def test_gamma_C(x_arr, y_arr, itraining, K_chk, y_chk,
get_ml, get_kernel=None):
shp = (len(y_arr), len(x_arr))
pnl = pd.Panel(data=[np.zeros(shp), np.zeros(shp)],
items=['error', 'accuracy'],
major_axis=y_arr, minor_axis=x_arr)
for g in x_arr:
for c in y_arr:
print "computing g=%r, c=%r" % (g, c)
ml = get_ml(g, c, get_kernel)
y_all = cls(ml, itraining, K_chk, y_chk)
pnl['error'][g][c] = mlpy.error(y_chk, y_all[y_chk.index])
pnl['accuracy'][g][c] = mlpy.accuracy(y_chk, y_all[y_chk.index])
return pnl
#ACCURACY measure CLASSIFICATION import mlpy t = [3,2,3,3,3,1,1,1] p = [3,2,1,3,3,2,1,1] mlpy.error(t, p) mlpy.accuracy(t, p) #Sensitivity, Specitivity, AUC import mlpy t = [1, 1, 1,-1, 1,-1,-1,-1] p = [1,-1, 1, 1, 1,-1, 1,-1] mlpy.error_p(t, p) mlpy.error_n(t, p) mlpy.sensitivity(t, p) mlpy.specificity(t, p) mlpy.ppv(t, p) mlpy.npv(t, p) mlpy.mcc(t, p) p = [2.3,-0.4, 1.6, 0.6, 3.2,-4.9, 1.3,-0.3] mlpy.auc_wmw(t, p) p = [2.3,0.4, 1.6, -0.6, 3.2,-4.9, -1.3,-0.3] mlpy.auc_wmw(t, p) #Mean Squared Error REGRESSION import mlpy t = [2.4,0.4,1.2,-0.2,3.3,-4.9,-1.1,-0.1] p = [2.3,0.4,1.6,-0.6,3.2,-4.9,-1.3,-0.3] mlpy.mse(t, p)
test_y = train_y[:split_index]
train_X = train_X[split_index:]
train_y = train_y[split_index:]
print strftime("%Y-%m-%d %H:%M:%S", gmtime()), ": Parsing complete!\n"
else:
print strftime("%Y-%m-%d %H:%M:%S", gmtime()), ": Parsing test data..."
# Parsing test input data
data_reader = csv.reader(open(input_test_file, 'rb'), delimiter = ",")
data_reader.next() # Skip the first line, since it contains the labels
test_X = []
for row in data_reader:
line_x = [1]
for i in range(len(row)):
line_x.append(float(row[i]))
test_X.append(line_x)
print strftime("%Y-%m-%d %H:%M:%S", gmtime()), ": Finished parsing test data!"
predicted_labels = Predict_svm(train_X, train_y, test_X)
print strftime("%Y-%m-%d %H:%M:%S", gmtime()), ": Finished predicting!"
if crossval == True:
# Only useful for cross-validation, otherwise test_y is unknown and we get error=1/accuracy=0
print "Error: ", mlpy.error(test_y, predicted_labels)
print "Accuracy: ", mlpy.accuracy(test_y, predicted_labels)
else:
print strftime("%Y-%m-%d %H:%M:%S", gmtime()), ": Writing predicted labels to file..."
prediction_filename = "test_" + strftime("%Y-%m-%d_%H-%M-%S", gmtime()) + ".csv"
prediction_writer = csv.writer(open(prediction_filename, 'wb'))
for row in predicted_labels:
list_row = [row]
prediction_writer.writerow(list_row)