diff --git a/aaanalysis/feature_engineering/_backend/cpp/sequence_feature.py b/aaanalysis/feature_engineering/_backend/cpp/sequence_feature.py
index e0afe54c..3860f5ed 100644
--- a/aaanalysis/feature_engineering/_backend/cpp/sequence_feature.py
+++ b/aaanalysis/feature_engineering/_backend/cpp/sequence_feature.py
@@ -136,6 +136,50 @@ def get_df_feat_(features=None, df_parts=None, labels=None,
return df
+# Scale-composition baseline featurization
+def get_scale_composition_(df_parts=None, df_scales=None):
+ """Per-sequence scale average over the concatenated sequence parts (vectorized).
+
+ For each row of ``df_parts`` the part strings are concatenated into one span; every
+ residue that is not a (single-letter) index label of ``df_scales`` (gaps ``'-'`` and
+ any other non-canonical symbol) is dropped, and the remaining residues' scale rows are
+ averaged column-wise into one value per scale, giving the ``(n_seq, n_scales)`` matrix.
+ A row whose span has no scored residue (empty / all-non-canonical) becomes all-``NaN``;
+ ``n_kept`` reports the number of averaged residues per row so the frontend can warn.
+
+ Implementation: all residues of all spans are flattened into one byte array and mapped
+ to scale rows via a 256-entry lookup table; a single ``np.bincount`` tallies a small
+ per-sequence residue-count matrix ``(n_seq, n_letters)``, and one BLAS matmul against
+ the scale matrix yields the per-sequence sums — no per-sequence Python loop or
+ ``DataFrame.loc``, and the wide part scales with ``n_scales`` as a matmul, not a scatter.
+ """
+ n_seq = len(df_parts)
+ scales_arr = df_scales.to_numpy(dtype=float) # (n_letters, n_scales)
+ n_letters = scales_arr.shape[0]
+ # Byte -> scale-row lookup (-1 = non-scored); only single-character labels can match a residue
+ lut = np.full(256, -1, dtype=np.intp)
+ for row, aa in enumerate(df_scales.index):
+ if len(aa) == 1 and ord(aa) < 256:
+ lut[ord(aa)] = row
+ # Flatten every residue of every span, tracking its owning sequence
+ # (df_parts columns are guaranteed str by the frontend's get_df_parts)
+ spans = df_parts.agg("".join, axis=1).to_list()
+ lengths = np.fromiter((len(s) for s in spans), dtype=np.intp, count=n_seq)
+ codes = np.frombuffer("".join(spans).encode("latin-1", "replace"), dtype=np.uint8)
+ seq_ids = np.repeat(np.arange(n_seq), lengths)
+ rows = lut[codes] # scale row per residue
+ valid = rows >= 0
+ seq_ids, rows = seq_ids[valid], rows[valid]
+ # Per-sequence residue-count matrix (n_seq, n_letters) from one bincount, then sums via matmul
+ counts = np.bincount(seq_ids * n_letters + rows,
+ minlength=n_seq * n_letters).reshape(n_seq, n_letters)
+ n_kept = counts.sum(axis=1).astype(int)
+ sums = counts @ scales_arr # (n_seq, n_scales)
+ with np.errstate(invalid="ignore"):
+ X = sums / n_kept[:, None] # 0 / 0 -> NaN (empty rows)
+ return X, n_kept
+
+
# Multi-class / regression label conversion
def get_labels_ovr_(labels=None, label_test=1, label_ref=0):
"""One-vs-rest: per class, a full-length binary array (class -> test, rest -> ref)."""
diff --git a/aaanalysis/feature_engineering/_sequence_feature.py b/aaanalysis/feature_engineering/_sequence_feature.py
index 7d8e1cdb..bce94a0e 100644
--- a/aaanalysis/feature_engineering/_sequence_feature.py
+++ b/aaanalysis/feature_engineering/_sequence_feature.py
@@ -26,7 +26,7 @@
get_positions_, get_amino_acids_,
get_df_pos_, get_df_pos_parts_)
from ._backend.cpp.sequence_feature import (get_split_kws_, get_features_, get_feature_names_,
- get_feature_descriptions_, get_df_feat_,
+ get_feature_descriptions_, get_df_feat_, get_scale_composition_,
get_labels_ovr_, get_labels_ovo_, get_labels_quantile_,
get_labels_tiered_)
from ._backend.cpp_run import _pick_feature_matrix_builder
@@ -853,6 +853,97 @@ def feature_matrix(self,
start += n
return list_X if batch else list_X[0]
+ def scale_composition(self,
+ df_seq: pd.DataFrame,
+ df_scales: Optional[pd.DataFrame] = None,
+ list_parts: Optional[Union[str, List[str]]] = None,
+ return_df: bool = False,
+ ) -> Union[ut.ArrayLike2D, pd.DataFrame]:
+ """
+ Create the scale-composition baseline matrix for given sequences and scales.
+
+ Builds the no-positional-split **scale-based baseline** featurization: for each
+ sequence the requested Parts are concatenated into one span, and every scale is
+ averaged over the residues of that span, yielding one value per scale. The result
+ is the ``(n_seq, n_scales)`` matrix ``X`` — the sequence's mean profile in
+ scale-space, the scale-based analogue of amino-acid composition. Unlike
+ :meth:`SequenceFeature.feature_matrix`, which averages each scale over the residues
+ of a specific Part-Split, this method averages each scale over the **whole span**
+ (a single mean per scale), with no positional information.
+
+ **Application.** Use this to build a **baseline feature set** for a prediction
+ model: fit the same classifier on this scale-composition ``X`` and on a
+ :class:`CPP` :meth:`feature_matrix` and compare the scores to show how much the
+ positional Part-Split-Scale features add over a plain scale-average encoding (the
+ "scale baseline vs CPP" comparison). It is *not* a positional feature set — reach
+ for :class:`CPP` when you need where-along-the-sequence information.
+
+ .. versionadded:: 1.1.0
+
+ Parameters
+ ----------
+ df_seq : pd.DataFrame, shape (n_samples, n_seq_info)
+ DataFrame containing an ``entry`` column with unique protein identifiers and sequence information
+ in a distinct format: **Position-based**, **Part-based**, **Sequence-based**, or **Sequence-TMD-based**
+ (the same input accepted by :meth:`SequenceFeature.get_df_parts`).
