vggish svm
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em474re
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139
src/svm_hand_vgg.py
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139
src/svm_hand_vgg.py
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from sklearn.svm import LinearSVC
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from sklearn.base import clone
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from sklearn.pipeline import Pipeline
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from sklearn.model_selection import PredefinedSplit, GridSearchCV
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from sklearn.preprocessing import StandardScaler
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from sklearn.metrics import classification_report, confusion_matrix, recall_score, make_scorer, plot_confusion_matrix
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# import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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RANDOM_SEED = 42
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GRID = [
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{'scaler': [StandardScaler(), None],
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'estimator': [LinearSVC(random_state=RANDOM_SEED)],
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'estimator__loss': ['squared_hinge'],
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'estimator__C': np.logspace(-1, -5, num=5),
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'estimator__class_weight': ['balanced', None],
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'estimator__max_iter': [1000]
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}
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]
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PIPELINE = Pipeline([('scaler', None), ('estimator', LinearSVC(dual=True))])
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if __name__ == '__main__':
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# load handcrafted and vggish features and labels
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devel_X_vgg = np.load(
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"./features/vgg_features/x_devel_data_vgg.npy", allow_pickle=True
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)
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test_X_vgg = np.load(
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"./features/vgg_features/x_test_data_vgg.npy", allow_pickle=True
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)
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train_X_vgg = np.load(
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"./features/vgg_features/x_train_data_vgg.npy", allow_pickle=True
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)
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devel_X_hand = np.load(
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"./features/hand_features/x_devel_data.npy", allow_pickle=True
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)
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test_X_hand=np.load(
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"./features/hand_features/x_test_data.npy", allow_pickle=True
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)
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train_X_hand=np.load(
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"./features/hand_features/x_train_data.npy", allow_pickle=True
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)
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devel_y=np.load(
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"./features/vgg_features/y_devel_label_vgg.npy", allow_pickle=True
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)
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test_y=np.load(
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"./features/vgg_features/y_test_label_vgg.npy", allow_pickle=True
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)
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train_y=np.load(
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"./features/vgg_features/y_train_label_vgg.npy", allow_pickle=True
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)
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num_train=train_X_vgg.shape[0]
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num_devel=devel_X_vgg.shape[0]
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split_indices=np.repeat([-1, 0], [num_train, num_devel])
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split=PredefinedSplit(split_indices)
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train_X_vgg=np.squeeze(train_X_vgg)
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devel_X_vgg=np.squeeze(devel_X_vgg)
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test_X_vgg=np.squeeze(test_X_vgg)
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devel_X=np.concatenate(
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(
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devel_X_hand,
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devel_X_vgg
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),
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axis=1,
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)
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test_X=np.concatenate(
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(
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test_X_hand,
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test_X_vgg
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),
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axis=1,
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)
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train_X=np.concatenate(
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(
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train_X_hand,
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train_X_vgg
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),
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axis=1,
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)
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X=np.append(train_X, devel_X, axis=0)
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y=np.append(train_y, devel_y, axis=0)
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grid_search=GridSearchCV(estimator=PIPELINE, param_grid=GRID,
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scoring=make_scorer(
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recall_score, average='macro'),
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n_jobs=-1, cv=split, refit=True, verbose=1,
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return_train_score=False)
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# find best estimator with grid search
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grid_search.fit(X, y)
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best_estimator=grid_search.best_estimator_
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# fit clone of best estimator on train again for devel predictions
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estimator=clone(best_estimator, safe=False)
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estimator.fit(train_X, train_y)
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preds=estimator.predict(devel_X)
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metrics={'dev': {}, 'test': {}}
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# devel results
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print('DEVEL')
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uar=recall_score(devel_y, preds, average='macro')
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cm=confusion_matrix(devel_y, preds)
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print(
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f'UAR: {uar}\n{classification_report(devel_y, preds)}\n\nConfusion Matrix:\n\n{cm}')
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# optional write grid_search to csv file
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# pd.DataFrame(grid_search.cv_results_).to_csv('grid_search.csv', index=False)
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# test results
