class SVMDetector:
#just the training() function changes, rest all remains same.
def __init__(self, subjects):
self.u_scores = []
self.i_scores = []
self.mean_vector = []
self.subjects = subjects
def training(self):
self.clf = OneClassSVM(kernel='rbf',gamma=26)
print(self.clf)
self.clf.fit(self.train)
print(self.clf.fit(self.train))
#print("lop")
def testing(self):
self.u_scores = -self.clf.decision_function(self.test_genuine)
self.i_scores = -self.clf.decision_function(self.test_imposter)
self.u_scores = list(self.u_scores)
self.i_scores = list(self.i_scores)
#print(self.u_scores)
#print("dog")
#
#print(self.i_scores)
#print("cat")
#print(len(self.i_scores[0]))
# specific .tie5Roanl
def evaluate(self):
eers = []
for subject in subjects:
genuine_user_data = data.loc[data.subject == "vaibhav", \
"H.period":"UD.l.Return"]
imposter_data = data.loc[data.subject != "vaibhav", :]
self.train = genuine_user_data[-1:]
self.test_genuine = genuine_user_data[:20]
self.test_imposter = imposter_data.groupby("subject"). \
head(20).loc[:, "H.period":"UD.l.Return"]
#total - selected genuine ,,,,, first five of every imposter displayed
#print(genuine_user_data[:200])
self.training()
self.testing()
eers.append(evaluateEER(self.u_scores, \
self.i_scores))
#print(evaluateEER(self.u_scores, \
# self.i_scores))
#print(np.mean(eers))
break
return np.mean(eers)
def odd_evaluate():
X_test = RTM.y_sampler.X_test
X_train = RTM.y_sampler.X_train
label_test = RTM.y_sampler.label_test
#one-class SVM
clf = OneClassSVM(gamma='auto').fit(X_train)
score_svm = clf.decision_function(X_test) #lower, more abnormal
pr_oneclassSVM = precision_at_K(score_svm, label_test)
#Isolation Forest
clf = IsolationForest()
clf.fit(X_train)
score_if = clf.decision_function(X_test) #lower, more abnormal
pr_iso_forest = precision_at_K(score_if, label_test)
#Roundtrip
py = RTM.estimate_py_with_IS(X_test,
epoch,
sd_y=best_sd,
scale=best_scale,
sample_size=sample_size,
log=True,
save=False)
pr_Roundtrip = precision_at_K(py, label_test)
print("The precision at K of Roundtrip model is %.4f" % pr_Roundtrip)
print("The precision at K of One-class SVM is %.4f" % pr_oneclassSVM)
print("The precision at K of Isolation forest is %.4f" % pr_iso_forest)
class SVMDetector(Detector):
def training(self):
self.clf = OneClassSVM(kernel='rbf', gamma=26)
self.clf.fit(self.train)
def testing(self):
self.user_scores = -self.clf.decision_function(self.test_genuine)
self.imposter_scores = -self.clf.decision_function(self.test_imposter)
self.user_scores = list(self.user_scores)
self.imposter_scores = list(self.imposter_scores)
def get_predictions(X, y, ktype):
'''Use a one-class SVM to get irregularity predictions from the inputs.