+ df_scales : pd.DataFrame, shape (n_letters, n_scales), optional
+ DataFrame of scales with letters (amino acids) as index. Default from :meth:`load_scales`
+ unless specified in ``options['df_scales']``. The index defines which residues are scored;
+ residues absent from it are dropped before averaging.
+ list_parts : str or list of str, optional
+ Names of the sequence parts to average over (see :class:`SequenceFeature` for valid parts).
+ If ``None`` (default), the whole TMD-JMD span ``jmd_n`` + ``tmd`` + ``jmd_c`` (the ``tmd_jmd``
+ part) is used. Multiple parts are concatenated per sequence before averaging.
+ return_df : bool, default=False
+ If ``True``, return a labeled ``pd.DataFrame`` (rows indexed like ``df_parts``, one column per
+ scale) instead of a plain numpy array.
+
+ Returns
+ -------
+ X : array-like, shape (n_samples, n_scales)
+ Scale-average matrix containing, per sequence, the mean of each scale over the span residues.
+ Returned as a ``pd.DataFrame`` (scale names as columns) when ``return_df=True``.
+
+ Notes
+ -----
+ * **Missing / non-canonical residues**: a residue is averaged only if it is an index label of
+ ``df_scales``. Gap symbols (``'-'``) and any other non-canonical symbol (e.g. ``'X'``) are
+ dropped per the package convention, so the mean is taken over the scored residues only.
+ * A sequence whose span has **no** scored residue (empty span, or all residues non-canonical)
+ yields an all-``NaN`` row; a ``UserWarning`` naming the count is emitted when ``verbose=True``.
+ * This is a no-positional-split mean only; positional splits remain the job of :class:`CPP`.
+
+ See Also
+ --------
+ * :meth:`SequenceFeature.feature_matrix` for the positional Part-Split-Scale feature matrix.
+ * :meth:`SequenceFeature.get_df_parts` for the part-slicing used to build the span.
+
+ Examples
+ --------
+ .. include:: examples/sf_scale_composition.rst
+ """
+ # Load defaults
+ if df_scales is None:
+ df_scales = ut.load_default_scales()
+ # Check input
+ ut.check_df_seq(df_seq=df_seq)
+ check_df_scales(df_scales=df_scales)
+ ut.check_bool(name="return_df", val=return_df)
+ list_parts = ut.check_list_parts(list_parts=list_parts, return_default=False, accept_none=True)
+ # Resolve parts: None -> whole TMD-JMD span (jmd_n + tmd + jmd_c, the "tmd_jmd" part)
+ list_parts_used = ["tmd_jmd"] if list_parts is None else list_parts
+ # Build the sequence parts and the scale-average matrix
+ df_parts = self.get_df_parts(df_seq=df_seq, list_parts=list_parts_used)
+ X, n_kept = get_scale_composition_(df_parts=df_parts, df_scales=df_scales)
+ # Warn about rows that had no scored residue (all-NaN output)
+ n_empty = int((n_kept == 0).sum())
+ if n_empty > 0 and self.verbose:
+ warnings.warn(f"{n_empty} sequence(s) have no scored residue in the selected parts "
+ f"(empty or all non-canonical); their scale-average row is all-NaN.")
+ if return_df:
+ return pd.DataFrame(X, index=df_parts.index, columns=list(df_scales.columns))
+ return X
+
def prune_by_variance(self,
df_feat: pd.DataFrame,
df_parts: Optional[pd.DataFrame] = None,
diff --git a/docs/source/index/release_notes.rst b/docs/source/index/release_notes.rst
index c715505d..f449801f 100644
--- a/docs/source/index/release_notes.rst
+++ b/docs/source/index/release_notes.rst
@@ -58,6 +58,15 @@ Added
- **SequenceFeature.get_labels_quantile / get_labels_tiered**: Discretize a continuous
target into binary ``labels`` — a single quantile cut, or a fixed positive set swept
against stepwise-lowered negative cuts (each tier row-matched).
+- **SequenceFeature.scale_composition**: Scale-composition baseline featurizer that turns
+ sequences + scales into a ``(n_seq, n_scales)`` matrix by averaging each scale over a
+ sequence span (``list_parts=None`` → whole ``jmd_n`` + ``tmd`` + ``jmd_c``), dropping
+ missing / non-canonical residues — the sequence's mean profile in scale-space (the
+ scale-based analogue of amino-acid composition). The no-positional-split baseline to
+ compare against ``feature_matrix`` / CPP; optional ``return_df=True`` for a labeled frame.
+- **SequenceFeature.get_df_parts_from_windows**: Assemble a reference ``df_parts`` from
+ per-part window sets (e.g. ``AAWindowSampler.sample_synthetic`` output).
+- **SequenceFeature.get_seq_kws**: Return one protein's ``{jmd_n_seq, tmd_seq, jmd_c_seq}``
- :meth:`~aaanalysis.SequenceFeature.get_df_parts_from_windows`: Assemble a reference ``df_parts`` from
per-part window sets (e.g. :meth:`~aaanalysis.AAWindowSampler.sample_synthetic` output).
