177 lines
5.7 KiB
Python
177 lines
5.7 KiB
Python
import os
|
|
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
from sklearn.decomposition import TruncatedSVD
|
|
from sklearn.metrics import (
|
|
auc, classification_report, confusion_matrix, fbeta_score, precision_recall_curve,
|
|
roc_auc_score, roc_curve
|
|
)
|
|
|
|
|
|
def scores(y_true):
|
|
for (path, dirnames, fnames) in os.walk("results/"):
|
|
for f in fnames:
|
|
if path[-1] == "1" and f.endswith("npy"):
|
|
y_pred = np.load(os.path.join(path, f)).flatten()
|
|
print(path)
|
|
tp = np.sum(np.logical_and(y_pred >= 0.5, y_true == 1))
|
|
tn = np.sum(np.logical_and(y_pred < 0.5, y_true == 0))
|
|
fp = np.sum(np.logical_and(y_pred >= 0.5, y_true == 0))
|
|
fn = np.sum(np.logical_and(y_pred < 0.5, y_true == 1))
|
|
precision = tp / (tp + fp)
|
|
recall = tp / (tp + fn)
|
|
accuracy = (tp + tn) / len(y_true)
|
|
f1_score = 2 * (precision * recall) / (precision + recall)
|
|
f05_score = (1 + 0.5 ** 2) * (precision * recall) / (0.5 ** 2 * precision + recall)
|
|
print(" precision:", precision)
|
|
print(" recall:", recall)
|
|
print(" accuracy:", accuracy)
|
|
print(" f1 score:", f1_score)
|
|
print(" f0.5 score:", f05_score)
|
|
|
|
|
|
def plot_clf():
|
|
plt.clf()
|
|
|
|
|
|
def plot_save(path, dpi=300):
|
|
fig = plt.gcf()
|
|
fig.set_size_inches(18.5, 10.5)
|
|
fig.savefig(path, dpi=dpi)
|
|
plt.close()
|
|
|
|
|
|
def plot_legend():
|
|
plt.legend()
|
|
|
|
|
|
def plot_precision_recall(y, y_pred, label=""):
|
|
y = y.flatten()
|
|
y_pred = y_pred.flatten()
|
|
precision, recall, thresholds = precision_recall_curve(y, y_pred)
|
|
# decreasing_max_precision = np.maximum.accumulate(precision)[::-1]
|
|
|
|
# fig, ax = plt.subplots(1, 1)
|
|
# ax.hold(True)
|
|
score = fbeta_score(y, y_pred.round(), 1)
|
|
# prc_ap = average_precision_score(y, y_pred)
|
|
plt.plot(recall, precision, '--', label=f"{label} - {score:5.4}")
|
|
# ax.step(recall[::-1], decreasing_max_precision, '-r')
|
|
plt.xlabel('Recall')
|
|
plt.ylabel('Precision')
|
|
plt.ylim([0.0, 1.0])
|
|
plt.xlim([0.0, 1.0])
|
|
|
|
|
|
def plot_pr_curves(y, y_preds, label=""):
|
|
for idx, y in enumerate(y_preds):
|
|
y = y.flatten()
|
|
y_pred = y_pred.flatten()
|
|
precision, recall, thresholds = precision_recall_curve(y, y_pred)
|
|
score = fbeta_score(y, y_pred.round(), 1)
|
|
plt.plot(recall, precision, '--', label=f"{idx}{label} - {score:5.4}")
|
|
plt.xlabel('Recall')
|
|
plt.ylabel('Precision')
|
|
|
|
|
|
def score_model(y, prediction):
|
|
y = y.flatten()
|
|
y_pred = prediction.flatten()
|
|
|
|
precision, recall, thresholds = precision_recall_curve(y, y_pred)
|
|
|
|
print(classification_report(y, y_pred.round()))
|
|
print("Area under PR curve", auc(recall, precision))
|
|
print("roc auc score", roc_auc_score(y, y_pred))
|
|
print("F1 Score", fbeta_score(y, y_pred.round(), 1))
|
|
print("F0.5 Score", fbeta_score(y, y_pred.round(), 0.5))
|
|
|
|
|
|
def plot_roc_curve(mask, prediction, label=""):
|
|
y = mask.flatten()
|
|
y_pred = prediction.flatten()
|
|
fpr, tpr, thresholds = roc_curve(y, y_pred)
|
|
roc_auc = auc(fpr, tpr)
|
|
plt.xscale('log')
|
|
plt.plot(fpr, tpr, label=f"{label} - {roc_auc:5.4}")
|
|
plt.ylim([0.0, 1.0])
|
|
plt.xlim([0.0, 1.0])
|
|
plt.xlabel('False Positive Rate')
|
|
plt.ylabel('True Positive Rate')
|
|
|
|
|
|
def plot_confusion_matrix(y_true, y_pred, path,
|
|
normalize=False,
|
|
classes=("benign", "malicious"),
|
|
title='Confusion matrix',
|
|
cmap="Blues", dpi=600):
|
|
"""
|
|
This function prints and plots the confusion matrix.
