def PolynomialSVMTest(pca_option):
import SVM
SVM.SVMSimulation(SVM.svm_poly, processing.linear_pca,
processing.overall_training_data, pca_option)
processing.final_validation = np.array(processing.final_validation)
FV_features = []
FV_labels = []
FV_features, FV_labels = processing.createFeatures_Labels(
processing.final_validation)
FV_features_data = None
FV_labels_data = None
FV_features_data, FV_labels_data = processing.convertToDataFrame(
FV_features, FV_labels, processing.column_titles)
global SVM_POLY_final_predictions
if(pca_option == 'yes' or pca_option == 'both'):
transformed_FV = processing.linear_pca.transform(FV_features_data)
final_predictions = SVM.svm_poly.predict(transformed_FV)
SVM_GAUS_final_predictions = final_predictions
accuracy = metrics.accuracy_score(final_predictions, FV_labels)
precision = metrics.precision_score(
FV_labels, final_predictions, average='micro')
recall = metrics.recall_score(
FV_labels, final_predictions, average='micro')
print('POLYNOMIAL SVM MODEL FINAL TEST DATA ACCURACY: ', 100 * accuracy)
print('POLYNOMIAL SVM MODEL FINAL TEST DATA PRECISION: ', 100 * precision)
print('POLYNOMIAL SVM MODEL FINAL TEST DATA RECALL: ', 100 * recall)
print()
return accuracy, precision, recall
else:
final_predictions = SVM.svm_poly.predict(FV_features_data)
SVM_GAUS_final_predictions = final_predictions
accuracy = metrics.accuracy_score(final_predictions, FV_labels)
precision = metrics.precision_score(
FV_labels, final_predictions, average='micro')
recall = metrics.recall_score(
FV_labels, final_predictions, average='micro')
print('POLYNOMIAL SVM MODEL FINAL TEST DATA ACCURACY: ', 100 * accuracy)
print('POLYNOMIAL SVM MODEL FINAL TEST DATA PRECISION: ', 100 * precision)
print('POLYNOMIAL SVM MODEL FINAL TEST DATA RECALL: ', 100 * recall)
print()
return accuracy, precision, recall