- :meth:`~aaanalysis.SequenceFeature.get_seq_kws`: Return one protein's ``{jmd_n_seq, tmd_seq, jmd_c_seq}``
diff --git a/examples/feature_engineering/sf_scale_composition.ipynb b/examples/feature_engineering/sf_scale_composition.ipynb
new file mode 100644
index 00000000..da2749a5
--- /dev/null
+++ b/examples/feature_engineering/sf_scale_composition.ipynb
@@ -0,0 +1,7299 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "9acb0004",
+ "metadata": {},
+ "source": [
+ "``SequenceFeature().scale_composition()`` builds a **baseline feature set** for a prediction model. For each sequence it averages every scale over the residues of a span, giving the ``(n_seq, n_scales)`` matrix ``X`` — the sequence's mean profile in scale-space (the scale-based analogue of amino-acid composition), with no positional information. Its purpose is **comparison**: fit the same classifier on this ``X`` and on a :class:`CPP` ``feature_matrix``, and compare the scores to see how much CPP's positional Part-Split-Scale features add over a plain scale average. Here we load the ``DOM_GSEC`` example dataset and the bundled scales (see [Breimann25]_):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "8e60701a",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-07-03T19:32:57.502737Z",
+ "iopub.status.busy": "2026-07-03T19:32:57.502420Z",
+ "iopub.status.idle": "2026-07-03T19:32:58.952045Z",
+ "shell.execute_reply": "2026-07-03T19:32:58.951746Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import aaanalysis as aa\n",
+ "aa.options[\"verbose\"] = False\n",
+ "df_seq = aa.load_dataset(name=\"DOM_GSEC\")\n",
+ "df_scales = aa.load_scales()\n",
+ "sf = aa.SequenceFeature()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a4a1a500",
+ "metadata": {},
+ "source": [
+ "By default (``list_parts=None``) the whole TMD-JMD span (``jmd_n`` + ``tmd`` + ``jmd_c``) is averaged. Residues that are not in the scale index (gaps ``'-'`` and other non-canonical symbols) are dropped before averaging:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "3bbd88cf",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-07-03T19:32:58.953279Z",
+ "iopub.status.busy": "2026-07-03T19:32:58.953208Z",
+ "iopub.status.idle": "2026-07-03T19:32:58.970492Z",
+ "shell.execute_reply": "2026-07-03T19:32:58.970301Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "n samples: 126\n",
+ "n scales: 586\n",
+ "Shape of X: (126, 586)\n"
+ ]
+ }
+ ],
+ "source": [
+ "X = sf.scale_composition(df_seq=df_seq, df_scales=df_scales)\n",
+ "print(f\"n samples: {X.shape[0]}\")\n",
+ "print(f\"n scales: {X.shape[1]}\")\n",
+ "print(f\"Shape of X: {X.shape}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3f9c4c67",
+ "metadata": {},
+ "source": [
+ "With ``return_df=True`` the matrix is returned as a labeled ``pd.DataFrame`` (rows indexed by protein entry, one column per scale):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "2deceb47",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-07-03T19:32:58.971539Z",
+ "iopub.status.busy": "2026-07-03T19:32:58.971465Z",
+ "iopub.status.idle": "2026-07-03T19:32:59.068314Z",
+ "shell.execute_reply": "2026-07-03T19:32:59.068111Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DataFrame shape: (126, 586)\n"
+ ]
+ },
+ {
+ "data": {
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+ " PRAM900101 | \n",
+ " PRAM900102 | \n",
+ " PRAM900103 | \n",
+ " PRAM900104 | \n",
+ " PTIO830101 | \n",
+ " PTIO830102 | \n",
+ " QIAN880101 | \n",
+ " QIAN880102 | \n",
+ " QIAN880103 | \n",
+ " QIAN880104 | \n",
+ " QIAN880105 | \n",
+ " QIAN880106 | \n",
+ " QIAN880107 | \n",
+ " QIAN880108 | \n",
+ " QIAN880109 | \n",
+ " QIAN880110 | \n",
+ " QIAN880111 | \n",
+ " QIAN880112 | \n",
+ " QIAN880113 | \n",
+ " QIAN880114 | \n",
+ " QIAN880115 | \n",
+ " QIAN880116 | \n",
+ " QIAN880117 | \n",
+ " QIAN880118 | \n",
+ " QIAN880119 | \n",
+ " QIAN880120 | \n",
+ " QIAN880121 | \n",
+ " QIAN880122 | \n",
+ " QIAN880123 | \n",
+ " QIAN880124 | \n",
+ " QIAN880125 | \n",
+ " QIAN880126 | \n",
+ " QIAN880127 | \n",
+ " QIAN880128 | \n",
+ " QIAN880129 | \n",
+ " QIAN880130 | \n",
+ " QIAN880131 | \n",
+ " QIAN880132 | \n",
+ " QIAN880133 | \n",
+ " QIAN880134 | \n",
+ " QIAN880135 | \n",
+ " QIAN880136 | \n",
+ " QIAN880137 | \n",
+ " QIAN880138 | \n",
+ " QIAN880139 | \n",
+ " RACS770101 | \n",
+ " RACS770102 | \n",
+ " RACS770103 | \n",
+ " RACS820101 | \n",
+ " RACS820102 | \n",
+ " RACS820103 | \n",
+ " RACS820104 | \n",
+ " RACS820105 | \n",
+ " RACS820106 | \n",
+ " RACS820107 | \n",
+ " RACS820108 | \n",
+ " RACS820109 | \n",
+ " RACS820110 | \n",
+ " RACS820111 | \n",
+ " RACS820112 | \n",
+ " RACS820113 | \n",
+ " RACS820114 | \n",
+ " RADA880101 | \n",
+ " RADA880102 | \n",
+ " RADA880103 | \n",
+ " RADA880104 | \n",
+ " RADA880105 | \n",
+ " RADA880106 | \n",
+ " RADA880107 | \n",
+ " RADA880108 | \n",
+ " RICJ880101 | \n",
+ " RICJ880102 | \n",
+ " RICJ880103 | \n",
+ " RICJ880104 | \n",
+ " RICJ880105 | \n",
+ " RICJ880106 | \n",
+ " RICJ880107 | \n",
+ " RICJ880108 | \n",
+ " RICJ880109 | \n",
+ " RICJ880110 | \n",
+ " RICJ880111 | \n",
+ " RICJ880112 | \n",
+ " RICJ880113 | \n",
+ " RICJ880114 | \n",
+ " RICJ880115 | \n",
+ " RICJ880116 | \n",
+ " RICJ880117 | \n",
