds + auc_score, _ = auc_td( - e_val, t_val, risk_preds_2d, times, time, - km=(e_train, t_train), primary_risk=k+1 + e_val, + t_val, + risk_preds_2d, + times, + time, + km=(e_train, t_train), + primary_risk=k + 1, ) - metrics[f"auc_event{k+1}_t{time:.2f}"].append(float(auc_score)) + metrics[f"auc_event{k + 1}_t{time:.2f}"].append(float(auc_score)) except Exception as ex: - print(f"[Warning] AUC failed at t={time:.2f}, event={k+1}: {ex}") - metrics[f"auc_event{k+1}_t{time:.2f}"].append(np.nan) + print(f"[Warning] AUC failed at t={time:.2f}, event={k + 1}: {ex}") + metrics[f"auc_event{k + 1}_t{time:.2f}"].append(np.nan) try: brier_score_val, _ = brier_score( - e_val, t_val, risk_preds_2d, times, time, - km=(e_train, t_train), primary_risk=k+1 + e_val, + t_val, + risk_preds_2d, + times, + time, + km=(e_train, t_train), + primary_risk=k + 1, + ) + metrics[f"brier_event{k + 1}_t{time:.2f}"].append( + float(brier_score_val) ) - metrics[f"brier_event{k+1}_t{time:.2f}"].append(float(brier_score_val)) except Exception as ex: - print(f"[Warning] Brier failed at t={time:.2f}, event={k+1}: {ex}") - metrics[f"brier_event{k+1}_t{time:.2f}"].append(np.nan) + print(f"[Warning] Brier failed at t={time:.2f}, event={k + 1}: {ex}") + metrics[f"brier_event{k + 1}_t{time:.2f}"].append(np.nan) try: tdci_result = concordance_index_ipcw( @@ -242,112 +327,120 @@ def evaluate_model(model, x_val, t_val, e_val, t_train, e_train, abs_risks, time if isinstance(tdci_result, tuple): tdci_score = tdci_result[0] else: - tdci_score = tdci_result - metrics[f"tdci_event{k+1}_t{time:.2f}"].append(float(tdci_score)) + tdci_score = tdci_result + metrics[f"tdci_event{k + 1}_t{time:.2f}"].append(float(tdci_score)) except Exception as ex: - print(f"[Warning] td-CI failed at t={time:.2f}, event={k+1}: {ex}") - metrics[f"tdci_event{k+1}_t{time:.2f}"].append(np.nan) + print(f"[Warning] td-CI failed at t={time:.2f}, event={k + 1}: {ex}") + metrics[f"tdci_event{k + 1}_t{time:.2f}"].append(np.nan) return metrics -def display_metrics_table(metrics_dict, quantiles=[0.25, 0.5, 0.75], dataset_name="dataset"): +def display_metrics_table( + metrics_dict, quantiles=[0.25, 0.5, 0.75], dataset_name="dataset" +): """Display evaluation metrics table and return processed data""" time_points_by_metric = {} event_types = set() - metric_types = ['auc', 'tdci', 'brier'] - + metric_types = ["auc", "tdci", "brier"] + for key, values in metrics_dict.items(): if any(metric in key for metric in metric_types): - parts = key.split('_') + parts = key.split("_") metric_type = parts[0] event_info = parts[1] - - event_type = int(event_info.replace('event', '')) + + event_type = int(event_info.replace("event", "")) event_types.add(event_type) - - if len(parts) > 2 and parts[2].startswith('t'): - time_point = float(parts[2].replace('t', '')) - + + if len(parts) > 2 and parts[2].startswith("t"): + time_point = float(parts[2].replace("t", "")) + if (event_type, metric_type) not in time_points_by_metric: time_points_by_metric[(event_type, metric_type)] = {} - + time_points_by_metric[(event_type, metric_type)][time_point] = values - + # Create summary table summary_results = [] detailed_results = [] - + for event_type in sorted(event_types): - row = {'Risk': f"Type {event_type}"} - + row = {"Risk": f"Type {event_type}"} + for metric_type in metric_types: if (event_type, metric_type) not in time_points_by_metric: for q in quantiles: row[f"{metric_type.upper()}_q{q:.2f}"] = "N/A" continue - + time_data = time_points_by_metric[(event_type, metric_type)] sorted_times = sorted(time_data.keys()) - + for q in quantiles: q_idx = max(0, min(len(sorted_times) - 1, int(len(sorted_times) * q))) q_time = sorted_times[q_idx] q_values = time_data[q_time] - + if q_values: value_array = np.array(q_values) mean_val = np.nanmean(value_array) std_val = np.nanstd(value_array) - row[f"{metric_type.upper()}_q{q:.2f}"] = f"{mean_val:.3f} ± {std_val:.3f}" - + row[f"{metric_type.upper()}_q{q:.2f}"] = ( + f"{mean_val:.3f} ± {std_val:.3f}" + ) + # Add to detailed results for separate CSV - detailed_results.append({ - 'Dataset': dataset_name, - 'Risk_Type': event_type, - 'Metric': metric_type.upper(), - 'Time_Quantile': f"q{q:.2f}", - 'Time_Value': q_time, - 'Mean': mean_val, - 'Std': std_val, - 'N_Folds': len(q_values), - 'Raw_Values': ';'.join(map(str, q_values)) - }) + detailed_results.append( + { + "Dataset": dataset_name, + "Risk_Type": event_type, + "Metric": metric_type.upper(), + "Time_Quantile": f"q{q:.2f}", + "Time_Value": q_time, + "Mean": mean_val, + "Std": std_val, + "N_Folds": len(q_values), + "Raw_Values": ";".join(map(str, q_values)), + } + ) else: row[f"{metric_type.upper()}_q{q:.2f}"] = "N/A" - + summary_results.append(row) - + # Create DataFrames summary_df = pd.DataFrame(summary_results) detailed_df = pd.DataFrame(detailed_results) - - columns = ['Risk'] - for metric in ['AUC', 'TDCI', 'BRIER']: + + columns = ["Risk"] + for metric in ["AUC", "TDCI", "BRIER"]: for q in quantiles: columns.append(f"{metric}_q{q:.2f}") - + summary_df = summary_df[[col for col in columns if col in summary_df.columns]] - + print("\nNested CV Performance Metrics:") - print(tabulate(summary_df, headers='keys', tablefmt='pretty', showindex=False)) - + print(tabulate(summary_df, headers="keys", tablefmt="pretty", showindex=False)) + print("\nInterpretation:") print("- AUC: 0.5=random, >0.7=good, >0.8=excellent") - print("- TDCI (Time-Dependent C-Index): 0.5=random, >0.7=good, >0.8=excellent") + print("- TDCI (Time-Dependent C-Index): 0.5=random, >0.7=good, >0.8=excellent") print("- Brier Score: 0=perfect, <0.25=good, >0.25=poor") - + return summary_df, detailed_df def main(): args = parse_args() - print(f"Running Nested Cross-Validation with {args.outer_folds} outer folds and {args.inner_folds} inner folds") + print( + f"Running Nested Cross-Validation with {args.outer_folds} outer folds and {args.inner_folds} inner folds" + ) print(args) - + # Set random seed set_seed(args.seed) - + # Load dataset if args.dataset.lower() == "framingham": x, t, e, feature_names, n_cont, _ = load_framingham() @@ -359,142 +452,193 @@ def main(): x, t, e, feature_names, n_cont, _ = load_synthetic_dataset() else: raise ValueError(f"Dataset {args.dataset} not supported") - + num_competing_risks = len(np.unique(e)) - 1 - device = ("cuda" if torch.cuda.is_available() else "cpu") - + device = "cuda" if torch.cuda.is_available() else "cpu" + # Outer cross-validation loop - outer_cv = StratifiedKFold(n_splits=args.outer_folds, shuffle=True, random_state=args.seed) + outer_cv = StratifiedKFold( + n_splits=args.outer_folds, shuffle=True, random_state=args.seed + ) all_metrics = defaultdict(list) all_best_params = [] - + quantiles = [0.25, 0.5, 0.75] safe_max = 0.99 * np.max(t) eval_times = np.quantile(t[t <= safe_max], quantiles) - + print(f"Evaluation times: {eval_times}") - + for outer_fold, (train_idx, test_idx) in enumerate(outer_cv.split(x, e)): print(f"\n=== Outer Fold {outer_fold + 1}/{args.outer_folds} ===") - + # Split data for outer fold x_train_outer, x_test_outer = x[train_idx].copy(), x[test_idx].copy() t_train_outer, t_test_outer = t[train_idx], t[test_idx] e_train_outer, e_test_outer = e[train_idx], e[test_idx] - + # Apply scaling within outer fold if args.scaling.lower() == "standard": scaler = StandardScaler() - x_train_outer[:, -n_cont:] = scaler.fit_transform(x_train_outer[:, -n_cont:]) + x_train_outer[:, -n_cont:] = scaler.fit_transform( + x_train_outer[:, -n_cont:] + ) x_test_outer[:, -n_cont:] = scaler.transform(x_test_outer[:, -n_cont:]) elif args.scaling.lower() == "minmax": scaler = MinMaxScaler() - x_train_outer[:, -n_cont:] = scaler.fit_transform(x_train_outer[:, -n_cont:]) + x_train_outer[:, -n_cont:] = scaler.fit_transform( + x_train_outer[:, -n_cont:] + ) x_test_outer[:, -n_cont:] = scaler.transform(x_test_outer[:, -n_cont:]) - + # Inner cross-validation for hyperparameter tuning - inner_cv = StratifiedKFold(n_splits=args.inner_folds, shuffle=True, random_state=args.seed) + inner_cv = StratifiedKFold( + n_splits=args.inner_folds, shuffle=True, random_state=args.seed + ) inner_scores = [] inner_params = [] - - for inner_fold, (train_inner_idx, val_inner_idx) in enumerate(inner_cv.split(x_train_outer, e_train_outer)): + + for inner_fold, (train_inner_idx, val_inner_idx) in enumerate( + inner_cv.split(x_train_outer, e_train_outer) + ): print(f" Inner Fold {inner_fold + 1}/{args.inner_folds}") - + # Split inner training data x_train_inner = x_train_outer[train_inner_idx].copy() x_val_inner = x_train_outer[val_inner_idx].copy() - t_train_inner, t_val_inner = t_train_outer[train_inner_idx], t_train_outer[val_inner_idx] - e_train_inner, e_val_inner = e_train_outer[train_inner_idx], e_train_outer[val_inner_idx] - + t_train_inner, t_val_inner = ( + t_train_outer[train_inner_idx], + t_train_outer[val_inner_idx], + ) + e_train_inner, e_val_inner = ( + e_train_outer[train_inner_idx], + e_train_outer[val_inner_idx], + ) + # Calculate event weights for this inner fold event_weights = None if args.event_weighting == "balanced": event_counts = np.zeros(num_competing_risks) for k in range(1, num_competing_risks + 1): - event_counts[k-1] = np.sum(e_train_inner == k) + event_counts[k - 1] = np.sum(e_train_inner == k) event_counts = np.maximum(event_counts, 1) event_weights = 1.0 / event_counts - event_weights = event_weights * (num_competing_risks / event_weights.sum()) - event_weights = torch.tensor(event_weights, dtype=torch.float32, device=device) - + event_weights = event_weights * ( + num_competing_risks / event_weights.sum() + ) + event_weights = torch.tensor( + event_weights, dtype=torch.float32, device=device + ) + # Hyperparameter optimization best_params = hyperparameter_optimization( - x_train_inner, t_train_inner, e_train_inner, - x_val_inner, t_val_inner, e_val_inner, - num_competing_risks, device, args, event_weights, n_cont + x_train_inner, + t_train_inner, + e_train_inner, + x_val_inner, + t_val_inner, + e_val_inner, + num_competing_risks, + device, + args, + event_weights, + n_cont, ) - + inner_params.append(best_params) - + # Select best hyperparameters (could use ensemble or average, here we take most common) # For simplicity, we'll use the first inner fold's best params best_params = inner_params[0] all_best_params.append(best_params) - - print(f" Selected hyperparameters for outer fold {outer_fold + 1}: {best_params}") - + + print( + f" Selected hyperparameters for outer fold {outer_fold + 1}: {best_params}" + ) + # Train final model on all outer training data with best hyperparameters - n_layers = best_params['n_layers'] - hidden_dimensions = [best_params[f'hidden_dim_{i}'] for i in range(n_layers)] - + n_layers = best_params["n_layers"] + hidden_dimensions = [best_params[f"hidden_dim_{i}"] for i in range(n_layers)] + # Calculate event weights for outer training data event_weights = None if args.event_weighting == "balanced": event_counts = np.zeros(num_competing_risks) for k in range(1, num_competing_risks + 1): - event_counts[k-1] = np.sum(e_train_outer == k) + event_counts[k - 1] = np.sum(e_train_outer == k) event_counts = np.maximum(event_counts, 1) event_weights = 1.0 / event_counts event_weights = event_weights * (num_competing_risks / event_weights.sum()) - event_weights = torch.tensor(event_weights, dtype=torch.float32, device=device) - + event_weights = torch.tensor( + event_weights, dtype=torch.float32, device=device + ) + # Create final model final_model = CrispNamModel( num_features=x.shape[1], num_competing_risks=num_competing_risks, hidden_sizes=hidden_dimensions, - dropout_rate=best_params['dropout_rate'], - feature_dropout=best_params['feature_dropout'], - batch_norm=best_params['batch_norm'] + dropout_rate=best_params["dropout_rate"], + feature_dropout=best_params["feature_dropout"], + batch_norm=best_params["batch_norm"], ).to(device) - + # Train final model train_dataset = SurvivalDataset(x_train_outer, t_train_outer, e_train_outer) - train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True) - + train_loader = DataLoader( + train_dataset, batch_size=args.batch_size, shuffle=True + ) + train_model( - final_model, train_loader, None, - num_epochs=args.num_epochs, - learning_rate=best_params['learning_rate'], - l2_reg=best_params['l2_reg'], + final_model, + train_loader, + None, + num_epochs=args.num_epochs, + learning_rate=best_params["learning_rate"], + l2_reg=best_params["l2_reg"], patience=args.patience, event_weights=event_weights, - verbose=True + verbose=True, ) - + # Evaluate on test data - baseline_cifs = {k: compute_baseline_cif(t_train_outer, e_train_outer, eval_times, k + 1) - for k in range(num_competing_risks)} - - abs_risks = predict_absolute_risk(final_model, x_test_outer, baseline_cifs, eval_times, device=device) - - fold_metrics = evaluate_model(final_model, x_test_outer, t_test_outer, e_test_outer, - t_train_outer, e_train_outer, abs_risks, eval_times) - + baseline_cifs = { + k: compute_baseline_cif(t_train_outer, e_train_outer, eval_times, k + 1) + for k in range(num_competing_risks) + } + + abs_risks = predict_absolute_risk( + final_model, x_test_outer, baseline_cifs, eval_times, device=device + ) + + fold_metrics = evaluate_model( + final_model, + x_test_outer, + t_test_outer, + e_test_outer, + t_train_outer, + e_train_outer, + abs_risks, + eval_times, + ) + for k, v in fold_metrics.items(): all_metrics[k].extend(v) - + # Display final results and get DataFrames for saving - summary_df, detailed_df = display_metrics_table(all_metrics, dataset_name=args.dataset) - + summary_df, detailed_df = display_metrics_table( + all_metrics, dataset_name=args.dataset + ) + # Generate shape plots using the last trained model (from the last outer fold) print("\n=== Generating Shape Function Plots ===") - + # Create figs subdirectory if not present import os + if not os.path.exists("figs"): os.makedirs("figs") - + # Use the entire dataset for plotting (with proper scaling) x_plot = x.copy() if args.scaling.lower() == "standard": @@ -503,11 +647,11 @@ def main(): elif args.scaling.lower() == "minmax": scaler = MinMaxScaler() x_plot[:, -n_cont:] = scaler.fit_transform(x_plot[:, -n_cont:]) - + # Generate plots for each risk type for risk in range(1, num_competing_risks + 1): print(f"Generating plots for risk type {risk}") - + # Generate feature importance plot fig, _, top_positive, top_negative = plot_feature_importance( model=final_model, @@ -515,131 +659,150 @@ def main(): feature_names=feature_names, n_top=5, # Show top 5 positive contributors n_bottom=5, # Show top 5 negative contributors - risk_idx=risk, + risk_idx=risk, figsize=(6, 4), - output_file=f"results/plots/nested_cv_feature_importance_risk_{risk}_{args.dataset}.png" + output_file=f"results/plots/nested_cv_feature_importance_risk_{risk}_{args.dataset}.png", ) - + # Get top features for shape function plots top_features = top_positive + top_negative - + # Generate shape function plots for top features + print("Ananya: Generating shape function plots for top features...") + print(f'{feature_names}') + print(f'{top_features}') fig, _ = plot_coxnam_shape_functions( model=final_model, - X=x_plot, + X=x_plot, risk_to_plot=risk, - feature_names=feature_names, - top_features=top_features, + feature_names=feature_names, + top_features=top_features, ncols=5, figsize=(12, 6), - output_file=f"results/plots/nested_cv_shape_functions_risk_{risk}_{args.dataset}.png" + output_file=f"results/plots/nested_cv_shape_functions_risk_{risk}_{args.dataset}.png", ) plt.close(fig) - - print(f"Shape function plots saved to results/plots/ directory") - + + print("Shape function plots saved to results/plots/ directory") + # Save metrics to files print("\n=== Saving Metrics to Files ===") - + # Save summary metrics (formatted table) summary_filename = f"nested_cv_summary_metrics_{args.dataset}.csv" summary_df.to_csv(summary_filename, index=False) print(f"Summary metrics saved to: {summary_filename}") - + # Save detailed metrics (all individual fold results) detailed_filename = f"nested_cv_detailed_metrics_{args.dataset}.csv" detailed_df.to_csv(detailed_filename, index=False) print(f"Detailed metrics saved to: {detailed_filename}") - + # Save to Excel with multiple sheets excel_filename = f"nested_cv_metrics_{args.dataset}.xlsx" - with pd.ExcelWriter(excel_filename, engine='openpyxl') as writer: - summary_df.to_excel(writer, sheet_name='Summary', index=False) - detailed_df.to_excel(writer, sheet_name='Detailed', index=False) - + with pd.ExcelWriter(excel_filename, engine="openpyxl") as writer: + summary_df.to_excel(writer, sheet_name="Summary", index=False) + detailed_df.to_excel(writer, sheet_name="Detailed", index=False) + # Create a metadata sheet - metadata_df = pd.DataFrame([ - {'Parameter': 'Dataset', 'Value': args.dataset}, - {'Parameter': 'Outer Folds', 'Value': args.outer_folds}, - {'Parameter': 'Inner Folds', 'Value': args.inner_folds}, - {'Parameter': 'Number of Trials', 'Value': args.n_trials}, - {'Parameter': 'Number of Epochs', 'Value': args.num_epochs}, - {'Parameter': 'Batch Size', 'Value': args.batch_size}, - {'Parameter': 'Event Weighting', 'Value': args.event_weighting}, - {'Parameter': 'Scaling', 'Value': args.scaling}, - {'Parameter': 'Random Seed', 'Value': args.seed} - ]) - metadata_df.to_excel(writer, sheet_name='Metadata', index=False) - + metadata_df = pd.DataFrame( + [ + {"Parameter": "Dataset", "Value": args.dataset}, + {"Parameter": "Outer Folds", "Value": args.outer_folds}, + {"Parameter": "Inner Folds", "Value": args.inner_folds}, + {"Parameter": "Number of Trials", "Value": args.n_trials}, + {"Parameter": "Number of Epochs", "Value": args.num_epochs}, + {"Parameter": "Batch Size", "Value": args.batch_size}, + {"Parameter": "Event Weighting", "Value": args.event_weighting}, + {"Parameter": "Scaling", "Value": args.scaling}, + {"Parameter": "Random Seed", "Value": args.seed}, + ] + ) + metadata_df.to_excel(writer, sheet_name="Metadata", index=False) + print(f"Excel file with multiple sheets saved to: {excel_filename}") - + # Save raw metrics dictionary as JSON for complete reproducibility import json - + # Convert numpy arrays to lists for JSON serialization serializable_metrics = {} for key, values in all_metrics.items(): - serializable_metrics[key] = [float(v) if not np.isnan(v) else None for v in values] - + serializable_metrics[key] = [ + float(v) if not np.isnan(v) else None for v in values + ] + json_filename = f"nested_cv_raw_metrics_{args.dataset}.json" - with open(json_filename, 'w') as f: - json.dump({ - 'metrics': serializable_metrics, - 'metadata': { - 'dataset': args.dataset, - 'outer_folds': args.outer_folds, - 'inner_folds': args.inner_folds, - 'n_trials': args.n_trials, - 'num_epochs': args.num_epochs, - 'batch_size': args.batch_size, - 'event_weighting': args.event_weighting, - 'scaling': args.scaling, - 'seed': args.seed - } - }, f, indent=2) - + with open(json_filename, "w") as f: + json.dump( + { + "metrics": serializable_metrics, + "metadata": { + "dataset": args.dataset, + "outer_folds": args.outer_folds, + "inner_folds": args.inner_folds, + "n_trials": args.n_trials, + "num_epochs": args.num_epochs, + "batch_size": args.batch_size, + "event_weighting": args.event_weighting, + "scaling": args.scaling, + "seed": args.seed, + }, + }, + f, + indent=2, + ) + print(f"Raw metrics (JSON) saved to: {json_filename}") - + # Save aggregated best parameters param_summary = {} for param_name in all_best_params[0].keys(): param_values = [params[param_name] for params in all_best_params] - + # Check if all values are numeric if all(isinstance(val, (int, float)) for val in param_values): param_summary[param_name] = np.mean(param_values) else: # For categorical parameters, take the most common param_summary[param_name] = max(set(param_values), key=param_values.count) - - print(f"\nAggregated best hyperparameters across all outer folds:") + + print("\nAggregated best hyperparameters across all outer folds:") for param, value in param_summary.items(): print(f"{param}: {value}") - + # Save to YAML config_dict = { "dataset": args.dataset, "scaling": args.scaling, "num_epochs": args.num_epochs, "batch_size": args.batch_size, - "learning_rate": param_summary['learning_rate'], - "l2_reg": param_summary['l2_reg'], + "learning_rate": param_summary["learning_rate"], + "l2_reg": param_summary["l2_reg"], "patience": args.patience, - "dropout_rate": param_summary['dropout_rate'], - "feature_dropout": param_summary['feature_dropout'], - "hidden_dimensions": ",".join(map(str, [param_summary[f'hidden_dim_{i}'] for i in range(int(param_summary['n_layers']))])), - "batch_norm": str(param_summary['batch_norm']), + "dropout_rate": param_summary["dropout_rate"], + "feature_dropout": param_summary["feature_dropout"], + "hidden_dimensions": ",".join( + map( + str, + [ + param_summary[f"hidden_dim_{i}"] + for i in range(int(param_summary["n_layers"])) + ], + ) + ), + "batch_norm": str(param_summary["batch_norm"]), "event_weighting": args.event_weighting, "seed": args.seed, - "n_folds": args.outer_folds + "n_folds": args.outer_folds, } - + output_file = f"nested_cv_best_params_{args.dataset}.yaml" - with open(output_file, 'w') as file: + with open(output_file, "w") as file: yaml.dump(config_dict, file, default_flow_style=False) - + print(f"\nBest hyperparameters saved to {output_file}") if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/training_scripts/tune_optuna.py b/training_scripts/tune_optuna.py index 3711de49..b6494f4d 100644 --- a/training_scripts/tune_optuna.py +++ b/training_scripts/tune_optuna.py @@ -1,74 +1,108 @@ -import yaml -import configargparse from collections import defaultdict +import configargparse +import numpy as np import optuna import torch -import numpy as np -import torch.optim as optim -from torch.utils.data import DataLoader +import yaml +from model_utils import EarlyStopping, set_seed from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler, MinMaxScaler +from sklearn.preprocessing import MinMaxScaler, StandardScaler +from sksurv.metrics import brier_score, concordance_index_ipcw, cumulative_dynamic_auc from sksurv.util import Surv -from sksurv.metrics import concordance_index_ipcw, cumulative_dynamic_auc, brier_score +from torch import optim +from torch.utils.data import DataLoader from crisp_nam.models import CrispNamModel from crisp_nam.utils import ( - weighted_negative_log_likelihood_loss, + compute_baseline_cif, + compute_l2_penalty, negative_log_likelihood_loss, - compute_l2_penalty + predict_absolute_risk, + weighted_negative_log_likelihood_loss, ) from data_utils import * -from model_utils import EarlyStopping, set_seed -from crisp_nam.utils import predict_absolute_risk, compute_baseline_cif + def parse_args(): parser = configargparse.ArgumentParser( description="Training script for MultiTaskCoxNAM model with Optuna", default_config_files=["config.yaml"], - config_file_parser_class=configargparse.YAMLConfigFileParser + config_file_parser_class=configargparse.YAMLConfigFileParser, ) - - + # Dataset - parser.add_argument("--dataset", type=str, default="framingham", choices=["framingham", "support", "pbc", "synthetic"], - help="Dataset to use") - + parser.add_argument( + "--dataset", + type=str, + default="framingham", + choices=["framingham", "support", "pbc", "synthetic"], + help="Dataset to use", + ) + # Data scaling - parser.add_argument("--scaling", type=str, default="standard", choices=["minmax", "standard", "none"], - help="Data scaling method for continuous features") - + parser.add_argument( + "--scaling", + type=str, + default="standard", + choices=["minmax", "standard", "none"], + help="Data scaling method for continuous features", + ) + # Training parameters - parser.add_argument("--num_epochs", type=int, default=500, - help="Number of training epochs") - parser.add_argument("--batch_size", type=int, default=256, - help="Batch size for training") - parser.add_argument("--patience", type=int, default=10, - help="Patience for early stopping") - + parser.add_argument( + "--num_epochs", type=int, default=500, help="Number of training epochs" + ) + parser.add_argument( + "--batch_size", type=int, default=256, help="Batch size for training" + ) + parser.add_argument( + "--patience", type=int, default=10, help="Patience for early stopping" + ) + # Optuna parameters - parser.add_argument("--n_trials", type=int, default=50, - help="Number of Optuna trials") - + parser.add_argument( + "--n_trials", type=int, default=50, help="Number of Optuna trials" + ) + # Weight parameters - parser.add_argument("--event_weighting", type=str, default="none", - choices=["none", "balanced", "custom"], - help="Event weighting strategy (none, balanced, custom)") - parser.add_argument("--custom_event_weights", type=str, default=None, - help="Custom weights for events (comma-separated, e.g., '1.0,2.0')") - + parser.add_argument( + "--event_weighting", + type=str, + default="none", + choices=["none", "balanced", "custom"], + help="Event weighting strategy (none, balanced, custom)", + ) + parser.add_argument( + "--custom_event_weights", + type=str, + default=None, + help="Custom weights for events (comma-separated, e.g., '1.0,2.0')", + ) + # Other parameters - parser.add_argument("--seed", type=int, default=42, - help="Random seed for reproducibility") - + parser.add_argument( + "--seed", type=int, default=42, help="Random seed for reproducibility" + ) + return parser.parse_args() -def train_model(model, train_loader, val_loader=None, num_epochs=500, learning_rate=1e-3, - l2_reg=0.01, patience=10, event_weights=None, verbose=True): + +def train_model( + model, + train_loader, + val_loader=None, + num_epochs=500, + learning_rate=1e-3, + l2_reg=0.01, + patience=10, + event_weights=None, + verbose=True, +): optimizer = optim.AdamW(model.parameters(), lr=learning_rate) early_stopper = EarlyStopping(patience=patience) device = next(model.parameters()).device - best_val_loss = float('inf') + best_val_loss = float("inf") for epoch in range(num_epochs): model.train() @@ -79,12 +113,18 @@ def train_model(model, train_loader, val_loader=None, num_epochs=500, learning_r # Use weighted loss if event_weights is provided if event_weights is not None: - loss = weighted_negative_log_likelihood_loss(risk_scores, t, e, - model.num_competing_risks, - event_weights=event_weights) + loss = weighted_negative_log_likelihood_loss( + risk_scores, + t, + e, + model.num_competing_risks, + event_weights=event_weights, + ) else: - loss = negative_log_likelihood_loss(risk_scores, t, e, model.num_competing_risks) - + loss = negative_log_likelihood_loss( + risk_scores, t, e, model.num_competing_risks + ) + reg = compute_l2_penalty(model) * l2_reg total = loss + reg @@ -102,25 +142,32 @@ def train_model(model, train_loader, val_loader=None, num_epochs=500, learning_r for x, t, e, _ in val_loader: x, t, e = x.to(device), t.to(device), e.to(device) risk_scores, _ = model(x) - + # Use same loss function as in training if event_weights is not None: - loss = weighted_negative_log_likelihood_loss(risk_scores, t, e, - model.num_competing_risks, - event_weights=event_weights) + loss = weighted_negative_log_likelihood_loss( + risk_scores, + t, + e, + model.num_competing_risks, + event_weights=event_weights, + ) else: - loss = negative_log_likelihood_loss(risk_scores, t, e, model.num_competing_risks) - + loss = negative_log_likelihood_loss( + risk_scores, t, e, model.num_competing_risks + ) + reg = compute_l2_penalty(model) * l2_reg val_loss += (loss + reg).item() avg_val_loss = val_loss / len(val_loader) current_best = early_stopper.step(avg_val_loss) - - if avg_val_loss < best_val_loss: - best_val_loss = avg_val_loss + + best_val_loss = min(best_val_loss, avg_val_loss) if verbose: - print(f"Epoch {epoch + 1} | Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f}") + print( + f"Epoch {epoch + 1} | Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f}" + ) if early_stopper.should_stop: if verbose: @@ -128,7 +175,7 @@ def train_model(model, train_loader, val_loader=None, num_epochs=500, learning_r break elif verbose: print(f"Epoch {epoch + 1} | Train Loss: {avg_train_loss:.4f}") - + return best_val_loss @@ -151,17 +198,14 @@ def evaluate_model(model, x_val, t_val, e_val, t_train, e_train, abs_risks, time # ---- AUC ---- try: auc_score, _ = cumulative_dynamic_auc( - survival_train, - survival_val, - risk_preds, - times=[time] + survival_train, survival_val, risk_preds, times=[time] ) - metrics[f"auc_event{k+1}_t{time:.2f}"].append(float(auc_score[0])) + metrics[f"auc_event{k + 1}_t{time:.2f}"].append(float(auc_score[0])) avg_auc += float(auc_score[0]) count += 1 except Exception as ex: - print(f"[Warning] AUC failed at t={time:.2f}, event={k+1}: {ex}") - metrics[f"auc_event{k+1}_t{time:.2f}"].append(np.nan) + print(f"[Warning] AUC failed at t={time:.2f}, event={k + 1}: {ex}") + metrics[f"auc_event{k + 1}_t{time:.2f}"].append(np.nan) # ---- Brier Score ---- try: @@ -169,153 +213,176 @@ def evaluate_model(model, x_val, t_val, e_val, t_train, e_train, abs_risks, time surv_probs = surv_probs.reshape(-1, 1) # Shape: (n_samples, n_times) _, brier_scores = brier_score( - survival_train, - survival_val, - surv_probs, - times=np.array([time]) + survival_train, survival_val, surv_probs, times=np.array([time]) + ) + metrics[f"brier_event{k + 1}_t{time:.2f}"].append( + float(brier_scores[0]) ) - metrics[f"brier_event{k+1}_t{time:.2f}"].append(float(brier_scores[0])) except Exception as ex: - print(f"[Warning] Brier failed at t={time:.2f}, event={k+1}: {ex}") - metrics[f"brier_event{k+1}_t{time:.2f}"].append(np.nan) + print(f"[Warning] Brier failed at t={time:.2f}, event={k + 1}: {ex}") + metrics[f"brier_event{k + 1}_t{time:.2f}"].append(np.nan) # ---- Time-dependent Concordance Index ---- try: tdci_result = concordance_index_ipcw( - survival_train, - survival_val, - estimate=risk_preds, - tau=time + survival_train, survival_val, estimate=risk_preds, tau=time ) if isinstance(tdci_result, tuple): tdci_score = tdci_result[0] else: tdci_score = tdci_result # fallback - metrics[f"tdci_event{k+1}_t{time:.2f}"].append(float(tdci_score)) + metrics[f"tdci_event{k + 1}_t{time:.2f}"].append(float(tdci_score)) except Exception as ex: - print(f"[Warning] td-CI failed at t={time:.2f}, event={k+1}: {ex}") - metrics[f"tdci_event{k+1}_t{time:.2f}"].append(np.nan) - + print(f"[Warning] td-CI failed at t={time:.2f}, event={k + 1}: {ex}") + metrics[f"tdci_event{k + 1}_t{time:.2f}"].append(np.nan) + # Calculate average AUC if we have valid measurements if count > 0: avg_auc = avg_auc / count else: avg_auc = 0.0 - + return metrics, avg_auc def display_metrics_table(metrics_dict, quantiles=[0.25, 0.5, 0.75]): import pandas as pd from tabulate import tabulate - + time_points_by_metric = {} event_types = set() - metric_types = ['auc', 'tdci', 'brier'] - + metric_types = ["auc", "tdci", "brier"] + for key, values in metrics_dict.items(): if any(metric in key for metric in metric_types): - parts = key.split('_') + parts = key.split("_") metric_type = parts[0] event_info = parts[1] - + # Extract event type - event_type = int(event_info.replace('event', '')) + event_type = int(event_info.replace("event", "")) event_types.add(event_type) - + # Extract time point - if len(parts) > 2 and parts[2].startswith('t'): - time_point = float(parts[2].replace('t', '')) - + if len(parts) > 2 and parts[2].startswith("t"): + time_point = float(parts[2].replace("t", "")) + # Initialize nested dictionaries if needed if (event_type, metric_type) not in time_points_by_metric: time_points_by_metric[(event_type, metric_type)] = {} - + # Store metrics by time point time_points_by_metric[(event_type, metric_type)][time_point] = values - + results = [] - + for event_type in sorted(event_types): - row = {'Risk': f"Type {event_type}"} - + row = {"Risk": f"Type {event_type}"} + for metric_type in metric_types: if (event_type, metric_type) not in time_points_by_metric: # Skip metrics that don't exist for this event type for q in quantiles: row[f"{metric_type.upper()}_q{q:.2f}"] = "N/A" continue - + # Get all time points for this event/metric time_data = time_points_by_metric[(event_type, metric_type)] sorted_times = sorted(time_data.keys()) - + # For each quantile for q in quantiles: # Calculate the index for this quantile q_idx = max(0, min(len(sorted_times) - 1, int(len(sorted_times) * q))) - + # Get the corresponding time point for this quantile q_time = sorted_times[q_idx] - + # Get the metrics for this time point q_values = time_data[q_time] - + # Calculate and format statistics if q_values: value_array = np.array(q_values) mean_val = np.nanmean(value_array) std_val = np.nanstd(value_array) - row[f"{metric_type.upper()}_q{q:.2f}"] = f"{mean_val:.3f} ± {std_val:.3f}" + row[f"{metric_type.upper()}_q{q:.2f}"] = ( + f"{mean_val:.3f} ± {std_val:.3f}" + ) else: row[f"{metric_type.upper()}_q{q:.2f}"] = "N/A" - + results.append(row) - + df = pd.DataFrame(results) - + # Define column order - columns = ['Risk'] - for metric in ['AUC', 'TDCI', 'BRIER']: + columns = ["Risk"] + for metric in ["AUC", "TDCI", "BRIER"]: for q in quantiles: columns.append(f"{metric}_q{q:.2f}") - + # Select columns in the right order (only those that exist) df = df[[col for col in columns if col in df.columns]] - + print("\nSummary Performance Metrics:") - print(tabulate(df, headers='keys', tablefmt='pretty', showindex=False)) - + print(tabulate(df, headers="keys", tablefmt="pretty", showindex=False)) + print("\nInterpretation:") print("- AUC: 0.5=random, >0.7=good, >0.8=excellent") - print("- TDCI (Time-Dependent C-Index): 0.5=random, >0.7=good, >0.8=excellent") + print("- TDCI (Time-Dependent C-Index): 0.5=random, >0.7=good, >0.8=excellent") print("- Brier Score: 0=perfect, <0.25=good, >0.25=poor") -def objective(trial, x, t, e, x_train, t_train, e_train, x_val, t_val, e_val, feature_names, n_cont, - num_competing_risks, device, args, event_weights=None): - +def objective( + trial, + x, + t, + e, + x_train, + t_train, + e_train, + x_val, + t_val, + e_val, + feature_names, + n_cont, + num_competing_risks, + device, + args, + event_weights=None, +): # Define hyperparameters to optimize - learning_rate = trial.suggest_float('learning_rate', 1e-5, 1e-1, log=True) - l2_reg = trial.suggest_float('l2_reg', 1e-5, 1e-1, log=True) - dropout_rate = trial.suggest_float('dropout_rate', 0.0, 0.8) - feature_dropout = trial.suggest_float('feature_dropout', 0.0, 0.5) - + learning_rate = trial.suggest_float("learning_rate", 1e-5, 1e-1, log=True) + l2_reg = trial.suggest_float("l2_reg", 1e-5, 1e-1, log=True) + dropout_rate = trial.suggest_float("dropout_rate", 0.0, 0.8) + feature_dropout = trial.suggest_float("feature_dropout", 0.0, 0.5) + # For hidden_dimensions - n_layers = trial.suggest_int('n_layers', 1, 3) + n_layers = trial.suggest_int("n_layers", 1, 3) hidden_dimensions = [] for i in range(n_layers): - hidden_dimensions.append(trial.suggest_categorical(f'hidden_dim_{i}', [32, 64, 128, 256])) - - batch_norm = trial.suggest_categorical('batch_norm', [True, False]) - + hidden_dimensions.append( + trial.suggest_categorical(f"hidden_dim_{i}", [32, 64, 128, 256]) + ) + + batch_norm = trial.suggest_categorical("batch_norm", [True, False]) + # Create data loaders train_dataset = SurvivalDataset(x_train, t_train, e_train) val_dataset = SurvivalDataset(x_val, t_val, e_val) - - train_loader = DataLoader(train_dataset, batch_size=args.batch_size, num_workers=4, pin_memory=True, shuffle=True) - val_loader = DataLoader(val_dataset, batch_size=args.batch_size,num_workers=4, pin_memory=True) - + + train_loader = DataLoader( + train_dataset, + batch_size=args.batch_size, + num_workers=4, + pin_memory=True, + shuffle=True, + ) + val_loader = DataLoader( + val_dataset, batch_size=args.batch_size, num_workers=4, pin_memory=True + ) + # Initialize model with the hyperparameters model = CrispNamModel( num_features=x.shape[1], @@ -323,40 +390,40 @@ def objective(trial, x, t, e, x_train, t_train, e_train, x_val, t_val, e_val, fe hidden_sizes=hidden_dimensions, dropout_rate=dropout_rate, feature_dropout=feature_dropout, - batch_norm=batch_norm + batch_norm=batch_norm, ).to(device) - + # Train model best_val_loss = train_model( - model, - train_loader, - val_loader, - num_epochs=args.num_epochs, + model, + train_loader, + val_loader, + num_epochs=args.num_epochs, learning_rate=learning_rate, - l2_reg=l2_reg, + l2_reg=l2_reg, patience=args.patience, event_weights=event_weights, - verbose=False # Set to False to reduce output during Optuna trials + verbose=False, # Set to False to reduce output during Optuna trials ) - + # We can also evaluate with metrics like AUC # If needed, uncomment this code to optimize for AUC instead of validation loss """ # Calculate evaluation times safe_max = 0.99 * np.max(t_train) eval_times = np.quantile(t_train[t_train <= safe_max], [0.25, 0.5, 0.75]) - + # Calculate baseline CIFs - baseline_cifs = {k: compute_baseline_cif(t_train, e_train, eval_times, k + 1) + baseline_cifs = {k: compute_baseline_cif(t_train, e_train, eval_times, k + 1) for k in range(num_competing_risks)} - + # Predict and evaluate abs_risks = predict_absolute_risk(model, x_val, baseline_cifs, eval_times, device=device) _, avg_auc = evaluate_model(model, x_val, t_val, e_val, t_train, e_train, abs_risks, eval_times) - + return avg_auc # Maximize AUC """ - + # For now, we'll optimize for validation loss (minimize) return best_val_loss # Minimize validation loss @@ -364,10 +431,10 @@ def objective(trial, x, t, e, x_train, t_train, e_train, x_val, t_val, e_val, fe def main(): args = parse_args() print(args) - + # Set random seed for reproducibility set_seed(args.seed) - + # Load the dataset if args.dataset.lower() == "framingham": x, t, e, feature_names, n_cont, _ = load_framingham() @@ -379,21 +446,18 @@ def main(): x, t, e, feature_names, n_cont, _ = load_synthetic_dataset() else: raise ValueError(f"Dataset {args.dataset} not supported") - + # Note: Scaling will be done after train/validation split to prevent data leakage # Compute number of competing risks num_competing_risks = len(np.unique(e)) - 1 # Excluding censoring (0) - device = ("cuda" if torch.cuda.is_available() else "cpu") - - + device = "cuda" if torch.cuda.is_available() else "cpu" - # Split data into train and validation sets x_train, x_val, t_train, t_val, e_train, e_val = train_test_split( x, t, e, test_size=0.2, random_state=args.seed, stratify=e ) - + # Apply scaling after split to prevent data leakage if args.scaling.lower() == "standard": scaler = StandardScaler() @@ -407,148 +471,183 @@ def main(): pass else: raise ValueError(f"Scaling method {args.scaling} not supported") - + # Initialize event weights event_weights = None - + if args.event_weighting != "none": if args.event_weighting == "balanced": # Compute balanced weights (inverse of class frequencies) event_counts = np.zeros(num_competing_risks) for k in range(1, num_competing_risks + 1): - event_counts[k-1] = np.sum(e_train == k) - + event_counts[k - 1] = np.sum(e_train == k) + # Avoid division by zero event_counts = np.maximum(event_counts, 1) - + # Inverse frequency weighting event_weights = 1.0 / event_counts - + # Normalize weights to sum to num_competing_risks event_weights = event_weights * (num_competing_risks / event_weights.sum()) - + print(f"Computed balanced event weights: {event_weights}") - + elif args.event_weighting == "custom": if args.custom_event_weights is None: - raise ValueError("Custom event weights must be provided when using custom weighting") - + raise ValueError( + "Custom event weights must be provided when using custom weighting" + ) + custom_weights = [float(w) for w in args.custom_event_weights.split(",")] if len(custom_weights) != num_competing_risks: - raise ValueError(f"Expected {num_competing_risks} weights, got {len(custom_weights)}") - + raise ValueError( + f"Expected {num_competing_risks} weights, got {len(custom_weights)}" + ) + event_weights = np.array(custom_weights) print(f"Using custom event weights: {event_weights}") - + # Convert to torch tensor event_weights = torch.tensor(event_weights, dtype=torch.float32, device=device) - + # Create Optuna study study = optuna.create_study(direction="minimize") # Minimize validation loss - + # Run optimization print("\n=== Starting Optuna hyperparameter optimization ===") - study.optimize(lambda trial: objective( - trial, x, t, e, x_train, t_train, e_train, x_val, t_val, e_val, - feature_names, n_cont, num_competing_risks, device, args, event_weights - ), n_trials=args.n_trials) - + study.optimize( + lambda trial: objective( + trial, + x, + t, + e, + x_train, + t_train, + e_train, + x_val, + t_val, + e_val, + feature_names, + n_cont, + num_competing_risks, + device, + args, + event_weights, + ), + n_trials=args.n_trials, + ) + # Get best parameters best_params = study.best_params print("\n=== Best Hyperparameters ===") for param, value in best_params.items(): print(f"{param}: {value}") - # Extract hidden dimensions from best params - n_layers = best_params.pop('n_layers') + n_layers = best_params.pop("n_layers") hidden_dimensions = [] for i in range(n_layers): - hidden_dimensions.append(best_params.pop(f'hidden_dim_{i}')) - + hidden_dimensions.append(best_params.pop(f"hidden_dim_{i}")) + # Save best hyperparameters to YAML file config_dict = { "dataset": args.dataset, "scaling": args.scaling, "num_epochs": args.num_epochs, "batch_size": args.batch_size, - "learning_rate": best_params['learning_rate'], - "l2_reg": best_params['l2_reg'], + "learning_rate": best_params["learning_rate"], + "l2_reg": best_params["l2_reg"], "patience": args.patience, - "dropout_rate": best_params['dropout_rate'], - "feature_dropout": best_params['feature_dropout'], - "hidden_dimensions": ",".join(map(str, hidden_dimensions)), # Format as comma-separated string - "batch_norm": str(best_params['batch_norm']), # Convert to string "True" or "False" + "dropout_rate": best_params["dropout_rate"], + "feature_dropout": best_params["feature_dropout"], + "hidden_dimensions": ",".join( + map(str, hidden_dimensions) + ), # Format as comma-separated string + "batch_norm": str( + best_params["batch_norm"] + ), # Convert to string "True" or "False" "event_weighting": args.event_weighting, "seed": args.seed, - "n_folds": 5 # Default for k-fold script + "n_folds": 5, # Default for k-fold script } - + # Add custom event weights if applicable if args.event_weighting == "custom" and args.custom_event_weights is not None: config_dict["custom_event_weights"] = args.custom_event_weights - + # Save to YAML file output_file = f"best_params_{args.dataset}.yaml" - with open(output_file, 'w') as file: + with open(output_file, "w") as file: yaml.dump(config_dict, file, default_flow_style=False) - + print(f"\nBest hyperparameters saved to {output_file}") - + # Train final model with best parameters print("\n=== Training final model with best parameters ===") - + # Create data loaders with all training data train_dataset = SurvivalDataset(x_train, t_train, e_train) val_dataset = SurvivalDataset(x_val, t_val, e_val) - - - train_loader = DataLoader(train_dataset, batch_size=args.batch_size, num_workers=4, pin_memory=True, shuffle=True) - val_loader = DataLoader(val_dataset, batch_size=args.batch_size,num_workers=4, pin_memory=True) - - + + train_loader = DataLoader( + train_dataset, + batch_size=args.batch_size, + num_workers=4, + pin_memory=True, + shuffle=True, + ) + val_loader = DataLoader( + val_dataset, batch_size=args.batch_size, num_workers=4, pin_memory=True + ) + # Initialize final model with best parameters final_model = CrispNamModel( num_features=x.shape[1], num_competing_risks=num_competing_risks, hidden_sizes=hidden_dimensions, - dropout_rate=best_params['dropout_rate'], - feature_dropout=best_params['feature_dropout'], - batch_norm=best_params['batch_norm'] + dropout_rate=best_params["dropout_rate"], + feature_dropout=best_params["feature_dropout"], + batch_norm=best_params["batch_norm"], ).to(device) - + # Train final model train_model( - final_model, - train_loader, - val_loader, - num_epochs=args.num_epochs, - learning_rate=best_params['learning_rate'], - l2_reg=best_params['l2_reg'], + final_model, + train_loader, + val_loader, + num_epochs=args.num_epochs, + learning_rate=best_params["learning_rate"], + l2_reg=best_params["l2_reg"], patience=args.patience, event_weights=event_weights, - verbose=True + verbose=True, ) - + # Evaluate final model # Calculate evaluation times safe_max = 0.99 * np.max(t_train) eval_times = np.quantile(t_train[t_train <= safe_max], [0.25, 0.5, 0.75]) - + print(f"Evaluation times: {eval_times}") - + # Calculate baseline CIFs - baseline_cifs = {k: compute_baseline_cif(t_train, e_train, eval_times, k + 1) - for k in range(num_competing_risks)} - + baseline_cifs = { + k: compute_baseline_cif(t_train, e_train, eval_times, k + 1) + for k in range(num_competing_risks) + } + # Predict and evaluate - abs_risks = predict_absolute_risk(final_model, x_val, baseline_cifs, eval_times, device=device) - final_metrics, _ = evaluate_model(final_model, x_val, t_val, e_val, t_train, e_train, abs_risks, eval_times) - + abs_risks = predict_absolute_risk( + final_model, x_val, baseline_cifs, eval_times, device=device + ) + final_metrics, _ = evaluate_model( + final_model, x_val, t_val, e_val, t_train, e_train, abs_risks, eval_times + ) + # Display metrics display_metrics_table(final_metrics) - - + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/training_scripts/tune_optuna_optimized.py b/training_scripts/tune_optuna_optimized.py index eb226813..7daabf4c 100644 --- a/training_scripts/tune_optuna_optimized.py +++ b/training_scripts/tune_optuna_optimized.py @@ -1,35 +1,29 @@ -import yaml -import configargparse from collections import defaultdict -import torch -import optuna +import configargparse import numpy as np -import torch.nn as nn +import optuna +import torch +import yaml +from model_utils import EarlyStopping, set_seed +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import MinMaxScaler, StandardScaler +from sksurv.metrics import brier_score, concordance_index_ipcw, cumulative_dynamic_auc from sksurv.util import Surv -import torch.optim as optim from tabulate import tabulate +from torch import nn, optim from torch.utils.data import DataLoader, TensorDataset -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler, MinMaxScaler -from sksurv.metrics import ( - cumulative_dynamic_auc, - brier_score, - concordance_index_ipcw -) -from data_utils import * -from model_utils import EarlyStopping, set_seed from crisp_nam.models import CrispNamModel from crisp_nam.utils import ( - weighted_negative_log_likelihood_loss, - negative_log_likelihood_loss, + compute_baseline_cif, compute_l2_penalty, -) -from crisp_nam.utils import ( + negative_log_likelihood_loss, predict_absolute_risk, - compute_baseline_cif + weighted_negative_log_likelihood_loss, ) +from data_utils import * + def train_model( model: nn.Module, @@ -45,7 +39,7 @@ def train_model( device = next(model.parameters()).device optimizer = optim.AdamW(model.parameters(), lr=lr) early_stop = EarlyStopping(patience) - scaler = torch.amp.GradScaler('cuda') + scaler = torch.amp.GradScaler("cuda") for epoch in range(1, num_epochs + 1): model.train() @@ -56,14 +50,16 @@ def train_model( eb = eb.to(device, non_blocking=True) optimizer.zero_grad() - with torch.amp.autocast('cuda'): + with torch.amp.autocast("cuda"): scores, _ = model(xb) if event_weights is not None: loss = weighted_negative_log_likelihood_loss( - scores, tb, eb, model.num_competing_risks, event_weights) + scores, tb, eb, model.num_competing_risks, event_weights + ) else: loss = negative_log_likelihood_loss( - scores, tb, eb, model.num_competing_risks) + scores, tb, eb, model.num_competing_risks + ) loss = loss + compute_l2_penalty(model) * l2_reg scaler.scale(loss).backward() @@ -81,26 +77,30 @@ def train_model( xb = xb.to(device, non_blocking=True) tb = tb.to(device, non_blocking=True) eb = eb.to(device, non_blocking=True) - with torch.amp.autocast('cuda'): + with torch.amp.autocast("cuda"): scores, _ = model(xb) if event_weights is not None: loss = weighted_negative_log_likelihood_loss( - scores, tb, eb, model.num_competing_risks, event_weights) + scores, tb, eb, model.num_competing_risks, event_weights + ) else: loss = negative_log_likelihood_loss( - scores, tb, eb, model.num_competing_risks) + scores, tb, eb, model.num_competing_risks + ) loss = loss + compute_l2_penalty(model) * l2_reg val_loss += loss.item() avg_val_loss = val_loss / len(val_loader) if verbose: - print(f"Epoch {epoch} | Train: {avg_train_loss:.4f} | Val: {avg_val_loss:.4f}") + print( + f"Epoch {epoch} | Train: {avg_train_loss:.4f} | Val: {avg_val_loss:.4f}" + ) if early_stop.step(avg_val_loss): - if verbose: print("Early stopping.") + if verbose: + print("Early stopping.") return avg_val_loss - else: - if verbose: - print(f"Epoch {epoch} | Train Loss: {avg_train_loss:.4f}") + elif verbose: + print(f"Epoch {epoch} | Train Loss: {avg_train_loss:.4f}") return avg_val_loss if val_loader is not None else avg_train_loss @@ -115,15 +115,16 @@ def objective( args, event_weights, ): - - lr = trial.suggest_float('learning_rate', 1e-5, 1e-1, log=True) - l2 = trial.suggest_float('l2_reg', 1e-5, 1e-1, log=True) - dropout = trial.suggest_float('dropout_rate', 0.0, 0.8) - feat_drop = trial.suggest_float('feature_dropout', 0.0, 0.5) - n_layers = trial.suggest_int('n_layers', 1, 3) - hidden_dims = [trial.suggest_categorical(f'hidden_dim_{i}', [32,64,128,256]) - for i in range(n_layers)] - batch_norm = trial.suggest_categorical('batch_norm', [True, False]) + lr = trial.suggest_float("learning_rate", 1e-5, 1e-1, log=True) + l2 = trial.suggest_float("l2_reg", 1e-5, 1e-1, log=True) + dropout = trial.suggest_float("dropout_rate", 0.0, 0.8) + feat_drop = trial.suggest_float("feature_dropout", 0.0, 0.5) + n_layers = trial.suggest_int("n_layers", 1, 3) + hidden_dims = [ + trial.suggest_categorical(f"hidden_dim_{i}", [32, 64, 128, 256]) + for i in range(n_layers) + ] + batch_norm = trial.suggest_categorical("batch_norm", [True, False]) loader_kwargs = dict( batch_size=args.batch_size, @@ -134,7 +135,7 @@ def objective( ) train_loader = DataLoader(train_ds, shuffle=True, **loader_kwargs) - val_loader = DataLoader(val_ds, shuffle=False, **loader_kwargs) + val_loader = DataLoader(val_ds, shuffle=False, **loader_kwargs) model = CrispNamModel( num_features=num_features, @@ -171,12 +172,12 @@ def evaluate_model( device: torch.device, ): surv_train = Surv.from_arrays(e_train != 0, t_train) - surv_val = Surv.from_arrays(e_val != 0, t_val) + surv_val = Surv.from_arrays(e_val != 0, t_val) abs_risk = predict_absolute_risk(model, X, baseline_cifs, eval_times, device=device) metrics = defaultdict(list) for k_idx, t0 in enumerate(eval_times, start=1): - preds = abs_risk[:, k_idx-1, :] + preds = abs_risk[:, k_idx - 1, :] for ti_idx, ti in enumerate(eval_times): r = preds[:, ti_idx] try: @@ -185,7 +186,9 @@ def evaluate_model( except: metrics[f"auc_event{k_idx}_t{ti:.2f}"].append(np.nan) try: - _, bsc = brier_score(surv_train, surv_val, 1 - r.reshape(-1,1), times=np.array([ti])) + _, bsc = brier_score( + surv_train, surv_val, 1 - r.reshape(-1, 1), times=np.array([ti]) + ) metrics[f"brier_event{k_idx}_t{ti:.2f}"].append(float(bsc[0])) except: metrics[f"brier_event{k_idx}_t{ti:.2f}"].append(np.nan) @@ -204,21 +207,34 @@ def parse_args(): default_config_files=["config.yml"], config_file_parser_class=configargparse.YAMLConfigFileParser, ) - parser.add_argument("--dataset", type=str, default="framingham", - choices=["framingham","support","pbc","synthetic"] ) - parser.add_argument("--scaling", type=str, default="standard", - choices=["minmax","standard","none"] ) + parser.add_argument( + "--dataset", + type=str, + default="framingham", + choices=["framingham", "support", "pbc", "synthetic"], + ) + parser.add_argument( + "--scaling", + type=str, + default="standard", + choices=["minmax", "standard", "none"], + ) parser.add_argument("--num_epochs", type=int, default=500) parser.add_argument("--batch_size", type=int, default=256) parser.add_argument("--patience", type=int, default=10) parser.add_argument("--num_workers", type=int, default=8) parser.add_argument("--n_trials", type=int, default=50) - parser.add_argument("--event_weighting", type=str, default="none", - choices=["none","balanced","custom"] ) + parser.add_argument( + "--event_weighting", + type=str, + default="none", + choices=["none", "balanced", "custom"], + ) parser.add_argument("--custom_event_weights", type=str, default=None) parser.add_argument("--seed", type=int, default=42) return parser.parse_args() + # ----------------------- Main ----------------------- # def main(): args = parse_args() @@ -226,74 +242,81 @@ def main(): set_seed(args.seed) # Load dataset - if args.dataset == 'framingham': + if args.dataset == "framingham": x, t, e, feature_names, n_cont, _ = load_framingham() - elif args.dataset == 'support': + elif args.dataset == "support": x, t, e, feature_names, n_cont, _ = load_support_dataset() - elif args.dataset == 'pbc': + elif args.dataset == "pbc": x, t, e, feature_names, n_cont, _ = load_pbc2_dataset() else: x, t, e, feature_names, n_cont, _ = load_synthetic_dataset() # Scale continuous features - if args.scaling == 'standard': + if args.scaling == "standard": x[:, -n_cont:] = StandardScaler().fit_transform(x[:, -n_cont:]) - elif args.scaling == 'minmax': + elif args.scaling == "minmax": x[:, -n_cont:] = MinMaxScaler().fit_transform(x[:, -n_cont:]) # Bulk convert to torch tensors on CPU - X = torch.from_numpy(x.astype('float32')) - T = torch.from_numpy(t.astype('float32')) - E = torch.from_numpy(e.astype('int64')) + X = torch.from_numpy(x.astype("float32")) + T = torch.from_numpy(t.astype("float32")) + E = torch.from_numpy(e.astype("int64")) # Train/validation split (fixed) idx_train, idx_val = train_test_split( np.arange(len(e)), test_size=0.2, random_state=args.seed, stratify=e ) train_ds = TensorDataset(X[idx_train], T[idx_train], E[idx_train]) - val_ds = TensorDataset(X[idx_val], T[idx_val], E[idx_val]) + val_ds = TensorDataset(X[idx_val], T[idx_val], E[idx_val]) - device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") num_competing_risks = len(np.unique(e)) - 1 # Event weighting event_weights = None - if args.event_weighting == 'balanced': - counts = np.array([(e[idx_train]==k).sum() for k in range(1, num_competing_risks+1)]) + if args.event_weighting == "balanced": + counts = np.array( + [(e[idx_train] == k).sum() for k in range(1, num_competing_risks + 1)] + ) counts = np.maximum(counts, 1) w = 1.0 / counts w *= num_competing_risks / w.sum() - event_weights = torch.from_numpy(w.astype('float32')).to(device) - elif args.event_weighting == 'custom': - w = np.array(list(map(float, args.custom_event_weights.split(',')))) - event_weights = torch.from_numpy(w.astype('float32')).to(device) + event_weights = torch.from_numpy(w.astype("float32")).to(device) + elif args.event_weighting == "custom": + w = np.array(list(map(float, args.custom_event_weights.split(",")))) + event_weights = torch.from_numpy(w.astype("float32")).to(device) # Optuna study - study = optuna.create_study(direction='minimize') + study = optuna.create_study(direction="minimize") study.optimize( lambda trial: objective( - trial, train_ds, val_ds, - x.shape[1], num_competing_risks, - device, args, event_weights + trial, + train_ds, + val_ds, + x.shape[1], + num_competing_risks, + device, + args, + event_weights, ), - n_trials=args.n_trials + n_trials=args.n_trials, ) best = study.best_params print("Best hyperparameters:", best) - with open(f"best_params_{args.dataset}.yaml", 'w') as f: + with open(f"best_params_{args.dataset}.yaml", "w") as f: yaml.dump(best, f) # Final model training with best params - n_layers = best['n_layers'] - hidden_dims = [best[f'hidden_dim_{i}'] for i in range(n_layers)] + n_layers = best["n_layers"] + hidden_dims = [best[f"hidden_dim_{i}"] for i in range(n_layers)] final_model = CrispNamModel( num_features=x.shape[1], num_competing_risks=num_competing_risks, hidden_sizes=hidden_dims, - dropout_rate=best['dropout_rate'], - feature_dropout=best['feature_dropout'], - batch_norm=best['batch_norm'], + dropout_rate=best["dropout_rate"], + feature_dropout=best["feature_dropout"], + batch_norm=best["batch_norm"], ).to(device) loader_kwargs = dict( @@ -304,15 +327,15 @@ def main(): prefetch_factor=2, ) final_train_loader = DataLoader(train_ds, shuffle=True, **loader_kwargs) - final_val_loader = DataLoader(val_ds, shuffle=False, **loader_kwargs) + final_val_loader = DataLoader(val_ds, shuffle=False, **loader_kwargs) train_model( final_model, final_train_loader, val_loader=final_val_loader, num_epochs=args.num_epochs, - lr=best['learning_rate'], - l2_reg=best['l2_reg'], + lr=best["learning_rate"], + l2_reg=best["l2_reg"], patience=args.patience, event_weights=event_weights, verbose=True, @@ -322,29 +345,36 @@ def main(): safe_max = 0.99 * np.max(t[idx_train]) eval_times = np.quantile(t[idx_train][t[idx_train] <= safe_max], [0.25, 0.5, 0.75]) baseline_cifs = { - k: compute_baseline_cif( - t[idx_train], e[idx_train], eval_times, k+1 - ) for k in range(num_competing_risks) + k: compute_baseline_cif(t[idx_train], e[idx_train], eval_times, k + 1) + for k in range(num_competing_risks) } metrics = evaluate_model( final_model, - x[idx_val], t[idx_val], e[idx_val], - t[idx_train], e[idx_train], - baseline_cifs, eval_times, device + x[idx_val], + t[idx_val], + e[idx_val], + t[idx_train], + e[idx_train], + baseline_cifs, + eval_times, + device, ) # Reporting rows = [] - for k in range(1, num_competing_risks+1): - row = {'Risk': f'Type {k}'} - for metric in ['auc', 'tdci', 'brier']: + for k in range(1, num_competing_risks + 1): + row = {"Risk": f"Type {k}"} + for metric in ["auc", "tdci", "brier"]: for q, ti in zip([0.25, 0.5, 0.75], eval_times): key = f"{metric}_event{k}_t{ti:.2f}" vals = np.array(metrics[key], dtype=float) - row[f"{metric.upper()}_q{q:.2f}"] = 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docs/overrides/partials/copyright.html | 22 +++ docs/overrides/partials/logo.html | 5 + docs/stylesheets/extra.css | 235 +++++++++++++++++++++++++ docs/usage.md | 72 ++++++++ mkdocs.yml | 98 +++++++++++ 13 files changed, 812 insertions(+) create mode 100644 .github/workflows/docs.yml create mode 100644 docs/Makefile create mode 100644 docs/api.md create mode 100644 docs/assets/favicon-48x48.svg create mode 100644 docs/assets/favicon.ico create mode 100644 docs/assets/launch.png create mode 100644 docs/assets/vector-logo.svg create mode 100644 docs/index.md create mode 100644 docs/overrides/partials/copyright.html create mode 100644 docs/overrides/partials/logo.html create mode 100644 docs/stylesheets/extra.css create mode 100644 docs/usage.md create mode 100644 mkdocs.yml diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 00000000..a36463e0 --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,100 @@ +name: docs +permissions: + contents: write + pull-requests: write + +on: + push: + branches: + - main + paths: + - .pre-commit-config.yaml + - .github/workflows/docs.yml + - '**.py' + - '**.ipynb' + - '**.html' + - '**.js' + - '**.md' + - uv.lock + - pyproject.toml + - mkdocs.yml + - '**.png' + - '**.svg' + pull_request: + branches: + - main + paths: + - .pre-commit-config.yaml + - .github/workflows/docs.yml + - '**.py' + - '**.ipynb' + - '**.js' + - '**.html' + - uv.lock + - pyproject.toml + - '**.md' + - mkdocs.yml + - '**.png' + - '**.svg' + +jobs: + build: + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v6.0.1 + + - name: Install uv + uses: astral-sh/setup-uv@v7.2.0 + with: + version: "0.9.11" + enable-cache: true + + - name: Set up Python + uses: actions/setup-python@v6.1.0 + with: + python-version-file: ".python-version" + + - name: Install the project + run: uv sync --all-extras --group docs + + - name: Build docs + run: uv run mkdocs build + + - name: Create .nojekyll file + run: touch site/.nojekyll + + - name: Upload artifact + uses: actions/upload-artifact@v6 + with: + name: docs-site + path: site/ + retention-days: 1 + + deploy: + needs: build + if: github.event_name == 'push' && github.ref == 'refs/heads/main' + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v6.0.1 + + - name: Configure Git Credentials + run: | + git config user.name github-actions[bot] + git config user.email 41898282+github-actions[bot]@users.noreply.github.com + + - name: Download artifact + uses: actions/download-artifact@v7 + with: + name: docs-site + path: site + + - name: Ensure .nojekyll exists + run: touch site/.nojekyll + + - name: Deploy to Github pages + uses: JamesIves/github-pages-deploy-action@v4.8.0 + with: + branch: gh-pages + folder: site diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 00000000..e1360093 --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,24 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +# NOTE: This is not generated from `sphinx-quickstart` and manually added +serve: + sphinx-autobuild $(SOURCEDIR) $(BUILDDIR) \ No newline at end of file diff --git a/docs/api.md b/docs/api.md new file mode 100644 index 00000000..432868f3 --- /dev/null +++ b/docs/api.md @@ -0,0 +1,47 @@ +# Python API Reference + +This section documents the Python API for CRISP-NAM. + +## Model + +::: crisp_nam.models.crisp_nam_model + options: + show_root_heading: true + members: + - CrispNamModel +::: crisp_nam.models.deephit_model + options: + show_root_heading: true + filters: + - "!^_" + members: + - DeepHit + + ## Metrics + +::: crisp_nam.metrics.calibration + options: + show_root_heading: true + members: true +::: crisp_nam.metrics.discrimination + options: + show_root_heading: true + members: true +::: crisp_nam.metrics.ipcw + options: + show_root_heading: true + members: true + + ## Utilities +::: crisp_nam.utils.plotting + options: + show_root_heading: true + members: true +::: crisp_nam.utils.risk_cif + options: + show_root_heading: true + members: true +::: crisp_nam.utils.loss + options: + show_root_heading: true + members: true \ No newline at end of file diff --git a/docs/assets/favicon-48x48.svg b/docs/assets/favicon-48x48.svg new file mode 100644 index 00000000..3cd92e57 --- /dev/null +++ b/docs/assets/favicon-48x48.svg @@ -0,0 +1,9 @@ + + + + + + + + + diff --git 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Models](https://ceur-ws.org/Vol-4059/paper5.pdf). +It includes the Python code for the following: + +- Models: `CRISP-NAM` and `DeepHIT` +- Data loading utilities for 4 datasets: Framingham, PBC, Support2, Synthetic +- Training scripts: Standard training, Hyperparameter optimization via Optuna, Nested cross validation +- Metrics: Loss and risk functions for survival analysis. +- Plotting: Feature importance and Shape functions for interpretability. + +## PyPI package +The core files of research: models, metrics and plotting utilities. + +## Installation +You can install the package via the following pip command: +```bash +pip install crisp_nam +``` + +## Citation +> @inproceedings{ramachandram2025crispnam,
    +title={CRISP-NAM: Competing Risks Interpretable Survival Prediction with Neural Additive Models},
    +author={Ramachandram, Dhanesh and Raval, Ananya},
    +booktitle={EXPLIMED 2025 - Second Workshop on Explainable AI for the Medical Domain},
    +year={2025}
    +}     \ No newline at end of file diff --git a/docs/overrides/partials/copyright.html b/docs/overrides/partials/copyright.html new file mode 100644 index 00000000..776166c1 --- /dev/null +++ b/docs/overrides/partials/copyright.html @@ -0,0 +1,22 @@ +

