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metalearner_plots.py
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metalearner_plots.py
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import json
from pathlib import Path
from typing import List
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from utils import get_plot_folder
def build_info(scores):
return [{'dataset': dataset, **dscores} for dataset, dscores in scores.items()]
def extract_scores(strategy):
"""Extracts a feature from an specific task info"""
info_folder = Path('results') / strategy / 'results'
data_dict = []
for fn in info_folder.glob('*.json'):
info = json.load(open(fn, 'r+'))
data_dict.append(build_info(info))
return [pd.DataFrame(data) for data in data_dict]
def get_globals(data):
"""Get the globals of different iterations"""
return pd.DataFrame([df.iloc[-1] for df in data])
def plot_boxplot(data: pd.DataFrame, metric: str, fig_path: Path):
"""Plots the data in a given metric and stores it in the figure path"""
plt.figure(metric)
sns.boxplot(data=data, y=metric)
plt.savefig(fig_path)
plt.close()
def plot_results(strategies: List[str], metrics: List[str], plot_folder: Path):
"""
Plots the results of a list of strategies by a given metrics
and results are stored in plot folder.
"""
for strategy in strategies:
data = extract_scores(strategy)
plot_folder = get_plot_folder(plot_folder / strategy)
globl = get_globals(data)
for metric in metrics:
for i, df in enumerate(data, 1):
plot_boxplot(df, metric, plot_folder / f'{metric}_{i}.pdf')
plot_boxplot(globl, metric, plot_folder / f'global_{metric}.pdf')
def main():
"""Configures everything to save all plots"""
strategies = ['xgb_metalearner'] #, 'nn_metalearner']
metrics = ['srcc_score', 'wrc_score', 'dcg_score', 'ndcg_score']
plot_folder = get_plot_folder('plots/meta_learners')
plot_results(strategies, metrics, plot_folder)
if __name__ == '__main__':
main()