changes according to reviewer comments

src/seismometer/data/binary_performance.py

@@ -3,7 +3,7 @@
 
 import numpy as np
 from numpy.typing import ArrayLike
-from sklearn.metrics import average_precision_score, roc_auc_score
+from sklearn.metrics import auc
 
 from . import calculate_bin_stats
 
@@ -69,7 +69,7 @@ def calculate_stats(
             - 'Prevalence': Prevalence of positive samples.
             - 'AUROC': Area under the receiver operating characteristic curve.
             - 'AUPRC': Area under the precision-recall curve.
-            - Additional metrics (PPV, Flag Rate, Sensitivity, Specificity, Threshold).
+            - Additional metrics (PPV, Flag Rate, Sensitivity, Specificity, Threshold, etc.).
     """
     # Check if metric is a valid name.
     try:
@@ -85,22 +85,22 @@ def calculate_stats(
     metrics_to_display = metrics_to_display if metrics_to_display else list(GENERATED_COLUMNS.keys())
     _metrics_to_display_lower = [metric_to_display.lower() for metric_to_display in metrics_to_display]
 
+    stats = calculate_bin_stats(y_true, y_pred)
+
     # Calculate overall statistics
     if "positives" in _metrics_to_display_lower:
-        row_data["Positives"] = sum(y_true)
+        row_data["Positives"] = stats["TP"].iloc[-1]
     if "prevalence" in _metrics_to_display_lower:
-        row_data["Prevalence"] = sum(y_true) / len(y_true)
+        row_data["Prevalence"] = stats["TP"].iloc[-1] / len(y_true)
     if "auroc" in _metrics_to_display_lower:
-        row_data["AUROC"] = roc_auc_score(y_true, y_pred)
+        row_data["AUROC"] = auc(1 - stats["Specificity"], stats["Sensitivity"])
     if "auprc" in _metrics_to_display_lower:
-        row_data["AUPRC"] = average_precision_score(y_true, y_pred)
+        row_data["AUPRC"] = auc(stats["Sensitivity"], stats["PPV"])
 
     # Order/round metric values
     metric_values = sorted([round(num, decimals) for num in metric_values])
     metric_values = [0 if val == 0.0 else val for val in metric_values]
 
-    stats = calculate_bin_stats(y_true, y_pred)
-
     metric_data = stats[GENERATED_COLUMNS[metric]].to_numpy()
     thresholds = stats["Threshold"].to_numpy()
 
@@ -123,10 +123,3 @@ def calculate_stats(
             )
 
     return row_data
-
-
-def is_binary_array(arr):
-    # Convert the input to a NumPy array if it isn't already
-    arr = np.asarray(arr)
-    # Check if all elements are either 0 or 1
-    return np.all((arr == 0) | (arr == 1))

src/seismometer/seismogram.py

@@ -2,6 +2,7 @@
 import logging
 from typing import Optional
 
+import numpy as np
 import pandas as pd
 
 from seismometer.configuration import AggregationStrategies, ConfigProvider, MergeStrategies
@@ -376,4 +377,17 @@ def create_cohorts(self) -> None:
             self.cohort_cols.append(disp_attr)
         logger.debug(f"Created cohorts: {', '.join(self.cohort_cols)}")
 
+    def _is_binary_array(self, arr):
+        # Convert the input to a NumPy array if it isn't already
+        arr = np.asarray(arr)
+        # Check if all elements are either 0 or 1
+        return np.all((arr == 0) | (arr == 1))
+
+    def get_binary_targets(self):
+        return [
+            pdh.event_value(target_col)
+            for target_col in self.target_cols
+            if self._is_binary_array(self.dataframe[[pdh.event_value(target_col)]])
+        ]
+
     # endregion

src/seismometer/table/analytics_table.py

@@ -1,6 +1,6 @@
 import itertools
 from enum import Enum
-from typing import Any, List, Optional
+from typing import List, Optional
 
 import pandas as pd
 
@@ -14,7 +14,7 @@
 from seismometer.controls.selection import MultiselectDropdownWidget
 from seismometer.controls.styles import BOX_GRID_LAYOUT, WIDE_LABEL_STYLE
 from seismometer.data import pandas_helpers as pdh
-from seismometer.data.binary_performance import GENERATED_COLUMNS, Metric, calculate_stats, is_binary_array
+from seismometer.data.binary_performance import GENERATED_COLUMNS, Metric, calculate_stats
 from seismometer.data.performance import (  # MetricGenerator,
     OVERALL_PERFORMANCE,
     STATNAMES,
@@ -109,15 +109,7 @@ def __init__(
         self.score_columns = score_columns if score_columns else sg.output_list
         self.target_columns = target_columns
         if sg.dataframe is not None:
-            self.target_columns = (
-                self.target_columns
-                if self.target_columns
-                else [
-                    pdh.event_value(target_col)
-                    for target_col in sg.target_cols
-                    if is_binary_array(sg.dataframe[[pdh.event_value(target_col)]])
-                ]
-            )
+            self.target_columns = self.target_columns if self.target_columns else sg.get_binary_targets()
         self.statistics_data = statistics_data
         if self.df is None and self.statistics_data is None:
             raise ValueError("At least one of 'df' or 'statistics_data' needs to be provided.")
@@ -144,20 +136,6 @@ def __init__(
         self.num_of_rows = len(self.score_columns) * len(self.target_columns)
         self.per_context = per_context
 
-        # If polars package is not installed, overwrite is_na function in great_tables package to treat Agnostic
-        # as pandas dataframe.
-        try:
-            import polars as pl
-
-            # Use 'pl' to avoid the F401 error
-            _ = pl.DataFrame()
-        except ImportError:
-            from great_tables._tbl_data import Agnostic, PdDataFrame, is_na
-
-            @is_na.register(Agnostic)
-            def _(df: PdDataFrame, x: Any) -> bool:
-                return pd.isna(x)
-
     def _validate_df_statistics_data(self):
         if not self._initializing:  # Skip validation during initial setup
             if self.df is None and self.statistics_data is None:
@@ -506,15 +484,6 @@ def binary_analytics_table(
     HTML
         The HTML table for the fairness evaluation.
     """
-    from seismometer.seismogram import Seismogram
-
-    sg = Seismogram()
-    target_cols = [
-        pdh.event_value(target_col)
-        for target_col in target_cols
-        if is_binary_array(sg.dataframe[[pdh.event_value(target_col)]])
-    ]
-
     table_config = AnalyticsTableConfig(**COLORING_CONFIG_DEFAULT)
     performance_metrics = PerformanceMetrics(
         df=None,
@@ -607,13 +576,11 @@ def __init__(
         sg = Seismogram()
         self.model_options_widget = model_options_widget
         self.title = title
-        self.binary_targets = [
-            target_col for target_col in target_cols if is_binary_array(sg.dataframe[[pdh.event_value(target_col)]])
-        ]
+
         # Multiple select dropdowns for targets and scores
         self._target_cols = MultiselectDropdownWidget(
-            self.binary_targets,
-            value=self.binary_targets,
+            sg.get_binary_targets(),
+            value=sg.get_binary_targets(),
             title="Targets",
         )
         self._score_cols = MultiselectDropdownWidget(