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performance_eval.py
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performance_eval.py
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import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import csv
from utils import plot_confusion_matrix
"""
this script contains the utils functions to evaluate model's performance
to use the functions in this script, we are expecting the users have already generated a csv
table in the following format
---------------------------------------------------------------------------
| ID | filename | COG_score | ADD_score | COG_pred | ADD_pred | COG | ADD |
---------------------------------------------------------------------------
where the COG_score is the continuous regression score from the model
the ADD_score is the probability of being ADD
the COG_pred is the predicted label (NC=0, MCI=1, DE=2)
the ADD_pred is the predicted label (ADD=1, nADD=0)
the COG is the true label (NC=0, MCI=1, DE=2)
the ADD is the true label (ADD=1, nADD=0)
"""
import numpy as np
import collections
from tabulate import tabulate
from sklearn.metrics import confusion_matrix
from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score
from matplotlib import rc
rc('axes', linewidth=1.5)
rc('font', weight='bold', size=15)
# generate confusion matrix from the a csv_file
# ----------------------------------------------------------------------------------
def cm_3_3(csv_file):
# this function generate a 3 by 3 confusion matrix for the COG task which is a
# 3 way classification between NC=0, MCI=1, DE=2
pred, label = [], []
with open(csv_file, 'r') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
if not row['COG']: continue
pred.append(int(row['COG_pred']))
label.append(int(row['COG'][0]))
return confusion_matrix(label, pred, labels=[0, 1, 2])
def cm_2_2(csv_file):
# this function generate a 2 by 2 confusion matrix for the ADD task which is a
# binary classification between ADD=1 and nADD=0
pred, label = [], []
with open(csv_file, 'r') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
if not row['ADD']: continue
pred.append(int(row['ADD_pred']))
label.append(int(row['ADD'][0]))
return confusion_matrix(label, pred, labels=[0, 1])
def cm_4_4(csv_file):
# this function generate a 4 by 4 confusion matrix for the ADD task which is a
# 4 ways classification between NC=0, MCI=1, ADD=2 and nADD=3
pred, label = [], []
with open(csv_file, 'r') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
if not row['COG']: continue
pred.append(to_4labels(row['COG_pred'], row['ADD_pred']))
label.append(to_4labels(row['COG'], row['ADD']))
return confusion_matrix(label, pred, labels=[0, 1, 2, 3])
def to_4labels(cog, add):
if cog[0] == '0': return 0 # NC
if cog[0] == '1': return 1 # MCI
if cog[0] == '2':
if add[0] == '1': return 2 # ADD
if add[0] == '0': return 3 # nADD
def get_accuracy(cm):
total, correct = 0, 0
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
total += cm[i, j]
if i == j: correct += cm[i, j]
return float(correct) / float(total)
# generate confusion matrix for a five fold cross validation experiment
# pass in csv_files which is a list of csv_file representing each fold
def crossValid_cm_3_3(csv_files):
cm = np.zeros((len(csv_files), 3, 3))
for i, csvfile in enumerate(csv_files):
cm[i] = cm_3_3(csvfile)
return cm.mean(axis=0)
def crossValid_cm_2_2(csv_files):
cm = np.zeros((len(csv_files), 2, 2))
for i, csvfile in enumerate(csv_files):
cm[i] = cm_2_2(csvfile)
return cm.mean(axis=0)
def crossValid_cm_4_4(csv_files):
cm = np.zeros((len(csv_files), 4, 4))
for i, csvfile in enumerate(csv_files):
cm[i] = cm_4_4(csvfile)
return cm.mean(axis=0)
def crossValid_cm(csv_files, stage):