Parameters:
X : DataFrame
input features
y : Series
labels
ktype : str
SVM kernel type
Returns:
result : DataFrame
containing the centre, its predicted label using the built-in
decision function, the predictive score, and the true label
'''
svm = OneClassSVM(kernel=ktype, gamma='auto').fit(X)
y_pred = svm.predict(X)
y_pred = ((1 - y_pred) / 2).astype(int)
y_score = -svm.decision_function(X)
result = pd.DataFrame({
'centre': X.index,
'pred': y_pred,
'score': y_score,
'anomalous': y
})
return result
class OCSVM():
def __init__(self, kernel='rbf', nu=.01, gamma=.01):
self.svm = OneClassSVM(nu=nu, gamma=gamma, kernel=kernel)
def predict(self, X):
X_prime = self.mapping(X)
return self.svm.predict(X_prime)
def fit(self, X):
X_prime = self.mapping(X)
return self.svm.fit(X_prime)
def decision_function(self, X):
X_prime = self.mapping(X)
return self.svm.decision_function(X_prime)
def mapping(self, X):
X = np.array(X)
clutter1_com = X[:, -3:]
clutter2_com = X[:, -6:-3]
obj_com = X[:, -12:-9]
gripper_com = X[:, -15:-12]
X_prime = np.hstack((clutter1_com, clutter2_com, obj_com, gripper_com))
return X_prime
def _raw_ocsvm_experiment(dataset_load_fn, dataset_name, single_class_ind):
(x_train, y_train), (x_test, y_test) = dataset_load_fn()
x_train = x_train.reshape((len(x_train), -1))
x_test = x_test.reshape((len(x_test), -1))
x_train_task = x_train[y_train.flatten() == single_class_ind]
if dataset_name in ['cats-vs-dogs']: # OC-SVM is quadratic on the number of examples, so subsample training set
subsample_inds = np.random.choice(len(x_train_task), 5000, replace=False)
x_train_task = x_train_task[subsample_inds]
pg = ParameterGrid({'nu': np.linspace(0.1, 0.9, num=9),
'gamma': np.logspace(-7, 2, num=10, base=2)})
results = Parallel(n_jobs=6)(
delayed(_train_ocsvm_and_score)(d, x_train_task, y_test.flatten() == single_class_ind, x_test)
for d in pg)
best_params, best_auc_score = max(zip(pg, results), key=lambda t: t[-1])
best_ocsvm = OneClassSVM(**best_params).fit(x_train_task)
scores = best_ocsvm.decision_function(x_test)
labels = y_test.flatten() == single_class_ind
res_file_name = '{}_raw-oc-svm_{}_{}.npz'.format(dataset_name,
get_class_name_from_index(single_class_ind, dataset_name),
datetime.now().strftime('%Y-%m-%d-%H%M'))
res_file_path = os.path.join(RESULTS_DIR, dataset_name, res_file_name)
save_roc_pr_curve_data(scores, labels, res_file_path)
def test_score_samples_estimators():
"""Check the values of score_samples methods derived from sklearn.
Check that the values are the same than sklearn decision_function methods.
This only concerns OCSVM and IsolationForest.
"""
X = np.random.randn(50, 2)
clf1 = IsolationForest(random_state=88)
clf1.fit(X)
clf2 = ensemble.IsolationForest(random_state=88)
clf2.fit(X)
assert_array_equal(clf1.score_samples(X), clf2.decision_function(X))
nu = 0.4
sigma = 3.0
gamma = gamma = 1. / (2. * sigma**2)
clf1 = OCSVM(sigma=sigma, nu=nu)
clf1.fit(X)
clf2 = OneClassSVM(gamma=gamma, nu=nu)
clf2.fit(X)
assert_array_equal(clf1.score_samples(X),
clf2.decision_function(X).ravel())
def dist_ocsvm(X_train, X_test, gamma=0.1):
"""
Calculation of data density by OCSVM
Parameters
----------
X_train : array-like, shape = [n_samples, n_features]
X training data
X_test : array-like, shape = [n_samples, n_features]
X test data
fact :
dumping factor
Returns
-------
array-like, shape = [n_samples]
data density calculated by OCSVM
"""
clf = OneClassSVM(nu=0.003, kernel="rbf", gamma=gamma)
clf.fit(X_train)
func = clf.decision_function(X_test)
func = func.ravel()
dens = abs(func - max(func))
# Normalization: dens = 0 ~ 1
dens = dens / max(dens)
return dens
class OCSVM():
def __init__(self, kernel='rbf', nu=.01, gamma=.01):
self.svm = OneClassSVM(nu=nu, gamma=gamma, kernel=kernel)
def predict(self, X):
X_prime = self.mapping(X)
return self.svm.predict(X_prime)
def fit(self, X):
X_prime = self.mapping(X)
return self.svm.fit(X_prime)
def decision_function(self, X):
X_prime = self.mapping(X)
