pretrained_DN_dependency - TobiasSchmidtDE/DeepL-MedicalImaging GitHub Wiki
Version: 1
Trained pretrained_DN_dependency architecture using the 'Chexpert_BCE_E5_B32_C1_N5' benchmark. The benchmark was initialized for the chexpert_preprocessed-256-crop dataset with batch size of 32, shuffle set to True and images rescaled to dimension (256, 256). The training was done for 5 epochs using the Adam optimizer and binary_crossentropy loss. A total of 5 labels/pathologies were included in the training and encoded using the 'uzeroes' method. The traing set included 142320 number of sample, the validation set 36162, and the test set 44932.
from pathlib import Path
from dotenv import load_dotenv, find_dotenv
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
import json
import os
import re
import pprint
basepath = Path(os.getcwd())
if basepath.name != "idp-radio-1":
os.chdir(basepath.parent.parent)
print(os.getcwd())
load_dotenv(find_dotenv())
from src.preprocessing.split.train_test_split import train_test_split/srv/idp-radio-1
data = json.loads(os.environ['EXP_DATA'])
history = data['history']for s in data["description"].split(".")[:-1]:
print(s + ".\n")Trained pretrained_DN_dependency architecture using the 'Chexpert_BCE_E5_B32_C1_N5' benchmark.
The benchmark was initialized for the chexpert_preprocessed-256-crop dataset with batch size of 32, shuffle set to True and images rescaled to dimension (256, 256).
The training was done for 5 epochs using the Adam optimizer and binary_crossentropy loss.
A total of 5 labels/pathologies were included in the training and encoded using the 'uzeroes' method.
The traing set included 142320 number of sample, the validation set 36162, and the test set 44932.
# if there are any metrics that were renamed, add this new name here as ("default_name":"new_name")
metric_custom_names={"auc":"AUC_ROC"}
metric_names = [re.sub("([a-z0-9])([A-Z])","\g<1> \g<2>",name) for name in data["benchmark"]["metrics"]]
metric_keys = [re.sub("([a-z0-9])([A-Z])","\g<1>_\g<2>",name).lower() for name in data["benchmark"]["metrics"]]
for default_name, custom_name in metric_custom_names.items():
if not default_name in history.keys() and default_name in metric_keys:
#replace default name with custom name
metric_keys[metric_keys.index(default_name)]=custom_namedef print_or_plot_metric(metric_key, metric_name, figure_name):
if len(history[metric_key]) == 1:
print("Data for {m_name} only available for a single epoch. \nSkipping plot and printing data...".format(m_name=metric_name))
print('Train {}: '.format(metric_name), history[metric_key])
print('Validation {}: '.format(metric_name), history['val_'+metric_key])
print()
else:
plot_epoch_metric(metric_key, metric_name, figure_name)
def plot_epoch_metric(metric_key, metric_name, figure_name):
figure(num=None, figsize=(10, 6))
plt.plot(history[metric_key])
if 'val_'+metric_key in history.keys():
plt.plot(history['val_'+metric_key])
plt.title(figure_name)
plt.ylabel(metric_name)
plt.xlabel('Epoch')
if 'val_'+metric_key in history.keys():
plt.legend(['Train', 'Validation'], loc='upper left')
plt.show()
for i, metric_key in enumerate(metric_keys):
print_or_plot_metric(metric_key, metric_names[i], "Model "+metric_names[i])
print_or_plot_metric("loss", "Loss", "Model loss")
if "lr" in history.keys():
plot_epoch_metric("lr", "Learning Rate", "Learning Rate")
if 'classification_report' in data.keys() and data['classification_report']:
print(data['classification_report']) precision recall f1-score support
Edema 0.24 0.11 0.15 10461
Atelectasis 0.00 0.00 0.00 6912
Pneumonia 0.00 0.00 0.00 1225
Lung Opacity 0.48 0.57 0.52 21324
Consolidation 0.00 0.00 0.00 3063
micro avg 0.44 0.31 0.36 42985
macro avg 0.14 0.14 0.13 42985
weighted avg 0.29 0.31 0.29 42985
samples avg 0.26 0.23 0.23 42985
if 'test' in data.keys() and data['test']:
for score_name, score in data["test"].items():
print('Test {}: '.format(score_name), score)
Test loss: 0.3730371296405792
Test auc: 0.6802114844322205
Test precision: 0.5793725848197937
Test recall: 0.41374897956848145
Test f2_score: 0.4388388991355896
Test binary_accuracy: 0.8304959535598755
pp = pprint.PrettyPrinter(indent=4)