+ " ROBB760101 | \n",
+ " ROBB760102 | \n",
+ " ROBB760103 | \n",
+ " ROBB760104 | \n",
+ " ROBB760105 | \n",
+ " ROBB760106 | \n",
+ " ROBB760107 | \n",
+ " ROBB760108 | \n",
+ " ROBB760109 | \n",
+ " ROBB760110 | \n",
+ " ROBB760111 | \n",
+ " ROBB760112 | \n",
+ " ROBB760113 | \n",
+ " ROBB790101 | \n",
+ " ROSG850101 | \n",
+ " ROSG850102 | \n",
+ " ROSM880101 | \n",
+ " ROSM880102 | \n",
+ " ROSM880103 | \n",
+ " SIMZ760101 | \n",
+ " SNEP660101 | \n",
+ " SNEP660102 | \n",
+ " SNEP660103 | \n",
+ " SNEP660104 | \n",
+ " SUEM840101 | \n",
+ " SUEM840102 | \n",
+ " SWER830101 | \n",
+ " TANS770101 | \n",
+ " TANS770102 | \n",
+ " TANS770103 | \n",
+ " TANS770104 | \n",
+ " TANS770105 | \n",
+ " TANS770106 | \n",
+ " TANS770107 | \n",
+ " TANS770108 | \n",
+ " TANS770109 | \n",
+ " TANS770110 | \n",
+ " VASM830101 | \n",
+ " VASM830102 | \n",
+ " VASM830103 | \n",
+ " VELV850101 | \n",
+ " VENT840101 | \n",
+ " VHEG790101 | \n",
+ " WARP780101 | \n",
+ " WEBA780101 | \n",
+ " WERD780101 | \n",
+ " WERD780102 | \n",
+ " WERD780103 | \n",
+ " WERD780104 | \n",
+ " WOEC730101 | \n",
+ " WOLR810101 | \n",
+ " WOLS870101 | \n",
+ " WOLS870102 | \n",
+ " WOLS870103 | \n",
+ " YUTK870101 | \n",
+ " YUTK870102 | \n",
+ " YUTK870103 | \n",
+ " YUTK870104 | \n",
+ " ZASB820101 | \n",
+ " ZIMJ680101 | \n",
+ " ZIMJ680102 | \n",
+ " ZIMJ680103 | \n",
+ " ZIMJ680104 | \n",
+ " ZIMJ680105 | \n",
+ " AURR980101 | \n",
+ " AURR980102 | \n",
+ " AURR980103 | \n",
+ " AURR980104 | \n",
+ " AURR980105 | \n",
+ " AURR980106 | \n",
+ " AURR980107 | \n",
+ " AURR980108 | \n",
+ " AURR980109 | \n",
+ " AURR980110 | \n",
+ " AURR980111 | \n",
+ " AURR980112 | \n",
+ " AURR980113 | \n",
+ " AURR980114 | \n",
+ " AURR980115 | \n",
+ " AURR980116 | \n",
+ " AURR980117 | \n",
+ " AURR980118 | \n",
+ " AURR980119 | \n",
+ " AURR980120 | \n",
+ " ONEK900101 | \n",
+ " ONEK900102 | \n",
+ " VINM940101 | \n",
+ " VINM940102 | \n",
+ " VINM940103 | \n",
+ " VINM940104 | \n",
+ " MUNV940101 | \n",
+ " MUNV940102 | \n",
+ " MUNV940103 | \n",
+ " MUNV940104 | \n",
+ " MUNV940105 | \n",
+ " WIMW960101 | \n",
+ " KIMC930101 | \n",
+ " MONM990101 | \n",
+ " BLAM930101 | \n",
+ " PARS000101 | \n",
+ " PARS000102 | \n",
+ " KUMS000101 | \n",
+ " KUMS000102 | \n",
+ " KUMS000103 | \n",
+ " KUMS000104 | \n",
+ " TAKK010101 | \n",
+ " FODM020101 | \n",
+ " NADH010101 | \n",
+ " NADH010102 | \n",
+ " NADH010103 | \n",
+ " NADH010104 | \n",
+ " NADH010105 | \n",
+ " NADH010106 | \n",
+ " NADH010107 | \n",
+ " MONM990201 | \n",
+ " KOEP990101 | \n",
+ " KOEP990102 | \n",
+ " CEDJ970101 | \n",
+ " CEDJ970102 | \n",
+ " CEDJ970103 | \n",
+ " CEDJ970104 | \n",
+ " CEDJ970105 | \n",
+ " FUKS010101 | \n",
+ " FUKS010102 | \n",
+ " FUKS010103 | \n",
+ " FUKS010104 | \n",
+ " FUKS010105 | \n",
+ " FUKS010106 | \n",
+ " FUKS010107 | \n",
+ " FUKS010108 | \n",
+ " FUKS010109 | \n",
+ " FUKS010110 | \n",
+ " FUKS010111 | \n",
+ " FUKS010112 | \n",
+ " MITS020101 | \n",
+ " TSAJ990101 | \n",
+ " TSAJ990102 | \n",
+ " COSI940101 | \n",
+ " PONP930101 | \n",
+ " WILM950101 | \n",
+ " WILM950102 | \n",
+ " WILM950103 | \n",
+ " WILM950104 | \n",
+ " KUHL950101 | \n",
+ " GUOD860101 | \n",
+ " JURD980101 | \n",
+ " BASU050101 | \n",
+ " BASU050102 | \n",
+ " BASU050103 | \n",
+ " SUYM030101 | \n",
+ " PUNT030101 | \n",
+ " PUNT030102 | \n",
+ " GEOR030101 | \n",
+ " GEOR030102 | \n",
+ " GEOR030103 | \n",
+ " GEOR030104 | \n",
+ " GEOR030105 | \n",
+ " GEOR030106 | \n",
+ " GEOR030107 | \n",
+ " GEOR030108 | \n",
+ " GEOR030109 | \n",
+ " ZHOH040101 | \n",
+ " ZHOH040102 | \n",
+ " ZHOH040103 | \n",
+ " BAEK050101 | \n",
+ " HARY940101 | \n",
+ " PONJ960101 | \n",
+ " DIGM050101 | \n",
+ " WOLR790101 | \n",
+ " OLSK800101 | \n",
+ " KIDA850101 | \n",
+ " GUYH850102 | \n",
+ " GUYH850104 | \n",
+ " GUYH850105 | \n",
+ " JACR890101 | \n",
+ " COWR900101 | \n",
+ " BLAS910101 | \n",
+ " CASG920101 | \n",
+ " CORJ870101 | \n",
+ " CORJ870102 | \n",
+ " CORJ870103 | \n",
+ " CORJ870104 | \n",
+ " CORJ870105 | \n",
+ " CORJ870106 | \n",
+ " CORJ870107 | \n",
+ " CORJ870108 | \n",
+ " MIYS990101 | \n",
+ " MIYS990102 | \n",
+ " MIYS990103 | \n",
+ " MIYS990104 | \n",
+ " MIYS990105 | \n",
+ " ENGD860101 | \n",
+ " FASG890101 | \n",
+ " KARS160101 | \n",
+ " KARS160102 | \n",
+ " KARS160103 | \n",
+ " KARS160104 | \n",
+ " KARS160105 | \n",
+ " KARS160106 | \n",
+ " KARS160107 | \n",
+ " KARS160108 | \n",
+ " KARS160109 | \n",
+ " KARS160110 | \n",
+ " KARS160111 | \n",
+ " KARS160112 | \n",
+ " KARS160113 | \n",
+ " KARS160114 | \n",
+ " KARS160115 | \n",
+ " KARS160116 | \n",
+ " KARS160117 | \n",
+ " KARS160118 | \n",
+ " KARS160119 | \n",
+ " KARS160120 | \n",
+ " KARS160121 | \n",
+ " KARS160122 | \n",
+ " LINS030101 | \n",
+ " LINS030102 | \n",
+ " LINS030103 | \n",
+ " LINS030104 | \n",
+ " LINS030105 | \n",
+ " LINS030106 | \n",
+ " LINS030107 | \n",
+ " LINS030108 | \n",
+ " LINS030109 | \n",
+ " LINS030110 | \n",
+ " LINS030111 | \n",
+ " LINS030112 | \n",
+ " LINS030113 | \n",
+ " LINS030114 | \n",
+ " LINS030115 | \n",
+ " LINS030116 | \n",
+ " LINS030117 | \n",
+ " LINS030118 | \n",
+ " LINS030119 | \n",
+ " LINS030120 | \n",
+ " LINS030121 | \n",
+ " KOEH090101 | \n",
+ " KOEH090102 | \n",
