import argparse
import matplotlib.pyplot as plt
import logging
import seaborn as sns
import os
import pandas as pd


def read_cmd():
    parser = argparse.ArgumentParser()
    parser.add_argument('--fanova_result_file',
                        default=os.path.expanduser('~/experiments/openml-pimp/fanova_adaboost_depth_1.csv'),
                        type=str)
    parser.add_argument('--output_directory', default=os.path.expanduser('~/experiments/openml-pimp'), type=str)
    parser.add_argument('--measure', default='predictive_accuracy', type=str)
    args_, misc = parser.parse_known_args()

    return args_


def run(args):
    root = logging.getLogger()
    root.setLevel(logging.INFO)

    df = pd.read_csv(args.fanova_result_file)
    medians = df.groupby('hyperparameter')['importance_variance', 'importance_max_min'].median()
    print(df.dtypes)
    print(medians.dtypes)
    df = df.join(medians, on='hyperparameter', how='left', rsuffix='median_')
    print(df)


if __name__ == '__main__':
    run(read_cmd())

import argparse
import matplotlib
matplotlib.use("TkAgg")
import matplotlib.pyplot as plt
import logging
import seaborn as sns
import os
import pandas as pd
import typing


def read_cmd():
    parser = argparse.ArgumentParser()
    parser.add_argument('--fanova_result_file',
                        default=os.path.expanduser('~/experiments/openml-pimp/fanova_adaboost_depth_1.csv'),
                        type=str)
    parser.add_argument('--output_directory', default=os.path.expanduser('~/experiments/openml-pimp'), type=str)
    parser.add_argument('--measure', default='predictive_accuracy', type=str)
    parser.add_argument('--n_combi_params', default=3, type=int)
    args_, misc = parser.parse_known_args()

    return args_


def calculate_cutoff_value(medians: pd.DataFrame, column_name: str, n_combi_params: typing.Optional[int]):
    medians_sorted = medians[medians['n_hyperparameters'] > 1].sort_values(column_name)
    cutoff = 0.0
    if n_combi_params is not None and len(medians_sorted) > n_combi_params:
        cutoff = medians_sorted[column_name][-1 * n_combi_params]
    return cutoff


def run(args):
    root = logging.getLogger()
    root.setLevel(logging.INFO)

    df = pd.read_csv(args.fanova_result_file)
    medians = df.groupby('hyperparameter')['n_hyperparameters', 'importance_variance', 'importance_max_min'].median()
    df = df.join(medians, on='hyperparameter', how='left', rsuffix='median_')

    cutoff_value = calculate_cutoff_value(medians, 'importance_variance', args.n_combi_params)
    df = df.query('n_hyperparameters == 1 or importance_variance > %f' % cutoff_value)

    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))

    sns.boxplot(x='hyperparameter', y='importance_variance', data=df, ax=ax1)
    ax1.set_title('fanova (variance)')
    ax1.set_xticklabels(ax1.get_xticklabels(), rotation=45, ha='right')

    sns.boxplot(x='hyperparameter', y='importance_max_min', data=df, ax=ax2)
    ax2.set_title('fanova (max-min)')
    ax2.set_xticklabels(ax2.get_xticklabels(), rotation=45, ha='right')

    os.makedirs(args.output_directory, exist_ok=True)
    output_file = os.path.join(args.output_directory, '%s.png' % os.path.basename(args.fanova_result_file))
    # ax1.set_yscale('log')
    plt.tight_layout()
    plt.savefig(output_file)
    logging.info('stored figure to %s' % output_file)


if __name__ == '__main__':
    run(read_cmd())

update plot script