Source code for neptunecontrib.monitoring.skopt

# Copyright (c) 2019, Neptune Labs Sp. z o.o.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
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import warnings

import matplotlib.pyplot as plt
import neptune
import numpy as np
import skopt.plots as sk_plots
from skopt.utils import dump

from neptunecontrib.monitoring.utils import axes2fig

[docs]class NeptuneCallback: """Logs hyperparameter optimization process to Neptune. Specifically using NeptuneCallback will log: run metrics and run parameters, best run metrics so far, and the current results checkpoint. Examples: Initialize NeptuneCallback:: import neptune import neptunecontrib.monitoring.skopt as sk_utils neptune.init(api_token='ANONYMOUS', project_qualified_name='shared/showroom') neptune.create_experiment(name='optuna sweep') neptune_callback = sk_utils.NeptuneCallback() Run skopt training passing neptune_callback as a callback:: ... results = skopt.forest_minimize(objective, space, callback=[neptune_callback], base_estimator='ET', n_calls=100, n_random_starts=10) You can explore an example experiment in Neptune: """ def __init__(self, experiment=None, log_checkpoint=True): self._exp = experiment if experiment else neptune self.log_checkpoint = log_checkpoint self._iteration = 0 def __call__(self, res): self._exp.log_metric('run_score', x=self._iteration, y=res.func_vals[-1]) self._exp.log_metric('best_so_far_run_score', x=self._iteration, y=np.min(res.func_vals)) self._exp.log_text('run_parameters', x=self._iteration, y=NeptuneCallback._get_last_params(res)) if self.log_checkpoint: self._exp.log_artifact(_export_results_object(res), 'results.pkl') self._iteration += 1 @staticmethod def _get_last_params(res): param_vals = res.x_iters[-1] named_params = _format_to_named_params(param_vals, res) return str(named_params)
[docs]def log_results(results, experiment=None, log_plots=True, log_pickle=True): """Logs runs results and parameters to neptune. Logs all hyperparameter optimization results to Neptune. Those include best score ('best_score' metric), best parameters ('best_parameters' property), convergence plot ('diagnostics' log), evaluations plot ('diagnostics' log), and objective plot ('diagnostics' log). Args: results('scipy.optimize.OptimizeResult'): Results object that is typically an output of the function like `skopt.forest_minimize(...)` experiment(`neptune.experiments.Experiment`): Neptune experiment. Default is None. log_plots: ('bool'): If True skopt plots will be logged to Neptune. log_pickle: ('bool'): if True pickled skopt results object will be logged to Neptune. Examples: Run skopt training:: ... results = skopt.forest_minimize(objective, space, base_estimator='ET', n_calls=100, n_random_starts=10) Initialize Neptune:: import neptune neptune.init(api_token='ANONYMOUS', project_qualified_name='shared/showroom') neptune.create_experiment(name='optuna sweep') Send best parameters to Neptune:: import neptunecontrib.monitoring.skopt as sk_utils sk_utils.log_results(results) You can explore an example experiment in Neptune: """ _exp = experiment if experiment else neptune _log_best_score(results, _exp) _log_best_parameters(results, _exp) if log_plots: _log_plot_convergence(results, _exp) _log_plot_evaluations(results, _exp) _log_plot_regret(results, _exp) _log_plot_objective(results, _exp) if log_pickle: _log_results_object(results, _exp)
def NeptuneMonitor(*args, **kwargs): message = """NeptuneMonitor was renamed to NeptuneCallback and will be removed in future releases. """ warnings.warn(message) return NeptuneCallback(*args, **kwargs) def _log_best_parameters(results, experiment): named_params = ([(, param) for dimension, param in zip(, results.x)]) experiment.set_property('best_parameters', str(named_params)) def _log_best_score(results, experiment): experiment.log_metric('best_score', def _log_plot_convergence(results, experiment, name='diagnostics'): fig, ax = plt.subplots() sk_plots.plot_convergence(results, ax=ax) experiment.log_image(name, fig) def _log_plot_regret(results, experiment, name='diagnostics'): fig, ax = plt.subplots() sk_plots.plot_regret(results, ax=ax) experiment.log_image(name, fig) def _log_plot_evaluations(results, experiment, name='diagnostics'): fig = plt.figure(figsize=(16, 12)) fig = axes2fig(sk_plots.plot_evaluations(results, bins=10), fig=fig) experiment.log_image(name, fig) def _log_plot_objective(results, experiment, name='diagnostics'): try: fig = plt.figure(figsize=(16, 12)) fig = axes2fig(sk_plots.plot_objective(results), fig=fig) experiment.log_image(name, fig) except Exception as e: print('Could not create the objective chart due to error: {}'.format(e)) def _log_results_object(results, experiment=None): experiment.log_artifact(_export_results_object(results), 'results.pkl') def _export_results_object(results): from io import BytesIO results.specs['args'].pop('callback', None) buffer = BytesIO() dump(results, buffer, store_objective=False) return buffer def _format_to_named_params(params, result): return [(, param) for dimension, param in zip(, params)]