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0.5rem; +} + +.md-select__label { + font-size: 0.7rem; + font-weight: 600; + color: var(--md-primary-bg-color); + cursor: pointer; + padding: 0.4rem 0.8rem; + background-color: rgba(255, 255, 255, 0.1); + border-radius: 4px; + border: 1px solid rgba(255, 255, 255, 0.2); + transition: all 0.15s ease-in-out; +} + +.md-select__label:hover { + background-color: rgba(255, 255, 255, 0.2); + box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1); +} + +/* Version selector in Material 9.2+ */ +.md-header__button.md-select { + display: inline-flex; + align-items: center; + margin: 0 0.8rem; +} + +/* For Material 9.x+ with specific version selector */ +.md-typeset .md-version-warn { + padding: 0.6rem 1rem; + margin: 1.5rem 0; + background-color: rgba(235, 8, 138, 0.1); + border-left: 4px solid #eb088a; + border-radius: 0.2rem; + color: var(--md-default-fg-color); + font-size: 0.8rem; +} diff --git a/docs/usage.md b/docs/usage.md new file mode 100644 index 00000000..e406fcb6 --- /dev/null +++ b/docs/usage.md @@ -0,0 +1,72 @@ +Example code to show how to use two classes in the package. + +## Use of `CrispNamModel` model class + +```python +import torch + +from crisp_nam.models import CrispNamModel + +# Example usage and testing +if __name__ == "__main__": + + # Generate some test data + torch.manual_seed(42) + test_data = torch.randn(100, 5) + + # Create model with L2 normalized projections + model = CrispNamModel( + num_features=5, + num_competing_risks=3, + hidden_sizes=[32, 32], + normalize_projections=True, + ) + + # Forward pass + risk_predictions = model(test_data) + + print("First 5 risk predictions:", risk_predictions[:5]) + + #Calculate feature importance + feature_importance = model.calculate_feature_importance(test_data) + print("First 5 feature importance values:", feature_importance) + + # Analyze projection weights + print('Analyzing projection weights...') + projection_weights = model.analyze_projection_weights() +``` + +## Use of `DeepHIT` model class + +```python +import torch + +from crisp_nam.models.deephit_model import DeepHit + +# Example usage and testing +if __name__ == "__main__": + + # Generate some test data + torch.manual_seed(42) + test_data = torch.randn(100, 5) + + input_dims = { + 'x_dim': test_data.shape[1], + 'num_Event': 2, + 'num_Category': 100 + } + + network_settings = { + 'h_dim_shared': 128, + 'h_dim_CS': 32, + 'num_layers_shared': 1, + 'num_layers_CS': 2, + 'active_fn': 'tanh', + 'keep_prob': 1.0 - 0.3 #1.0 - dropout_rate + } + + model = DeepHit(input_dims, network_settings) + + out = model.predict(test_data) + print("Output shape:", out.shape) +``` diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 00000000..c35ba56b --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1,98 @@ +extra_css: + - stylesheets/extra.css +extra: + generator: false + social: + - icon: fontawesome/brands/discord + link: 404.html + - icon: fontawesome/brands/github + link: https://github.com/VectorInstitute/crisp-nam + version: + provider: mike + default: 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z`NJi^iM;HRBSujxulUrQZ;O#oYl2MbO$fTy^1yHIv_#@*u;i-Z;vjYwd(&;tqK_RwTT?b6}Wn7Ke#UG81;0jAr0X?c}}&47ZkzT)EP z?h>@aAQO-h5yh~FT&wCwhq##1_83W>aeP>Rg02@Aq=m!ND6`iL(6R0g-3%SYGg1G1kMTM_*-{;h_{`U<0EZTSMDVu4I_+ls z(#03_zy-QijW1mRZYSA8?%Ee!yi+?Zl3ycV%reK(%*6%!wH(Zh%Jr!R=EF9;p?1dU ze#gVHp!K`lSKG$Gk$=nD@n~D2J_7d3hP?bh&gI9BAr}8zpm;^jdblZhb`(m8>R)zE zx}U;#ouuiU0-PkGD=LEPR>`8WuV?kdXG(KdniZO-Xn$wJ3ewNCd%A9NhM55#tbtn# z^Lx1_lWHB5G0;8<3QWiz%!TQsoza+SrGTVa+o8S!@4loECrlGrh_9D|9OUZ{-3oq; zq1isdfUbTja;zTGadj Date: Mon, 2 Mar 2026 15:09:29 -0500 Subject: [PATCH 17/47] .python-version --- .python-version | 1 + 1 file changed, 1 insertion(+) create mode 100644 .python-version diff --git a/.python-version b/.python-version new file mode 100644 index 00000000..c8cfe395 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.10 From 412caba7005f2a8f14d54627fc692e06364493ef Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Mon, 2 Mar 2026 18:31:35 -0500 Subject: [PATCH 18/47] Fix docstring to replace ARgs with Parameters --- crisp_nam/metrics/__init__.py | 1 - crisp_nam/metrics/calibration.py | 1 - crisp_nam/models/crisp_nam_model.py | 17 +- crisp_nam/models/deephit_model.py | 46 ++--- crisp_nam/models/deephit_org.py | 251 ++++++++++++++++++++++++++++ crisp_nam/utils/loss.py | 9 +- crisp_nam/utils/plotting.py | 6 +- crisp_nam/utils/risk_cif.py | 6 +- 8 files changed, 303 insertions(+), 34 deletions(-) create mode 100644 crisp_nam/models/deephit_org.py diff --git a/crisp_nam/metrics/__init__.py b/crisp_nam/metrics/__init__.py index 7e34cb25..01a720b3 100644 --- a/crisp_nam/metrics/__init__.py +++ b/crisp_nam/metrics/__init__.py @@ -4,7 +4,6 @@ from .discrimination import auc_td, cumulative_dynamic_auc, truncated_concordance_td from .ipcw import estimate_ipcw - __all__ = [ "brier_score", "integrated_brier_score", diff --git a/crisp_nam/metrics/calibration.py b/crisp_nam/metrics/calibration.py index 8d941746..26556604 100644 --- a/crisp_nam/metrics/calibration.py +++ b/crisp_nam/metrics/calibration.py @@ -12,7 +12,6 @@ # A small constant to avoid division by zero epsilon = 1e-4 - def brier_score( e_test: np.ndarray, t_test: np.ndarray, diff --git a/crisp_nam/models/crisp_nam_model.py b/crisp_nam/models/crisp_nam_model.py index 00e1d508..0e5601aa 100644 --- a/crisp_nam/models/crisp_nam_model.py +++ b/crisp_nam/models/crisp_nam_model.py @@ -60,7 +60,7 @@ def __init__( def forward(self, x: torch.Tensor) -> torch.Tensor: """Forward pass through the network. - Args: + Parameters x: Input tensor of shape Returns @@ -84,7 +84,8 @@ def _forward_singleton(self, x: torch.Tensor) -> torch.Tensor: """Handle the case of a single sample (batch_size=1) where BatchNorm1d would fail. - Args: + Parameters + ----------- x: Input tensor of shape (1, num_features) Returns @@ -119,7 +120,8 @@ def __init__( def forward(self, x: torch.Tensor) -> torch.Tensor: """Apply the linear transformation with L2 normalized weights. - Args: + Parameters + ----------- x: Input tensor of shape (batch_size, in_features) Returns @@ -201,7 +203,8 @@ def __init__( def forward(self, x: torch.Tensor) -> Tuple[List[torch.Tensor], List[torch.Tensor]]: """Forward pass to compute risk scores for all competing risks. - Args: + Parameters + ----------- x: Tensor of shape (batch_size, num_features) Returns @@ -303,7 +306,8 @@ def calculate_feature_importance( With L2 normalized weights, this gives a fair comparison across features. - Args: + Parameters + ----------- x_data: Input data tensor or numpy array feature_idx: Optional; if provided, only calculate importance for this feature @@ -351,7 +355,8 @@ def calculate_feature_importance( def analyze_projection_weights(self) -> dict: """Analyze the L2 norms and statistics of projection weights. - Args: + Parameters + ----------- None Returns diff --git a/crisp_nam/models/deephit_model.py b/crisp_nam/models/deephit_model.py index ebaa7174..5d1d3a75 100644 --- a/crisp_nam/models/deephit_model.py +++ b/crisp_nam/models/deephit_model.py @@ -35,7 +35,8 @@ def __init__( def forward(self, x: torch.Tensor) -> torch.Tensor: """Forward pass through the layer. - Args: + Parameters + ---------- x: Tensor of shape (batch_size, in_dim) Returns @@ -100,7 +101,8 @@ def __init__( def forward(self, x: torch.Tensor) -> Optional[nn.Module]: """Forward pass through the network. - Args: + Parameters + ----------- x: Tensor of shape (batch_size, in_dim) Returns @@ -148,7 +150,8 @@ def __init__(self, input_dims: dict, network_settings: dict): def _build_network(self) -> None: """Build the shared and cause-specific networks. - Args: + Parameters + ---------- None Returns @@ -187,7 +190,8 @@ def _build_network(self) -> None: def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, None]: """Forward pass through the network. - Args: + Parameters + ---------- x: Tensor of shape (batch_size, num_Event, num_Category) Returns @@ -229,7 +233,7 @@ def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, None]: # In this model, we don't have separate shape functions, so just return None return out, None - def log_likelihood_loss( + def _log_likelihood_loss( self, out: torch.Tensor, t: Optional[torch.Tensor | np.ndarray], @@ -239,7 +243,8 @@ def log_likelihood_loss( ) -> torch.Tensor: """Log-likelihood loss (including log-likelihood of censored subjects). - Args: + Parameters + ---------- out: Torch.tensor t: Torch.tensor or numpy array k: Torch.tensor or numpy array @@ -271,7 +276,7 @@ def log_likelihood_loss( return -torch.mean(tmp1 + tmp2) - def ranking_loss( + def _ranking_loss( self, out: torch.Tensor, t: Optional[torch.Tensor | np.ndarray], @@ -280,7 +285,8 @@ def ranking_loss( ) -> torch.Tensor: """Ranking loss (calculated only for acceptable pairs). - Args: + Parameters + ---------- out: Torch.tensor t: Torch.tensor or numpy array k: Torch.tensor or numpy array @@ -302,7 +308,6 @@ def ranking_loss( one_vector = torch.ones_like(t) for e in range(self.num_Event): - # Indicator for current event type i_2 = (k == e + 1).float() i_2_diag = torch.diag(i_2.squeeze()) @@ -318,7 +323,7 @@ def ranking_loss( r = r.transpose(0, 1) # Now R_ij = r_i(T_i) - r_j(T_i) # Time comparison matrix - t = F.relu( + time = F.relu( torch.sign( torch.matmul(one_vector, t.transpose(0, 1)) - torch.matmul(t, one_vector.transpose(0, 1)) @@ -326,10 +331,10 @@ def ranking_loss( ) # Filter by event occurrence - t = torch.matmul(i_2_diag, t) + time = torch.matmul(i_2_diag, time) # Calculate ranking loss for current event - tmp_eta = torch.mean(t * torch.exp(-r / sigma1), dim=1, keepdim=True) + tmp_eta = torch.mean(time * torch.exp(-r / sigma1), dim=1, keepdim=True) eta.append(tmp_eta) eta = torch.stack(eta, dim=1) # [batch_size, num_Event] @@ -337,7 +342,7 @@ def ranking_loss( return torch.sum(eta_mean) - def calibration_loss( + def _calibration_loss( self, out: torch.Tensor, t: Optional[torch.Tensor | np.ndarray], @@ -346,7 +351,8 @@ def calibration_loss( ) -> torch.Tensor: """Calibration loss. - Args: + Parameters + ---------- out: Torch.tensor t: Torch.tensor or numpy array k: Torch.tensor or numpy array @@ -392,7 +398,8 @@ def compute_loss( ) -> torch.Tensor: """Compute total loss. - Args: + Parameters + ---------- out: Torch.tensor t: Torch.tensor or numpy array k: Torch.tensor or numpy array @@ -406,9 +413,9 @@ def compute_loss( ------- total_loss: Torch.tensor """ - loss1 = self.log_likelihood_loss(out, t, k, mask1, mask2) - loss2 = self.ranking_loss(out, t, k, mask2) - loss3 = self.calibration_loss(out, t, k, mask2) + loss1 = self._log_likelihood_loss(out, t, k, mask1, mask2) + loss2 = self._ranking_loss(out, t, k, mask2) + loss3 = self._calibration_loss(out, t, k, mask2) # L2 regularization is handled by optimizer (weight_decay) return alpha * loss1 + beta * loss2 + gamma * loss3 @@ -416,7 +423,8 @@ def compute_loss( def predict(self, x: torch.Tensor) -> torch.Tensor: """Predict risk scores for input x. - Args: + Parameters + ---------- x: Tensor of shape (batch_size, num_Event, num_Category) Returns diff --git a/crisp_nam/models/deephit_org.py b/crisp_nam/models/deephit_org.py new file mode 100644 index 00000000..cc3394f3 --- /dev/null +++ b/crisp_nam/models/deephit_org.py @@ -0,0 +1,251 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class FCLayer(nn.Module): + """Fully connected layer with optional batch norm, dropout, and activation.""" + def __init__(self, in_dim, out_dim, activation=nn.ReLU(), batch_norm=False, dropout_rate=0.0, + init_fn=nn.init.xavier_normal_): + super(FCLayer, self).__init__() + self.fc = nn.Linear(in_dim, out_dim) + self.batch_norm = nn.BatchNorm1d(out_dim) if batch_norm else None + self.dropout = nn.Dropout(dropout_rate) if dropout_rate > 0 else None + self.activation = activation + + # Initialize weights + if init_fn: + init_fn(self.fc.weight) + nn.init.zeros_(self.fc.bias) + + def forward(self, x): + x = self.fc(x) + if self.batch_norm: + x = self.batch_norm(x) + if self.activation: + x = self.activation(x) + if self.dropout: + x = self.dropout(x) + return x + + +class FCNet(nn.Module): + """Multi-layer fully connected network.""" + def __init__(self, in_dim, num_layers, h_dim, activation=nn.ReLU(), + out_dim=None, out_activation=None, batch_norm=False, + dropout_rate=0.0, init_fn=nn.init.xavier_normal_): + super(FCNet, self).__init__() + + layers = [] + prev_dim = in_dim + + # Hidden layers + for i in range(num_layers): + curr_dim = out_dim if (i == num_layers - 1 and out_dim) else h_dim + curr_act = out_activation if (i == num_layers - 1 and out_activation) else activation + + layers.append(FCLayer( + prev_dim, curr_dim, activation=curr_act, + batch_norm=batch_norm, dropout_rate=dropout_rate, init_fn=init_fn + )) + prev_dim = curr_dim + + self.network = nn.Sequential(*layers) + + def forward(self, x): + return self.network(x) + + +class DeepHit(nn.Module): + """PyTorch implementation of DeepHit for competing risks survival analysis.""" + + def __init__(self, input_dims, network_settings): + super(DeepHit, self).__init__() + + # Input dimensions + self.x_dim = input_dims['x_dim'] + self.num_Event = input_dims['num_Event'] + self.num_Category = input_dims['num_Category'] + + # Network settings + self.h_dim_shared = network_settings['h_dim_shared'] + self.h_dim_CS = network_settings['h_dim_CS'] + self.num_layers_shared = network_settings['num_layers_shared'] + self.num_layers_CS = network_settings['num_layers_CS'] + + # Activation function + if network_settings['active_fn'] == 'relu': + self.active_fn = nn.ReLU() + elif network_settings['active_fn'] == 'elu': + self.active_fn = nn.ELU() + elif network_settings['active_fn'] == 'tanh': + self.active_fn = nn.Tanh() + else: + self.active_fn = nn.ReLU() + + # Regularization + self.keep_prob = network_settings.get('keep_prob', 0.5) + self.dropout_rate = 1.0 - self.keep_prob + + # Initialize networks + self._build_network() + + def _build_network(self): + # Shared network + self.shared_net = FCNet( + in_dim=self.x_dim, + num_layers=self.num_layers_shared, + h_dim=self.h_dim_shared, + activation=self.active_fn, + dropout_rate=self.dropout_rate + ) + + # Cause-specific networks + self.cs_nets = nn.ModuleList([ + FCNet( + in_dim=self.x_dim + self.h_dim_shared, # Concatenate input and shared output + num_layers=self.num_layers_CS, + h_dim=self.h_dim_CS, + activation=self.active_fn, + dropout_rate=self.dropout_rate + ) for _ in range(self.num_Event) + ]) + + # Output layer + self.output_layer = nn.Linear(self.num_Event * self.h_dim_CS, self.num_Event * self.num_Category) + + def forward(self, x): + # Shared network + shared_out = self.shared_net(x) + + # Concatenate input with shared output + h = torch.cat([x, shared_out], dim=1) + + # Cause-specific networks + cs_outputs = [] + for cs_net in self.cs_nets: + cs_out = cs_net(h) + cs_outputs.append(cs_out) + + # Stack outputs + stacked_out = torch.stack(cs_outputs, dim=1) # [batch_size, num_Event, h_dim_CS] + reshaped_out = stacked_out.view(-1, self.num_Event * self.h_dim_CS) # [batch_size, num_Event * h_dim_CS] + + # Final output layer + logits = self.output_layer(F.dropout(reshaped_out, self.dropout_rate, self.training)) + out = F.softmax(logits.view(-1, self.num_Event * self.num_Category), dim=1) + + # Reshape to [batch_size, num_Event, num_Category] + out = out.view(-1, self.num_Event, self.num_Category) + + # For compatibility with the training script, return both raw risks and shape functions + # In this model, we don't have separate shape functions, so just return None + return out, None + + def log_likelihood_loss(self, out, t, k, mask1, mask2): + """Log-likelihood loss (including log-likelihood of censored subjects)""" + batch_size = out.size(0) + + # Convert to PyTorch tensors if necessary + if not isinstance(k, torch.Tensor): + k = torch.tensor(k, device=out.device) + if not isinstance(t, torch.Tensor): + t = torch.tensor(t, device=out.device) + + # Indicator for uncensored subjects + I_1 = (k > 0).float().view(-1, 1) + + # For uncensored: log P(T=t, K=k|x) + tmp1 = torch.sum(torch.sum(mask1 * out, dim=2), dim=1, keepdim=True) + tmp1 = I_1 * torch.log(tmp1 + 1e-8) + + # For censored: log ∑ P(T>t|x) + tmp2 = torch.sum(torch.sum(mask2.unsqueeze(1) * out, dim=2), dim=1, keepdim=True) + tmp2 = (1.0 - I_1) * torch.log(tmp2 + 1e-8) + + return -torch.mean(tmp1 + tmp2) + + def ranking_loss(self, out, t, k, mask2): + """Ranking loss (calculated only for acceptable pairs)""" + batch_size = out.size(0) + sigma1 = 0.1 + eta = [] + + # Convert to PyTorch tensors if necessary + if not isinstance(k, torch.Tensor): + k = torch.tensor(k, device=out.device) + if not isinstance(t, torch.Tensor): + t = torch.tensor(t, device=out.device) + + one_vector = torch.ones_like(t) + + for e in range(self.num_Event): + # Indicator for current event type + I_2 = (k == e+1).float() + I_2_diag = torch.diag(I_2.squeeze()) + + # Extract event-specific probabilities + tmp_e = out[:, e, :] # [batch_size, num_Category] + + # Calculate risk scores + R = torch.matmul(tmp_e, mask2.transpose(0, 1)) # [batch_size, batch_size] + diag_R = torch.diag(R).unsqueeze(1) # [batch_size, 1] + R = torch.matmul(one_vector, diag_R.transpose(0, 1)) - R # [batch_size, batch_size] + R = R.transpose(0, 1) # Now R_ij = r_i(T_i) - r_j(T_i) + + # Time comparison matrix + T = F.relu(torch.sign(torch.matmul(one_vector, t.transpose(0, 1)) - + torch.matmul(t, one_vector.transpose(0, 1)))) + + # Filter by event occurrence + T = torch.matmul(I_2_diag, T) + + # Calculate ranking loss for current event + tmp_eta = torch.mean(T * torch.exp(-R / sigma1), dim=1, keepdim=True) + eta.append(tmp_eta) + + eta = torch.stack(eta, dim=1) # [batch_size, num_Event] + eta = torch.mean(eta.reshape(-1, self.num_Event), dim=1, keepdim=True) + + return torch.sum(eta) + + def calibration_loss(self, out, t, k, mask2): + """Calibration loss""" + batch_size = out.size(0) + eta = [] + + # Convert to PyTorch tensors if necessary + if not isinstance(k, torch.Tensor): + k = torch.tensor(k, device=out.device) + + for e in range(self.num_Event): + # Indicator for current event type + I_2 = (k == e+1).float() + + # Extract event-specific probabilities + tmp_e = out[:, e, :] # [batch_size, num_Category] + + # Calculate calibration loss + r = torch.sum(tmp_e * mask2, dim=1) + tmp_eta = torch.mean((r - I_2) ** 2, dim=0, keepdim=True) + eta.append(tmp_eta) + + eta = torch.stack(eta, dim=1) # [1, num_Event] + eta = torch.mean(eta.reshape(-1, self.num_Event), dim=1, keepdim=True) + + return torch.sum(eta) + + def compute_loss(self, out, t, k, mask1, mask2, alpha=1.0, beta=1.0, gamma=1.0): + """Compute total loss""" + loss1 = self.log_likelihood_loss(out, t, k, mask1, mask2) + loss2 = self.ranking_loss(out, t, k, mask2) + loss3 = self.calibration_loss(out, t, k, mask2) + + # L2 regularization is handled by optimizer (weight_decay) + return alpha * loss1 + beta * loss2 + gamma * loss3 + + def predict(self, x): + """Predict risk scores for input x""" + self.eval() + with torch.no_grad(): + out, _ = self.forward(x) + return out \ No newline at end of file diff --git a/crisp_nam/utils/loss.py b/crisp_nam/utils/loss.py index 9d3856d2..5055387e 100644 --- a/crisp_nam/utils/loss.py +++ b/crisp_nam/utils/loss.py @@ -19,7 +19,8 @@ def weighted_negative_log_likelihood_loss( """ Compute the weighted negative log-likelihood loss for competing risks Cox model. - Args: + Parameters + ---------- risk_scores: List of tensors with shape (batch_size, 1) for each competing risk times: Event/censoring times (batch_size,) events: Event indicators (0=censored, 1...K=event types) (batch_size,) @@ -96,7 +97,8 @@ def negative_log_likelihood_loss( """ Compute the negative log-likelihood loss for competing risks Cox model. - Args: + Parameters + ---------- risk_scores: List of tensors with shape (batch_size, 1) for each competing risk times: Event/censoring times (batch_size,) events: Event indicators (0=censored, 1...K=event types) (batch_size,) @@ -154,7 +156,8 @@ def compute_l2_penalty( """ Compute L2 regularization penalty on model parameters. - Args: + Parameters + ---------- model: Neural network model include_bias: Whether to include bias terms in regularization diff --git a/crisp_nam/utils/plotting.py b/crisp_nam/utils/plotting.py index 62c78107..184e7718 100644 --- a/crisp_nam/utils/plotting.py +++ b/crisp_nam/utils/plotting.py @@ -27,7 +27,8 @@ def plot_feature_importance( """Plot feature importance with both top positive and negative influences, handling both CPU and CUDA devices automatically. - Args: + Parameters + ---------- - model: A trained CoxNAM model (torch.nn.Module) - x_data: Input data (numpy array or torch tensor) to compute contributions - feature_names: Optional list of feature names (default: generic names) @@ -131,7 +132,8 @@ def plot_coxnam_shape_functions( """Plot shape functions for each feature in a CoxNAM model, automatically handling CPU vs CUDA inputs. - Args: + Parameters + ---------- - model: A trained CoxNAM model (torch.nn.Module) - X: Input data (numpy array or torch tensor) to compute shape functions - risk_to_plot: Index of the competing risk to visualize diff --git a/crisp_nam/utils/risk_cif.py b/crisp_nam/utils/risk_cif.py index 060c2ffc..4c043890 100644 --- a/crisp_nam/utils/risk_cif.py +++ b/crisp_nam/utils/risk_cif.py @@ -58,7 +58,8 @@ def predict_cif( """ Predict cumulative incidence function for a specific competing risk. - Args: + Parameters + ---------- model: Trained model. x: Input tensor of shape (n_samples, n_features). baseline_cif: Array of shape (len(times),) — @@ -122,7 +123,8 @@ def predict_absolute_risk( """ Predict absolute risk (CIF) for each competing event by given time points. - Args: + Parameters + ---------- model: Trained model. x_input (np.ndarray or Tensor): Input features, shape (n_samples, n_features). baseline_cifs (dict): Mapping of event index to baseline CIF From d851188213fc1701e616972e55418615cbf76359 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Wed, 18 Mar 2026 11:30:18 -0400 Subject: [PATCH 19/47] Remove extra deephit file --- crisp_nam/models/deephit_org.py | 251 -------------------------------- 1 file changed, 251 deletions(-) delete mode 100644 crisp_nam/models/deephit_org.py diff --git a/crisp_nam/models/deephit_org.py b/crisp_nam/models/deephit_org.py deleted file mode 100644 index cc3394f3..00000000 --- a/crisp_nam/models/deephit_org.py +++ /dev/null @@ -1,251 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -class FCLayer(nn.Module): - """Fully connected layer with optional batch norm, dropout, and activation.""" - def __init__(self, in_dim, out_dim, activation=nn.ReLU(), batch_norm=False, dropout_rate=0.0, - init_fn=nn.init.xavier_normal_): - super(FCLayer, self).__init__() - self.fc = nn.Linear(in_dim, out_dim) - self.batch_norm = nn.BatchNorm1d(out_dim) if batch_norm else None - self.dropout = nn.Dropout(dropout_rate) if dropout_rate > 0 else None - self.activation = activation - - # Initialize weights - if init_fn: - init_fn(self.fc.weight) - nn.init.zeros_(self.fc.bias) - - def forward(self, x): - x = self.fc(x) - if self.batch_norm: - x = self.batch_norm(x) - if self.activation: - x = self.activation(x) - if self.dropout: - x = self.dropout(x) - return x - - -class FCNet(nn.Module): - """Multi-layer fully connected network.""" - def __init__(self, in_dim, num_layers, h_dim, activation=nn.ReLU(), - out_dim=None, out_activation=None, batch_norm=False, - dropout_rate=0.0, init_fn=nn.init.xavier_normal_): - super(FCNet, self).__init__() - - layers = [] - prev_dim = in_dim - - # Hidden layers - for i in range(num_layers): - curr_dim = out_dim if (i == num_layers - 1 and out_dim) else h_dim - curr_act = out_activation if (i == num_layers - 1 and out_activation) else activation - - layers.append(FCLayer( - prev_dim, curr_dim, activation=curr_act, - batch_norm=batch_norm, dropout_rate=dropout_rate, init_fn=init_fn - )) - prev_dim = curr_dim - - self.network = nn.Sequential(*layers) - - def forward(self, x): - return self.network(x) - - -class DeepHit(nn.Module): - """PyTorch implementation of DeepHit for competing risks survival analysis.""" - - def __init__(self, input_dims, network_settings): - super(DeepHit, self).__init__() - - # Input dimensions - self.x_dim = input_dims['x_dim'] - self.num_Event = input_dims['num_Event'] - self.num_Category = input_dims['num_Category'] - - # Network settings - self.h_dim_shared = network_settings['h_dim_shared'] - self.h_dim_CS = network_settings['h_dim_CS'] - self.num_layers_shared = network_settings['num_layers_shared'] - self.num_layers_CS = network_settings['num_layers_CS'] - - # Activation function - if network_settings['active_fn'] == 'relu': - self.active_fn = nn.ReLU() - elif network_settings['active_fn'] == 'elu': - self.active_fn = nn.ELU() - elif network_settings['active_fn'] == 'tanh': - self.active_fn = nn.Tanh() - else: - self.active_fn = nn.ReLU() - - # Regularization - self.keep_prob = network_settings.get('keep_prob', 0.5) - self.dropout_rate = 1.0 - self.keep_prob - - # Initialize networks - self._build_network() - - def _build_network(self): - # Shared network - self.shared_net = FCNet( - in_dim=self.x_dim, - num_layers=self.num_layers_shared, - h_dim=self.h_dim_shared, - activation=self.active_fn, - dropout_rate=self.dropout_rate - ) - - # Cause-specific networks - self.cs_nets = nn.ModuleList([ - FCNet( - in_dim=self.x_dim + self.h_dim_shared, # Concatenate input and shared output - num_layers=self.num_layers_CS, - h_dim=self.h_dim_CS, - activation=self.active_fn, - dropout_rate=self.dropout_rate - ) for _ in range(self.num_Event) - ]) - - # Output layer - self.output_layer = nn.Linear(self.num_Event * self.h_dim_CS, self.num_Event * self.num_Category) - - def forward(self, x): - # Shared network - shared_out = self.shared_net(x) - - # Concatenate input with shared output - h = torch.cat([x, shared_out], dim=1) - - # Cause-specific networks - cs_outputs = [] - for cs_net in self.cs_nets: - cs_out = cs_net(h) - cs_outputs.append(cs_out) - - # Stack outputs - stacked_out = torch.stack(cs_outputs, dim=1) # [batch_size, num_Event, h_dim_CS] - reshaped_out = stacked_out.view(-1, self.num_Event * self.h_dim_CS) # [batch_size, num_Event * h_dim_CS] - - # Final output layer - logits = self.output_layer(F.dropout(reshaped_out, self.dropout_rate, self.training)) - out = F.softmax(logits.view(-1, self.num_Event * self.num_Category), dim=1) - - # Reshape to [batch_size, num_Event, num_Category] - out = out.view(-1, self.num_Event, self.num_Category) - - # For compatibility with the training script, return both raw risks and shape functions - # In this model, we don't have separate shape functions, so just return None - return out, None - - def log_likelihood_loss(self, out, t, k, mask1, mask2): - """Log-likelihood loss (including log-likelihood of censored subjects)""" - batch_size = out.size(0) - - # Convert to PyTorch tensors if necessary - if not isinstance(k, torch.Tensor): - k = torch.tensor(k, device=out.device) - if not isinstance(t, torch.Tensor): - t = torch.tensor(t, device=out.device) - - # Indicator for uncensored subjects - I_1 = (k > 0).float().view(-1, 1) - - # For uncensored: log P(T=t, K=k|x) - tmp1 = torch.sum(torch.sum(mask1 * out, dim=2), dim=1, keepdim=True) - tmp1 = I_1 * torch.log(tmp1 + 1e-8) - - # For censored: log ∑ P(T>t|x) - tmp2 = torch.sum(torch.sum(mask2.unsqueeze(1) * out, dim=2), dim=1, keepdim=True) - tmp2 = (1.0 - I_1) * torch.log(tmp2 + 1e-8) - - return -torch.mean(tmp1 + tmp2) - - def ranking_loss(self, out, t, k, mask2): - """Ranking loss (calculated only for acceptable pairs)""" - batch_size = out.size(0) - sigma1 = 0.1 - eta = [] - - # Convert to PyTorch tensors if necessary - if not isinstance(k, torch.Tensor): - k = torch.tensor(k, device=out.device) - if not isinstance(t, torch.Tensor): - t = torch.tensor(t, device=out.device) - - one_vector = torch.ones_like(t) - - for e in range(self.num_Event): - # Indicator for current event type - I_2 = (k == e+1).float() - I_2_diag = torch.diag(I_2.squeeze()) - - # Extract event-specific probabilities - tmp_e = out[:, e, :] # [batch_size, num_Category] - - # Calculate risk scores - R = torch.matmul(tmp_e, mask2.transpose(0, 1)) # [batch_size, batch_size] - diag_R = torch.diag(R).unsqueeze(1) # [batch_size, 1] - R = torch.matmul(one_vector, diag_R.transpose(0, 1)) - R # [batch_size, batch_size] - R = R.transpose(0, 1) # Now R_ij = r_i(T_i) - r_j(T_i) - - # Time comparison matrix - T = F.relu(torch.sign(torch.matmul(one_vector, t.transpose(0, 1)) - - torch.matmul(t, one_vector.transpose(0, 1)))) - - # Filter by event occurrence - T = torch.matmul(I_2_diag, T) - - # Calculate ranking loss for current event - tmp_eta = torch.mean(T * torch.exp(-R / sigma1), dim=1, keepdim=True) - eta.append(tmp_eta) - - eta = torch.stack(eta, dim=1) # [batch_size, num_Event] - eta = torch.mean(eta.reshape(-1, self.num_Event), dim=1, keepdim=True) - - return torch.sum(eta) - - def calibration_loss(self, out, t, k, mask2): - """Calibration loss""" - batch_size = out.size(0) - eta = [] - - # Convert to PyTorch tensors if necessary - if not isinstance(k, torch.Tensor): - k = torch.tensor(k, device=out.device) - - for e in range(self.num_Event): - # Indicator for current event type - I_2 = (k == e+1).float() - - # Extract event-specific probabilities - tmp_e = out[:, e, :] # [batch_size, num_Category] - - # Calculate calibration loss - r = torch.sum(tmp_e * mask2, dim=1) - tmp_eta = torch.mean((r - I_2) ** 2, dim=0, keepdim=True) - eta.append(tmp_eta) - - eta = torch.stack(eta, dim=1) # [1, num_Event] - eta = torch.mean(eta.reshape(-1, self.num_Event), dim=1, keepdim=True) - - return torch.sum(eta) - - def compute_loss(self, out, t, k, mask1, mask2, alpha=1.0, beta=1.0, gamma=1.0): - """Compute total loss""" - loss1 = self.log_likelihood_loss(out, t, k, mask1, mask2) - loss2 = self.ranking_loss(out, t, k, mask2) - loss3 = self.calibration_loss(out, t, k, mask2) - - # L2 regularization is handled by optimizer (weight_decay) - return alpha * loss1 + beta * loss2 + gamma * loss3 - - def predict(self, x): - """Predict risk scores for input x""" - self.eval() - with torch.no_grad(): - out, _ = self.forward(x) - return out \ No newline at end of file From b69f57d0bd5b62aca5d64b9e86987c04d20c7471 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Wed, 18 Mar 2026 21:40:22 -0400 Subject: [PATCH 20/47] Update readme with new repo structure; Add datasets.md for detailed info on datasets --- README.md | 105 +++++++++++++++++++++------------------------------- datasets.md | 40 ++++++++++++++++++++ 2 files changed, 83 insertions(+), 62 deletions(-) create mode 100644 datasets.md diff --git a/README.md b/README.md index f7e06772..df90103b 100644 --- a/README.md +++ b/README.md @@ -17,37 +17,7 @@ This repository provides a comprehensive framework for competing risks survival - **Baseline Comparisons**: DeepHit implementation for benchmarking against state-of-the-art methods ### Available Datasets - -The repository includes four well-established survival analysis datasets: - -1. **Framingham Heart Study**: Cardiovascular disease prediction with competing events (CVD vs. death) - - Features: Demographics, clinical measurements, lifestyle factors - - Events: Cardiovascular disease, death from other causes - -2. **PBC (Primary Biliary Cirrhosis)**: Liver disease progression study - - Features: Clinical laboratory values, demographic information - - Events: Death, liver transplantation - -3. **SUPPORT**: Study to understand prognoses and preferences for outcomes - - Features: Comprehensive clinical and demographic variables - - Events: Cancer death, non-cancer death - -4. **Synthetic Dataset**: Controlled simulation for method validation - - Features: Simulated clinical variables with known ground truth - - Events: Multiple competing risks with controllable hazard functions - -All datasets come with preprocessing pipelines that handle missing values, feature encoding, and proper train/test splitting to prevent data leakage. - -### Training Scripts - -The repository provides several specialized training scripts: - -- **`train.py`**: Standard model training with cross-validation and comprehensive evaluation -- **`train_nested_cv.py`**: Robust nested cross-validation for unbiased performance estimation -- **`tune_optuna.py`**: Hyperparameter optimization using Optuna's advanced algorithms -- **`train_deephit.py`**: DeepHit baseline implementation for comparative studies - -Each script supports extensive configuration through command-line arguments and YAML config files, enabling reproducible experiments and easy parameter sweeps. +This repository includes 4 datasets: Framingham Heart Study, PBC, Support2 and Synthetic datasets. Detailed information is available in [datasets.md](./datasets.md). ## Requirements Python >=3.10 @@ -56,6 +26,7 @@ Python >=3.10 ``` crisp_nam/ +├── blog/ # Blog ├── crisp_nam/ # Main package │ ├── metrics/ │ │ ├── __init__.py @@ -89,6 +60,7 @@ crisp_nam/ │ ├── SurvivalDataset.py │ ├── synthetic_comprisk.csv │ └── synthetic_dataset.py +├── docs/ # Documentation of Pypi package. ├── results/ # Results and outputs │ ├── best_params/ # Best parameters for dataset and model combinations │ │ ├── best_params_framingham_deephit.yaml @@ -117,11 +89,6 @@ crisp_nam/ │ ├── train.py │ ├── tune_optuna_optimized.py │ └── tune_optuna.py -└── utils/ # Legacy utils (duplicate of crisp_nam/utils) - ├── __init__.py - ├── loss.py - ├── plotting.py - └── risk_cif.py ``` ## Install from source @@ -146,7 +113,20 @@ cd crisp-nam uv sync ``` -## Running training scripts +## Training Scripts + +The repository provides several specialized training scripts: + +- **`train.py`**: Standard model training with cross-validation and comprehensive evaluation +- **`train_nested_cv.py`**: Robust nested cross-validation for unbiased performance estimation +- **`tune_optuna.py`**: Hyperparameter optimization using Optuna's advanced algorithms +- **`tune_optuna_optimized.py`**: Hyperparameter optimization using Optuna on a GPU. +- **`train_deephit.py`**: DeepHit baseline implementation for comparative studies +- **`train_deephit_cuda.py`**: DeepHit baseline implementation optimized for running on a GPU. + +Each script supports extensive configuration through command-line arguments and YAML config files, enabling reproducible experiments and easy parameter sweeps. + +### Running training scripts 1. Modify training parameters in `training_scripts/train.py` OR @@ -162,65 +142,65 @@ uv sync 2. Run the training script - via `python` + 1. via `python` ```bash source .venv/bin/activate python training_scripts/train.py --dataset framingham ``` - via `uv` + 2. via `uv` ```bash uv run training_scripts/train.py --dataset framingham ``` -## Running Nested Cross-Validation +### Running Nested Cross-Validation The nested cross-validation script performs robust model evaluation with hyperparameter optimization using inner and outer cross-validation loops. It automatically generates performance metrics, feature importance plots, and shape function visualizations. -### Basic Usage - +via `python` ```bash -# Using python python training_scripts/train_nested_cv.py --dataset framingham +``` -# Using uv +via `uv` +```bash uv run training_scripts/train_nested_cv.py --dataset framingham ``` -### Configuration Parameters +## Configuration Parameters All parameters can be passed via command line or specified in a YAML config file: -#### Dataset Configuration +1. Dataset Configuration - `--dataset` (str): Dataset to use (choices: `framingham`, `support`, `pbc`, `synthetic`, default: `framingham`) - `--scaling` (str): Data scaling method for continuous features (choices: `minmax`, `standard`, `none`, default: `standard`) -#### Training Parameters +2. Training Parameters - `--num_epochs` (int): Number of training epochs (default: `250`) - `--batch_size` (int): Batch size for training (default: `512`) - `--patience` (int): Patience for early stopping (default: `10`) -#### Cross-Validation Configuration +3. Cross-Validation Configuration - `--outer_folds` (int): Number of outer CV folds (default: `5`) - `--inner_folds` (int): Number of inner CV folds for hyperparameter tuning (default: `3`) - `--n_trials` (int): Number of Optuna trials per inner fold (default: `20`) -#### Event Weighting +4. Event Weighting - `--event_weighting` (str): Event weighting strategy (choices: `none`, `balanced`, `custom`, default: `none`) - `--custom_event_weights` (str): Custom weights for events (comma-separated, default: `None`) -#### Other Parameters +5. Other Parameters - `--seed` (int): Random seed for reproducibility (default: `42`) - `--config` (str): Path to YAML config file (default: looks for `config.yaml`) ### Examples -#### Basic nested CV with default parameters: +1. **Basic nested CV with default parameters:** ```bash python training_scripts/train_nested_cv.py --dataset pbc ``` -#### Customized nested CV with specific parameters: +2. **Customized nested CV with specific parameters:** ```bash python training_scripts/train_nested_cv.py \ --dataset support \ @@ -233,38 +213,39 @@ python training_scripts/train_nested_cv.py \ --seed 123 ``` -#### Using a config file: +3. **Using a config file:** ```bash python training_scripts/train_nested_cv.py --config my_config.yaml ``` -### Output Files +## Output Files The script generates several output files in the current directory: -#### Performance Metrics +1. **Performance Metrics** - `nested_cv_summary_metrics_{dataset}.csv`: Summary table with mean ± std metrics - `nested_cv_detailed_metrics_{dataset}.csv`: Detailed results for each fold - `nested_cv_metrics_{dataset}.xlsx`: Excel file with multiple sheets (Summary, Detailed, Metadata) - `nested_cv_raw_metrics_{dataset}.json`: Raw metrics dictionary for reproducibility -#### Model Configuration +2. **Model Configuration** - `nested_cv_best_params_{dataset}.yaml`: Aggregated best hyperparameters across all folds -#### Visualizations -Results are saved to `results/plots/`: +3. **Visualizations** - `nested_cv_feature_importance_risk_{risk}_{dataset}.png`: Feature importance plots - `nested_cv_shape_functions_risk_{risk}_{dataset}.png`: Shape function plots for top features -### Evaluation Metrics +Results are saved to `results/plots/`: + +## Evaluation Metrics The script computes the following metrics at different time quantiles (25%, 50%, 75%): -- **AUC (Area Under the ROC Curve)**: Time-dependent AUC for discrimination +1. **AUC (Area Under the ROC Curve)**: Time-dependent AUC for discrimination - 0.5 = random, >0.7 = good, >0.8 = excellent -- **TDCI (Time-Dependent Concordance Index)**: Harrell's C-index adapted for competing risks +2. **TDCI (Time-Dependent Concordance Index)**: Harrell's C-index adapted for competing risks - 0.5 = random, >0.7 = good, >0.8 = excellent -- **Brier Score**: Calibration metric measuring prediction accuracy +3. **Brier Score**: Calibration metric measuring prediction accuracy - 0 = perfect, <0.25 = good, >0.25 = poor > [!NOTE] diff --git a/datasets.md b/datasets.md new file mode 100644 index 00000000..47f95d3d --- /dev/null +++ b/datasets.md @@ -0,0 +1,40 @@ +## Datasets overview +The repository includes four well-established survival analysis datasets: + +1. **Framingham Heart Study**: Cardiovascular disease prediction with competing events (CVD vs. death) + - Features: Demographics, clinical measurements, lifestyle factors + - Events: Cardiovascular disease, death from other causes + +2. **PBC (Primary Biliary Cirrhosis)**: Liver disease progression study + - Features: Clinical laboratory values, demographic information + - Events: Death, liver transplantation + +3. **SUPPORT**: Study to understand prognoses and preferences for outcomes + - Features: Comprehensive clinical and demographic variables + - Events: Cancer death, non-cancer death + +4. **Synthetic Dataset**: Controlled simulation for method validation + - Features: Simulated clinical variables with known ground truth + - Events: Multiple competing risks with controllable hazard functions + +CSV files for all datasets are available in the repository within `crisp-nam/datasets` folder. + +## Data loading scripts +The repository contains preprocessing scripts within `datasets` folder that handle missing values, feature encoding, and proper train/test splitting to prevent data leakage for each dataset. + +- **`framingham_dataset.py`**: Preprocess and load Framingham dataset. +- **`pbc_dataset.py`**: Preprocess and load PBC dataset. +- **`support_dataset.py`**: Preprocess and load Support2 dataset. +- **`synthetic_dataset.py`**: Preprocess and load synthetically generated dataset. + +## Return format +Each script returns the following values for use within training scripts: +1. `x`: Feature matrix after preprocessing +2. `t`: Array of time to event values +3. `event_type`: Categorical data depicting event types +4. `feature_names`: Array of feature names +5. `n_continuous`: Number of continuous features +6. `feature_ranges`: List of (min, max) ranges for each feature. +``` +> [!NOTE] +> If introducing a new dataset, the above mentioned return format is needed to run the training scripts. \ No newline at end of file From d5ffde9a5b9ad901686123311696add71cf945c4 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Wed, 18 Mar 2026 22:07:30 -0400 Subject: [PATCH 21/47] Try adding blog within documentation folder --- .github/workflows/blog.yml | 34 +- documentation/blog/package-lock.json | 7563 + documentation/blog/package.json | 31 + .../blog/src/assets/ism-overview-new.svg | 2 + documentation/blog/src/diagrams/.gitkeep | 0 .../blog/src/diagrams/architecture.html | 266 + .../src/diagrams/crisp-nam-architecture.js | 369 + .../blog/src/diagrams/feature_importance.html | 372 + documentation/blog/src/diagrams/index.js | 281 + .../blog/src/diagrams/nam-architecture.js | 256 + .../blog/src/diagrams/results-table.html | 147 + documentation/blog/src/diagrams/shape_plot.js | 164 + .../blog/src/diagrams/shape_plots.html | 72 + documentation/blog/src/diagrams/toc.js | 244 + documentation/blog/src/diagrams/venn.html | 49 + documentation/blog/src/index.ejs | 374 + documentation/blog/src/index.js | 10 + documentation/blog/src/style.css | 250 + documentation/blog/src/util.js | 3 + documentation/blog/static/final_results.json | 328505 +++++++++++++++ documentation/blog/static/references.bib | 156 + documentation/blog/webpack.config.js | 179 + 22 files changed, 339313 insertions(+), 14 deletions(-) create mode 100644 documentation/blog/package-lock.json create mode 100644 documentation/blog/package.json create mode 100644 documentation/blog/src/assets/ism-overview-new.svg create mode 100644 documentation/blog/src/diagrams/.gitkeep create mode 100644 documentation/blog/src/diagrams/architecture.html create mode 100644 documentation/blog/src/diagrams/crisp-nam-architecture.js create mode 100644 documentation/blog/src/diagrams/feature_importance.html create mode 100644 documentation/blog/src/diagrams/index.js create mode 100644 documentation/blog/src/diagrams/nam-architecture.js create mode 100644 documentation/blog/src/diagrams/results-table.html create mode 100644 documentation/blog/src/diagrams/shape_plot.js create mode 100644 documentation/blog/src/diagrams/shape_plots.html create mode 100644 documentation/blog/src/diagrams/toc.js create mode 100644 documentation/blog/src/diagrams/venn.html create mode 100644 documentation/blog/src/index.ejs create mode 100644 documentation/blog/src/index.js create mode 100644 documentation/blog/src/style.css create mode 100644 documentation/blog/src/util.js create mode 100644 documentation/blog/static/final_results.json create mode 100644 documentation/blog/static/references.bib create mode 100644 documentation/blog/webpack.config.js diff --git a/.github/workflows/blog.yml b/.github/workflows/blog.yml index b797520a..60cef9aa 