fix(hds): override the fit_predict and fit_predict_proba methods

crowdkit/aggregation/classification/dawid_skene.py

@@ -578,3 +578,31 @@ def fit(self, data: pd.DataFrame) -> "OneCoinDawidSkene":  # type: ignore[overri
         self.labels_ = get_most_probable_labels(probas)
 
         return self
+
+    def fit_predict_proba(self, data: pd.DataFrame) -> pd.DataFrame:  # type: ignore[override]
+        """Fits the model to the training data and returns probability distributions of labels for each task.
+        Args:
+            data (DataFrame): The training dataset of workers' labeling results
+                which is represented as the `pandas.DataFrame` data containing `task`, `worker`, and `label` columns.
+        Returns:
+            DataFrame: Probability distributions of task labels.
+                The `pandas.DataFrame` data is indexed by `task` so that `result.loc[task, label]` is the probability that the `task` true label is equal to `label`.
+                Each probability is in the range from 0 to 1, all task probabilities must sum up to 1.
+        """
+
+        self.fit(data)
+        assert self.probas_ is not None, "no probas_"
+        return self.probas_
+
+    def fit_predict(self, data: pd.DataFrame) -> "pd.Series[Any]":  # type: ignore[override]
+        """Fits the model to the training data and returns the aggregated results.
+        Args:
+            data (DataFrame): The training dataset of workers' labeling results
+                which is represented as the `pandas.DataFrame` data containing `task`, `worker`, and `label` columns.
+        Returns:
+            Series: Task labels. The `pandas.Series` data is indexed by `task` so that `labels.loc[task]` is the most likely true label of tasks.
+        """
+
+        self.fit(data)
+        assert self.labels_ is not None, "no labels_"
+        return self.labels_

tests/aggregation/test_ds_aggregation.py

@@ -404,6 +404,16 @@ def test_aggregate_hds_on_toy_ysda(
         toy_ground_truth_df.sort_index(),
     )
 
+    assert_series_equal(
+        OneCoinDawidSkene(n_iter=n_iter, tol=tol)
+        .fit_predict(toy_answers_df)
+        .sort_index(),
+        toy_ground_truth_df.sort_index(),
+    )
+
+    probas = OneCoinDawidSkene(n_iter=n_iter, tol=tol).fit_predict_proba(toy_answers_df)
+    assert ((probas >= 0) & (probas <= 1)).all().all()
+
 
 @pytest.mark.parametrize("n_iter, tol", [(10, 0), (100500, 1e-5)])
 def test_aggregate_ds_on_simple(
@@ -432,6 +442,18 @@ def test_aggregate_hds_on_simple(
         simple_ground_truth.sort_index(),
     )
 
+    assert_series_equal(
+        OneCoinDawidSkene(n_iter=n_iter, tol=tol)
+        .fit_predict(simple_answers_df)
+        .sort_index(),
+        simple_ground_truth.sort_index(),
+    )
+
+    probas = OneCoinDawidSkene(n_iter=n_iter, tol=tol).fit_predict_proba(
+        simple_answers_df
+    )
+    assert ((probas >= 0) & (probas <= 1)).all().all()
+
 
 def _make_probas(data: List[List[Any]]) -> pd.DataFrame:
     # TODO: column should not be an index!