intermediate backup
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@ -1,5 +1,26 @@
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"""
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IO utilities for the forecasting model.
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Input/Output utilities for the forecasting model package.
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Currently, primarily includes plotting functions used internally by evaluation.
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"""
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This package contains utilities for data loading, saving, and visualization.
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"""
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# Expose plotting utilities if intended for external use
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# from .plotting import (
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# setup_plot_style,
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# save_plot,
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# create_time_series_plot,
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# create_scatter_plot,
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# create_residuals_plot,
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# create_residuals_distribution_plot
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# )
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# __all__ = [
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# "setup_plot_style",
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# "save_plot",
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# "create_time_series_plot",
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# "create_scatter_plot",
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# "create_residuals_plot",
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# "create_residuals_distribution_plot",
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# ]
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# If nothing is intended for public API from this submodule, leave this file empty
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# or with just a docstring.
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@ -1,75 +1,307 @@
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import matplotlib.pyplot as plt
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import seaborn as sns
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import numpy as np
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from typing import Optional
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from typing import Optional, Union
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import logging
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from pathlib import Path
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logger = logging.getLogger(__name__)
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def setup_plot_style() -> None:
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def setup_plot_style(use_seaborn: bool = True) -> None:
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"""
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Set up consistent plotting style.
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"""
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# TODO: Implement plot style configuration
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pass
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Set up a consistent plotting style using seaborn if enabled.
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def save_plot(fig: plt.Figure, filename: str) -> None:
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Args:
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use_seaborn: Whether to apply seaborn styling.
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"""
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Save plot to file with proper error handling.
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if use_seaborn:
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try:
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sns.set_theme(style="whitegrid", palette="muted")
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plt.rcParams['figure.figsize'] = (12, 6) # Default figure size
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logger.debug("Seaborn plot style set.")
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except Exception as e:
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logger.warning(f"Failed to set seaborn theme: {e}. Using default matplotlib style.")
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else:
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# Optional: Define a default matplotlib style if seaborn is not used
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plt.style.use('default')
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logger.debug("Using default matplotlib plot style.")
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def save_plot(fig: plt.Figure, filename: Union[str, Path]) -> None:
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"""
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# TODO: Implement plot saving with error handling
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pass
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Save matplotlib figure to a file with directory creation and error handling.
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Args:
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fig: The matplotlib Figure object to save.
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filename: The full path (including filename and extension) to save the plot to.
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Can be a string or Path object.
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Raises:
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OSError: If the directory cannot be created.
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Exception: For other file saving errors.
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"""
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filepath = Path(filename)
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try:
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# Create the parent directory if it doesn't exist
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filepath.parent.mkdir(parents=True, exist_ok=True)
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fig.savefig(filepath, bbox_inches='tight', dpi=150) # Save with tight bounding box and decent resolution
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logger.info(f"Plot saved successfully to: {filepath}")
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except OSError as e:
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logger.error(f"Failed to create directory for plot {filepath}: {e}", exc_info=True)
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raise # Re-raise OSError for directory creation issues
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except Exception as e:
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logger.error(f"Failed to save plot to {filepath}: {e}", exc_info=True)
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raise # Re-raise other saving errors
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finally:
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# Close the figure to free up memory, regardless of saving success
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plt.close(fig)
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def create_time_series_plot(
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x: np.ndarray,
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y_true: np.ndarray,
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y_pred: np.ndarray,
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title: str,
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xlabel: str,
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ylabel: str,
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xlabel: str = "Time Index",
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ylabel: str = "Value",
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max_points: Optional[int] = None
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) -> plt.Figure:
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"""
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Create a time series plot with actual vs predicted values.
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Create a time series plot comparing actual vs predicted values.
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Args:
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x: The array for the x-axis (e.g., time steps, indices).
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y_true: Ground truth values (1D array).
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y_pred: Predicted values (1D array).
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title: Title for the plot.
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xlabel: Label for the x-axis.
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ylabel: Label for the y-axis.
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max_points: Maximum number of points to display (subsamples if needed).
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Returns:
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The generated matplotlib Figure object.
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Raises:
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ValueError: If input array shapes are incompatible.
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"""
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# TODO: Implement time series plot creation
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pass
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if not (x.shape == y_true.shape == y_pred.shape and x.ndim == 1):
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raise ValueError("Input arrays (x, y_true, y_pred) must be 1D and have the same shape.")