100644 --- a/.github/workflows/blog.yml +++ b/.github/workflows/blog.yml @@ -25,36 +25,36 @@ jobs: cache-dependency-path: blog/package-lock.json - name: Install dependencies - run: cd blog && npm ci + run: cd documentation/blog && npm ci - name: Build - run: cd ./blog && npm run build + run: cd ./documentation/blog && npm run build - name: Upload build artifact if: github.event_name == 'push' && github.ref == 'refs/heads/main' uses: actions/upload-artifact@v6 with: name: blog-build - path: blog/public + path: documentation/blog/public retention-days: 1 deploy: needs: build if: github.event_name == 'push' && github.ref == 'refs/heads/main' runs-on: ubuntu-latest - environment: - name: github-pages - url: ${{ steps.deployment.outputs.page_url }} - concurrency: - group: pages - cancel-in-progress: false + # environment: + # name: github-pages + # url: ${{ steps.deployment.outputs.page_url }} + # concurrency: + # group: pages + # cancel-in-progress: false steps: - name: Download build artifact uses: actions/download-artifact@v7 with: name: blog-build - path: blog/public + path: documentation/blog/public - name: Setup Pages uses: actions/configure-pages@v5 @@ -62,9 +62,15 @@ jobs: - name: Upload Pages artifact uses: actions/upload-pages-artifact@v4 with: - path: blog/public + path: documentation/blog/public retention-days: 1 - - name: Deploy to GitHub Pages - id: deployment - uses: actions/deploy-pages@v4 + # - name: Deploy to GitHub Pages + # id: deployment + # uses: actions/deploy-pages@v4 + + - name: Deploy to Github pages + uses: JamesIves/github-pages-deploy-action@v4.8.0 + with: + 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/dev/null +++ b/documentation/blog/package.json @@ -0,0 +1,31 @@ +{ + "name": "ai-safety-needs-social-scientists", + "private": true, + "scripts": { + "dev": "cross-env NODE_ENV=development webpack-dev-server --hot", + "build": "cross-env NODE_ENV=production webpack --progress", + "profile": "cross-env NODE_ENV=production webpack --profile --json > webpack-stats.json" + }, + "devDependencies": { + "@babel/core": "^7.23.0", + "@babel/preset-env": "^7.26.0", + "babel-loader": "^9.1.3", + "copy-webpack-plugin": "^11.0.0", + "cross-env": "^7.0.3", + "css-loader": "^6.8.1", + "eases-jsnext": "^1.0.10", + "file-loader": "^6.2.0", + "html-replace-webpack-plugin": "^2.6.0", + "html-string-replace-webpack-plugin": "^1.0.5", + "html-webpack-plugin": "^5.5.0", + "lucid-components": "0.0.6", + "raw-loader": "^4.0.2", + "svg-inline-loader": "^0.8.2", + "webpack": "^5.89.0", + "webpack-cli": "^5.1.4", + "webpack-dev-server": "^5.2.1" + }, + "dependencies": { + "d3": "^7.9.0" + } +} diff --git a/documentation/blog/src/assets/ism-overview-new.svg b/documentation/blog/src/assets/ism-overview-new.svg new file mode 100644 index 00000000..09abbb22 --- /dev/null +++ b/documentation/blog/src/assets/ism-overview-new.svg @@ -0,0 +1,2 @@ +BMIGlucoseHazard functionCardiovascular diseaseDeathBP MedsCompeting RisksPatient covariatesHazard function \ No newline at end of file diff --git a/documentation/blog/src/diagrams/.gitkeep b/documentation/blog/src/diagrams/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/documentation/blog/src/diagrams/architecture.html b/documentation/blog/src/diagrams/architecture.html new file mode 100644 index 00000000..70b7347c --- /dev/null +++ b/documentation/blog/src/diagrams/architecture.html @@ -0,0 +1,266 @@ + + + + + + CRISP-NAM Architecture + + + + + + + + + \ No newline at end of file diff --git a/documentation/blog/src/diagrams/crisp-nam-architecture.js b/documentation/blog/src/diagrams/crisp-nam-architecture.js new file mode 100644 index 00000000..9babe602 --- /dev/null +++ b/documentation/blog/src/diagrams/crisp-nam-architecture.js @@ -0,0 +1,369 @@ +import * as d3 from 'd3'; + +export function initCrispNamStructure(containerId) { + const container = document.getElementById(containerId); + if (!container) return; + + // Clear previous + container.innerHTML = ''; + + const width = 1000; + const height = 450; + const margin = { top: 40, right: 20, bottom: 40, left: 20 }; + const figureTitle = 'Figure 4: Schematic of CRISP-NAM Architecture'; + + const svg = d3.select(container) + .append('svg') + .attr('width', '100%') + .attr('height', 'auto') + .attr('viewBox', `0 0 ${width} ${height}`) + .style('max-width', '100%') + .style('height', 'auto'); + + // Accessibility title + svg.append('title').text(figureTitle); + + // // Visible title + svg.append('text') + .attr('class', 'figure-title') + .attr('x', width / 2) + .attr('y', height - margin.bottom + 30) + .attr('text-anchor', 'middle') + .attr('font-size', '20px') + .attr('font-weight', '600') + .attr('fill', '#111') + .text(figureTitle); + + + // Definitions for markers + const defs = svg.append('defs'); + defs.append('marker') + .attr('id', 'arrowhead') + .attr('viewBox', '0 -5 10 10') + .attr('refX', 8) + .attr('refY', 0) + .attr('markerWidth', 6) + .attr('markerHeight', 6) + .attr('orient', 'auto') + .append('path') + .attr('d', 'M0,-5L10,0L0,5') + .attr('fill', '#999'); + + defs.append('marker') + .attr('id', 'arrowhead-blue') + .attr('viewBox', '0 -5 10 10') + .attr('refX', 8) + .attr('refY', 0) + .attr('markerWidth', 6) + .attr('markerHeight', 6) + .attr('orient', 'auto') + .append('path') + .attr('d', 'M0,-5L10,0L0,5') + .attr('fill', '#4a90e2'); + + defs.append('marker') + .attr('id', 'arrowhead-red') + .attr('viewBox', '0 -5 10 10') + .attr('refX', 8) + .attr('refY', 0) + .attr('markerWidth', 6) + .attr('markerHeight', 6) + .attr('orient', 'auto') + .append('path') + .attr('d', 'M0,-5L10,0L0,5') + .attr('fill', '#e24a4a'); + + // Layout parameters + const xStart = 50; + const yStart = 80; + const xGap = 180; + const yGap = 100; + + // Data structure for the diagram + const features = [ + { id: 'x1', label: 'Covariate x₁', y: yStart }, + { id: 'x2', label: 'Covariate x₂', y: yStart + yGap }, + { id: 'xp', label: 'Covariate xₚ', y: yStart + yGap * 2.2 } // Gap for ellipsis + ]; + + // Draw layers + + // 1. Inputs + const featureGroup = svg.append('g').attr('class', 'features'); + features.forEach(f => { + drawBox(featureGroup, xStart, f.y, f.label, '#e3f2fd', '#2196f3', f.id); + }); + // Ellipsis + featureGroup.append('text').attr('x', xStart + 50).attr('y', yStart + yGap * 1.8).text('⋮').attr('class', 'arch-text'); + // Inputs + featureGroup.append('text') + .attr('x', xStart + 50) + .attr('y', yStart - 10) + .attr('text-anchor', 'middle') + .attr('font-size', '18px') + .attr('font-weight', '600') + .attr('fill', '#333') + .text('Input'); + + // 2. FeatureNets + const netX = xStart + xGap; + const netY = yStart; + const netGroup = svg.append('g').attr('class', 'feature-nets'); + drawContainer(netGroup, netX - 15, netY - 15, 'FeatureNets', 'none', '#4caf50', 'feature-box', 100 + 30, yGap * 2.6 + 30); + features.forEach(f => { + drawBox(netGroup, netX, f.y, `f(${f.label.split(' ')[1]})`, '#e8f5e9', '#4caf50', 'net-' + f.id); + }); + netGroup.append('text').attr('x', netX + 40).attr('y', yStart + yGap * 1.8).text('⋮').attr('class', 'arch-text'); + + // 3. Projections (Split) + const projX = netX + xGap; + const projY = netY; + const projGroup = svg.append('g').attr('class', 'projections'); + drawContainer(projGroup, projX - 15, projY - 23 - 15, 'Risk Projections', 'none', '#2196f3', 'proj-box', 100 + 30, yGap * 3 + 33); + features.forEach((f, i) => { + // Risk 1 (Blue, Up) + drawBox(projGroup, projX, f.y - 23, `proj${i === 2 ? 'p' : i + 1},1`, 'white', '#2196f3', `proj1-${f.id}`); + // Risk 2 (Red, Down) + drawBox(projGroup, projX, f.y + 22, `proj${i === 2 ? 'p' : i + 1},2`, 'white', '#f44336', `proj2-${f.id}`); + }); + + projGroup.append('text').attr('x', projX + 32).attr('y', yStart + yGap * 1.85).text('⋮').attr('class', 'arch-text'); + + + // 4. Sums + const sumX = projX + xGap + 80; + const sumGroup = svg.append('g').attr('class', 'sums'); + const sum1Y = yStart + yGap * 0.5; + const sum2Y = yStart + yGap * 2.0; + drawContainer(sumGroup, sumX - 37, sum1Y - 30, '', 'none', '#f44336', 'sum-box', 70, yGap * 2 + 10); + sumGroup.append('text') + .attr('x', sumX) + .attr('y', sum1Y - 60) + .attr('text-anchor', 'middle') + .attr('font-size', '18px') + .attr('font-weight', '600') + .attr('fill', '#333') + .text('Additive Risk'); + sumGroup.append('text') + .attr('x', sumX) + .attr('y', sum1Y - 40) + .attr('text-anchor', 'middle') + .attr('font-size', '18px') + .attr('font-weight', '600') + .attr('fill', '#333') + .text('Aggregation'); + drawCircle(sumGroup, sumX, sum1Y, '+', '#f5f5f5', '#2196f3', 'sum1'); + drawCircle(sumGroup, sumX, sum2Y, '+', '#f5f5f5', '#f44336', 'sum2'); + + // 5. Final Risks + const riskX = sumX + xGap - 50; + const riskGroup = svg.append('g').attr('class', 'risks'); + drawContainer(riskGroup, riskX - 15, sum1Y, '', 'none', '#2196f3', 'risk-box', 110, yGap * 1.5); + riskGroup.append('text') + .attr('x', riskX + 45) + .attr('y', sum1Y - 30) + .attr('text-anchor', 'middle') + .attr('font-size', '18px') + .attr('font-weight', '600') + .attr('fill', '#333') + .text('Cause Specific'); + riskGroup.append('text') + .attr('x', riskX + 45) + .attr('y', sum1Y - 10) + .attr('text-anchor', 'middle') + .attr('font-size', '18px') + .attr('font-weight', '600') + .attr('fill', '#333') + .text('Log hazard Calculation'); + drawBox(riskGroup, riskX, sum1Y + 20, 'η₁(x)', '#e3f2fd', '#2196f3', 'eta1', 80); + drawBox(riskGroup, riskX, sum2Y - 60, 'η₂(x)', '#ffebee', '#f44336', 'eta2', 80); + + + // --- Connections --- + const edgesLayer = svg.append('g').attr('class', 'edges').lower(); + + // x -> Net -> h + features.forEach(f => { + drawArrow(edgesLayer, xStart + 100, f.y + 20, netX, f.y + 20, '#999'); // x -> net + drawArrow(edgesLayer, netX, f.y + 20, projX, f.y + 20, '#999'); // net -> proj + + // proj -> sum1 + const g1Box = { x: projX + 80, y: f.y - 5 }; + drawArrowCurve(edgesLayer, g1Box.x, g1Box.y, sumX - 22, sum1Y, '#4a90e2'); + + // proj -> sum2 + const g2Box = { x: projX + 80, y: f.y + 45 }; + drawArrowCurve(edgesLayer, g2Box.x, g2Box.y, sumX - 22, sum2Y, '#e24a4a'); + }); + + // sum -> eta + drawArrow(edgesLayer, sumX + 20, sum1Y, riskX, sum1Y + 43, '#4a90e2', 'arrowhead-blue'); + + drawArrow(edgesLayer, sumX + 20, sum2Y, riskX, sum2Y - 43, '#e24a4a', 'arrowhead-red'); + + // Helpers + function drawBox(group, x, y, text, fill, stroke, id, w = 100, h = 40) { + const g = group.append('g').attr('id', id); + g.append('rect') + .attr('x', x) + .attr('y', y) + .attr('width', w) + .attr('height', h) + .attr('rx', 5) + .attr('ry', 5) + .attr('fill', fill) + .attr('stroke', stroke) + .attr('stroke-width', 2); + + g.append('text') + .attr('x', x + w / 2) + .attr('y', y + h / 2 + 5) + .attr('text-anchor', 'middle') + .attr('font-size', '14px') + .attr('fill', '#333') + .text(text); + + return g; + } + + function drawContainer(group, x, y, text, fill, stroke, id, w = 100, h = 40) { + const g = group.append('g').attr('id', id); + g.append('rect') + .attr('x', x) + .attr('y', y) + .attr('width', w) + .attr('height', h) + .attr('rx', 5) + .attr('ry', 5) + .attr('fill', fill) + .attr('stroke', stroke) + .attr('stroke-width', 2) + .attr('stroke-dasharray', '6 4'); + + g.append('text') + .attr('x', x + w / 2) + .attr('y', y - 10) + .attr('text-anchor', 'middle') + .attr('font-size', '18px') + .attr('font-weight', '600') + .attr('fill', '#333') + .text(text); + return g; + } + + function drawCircle(group, x, y, text, fill, stroke, id, r = 20) { + const g = group.append('g').attr('id', id); + g.append('circle') + .attr('cx', x) + .attr('cy', y) + .attr('r', r) + .attr('fill', fill) + .attr('stroke', stroke) + .attr('stroke-width', 2); + + g.append('text') + .attr('x', x) + .attr('y', y + 5) + .attr('text-anchor', 'middle') + .attr('font-size', '20px') + .text(text); + } + + function drawArrow(group, x1, y1, x2, y2, color, markerId = 'arrowhead') { + const line = group.append('line') + .attr('x1', x1) + .attr('y1', y1) + .attr('x2', x2) + .attr('y2', y2) + .attr('stroke', color) + .attr('stroke-width', 2) + .attr('marker-end', `url(#${markerId})`) + .attr('stroke-linecap', 'round') + .attr('stroke-dasharray', '8 12') // dash pattern + .attr('stroke-dashoffset', 0); + + // Continuous motion from start to end + const speed = 80; // px per second; increase for quicker motion + d3.timer((elapsed) => { + const offset = -(elapsed / 1000) * speed; + line.attr('stroke-dashoffset', offset); + }); + } + + function drawArrowCurve(group, x1, y1, x2, y2, color) { + const midX = (x1 + x2) / 2; + const path = d3.path(); + path.moveTo(x1, y1); + path.bezierCurveTo(midX, y1, midX, y2, x2, y2); + + const curve = group.append('path') + .attr('d', path.toString()) + .attr('stroke', color) + .attr('stroke-width', 2) + .attr('fill', 'none') + .attr('marker-end', color.includes('e24a4a') ? 'url(#arrowhead-red)' : 'url(#arrowhead-blue)') + .attr('stroke-linecap', 'round') + .attr('stroke-dasharray', '8 12') + .attr('stroke-dashoffset', 0); + + // Continuous motion along the curve + const speed = 80; // px per second + d3.timer((elapsed) => { + const offset = -(elapsed / 1000) * speed; + curve.attr('stroke-dashoffset', offset); + }); + } + + // Animation: Flow particles + function animateParticles() { + features.forEach((f, i) => { + const particle = svg.append('circle') + .attr('r', 4) + .attr('fill', '#333') + .attr('cx', xStart + 100) + .attr('cy', f.y + 20); + + particle.transition() + .duration(1000) + .attr('cx', netX) + .transition() + .duration(1000) + .attr('cx', projX) + .transition() + .on('end', () => { + // Split + const p1 = svg.append('circle').attr('r', 3).attr('fill', '#2196f3').attr('cx', projX + 60).attr('cy', f.y + 20); + const p2 = svg.append('circle').attr('r', 3).attr('fill', '#f44336').attr('cx', projX + 60).attr('cy', f.y + 20); + + p1.transition().duration(1000) + .attrTween('transform', function () { + const startX = projX + 60; const startY = f.y + 20; + const endX = sumX; const endY = sum1Y; + return (t) => { + const x = startX + (endX - startX) * t; + // Simple easing for y + const y = startY + (endY - startY) * t; + return `translate(${x - startX}, ${y - startY})`; + }; + }) + .remove(); + + p2.transition().duration(1000) + .attrTween('transform', function () { + const startX = projX + 60; const startY = f.y + 20; + const endX = sumX; const endY = sum2Y; + return (t) => { + const x = startX + (endX - startX) * t; + const y = startY + (endY - startY) * t; + return `translate(${x - startX}, ${y - startY})`; + }; + }) + .remove(); + + particle.remove(); + }); + }); + + setTimeout(animateParticles, 4000); + } +} diff --git a/documentation/blog/src/diagrams/feature_importance.html b/documentation/blog/src/diagrams/feature_importance.html new file mode 100644 index 00000000..14ad8777 --- /dev/null +++ b/documentation/blog/src/diagrams/feature_importance.html @@ -0,0 +1,372 @@ + + + + + + Feature Importance - Both Risks + + + +
        +
        + +
        +
        + Risk 1: Death due to Cancer +
        +
        Feature Importance for Cancer Death
        + +
        +
        adls
        +
        +
        + -2.01 +
        +
        +
        +
        +
        sex_male
        +
        +
        + -1.94 +
        +
        +
        +
        +
        sps
        +
        +
        + -1.513 +
        +
        +
        +
        +
        resp
        +
        +
        + -1.28 +
        +
        +
        +
        +
        dnr_no dnr
        +
        +
        + -1.09 +
        +
        +
        +
        +
        age
        +
        +
        + 1.94 +
        +
        +
        + +
        +
        race_hispanic
        +
        +
        + 1.419 +
        +
        +
        + +
        +
        scoma
        +
        +
        + 1.71 +
        +
        +
        + +
        +
        temp
        +
        +
        + 1.92 +
        +
        +
        + +
        +
        dnr_dnr before sadm
        +
        +
        + 2.41 +
        +
        +
        + +
        ← Decreases Risk | Increases Risk →
        +
        +
        + + +
        +
        + Risk 2: Death due to Other Causes +
        +
        Feature Importance for Other Causes Death
        + +
        +
        sod
        +
        +
        + -1.710 +
        +
        +
        +
        +
        bili
        +
        +
        + -1.20 +
        +
        +
        +
        +
        pafi
        +
        +
        + -0.80 +
        +
        +
        +
        +
        race_white
        +
        +
        + -0.64 +
        +
        +
        +
        +
        sex_male
        +
        +
        + -0.59 +
        +
        +
        + +
        +
        crea
        +
        +
        + 1.028 +
        +
        +
        + +
        +
        hrt
        +
        +
        + 1.102 +
        +
        +
        + +
        +
        wblc
        +
        +
        + 1.23 +
        +
        +
        + +
        +
        temp
        +
        +
        + 1.35 +
        +
        +
        + +
        +
        charges
        +
        +
        + 1.91 +
        +
        +
        +
        ← Decreases Risk | Increases Risk →
        +
        +
        + + +
        +
        +
        + Increases Death Risk +
        +
        +
        + Decreases Death Risk +
        +
        + + + \ No newline at end of file diff --git a/documentation/blog/src/diagrams/index.js b/documentation/blog/src/diagrams/index.js new file mode 100644 index 00000000..84f42a39 --- /dev/null +++ b/documentation/blog/src/diagrams/index.js @@ -0,0 +1,281 @@ +/** + * Main diagrams module - imports and initializes all visualizations + */ + +import { initNamStructure } from './nam-architecture'; +import { initCrispNamStructure } from './crisp-nam-architecture'; + + +// Simple HTML escape function to prevent XSS when inserting text as HTML +function escapeHTML(str) { + return String(str) + .replace(/&/g, '&') + .replace(//g, '>') + .replace(/"/g, '"') + .replace(/'/g, '''); + } + + // Import diagram script path for iframe reference + export const diagramPath = './diagrams/diagram.js'; + + // Initialize all visualizations + export function initVisualizations() { + // Any interactive visualizations to initialize would go here + // The main interactive visualization is already handled in the iframe + try { initNamStructure('nam-structure-diagram'); } catch (e) { console.error(e); } + try { initCrispNamStructure('crisp-nam-structure-diagram'); } catch (e) { console.error(e); } + } + + /** + * Custom Table of Contents with enhanced styling + */ + + export function initCustomTOC() { + // Wait for the DOM to be fully loaded + const tocContainer = document.querySelector('d-contents'); + if (!tocContainer) return; + + // Apply custom styling to the TOC + const style = document.createElement('style'); + style.textContent = ` + d-contents { + background: transparent; + border-radius: 0; + box-shadow: none; + padding: 10px 0 !important; + width: 100% !important; + max-width: 100% !important; + left: 0 !important; + right: 0 !important; + margin-left: 0 !important; + position: relative !important; + top: 0 !important; + bottom: auto !important; + margin-bottom: 10px !important; + border: none !important; + } + + d-article { + padding-left: 0 !important; + } + + /* Remove hover effect */ + + d-contents nav h4 { + font-size: 1.4em !important; + color: #333 !important; + margin-bottom: 0.8em !important; + padding-bottom: 0.25em !important; + position: relative !important; + font-weight: 600 !important; + display: block !important; + width: 100% !important; + text-align: left !important; + } + + d-contents .toc-line { + display: none !important; + } + + d-contents nav { + display: block !important; + width: 100% !important; + max-width: 100% !important; + } + + d-contents nav > div.toc-section { + margin-bottom: 0.5em !important; + padding: 5px 15px !important; + transition: all 0.3s ease !important; + border-left: none !important; + margin-right: 0 !important; + width: 100% !important; + display: block !important; + background-color: transparent !important; + box-shadow: none !important; + } + + d-contents nav > div:hover { + background-color: transparent !important; + } + + d-contents nav > div > a { + display: block !important; + font-size: 16px !important; + font-weight: 600 !important; + color: #2c3e50 !important; + padding: 3px 0 !important; + margin: 0 !important; + position: relative !important; + transition: all 0.3s ease !important; + text-decoration: none !important; + } + + d-contents nav > div > a:hover { + color: #8e44ad !important; + } + + d-contents ul { + padding-left: 0 !important; + margin-top: 3px !important; + margin-bottom: 3px !important; + margin-left: 28px !important; + list-style: none !important; + } + + d-contents nav ul li { + margin-bottom: 4px !important; + position: relative !important; + transition: all 0.3s ease !important; + padding: 2px 5px 2px 20px !important; + border-radius: 0 !important; + } + + d-contents nav ul li::before { + content: ""; + } + + d-contents nav ul li:hover { + background-color: transparent !important; + } + + d-contents nav a { + color: #555 !important; + transition: all 0.3s ease !important; + font-size: 14px !important; + display: block !important; + text-decoration: none !important; + } + + d-contents nav ul li a { + color: #555 !important; + } + + d-contents nav ul li:hover a { + color: #8e44ad !important; + } + `; + + document.head.appendChild(style); + + // Enhance the TOC with section markers and icons + const tocDivs = tocContainer.querySelectorAll('nav > div'); + + tocDivs.forEach((div, index) => { + const sectionNum = index + 1; + const link = div.querySelector('a'); + if (link) { + const originalText = link.textContent; + link.innerHTML = `${sectionNum}. ${escapeHTML(originalText)}`; + } + + // Add toc-section class to all divs + div.classList.add('toc-section'); + }); + + // Fix the double bullet issue for sub-headings with a more aggressive approach + const fixBullets = () => { + // Apply a full reset to ensure correct layout + const tocSections = tocContainer.querySelectorAll('.toc-section'); + tocSections.forEach(section => { + section.style.display = 'block'; + section.style.width = '100%'; + }); + + // Remove all default list styling from ULs + const allULs = tocContainer.querySelectorAll('ul'); + allULs.forEach(ul => { + ul.style.listStyleType = 'none'; + ul.style.paddingLeft = '20px'; + ul.style.marginLeft = '0'; + ul.style.marginTop = '3px'; + ul.style.marginBottom = '3px'; + ul.setAttribute('role', 'list'); // For accessibility + }); + + // Process each list item to ensure no browser default bullets + const listItems = tocContainer.querySelectorAll('li'); + listItems.forEach(li => { + // Apply inline styles to override any browser defaults + li.style.listStyleType = 'none'; + li.style.display = 'block'; + li.style.marginBottom = '4px'; + + // Add a class for specific targeting + li.classList.add('custom-toc-item'); + + const link = li.querySelector('a'); + if (link) { + // Style the link directly + link.style.fontSize = '15px'; + link.style.color = '#444'; + link.style.textDecoration = 'none'; + link.style.display = 'inline-block'; + } + }); + + // Add one more style that will override all bullets + // Check if the style element already exists + let extraStyle = document.getElementById('custom-toc-style'); + if (!extraStyle) { + extraStyle = document.createElement('style'); + extraStyle.id = 'custom-toc-style'; + extraStyle.textContent = ` + d-contents li, + d-contents ul li, + d-contents nav ul li, + d-contents li::marker, + d-contents ul li::marker, + d-contents nav ul li::marker, + .custom-toc-item::marker { + list-style: none !important; + list-style-type: none !important; + list-style-position: initial !important; + list-style-image: initial !important; + } + + /* Simple hover effect for links with purple color */ + d-contents a:hover { + color: #8e44ad !important; + } + + /* Fix for the TOC layout and positioning */ + @media (min-width: 1024px) { + d-article { + padding-top: 2rem !important; + } + + d-contents + * { + margin-top: 2rem !important; + } + } + `; + document.head.appendChild(extraStyle); + } + + // Add classes for easier styling + const nav = tocContainer.querySelector('nav'); + if (nav) { + nav.classList.add('clean-nav-layout'); + + // Remove any potential borders or styling + const tocElements = tocContainer.querySelectorAll('*'); + tocElements.forEach(el => { + el.style.border = 'none'; + el.style.borderBottom = 'none'; + el.style.borderLeft = 'none'; + el.style.borderRight = 'none'; + el.style.borderTop = 'none'; + el.style.boxShadow = 'none'; + }); + } + }; + + // Run the fix immediately and after a short delay to ensure it applies + // after the browser has rendered the list items + fixBullets(); + setTimeout(fixBullets, 100); + // Try again after all resources are loaded + window.addEventListener('load', fixBullets); + } \ No newline at end of file diff --git a/documentation/blog/src/diagrams/nam-architecture.js b/documentation/blog/src/diagrams/nam-architecture.js new file mode 100644 index 00000000..9f33bd60 --- /dev/null +++ b/documentation/blog/src/diagrams/nam-architecture.js @@ -0,0 +1,256 @@ +import * as d3 from 'd3'; + +export function initNamStructure(containerId) { + const container = document.getElementById(containerId); + if (!container) return; + + // Clear previous + container.innerHTML = ''; + + const width = 800; + const height = 400; // Compact height + const margin = { top: 40, right: 40, bottom: 80, left: 40 }; + // --- Figure title and description (bottom center) --- + const figureTitle = 'Figure 3: Schematic of Neural Additive Model (NAM) Architecture' + const svg = d3.select(container) + .append('svg') + .attr('width', '100%') + .attr('height', 'auto') + .attr('viewBox', `0 0 ${width} ${height}`) + .style('max-width', '100%') + .style('height', 'auto'); + + // Accessibility title + svg.append('title').text(figureTitle); + + // Definitions for markers + const defs = svg.append('defs'); + defs.append('marker') + .attr('id', 'arrowhead-nam') + .attr('viewBox', '0 -5 10 10') + .attr('refX', 8) + .attr('refY', 0) + .attr('markerWidth', 6) + .attr('markerHeight', 6) + .attr('orient', 'auto') + .append('path') + .attr('d', 'M0,-5L10,0L0,5') + .attr('fill', '#666'); + + // // Visible title + svg.append('text') + .attr('class', 'figure-title') + .attr('x', width / 2) + .attr('y', height - margin.bottom + 30) + .attr('text-anchor', 'middle') + .attr('font-size', '16px') + .attr('font-weight', '600') + .attr('fill', '#111') + .text(figureTitle); + + // Layout + const xStart = 50; + const yStart = 60; + const yGap = 80; + + // Nodes Data + const features = [ + { id: 'x1', label: 'Covariate 1', y: yStart }, + { id: 'x2', label: 'Covariate 2', y: yStart + yGap }, + { id: 'x3', label: 'Covariate 3', y: yStart + yGap * 2 } + ]; + + const finalFeature = { id: 'xN', label: 'Covariate N', y: yStart + yGap * 2.8 }; + + + // Layers X Coordinates + const xFeatures = xStart; + const xNets = xStart + 180; + const xSum = xNets + 280; + const xPred = xSum + 120; + + // Draw Groups + const edgesGroup = svg.append('g').attr('class', 'edges'); + const nodesGroup = svg.append('g').attr('class', 'nodes'); + + // --- Draw Nodes --- + + // 1. Features + features.forEach(f => { + drawBox(nodesGroup, xFeatures, f.y, f.label, '#e3f2fd', '#2196f3', f.id); + }); + // Ellipsis for features + nodesGroup.append('text').attr('x', xFeatures + 50).attr('y', yStart + yGap * 2.7).text('⋮').attr('class', 'arch-text').attr('text-anchor', 'middle'); + nodesGroup.append('text') + .attr('x', xStart + 50) + .attr('y', yStart - 10) + .attr('text-anchor', 'middle') + .attr('font-size', '14px') + .attr('font-weight', '600') + .attr('fill', '#333') + .text('Input'); + //Draw final feature + drawBox(nodesGroup, xFeatures, finalFeature.y, finalFeature.label, '#e3f2fd', '#2196f3', finalFeature.id); + + // 2. Neural Nets + features.forEach(f => { + drawBox(nodesGroup, xNets, f.y, `Neural Net ${f.id.replace('x', '')}`, '#e8f5e9', '#4caf50', `net-${f.id}`, 120); + }); + nodesGroup.append('text').attr('x', xNets + 60).attr('y', yStart + yGap * 2.7).text('⋮').attr('class', 'arch-text').attr('text-anchor', 'middle'); + + // Draw final neural net + drawBox(nodesGroup, xNets, finalFeature.y, `Neural Net ${finalFeature.id.replace('x', '')}`, '#e8f5e9', '#4caf50', `net-${finalFeature.id}`, 120); + + // 3. Sum Node + const ySum = yStart + yGap * 1.5; // Centered + const sumBoxSize = 50; + // Transparent box centered at (xSum, ySum) + drawContainer(nodesGroup, xSum - sumBoxSize / 2, ySum - sumBoxSize / 2, 'Aggregate', 'none', '#9e9e9e', 'sum-box', sumBoxSize, sumBoxSize); + drawCircle(nodesGroup, xSum, ySum, '+', '#f5f5f5', '#9e9e9e', 'sum'); + + // 4. Prediction Node + const yPred = ySum; + const predBoxSize = 50; + const predRadius = 20; + + drawContainer(nodesGroup, xPred - predBoxSize / 2, yPred - predBoxSize / 2, 'Prediction', 'none', '#e91e63', 'pred-box', predBoxSize, predBoxSize); + drawCircle(nodesGroup, xPred, yPred, 'σ', '#fce4ec', '#e91e63', 'pred', predRadius); + + // --- Draw Edges --- + + features.forEach(f => { + // Feature -> Net + drawArrow(edgesGroup, xFeatures + 100, f.y + 20, xNets, f.y + 20); + + // Net -> Function + drawArrowCurve(edgesGroup, xNets + 120, f.y + 20, xSum-27, ySum); + + }); + + // Final Feature -> Final Net Arrows + drawArrow(edgesGroup, xFeatures + 100, finalFeature.y + 20, xNets, finalFeature.y + 20); + drawArrowCurve(edgesGroup, xNets + 120, finalFeature.y + 20, xSum-27, ySum); + + + // Sum -> Prediction + drawArrow(edgesGroup, xSum + 27, ySum, xPred - 27, yPred); + + + // --- Helpers --- + + function drawBox(group, x, y, text, fill, stroke, id, w = 100, h = 40) { + const g = group.append('g').attr('id', id); + g.append('rect') + .attr('x', x) + .attr('y', y) + .attr('width', w) + .attr('height', h) + .attr('rx', 5) + .attr('ry', 5) + .attr('fill', fill) + .attr('stroke', stroke) + .attr('stroke-width', 2); + + g.append('text') + .attr('x', x + w / 2) + .attr('y', y + h / 2 + 5) + .attr('text-anchor', 'middle') + .attr('font-size', '14px') + .attr('fill', '#333') + .text(text); + return g; + } + + function drawContainer(group, x, y, text, fill, stroke, id, w = 100, h = 40) { + const g = group.append('g').attr('id', id); + g.append('rect') + .attr('x', x) + .attr('y', y) + .attr('width', w) + .attr('height', h) + .attr('rx', 5) + .attr('ry', 5) + .attr('fill', fill) + .attr('stroke', stroke) + .attr('stroke-width', 2) + .attr('stroke-dasharray', '6 4'); + + g.append('text') + .attr('x', x + w / 2) + .attr('y', y - 5) + .attr('text-anchor', 'middle') + .attr('font-size', '14px') + .attr('font-weight', '600') + .attr('fill', '#333') + .text(text); + return g; + } + + function drawCircle(group, x, y, text, fill, stroke, id, r = 20) { + const g = group.append('g').attr('id', id); + g.append('circle') + .attr('cx', x) + .attr('cy', y) + .attr('r', r) + .attr('fill', fill) + .attr('stroke', stroke) + .attr('stroke-width', 2); + + g.append('text') + .attr('x', x) + .attr('y', y + 5) + .attr('text-anchor', 'middle') + .attr('font-size', '20px') + .text(text); + return g; + } + + function drawArrow(group, x1, y1, x2, y2) { + const line = group.append('line') + .attr('x1', x1) + .attr('y1', y1) + .attr('x2', x2) + .attr('y2', y2) + .attr('stroke', '#666') + .attr('stroke-width', 2) + .attr('marker-end', 'url(#arrowhead-nam)') + .attr('stroke-linecap', 'round') + .attr('stroke-dasharray', '8 12') // dash pattern + .attr('stroke-dashoffset', 0); + + // Continuous motion from start to end + const speed = 80; // px per second; increase for quicker motion + d3.timer((elapsed) => { + const offset = -(elapsed / 1000) * speed; + line.attr('stroke-dashoffset', offset); + }); + } + + function drawArrowCurve(group, x1, y1, x2, y2) { + const midX = (x1 + x2) / 2; + const path = d3.path(); + path.moveTo(x1, y1); + path.bezierCurveTo(midX, y1, midX, y2, x2, y2); + + const curve = group.append('path') + .attr('d', path.toString()) + .attr('stroke', '#666') + .attr('stroke-width', 2) + .attr('fill', 'none') + .attr('marker-end', 'url(#arrowhead-nam)') + .attr('stroke-linecap', 'round') + .attr('stroke-dasharray', '8 12') + .attr('stroke-dashoffset', 0); + + + // Continuous motion along the curve + const speed = 80; // px per second + d3.timer((elapsed) => { + const offset = -(elapsed / 1000) * speed; + curve.attr('stroke-dashoffset', offset); + }); + } + + // Optional simple animation on load + d3.selectAll('.nodes rect').style('opacity', 0).transition().duration(800).style('opacity', 1); +} diff --git a/documentation/blog/src/diagrams/results-table.html b/documentation/blog/src/diagrams/results-table.html new file mode 100644 index 00000000..f6d6953e --- /dev/null +++ b/documentation/blog/src/diagrams/results-table.html @@ -0,0 +1,147 @@ + + + + + + CRISP-NAM Results Table + + + +
        + + + + + + + + + + + + + + + + + + + + + + + + + + +
        Risk 1: Death due to Cancer
        ModelTD-AUCTD-CIBrier Score
        CRISP-NAM0.802 ± 0.0920.624 ± 0.0290.380 ± 0.242
        DeepHIT0.640 ± 0.2220.540 ± 0.0100.089 ± 0.002
        +
        + +
        + + + + + + + + + + + + + + + + + + + + + + + + + + +
        Risk 2: Death due to Other Causes
        ModelTD-AUCTD-CIBrier Score
        CRISP-NAM0.838 ± 0.1790.713 ± 0.0970.308 ± 0.188
        DeepHIT0.893 ± 0.0690.745 ± 0.0050.144 ± 0.006
        +
        + + \ No newline at end of file diff --git a/documentation/blog/src/diagrams/shape_plot.js b/documentation/blog/src/diagrams/shape_plot.js new file mode 100644 index 00000000..8fb2d22e --- /dev/null +++ b/documentation/blog/src/diagrams/shape_plot.js @@ -0,0 +1,164 @@ +let DATA; +let currentDataset = 'support'; +let currentRisk = 'risk1'; +let currentFeature = 'sps' +let features = []; +let risks = ['risk1', 'risk2'].map(r => ({ id: r })); + +/* ---------- Load JSON ---------- */ +fetch("../final_results.json") + .then(res => res.json()) + .then(json => { + DATA = json; + currentDataset = Object.keys(DATA)[0]; + + // Infer risks from dataset + risks = Object.keys(DATA[currentDataset]); + currentRisk = risks[0]; + + // Infer features from risk + features = Object.keys(DATA[currentDataset][currentRisk]); + currentFeature = features[0]; + populateDatasetDropdown(); + populateRiskDropdown(); + populateFeatureDropdown(); + drawPlot(); + }); + +/* ---------- Populate UI ---------- */ +function populateDatasetDropdown() { + const select = document.getElementById("datasetSelect"); + select.innerHTML = ""; + + Object.keys(DATA).forEach(name => { + const opt = document.createElement("option"); + opt.value = name; + opt.textContent = name; + select.appendChild(opt); + }); + + select.value = currentDataset; + + select.onchange = e => { + currentDataset = e.target.value; + + risks = Object.keys(DATA[currentDataset]); + currentRisk = risks[0]; + + features = Object.keys(DATA[currentDataset][currentRisk]); + currentFeature = features[0]; + + populateRiskDropdown(); + populateFeatureDropdown(); + drawPlot(); + }; +} + +function populateRiskDropdown() { + const select = document.getElementById("riskSelect"); + select.innerHTML = ""; + + risks.forEach(risk => { + const opt = document.createElement("option"); + opt.value = risk; + opt.textContent = risk; + select.appendChild(opt); + }); + + select.value = currentRisk; + + select.onchange = e => { + currentRisk = e.target.value; + + features = Object.keys(DATA[currentDataset][currentRisk]); + currentFeature = features[0]; + populateDatasetDropdown(); + populateFeatureDropdown(); + drawPlot(); + }; +} + +function populateFeatureDropdown() { + const select = document.getElementById("featureSelect"); + select.innerHTML = ""; + + features.forEach(feature => { + const opt = document.createElement("option"); + opt.value = feature; + opt.textContent = feature; + select.appendChild(opt); + }); + + select.value = currentFeature; + + select.onchange = e => { + currentFeature = e.target.value; + populateDatasetDropdown(); + populateRiskDropdown(); + drawPlot(); + }; +} + +/* ----- Shape function data ----- */ +function drawPlot() { + + const entry = DATA[currentDataset][currentRisk][currentFeature]; + + if (!entry || !entry.shape_plots?.pts?.length) { + Plotly.purge("plot"); + return; + } + + /* ----- Traces ----- */ + const curve = { + x: entry.shape_plots.pts, + y: entry.shape_plots.shp, + type: "scatter", + mode: "lines", + line: { color: "#1f77b4", width: 6 }, + name : "Shape Function", + }; + + const zeroLine = { + x: [Math.min(...entry.shape_plots.pts), Math.max(...entry.shape_plots.pts)], + y: [0, 0], + type: "scatter", + mode: "lines", + line: { color: "#1f77b4", width: 3, dash: "dash" }, + hoverinfo: "skip", + showlegend: false + }; + + const rugBars = { + x: entry.values, + y: Array(entry.values.length).fill(-0.08), + type: "scatter", + mode: "markers", + marker: { + symbol: "line-ns-open", + size: 14, + color: "rgba(255,127,14,0.45)", + opacity: 1.0 + }, + cliponaxis: false, + hoverinfo: "skip", + showlegend: false + }; + + /* ----- Render ----- */ + Plotly.react( + "plot", + [curve, zeroLine, rugBars], + { + title: `${currentDataset} — ${currentRisk} — ${currentFeature}`, + xaxis: { title: "Feature Value" }, + yaxis: { title: "Contribution", range: [[Math.min(...entry.shape_plots.shp), Math.max(...entry.shape_plots.shp)]] }, + margin: { l: 90, r: 30, t: 80, b: 80 }, + paper_bgcolor: "white", + plot_bgcolor: "white", + showlegend: false + }, + { displayModeBar: false } + ); +} + diff --git a/documentation/blog/src/diagrams/shape_plots.html b/documentation/blog/src/diagrams/shape_plots.html new file mode 100644 index 00000000..77136462 --- /dev/null +++ b/documentation/blog/src/diagrams/shape_plots.html @@ -0,0 +1,72 @@ + + + + + Feature Shape Plot + + + + + + + + + + + +
        + + + +
        + + +
        +
        +
        + + + + \ No newline at end of file diff --git a/documentation/blog/src/diagrams/toc.js b/documentation/blog/src/diagrams/toc.js new file mode 100644 index 00000000..7a837dcd --- /dev/null +++ b/documentation/blog/src/diagrams/toc.js @@ -0,0 +1,244 @@ +/** + * Custom Table of Contents with enhanced styling + */ + +export function initCustomTOC() { + // Wait for the DOM to be fully loaded + const tocContainer = document.querySelector('d-contents'); + if (!tocContainer) return; + + // Apply custom styling to the TOC + const style = document.createElement('style'); + style.textContent = ` + d-contents { + background: transparent; + border-radius: 0; + box-shadow: none; + padding: 10px 0 !important; + width: 100% !important; + max-width: 100% !important; + left: 0 !important; + right: 0 !important; + margin-left: 0 !important; + position: relative !important; + top: 0 !important; + bottom: auto !important; + margin-bottom: 10px !important; + border: none !important; + } + + d-article { + padding-left: 0 !important; + } + + /* Remove hover effect */ + + d-contents nav h4 { + font-size: 1.4em !important; + color: #333 !important; + margin-bottom: 0.8em !important; + padding-bottom: 0.25em !important; + position: relative !important; + font-weight: 600 !important; + display: block !important; + width: 100% !important; + text-align: left !important; + } + + d-contents .toc-line { + display: none !important; + } + + d-contents nav { + display: block !important; + width: 100% !important; + max-width: 100% !important; + } + + d-contents nav > div.toc-section { + margin-bottom: 0.5em !important; + padding: 5px 15px !important; + transition: all 0.3s ease !important; + border-left: none !important; + margin-right: 0 !important; + width: 100% !important; + display: block !important; + background-color: transparent !important; + box-shadow: none !important; + } + + d-contents nav > div:hover { + background-color: transparent !important; + } + + d-contents nav > div > a { + display: block !important; + font-size: 16px !important; + font-weight: 600 !important; + color: #2c3e50 !important; + padding: 3px 0 !important; + margin: 0 !important; + position: relative !important; + transition: all 0.3s ease !important; + text-decoration: none !important; + } + + d-contents nav > div > a:hover { + color: #8e44ad !important; + } + + d-contents ul { + padding-left: 0 !important; + margin-top: 3px !important; + margin-bottom: 3px !important; + margin-left: 28px !important; + list-style: none !important; + } + + d-contents nav ul li { + margin-bottom: 4px !important; + position: relative !important; + transition: all 0.3s ease !important; + padding: 2px 5px 2px 20px !important; + border-radius: 0 !important; + } + + d-contents nav ul li::before { + content: ""; + } + + d-contents nav ul li:hover { + background-color: transparent !important; + } + + d-contents nav a { + color: #555 !important; + transition: all 0.3s ease !important; + font-size: 14px !important; + display: block !important; + text-decoration: none !important; + } + + d-contents nav ul li a { + color: #555 !important; + } + + d-contents nav ul li:hover a { + color: #8e44ad !important; + } + `; + + document.head.appendChild(style); + + // Enhance the TOC with section markers and icons + const tocDivs = tocContainer.querySelectorAll('nav > div'); + + tocDivs.forEach((div, index) => { + const sectionNum = index + 1; + const link = div.querySelector('a'); + if (link) { + const originalText = link.textContent; + link.innerHTML = `${sectionNum}. ${originalText}`; + } + }); + + // Fix the double bullet issue for sub-headings with a more aggressive approach + const fixBullets = () => { + // Apply a full reset to ensure correct layout + const tocSections = tocContainer.querySelectorAll('.toc-section'); + tocSections.forEach(section => { + section.style.display = 'block'; + section.style.width = '100%'; + }); + + // Remove all default list styling from ULs + const allULs = tocContainer.querySelectorAll('ul'); + allULs.forEach(ul => { + ul.style.listStyleType = 'none'; 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+ list-style-type: none !important; + list-style-position: initial !important; + list-style-image: initial !important; + } + + /* Simple hover effect for links with purple color */ + d-contents a:hover { + color: #8e44ad !important; + } + + /* Fix for the TOC layout and positioning */ + @media (min-width: 1024px) { + d-article { + padding-top: 2rem !important; + } + + d-contents + * { + margin-top: 2rem !important; + } + } + `; + document.head.appendChild(extraStyle); + + // Add classes for easier styling + const nav = tocContainer.querySelector('nav'); + if (nav) { + nav.classList.add('clean-nav-layout'); + + // Remove any potential borders or styling + const tocElements = tocContainer.querySelectorAll('*'); + tocElements.forEach(el => { + el.style.border = 'none'; + el.style.borderBottom = 'none'; + el.style.borderLeft = 'none'; + el.style.borderRight = 'none'; + el.style.borderTop = 'none'; + el.style.boxShadow = 'none'; + }); + } + }; + + // Run the fix immediately and after a short delay to ensure it applies + // after the browser has rendered the list items + fixBullets(); + setTimeout(fixBullets, 100); + // Try again after all resources are loaded + window.addEventListener('load', fixBullets); +} \ No newline at end of file diff --git a/documentation/blog/src/diagrams/venn.html b/documentation/blog/src/diagrams/venn.html new file mode 100644 index 00000000..b64e6b61 --- /dev/null +++ b/documentation/blog/src/diagrams/venn.html @@ -0,0 +1,49 @@ + + + + + + CRISP-NAM Venn Diagram + + + +
        +
        + + + + Interpretability + + + + Non-linear + Modeling + + + + Competing + Risks + + + CRISP- + NAM + +
        +
        + + \ No newline at end of file diff --git a/documentation/blog/src/index.ejs b/documentation/blog/src/index.ejs new file mode 100644 index 00000000..9a04fe1d --- /dev/null +++ b/documentation/blog/src/index.ejs @@ -0,0 +1,374 @@ + + + + + + + + + + + + + + + +