return self.svm.decision_function(X_prime)
def mapping(self, X):
X = np.array(X)
clutter1_com = X[:, -3:]
clutter2_com = X[:, -6:-3]
obj_com = X[:, -12:-9]
gripper_com = X[:, -15:-12]
diff1 = clutter1_com - obj_com
diff2 = clutter2_com - obj_com
norm1 = np.linalg.norm(diff1, axis=1)
norm2 = np.linalg.norm(diff2, axis=1)
X_prime = np.array([norm1, norm2]).T
X_prime = np.hstack((X_prime, gripper_com))
return X_prime
class OCSVM(AnomalyDetector):
"""
Anomaly detector based on one-class SVM
"""
def __init__(self, kernel="rbf"):
self._model = OneClassSVM(gamma='scale', kernel=kernel)
# self._thresholds = None
# TODO : tester gamma="auto"
def learn(self, data):
self._model.fit(data)
def get_score(self, data, epoch=None):
assert len(data) == 1, "len(data) = " + str(len(data))
return self._model.decision_function(data)
def anomalies_have_high_score(self):
return True
def predict(self, data, obs):
return self._model.predict(obs) == -1
def get_memory_size(self):
return 0
def save(self, filename):
joblib.dump(self._model, filename)
def load(self, filename):
self._model = joblib.load(filename)
def support_vectors(self, X, n, **kwargs):
model = OneClassSVM(gamma=X.shape[1], nu=1 / X.shape[0])
model.fit(X)
sv = model.support_vectors_
distance_from_hyperplane = model.decision_function(sv).reshape(-1)
idx = np.argsort(np.abs(distance_from_hyperplane))[:n]
return sv[idx, :]
def occ_onehot(pokemon, combats):
training, labels = get_occ_feature_matrix_onehot(pokemon, combats)
training = np.array(training)
labels = np.array(labels)
kf = KFold(n_splits=5)
clf = OneClassSVM()
all_scores = list()
for train_index, test_index in kf.split(training):
X_train, X_test = training[train_index], training[test_index]
Y_test = np.ones(len(test_index))
clf.fit(X_train)
y_pred = clf.predict(X_test)
prob_pos = clf.decision_function(X_test)
prob_pos = (prob_pos - prob_pos.min()) / (prob_pos.max() -
prob_pos.min())
y_pred = [0 if x == -1 else 1 for x in y_pred]
Y_test = [int(x) for x in Y_test]
scores = [
precision_score(Y_test, y_pred),
recall_score(Y_test, y_pred),
f1_score(Y_test, y_pred)
]
all_scores.append(scores)
print(all_scores)
pickle.dump(all_scores, open('scores_occ_oneoht.pickle', 'wb'))
class OCSVM(object):
def __init__(self, file_name, config):
self.dataset = config.dataset
self.file_name = file_name
self.x_dim = config.x_dim
self.kernel = config.kernel
self.degree = config.degree
self.gamma = config.gamma
self.coef0 = config.coef0
self.tol = config.tol
self.nu = config.nu
self.pid = config.pid
self.model = OneClassSVM(kernel=self.kernel, degree=self.degree, gamma=self.gamma, coef0=self.coef0,
tol=self.tol, nu=self.nu)
def fit(self, train_input, train_label, test_input, test_label):
# Perform fit on X and returns labels for X.
# Returns -1 for outliers and 1 for inliers.
y_pred = self.model.fit_predict(train_input)
decision_function = self.model.decision_function(train_input)
ocsvm_output = OCSVMOutput(y_hat=y_pred, decision_function=decision_function)
return ocsvm_output
def ocsvm(self, X_train, kernel=None, gamma=None, nu=None):
"""
Train OCSVM model from Sklearn
Parameters
__________
X_train: scaled training data
kernel: kernel funcs: ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’
gamma: kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’
nu: regularization parameter btw [0,1]
Returns
________
Anomaly scores
"""
model = OCSVM(kernel=kernel, gamma=gamma, nu=nu)
model.fit(X_train)
# Predict raw anomaly score
labels = model.predict(X_train) # Outlier labels (-1 or 1)
labels = (labels.max() -
labels) // 2 # rescaled labels (1: outliers, 0: inliers)
ocsvm_anomaly_scores = model.decision_function(
X_train) * -1 # Outlier scores
ocsvm_anomaly_scores = self.min_max_scaler(ocsvm_anomaly_scores)
return ocsvm_anomaly_scores, labels
def oneClassSVM(dataset):
classifier = OneClassSVM(nu=outlierFraction, gamma=0.03)
classifier.fit(dataset)
predScore = classifier.decision_function(dataset).T[0]
pred = classifier.predict(dataset)