print('2 by 2 confusion matrix is:')
cm = crossValid_cm_2_2(csv_files)
fig = plot_confusion_matrix(cm, ['nADD', 'ADD'])
fig.savefig(csv_files[0].replace(stage + '_eval.csv', stage + '_cm2.jpg'))
print(cm)
print('---------------------------------')
print('3 by 3 confusion matrix is:')
cm = crossValid_cm_3_3(csv_files)
fig = plot_confusion_matrix(cm, ['NC', 'MCI', 'DE'])
fig.savefig(csv_files[0].replace(stage + '_eval.csv', stage + '_cm3.jpg'))
print(cm)
print('---------------------------------')
print('4 by 4 confusion matrix is:')
cm = crossValid_cm_4_4(csv_files)
fig = plot_confusion_matrix(cm, ['NC', 'MCI', 'ADD', 'nADD'])
fig.savefig(csv_files[0].replace(stage + '_eval.csv', stage + '_cm4.jpg'))
print(cm)
print('---------------------------------')
# generate ROC and PR curves
# ----------------------------------------------------------------------------------
def generate_roc(csv_files, positive_label, color, out_file):
"""
:param csv_files: a list of csv files as above format
:param positive_label: if positive_label == 'NC', the curve is about NC vs not NC
if positive_label == 'DE', the curve is about DE vs not DE
if positive_label =='ADD', the curve is about ADD vs nADD
:param color: color of the roc curve
:param out_file: image filename you want to save as
:return:
"""
lw = 2
text_size = 20
fig, ax = plt.subplots(dpi=100)
tprs = []
aucs = []
mean_fpr = np.linspace(0, 1, 100)
for csvfile in csv_files:
scores, labels = get_score_label(csvfile, positive_label)
fpr, tpr, thres = roc_curve(labels, scores, pos_label=1)
AUC = auc(fpr, tpr)
ax.plot(fpr, tpr, lw=lw/2, alpha=0.15)
interp_tpr = np.interp(mean_fpr, fpr, tpr)
interp_tpr[0] = 0.0
tprs.append(interp_tpr)
aucs.append(AUC)
mean_tpr = np.mean(tprs, axis=0)
mean_tpr[-1] = 1.0
mean_auc = auc(mean_fpr, mean_tpr)
std_auc = np.std(aucs)
ax.plot(mean_fpr, mean_tpr, color=color,
label=r'AUC=%0.3f$\pm$%0.3f' % (mean_auc, std_auc),
lw=2, alpha=.8)
ax.plot([0, 1], [0, 1], 'k--', lw=lw)
std_tpr = np.std(tprs, axis=0)
tprs_upper = np.minimum(mean_tpr + std_tpr, 1)
tprs_lower = np.maximum(mean_tpr - std_tpr, 0)
ax.fill_between(mean_fpr, tprs_lower, tprs_upper, color=color, alpha=.2,
label=r'$\pm$ 1 std. dev.')
ax.set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05])
legend_properties = {'weight': 'bold', 'size': text_size}
ax.legend(loc="lower right", prop=legend_properties)
ax.set_xlabel('False Positive Rate', fontsize=text_size, fontweight='bold')
ax.set_ylabel('True Positive Rate', fontsize=text_size, fontweight='bold')
ax.tick_params(axis='both', which='major', labelsize=16)
fig.savefig(out_file, bbox_inches='tight')
fig.clf()
plt.close()
return mean_auc
def generate_pr(csv_files, positive_label, color, out_file):
lw = 2
text_size = 20
fig, ax = plt.subplots(dpi=100)
prs = []
aucs = []
mean_rc = np.linspace(0, 1, 100)
for csvfile in csv_files:
scores, labels = get_score_label(csvfile, positive_label)
pr, rc, thres = precision_recall_curve(labels, scores, pos_label=1)
pr, rc = pr[::-1], rc[::-1]
AUC = average_precision_score(labels, scores, pos_label=1)
ax.plot(rc, pr, lw=lw/2, alpha=0.15)
interp_pr = np.interp(mean_rc, rc, pr)
prs.append(interp_pr)
aucs.append(AUC)
mean_pr = np.mean(prs, axis=0)
mean_auc = np.mean(aucs) # is this right?
std_auc = np.std(aucs)
ax.plot(mean_rc, mean_pr, color=color,
label=r'AP=%0.3f$\pm$%0.3f' % (mean_auc, std_auc),
lw=2, alpha=.8)
count = collections.Counter(labels)
ratio = count[1] / (count[1] + count[0])
ax.plot([0, 1], [ratio, ratio], 'k--', lw=lw)
std_pr = np.std(prs, axis=0)
prs_upper = np.minimum(mean_pr + std_pr, 1)
prs_lower = np.maximum(mean_pr - std_pr, 0)
ax.fill_between(mean_rc, prs_lower, prs_upper, color=color, alpha=.2,
label=r'$\pm$ 1 std. dev.')
