Fix type annotations and formatting issues

Author: fcogidi

cyclops/evaluate/fairness/evaluator.py

@@ -744,7 +744,7 @@ def _compute_metrics(  # noqa: C901, PLR0912
                         "threshold",
                     ):
                         metric.metric_a.threshold = threshold
-                        metric.metric_b.threshold = threshold
+                        metric.metric_b.threshold = threshold  # type: ignore[union-attr]
                 else:
                     LOGGER.warning(
                         "Metric %s does not have a threshold attribute. "

cyclops/evaluate/metrics/experimental/auroc.py

@@ -73,7 +73,7 @@ def __init__(
         self.max_fpr = max_fpr
 
     def _compute_metric(self) -> Array:  # type: ignore[override]
-        """Compute the AUROC.""" ""
+        """Compute the AUROC."""
         state = (
             (dim_zero_cat(self.target), dim_zero_cat(self.preds))  # type: ignore[attr-defined]
             if self.thresholds is None

cyclops/evaluate/metrics/experimental/average_precision.py

@@ -43,18 +43,18 @@ class BinaryAveragePrecision(
     >>> metric(target, preds)
     Array(0.75, dtype=float32)
     >>> metric.reset()
-    >>> target = anp.asarray([[0, 1, 0, 1], [1, 1, 0, 0]])
-    >>> preds = anp.asarray([[0.1, 0.4, 0.35, 0.8], [0.6, 0.3, 0.1, 0.7]])
+    >>> target = [[0, 1, 0, 1], [1, 1, 0, 0]]
+    >>> preds = [[0.1, 0.4, 0.35, 0.8], [0.6, 0.3, 0.1, 0.7]]
     >>> for t, p in zip(target, preds):
-    ...     metric.update(t, p)
+    ...     metric.update(anp.asarray(t), anp.asarray(p))
     >>> metric.compute()
-    Array(0.5833333333333333, dtype=float32)
+    Array(0.5833334, dtype=float32)
 
     """
 
     name: str = "Average Precision"
 
-    def _compute_metric(self) -> Array:
+    def _compute_metric(self) -> Array:  # type: ignore[override]
         """Compute the metric."""
         state = (
             (dim_zero_cat(self.target), dim_zero_cat(self.preds))  # type: ignore[attr-defined]

cyclops/evaluate/metrics/experimental/functional/average_precision.py

@@ -50,7 +50,7 @@ def _binary_average_precision_compute(
         pos_label,
     )
     xp = apc.array_namespace(precision, recall)
-    return -xp.sum(_diff(recall) * precision[:-1])  # type: ignore
+    return -xp.sum(_diff(recall) * precision[:-1], dtype=xp.float32)  # type: ignore
 
 
 def binary_average_precision(
@@ -128,13 +128,13 @@ def binary_average_precision(
     Examples
     --------
     >>> import numpy.array_api as anp
-    >>> from cyclops.evaluate.metrics..experimental.functional import (
+    >>> from cyclops.evaluate.metrics.experimental.functional import (
     ...     binary_average_precision
     ... )
     >>> target = anp.asarray([0, 1, 1, 0])
     >>> preds = anp.asarray([0, 0.5, 0.7, 0.8])
     >>> binary_average_precision(target, preds, thresholds=None)
-    Array(0.5833333333333333, dtype=float32)
+    Array(0.5833334, dtype=float32)
 
     """
     _binary_precision_recall_curve_validate_args(thresholds, ignore_index)

cyclops/tasks/classification.py

@@ -348,9 +348,11 @@ def evaluate(
 
             # select the probability scores of the positive class since metrics
             # expect a single column of probabilities
-            dataset = dataset.map(
+            dataset = dataset.map(  # type: ignore[union-attr]
                 lambda examples: {
-                    f"{prediction_column_prefix}.{model_name}": np.array(examples)[
+                    f"{prediction_column_prefix}.{model_name}": np.array(  # noqa: B023
+                        examples,
+                    )[
                         :,
                         1,
                     ].tolist(),

docs/source/tutorials/kaggle/heart_failure_prediction.ipynb

@@ -858,8 +858,9 @@
     "    metrics=MetricDict(\n",
     "        {\n",
     "            \"BinaryAccuracy\": create_metric(\n",
-    "                metric_name=\"binary_accuracy\", experimental=True\n",
-    "            )\n",
+    "                metric_name=\"binary_accuracy\",\n",
+    "                experimental=True,\n",
+    "            ),\n",
     "        },\n",
     "    ),\n",
     "    model_names=model_name,\n",

docs/source/tutorials/nihcxr/cxr_classification.ipynb

@@ -29,7 +29,6 @@
     "\n",
     "import shutil\n",
     "from functools import partial\n",
-    "from typing import Optional\n",
     "\n",
     "import numpy as np\n",
     "import plotly.express as px\n",