before pytest

Author: MaaTi-project

.gitignore

@@ -1,4 +1,5 @@
 # Byte-compiled / optimized / DLL files
+.idea/*
 __pycache__/
 *.py[cod]
 *$py.class

tests/feature_importance/feature_importance.py eis_toolkit/exploratory_analyses/feature_importance.py

@@ -1,5 +1,6 @@
+from typing import Sequence
+
 import numpy as np
-import pandas
 import pandas as pd
 import sklearn.neural_network
 from sklearn.inspection import permutation_importance
@@ -8,41 +9,40 @@
 
 
 def evaluate_feature_importance(
-    clf: sklearn.neural_network or sklearn.linear_model,
+    classifier: sklearn.base.BaseEstimator,
     x_test: np.ndarray,
     y_test: np.ndarray,
-    feature_names: list[str],
+    feature_names: Sequence[str],
     number_of_repetition: int = 50,
     random_state: int = 0,
-) -> (pandas.DataFrame, dict):
+) -> (pd.DataFrame, dict):
     """
     Evaluate the feature importance of a sklearn classifier or linear model.
 
     Parameters:
-        clf (Any sklearn nn model or lm model): Trained classifier.
-        x_test (np.ndarray): Testing feature data (X data need to be normalized / standardized).
-        y_test (np.ndarray): Testing target data.
-        feature_names (list): Names of the feature columns.
-        number_of_repetition (int): Number of iteration used when calculate feature importance (default 50).
-        random_state (int): random state for repeatability of results (Default 0).
+        classifier: Trained classifier.
+        x_test: Testing feature data (X data need to be normalized / standardized).
+        y_test: Testing target data.
+        feature_names: Names of the feature columns.
+        number_of_repetition: Number of iteration used when calculate feature importance (default 50).
+        random_state: random state for repeatability of results (Default 0).
     Return:
-        feature_importance (pd.Dataframe): A dataframe composed by features name and Importance value
-        result (dict[object]): The resulted object with importance mean, importance std, and overall importance
-    Raise:
+        A dataframe composed by features name and Importance value
+        The resulted object with importance mean, importance std, and overall importance
+    Raises:
         InvalidDatasetException: When the dataset is None.
     """
 
     if x_test is None or y_test is None:
         raise InvalidDatasetException
 
     result = permutation_importance(
-        clf, x_test, y_test.ravel(), n_repeats=number_of_repetition, random_state=random_state
+        classifier, x_test, y_test.ravel(), n_repeats=number_of_repetition, random_state=random_state
     )
 
     feature_importance = pd.DataFrame({"Feature": feature_names, "Importance": result.importances_mean})
 
     feature_importance["Importance"] = feature_importance["Importance"] * 100
     feature_importance = feature_importance.sort_values(by="Importance", ascending=False)
-    # feature_importance['Importance'] = feature_importance['Importance'].apply(lambda x: '{:.6f}%'.format(x))
 
     return feature_importance, result