def test_raises_type_error(mock_fit):
"""
If a fit method with required X and y arguments raises a TypeError, it's
re-raised (for a different reason) when it's called with one argument
"""
with pytest.raises(TypeError):
_call_fit(Trans().fit, 'X', 'y', kwarg='kwarg')
def test_called_with_x_and_y(mock_fit):
"""
Fit method with required X and y arguments is called with both and with
any additional keywords
"""
_call_fit(Trans().fit, 'X', 'y', kwarg='kwarg')
mock_fit.assert_called_with('X', 'y', kwarg='kwarg')
def fit_predict(self, factor_container, y=None, **fit_params):
fc_fit, fit_params = self._pre_transform(factor_container, y, **fit_params)
if hasattr(self.steps[-1][-1], "fit_predict"):
return _call_fit(self.steps[-1][-1].fit_predict,
fc_fit, y, **fit_params)
else:
return _call_fit(self.steps[-1][-1].fit,
fc_fit, y, **fit_params).predict(fc_fit)
def fit_predict(self, factor_container, y=None, **fit_params):
fc_fit, fit_params = self._pre_transform(factor_container, y,
**fit_params)
if hasattr(self.steps[-1][-1], "fit_predict"):
return _call_fit(self.steps[-1][-1].fit_predict, fc_fit, y,
**fit_params)
else:
return _call_fit(self.steps[-1][-1].fit, fc_fit, y,
**fit_params).predict(fc_fit)
def _pre_transform(self, factor_container, y=None, **fit_params):
fit_params_steps = dict((step, {}) for step, _ in self.steps)
for pname, pval in six.iteritems(fit_params):
step, param = pname.split('__', 1)
fit_params_steps[step][param] = pval
fc = factor_container
for name, transform in self.steps[:-1]:
if hasattr(transform, "fit_transform"):
fc_fit = _call_fit(transform.fit_transform,
fc, y, **fit_params_steps[name])
else:
fc_fit = _call_fit(transform.fit,
fc, y, **fit_params_steps[name]).transform(fc)
if not isinstance(fc_fit, FactorContainer):
try:
fc.replace_data(fc_fit)
except ValueError:
raise ValueError(
'Failed in chain step {0}, please set out_container=True explicitly'.format(transform))
else:
fc = fc_fit
return fc, fit_params_steps[self.steps[-1][0]]
def _pre_transform(self, factor_container, y=None, **fit_params):
fit_params_steps = dict((step, {}) for step, _ in self.steps)
for pname, pval in six.iteritems(fit_params):
step, param = pname.split('__', 1)
fit_params_steps[step][param] = pval
fc = factor_container
for name, transform in self.steps[:-1]:
if hasattr(transform, "fit_transform"):
fc_fit = _call_fit(transform.fit_transform, fc, y,
**fit_params_steps[name])
else:
fc_fit = _call_fit(transform.fit, fc, y,
**fit_params_steps[name]).transform(fc)
if not isinstance(fc_fit, FactorContainer):
try:
fc.replace_data(fc_fit)
except ValueError:
raise ValueError(
'Failed in chain step {0}, please set out_container=True explicitly'
.format(transform))
else:
fc = fc_fit
return fc, fit_params_steps[self.steps[-1][0]]
def fit(self, factor_container, y=None, **fit_params):
fc_fit, fit_params = self._pre_transform(factor_container, y,
**fit_params)
_call_fit(self.steps[-1][-1].fit, factor_container, y, **fit_params)
return self
def fit(self, factor_container, y=None, **fit_params):
fc_fit, fit_params = self._pre_transform(factor_container, y, **fit_params)
_call_fit(self.steps[-1][-1].fit, factor_container, y, **fit_params)
return self
