def test_1d(self):
# one observation
res = self.res
data = self.data
pred = res.predict(data.iloc[:1])
pdt.assert_index_equal(pred.index, data.iloc[:1].index)
assert_allclose(pred.values, res.fittedvalues[0], rtol=1e-13)
fittedm = res.fittedvalues.mean()
xmean = data.mean()
pred = res.predict(xmean.to_frame().T)
assert_equal(pred.index, np.arange(1))
assert_allclose(pred, fittedm, rtol=1e-13)
# Series
pred = res.predict(data.mean())
assert_equal(pred.index, np.arange(1))
assert_allclose(pred.values, fittedm, rtol=1e-13)
# dict with scalar value (is plain dict)
# Note: this warns about dropped nan, even though there are None -FIXED
pred = res.predict(data.mean().to_dict())
assert_equal(pred.index, np.arange(1))
assert_allclose(pred.values, fittedm, rtol=1e-13)
def test_nopatsy(self):
res = self.res
data = self.data
fitted = res.fittedvalues.iloc[1:10:2]
# plain numpy array
pred = res.predict(res.model.exog[1:10:2], transform=False)
assert_allclose(pred, fitted.values, rtol=1e-13)
# pandas DataFrame
x = pd.DataFrame(res.model.exog[1:10:2],
index = data.index[1:10:2],
columns=res.model.exog_names)
pred = res.predict(x)
pdt.assert_index_equal(pred.index, fitted.index)
assert_allclose(pred.values, fitted.values, rtol=1e-13)
# one observation - 1-D
pred = res.predict(res.model.exog[1], transform=False)
assert_allclose(pred, fitted.values[0], rtol=1e-13)
# one observation - pd.Series
pred = res.predict(x.iloc[0])
pdt.assert_index_equal(pred.index, fitted.index[:1])
assert_allclose(pred.values[0], fitted.values[0], rtol=1e-13)
def test_nopatsy(self):
res = self.res
data = self.data
fitted = res.fittedvalues.iloc[1:10:2]
# plain numpy array
pred = res.predict(res.model.exog[1:10:2], transform=False)
assert_allclose(pred, fitted.values, rtol=1e-13)
# pandas DataFrame
x = pd.DataFrame(res.model.exog[1:10:2],
index=data.index[1:10:2],
columns=res.model.exog_names)
pred = res.predict(x)
pdt.assert_index_equal(pred.index, fitted.index)
assert_allclose(pred.values, fitted.values, rtol=1e-13)
# one observation - 1-D
pred = res.predict(res.model.exog[1], transform=False)
assert_allclose(pred, fitted.values[0], rtol=1e-13)
# one observation - pd.Series
pred = res.predict(x.iloc[0])
pdt.assert_index_equal(pred.index, fitted.index[:1])
assert_allclose(pred.values[0], fitted.values[0], rtol=1e-13)
def test_1d(self):
# one observation
res = self.res
data = self.data
pred = res.predict(data.iloc[:1])
pdt.assert_index_equal(pred.index, data.iloc[:1].index)
assert_allclose(pred.values, res.fittedvalues[0], rtol=1e-13)
fittedm = res.fittedvalues.mean()
xmean = data.mean()
pred = res.predict(xmean.to_frame().T)
assert_equal(pred.index, np.arange(1))
assert_allclose(pred, fittedm, rtol=1e-13)
# Series
pred = res.predict(data.mean())
assert_equal(pred.index, np.arange(1))
assert_allclose(pred.values, fittedm, rtol=1e-13)
# dict with scalar value (is plain dict)
# Note: this warns about dropped nan, even though there are None -FIXED
pred = res.predict(data.mean().to_dict())
assert_equal(pred.index, np.arange(1))
assert_allclose(pred.values, fittedm, rtol=1e-13)
def test_summary(self):
infl1 = self.infl1
infl0 = self.infl0
df0 = infl0.summary_frame()
df1 = infl1.summary_frame()
assert_allclose(df0.values, df1.values, rtol=5e-5, atol=1e-14)
pdt.assert_index_equal(df0.index, df1.index)
def test_summary(self):
infl1 = self.infl1
infl0 = self.infl0
df0 = infl0.summary_frame()
df1 = infl1.summary_frame()
assert_allclose(df0.values, df1.values, rtol=5e-5)
pdt.assert_index_equal(df0.index, df1.index)
def assert_equal(actual, desired, err_msg='', verbose=True, **kwds):
if isinstance(desired, pandas.Index):
pdt.assert_index_equal(actual, desired)
elif isinstance(desired, pandas.Series):
pdt.assert_series_equal(actual, desired, **kwds)
elif isinstance(desired, pandas.DataFrame):
pdt.assert_frame_equal(actual, desired, **kwds)
else:
npt.assert_equal(actual, desired, err_msg='', verbose=True)
def assert_equal(actual, desired, err_msg='', verbose=True, **kwds):
if isinstance(desired, pandas.Index):
pdt.assert_index_equal(actual, desired)
