def test_dtype_object(): # GH#880 X = np.random.random((40, 2)) df = pd.DataFrame(X) df[2] = np.random.randint(2, size=40).astype('object') df['constant'] = 1 y = pd.Series(np.random.randint(2, size=40)) with pytest.raises(ValueError): sm_data.handle_data(y, df)
def test_extra_kwargs_2d(self): sigma = np.random.random((25, 25)) sigma = sigma + sigma.T - np.diag(np.diag(sigma)) data = sm_data.handle_data(self.y, self.X, 'drop', sigma=sigma) idx = ~np.isnan(np.c_[self.y, self.X]).any(axis=1) sigma = sigma[idx][:, idx] np.testing.assert_array_equal(data.sigma, sigma)
def setup_class(cls): cls.endog = endog = pd.DataFrame(np.random.random((10, 4)), columns=['y_1', 'y_2', 'y_3', 'y_4']) exog = pd.DataFrame(np.random.random((10, 2)), columns=['x_1', 'x_2']) exog.insert(0, 'const', 1) cls.exog = exog cls.data = sm_data.handle_data(cls.endog, cls.exog) nrows = 10 nvars = 3 neqs = 4 cls.col_input = np.random.random(nvars) cls.col_result = pd.Series(cls.col_input, index=exog.columns) cls.row_input = np.random.random(nrows) cls.row_result = pd.Series(cls.row_input, index=exog.index) cls.cov_input = np.random.random((nvars, nvars)) cls.cov_result = pd.DataFrame(cls.cov_input, index=exog.columns, columns=exog.columns) cls.cov_eq_input = np.random.random((neqs, neqs)) cls.cov_eq_result = pd.DataFrame(cls.cov_eq_input, index=endog.columns, columns=endog.columns) cls.col_eq_input = np.random.random((nvars, neqs)) cls.col_eq_result = pd.DataFrame(cls.col_eq_input, index=exog.columns, columns=endog.columns) cls.xnames = ['const', 'x_1', 'x_2'] cls.ynames = ['y_1', 'y_2', 'y_3', 'y_4'] cls.row_labels = cls.exog.index
def setup_class(cls): super(TestArrays1dExog, cls).setup_class() cls.endog = np.random.random(10) exog = np.random.random(10) cls.data = sm_data.handle_data(cls.endog, exog) cls.exog = exog[:, None] cls.xnames = ['x1'] cls.ynames = 'y'
def test_labels(self): 2, 10, 14 # WTF labels = pd.Index([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24 ]) data = sm_data.handle_data(self.y, self.X, 'drop') assert data.row_labels.equals(labels)
def setup_class(cls): super(TestStructarrays, cls).setup_class() cls.endog = np.random.random(9).view([('y_1', 'f8')]).view(np.recarray) exog = np.random.random(9 * 3).view([('const', 'f8'), ('x_1', 'f8'), ('x_2', 'f8')]).view(np.recarray) exog['const'] = 1 cls.exog = exog cls.data = sm_data.handle_data(cls.endog, cls.exog) cls.xnames = ['const', 'x_1', 'x_2'] cls.ynames = 'y_1'
def test_drop(self): y = self.y X = self.X combined = np.c_[y, X] idx = ~np.isnan(combined).any(axis=1) y = y[idx] X = X[idx] data = sm_data.handle_data(self.y, self.X, 'drop') np.testing.assert_array_equal(data.endog, y) np.testing.assert_array_equal(data.exog, X)
def test_drop(self): y = self.y X = self.X combined = np.c_[y, X] idx = ~np.isnan(combined).any(axis=1) y = y.loc[idx] X = X.loc[idx] data = sm_data.handle_data(self.y, self.X, 'drop') np.testing.assert_array_equal(data.endog, y.values) tm.assert_series_equal(data.orig_endog, self.y.loc[idx]) np.testing.assert_array_equal(data.exog, X.values) tm.assert_frame_equal(data.orig_exog, self.X.loc[idx])
def setup_class(cls): cls.endog = np.random.random(10) cls.exog = np.c_[np.ones(10), np.random.random((10, 2))] cls.data = sm_data.handle_data(cls.endog, cls.exog) nrows = 10 nvars = 3 cls.col_result = cls.col_input = np.random.random(nvars) cls.row_result = cls.row_input = np.random.random(nrows) cls.cov_result = cls.cov_input = np.random.random((nvars, nvars)) cls.xnames = ['const', 'x1', 'x2'] cls.ynames = 'y' cls.row_labels = None
