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9159c06 | 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 | """Analyzer for machine-learning and deep-learning code."""
from __future__ import annotations
from typing import Any, Dict
from schemas.response import AnalysisIssue, DomainAnalysis
def analyze_ml_code(code: str, parsed: Dict[str, Any], complexity: Dict[str, Any]) -> DomainAnalysis:
"""Inspect training and inference logic for common ML / DL mistakes."""
issues = []
suggestions = []
score = 0.74
if "torch" in code and "model.eval()" not in code and "predict" in code.lower():
issues.append(
AnalysisIssue(
title="Inference path may be missing eval mode",
severity="high",
description="Inference code should place the model in eval mode before prediction.",
)
)
suggestions.append("Call model.eval() before inference to disable training-time behavior such as dropout.")
score -= 0.18
if "torch" in code and "no_grad" not in code and "predict" in code.lower():
suggestions.append("Wrap inference in torch.no_grad() to reduce memory usage and avoid unnecessary gradient tracking.")
score -= 0.12
if parsed.get("calls_backward") and not parsed.get("calls_optimizer_step"):
issues.append(
AnalysisIssue(
title="Backward pass without optimizer step",
severity="medium",
description="Gradients are computed, but the optimizer step is not obvious in the snippet.",
)
)
suggestions.append("Ensure optimizer.step() and optimizer.zero_grad() are placed correctly in the training loop.")
score -= 0.12
if "CrossEntropyLoss" in code and "softmax(" in code:
suggestions.append("CrossEntropyLoss expects raw logits; remove the explicit softmax before the loss when possible.")
score -= 0.05
if not suggestions:
suggestions.append("Add explicit train/eval mode transitions and log validation metrics during training.")
return DomainAnalysis(
domain="ml_dl",
domain_score=max(0.05, round(score, 4)),
issues=issues,
suggestions=suggestions,
highlights={
"uses_torch": float(parsed.get("uses_torch", False)),
"has_eval_mode": float("model.eval()" in code),
"has_no_grad": float("no_grad" in code),
"time_complexity": complexity["time_complexity"],
},
)
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