ax.set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05])
legend_properties = {'weight': 'bold', 'size': text_size}
ax.legend(loc="lower left", prop=legend_properties)
ax.set_xlabel('Recall', fontsize=text_size, fontweight='bold')
ax.set_ylabel('Precision', fontsize=text_size, fontweight='bold')
ax.tick_params(axis='both', which='major', labelsize=16)
fig.savefig(out_file, bbox_inches='tight')
fig.clf()
plt.close()
return mean_auc
def get_score_label(csv_file, positive_label):
# get the raw scores and labels from the csvfile for the ROC PR curves
score, label = [], []
with open(csv_file, 'r') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
if not row['COG']: continue
if positive_label == 'NC':
score.append(-float(row['COG_score']))
label.append(1 if row['COG'][0] == '0' else 0)
if positive_label == 'DE':
score.append(float(row['COG_score']))
label.append(1 if row['COG'][0] == '2' else 0)
if positive_label == 'ADD':
if not row['ADD']: continue
score.append(float(row['ADD_score']))
label.append(int(row['ADD'][0]))
return score, label
def ROC_PR_curves(csv_files, stage):
nc_roc = generate_roc(csv_files, 'NC', 'g', csv_files[0].replace(stage + '_eval.csv', stage + '_NC_roc.jpg'))
de_roc = generate_roc(csv_files, 'DE', 'darkorange', csv_files[0].replace(stage + '_eval.csv', stage + '_DE_roc.jpg'))
add_roc = generate_roc(csv_files, 'ADD', 'r', csv_files[0].replace(stage + '_eval.csv', stage + '_ADD_roc.jpg'))
nc_pr = generate_pr(csv_files, 'NC', 'g', csv_files[0].replace(stage + '_eval.csv', stage + '_NC_pr.jpg'))
de_pr = generate_pr(csv_files, 'DE', 'darkorange', csv_files[0].replace(stage + '_eval.csv', stage + '_DE_pr.jpg'))
add_pr = generate_pr(csv_files, 'ADD', 'r', csv_files[0].replace(stage + '_eval.csv', stage + '_ADD_pr.jpg'))
return (nc_pr + nc_roc) + (de_roc + de_pr) + (add_roc + add_pr) * 2
# generate performance metrics table
# ----------------------------------------------------------------------------------
"""
a. NC (sensitivity; specificity, etc)
b. MCI (sensitivity; specificity, etc)
c. DE (sensitivity; specificity, etc)
d. ADD (sensitivity; specificity, etc)
e. nADD (sensitivity; specificity, etc)
f. ADD | DE (sensitivity; specificity, etc)
g. nADD | DE (sensitivity; specificity, etc)
"""
def perform_table(csv_files, output_name):
ans = 0
content = []
cache = {}
for metric in ['Accuracy', 'F-1', 'Sensitivity', 'Specificity', 'MCC']:
row = [metric]
cache[metric] = {}
for task in ['NC', 'MCI', 'DE', 'ADD', 'nADD', 'ADD|DE', 'nADD|DE', 'macro-3ways', 'macro-4ways']:
metric_list = []
cache[metric][task] = {}
for csv_file in csv_files:
pd, gt = get_pd_gt(csv_file, task)
if metric == 'Accuracy':
if task == 'macro-3ways':
metric_list.append(get_accuracy(cm_3_3(csv_file)))
elif task == 'macro-4ways':
metric_list.append(get_accuracy(cm_4_4(csv_file)))
else:
metric_list.append(accu_(gt, pd))
elif metric == 'F-1':
if task == 'macro-3ways':
val = sum(cache[metric][key][csv_file] for key in ['NC', 'MCI', 'DE']) / 3.0
elif task == 'macro-4ways':
val = sum(cache[metric][key][csv_file] for key in ['NC', 'MCI', 'ADD', 'nADD']) / 4.0
else:
val = f1_(gt, pd)
metric_list.append(val)
elif metric == 'Sensitivity':
if task == 'macro-3ways':
val = sum(cache[metric][key][csv_file] for key in ['NC', 'MCI', 'DE']) / 3.0
elif task == 'macro-4ways':
val = sum(cache[metric][key][csv_file] for key in ['NC', 'MCI', 'ADD', 'nADD']) / 4.0
else:
val = sens_(gt, pd)
metric_list.append(val)
elif metric == 'Specificity':
if task == 'macro-3ways':
val = sum(cache[metric][key][csv_file] for key in ['NC', 'MCI', 'DE']) / 3.0
elif task == 'macro-4ways':
