def predict(self,
beta=None,
x=None,
fill_value=None,
fill_method=None,
axis=0):
"""
Parameters
----------
beta : Series
x : Series or DataFrame
fill_value : scalar or dict, default None
fill_method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
axis : {0, 1}, default 0
See DataFrame.fillna for more details
Notes
-----
1. If both fill_value and fill_method are None then NaNs are dropped
(this is the default behavior)
2. An intercept will be automatically added to the new_y_values if
the model was fitted using an intercept
Returns
-------
Series of predicted values
"""
if beta is None and x is None:
return self.y_predict
if beta is None:
beta = self.beta
else:
beta = beta.reindex(self.beta.index)
if isnull(beta).any():
raise ValueError('Must supply betas for same variables')
if x is None:
x = self._x
orig_x = x
else:
orig_x = x
if fill_value is None and fill_method is None:
x = x.dropna(how='any')
else:
x = x.fillna(value=fill_value, method=fill_method, axis=axis)
if isinstance(x, Series):
x = DataFrame({'x': x})
if self._intercept:
x['intercept'] = 1.
x = x.reindex(columns=self._x.columns)
rs = np.dot(x.values, beta.values)
return Series(rs, x.index).reindex(orig_x.index)
def test_groupby_preserve_categorical_dtype(self):
# GH13743, GH13854
df = DataFrame({'A': [1, 2, 1, 1, 2],
'B': [10, 16, 22, 28, 34],
'C1': Categorical(list("abaab"),
categories=list("bac"),
ordered=False),
'C2': Categorical(list("abaab"),
categories=list("bac"),
ordered=True)})
# single grouper
exp_full = DataFrame({'A': [2.0, 1.0, np.nan],
'B': [25.0, 20.0, np.nan],
'C1': Categorical(list("bac"),
categories=list("bac"),
ordered=False),
'C2': Categorical(list("bac"),
categories=list("bac"),
ordered=True)})
for col in ['C1', 'C2']:
result1 = df.groupby(by=col, as_index=False).mean()
result2 = df.groupby(by=col, as_index=True).mean().reset_index()
expected = exp_full.reindex(columns=result1.columns)
tm.assert_frame_equal(result1, expected)
tm.assert_frame_equal(result2, expected)
# multiple grouper
exp_full = DataFrame({'A': [1, 1, 1, 2, 2, 2],
'B': [np.nan, 20.0, np.nan, 25.0, np.nan,
np.nan],
'C1': Categorical(list("bacbac"),
categories=list("bac"),
ordered=False),
'C2': Categorical(list("bacbac"),
categories=list("bac"),
ordered=True)})
for cols in [['A', 'C1'], ['A', 'C2']]:
result1 = df.groupby(by=cols, as_index=False).mean()
result2 = df.groupby(by=cols, as_index=True).mean().reset_index()
expected = exp_full.reindex(columns=result1.columns)
tm.assert_frame_equal(result1, expected)
tm.assert_frame_equal(result2, expected)
def test_groupby_preserve_categorical_dtype(self):
# GH13743, GH13854
df = DataFrame({
'A': [1, 2, 1, 1, 2],
'B': [10, 16, 22, 28, 34],
'C1':
Categorical(list("abaab"), categories=list("bac"), ordered=False),
'C2':
Categorical(list("abaab"), categories=list("bac"), ordered=True)
})
# single grouper
exp_full = DataFrame({
'A': [2.0, 1.0, np.nan],
'B': [25.0, 20.0, np.nan],
'C1':
Categorical(list("bac"), categories=list("bac"), ordered=False),
'C2':
Categorical(list("bac"), categories=list("bac"), ordered=True)
})
for col in ['C1', 'C2']:
result1 = df.groupby(by=col, as_index=False).mean()
result2 = df.groupby(by=col, as_index=True).mean().reset_index()
expected = exp_full.reindex(columns=result1.columns)
tm.assert_frame_equal(result1, expected)
tm.assert_frame_equal(result2, expected)
# multiple grouper
exp_full = DataFrame({
'A': [1, 1, 1, 2, 2, 2],
'B': [np.nan, 20.0, np.nan, 25.0, np.nan, np.nan],
'C1':
Categorical(list("bacbac"), categories=list("bac"), ordered=False),
'C2':
Categorical(list("bacbac"), categories=list("bac"), ordered=True)
})
for cols in [['A', 'C1'], ['A', 'C2']]:
result1 = df.groupby(by=cols, as_index=False).mean()
result2 = df.groupby(by=cols, as_index=True).mean().reset_index()
expected = exp_full.reindex(columns=result1.columns)
tm.assert_frame_equal(result1, expected)
tm.assert_frame_equal(result2, expected)
def predict(self, beta=None, x=None, fill_value=None,
fill_method=None, axis=0):
"""
Parameters
----------
beta : Series
x : Series or DataFrame
fill_value : scalar or dict, default None
fill_method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
axis : {0, 1}, default 0
See DataFrame.fillna for more details
Notes
-----
1. If both fill_value and fill_method are None then NaNs are dropped
(this is the default behavior)
2. An intercept will be automatically added to the new_y_values if
the model was fitted using an intercept
Returns
-------
Series of predicted values
"""
if beta is None and x is None:
return self.y_predict
if beta is None:
beta = self.beta
else:
beta = beta.reindex(self.beta.index)
if isnull(beta).any():
raise ValueError('Must supply betas for same variables')
if x is None:
x = self._x
orig_x = x
else:
orig_x = x
if fill_value is None and fill_method is None:
x = x.dropna(how='any')
else:
x = x.fillna(value=fill_value, method=fill_method, axis=axis)
if isinstance(x, Series):
x = DataFrame({'x': x})
if self._intercept:
x['intercept'] = 1.
x = x.reindex(columns=self._x.columns)
rs = np.dot(x.values, beta.values)
return Series(rs, x.index).reindex(orig_x.index)
