55 lines
2.3 KiB
Python
55 lines
2.3 KiB
Python
import pandas as pd
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import joblib
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from typing import List, Dict
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from schemas.schemas import LaptopCreate, LaptopResponse, PredictPriceResponse
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class LaptopService:
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def __init__(self, model_path: str, feature_columns_path: str, poly_path: str, scaler_path: str):
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try:
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self.model = joblib.load(model_path)
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except FileNotFoundError:
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raise Exception(f"Model file not found at {model_path}")
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except Exception as e:
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raise Exception(f"Error loading model: {str(e)}")
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try:
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self.feature_columns = joblib.load(feature_columns_path)
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except FileNotFoundError:
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raise Exception(f"Feature columns file not found at {feature_columns_path}")
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except Exception as e:
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raise Exception(f"Error loading feature columns: {str(e)}")
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try:
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self.poly_transformer = joblib.load(poly_path)
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self.scaler = joblib.load(scaler_path)
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except FileNotFoundError:
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raise Exception("Polynomial transformer or scaler file not found.")
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except Exception as e:
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raise Exception(f"Error loading polynomial transformer or scaler: {str(e)}")
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def predict_price(self, data: Dict[str, any]) -> PredictPriceResponse:
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# Преобразование данных в DataFrame
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input_df = pd.DataFrame([data])
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# Применение One-Hot Encoding к категориальным признакам
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input_df = pd.get_dummies(input_df, columns=['processor', 'os'], drop_first=True)
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# Добавление отсутствующих признаков
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for col in self.feature_columns:
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if col not in input_df.columns and col != 'price':
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input_df[col] = 0
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# Упорядочивание колонок
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input_df = input_df[self.feature_columns]
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# Преобразование с использованием PolynomialFeatures
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input_poly = self.poly_transformer.transform(input_df)
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# Масштабирование данных
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input_scaled = self.scaler.transform(input_poly)
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# Предсказание цены
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predicted_price = self.model.predict(input_scaled)[0]
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return PredictPriceResponse(predicted_price=round(predicted_price, 2))
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