xgboost interpretable dimensions

sinr/text/evaluate.py

@@ -356,3 +356,39 @@ def clf_score(clf, X_test, y_test, scoring='accuracy', params={}):
     score = getattr(metrics, scoring+'_score')
     y_pred = clf.predict(X_test)
     return score(y_test, y_pred, **params)
+
+def clf_xgb_interpretability(sinr_vec, xgb, interpreter,topk_dim=10, topk=5, importance_type='gain'):
+    """Interpretability of main dimensions used by the xgboost classifier
+    :param sinr_vec: SINrVectors object from which datas were vectorized
+    :type sinr_vec: SINrVectors
+    :param xgb: fitted xgboost classifier
+    :type xgb: xgboost.XGBClassifier
+    :param interpreter: whether stereotypes or descriptors are requested
+    :type interpreter: str
+    :param topk_dim: Number of features requested among the main features used by the classifier (Default value = 10)
+    :type topk_dim: int
+    :param topk: `topk` value to consider on each dimension (Default value = 5)
+    :type topk: int
+    :param importance_type: ‘weight’: the number of times a feature is used to split the data across all trees,
+                            ‘gain’: the average gain across all splits the feature is used in,
+                            ‘cover’: the average coverage across all splits the feature is used in,
+                            ‘total_gain’: the total gain across all splits the feature is used in
+                            ‘total_cover’: the total coverage across all splits the feature is used in
+    :type importance_type: str
+    
+    :returns: Interpreters of dimensions, importance of dimensions
+    :rtype: list of set of object, list of tuple
+
+    """
+
+    features = xgb.get_booster().get_score(importance_type=importance_type)
+    features = dict(sorted(features.items(), key=lambda x: x[1], reverse=True))
+    features_index = [int(f[1:]) for f in list(features.keys())[:topk_dim]]
+    features_importance = list(features.items())[:topk_dim]
+
+    if interpreter=='descriptors':
+        dim = [sinr_vec.get_dimension_descriptors_idx(index, topk=topk) for index in features_index]
+    elif interpreter=='stereotypes':
+        dim = [sinr_vec.get_dimension_stereotypes_idx(index, topk=topk) for index in features_index]
+
+    return dim, features_importance