Move files

src/great_ai/utilities/evaluate_ranking/evaluate_ranking.py

@@ -1,90 +0,0 @@
-from pathlib import Path
-from typing import Dict, List, Optional, Union
-
-import matplotlib
-import matplotlib.pyplot as plt
-import numpy as np
-from sklearn.metrics import average_precision_score, precision_recall_curve
-
-from ..unique import unique
-from .draw_f1_iso_lines import draw_f1_iso_lines
-
-
-def evaluate_ranking(
-    expected: List[Union[str, float]],
-    actual_scores: List[float],
-    target_recall: float,
-    title: Optional[str] = "",
-    disable_interpolation: bool = False,
-    axes: Optional[plt.Axes] = None,
-    output_svg: Optional[Path] = None,
-    reverse_order: bool = False,
-    plot: bool = True,
-) -> Dict[Union[str, float], float]:
-    assert 0 <= target_recall <= 1
-
-    if plot and axes is None:
-        fig = plt.figure(figsize=(20, 10))
-        fig.patch.set_facecolor("white")
-        ax = plt.axes()
-    else:
-        ax = axes
-
-    classes = sorted(unique(expected), reverse=reverse_order)
-    str_classes = [str(c) for c in classes]
-
-    with matplotlib.rc_context({"font.size": 20}):
-        if plot:
-            ax.set_xmargin(0)
-
-            draw_f1_iso_lines(axes=ax)
-
-        results: Dict[Union[str, float], float] = {}
-        for i in range(len(classes) - 1):
-            binarized_expected = [
-                (v < classes[i]) if reverse_order else (v > classes[i])
-                for v in expected
-            ]
-            precision, recall, _ = precision_recall_curve(
-                binarized_expected, actual_scores
-            )
-
-            if not disable_interpolation:
-                for j in range(1, len(precision)):
-                    precision[j] = max(precision[j - 1], precision[j])
-
-            closest_recall_index = np.argmin(np.abs(recall - target_recall))
-            precision_at_closest_recall = precision[closest_recall_index]
-            average_precision = average_precision_score(
-                binarized_expected, actual_scores
-            )
-            results[classes[i]] = precision_at_closest_recall
-
-            if plot:
-                ax.plot(
-                    recall,
-                    precision,
-                    label=f"{'|'.join(str_classes[:i + 1])} ↔ {'|'.join(str_classes[i+1:])} (P@{target_recall:.2f}={precision_at_closest_recall:.2f}, AP={average_precision:.2f})",
-                )
-
-        if plot:
-            ax.legend(loc="upper right")
-            ax.axvline(x=target_recall, linestyle="--", color="#55c6bb", linewidth=2.0)
-
-            if title is None:
-                title = "Ranking evaluation"
-
-            ax.set_title(f'{title} ({" < ".join(str_classes)})', pad=20)
-
-            ax.set_xlabel("Recall")
-            ax.set_ylabel("Precision")
-
-            ax.set_xticks([target_recall] + sorted(ax.get_xticks()))
-
-        if plot and output_svg is None:
-            if axes is None:
-                plt.show()
-        elif output_svg:
-            plt.savefig(output_svg, format="svg")
-
-    return results