def test_feature_column_renaming_and_tag(): df = pd.DataFrame({"idx": ["id_1"], "y": [1], "x": [2]}) conv = DFtoVW( label=SimpleLabel("y"), tag="idx", features=Feature(value="x", rename_feature="col_x"), df=df, ) first_line = conv.convert_df()[0] assert first_line == "1 id_1| col_x:2"
def test_as_type_in_features(): df = pd.DataFrame({"y": [1], "a": [2], "b": [3], "c": ["4"]}) features = [ Feature("a", as_type="categorical"), Feature("b"), Feature("c", as_type="numerical") ] conv = DFtoVW(label=SimpleLabel("y"), features=features, df=df) first_line = conv.convert_df()[0] assert first_line == "1 | a=2 b:3 c:4"
def test_multiclasslabel_negative_weight_error(): df = pd.DataFrame({"y": [1], "w": [-0.5], "x": [2]}) with pytest.raises(ValueError) as value_error: DFtoVW( df=df, label=MulticlassLabel(label="y", weight="w"), features=Feature("x"), ) expected = "In argument 'weight' of 'MulticlassLabel', column 'w' must be >= 0." assert expected == str(value_error.value)
def test_multilabel_non_positive_label_error(): df = pd.DataFrame({"y": [0], "b": [1]}) with pytest.raises(ValueError) as value_error: DFtoVW( df=df, label=MultiLabel(label="y"), features=Feature("b"), ) expected = "In argument 'label' of 'MultiLabel', column 'y' must be >= 1." assert expected == str(value_error.value)
def test_multiclasslabel_non_positive_label_error(): df = pd.DataFrame({"a": [0], "b": [0.5], "c": ["x"]}) with pytest.raises(ValueError) as value_error: DFtoVW( df=df, label=MulticlassLabel(label="a", weight="b"), features=Feature("c"), ) expected = "In argument 'label' of 'MulticlassLabel', column 'a' must be >= 1." assert expected == str(value_error.value)
def test_absent_col_error(): with pytest.raises(ValueError) as value_error: df = pd.DataFrame({"a": [1]}) DFtoVW( df=df, label=SimpleLabel("a"), features=[Feature(col) for col in ["a", "c", "d"]], ) expected = "In 'Feature': column(s) 'c', 'd' not found in dataframe." assert expected == str(value_error.value)
def test_listlabel_mixed_label_error(): with pytest.raises(TypeError) as type_error: df = pd.DataFrame({"a": [1], "c": [-0.5], "p": [0.1], "b": [2], "x": [1]}) conv = DFtoVW( df=df, label=[ContextualbanditLabel("a", "c", "p"), MultiLabel("b")], features=Feature("x"), ) expected = "The list passed in 'label' has mixed label types." assert expected == str(type_error.value)
def test_contextualbanditlabel_non_positive_action(): df = pd.DataFrame({"a": [0], "c": [-0.5], "p": [0.5], "x": [1]}) with pytest.raises(ValueError) as value_error: DFtoVW( df=df, label=ContextualbanditLabel("a", "c", "p"), features=Feature("x"), ) expected = "In argument 'action' of 'ContextualbanditLabel', column 'a' must be >= 1." assert expected == str(value_error.value)
def test_contextualbanditlabel_non_float_proba_error(): df = pd.DataFrame({"a": [1], "c": [-0.5], "p": [1], "x": [1]}) with pytest.raises(TypeError) as value_error: DFtoVW( df=df, label=ContextualbanditLabel("a", "c", "p"), features=Feature("x"), ) expected = "In argument 'probability' of 'ContextualbanditLabel', column 'p' should be either of the following type(s): 'float'." assert expected == str(value_error.value)
def test_multiple_named_namespaces_multiple_features_multiple_lines(): df = pd.DataFrame({"y": [1, -1], "a": [2, 3], "b": ["x1", "x2"], "c": [36.4, 47.8]}) ns1 = Namespace(name="FirstNameSpace", features=Feature("a")) ns2 = Namespace(name="DoubleIt", value=2, features=[Feature("b"), Feature("c")]) label = SimpleLabel("y") conv = DFtoVW(df=df, label=label, namespaces=[ns1, ns2]) lines_list = conv.convert_df() assert lines_list == [ "1 |FirstNameSpace a:2 |DoubleIt:2 b=x1 c:36.4", "-1 |FirstNameSpace a:3 |DoubleIt:2 b=x2 c:47.8", ]
def test_contextualbanditlabel_multiple_label(): df = pd.DataFrame( {"a1": [1], "c1": [-0.5], "p1": [0.1], "a2": [2], "c2": [-1.5], "p2": [0.6], "x": [1]} ) conv = DFtoVW( df=df, label=[ContextualbanditLabel("a1", "c1", "p1"), ContextualbanditLabel("a2", "c2", "p2")], features=Feature("x"), ) first_line = conv.convert_df()[0] assert first_line == "1:-0.5:0.1 2:-1.5:0.6 | x:1"
def test_listlabel_not_allowed_label_error(): with pytest.raises(TypeError) as type_error: df = pd.DataFrame({"a": [1], "b": [2], "x": [1]}) conv = DFtoVW( df=df, label=[SimpleLabel("a"), SimpleLabel("b")], features=Feature("x"), ) expected = ( "The only labels that can be used with list are 'ContextualbanditLabel', 'MultiLabel'." ) assert expected == str(type_error.value)