        CRISP-NAM: Interpretable survival prediction

        +

        + Competing Risks Interpretable Survival + Prediction with Neural Additive Models - A model that enhances interpretability in survival analysis. +

        +         + + + + + +         +
        + + +

        AI In Healthcare

        + +

        In the past decade, AI has made numerous advances in the field of healthcare. Sophisticated algorithms are available to analyze and interpret medical images such as X-rays and CT scans, accelerating disease diagnosis. AI’s ability to synthesize and understand large amounts of patient data (genetics, biomarkers, clinical history) has led to personalized treatment plans and individualized therapies. Drug discovery, disease risk prediction and assessment, and survival analysis are other areas where new AI models have been developed. These AI-driven systems promise greater efficiency and accuracy in the healthcare ecosystem. However, many challenges remain for their widespread deployment and adoption.

        + +

        Challenges

        + +

        Current generative AI models achieve a very high accuracy in classification and prediction. However, they suffer from multiple limitations. +

          +
        • Interpretability: Deep learning models are black boxes, and it is challenging to determine how they make a particular prediction. In a high-stakes field such as healthcare, clinicians must understand the reasoning behind a model’s output. Misinterpretation can lead to incorrect treatment plans or adverse patient outcomes.
          • + +
        • Data privacy: Many AI models are trained using vast amounts of patient data - EHR, images, etc. It contains personally identifiable information that needs to be protected. This can require regulatory practises in place before more widespread adoption.
          • + +
        • Regulatory frameworks: Many policies have established explainability and interpretability constraints for high-risk AI systems. As an example, the Food and Drug Administration (FDA) guidance requires clear documentation of AI/ML medical device decision-making.
          • + +
        Our focus in this blog will be on developing interpretable techniques for survival analysis use cases. It is a robust framework for providing time-to-event predictions, especially in healthcare. +

        + +

        Survival Analysis: Primer

        + +

        Survival analysis is a collection of statistical procedures for data analysis where the outcome is time-to-event, meaning time until an event of interest occurs. For example, clinicians utilize patient data (covariates) to predict the timing of hospital readmissions for specific medical conditions. There can be multiple outcomes (such as cardiovascular risk, death, etc.) associated with patient data, and multiple mutually exclusive events that can lead to any outcome. These outcomes are referred to as competing risks. Survival modelling learns functions to map a patient’s data (covariates) to the time of occurrence of a single outcome (time-to-event). See Appendix for details.