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if len(x) == 0:
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logger.warning("Attempting to create time series plot with empty data.")
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# Return an empty figure or raise error? Let's return empty.
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return plt.figure()
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logger.debug(f"Creating time series plot: {title}")
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fig, ax = plt.subplots(figsize=(15, 6)) # Consistent size
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n_points = len(x)
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indices = np.arange(n_points) # Use internal indices for potential slicing
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if max_points and n_points > max_points:
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step = max(1, n_points // max_points)
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plot_indices = indices[::step]
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plot_x = x[::step]
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plot_y_true = y_true[::step]
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plot_y_pred = y_pred[::step]
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effective_title = f'{title} (Sampled {len(plot_indices)} points)'
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else:
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plot_x = x
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plot_y_true = y_true
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plot_y_pred = y_pred
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effective_title = title
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ax.plot(plot_x, plot_y_true, label='Actual', marker='.', linestyle='-', markersize=4, linewidth=1.5)
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ax.plot(plot_x, plot_y_pred, label='Predicted', marker='x', linestyle='--', markersize=4, alpha=0.8, linewidth=1)
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ax.set_title(effective_title, fontsize=14)
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ax.set_xlabel(xlabel, fontsize=12)
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ax.set_ylabel(ylabel, fontsize=12)
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ax.legend()
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ax.grid(True, linestyle='--', alpha=0.6)
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fig.tight_layout()
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return fig
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def create_scatter_plot(
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y_true: np.ndarray,
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y_pred: np.ndarray,
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title: str,
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xlabel: str,
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ylabel: str
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xlabel: str = "Actual Values",
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ylabel: str = "Predicted Values"
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) -> plt.Figure:
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"""
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Create a scatter plot of actual vs predicted values.
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Args:
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y_true: Ground truth values (1D array).
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y_pred: Predicted values (1D array).
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title: Title for the plot.
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xlabel: Label for the x-axis.
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ylabel: Label for the y-axis.
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Returns:
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The generated matplotlib Figure object.
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Raises:
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ValueError: If input array shapes are incompatible.
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"""
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# TODO: Implement scatter plot creation
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pass
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if not (y_true.shape == y_pred.shape and y_true.ndim == 1):
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raise ValueError("Input arrays (y_true, y_pred) must be 1D and have the same shape.")
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if len(y_true) == 0:
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logger.warning("Attempting to create scatter plot with empty data.")
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return plt.figure()
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logger.debug(f"Creating scatter plot: {title}")
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fig, ax = plt.subplots(figsize=(8, 8)) # Square figure common for scatter
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# Determine plot limits, handle potential NaNs
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valid_mask = ~np.isnan(y_true) & ~np.isnan(y_pred)
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if not np.any(valid_mask):
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logger.warning(f"No valid (non-NaN) data points found for scatter plot '{title}'.")
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# Return empty figure, plot would be blank
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return fig
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y_true_valid = y_true[valid_mask]
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y_pred_valid = y_pred[valid_mask]
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min_val = min(y_true_valid.min(), y_pred_valid.min())
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max_val = max(y_true_valid.max(), y_pred_valid.max())
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plot_range = max_val - min_val
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if plot_range < 1e-6: # Handle cases where all points are identical
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plot_range = 1.0 # Avoid zero range
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lim_min = min_val - 0.05 * plot_range
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lim_max = max_val + 0.05 * plot_range
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ax.scatter(y_true_valid, y_pred_valid, alpha=0.5, s=10, label='Predictions')
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ax.plot([lim_min, lim_max], [lim_min, lim_max], 'r--', label='Ideal (y=x)', linewidth=1.5)
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ax.set_title(title, fontsize=14)
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ax.set_xlabel(xlabel, fontsize=12)
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ax.set_ylabel(ylabel, fontsize=12)
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ax.set_xlim(lim_min, lim_max)
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ax.set_ylim(lim_min, lim_max)
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ax.legend()
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ax.grid(True, linestyle='--', alpha=0.6)
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ax.set_aspect('equal', adjustable='box') # Ensure square scaling
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fig.tight_layout()
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return fig
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def create_residuals_plot(
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x: np.ndarray,
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residuals: np.ndarray,
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title: str,
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xlabel: str,
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ylabel: str,
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xlabel: str = "Time Index",
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ylabel: str = "Residual (Actual - Predicted)",
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max_points: Optional[int] = None
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) -> plt.Figure:
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"""
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Create a plot of residuals over time.
