import unittest
from boruta import BorutaPy
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
import numpy as np
class BorutaTestCases(unittest.TestCase):
def test_get_tree_num(self):
rfc = RandomForestClassifier(max_depth=10)
bt = BorutaPy(rfc)
self.assertEqual(bt._get_tree_num(10), 44, "Tree Est. Math Fail")
self.assertEqual(bt._get_tree_num(100), 141, "Tree Est. Math Fail")
def test_if_boruta_extracts_relevant_features(self):
np.random.seed(42)
y = np.random.binomial(1, 0.5, 1000)
X = np.zeros((1000, 10))
z = y - np.random.binomial(1, 0.1, 1000) + np.random.binomial(1, 0.1, 1000)
z[z == -1] = 0
z[z == 2] = 1
# 5 relevant features
X[:, 0] = z
X[:, 1] = y * np.abs(np.random.normal(0, 1, 1000)) + np.random.normal(0, 0.1, 1000)
X[:, 2] = y + np.random.normal(0, 1, 1000)
X[:, 3] = y ** 2 + np.random.normal(0, 1, 1000)
X[:, 4] = np.sqrt(y) + np.random.binomial(2, 0.1, 1000)
# 5 irrelevant features
X[:, 5] = np.random.normal(0, 1, 1000)
X[:, 6] = np.random.poisson(1, 1000)
X[:, 7] = np.random.binomial(1, 0.3, 1000)
X[:, 8] = np.random.normal(0, 1, 1000)
X[:, 9] = np.random.poisson(1, 1000)
rfc = RandomForestClassifier()
bt = BorutaPy(rfc)
bt.fit(X, y)
# make sure that only all the relevant features are returned
self.assertListEqual(list(range(5)), list(np.where(bt.support_)[0]))
# test if this works as expected for dataframe input
X_df, y_df = pd.DataFrame(X), pd.Series(y)
bt.fit(X_df, y_df)
self.assertListEqual(list(range(5)), list(np.where(bt.support_)[0]))
# check it dataframe is returned when return_df=True
self.assertIsInstance(bt.transform(X_df, return_df=True), pd.DataFrame)
if __name__ == '__main__':
unittest.main()