+ " KOEH090103 | \n",
+ " KOEH090104 | \n",
+ " KOEH090105 | \n",
+ " KOEH090106 | \n",
+ " KOEH090107 | \n",
+ " KOEH090108 | \n",
+ " KOEH090109 | \n",
+ " KOEH090110 | \n",
+ " KOEH090111 | \n",
+ " KOEH090112 | \n",
+ "
\n",
+ " \n",
+ " | entry | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
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+ " | \n",
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+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
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+ " | \n",
+ " | \n",
+ " | \n",
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+ " | \n",
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+ " | \n",
+ " | \n",
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+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
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+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
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+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
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+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
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+ " | \n",
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+ " | \n",
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+ " | \n",
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+ " | \n",
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+ " | \n",
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+ " | \n",
+ " | \n",
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+ " | \n",
+ " | \n",
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+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
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+ " | \n",
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+ " | \n",
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+ " | \n",
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+ " | \n",
+ " | \n",
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+ " | \n",
+ " | \n",
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+ " | \n",
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+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
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+ " | \n",
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+ " | \n",
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+ " | \n",
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+ " | \n",
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+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
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+ " | \n",
+ " | \n",
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+ " | \n",
+ " | \n",
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+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
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+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
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+ " | \n",
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+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
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+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
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+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
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+ " 0.573744 | \n",
+ " 0.105791 | \n",
+ " 0.472814 | \n",
+ " 0.473116 | \n",
+ " 0.258535 | \n",
+ " 0.492884 | \n",
+ " 0.483535 | \n",
+ " 0.428767 | \n",
+ " 0.319395 | \n",
+ " 0.463163 | \n",
+ " 0.312977 | \n",
+ " 0.492953 | \n",
+ " 0.580605 | \n",
+ " 0.571372 | \n",
+ " 0.473140 | \n",
+ " 0.538326 | \n",
+ " 0.473326 | \n",
+ " 0.479256 | \n",
+ " 0.376884 | \n",
+ " 0.442302 | \n",
+ " 0.355302 | \n",
+ " 0.528070 | \n",
+ " 0.615581 | \n",
+ " 0.645465 | \n",
+ " 0.546023 | \n",
+ " 0.412442 | \n",
+ " 0.589000 | \n",
+ " 0.157279 | \n",
+ " 0.379837 | \n",
+ " 0.502744 | \n",
+ " 0.450256 | \n",
+ " 0.480860 | \n",
+ " 0.466512 | \n",
+ " 0.447442 | \n",
+ " 0.618372 | \n",
+ " 0.762186 | \n",
+ " 0.501814 | \n",
+ " 0.432326 | \n",
+ " 0.541744 | \n",
+ " 0.335163 | \n",
+ " 0.320953 | \n",
+ " 0.800860 | \n",
+ " 0.663116 | \n",
+ " 0.607047 | \n",
+ " 0.480977 | \n",
+ " 0.517070 | \n",
+ " 0.439953 | \n",
+ " 0.545814 | \n",
+ " 0.553837 | \n",
+ " 0.576279 | \n",
+ " 0.546093 | \n",
+ " 0.573395 | \n",
+ " 0.429581 | \n",
+ " 0.518860 | \n",
+ " 0.512000 | \n",
+ " 0.417256 | \n",
+ " 0.406093 | \n",
+ " 0.401209 | \n",
+ " 0.279093 | \n",
+ " 0.475605 | \n",
+ " 0.369767 | \n",
+ " 0.317698 | \n",
+ " 0.309977 | \n",
+ " 0.365674 | \n",
+ " 0.380349 | \n",
+ " 0.338349 | \n",
+ " 0.405395 | \n",
+ " 0.671465 | \n",
+ " 0.616721 | \n",
+ " 0.387930 | \n",
+ " 0.593651 | \n",
+ " 0.469698 | \n",
+ " 0.423442 | \n",
+ " 0.450070 | \n",
+ " 0.566698 | \n",
+ " 0.413581 | \n",
+ " 0.362233 | \n",
+ " 0.437581 | \n",
+ " 0.635698 | \n",
+ " 0.876698 | \n",
+ " 0.579279 | \n",
+ " 0.299535 | \n",
+ " 0.501326 | \n",
+ " 0.471326 | \n",
+ " 0.255953 | \n",
+ " 0.299581 | \n",
+ " 0.254744 | \n",
+ " 0.268744 | \n",
+ " 0.359023 | \n",
+ " 0.369442 | \n",
+ " 0.308698 | \n",
+ " 0.375907 | \n",
