Add predictions / models functions for da index calculation

daindex/ml.py

@@ -12,7 +12,7 @@ class ProbabilisticModel(Protocol):
     def predict_proba(self, X: np.ndarray) -> np.ndarray: ...
 
 
-def obtain_det_alo_index(
+def obtain_da_index(
     df,
     cohort_name,
     scores,
@@ -112,7 +112,7 @@ def get_scores(models: list[ProbabilisticModel], df: pd.DataFrame, feature_list:
     return predicted_probs[:, :, 1].mean(axis=0)
 
 
-def get_da_values_on_predictions(
+def get_da_values_on_models(
     df: pd.DataFrame,
     cohort_name: str,
     model: list[ProbabilisticModel] | ProbabilisticModel,
@@ -145,20 +145,20 @@ def get_da_values_on_predictions(
         is_discrete: Flag indicating if the deterioration feature is discrete. Defaults to False.
         reverse: Flag indicating if the deterioration index should be reversed. Defaults to False.
         optimise_bandwidth: Flag indicating if the bandwidth of the kde should be searched. Defaults to True.
-        score_margin_multiplier: The multiplier to be used for the score margin, 1.0 results in no overlap, some overlap works well, essentially this smooths. Defaults to 1.5.
+        score_margin_multiplier: The multiplier to be used for the score margin, 1.0 results in no overlap, some overlap works well, essentially this smooths. Defaults to 5.0.
 
     Returns:
         A list of deterioration index values computed at each step,
         the length of the list of scores within the score range (there is a small upper and lower bound applied)
         and finally the da index value itself.
     """
 
-    det_label = det_label if det_label is not None else det_feature
+    det_label = det_label or det_feature
     models = model if isinstance(model, list) else [model]
     ret = []
 
-    min_det_v = min(np.min(df[det_feature]), np.min(df[det_feature]))
-    max_det_v = max(np.max(df[det_feature]), np.max(df[det_feature]))
+    min_det_v = np.min(df[det_feature])
+    max_det_v = np.max(df[det_feature])
 
     scores = get_scores(models, df, feature_list)
     step_scores = np.linspace(0, 1, steps + 1)[1:] - 1 / (2 * steps)
@@ -171,7 +171,85 @@ def get_da_values_on_predictions(
         ret.append(
             (
                 s,
-                *obtain_det_alo_index(
+                *obtain_da_index(
+                    df,
+                    cohort_name,
+                    scores,
+                    step_score_bounds,
+                    det_feature,
+                    det_threshold,
+                    det_label,
+                    det_feature_func,
+                    det_list_lengths,
+                    min_det_v,
+                    max_det_v,
+                    is_discrete,
+                    reverse,
+                    optimise_bandwidth,
+                ),
+            )
+        )
+
+    return ret
+
+
+def get_da_values_on_predictions(
+    df: pd.DataFrame,
+    cohort_name: str,
+    preds_col: str,
+    det_feature: str,
+    det_threshold: float,
+    det_label: str = None,
+    det_feature_func: Callable = None,
+    det_list_lengths: list[int] = [20, 10, 5],
+    steps: int = 50,
+    is_discrete: bool = False,
+    reverse: bool = False,
+    optimise_bandwidth: bool = True,
+    score_margin_multiplier: float = 5.0,
+) -> list[tuple[int, float]]:
+    """
+    Compute the deterioration index on the predictions of the model.
+
+    Args:
+        df: The DataFrame containing the data.
+        cohort_name: The name of the cohort being analyzed.
+        preds_col: The column name containing the predictions.
+        det_feature: The feature name representing the deterioration metric.
+        det_threshold: The threshold value for determining deterioration.
+        det_label: The label for the deterioration index. Defaults to None to be replaced by `det_feature`.
+        det_feature_func: The function to be used to extract the deterioration feature. Defaults to None as we assume it is in the `df`.
+        det_list_lengths: The list of acceptable lengths to be used for the deterioration index calculation. The first element is the preferred minimum length. Defaults to [20, 10, 5].
+        steps: The number of steps for the deterioration index calculation. Defaults to 50.
+        is_discrete: Flag indicating if the deterioration feature is discrete. Defaults to False.
+        reverse: Flag indicating if the deterioration index should be reversed. Defaults to False.
+        optimise_bandwidth: Flag indicating if the bandwidth of the kde should be searched. Defaults to True.
+        score_margin_multiplier: The multiplier to be used for the score margin, 1.0 results in no overlap, some overlap works well, essentially this smooths. Defaults to 1.5.
+
+    Returns:
+        A list of deterioration index values computed at each step,
+        the length of the list of scores within the score range (there is a small upper and lower bound applied)
+        and finally the da index value itself.
+    """
+
+    det_label = det_label or det_feature
+    ret = []
+
+    min_det_v = np.min(df[det_feature])
+    max_det_v = np.max(df[det_feature])
+
+    scores = df[preds_col].values
+    step_scores = np.linspace(0, 1, steps + 1)[1:] - 1 / (2 * steps)
+
+    for s in step_scores:
+        step_score_bounds = (
+            max(0.0, s - (1 / (2 * steps)) * score_margin_multiplier),
+            min(1.0, s + (1 / (2 * steps)) * score_margin_multiplier),
+        )
+        ret.append(
+            (
+                s,
+                *obtain_da_index(
                     df,
                     cohort_name,
                     scores,