89 lines
2.9 KiB
Python
89 lines
2.9 KiB
Python
import itertools
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import os.path
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from Retrieval.experiments import methods
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from Retrieval.commons import CLASS_NAMES, Ks, DATA_SIZES
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import matplotlib.pyplot as plt
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from Retrieval.plot_mrae_xaxis_k import load_all_results
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data_home = 'data'
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class_mode = 'multiclass'
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method_names = [name for name, *other in methods(None)]
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all_results = {}
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class_name_label = {
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'continent': 'Geographic Location',
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'gender': 'Gender',
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'years_category': 'Age of Topic'
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}
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# loads all MRAE results, and returns a dictionary containing the values, which is indexed by:
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# class_name -> data_size -> method_name -> k -> stat -> float
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results = load_all_results()
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# generates the class-independent, size-independent plots for y-axis=MRAE in which:
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# - the x-axis displays the Ks
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# X_DATA_SIZES = [int(x.replace('K', '000').replace('M', '000000').replace('FULL', '3250000')) for x in DATA_SIZES]
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X_DATA_SIZES = [x.replace('FULL', '3.25M') for x in DATA_SIZES]
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for class_name in CLASS_NAMES:
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for k in [100]: #Ks:
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log = class_name=='gender'
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fig, ax = plt.subplots()
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max_means = []
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markers = itertools.cycle(['o', 's', '^', 'D', 'v', '*', '+'])
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for method_name in method_names:
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# class_name -> data_size -> method_name -> k -> stat -> float
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means = [
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results[class_name][data_size][method_name][k]['mean'] for data_size in DATA_SIZES
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]
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stds = [
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results[class_name][data_size][method_name][k]['std'] for data_size in DATA_SIZES
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]
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# max_mean = np.max([
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# results[class_name][data_size][method_name][k]['max'] for data_size in DATA_SIZE
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# ])
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max_means.append(max(means))
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style = 'o-' if method_name != 'CC' else '--'
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method_name = method_name.replace('NaiveQuery', 'Naive@$k$')
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method_name = method_name.replace('KDEy-ML', 'KDEy')
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marker=next(markers)
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line = ax.plot(X_DATA_SIZES, means, style, label=method_name, color=None, linewidth=3, markersize=10, marker=marker)
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color = line[-1].get_color()
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if log:
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ax.set_yscale('log')
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# ax.fill_between(Ks, means - stds, means + stds, alpha=0.3, color=color)
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ax.grid(True, which='both', axis='y', color='gray', linestyle='--', linewidth=0.3)
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ax.set_xlabel('training pool size')
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ax.set_ylabel('RAE' + (' (log scale)' if log else ''))
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ax.set_title(f'{class_name_label[class_name]} at exposure {k=}')
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ax.set_ylim([0, max(max_means)*1.05])
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if class_name == 'years_category':
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ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
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os.makedirs(f'plots/var_size/{class_name}', exist_ok=True)
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plotpath = f'plots/var_size/{class_name}/{k}_mrae.pdf'
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print(f'saving plot in {plotpath}')
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plt.savefig(plotpath, bbox_inches='tight')
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