ma_cisco_malware/visualize.py

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Python
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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=600):
plt.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,
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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")
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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)