Source code for sklearn_genetic.callbacks.loggers

import logging
import os
import sys
import time
from copy import deepcopy
from joblib import dump

from import tqdm

from .base import BaseCallback
from ..parameters import Metrics

logger = logging.getLogger(__name__)  # noqa

    import tensorflow as tf
except ModuleNotFoundError:  # noqa
    tf = None  # noqa

[docs]class ProgressBar(BaseCallback): """Displays a tqdm progress bar with the training progress.""" def __init__(self, **kwargs): """ Parameters ---------- kwargs: dict, default = {"file": sys.stdout} A dict with valid arguments from """ if not kwargs.get("file"): kwargs["file"] = sys.stdout self.kwargs = kwargs self.progress_bar = None
[docs] def on_start(self, estimator=None): """Initializes the progress bar with the kwargs and total generations""" self.kwargs["total"] = estimator._n_iterations self.progress_bar = tqdm(**self.kwargs) self.progress_bar.update(1)
[docs] def on_step(self, record=None, logbook=None, estimator=None): """Increases the progress bar by one step""" self.progress_bar.update(1)
[docs] def on_end(self, logbook=None, estimator=None): """Closes the progress bar""" self.progress_bar.close()
[docs]class LogbookSaver(BaseCallback): """ Saves the estimator.logbook parameter chapter object in a local file system. """ def __init__(self, checkpoint_path, **dump_options): """ Parameters ---------- checkpoint_path: str Location where checkpoint will be saved to dump_options, str Valid kwargs from joblib :class:`~joblib.dump` """ self.checkpoint_path = checkpoint_path self.dump_options = dump_options
[docs] def on_step(self, record=None, logbook=None, estimator=None): try: dump_logbook = deepcopy(estimator.logbook.chapters["parameters"]) dump(dump_logbook, self.checkpoint_path, **self.dump_options) except Exception as e: logger.error("Could not save the Logbook in the checkpoint") return False
[docs]class TensorBoard(BaseCallback): """Log all the fitness metrics to Tensorboard into log_dir/run_id folder""" def __init__(self, log_dir="./logs", run_id=None): """ Parameters ---------- log_dir: str, default="./logs" Path to the main folder where the data will be log run_id: str, default=None Subfolder where the data will be log, if None it will create a folder with the current datetime with format time.strftime("%Y_%m_%d-%H_%M_%S") """ if tf is None: logger.error( "Tensorflow not found, pip install tensorflow to use TensorBoard callback" ) # noqa self.log_dir = log_dir if run_id is None: self.run_id = time.strftime("%Y_%m_%d-%H_%M_%S") else: self.run_id = run_id self.path = os.path.join(log_dir, self.run_id)
[docs] def on_step(self, record=None, logbook=None, estimator=None): # Get the last metric value stats = logbook[-1] # Create logs files placeholder writer = tf.summary.create_file_writer(self.path) # Log the metrics with writer.as_default(): for metric in Metrics.list(): tf.summary.scalar(name=metric, data=stats[metric], step=stats["gen"]) writer.flush() return False