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src/evaluate.py
---------------
Run full test-set evaluation on the fine-tuned DistilBERT model.
Saves per-example predictions + probabilities to results/test_predictions.csv
for use in the error analysis notebook.
Usage:
python -m src.evaluate
"""
import os
import json
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset, DataLoader
from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification
from sklearn.metrics import f1_score, hamming_loss, precision_score, recall_score
from src.config import (
DISTORTION_LABELS, NUM_LABELS,
DATA_PROC_DIR, MODELS_DIR, RESULTS_DIR,
BATCH_SIZE, SEED
)
MODEL_PATH = os.path.join(MODELS_DIR, "distilbert_cognitive_distortion")
MAX_LEN = 128
THRESHOLD = 0.2 # matches Colab training
class CognitiveDistortionDataset(Dataset):
def __init__(self, df, tokenizer, max_len):
self.texts = df["text"].astype(str).tolist()
self.labels = df[DISTORTION_LABELS].values.astype(np.float32)
self.tokenizer = tokenizer
self.max_len = max_len
def __len__(self):
return len(self.texts)
def __getitem__(self, idx):
enc = self.tokenizer(
self.texts[idx],
max_length=self.max_len,
padding="max_length",
truncation=True,
return_tensors="pt"
)
return {
"input_ids" : enc["input_ids"].squeeze(0),
"attention_mask": enc["attention_mask"].squeeze(0),
"labels" : torch.tensor(self.labels[idx], dtype=torch.float32)
}
def load_baseline():
path = os.path.join(RESULTS_DIR, "baseline_results.json")
if not os.path.exists(path):
return {}
with open(path) as f:
return json.load(f)
def run():
torch.manual_seed(SEED)
np.random.seed(SEED)
if not os.path.isdir(MODEL_PATH):
raise FileNotFoundError(
f"Model not found at: {MODEL_PATH}\n"
"Extract the Colab zip and place the folder there."
)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Device : {device}")
# ── Load data ─────────────────────────────────────────────────────────────
test_df = pd.read_csv(os.path.join(DATA_PROC_DIR, "test.csv"))
print(f"Test set : {len(test_df)} rows")
# ── Load model ────────────────────────────────────────────────────────────
print(f"Loading : {MODEL_PATH}")
tokenizer = DistilBertTokenizerFast.from_pretrained(MODEL_PATH)
model = DistilBertForSequenceClassification.from_pretrained(
MODEL_PATH, num_labels=NUM_LABELS
)
model = model.to(device)
model.eval()
dataset = CognitiveDistortionDataset(test_df, tokenizer, MAX_LEN)
loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=False)
# ── Inference ─────────────────────────────────────────────────────────────
all_probs = []
all_preds = []
all_labels = []
with torch.no_grad():
for batch in loader:
input_ids = batch["input_ids"].to(device)
attention_mask = batch["attention_mask"].to(device)
labels = batch["labels"]
outputs = model(input_ids=input_ids, attention_mask=attention_mask)
probs = torch.sigmoid(outputs.logits)
preds = (probs >= THRESHOLD).int()
all_probs.append(probs.cpu().numpy())
all_preds.append(preds.cpu().numpy())
all_labels.append(labels.numpy())
all_probs = np.vstack(all_probs)
all_preds = np.vstack(all_preds)
all_labels = np.vstack(all_labels).astype(int)
# ── Metrics ───────────────────────────────────────────────────────────────
per_label_f1 = f1_score(all_labels, all_preds, average=None, zero_division=0)
per_label_p = precision_score(all_labels, all_preds, average=None, zero_division=0)
per_label_r = recall_score(all_labels, all_preds, average=None, zero_division=0)
micro_f1 = f1_score(all_labels, all_preds, average="micro", zero_division=0)
macro_f1 = f1_score(all_labels, all_preds, average="macro", zero_division=0)
h_loss = hamming_loss(all_labels, all_preds)
# ── Print comparison table ────────────────────────────────────────────────
baseline = load_baseline()
base_labels = baseline.get("per_label_f1", {})
print("\n" + "=" * 72)
print("DISTILBERT vs BASELINE — TEST SET EVALUATION")
print("=" * 72)
print(f" {'Label':<35} {'Base F1':>7} {'P':>6} {'R':>6} {'F1':>6} {'Delta':>7}")
print(f" {'-'*35} {'-'*7} {'-'*6} {'-'*6} {'-'*6} {'-'*7}")
for label, p, r, f1 in zip(DISTORTION_LABELS, per_label_p, per_label_r, per_label_f1):
base = base_labels.get(label, float("nan"))
delta = f1 - base if not np.isnan(base) else float("nan")
sign = "+" if delta >= 0 else ""
d_str = f"{sign}{delta:.4f}" if not np.isnan(delta) else " N/A"
print(f" {label:<35} {base:>7.4f} {p:>6.4f} {r:>6.4f} {f1:>6.4f} {d_str:>7}")
print("=" * 72)
print(f" {'Micro F1':<35} {baseline.get('micro_f1', float('nan')):>7.4f} {'':>6} {'':>6} {micro_f1:>6.4f}")
print(f" {'Macro F1':<35} {baseline.get('macro_f1', float('nan')):>7.4f} {'':>6} {'':>6} {macro_f1:>6.4f}")
print(f" {'Hamming Loss':<35} {baseline.get('hamming_loss', float('nan')):>7.4f} {'':>6} {'':>6} {h_loss:>6.4f}")
print("=" * 72)
# ── Save results JSON ─────────────────────────────────────────────────────
os.makedirs(RESULTS_DIR, exist_ok=True)
results = {
"model" : "DistilBERT fine-tuned",
"threshold" : THRESHOLD,
"micro_f1" : round(float(micro_f1), 4),
"macro_f1" : round(float(macro_f1), 4),
"hamming_loss" : round(float(h_loss), 4),
"per_label_f1" : {l: round(float(s), 4) for l, s in zip(DISTORTION_LABELS, per_label_f1)},
"per_label_precision": {l: round(float(s), 4) for l, s in zip(DISTORTION_LABELS, per_label_p)},
"per_label_recall" : {l: round(float(s), 4) for l, s in zip(DISTORTION_LABELS, per_label_r)},
}
results_path = os.path.join(RESULTS_DIR, "distilbert_results.json")
with open(results_path, "w") as f:
json.dump(results, f, indent=2)
print(f"\nSaved: {results_path}")
# ── Save per-example predictions CSV (for error analysis notebook) ─────────
pred_df = test_df[["text"]].copy()
for i, label in enumerate(DISTORTION_LABELS):
short = label.replace("/", "_").replace(" ", "_").replace("-", "_")
pred_df[f"true_{short}"] = all_labels[:, i]
pred_df[f"pred_{short}"] = all_preds[:, i]
pred_df[f"prob_{short}"] = all_probs[:, i].round(4)
preds_path = os.path.join(RESULTS_DIR, "test_predictions.csv")
pred_df.to_csv(preds_path, index=False)
print(f"Saved: {preds_path}")
print("Evaluation complete.")
if __name__ == "__main__":
run()
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