def test_with_pandas_df(self): x, y = make_classification(random_state=1105) df = pd.DataFrame(x) df['y'] = y m = LogitNet(n_folds=3, random_state=123) m = m.fit(df.drop(['y'], axis=1), df.y) sanity_check_logistic(m, x)
def test_with_pandas_df(self): x, y = make_classification(random_state=1105) df = pd.DataFrame(x) df['y'] = y m = LogitNet(n_splits=3, random_state=123) m = m.fit(df.drop(['y'], axis=1), df.y) sanity_check_logistic(m, x)
def test_n_splits(self): x, y = self.binomial[0] for n in self.n_splits: m = LogitNet(n_splits=n, random_state=46657) if n > 0 and n < 3: with self.assertRaisesRegexp(ValueError, "n_splits must be at least 3"): m = m.fit(x, y) else: m = m.fit(x, y) sanity_check_logistic(m, x)
def test_n_folds(self): x, y = self.binomial[0] for n in self.n_folds: m = LogitNet(n_folds=n, random_state=46657) if n > 0 and n < 3: with self.assertRaisesRegexp(ValueError, "n_folds must be at least 3"): m = m.fit(x, y) else: m = m.fit(x, y) sanity_check_logistic(m, x)
def test_with_defaults(self): m = LogitNet(random_state=29341) for x, y in itertools.chain(self.binomial, self.multinomial): m = m.fit(x, y) sanity_check_logistic(m, x) # check selection of lambda_best assert m.lambda_best_inx_ <= m.lambda_max_inx_ # check full path predict p = m.predict(x, lamb=m.lambda_path_) assert p.shape[-1] == m.lambda_path_.size
def test_with_defaults(self): m = LogitNet(random_state=29341) for x, y in itertools.chain(self.binomial, self.multinomial): m = m.fit(x, y) sanity_check_logistic(m, x) # check selection of lambda_best ok_(m.lambda_best_inx_ <= m.lambda_max_inx_) # check full path predict p = m.predict(x, lamb=m.lambda_path_) eq_(p.shape[-1], m.lambda_path_.size)