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    • + +

    Competing risks: Multiple causes for the same outcome

    +

    + Consider a clinician managing patients with advanced liver disease. The goal is to predict outcomes, but patients + face more than one possible endpoint. Some will pass away while waiting for treatment; others will receive a liver + transplant. These events compete with each other: once a patient receives a transplant, they exit the waiting-list , and vice versa. +

    +

    + This is the problem of competing risks, and it appears throughout the healthcare sector: +

    +
      +
    • Cancer patients may die from their malignancy or from other causes
      • +
    • Heart disease patients face cardiovascular events or non-cardiac mortality
      • +
    • Hospitalized patients may be discharged or die during their stay
      • +
    +

    + Traditional survival models that predict time-to-event were not designed to handle this complexity. While deep + learning has introduced powerful approaches to survival analysis, most operate as black boxes, providing + predictions without explanations. +

    +

    + CRISP-NAM addresses this gap by handling competing risks + while maintaining interpretability, and allowing practitioners to examine exactly how each feature influences each risk. Below is a figure illustrating how competing risks are modeled in survival analysis. +

    +
    + Survival Model Overview +
    Figure 1: Sketch of modeling competing risks in survival analysis.
    +
    + +

    Existing Models

    +

    Many statistical and machine learning techniques have been used to capture the interactions between covariates and time-to-event variables. The following are a few classes of models that exist in the literature: +

      +
    • Traditional: Cox Proportional Hazards(CoxPH) is a popular model. It linearly maps covariates to time-to-event prediction. It assumes a multiplicative effect of covariates on the prediction and that their effect is constant over time.
      • +
    • Deep Learning: This class of models uses a feed-forward neural network for mapping covariates to the risk of the outcome. This captures non-linear and high-dimensional relationships between the two variables. DeepSurv, CoxNAM, and SurvNam are a few examples.
      • +
    • Models for competing risks: Such models follow a cause-specific hazard framework to model multiple risks. These models map covariates to each competing risk separately and learn the joint distribution of survival times and the risks directly. DeepHit and Neural Fine Gray are a few examples.
      • +
    +

    +

    Limitations of existing approaches

    +

    There are multiple limitations to existing approaches: +

      +
    • Insufficient parameters: Many traditional models(CoxPH) are linear. They fail to capture the non-linearity between the covariates and time-to-event variables.
      • +
    • Lack of interpretability: Deep learning survival models (DeepSurv) are black-box and suffer from a lack of interpretability. This makes it difficult to understand how individual features contribute to predictions for different competing risks.
      • +
    • Require >1 model: Cause-specific modelling requires learning multiple models - one for each outcome. This can be computationally expensive.
      • +
    +

    + +

    Our solution: CRISP-NAM

    +

    To address these limitations, we developed a novel approach for modeling competing risks, CRISP-NAM - Competing Risks Interpretable Survival Prediction with Neural Additive Models. It extends the NAM architecture approach to enable flexible, interpretable, and non-linear modeling. +

    +
    + +
    Figure 2: CRISP-NAM combines advantages from multiple modeling approaches.
    +
    +

    CRISP-NAM - Background

    +

    + CRISP-NAM is a derivative of Generalized Additive Model (GAM) family. GAMs have been a statistical mainstay for decades. Rather than assuming linear covariate-outcome relationships, they allow each covariate to have its own contribution via a separate spline function. Spline functions capture non-linear relationships such as thresholds, curves and saturation effects that linear models cannot represent. Since covariate has its own function, and they are added together, GAMs can provide insights into how each feature affects the outcome. +

    +

    + Neural Networks as Shape Functions: NAMs replace traditional spline-based functions with small neural networks, one per feature. Each neural network takes a single feature and models its contribution to the outcome. Training NAMs is easier and offers multiple advantages such as automatic weight learning, standard regularization techniques, and GPU acceleration. Even though NAMs don't model covariate interactions, their ability to model single covariate's effect is what makes them interpretable. +

    +
    +

    Architecture

    +

    The CRISP-NAM architecture consists of 4 components: +

      +
    • FeatureNets: Each input feature is processed by its own dedicated neural network called FeatureNet. This sub-network is designed to learn the feature’s non-linear contribution to the overall outcome. Since it isolates the effects of each feature, it is more interpretable than existing approaches.
      • +
    • Risk-Specific Projections: Each covariate and competing risk pair is then linearly transformed by a dedicated projection layer to measure its contribution to the outcome. Normalization constraint is added to ensure that contribution scales are comparable across risks enabling fair comparisonof feature importance between different event types.
      • +
    • Additive Risk Aggregation: Individual risk contribution to the hazard function is computed as the sum of individual feature contributions, preserving the additive nature of the original NAM model.
      • +
    • Cause-specific Log Hazard Calculation: This calculates the absolute probability for each risk by capturing the underlying temporal pattern independent of the covariates. It calculates the final risk probability with patients experiencing it within a specified time-interval and a cause-specific baseline measure.
      • +
    +

    +
    +
    +

    Advantages

    +
      +
    • It retains the interpretability and feature-wise transparency of NAMs, allowing for flexible and non-linear modelling in competing risk scenarios. This enables us to visualize each feature’s contribution via shape plots, enhancing interpretability.
      • +
    • The calculation of absolute risks is done independently of patient covariates. This provides predictions for clinically relevant time horizons (1-year, 5-year) and facilitates better patient care.
      • +
    +

    Limitations

    +

    + By design, the model excludes feature interactions. This is + intentional, as interactions make interpretation exponentially more difficult. With p + features, there are O(p^2) pairwise interactions, and visualization beyond pairwise terms + becomes impractical. We accept some reduction in predictive power in exchange for guaranteed interpretability. +

    +
    +
    + + Vector Institute Logo + +
    + {% if config.copyright %} +
    {{ config.copyright }}
    + {% endif %} {% if not config.extra.generator == false %} Made with + + Material for MkDocs + + {% endif %} +

    +

    Evaluation

    +

    + The dataset was evaluated using a mix of both discriminative and calibration metrics. +

      +
    1. Time-Dependent Area-Under-the-Curve (TD-AUC): It is an extension of the standard ROC-AUC designed specifically for time-to-event (survival) data, where outcomes happen over time. It measures how well the model ranks patients for an event that happened at time t over those who didn’t experience the events or were censored. +

      + \widehat{\mathrm{AUC}}(t) = + \frac{\sum_{i=1}^n \sum_{j=1}^n I(y_j > t) I(y_i \leq t) \omega_i + I(\hat{f}(\mathbf{x}_j) \leq \hat{f}(\mathbf{x}_i))} + {(\sum_{i=1}^n I(y_i > t)) (\sum_{i=1}^n I(y_i \leq t) \omega_i)} + +

      + n: Number of patients
      + x_i, x_j: Covariates of patient i or j
      + y_i, y_j: Time to event response for patient i or j
      + \hat{f}(x_i), \hat{f}(x_j): Risk score assigned by the model for patient i or j
      + \omega_i, \omega_j: Inverse probability of censoring weight for patient i or j
      + I{.} Indicator function that denotes whether the element inside belongs to the set (= 1) or not(= 0). +

      + +

      2. +
    2. Time-Dependent Concordance Index (TD-CI): Concordance Index (CI) measures the proportion of pairs for which the model correctly predicts the survival time, among all pairs for which this can be acurately determined. Time-depenent CI calculates this count within a common observation time interval. It is a good metric to assess model performance over time. +

      + \text{TD-CI} = \frac{\sum{i \neq j} I\{\eta_i \leq \eta_j\} I\{T_i \gt T_j\}{d_j}}{\sum{i \neq j} I\{{T_i \gt T_j}\}{d_j}} + i, j: patient id i or j
      + \eta_i, \eta_j: Risk score assigned by the model for patient i or j
      + T_i, T_j Time to event response for patient i or j
      + d_i, d_j: indicates whether + T_i has been fully observed (= 1) or not (= 0).
      + I{.} Indicator function that denotes whether the element belongs to the set (= 1) or not(= 0). +

      +
      3. +
    3. Brier score (BS): This score evaluates the accuracy of a survival function at a given time t. It calculates the average of squared distances between the observed survival status and the predicted survival probability. This is a combined measure of caliberation and discrimination.
      +

      + \text{Brier Score} = \frac{1}{N} \sum_{i=1}^{N} (\hat{y}_i - y_i)^2 + N: Number of subjects
      + \hat{y}_i: Predicted survival probability by the model for ith subject
      + y_i: Observed outcome in the ith subject (1 for event, 0 for non-event)
      +

      +
      4. +

      +

      Support 2 dataset

      + The SUPPORT2 dataset originates from the Study to Understand Prognoses + and Preferences for Outcomes and Risks of Treatments (SUPPORT2), a comprehensive investigation conducted across five U.S. medical centers between 1989 and 1994. This dataset contains 9,105 records of critically ill hospitalized adults, each characterized by 42 variables containing the following classes: +
        +
      • Demographics: age, death, sex, income
        • +
      • Physiological measurements: ph, glucose, urine
        • +
      • Disease severity indicators: diabetes, dementia
        • +
      + This dataset was chosen as it contains 2 competing risks: Death due to cancer and Death due to other reasons. + +

      Results

      +

      + Below are the performance metrics comparing CRISP-NAM and the state-of-the-art DeepHIT model for the two competing risks: Death due to cancer (Risk 1) and death due to other causes (Risk 2). +

      +
      + +
      Table 1: Performance comparison between CRISP-NAM and DeepHIT on SUPPORT2 dataset. Best scores are highlighted in bold green.
      +
      +

      + CRISP-NAM shows better performance in Risk 1, and DeepHIT is better in Risk 2. Across more datasets, such as Framingham Heart Study and Primary Biliary Cholangitis (PBC), DeepHit generally achieves a higher performance, and CRISP-NAM demonstrates competitive discrimination with the added benefit of interpretability. + + The main point to note here is that CRISP-NAM achieves comparable performance to DeepHIT while providing interpretability, which is crucial for clinical adoption. +

      + +

      Interpretability

      +

      +

        +
      1. Feature importance plots: These plots summarize a covariate’s contribution to the final output of the model. In CRISP-NAM, it is the mean of the covariates risk projection vector for a particular risk. Its value determines the positive and negative contributions to the risk.
        2. +
        + +
        Figure 4: Feature Importance for Both Competing Risks.
        +
        +

        + NOTE: It is important to note that the feature importance plot provides a global view of how each covariate affects the risk. To determine its accuracy, physicians can validate it against their knowledge. This, along with covariate specific shape plots, provide an overall picture of how an individual covariate affects the risk as determined by the model. +

        + +
      2. Shape plots: Plotted for every covariate, it shows its individual contribution to the overall probability of the risk. Beneath each curve, rug plots illustrate the empirical distribution of feature values, highlighting regions where data is sparse. For CRISP-NAM, this plot is the risk projection vector of every covariate per risk. This enables ranking features by their impact on each competing risk, providing valuable insights into risk-specific predictor importance.
        3. +
      +

      Shape Plots for Individual Features

      +
      + +
      + Figure 5: Shape plots for individual features. Select dataset, risk type, and feature to view the plot. +
      +
      +

      Conclusion

      +

      + CRISP-NAM demonstrates that interpretability and competitive predictive performance are not mutually exclusive, even for complex problems like competing risks survival analysis. By extending Neural Additive Models to handle multiple event types, we provide clinicians and researchers with a tool that achieves strong discrimination while fully explaining its predictions. +

      +

      + In high-stakes healthcare applications, understanding why a model makes its predictions is not a luxury but a requirement. CRISP-NAM makes that understanding achievable. +

      + +

      The source code for reproducing all experiments and implementations described in this article is available in our CRISP-NAM repository.

      + +
      +

      Citation

      + 
      @inproceedings{ramachandram2025crispnam,
+    title={CRISP-NAM: Competing Risks Interpretable Survival Prediction with Neural Additive Models},
+    author={Ramachandram, Dhanesh and Raval, Ananya},
+    booktitle={EXPLIMED 2025 - Second Workshop on Explainable AI for the Medical Domain},
+    year={2025}
+    }
      +
      + + + +

      Acknowledgments

      +

      + We would like to thank the Vector Institute for supporting this research, and the open-source community for providing valuable datasets, tools and frameworks that made this work possible. +

      + +

      Survival analysis: Primer

      +

      The Hazard Function

      +

      + Central to survival analysis is the hazard function h(t), which represents the + instantaneous risk of an event at time t, conditional on survival up to that point. It answers: + "Given survival to time t, what is the instantaneous failure rate?" +

      +

      Currently, the classic Cox Proportional Hazards model relates covariates to this hazard:

      + h(t \mid X) = h_0(t) \cdot \exp(\beta^\top X) +

      + Here, h_0(t) is the baseline hazard common to all subjects, and \exp(\beta^\top + X) is a subject-specific multiplier based on covariates. A coefficient \beta_i = 0.7 + corresponds to a hazard ratio of \exp(0.7) \approx 2, indicating a doubling of risk. +

      +
      + The proportional hazards assumption: This model assumes that the hazard ratio between any two + subjects remains constant over time. Covariates may double your risk relative to another subject, but that + multiplicative effect persists regardless of the time horizon. +
      + +

      The Competing Risks Extension

      +

      + With competing risks, we require separate hazard functions for each event type. For event type k, the cause-specific hazard is defined as: +

      + \lambda_k(t|\mathbf{x}) = \lim_{\Delta t \to 0} \frac{P(t \leq T \leq t + \Delta t, E=k \mid T \geq t, \mathbf{x})}{\Delta t} +

      This represents the instantaneous rate of experiencing event k at time t, among subjects still at risk.

      +

      + The key insight is that these hazards interact through the risk set. A high hazard for one event can effectively reduce the observed rate of another, not through a causal mechanism, but because experiencing one event removes subjects from the risk pool for competing events. +

      +

      Cumulative Incidence Function

      +

      To compute absolute risk predictions, we require the cumulative incidence function (CIF):

      + F_k(t|\mathbf{x}) = \int_0^t S(u|\mathbf{x}) \cdot \lambda_k(u|\mathbf{x}) \, du +

      where the overall survival function is:

      + S(t|\mathbf{x}) = \exp\left(-\sum_{k=1}^{K} \int_0^t \lambda_k(u|\mathbf{x}) \, du\right) +

      In practice, we discretize time and compute:

      + \hat{F}_k(t|\mathbf{x}) \approx \sum_{t_m \leq t} \hat{S}(t_{m-1}|\mathbf{x}) \cdot + \hat{\lambda}_k(t_m|\mathbf{x}) +

      Breslow Estimator for Baseline Hazard

      +

      After training, we estimate the cumulative baseline hazard using:

      + \hat{\Lambda}_{0k}(t) = \sum_{n: T_n \leq t, E_n = k} \frac{1}{\sum_{j: T_j \geq T_n}\exp(\eta_k(\mathbf{x}_j))} +

      This non-parametric estimator makes no assumptions about the functional form of the baseline hazard.

