Drop evaluated and used feature names and importances attributes

- the evaluated features characterize the analysis process, not the
  model itself
- the used features characterize the model, but:
  - they are easily accessible through the `model_report_` attribute
  - they are not expected by the Scikit-learn ecosystem.

Author: popescu-v

doc/samples/samples_sklearn.rst

@@ -68,14 +68,6 @@ Samples
     # Train the classifier
     khc.fit(X_train, y_train)
 
-    # Show the feature importance info
-    print(f"Features evaluated: {khc.n_features_evaluated_}")
-    print(f"Features selected : {khc.n_features_used_}")
-    print("Top 3 used features")
-    for i, feature in enumerate(khc.feature_used_names_[:3]):
-        print(f"{feature} - Importance: {khc.feature_used_importances_[i][2]}")
-    print("---")
-
     # Predict the classes on the test dataset
     y_test_pred = khc.predict(X_test)
     print("Predicted classes (first 10):")
@@ -186,14 +178,6 @@ Samples
     # Train the classifier
     khc.fit(X_train, y_train)
 
-    # Show the feature importance info
-    print(f"Features evaluated: {khc.n_features_evaluated_}")
-    print(f"Features selected : {khc.n_features_used_}")
-    print("Top 3 used features")
-    for i, feature in enumerate(khc.feature_used_names_[:3]):
-        print(f"{feature} - Importance: {khc.feature_used_importances_[i][2]}")
-    print("---")
-
     # Predict the classes on the test dataset
     y_test_pred = khc.predict(X_test)
     print("Predicted classes (first 10):")
@@ -307,14 +291,6 @@ Samples
     khc = KhiopsClassifier(n_trees=0)
     khc.fit(X_train, y_train)
 
-    # Show the feature importance info
-    print(f"Features evaluated: {khc.n_features_evaluated_}")
-    print(f"Features selected : {khc.n_features_used_}")
-    print("Top 3 used features")
-    for i, feature in enumerate(khc.feature_used_names_[:3]):
-        print(f"{feature} - Importance: {khc.feature_used_importances_[i][2]}")
-    print("---")
-
     # Predict the class on the test dataset
     y_test_pred = khc.predict(X_test)
     print("Predicted classes (first 10):")
@@ -539,14 +515,6 @@ Samples
     # Train the regressor
     khr.fit(X_train, y_train)
 
-    # Show the feature importance info
-    print(f"Features evaluated: {khr.n_features_evaluated_}")
-    print(f"Features selected : {khr.n_features_used_}")
-    print("Top 3 used features")
-    for i, feature in enumerate(khr.feature_used_names_[:3]):
-        print(f"{feature} - Importance: {khr.feature_used_importances_[i][2]}")
-    print("---")
-
     # Predict the values on the test dataset
     y_test_pred = khr.predict(X_test)
     print("Predicted values for 'age' (first 10):")
@@ -667,13 +635,6 @@ Samples
     khe = KhiopsEncoder(n_features=10)
     khe.fit(X, y)
 
-    # Show the feature importance info
-    print(f"Features evaluated: {khe.n_features_evaluated_}")
-    print("Top 3 evaluated features")
-    for i, feature in enumerate(khe.feature_evaluated_names_[:3]):
-        print(f"{feature} - Level: {khe.feature_evaluated_importances_[i]}")
-    print("---")
-
     # Transform the train dataset
     print("Encoded feature names:")
     print(khe.feature_names_out_)

