def test_patsy_lazy_dict():
class LazyDict(dict):
def __init__(self, data):
self.data = data
def __missing__(self, key):
return np.array(self.data[key])
data = cpunish.load_pandas().data
data = LazyDict(data)
res = ols('EXECUTIONS ~ SOUTH + INCOME', data=data).fit()
res2 = res.predict(data)
npt.assert_allclose(res.fittedvalues, res2)
data = cpunish.load_pandas().data
data['INCOME'].loc[0] = None
data = LazyDict(data)
data.index = cpunish.load_pandas().data.index
res = ols('EXECUTIONS ~ SOUTH + INCOME', data=data).fit()
res2 = res.predict(data)
assert_equal(res.fittedvalues, res2) # Should lose a record
assert_equal(len(res2) + 1, len(cpunish.load_pandas().data))
def test_patsy_lazy_dict():
class LazyDict(dict):
def __init__(self, data):
self.data = data
def __missing__(self, key):
return np.array(self.data[key])
data = cpunish.load_pandas().data
data = LazyDict(data)
res = ols('EXECUTIONS ~ SOUTH + INCOME', data=data).fit()
res2 = res.predict(data)
npt.assert_allclose(res.fittedvalues, res2)
data = cpunish.load_pandas().data
data['INCOME'].loc[0] = None
data = LazyDict(data)
data.index = cpunish.load_pandas().data.index
res = ols('EXECUTIONS ~ SOUTH + INCOME', data=data).fit()
res2 = res.predict(data)
assert_equal(res.fittedvalues, res2) # Should lose a record
assert_equal(len(res2) + 1, len(cpunish.load_pandas().data))
def test_patsy_missing_data():
# Test pandas-style first
data = cpunish.load_pandas().data
data['INCOME'].loc[0] = None
res = ols('EXECUTIONS ~ SOUTH + INCOME', data=data).fit()
res2 = res.predict(data)
# First record will be dropped during fit, but not during predict
assert_equal(res.fittedvalues, res2[1:])
# Non-pandas version
data = cpunish.load_pandas().data
data['INCOME'].loc[0] = None
data = data.to_records(index=False)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
res2 = res.predict(data)
assert 'ValueWarning' in repr(w[-1].message)
assert 'nan values have been dropped' in repr(w[-1].message)
# Frist record will be dropped in both cases
assert_equal(res.fittedvalues, res2)
def test_patsy_missing_data():
# Test pandas-style first
data = cpunish.load_pandas().data
data['INCOME'].loc[0] = None
res = ols('EXECUTIONS ~ SOUTH + INCOME', data=data).fit()
res2 = res.predict(data)
# First record will be dropped during fit, but not during predict
assert_equal(res.fittedvalues, res2[1:])
# Non-pandas version
data = cpunish.load_pandas().data
data['INCOME'].loc[0] = None
data = data.to_records(index=False)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
res2 = res.predict(data)
assert 'ValueWarning' in repr(w[-1].message)
assert 'nan values have been dropped' in repr(w[-1].message)
# Frist record will be dropped in both cases
assert_equal(res.fittedvalues, res2)