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print('TEST')
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preds=best_estimator.predict(test_X)
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uar=recall_score(test_y, preds, average='macro')
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cm=confusion_matrix(test_y, preds)
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print(f'UAR: {uar}\n{classification_report(test_y, preds)}\n\nConfusion Matrix:\n\n{cm}')
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fig = plt.figure()
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plot_confusion_matrix(best_estimator,X= test_X, y_true=test_y,cmap=plt.cm.Blues,display_labels=['Negative','Positive'],normalize='true')
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plt.ylabel('True Label')
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plt.xlabel('Predicated Label')
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plt.savefig('cm_svm_hand_vgg.jpg')
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101
src/svm_vgg.py
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101
src/svm_vgg.py
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@ -0,0 +1,101 @@
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from sklearn.svm import LinearSVC
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from sklearn.base import clone
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from sklearn.pipeline import Pipeline
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from sklearn.model_selection import PredefinedSplit, GridSearchCV
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from sklearn.preprocessing import StandardScaler
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from sklearn.metrics import classification_report, confusion_matrix, recall_score, make_scorer, plot_confusion_matrix
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# import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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RANDOM_SEED = 42
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GRID = [
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{'scaler': [StandardScaler(), None],
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'estimator': [LinearSVC(random_state=RANDOM_SEED)],
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'estimator__loss': ['squared_hinge'],
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'estimator__C': np.logspace(-1, -5, num=5),
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'estimator__class_weight': ['balanced', None],
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'estimator__max_iter': [1000]
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}
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]
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PIPELINE = Pipeline([('scaler', None), ('estimator', LinearSVC(dual=True))])
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if __name__=='__main__':
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# load vggish features and labels
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devel_X_vgg = np.load(
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"./features/vgg_features/x_devel_data_vgg.npy", allow_pickle=True
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)
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test_X_vgg = np.load(
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"./features/vgg_features/x_test_data_vgg.npy", allow_pickle=True
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)
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train_X_vgg = np.load(
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"./features/vgg_features/x_train_data_vgg.npy", allow_pickle=True
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)
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devel_y = np.load(
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"./features/vgg_features/y_devel_label_vgg.npy", allow_pickle=True
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)
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test_y = np.load(
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"./features/vgg_features/y_test_label_vgg.npy", allow_pickle=True
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)
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train_y = np.load(
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"./features/vgg_features/y_train_label_vgg.npy", allow_pickle=True
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)
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num_train = train_X_vgg.shape[0]
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num_devel = devel_X_vgg.shape[0]
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split_indices = np.repeat([-1, 0], [num_train, num_devel])
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split = PredefinedSplit(split_indices)
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train_X_vgg = np.squeeze(train_X_vgg)
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devel_X_vgg = np.squeeze(devel_X_vgg)
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test_X_vgg = np.squeeze(test_X_vgg)
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X = np.append(train_X_vgg, devel_X_vgg, axis=0)
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y = np.append(train_y, devel_y, axis=0)
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grid_search = GridSearchCV(estimator=PIPELINE, param_grid=GRID,
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scoring=make_scorer(recall_score, average='macro'),
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n_jobs=-1, cv=split, refit=True, verbose=1,
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return_train_score=False)
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# find best estimator with grid search
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grid_search.fit(X, y)
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best_estimator = grid_search.best_estimator_
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# fit clone of best estimator on train again for devel predictions
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estimator = clone(best_estimator, safe=False)
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estimator.fit(train_X_vgg, train_y)
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preds = estimator.predict(devel_X_vgg)
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metrics = {'dev': {}, 'test': {}}
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# devel results
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print('DEVEL')
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uar = recall_score(devel_y, preds, average='macro')
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cm = confusion_matrix(devel_y, preds)
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print(f'UAR: {uar}\n{classification_report(devel_y, preds)}\n\nConfusion Matrix:\n\n{cm}')
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# optional write grid_search to csv file
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# pd.DataFrame(grid_search.cv_results_).to_csv('grid_search.csv', index=False)
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# test results
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print('TEST')
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preds = best_estimator.predict(test_X_vgg)
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uar = recall_score(test_y, preds, average='macro')
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cm = confusion_matrix(test_y, preds)
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print(f'UAR: {uar}\n{classification_report(test_y, preds)}\n\nConfusion Matrix:\n\n{cm}')
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fig = plt.figure()
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plot_confusion_matrix(best_estimator,X= test_X_vgg, y_true=test_y,cmap=plt.cm.Blues,display_labels=['Negative','Positive'],normalize='true')
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plt.ylabel('True Label')
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plt.xlabel('Predicated Label')
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plt.savefig('cm_svm_vgg.jpg')
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