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Improve the PR-curve's semantics

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Andras Schmelczer 2022-07-24 15:42:23 +02:00
parent e881bfbfbe
commit e8b07b4f5a
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1 changed files with 14 additions and 6 deletions
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20
great_ai/utilities/evaluate_ranking/evaluate_ranking.py
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@ -4,7 +4,7 @@ from typing import Dict, List, Optional, TypeVar
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import average_precision_score, precision_recall_curve
from sklearn.metrics import average_precision_score
from ..unique import unique
from .draw_f1_iso_lines import draw_f1_iso_lines
@ -67,15 +67,23 @@ def evaluate_ranking(
(v < classes[i]) if reverse_order else (v > classes[i])
for v in expected
]
precision, recall, _ = precision_recall_curve(
binarized_expected, actual_scores
sorted_expected_actual = sorted(
zip(binarized_expected, actual_scores), key=lambda v: v[1], reverse=True
)
precision = []
recall = []
correct = 0
for all, (e, score) in enumerate(sorted_expected_actual, start=1):
correct += int(e)
precision.append(correct / all)
recall.append(all / len(sorted_expected_actual))
if not disable_interpolation:
for j in range(1, len(precision)):
precision[j] = max(precision[j - 1], precision[j])
for j in range(len(precision) - 2, -1, -1):
precision[j] = max(precision[j], precision[j + 1])
closest_recall_index = np.argmin(np.abs(recall - target_recall))
closest_recall_index = np.argmin(np.abs(np.array(recall) - target_recall))
precision_at_closest_recall = precision[closest_recall_index]
average_precision = average_precision_score(
binarized_expected, actual_scores