wehub-resource-sync
47 lines
1.2 KiB
Python
47 lines
1.2 KiB
Python
# coding=utf-8
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import pytest
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from sklearn.datasets import make_classification
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from sklearn.metrics import roc_auc_score
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try:
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from sklearn.model_selection import train_test_split
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except ImportError:
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from sklearn.cross_validation import train_test_split
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from mla.ensemble import RandomForestClassifier
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from mla.pca import PCA
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@pytest.fixture
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def dataset():
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# Generate a random binary classification problem.
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return make_classification(
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n_samples=1000,
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n_features=100,
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n_informative=75,
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random_state=1111,
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n_classes=2,
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class_sep=2.5,
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)
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# TODO: fix
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@pytest.mark.skip()
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def test_PCA(dataset):
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X, y = dataset
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.25, random_state=1111
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)
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p = PCA(50, solver="eigen")
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# fit PCA with training set, not the entire dataset
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p.fit(X_train)
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X_train_reduced = p.transform(X_train)
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X_test_reduced = p.transform(X_test)
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model = RandomForestClassifier(n_estimators=25, max_depth=5)
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model.fit(X_train_reduced, y_train)
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predictions = model.predict(X_test_reduced)[:, 1]
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score = roc_auc_score(y_test, predictions)
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assert score >= 0.75
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