67 lines
2.2 KiB
Python
67 lines
2.2 KiB
Python
import numpy as np
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import pandas as pd
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import torch
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from torch.utils.data import Dataset, DataLoader
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from sklearn.preprocessing import StandardScaler, MinMaxScaler
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from typing import Tuple, Generator, List, Optional
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from utils.config_model import DataConfig, FeatureConfig, TrainingConfig, EvaluationConfig
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# --- Data Loading ---
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def load_raw_data(config: DataConfig) -> pd.DataFrame:
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"""
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Load and preprocess raw data from CSV.
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"""
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# TODO: Implement CSV loading and datetime parsing
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pass
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# --- Feature Engineering ---
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def engineer_features(df: pd.DataFrame, target_col: str, feature_config: FeatureConfig) -> pd.DataFrame:
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"""
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Create features from the target column and datetime index.
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"""
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# TODO: Implement feature engineering (lags, rolling stats, time features, wavelets)
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pass
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# --- Cross Validation ---
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class TimeSeriesCrossValidationSplitter:
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def __init__(self, config: CrossValidationConfig, n_samples: int):
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self.config = config
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self.n_samples = n_samples
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def split(self) -> Generator[Tuple[np.ndarray, np.ndarray, np.ndarray], None, None]:
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"""
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Generate train/val/test splits using expanding window approach.
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"""
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# TODO: Implement expanding window CV splitter
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pass
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# --- Dataset Class ---
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class TimeSeriesDataset(Dataset):
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def __init__(self, data_array: np.ndarray, sequence_length: int, forecast_horizon: int):
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self.data = data_array
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self.sequence_length = sequence_length
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self.forecast_horizon = forecast_horizon
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def __len__(self) -> int:
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# TODO: Implement length calculation
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pass
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def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]:
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# TODO: Implement sequence extraction
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pass
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# --- Data Preparation ---
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def prepare_fold_data_and_loaders(
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full_df: pd.DataFrame,
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train_idx: np.ndarray,
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val_idx: np.ndarray,
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test_idx: np.ndarray,
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feature_config: FeatureConfig,
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train_config: TrainingConfig,
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eval_config: EvaluationConfig
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) -> Tuple[DataLoader, DataLoader, DataLoader, object, int]:
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"""
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Prepare data loaders for a single fold.
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"""
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# TODO: Implement data preparation pipeline
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pass |