def test_scope_model():
data = patsy.demo_data("x1", "x2", "x3", "y")
def myfunc(x):
tmp = np.ones_like(x)
tmp.fill(42)
return tmp
def check_X(X):
return np.all(X[:, 1] == 42)
# checking classifier raises error if check_X doesn't return true.
# this checks that myfunc was actually applied
est = PatsyModel(CheckingClassifier(check_X=check_X), "y ~ x1 + myfunc(x2)")
est.fit(data)
def test_stateful_model():
data_train = patsy.demo_data("x1", "x2", "y")
data_train['x1'][:] = 1
# mean of x1 is 1
data_test = patsy.demo_data("x1", "x2", "y")
data_test['x1'][:] = 0
# center x1
est = PatsyModel(CheckingClassifier(), "y ~ center(x1) + x2")
est.fit(data_train)
def check_centering(X):
return np.all(X[:, 0] == -1)
est.estimator_.check_X = check_centering
# make sure that mean of training, not test data was removed
est.predict(data_test)
def test_scope_model():
data = patsy.demo_data("x1", "x2", "x3", "y")
def myfunc(x):
tmp = np.ones_like(x)
tmp.fill(42)
return tmp
def check_X(X):
return np.all(X[:, 1] == 42)
# checking classifier raises error if check_X doesn't return true.
# this checks that myfunc was actually applied
est = PatsyModel(CheckingClassifier(check_X=check_X),
"y ~ x1 + myfunc(x2)")
est.fit(data)
# test feature names
assert_equal(est.feature_names_, ["x1", "myfunc(x2)"])
def test_intercept_model():
data = patsy.demo_data("x1", "x2", "x3", "y")
def check_X_no_intercept(X):
return X.shape[1] == 2
# check wether X contains only the two features, no intercept
est = PatsyModel(CheckingClassifier(check_X=check_X_no_intercept), "y ~ x1 + x2")
est.fit(data)
# predict checks applying to new data
est.predict(data)
def check_X_intercept(X):
shape_correct = X.shape[1] == 3
first_is_intercept = np.all(X[:, 0] == 1)
return shape_correct and first_is_intercept
# check wether X does contain intercept
est = PatsyModel(CheckingClassifier(check_X=check_X_intercept), "y ~ x1 + x2", add_intercept=True)
est.fit(data)
est.predict(data)
def test_stateful_model():
data_train = patsy.demo_data("x1", "x2", "y")
data_train['x1'][:] = 1
# mean of x1 is 1
data_test = patsy.demo_data("x1", "x2", "y")
data_test['x1'][:] = 0
# center x1
est = PatsyModel(CheckingClassifier(), "y ~ center(x1) + x2")
est.fit(data_train)
def check_centering(X):
return np.all(X[:, 0] == -1)
est.estimator_.check_X = check_centering
# make sure that mean of training, not test data was removed
est.predict(data_test)
def test_intercept_model():
data = patsy.demo_data("x1", "x2", "x3", "y")
def check_X_no_intercept(X):
return X.shape[1] == 2
# check wether X contains only the two features, no intercept
est = PatsyModel(CheckingClassifier(check_X=check_X_no_intercept),
"y ~ x1 + x2")
est.fit(data)
# predict checks applying to new data
est.predict(data)
def check_X_intercept(X):
shape_correct = X.shape[1] == 3
first_is_intercept = np.all(X[:, 0] == 1)
return shape_correct and first_is_intercept
# check wether X does contain intercept
est = PatsyModel(CheckingClassifier(check_X=check_X_intercept),
"y ~ x1 + x2",
add_intercept=True)
est.fit(data)
est.predict(data)