def test_multiple_named_namespaces(): df = pd.DataFrame({"y": [1], "a": [2], "b": [3]}) conv = DFtoVW( df=df, label=SimpleLabel("y"), namespaces=[ Namespace(name="FirstNameSpace", features=Feature("a")), Namespace(name="DoubleIt", value=2, features=Feature("b")), ], ) first_line = conv.convert_df()[0] assert first_line == "1 |FirstNameSpace a:2 |DoubleIt:2 b:3"
def test_contextualbanditlabel_negative_proba_error(): df = pd.DataFrame({"a": [1], "c": [-0.5], "p": [-0.1], "x": [1]}) with pytest.raises(ValueError) as value_error: DFtoVW( df=df, label=ContextualbanditLabel("a", "c", "p"), features=Feature("x"), ) expected = ( "In argument 'probability' of 'ContextualbanditLabel', column 'p' must be >= 0 and <= 1." ) assert expected == str(value_error.value)
def test_dirty_colname_feature(): df = pd.DataFrame({ "target :": [1], " my first feature:": ["x"], "white space at the end ": [2] }) features = [ Feature(colname) for colname in [" my first feature:", "white space at the end "] ] conv = DFtoVW(label=SimpleLabel("target :"), features=features, df=df) first_line = conv.convert_df()[0] assert first_line == "1 | my_first_feature=x white_space_at_the_end:2"
def test_non_default_index(): df = pd.DataFrame({"y": [0], "x": [1]}, index=[1]) conv = DFtoVW(df=df, label=SimpleLabel("y"), features=Feature("x")) first_line = conv.convert_df()[0] assert first_line == "0 | x:1"
def test_no_label_multiple_features(): df = pd.DataFrame({"a": [2], "b": [3]}) conv = DFtoVW(df=df, features=[Feature(col) for col in ["a", "b"]]) first_line = conv.convert_df()[0] assert first_line == "| a:2 b:3"
def test_contextualbanditlabel_one_label(): df = pd.DataFrame({"a": [1], "c": [-0.5], "p": [0.1], "x": [1]}) conv = DFtoVW(df=df, label=ContextualbanditLabel("a", "c", "p"), features=Feature("x")) first_line = conv.convert_df()[0] assert first_line == "1:-0.5:0.1 | x:1"
def test_multilabel_with_listlabel_builder(): df = pd.DataFrame({"y1": [1], "y2": [2], "x": [3]}) conv = DFtoVW(df=df, label=[MultiLabel("y1"), MultiLabel("y2")], features=Feature("x")) first_line = conv.convert_df()[0] assert first_line == "1,2 | x:3"
def test_multilabel_list_of_len_1(): df = pd.DataFrame({"y": [1], "x": [2]}) conv1 = DFtoVW(df=df, label=MultiLabel(["y"]), features=Feature("x")) conv2 = DFtoVW(df=df, label=MultiLabel("y"), features=Feature("x")) assert conv1.convert_df()[0] == conv2.convert_df()[0]
def test_multilabel(): df = pd.DataFrame({"y1": [1], "y2": [2], "x": [3]}) conv = DFtoVW(df=df, label=MultiLabel(["y1", "y2"]), features=Feature("x")) first_line = conv.convert_df()[0] assert first_line == "1,2 | x:3"
def test_no_label_rename_feature(): df = pd.DataFrame({"a": [1]}) first_line = DFtoVW(df=df, features=Feature("a", rename_feature="")).convert_df()[0] assert first_line == "| :1"
def test_multiclasslabel(): df = pd.DataFrame({"a": [1], "b": [0.5], "c": [-3]}) conv = DFtoVW(df=df, label=MulticlassLabel(label="a", weight="b"), features=Feature("c")) first_line = conv.convert_df()[0] assert first_line == "1 0.5 | c:-3"
def test_wrong_weight_type_error(): df = pd.DataFrame({"y": [1], "x": [2], "w": ["a"]}) with pytest.raises(TypeError) as type_error: DFtoVW(df=df, label=SimpleLabel(label="y", weight="w"), features=Feature("x")) expected = "In argument 'weight' of 'SimpleLabel', column 'w' should be either of the following type(s): 'int', 'float'." assert expected == str(type_error.value)
def test_wrong_feature_type_error(): df = pd.DataFrame({"y": [1], "x": [2]}) with pytest.raises(TypeError) as type_error: DFtoVW(df=df, label=SimpleLabel("y"), features="x") expected = "Argument 'features' should be a Feature or a list of Feature." assert expected == str(type_error.value)
def test_multiple_lines(): df = pd.DataFrame({"y": [1, -1], "x": [1, 2]}) conv = DFtoVW(label=SimpleLabel("y"), features=Feature(value="x"), df=df) lines_list = conv.convert_df() assert lines_list == ["1 | x:1", "-1 | x:2"]
def test_non_numerical_error(): df = pd.DataFrame({"y": ["a"], "x": ["featX"]}) with pytest.raises(TypeError) as type_error: DFtoVW(df=df, label=SimpleLabel(label="y"), features=Feature("x")) expected = "In argument 'label' of 'SimpleLabel', column 'y' should be either of the following type(s): 'int', 'float'." assert expected == str(type_error.value)