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_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_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_mixed_type_features(): df = pd.DataFrame({"y": [1], "x1": ["a"], "x2": [2]}) conv = DFtoVW( label=SimpleLabel("y"), features=[Feature(value=colname) for colname in ["x1", "x2"]], df=df, ) first_line = conv.convert_df()[0] assert first_line == "1 | x1=a x2:2"
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_multiple_lines_with_weight(): df = pd.DataFrame({ "y": [1, 2, -1], "w": [2.5, 1.2, 3.75], "x": ["a", "b", "c"] }) conv = DFtoVW(df=df, label=SimpleLabel(label="y", weight="w"), features=Feature("x")) lines_list = conv.convert_df() assert lines_list == ["1 2.5 | x=a", "2 1.2 | x=b", "-1 3.75 | x=c"]
def test_feature_with_nan(): df = pd.DataFrame({ "y": [-1, 1, 1], "x1": [1, 2, None], "x2": [3, None, 2] }) conv = DFtoVW(df=df, features=[Feature("x1"), Feature("x2")], label=SimpleLabel("y")) lines = conv.convert_df() assert lines == ["-1 | x1:1.0 x2:3.0", "1 | x1:2.0 ", "1 | x2:2.0"]
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_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_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_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_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_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_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_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"