93 lines
3.7 KiB
Python
93 lines
3.7 KiB
Python
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import pandas as pd
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import numpy as np
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from catboost import CatBoostClassifier
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class MCCPredictor:
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def __init__(self, model_path='mcc_classifier.cbm'):
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self.model = CatBoostClassifier()
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self.model.load_model(model_path)
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# Порядок колонок должен быть В ТОЧНОСТИ как при обучении
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self.feature_order = [
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'terminal_name', 'terminal_description', 'terminal_city', 'items_text', 'text',
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'amount', 'items_count', 'items_total_price', 'items_max_price', 'items_min_price',
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'terminal_id'
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]
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def _preprocess_json(self, data):
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"""Конвертирует входящий JSON в плоскую структуру для модели"""
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# 1. Агрегируем данные из списка items
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items = data.get('items', [])
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item_names = [str(i.get('name', '')) for i in items]
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item_prices = [float(i.get('price', 0)) for i in items]
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items_text = " ".join(item_names)
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items_count = len(items)
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items_total_price = sum(item_prices)
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items_max_price = max(item_prices) if item_prices else 0
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items_min_price = min(item_prices) if item_prices else 0
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# 2. Формируем ту самую склеенную колонку 'text'
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# Важно: используй тот же формат, что был в train.csv
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combined_text = f"{data.get('terminal_name', '')} {data.get('terminal_description', '')} {data.get('city', '')} items {items_text}"
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# 3. Собираем финальный словарь
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flat_data = {
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'terminal_name': str(data.get('terminal_name', '')),
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'terminal_description': str(data.get('terminal_description', '')),
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'terminal_city': str(data.get('city', '')), # city -> terminal_city
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'items_text': items_text,
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'text': combined_text.lower(),
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'amount': float(data.get('amount', 0)),
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'items_count': float(items_count),
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'items_total_price': float(items_total_price),
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'items_max_price': float(items_max_price),
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'items_min_price': float(items_min_price),
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'terminal_id': 'unknown' # В запросе нет ID, ставим заглушку
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}
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return flat_data
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def predict(self, raw_json):
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# Если пришла одна транзакция, оборачиваем в список
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if isinstance(raw_json, dict):
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raw_json = [raw_json]
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# Препроцессинг всех транзакций в списке
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processed_data = [self._preprocess_json(t) for t in raw_json]
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df = pd.DataFrame(processed_data)
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# Проверка порядка колонок
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df = df[self.feature_order]
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# Предсказание
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mcc_codes = self.model.predict(df)
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probs = self.model.predict_proba(df)
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results = []
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for i in range(len(raw_json)):
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results.append({
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"transaction_id": raw_json[i].get('transaction_id'),
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"mcc": int(mcc_codes[i][0]),
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"confidence": round(float(np.max(probs[i])), 4)
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})
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return results
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# --- ТЕСТ ---
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predictor = MCCPredictor('mcc_classifier.cbm')
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request_data = {
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"transaction_id": "TX00001116",
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"terminal_name": "STORE001",
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"terminal_description": "common common common thing",
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"city": "NYC",
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"amount": 272.80,
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"items": [
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{"name": "basic loyalty", "price": 58.20},
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{"name": "Bringiong item lifes", "price": 28.99},
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{"name": "regular item basic item", "price": 56.91}
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]
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}
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res = predictor.predict(request_data)
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print(res)
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