khiops/samples/samples_sklearn.ipynb

@@ -54,14 +54,6 @@
     "# Train the classifier\n",
     "khc.fit(X_train, y_train)\n",
     "\n",
-    "# Show the feature importance info\n",
-    "print(f\"Features evaluated: {khc.n_features_evaluated_}\")\n",
-    "print(f\"Features selected : {khc.n_features_used_}\")\n",
-    "print(\"Top 3 used features\")\n",
-    "for i, feature in enumerate(khc.feature_used_names_[:3]):\n",
-    "    print(f\"{feature} - Importance: {khc.feature_used_importances_[i][2]}\")\n",
-    "print(\"---\")\n",
-    "\n",
     "# Predict the classes on the test dataset\n",
     "y_test_pred = khc.predict(X_test)\n",
     "print(\"Predicted classes (first 10):\")\n",
@@ -198,14 +190,6 @@
     "# Train the classifier\n",
     "khc.fit(X_train, y_train)\n",
     "\n",
-    "# Show the feature importance info\n",
-    "print(f\"Features evaluated: {khc.n_features_evaluated_}\")\n",
-    "print(f\"Features selected : {khc.n_features_used_}\")\n",
-    "print(\"Top 3 used features\")\n",
-    "for i, feature in enumerate(khc.feature_used_names_[:3]):\n",
-    "    print(f\"{feature} - Importance: {khc.feature_used_importances_[i][2]}\")\n",
-    "print(\"---\")\n",
-    "\n",
     "# Predict the classes on the test dataset\n",
     "y_test_pred = khc.predict(X_test)\n",
     "print(\"Predicted classes (first 10):\")\n",
@@ -345,14 +329,6 @@
     "khc = KhiopsClassifier(n_trees=0)\n",
     "khc.fit(X_train, y_train)\n",
     "\n",
-    "# Show the feature importance info\n",
-    "print(f\"Features evaluated: {khc.n_features_evaluated_}\")\n",
-    "print(f\"Features selected : {khc.n_features_used_}\")\n",
-    "print(\"Top 3 used features\")\n",
-    "for i, feature in enumerate(khc.feature_used_names_[:3]):\n",
-    "    print(f\"{feature} - Importance: {khc.feature_used_importances_[i][2]}\")\n",
-    "print(\"---\")\n",
-    "\n",
     "# Predict the class on the test dataset\n",
     "y_test_pred = khc.predict(X_test)\n",
     "print(\"Predicted classes (first 10):\")\n",
@@ -629,14 +605,6 @@
     "# Train the regressor\n",
     "khr.fit(X_train, y_train)\n",
     "\n",
-    "# Show the feature importance info\n",
-    "print(f\"Features evaluated: {khr.n_features_evaluated_}\")\n",
-    "print(f\"Features selected : {khr.n_features_used_}\")\n",
-    "print(\"Top 3 used features\")\n",
-    "for i, feature in enumerate(khr.feature_used_names_[:3]):\n",
-    "    print(f\"{feature} - Importance: {khr.feature_used_importances_[i][2]}\")\n",
-    "print(\"---\")\n",
-    "\n",
     "# Predict the values on the test dataset\n",
     "y_test_pred = khr.predict(X_test)\n",
     "print(\"Predicted values for 'age' (first 10):\")\n",
@@ -796,13 +764,6 @@
     "khe = KhiopsEncoder(n_features=10)\n",
     "khe.fit(X, y)\n",
     "\n",
-    "# Show the feature importance info\n",
-    "print(f\"Features evaluated: {khe.n_features_evaluated_}\")\n",
-    "print(\"Top 3 evaluated features\")\n",
-    "for i, feature in enumerate(khe.feature_evaluated_names_[:3]):\n",
-    "    print(f\"{feature} - Level: {khe.feature_evaluated_importances_[i]}\")\n",
-    "print(\"---\")\n",
-    "\n",
     "# Transform the train dataset\n",
     "print(\"Encoded feature names:\")\n",
     "print(khe.feature_names_out_)\n",

khiops/samples/samples_sklearn.py

@@ -57,14 +57,6 @@ def khiops_classifier():
     # Train the classifier
     khc.fit(X_train, y_train)
 
-    # Show the feature importance info
-    print(f"Features evaluated: {khc.n_features_evaluated_}")
-    print(f"Features selected : {khc.n_features_used_}")
-    print("Top 3 used features")
-    for i, feature in enumerate(khc.feature_used_names_[:3]):
-        print(f"{feature} - Importance: {khc.feature_used_importances_[i][2]}")
-    print("---")
-
     # Predict the classes on the test dataset
     y_test_pred = khc.predict(X_test)
     print("Predicted classes (first 10):")
@@ -181,14 +173,6 @@ def khiops_classifier_text():
     # Train the classifier
     khc.fit(X_train, y_train)
 
-    # Show the feature importance info
-    print(f"Features evaluated: {khc.n_features_evaluated_}")
-    print(f"Features selected : {khc.n_features_used_}")
-    print("Top 3 used features")
-    for i, feature in enumerate(khc.feature_used_names_[:3]):
-        print(f"{feature} - Importance: {khc.feature_used_importances_[i][2]}")
-    print("---")
-
     # Predict the classes on the test dataset
     y_test_pred = khc.predict(X_test)
     print("Predicted classes (first 10):")
@@ -310,14 +294,6 @@ def khiops_classifier_multitable_snowflake():
     khc = KhiopsClassifier(n_trees=0)
     khc.fit(X_train, y_train)
 
-    # Show the feature importance info
-    print(f"Features evaluated: {khc.n_features_evaluated_}")
-    print(f"Features selected : {khc.n_features_used_}")
-    print("Top 3 used features")
-    for i, feature in enumerate(khc.feature_used_names_[:3]):
-        print(f"{feature} - Importance: {khc.feature_used_importances_[i][2]}")
-    print("---")
-
     # Predict the class on the test dataset
     y_test_pred = khc.predict(X_test)
     print("Predicted classes (first 10):")
@@ -550,14 +526,6 @@ def khiops_regressor():
     # Train the regressor
     khr.fit(X_train, y_train)
 
-    # Show the feature importance info
-    print(f"Features evaluated: {khr.n_features_evaluated_}")
-    print(f"Features selected : {khr.n_features_used_}")
-    print("Top 3 used features")
-    for i, feature in enumerate(khr.feature_used_names_[:3]):
-        print(f"{feature} - Importance: {khr.feature_used_importances_[i][2]}")
-    print("---")
-
     # Predict the values on the test dataset
     y_test_pred = khr.predict(X_test)
     print("Predicted values for 'age' (first 10):")
@@ -696,13 +664,6 @@ def khiops_encoder_multitable_snowflake():
     khe = KhiopsEncoder(n_features=10)
     khe.fit(X, y)
 
-    # Show the feature importance info
-    print(f"Features evaluated: {khe.n_features_evaluated_}")
-    print("Top 3 evaluated features")
-    for i, feature in enumerate(khe.feature_evaluated_names_[:3]):
-        print(f"{feature} - Level: {khe.feature_evaluated_importances_[i]}")
-    print("---")
-
     # Transform the train dataset
     print("Encoded feature names:")
     print(khe.feature_names_out_)