val = sum(cache[metric][key][csv_file] for key in ['NC', 'MCI', 'ADD', 'nADD']) / 4.0
else:
val = spec_(gt, pd)
metric_list.append(val)
elif metric == 'MCC':
if task == 'macro-3ways':
val = sum(cache[metric][key][csv_file] for key in ['NC', 'MCI', 'DE']) / 3.0
elif task == 'macro-4ways':
val = sum(cache[metric][key][csv_file] for key in ['NC', 'MCI', 'ADD', 'nADD']) / 4.0
else:
val = mcc_(gt, pd)
metric_list.append(val)
cache[metric][task][csv_file] = metric_list[-1]
metric_list = np.array(metric_list)
mean, std = np.mean(metric_list), np.std(metric_list)
left, right = confidence_intervals(mean, std)
row.append("{0:.3f}±{1:.3f} [{2:.3f}-{3:.3f}]".format(mean, std, left, right))
if metric == 'MCC': ans += np.mean(metric_list)
content.append(row)
headers = [' ', 'NC', 'MCI', 'DE', 'ADD', 'nADD', 'ADD|DE', 'nADD|DE', 'macro-3ways', 'macro-4ways']
print(tabulate(content,
headers=headers))
print('-' * 147)
with open(output_name + ".csv", "w") as f:
f.write(tabulate(content, headers=headers, tablefmt="csv"))
return ans
def confidence_intervals(mean, std, dof=4):
z = 2.776
if dof == 16: # for 17 neurologists
z = 2.120
elif dof == 6: # for 7 radiologists
z = 2.447
return mean - z * std / ((dof + 1) ** 0.5), mean + z * std / ((dof + 1) ** 0.5)
def get_pd_gt(csv_file, task):
pred, label = [], []
with open(csv_file, 'r') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
if not row['COG']: continue
if task == 'NC':
pred.append(1 if row['COG_pred'] == '0' else 0)
label.append(1 if row['COG'][0] == '0' else 0)
elif task == 'MCI':
pred.append(1 if row['COG_pred'] == '1' else 0)
label.append(1 if row['COG'][0] == '1' else 0)
elif task == 'DE':
pred.append(1 if row['COG_pred'] == '2' else 0)
label.append(1 if row['COG'][0] == '2' else 0)
elif task == 'ADD':
pred.append(1 if (row['COG_pred']=='2' and row['ADD_pred']=='1') else 0)
label.append(1 if (row['COG'][0]=='2' and row['ADD'][0]=='1') else 0)
elif task == 'nADD':
pred.append(1 if (row['COG_pred']=='2' and row['ADD_pred']=='0') else 0)
label.append(1 if (row['COG'][0]=='2' and row['ADD'][0]=='0') else 0)
elif task == 'ADD|DE':
if not row['ADD']: continue
pred.append(1 if row['ADD_pred']=='1' else 0)
label.append(1 if row['ADD'][0]=='1' else 0)
elif task == 'nADD|DE':
if not row['ADD']: continue
pred.append(1 if row['ADD_pred']=='0' else 0)
label.append(1 if row['ADD'][0]=='0' else 0)
return pred, label
def accu_(gt, pd):
cm = confusion_matrix(gt, pd, labels=[0, 1])
tp, tn, fp, fn = cm[1][1], cm[0][0], cm[0][1], cm[1][0]
return float(tp + tn) / (tp + fp + fn + tn)
def f1_(gt, pd):
cm = confusion_matrix(gt, pd, labels=[0, 1])
tp, fp, fn = cm[1][1], cm[0][1], cm[1][0]
return float(tp) / (tp + 0.5 * (fp + fn) + 0.000001)
def sens_(gt, pd):
cm = confusion_matrix(gt, pd, labels=[0, 1])
tp, fp, fn = cm[1][1], cm[0][1], cm[1][0]
return float(tp) / (tp + fn + 0.000001)
def spec_(gt, pd):
cm = confusion_matrix(gt, pd, labels=[0, 1])
tp, fp, fn, tn = cm[1][1], cm[0][1], cm[1][0], cm[0][0]
return float(tn) / (tn + fp + 0.000001)
def mcc_(gt, pd):
cm = confusion_matrix(gt, pd, labels=[0, 1])
TP, FP, FN, TN = cm[1][1], cm[0][1], cm[1][0], cm[0][0]
upper = TP * TN - FP * FN
lower = (TP + FP) * (TP + FN) * (TN + FP) * (TN + FN)
return float(upper) / (lower ** 0.5 + 0.000000001)
def whole_eval_package(model_name, stage, performTableFileName):
print("evaluating the model performance of "+stage)
common_path = 'tb_log/' + model_name
name = '/{}_eval.csv'.format(stage)
csv_files = [common_path + '_cross{}'.format(i) + name for i in range(5)]
crossValid_cm(csv_files, stage)
perform_table(csv_files, performTableFileName)
ROC_PR_curves(csv_files, stage)
if __name__ == "__main__":
pass