outlierRows = [i for i in range(len(pred)) if pred[i] == -1]
return predScore, outlierRows
def _cae_ocsvm_experiment(dataset_load_fn, dataset_name, single_class_ind,
gpu_q):
# gpu_to_use = gpu_q.get()
# os.environ["CUDA_VISIBLE_DEVICES"] = gpu_to_use
(x_train, y_train), (x_test, y_test) = dataset_load_fn()
print('data_shape', x_train.shape)
n_channels = x_train.shape[get_channels_axis()]
input_side = x_train.shape[2] # channel side will always be at shape[2]
enc = conv_encoder(input_side, n_channels)
dec = conv_decoder(input_side, n_channels)
# print(input_side)
# print(dec.summary())
x_in = Input(shape=x_train.shape[1:])
x_rec = dec(enc(x_in))
cae = Model(x_in, x_rec)
cae.compile('adam', 'mse')
x_train_task = x_train[y_train.flatten() == single_class_ind]
x_test_task = x_test[y_test.flatten(
) == single_class_ind] # This is just for visual monitoring
cae.fit(x=x_train_task,
y=x_train_task,
batch_size=128,
epochs=200,
validation_data=(x_test_task, x_test_task))
x_train_task_rep = enc.predict(x_train_task, batch_size=128)
if dataset_name in LARGE_DATASET_NAMES: # OC-SVM is quadratic on the number of examples, so subsample training set
subsample_inds = np.random.choice(len(x_train_task_rep),
2500,
replace=False)
x_train_task_rep_temp = x_train_task_rep[subsample_inds]
x_test_rep = enc.predict(x_test, batch_size=128)
pg = ParameterGrid({
'nu': np.linspace(0.1, 0.9, num=9),
'gamma': np.logspace(-7, 2, num=10, base=2)
})
results = Parallel(n_jobs=PARALLEL_N_JOBS)(delayed(
_train_ocsvm_and_score)(d, x_train_task_rep_temp, y_test.flatten() ==
single_class_ind, x_test_rep) for d in pg)
best_params, best_auc_score = max(zip(pg, results), key=lambda t: t[-1])
print(best_params)
best_ocsvm = OneClassSVM(**best_params).fit(x_train_task_rep)
scores = best_ocsvm.decision_function(x_test_rep)
labels = y_test.flatten() == single_class_ind
res_file_name = '{}_cae-oc-svm_{}_{}.npz'.format(
dataset_name, get_class_name_from_index(single_class_ind,
dataset_name),
datetime.datetime.now().strftime('%Y-%m-%d-%H%M'))
res_file_path = os.path.join(RESULTS_DIR, dataset_name, res_file_name)
save_roc_pr_curve_data(scores, labels, res_file_path)
def find_anomaly(label1, label2, winsize):
print("Find anomaly in channel",
label1 + '-' + label2 + '...',
file=sys.stderr)
print("-" * 80)
print("Channel [" + label1 + '-' + label2 + ']')
print("-" * 80)
# find difference
electrode1 = eeg.chan_lab.index(label1)
electrode2 = eeg.chan_lab.index(label2)
wave = eeg.X[electrode1] - eeg.X[electrode2]
# # import random
# wave = [random.uniform(-20,20) for _ in range(400*30)] + [random.uniform(-2000,2000) for _ in range(5*30)]
# wave = np.array(wave)
print("Splitting into windows...", file=sys.stderr)
wave_windows = np.array_split(wave,
len(wave) / eeg.sample_rate / winsize)
# wave_windows = np.array_split(wave, len(wave)/winsize)
print("Extracting features...", file=sys.stderr)
def extract_features(wave_window):
max_val = max(wave_window)
min_val = min(wave_window)
stdev = np.std(wave_window)
sum_val = sum(wave_window)
sum_pos_val = sum([x for x in wave_window if x > 0])
sum_abs_val = sum([abs(x) for x in wave_window])
return [max_val, min_val, stdev, sum_val, sum_pos_val, sum_abs_val]
Examples = np.array(map(extract_features, wave_windows))
print("Training model, assuming no more than",
CONTAMINATION,
"anomaly...",
file=sys.stderr)
od = OneClassSVM(nu=CONTAMINATION,
kernel='poly',
gamma=0.05,
max_iter=100000)
od.fit(Examples)
decisions = od.decision_function(Examples)
# print decisions
# print max(decisions), min(decisions)
print("Most likely windows with anomaly:")
# find most likely windows, in desc order
largest_indices = np.argsort((-np.absolute(decisions)).ravel())[:20]
for large_index in largest_indices:
print(large_index * winsize / 60, "min (score:",
decisions[large_index][0], ")")
sys.stdout.flush()
def ocsvm(features_train, features_test):
# One Class Support Vector Machines
# fit the model
ocsvm = OneClassSVM().fit(features_train)
