print_results.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# */AIPND-revision/intropyproject-classify-pet-images/print_results.py
#
# PROGRAMMER:
# DATE CREATED:
# REVISED DATE:
# PURPOSE: Create a function print_results that prints the results statistics
#          from the results statistics dictionary (results_stats_dic). It
#          should also allow the user to be able to print out cases of misclassified
#          dogs and cases of misclassified breeds of dog using the Results
#          dictionary (results_dic).
#         This function inputs:
#            -The results dictionary as results_dic within print_results
#             function and results for the function call within main.
#            -The results statistics dictionary as results_stats_dic within
#             print_results function and results_stats for the function call within main.
#            -The CNN model architecture as model wihtin print_results function
#             and in_arg.arch for the function call within main.
#            -Prints Incorrectly Classified Dogs as print_incorrect_dogs within
#             print_results function and set as either boolean value True or
#             False in the function call within main (defaults to False)
#            -Prints Incorrectly Classified Breeds as print_incorrect_breed within
#             print_results function and set as either boolean value True or
#             False in the function call within main (defaults to False)
#         This function does not output anything other than printing a summary
#         of the final results.
##
# TODO 6: Define print_results function below, specifically replace the None
#       below by the function definition of the print_results function.
#       Notice that this function doesn't to return anything because it
#       prints a summary of the results using results_dic and results_stats_dic
#
def print_results(results_dic, results_stats_dic, model,
                  print_incorrect_dogs = False, print_incorrect_breed = False):
    """
    Prints summary results on the classification and then prints incorrectly
    classified dogs and incorrectly classified dog breeds if user indicates
    they want those printouts (use non-default values)
    Parameters:
      results_dic - Dictionary with key as image filename and value as a List
             (index)idx 0 = pet image label (string)
                    idx 1 = classifier label (string)
                    idx 2 = 1/0 (int)  where 1 = match between pet image and
                            classifer labels and 0 = no match between labels
                    idx 3 = 1/0 (int)  where 1 = pet image 'is-a' dog and
                            0 = pet Image 'is-NOT-a' dog.
                    idx 4 = 1/0 (int)  where 1 = Classifier classifies image
                            'as-a' dog and 0 = Classifier classifies image
                            'as-NOT-a' dog.
      results_stats_dic - Dictionary that contains the results statistics (either
                   a  percentage or a count) where the key is the statistic's
                     name (starting with 'pct' for percentage or 'n' for count)
                     and the value is the statistic's value
      model - Indicates which CNN model architecture will be used by the
              classifier function to classify the pet images,
              values must be either: resnet alexnet vgg (string)
      print_incorrect_dogs - True prints incorrectly classified dog images and
                             False doesn't print anything(default) (bool)
      print_incorrect_breed - True prints incorrectly classified dog breeds and
                              False doesn't print anything(default) (bool)
    Returns:
           None - simply printing results.
    """
    print("\n\n*** Results Summary for CNN Model Architecture",model.upper(),
          "***")
    print("{:20}: {:3d}".format('N Images', results_stats_dic['n_images']))
    print("{:20}: {:3d}".format('N Dog Images', results_stats_dic['n_dogs_img']))
    print("{:20}: {:3d}".format('N Not-Dog Images', results_stats_dic['n_notdogs_img']))

    print("{:20}: {:3f}".format('P correct match', results_stats_dic['pct_match']))
    print("{:20}: {:3f}".format('P correct clasf dogs', results_stats_dic['pct_correct_dogs']))
    print("{:20}: {:3f}".format('P correct clasf dogs breed', results_stats_dic['pct_correct_breed']))
    print("{:20}: {:3f}".format('P correct clasf non dogs', results_stats_dic['pct_correct_notdogs']))

    for key in results_stats_dic:
        if key[0] == "p":
            print("{} = {}".format(key,results_stats_dic[key]))

    if (print_incorrect_dogs and
        ( (results_stats_dic['n_correct_dogs'] + results_stats_dic['n_correct_notdogs'])
          != results_stats_dic['n_images'] )
       ):
        print("\nINCORRECT Dog/NOT Dog Assignments:")

    for key in results_dic:
        if results_dic[key][3] != results_dic[key][4]:
            print("pet image label = {}, classifier image label = {}".format(results_dic[key][0],results_dic[key][1]))

    if print_incorrect_breed and results_stats_dic['n_correct_dogs'] != results_stats_dic['n_correct_breed']:
        print("\nINCORRECT Dog Breed Assignment:")

    for key in results_dic:

            if ( sum(results_dic[key][3:]) == 2 and
                results_dic[key][2] == 0 ):
                print("Real: {:>26}   Classifier: {:>30}".format(results_dic[key][0],
                                                          results_dic[key][1]))