Improving SHAP Explainer with AI-Based features for Modern Model

ai_enhanced_shap.py

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+from __future__ import print_function
+import unittest
+import sklearn
+import sklearn.datasets
+import sklearn.ensemble
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.model_selection import train_test_split
+import numpy as np
+import keras
+from keras.applications.vgg16 import VGG16
+from keras.applications.vgg16 import preprocess_input, decode_predictions
+from keras.datasets import mnist
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Flatten
+from keras.layers import Conv2D, MaxPooling2D
+import keras.backend as K
+import json
+import xgboost
+from aix360.algorithms.shap import KernelExplainer, LinearExplainer, GradientExplainer, DeepExplainer, TreeExplainer
+import shap
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
+
+class TestShapExplainer(unittest.TestCase):
+
+ def test_Shap(self):
+ np.random.seed(1)
+ X_train, X_test, Y_train, Y_test = train_test_split(*shap.datasets.iris(), test_size=0.2, random_state=0)
+
+ # K-nearest neighbors
+ knn = sklearn.neighbors.KNeighborsClassifier()
+ knn.fit(X_train, Y_train)
+
+ # AI-driven feature: Automatically calculate additional performance metrics
+ Y_pred = knn.predict(X_test)
+ accuracy = accuracy_score(Y_test, Y_pred)
+ precision = precision_score(Y_test, Y_pred, average='macro')
+ recall = recall_score(Y_test, Y_pred, average='macro')
+ f1 = f1_score(Y_test, Y_pred, average='macro')
+
+ print(f"Accuracy = {accuracy * 100:.2f}%")
+ print(f"Precision = {precision:.2f}")
+ print(f"Recall = {recall:.2f}")
+ print(f"F1 Score = {f1:.2f}")
+
+ # Explain a single prediction from the test set
+ shapexplainer = KernelExplainer(knn.predict_proba, X_train)
+ shap_values = shapexplainer.explain_instance(X_test.iloc[0,:]) # AI-driven: Debugging output
+ print('knn X_test iloc_0 SHAP values:', shap_values)
+
+ # AI-driven feature: Enhanced visualization for SHAP values
+ shap.summary_plot(shap_values, X_test)
+
+ # SV machine with a linear kernel
+ svc_linear = sklearn.svm.SVC(kernel='linear', probability=True)
+ svc_linear.fit(X_train, Y_train)
+
+ # Calculate additional metrics for SVC
+ Y_pred_svc = svc_linear.predict(X_test)
+ svc_accuracy = accuracy_score(Y_test, Y_pred_svc)
+ svc_precision = precision_score(Y_test, Y_pred_svc, average='macro')
+ svc_recall = recall_score(Y_test, Y_pred_svc, average='macro')
+ svc_f1 = f1_score(Y_test, Y_pred_svc, average='macro')
+
+ print(f"SVC Accuracy = {svc_accuracy * 100:.2f}%")
+ print(f"SVC Precision = {svc_precision:.2f}")
+ print(f"SVC Recall = {svc_recall:.2f}")
+ print(f"SVC F1 Score = {svc_f1:.2f}")
+
+ # Explain all the predictions in the test set
+ shapexplainer = KernelExplainer(svc_linear.predict_proba, X_train)
+ shap_values = shapexplainer.explain_instance(X_test)
+ print('svc X_test SHAP values:', shap_values)
+
+ # Enhanced visualization
+ shap.summary_plot(shap_values, X_test)
+
+ def test_ShapLinearExplainer(self):
+ corpus, y = shap.datasets.imdb()
+ corpus_train, corpus_test, y_train, y_test = train_test_split(corpus, y, test_size=0.2, random_state=7)
+
+ vectorizer = TfidfVectorizer(min_df=10)
+ X_train = vectorizer.fit_transform(corpus_train)
+ X_test = vectorizer.transform(corpus_test)
+
+ model = sklearn.linear_model.LogisticRegression(penalty="l1", C=0.1, solver='liblinear')
+ model.fit(X_train, y_train)
+
+ shapexplainer = LinearExplainer(model, X_train, feature_dependence="independent")
+ shap_values = shapexplainer.explain_instance(X_test)
+ print("Invoked Shap LinearExplainer")
+
+ # AI-driven feature: Performance metrics for the linear model
+ Y_pred = model.predict(X_test)
+ linear_accuracy = accuracy_score(y_test, Y_pred)
+ linear_precision = precision_score(y_test, Y_pred, average='macro')
+ linear_recall = recall_score(y_test, Y_pred, average='macro')