# predict
start = time.time()
anomalyScores = ocsvm.decision_function(features_test)
test_runtime = time.time() - start
return anomalyScores, test_runtime
def main():
print('------------01')
iris = load_iris()
pca = PCA(n_components=2)
data = pca.fit_transform(iris.data)
print(type(data))
print(data)
# nuで異常値の割合を指定。predictすると正常値=1,異常値=-1。
ocsvm = OneClassSVM(nu=0.1, gamma="auto")
ocsvm.fit(data)
preds = ocsvm.predict(data)
print(preds)
plt.scatter(data[:, 0], data[:, 1], c=preds, cmap=plt.cm.RdBu)
plt.show()
print('------------02A')
x = np.linspace(-5, 5, 500)
y = np.linspace(-1.5, 1.5, 250)
X, Y = np.meshgrid(x, y)
print('X.ravel():')
print(X.ravel())
print(X.shape)
print(Y.shape)
z1 = np.array([X.ravel(), Y.ravel()])
print(z1.shape)
z2 = ocsvm.decision_function(np.array([X.ravel(), Y.ravel()]).T)
print(z2.shape)
# (250, 500)
# (250, 500)
# (2, 125000)
# (125000,)
# (250, 500)
print(z2.reshape(X.shape).shape)
df = ocsvm.decision_function(np.array([X.ravel(),
Y.ravel()]).T).reshape(X.shape)
plt.scatter(data[:, 0], data[:, 1], c=preds, cmap=plt.cm.RdBu, alpha=0.8)
r = max([abs(df.min()), abs(df.max())])
print('------------02B')
print(df.min())
print(max([abs(df.min()), abs(df.max())]))
print(df)
plt.contourf(X, Y, df, 10, vmin=-r, vmax=r, cmap=plt.cm.RdBu, alpha=.5)
plt.show()
def __generate_probas(self, samples, resolution, affinity_matrix, number_of_questions):
print(
f"📞 Looks like there's a probability distribution ({self.name}) that wants to phone in an expert (that's "
f"you)\n"
)
clf = OneClassSVM(kernel='precomputed')
samples_and_weights = {0: 0.5}
for nq in range(number_of_questions):
indices = list(samples_and_weights.keys())
if nq == 0:
idx = np.random.choice(range(1, len(samples)))
else:
preds = clf.decision_function(affinity_matrix[:, indices])
idx = [i for i, _ in sorted(enumerate(preds), key=lambda x: x[1]) if i not in samples_and_weights][
0]
sample = samples[idx]
print('Score the sample below with a number between 0 and 1 (higher is better)\n')
if hasattr(sample, '_repr_html_'):
print(sample)
else:
print(sample)
weight = float(input('Score: '))
assert 0 <= weight <= 1
samples_and_weights[idx] = weight
indices = list(samples_and_weights.keys())
clf.fit(
affinity_matrix[indices, :][:, indices],
sample_weight=list(samples_and_weights.values())
)
indices = list(samples_and_weights.keys())
preds = clf.decision_function(affinity_matrix[:, indices])
scores = KernelDiscretizedMethod.discretized_scores(
resolution,
samples,
affinity_matrix,
lambda mask, _idx: preds[mask].mean())
Z = logsumexp([s for s in scores.values()])
return {idx: s - Z for idx, s in scores.items()}
def run(opt):
output={}
cname = opt.cname
datatrain, test_loader = get_loader(opt,classname = opt.cname)
model = create_model(opt)
opt.load = False
#model.setup(opt)
model = vgg_face_dag(weights_path='vgg_face_dag.pth')
model.eval()
f = []
g = []
tlbl = []
model.eval()
cnt = 0
output={}
for data, lbl in datatrain:
cnt +=1
code, c = model(data)
if cnt ==1:
f = code.view(code.size(0), -1).detach().cpu().numpy().tolist()
else:
f += code.view(code.size(0), -1).detach().cpu().numpy().tolist()
output['train']=f
cnt = 0
for data, lbl in test_loader:
cnt +=1
code, c = model(data)
#out, code = model.test()
if cnt ==1:
tlbl = lbl.cpu().numpy().tolist()
g = code.view(code.size(0), -1).detach().cpu().numpy().tolist()
#tloss = np.mean(((out.cpu()-model.real_A.detach().cpu()).numpy())**2,(1,2,3)).tolist()
else:
g += code.view(code.size(0), -1).detach().cpu().numpy().tolist()
tlbl += lbl.cpu().numpy().tolist()
#tloss += np.mean(((out.cpu()-model.real_A.detach().cpu()).numpy())**2,(1,2,3)).tolist()
output['test']=g
output['lbl']=tlbl
print(len(tlbl))
print(len(g))
clf = OneClassSVM(gamma='auto')
clf.fit(output['train'])
scores = clf.decision_function(output['test'])
print(sklearn.metrics.roc_auc_score(output['lbl'], scores))
'''cnt = 0
def one_class_svm_core(x_train, x_test, y_test, x_test_names, version=0):
clf = OneClassSVM()
print 'svm begin...'
