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a639402 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | import torch
import pandas as pd
import numpy as np
import random
from sklearn.utils.class_weight import compute_class_weight
from sklearn.preprocessing import LabelEncoder
def log_scalar_values(file_path : str, data_dict : dict):
## Create a CSV file with results
try:
check = pd.read_csv(file_path)
df = pd.DataFrame(data_dict, index=[0])
df.to_csv(file_path, mode='a', index=False, header=False)
except:
df = pd.DataFrame(data_dict, index=[0])
df.to_csv(file_path, index=False, header = True)
def count_class_distribution(file, which_labels):
## Old Code. Check
read_file = pd.read_excel(file)
labels = read_file[which_labels].tolist()
values, counts = np.unique(labels, return_counts=True)
return counts[::-1]
def set_seed(seed):
## Random Seeds
np.random.seed(seed)
random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def label_encoder(labels, class_type = 'subtype_class'):
## Keeping this for easier understanding of the labels. Not needed; Only when labels are strings
if class_type in ['Calc_Mass_Malignant_MultiLabel']:
if labels == 'calcification_Benign':
label = 0
elif labels == 'calcification_Malignant':
label = 1
elif labels == 'mass_Benign' :
label = 2
elif labels == 'mass_Malignant':
label = 3
elif labels == 'both_Benign':
label = 4
elif labels == 'both_Malignant':
label = 5
elif class_type in ['CMMD_Malignant']:
if labels == 'Benign':
label = 0
elif labels == 'Malignant':
label = 1
elif class_type in ['2i', '3i', 'Skinfolds', 'Skinfold_MultiLabel', 'Skinfold_Defect_MultiLabel']:
if labels == 0 or labels == '0':
label = 0
elif labels == 1 or labels == '1':
label = 1
elif class_type in ['No Defects']:
if labels == 0 or labels == '0':
label = 1
elif labels == 1 or labels == '1':
label = 0
elif class_type in ['BreastDensity']:
if labels in ['DENSITY A']:
label = 0
elif labels in ['DENSITY B']:
label = 1
elif labels in ['DENSITY C']:
label = 2
elif labels in ['DENSITY D']:
label = 3
elif class_type in ['CM', 'Calc', 'Mass', 'CM_Malignant']:
if labels in ['Normal', 'BENIGN_WITHOUT_CALLBACK']:
label = 0
elif labels in ['Benign', 'BENIGN']:
label = 1
elif labels in ['Malignant', 'MALIGNANT']:
label = 2
elif class_type in ['MassRest']:
if 'Mass' in labels:
label = 0
else:
label = 1
elif class_type in ['MassNormal']:
if 'Mass' in labels:
label = 0
elif 'No Finding' in labels:
label = 1
else:
label = 2
else:
raise ValueError("Problem in label_encoder in util functions. Check the method")
return label
def compute_sample_weights(csv_file:str, class_type:str, view:str):
## Compute sample weights and weights for Weighted Random Sampler and Class-Weighted Cross Entropy
read_csv = pd.read_csv(csv_file)
if view in ['CC','MLO']:
read_csv = read_csv[read_csv['View Position'] == view]
if class_type in ['Calc_Mass_Malignant_MultiLabel']:
abnormality = read_csv['abnormality']
classification = read_csv['classification']
labels = abnormality + '_' + classification
elif class_type in ['CMMD_Malignant']:
labels = read_csv['classification']
elif class_type in ['MassRest', 'MassNormal']:
labels = read_csv['finding_categories']
elif class_type in ['2i']:
labels = read_csv['2i']
elif class_type in ['3i']:
labels = read_csv['3i']
elif class_type in ['Skinfolds']:
labels = read_csv['Skinfolds']
elif class_type in ['No Defects']:
labels = read_csv['no_defect']
elif class_type in ['Skinfold_MultiLabel', 'Skinfold_Defect_MultiLabel']:
labels = read_csv['Skinfolds'] ## No better idea currently : TODO
elif class_type in ['BreastDensity']:
labels = read_csv['breast_density']
elif class_type in ['CM', 'CM_Malignant']:
labels = read_csv['Pathology Classification/ Follow up']
elif class_type in ['Calc', 'Mass']:
labels = read_csv['pathology']
elif class_type in ['MRI', 'MRI_MIL']:
read_csv = read_csv[read_csv['Split_patient'] == "train"].reset_index(drop=True)
labels = read_csv['Lesion'].astype(str) + '_' + read_csv['Institution'].astype(str)
targets = labels.tolist()
if class_type not in ['MRI', 'MRI_MIL']:
tar = [label_encoder(labels, class_type = class_type) for labels in targets]
target = np.array(tar)
class_sample_count = np.unique(target, return_counts=True)[1]
print('Class Sample Count : ', class_sample_count)
class_weights=compute_class_weight(class_weight ='balanced', classes = np.unique(tar),y = tar)
class_weights_torch=torch.tensor(class_weights,dtype=torch.float)
print('Class Weights: ', class_weights)
samples_weight = class_weights[target]
return class_weights_torch, samples_weight
else:
le = LabelEncoder()
targets = le.fit_transform(labels)
# 4) Compute how many samples per class
class_counts = np.bincount(targets)
print("Class sample counts:", class_counts)
# 5) Compute balanced class weights
class_weights = compute_class_weight(
class_weight='balanced',
classes=np.unique(targets),
y=targets
)
print("Class weights:", class_weights)
# 6) Torch tensor of per-class weights
class_weights_torch = torch.tensor(class_weights, dtype=torch.float)
# 7) Per-sample weights array
samples_weight = class_weights[targets]
return class_weights_torch, samples_weight
def labels_for_classification(labels):
if labels[0].detach().cpu().numpy() == 0:
title = 'HRS Positive'
elif labels[0].detach().cpu().numpy() == 1:
title = 'HRS Negative'
return title
def labels_for_classification2(labels):
if labels[0].detach().cpu().numpy() == 0:
title = 'Follow Up'
elif labels[0].detach().cpu().numpy() == 1:
title = 'No Follow Up'
return title
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