def test_zero_count():
"""Ensure that the TPOT _zero_count preprocessor outputs the input dataframe when no. of training features is 0"""
tpot_obj = TPOT()
assert np.array_equal(
tpot_obj._zero_count(training_testing_data.ix[:, -3:]),
training_testing_data.ix[:, -3:])
def test_zero_count_2():
"""Assert that the Zero Count preprocessor adds two columns to the dataframe"""
tpot_obj = TPOT()
input_df = training_testing_data
output_df = tpot_obj._zero_count(input_df)
assert type(input_df) == type(output_df)
(in_rows, in_cols) = input_df.shape
(out_rows, out_cols) = output_df.shape
assert in_rows == out_rows
assert in_cols == (out_cols - 2)
def test_zero_count_2():
"""Assert that the Zero Count preprocessor adds two columns to the dataframe"""
tpot_obj = TPOT()
input_df = training_testing_data
output_df = tpot_obj._zero_count(input_df)
assert type(input_df) == type(output_df)
(in_rows, in_cols) = input_df.shape
(out_rows, out_cols) = output_df.shape
assert in_rows == out_rows
assert in_cols == (out_cols - 2)
def test_zero_count():
"""Ensure that the TPOT _zero_count preprocessor outputs the input dataframe when no. of training features is 0"""
tpot_obj = TPOT()
assert np.array_equal(tpot_obj._zero_count(training_testing_data.ix[:, -3:]), training_testing_data.ix[:, -3:])
