112 lines
3.7 KiB
Python
112 lines
3.7 KiB
Python
from pathlib import Path
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from typing import Dict, List, Optional, TypeVar
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import matplotlib
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.metrics import average_precision_score, precision_recall_curve
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from ..unique import unique
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from .draw_f1_iso_lines import draw_f1_iso_lines
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T = TypeVar("T", str, float)
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def evaluate_ranking(
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expected: List[T],
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actual_scores: List[float],
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target_recall: float,
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title: Optional[str] = "",
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disable_interpolation: bool = False,
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axes: Optional[plt.Axes] = None,
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output_svg: Optional[Path] = None,
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reverse_order: bool = False,
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plot: bool = True,
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) -> Dict[T, float]:
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"""Render the Precision-Recall curve of a ranking.
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And improved version of scikit-learn's [PR-curve](https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py)
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Args:
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expected: Expected ordering of the elements
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(rank if it's an integer, alphabetical if a string)
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actual_scores: Actual ranking scores (need not be on the same scale as
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`expected`)
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title: Title of the plot.
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disable_interpolation: Do not interpolate.
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axes: Matplotlib axes for ploting inside a subplot.
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output_svg: If specified, save the chart as an svg to the given Path.
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reverse_order: Reverse the ranking specified by `expected`.
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plot: Display a plot on the screen.
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Returns:
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Precision values at given recall.
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"""
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assert 0 <= target_recall <= 1
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if plot and axes is None:
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fig = plt.figure(figsize=(20, 10))
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fig.patch.set_facecolor("white")
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ax = plt.axes()
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else:
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ax = axes
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classes = sorted(unique(expected), reverse=reverse_order)
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str_classes = [str(c) for c in classes]
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with matplotlib.rc_context({"font.size": 20}):
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if plot:
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ax.set_xmargin(0)
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draw_f1_iso_lines(axes=ax)
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results: Dict[T, float] = {}
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for i in range(len(classes) - 1):
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binarized_expected = [
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(v < classes[i]) if reverse_order else (v > classes[i])
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for v in expected
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]
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precision, recall, _ = precision_recall_curve(
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binarized_expected, actual_scores
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)
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if not disable_interpolation:
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for j in range(1, len(precision)):
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precision[j] = max(precision[j - 1], precision[j])
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closest_recall_index = np.argmin(np.abs(recall - target_recall))
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precision_at_closest_recall = precision[closest_recall_index]
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average_precision = average_precision_score(
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binarized_expected, actual_scores
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)
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results[classes[i]] = precision_at_closest_recall
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if plot:
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ax.plot(
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recall,
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precision,
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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})",
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)
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if plot:
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ax.legend(loc="upper right")
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ax.axvline(x=target_recall, linestyle="--", color="#55c6bb", linewidth=2.0)
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if title is None:
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title = "Ranking evaluation"
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ax.set_title(f'{title} ({" < ".join(str_classes)})', pad=20)
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ax.set_xlabel("Recall")
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ax.set_ylabel("Precision")
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ax.set_xticks([target_recall] + sorted(ax.get_xticks()))
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if plot and output_svg is None:
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if axes is None:
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plt.show()
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elif output_svg:
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plt.savefig(output_svg, format="svg")
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return results
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