+ " 0.357860 | \n",
+ " 0.286209 | \n",
+ " 0.481674 | \n",
+ " 0.480140 | \n",
+ " 0.348442 | \n",
+ " 0.313488 | \n",
+ " 0.286186 | \n",
+ " 0.271930 | \n",
+ " 0.351953 | \n",
+ " 0.394163 | \n",
+ " 0.332395 | \n",
+ " 0.398302 | \n",
+ " 0.318721 | \n",
+ " 0.314395 | \n",
+ " 0.429465 | \n",
+ " 0.511047 | \n",
+ " 0.344442 | \n",
+ " 0.479256 | \n",
+ " 0.376884 | \n",
+ " 0.375558 | \n",
+ " 0.348558 | \n",
+ " 0.501419 | \n",
+ " 0.357140 | \n",
+ "
\n",
+ " \n",
+ "
\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_scale_composition = sf.scale_composition(df_seq=df_seq, df_scales=df_scales, return_df=True)\n",
+ "aa.display_df(df_scale_composition, n_rows=10, show_shape=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "befac034",
+ "metadata": {},
+ "source": [
+ "Use ``list_parts`` to average only over selected sequence parts (concatenated per sequence). For example, restrict the baseline to the TMD:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "ff9e0690",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-07-03T19:32:59.069547Z",
+ "iopub.status.busy": "2026-07-03T19:32:59.069490Z",
+ "iopub.status.idle": "2026-07-03T19:32:59.086766Z",
+ "shell.execute_reply": "2026-07-03T19:32:59.086591Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Shape of X (TMD only): (126, 586)\n"
+ ]
+ }
+ ],
+ "source": [
+ "X_tmd = sf.scale_composition(df_seq=df_seq, df_scales=df_scales, list_parts=\"tmd\")\n",
+ "print(f\"Shape of X (TMD only): {X_tmd.shape}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a573e32c",
+ "metadata": {},
+ "source": [
+ "**Further parameters.** ``df_scales`` defaults to the bundled scales (or ``options['df_scales']``) when omitted, so ``sf.scale_composition(df_seq=df_seq)`` returns the scale-average matrix over the default scale set."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "3c84c7e2",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-07-03T19:32:59.087779Z",
+ "iopub.status.busy": "2026-07-03T19:32:59.087725Z",
+ "iopub.status.idle": "2026-07-03T19:32:59.105005Z",
+ "shell.execute_reply": "2026-07-03T19:32:59.104814Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Shape of X (default scales): (126, 586)\n"
+ ]
+ }
+ ],
+ "source": [
+ "X_default = sf.scale_composition(df_seq=df_seq)\n",
+ "print(f\"Shape of X (default scales): {X_default.shape}\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.13.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tests/unit/sequence_feature_tests/test_sf_scale_composition.py b/tests/unit/sequence_feature_tests/test_sf_scale_composition.py
new file mode 100644
index 00000000..5fab2a5f
--- /dev/null
+++ b/tests/unit/sequence_feature_tests/test_sf_scale_composition.py
@@ -0,0 +1,268 @@
+"""This is a script to test the SequenceFeature().scale_composition() method."""
+from hypothesis import given, settings
+import hypothesis.strategies as st
+import warnings
+import pytest
+import numpy as np
+import pandas as pd
+import aaanalysis as aa
+
+# Set default deadline from 200 to 400
+settings.register_profile("ci", deadline=None)
+settings.load_profile("ci")
+
+aa.options["verbose"] = False
+
+LIST_PARTS = ['tmd', 'tmd_e', 'tmd_n', 'tmd_c', 'jmd_n', 'jmd_c', 'ext_c', 'ext_n',
+ 'tmd_jmd', 'jmd_n_tmd_n', 'tmd_c_jmd_c', 'ext_n_tmd_n', 'tmd_c_ext_c']
+
+
+def _get_input(n_samples=10):
+ """Load a small DOM_GSEC fixture and the default scales."""
+ aa.options["verbose"] = False
+ df_seq = aa.load_dataset(name="DOM_GSEC", n=n_samples)
+ df_scales = aa.load_scales()
+ return df_seq, df_scales
+
+
+def _manual_scale_X(df_parts, df_scales):
+ """Cell-27 comprehension over the concatenated jmd_n + tmd + jmd_c span."""
+ seqs = (df_parts["jmd_n"] + df_parts["tmd"] + df_parts["jmd_c"]).to_list()
+ return np.array([df_scales.loc[[a for a in s if a in df_scales.index]].mean(axis=0).values
+ for s in seqs])
+
+
+class TestScaleMean:
+ """Test class for the 'scale_composition' method of the SequenceFeature class."""
+
+ # Positive tests
+ def test_valid_df_seq(self):
+ """Positive test for valid 'df_seq' input (several sizes)."""
+ for n in [3, 5, 10]:
+ df_seq, df_scales = _get_input(n_samples=n)
+ sf = aa.SequenceFeature()
+ X = sf.scale_composition(df_seq=df_seq, df_scales=df_scales)
+ assert isinstance(X, np.ndarray)
+ assert X.shape == (len(df_seq), df_scales.shape[1])
+
+ def test_valid_df_scales(self):
+ """Positive test for valid 'df_scales' input, incl. a column subset and the default."""
+ df_seq, df_scales = _get_input()
+ sf = aa.SequenceFeature()
+ # Full scales
+ X = sf.scale_composition(df_seq=df_seq, df_scales=df_scales)
+ assert X.shape[1] == df_scales.shape[1]
+ # Subset of scales
+ sub = df_scales.iloc[:, :5]
+ X_sub = sf.scale_composition(df_seq=df_seq, df_scales=sub)
+ assert X_sub.shape == (len(df_seq), 5)
+ # Default (df_scales=None) loads the bundled scales
+ X_def = sf.scale_composition(df_seq=df_seq)
+ assert isinstance(X_def, np.ndarray)
+ assert X_def.shape[0] == len(df_seq)
+
+ @settings(max_examples=6, deadline=None)
+ @given(list_parts=st.lists(st.sampled_from(LIST_PARTS), min_size=1, max_size=3, unique=True))
+ def test_valid_list_parts(self, list_parts):
+ """Positive test for valid 'list_parts' inputs (single, str, multiple)."""