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Args:
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x: The array for the x-axis (e.g., time steps, indices).
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residuals: Array of residual values (1D array).
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title: Title for the plot.
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xlabel: Label for the x-axis.
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ylabel: Label for the y-axis.
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max_points: Maximum number of points to display (subsamples if needed).
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Returns:
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The generated matplotlib Figure object.
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Raises:
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ValueError: If input array shapes are incompatible.
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"""
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# TODO: Implement residuals plot creation
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pass
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if not (x.shape == residuals.shape and x.ndim == 1):
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raise ValueError("Input arrays (x, residuals) must be 1D and have the same shape.")
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if len(x) == 0:
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logger.warning("Attempting to create residuals time plot with empty data.")
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return plt.figure()
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logger.debug(f"Creating residuals time plot: {title}")
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fig, ax = plt.subplots(figsize=(15, 5)) # Often wider than tall
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n_points = len(x)
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indices = np.arange(n_points)
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if max_points and n_points > max_points:
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step = max(1, n_points // max_points)
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plot_indices = indices[::step]
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plot_x = x[::step]
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plot_residuals = residuals[::step]
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effective_title = f'{title} (Sampled {len(plot_indices)} points)'
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else:
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plot_x = x
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plot_residuals = residuals
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effective_title = title
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ax.plot(plot_x, plot_residuals, marker='.', linestyle='-', markersize=4, linewidth=1, label='Residuals')
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ax.axhline(0, color='red', linestyle='--', label='Zero Error', linewidth=1.5)
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ax.set_title(effective_title, fontsize=14)
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ax.set_xlabel(xlabel, fontsize=12)
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ax.set_ylabel(ylabel, fontsize=12)
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ax.legend()
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ax.grid(True, linestyle='--', alpha=0.6)
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fig.tight_layout()
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return fig
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def create_residuals_distribution_plot(
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residuals: np.ndarray,
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title: str,
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xlabel: str,
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ylabel: str
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xlabel: str = "Residual Value",
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ylabel: str = "Density"
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) -> plt.Figure:
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"""
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Create a distribution plot of residuals.
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Create a distribution plot (histogram and KDE) of residuals using seaborn.
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Args:
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residuals: Array of residual values (1D array).
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title: Title for the plot.
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xlabel: Label for the x-axis.
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ylabel: Label for the y-axis.
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Returns:
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The generated matplotlib Figure object.
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Raises:
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ValueError: If input array shape is invalid.
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"""
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# TODO: Implement residuals distribution plot creation
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pass
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if residuals.ndim != 1:
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raise ValueError("Input array (residuals) must be 1D.")
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if len(residuals) == 0:
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logger.warning("Attempting to create residuals distribution plot with empty data.")
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return plt.figure()
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logger.debug(f"Creating residuals distribution plot: {title}")
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fig, ax = plt.subplots(figsize=(8, 6))
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# Filter out NaNs before plotting and calculating stats
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residuals_valid = residuals[~np.isnan(residuals)]
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if len(residuals_valid) == 0:
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logger.warning(f"No valid (non-NaN) data points found for residual distribution plot '{title}'.")
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return fig # Return empty figure
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# Use seaborn histplot which combines histogram and KDE
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try:
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sns.histplot(residuals_valid, kde=True, bins=50, stat="density", ax=ax)
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except Exception as e:
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logger.error(f"Seaborn histplot failed for '{title}': {e}. Falling back to matplotlib hist.", exc_info=True)
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# Fallback to basic matplotlib histogram if seaborn fails
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ax.hist(residuals_valid, bins=50, density=True, alpha=0.7)
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ylabel = "Frequency" # Adjust label if only histogram shown
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mean_res = np.mean(residuals_valid)
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std_res = np.std(residuals_valid)
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ax.axvline(float(mean_res), color='red', linestyle='--', label=f'Mean: {mean_res:.3f}')
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ax.set_title(f'{title}\n(Std Dev: {std_res:.3f})', fontsize=14)
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ax.set_xlabel(xlabel, fontsize=12)
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ax.set_ylabel(ylabel, fontsize=12)
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ax.legend()
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ax.grid(True, axis='y', linestyle='--', alpha=0.6)
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fig.tight_layout()
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return fig
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