      + + + + +       + + diff --git a/documentation/blog/src/index.js b/documentation/blog/src/index.js new file mode 100644 index 00000000..e634a383 --- /dev/null +++ b/documentation/blog/src/index.js @@ -0,0 +1,10 @@ +/** + * Main entry point for the blog + */ + +import { initVisualizations } from './diagrams/index'; + +// Initialize all the interactive visualizations when the page loads +document.addEventListener('DOMContentLoaded', () => { + initVisualizations(); +}); \ No newline at end of file diff --git a/documentation/blog/src/style.css b/documentation/blog/src/style.css new file mode 100644 index 00000000..e078c989 --- /dev/null +++ b/documentation/blog/src/style.css @@ -0,0 +1,250 @@ +.subgrid { + grid-column: screen; + display: grid; + grid-template-columns: inherit; + grid-template-rows: inherit; + grid-column-gap: inherit; + grid-row-gap: inherit; +} + +d-figure.base-grid { + grid-column: screen; + background: hsl(0, 0%, 97%); + padding: 20px 0; + border-top: 1px solid 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b/documentation/blog/static/references.bib new file mode 100644 index 00000000..f1105170 --- /dev/null +++ b/documentation/blog/static/references.bib @@ -0,0 +1,156 @@ +@article{bajwa2021artificial, + title={Artificial intelligence in healthcare: transforming the practice of medicine}, + author={Bajwa, Junaid and Munir, Usman and Nori, Aditya and Williams, Bryan}, + journal={Future healthcare journal}, + volume={8}, + number={2}, + pages={e188--e194}, + year={2021}, + publisher={Elsevier} +} + +@inproceedings{agarwal2021neural, + title = {Neural additive models: interpretable machine learning with neural nets}, + author = {Agarwal, Rishabh and Melnick, Levi and Frosst, Nicholas and Zhang, Xuezhou and Lengerich, Ben and Caruana, Rich and Hinton, Geoffrey E.}, + booktitle={Advances in Neural Information Processing Systems}, + doi={10.5555/3540261.3540620}, + volume={34}, + pages={4699--4711}, + year={2021} +} + +@inproceedings{lee2018deephit, + title={Deephit: A deep learning approach to survival analysis with competing risks}, + author={Lee, Changhee and Zame, William and Yoon, Jinsung and Van Der Schaar, Mihaela}, + booktitle={Proceedings of the 32nd AAAI Conference on Artificial Intelligence}, + volume={32}, + number={1}, + year={2018} +} + +@inproceedings{jeanselme2023neural, + title={Neural Fine-Gray: Monotonic neural networks for competing risks}, + author={Jeanselme, Vincent and Yoon, Chang Ho and Tom, Brian and Barrett, Jessica}, + booktitle={Proceedings of the Conference on Health, Inference, and Learning}, + pages={379--392}, + year={2023}, + publisher={PMLR} +} + +@article{katzman2018deepsurv, + title={DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network}, + author={Katzman, Jared L. and Shaham, Uri and Cloninger, Alexander and Bates, Jonathan and Jiang, Tingting and Kluger, Yuval}, + journal={BMC Medical Research Methodology}, + doi={10.1186/s12874-018-0482-1}, + volume={18}, + pages={1--12}, + year={2018}, + publisher={Springer} +} + +@article{xu2023coxnam, + title={CoxNAM: An interpretable deep survival analysis model}, + author = {Xu, Liangchen and Guo, Chonghui}, + journal={Expert Systems with Applications}, + doi = {10.1016/j.eswa.2023.120218}, + volume={227}, + pages={120--218}, + year={2023} +} + + +@article{utkin2022survnam, + title={SurvNAM: The machine learning survival model explanation}, + author={Utkin, Lev V. and Satyukov, Egor D. and Konstantinov, Andrei V.}, + journal={Neural Networks}, + doi={10.1016/j.neunet.2021.12.015}, + volume={147}, + number = {C}, + pages={81--102}, + year={2022}, + publisher={Elsevier} +} + +@article{cox1972regression, + title = {Regression Models and Life-Tables}, + author={Cox, David R.}, + journal={Journal of the Royal Statistical Society: Series B (Methodological)}, + volume={34}, + number={2}, + pages={187--202}, + year={1972}, + publisher={JSTOR} +} + +@misc{fda_ai_software_medical_device, + title = {Artificial Intelligence/Software as a Medical Device}, + author = {U.S. Food and Drug Administration}, + url = {https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-software-medical-device}, + urldate = {2025-08-20}, + year = {2024}, + type = {Government Website} +} + +@article{2024frobt1444763, + author = {Ennab M, and Mcheick H.}, + title = {Enhancing interpretability and accuracy of AI models in healthcare: a comprehensive review on challenges and future directions.}, + journal = {Front Robot AI.}, + year = {2024}, + volume = {11}, + doi = {10.3389/frobt.2024.1444763} +} +@article{2021brier_score_ref, + author = {Park SY, Park JE, Kim H, Park SH.}, + title = {Review of Statistical Methods for Evaluating the Performance of Survival or Other Time-to-Event Prediction Models (from Conventional to Deep Learning Approaches).}, + journal = {Korean J Radiol.}, + year = {2021}, + volume = {22}, + number = {10}, + pages = {1697--1707}, + doi = {10.3348/kjr.2021.0223} +} + +@article{2021ai_medical_challenges, + author = {Jiang L, Wu Z, Xu X, et al.}, + title = {Opportunities and challenges of artificial intelligence in the medical field: current application, emerging problems, and problem-solving strategies.}, + journal = {J Int Med Res.}, + year = {2021}, + volume = {49}, + number = {3}, + doi = {10.1177/03000605211000157} +} + +@article{2016_c_index_ref, +author = {Schmid, Matthias and Wright, Marvin N. and Ziegler, Andreas}, +title = {On the use of Harrell's C for clinical risk prediction via random survival forests}, +year = {2016}, +issue_date = {November 2016}, +publisher = {Pergamon Press, Inc.}, +volume = {63}, +number = {C}, +doi = {10.1016/j.eswa.2016.07.018}, +journal = {Expert Syst. Appl.}, +pages = {450–459}, +} + +@article{2016_roc_citation, +author = {Jérôme Lambert and Sylvie Chevret}, +title ={Summary measure of discrimination in survival models based on cumulative/dynamic time-dependent ROC curves}, +journal = {Statistical Methods in Medical Research}, +volume = {25}, +number = {5}, +pages = {2088-2102}, +year = {2016}, +doi = {10.1177/0962280213515571} +} + +@article{sksurv, + author = {Sebastian P{\"o}lsterl}, + title = {scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn}, + journal = {Journal of Machine Learning Research}, + year = {2020}, + volume = {21}, + number = {212}, + pages = {1-6} +} + diff --git a/documentation/blog/webpack.config.js b/documentation/blog/webpack.config.js new file mode 100644 index 00000000..1bab2686 --- /dev/null +++ b/documentation/blog/webpack.config.js @@ -0,0 +1,179 @@ +const assert = require('assert') +const fs = require('fs') +const path = require("path") +const webpack = require("webpack") +const HtmlWebpackPlugin = require('html-webpack-plugin') +const CopyWebpackPlugin = require('copy-webpack-plugin') +const HtmlReplaceWebpackPlugin = require('html-replace-webpack-plugin') + +function format_debate(base_indent, name) { + // Slurp in file and split into lines + const lines = fs.readFileSync('src/' + name + '.txt', 'utf8').trim().split('\n') + + // Parse into tree based on indentation + const tree = [] + for (const [n, line] of lines.entries()) { + if (line.trim().length == 0) continue + const most = line.trimLeft() + const indent = line.length - most.length + assert(indent % 2 == 0) + let kids = tree + for (var i = 0; i < indent; i += 2) { + assert(kids.length) + kids = kids[kids.length - 1].kids + } + kids.push({n: n, text: most, kids:[]}) + } + + // Dump tree to check consistency + function dump_tree(indent, list) { + for (const {n, text, kids} of list) { + console.log(indent + n + ': ' + text) + dump_tree(indent + ' ', kids) + } + } + if (0) + dump_tree('', tree) + + // Output as html + const output = [] + + // Format a single line + function show_line(n, line) { + const m = line.match(/^([A-Za-z ]+):\s*(.*)$/) + assert(m, name + ':' + n + ': Bad line: ' + line) + kind = {Red: 'red', Blue: 'blue', Judge: 'judge', 'Note for judges': 'note', 'Wikipedia': 'wikipedia', 'Question': 'question'}[m[1]] + assert(kind, name + ':' + n + ': Bad line: ' + line) + return [kind, m[2]] + } + + // Recursively display our tree + function show_node(indent, node) { + const [kind, line] = show_line(node.n, node.text) + const start = indent + '
    4. ' + '' + line + '' + if (!node.kids.length) + output.push(start + '
      5. ') + else { + output.push(start) + show_nodes(indent + ' ', node.kids) + output.push(indent + '') + } + } + function show_nodes(indent, nodes) { + output.push(indent + '
        ') + for (const node of nodes) + show_node(indent + ' ', node) + output.push(indent + '
      ') + } + show_nodes(base_indent, tree) + return output.join('\n') +} + +module.exports = { + entry: { + "index": "./src/index.js", + }, + resolve: { + extensions: [ ".js", ".html", ".npy", ".json" ] + }, + output: { + path: path.join(__dirname, "public"), + filename: "[name].bundle.js", + chunkFilename: "[name].[id].js" + }, + module: { + rules: [ + { + test: /\.(html|js)$/, + exclude: /node_modules/, + loader: "babel-loader", + options: { + presets: ["@babel/preset-env"] + } + }, + { + test: /\.(npy|npc)$/, + exclude: /node_modules/, + loader: 'numpy-loader', + options: { + outputPath: 'data/' + } + }, + { + test: /\.(json)$/, + exclude: /node_modules/, + loader: 'json-loader', + options: { + outputPath: 'data/' + } + }, + { + test: /\.svg$/, + exclude: /node_modules/, + loader: 'svg-inline-loader', + options: { + removeSVGTagAttrs: true, + removingTagAttrs: ["font-family"] + } + }, + { + test: /\.(png|jpg|jpeg)$/, + exclude: /node_modules/, + loader: 'file', + options: { + outputPath: 'images/' + } + } + ] + }, + plugins: [ + new HtmlWebpackPlugin({ + template: "./src/index.ejs", + filename: "index.html", + chunks: ["index"] + }), + new CopyWebpackPlugin({ + patterns: [ + { from: 'static/'}, + { from: 'src/assets/', to: 'assets/' }, + { from: 'src/diagrams/', to: 'diagrams/' } + ] + }), + new HtmlReplaceWebpackPlugin([ + { + pattern: //g, + replacement: (_, key) => '' + }, + { + pattern: /<(\/?)todo>/g, + replacement: (_, slash) => slash ? '' : '' + }, + { + pattern: /<(\/?)dtodo>/g, + replacement: (_, slash) => slash ? '' : '
      ' + }, + { + pattern: /( *)/g, + replacement: (_, indent, name) => format_debate(indent, name) + }, + { + pattern: '', + replacement: '' + } + ]) + ], + devServer: { + static: { + directory: path.join(__dirname, 'public') + }, + historyApiFallback: true, + hot: true, + client: { + overlay: true + }, + devMiddleware: { + stats: "minimal" + } + }, + devtool: "inline-source-map" +} From 9cb3dcb91501ab3a8cb194f7573ec21334cd33bd Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Fri, 20 Mar 2026 15:07:23 -0400 Subject: [PATCH 22/47] Move docs and blog folder to documentation; Add build steps for both into single documentation.yml --- .github/workflows/documentation.yml | 72 + blog/package-lock.json | 7563 - blog/package.json | 31 - blog/src/assets/ism-overview-new.svg | 2 - blog/src/diagrams/.gitkeep | 0 blog/src/diagrams/architecture.html | 266 - blog/src/diagrams/crisp-nam-architecture.js | 369 - blog/src/diagrams/feature_importance.html | 372 - blog/src/diagrams/index.js | 281 - blog/src/diagrams/nam-architecture.js | 256 - blog/src/diagrams/results-table.html | 147 - blog/src/diagrams/shape_plot.js | 164 - blog/src/diagrams/shape_plots.html | 72 - blog/src/diagrams/toc.js | 244 - blog/src/diagrams/venn.html | 49 - blog/src/index.ejs | 374 - blog/src/index.js | 10 - blog/src/style.css | 250 - blog/src/util.js | 3 - blog/static/final_results.json | 328505 --------------- blog/static/references.bib | 156 - blog/webpack.config.js | 179 - {docs => documentation/docs}/Makefile | 0 {docs => documentation/docs}/api.md | 0 .../docs}/assets/favicon-48x48.svg | 0 .../docs}/assets/favicon.ico | Bin .../docs}/assets/launch.png | Bin .../docs}/assets/vector-logo.svg | 0 {docs => documentation/docs}/index.md | 0 .../docs}/overrides/partials/copyright.html | 0 .../docs}/overrides/partials/logo.html | 0 .../docs}/stylesheets/extra.css | 0 {docs => documentation/docs}/usage.md | 0 33 files changed, 72 insertions(+), 339293 deletions(-) create mode 100644 .github/workflows/documentation.yml delete mode 100644 blog/package-lock.json delete mode 100644 blog/package.json delete mode 100644 blog/src/assets/ism-overview-new.svg delete mode 100644 blog/src/diagrams/.gitkeep delete mode 100644 blog/src/diagrams/architecture.html delete mode 100644 blog/src/diagrams/crisp-nam-architecture.js delete mode 100644 blog/src/diagrams/feature_importance.html delete mode 100644 blog/src/diagrams/index.js delete mode 100644 blog/src/diagrams/nam-architecture.js delete mode 100644 blog/src/diagrams/results-table.html delete mode 100644 blog/src/diagrams/shape_plot.js delete mode 100644 blog/src/diagrams/shape_plots.html delete mode 100644 blog/src/diagrams/toc.js delete mode 100644 blog/src/diagrams/venn.html delete mode 100644 blog/src/index.ejs delete mode 100644 blog/src/index.js delete mode 100644 blog/src/style.css delete mode 100644 blog/src/util.js delete mode 100644 blog/static/final_results.json delete mode 100644 blog/static/references.bib delete mode 100644 blog/webpack.config.js rename {docs => documentation/docs}/Makefile (100%) rename {docs => documentation/docs}/api.md (100%) rename {docs => documentation/docs}/assets/favicon-48x48.svg (100%) rename {docs => documentation/docs}/assets/favicon.ico (100%) rename {docs => documentation/docs}/assets/launch.png (100%) rename {docs => documentation/docs}/assets/vector-logo.svg (100%) rename {docs => documentation/docs}/index.md (100%) rename {docs => documentation/docs}/overrides/partials/copyright.html (100%) rename {docs => documentation/docs}/overrides/partials/logo.html (100%) rename {docs => documentation/docs}/stylesheets/extra.css (100%) rename {docs => documentation/docs}/usage.md (100%) diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml new file mode 100644 index 00000000..69164905 --- /dev/null +++ b/.github/workflows/documentation.yml @@ -0,0 +1,72 @@ +name: Build and Deploy documentation + +on: + push: + branches: [ "main" ] + pull_request: + branches: [ "main" ] + +permissions: + contents: write + pull-requests: write + +jobs: + build-blog: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6 + + - name: Setup Node.js + uses: actions/setup-node@v6 + with: + node-version: 'latest' + cache: 'npm' + cache-dependency-path: blog/package-lock.json + + - name: Install dependencies + run: cd documentation/blog && npm ci + + - name: Build + run: cd ./documentation/blog && npm run build + + - name: Upload blog build artifact + uses: actions/upload-artifact@v6 + with: + name: blog-build + path: ./documentation/blog/public + retention-days: 1 + + build-docs: + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v6.0.1 + + - name: Install uv + uses: astral-sh/setup-uv@v7.2.0 + with: + version: "0.9.11" + enable-cache: true + + - name: Set up Python + uses: actions/setup-python@v6.1.0 + with: + python-version-file: ".python-version" + + - name: Install the project + run: uv sync --all-extras --group docs + + - name: Build docs + run: uv run mkdocs build + + - name: Create .nojekyll file + run: touch site/.nojekyll + + - name: Upload docs site artifact + uses: actions/upload-artifact@v6 + with: + name: docs-site + path: site/ + retention-days: 1 + + diff --git a/blog/package-lock.json b/blog/package-lock.json deleted file mode 100644 index 0ff24d8d..00000000 --- a/blog/package-lock.json +++ /dev/null @@ -1,7563 +0,0 @@ -{ - 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"name": "ai-safety-needs-social-scientists", - "private": true, - "scripts": { - "dev": "cross-env NODE_ENV=development webpack-dev-server --hot", - "build": "cross-env NODE_ENV=production webpack --progress", - "profile": "cross-env NODE_ENV=production webpack --profile --json > webpack-stats.json" - }, - "devDependencies": { - "@babel/core": "^7.23.0", - "@babel/preset-env": "^7.26.0", - "babel-loader": "^9.1.3", - "copy-webpack-plugin": "^11.0.0", - "cross-env": "^7.0.3", - "css-loader": "^6.8.1", - "eases-jsnext": "^1.0.10", - "file-loader": "^6.2.0", - "html-replace-webpack-plugin": "^2.6.0", - "html-string-replace-webpack-plugin": "^1.0.5", - "html-webpack-plugin": "^5.5.0", - "lucid-components": "0.0.6", - "raw-loader": "^4.0.2", - "svg-inline-loader": "^0.8.2", - "webpack": "^5.89.0", - "webpack-cli": "^5.1.4", - "webpack-dev-server": "^5.2.1" - }, - "dependencies": { - "d3": "^7.9.0" - } -} diff --git a/blog/src/assets/ism-overview-new.svg b/blog/src/assets/ism-overview-new.svg deleted file mode 100644 index 09abbb22..00000000 --- a/blog/src/assets/ism-overview-new.svg +++ /dev/null @@ -1,2 +0,0 @@ -BMIGlucoseHazard functionCardiovascular diseaseDeathBP MedsCompeting RisksPatient covariatesHazard function \ No newline at end of file diff --git a/blog/src/diagrams/.gitkeep b/blog/src/diagrams/.gitkeep deleted file mode 100644 index e69de29b..00000000 diff --git a/blog/src/diagrams/architecture.html b/blog/src/diagrams/architecture.html deleted file mode 100644 index 70b7347c..00000000 --- a/blog/src/diagrams/architecture.html +++ /dev/null @@ -1,266 +0,0 @@ - - - - - - CRISP-NAM Architecture - - - - - - - - - \ No newline at end of file diff --git a/blog/src/diagrams/crisp-nam-architecture.js b/blog/src/diagrams/crisp-nam-architecture.js deleted file mode 100644 index 9babe602..00000000 --- a/blog/src/diagrams/crisp-nam-architecture.js +++ /dev/null @@ -1,369 +0,0 @@ -import * as d3 from 'd3'; - -export function initCrispNamStructure(containerId) { - const container = document.getElementById(containerId); - if (!container) return; - - // Clear previous - container.innerHTML = ''; - - const width = 1000; - const height = 450; - const margin = { top: 40, right: 20, bottom: 40, left: 20 }; - const figureTitle = 'Figure 4: Schematic of CRISP-NAM Architecture'; - - const svg = d3.select(container) - .append('svg') - .attr('width', '100%') - .attr('height', 'auto') - .attr('viewBox', `0 0 ${width} ${height}`) - .style('max-width', '100%') - .style('height', 'auto'); - - // Accessibility title - svg.append('title').text(figureTitle); - - // // Visible title - svg.append('text') - .attr('class', 'figure-title') - .attr('x', width / 2) - .attr('y', height - margin.bottom + 30) - .attr('text-anchor', 'middle') - .attr('font-size', '20px') - .attr('font-weight', '600') - .attr('fill', '#111') - .text(figureTitle); - - - // Definitions for markers - const defs = svg.append('defs'); - defs.append('marker') - .attr('id', 'arrowhead') - .attr('viewBox', '0 -5 10 10') - .attr('refX', 8) - .attr('refY', 0) - .attr('markerWidth', 6) - .attr('markerHeight', 6) - .attr('orient', 'auto') - .append('path') - .attr('d', 'M0,-5L10,0L0,5') - .attr('fill', '#999'); - - defs.append('marker') - .attr('id', 'arrowhead-blue') - .attr('viewBox', '0 -5 10 10') - .attr('refX', 8) - .attr('refY', 0) - .attr('markerWidth', 6) - .attr('markerHeight', 6) - .attr('orient', 'auto') - .append('path') - .attr('d', 'M0,-5L10,0L0,5') - .attr('fill', '#4a90e2'); - - defs.append('marker') - .attr('id', 'arrowhead-red') - .attr('viewBox', '0 -5 10 10') - .attr('refX', 8) - .attr('refY', 0) - .attr('markerWidth', 6) - .attr('markerHeight', 6) - .attr('orient', 'auto') - .append('path') - .attr('d', 'M0,-5L10,0L0,5') - .attr('fill', '#e24a4a'); - - // Layout parameters - const xStart = 50; - const yStart = 80; - const xGap = 180; - const yGap = 100; - - // Data structure for the diagram - const features = [ - { id: 'x1', label: 'Covariate x₁', y: yStart }, - { id: 'x2', label: 'Covariate x₂', y: yStart + yGap }, - { id: 'xp', label: 'Covariate xₚ', y: yStart + yGap * 2.2 } // Gap for ellipsis - ]; - - // Draw layers - - // 1. Inputs - const featureGroup = svg.append('g').attr('class', 'features'); - features.forEach(f => { - drawBox(featureGroup, xStart, f.y, f.label, '#e3f2fd', '#2196f3', f.id); - }); - // Ellipsis - featureGroup.append('text').attr('x', xStart + 50).attr('y', yStart + yGap * 1.8).text('⋮').attr('class', 'arch-text'); - // Inputs - featureGroup.append('text') - .attr('x', xStart + 50) - .attr('y', yStart - 10) - .attr('text-anchor', 'middle') - .attr('font-size', '18px') - .attr('font-weight', '600') - .attr('fill', '#333') - .text('Input'); - - // 2. FeatureNets - const netX = xStart + xGap; - const netY = yStart; - const netGroup = svg.append('g').attr('class', 'feature-nets'); - drawContainer(netGroup, netX - 15, netY - 15, 'FeatureNets', 'none', '#4caf50', 'feature-box', 100 + 30, yGap * 2.6 + 30); - features.forEach(f => { - drawBox(netGroup, netX, f.y, `f(${f.label.split(' ')[1]})`, '#e8f5e9', '#4caf50', 'net-' + f.id); - }); - netGroup.append('text').attr('x', netX + 40).attr('y', yStart + yGap * 1.8).text('⋮').attr('class', 'arch-text'); - - // 3. Projections (Split) - const projX = netX + xGap; - const projY = netY; - const projGroup = svg.append('g').attr('class', 'projections'); - drawContainer(projGroup, projX - 15, projY - 23 - 15, 'Risk Projections', 'none', '#2196f3', 'proj-box', 100 + 30, yGap * 3 + 33); - features.forEach((f, i) => { - // Risk 1 (Blue, Up) - drawBox(projGroup, projX, f.y - 23, `proj${i === 2 ? 'p' : i + 1},1`, 'white', '#2196f3', `proj1-${f.id}`); - // Risk 2 (Red, Down) - drawBox(projGroup, projX, f.y + 22, `proj${i === 2 ? 'p' : i + 1},2`, 'white', '#f44336', `proj2-${f.id}`); - }); - - projGroup.append('text').attr('x', projX + 32).attr('y', yStart + yGap * 1.85).text('⋮').attr('class', 'arch-text'); - - - // 4. Sums - const sumX = projX + xGap + 80; - const sumGroup = svg.append('g').attr('class', 'sums'); - const sum1Y = yStart + yGap * 0.5; - const sum2Y = yStart + yGap * 2.0; - drawContainer(sumGroup, sumX - 37, sum1Y - 30, '', 'none', '#f44336', 'sum-box', 70, yGap * 2 + 10); - sumGroup.append('text') - .attr('x', sumX) - .attr('y', sum1Y - 60) - .attr('text-anchor', 'middle') - .attr('font-size', '18px') - .attr('font-weight', '600') - .attr('fill', '#333') - .text('Additive Risk'); - sumGroup.append('text') - .attr('x', sumX) - .attr('y', sum1Y - 40) - .attr('text-anchor', 'middle') - .attr('font-size', '18px') - .attr('font-weight', '600') - .attr('fill', '#333') - .text('Aggregation'); - drawCircle(sumGroup, sumX, sum1Y, '+', '#f5f5f5', '#2196f3', 'sum1'); - drawCircle(sumGroup, sumX, sum2Y, '+', '#f5f5f5', '#f44336', 'sum2'); - - // 5. Final Risks - const riskX = sumX + xGap - 50; - const riskGroup = svg.append('g').attr('class', 'risks'); - drawContainer(riskGroup, riskX - 15, sum1Y, '', 'none', '#2196f3', 'risk-box', 110, yGap * 1.5); - riskGroup.append('text') - .attr('x', riskX + 45) - .attr('y', sum1Y - 30) - .attr('text-anchor', 'middle') - .attr('font-size', '18px') - .attr('font-weight', '600') - .attr('fill', '#333') - .text('Cause Specific'); - riskGroup.append('text') - .attr('x', riskX + 45) - .attr('y', sum1Y - 10) - .attr('text-anchor', 'middle') - .attr('font-size', '18px') - .attr('font-weight', '600') - .attr('fill', '#333') - .text('Log hazard Calculation'); - drawBox(riskGroup, riskX, sum1Y + 20, 'η₁(x)', '#e3f2fd', '#2196f3', 'eta1', 80); - drawBox(riskGroup, riskX, sum2Y - 60, 'η₂(x)', '#ffebee', '#f44336', 'eta2', 80); - - - // --- Connections --- - const edgesLayer = svg.append('g').attr('class', 'edges').lower(); - - // x -> Net -> h - features.forEach(f => { - drawArrow(edgesLayer, xStart + 100, f.y + 20, netX, f.y + 20, '#999'); // x -> net - drawArrow(edgesLayer, netX, f.y + 20, projX, f.y + 20, '#999'); // net -> proj - - // proj -> sum1 - const g1Box = { x: projX + 80, y: f.y - 5 }; - drawArrowCurve(edgesLayer, g1Box.x, g1Box.y, sumX - 22, sum1Y, '#4a90e2'); - - // proj -> sum2 - const g2Box = { x: projX + 80, y: f.y + 45 }; - drawArrowCurve(edgesLayer, g2Box.x, g2Box.y, sumX - 22, sum2Y, '#e24a4a'); - }); - - // sum -> eta - drawArrow(edgesLayer, sumX + 20, sum1Y, riskX, sum1Y + 43, '#4a90e2', 'arrowhead-blue'); - - drawArrow(edgesLayer, sumX + 20, sum2Y, riskX, sum2Y - 43, '#e24a4a', 'arrowhead-red'); - - // Helpers - function drawBox(group, x, y, text, fill, stroke, id, w = 100, h = 40) { - const g = group.append('g').attr('id', id); - g.append('rect') - .attr('x', x) - .attr('y', y) - .attr('width', w) - .attr('height', h) - .attr('rx', 5) - .attr('ry', 5) - .attr('fill', fill) - .attr('stroke', stroke) - .attr('stroke-width', 2); - - g.append('text') - .attr('x', x + w / 2) - .attr('y', y + h / 2 + 5) - .attr('text-anchor', 'middle') - .attr('font-size', '14px') - .attr('fill', '#333') - .text(text); - - return g; - } - - function drawContainer(group, x, y, text, fill, stroke, id, w = 100, h = 40) { - const g = group.append('g').attr('id', id); - g.append('rect') - .attr('x', x) - .attr('y', y) - .attr('width', w) - .attr('height', h) - .attr('rx', 5) - .attr('ry', 5) - .attr('fill', fill) - .attr('stroke', stroke) - .attr('stroke-width', 2) - .attr('stroke-dasharray', '6 4'); - - g.append('text') - .attr('x', x + w / 2) - .attr('y', y - 10) - .attr('text-anchor', 'middle') - .attr('font-size', '18px') - .attr('font-weight', '600') - .attr('fill', '#333') - .text(text); - return g; - } - - function drawCircle(group, x, y, text, fill, stroke, id, r = 20) { - const g = group.append('g').attr('id', id); - g.append('circle') - .attr('cx', x) - .attr('cy', y) - .attr('r', r) - .attr('fill', fill) - .attr('stroke', stroke) - .attr('stroke-width', 2); - - g.append('text') - .attr('x', x) - .attr('y', y + 5) - .attr('text-anchor', 'middle') - .attr('font-size', '20px') - .text(text); - } - - function drawArrow(group, x1, y1, x2, y2, color, markerId = 'arrowhead') { - const line = group.append('line') - .attr('x1', x1) - .attr('y1', y1) - .attr('x2', x2) - .attr('y2', y2) - .attr('stroke', color) - .attr('stroke-width', 2) - .attr('marker-end', `url(#${markerId})`) - .attr('stroke-linecap', 'round') - .attr('stroke-dasharray', '8 12') // dash pattern - .attr('stroke-dashoffset', 0); - - // Continuous motion from start to end - const speed = 80; // px per second; increase for quicker motion - d3.timer((elapsed) => { - const offset = -(elapsed / 1000) * speed; - line.attr('stroke-dashoffset', offset); - }); - } - - function drawArrowCurve(group, x1, y1, x2, y2, color) { - const midX = (x1 + x2) / 2; - const path = d3.path(); - path.moveTo(x1, y1); - path.bezierCurveTo(midX, y1, midX, y2, x2, y2); - - const curve = group.append('path') - .attr('d', path.toString()) - .attr('stroke', color) - .attr('stroke-width', 2) - .attr('fill', 'none') - .attr('marker-end', color.includes('e24a4a') ? 'url(#arrowhead-red)' : 'url(#arrowhead-blue)') - .attr('stroke-linecap', 'round') - .attr('stroke-dasharray', '8 12') - .attr('stroke-dashoffset', 0); - - // Continuous motion along the curve - const speed = 80; // px per second - d3.timer((elapsed) => { - const offset = -(elapsed / 1000) * speed; - curve.attr('stroke-dashoffset', offset); - }); - } - - // Animation: Flow particles - function animateParticles() { - features.forEach((f, i) => { - const particle = svg.append('circle') - .attr('r', 4) - .attr('fill', '#333') - .attr('cx', xStart + 100) - .attr('cy', f.y + 20); - - particle.transition() - .duration(1000) - .attr('cx', netX) - .transition() - .duration(1000) - .attr('cx', projX) - .transition() - .on('end', () => { - // Split - const p1 = svg.append('circle').attr('r', 3).attr('fill', '#2196f3').attr('cx', projX + 60).attr('cy', f.y + 20); - const p2 = svg.append('circle').attr('r', 3).attr('fill', '#f44336').attr('cx', projX + 60).attr('cy', f.y + 20); - - p1.transition().duration(1000) - .attrTween('transform', function () { - const startX = projX + 60; const startY = f.y + 20; - const endX = sumX; const endY = sum1Y; - return (t) => { - const x = startX + (endX - startX) * t; - // Simple easing for y - const y = startY + (endY - startY) * t; - return `translate(${x - startX}, ${y - startY})`; - }; - }) - .remove(); - - p2.transition().duration(1000) - .attrTween('transform', function () { - const startX = projX + 60; const startY = f.y + 20; - const endX = sumX; const endY = sum2Y; - return (t) => { - const x = startX + (endX - startX) * t; - const y = startY + (endY - startY) * t; - return `translate(${x - startX}, ${y - startY})`; - }; - }) - .remove(); - - particle.remove(); - }); - }); - - setTimeout(animateParticles, 4000); - } -} diff --git a/blog/src/diagrams/feature_importance.html b/blog/src/diagrams/feature_importance.html deleted file mode 100644 index 14ad8777..00000000 --- a/blog/src/diagrams/feature_importance.html +++ /dev/null @@ -1,372 +0,0 @@ - - - - - - Feature Importance - Both Risks - - - -
      -
      - -
      -
      - Risk 1: Death due to Cancer -
      -
      Feature Importance for Cancer Death
      - -
      -
      adls
      -
      -
      - -2.01 -
      -
      -
      -
      -
      sex_male
      -
      -
      - -1.94 -
      -
      -
      -
      -
      sps
      -
      -
      - -1.513 -
      -
      -
      -
      -
      resp
      -
      -
      - -1.28 -
      -
      -
      -
      -
      dnr_no dnr
      -
      -
      - -1.09 -
      -
      -
      -
      -
      age
      -
      -
      - 1.94 -
      -
      -
      - -
      -
      race_hispanic
      -
      -
      - 1.419 -
      -
      -
      - -
      -
      scoma
      -
      -
      - 1.71 -
      -
      -
      - -
      -
      temp
      -
      -
      - 1.92 -
      -
      -
      - -
      -
      dnr_dnr before sadm
      -
      -
      - 2.41 -
      -
      -
      - -
      ← Decreases Risk | Increases Risk →
      -
      -
      - - -
      -
      - Risk 2: Death due to Other Causes -
      -
      Feature Importance for Other Causes Death
      - -
      -
      sod
      -
      -
      - -1.710 -
      -
      -
      -
      -
      bili
      -
      -
      - -1.20 -
      -
      -
      -
      -
      pafi
      -
      -
      - -0.80 -
      -
      -
      -
      -
      race_white
      -
      -
      - -0.64 -
      -
      -
      -
      -
      sex_male
      -
      -
      - -0.59 -
      -
      -
      - -
      -
      crea
      -
      -
      - 1.028 -
      -
      -
      - -
      -
      hrt
      -
      -
      - 1.102 -
      -
      -
      - -
      -
      wblc
      -
      -
      - 1.23 -
      -
      -
      - -
      -
      temp
      -
      -
      - 1.35 -
      -
      -
      - -
      -
      charges
      -
      -
      - 1.91 -
      -
      -
      -
      ← Decreases Risk | Increases Risk →
      -
      -
      -
      - -
      -
      -
      - Increases Death Risk -
      -
      -
      - Decreases Death Risk -
      -
      - - - \ No newline at end of file diff --git a/blog/src/diagrams/index.js b/blog/src/diagrams/index.js deleted file mode 100644 index 84f42a39..00000000 --- a/blog/src/diagrams/index.js +++ /dev/null @@ -1,281 +0,0 @@ -/** - * Main diagrams module - imports and initializes all visualizations - */ - -import { initNamStructure } from './nam-architecture'; -import { initCrispNamStructure } from './crisp-nam-architecture'; - - -// Simple HTML escape function to prevent XSS when inserting text as HTML -function escapeHTML(str) { - return String(str) - .replace(/&/g, '&') - .replace(//g, '>') - .replace(/"/g, '"') - .replace(/'/g, '''); - } - - // Import diagram script path for iframe reference - export const diagramPath = './diagrams/diagram.js'; - - // Initialize all visualizations - export function initVisualizations() { - // Any interactive visualizations to initialize would go here - // The main interactive visualization is already handled in the iframe - try { initNamStructure('nam-structure-diagram'); } catch (e) { console.error(e); } - try { initCrispNamStructure('crisp-nam-structure-diagram'); } catch (e) { console.error(e); } - } - - /** - * Custom Table of Contents with enhanced styling - */ - - export function initCustomTOC() { - // Wait for the DOM to be fully loaded - const tocContainer = document.querySelector('d-contents'); - if (!tocContainer) return; - - // Apply custom styling to the TOC - const style = document.createElement('style'); - style.textContent = ` - d-contents { - background: transparent; - border-radius: 0; - box-shadow: none; - padding: 10px 0 !important; - width: 100% !important; - max-width: 100% !important; - left: 0 !important; - right: 0 !important; - margin-left: 0 !important; - position: relative !important; - top: 0 !important; - bottom: auto !important; - margin-bottom: 10px !important; - border: none !important; - } - - d-article { - padding-left: 0 !important; - } - - /* Remove hover effect */ - - d-contents nav h4 { - font-size: 1.4em !important; - color: #333 !important; - margin-bottom: 0.8em !important; - padding-bottom: 0.25em !important; - position: relative !important; - font-weight: 600 !important; - display: block !important; - width: 100% !important; - text-align: left !important; - } - - d-contents .toc-line { - display: none !important; - } - - d-contents nav { - display: block !important; - width: 100% !important; - max-width: 100% !important; - } - - d-contents nav > div.toc-section { - margin-bottom: 0.5em !important; - padding: 5px 15px !important; - transition: all 0.3s ease !important; - border-left: none !important; - margin-right: 0 !important; - width: 100% !important; - display: block !important; - background-color: transparent !important; - box-shadow: none !important; - } - - d-contents nav > div:hover { - background-color: transparent !important; - } - - d-contents nav > div > a { - display: block !important; - font-size: 16px !important; - font-weight: 600 !important; - color: #2c3e50 !important; - padding: 3px 0 !important; - margin: 0 !important; - position: relative !important; - transition: all 0.3s ease !important; - text-decoration: none !important; - } - - d-contents nav > div > a:hover { - color: #8e44ad !important; - } - - d-contents ul { - padding-left: 0 !important; - margin-top: 3px !important; - margin-bottom: 3px !important; - margin-left: 28px !important; - list-style: none !important; - } - - d-contents nav ul li { - margin-bottom: 4px !important; - position: relative !important; - transition: all 0.3s ease !important; - padding: 2px 5px 2px 20px !important; - border-radius: 0 !important; - } - - d-contents nav ul li::before { - content: ""; - } - - d-contents nav ul li:hover { - background-color: transparent !important; - } - - d-contents nav a { - color: #555 !important; - transition: all 0.3s ease !important; - font-size: 14px !important; - display: block !important; - text-decoration: none !important; - } - - d-contents nav ul li a { - color: #555 !important; - } - - d-contents nav ul li:hover a { - color: #8e44ad !important; - } - `; - - document.head.appendChild(style); - - // Enhance the TOC with section markers and icons - const tocDivs = tocContainer.querySelectorAll('nav > div'); - - tocDivs.forEach((div, index) => { - const sectionNum = index + 1; - const link = div.querySelector('a'); - if (link) { - const originalText = link.textContent; - link.innerHTML = `${sectionNum}. ${escapeHTML(originalText)}`; - } - - // Add toc-section class to all divs - div.classList.add('toc-section'); - }); - - // Fix the double bullet issue for sub-headings with a more aggressive approach - const fixBullets = () => { - // Apply a full reset to ensure correct layout - const tocSections = tocContainer.querySelectorAll('.toc-section'); - tocSections.forEach(section => { - section.style.display = 'block'; - section.style.width = '100%'; - }); - - // Remove all default list styling from ULs - const allULs = tocContainer.querySelectorAll('ul'); - allULs.forEach(ul => { - ul.style.listStyleType = 'none'; - ul.style.paddingLeft = '20px'; - ul.style.marginLeft = '0'; - ul.style.marginTop = '3px'; - ul.style.marginBottom = '3px'; - ul.setAttribute('role', 'list'); // For accessibility - }); - - // Process each list item to ensure no browser default bullets - const listItems = tocContainer.querySelectorAll('li'); - listItems.forEach(li => { - // Apply inline styles to override any browser defaults - li.style.listStyleType = 'none'; - li.style.display = 'block'; - li.style.marginBottom = '4px'; - - // Add a class for specific targeting - li.classList.add('custom-toc-item'); - - const link = li.querySelector('a'); - if (link) { - // Style the link directly - link.style.fontSize = '15px'; - link.style.color = '#444'; - link.style.textDecoration = 'none'; - link.style.display = 'inline-block'; - } - }); - - // Add one more style that will override all bullets - // Check if the style element already exists - let extraStyle = document.getElementById('custom-toc-style'); - if (!extraStyle) { - extraStyle = document.createElement('style'); - extraStyle.id = 'custom-toc-style'; - extraStyle.textContent = ` - d-contents li, - d-contents ul li, - d-contents nav ul li, - d-contents li::marker, - d-contents ul li::marker, - d-contents nav ul li::marker, - .custom-toc-item::marker { - list-style: none !important; - list-style-type: none !important; - list-style-position: initial !important; - list-style-image: initial !important; - } - - /* Simple hover effect for links with purple color */ - d-contents a:hover { - color: #8e44ad !important; - } - - /* Fix for the TOC layout and positioning */ - @media (min-width: 1024px) { - d-article { - padding-top: 2rem !important; - } - - d-contents + * { - margin-top: 2rem !important; - } - } - `; - document.head.appendChild(extraStyle); - } - - // Add classes for easier styling - const nav = tocContainer.querySelector('nav'); - if (nav) { - nav.classList.add('clean-nav-layout'); - - // Remove any potential borders or styling - const tocElements = tocContainer.querySelectorAll('*'); - tocElements.forEach(el => { - el.style.border = 'none'; - el.style.borderBottom = 'none'; - el.style.borderLeft = 'none'; - el.style.borderRight = 'none'; - el.style.borderTop = 'none'; - el.style.boxShadow = 'none'; - }); - } - }; - - // Run the fix immediately and after a short delay to ensure it applies - // after the browser has rendered the list items - fixBullets(); - setTimeout(fixBullets, 100); - // Try again after all resources are loaded - window.addEventListener('load', fixBullets); - } \ No newline at end of file diff --git a/blog/src/diagrams/nam-architecture.js b/blog/src/diagrams/nam-architecture.js deleted file mode 100644 index 9f33bd60..00000000 --- a/blog/src/diagrams/nam-architecture.js +++ /dev/null @@ -1,256 +0,0 @@ -import * as d3 from 'd3'; - -export function initNamStructure(containerId) { - const container = document.getElementById(containerId); - if (!container) return; - - // Clear previous - container.innerHTML = ''; - - const width = 800; - const height = 400; // Compact height - const margin = { top: 40, right: 40, bottom: 80, left: 40 }; - // --- Figure title and description (bottom center) --- - const figureTitle = 'Figure 3: Schematic of Neural Additive Model (NAM) Architecture' - const svg = d3.select(container) - .append('svg') - .attr('width', '100%') - .attr('height', 'auto') - .attr('viewBox', `0 0 ${width} ${height}`) - .style('max-width', '100%') - .style('height', 'auto'); - - // Accessibility title - svg.append('title').text(figureTitle); - - // Definitions for markers - const defs = svg.append('defs'); - defs.append('marker') - .attr('id', 'arrowhead-nam') - .attr('viewBox', '0 -5 10 10') - .attr('refX', 8) - .attr('refY', 0) - .attr('markerWidth', 6) - .attr('markerHeight', 6) - .attr('orient', 'auto') - .append('path') - .attr('d', 'M0,-5L10,0L0,5') - .attr('fill', '#666'); - - // // Visible title - svg.append('text') - .attr('class', 'figure-title') - .attr('x', width / 2) - .attr('y', height - margin.bottom + 30) - .attr('text-anchor', 'middle') - .attr('font-size', '16px') - .attr('font-weight', '600') - .attr('fill', '#111') - .text(figureTitle); - - // Layout - const xStart = 50; - const yStart = 60; - const yGap = 80; - - // Nodes Data - const features = [ - { id: 'x1', label: 'Covariate 1', y: yStart }, - { id: 'x2', label: 'Covariate 2', y: yStart + yGap }, - { id: 'x3', label: 'Covariate 3', y: yStart + yGap * 2 } - ]; - - const finalFeature = { id: 'xN', label: 'Covariate N', y: yStart + yGap * 2.8 }; - - - // Layers X Coordinates - const xFeatures = xStart; - const xNets = xStart + 180; - const xSum = xNets + 280; - const xPred = xSum + 120; - - // Draw Groups - const edgesGroup = svg.append('g').attr('class', 'edges'); - const nodesGroup = svg.append('g').attr('class', 'nodes'); - - // --- Draw Nodes --- - - // 1. Features - features.forEach(f => { - drawBox(nodesGroup, xFeatures, f.y, f.label, '#e3f2fd', '#2196f3', f.id); - }); - // Ellipsis for features - nodesGroup.append('text').attr('x', xFeatures + 50).attr('y', yStart + yGap * 2.7).text('⋮').attr('class', 'arch-text').attr('text-anchor', 'middle'); - nodesGroup.append('text') - .attr('x', xStart + 50) - .attr('y', yStart - 10) - .attr('text-anchor', 'middle') - .attr('font-size', '14px') - .attr('font-weight', '600') - .attr('fill', '#333') - .text('Input'); - //Draw final feature - drawBox(nodesGroup, xFeatures, finalFeature.y, finalFeature.label, '#e3f2fd', '#2196f3', finalFeature.id); - - // 2. Neural Nets - features.forEach(f => { - drawBox(nodesGroup, xNets, f.y, `Neural Net ${f.id.replace('x', '')}`, '#e8f5e9', '#4caf50', `net-${f.id}`, 120); - }); - nodesGroup.append('text').attr('x', xNets + 60).attr('y', yStart + yGap * 2.7).text('⋮').attr('class', 'arch-text').attr('text-anchor', 'middle'); - - // Draw final neural net - drawBox(nodesGroup, xNets, finalFeature.y, `Neural Net ${finalFeature.id.replace('x', '')}`, '#e8f5e9', '#4caf50', `net-${finalFeature.id}`, 120); - - // 3. Sum Node - const ySum = yStart + yGap * 1.5; // Centered - const sumBoxSize = 50; - // Transparent box centered at (xSum, ySum) - drawContainer(nodesGroup, xSum - sumBoxSize / 2, ySum - sumBoxSize / 2, 'Aggregate', 'none', '#9e9e9e', 'sum-box', sumBoxSize, sumBoxSize); - drawCircle(nodesGroup, xSum, ySum, '+', '#f5f5f5', '#9e9e9e', 'sum'); - - // 4. Prediction Node - const yPred = ySum; - const predBoxSize = 50; - const predRadius = 20; - - drawContainer(nodesGroup, xPred - predBoxSize / 2, yPred - predBoxSize / 2, 'Prediction', 'none', '#e91e63', 'pred-box', predBoxSize, predBoxSize); - drawCircle(nodesGroup, xPred, yPred, 'σ', '#fce4ec', '#e91e63', 'pred', predRadius); - - // --- Draw Edges --- - - features.forEach(f => { - // Feature -> Net - drawArrow(edgesGroup, xFeatures + 100, f.y + 20, xNets, f.y + 20); - - // Net -> Function - drawArrowCurve(edgesGroup, xNets + 120, f.y + 20, xSum-27, ySum); - - }); - - // Final Feature -> Final Net Arrows - drawArrow(edgesGroup, xFeatures + 100, finalFeature.y + 20, xNets, finalFeature.y + 20); - drawArrowCurve(edgesGroup, xNets + 120, finalFeature.y + 20, xSum-27, ySum); - - - // Sum -> Prediction - drawArrow(edgesGroup, xSum + 27, ySum, xPred - 27, yPred); - - - // --- Helpers --- - - function drawBox(group, x, y, text, fill, stroke, id, w = 100, h = 40) { - const g = group.append('g').attr('id', id); - g.append('rect') - .attr('x', x) - .attr('y', y) - .attr('width', w) - .attr('height', h) - .attr('rx', 5) - .attr('ry', 5) - .attr('fill', fill) - .attr('stroke', stroke) - .attr('stroke-width', 2); - - g.append('text') - .attr('x', x + w / 2) - .attr('y', y + h / 2 + 5) - .attr('text-anchor', 'middle') - .attr('font-size', '14px') - .attr('fill', '#333') - .text(text); - return g; - } - - function drawContainer(group, x, y, text, fill, stroke, id, w = 100, h = 40) { - const g = group.append('g').attr('id', id); - g.append('rect') - .attr('x', x) - .attr('y', y) - .attr('width', w) - .attr('height', h) - .attr('rx', 5) - .attr('ry', 5) - .attr('fill', fill) - .attr('stroke', stroke) - .attr('stroke-width', 2) - .attr('stroke-dasharray', '6 4'); - - g.append('text') - .attr('x', x + w / 2) - .attr('y', y - 5) - .attr('text-anchor', 'middle') - .attr('font-size', '14px') - .attr('font-weight', '600') - .attr('fill', '#333') - .text(text); - return g; - } - - function drawCircle(group, x, y, text, fill, stroke, id, r = 20) { - const g = group.append('g').attr('id', id); - g.append('circle') - .attr('cx', x) - .attr('cy', y) - .attr('r', r) - .attr('fill', fill) - .attr('stroke', stroke) - .attr('stroke-width', 2); - - g.append('text') - .attr('x', x) - .attr('y', y + 5) - .attr('text-anchor', 'middle') - .attr('font-size', '20px') - .text(text); - return g; - } - - function drawArrow(group, x1, y1, x2, y2) { - const line = group.append('line') - .attr('x1', x1) - .attr('y1', y1) - .attr('x2', x2) - .attr('y2', y2) - .attr('stroke', '#666') - .attr('stroke-width', 2) - .attr('marker-end', 'url(#arrowhead-nam)') - .attr('stroke-linecap', 'round') - .attr('stroke-dasharray', '8 12') // dash pattern - .attr('stroke-dashoffset', 0); - - // Continuous motion from start to end - const speed = 80; // px per second; increase for quicker motion - d3.timer((elapsed) => { - const offset = -(elapsed / 1000) * speed; - line.attr('stroke-dashoffset', offset); - }); - } - - function drawArrowCurve(group, x1, y1, x2, y2) { - const midX = (x1 + x2) / 2; - const path = d3.path(); - path.moveTo(x1, y1); - path.bezierCurveTo(midX, y1, midX, y2, x2, y2); - - const curve = group.append('path') - .attr('d', path.toString()) - .attr('stroke', '#666') - .attr('stroke-width', 2) - .attr('fill', 'none') - .attr('marker-end', 'url(#arrowhead-nam)') - .attr('stroke-linecap', 'round') - .attr('stroke-dasharray', '8 12') - .attr('stroke-dashoffset', 0); - - - // Continuous motion along the curve - const speed = 80; // px per second - d3.timer((elapsed) => { - const offset = -(elapsed / 1000) * speed; - curve.attr('stroke-dashoffset', offset); - }); - } - - // Optional simple animation on load - d3.selectAll('.nodes rect').style('opacity', 0).transition().duration(800).style('opacity', 1); -} diff --git a/blog/src/diagrams/results-table.html b/blog/src/diagrams/results-table.html deleted file mode 100644 index f6d6953e..00000000 --- a/blog/src/diagrams/results-table.html +++ /dev/null @@ -1,147 +0,0 @@ - - - - - - CRISP-NAM Results Table - - - -
      - - - - - - - - - - - - - - - - - - - - - - - - - - -
      Risk 1: Death due to Cancer
      ModelTD-AUCTD-CIBrier Score
      CRISP-NAM0.802 ± 0.0920.624 ± 0.0290.380 ± 0.242
      DeepHIT0.640 ± 0.2220.540 ± 0.0100.089 ± 0.002
      -
      - -
      - - - - - - - - - - - - - - - - - - - - - - - - - - -
      Risk 2: Death due to Other Causes
      ModelTD-AUCTD-CIBrier Score
      CRISP-NAM0.838 ± 0.1790.713 ± 0.0970.308 ± 0.188
      DeepHIT0.893 ± 0.0690.745 ± 0.0050.144 ± 0.006
      -
      - - \ No newline at end of file diff --git a/blog/src/diagrams/shape_plot.js b/blog/src/diagrams/shape_plot.js deleted file mode 100644 index 8fb2d22e..00000000 --- a/blog/src/diagrams/shape_plot.js +++ /dev/null @@ -1,164 +0,0 @@ -let DATA; -let currentDataset = 'support'; -let currentRisk = 'risk1'; -let currentFeature = 'sps' -let features = []; -let risks = ['risk1', 'risk2'].map(r => ({ id: r })); - -/* ---------- Load JSON ---------- */ -fetch("../final_results.json") - .then(res => res.json()) - .then(json => { - DATA = json; - currentDataset = Object.keys(DATA)[0]; - - // Infer risks from dataset - risks = Object.keys(DATA[currentDataset]); - currentRisk = risks[0]; - - // Infer features from risk - features = Object.keys(DATA[currentDataset][currentRisk]); - currentFeature = features[0]; - populateDatasetDropdown(); - populateRiskDropdown(); - populateFeatureDropdown(); - drawPlot(); - }); - -/* ---------- Populate UI ---------- */ -function populateDatasetDropdown() { - const select = document.getElementById("datasetSelect"); - select.innerHTML = ""; - - Object.keys(DATA).forEach(name => { - const opt = document.createElement("option"); - opt.value = name; - opt.textContent = name; - select.appendChild(opt); - }); - - select.value = currentDataset; - - select.onchange = e => { - currentDataset = e.target.value; - - risks = Object.keys(DATA[currentDataset]); - currentRisk = risks[0]; - - features = Object.keys(DATA[currentDataset][currentRisk]); - currentFeature = features[0]; - - populateRiskDropdown(); - populateFeatureDropdown(); - drawPlot(); - }; -} - -function populateRiskDropdown() { - const select = document.getElementById("riskSelect"); - select.innerHTML = ""; - - risks.forEach(risk => { - const opt = document.createElement("option"); - opt.value = risk; - opt.textContent = risk; - select.appendChild(opt); - }); - - select.value = currentRisk; - - select.onchange = e => { - currentRisk = e.target.value; - - features = Object.keys(DATA[currentDataset][currentRisk]); - currentFeature = features[0]; - populateDatasetDropdown(); - populateFeatureDropdown(); - drawPlot(); - }; -} - -function populateFeatureDropdown() { - const select = document.getElementById("featureSelect"); - select.innerHTML = ""; - - features.forEach(feature => { - const opt = document.createElement("option"); - opt.value = feature; - opt.textContent = feature; - select.appendChild(opt); - }); - - select.value = currentFeature; - - select.onchange = e => { - currentFeature = e.target.value; - populateDatasetDropdown(); - populateRiskDropdown(); - drawPlot(); - }; -} - -/* ----- Shape function data ----- */ -function drawPlot() { - - const entry = DATA[currentDataset][currentRisk][currentFeature]; - - if (!entry || !entry.shape_plots?.pts?.length) { - Plotly.purge("plot"); - return; - } - - /* ----- Traces ----- */ - const curve = { - x: entry.shape_plots.pts, - y: entry.shape_plots.shp, - type: "scatter", - mode: "lines", - line: { color: "#1f77b4", width: 6 }, - name : "Shape Function", - }; - - const zeroLine = { - x: [Math.min(...entry.shape_plots.pts), Math.max(...entry.shape_plots.pts)], - y: [0, 0], - type: "scatter", - mode: "lines", - line: { color: "#1f77b4", width: 3, dash: "dash" }, - hoverinfo: "skip", - showlegend: false - }; - - const rugBars = { - x: entry.values, - y: Array(entry.values.length).fill(-0.08), - type: "scatter", - mode: "markers", - marker: { - symbol: "line-ns-open", - size: 14, - color: "rgba(255,127,14,0.45)", - opacity: 1.0 - }, - cliponaxis: false, - hoverinfo: "skip", - showlegend: false - }; - - /* ----- Render ----- */ - Plotly.react( - "plot", - [curve, zeroLine, rugBars], - { - title: `${currentDataset} — ${currentRisk} — ${currentFeature}`, - xaxis: { title: "Feature Value" }, - yaxis: { title: "Contribution", range: [[Math.min(...entry.shape_plots.shp), Math.max(...entry.shape_plots.shp)]] }, - margin: { l: 90, r: 30, t: 80, b: 80 }, - paper_bgcolor: "white", - plot_bgcolor: "white", - showlegend: false - }, - { displayModeBar: false } - ); -} - diff --git a/blog/src/diagrams/shape_plots.html b/blog/src/diagrams/shape_plots.html deleted file mode 100644 index 77136462..00000000 --- a/blog/src/diagrams/shape_plots.html +++ /dev/null @@ -1,72 +0,0 @@ - - - - - Feature Shape Plot - - - - - - - - - - - -
      - - - -
      - - -
      -
      -
      - - - - \ No newline at end of file diff --git a/blog/src/diagrams/toc.js b/blog/src/diagrams/toc.js deleted file mode 100644 index 7a837dcd..00000000 --- a/blog/src/diagrams/toc.js +++ /dev/null @@ -1,244 +0,0 @@ -/** - * Custom Table of Contents with enhanced styling - */ - -export function initCustomTOC() { - // Wait for the DOM to be fully loaded - const tocContainer = document.querySelector('d-contents'); - if (!tocContainer) return; - - // Apply custom styling to the TOC - const style = document.createElement('style'); - style.textContent = ` - d-contents { - background: transparent; - border-radius: 0; - box-shadow: none; - padding: 10px 0 !important; - width: 100% !important; - max-width: 100% !important; - left: 0 !important; - right: 0 !important; - margin-left: 0 !important; - position: relative !important; - top: 0 !important; - bottom: auto !important; - margin-bottom: 10px !important; - border: none !important; - } - - d-article { - padding-left: 0 !important; - } - - /* Remove hover effect */ - - d-contents nav h4 { - font-size: 1.4em !important; - color: #333 !important; - margin-bottom: 0.8em !important; - padding-bottom: 0.25em !important; - position: relative !important; - font-weight: 600 !important; - display: block !important; - width: 100% !important; - text-align: left !important; - } - - d-contents .toc-line { - display: none !important; - } - - d-contents nav { - display: block !important; - width: 100% !important; - max-width: 100% !important; - } - - d-contents nav > div.toc-section { - margin-bottom: 0.5em !important; - padding: 5px 15px !important; - transition: all 0.3s ease !important; - border-left: none !important; - margin-right: 0 !important; - width: 100% !important; - display: block !important; - background-color: transparent !important; - box-shadow: none !important; - } - - d-contents nav > div:hover { - background-color: transparent !important; - } - - d-contents nav > div > a { - display: block !important; - font-size: 16px !important; - font-weight: 600 !important; - color: #2c3e50 !important; - padding: 3px 0 !important; - margin: 0 !important; - position: relative !important; - transition: all 0.3s ease !important; - text-decoration: none !important; - } - - d-contents nav > div > a:hover { - color: #8e44ad !important; - } - - d-contents ul { - padding-left: 0 !important; - margin-top: 3px !important; - margin-bottom: 3px !important; - margin-left: 28px !important; - list-style: none !important; - } - - d-contents nav ul li { - margin-bottom: 4px !important; - position: relative !important; - transition: all 0.3s ease !important; - padding: 2px 5px 2px 20px !important; - border-radius: 0 !important; - } - - d-contents nav ul li::before { - content: ""; - } - - d-contents nav ul li:hover { - background-color: transparent !important; - } - - d-contents nav a { - color: #555 !important; - transition: all 0.3s ease !important; - font-size: 14px !important; - display: block !important; - text-decoration: none !important; - } - - d-contents nav ul li a { - color: #555 !important; - } - - d-contents nav ul li:hover a { - color: #8e44ad !important; - } - `; - - document.head.appendChild(style); - - // Enhance the TOC with section markers and icons - const tocDivs = tocContainer.querySelectorAll('nav > div'); - - tocDivs.forEach((div, index) => { - const sectionNum = index + 1; - const link = div.querySelector('a'); - if (link) { - const originalText = link.textContent; - link.innerHTML = `${sectionNum}. ${originalText}`; - } - }); - - // Fix the double bullet issue for sub-headings with a more aggressive approach - const fixBullets = () => { - // Apply a full reset to ensure correct layout - const tocSections = tocContainer.querySelectorAll('.toc-section'); - tocSections.forEach(section => { - section.style.display = 'block'; - section.style.width = '100%'; - }); - - // Remove all default list styling from ULs - const allULs = tocContainer.querySelectorAll('ul'); - allULs.forEach(ul => { - ul.style.listStyleType = 'none'; - ul.style.paddingLeft = '20px'; - ul.style.marginLeft = '0'; - ul.style.marginTop = '3px'; - ul.style.marginBottom = '3px'; - ul.setAttribute('role', 'list'); // For accessibility - }); - - // Process each list item to ensure no browser default bullets - const listItems = tocContainer.querySelectorAll('li'); - listItems.forEach(li => { - // Apply inline styles to override any browser defaults - li.style.listStyleType = 'none'; - li.style.display = 'block'; - li.style.marginBottom = '4px'; - - // Add a class for specific targeting - li.classList.add('custom-toc-item'); - - const link = li.querySelector('a'); - if (link) { - // Style the link directly - link.style.fontSize = '15px'; - link.style.color = '#444'; - link.style.textDecoration = 'none'; - link.style.display = 'inline-block'; - } - }); - - // Add one more style that will override all bullets - const extraStyle = document.createElement('style'); - extraStyle.textContent = ` - d-contents li, - d-contents ul li, - d-contents nav ul li, - d-contents li::marker, - d-contents ul li::marker, - d-contents nav ul li::marker, - .custom-toc-item::marker { - list-style: none !important; - list-style-type: none !important; - list-style-position: initial !important; - list-style-image: initial !important; - } - - /* Simple hover effect for links with purple color */ - d-contents a:hover { - color: #8e44ad !important; - } - - /* Fix for the TOC layout and positioning */ - @media (min-width: 1024px) { - d-article { - padding-top: 2rem !important; - } - - d-contents + * { - margin-top: 2rem !important; - } - } - `; - document.head.appendChild(extraStyle); - - // Add classes for easier styling - const nav = tocContainer.querySelector('nav'); - if (nav) { - nav.classList.add('clean-nav-layout'); - - // Remove any potential borders or styling - const tocElements = tocContainer.querySelectorAll('*'); - tocElements.forEach(el => { - el.style.border = 'none'; - el.style.borderBottom = 'none'; - el.style.borderLeft = 'none'; - el.style.borderRight = 'none'; - el.style.borderTop = 'none'; - el.style.boxShadow = 'none'; - }); - } - }; - - // Run the fix immediately and after a short delay to ensure it applies - // after the browser has rendered the list items - fixBullets(); - setTimeout(fixBullets, 100); - // Try again after all resources are loaded - window.addEventListener('load', fixBullets); -} \ No newline at end of file diff --git a/blog/src/diagrams/venn.html b/blog/src/diagrams/venn.html deleted file mode 100644 index b64e6b61..00000000 --- a/blog/src/diagrams/venn.html +++ /dev/null @@ -1,49 +0,0 @@ - - - - - - CRISP-NAM Venn Diagram - - - -
      -
      - - - - Interpretability - - - - Non-linear - Modeling - - - - Competing - Risks - - - CRISP- - NAM - -
      -
      - - \ No newline at end of file diff --git a/blog/src/index.ejs b/blog/src/index.ejs deleted file mode 100644 index 9a04fe1d..00000000 --- a/blog/src/index.ejs +++ /dev/null @@ -1,374 +0,0 @@ - - - - - - - - - - - - - - - -

      CRISP-NAM: Interpretable survival prediction

      -

      - Competing Risks Interpretable Survival - Prediction with Neural Additive Models - A model that enhances interpretability in survival analysis. -

      -       - - - - - - -
      -       - -

      AI In Healthcare

      - -

      In the past decade, AI has made numerous advances in the field of healthcare. Sophisticated algorithms are available to analyze and interpret medical images such as X-rays and CT scans, accelerating disease diagnosis. AI’s ability to synthesize and understand large amounts of patient data (genetics, biomarkers, clinical history) has led to personalized treatment plans and individualized therapies. Drug discovery, disease risk prediction and assessment, and survival analysis are other areas where new AI models have been developed. These AI-driven systems promise greater efficiency and accuracy in the healthcare ecosystem. However, many challenges remain for their widespread deployment and adoption.

      - -

      Challenges

      - -

      Current generative AI models achieve a very high accuracy in classification and prediction. However, they suffer from multiple limitations. -

        -
      • Interpretability: Deep learning models are black boxes, and it is challenging to determine how they make a particular prediction. In a high-stakes field such as healthcare, clinicians must understand the reasoning behind a model’s output. Misinterpretation can lead to incorrect treatment plans or adverse patient outcomes.
        • - -
      • Data privacy: Many AI models are trained using vast amounts of patient data - EHR, images, etc. It contains personally identifiable information that needs to be protected. This can require regulatory practises in place before more widespread adoption.
        • - -
      • Regulatory frameworks: Many policies have established explainability and interpretability constraints for high-risk AI systems. As an example, the Food and Drug Administration (FDA) guidance requires clear documentation of AI/ML medical device decision-making.
        • - -
      Our focus in this blog will be on developing interpretable techniques for survival analysis use cases. It is a robust framework for providing time-to-event predictions, especially in healthcare. -

      - -

      Survival Analysis: Primer

      - -

      Survival analysis is a collection of statistical procedures for data analysis where the outcome is time-to-event, meaning time until an event of interest occurs. For example, clinicians utilize patient data (covariates) to predict the timing of hospital readmissions for specific medical conditions. There can be multiple outcomes (such as cardiovascular risk, death, etc.) associated with patient data, and multiple mutually exclusive events that can lead to any outcome. These outcomes are referred to as competing risks. Survival modelling learns functions to map a patient’s data (covariates) to the time of occurrence of a single outcome (time-to-event). See Appendix for details.

