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print_results.py
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#!/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.
"""
# Prints summary statistics over the run
print("\n\n*** Results Summary for CNN Model Architecture",model,
"***")
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']))
# Prints summary statistics (percentages) on Model Run
print(" ")
for key in results_stats_dic:
## print the key and value associated to each key starting with "p" :
if key.startswith('p'):
print( "{:20}: {:3f}".format(key, results_stats_dic[key]) )
# IF print_incorrect_dogs == True AND there were images incorrectly
# classified as dogs or vice versa - print out these cases
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:")
# process through results dict, printing incorrectly classified dogs
for key in results_dic:
# TODO: 6c. REPLACE pass with CODE that prints out the pet label
# and the classifier label from results_dic dictionary
# ONLY when the classifier function (classifier label)
# misclassified dogs specifically:
# pet label is-a-dog and classifier label is-NOT-a-dog
# -OR-
# pet label is-NOT-a-dog and classifier label is-a-dog
# You will need to write a conditional statement that
# determines if the classifier function misclassified dogs
# See 'Adjusting Results Dictionary' section in
# 'Classifying Labels as Dogs' for details on the
# format of the results_dic dictionary. Remember the value
# is accessed by results_dic[key] and the value is a list
# so results_dic[key][idx] - where idx represents the
# index value of the list and can have values 0-4.
#
# Pet Image Label is a Dog - Classified as NOT-A-DOG -OR-
# Pet Image Label is NOT-a-Dog - Classified as a-DOG
if sum(results_dic[key][3:]) == (1):
print("Pet label: {} Classifier: {}".format(results_dic[key][0],results_dic[key][1]))
# print('classifier label',results_dic[key][2])
# print('pet label',results_dic[key][1])
# IF print_incorrect_breed == True AND there were dogs whose breeds
# were incorrectly classified - print out these cases
if (print_incorrect_breed and
(results_stats_dic['n_correct_dogs'] != results_stats_dic['n_correct_breed'])
):
print("\nINCORRECT Dog Breed Assignment:")
# process through results dict, printing incorrectly classified breeds
for key in results_dic:
# Pet Image Label is-a-Dog, classified as-a-dog but is WRONG breed
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]))