File size: 4,676 Bytes
0b55df4 | 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 | import csv
import json
import time
from pathlib import Path
import numpy as np
import onnxruntime as ort
from tokenizers import Tokenizer
# Resolve file paths relative to script location
TESTS_DIR = Path(__file__).resolve().parent
PROJECT_ROOT = TESTS_DIR.parent
MODEL_PATH = PROJECT_ROOT / "model.onnx"
TOK_PATH = PROJECT_ROOT / "tokenizer.json"
DATASET_PATH = TESTS_DIR / "test_dataset.csv"
LABEL_MAP = {"0": "low", "1": "medium", "2": "hard"}
def main():
if not MODEL_PATH.exists() or not TOK_PATH.exists():
raise FileNotFoundError(f"Model artifacts not found in {PROJECT_ROOT}")
if not DATASET_PATH.exists():
raise FileNotFoundError(f"Test dataset not found at {DATASET_PATH}")
print(f"Loading tokenizer from {TOK_PATH}...")
tokenizer = Tokenizer.from_file(str(TOK_PATH))
print(f"Loading ONNX session from {MODEL_PATH}...")
session = ort.InferenceSession(str(MODEL_PATH), providers=["CPUExecutionProvider"])
# Read test dataset
queries = []
targets = []
with open(DATASET_PATH, "r", encoding="utf-8") as f:
reader = csv.DictReader(f)
for row in reader:
queries.append(row["query"])
targets.append(row["label"])
print(f"Loaded {len(queries)} test queries from {DATASET_PATH.name}.")
predictions = []
latencies = []
# Warmup session
encoded_warmup = tokenizer.encode("Warmup query")
w_ids = np.array([[encoded_warmup.ids[0]]], dtype=np.int64)
w_mask = np.array([[1]], dtype=np.int64)
_ = session.run(None, {"input_ids": w_ids, "attention_mask": w_mask})
for q in queries:
t0 = time.perf_counter()
encoded = tokenizer.encode(q)
input_ids = np.array([encoded.ids], dtype=np.int64)
attention_mask = np.array([encoded.attention_mask], dtype=np.int64)
outputs = session.run(None, {"input_ids": input_ids, "attention_mask": attention_mask})
logits = outputs[0][0]
exp_l = np.exp(logits - np.max(logits))
probs = exp_l / np.sum(exp_l)
pred_idx = int(np.argmax(probs))
t1 = time.perf_counter()
latencies.append((t1 - t0) * 1000.0)
predictions.append(LABEL_MAP[str(pred_idx)])
# Compute Metrics
labels = ["low", "medium", "hard"]
conf_matrix = {t: {p: 0 for p in labels} for t in labels}
correct = 0
for target, pred in zip(targets, predictions):
conf_matrix[target][pred] += 1
if target == pred:
correct += 1
accuracy = correct / len(targets)
metrics = {}
f1_scores = []
for label in labels:
tp = conf_matrix[label][label]
fp = sum(conf_matrix[other][label] for other in labels if other != label)
fn = sum(conf_matrix[label][other] for other in labels if other != label)
support = sum(conf_matrix[label].values())
precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0
recall = tp / (tp + fn) if (tp + fn) > 0 else 0.0
f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0.0
f1_scores.append(f1)
metrics[label] = {
"precision": precision,
"recall": recall,
"f1_score": f1,
"support": support
}
macro_f1 = float(np.mean(f1_scores))
p50_lat = float(np.percentile(latencies, 50))
mean_lat = float(np.mean(latencies))
p90_lat = float(np.percentile(latencies, 90))
p99_lat = float(np.percentile(latencies, 99))
qps = 1000.0 / mean_lat if mean_lat > 0 else 0.0
print("\n" + "="*55)
print("HELD-OUT EVALUATION RESULTS (301 SAMPLES)")
print("="*55)
print(f"Overall Accuracy: {accuracy*100:.2f}%")
print(f"Macro F1-Score: {macro_f1:.4f}")
print("\nPer-Class Breakdown:")
for label in labels:
m = metrics[label]
print(f" [{label.upper():<6}] Precision: {m['precision']*100:6.2f}% | Recall: {m['recall']*100:6.2f}% | F1: {m['f1_score']:.4f} | Support: {m['support']}")
print("\nConfusion Matrix (Rows=Actual, Cols=Predicted):")
print(f"{'Actual \\ Pred':<15} {'low':<8} {'medium':<8} {'hard':<8}")
for t in labels:
row_str = f"{t:<15} " + " ".join(f"{conf_matrix[t][p]:<8}" for p in labels)
print(row_str)
print("\nLatency Profile (CPU):")
print(f" p50: {p50_lat:.2f} ms")
print(f" Mean: {mean_lat:.2f} ms")
print(f" p90: {p90_lat:.2f} ms")
print(f" p99: {p99_lat:.2f} ms")
print(f" Throughput: {qps:.1f} queries/sec")
if __name__ == "__main__":
main()
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