def compute_print_scores(normal_users, queue):
K_GMM_n, K_KMeans_n, K_GMM_s, K_KMeans_s = Ks
print 'novelty score GMM'
B = GMM(covariance_type='full', n_components = 1)
B.fit(queue)
x = [B.score([i]).mean() for i in queue]
print get_score_last_item(x, K_GMM_n)
print 'novelty score OneClassSVM'
x = anom_one_class(queue, [queue[-1]])
print x[-1]
print 'novelty score LSA'
anomalymodel = lsanomaly.LSAnomaly()
X = np.array(queue)
anomalymodel.fit(X)
print anomalymodel.predict(np.array([queue[-1]]))
print 'novelty score degree K_means'
K = KMeans(n_clusters=1)
K.fit(queue)
x = [K.score([i]) for i in queue]
print get_score_last_item(x, K_KMeans_n)
normal_and_new = normal_users + [queue[-1]]
print 'degree of belonging to known class GMM'
B = GMM(covariance_type='full', n_components = 1)
B.fit(normal_users)
x = [B.score([i]).mean() for i in normal_and_new]
print get_score_last_item(x, K_GMM_s)
print 'degree of belonging to known class OneClassSVM'
x = anom_one_class(normal_users, [queue[-1]])
print x[-1]
print 'degree of belonging to known class LSA'
anomalymodel = lsanomaly.LSAnomaly()
X = np.array(normal_users)
anomalymodel.fit(X)
print anomalymodel.predict(np.array([queue[-1]]))
print 'degree of belonging to known class K_means'
K = KMeans(n_clusters=1)
K.fit(normal_users)
x = [K.score([i]) for i in normal_and_new]
print get_score_last_item(x, K_KMeans_s)
def plot_results(X,
xx,
yy,
threshold=0.5,
sigma_candidates=None,
rho_candidates=None):
_ = plt.figure(figsize=(16, 10))
for row, sigma in enumerate(sigma_candidates):
for col, rho in enumerate(rho_candidates):
# Train the anomaly model
clf = lsanomaly.LSAnomaly(sigma=sigma, rho=rho)
clf.fit(X)
# Get anomaly scores across the grid
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
# Plot the training data, anomaly model response and decision
# boundary at threshold 0.5.
subplot = plt.subplot(len(sigma_candidates), len(rho_candidates),
row * 3 + col + 1)
plt.contourf(
xx,
yy,
Z,
levels=np.linspace(0, 1, 11),
cmap=plt.cm.get_cmap("GnBu"),
)
subplot.contour(xx,
yy,
Z,
levels=[threshold],
linewidths=2,
colors="red")
cb = plt.colorbar()
for t in cb.ax.get_yticklabels():
t.set_fontsize(10)
plt.scatter(X[:, 0],
X[:, 1],
c="black",
marker="+",
s=50,
linewidth=2)
subplot.set_title(
"$\sigma = $ %.3g, $\\rho$ = %.3g" % (sigma, rho),
fontsize=14,
usetex=True,
)
subplot.axes.get_xaxis().set_ticks([])
subplot.axes.get_yaxis().set_ticks([])
plt.xlim((-7, 7))
plt.ylim((-7, 7))
plt.show()
def train_with_lsanomaly(self, trainX, testX):
anomalymodel = lsanomaly.LSAnomaly()
anomalymodel.fit(trainX)
y_pred_train = anomalymodel.predict(trainX)
y_pred_test = anomalymodel.predict(testX)
# Process results
self.replace_in_list(y_pred_train, 'anomaly', -1)
self.replace_in_list(y_pred_test, 'anomaly', -1)
n_error_train = y_pred_train.count(-1)
n_error_test = y_pred_test.count(-1)
return y_pred_train, y_pred_test, n_error_train, n_error_test
def use_model(model, df_list, x_columns, params):
predicted = []
if model == 'knn':
neigh = NearestNeighbors(n_neighbors=params['n'], p=params['p'])
neigh.fit(df_list[0][x_columns])
