✅ Refactored melusine tests

tests/pipeline/test_pipeline.py

@@ -35,7 +35,7 @@ def test_pipeline_with_processors():
     df = pd.DataFrame({"a": [dum0, dum0]})
 
     # Fit the pipeline and transform the data
-    df_transformed = pipe.fit_transform(df)
+    df_transformed = pipe.transform(df)
 
     # Most basic test, check that the pipeline returns a pandas DataFrame
     assert isinstance(df_transformed, pd.DataFrame)
@@ -48,39 +48,6 @@ def test_pipeline_with_processors():
     assert df_transformed["c"].iloc[0] == dum2
 
 
-def test_pipeline_with_arbitrary_transformer():
-    class ArbitraryTransformer:
-        def __init__(self, dummy_attr=dum1):
-            self.dummy_attr = dummy_attr
-
-        def add_dummy_col(self, col_a_data):
-            return self.dummy_attr
-
-        def fit(self, x, y=None):
-            return self
-
-        def transform(self, x):
-            x["b"] = x["a"].apply(self.add_dummy_col)
-
-            return x
-
-    d1 = ArbitraryTransformer()
-
-    # Create pipeline
-    pipe = MelusinePipeline(steps=[("d1", d1)], verbose=True)
-
-    # Create data
-    df = pd.DataFrame({"a": [dum0, dum0]})
-
-    # Fit the pipeline and transform the data
-    df_transformed = pipe.fit_transform(df)
-
-    # Most basic test, check that the pipeline returns a pandas DataFrame
-    assert isinstance(df_transformed, pd.DataFrame)
-    assert "a" in df_transformed.columns
-    assert "b" in df_transformed.columns
-
-
 def test_meta_pipeline():
     d1 = DummyProcessor()
     d2 = DummyProcessor(output_columns=("c",), dummy_attr=dum2)
@@ -95,7 +62,7 @@ def test_meta_pipeline():
     df = pd.DataFrame({"a": [dum0, dum0]})
 
     # Fit the pipeline and transform the data
-    df_transformed = meta_pipe.fit_transform(df)
+    df_transformed = meta_pipe.transform(df)
 
     # Most basic test, check that the pipeline returns a pandas DataFrame
     assert isinstance(df_transformed, pd.DataFrame)

tests/pipeline/test_pipeline_basic.py

@@ -24,7 +24,7 @@ def test_pipeline_basic(dataframe_basic):
     pipe = MelusinePipeline(steps=[("normalizer", normalizer), ("tokenizer", tokenizer)], verbose=True)
 
     # Fit the pipeline and transform the data
-    df_transformed = pipe.fit_transform(df)
+    df_transformed = pipe.transform(df)
 
     # Most basic test, check that the pipeline returns a pandas DataFrame
     assert isinstance(df_transformed, pd.DataFrame)
@@ -67,7 +67,7 @@ def test_pipeline_from_config(dataframe_basic):
     pipe = MelusinePipeline.from_config(config_key=pipeline_key, verbose=True)
 
     # Fit the pipeline and transform the data
-    df_transformed = pipe.fit_transform(df)
+    df_transformed = pipe.transform(df)
 
     # Make basic tests
     assert isinstance(df_transformed, pd.DataFrame)
@@ -113,7 +113,7 @@ def test_pipeline_from_dict(dataframe_basic):
     pipe = MelusinePipeline.from_config(config_dict=conf_pipeline_basic, verbose=True)
 
     # Fit the pipeline and transform the data
-    df_transformed = pipe.fit_transform(df)
+    df_transformed = pipe.transform(df)
 
     # Make basic tests
     assert isinstance(df_transformed, pd.DataFrame)