if "benchmark" in data.keys():
pp.pprint(data["benchmark"]){ 'batch_size': 32,
'benchmark_name': 'Chexpert_BCE_E5_B32_C1_N5',
'crop': False,
'dataset_folder': 'data/chexpert/preprocessed-256-crop',
'dataset_name': 'chexpert_preprocessed-256-crop',
'dim': [256, 256],
'drop_last': True,
'epochs': 5,
'label_columns': [ 'Edema',
'Atelectasis',
'Pneumonia',
'Lung Opacity',
'Consolidation'],
'loss': 'binary_crossentropy',
'metrics': ['auc', 'precision', 'recall', 'f2_score', 'binary_accuracy'],
'models_dir': 'models',
'n_channels': 3,
'nan_replacement': 0,
'negative_weights': [ 1.3037590980529785,
1.1762800216674805,
1.0274509191513062,
1.8930201530456543,
1.0706391334533691],
'optimizer': 'Adam',
'path_column': 'Path',
'path_column_prefix': '',
'positive_weights': [ 4.292082786560059,
6.672793865203857,
37.4287223815918,
2.119795560836792,
15.15645980834961],
'shuffle': True,
'split_seed': 6122156,
'test_num_samples': 44932,
'train_num_samples': 142320,
'u_enc': 'uzeroes',
'unc_value': -1,
'use_class_weights': False,
'valid_num_samples': 36162}
if 'benchmark' in data.keys() and 'split_seed' in data['benchmark']:
benchmark = data['benchmark']
dataset_path = Path(benchmark['dataset_folder'])
train_labels = benchmark['train_labels'] if 'train_labels' in benchmark.keys() else 'train.csv'
split_test_size = benchmark['split_test_size'] if 'split_test_size' in benchmark.keys() else 0.2
split_valid_size = benchmark['split_valid_size'] if 'split_valid_size' in benchmark.keys() else 0.2
split_group = benchmark['split_group'] if 'split_group' in benchmark.keys() else 'patient_id'
split_seed = benchmark['split_seed']
all_labels = pd.read_csv(dataset_path / train_labels)
train_labels, test_labels = train_test_split(all_labels, test_size=split_test_size, group=split_group, seed=split_seed)
train_labels, validation_labels = train_test_split(train_labels, test_size=split_valid_size, group=split_group, seed=split_seed)from src.datasets.u_encoding import uencode
def get_distribution(labels):
if 'nan_replacement' in benchmark.keys():
labels = labels.fillna(benchmark['nan_replacement'])
data = labels.to_numpy()
data = uencode(benchmark['u_enc'], data, unc_value=benchmark['unc_value'])
data = pd.DataFrame(data, columns=labels.columns)
labels = data[benchmark['label_columns']]
d = {'Pathology': [], 'Positive': [], 'Positive %': [], 'Negative': [], 'Negative %': [],}
for label in labels.columns:
values = labels.groupby(label)
d['Pathology'].append(label)
positive = values.size()[1.0] if 1.0 in values.size() else 0
positive_percent = positive / labels.shape[0] * 100
d['Positive'].append(positive)
d['Positive %'].append(round(positive_percent))
negative = values.size()[-0.0] if -0.0 in values.size() else 0
negative_percent = negative / labels.shape[0] * 100
d['Negative'].append(negative)
d['Negative %'].append(round(negative_percent))
df = pd.DataFrame(d)
df = df.set_index('Pathology')
return dfif 'benchmark' in data.keys() and 'split_seed' in data['benchmark']:
train = get_distribution(train_labels)
val = get_distribution(validation_labels)
test = get_distribution(test_labels)
positives = train[['Positive %']].merge(val[['Positive %']], left_index=True, right_index=True).merge(test[['Positive %']], left_index=True, right_index=True).rename(columns={"Positive %_x": "Positives Train", "Positive %_y": "Positives Validation", "Positive %": "Positives Test", })
positives.copy().plot(kind='bar', figsize=(10,7), title="Positive Labels Distribution")
negatives = train[['Negative %']].merge(val[['Negative %']], left_index=True, right_index=True).merge(test[['Negative %']], left_index=True, right_index=True).rename(columns={"Negative %_x": "Negative Train", "Negative %_y": "Negative Validation", "Negative %": "Negative Test", })
negatives.copy().plot(kind='bar', figsize=(10,7), title="Negative Labels Distribution")
train[['Positive %', 'Negative %']].copy().plot(kind='bar', figsize=(10,7), title="Training set")
val[['Positive %', 'Negative %']].copy().plot(kind='bar', figsize=(10,7), title="Validation set")
test[['Positive %', 'Negative %']].copy().plot(kind='bar', figsize=(10,7), title="Test set")