for i in range(len(df_list)):
pred = neigh.kneighbors(df_list[i][x_columns])
pred = [np.mean(i) for i in pred[0]]
predicted.append(pred)
elif model == 'svm':
svm = OneClassSVM(kernel=params['kernel'])
svm.fit(df_list[0][x_columns])
for i in range(len(df_list)):
pred = svm.score_samples(df_list[i][x_columns])
maximum = max(pred)
pred = [(x * -1) + maximum for x in pred]
predicted.append(pred)
elif model == 'ísolationForest':
clf = IsolationForest(n_estimators=params['n_estimators'],
random_state=0)
clf.fit(df_list[0][x_columns])
for i in range(len(df_list)):
pred = clf.score_samples(df_list[i][x_columns])
pred = list(map(abs, pred))
predicted.append(pred)
elif model == 'autoencoder':
clf = AutoEncoder(hidden_neurons=params['hidden_neurons'],
verbose=0,
random_state=0)
clf.fit(df_list[0][x_columns])
for i in range(len(df_list)):
pred = clf.decision_function(df_list[i][x_columns])
predicted.append(pred)
elif model == 'lsanomaly':
anomalymodel = lsanomaly.LSAnomaly(sigma=params['sigma'],
rho=params['rho'])
anomalymodel.fit(df_list[0][x_columns].to_numpy())
for i in range(len(df_list)):
pred = anomalymodel.predict_proba(df_list[i][x_columns].to_numpy())
pred = [a[1] for a in pred]
predicted.append(pred)
return predicted
digits = datasets.load_digits()
X = digits.data
y = digits.target
# Split data into training and test sets, then remove all examples of
# class 9 from the training set, leaving only examples of 0-8.
X_train, X_test, y_train, y_test = cross_validation.train_test_split(
X, y, test_size=0.5)
train_inlier_idx = y_train < 9
X_train = X_train[train_inlier_idx, :]
y_train = y_train[train_inlier_idx]
# Fit the model for inlier classes
anomalymodel = lsanomaly.LSAnomaly()
anomalymodel.fit(X_train, y_train)
# Use the outlier score as a prediction of whether each test point
# belongs to class 9, for which no training data was given.
predictions = anomalymodel.predict_proba(X_test)
fpr, tpr, thresholds = metrics.roc_curve(y_test == 9, predictions[:, -1])
print('AUC=%f' % (metrics.auc(fpr, tpr)))
# Try to assign each test point to classes 0-9, given only test data
# for classes 0-8.
y_pred = anomalymodel.predict(X_test)
y_pred = [w if np.isreal(w) else 9 for w in y_pred]
print 'Confusion matrix for all classes:'
print metrics.confusion_matrix(y_test, y_pred)
def evaluate(
X_train,
y_train,
X_test,
y_test,
outlier_class,
method_name,
current_method_aucs,
sigma,
rho=0.1,
nu=0.5,
):
"""
Evaluation for a method and data set. Calculates the AUC for a single
evaluation fold.
Args:
X_train (numpy.ndarray): independent training variables
y_train (numpy.ndarray): training labels
X_test (numpy.ndarray): independent test variables
y_test (numpy.ndarray): test labels
outlier_class (int): index of the outlier class
method_name (str): method being run
current_method_aucs (list): input to the *results* dictionary
sigma (float): kernel lengthscale for LSAD and OCSVM
rho (float): smoothness parameter for LSAD
nu (float): OCSVM parameter - see *scikit-learn* documentation
Raises:
ValueError: if a `NaN` is encountered in the AUC calculation.
