inbreast_preprocess_script_pos_neg.py

import os
import matplotlib.pyplot as plt
import numpy as np
import json
import seaborn as sns;

sns.set()
import glob2
import argparse
import glob
import pandas as pd


##################    THIS IS JUST SAMPLE CODE TO READ FROM CSV FILES  ###############################
def smooth_reward_curve(x, y):
    halfwidth = int(np.ceil(len(x) / 60))  # Halfwidth of our smoothing convolution
    k = halfwidth
    xsmoo = x
    ysmoo = np.convolve(y, np.ones(2 * k + 1), mode='same') / np.convolve(np.ones_like(y), np.ones(2 * k + 1),
                                                                          mode='same')
    return xsmoo, ysmoo


def load_results(file):
    if not os.path.exists(file):
        return None
    with open(file, 'r') as f:
        lines = [line for line in f]
    if len(lines) < 2:
        return None
    keys = [name.strip() for name in lines[0].split(',')]
    data = np.genfromtxt(file, delimiter=',', skip_header=1, filling_values=0.)
    if data.ndim == 1:
        data = data.reshape(1, -1)
    assert data.ndim == 2
    assert data.shape[-1] == len(keys)
    result = {}
    for idx, key in enumerate(keys):
        result[key] = data[:, idx]
    return result


def pad(xs, value=np.nan):
    maxlen = np.max([len(x) for x in xs])

    padded_xs = []
    for x in xs:
        if x.shape[0] >= maxlen:
            padded_xs.append(x)

        padding = np.ones((maxlen - x.shape[0],) + x.shape[1:]) * value
        x_padded = np.concatenate([x, padding], axis=0)
        assert x_padded.shape[1:] == x.shape[1:]
        assert x_padded.shape[0] == maxlen
        padded_xs.append(x_padded)
    return np.array(padded_xs)


parser = argparse.ArgumentParser()
parser.add_argument('dir', type=str)
parser.add_argument('dir2', type=str)
parser.add_argument('--smooth', type=int, default=1)
args = parser.parse_args()

# Load all data.
data = {}
epochs = 30
averages = np.zeros(epochs)
epoch_list = np.zeros(epochs)
averages2 = np.zeros(epochs)

csv_files = glob.glob(os.path.join(args.dir, "*.csv"))
csv_files2 = glob.glob(os.path.join(args.dir2, '*.csv'))
idx = 0
file_count = 0
# loop over the list of csv files
for f in csv_files:
    file_count += 1
    # read the csv file
    df = pd.read_csv(f)
    # print(df)
    for i in range(epochs):
        averages[i] += df['test/success_rate'][i]
        epoch_list[i] = df['epoch'][i]

final_average_list = averages / file_count

print(final_average_list.shape)
print(epoch_list.shape)

for f in csv_files2:
    df = pd.read_csv(f)
    for i in range(epochs):
        averages2[i] += df['test/success_rate'][i]

final_average_list2 = averages2 / file_count

plt.clf()
x, y = smooth_reward_curve(epoch_list, final_average_list)
x2, y2 = smooth_reward_curve(epoch_list, final_average_list2)

plt.plot(x2, y2, label='DRL')
plt.plot(x, y, label='GA-DRL')
plt.plot()
plt.title('Success rate vs epochs')
plt.xlabel('epochs')
plt.ylabel('Median success rate')
plt.grid(False)
plt.legend()
plt.savefig(os.path.join(args.dir, 'Aubo_Reach_Averages'))