dmelodies_benchmarking/script_factor_analysis.py at master · ashispati/dmelodies_benchmarking · GitHub

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import os
import json
import numpy as np
import pandas as pd
from script_create_plots import get_results_path,\
    get_annealed_vae_results_file, \
    get_beta_vae_results_file, \
    get_factor_vae_results_file, vae_type_dict, vae_param_dict, vae_param__values_dict, model_type_dict, d1, d2, d3
import seaborn as sns
import matplotlib.pyplot as plt
from src.utils.plotting import create_box_plot


dark_colors = [d1, d2]


def main():
    # create plots folder if it doesn't exist
    cur_dir = os.path.dirname(os.path.realpath(__file__))
    if not os.path.exists(os.path.join(cur_dir, "plots")):
        os.mkdir(os.path.join(cur_dir, "plots"))

    for v in vae_type_dict.keys():
        if v == 'Factor-VAE':
            continue
        data = []
        model_list = []
        for m in model_type_dict.keys():
            # if m == 'dmelCNN':
            #     continue
            # if m == 'dmelRNN':
            #     continue
            if m == 'dsprCNN':
                continue
            model_list.append(model_type_dict[m])
            fp_function = vae_type_dict[v]
            a = vae_param_dict[v]
            for p in a:
                results_fp = fp_function(m, p)
                with open(results_fp, 'r') as infile:
                    results_dict = json.load(infile)
                f_dict = results_dict['mig_factors']
                m_list = []
                c_list = []
                temp_list = []
                num_factors = 0
                for f in f_dict.keys():
                    m_list.append(f_dict[f])
                    if f == 'arp_chord8':
                        c_list.append('arp_chord4')
                    else:
                        c_list.append(f)
                    num_factors += 1
                if len(m_list) != 0:
                    temp_list.append(m_list)
                    temp_list.append(c_list)
                    temp_list.append(num_factors * [model_type_dict[m]])
                    data.append(temp_list)
        data = np.concatenate(data, axis=1).T
        df = pd.DataFrame(columns=['MIG', 'Attribute', 'Model'], data=data)
        df['MIG'] = df['MIG'].astype(float)
        y_axis_range = None
        if v == r'$\beta$-VAE':
            y_axis_range = (0, 0.2)
        fig, ax = create_box_plot(
            data_frame=df,
            model_list=model_list,
            d_list=dark_colors,
            x_axis='Attribute',
            y_axis='MIG',
            grouping='Model',
            legend_on=False,
            alpha=0.8,
            stripplot_on=False,
            y_axis_range=y_axis_range
        )
        plt.setp(ax.get_xticklabels(), rotation=45)
        if v == r'$\beta$-VAE':
            v = 'beta-VAE'
        save_path = os.path.join(
            os.path.realpath(os.path.dirname(__file__)), 'plots', f'factor_analysis_{v}_dmel.pdf'
        )
        plt.tight_layout()
        plt.savefig(save_path)

    for v in vae_type_dict.keys():
        if v == 'Factor-VAE':
            continue
        data = []
        model_list = []
        for m in model_type_dict.keys():
            if m == 'dmelCNN' or  m == 'dmelRNN':
                continue
            # if m == 'dsprCNN':
            #     continue
            model_list.append(model_type_dict[m])
            fp_function = vae_type_dict[v]
            a = vae_param_dict[v]
            for p in a:
                results_fp = fp_function(m, p)
                with open(results_fp, 'r') as infile:
                    results_dict = json.load(infile)
                f_dict = results_dict['mig_factors']
                m_list = []
                c_list = []
                temp_list = []
                num_factors = 0
                for f in f_dict.keys():
                    m_list.append(f_dict[f])
                    c_list.append(f)
                    num_factors += 1
                if len(m_list) != 0:
                    temp_list.append(m_list)
                    temp_list.append(c_list)
                    temp_list.append(num_factors * [model_type_dict[m]])
                    data.append(temp_list)
        data = np.concatenate(data, axis=1).T
        df = pd.DataFrame(columns=['MIG', 'Attribute', 'Model'], data=data)
        df['MIG'] = df['MIG'].astype(float)
        fig, ax = create_box_plot(
            data_frame=df,
            model_list=model_list,
            d_list=dark_colors,
            x_axis='Attribute',
            y_axis='MIG',
            grouping='Model',
            legend_on=False,
            alpha=0.8,
            stripplot_on=False,
            # y_axis_range=(0., 0.)
        )
        plt.setp(ax.get_xticklabels(), rotation=45)
        if v == r'$\beta$-VAE':
            v = 'beta-VAE'
        save_path = os.path.join(
            os.path.realpath(os.path.dirname(__file__)), 'plots', f'factor_analysis_{v}_dspr.pdf'
        )
        plt.tight_layout()
        plt.savefig(save_path)


if __name__ == '__main__':
    main()