def setup_class(cls): cls.endog = np.random.random((10, 4)) cls.exog = np.c_[np.ones(10), np.random.random((10, 2))] cls.data = sm_data.handle_data(cls.endog, cls.exog) nrows = 10 nvars = 3 neqs = 4 cls.col_result = cls.col_input = np.random.random(nvars) cls.row_result = cls.row_input = np.random.random(nrows) cls.cov_result = cls.cov_input = np.random.random((nvars, nvars)) cls.cov_eq_result = cls.cov_eq_input = np.random.random((neqs, neqs)) cls.col_eq_result = cls.col_eq_input = np.array((neqs, nvars)) cls.xnames = ['const', 'x1', 'x2'] cls.ynames = ['y1', 'y2', 'y3', 'y4'] cls.row_labels = None
def setup_class(cls): cls.endog = pd.Series(np.random.random(10), name='y_1') exog = pd.Series(np.random.random(10), name='x_1') cls.exog = exog cls.data = sm_data.handle_data(cls.endog, cls.exog) nrows = 10 nvars = 1 cls.col_input = np.random.random(nvars) cls.col_result = pd.Series(cls.col_input, index=[exog.name]) cls.row_input = np.random.random(nrows) cls.row_result = pd.Series(cls.row_input, index=exog.index) cls.cov_input = np.random.random((nvars, nvars)) cls.cov_result = pd.DataFrame(cls.cov_input, index=[exog.name], columns=[exog.name]) cls.xnames = ['x_1'] cls.ynames = 'y_1' cls.row_labels = cls.exog.index
def setup_class(cls): cls.endog = pd.DataFrame(np.random.random(10), columns=['y_1']) exog = pd.DataFrame(np.random.random((10, 2)), columns=['x1', 'x2']) exog.insert(0, 'const', 1) cls.exog = exog.values.tolist() cls.data = sm_data.handle_data(cls.endog, cls.exog) nrows = 10 nvars = 3 cls.col_input = np.random.random(nvars) cls.col_result = pd.Series(cls.col_input, index=exog.columns) cls.row_input = np.random.random(nrows) cls.row_result = pd.Series(cls.row_input, index=exog.index) cls.cov_input = np.random.random((nvars, nvars)) cls.cov_result = pd.DataFrame(cls.cov_input, index=exog.columns, columns=exog.columns) cls.xnames = ['const', 'x1', 'x2'] cls.ynames = 'y_1' cls.row_labels = cls.endog.index
def test_endog_only_raise(self): with pytest.raises(Exception): sm_data.handle_data(self.y, None, 'raise')
def test_none(self): data = sm_data.handle_data(self.y, self.X, 'none', hasconst=False) np.testing.assert_array_equal(data.endog, self.y.values) np.testing.assert_array_equal(data.exog, self.X.values)
def test_endog_only_drop(self): y = self.y y = y[~np.isnan(y)] data = sm_data.handle_data(self.y, None, 'drop') np.testing.assert_array_equal(data.endog, y)
def test_raise_no_missing(self): # smoke test for GH#1700 sm_data.handle_data(np.random.random(20), np.random.random((20, 2)), 'raise')
def test_endog_only_drop(self): y = self.y y = y.dropna() data = sm_data.handle_data(self.y, None, 'drop') np.testing.assert_array_equal(data.endog, y.values)
def test_mv_endog(self): y = self.X y = y.loc[~np.isnan(y.values).any(axis=1)] data = sm_data.handle_data(self.X, None, 'drop') np.testing.assert_array_equal(data.endog, y.values)
def test_pandas_noconstant(self): exog = self.data.exog.copy() data = sm_data.handle_data(self.data.endog, exog) np.testing.assert_equal(data.k_constant, 0) np.testing.assert_equal(data.const_idx, None)
def test_pandas_constant(self): exog = self.data.exog.copy() exog['const'] = 1 data = sm_data.handle_data(self.data.endog, exog) np.testing.assert_equal(data.k_constant, 1) np.testing.assert_equal(data.const_idx, 6)
