-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathadjust_results4_isadog.py
114 lines (109 loc) · 6.3 KB
/
adjust_results4_isadog.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# */AIPND-revision/intropyproject-classify-pet-images/adjust_results4_isadog.py
#
# PROGRAMMER: Biraj Karki
# DATE CREATED: 10/11/2023
# REVISED DATE: 11/11/2023
# 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
dognames_dic = dict()
# Reads in dognames from file, 1 name per line & automatically closes file
with open(dogfile, "r") as infile:
line = infile.readline().strip() # Read the first line and strip newline
# Process each line in the file until reaching EOF
while line != "":
# Remove newline character and add the dogname to dognames_dic
dognames_dic[line] = 1
# Read the next line in the file
line = infile.readline().strip()
# Add to whether pet labels & classifier labels are dogs by appending
# two items to the end of the value (list) in results_dic.
# List Index 3 = whether(1) or not(0) Pet Image Label is a dog AND
# List Index 4 = whether(1) or not(0) Classifier Label is a dog
# Iterate through results_dic, if labels are found in dognames_dic, then
# label "is a dog" index 3/4=1; otherwise, index 3/4=0 "not a dog".
for key in results_dic:
pet_label = results_dic[key][0]
classifier_label = results_dic[key][1]
# Pet Image Label IS of Dog (e.g. found in dognames_dic)
if pet_label in dognames_dic:
# Classifier Label IS image of Dog (e.g. found in dognames_dic)
# appends (1, 1) because both labels are dogs
if classifier_label in dognames_dic:
results_dic[key].extend((1, 1))
else:
# Classifier Label IS NOT image of dog (e.g. NOT in dognames_dic)
# appends (1, 0) because only the pet label is a dog
results_dic[key].extend((1, 0))
else:
# Pet Image Label IS NOT a Dog image (e.g. NOT found in dognames_dic)
# Classifier Label IS image of Dog (e.g. found in dognames_dic)
# appends (0, 1) because only the classifier label is a dog
if results_dic[key][1] in dognames_dic:
results_dic[key].extend((0, 1))
else:
# Classifier Label IS NOT image of Dog (e.g. NOT in dognames_dic)
# appends (0, 0) because both labels aren't dogs
results_dic[key].extend((0, 0))