      - -

      Competing risks: Multiple causes for the same outcome

      -

      - Consider a clinician managing patients with advanced liver disease. The goal is to predict outcomes, but patients - face more than one possible endpoint. Some will pass away while waiting for treatment; others will receive a liver - transplant. These events compete with each other: once a patient receives a transplant, they exit the waiting-list , and vice versa. -

      -

      - This is the problem of competing risks, and it appears throughout the healthcare sector: -

      -
        -
      • Cancer patients may die from their malignancy or from other causes
        • -
      • Heart disease patients face cardiovascular events or non-cardiac mortality
        • -
      • Hospitalized patients may be discharged or die during their stay
        • -
      -

      - Traditional survival models that predict time-to-event were not designed to handle this complexity. While deep - learning has introduced powerful approaches to survival analysis, most operate as black boxes, providing - predictions without explanations. -

      -

      - CRISP-NAM addresses this gap by handling competing risks - while maintaining interpretability, and allowing practitioners to examine exactly how each feature influences each risk. Below is a figure illustrating how competing risks are modeled in survival analysis. -

      -
      - Survival Model Overview -
      Figure 1: Sketch of modeling competing risks in survival analysis.
      -
      - -

      Existing Models

      -

      Many statistical and machine learning techniques have been used to capture the interactions between covariates and time-to-event variables. The following are a few classes of models that exist in the literature: -

        -
      • Traditional: Cox Proportional Hazards(CoxPH) is a popular model. It linearly maps covariates to time-to-event prediction. It assumes a multiplicative effect of covariates on the prediction and that their effect is constant over time.
        • -
      • Deep Learning: This class of models uses a feed-forward neural network for mapping covariates to the risk of the outcome. This captures non-linear and high-dimensional relationships between the two variables. DeepSurv, CoxNAM, and SurvNam are a few examples.
        • -
      • Models for competing risks: Such models follow a cause-specific hazard framework to model multiple risks. These models map covariates to each competing risk separately and learn the joint distribution of survival times and the risks directly. DeepHit and Neural Fine Gray are a few examples.
        • -
      -

      -

      Limitations of existing approaches

      -

      There are multiple limitations to existing approaches: -

        -
      • Insufficient parameters: Many traditional models(CoxPH) are linear. They fail to capture the non-linearity between the covariates and time-to-event variables.
        • -
      • Lack of interpretability: Deep learning survival models (DeepSurv) are black-box and suffer from a lack of interpretability. This makes it difficult to understand how individual features contribute to predictions for different competing risks.
        • -
      • Require >1 model: Cause-specific modelling requires learning multiple models - one for each outcome. This can be computationally expensive.
        • -
      -

      - -

      Our solution: CRISP-NAM

      -

      To address these limitations, we developed a novel approach for modeling competing risks, CRISP-NAM - Competing Risks Interpretable Survival Prediction with Neural Additive Models. It extends the NAM architecture approach to enable flexible, interpretable, and non-linear modeling. -

      -
      - -
      Figure 2: CRISP-NAM combines advantages from multiple modeling approaches.
      -
      -

      CRISP-NAM - Background

      -

      - CRISP-NAM is a derivative of Generalized Additive Model (GAM) family. GAMs have been a statistical mainstay for decades. Rather than assuming linear covariate-outcome relationships, they allow each covariate to have its own contribution via a separate spline function. Spline functions capture non-linear relationships such as thresholds, curves and saturation effects that linear models cannot represent. Since covariate has its own function, and they are added together, GAMs can provide insights into how each feature affects the outcome. -

      -

      - Neural Networks as Shape Functions: NAMs replace traditional spline-based functions with small neural networks, one per feature. Each neural network takes a single feature and models its contribution to the outcome. Training NAMs is easier and offers multiple advantages such as automatic weight learning, standard regularization techniques, and GPU acceleration. Even though NAMs don't model covariate interactions, their ability to model single covariate's effect is what makes them interpretable. -

      -
      -

      Architecture

      -

      The CRISP-NAM architecture consists of 4 components: -

        -
      • FeatureNets: Each input feature is processed by its own dedicated neural network called FeatureNet. This sub-network is designed to learn the feature’s non-linear contribution to the overall outcome. Since it isolates the effects of each feature, it is more interpretable than existing approaches.
        • -
      • Risk-Specific Projections: Each covariate and competing risk pair is then linearly transformed by a dedicated projection layer to measure its contribution to the outcome. Normalization constraint is added to ensure that contribution scales are comparable across risks enabling fair comparisonof feature importance between different event types.
        • -
      • Additive Risk Aggregation: Individual risk contribution to the hazard function is computed as the sum of individual feature contributions, preserving the additive nature of the original NAM model.
        • -
      • Cause-specific Log Hazard Calculation: This calculates the absolute probability for each risk by capturing the underlying temporal pattern independent of the covariates. It calculates the final risk probability with patients experiencing it within a specified time-interval and a cause-specific baseline measure.
        • -
      -

      -
      -
      -

      Advantages

      -
        -
      • It retains the interpretability and feature-wise transparency of NAMs, allowing for flexible and non-linear modelling in competing risk scenarios. This enables us to visualize each feature’s contribution via shape plots, enhancing interpretability.
        • -
      • The calculation of absolute risks is done independently of patient covariates. This provides predictions for clinically relevant time horizons (1-year, 5-year) and facilitates better patient care.
        • -
      -

      Limitations

      -

      - By design, the model excludes feature interactions. This is - intentional, as interactions make interpretation exponentially more difficult. With p - features, there are O(p^2) pairwise interactions, and visualization beyond pairwise terms - becomes impractical. We accept some reduction in predictive power in exchange for guaranteed interpretability. -

      - -

      Evaluation

      -

      - The dataset was evaluated using a mix of both discriminative and calibration metrics. -

        -
      1. Time-Dependent Area-Under-the-Curve (TD-AUC): It is an extension of the standard ROC-AUC designed specifically for time-to-event (survival) data, where outcomes happen over time. It measures how well the model ranks patients for an event that happened at time t over those who didn’t experience the events or were censored. -

        - \widehat{\mathrm{AUC}}(t) = - \frac{\sum_{i=1}^n \sum_{j=1}^n I(y_j > t) I(y_i \leq t) \omega_i - I(\hat{f}(\mathbf{x}_j) \leq \hat{f}(\mathbf{x}_i))} - {(\sum_{i=1}^n I(y_i > t)) (\sum_{i=1}^n I(y_i \leq t) \omega_i)} - -

        - n: Number of patients
        - x_i, x_j: Covariates of patient i or j
        - y_i, y_j: Time to event response for patient i or j
        - \hat{f}(x_i), \hat{f}(x_j): Risk score assigned by the model for patient i or j
        - \omega_i, \omega_j: Inverse probability of censoring weight for patient i or j
        - I{.} Indicator function that denotes whether the element inside belongs to the set (= 1) or not(= 0). -

        - -

        2. -
      2. Time-Dependent Concordance Index (TD-CI): Concordance Index (CI) measures the proportion of pairs for which the model correctly predicts the survival time, among all pairs for which this can be acurately determined. Time-depenent CI calculates this count within a common observation time interval. It is a good metric to assess model performance over time. -

        - \text{TD-CI} = \frac{\sum{i \neq j} I\{\eta_i \leq \eta_j\} I\{T_i \gt T_j\}{d_j}}{\sum{i \neq j} I\{{T_i \gt T_j}\}{d_j}} - i, j: patient id i or j
        - \eta_i, \eta_j: Risk score assigned by the model for patient i or j
        - T_i, T_j Time to event response for patient i or j
        - d_i, d_j: indicates whether - T_i has been fully observed (= 1) or not (= 0).
        - I{.} Indicator function that denotes whether the element belongs to the set (= 1) or not(= 0). -

        -
        3. -
      3. Brier score (BS): This score evaluates the accuracy of a survival function at a given time t. It calculates the average of squared distances between the observed survival status and the predicted survival probability. This is a combined measure of caliberation and discrimination.
        -

        - \text{Brier Score} = \frac{1}{N} \sum_{i=1}^{N} (\hat{y}_i - y_i)^2 - N: Number of subjects
        - \hat{y}_i: Predicted survival probability by the model for ith subject
        - y_i: Observed outcome in the ith subject (1 for event, 0 for non-event)
        -

        -
        4. -

        -

        Support 2 dataset

        - The SUPPORT2 dataset originates from the Study to Understand Prognoses - and Preferences for Outcomes and Risks of Treatments (SUPPORT2), a comprehensive investigation conducted across five U.S. medical centers between 1989 and 1994. This dataset contains 9,105 records of critically ill hospitalized adults, each characterized by 42 variables containing the following classes: -
          -
        • Demographics: age, death, sex, income
          • -
        • Physiological measurements: ph, glucose, urine
          • -
        • Disease severity indicators: diabetes, dementia
          • -
        - This dataset was chosen as it contains 2 competing risks: Death due to cancer and Death due to other reasons. - -

        Results

        -

        - Below are the performance metrics comparing CRISP-NAM and the state-of-the-art DeepHIT model for the two competing risks: Death due to cancer (Risk 1) and death due to other causes (Risk 2). -

        -
        - -
        Table 1: Performance comparison between CRISP-NAM and DeepHIT on SUPPORT2 dataset. Best scores are highlighted in bold green.
        -
        -

        - CRISP-NAM shows better performance in Risk 1, and DeepHIT is better in Risk 2. Across more datasets, such as Framingham Heart Study and Primary Biliary Cholangitis (PBC), DeepHit generally achieves a higher performance, and CRISP-NAM demonstrates competitive discrimination with the added benefit of interpretability. - - The main point to note here is that CRISP-NAM achieves comparable performance to DeepHIT while providing interpretability, which is crucial for clinical adoption. -

        - -

        Interpretability

        -

        -

          -
        1. Feature importance plots: These plots summarize a covariate’s contribution to the final output of the model. In CRISP-NAM, it is the mean of the covariates risk projection vector for a particular risk. Its value determines the positive and negative contributions to the risk.
          2. -
          - -
          Figure 4: Feature Importance for Both Competing Risks.
          -
          -

          - NOTE: It is important to note that the feature importance plot provides a global view of how each covariate affects the risk. To determine its accuracy, physicians can validate it against their knowledge. This, along with covariate specific shape plots, provide an overall picture of how an individual covariate affects the risk as determined by the model. -

          - -
        2. Shape plots: Plotted for every covariate, it shows its individual contribution to the overall probability of the risk. Beneath each curve, rug plots illustrate the empirical distribution of feature values, highlighting regions where data is sparse. For CRISP-NAM, this plot is the risk projection vector of every covariate per risk. This enables ranking features by their impact on each competing risk, providing valuable insights into risk-specific predictor importance.
          3. -
        -

        Shape Plots for Individual Features

        -
        - -
        - Figure 5: Shape plots for individual features. Select dataset, risk type, and feature to view the plot. -
        -
        -

        Conclusion

        -

        - CRISP-NAM demonstrates that interpretability and competitive predictive performance are not mutually exclusive, even for complex problems like competing risks survival analysis. By extending Neural Additive Models to handle multiple event types, we provide clinicians and researchers with a tool that achieves strong discrimination while fully explaining its predictions. -

        -

        - In high-stakes healthcare applications, understanding why a model makes its predictions is not a luxury but a requirement. CRISP-NAM makes that understanding achievable. -

        - -

        The source code for reproducing all experiments and implementations described in this article is available in our CRISP-NAM repository.

        - -
        -

        Citation

        - 
        @inproceedings{ramachandram2025crispnam,
-    title={CRISP-NAM: Competing Risks Interpretable Survival Prediction with Neural Additive Models},
-    author={Ramachandram, Dhanesh and Raval, Ananya},
-    booktitle={EXPLIMED 2025 - Second Workshop on Explainable AI for the Medical Domain},
-    year={2025}
-    }
        -
        - - - -

        Acknowledgments

        -

        - We would like to thank the Vector Institute for supporting this research, and the open-source community for providing valuable datasets, tools and frameworks that made this work possible. -

        - -

        Survival analysis: Primer

        -

        The Hazard Function

        -

        - Central to survival analysis is the hazard function h(t), which represents the - instantaneous risk of an event at time t, conditional on survival up to that point. It answers: - "Given survival to time t, what is the instantaneous failure rate?" -

        -

        Currently, the classic Cox Proportional Hazards model relates covariates to this hazard:

        - h(t \mid X) = h_0(t) \cdot \exp(\beta^\top X) -

        - Here, h_0(t) is the baseline hazard common to all subjects, and \exp(\beta^\top - X) is a subject-specific multiplier based on covariates. A coefficient \beta_i = 0.7 - corresponds to a hazard ratio of \exp(0.7) \approx 2, indicating a doubling of risk. -

        -
        - The proportional hazards assumption: This model assumes that the hazard ratio between any two - subjects remains constant over time. Covariates may double your risk relative to another subject, but that - multiplicative effect persists regardless of the time horizon. -
        - -

        The Competing Risks Extension

        -

        - With competing risks, we require separate hazard functions for each event type. For event type k, the cause-specific hazard is defined as: -

        - \lambda_k(t|\mathbf{x}) = \lim_{\Delta t \to 0} \frac{P(t \leq T \leq t + \Delta t, E=k \mid T \geq t, \mathbf{x})}{\Delta t} -

        This represents the instantaneous rate of experiencing event k at time t, among subjects still at risk.

        -

        - The key insight is that these hazards interact through the risk set. A high hazard for one event can effectively reduce the observed rate of another, not through a causal mechanism, but because experiencing one event removes subjects from the risk pool for competing events. -

        -

        Cumulative Incidence Function

        -

        To compute absolute risk predictions, we require the cumulative incidence function (CIF):

        - F_k(t|\mathbf{x}) = \int_0^t S(u|\mathbf{x}) \cdot \lambda_k(u|\mathbf{x}) \, du -

        where the overall survival function is:

        - S(t|\mathbf{x}) = \exp\left(-\sum_{k=1}^{K} \int_0^t \lambda_k(u|\mathbf{x}) \, du\right) -

        In practice, we discretize time and compute:

        - \hat{F}_k(t|\mathbf{x}) \approx \sum_{t_m \leq t} \hat{S}(t_{m-1}|\mathbf{x}) \cdot - \hat{\lambda}_k(t_m|\mathbf{x}) -

        Breslow Estimator for Baseline Hazard

        -

        After training, we estimate the cumulative baseline hazard using:

        - \hat{\Lambda}_{0k}(t) = \sum_{n: T_n \leq t, E_n = k} \frac{1}{\sum_{j: T_j \geq T_n}\exp(\eta_k(\mathbf{x}_j))} -

        This non-parametric estimator makes no assumptions about the functional form of the baseline hazard.

