data_ml_classifiers.py

import numpy as np

#Classifiers
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier #KNN
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.neural_network import MLPClassifier

RANDOM_SED: int = 42

""" Classifiers with parameters used in GridSearchCV 
"""
ml_classifiers_params = {

    'Decision Tree': {
        'classifier': DecisionTreeClassifier(random_state=RANDOM_SED),
        'params': {
            'criterion': ['gini', 'entropy'],
            'splitter': ['best','random'],
            'min_samples_split': [2, 5, 10],
            'min_samples_leaf': [1, 5, 10]
        }
    },

    'SVM': {
        'classifier': SVC(random_state=RANDOM_SED),
        'params': {
            'tol': [0.001, 0.0001, 0.00001],
            'C': [1.0, 1.5, 2.0],
            'kernel': ['rbf', 'linear', 'poly', 'sigmoid']
        }
    },
    
    'Nearest Neighbors': {
        'classifier': KNeighborsClassifier(),
        'params': {
            'n_neighbors': [2, 5, 10, 20],
            'p': [1, 2],
            'algorithm': ['ball_tree', 'kd_tree', 'brute'],
            'metric': ['euclidean', 'manhattan', 'chebyshev', 'minkowski'],
        }
    },

    'Random Forest': {
        'classifier': RandomForestClassifier(random_state=RANDOM_SED),
        'params': {
            'criterion': ['gini', 'entropy'],
            #'splitter': ['best','random'],
            'min_samples_split': [10, 40, 100, 150],
            'min_samples_leaf': [1, 5, 10]
        }  
    },

    'Gaussian Naive Bayes': {
        'classifier': GaussianNB(),
        'params': {
            'var_smoothing' : np.logspace(0,-9, num=100)
        }
    },

    'Logistic Regression': {
        'classifier': LogisticRegression(random_state=RANDOM_SED),
        'params': {
            'tol': [0.0001, 0.00001, 0.000001],
            'max_iter': [1000, 10000],
            'C': [1.0, 1.5, 2.0],
            'solver': ['lbfgs', 'sag', 'saga']
        }
    },
    
    'Extra Trees': {
        'classifier': ExtraTreesClassifier(random_state=RANDOM_SED),
        'params': {
            'criterion': ['gini', 'entropy'],
            'n_estimators': [10, 50, 100, 200],
            'min_samples_split': [2, 5, 10],
            'min_samples_leaf': [1, 5, 10]
        }
    },

    'MLP': {
        'classifier': MLPClassifier(random_state=RANDOM_SED),
        'params': {
            'activation': ['relu', 'logistic', 'tanh'],
            'solver': ['adam', 'sgd'],
            'batch_size': [8, 16, 32, 64]
        }
    }   
}