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Arianna Di Serio 2026-03-06 12:03:53 +01:00
parent 4e68ffc3dc
commit f5c16bbf3d
1 changed files with 14 additions and 16 deletions
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annotation.py
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@ -70,7 +70,7 @@ print("Shape embeddings:", embeddings.shape)
# ----- Step3: Creazione indice FAISS --- # ----- Step3: Creazione indice FAISS e calcolo similarity ---
faiss.normalize_L2(embeddings) faiss.normalize_L2(embeddings)
dimension = embeddings.shape[1] dimension = embeddings.shape[1]
index = faiss.IndexFlatIP(dimension) index = faiss.IndexFlatIP(dimension)
@ -78,13 +78,13 @@ index.add(embeddings)
print(f"\n***Step 3: Indice FAISS creato***.\nNumero di vettori nell'indice: {index.ntotal}") print(f"\n***Step 3: Indice FAISS creato***.\nNumero di vettori nell'indice: {index.ntotal}")
# ----- Step 4: Retrieval (similarità cosine) ----- # ----- Step 4: Retrieval: 5 automazioni più simili -----
k = 5 k = 5
output_rows = [] output_rows = []
df_sample = df_unlabeled_filtered.head(50).reset_index(drop=True) df_sample = df_unlabeled_filtered.head(50).reset_index(drop=True)
llm_rows = [] llm_rows = []
# label in bale alla similarity
def sim_label(sim: float) -> str: def sim_label(sim: float) -> str:
# più alto = più simile # più alto = più simile
if sim >= 0.80: if sim >= 0.80:
@ -122,8 +122,8 @@ for count, (_, row) in enumerate(df_sample.iterrows(), start=1):
rank1_category = topk_cats[0] if topk_cats else "" rank1_category = topk_cats[0] if topk_cats else ""
majority_category = Counter(topk_cats).most_common(1)[0][0] if topk_cats else "" majority_category = Counter(topk_cats).most_common(1)[0][0] if topk_cats else ""
consistency = (sum(c == majority_category for c in topk_cats) / len(topk_cats)) if topk_cats else 0.0 consistency = (sum(c == majority_category for c in topk_cats) / len(topk_cats)) if topk_cats else 0.0
print(consistency)
# per ognuna delle 5 automazioni simili
for rank in range(k): for rank in range(k):
idx = int(indices[0][rank]) idx = int(indices[0][rank])
sim = float(sims[0][rank]) sim = float(sims[0][rank])
@ -185,7 +185,7 @@ for count, (_, row) in enumerate(df_sample.iterrows(), start=1):
# (4) Salvataggio di 1 riga per automazione con: # (4) Salvataggio di 1 riga per automazione con:
# - metriche retrieval (rank1/majority/consistency) # - metriche retrieval (rank1/majority/consistency)
# - output dell'LLM (scores + label finale + review flag) # - output dell'LLM
llm_category = str(parsed.get("category", "")).strip() llm_category = str(parsed.get("category", "")).strip()
llm_subcategory = str(parsed.get("subcategory", "")).strip() llm_subcategory = str(parsed.get("subcategory", "")).strip()
llm_problem_type = str(parsed.get("problem_type", "")).strip() llm_problem_type = str(parsed.get("problem_type", "")).strip()
@ -194,21 +194,20 @@ for count, (_, row) in enumerate(df_sample.iterrows(), start=1):
llm_subcategory = "" llm_subcategory = ""
llm_problem_type = "none" llm_problem_type = "none"
llm_gravity = "NONE" llm_gravity = "NONE"
# di default l'etichetta assegnata è quella del LLM - rivista se review=true # di default l'etichetta finale assegnata è quella del LLM - revisionata se review=true
final_category = llm_category final_category = llm_category
final_subcategory = llm_subcategory final_subcategory = llm_subcategory
final_problem_type = llm_problem_type final_problem_type = llm_problem_type
final_gravity = llm_gravity final_gravity = llm_gravity
# ================= REVIEW LOGIC =================
if top1_similarity_label == "Debole" or top1_similarity_label == "Similarità instabile": if top1_similarity_label == "Debole" or top1_similarity_label == "Similarità instabile":
needs_human_review = True needs_human_review = True
else: else:
needs_human_review = False needs_human_review = False
final_needs_human_review = needs_human_review final_needs_human_review = needs_human_review
# ================= HUMAN REVIEW LOGIC =================
aligned_strong = ( aligned_strong = (
llm_category == majority_category llm_category == majority_category
and llm_category == rank1_category and llm_category == rank1_category
@ -229,7 +228,7 @@ for count, (_, row) in enumerate(df_sample.iterrows(), start=1):
llm_rows.append({ llm_rows.append({
"automation_id": row.get("automation_id", ""), "id": row.get("automation_id", ""),
"folder": row.get("folder", ""), "folder": row.get("folder", ""),
"automation_text": query_text, "automation_text": query_text,
@ -268,12 +267,11 @@ df_out.to_excel(out_path, index=False)
wb = load_workbook(out_path) wb = load_workbook(out_path)
ws = wb.active ws = wb.active
# colore delle colonne review
true_fill = PatternFill(start_color="FF6347", end_color="FF6347", fill_type="solid") # rosso true_fill = PatternFill(start_color="FF6347", end_color="FF6347", fill_type="solid") # rosso
false_fill = PatternFill(start_color="90EE90", end_color="90EE90", fill_type="solid") # verde false_fill = PatternFill(start_color="90EE90", end_color="90EE90", fill_type="solid") # verde
col_index = {cell.value: idx for idx, cell in enumerate(ws[1], start=1)} col_index = {cell.value: idx for idx, cell in enumerate(ws[1], start=1)}
for col_name in ["needs_review", "final_needs_review"]:
for col_name in ["needs_human_review", "final_needs_human_review"]:
if col_name in col_index: if col_name in col_index:
c = col_index[col_name] c = col_index[col_name]
for r in range(2, ws.max_row + 1): for r in range(2, ws.max_row + 1):
@ -286,8 +284,8 @@ for col_name in ["needs_human_review", "final_needs_human_review"]:
wb.save(out_path) wb.save(out_path)
print(f"\n***Step 6: Excel salvato in {out_path}") print(f"\n***Step 6: Excel salvato in {out_path}")
# --- Conteggio needs_human_review --- # --- Conteggio final_needs_review ---
review_counts = df_out["final_needs_human_review"].value_counts(dropna=False) review_counts = df_out["final_needs_review"].value_counts(dropna=False)
true_count = review_counts.get(True, 0) true_count = review_counts.get(True, 0)
false_count = review_counts.get(False, 0) false_count = review_counts.get(False, 0)
print("\n--- Needs human review summary ---") print("\n--- Needs human review summary ---")