increase coverage 2

README.md

@@ -101,6 +101,7 @@ Example steps taken by ATOM's pipeline:
 * [50+ plots to analyze the data and model performance](https://tvdboom.github.io/ATOM/latest/user_guide/plots/#available-plots)
 * [Avoid refactoring to test new pipelines](https://tvdboom.github.io/ATOM/latest/user_guide/data_management/#branches)
 * [Native support for GPU training](https://tvdboom.github.io/ATOM/latest/user_guide/accelerating/#gpu-acceleration)
+* [Integration with polars, pyspark and pyarrow](https://tvdboom.github.io/ATOM/latest/user_guide/data_management/#data-engines)
 * [25+ example notebooks to get you started](https://tvdboom.github.io/ATOM/latest/examples/accelerating_cuml/)
 * [Full integration with multilabel and multioutput datasets](https://tvdboom.github.io/ATOM/latest/user_guide/data_management/#multioutput-tasks)
 * [Native support for sparse datasets](https://tvdboom.github.io/ATOM/latest/user_guide/data_management/#sparse-datasets)

atom/basemodel.py

@@ -779,6 +779,8 @@ def _score_from_est(
                 data=_check_response_method(estimator, scorer._response_method)(X),
                 index=y.index,
             )
+            if isinstance(y_pred, pd.DataFrame) and self.task is Task.binary_classification:
+                y_pred = y_pred.iloc[:, 1]  # Return probability of the positive class
 
         return self._score_from_pred(scorer, y, y_pred, **kwargs)
 

atom/data_cleaning.py

@@ -2621,11 +2621,6 @@ def fit(self, X: XConstructor, y: YConstructor | None = None) -> Self:
                 random_state=kwargs.pop("random_state", self.random_state),
                 **kwargs,
             )
-        else:
-            raise ValueError(
-                f"Invalid value for the strategy parameter, got {self.strategy}. "
-                f"Choose from: {', '.join(strategies)}."
-            )
 
         num_cols = Xt.select_dtypes(include="number")
 
@@ -2889,11 +2884,6 @@ def transform(
         }
 
         for strat in lst(self.strategy):
-            if strat not in ["zscore", *strategies]:
-                raise ValueError(
-                    "Invalid value for the strategy parameter. "
-                    f"Choose from: zscore, {', '.join(strategies)}."
-                )
             if strat != "zscore" and str(self.method) != "drop":
                 raise ValueError(
                     "Invalid value for the method parameter. Only the zscore "
@@ -2986,12 +2976,8 @@ def transform(
                 yt = yt[outlier_rows]
 
         else:
-            # Replace the columns in X and y with the new values from objective
+            # Replace the columns in X with the new values from objective
             Xt.update(objective)
-            if isinstance(yt, pd.Series) and yt.name in objective:
-                yt.update(objective[str(yt.name)])
-            elif isinstance(yt, pd.DataFrame):
-                yt.update(objective)
 
         return variable_return(self._convert(Xt), self._convert(yt))
 

atom/nlp.py

@@ -955,10 +955,6 @@ def fit(self, X: XConstructor, y: YConstructor | None = None) -> Self:
         )
         self._estimator = estimator(**self.kwargs)
 
-        if hasattr(self._estimator, "set_output"):
-            # transform="pandas" fails for sparse output
-            self._estimator.set_output(transform="default")
-
         self._log("Fitting Vectorizer...", 1)
         self._estimator.fit(Xt[self._corpus])
 

atom/pipeline.py

@@ -354,11 +354,6 @@ def _fit(
             else:
                 cloned = clone(transformer)
 
-                # Attach internal attrs otherwise wiped by clone
-                for attr in ("_cols", "_train_only"):
-                    if hasattr(transformer, attr):
-                        setattr(cloned, attr, getattr(transformer, attr))
-
             with adjust(cloned, verbose=self._verbose):
                 # Fit or load the current estimator from cache
                 # Type ignore because routed_params is never None but

atom/training.py

@@ -29,6 +29,14 @@
 )
 
 
+__all__ = [
+    "DirectClassifier", "DirectForecaster", "DirectRegressor",
+    "SuccessiveHalvingClassifier", "SuccessiveHalvingForecaster",
+    "SuccessiveHalvingRegressor", "TrainSizingClassifier",
+    "TrainSizingForecaster", "TrainSizingRegressor",
+]
+
+
 class Direct(BaseEstimator, BaseTrainer):
     """Direct training approach.
 

atom/utils/utils.py

@@ -2023,17 +2023,14 @@ def check_is_fitted(
         Whether the estimator is fitted.
 
     """
-    if not _is_fitted(obj, attributes):
-        if exception:
-            raise NotFittedError(
-                f"This {type(obj).__name__} instance is not yet fitted. "
-                f"Call {'run' if hasattr(obj, 'run') else 'fit'} with "
-                "appropriate arguments before using this object."
-            )
-        else:
-            return False
+    if not (is_fitted := _is_fitted(obj, attributes)) and exception:
+        raise NotFittedError(
+            f"This {type(obj).__name__} instance is not yet fitted. "
+            f"Call {'run' if hasattr(obj, 'run') else 'fit'} with "
+            "appropriate arguments before using this object."
+        )
 
-    return True
+    return is_fitted
 
 
 def get_custom_scorer(metric: str | MetricFunction | Scorer) -> Scorer:

docs_sources/dependencies.md

@@ -40,7 +40,7 @@ packages are necessary for its correct functioning.
 * **[optuna](https://optuna.org/)** (>=3.4.0)
 * **[pandas](https://pandas.pydata.org/)** (>=2.1.2)
 * **[plotly](https://plotly.com/python/)** (>=5.18.0)
-* **[scikit-learn](https://scikit-learn.org/stable/)** (>=1.4.0)
+* **[scikit-learn](https://scikit-learn.org/stable/)** (>=1.4.1.post1)
 * **[scipy](https://www.scipy.org/)** (>=1.10.1)
 * **[shap](https://github.com/slundberg/shap/)** (>=0.43.0)
 * **[sktime[forecasting]](http://www.sktime.net/en/latest/)** (>=0.26.0)