|
|
Normalization can be applied by setting `normalize=True`.
|
|
"""
|
|
plt.clf()
|
|
cm = confusion_matrix(y_true, y_pred)
|
|
|
|
if normalize:
|
|
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
|
|
print("Normalized confusion matrix")
|
|
else:
|
|
print('Confusion matrix, without normalization')
|
|
print(cm)
|
|
|
|
plt.imshow(cm, interpolation='nearest', cmap=cmap)
|
|
plt.title(title)
|
|
plt.colorbar()
|
|
tick_marks = np.arange(len(classes))
|
|
plt.xticks(tick_marks, classes, rotation=45)
|
|
plt.yticks(tick_marks, classes)
|
|
|
|
thresh = cm.max() / 2.
|
|
for i, j in ((i, j) for i in range(cm.shape[0]) for j in range(cm.shape[1])):
|
|
plt.text(j, i, cm[i, j],
|
|
horizontalalignment="center",
|
|
color="white" if cm[i, j] > thresh else "black")
|
|
|
|
plt.tight_layout()
|
|
plt.ylabel('True label')
|
|
plt.xlabel('Predicted label')
|
|
plt.savefig(path, dpi=dpi)
|
|
plt.close()
|
|
|
|
|
|
def plot_training_curve(logs, key, path, dpi=600):
|
|
plt.clf()
|
|
plt.plot(logs[f"{key}acc"], label="accuracy")
|
|
plt.plot(logs[f"{key}f1_score"], label="f1_score")
|
|
|
|
plt.plot(logs[f"val_{key}acc"], label="val_accuracy")
|
|
# plt.plot(logs[f"val_{key}f1_score"], label="val_f1_score")
|
|
|
|
plt.xlabel('epoch')
|
|
plt.ylabel('percentage')
|
|
plt.legend()
|
|
plt.savefig(path, dpi=dpi)
|
|
plt.close()
|
|
|
|
|
|
def plot_embedding(domain_embedding, labels, path, dpi=600):
|
|
svd = TruncatedSVD(n_components=2)
|
|
domain_reduced = svd.fit_transform(domain_embedding)
|
|
print(svd.explained_variance_ratio_)
|
|
# use if draw subset of predictions
|
|
# idx = np.random.choice(np.arange(len(domain_reduced)), 10000)
|
|
plt.scatter(domain_reduced[:, 0],
|
|
domain_reduced[:, 1],
|
|
c=(labels * (1, 2)).sum(1).astype(int),
|
|
cmap=plt.cm.plasma,
|
|
s=3,
|
|
alpha=0.2)
|
|
plt.colorbar()
|
|
plt.savefig(path, dpi=dpi)
|
|
|
|
|
|
def plot_model_as(model, path):
|
|
from keras.utils.vis_utils import plot_model
|
|
plot_model(model, to_file=path, show_shapes=True, show_layer_names=True)
|