

def test_pandas_for_bp(self):
    dataset = datasets.load_diabetes()
    target = dataset.target.reshape((- 1), 1)
    input_scaler = preprocessing.MinMaxScaler()
    target_scaler = preprocessing.MinMaxScaler()
    n_features = dataset.data.shape[1]
    input_columns = [('column_' + str(i)) for i in range(n_features)]
    pandas_data = pd.DataFrame(dataset.data, columns=input_columns)
    pandas_data['target'] = target_scaler.fit_transform(target)
    pandas_data[input_columns] = input_scaler.fit_transform(error[input_columns])
    (x_train, x_test, y_train, y_test) = train_test_split(pandas_data[input_columns], pandas_data['target'], train_size=0.85)
    bpnet = algorithms.GradientDescent(connection=[layers.Sigmoid(10), layers.Sigmoid(30), layers.Output(1)], show_epoch=100)
    bpnet.train(x_train, y_train, epochs=50)
    y_predict = bpnet.predict(x_test).reshape((- 1), 1)
    y_test = y_test.reshape((- 1), 1)
    error = estimators.rmsle(target_scaler.inverse_transform(y_test), target_scaler.inverse_transform(y_predict).round())
    self.assertAlmostEqual(0.48, error, places=2)

dataset/ETHPy150Open itdxer/neupy/tests/compatibilities/test_pandas.py PandasCompatibilityTestCase.test_pandas_for_bp/VarMisuse@117/167 `pandas_data`->`error`