"""
try:
if method_name == "LSAD":
lsanomaly_model = lsanomaly.LSAnomaly(n_kernels_max=500,
gamma=sigma**-2,
rho=rho)
lsanomaly_model.fit(X_train, y_train)
predictions = lsanomaly_model.predict_proba(X_test)[:, -1]
elif method_name == "OCSVM":
svm_anomaly_model = svm.OneClassSVM(gamma=sigma**-2, nu=nu)
svm_anomaly_model.fit(X_train)
predictions = 1 - svm_anomaly_model.decision_function(X_test)
elif method_name == "KNN":
anomaly_model = neighbors.NearestNeighbors(10)
anomaly_model.fit(X_train)
dists, idx = anomaly_model.kneighbors(X_test)
predictions = dists[:, -1]
elif method_name == "KM":
km = cluster.KMeans(min(X_train.shape[0], 20))
km.fit(X_train)
nn = neighbors.NearestNeighbors(1)
nn.fit(km.cluster_centers_)
dists, idx = nn.kneighbors(X_test)
predictions = dists[:, 0]
else:
raise ValueError("unknown method: {}".format(method_name))
fpr, tpr, thresholds = metrics.roc_curve(y_test == outlier_class,
predictions)
metric_auc = metrics.auc(fpr, tpr)
logger.debug("\tAUC: {:>6.4f}".format(metric_auc))
if not math.isnan(metric_auc):
current_method_aucs.append(metric_auc)
else:
raise ValueError("NaN encountered in {}".format(method_name))
except (IndexError, ValueError, Exception) as e:
logger.exception("\t{} {}: {}".format(method_name, type(e), str(e)),
exc_info=True)
raise
def compute_scores(normal_users, queue, Ks=[]):
'''
Calculates the novelty scores (noise and strangeness) for the 4 algotithms
Receives the list of normal users and the queue (all users) and the list of curiosity factors Ks
Updates the global variables GMM_n, one_n, lsa_n, K_n, GMM_s, one_s, lsa_s with the results
'''
global GMM_n, one_n, lsa_n, K_n, GMM_s, one_s, lsa_s, K_s #Novelty Scores for each algorithm, those ''_n are for noise score, ''_s are for strangeness score
GMM_n = []
one_n = []
lsa_n = []
K_n = []
GMM_s = []
one_s = []
lsa_s = []
K_s = []
K_GMM_n, K_KMeans_n, K_GMM_s, K_KMeans_s = Ks #K_GMM_n, K_KMeans_n are the noise curiosity factors for each algorithm
#K_GMM_s, K_KMeans_s are the strangeness curiosity factors for each algorithm
#Ks is a list containing the 4 above mentioned parameters
'''
For One_class_SVM and LSA, when asked to predict the new entry, a label is directly returned
LSA: 'anomaly' or '0' (normal)
One One_class_SVM: -1 (anomaly) or 1 (normal)
GMM and K means predict a fitting score. The novelty score is obtained calculating the zscore of the entry compared with the scores of all other entries, calling
the function get_score_last_item
If the zscore returned >= 1 the new entry is anomalous
'''
'''
Noise scores are computed with the queue as the base of knowledge, fitting all the entries but the last to the algorithm
'''
B = GMM(covariance_type='full', n_components = 1)
B.fit(queue[0:-1])
x = [B.score([i]).mean() for i in queue]
GMM_n.append(get_score_last_item(x, K_GMM_n))
K = KMeans(n_clusters=1)
K.fit(queue[0:-1])
x = [K.score([i]) for i in queue]
K_n.append(get_score_last_item(x, K_KMeans_n))
oneClassSVM = OneClassSVM(nu=0.1)
oneClassSVM.fit(queue[0:-1])
x = oneClassSVM.predict(np.array([queue[-1]]))
if x == -1:
one_n.append(1)
if x == 1:
one_n.append(0)
X = np.array(queue[0:-1])
anomalymodel = lsanomaly.LSAnomaly()
anomalymodel.fit(X)
x = anomalymodel.predict(np.array([queue[-1]]))
if x == ['anomaly']:
lsa_n.append(1)
if x == [0]:
lsa_n.append(0)
