Create carb/pr_plot.py

evaluation_data/carb/pr_plot.py

@@ -0,0 +1,58 @@
+""" Usage:
+   pr_plot --in=DIR_NAME --out=OUTPUT_FILENAME 
+
+Options:
+  --in=DIR_NAME            Folder in which to search for *.dat files, all of which should be in a P/R column format (outputs from benchmark.py)
+  --out=OUTPUT_FILENAME    Output filename, filetype will determine the format. Possible formats: pdf, pgf, png
+
+
+"""
+
+import os
+import ntpath
+import numpy as np
+from glob import glob
+from docopt import docopt
+import matplotlib.pyplot as plt
+import logging
+import ipdb
+logging.basicConfig(level = logging.INFO)
+
+plt.rcParams.update({'font.size': 14})
+
+def trend_name(path):
+    ''' return a system trend name from dat file path '''
+    head, tail = ntpath.split(path)
+    ret = tail or ntpath.basename(head)
+    return ret.split('.')[0]
+
+def get_pr(path):
+    ''' get PR curve from file '''
+    with open(path) as fin:
+        # remove header line
+        fin.readline()
+        prc = list(zip(*[[float(x) for x in line.strip().split('\t')] for line in fin]))
+        p = prc[0]
+        r = prc[1]
+        return p, r
+    
+if __name__ == '__main__':
+    args = docopt(__doc__)
+    input_folder = args['--in']
+    output_file = args['--out']
+    
+    # plot graphs for all *.dat files in input path
+    files = glob(os.path.join(input_folder, '*.dat'))
+    for _file in files:
+        p, r = get_pr(_file)
+        name = trend_name(_file)
+        plt.plot(r, p, label = name)
+
+    # Set figure properties and save
+    logging.info("Plotting P/R graph to {}".format(output_file))
+    plt.ylim([0.0, 1.05])
+    plt.xlim([0.0, 0.8])
+    plt.xlabel('Recall')
+    plt.ylabel('Precision')
+    plt.legend(loc="lower right")
+    plt.savefig(output_file)