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Limit scikit-learn version because of API changes (#81)

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* Limit scikit-learn versions between 0.22.2 and 1.1.3, remove deprecated load_boston().

* Set pytest configuration option to show test progress in detail.

* Change np.int to int according to DeprecationWarning

Signed-off-by: Maya Anderson <mayaa@il.ibm.com>
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andersonm-ibm 2023-05-14 08:52:06 +03:00 • committed by GitHub
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5 changed files with 84 additions and 79 deletions
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tests/test_minimizer.py
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@ -4,7 +4,7 @@ import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.datasets import load_boston, load_diabetes
from sklearn.datasets import load_diabetes
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
@ -24,11 +24,11 @@ tf.compat.v1.disable_eager_execution()
@pytest.fixture
def data():
return load_boston(return_X_y=True)
def dataset():
return load_diabetes()
def test_minimizer_params(data):
def test_minimizer_params():
# Assume two features, age and height, and boolean label
cells = [{"id": 1, "ranges": {"age": {"start": None, "end": 38}, "height": {"start": None, "end": 170}}, "label": 0,
'categories': {}, "representative": {"age": 26, "height": 149}},
@ -54,7 +54,7 @@ def test_minimizer_params(data):
gen.transform(dataset=ArrayDataset(X, features_names=features))
def test_minimizer_fit(data):
def test_minimizer_fit():
features = ['age', 'height']
X = np.array([[23, 165],
[45, 158],
@ -108,7 +108,7 @@ def test_minimizer_fit(data):
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
def test_minimizer_fit_pandas(data):
def test_minimizer_fit_pandas():
features = ['age', 'height', 'sex', 'ola']
X = [[23, 165, 'f', 'aa'],
[45, 158, 'f', 'aa'],
@ -179,7 +179,7 @@ def test_minimizer_fit_pandas(data):
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
def test_minimizer_params_categorical(data):
def test_minimizer_params_categorical():
# Assume three features, age, sex and height, and boolean label
cells = [{'id': 1, 'label': 0, 'ranges': {'age': {'start': None, 'end': None}},
'categories': {'sex': ['f', 'm']}, 'hist': [2, 0],
@ -246,7 +246,7 @@ def test_minimizer_params_categorical(data):
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
def test_minimizer_fit_QI(data):
def test_minimizer_fit_QI():
features = ['age', 'height', 'weight']
X = np.array([[23, 165, 70],
[45, 158, 67],
@ -301,7 +301,7 @@ def test_minimizer_fit_QI(data):
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
def test_minimizer_fit_pandas_QI(data):
def test_minimizer_fit_pandas_QI():
features = ['age', 'height', 'weight', 'sex', 'ola']
X = [[23, 165, 65, 'f', 'aa'],
[45, 158, 76, 'f', 'aa'],
@ -577,8 +577,7 @@ def test_german_credit_pandas():
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
def test_regression():
dataset = load_diabetes()
def test_regression(dataset):
x_train, x_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.5, random_state=14)
base_est = DecisionTreeRegressor(random_state=10, min_samples_split=2)
@ -651,7 +650,7 @@ def test_regression():
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
def test_X_y(data):
def test_X_y():
features = [0, 1, 2]
X = np.array([[23, 165, 70],
[45, 158, 67],
@ -705,7 +704,7 @@ def test_X_y(data):
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
def test_X_y_features_names(data):
def test_X_y_features_names():
features = ['age', 'height', 'weight']
X = np.array([[23, 165, 70],
[45, 158, 67],
@ -759,7 +758,7 @@ def test_X_y_features_names(data):
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
def test_BaseEstimator_classification(data):
def test_BaseEstimator_classification():
features = ['age', 'height', 'weight', 'sex', 'ola']
X = [[23, 165, 65, 'f', 'aa'],
[45, 158, 76, 'f', 'aa'],
@ -833,8 +832,7 @@ def test_BaseEstimator_classification(data):
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
def test_BaseEstimator_regression():
dataset = load_diabetes()
def test_BaseEstimator_regression(dataset):
x_train, x_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.5, random_state=14)
base_est = DecisionTreeRegressor(random_state=10, min_samples_split=2)