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calculates_results_stats.py
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calculates_results_stats.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# */AIPND-revision/intropyproject-classify-pet-images/calculates_results_stats.py
#
# PROGRAMMER: Husam Jehad AlSabbah
# DATE CREATED: 17/6/2023
# REVISED DATE:
# PURPOSE: Create a function calculates_results_stats that calculates the
# statistics of the results of the programrun using the classifier's model
# architecture to classify the images. This function will use the
# results in the results dictionary to calculate these statistics.
# This function will then put the results statistics in a dictionary
# (results_stats_dic) that's created and returned by this function.
# This will allow the user of the program to determine the 'best'
# model for classifying the images. The statistics that are calculated
# will be counts and percentages. Please see "Intro to Python - Project
# classifying Images - xx Calculating Results" for details on the
# how to calculate the counts and percentages for this function.
# This function inputs:
# -The results dictionary as results_dic within calculates_results_stats
# function and results for the function call within main.
# This function creates and returns the Results Statistics Dictionary -
# results_stats_dic. This dictionary 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 value
# is the statistic's value. This dictionary should contain the
# following keys:
# n_images - number of images
# n_dogs_img - number of dog images
# n_notdogs_img - number of NON-dog images
# n_match - number of matches between pet & classifier labels
# n_correct_dogs - number of correctly classified dog images
# n_correct_notdogs - number of correctly classified NON-dog images
# n_correct_breed - number of correctly classified dog breeds
# pct_match - percentage of correct matches
# pct_correct_dogs - percentage of correctly classified dogs
# pct_correct_breed - percentage of correctly classified dog breeds
# pct_correct_notdogs - percentage of correctly classified NON-dogs
#
##
# TODO 5: Define calculates_results_stats function below, please be certain to replace None
# in the return statement with the results_stats_dic dictionary that you create
# with this function
#
def calculates_results_stats(results_dic):
"""
Calculates statistics of the results of the program run using classifier's model
architecture to classifying pet images. Then puts the results statistics in a
dictionary (results_stats_dic) so that it's returned for printing as to help
the user to determine the 'best' model for classifying images. Note that
the statistics calculated as the results are either percentages or counts.
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.
Returns:
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. See comments above
and the previous topic Calculating Results in the class for details
on how to calculate the counts and statistics.
"""
# Replace None with the results_stats_dic dictionary that you created with
# this function
results_stats_dic = dict()
n_images = len(results_dic)
n_dogs_img = len(list(result for result in results_dic.values() if result[3] == 1))
n_notdogs_img = len(list(result for result in results_dic.values() if result[3] == 0))
n_match = len(list(result for result in results_dic.values() if result[2] == 1))
n_correct_dogs = len(list(result for result in results_dic.values() if result[3] == 1 and result[4] == 1))
n_correct_notdogs = len(list(result for result in results_dic.values() if result[3] == 0 and result[4] == 0))
n_correct_breed = len(list(result for result in results_dic.values() if result[2] == 1 and result[3] == 1))
if n_images == 0:
pct_match = 0
else:
pct_match = (n_match / n_images) * 100
if n_dogs_img == 0:
pct_correct_dogs = 0
else:
pct_correct_dogs = (n_correct_dogs / n_dogs_img) * 100
if n_correct_dogs == 0:
pct_correct_breed = 0
else:
pct_correct_breed = (n_correct_breed / n_correct_dogs) * 100
if n_notdogs_img == 0:
pct_correct_notdogs = 0
else:
pct_correct_notdogs = (n_correct_notdogs / n_notdogs_img) * 100
results_stats_dic["n_images"] = n_images
results_stats_dic["n_dogs_img"] = n_dogs_img
results_stats_dic["n_notdogs_img"] = n_notdogs_img
results_stats_dic["n_match"] = n_match
results_stats_dic["n_correct_dogs"] = n_correct_dogs
results_stats_dic["n_correct_notdogs"] = n_correct_notdogs
results_stats_dic["n_correct_breed"] = n_correct_breed
results_stats_dic["pct_match"] = pct_match
results_stats_dic["pct_correct_dogs"] = pct_correct_dogs
results_stats_dic["pct_correct_breed"] = pct_correct_breed
results_stats_dic["pct_correct_notdogs"] = pct_correct_notdogs
return results_stats_dic