43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
"""
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Time Series Forecasting Module with LSTM
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This module provides a configurable PyTorch-based LSTM model for time series forecasting,
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with support for feature engineering, cross-validation, and evaluation.
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"""
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__version__ = "0.1.0"
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# Expose core components for easier import
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from .data_processing import (
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load_raw_data,
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engineer_features,
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TimeSeriesCrossValidationSplitter,
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prepare_fold_data_and_loaders,
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TimeSeriesDataset
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)
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from .model import LSTMForecastLightningModule
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from .evaluation import (
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evaluate_fold_predictions,
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# Optionally expose the standalone evaluation utility if needed externally
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# evaluate_model_on_fold_test_set
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)
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# Expose main configuration class from utils
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from .utils import MainConfig
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# Expose the main execution script function if it's intended to be callable as a function
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# from .forecasting_model import run # Assuming the main script is named forecasting_model.py
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# Define __all__ for explicit public API (optional but good practice)
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__all__ = [
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"load_raw_data",
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"engineer_features",
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"TimeSeriesCrossValidationSplitter",
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"prepare_fold_data_and_loaders",
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"TimeSeriesDataset",
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"LSTMForecastLightningModule",
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"evaluate_fold_predictions",
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# "evaluate_model_on_fold_test_set", # Uncomment if exposed
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"MainConfig",
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# "run", # Uncomment if exposed
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] |