File size: 4,292 Bytes
877049d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 | import argparse
import json
from pathlib import Path
from typing import Iterable
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.metrics import (
accuracy_score,
classification_report,
confusion_matrix,
precision_recall_fscore_support,
)
from utils import ensure_dir
def compute_metrics(y_true: Iterable[int], y_pred: Iterable[int]) -> dict:
y_true = np.asarray(list(y_true))
y_pred = np.asarray(list(y_pred))
precision, recall, f1, _ = precision_recall_fscore_support(
y_true,
y_pred,
average="macro",
zero_division=0,
)
return {
"accuracy": float(accuracy_score(y_true, y_pred)),
"precision_macro": float(precision),
"recall_macro": float(recall),
"f1_macro": float(f1),
"support": int(len(y_true)),
}
def plot_confusion(cm: np.ndarray, label_names: list[str], output_path: str | Path, title: str) -> None:
plt.figure(figsize=(6, 5))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", xticklabels=label_names, yticklabels=label_names)
plt.xlabel("Predicted")
plt.ylabel("True")
plt.title(title)
plt.tight_layout()
plt.savefig(output_path, dpi=200)
plt.close()
def save_evaluation_bundle(
output_dir: str | Path,
split_name: str,
y_true: Iterable[int],
y_pred: Iterable[int],
label_names: list[str],
dataframe: pd.DataFrame | None = None,
probabilities: np.ndarray | None = None,
) -> dict:
output_dir = ensure_dir(output_dir)
y_true = np.asarray(list(y_true))
y_pred = np.asarray(list(y_pred))
metrics = compute_metrics(y_true, y_pred)
report_dict = classification_report(
y_true,
y_pred,
target_names=label_names,
zero_division=0,
output_dict=True,
)
report_text = classification_report(
y_true,
y_pred,
target_names=label_names,
zero_division=0,
)
cm = confusion_matrix(y_true, y_pred)
with (Path(output_dir) / f"{split_name}_metrics.json").open("w", encoding="utf-8") as file:
json.dump(
{
"metrics": metrics,
"classification_report": report_dict,
"confusion_matrix": cm.tolist(),
},
file,
indent=2,
ensure_ascii=False,
)
with (Path(output_dir) / f"{split_name}_cls_report.txt").open("w", encoding="utf-8") as file:
file.write(report_text)
plot_confusion(cm, label_names, Path(output_dir) / f"{split_name}_confusion_matrix.png", f"{split_name} confusion matrix")
if split_name == "test":
with (Path(output_dir) / "cls_report.txt").open("w", encoding="utf-8") as file:
file.write(report_text)
plot_confusion(cm, label_names, Path(output_dir) / "confusion_matrix.png", "Test confusion matrix")
if dataframe is not None:
predictions = dataframe.copy()
predictions["y_true"] = y_true
predictions["y_pred"] = y_pred
predictions["y_pred_label"] = [label_names[index] for index in y_pred]
if probabilities is not None:
for class_index, label_name in enumerate(label_names):
predictions[f"prob_{label_name}"] = probabilities[:, class_index]
split_path = Path(output_dir) / f"pred_{split_name}.csv"
predictions.to_csv(split_path, index=False)
if split_name == "test":
predictions.to_csv(Path(output_dir) / "pred_test.csv", index=False)
return metrics
def main() -> None:
parser = argparse.ArgumentParser(description="Evaluate a predictions CSV file.")
parser.add_argument("--input", required=True, help="CSV file with y_true and y_pred columns.")
parser.add_argument("--output_dir", required=True)
parser.add_argument("--label_names", nargs="+", default=["non-clickbait", "clickbait"])
args = parser.parse_args()
frame = pd.read_csv(args.input)
save_evaluation_bundle(
output_dir=args.output_dir,
split_name="custom",
y_true=frame["y_true"].tolist(),
y_pred=frame["y_pred"].tolist(),
label_names=args.label_names,
dataframe=frame,
)
if __name__ == "__main__":
main()
|