'''
Strangeness scores are computed with the normal users as the base of knowledge, fitting normal users to the algorithm
'''
normal_and_new = normal_users + [queue[-1]] #List to be passed to get_score_last_item to calculate the zscore of the last item, the new entry
B = GMM(covariance_type='full', n_components = 1)
B.fit(normal_users)
x = [B.score([i]).mean() for i in normal_and_new]
GMM_s.append(get_score_last_item(x, K_GMM_s))
K = KMeans(n_clusters=1)
K.fit(normal_users)
x = [K.score([i]) for i in normal_and_new]
K_s.append(get_score_last_item(x, K_KMeans_s))
oneClassSVM = OneClassSVM(nu=0.1)
oneClassSVM.fit(normal_users)
x = oneClassSVM.predict(np.array([queue[-1]]))
if x == -1:
one_s.append(1)
if x == 1:
one_s.append(0)
anomalymodel = lsanomaly.LSAnomaly()
X = np.array(normal_users)
anomalymodel.fit(X)
x = anomalymodel.predict(np.array([queue[-1]]))
if x == ['anomaly']:
lsa_s.append(1)
if x == [0]:
lsa_s.append(0)
return GMM_n, one_n, lsa_n, K_n, GMM_s, one_s, lsa_s, K_s
def eval(
X_train,
y_train,
X_test,
y_test,
outlier_class,
method,
method_name,
current_method_aucs,
sigma,
rho=0.1,
nu=0.5,
):
predictions = None
try:
if method_name == "LSAD":
lsanomaly_model = lsanomaly.LSAnomaly(n_kernels_max=500,
gamma=sigma**-2,
rho=rho)
lsanomaly_model.fit(X_train, y_train)
predictions = lsanomaly_model.predict_proba(X_test)[:, -1]
elif method_name == "OCSVM":
svm_anomaly_model = svm.OneClassSVM(gamma=sigma**-2, nu=nu)
svm_anomaly_model.fit(X_train)
predictions = 1 - svm_anomaly_model.decision_function(X_test)
elif method_name == "KNN":
anomaly_model = neighbors.NearestNeighbors(10)
anomaly_model.fit(X_train)
dists, idx = anomaly_model.kneighbors(X_test)
predictions = dists[:, -1]
elif method_name == "KM":
km = cluster.KMeans(min(X_train.shape[0], 20))
km.fit(X_train)
nn = neighbors.NearestNeighbors(1)
nn.fit(km.cluster_centers_)
dists, idx = nn.kneighbors(X_test)
predictions = dists[:, 0]
elif method_name == "DBS":
dbs_anomaly_model = cluster.DBSCAN(eps=sigma, min_samples=3)
clusters = dbs_anomaly_model.fit_predict(X_test)
else:
raise ValueError("unknown method: {}".format(method_name))
fpr, tpr, thresholds = metrics.roc_curve(y_test == outlier_class,
predictions)
metric_auc = metrics.auc(fpr, tpr)
logger.debug("\tAUC: {:>6.4f}".format(metric_auc))
if not math.isnan(metric_auc):
current_method_aucs.append(metric_auc)
else:
raise ValueError("NaN encountered in {}".format(method_name))
except (IndexError, Exception) as e:
logger.exception(
"\t{} {}: {}".format(method_name, type(e), str(e)),
exc_info=True,
)
raise
Y_test = Y2[0:319, :] # In[159]: X_train = X2[:5000, :] X_test = X2[10000:15000, :] # In[160]: plt.plot(X_test[:, 0]) plt.plot(X_test[:, 1]) # In[161]: # Train the model anomalymodel = lsanomaly.LSAnomaly(rho=1, sigma=.5) anomalymodel.fit(Y_train) # Predict anomalies statically (assuming iid samples) y_pred_static = anomalymodel.predict_proba(Y_test) # Predict anomalies sequentially (assume known transition matrix and # initial probabilities) A = np.array([[.999, .001], [.01, .99]]) pi = np.array([.5, .5]) y_pred_dynamic = anomalymodel.predict_sequence(Y_test, A, pi) # In[162]: plt.clf() plt.figure(figsize=(10, 6))
def generate_neural_classifier(data_list):
x_train = np.array(data_list)
clf = lsanomaly.LSAnomaly()
clf.fit(x_train)
return clf