def setup_class(cls): super(TestArrays2dEndog, cls).setup_class() cls.endog = np.random.random((10, 1)) cls.exog = np.c_[np.ones(10), np.random.random((10, 2))] cls.data = sm_data.handle_data(cls.endog, cls.exog)
def test_array_noconstant(self): exog = self.data.exog.copy() data = sm_data.handle_data(self.data.endog.values, exog.values) np.testing.assert_equal(data.k_constant, 0) np.testing.assert_equal(data.const_idx, None)
def test_extra_kwargs_1d(self): weights = np.random.random(25) data = sm_data.handle_data(self.y, self.X, 'drop', weights=weights) idx = ~np.isnan(np.c_[self.y, self.X]).any(axis=1) weights = weights[idx] np.testing.assert_array_equal(data.weights, weights)
def test_formula_missing_extra_arrays(): np.random.seed(1) # because patsy can't turn off missing data-handling as of 0.3.0, we need # separate tests to make sure that missing values are handled correctly # when going through formulas # there is a handle_formula_data step # then there is the regular handle_data step # see GH#2083 # the untested cases are endog/exog have missing. extra has missing. # endog/exog are fine. extra has missing. # endog/exog do or do not have missing and extra has wrong dimension y = np.random.randn(10) y_missing = y.copy() y_missing[[2, 5]] = np.nan X = np.random.randn(10) X_missing = X.copy() X_missing[[1, 3]] = np.nan weights = np.random.uniform(size=10) weights_missing = weights.copy() weights_missing[[6]] = np.nan weights_wrong_size = np.random.randn(12) data = { 'y': y, 'X': X, 'y_missing': y_missing, 'X_missing': X_missing, 'weights': weights, 'weights_missing': weights_missing } data = pd.DataFrame.from_dict(data) data['constant'] = 1 formula = 'y_missing ~ X_missing' ((endog, exog), missing_idx, design_info) = handle_formula_data(data, None, formula, depth=2, missing='drop') kwargs = { 'missing_idx': missing_idx, 'missing': 'drop', 'weights': data['weights_missing'] } model_data = sm_data.handle_data(endog, exog, **kwargs) data_nona = data.dropna() np.testing.assert_equal(data_nona['y'].values, model_data.endog) np.testing.assert_equal(data_nona[['constant', 'X']].values, model_data.exog) np.testing.assert_equal(data_nona['weights'].values, model_data.weights) tmp = handle_formula_data(data, None, formula, depth=2, missing='drop') (endog, exog), missing_idx, design_info = tmp weights_2d = np.random.randn(10, 10) weights_2d[[8, 7], [7, 8]] = np.nan # symmetric missing values kwargs.update({'weights': weights_2d, 'missing_idx': missing_idx}) model_data2 = sm_data.handle_data(endog, exog, **kwargs) good_idx = [0, 4, 6, 9] np.testing.assert_equal(data.loc[good_idx, 'y'], model_data2.endog) np.testing.assert_equal(data.loc[good_idx, ['constant', 'X']], model_data2.exog) np.testing.assert_equal(weights_2d[good_idx][:, good_idx], model_data2.weights) tmp = handle_formula_data(data, None, formula, depth=2, missing='drop') (endog, exog), missing_idx, design_info = tmp kwargs.update({'weights': weights_wrong_size, 'missing_idx': missing_idx}) with pytest.raises(ValueError): sm_data.handle_data(endog, exog, **kwargs)
def test_raise_no_missing(self): # smoke test for GH#1700 sm_data.handle_data(pd.Series(np.random.random(20)), pd.DataFrame(np.random.random((20, 2))), 'raise')
def test_raise(self): with pytest.raises(Exception): sm_data.handle_data(self.y, self.X, 'raise')
def setup_class(cls): super(TestLists, cls).setup_class() cls.endog = np.random.random(10).tolist() cls.exog = np.c_[np.ones(10), np.random.random((10, 2))].tolist() cls.data = sm_data.handle_data(cls.endog, cls.exog)