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calculates_results_stats_hints.py
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calculates_results_stats_hints.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# */AIPND-revision/intropyproject-classify-pet-images/calculates_results_stats_hints.py
#
# PROGRAMMER: Aman Aggarwal
# DATE CREATED: 06-07-2023
# REVISED DATE:
# PURPOSE: This is a *hints* file to help guide students in creating the
# 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: EDIT and ADD code BELOW to do the following that's stated in the
# comments below that start with "TODO: 5" for the calculates_results_stats
# function. 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 classroom Item XX Calculating Results for details
on how to calculate the counts and statistics.
"""
# Creates empty dictionary for results_stats_dic
results_stats_dic = dict()
# Sets all counters to initial values of zero so that they can
# be incremented while processing through the images in results_dic
results_stats_dic['n_dogs_img'] = 0
results_stats_dic['n_match'] = 0
results_stats_dic['n_correct_dogs'] = 0
results_stats_dic['n_correct_notdogs'] = 0
results_stats_dic['n_correct_breed'] = 0
# process through the results dictionary
for key in results_dic:
# Labels Match Exactly
if results_dic[key][2] == 1:
results_stats_dic['n_match'] += 1
# TODO: 5a. REPLACE pass with CODE that counts how many pet images of
# dogs had their breed correctly classified. This happens
# when the pet image label indicates the image is-a-dog AND
# the pet image label and the classifier label match. You
# will need to write a conditional statement that determines
# when the dog breed is correctly classified and then
# increments 'n_correct_breed' by 1. Recall 'n_correct_breed'
# is a key in the results_stats_dic dictionary with it's value
# representing the number of correctly classified dog breeds.
#
# Pet Image Label is a Dog AND Labels match- counts Correct Breed
pass
# Pet Image Label is a Dog - counts number of dog images
if results_dic[key][3] == 1:
results_stats_dic['n_dogs_img'] += 1
# Classifier classifies image as Dog (& pet image is a dog)
# counts number of correct dog classifications
if results_dic[key][4] == 1:
results_stats_dic['n_correct_dogs'] += 1
# TODO: 5b. REPLACE pass with CODE that counts how many pet images
# that are NOT dogs were correctly classified. This happens
# when the pet image label indicates the image is-NOT-a-dog
# AND the classifier label indicates the images is-NOT-a-dog.
# You will need to write a conditional statement that
# determines when the classifier label indicates the image
# is-NOT-a-dog and then increments 'n_correct_notdogs' by 1.
# Recall the 'else:' above 'pass' already indicates that the
# pet image label indicates the image is-NOT-a-dog and
# 'n_correct_notdogs' is a key in the results_stats_dic dictionary
# with it's value representing the number of correctly
# classified NOT-a-dog images.
#
# Pet Image Label is NOT a Dog
else:
# Classifier classifies image as NOT a Dog(& pet image isn't a dog)
# counts number of correct NOT dog clasifications.
pass
# Calculates run statistics (counts & percentages) below that are calculated
# using the counters from above.
# calculates number of total images
results_stats_dic['n_images'] = len(results_dic)
# calculates number of not-a-dog images using - images & dog images counts
results_stats_dic['n_notdogs_img'] = (results_stats_dic['n_images'] -
results_stats_dic['n_dogs_img'])
# TODO: 5c. REPLACE zero(0.0) with CODE that calculates the % of correctly
# matched images. Recall that this can be calculated by the
# number of correctly matched images ('n_match') divided by the
# number of images('n_images'). This result will need to be
# multiplied by 100.0 to provide the percentage.
#
# Calculates % correct for matches
results_stats_dic['pct_match'] = 0.0
# TODO: 5d. REPLACE zero(0.0) with CODE that calculates the % of correctly
# classified dog images. Recall that this can be calculated by
# the number of correctly classified dog images('n_correct_dogs')
# divided by the number of dog images('n_dogs_img'). This result
# will need to be multiplied by 100.0 to provide the percentage.
#
# Calculates % correct dogs
results_stats_dic['pct_correct_dogs'] = 0.0
# TODO: 5e. REPLACE zero(0.0) with CODE that calculates the % of correctly
# classified breeds of dogs. Recall that this can be calculated
# by the number of correctly classified breeds of dog('n_correct_breed')
# divided by the number of dog images('n_dogs_img'). This result
# will need to be multiplied by 100.0 to provide the percentage.
#
# Calculates % correct breed of dog
results_stats_dic['pct_correct_breed'] = 0.0
# Calculates % correct not-a-dog images
# Uses conditional statement for when no 'not a dog' images were submitted
if results_stats_dic['n_notdogs_img'] > 0:
results_stats_dic['pct_correct_notdogs'] = (results_stats_dic['n_correct_notdogs'] /
results_stats_dic['n_notdogs_img'])*100.0
else:
results_stats_dic['pct_correct_notdogs'] = 0.0
# TODO 5f. REPLACE None with the results_stats_dic dictionary that you
# created with this function
return None