autogen/flaml/tune/result.py

'''
Copyright 2020 The Ray Authors.

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

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

This source file is adapted here because ray does not fully support Windows.

Copyright (c) Microsoft Corporation.
'''
import os

# yapf: disable
# __sphinx_doc_begin__
# (Optional/Auto-filled) training is terminated. Filled only if not provided.
DONE = "done"

# (Optional) Enum for user controlled checkpoint
SHOULD_CHECKPOINT = "should_checkpoint"

# (Auto-filled) The hostname of the machine hosting the training process.
HOSTNAME = "hostname"

# (Auto-filled) The auto-assigned id of the trial.
TRIAL_ID = "trial_id"

# (Auto-filled) The auto-assigned id of the trial.
EXPERIMENT_TAG = "experiment_tag"

# (Auto-filled) The node ip of the machine hosting the training process.
NODE_IP = "node_ip"

# (Auto-filled) The pid of the training process.
PID = "pid"

# (Optional) Default (anonymous) metric when using tune.report(x)
DEFAULT_METRIC = "_metric"

# (Optional) Mean reward for current training iteration
EPISODE_REWARD_MEAN = "episode_reward_mean"

# (Optional) Mean loss for training iteration
MEAN_LOSS = "mean_loss"

# (Optional) Mean loss for training iteration
NEG_MEAN_LOSS = "neg_mean_loss"

# (Optional) Mean accuracy for training iteration
MEAN_ACCURACY = "mean_accuracy"

# Number of episodes in this iteration.
EPISODES_THIS_ITER = "episodes_this_iter"

# (Optional/Auto-filled) Accumulated number of episodes for this trial.
EPISODES_TOTAL = "episodes_total"

# Number of timesteps in this iteration.
TIMESTEPS_THIS_ITER = "timesteps_this_iter"

# (Auto-filled) Accumulated number of timesteps for this entire trial.
TIMESTEPS_TOTAL = "timesteps_total"

# (Auto-filled) Time in seconds this iteration took to run.
# This may be overridden to override the system-computed time difference.
TIME_THIS_ITER_S = "time_this_iter_s"

# (Auto-filled) Accumulated time in seconds for this entire trial.
TIME_TOTAL_S = "time_total_s"

# (Auto-filled) The index of this training iteration.
TRAINING_ITERATION = "training_iteration"
# __sphinx_doc_end__
# yapf: enable

DEFAULT_EXPERIMENT_INFO_KEYS = ("trainable_name", EXPERIMENT_TAG, TRIAL_ID)

DEFAULT_RESULT_KEYS = (TRAINING_ITERATION, TIME_TOTAL_S, TIMESTEPS_TOTAL,
                       MEAN_ACCURACY, MEAN_LOSS)

# Make sure this doesn't regress
AUTO_RESULT_KEYS = (
    TRAINING_ITERATION,
    TIME_TOTAL_S,
    EPISODES_TOTAL,
    TIMESTEPS_TOTAL,
    NODE_IP,
    HOSTNAME,
    PID,
    TIME_TOTAL_S,
    TIME_THIS_ITER_S,
    "timestamp",
    "experiment_id",
    "date",
    "time_since_restore",
    "iterations_since_restore",
    "timesteps_since_restore",
    "config",
)

# __duplicate__ is a magic keyword used internally to
# avoid double-logging results when using the Function API.
RESULT_DUPLICATE = "__duplicate__"

# __trial_info__ is a magic keyword used internally to pass trial_info
# to the Trainable via the constructor.
TRIAL_INFO = "__trial_info__"

# __stdout_file__/__stderr_file__ are magic keywords used internally
# to pass log file locations to the Trainable via the constructor.
STDOUT_FILE = "__stdout_file__"
STDERR_FILE = "__stderr_file__"

# Where Tune writes result files by default
DEFAULT_RESULTS_DIR = (os.environ.get("TEST_TMPDIR")
                       or os.environ.get("TUNE_RESULT_DIR")
                       or os.path.expanduser("~/ray_results"))

# Meta file about status under each experiment directory, can be
# parsed by automlboard if exists.
JOB_META_FILE = "job_status.json"

# Meta file about status under each trial directory, can be parsed
# by automlboard if exists.
EXPR_META_FILE = "trial_status.json"

# File that stores parameters of the trial.
EXPR_PARAM_FILE = "params.json"

# Pickle File that stores parameters of the trial.
EXPR_PARAM_PICKLE_FILE = "params.pkl"

# File that stores the progress of the trial.
EXPR_PROGRESS_FILE = "progress.csv"

# File that stores results of the trial.
EXPR_RESULT_FILE = "result.json"

# Config prefix when using Analysis.
CONFIG_PREFIX = "config/"