fix model mappings,dash updates, trading
This commit is contained in:
Dobromir Popov
219
utils/tensorboard_logger.py
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219
utils/tensorboard_logger.py
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#!/usr/bin/env python3
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"""
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TensorBoard Logger Utility
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This module provides a centralized way to log training metrics to TensorBoard.
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It ensures consistent logging across different training components.
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"""
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import os
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import logging
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from pathlib import Path
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from datetime import datetime
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from typing import Dict, Any, Optional, Union, List
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# Import conditionally to handle missing dependencies gracefully
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try:
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from torch.utils.tensorboard import SummaryWriter
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TENSORBOARD_AVAILABLE = True
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except ImportError:
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TENSORBOARD_AVAILABLE = False
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logger = logging.getLogger(__name__)
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class TensorBoardLogger:
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"""
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Centralized TensorBoard logging utility for training metrics
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This class provides a consistent interface for logging metrics to TensorBoard
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across different training components.
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"""
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def __init__(self,
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log_dir: Optional[str] = None,
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experiment_name: Optional[str] = None,
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enabled: bool = True):
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"""
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Initialize TensorBoard logger
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Args:
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log_dir: Base directory for TensorBoard logs (default: 'runs')
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experiment_name: Name of the experiment (default: timestamp)
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enabled: Whether TensorBoard logging is enabled
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"""
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self.enabled = enabled and TENSORBOARD_AVAILABLE
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self.writer = None
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if not self.enabled:
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if not TENSORBOARD_AVAILABLE:
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logger.warning("TensorBoard not available. Install with: pip install tensorboard")
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return
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# Set up log directory
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if log_dir is None:
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log_dir = "runs"
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# Create experiment name if not provided
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if experiment_name is None:
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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experiment_name = f"training_{timestamp}"
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# Create full log path
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self.log_dir = os.path.join(log_dir, experiment_name)
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# Create writer
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try:
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self.writer = SummaryWriter(log_dir=self.log_dir)
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logger.info(f"TensorBoard logging enabled at: {self.log_dir}")
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except Exception as e:
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logger.error(f"Failed to initialize TensorBoard: {e}")
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self.enabled = False
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def log_scalar(self, tag: str, value: float, step: int) -> None:
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"""
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Log a scalar value to TensorBoard
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Args:
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tag: Metric name
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value: Metric value
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step: Training step
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"""
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if not self.enabled or self.writer is None:
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return
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try:
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self.writer.add_scalar(tag, value, step)
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except Exception as e:
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logger.warning(f"Failed to log scalar {tag}: {e}")
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def log_scalars(self, main_tag: str, tag_value_dict: Dict[str, float], step: int) -> None:
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"""
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Log multiple scalar values with the same main tag
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Args:
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main_tag: Main tag for the metrics
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tag_value_dict: Dictionary of tag names to values
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step: Training step
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"""
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if not self.enabled or self.writer is None:
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return
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try:
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self.writer.add_scalars(main_tag, tag_value_dict, step)
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except Exception as e:
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logger.warning(f"Failed to log scalars for {main_tag}: {e}")
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def log_histogram(self, tag: str, values, step: int) -> None:
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"""
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Log a histogram to TensorBoard
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Args:
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tag: Histogram name
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values: Values to create histogram from
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step: Training step
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"""
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if not self.enabled or self.writer is None:
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return
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try:
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self.writer.add_histogram(tag, values, step)
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except Exception as e:
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logger.warning(f"Failed to log histogram {tag}: {e}")
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def log_training_metrics(self,
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metrics: Dict[str, Any],
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step: int,
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prefix: str = "Training") -> None:
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"""
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Log training metrics to TensorBoard
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Args:
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metrics: Dictionary of metric names to values
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step: Training step
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prefix: Prefix for metric names
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"""
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if not self.enabled or self.writer is None:
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return
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for name, value in metrics.items():
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if isinstance(value, (int, float)):
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self.log_scalar(f"{prefix}/{name}", value, step)
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elif hasattr(value, "shape"): # For numpy arrays or tensors
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try:
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self.log_histogram(f"{prefix}/{name}", value, step)
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except:
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pass
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def log_model_metrics(self,
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model_name: str,
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metrics: Dict[str, Any],
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step: int) -> None:
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"""
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Log model-specific metrics to TensorBoard
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Args:
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model_name: Name of the model
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metrics: Dictionary of metric names to values
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step: Training step
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"""
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if not self.enabled or self.writer is None:
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return
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for name, value in metrics.items():
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if isinstance(value, (int, float)):
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self.log_scalar(f"Model/{model_name}/{name}", value, step)
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def log_reward_metrics(self,
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symbol: str,
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metrics: Dict[str, float],
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step: int) -> None:
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"""
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Log reward-related metrics to TensorBoard
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Args:
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symbol: Trading symbol
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metrics: Dictionary of metric names to values
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step: Training step
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"""
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if not self.enabled or self.writer is None:
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return
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for name, value in metrics.items():
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self.log_scalar(f"Rewards/{symbol}/{name}", value, step)
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def log_state_metrics(self,
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symbol: str,
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state_info: Dict[str, Any],
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step: int) -> None:
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"""
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Log state-related metrics to TensorBoard
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Args:
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symbol: Trading symbol
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state_info: Dictionary of state information
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step: Training step
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"""
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if not self.enabled or self.writer is None:
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return
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# Log state size
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if "size" in state_info:
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self.log_scalar(f"State/{symbol}/Size", state_info["size"], step)
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# Log state quality
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if "quality" in state_info:
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self.log_scalar(f"State/{symbol}/Quality", state_info["quality"], step)
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# Log feature counts
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if "feature_counts" in state_info:
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for feature_type, count in state_info["feature_counts"].items():
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self.log_scalar(f"State/{symbol}/Features/{feature_type}", count, step)
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def close(self) -> None:
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"""Close the TensorBoard writer"""
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if self.enabled and self.writer is not None:
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try:
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self.writer.close()
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logger.info("TensorBoard writer closed")
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except Exception as e:
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logger.warning(f"Error closing TensorBoard writer: {e}")
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