+ linear_f1 = f1_score(y_test, Y_pred, average='macro')
+
+ print(f"Linear Model Accuracy = {linear_accuracy * 100:.2f}%")
+ print(f"Linear Model Precision = {linear_precision:.2f}")
+ print(f"Linear Model Recall = {linear_recall:.2f}")
+ print(f"Linear Model F1 Score = {linear_f1:.2f}")
+
+ # Enhanced SHAP visualization
+ shap.summary_plot(shap_values, X_test)
+
+ # comment this test as travis runs out of resources
+ def test_ShapGradientExplainer(self):
+ print("Skipped Shap GradientExplainer")
+
+ def test_ShapDeepExplainer(self):
+ batch_size = 128
+ num_classes = 10
+ epochs = 2
+
+ # input image dimensions
+ img_rows, img_cols = 28, 28
+
+ # the data, split between train and test sets
+ (x_train, y_train), (x_test, y_test) = mnist.load_data()
+
+ if K.image_data_format() == 'channels_first':
+ x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
+ x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
+ input_shape = (1, img_rows, img_cols)
+ else:
+ x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
+ x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
+ input_shape = (img_rows, img_cols, 1)
+
+ x_train = x_train.astype('float32')
+ x_test = x_test.astype('float32')
+ x_train /= 255
+ x_test /= 255
+ print('x_train shape:', x_train.shape)
+ print(x_train.shape[0], 'train samples')
+ print(x_test.shape[0], 'test samples')
+
+ y_train = keras.utils.to_categorical(y_train, num_classes)
+ y_test = keras.utils.to_categorical(y_test, num_classes)
+
+ model = Sequential()
+ model.add(Conv2D(32, kernel_size=(3, 3),
+ activation='relu',
+ input_shape=input_shape))
+ model.add(Conv2D(64, (3, 3), activation='relu'))
+ model.add(MaxPooling2D(pool_size=(2, 2)))
+ model.add(Dropout(0.25))
+ model.add(Flatten())
+ model.add(Dense(128, activation='relu'))
+ model.add(Dropout(0.5))
+ model.add(Dense(num_classes, activation='softmax'))
+
+ model.compile(loss=keras.losses.categorical_crossentropy,
+ optimizer=keras.optimizers.Adadelta(),
+ metrics=['accuracy'])
+
+ model.fit(x_train, y_train,
+ batch_size=batch_size,
+ epochs=epochs,
+ verbose=1,
+ validation_data=(x_test, y_test))
+ score = model.evaluate(x_test, y_test, verbose=0)
+ print('Test loss:', score[0])
+ print('Test accuracy:', score[1])
+
+ # select a set of background examples to take an expectation over
+ background = x_train[np.random.choice(x_train.shape[0], 100, replace=False)]
+
+ # explain predictions of the model on three images
+ e = DeepExplainer(model, background)
+
+ shap_values = e.explain_instance(x_test[1:5])
+ print("Invoked Shap DeepExplainer")
+
+ # Enhanced visualization for image explanations
+ shap.image_plot(shap_values, x_test[1:5])
+
+ def test_ShapTreeExplainer(self):
+ X, y = shap.datasets.nhanesi()
+ X_display, y_display = shap.datasets.nhanesi(display=True) # human readable feature values
+
+ xgb_full = xgboost.DMatrix(X, label=y)
+
+ # create a train/test split
+ X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=7)
+ xgb_train = xgboost.DMatrix(X_train, label=y_train)
+ xgb_test = xgboost.DMatrix(X_test, label=y_test)
+
+ # use validation set to choose # of trees
+ params = {
+ "eta": 0.002,
+ "max_depth": 3,
+ "objective": "survival:cox",
+ "subsample": 0.5
+ }
+ model_train = xgboost.train(params, xgb_train, 10000, evals=[(xgb_test, "test")], verbose_eval=1000)
+
+ # train final model on the full data set
+ params = {
+ "eta": 0.002,
+ "max_depth": 3,
+ "objective": "survival:cox",
+ "subsample": 0.5
+ }
+ model_full = xgboost.train(params, xgb_full, 10000, evals=[(xgb_full, "test")], verbose_eval=1000)
+
+ explainer = shap.TreeExplainer(model_full)
+ shap_values = explainer.shap_values(X)
+
+ # AI-driven feature: Enhanced interpretation of Tree SHAP values
+ print("Tree SHAP values:", shap_values)
+ shap.summary_plot(shap_values, X_display)
+
+if __name__ == '__main__':
+ unittest.main()