def test_minmaxscaler_minimum_operation_success():
params = {
MinMaxScalerOperation.ATTRIBUTE_PARAM: ['col'],
}
n_in = {'input data': 'input_1'}
n_out = {'output data': 'output_1'}
in1 = n_in['input data']
out = n_out['output data']
instance = MinMaxScalerOperation(params,
named_inputs=n_in,
named_outputs=n_out)
code = instance.generate_code()
expected_code = dedent("""
output_1 = input_1
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(0,1))
X_train = input_1['col'].values.tolist()
output_1['col_norm'] = scaler.fit_transform(X_train).tolist()
""")
result, msg = compare_ast(ast.parse(code), ast.parse(expected_code))
assert result, msg + format_code_comparison(code, expected_code)
def test_min_max_scaler_no_output_implies_no_code_success():
arguments = {
'parameters': {
'attribute': ['sepalwidth', 'petalwidth'],
'multiplicity': {
'input data': 0
},
'min': 0,
'max': 1
},
'named_inputs': {
'input data': 'df',
},
'named_outputs': {}
}
instance = MinMaxScalerOperation(**arguments)
assert instance.generate_code() is None
def test_min_max_scaler_alias_param_success():
df = util.iris(['sepalwidth', 'petalwidth'], size=10)
arguments = {
'parameters': {
'attribute': ['sepalwidth', 'petalwidth'],
'multiplicity': {
'input data': 0
},
'min': 0,
'max': 1,
'alias': 'success'
},
'named_inputs': {
'input data': 'df',
},
'named_outputs': {
'output data': 'out'
}
}
instance = MinMaxScalerOperation(**arguments)
result = util.execute(instance.generate_code(), {'df': df})
assert result['out'].iloc[:, 2].name == "success"
def test_min_max_scaler_2_success():
df = util.iris(['sepalwidth', 'petalwidth'], size=10)
test_df = df.copy()
arguments = {
'parameters': {
'attribute': ['sepalwidth', 'petalwidth'],
'multiplicity': {
'input data': 0
},
'min': 2,
'max': 6
},
'named_inputs': {
'input data': 'df',
},
'named_outputs': {
'output data': 'out'
}
}
instance = MinMaxScalerOperation(**arguments)
result = util.execute(instance.generate_code(), {'df': df})
test_out = scaler(test_df, ['sepalwidth', 'petalwidth'], 2, 6)
assert result['out'].loc[:, 'scaled_1'].equals(test_out.loc[:, 'scaled_1'])
def test_min_max_scaler_invalid_min_max_param_fail():
df = util.iris(['sepalwidth', 'petalwidth'], size=10)
arguments = {
'parameters': {
'attribute': ['sepalwidth', 'petalwidth'],
'multiplicity': {
'input data': 0
},
'min': 1,
'max': 1
},
'named_inputs': {
'input data': 'df',
},
'named_outputs': {
'output data': 'out'
}
}
instance = MinMaxScalerOperation(**arguments)
with pytest.raises(ValueError) as val_err:
util.execute(instance.generate_code(), {'df': df})
assert "Minimum of desired feature range must be smaller than maximum." \
" Got (1, 1)." in str(val_err.value)
def test_min_max_scaler_invalid_attribute_param_fail():
df = util.iris(['sepalwidth', 'petalwidth', 'class'], size=10)
arguments = {
'parameters': {
'attribute': ['sepalwidth', 'petalwidth', 'class'],
'multiplicity': {
'input data': 0
},
'min': 0,
'max': 1
},
'named_inputs': {
'input data': 'df',
},
'named_outputs': {
'output data': 'out'
}
}
instance = MinMaxScalerOperation(**arguments)
with pytest.raises(ValueError) as val_err:
util.execute(instance.generate_code(), {'df': df})
assert "could not convert string to float: 'Iris-setosa'" in str(
val_err.value)
def test_min_max_scaler_missing_attribute_param_fail():
arguments = {
'parameters': {
'multiplicity': {
'input data': 0
},
'min': 0,
'max': 1
},
'named_inputs': {
'input data': 'df',
},
'named_outputs': {
'output data': 'out'
}
}
with pytest.raises(ValueError) as val_err:
MinMaxScalerOperation(**arguments)
assert "Parameters 'attribute' must be informed for task" in str(
val_err.value)
def test_minmaxscaler_operation_failure():
params = {MinMaxScalerOperation.ALIAS_PARAM: 'c'}
with pytest.raises(ValueError):
n_in = {'input data': 'input_1'}
n_out = {'output data': 'output_1'}
MinMaxScalerOperation(params, named_inputs=n_in, named_outputs=n_out)