+ df_seq, df_scales = _get_input(n_samples=8)
+ sf = aa.SequenceFeature()
+ X = sf.scale_composition(df_seq=df_seq, df_scales=df_scales, list_parts=list_parts)
+ assert isinstance(X, np.ndarray)
+ assert X.shape == (len(df_seq), df_scales.shape[1])
+ # A single part may also be passed as a bare string
+ X_str = sf.scale_composition(df_seq=df_seq, df_scales=df_scales, list_parts=list_parts[0])
+ assert X_str.shape == (len(df_seq), df_scales.shape[1])
+
+ def test_valid_list_parts_none_is_whole_span(self):
+ """list_parts=None equals the explicit jmd_n + tmd + jmd_c part list."""
+ df_seq, df_scales = _get_input()
+ sf = aa.SequenceFeature()
+ X_none = sf.scale_composition(df_seq=df_seq, df_scales=df_scales, list_parts=None)
+ X_explicit = sf.scale_composition(df_seq=df_seq, df_scales=df_scales,
+ list_parts=["jmd_n", "tmd", "jmd_c"])
+ assert np.allclose(X_none, X_explicit, equal_nan=True)
+
+ def test_valid_return_df(self):
+ """Positive test for valid 'return_df' input."""
+ df_seq, df_scales = _get_input()
+ sf = aa.SequenceFeature()
+ X = sf.scale_composition(df_seq=df_seq, df_scales=df_scales, return_df=False)
+ assert isinstance(X, np.ndarray)
+ df = sf.scale_composition(df_seq=df_seq, df_scales=df_scales, return_df=True)
+ assert isinstance(df, pd.DataFrame)
+ assert list(df.columns) == list(df_scales.columns)
+ assert df.shape == (len(df_seq), df_scales.shape[1])
+ # Values agree between the array and DataFrame forms
+ assert np.allclose(np.asarray(df.values, dtype=float), X, equal_nan=True)
+
+ # Negative tests
+ def test_invalid_df_seq(self):
+ """Negative test for invalid 'df_seq' input."""
+ df_seq, df_scales = _get_input()
+ sf = aa.SequenceFeature()
+ with pytest.raises(ValueError):
+ sf.scale_composition(df_seq=None, df_scales=df_scales)
+ with pytest.raises(ValueError):
+ sf.scale_composition(df_seq="invalid_input", df_scales=df_scales)
+ with pytest.raises(ValueError):
+ sf.scale_composition(df_seq=pd.DataFrame({"wrong": ["A"]}), df_scales=df_scales)
+
+ def test_invalid_df_scales(self):
+ """Negative test for invalid 'df_scales' input."""
+ df_seq, _ = _get_input()
+ sf = aa.SequenceFeature()
+ with pytest.raises(ValueError):
+ sf.scale_composition(df_seq=df_seq, df_scales="invalid_input")
+
+ def test_invalid_list_parts(self):
+ """Negative test for invalid 'list_parts' input."""
+ df_seq, df_scales = _get_input()
+ sf = aa.SequenceFeature()
+ with pytest.raises(ValueError):
+ sf.scale_composition(df_seq=df_seq, df_scales=df_scales, list_parts=["not_a_part"])
+ with pytest.raises(ValueError):
+ sf.scale_composition(df_seq=df_seq, df_scales=df_scales, list_parts=[])
+
+ def test_invalid_return_df(self):
+ """Negative test for invalid 'return_df' input."""
+ df_seq, df_scales = _get_input()
+ sf = aa.SequenceFeature()
+ with pytest.raises(ValueError):
+ sf.scale_composition(df_seq=df_seq, df_scales=df_scales, return_df="not_a_boolean")
+
+
+class TestScaleMeanComplex:
+ """Complex positive / negative tests for the 'scale_composition' method."""
+
+ def test_valid_combinations(self):
+ """Test with valid combinations of parameters."""
+ df_seq, df_scales = _get_input(n_samples=8)
+ sf = aa.SequenceFeature()
+ for list_parts in [None, "tmd", ["jmd_n", "jmd_c"], ["tmd_jmd"]]:
+ for return_df in [True, False]:
+ out = sf.scale_composition(df_seq=df_seq, df_scales=df_scales,
+ list_parts=list_parts, return_df=return_df)
+ if return_df:
+ assert isinstance(out, pd.DataFrame)
+ else:
+ assert isinstance(out, np.ndarray)
+ assert np.asarray(out).shape == (len(df_seq), df_scales.shape[1])
+
+ def test_invalid_combinations(self):
+ """Test with invalid combinations of parameters."""
+ df_seq, df_scales = _get_input()
+ sf = aa.SequenceFeature()
+ with pytest.raises(ValueError):
+ sf.scale_composition(df_seq=None, df_scales="invalid")
+ with pytest.raises(ValueError):
+ sf.scale_composition(df_seq=df_seq, df_scales=df_scales, list_parts="bad_part", return_df="bad")
+
+
+class TestScaleMeanGoldenValues:
+ """Hand-computed values, the notebook-comprehension match, and edge cases."""
+
+ def test_golden_matches_notebook_comprehension(self):
+ """Output equals the gamma-secretase notebook (cell 27) comprehension on DOM_GSEC."""
+ aa.options["verbose"] = False
+ df_seq = aa.load_dataset(name="DOM_GSEC")
+ df_scales = aa.load_scales()
+ sf = aa.SequenceFeature()
+ df_parts = sf.get_df_parts(df_seq=df_seq, list_parts=["jmd_n", "tmd", "jmd_c"])
+ X_manual = _manual_scale_X(df_parts=df_parts, df_scales=df_scales)
+ X = sf.scale_composition(df_seq=df_seq, df_scales=df_scales)
+ assert X.shape == X_manual.shape
+ assert np.allclose(X, X_manual, equal_nan=True)
+
+ def test_golden_hand_computed_mean(self):
+ """'AC' (jmd empty) over a single scale -> mean(S1[A]=1, S1[C]=2) == 1.5 exactly."""
