def test_006_logit_estimator(self):
""" Test running with different statsmodels estimators -
here logitistic
"""
n_samples = 1000
x_2 = np.random.randint(2, size=n_samples)
x_1 = np.random.random(size=n_samples)
x_3 = np.random.randint(3, size=n_samples)
x_4 = np.random.random(size=n_samples)
x_5 = x_1 + 0.05 * np.random.randn(n_samples)
x_beta = -1 + 3.5 * x_1 + 0.2 * x_2 + 0.3 * x_3
prob = 1 / (1 + np.exp(-x_beta))
y = np.random.binomial(n=1, p=prob, size=n_samples)
y2 = np.random.binomial(n=1, p=prob * 0.98, size=n_samples)
df = pd.DataFrame([x_1, x_2, x_3, x_4, x_5, y, y2],
['x_1', 'x_2', 'x_3', 'x_4', 'x_5', 'y', 'y2']).T
y_endog = ['y', 'y2']
x_exog = ['x_1', 'x_5']
controls = ['x_3', 'x_2', 'x_4']
sc = specy.SpecificationCurve(df,
y_endog,
x_exog,
controls,
cat_expand='x_3')
sc.fit(estimator=sm.Logit)
sc.plot()
sc.df_r.head()
def test_000_basic(self):
"""Test vanilla run."""
df = scdata.load_example_data2()
y_endog = 'y'
x_exog = 'x_1'
controls = ['x_2', 'x_3', 'x_4']
sc = specy.SpecificationCurve(df, y_endog, x_exog, controls)
sc.fit()
sc.plot()
sc.df_r.head()
def test_004_docs_feat_three(self):
"""Test docs feature 3: multiple dependent or independent variables"""
df = scdata.load_example_data1()
x_exog = ['x1', 'x2']
y_endog = 'y1'
controls = ['c1', 'c2', 'group1', 'group2']
sc = specy.SpecificationCurve(df, y_endog, x_exog, controls)
sc.fit()
sc.plot()
sc.df_r.head()
def test_010_preferred_specification(self):
"""Test of labelling preferred specification."""
df = scdata.load_example_data1()
x_exog = 'x1'
y_endog = 'y1'
controls = ['c2', 'group1', 'group2']
sc = specy.SpecificationCurve(df, y_endog, x_exog, controls)
sc.fit()
sc.plot(preferred_spec=['group1', 'x1', 'y1'])
sc.df_r.head()
def test_005_save_fig(self):
"""Test save fig.."""
df = scdata.load_example_data1()
x_exog = ['x1', 'x2']
y_endog = 'y1'
controls = ['c1', 'c2', 'group1', 'group2']
sc = specy.SpecificationCurve(df,
y_endog,
x_exog,
controls,
cat_expand=['group1'])
sc.fit()
sc.plot(save_path='test_fig.pdf')
os.remove('test_fig.pdf')
def test_002_docs_feat_two(self):
"""Test docs feature 2."""
df = scdata.load_example_data1()
y_endog = 'y1'
x_exog = 'x1'
controls = ['c1', 'c2', 'group1', 'group2']
sc = specy.SpecificationCurve(df,
y_endog,
x_exog,
controls,
exclu_grps=[['c1', 'c2']])
sc.fit()
sc.plot()
sc.df_r.head()
def test_001_fe_grp(self):
"""Test expand multiple FE groups. Docs feature 1."""
df = scdata.load_example_data1()
y_endog = 'y1'
x_exog = 'x1'
controls = ['c1', 'c2', 'group1', 'group2']
sc = specy.SpecificationCurve(df,
y_endog,
x_exog,
controls,
cat_expand=['group1', 'group2'])
sc.fit()
sc.plot()
sc.df_r.head()
def test_009_always_include(self):
"""Test of always include."""
df = scdata.load_example_data1()
x_exog = 'x1'
y_endog = 'y1'
controls = ['c2', 'group1', 'group2']
sc = specy.SpecificationCurve(df,
y_endog,
x_exog,
controls,
always_include='c1')
sc.fit()
sc.plot()
sc.df_r.head()
def test_008_large_no_specifications(self):
"""
Test a very large set of specifications
"""
n_samples = 400
# Number of dimensions of continuous
# random variables
n_dim = 8
c_rnd_vars = np.random.random(size=(n_dim, n_samples))
c_rnd_vars_names = [f'c_{i}' for i in range(np.shape(c_rnd_vars)[0])]
y_1 = (0.3 * c_rnd_vars[0, :] + 0.5 * c_rnd_vars[1, :])
y_2 = y_1 + 0.05 * np.random.randn(n_samples)
df = pd.DataFrame([y_1, y_2], ['y1', 'y2']).T
for i, col_name in enumerate(c_rnd_vars_names):
df[col_name] = c_rnd_vars[i, :]
controls = c_rnd_vars_names[1:]
sc = specy.SpecificationCurve(df, ['y1', 'y2'], c_rnd_vars_names[0],
controls)
sc.fit()
sc.plot()