elif isinstance(desired, pandas.Series):
pdt.assert_series_equal(actual, desired, **kwds)
elif isinstance(desired, pandas.DataFrame):
pdt.assert_frame_equal(actual, desired, **kwds)
else:
npt.assert_equal(actual, desired, err_msg='', verbose=True)
def test_summary(self):
infl1 = self.infl1
infl0 = self.infl0
df0 = infl0.summary_frame()
df1 = infl1.summary_frame()
# just some basic check on overlap except for dfbetas
cols = ['cooks_d', 'standard_resid', 'hat_diag', 'dffits_internal']
assert_allclose(df0[cols].values, df1[cols].values, rtol=1e-5)
pdt.assert_index_equal(df0.index, df1.index)
def test_summary(self):
infl1 = self.infl1
infl0 = self.infl0
df0 = infl0.summary_frame()
df1 = infl1.summary_frame()
# just some basic check on overlap except for dfbetas
cols = ['cooks_d', 'standard_resid', 'hat_diag', 'dffits_internal']
assert_allclose(df0[cols].values, df1[cols].values, rtol=1e-5)
pdt.assert_index_equal(df0.index, df1.index)
def test_2d(self):
res = self.res
data = self.data
fitted = res.fittedvalues.iloc[1:10:2]
pred = res.predict(data.iloc[1:10:2])
pdt.assert_index_equal(pred.index, fitted.index)
assert_allclose(pred.values, fitted.values, rtol=1e-13)
# plain dict
xd = dict(zip(data.columns, data.iloc[1:10:2].values.T))
pred = res.predict(xd)
assert_equal(pred.index, np.arange(len(pred)))
assert_allclose(pred.values, fitted.values, rtol=1e-13)
def test_2d(self):
res = self.res
data = self.data
fitted = res.fittedvalues.iloc[1:10:2]
pred = res.predict(data.iloc[1:10:2])
pdt.assert_index_equal(pred.index, fitted.index)
assert_allclose(pred.values, fitted.values, rtol=1e-13)
# plain dict
xd = dict(zip(data.columns, data.iloc[1:10:2].values.T))
pred = res.predict(xd)
assert_equal(pred.index, np.arange(len(pred)))
assert_allclose(pred.values, fitted.values, rtol=1e-13)
def test_predict_offset(self):
res = self.res
data = self.data
fitted = res.fittedvalues.iloc[1:10:2]
offset = np.arange(len(fitted))
fitted = fitted + offset
pred = res.predict(data.iloc[1:10:2], offset=offset)
pdt.assert_index_equal(pred.index, fitted.index)
assert_allclose(pred.values, fitted.values, rtol=1e-13)
# plain dict
xd = dict(zip(data.columns, data.iloc[1:10:2].values.T))
pred = res.predict(xd, offset=offset)
assert_equal(pred.index, np.arange(len(pred)))
assert_allclose(pred.values, fitted.values, rtol=1e-13)
# offset as pandas.Series
data2 = data.iloc[1:10:2].copy()
data2['offset'] = offset
pred = res.predict(data2, offset=data2['offset'])
pdt.assert_index_equal(pred.index, fitted.index)
assert_allclose(pred.values, fitted.values, rtol=1e-13)
# check nan in exog is ok, preserves index matching offset length
data2 = data.iloc[1:10:2].copy()
data2['offset'] = offset
data2.iloc[0, 1] = np.nan
pred = res.predict(data2, offset=data2['offset'])
pdt.assert_index_equal(pred.index, fitted.index)
fitted_nan = fitted.copy()
fitted_nan[0] = np.nan
assert_allclose(pred.values, fitted_nan.values, rtol=1e-13)
def test_predict_offset(self):
res = self.res
data = self.data
fitted = res.fittedvalues.iloc[1:10:2]
offset = np.arange(len(fitted))
fitted = fitted + offset
pred = res.predict(data.iloc[1:10:2], offset=offset)
pdt.assert_index_equal(pred.index, fitted.index)
assert_allclose(pred.values, fitted.values, rtol=1e-13)
# plain dict
xd = dict(zip(data.columns, data.iloc[1:10:2].values.T))
pred = res.predict(xd, offset=offset)
assert_equal(pred.index, np.arange(len(pred)))
assert_allclose(pred.values, fitted.values, rtol=1e-13)
# offset as pandas.Series
data2 = data.iloc[1:10:2].copy()
data2['offset'] = offset
pred = res.predict(data2, offset=data2['offset'])
pdt.assert_index_equal(pred.index, fitted.index)
assert_allclose(pred.values, fitted.values, rtol=1e-13)
# check nan in exog is ok, preserves index matching offset length
data2 = data.iloc[1:10:2].copy()
data2['offset'] = offset
data2.iloc[0, 1] = np.nan
pred = res.predict(data2, offset=data2['offset'])
pdt.assert_index_equal(pred.index, fitted.index)
fitted_nan = fitted.copy()
fitted_nan[0] = np.nan
assert_allclose(pred.values, fitted_nan.values, rtol=1e-13)
