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8bbb872 | 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 | import pandas as pd
import numpy as np
from xgboost import XGBClassifier
from sklearn.metrics import accuracy_score
from data_preparation.prepare_dataset import get_numpy_splits
import os
print("Loading dataset for evaluation...")
splits, _, _, _ = get_numpy_splits(
model_name="face_orientation",
split_ratios=(0.7, 0.15, 0.15),
seed=42,
scale=False
)
X_train, y_train = splits["X_train"], splits["y_train"]
X_val, y_val = splits["X_val"], splits["y_val"]
csv_path = 'models/xgboost/sweep_results_all_40.csv'
df = pd.read_csv(csv_path)
# We will calculate accuracy for each row
accuracies = []
print(f"Re-evaluating {len(df)} configurations for accuracy. This will take a few minutes...")
for idx, row in df.iterrows():
params = {
"n_estimators": int(row["n_estimators"]),
"max_depth": int(row["max_depth"]),
"learning_rate": float(row["learning_rate"]),
"subsample": float(row["subsample"]),
"colsample_bytree": float(row["colsample_bytree"]),
"reg_alpha": float(row["reg_alpha"]),
"reg_lambda": float(row["reg_lambda"]),
"random_state": 42,
"use_label_encoder": False,
"verbosity": 0,
"eval_metric": "logloss"
}
# Train the exact same model quickly
model = XGBClassifier(**params)
model.fit(X_train, y_train)
# Get validation predictions and calculate accuracy
val_preds = model.predict(X_val)
acc = accuracy_score(y_val, val_preds)
accuracies.append(round(acc, 4))
if (idx + 1) % 5 == 0:
print(f"Processed {idx + 1}/{len(df)} trials...")
# Add accuracy column and save back to CSV
df.insert(2, 'val_accuracy', accuracies)
df.to_csv(csv_path, index=False)
print(f"\nDone! Updated {csv_path} with 'val_accuracy'.")
# Display the top 5 by accuracy now just to see
top5_acc = df.nlargest(5, 'val_accuracy')[['task_id', 'val_accuracy', 'val_f1', 'val_loss']]
print("\nTop 5 Trials by Accuracy:")
print(top5_acc.to_string(index=False))
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