add unit tests for CLI and supervised learning components

Author: shankarpandala

tests/test_cli.py

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+import pytest
+from click.testing import CliRunner
+from lazypredict import cli
+
+def test_cli_main():
+    runner = CliRunner()
+    result = runner.invoke(cli.main)
+    assert result.exit_code == 0
+    assert "lazypredict.cli.main" in result.output
+
+def test_cli_help():
+    runner = CliRunner()
+    result = runner.invoke(cli.main, ["--help"])
+    assert result.exit_code == 0
+    assert "--help  Show this message and exit." in result.output

tests/test_helpers.py

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+import pytest
+import pandas as pd
+from lazypredict.Supervised import get_card_split
+
+def test_get_card_split():
+    df = pd.DataFrame({
+        'A': ['a', 'b', 'c', 'd', 'e'],
+        'B': ['f', 'g', 'h', 'i', 'j'],
+        'C': [1, 2, 3, 4, 5]
+    })
+    cols = ['A', 'B']
+    card_low, card_high = get_card_split(df, cols, n=3)
+    assert len(card_low) == 2
+    assert len(card_high) == 0

tests/test_init.py

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+import lazypredict
+
+def test_import():
+    assert lazypredict is not None

tests/test_supervised.py

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+import pytest
+import numpy as np
+import pandas as pd
+from lazypredict.Supervised import LazyClassifier, LazyRegressor
+from sklearn.datasets import load_breast_cancer, load_boston
+from sklearn.model_selection import train_test_split
+from sklearn.utils import shuffle
+
+def test_lazy_classifier_fit():
+    data = load_breast_cancer()
+    X = data.data
+    y = data.target
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=123)
+    clf = LazyClassifier(verbose=0, ignore_warnings=True, custom_metric=None)
+    models, predictions = clf.fit(X_train, X_test, y_train, y_test)
+    assert isinstance(models, pd.DataFrame)
+    assert isinstance(predictions, pd.DataFrame)
+
+def test_lazy_classifier_provide_models():
+    data = load_breast_cancer()
+    X = data.data
+    y = data.target
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=123)
+    clf = LazyClassifier(verbose=0, ignore_warnings=True, custom_metric=None)
+    clf.fit(X_train, X_test, y_train, y_test)
+    models = clf.provide_models(X_train, X_test, y_train, y_test)
+    assert isinstance(models, dict)
+
+def test_lazy_regressor_fit():
+    boston = load_boston()
+    X, y = shuffle(boston.data, boston.target, random_state=13)
+    X = X.astype(np.float32)
+    offset = int(X.shape[0] * 0.9)
+    X_train, y_train = X[:offset], y[:offset]
+    X_test, y_test = X[offset:], y[offset:]
+    reg = LazyRegressor(verbose=0, ignore_warnings=False, custom_metric=None)
+    models, predictions = reg.fit(X_train, X_test, y_train, y_test)
+    assert isinstance(models, pd.DataFrame)
+    assert isinstance(predictions, pd.DataFrame)
+
+def test_lazy_regressor_provide_models():
+    boston = load_boston()
+    X, y = shuffle(boston.data, boston.target, random_state=13)
+    X = X.astype(np.float32)
+    offset = int(X.shape[0] * 0.9)
+    X_train, y_train = X[:offset], y[:offset]
+    X_test, y_test = X[offset:], y[offset:]
+    reg = LazyRegressor(verbose=0, ignore_warnings=False, custom_metric=None)
+    reg.fit(X_train, X_test, y_train, y_test)
+    models = reg.provide_models(X_train, X_test, y_train, y_test)
+    assert isinstance(models, dict)