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visualization.py 3.00 KiB
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import pandas as pd
import matplotlib.pyplot as plt
import os
import re
import argparse

# which parameter to use for caption and order of the subplots
caption_regex = p = re.compile("--learning_rate (\d+\.\d+)")


def main():
    parser = argparse.ArgumentParser(
        description='Visualizes the results of a parameterscan')
    parser.add_argument("--input_dir", type=str, default="results",
                        required=True, action='store',
                        help="Input directory")
    parser.add_argument("--num_rows", type=int, default=4,
                        required=False, action='store',
                        help="Plot Layout: Number of rows")
    parser.add_argument("--max_loss", type=float, default=0,
                        required=False, action='store',
                        help="maximum loss to display (yscale of plot)")
    parser.add_argument("--output", type=str, default="plot.pdf",
                        required=False, action='store',
                        help="output filename")

    ARGS = parser.parse_args()

    cases = []

    for path, directories, files in os.walk(ARGS.input_dir):
        for dir in directories:
            if (os.path.isfile(os.path.join(path, dir, "train_hist.csv")) and
                    os.path.isfile(os.path.join(path, dir, "test_accuracy.txt")) and
                    os.path.isfile(os.path.join(path, dir, "input_parameter.params"))):
                cases.append(os.path.join(path, dir))
                pass
            else:
                print("%s has not the expected files" % dir)

    num_results = len(cases)
    if num_results == 0:
        print("Nothing to visualize")
        exit()

    data = {}
    for case in cases:
        param_string = "unknown"
        # read last line from file
        with open(os.path.join(case, "input_parameter.params"), 'r') as file:
            param_string = file.readline()

        train_hist = pd.read_csv(os.path.join(case, "train_hist.csv"))

        title = caption_regex.findall(param_string)[0]
        data[title] = train_hist

    num_cols = int(num_results / ARGS.num_rows)
    if num_results % ARGS.num_rows != 0:
        num_cols += 1
    fig, axes = plt.subplots(ARGS.num_rows, num_cols, sharex=True, sharey=True)
    if ARGS.max_loss != 0:
        plt.ylim([0, ARGS.max_loss])

    i = 0
    for title in sorted(data):
        train_hist = data[title]

        row = int(i / num_cols)
        col = i % num_cols
        if num_cols == 1:
            this_plot = axes[row]
        elif ARGS.num_rows == 1:
            this_plot = axes[col]
        else:
            this_plot = axes[row, col]

        this_plot.set_title(title)
        this_plot.plot(train_hist['epoch'], train_hist['loss'], 'bo-', label='Train loss')
        this_plot.plot(train_hist['epoch'], train_hist['val_loss'], 'rd--', label='Val loss')
        this_plot.set_xlabel('Epoch')
        this_plot.set_ylabel('Loss')
        this_plot.legend()

        i += 1

    plt.show()
    plt.savefig(ARGS.output)


if __name__ == "__main__":
    main()