visual_utils.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import pandas as pd
from pandas.api.types import is_string_dtype, is_numeric_dtype
import logging
import os
import os.path as osp
import numpy as np
import json

logger = logging.getLogger(__name__)


def _flatten_dict(dt):
    while any(type(v) is dict for v in dt.values()):
        remove = []
        add = {}
        for key, value in dt.items():
            if type(value) is dict:
                for subkey, v in value.items():
                    add[":".join([key, subkey])] = v
                remove.append(key)
        dt.update(add)
        for k in remove:
            del dt[k]
    return dt


def _parse_results(res_path):
    res_dict = {}
    try:
        with open(res_path) as f:
            # Get last line in file
            for line in f:
                pass
        res_dict = _flatten_dict(json.loads(line.strip()))
    except Exception:
        logger.exception("Importing %s failed...Perhaps empty?" % res_path)
    return res_dict


def _parse_configs(cfg_path):
    try:
        with open(cfg_path) as f:
            cfg_dict = _flatten_dict(json.load(f))
    except Exception:
        logger.exception("Config parsing failed.")
    return cfg_dict


def _resolve(directory, result_fname):
    try:
        resultp = osp.join(directory, result_fname)
        res_dict = _parse_results(resultp)
        cfgp = osp.join(directory, "params.json")
        cfg_dict = _parse_configs(cfgp)
        cfg_dict.update(res_dict)
        return cfg_dict
    except Exception:
        return None


def load_results_to_df(directory, result_name="result.json"):
    exp_directories = [
        dirpath for dirpath, dirs, files in os.walk(directory) for f in files
        if f == result_name
    ]
    data = [_resolve(d, result_name) for d in exp_directories]
    data = [d for d in data if d]
    return pd.DataFrame(data)


def generate_plotly_dim_dict(df, field):
    dim_dict = {}
    dim_dict["label"] = field
    column = df[field]
    if is_numeric_dtype(column):
        dim_dict["values"] = column
    elif is_string_dtype(column):
        texts = column.unique()
        dim_dict["values"] = [
            np.argwhere(texts == x).flatten()[0] for x in column
        ]
        dim_dict["tickvals"] = list(range(len(texts)))
        dim_dict["ticktext"] = texts
    else:
        raise Exception("Unidentifiable Type")

    return dim_dict