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Sven
60d4d5e1aa
* Remove all __future__ imports from RLlib. * Remove (object) again from tf_run_builder.py::TFRunBuilder. * Fix 2xLINT warnings. * Fix broken appo_policy import (must be appo_tf_policy) * Remove future imports from all other ray files (not just RLlib). * Remove future imports from all other ray files (not just RLlib). * Remove future import blocks that contain `unicode_literals` as well. Revert appo_tf_policy.py to appo_policy.py (belongs to another PR). * Add two empty lines before Schedule class. * Put back __future__ imports into determine_tests_to_run.py. Fails otherwise on a py2/print related error.
77 lines
2.0 KiB
Python
77 lines
2.0 KiB
Python
import pandas as pd
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from pandas.api.types import is_string_dtype, is_numeric_dtype
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import logging
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import os
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import os.path as osp
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import numpy as np
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import json
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from ray.tune.utils import flatten_dict
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logger = logging.getLogger(__name__)
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logger.warning("This module will be deprecated in a future version of Tune.")
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def _parse_results(res_path):
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res_dict = {}
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try:
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with open(res_path) as f:
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# Get last line in file
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for line in f:
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pass
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res_dict = flatten_dict(json.loads(line.strip()))
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except Exception:
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logger.exception("Importing %s failed...Perhaps empty?" % res_path)
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return res_dict
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def _parse_configs(cfg_path):
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try:
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with open(cfg_path) as f:
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cfg_dict = flatten_dict(json.load(f))
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except Exception:
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logger.exception("Config parsing failed.")
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return cfg_dict
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def _resolve(directory, result_fname):
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try:
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resultp = osp.join(directory, result_fname)
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res_dict = _parse_results(resultp)
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cfgp = osp.join(directory, "params.json")
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cfg_dict = _parse_configs(cfgp)
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cfg_dict.update(res_dict)
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return cfg_dict
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except Exception:
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return None
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def load_results_to_df(directory, result_name="result.json"):
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exp_directories = [
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dirpath for dirpath, dirs, files in os.walk(directory) for f in files
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if f == result_name
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]
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data = [_resolve(d, result_name) for d in exp_directories]
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data = [d for d in data if d]
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return pd.DataFrame(data)
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def generate_plotly_dim_dict(df, field):
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dim_dict = {}
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dim_dict["label"] = field
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column = df[field]
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if is_numeric_dtype(column):
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dim_dict["values"] = column
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elif is_string_dtype(column):
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texts = column.unique()
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dim_dict["values"] = [
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np.argwhere(texts == x).flatten()[0] for x in column
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]
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dim_dict["tickvals"] = list(range(len(texts)))
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dim_dict["ticktext"] = texts
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else:
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raise Exception("Unidentifiable Type")
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return dim_dict
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