+ order = "ACDEFGHIKLMNPQRSTVWY"
+ df_scales = pd.DataFrame({"S1": {a: float(i + 1) for i, a in enumerate(order)}})
+ # Part-based df_seq with jmd_n/jmd_c so the span is exactly the TMD 'AC'
+ df_seq = pd.DataFrame({"entry": ["E1"], "jmd_n": [""], "tmd": ["AC"], "jmd_c": [""]})
+ sf = aa.SequenceFeature()
+ X = sf.scale_composition(df_seq=df_seq, df_scales=df_scales)
+ assert X.shape == (1, 1)
+ assert float(X[0, 0]) == 1.5
+
+ def test_golden_frequency_weighted(self):
+ """Residues count by frequency: 'AAC' -> mean(1, 1, 2) == 4/3."""
+ order = "ACDEFGHIKLMNPQRSTVWY"
+ df_scales = pd.DataFrame({"S1": {a: float(i + 1) for i, a in enumerate(order)}})
+ df_seq = pd.DataFrame({"entry": ["E1"], "jmd_n": [""], "tmd": ["AAC"], "jmd_c": [""]})
+ sf = aa.SequenceFeature()
+ X = sf.scale_composition(df_seq=df_seq, df_scales=df_scales)
+ assert np.isclose(float(X[0, 0]), 4.0 / 3.0)
+
+ def test_non_canonical_residues_dropped(self):
+ """Non-canonical residues ('X') and gaps are ignored before averaging."""
+ order = "ACDEFGHIKLMNPQRSTVWY"
+ df_scales = pd.DataFrame({"S1": {a: float(i + 1) for i, a in enumerate(order)}})
+ # 'AXC' should equal 'AC' because 'X' is not in the scale index
+ df_seq = pd.DataFrame({"entry": ["E1", "E2"], "jmd_n": ["", ""],
+ "tmd": ["AXC", "AC"], "jmd_c": ["", ""]})
+ sf = aa.SequenceFeature()
+ X = sf.scale_composition(df_seq=df_seq, df_scales=df_scales)
+ assert float(X[0, 0]) == float(X[1, 0]) == 1.5
+
+ def test_multi_char_scale_label_never_matches_residue(self):
+ """A multi-character index label in df_scales must not be matched by a residue.
+
+ The byte lookup registers single-character labels only, mirroring the notebook's
+ per-character ``a in df_scales.index`` test, so a spurious ``'AC'`` scale row cannot
+ pull the span 'AC' toward its value.
+ """
+ order = "ACDEFGHIKLMNPQRSTVWY"
+ df_scales = pd.DataFrame({"S1": {a: float(i + 1) for i, a in enumerate(order)}})
+ df_scales.loc["AC"] = 99.0 # multi-character label that must be ignored
+ df_seq = pd.DataFrame({"entry": ["E1"], "jmd_n": [""], "tmd": ["AC"], "jmd_c": [""]})
+ sf = aa.SequenceFeature()
+ X = sf.scale_composition(df_seq=df_seq, df_scales=df_scales)
+ assert float(X[0, 0]) == 1.5 # mean(S1[A]=1, S1[C]=2), 'AC' label ignored
+
+ def test_non_latin1_residues_dropped(self):
+ """Codepoints above 255 (e.g. 'Ā') are treated as non-canonical and dropped, not crash."""
+ order = "ACDEFGHIKLMNPQRSTVWY"
+ df_scales = pd.DataFrame({"S1": {a: float(i + 1) for i, a in enumerate(order)}})
+ # 'AĀC' and lowercase 'AcC' both reduce to the canonical 'AC'
+ df_seq = pd.DataFrame({"entry": ["E1", "E2"], "jmd_n": ["", ""],
+ "tmd": ["AĀC", "AcC"], "jmd_c": ["", ""]})
+ sf = aa.SequenceFeature()
+ X = sf.scale_composition(df_seq=df_seq, df_scales=df_scales)
+ assert float(X[0, 0]) == float(X[1, 0]) == 1.5
+
+ def test_edge_all_non_canonical_is_nan(self):
+ """An all-non-canonical / empty span yields an all-NaN row and warns when verbose."""
+ order = "ACDEFGHIKLMNPQRSTVWY"
+ df_scales = pd.DataFrame({"S1": {a: float(i + 1) for i, a in enumerate(order)},
+ "S2": {a: float(i) for i, a in enumerate(order)}})
+ df_seq = pd.DataFrame({"entry": ["E1", "E2"], "jmd_n": ["", ""],
+ "tmd": ["XXXX", "AC"], "jmd_c": ["", ""]})
+ sf = aa.SequenceFeature(verbose=True)
+ with warnings.catch_warnings(record=True) as records:
+ warnings.simplefilter("always")
+ X = sf.scale_composition(df_seq=df_seq, df_scales=df_scales)
+ assert np.all(np.isnan(X[0])) # all-non-canonical -> NaN row
+ assert np.all(np.isfinite(X[1])) # canonical row stays finite
+ assert any("no scored residue" in str(r.message) for r in records)
+
+ def test_edge_all_non_canonical_silent_when_not_verbose(self):
+ """No warning is emitted for the NaN row when verbose=False."""
+ order = "ACDEFGHIKLMNPQRSTVWY"
+ df_scales = pd.DataFrame({"S1": {a: float(i + 1) for i, a in enumerate(order)}})
+ df_seq = pd.DataFrame({"entry": ["E1"], "jmd_n": [""], "tmd": ["XXXX"], "jmd_c": [""]})
+ sf = aa.SequenceFeature(verbose=False)
+ with warnings.catch_warnings(record=True) as records:
+ warnings.simplefilter("always")
+ X = sf.scale_composition(df_seq=df_seq, df_scales=df_scales)
+ assert np.all(np.isnan(X[0]))
+ assert not any("no scored residue" in str(r.message) for r in records)
+
+ def test_property_shape_invariant(self):
+ """Invariant: result is (n_samples, n_scales) for every input size."""
+ df_scales = aa.load_scales()
+ for n in [3, 7, 12]:
+ df_seq = aa.load_dataset(name="DOM_GSEC", n=n)
+ sf = aa.SequenceFeature()
+ X = np.asarray(sf.scale_composition(df_seq=df_seq, df_scales=df_scales))
+ assert X.shape == (len(df_seq), df_scales.shape[1])