forked from moreo/QuaPy
87 lines
2.4 KiB
Python
87 lines
2.4 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import sys, os, pathlib
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assert len(sys.argv) == 3, f'wrong args, syntax is: python {sys.argv[0]} <result_input_path> <dynamic (0|1)>'
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file = sys.argv[1]
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loop = bool(int(sys.argv[2]))
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plotname = pathlib.Path(file).name.replace(".csv", ".png")
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if not loop:
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plt.rcParams['figure.figsize'] = [12, 12]
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plt.rcParams['figure.dpi'] = 200
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# plot the data
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fig, axs = plt.subplots(5)
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try:
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while True:
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aXn = 0
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df = pd.read_csv(file, sep='\t')
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xs = df['it']
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y_r = df['R']
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y_rhat = df['Rhat']
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y_rhatCC = df['RhatCC']
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axs[aXn].plot(xs, y_rhat, label='$\hat{R}_{Q}$')
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axs[aXn].plot(xs, y_rhatCC, label='$\hat{R}_{CC}$')
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axs[aXn].plot(xs, y_r, label='$R$')
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axs[aXn].legend()
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axs[aXn].grid()
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axs[aXn].set_ylabel('Recall estimation')
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axs[aXn].set_ylim(0,1)
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aXn+=1
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y_r = df['te-prev']
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y_rhat = df['te-estim']
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y_rhatCC = df['te-estimCC']
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axs[aXn].plot(xs, y_rhat, label='te-$\hat{Pr}(\oplus)_{Q}$')
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axs[aXn].plot(xs, y_rhatCC, label='te-$\hat{Pr}(\oplus)_{CC}$')
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axs[aXn].plot(xs, y_r, label='te-$Pr(\oplus)$')
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axs[aXn].legend()
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axs[aXn].grid()
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axs[aXn].set_ylabel('Prevalence estimation')
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aXn += 1
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y_ae = df['AE']
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y_ae_cc = df['AE_CC']
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axs[aXn].plot(xs, y_ae, label='AE$_{Q}$')
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axs[aXn].plot(xs, y_ae_cc, label='AE$_{CC}$')
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axs[aXn].legend()
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axs[aXn].grid()
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axs[aXn].set_ylabel('Quantification error')
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aXn += 1
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axs[aXn].plot(xs, df['Shift'], label='tr-te shift (AE)')
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axs[aXn].plot(xs, df['tr-prev'], label='tr-$Pr(\oplus)$')
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axs[aXn].plot(xs, df['te-prev'], label='te-$Pr(\oplus)$')
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axs[aXn].legend()
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axs[aXn].grid()
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axs[aXn].set_ylabel('Train-Test Shift')
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aXn += 1
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axs[aXn].plot(xs, df['MF1_Q'], label='$F_1(clf(Q))$')
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axs[aXn].plot(xs, df['MF1_Clf'], label='$F_1(clf(CC))$')
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axs[aXn].legend()
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axs[aXn].grid()
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axs[aXn].set_ylabel('Classifiers performance')
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aXn += 1
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os.makedirs('./plots', exist_ok=True)
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plt.savefig(f'./plots/{plotname}')
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if not loop:
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break
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else:
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plt.pause(.5)
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for i in range(aXn):
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axs[i].cla()
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except KeyboardInterrupt:
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print("\n[exit]") |