def _get_x_y_report(data, jump=0.01,max_pos=None): if max_pos==None: max_pos=1 x = [] y = [] # build x and y for graph idx = 0 x_pos = 0 while data[idx][0]<x_pos-jump/2: idx+=1 while x_pos < max_pos: y_data = [] # get all the data between [current_jump-jump/2, current_jump+jump/2[ while idx<len(data) and data[idx][0] >= x_pos-jump/2 and data[idx][0] < x_pos+jump/2: y_data.append(data[idx]) idx+=1 if y_data: y_true_values = np.asarray(list(map(lambda t:t[1],y_data))) y_pred_values = np.asarray(list(map(lambda t:t[2],y_data))) report = dskc_modeling.EvaluationReport(y_true_values, y_pred_values) x.append(x_pos) y.append(report) x_pos += jump return x,y
def test(model): # read data x_test, y_test, _ = util.read_test_data() # predict y_pred = model.predict(x_test) # clean array = [] for x in y_pred: if x < 0: array.append(0) elif x > 1: array.append(1) else: array.append(x) y_pred = np.asarray(array) # evaluate report = dskc_modeling.EvaluationReport(y_test, y_pred, name="Bayesian Ridge") return report
def test(model): # read data x_test, y_test, _ = util.read_test_data() # predict y_pred = model.predict(x_test) # evaluate report = dskc_modeling.EvaluationReport(y_test, y_pred, name="Logistic Regression") return report
def test(model): x_test, y_test, _ = util.read_test_data() # predict y_pred = model.predict(x_test) # clean y_pred = np.asarray([x[0] for x in y_pred]) # evaluate report = dskc_modeling.EvaluationReport(y_test, y_pred, name="Neural Network") return report
def test(model): # read data x_test, y_test, _ = util.read_test_data() # predict y_pred = model.predict(x_test) # evaluate report = dskc_modeling.EvaluationReport(y_test, y_pred, name="Random Forest Regressor", to_binary=True) return report