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booktitle={Advances in Neural Information Processing Systems}, - doi={10.5555/3540261.3540620}, - volume={34}, - pages={4699--4711}, - year={2021} -} - -@inproceedings{lee2018deephit, - title={Deephit: A deep learning approach to survival analysis with competing risks}, - author={Lee, Changhee and Zame, William and Yoon, Jinsung and Van Der Schaar, Mihaela}, - booktitle={Proceedings of the 32nd AAAI Conference on Artificial Intelligence}, - volume={32}, - number={1}, - year={2018} -} - -@inproceedings{jeanselme2023neural, - title={Neural Fine-Gray: Monotonic neural networks for competing risks}, - author={Jeanselme, Vincent and Yoon, Chang Ho and Tom, Brian and Barrett, Jessica}, - booktitle={Proceedings of the Conference on Health, Inference, and Learning}, - pages={379--392}, - year={2023}, - publisher={PMLR} -} - -@article{katzman2018deepsurv, - title={DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network}, - author={Katzman, Jared L. and Shaham, Uri and Cloninger, Alexander and Bates, Jonathan and Jiang, Tingting and Kluger, Yuval}, - journal={BMC Medical Research Methodology}, - doi={10.1186/s12874-018-0482-1}, - volume={18}, - pages={1--12}, - year={2018}, - publisher={Springer} -} - -@article{xu2023coxnam, - title={CoxNAM: An interpretable deep survival analysis model}, - author = {Xu, Liangchen and Guo, Chonghui}, - journal={Expert Systems with Applications}, - doi = {10.1016/j.eswa.2023.120218}, - volume={227}, - pages={120--218}, - year={2023} -} - - -@article{utkin2022survnam, - title={SurvNAM: The machine learning survival model explanation}, - author={Utkin, Lev V. and Satyukov, Egor D. and Konstantinov, Andrei V.}, - journal={Neural Networks}, - doi={10.1016/j.neunet.2021.12.015}, - volume={147}, - number = {C}, - pages={81--102}, - year={2022}, - publisher={Elsevier} -} - -@article{cox1972regression, - title = {Regression Models and Life-Tables}, - author={Cox, David R.}, - journal={Journal of the Royal Statistical Society: Series B (Methodological)}, - volume={34}, - number={2}, - pages={187--202}, - year={1972}, - publisher={JSTOR} -} - -@misc{fda_ai_software_medical_device, - title = {Artificial Intelligence/Software as a Medical Device}, - author = {U.S. Food and Drug Administration}, - url = {https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-software-medical-device}, - urldate = {2025-08-20}, - year = {2024}, - type = {Government Website} -} - -@article{2024frobt1444763, - author = {Ennab M, and Mcheick H.}, - title = {Enhancing interpretability and accuracy of AI models in healthcare: a comprehensive review on challenges and future directions.}, - journal = {Front Robot AI.}, - year = {2024}, - volume = {11}, - doi = {10.3389/frobt.2024.1444763} -} -@article{2021brier_score_ref, - author = {Park SY, Park JE, Kim H, Park SH.}, - title = {Review of Statistical Methods for Evaluating the Performance of Survival or Other Time-to-Event Prediction Models (from Conventional to Deep Learning Approaches).}, - journal = {Korean J Radiol.}, - year = {2021}, - volume = {22}, - number = {10}, - pages = {1697--1707}, - doi = {10.3348/kjr.2021.0223} -} - -@article{2021ai_medical_challenges, - author = {Jiang L, Wu Z, Xu X, et al.}, - title = {Opportunities and challenges of artificial intelligence in the medical field: current application, emerging problems, and problem-solving strategies.}, - journal = {J Int Med Res.}, - year = {2021}, - volume = {49}, - number = {3}, - doi = {10.1177/03000605211000157} -} - -@article{2016_c_index_ref, -author = {Schmid, Matthias and Wright, Marvin N. and Ziegler, Andreas}, -title = {On the use of Harrell's C for clinical risk prediction via random survival forests}, -year = {2016}, -issue_date = {November 2016}, -publisher = {Pergamon Press, Inc.}, -volume = {63}, -number = {C}, -doi = {10.1016/j.eswa.2016.07.018}, -journal = {Expert Syst. Appl.}, -pages = {450–459}, -} - -@article{2016_roc_citation, -author = {Jérôme Lambert and Sylvie Chevret}, -title ={Summary measure of discrimination in survival models based on cumulative/dynamic time-dependent ROC curves}, -journal = {Statistical Methods in Medical Research}, -volume = {25}, -number = {5}, -pages = {2088-2102}, -year = {2016}, -doi = {10.1177/0962280213515571} -} - -@article{sksurv, - author = {Sebastian P{\"o}lsterl}, - title = {scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn}, - journal = {Journal of Machine Learning Research}, - year = {2020}, - volume = {21}, - number = {212}, - pages = {1-6} -} - diff --git a/blog/webpack.config.js b/blog/webpack.config.js deleted file mode 100644 index 1bab2686..00000000 --- a/blog/webpack.config.js +++ /dev/null @@ -1,179 +0,0 @@ -const assert = require('assert') -const fs = require('fs') -const path = require("path") -const webpack = require("webpack") -const HtmlWebpackPlugin = require('html-webpack-plugin') -const CopyWebpackPlugin = require('copy-webpack-plugin') -const HtmlReplaceWebpackPlugin = require('html-replace-webpack-plugin') - -function format_debate(base_indent, name) { - // Slurp in file and split into lines - const lines = fs.readFileSync('src/' + name + '.txt', 'utf8').trim().split('\n') - - // Parse into tree based on indentation - const tree = [] - for (const [n, line] of lines.entries()) { - if (line.trim().length == 0) continue - const most = line.trimLeft() - const indent = line.length - most.length - assert(indent % 2 == 0) - let kids = tree - for (var i = 0; i < indent; i += 2) { - assert(kids.length) - kids = kids[kids.length - 1].kids - } - kids.push({n: n, text: most, kids:[]}) - } - - // Dump tree to check consistency - function dump_tree(indent, list) { - for (const {n, text, kids} of list) { - console.log(indent + n + ': ' + text) - dump_tree(indent + ' ', kids) - } - } - if (0) - dump_tree('', tree) - - // Output as html - const output = [] - - // Format a single line - function show_line(n, line) { - const m = line.match(/^([A-Za-z ]+):\s*(.*)$/) - assert(m, name + ':' + n + ': Bad line: ' + line) - kind = {Red: 'red', Blue: 'blue', Judge: 'judge', 'Note for judges': 'note', 'Wikipedia': 'wikipedia', 'Question': 'question'}[m[1]] - assert(kind, name + ':' + n + ': Bad line: ' + line) - return [kind, m[2]] - } - - // Recursively display our tree - function show_node(indent, node) { - const [kind, line] = show_line(node.n, node.text) - const start = indent + '
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        ' - }, - { - pattern: /( *)/g, - replacement: (_, indent, name) => format_debate(indent, name) - }, - { - pattern: '', - replacement: '' - } - ]) - ], - devServer: { - static: { - directory: path.join(__dirname, 'public') - }, - historyApiFallback: true, - hot: true, - client: { - overlay: true - }, - devMiddleware: { - stats: "minimal" - } - }, - devtool: "inline-source-map" -} diff --git a/docs/Makefile b/documentation/docs/Makefile similarity index 100% rename from docs/Makefile rename to documentation/docs/Makefile diff --git a/docs/api.md b/documentation/docs/api.md similarity index 100% rename from docs/api.md rename to documentation/docs/api.md diff --git a/docs/assets/favicon-48x48.svg b/documentation/docs/assets/favicon-48x48.svg similarity index 100% rename from docs/assets/favicon-48x48.svg rename to documentation/docs/assets/favicon-48x48.svg diff --git a/docs/assets/favicon.ico b/documentation/docs/assets/favicon.ico similarity index 100% rename from docs/assets/favicon.ico rename to documentation/docs/assets/favicon.ico diff --git a/docs/assets/launch.png b/documentation/docs/assets/launch.png similarity index 100% rename from docs/assets/launch.png rename to documentation/docs/assets/launch.png diff --git a/docs/assets/vector-logo.svg b/documentation/docs/assets/vector-logo.svg similarity index 100% rename from docs/assets/vector-logo.svg rename to documentation/docs/assets/vector-logo.svg diff --git a/docs/index.md b/documentation/docs/index.md similarity index 100% rename from docs/index.md rename to documentation/docs/index.md diff --git a/docs/overrides/partials/copyright.html b/documentation/docs/overrides/partials/copyright.html similarity index 100% rename from docs/overrides/partials/copyright.html rename to documentation/docs/overrides/partials/copyright.html diff --git a/docs/overrides/partials/logo.html b/documentation/docs/overrides/partials/logo.html similarity index 100% rename from docs/overrides/partials/logo.html rename to documentation/docs/overrides/partials/logo.html diff --git a/docs/stylesheets/extra.css b/documentation/docs/stylesheets/extra.css similarity index 100% rename from docs/stylesheets/extra.css rename to documentation/docs/stylesheets/extra.css diff --git a/docs/usage.md b/documentation/docs/usage.md similarity index 100% rename from docs/usage.md rename to documentation/docs/usage.md From a6413869ed3b8f3ba06bc2c9745c7e2d270eaf25 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Fri, 20 Mar 2026 15:10:15 -0400 Subject: [PATCH 23/47] Remove docs.yml and blog.yml --- .github/workflows/blog.yml | 76 ---------------------------- .github/workflows/docs.yml | 100 ------------------------------------- 2 files changed, 176 deletions(-) delete mode 100644 .github/workflows/blog.yml delete mode 100644 .github/workflows/docs.yml diff --git a/.github/workflows/blog.yml b/.github/workflows/blog.yml deleted file mode 100644 index 60cef9aa..00000000 --- a/.github/workflows/blog.yml +++ /dev/null @@ -1,76 +0,0 @@ -name: Build and Deploy Blog - -on: - push: - branches: [ "main" ] - pull_request: - branches: [ "main" ] - -permissions: - contents: read - pages: write - id-token: write - -jobs: - build: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v6 - - - name: Setup Node.js - uses: actions/setup-node@v6 - with: - node-version: 'latest' - cache: 'npm' - cache-dependency-path: blog/package-lock.json - - - name: Install dependencies - run: cd documentation/blog && npm ci - - - name: Build - run: cd ./documentation/blog && npm run build - - - name: Upload build artifact - if: github.event_name == 'push' && github.ref == 'refs/heads/main' - uses: actions/upload-artifact@v6 - with: - name: blog-build - path: documentation/blog/public - retention-days: 1 - - deploy: - needs: build - if: github.event_name == 'push' && github.ref == 'refs/heads/main' - runs-on: ubuntu-latest - # environment: - # name: github-pages - # url: ${{ steps.deployment.outputs.page_url }} - # concurrency: - # group: pages - # cancel-in-progress: false - - steps: - - name: Download build artifact - uses: actions/download-artifact@v7 - with: - name: blog-build - path: documentation/blog/public - - - name: Setup Pages - uses: actions/configure-pages@v5 - - - name: Upload Pages artifact - uses: actions/upload-pages-artifact@v4 - with: - path: documentation/blog/public - retention-days: 1 - - # - name: Deploy to GitHub Pages - # id: deployment - # uses: actions/deploy-pages@v4 - - - name: Deploy to Github pages - uses: JamesIves/github-pages-deploy-action@v4.8.0 - with: - branch: gh-pages - folder: documentation/blog/public \ No newline at end of file diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml deleted file mode 100644 index a36463e0..00000000 --- a/.github/workflows/docs.yml +++ /dev/null @@ -1,100 +0,0 @@ -name: docs -permissions: - contents: write - pull-requests: write - -on: - push: - branches: - - main - paths: - - .pre-commit-config.yaml - - .github/workflows/docs.yml - - '**.py' - - '**.ipynb' - - '**.html' - - '**.js' - - '**.md' - - uv.lock - - pyproject.toml - - mkdocs.yml - - '**.png' - - '**.svg' - pull_request: - branches: - - main - paths: - - .pre-commit-config.yaml - - .github/workflows/docs.yml - - '**.py' - - '**.ipynb' - - '**.js' - - '**.html' - - uv.lock - - pyproject.toml - - '**.md' - - mkdocs.yml - - '**.png' - - '**.svg' - -jobs: - build: - runs-on: ubuntu-latest - steps: - - name: Checkout code - uses: actions/checkout@v6.0.1 - - - name: Install uv - uses: astral-sh/setup-uv@v7.2.0 - with: - version: "0.9.11" - enable-cache: true - - - name: Set up Python - uses: actions/setup-python@v6.1.0 - with: - python-version-file: ".python-version" - - - name: Install the project - run: uv sync --all-extras --group docs - - - name: Build docs - run: uv run mkdocs build - - - name: Create .nojekyll file - run: touch site/.nojekyll - - - name: Upload artifact - uses: actions/upload-artifact@v6 - with: - name: docs-site - path: site/ - retention-days: 1 - - deploy: - needs: build - if: github.event_name == 'push' && github.ref == 'refs/heads/main' - runs-on: ubuntu-latest - steps: - - name: Checkout code - uses: actions/checkout@v6.0.1 - - - name: Configure Git Credentials - run: | - git config user.name github-actions[bot] - git config user.email 41898282+github-actions[bot]@users.noreply.github.com - - - name: Download artifact - uses: actions/download-artifact@v7 - with: - name: docs-site - path: site - - - name: Ensure .nojekyll exists - run: touch site/.nojekyll - - - name: Deploy to Github pages - uses: JamesIves/github-pages-deploy-action@v4.8.0 - with: - branch: gh-pages - folder: site From a667e18af43fa5e3d02e46ea5c99d8e6676b43e5 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Fri, 20 Mar 2026 15:43:51 -0400 Subject: [PATCH 24/47] Fix issues in build steps of documentation workflow --- .github/workflows/documentation.yml | 2 +- mkdocs.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml index 69164905..8643c85d 100644 --- a/.github/workflows/documentation.yml +++ b/.github/workflows/documentation.yml @@ -21,7 +21,7 @@ jobs: with: node-version: 'latest' cache: 'npm' - cache-dependency-path: blog/package-lock.json + cache-dependency-path: documentation/blog/package-lock.json - name: Install dependencies run: cd documentation/blog && npm ci diff --git a/mkdocs.yml b/mkdocs.yml index c35ba56b..cb159921 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -63,7 +63,7 @@ site_name: CRISP-NAM Documentation site_url: https://vectorinstitute.github.io/crisp-nam-docs/ theme: name: material - custom_dir: docs/overrides + custom_dir: documentation/docs/overrides favicon: assets/favicon-48x48.svg features: - content.code.annotate From 3f2c992636dc8128c7c81e6a969ea9c8cd8324b7 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Fri, 20 Mar 2026 16:05:53 -0400 Subject: [PATCH 25/47] Add docs_dir config to fix workflow --- mkdocs.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/mkdocs.yml b/mkdocs.yml index cb159921..471dab07 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -61,6 +61,7 @@ repo_url: https://github.com/VectorInstitute/crisp-nam repo_name: VectorInstitute/crisp-nam site_name: CRISP-NAM Documentation site_url: https://vectorinstitute.github.io/crisp-nam-docs/ +docs_dir: documentation/docs theme: name: material custom_dir: documentation/docs/overrides From 156ae55ea9a26f9b4f934d0d786696a2d655582e Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Thu, 2 Apr 2026 14:29:52 -0400 Subject: [PATCH 26/47] Remove documentation folder; move docs and blog to repo home; update docs.yml and blog.yml to add target folder --- .github/workflows/blog.yml | 74 +++++++++++++ .github/workflows/docs.yml | 101 ++++++++++++++++++ .../blog => blog}/package-lock.json | 0 {documentation/blog => blog}/package.json | 0 .../src/assets/ism-overview-new.svg | 0 .../blog => blog}/src/diagrams/.gitkeep | 0 .../src/diagrams/architecture.html | 0 .../src/diagrams/crisp-nam-architecture.js | 0 .../src/diagrams/feature_importance.html | 0 .../blog => blog}/src/diagrams/index.js | 0 .../src/diagrams/nam-architecture.js | 0 .../src/diagrams/results-table.html | 0 .../blog => blog}/src/diagrams/shape_plot.js | 0 .../src/diagrams/shape_plots.html | 0 .../blog => blog}/src/diagrams/toc.js | 0 .../blog => blog}/src/diagrams/venn.html | 0 {documentation/blog => blog}/src/index.ejs | 0 {documentation/blog => blog}/src/index.js | 0 {documentation/blog => blog}/src/style.css | 0 {documentation/blog => blog}/src/util.js | 0 .../blog => blog}/static/final_results.json | 0 .../blog => blog}/static/references.bib | 0 .../blog => blog}/webpack.config.js | 0 {documentation/docs => docs}/Makefile | 0 {documentation/docs => docs}/api.md | 0 .../docs => docs}/assets/favicon-48x48.svg | 0 .../docs => docs}/assets/favicon.ico | Bin .../docs => docs}/assets/launch.png | Bin .../docs => docs}/assets/vector-logo.svg | 0 {documentation/docs => docs}/index.md | 0 .../overrides/partials/copyright.html | 0 .../overrides/partials/logo.html | 0 .../docs => docs}/stylesheets/extra.css | 0 {documentation/docs => docs}/usage.md | 0 34 files changed, 175 insertions(+) create mode 100644 .github/workflows/blog.yml create mode 100644 .github/workflows/docs.yml rename {documentation/blog => blog}/package-lock.json (100%) rename {documentation/blog => blog}/package.json (100%) rename {documentation/blog => blog}/src/assets/ism-overview-new.svg (100%) rename {documentation/blog => blog}/src/diagrams/.gitkeep (100%) rename {documentation/blog => blog}/src/diagrams/architecture.html (100%) rename {documentation/blog => blog}/src/diagrams/crisp-nam-architecture.js (100%) rename {documentation/blog => blog}/src/diagrams/feature_importance.html (100%) rename {documentation/blog => blog}/src/diagrams/index.js (100%) rename {documentation/blog => blog}/src/diagrams/nam-architecture.js (100%) rename {documentation/blog => blog}/src/diagrams/results-table.html (100%) rename {documentation/blog => blog}/src/diagrams/shape_plot.js (100%) rename {documentation/blog => blog}/src/diagrams/shape_plots.html (100%) rename {documentation/blog => blog}/src/diagrams/toc.js (100%) rename {documentation/blog => blog}/src/diagrams/venn.html (100%) rename {documentation/blog => blog}/src/index.ejs (100%) rename {documentation/blog => blog}/src/index.js (100%) rename {documentation/blog => blog}/src/style.css (100%) rename {documentation/blog => blog}/src/util.js (100%) rename {documentation/blog => blog}/static/final_results.json (100%) rename {documentation/blog => blog}/static/references.bib (100%) rename {documentation/blog => blog}/webpack.config.js (100%) rename {documentation/docs => docs}/Makefile (100%) rename {documentation/docs => docs}/api.md (100%) rename {documentation/docs => docs}/assets/favicon-48x48.svg (100%) rename {documentation/docs => docs}/assets/favicon.ico (100%) rename {documentation/docs => docs}/assets/launch.png (100%) rename {documentation/docs => docs}/assets/vector-logo.svg (100%) rename {documentation/docs => docs}/index.md (100%) rename {documentation/docs => docs}/overrides/partials/copyright.html (100%) rename {documentation/docs => docs}/overrides/partials/logo.html (100%) rename {documentation/docs => docs}/stylesheets/extra.css (100%) rename {documentation/docs => docs}/usage.md (100%) diff --git a/.github/workflows/blog.yml b/.github/workflows/blog.yml new file mode 100644 index 00000000..3f30b7c8 --- /dev/null +++ b/.github/workflows/blog.yml @@ -0,0 +1,74 @@ +name: Build and Deploy Blog + +on: + push: + branches: [ "main" ] + pull_request: + branches: [ "main" ] + +permissions: + contents: read + pages: write + id-token: write + +jobs: + build: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6 + + - name: Setup Node.js + uses: actions/setup-node@v6 + with: + node-version: 'latest' + cache: 'npm' + cache-dependency-path: blog/package-lock.json + + - name: Install dependencies + run: cd blog && npm ci + + - name: Build + run: cd ./blog && npm run build + + - name: Upload build artifact + if: github.event_name == 'push' && github.ref == 'refs/heads/main' + uses: actions/upload-artifact@v6 + with: + name: blog-build + path: blog/public + retention-days: 1 + + deploy: + needs: build + if: github.event_name == 'push' && github.ref == 'refs/heads/main' + runs-on: ubuntu-latest + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + concurrency: + group: pages + cancel-in-progress: false + + steps: + - name: Download build artifact + uses: actions/download-artifact@v7 + with: + name: blog-build + path: blog/public + + - name: Setup Pages + uses: actions/configure-pages@v5 + + - name: Upload Pages artifact + uses: actions/upload-pages-artifact@v4 + with: + path: blog/public + retention-days: 1 + + - name: Deploy to GitHub Pages + id: deployment + uses: JamesIves/github-pages-deploy-action@v4.8.0 + with: + branch: gh-pages + folder: blog/public + target-folder: blog diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 00000000..79cdc5fb --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,101 @@ +name: docs +permissions: + contents: write + pull-requests: write + +on: + push: + branches: + - main + paths: + - .pre-commit-config.yaml + - .github/workflows/docs.yml + - '**.py' + - '**.ipynb' + - '**.html' + - '**.js' + - '**.md' + - uv.lock + - pyproject.toml + - mkdocs.yml + - '**.png' + - '**.svg' + pull_request: + branches: + - main + paths: + - .pre-commit-config.yaml + - .github/workflows/docs.yml + - '**.py' + - '**.ipynb' + - '**.js' + - '**.html' + - uv.lock + - pyproject.toml + - '**.md' + - mkdocs.yml + - '**.png' + - '**.svg' + +jobs: + build: + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v6.0.1 + + - name: Install uv + uses: astral-sh/setup-uv@v7.2.0 + with: + version: "0.9.11" + enable-cache: true + + - name: Set up Python + uses: actions/setup-python@v6.1.0 + with: + python-version-file: ".python-version" + + - name: Install the project + run: uv sync --all-extras --group docs + + - name: Build docs + run: uv run mkdocs build + + - name: Create .nojekyll file + run: touch site/.nojekyll + + - name: Upload artifact + uses: actions/upload-artifact@v6 + with: + name: docs-site + path: site/ + retention-days: 1 + + deploy: + needs: build + if: github.event_name == 'push' && github.ref == 'refs/heads/main' + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v6.0.1 + + - name: Configure Git Credentials + run: | + git config user.name github-actions[bot] + git config user.email 41898282+github-actions[bot]@users.noreply.github.com + + - name: Download artifact + uses: actions/download-artifact@v7 + with: + name: docs-site + path: site + + - name: Ensure .nojekyll exists + run: touch site/.nojekyll + + - name: Deploy to Github pages + uses: JamesIves/github-pages-deploy-action@v4.8.0 + with: + branch: gh-pages + folder: site + target-folder: docs diff --git a/documentation/blog/package-lock.json b/blog/package-lock.json similarity index 100% rename from documentation/blog/package-lock.json rename to blog/package-lock.json diff --git a/documentation/blog/package.json b/blog/package.json similarity index 100% rename from documentation/blog/package.json rename to blog/package.json diff --git a/documentation/blog/src/assets/ism-overview-new.svg b/blog/src/assets/ism-overview-new.svg similarity index 100% rename from documentation/blog/src/assets/ism-overview-new.svg rename to blog/src/assets/ism-overview-new.svg diff --git a/documentation/blog/src/diagrams/.gitkeep b/blog/src/diagrams/.gitkeep similarity index 100% rename from documentation/blog/src/diagrams/.gitkeep rename to blog/src/diagrams/.gitkeep diff --git a/documentation/blog/src/diagrams/architecture.html b/blog/src/diagrams/architecture.html similarity index 100% rename from documentation/blog/src/diagrams/architecture.html rename to blog/src/diagrams/architecture.html diff --git a/documentation/blog/src/diagrams/crisp-nam-architecture.js b/blog/src/diagrams/crisp-nam-architecture.js similarity index 100% rename from documentation/blog/src/diagrams/crisp-nam-architecture.js rename to blog/src/diagrams/crisp-nam-architecture.js diff --git a/documentation/blog/src/diagrams/feature_importance.html b/blog/src/diagrams/feature_importance.html similarity index 100% rename from documentation/blog/src/diagrams/feature_importance.html rename to blog/src/diagrams/feature_importance.html diff --git a/documentation/blog/src/diagrams/index.js b/blog/src/diagrams/index.js similarity index 100% rename from documentation/blog/src/diagrams/index.js rename to blog/src/diagrams/index.js diff --git a/documentation/blog/src/diagrams/nam-architecture.js b/blog/src/diagrams/nam-architecture.js similarity index 100% rename from documentation/blog/src/diagrams/nam-architecture.js rename to blog/src/diagrams/nam-architecture.js diff --git a/documentation/blog/src/diagrams/results-table.html b/blog/src/diagrams/results-table.html similarity index 100% rename from documentation/blog/src/diagrams/results-table.html rename to blog/src/diagrams/results-table.html diff --git a/documentation/blog/src/diagrams/shape_plot.js b/blog/src/diagrams/shape_plot.js similarity index 100% rename from documentation/blog/src/diagrams/shape_plot.js rename to blog/src/diagrams/shape_plot.js diff --git a/documentation/blog/src/diagrams/shape_plots.html b/blog/src/diagrams/shape_plots.html similarity index 100% rename from documentation/blog/src/diagrams/shape_plots.html rename to blog/src/diagrams/shape_plots.html diff --git a/documentation/blog/src/diagrams/toc.js b/blog/src/diagrams/toc.js similarity index 100% rename from documentation/blog/src/diagrams/toc.js rename to blog/src/diagrams/toc.js diff --git a/documentation/blog/src/diagrams/venn.html b/blog/src/diagrams/venn.html similarity index 100% rename from documentation/blog/src/diagrams/venn.html rename to blog/src/diagrams/venn.html diff --git a/documentation/blog/src/index.ejs b/blog/src/index.ejs similarity index 100% rename from documentation/blog/src/index.ejs rename to blog/src/index.ejs diff --git a/documentation/blog/src/index.js b/blog/src/index.js similarity index 100% rename from documentation/blog/src/index.js rename to blog/src/index.js diff --git a/documentation/blog/src/style.css b/blog/src/style.css similarity index 100% rename from documentation/blog/src/style.css rename to blog/src/style.css diff --git a/documentation/blog/src/util.js b/blog/src/util.js similarity index 100% rename from documentation/blog/src/util.js rename to blog/src/util.js diff --git a/documentation/blog/static/final_results.json b/blog/static/final_results.json similarity index 100% rename from documentation/blog/static/final_results.json rename to blog/static/final_results.json diff --git a/documentation/blog/static/references.bib b/blog/static/references.bib similarity index 100% rename from documentation/blog/static/references.bib rename to blog/static/references.bib diff --git a/documentation/blog/webpack.config.js b/blog/webpack.config.js similarity index 100% rename from documentation/blog/webpack.config.js rename to blog/webpack.config.js diff --git a/documentation/docs/Makefile b/docs/Makefile similarity index 100% rename from documentation/docs/Makefile rename to docs/Makefile diff --git a/documentation/docs/api.md b/docs/api.md similarity index 100% rename from documentation/docs/api.md rename to docs/api.md diff --git a/documentation/docs/assets/favicon-48x48.svg b/docs/assets/favicon-48x48.svg similarity index 100% rename from documentation/docs/assets/favicon-48x48.svg rename to docs/assets/favicon-48x48.svg diff --git a/documentation/docs/assets/favicon.ico b/docs/assets/favicon.ico similarity index 100% rename from documentation/docs/assets/favicon.ico rename to docs/assets/favicon.ico diff --git a/documentation/docs/assets/launch.png b/docs/assets/launch.png similarity index 100% rename from documentation/docs/assets/launch.png rename to docs/assets/launch.png diff --git a/documentation/docs/assets/vector-logo.svg b/docs/assets/vector-logo.svg similarity index 100% rename from documentation/docs/assets/vector-logo.svg rename to docs/assets/vector-logo.svg diff --git a/documentation/docs/index.md b/docs/index.md similarity index 100% rename from documentation/docs/index.md rename to docs/index.md diff --git a/documentation/docs/overrides/partials/copyright.html b/docs/overrides/partials/copyright.html similarity index 100% rename from documentation/docs/overrides/partials/copyright.html rename to docs/overrides/partials/copyright.html diff --git a/documentation/docs/overrides/partials/logo.html b/docs/overrides/partials/logo.html similarity index 100% rename from documentation/docs/overrides/partials/logo.html rename to docs/overrides/partials/logo.html diff --git a/documentation/docs/stylesheets/extra.css b/docs/stylesheets/extra.css similarity index 100% rename from documentation/docs/stylesheets/extra.css rename to docs/stylesheets/extra.css diff --git a/documentation/docs/usage.md b/docs/usage.md similarity index 100% rename from documentation/docs/usage.md rename to docs/usage.md From 10f76218b3068df8456b4a8fc3c0bfa691d2267d Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Thu, 2 Apr 2026 14:32:53 -0400 Subject: [PATCH 27/47] Remove documentation.yml --- .github/workflows/documentation.yml | 72 ----------------------------- 1 file changed, 72 deletions(-) delete mode 100644 .github/workflows/documentation.yml diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml deleted file mode 100644 index 8643c85d..00000000 --- a/.github/workflows/documentation.yml +++ /dev/null @@ -1,72 +0,0 @@ -name: Build and Deploy documentation - -on: - push: - branches: [ "main" ] - pull_request: - branches: [ "main" ] - -permissions: - contents: write - pull-requests: write - -jobs: - build-blog: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v6 - - - name: Setup Node.js - uses: actions/setup-node@v6 - with: - node-version: 'latest' - cache: 'npm' - cache-dependency-path: documentation/blog/package-lock.json - - - name: Install dependencies - run: cd documentation/blog && npm ci - - - name: Build - run: cd ./documentation/blog && npm run build - - - name: Upload blog build artifact - uses: actions/upload-artifact@v6 - with: - name: blog-build - path: ./documentation/blog/public - retention-days: 1 - - build-docs: - runs-on: ubuntu-latest - steps: - - name: Checkout code - uses: actions/checkout@v6.0.1 - - - name: Install uv - uses: astral-sh/setup-uv@v7.2.0 - with: - version: "0.9.11" - enable-cache: true - - - name: Set up Python - uses: actions/setup-python@v6.1.0 - with: - python-version-file: ".python-version" - - - name: Install the project - run: uv sync --all-extras --group docs - - - name: Build docs - run: uv run mkdocs build - - - name: Create .nojekyll file - run: touch site/.nojekyll - - - name: Upload docs site artifact - uses: actions/upload-artifact@v6 - with: - name: docs-site - path: site/ - retention-days: 1 - - From ce8e2da467016873a382feaf48b516ceac87f6ec Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Thu, 2 Apr 2026 14:37:57 -0400 Subject: [PATCH 28/47] Fix mkdocs.yml to remove documentation folder after refactor --- mkdocs.yml | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/mkdocs.yml b/mkdocs.yml index 471dab07..c5d2e7a6 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -60,11 +60,10 @@ plugins: repo_url: https://github.com/VectorInstitute/crisp-nam repo_name: VectorInstitute/crisp-nam site_name: CRISP-NAM Documentation -site_url: https://vectorinstitute.github.io/crisp-nam-docs/ -docs_dir: documentation/docs +docs_dir: docs theme: name: material - custom_dir: documentation/docs/overrides + custom_dir: docs/overrides favicon: assets/favicon-48x48.svg features: - content.code.annotate From 01ab80b512da3268f5e5089b6d8946130f857ad0 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Thu, 2 Apr 2026 14:59:36 -0400 Subject: [PATCH 29/47] Temporary commit for github action workflow files --- .github/workflows/blog.yml | 2 +- .github/workflows/docs.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/blog.yml b/.github/workflows/blog.yml index 3f30b7c8..9b4ea701 100644 --- a/.github/workflows/blog.yml +++ b/.github/workflows/blog.yml @@ -40,7 +40,7 @@ jobs: deploy: needs: build - if: github.event_name == 'push' && github.ref == 'refs/heads/main' + if: github.event_name == 'push' && github.ref == 'refs/heads/feature/pypi' runs-on: ubuntu-latest environment: name: github-pages diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 79cdc5fb..26cc0d34 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -73,7 +73,7 @@ jobs: deploy: needs: build - if: github.event_name == 'push' && github.ref == 'refs/heads/main' + if: github.event_name == 'push' && github.ref == 'refs/heads/feature/pypi' runs-on: ubuntu-latest steps: - name: Checkout code From 40ec427b3d63cbedcca7835684463d314d8b681e Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Thu, 2 Apr 2026 15:09:29 -0400 Subject: [PATCH 30/47] Temporary commit to check URLS --- .github/workflows/blog.yml | 2 +- .github/workflows/docs.yml | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/blog.yml b/.github/workflows/blog.yml index 9b4ea701..f85c1ded 100644 --- a/.github/workflows/blog.yml +++ b/.github/workflows/blog.yml @@ -2,7 +2,7 @@ name: Build and Deploy Blog on: push: - branches: [ "main" ] + branches: [ "main", "feature/pypi" ] pull_request: branches: [ "main" ] diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 26cc0d34..57334c70 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -7,6 +7,7 @@ on: push: branches: - main + - feature/pypi paths: - .pre-commit-config.yaml - .github/workflows/docs.yml From 54d138f2db096dc50a69a5adc0d34d49be8bf4a0 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Thu, 2 Apr 2026 15:41:19 -0400 Subject: [PATCH 31/47] Add github-pages config to docs.yml --- .github/workflows/docs.yml | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 57334c70..25af9773 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -1,6 +1,7 @@ name: docs permissions: contents: write + pages: write pull-requests: write on: @@ -76,6 +77,12 @@ jobs: needs: build if: github.event_name == 'push' && github.ref == 'refs/heads/feature/pypi' runs-on: ubuntu-latest + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + concurrency: + group: pages + cancel-in-progress: false steps: - name: Checkout code uses: actions/checkout@v6.0.1 @@ -95,6 +102,7 @@ jobs: run: touch site/.nojekyll - name: Deploy to Github pages + id: deployment uses: JamesIves/github-pages-deploy-action@v4.8.0 with: branch: gh-pages From 56b0675a5a73a09cb27cf7b24aff632477b11d44 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Thu, 2 Apr 2026 15:46:00 -0400 Subject: [PATCH 32/47] Remove feature/pypi from yml files --- .github/workflows/blog.yml | 2 +- .github/workflows/docs.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/blog.yml b/.github/workflows/blog.yml index f85c1ded..9d68b556 100644 --- a/.github/workflows/blog.yml +++ b/.github/workflows/blog.yml @@ -2,7 +2,7 @@ name: Build and Deploy Blog on: push: - branches: [ "main", "feature/pypi" ] + branches: [ "main"] pull_request: branches: [ "main" ] diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 25af9773..fedd29e7 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -75,7 +75,7 @@ jobs: deploy: needs: build - if: github.event_name == 'push' && github.ref == 'refs/heads/feature/pypi' + if: github.event_name == 'push' && github.ref == 'refs/heads/main' runs-on: ubuntu-latest environment: name: github-pages From 6ee8bb12a0b5dfa273799f79599b9541c2a0efdc Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Thu, 2 Apr 2026 16:01:19 -0400 Subject: [PATCH 33/47] Read deploy for feature/pypi branch --- .github/workflows/blog.yml | 2 -- .github/workflows/docs.yml | 3 +-- 2 files changed, 1 insertion(+), 4 deletions(-) diff --git a/.github/workflows/blog.yml b/.github/workflows/blog.yml index 9d68b556..b61bfd83 100644 --- a/.github/workflows/blog.yml +++ b/.github/workflows/blog.yml @@ -31,7 +31,6 @@ jobs: run: cd ./blog && npm run build - name: Upload build artifact - if: github.event_name == 'push' && github.ref == 'refs/heads/main' uses: actions/upload-artifact@v6 with: name: blog-build @@ -44,7 +43,6 @@ jobs: runs-on: ubuntu-latest environment: name: github-pages - url: ${{ steps.deployment.outputs.page_url }} concurrency: group: pages cancel-in-progress: false diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index fedd29e7..310e6b16 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -75,11 +75,10 @@ jobs: deploy: needs: build - if: github.event_name == 'push' && github.ref == 'refs/heads/main' + if: github.event_name == 'push' && github.ref == 'refs/heads/feature/pypi' runs-on: ubuntu-latest environment: name: github-pages - url: ${{ steps.deployment.outputs.page_url }} concurrency: group: pages cancel-in-progress: false From b25696c34bfd6409bd213c8379b1751e1ede4546 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Thu, 2 Apr 2026 17:16:00 -0400 Subject: [PATCH 34/47] Add condition for deploy to run on push to feature/pypi --- .github/workflows/blog.yml | 4 ++-- .github/workflows/docs.yml | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/blog.yml b/.github/workflows/blog.yml index b61bfd83..383f08ae 100644 --- a/.github/workflows/blog.yml +++ b/.github/workflows/blog.yml @@ -2,7 +2,7 @@ name: Build and Deploy Blog on: push: - branches: [ "main"] + branches: [ "main", "feature/pypi"] pull_request: branches: [ "main" ] @@ -39,7 +39,7 @@ jobs: deploy: needs: build - if: github.event_name == 'push' && github.ref == 'refs/heads/feature/pypi' + if: github.event_name == 'push' && (github.ref == 'refs/heads/main' || github.ref == 'refs/heads/feature/pypi') runs-on: ubuntu-latest environment: name: github-pages diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 310e6b16..51188bf3 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -75,7 +75,7 @@ jobs: deploy: needs: build - if: github.event_name == 'push' && github.ref == 'refs/heads/feature/pypi' + if: github.event_name == 'push' && (github.ref == 'refs/heads/main' || github.ref == 'refs/heads/feature/pypi') runs-on: ubuntu-latest environment: name: github-pages From f6120c0f0cf0a87b029a59c266dd1f7976ca991b Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Thu, 2 Apr 2026 17:42:38 -0400 Subject: [PATCH 35/47] Update folder in deploy github pages step for blog and docs --- .github/workflows/blog.yml | 8 +------- .github/workflows/docs.yml | 2 +- 2 files changed, 2 insertions(+), 8 deletions(-) diff --git a/.github/workflows/blog.yml b/.github/workflows/blog.yml index 383f08ae..eecd1d2f 100644 --- a/.github/workflows/blog.yml +++ b/.github/workflows/blog.yml @@ -57,16 +57,10 @@ jobs: - name: Setup Pages uses: actions/configure-pages@v5 - - name: Upload Pages artifact - uses: actions/upload-pages-artifact@v4 - with: - path: blog/public - retention-days: 1 - - name: Deploy to GitHub Pages id: deployment uses: JamesIves/github-pages-deploy-action@v4.8.0 with: branch: gh-pages - folder: blog/public + folder: blog-build target-folder: blog diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 51188bf3..47cfe8e9 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -105,5 +105,5 @@ jobs: uses: JamesIves/github-pages-deploy-action@v4.8.0 with: branch: gh-pages - folder: site + folder: docs-site target-folder: docs From 90e1c259a78bb9ffbfeac34906091ea6120776d7 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Thu, 2 Apr 2026 17:47:52 -0400 Subject: [PATCH 36/47] Undo changes to the folder path --- .github/workflows/blog.yml | 2 +- .github/workflows/docs.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/blog.yml b/.github/workflows/blog.yml index eecd1d2f..599c7d50 100644 --- a/.github/workflows/blog.yml +++ b/.github/workflows/blog.yml @@ -62,5 +62,5 @@ jobs: uses: JamesIves/github-pages-deploy-action@v4.8.0 with: branch: gh-pages - folder: blog-build + folder: blog/public target-folder: blog diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 47cfe8e9..51188bf3 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -105,5 +105,5 @@ jobs: uses: JamesIves/github-pages-deploy-action@v4.8.0 with: branch: gh-pages - folder: docs-site + folder: site target-folder: docs From 714f096815f25da6ca629a89bff56b7b666e2d39 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Thu, 2 Apr 2026 17:57:21 -0400 Subject: [PATCH 37/47] Re-add url info to environment --- .github/workflows/blog.yml | 1 + .github/workflows/docs.yml | 1 + 2 files changed, 2 insertions(+) diff --git a/.github/workflows/blog.yml b/.github/workflows/blog.yml index 599c7d50..0ed1d2b9 100644 --- a/.github/workflows/blog.yml +++ b/.github/workflows/blog.yml @@ -43,6 +43,7 @@ jobs: runs-on: ubuntu-latest environment: name: github-pages + url: ${{ steps.deployment.outputs.page_url }} concurrency: group: pages cancel-in-progress: false diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 51188bf3..c670aa2a 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -79,6 +79,7 @@ jobs: runs-on: ubuntu-latest environment: name: github-pages + url: ${{ steps.deployment.outputs.page_url }} concurrency: group: pages cancel-in-progress: false From 3f2e87f4598603b66a98a1f4b5858b78e2d7c2cd Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Mon, 6 Apr 2026 17:32:45 -0400 Subject: [PATCH 38/47] Add blog link to docs --- .github/workflows/docs.yml | 39 ++++++++++++++++++++++++++++++++++---- docs/blog.md | 3 +++ mkdocs.yml | 1 + 3 files changed, 39 insertions(+), 4 deletions(-) create mode 100644 docs/blog.md diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index c670aa2a..321804e6 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -40,7 +40,7 @@ on: - '**.svg' jobs: - build: + build-docs: runs-on: ubuntu-latest steps: - name: Checkout code @@ -72,9 +72,32 @@ jobs: name: docs-site path: site/ retention-days: 1 + build-blog: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6 + + - name: Setup Node.js + uses: actions/setup-node@v6 + with: + node-version: 'latest' + cache: 'npm' + cache-dependency-path: blog/package-lock.json + + - name: Install dependencies + run: cd blog && npm ci + + - name: Build + run: cd ./blog && npm run build + - name: Upload build artifact + uses: actions/upload-artifact@v6 + with: + name: blog-build + path: blog/public + retention-days: 1 deploy: - needs: build + needs: [build-docs, build-blog] if: github.event_name == 'push' && (github.ref == 'refs/heads/main' || github.ref == 'refs/heads/feature/pypi') runs-on: ubuntu-latest environment: @@ -101,10 +124,18 @@ jobs: - name: Ensure .nojekyll exists run: touch site/.nojekyll + - name: Download blog artifact + uses: actions/download-artifact@v7 + with: + name: blog-build + - name: Move blog to site/blog + run: | + mkdir -p site/blog + mv blog/public/* site/blog/ + - name: Deploy to Github pages id: deployment uses: JamesIves/github-pages-deploy-action@v4.8.0 with: branch: gh-pages - folder: site - target-folder: docs + folder: site \ No newline at end of file diff --git a/docs/blog.md b/docs/blog.md new file mode 100644 index 00000000..8ceecd18 --- /dev/null +++ b/docs/blog.md @@ -0,0 +1,3 @@ +There is a blog explaining the architecture of CRISP-NAM and showcasing the results. + +Head [here](blog/index.html) to read it! \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index c5d2e7a6..193ed034 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -33,6 +33,7 @@ nav: - Home: index.md - API Reference: api.md - Usage: usage.md + - Blog: blog.md plugins: - search - mike: From 43a48c4846e077613c9925fcd0f05183c5cc7157 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Mon, 6 Apr 2026 17:45:28 -0400 Subject: [PATCH 39/47] Fix docs.yml to put blog in docs folder --- .github/workflows/docs.yml | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 321804e6..7398e44c 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -97,7 +97,9 @@ jobs: path: blog/public retention-days: 1 deploy: - needs: [build-docs, build-blog] + needs: + - build-docs + - build-blog if: github.event_name == 'push' && (github.ref == 'refs/heads/main' || github.ref == 'refs/heads/feature/pypi') runs-on: ubuntu-latest environment: @@ -115,24 +117,20 @@ jobs: git config user.name github-actions[bot] git config user.email 41898282+github-actions[bot]@users.noreply.github.com - - name: Download artifact + - name: Download docs artifact uses: actions/download-artifact@v7 with: name: docs-site path: site - - name: Ensure .nojekyll exists run: touch site/.nojekyll - + - name: Create a folder for blog + run: mkdir -p site/blog - name: Download blog artifact uses: actions/download-artifact@v7 with: name: blog-build - - name: Move blog to site/blog - run: | - mkdir -p site/blog - mv blog/public/* site/blog/ - + path: site/blog - name: Deploy to Github pages id: deployment uses: JamesIves/github-pages-deploy-action@v4.8.0 From 3437d3278aa916e81b16035472d7de32d3343dd2 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Mon, 6 Apr 2026 18:03:55 -0400 Subject: [PATCH 40/47] Remove blog.yml --- .github/workflows/blog.yml | 67 -------------------------------------- 1 file changed, 67 deletions(-) delete mode 100644 .github/workflows/blog.yml diff --git a/.github/workflows/blog.yml b/.github/workflows/blog.yml deleted file mode 100644 index 0ed1d2b9..00000000 --- a/.github/workflows/blog.yml +++ /dev/null @@ -1,67 +0,0 @@ -name: Build and Deploy Blog - -on: - push: - branches: [ "main", "feature/pypi"] - pull_request: - branches: [ "main" ] - -permissions: - contents: read - pages: write - id-token: write - -jobs: - build: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v6 - - - name: Setup Node.js - uses: actions/setup-node@v6 - with: - node-version: 'latest' - cache: 'npm' - cache-dependency-path: blog/package-lock.json - - - name: Install dependencies - run: cd blog && npm ci - - - name: Build - run: cd ./blog && npm run build - - - name: Upload build artifact - uses: actions/upload-artifact@v6 - with: - name: blog-build - path: blog/public - retention-days: 1 - - deploy: - needs: build - if: github.event_name == 'push' && (github.ref == 'refs/heads/main' || github.ref == 'refs/heads/feature/pypi') - runs-on: ubuntu-latest - environment: - name: github-pages - url: ${{ steps.deployment.outputs.page_url }} - concurrency: - group: pages - cancel-in-progress: false - - steps: - - name: Download build artifact - uses: actions/download-artifact@v7 - with: - name: blog-build - path: blog/public - - - name: Setup Pages - uses: actions/configure-pages@v5 - - - name: Deploy to GitHub Pages - id: deployment - uses: JamesIves/github-pages-deploy-action@v4.8.0 - with: - branch: gh-pages - folder: blog/public - target-folder: blog From 840bf8183b45ef4020fc77b1b346c0e246e95e30 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Mon, 6 Apr 2026 18:05:31 -0400 Subject: [PATCH 41/47] Remove feature/pypi from docs.yml actions --- .github/workflows/docs.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 7398e44c..a558b951 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -8,7 +8,6 @@ on: push: branches: - main - - feature/pypi paths: - .pre-commit-config.yaml - .github/workflows/docs.yml @@ -100,7 +99,7 @@ jobs: needs: - build-docs - build-blog - if: github.event_name == 'push' && (github.ref == 'refs/heads/main' || github.ref == 'refs/heads/feature/pypi') + if: github.event_name == 'push' && github.ref == 'refs/heads/main' runs-on: ubuntu-latest environment: name: github-pages From 8884afba3ba347f13700c45b798a99586559298c Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Mon, 13 Apr 2026 12:37:00 -0400 Subject: [PATCH 42/47] Add publish.yml --- .github/workflows/publish.yml | 50 +++++++++++++++++++++++++++++++++++ 1 file changed, 50 insertions(+) create mode 100644 .github/workflows/publish.yml diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml new file mode 100644 index 00000000..bdfa60ae --- /dev/null +++ b/.github/workflows/publish.yml @@ -0,0 +1,50 @@ +name: publish package +permissions: + contents: read + pull-requests: write + +on: + push: + tags: + - "v*" + +jobs: + deploy: + runs-on: ubuntu-latest + steps: + - name: Install apt dependencies + run: | + sudo apt-get update + sudo apt-get install libcurl4-openssl-dev libssl-dev + - uses: actions/checkout@v6.0.1 + + - name: Install uv + uses: astral-sh/setup-uv@v7.2.0 + with: + # Install a specific version of uv. + version: "0.9.11" + enable-cache: true + + - name: "Set up Python" + uses: actions/setup-python@83679a892e2d95755f2dac6acb0bfd1e9ac5d548 + with: + python-version-file: ".python-version" + + - name: Install the project + run: uv sync --all-extras --dev + + - name: Build package + run: uv build + + - name: Publish package + uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e + with: + user: __token__ + password: ${{ secrets.PYPI_API_TOKEN }} + + - name: Create GitHub Release + id: create_release + uses: ncipollo/release-action@b7eabc95ff50cbeeedec83973935c8f306dfcd0b # v1.20.0 + with: + artifacts: "dist/*" + generateReleaseNotes: true From ec78a73057a153e1a303120f5efe4e1ab6175a1d Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Mon, 13 Apr 2026 12:43:49 -0400 Subject: [PATCH 43/47] Trigger publish package workflow --- .github/workflows/publish.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index bdfa60ae..548f61fb 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -47,4 +47,4 @@ jobs: uses: ncipollo/release-action@b7eabc95ff50cbeeedec83973935c8f306dfcd0b # v1.20.0 with: artifacts: "dist/*" - generateReleaseNotes: true + generateReleaseNotes: true \ No newline at end of file From 6ecd6be634141103a361ab06bf60258db5dc5a16 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Mon, 13 Apr 2026 13:01:01 -0400 Subject: [PATCH 44/47] Add release job separately with write permissions --- .github/workflows/publish.yml | 19 +++++++++++++------ 1 file changed, 13 insertions(+), 6 deletions(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 548f61fb..3dc40099 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -42,9 +42,16 @@ jobs: user: __token__ password: ${{ secrets.PYPI_API_TOKEN }} - - name: Create GitHub Release - id: create_release - uses: ncipollo/release-action@b7eabc95ff50cbeeedec83973935c8f306dfcd0b # v1.20.0 - with: - artifacts: "dist/*" - generateReleaseNotes: true \ No newline at end of file + release_github: + needs: deploy + runs-on: ubuntu-latest + permissions: + contents: write # To create a github release + steps: + - name: Create GitHub Release + id: create_release + uses: ncipollo/release-action@v1.21.0 + with: + artifacts: "dist/*" + generateReleaseNotes: true + \ No newline at end of file From d51b18a3f66160e19b3f598d992419718ec5bbb6 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Mon, 13 Apr 2026 13:34:45 -0400 Subject: [PATCH 45/47] Add separate training.md file; Fix README.md to have only package information --- README.md | 222 ++++------------------------------------------------ training.md | 214 ++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 231 insertions(+), 205 deletions(-) create mode 100644 training.md diff --git a/README.md b/README.md index df90103b..d30eec6f 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ CRISP-NAM (Competing Risks Interpretable Survival Prediction with Neural Additiv ## Overview -This repository provides a comprehensive framework for competing risks survival analysis with interpretable neural additive models. CRISP-NAM combines the predictive power of deep learning with interpretability through feature-level shape functions, making it suitable for clinical and biomedical applications where understanding feature contributions is crucial. +This package provides a comprehensive framework for competing risks survival analysis with interpretable neural additive models. CRISP-NAM combines the predictive power of deep learning with interpretability through feature-level shape functions, making it suitable for clinical and biomedical applications where understanding feature contributions is crucial. ### Key Features @@ -16,79 +16,14 @@ This repository provides a comprehensive framework for competing risks survival - **Multiple Training Modes**: Standard training, hyperparameter tuning, and nested cross-validation - **Baseline Comparisons**: DeepHit implementation for benchmarking against state-of-the-art methods -### Available Datasets -This repository includes 4 datasets: Framingham Heart Study, PBC, Support2 and Synthetic datasets. Detailed information is available in [datasets.md](./datasets.md). - ## Requirements + Python >=3.10 -## Repository Structure +## Install the package -``` -crisp_nam/ -├── blog/ # Blog -├── crisp_nam/ # Main package -│ ├── metrics/ -│ │ ├── __init__.py -│ │ ├── calibration.py -│ │ ├── discrimination.py -│ │ └── ipcw.py -│ ├── models/ -│ │ ├── __init__.py -│ │ ├── crisp_nam_model.py -│ │ └── deephit_model.py -│ ├── utils/ -│ │ ├── __init__.py -│ │ ├── loss.py -│ │ ├── plotting.py -│ │ └── risk_cif.py -│ └── __init__.py -├── data_utils/ # Data utilities -│ ├── __init__.py -│ ├── load_datasets.py -│ └── survival_datasets.py -├── datasets/ # Dataset files and loaders -│ ├── metabric/ -│ │ ├── cleaned_features_final.csv -│ │ └── label.csv -│ ├── framingham_dataset.py -│ ├── framingham.csv -│ ├── pbc_dataset.py -│ ├── pbc2.csv -│ ├── support_dataset.py -│ ├── support2.csv -│ ├── SurvivalDataset.py -│ ├── synthetic_comprisk.csv -│ └── synthetic_dataset.py -├── docs/ # Documentation of Pypi package. -├── results/ # Results and outputs -│ ├── best_params/ # Best parameters for dataset and model combinations -│ │ ├── best_params_framingham_deephit.yaml -│ │ ├── best_params_framingham.yaml -│ │ ├── best_params_pbc_deephit.yaml -│ │ ├── best_params_pbc.yaml -│ │ ├── best_params_support.yaml -│ │ ├── best_params_support2_deephit.yaml -│ │ ├── best_params_synthetic_deephit.yaml -│ │ └── best_params_synthetic.yaml -│ ├── logs/ # Nested CV results and logs -│ │ ├── nested_cv_best_params_*.yaml -│ │ ├── nested_cv_detailed_metrics_*.csv -│ │ ├── nested_cv_metrics_*.xlsx -│ │ ├── nested_cv_raw_metrics_*.json -│ │ └── nested_cv_summary_metrics_*.csv -│ └── plots/ # Generated plots -│ ├── nested_cv_feature_importance_risk_*_*.png -│ └── nested_cv_shape_functions_risk_*_*.png -├── training_scripts/ # Training scripts -│ ├── config.yaml -│ ├── model_utils.py -│ ├── train_deephit_cuda.py -│ ├── train_deephit.py -│ ├── train_nested_cv.py # Nested cross-validation script -│ ├── train.py -│ ├── tune_optuna_optimized.py -│ └── tune_optuna.py +```bash +pip install crisp-nam ``` ## Install from source @@ -112,149 +47,26 @@ via `uv` cd crisp-nam uv sync ``` +## Research details -## Training Scripts - -The repository provides several specialized training scripts: - -- **`train.py`**: Standard model training with cross-validation and comprehensive evaluation -- **`train_nested_cv.py`**: Robust nested cross-validation for unbiased performance estimation -- **`tune_optuna.py`**: Hyperparameter optimization using Optuna's advanced algorithms -- **`tune_optuna_optimized.py`**: Hyperparameter optimization using Optuna on a GPU. -- **`train_deephit.py`**: DeepHit baseline implementation for comparative studies -- **`train_deephit_cuda.py`**: DeepHit baseline implementation optimized for running on a GPU. - -Each script supports extensive configuration through command-line arguments and YAML config files, enabling reproducible experiments and easy parameter sweeps. - -### Running training scripts - -1. Modify training parameters in `training_scripts/train.py` - OR - Run either of following commands to see CLI arguments for passing training parameters: - - ```bash - python training_scripts/train.py --help - ``` - - ```bash - uv run training_scripts/train.py --help - ``` - -2. Run the training script - - 1. via `python` - ```bash - source .venv/bin/activate - python training_scripts/train.py --dataset framingham - ``` - - 2. via `uv` - ```bash - uv run training_scripts/train.py --dataset framingham - ``` - -### Running Nested Cross-Validation - -The nested cross-validation script performs robust model evaluation with hyperparameter optimization using inner and outer cross-validation loops. It automatically generates performance metrics, feature importance plots, and shape function visualizations. - -via `python` -```bash -python training_scripts/train_nested_cv.py --dataset framingham -``` - -via `uv` -```bash -uv run training_scripts/train_nested_cv.py --dataset framingham -``` - -## Configuration Parameters - -All parameters can be passed via command line or specified in a YAML config file: +For more details regarding the research work, please refer to `datasets.md` and `training.md` within the project repository. -1. Dataset Configuration -- `--dataset` (str): Dataset to use (choices: `framingham`, `support`, `pbc`, `synthetic`, default: `framingham`) -- `--scaling` (str): Data scaling method for continuous features (choices: `minmax`, `standard`, `none`, default: `standard`) - -2. Training Parameters -- `--num_epochs` (int): Number of training epochs (default: `250`) -- `--batch_size` (int): Batch size for training (default: `512`) -- `--patience` (int): Patience for early stopping (default: `10`) - -3. Cross-Validation Configuration -- `--outer_folds` (int): Number of outer CV folds (default: `5`) -- `--inner_folds` (int): Number of inner CV folds for hyperparameter tuning (default: `3`) -- `--n_trials` (int): Number of Optuna trials per inner fold (default: `20`) - -4. Event Weighting -- `--event_weighting` (str): Event weighting strategy (choices: `none`, `balanced`, `custom`, default: `none`) -- `--custom_event_weights` (str): Custom weights for events (comma-separated, default: `None`) +## Contributing -5. Other Parameters -- `--seed` (int): Random seed for reproducibility (default: `42`) -- `--config` (str): Path to YAML config file (default: looks for `config.yaml`) +Contributions are welcome! Please open issues or submit pull requests. -### Examples +## Citation -1. **Basic nested CV with default parameters:** -```bash -python training_scripts/train_nested_cv.py --dataset pbc +If you use our package, kindly acknowledge by citing our research. ``` - -2. **Customized nested CV with specific parameters:** -```bash -python training_scripts/train_nested_cv.py \ - --dataset support \ - --outer_folds 10 \ - --inner_folds 5 \ - --n_trials 50 \ - --num_epochs 500 \ - --event_weighting balanced \ - --scaling minmax \ - --seed 123 -``` - -3. **Using a config file:** -```bash -python training_scripts/train_nested_cv.py --config my_config.yaml +@inproceedings{ramachandram2025crispnam, + title={CRISP-NAM: Competing Risks Interpretable Survival Prediction with Neural Additive Models}, + author={Ramachandram, Dhanesh and Raval, Ananya}, + booktitle={EXPLIMED 2025 - Second Workshop on Explainable AI for the Medical Domain}, + year={2025} +} ``` -## Output Files - -The script generates several output files in the current directory: - -1. **Performance Metrics** -- `nested_cv_summary_metrics_{dataset}.csv`: Summary table with mean ± std metrics -- `nested_cv_detailed_metrics_{dataset}.csv`: Detailed results for each fold -- `nested_cv_metrics_{dataset}.xlsx`: Excel file with multiple sheets (Summary, Detailed, Metadata) -- `nested_cv_raw_metrics_{dataset}.json`: Raw metrics dictionary for reproducibility - -2. **Model Configuration** -- `nested_cv_best_params_{dataset}.yaml`: Aggregated best hyperparameters across all folds - -3. **Visualizations** -- `nested_cv_feature_importance_risk_{risk}_{dataset}.png`: Feature importance plots -- `nested_cv_shape_functions_risk_{risk}_{dataset}.png`: Shape function plots for top features - -Results are saved to `results/plots/`: - -## Evaluation Metrics - -The script computes the following metrics at different time quantiles (25%, 50%, 75%): - -1. **AUC (Area Under the ROC Curve)**: Time-dependent AUC for discrimination - - 0.5 = random, >0.7 = good, >0.8 = excellent -2. **TDCI (Time-Dependent Concordance Index)**: Harrell's C-index adapted for competing risks - - 0.5 = random, >0.7 = good, >0.8 = excellent -3. **Brier Score**: Calibration metric measuring prediction accuracy - - 0 = perfect, <0.25 = good, >0.25 = poor - -> [!NOTE] -> For `uv` installation, please visit follow instructions in their [official page](https://docs.astral.sh/uv/getting-started/installation/). - -## Contributing - -Contributions are welcome! Please open issues or submit pull requests. - ## License This project is licensed under the MIT License. diff --git a/training.md b/training.md new file mode 100644 index 00000000..2b13b81d --- /dev/null +++ b/training.md @@ -0,0 +1,214 @@ +# Training Instructions + +This file contains details information about training models within the `crisp_nam` repository. + +## Repository Structure +The following is the structure for the repository. Files within the `crisp_nam` folder are available as a python package. + +``` +crisp_nam/ +├── blog/ # Blog +├── crisp_nam/ # Main package +│ ├── metrics/ +│ │ ├── __init__.py +│ │ ├── calibration.py +│ │ ├── discrimination.py +│ │ └── ipcw.py +│ ├── models/ +│ │ ├── __init__.py +│ │ ├── crisp_nam_model.py +│ │ └── deephit_model.py +│ ├── utils/ +│ │ ├── __init__.py +│ │ ├── loss.py +│ │ ├── plotting.py +│ │ └── risk_cif.py +│ └── __init__.py +├── data_utils/ # Data utilities +│ ├── __init__.py +│ ├── load_datasets.py +│ └── survival_datasets.py +├── datasets/ # Dataset files and loaders +│ ├── metabric/ +│ │ ├── cleaned_features_final.csv +│ │ └── label.csv +│ ├── framingham_dataset.py +│ ├── framingham.csv +│ ├── pbc_dataset.py +│ ├── pbc2.csv +│ ├── support_dataset.py +│ ├── support2.csv +│ ├── SurvivalDataset.py +│ ├── synthetic_comprisk.csv +│ └── synthetic_dataset.py +├── docs/ # Documentation of Pypi package. +├── results/ # Results and outputs +│ ├── best_params/ # Best parameters for dataset and model combinations +│ │ ├── best_params_framingham_deephit.yaml +│ │ ├── best_params_framingham.yaml +│ │ ├── best_params_pbc_deephit.yaml +│ │ ├── best_params_pbc.yaml +│ │ ├── best_params_support.yaml +│ │ ├── best_params_support2_deephit.yaml +│ │ ├── best_params_synthetic_deephit.yaml +│ │ └── best_params_synthetic.yaml +│ ├── logs/ # Nested CV results and logs +│ │ ├── nested_cv_best_params_*.yaml +│ │ ├── nested_cv_detailed_metrics_*.csv +│ │ ├── nested_cv_metrics_*.xlsx +│ │ ├── nested_cv_raw_metrics_*.json +│ │ └── nested_cv_summary_metrics_*.csv +│ └── plots/ # Generated plots +│ ├── nested_cv_feature_importance_risk_*_*.png +│ └── nested_cv_shape_functions_risk_*_*.png +├── training_scripts/ # Training scripts +│ ├── config.yaml +│ ├── model_utils.py +│ ├── train_deephit_cuda.py +│ ├── train_deephit.py +│ ├── train_nested_cv.py # Nested cross-validation script +│ ├── train.py +│ ├── tune_optuna_optimized.py +│ └── tune_optuna.py +``` + +### Available Datasets +This repository includes 4 datasets: Framingham Heart Study, PBC, Support2 and Synthetic datasets. Detailed information is available in [datasets.md](./datasets.md). + +## Training Scripts + +The repository provides several specialized training scripts: + +- **`train.py`**: Standard model training with cross-validation and comprehensive evaluation +- **`train_nested_cv.py`**: Robust nested cross-validation for unbiased performance estimation +- **`tune_optuna.py`**: Hyperparameter optimization using Optuna's advanced algorithms +- **`tune_optuna_optimized.py`**: Hyperparameter optimization using Optuna on a GPU. +- **`train_deephit.py`**: DeepHit baseline implementation for comparative studies +- **`train_deephit_cuda.py`**: DeepHit baseline implementation optimized for running on a GPU. + +Each script supports extensive configuration through command-line arguments and YAML config files, enabling reproducible experiments and easy parameter sweeps. + +### Running training scripts + +1. Modify training parameters in `training_scripts/train.py` + OR + Run either of following commands to see CLI arguments for passing training parameters: + + ```bash + python training_scripts/train.py --help + ``` + + ```bash + uv run training_scripts/train.py --help + ``` + +2. Run the training script + + 1. via `python` + ```bash + source .venv/bin/activate + python training_scripts/train.py --dataset framingham + ``` + + 2. via `uv` + ```bash + uv run training_scripts/train.py --dataset framingham + ``` + +### Running Nested Cross-Validation + +The nested cross-validation script performs robust model evaluation with hyperparameter optimization using inner and outer cross-validation loops. It automatically generates performance metrics, feature importance plots, and shape function visualizations. + +via `python` +```bash +python training_scripts/train_nested_cv.py --dataset framingham +``` + +via `uv` +```bash +uv run training_scripts/train_nested_cv.py --dataset framingham +``` + +## Configuration Parameters + +All parameters can be passed via command line or specified in a YAML config file: + +1. Dataset Configuration +- `--dataset` (str): Dataset to use (choices: `framingham`, `support`, `pbc`, `synthetic`, default: `framingham`) +- `--scaling` (str): Data scaling method for continuous features (choices: `minmax`, `standard`, `none`, default: `standard`) + +2. Training Parameters +- `--num_epochs` (int): Number of training epochs (default: `250`) +- `--batch_size` (int): Batch size for training (default: `512`) +- `--patience` (int): Patience for early stopping (default: `10`) + +3. Cross-Validation Configuration +- `--outer_folds` (int): Number of outer CV folds (default: `5`) +- `--inner_folds` (int): Number of inner CV folds for hyperparameter tuning (default: `3`) +- `--n_trials` (int): Number of Optuna trials per inner fold (default: `20`) + +4. Event Weighting +- `--event_weighting` (str): Event weighting strategy (choices: `none`, `balanced`, `custom`, default: `none`) +- `--custom_event_weights` (str): Custom weights for events (comma-separated, default: `None`) + +5. Other Parameters +- `--seed` (int): Random seed for reproducibility (default: `42`) +- `--config` (str): Path to YAML config file (default: looks for `config.yaml`) + +### Examples + +1. **Basic nested CV with default parameters:** +```bash +python training_scripts/train_nested_cv.py --dataset pbc +``` + +2. **Customized nested CV with specific parameters:** +```bash +python training_scripts/train_nested_cv.py \ + --dataset support \ + --outer_folds 10 \ + --inner_folds 5 \ + --n_trials 50 \ + --num_epochs 500 \ + --event_weighting balanced \ + --scaling minmax \ + --seed 123 +``` + +3. **Using a config file:** +```bash +python training_scripts/train_nested_cv.py --config my_config.yaml +``` + +## Output Files + +The script generates several output files in the current directory: + +1. **Performance Metrics** +- `nested_cv_summary_metrics_{dataset}.csv`: Summary table with mean ± std metrics +- `nested_cv_detailed_metrics_{dataset}.csv`: Detailed results for each fold +- `nested_cv_metrics_{dataset}.xlsx`: Excel file with multiple sheets (Summary, Detailed, Metadata) +- `nested_cv_raw_metrics_{dataset}.json`: Raw metrics dictionary for reproducibility + +2. **Model Configuration** +- `nested_cv_best_params_{dataset}.yaml`: Aggregated best hyperparameters across all folds + +3. **Visualizations** +- `nested_cv_feature_importance_risk_{risk}_{dataset}.png`: Feature importance plots +- `nested_cv_shape_functions_risk_{risk}_{dataset}.png`: Shape function plots for top features + +Results are saved to `results/plots/`: + +## Evaluation Metrics + +The script computes the following metrics at different time quantiles (25%, 50%, 75%): + +1. **AUC (Area Under the ROC Curve)**: Time-dependent AUC for discrimination + - 0.5 = random, >0.7 = good, >0.8 = excellent +2. **TDCI (Time-Dependent Concordance Index)**: Harrell's C-index adapted for competing risks + - 0.5 = random, >0.7 = good, >0.8 = excellent +3. **Brier Score**: Calibration metric measuring prediction accuracy + - 0 = perfect, <0.25 = good, >0.25 = poor + +> [!NOTE] +> For `uv` installation, please visit follow instructions in their [official page](https://docs.astral.sh/uv/getting-started/installation/). \ No newline at end of file From 7f4da4cb32541e3f47602b65e671a24882414737 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Mon, 13 Apr 2026 17:15:33 -0400 Subject: [PATCH 46/47] Replace version in pyproject.toml --- pyproject.toml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index bc1fd841..206ae945 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,6 +34,9 @@ docs = [ "click<=8.2.1", ] +[project.urls] +Homepage = "https://github.com/VectorInstitute/crisp-nam" + [tool.mypy] follow_imports = "normal" ignore_missing_imports = false @@ -120,4 +123,4 @@ convention = "numpy" max-doc-length = 88 [tool.hatch.build.targets.sdist] -only-include = ["crisp_nam"] +only-include = ["crisp_nam"] \ No newline at end of file From 6813703620da63a76c0434ae68d29dece4960036 Mon Sep 17 00:00:00 2001 From: Ananya Raval Date: Mon, 13 Apr 2026 17:33:15 -0400 Subject: [PATCH 47/47] Add v0.1.1 --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 206ae945..64364dbe 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "hatchling.build" [project] name = "crisp-nam" -version = "0.1.0" +version = "0.1.1" description = "CRISP-NAM: Competing Risks Interpretable Survival Prediction with Neural Additive Models" readme = "README.md" requires-python = ">=3.10"