def test_logistic_regression2(self):
train_out = logistic_regression_train(table=self.iris, feature_cols=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], label_col='species', random_state=12345)
predict_out = logistic_regression_predict(table=self.iris, model=train_out['model'])
table = predict_out['out_table'].values.tolist()
self.assertListEqual(table[0][:6], [5.1, 3.5, 1.4, 0.2, 'setosa', 'setosa'])
self.assertAlmostEqual(table[0][6], 0.8796816489561853, places=10)
self.assertAlmostEqual(table[0][7], 0.1203075379065891, places=10)
self.assertAlmostEqual(table[0][8], 1.0813137225508066e-05, places=10)
self.assertListEqual(table[1][:6], [4.9, 3.0, 1.4, 0.2, 'setosa', 'setosa'])
self.assertAlmostEqual(table[1][6], 0.7997063251281568, places=10)
self.assertAlmostEqual(table[1][7], 0.2002632923353134, places=10)
self.assertAlmostEqual(table[1][8], 3.0382536530016292e-05, places=10)
self.assertListEqual(table[2][:6], [4.7, 3.2, 1.3, 0.2, 'setosa', 'setosa'])
self.assertAlmostEqual(table[2][6], 0.853796794849413, places=10)
self.assertAlmostEqual(table[2][7], 0.14617730202211324, places=10)
self.assertAlmostEqual(table[2][8], 2.590312847381948e-05, places=10)
self.assertListEqual(table[3][:6], [4.6, 3.1, 1.5, 0.2, 'setosa', 'setosa'])
self.assertAlmostEqual(table[3][6], 0.8253831268363401, places=10)
self.assertAlmostEqual(table[3][7], 0.17455893749671547, places=10)
self.assertAlmostEqual(table[3][8], 5.793566694451843e-05, places=10)
self.assertListEqual(table[4][:6], [5.0, 3.6, 1.4, 0.2, 'setosa', 'setosa'])
self.assertAlmostEqual(table[4][6], 0.8973236276177116, places=10)
self.assertAlmostEqual(table[4][7], 0.10266516737872604, places=10)
self.assertAlmostEqual(table[4][8], 1.1205003562481428e-05, places=10)
def test_groupby1(self):
df = load_iris()
random_group = []
for _ in range(len(df)):
random_group.append(random.randint(1, 2))
df['random_group'] = random_group
train_out = logistic_regression_train(table=df, feature_cols=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], label_col='species', group_by=['random_group'])
predict_out = logistic_regression_predict(table=df, model=train_out['model'])
def test_logistic_regression1(self):
train_out = logistic_regression_train(table=self.iris, feature_cols=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], label_col='species', random_state=12345)
table = train_out['model']
self.assertAlmostEqual(table['aic'], 102.8061763653174, places=10)
self.assertAlmostEqual(table['bic'], 117.85935283579867, places=10)
self.assertAlmostEqual(table['intercept'][0], 0.265606167975517, places=10)
self.assertAlmostEqual(table['intercept'][1], 1.0854237423889328, places=10)
self.assertAlmostEqual(table['intercept'][2], -1.2147145780786375, places=10)
self.assertAlmostEqual(table['coefficients'][0][0], 0.41498832829570176, places=10)
self.assertAlmostEqual(table['coefficients'][1][0], 0.41663968559519354, places=10)
self.assertAlmostEqual(table['coefficients'][2][0], -1.7075251538239118, places=10)
def groupby1(self):
df = get_iris()
random_group = []
for i in range(len(df)):
random_group.append(random.randint(1, 2))
df['random_group'] = random_group
train_out = logistic_regression_train(df,
feature_cols=[
'sepal_length',
'sepal_width',
'petal_length', 'petal_width'
],
label_col='species',
group_by=['random_group'])
predict_out = logistic_regression_predict(df, train_out['model'])
print(predict_out['out_table'][['species', 'prediction']])
def test_logistic_regression3(self):
train_out = logistic_regression_train(table=self.iris, feature_cols=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], label_col='species', random_state=12345, C=2.0, group_by=['petal_width'])
predict_out = logistic_regression_predict(table=self.iris, model=train_out['model'])
table = predict_out['out_table'].values.tolist()
self.assertListEqual(table[0][:6], [7.0, 3.2, 4.7, 1.4, 'versicolor', 'versicolor'])
self.assertAlmostEqual(table[0][6], 0.9352574891997213, places=10)
self.assertAlmostEqual(table[0][7], 0.06474251080027872, places=10)
self.assertListEqual(table[1][:6], [5.2, 2.7, 3.9, 1.4, 'versicolor', 'versicolor'])
self.assertAlmostEqual(table[1][6], 0.8639068307342562, places=10)
self.assertAlmostEqual(table[1][7], 0.13609316926574377, places=10)
self.assertListEqual(table[2][:6], [6.1, 2.9, 4.7, 1.4, 'versicolor', 'versicolor'])
self.assertAlmostEqual(table[2][6], 0.8587139003988119, places=10)
self.assertAlmostEqual(table[2][7], 0.14128609960118807, places=10)
self.assertListEqual(table[3][:6], [6.7, 3.1, 4.4, 1.4, 'versicolor', 'versicolor'])
self.assertAlmostEqual(table[3][6], 0.9368424584237718, places=10)
self.assertAlmostEqual(table[3][7], 0.06315754157622822, places=10)
self.assertListEqual(table[4][:6], [6.6, 3.0, 4.4, 1.4, 'versicolor', 'versicolor'])
self.assertAlmostEqual(table[4][6], 0.9278851167206701, places=10)
self.assertAlmostEqual(table[4][7], 0.07211488327932994, places=10)