def test_predict_series(df): clusterer = KMeansClusterer() clusterer.fit(df) new = {'feature1': 2, 'feature2': 2} series = pd.Series(data=new) clusterer.predict(series)
def test_predict_not_fitted(): clusterer = KMeansClusterer() new = {'feature1': 2, 'feature2': 2} series = pd.Series(data=new) with pytest.raises(AttributeError): clusterer.predict(series)
def test_predict_dataframe(df): clusterer = KMeansClusterer() clusterer.fit(df) new = {'feature1': [2], 'feature2': [2]} dataframe = pd.DataFrame(data=new) clusterer.predict(dataframe)
def test_predict_featuresets(df): clusterer = KMeansClusterer() clusterer.fit(df) new = [{'feature1': 2, 'feature2': 2}] clusterer.predict(new)
def test_predict_dict(df): clusterer = KMeansClusterer() clusterer.fit(df) new = {'feature1': 2, 'feature2': 2} clusterer.predict(new)
def test_predict_bad_input_value(df): clusterer = KMeansClusterer() clusterer.fit(df) with pytest.raises(ValueError): clusterer.predict(pd.Series([0, 1, 2]))
def test_predict_bad_input_type(df): clusterer = KMeansClusterer() clusterer.fit(df) with pytest.raises(TypeError): clusterer.predict([0, 1])