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])