SVM using kernel sigmoid

sigmoid_svm_audio.py

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+import sys
+from sklearn.model_selection import GridSearchCV
+from sklearn import svm
+from sklearn import preprocessing
+from sklearn.model_selection import StratifiedShuffleSplit
+from sklearn.metrics import accuracy_score, precision_score, recall_score
+import numpy as np
+from csv import reader
+from sklearn.manifold.t_sne import TSNE
+
+def load_csv(filename):
+    file = open(filename, "rt")
+    lines = reader(file)
+    dataset = list(lines)
+    dataset = np.array(dataset).astype('float')
+    predictors = dataset[:,0:13]
+    labels = dataset[:,13].astype('int')
+    return predictors, labels
+
+def SVM(train_data, train_labels, test_data, test_labels):
+    C_range = np.outer(np.logspace(-1, 1, 3),np.array([1,5]))
+    print C_range
+    C_range = C_range.flatten()
+
+    gamma_range = np.outer(np.logspace(-3, 0, 4),np.array([1,5]))
+    gamma_range = gamma_range.flatten()
+    parameters = {'kernel':['sigmoid'], 'C':C_range, 'gamma': gamma_range}
+    svm_clsf = svm.SVC()
+
+    grid_clsf = GridSearchCV(estimator=svm_clsf,param_grid=parameters,n_jobs=1, verbose=2)
+    grid_clsf.fit(train_data, train_labels)
+
+    classifier = grid_clsf.best_estimator_
+    print grid_clsf.best_estimator_
+
+    train_predictions = classifier.predict(train_data)
+    train_accuracy = accuracy_score(train_labels, train_predictions)
+
+    print "Training Accuracy: %.4f" % (train_accuracy)
+
+    test_predictions = classifier.predict(test_data)
+    print test_predictions
+    accuracy = accuracy_score(test_labels, test_predictions)
+    print "Test Accuracy: %.4f" % (accuracy)
+
+    precision = precision_score(test_labels, test_predictions, average='weighted')
+    recall = recall_score(test_labels, test_predictions, average='weighted')
+    f1 = 2.0 * (precision * recall) / (precision + recall)
+
+    print "Test Precision: %.4f" % (precision)
+    print "Test Recall: %.4f" % (recall)
+    print "Test f1_score: %.4f" % (f1)
+
+    return accuracy, precision, recall, f1
+
+
+filename = sys.argv[1]
+X_data, Y_data = load_csv(filename)
+
+sss = StratifiedShuffleSplit(n_splits=5, test_size=0.125)
+metrics = []
+fold = 1
+for train_indices, test_indices in sss.split(X_data, Y_data):
+    train_data, test_data = X_data[train_indices], X_data[test_indices]
+    train_labels, test_labels = Y_data[train_indices], Y_data[test_indices]
+    metrics.append(SVM(train_data, train_labels, test_data, test_labels))
+    fold += 1
+
+    accuracy = 0.00
+    precision = 0.00
+    recall = 0.00
+    fi = 0.00
+    for i in metrics:
+        accuracy += i[0]
+        precision += i[1]
+        recall += i[2]
+        fi += i[3]
+    accuracy = accuracy/5.0
+    precision = precision/5.0
+    recall = recall/5.0
+    fi = fi/5.0
+    print (accuracy),(","),(precision),(", "),(recall),(", "),(fi)