Remove the possibility in DFtoVW to provide constant values (#2601)

This commit also:
- removes unnecessary code linked with *_from_df
- removes related tests
- cleans docstring
- In Feature, change arg name to rename_feature
- In *Label class, change name to label

python/tests/test_pyvw.py

@@ -396,36 +396,25 @@ def test_feature_column_renaming_and_tag():
     conv = DFtoVW(
         label=SimpleLabel("y"),
         tag="idx",
-        features=Feature(name="col_x", value="x"),
+        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_constant_feature_value_with_empty_name():
+def test_feature_value_with_empty_name():
     df = pd.DataFrame({"idx": ["id_1"], "y": [1], "x": [2]})
     conv = DFtoVW(
         label=SimpleLabel("y"),
         tag="idx",
-        features=Feature(name="", value=2, value_from_df=False),
+        features=Feature(value="x", rename_feature=""),
         df=df,
     )
     first_line = conv.convert_df()[0]
     assert first_line == "1 id_1| :2"
 
 
-def test_variable_feature_name():
-    df = pd.DataFrame({"y": [1], "x": [2], "a": ["col_x"]})
-    conv = DFtoVW(
-        label=SimpleLabel("y"),
-        features=Feature(name="a", value="x", name_from_df=True),
-        df=df,
-    )
-    first_line = conv.convert_df()[0]
-    assert first_line == "1 | col_x:2"
-
-
 def test_multiple_lines():
     df = pd.DataFrame({"y": [1, -1], "x": [1, 2]})
     conv = DFtoVW(label=SimpleLabel("y"), features=Feature(value="x"), df=df,)
@@ -457,7 +446,7 @@ def test_without_target_multiple_features():
 def test_multiclasslabel():
     df = pd.DataFrame({"a": [1], "b": [0.5], "c": ["x"]})
     conv = DFtoVW(
-        df=df, label=MulticlassLabel(name="a", weight="b"), features=Feature("c")
+        df=df, label=MulticlassLabel(label="a", weight="b"), features=Feature("c")
     )
     first_line = conv.convert_df()[0]
     assert first_line == "1 0.5 | x"
@@ -498,8 +487,8 @@ def test_absent_col_error():
 def test_non_numerical_simplelabel_error():
     df = pd.DataFrame({"y": ["a"], "x": ["featX"]})
     with pytest.raises(TypeError) as type_error:
-        DFtoVW(df=df, label=SimpleLabel(name="y"), features=Feature("x"))
-    expected = "In argument 'name' of 'SimpleLabel', column 'y' should be either of the following type(s): 'int', 'float', 'int64'."
+        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', 'int64'."
     assert expected == str(type_error.value)
 
 
@@ -511,15 +500,15 @@ def test_wrong_feature_type_error():
     assert expected == str(type_error.value)
 
 
-def test_multiclasslabel_non_positive_name_error():
+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(name="a", weight="b"),
+            label=MulticlassLabel(label="a", weight="b"),
             features=Feature("c"),
         )
-    expected = "In argument 'name' of 'MulticlassLabel', column 'a' must be >= 1."
+    expected = "In argument 'label' of 'MulticlassLabel', column 'a' must be >= 1."
     assert expected == str(value_error.value)
 
 
@@ -528,44 +517,20 @@ def test_multiclasslabel_negative_weight_error():
     with pytest.raises(ValueError) as value_error:
         DFtoVW(
             df=df,
-            label=MulticlassLabel(name="y", weight="w"),
+            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_multiclasslabel_non_positive_constant_label_error():
-    df = pd.DataFrame({"a": [0], "b": [0.5], "c": ["x"]})
-    with pytest.raises(ValueError) as value_error:
-        DFtoVW(
-            df=df,
-            label=MulticlassLabel(name=-1, weight="b", name_from_df=False),
-            features=Feature("c"),
-        )
-    expected = "In 'MulticlassLabel', argument 'name' must be >= 1."
-    assert expected == str(value_error.value)
-
-
-def test_multiclasslabel_constant_label_type_error():
-    df = pd.DataFrame({"a": [0], "b": [0.5], "c": ["x"]})
-    with pytest.raises(TypeError) as type_error:
-        DFtoVW(
-            df=df,
-            label=MulticlassLabel(name="a", weight="b", weight_from_df=False),
-            features=Feature("c"),
-        )
-    expected = "In 'MulticlassLabel', when weight_from_df=False, argument 'weight' should be either of the following type(s): 'int', 'float'."
-    assert expected == str(type_error.value)
-
-
-def test_multilabel_non_positive_name_error():
+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(name="y"),
+            label=MultiLabel(label="y"),
             features=Feature("b"),
         )
-    expected = "In argument 'name' of 'MultiLabel', column 'y' must be >= 1."
+    expected = "In argument 'label' of 'MultiLabel', column 'y' must be >= 1."
     assert expected == str(value_error.value)