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adjust_results4_isadog.py
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adjust_results4_isadog.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# */AIPND-revision/intropyproject-classify-pet-images/adjust_results4_isadog.py
#
# PROGRAMMER: Masood Khan Patel
# DATE CREATED: 9th Feb 2019
# REVISED DATE:
# PURPOSE: Create a function adjust_results4_isadog that adjusts the results
# dictionary to indicate whether or not the pet image label is of-a-dog,
# and to indicate whether or not the classifier image label is of-a-dog.
# All dog labels from both the pet images and the classifier function
# will be found in the dognames.txt file. We recommend reading all the
# dog names in dognames.txt into a dictionary where the 'key' is the
# dog name (from dognames.txt) and the 'value' is one. If a label is
# found to exist within this dictionary of dog names then the label
# is of-a-dog, otherwise the label isn't of a dog. Alternatively one
# could also read all the dog names into a list and then if the label
# is found to exist within this list - the label is of-a-dog, otherwise
# the label isn't of a dog.
# This function inputs:
# -The results dictionary as results_dic within adjust_results4_isadog
# function and results for the function call within main.
# -The text file with dog names as dogfile within adjust_results4_isadog
# function and in_arg.dogfile for the function call within main.
# This function uses the extend function to add items to the list
# that's the 'value' of the results dictionary. You will be adding the
# whether or not the pet image label is of-a-dog as the item at index
# 3 of the list and whether or not the classifier label is of-a-dog as
# the item at index 4 of the list. Note we recommend setting the values
# at indices 3 & 4 to 1 when the label is of-a-dog and to 0 when the
# label isn't a dog.
#
##
# TODO 4: Define adjust_results4_isadog function below, specifically replace the None
# below by the function definition of the adjust_results4_isadog function.
# Notice that this function doesn't return anything because the
# results_dic dictionary that is passed into the function is a mutable
# data type so no return is needed.
#
def adjust_results4_isadog(results_dic, dogfile):
"""
Adjusts the results dictionary to determine if classifier correctly
classified images 'as a dog' or 'not a dog' especially when not a match.
Demonstrates if model architecture correctly classifies dog images even if
it gets dog breed wrong (not a match).
Parameters:
results_dic - Dictionary with 'key' as image filename and 'value' as a
List. Where the list will contain the following items:
index 0 = pet image label (string)
index 1 = classifier label (string)
index 2 = 1/0 (int) where 1 = match between pet image
and classifer labels and 0 = no match between labels
------ where index 3 & index 4 are added by this function -----
NEW - index 3 = 1/0 (int) where 1 = pet image 'is-a' dog and
0 = pet Image 'is-NOT-a' dog.
NEW - index 4 = 1/0 (int) where 1 = Classifier classifies image
'as-a' dog and 0 = Classifier classifies image
'as-NOT-a' dog.
dogfile - A text file that contains names of all dogs from the classifier
function and dog names from the pet image files. This file has
one dog name per line dog names are all in lowercase with
spaces separating the distinct words of the dog name. Dog names
from the classifier function can be a string of dog names separated
by commas when a particular breed of dog has multiple dog names
associated with that breed (ex. maltese dog, maltese terrier,
maltese) (string - indicates text file's filename)
Returns:
None - results_dic is mutable data type so no return needed.
"""
# Creates dognames dictionary for quick matching to results_dic labels from
# real answer & classifier's answer
dognames_dic = dict()
# Reads in dognames from file, 1 name per line & automatically closes file
with open(dogfile, "r") as infile:
# Reads in dognames from first line in file
line = infile.readline()
# Processes each line in file until reaching EOF (end-of-file) by
# processing line and adding dognames to dognames_dic with while loop
while line != "":
# Process line by striping newline from line
line = line.rstrip('\n')
# adds dogname(line) to dogsnames_dic if it doesn't already exist
# in the dogsnames_dic dictionary
if line not in dognames_dic:
dognames_dic[line] = 1
# Reads in next line in file to be processed with while loop
# if this line isn't empty (EOF)
line = infile.readline()
# Iterate through results_dic if labels are found in dognames_dic
# then label "is a dog" index3/4=1 otherwise index3/4=0 "not a dog"
for key in results_dic:
# Pet Image Label IS of Dog (e.g. found in dognames_dic)
if results_dic[key][0] in dognames_dic:
# Classifier Label IS image of Dog (e.g. found in dognames_dic)
# appends (1, 1) because both labels are dogs
if results_dic[key][1] in dognames_dic:
results_dic[key].extend((1, 1))
# Classifier Label IS NOT image of dog (e.g. NOT in dognames_dic)
# appends (1,0) because only pet label is a dog
else:
results_dic[key].extend([1, 0])
# Pet Image Label IS NOT a Dog image (e.g. NOT found in dognames_dic)
else:
# Classifier Label IS image of Dog (e.g. found in dognames_dic)
# appends (0, 1)because only Classifier labe is a dog
if results_dic[key][1] in dognames_dic:
results_dic[key].extend([0, 1])
# Classifier Label IS NOT image of Dog (e.g. NOT in dognames_dic)
# appends (0, 0) because both labels aren't dogs
else:
results_dic[key].extend([0, 0])