start = time.time()
clf.fit(x_train)
joblib.dump(clf, 'one_class_model.m')
print 'begin compute', time.time() - start
y_distance = clf.decision_function(x_test)
y_score = (np.clip(y_distance, -1, 1) + 1) / 2
print 'svm complete..'
show_result(y_score, y_test, x_test_names, version=version)
class OneClsSVM(AbstractDetector):
name = "OneClassSVM"
data_type = "REAL"
def compute_scores(self, dataframe: pd.DataFrame, classes: np.array):
bin_dataframe = dataframe._binarize_categorical_values()
self.clf = OneClassSVM(**self.settings)
self.clf.fit(bin_dataframe.values)
self.values = -self.clf.decision_function(bin_dataframe.values)
return self
def evaluate_features(train, test, labels, dataset, model=''):
params = svm_parameters_dict(dataset)
clf = OneClassSVM(kernel='rbf',
gamma=params[1],
nu=params[0],
verbose=True)
clf.fit(train)
decision_f = clf.decision_function(test)
new_decision_f = filter_scores(decision_f, dataset, params[2])
_auc = calc_auc(labels, new_decision_f)
print "Area under ROC: ", _auc
def runClassifier(self, _driverId, numComponents=0):
X = self.featuresHash.values()
self.ids = self.featuresHash.keys()
if self.runDimRed:
X = self.dimRed(X, numComponents)
clf = OCSVM(nu=self.nu, gamma=self.gamma)
clf.fit(X)
y_pred = clf.decision_function(X).ravel()
threshold = stats.scoreatpercentile(y_pred, 100 * self.outliers_fraction)
self.label = y_pred > threshold
self.label = map(int, self.label)
def SVM(person):
# Consider current subject as real and rest as fake
realUser_data = data.loc[data.subject == person, "H.period":"H.Return"]
if (len(realUser_data) == 0):
print("No data found for the given user")
return 0
real_train = np.array((realUser_data[:200]).values)
# True test set (200 records)
real_test = np.array((realUser_data[200:]).values)
fakeUser_data = data.loc[data.subject != person, :]
# False set (250 records, 5 per fake user, 50 fake users in all)
fake_test = np.array(
(fakeUser_data.groupby("subject").head(5).loc[:, "H.period":"H.Return"]
).values)
clf = OneClassSVM(kernel='rbf', gamma=26)
clf.fit(real_train)
realUser_scores = [] # real user score
fakeUser_scores = [] # imposter user score
# Calculate score for real user test data
realUser_scores = list(-clf.decision_function(real_test))
# Calculate score for fake user test data
fakeUser_scores = list(-clf.decision_function(fake_test))
# true label
labels = [0] * len(realUser_scores) + [1] * len(fakeUser_scores)
Equal_err_rate = Calc_equal_err_rate(realUser_scores, fakeUser_scores,
labels)
print("Equal err rate:: ", Equal_err_rate)
return Equal_err_rate
class SVMDetector:
# just the training() function changes, rest all remains same.
def __init__(self, subjects):
self.u_scores = []
self.i_scores = []
self.mean_vector = []
self.subjects = subjects
def training(self):
self.clf = OneClassSVM(kernel='rbf', gamma=26)
self.clf.fit(self.train)
def testing(self):
self.u_scores = -self.clf.decision_function(self.test_genuine)
self.i_scores = -self.clf.decision_function(self.test_imposter)
self.u_scores = list(self.u_scores)
self.i_scores = list(self.i_scores)
def evaluate(self):
eers = []
for subject in subjects:
genuine_user_data = data.loc[data.subject ==
tempvalue, "H.period":"UD.l.Return"]
imposter_data = data.loc[data.subject != tempvalue, :]
self.train = genuine_user_data[-1:]
self.test_genuine = genuine_user_data[:20]
self.test_imposter = imposter_data.groupby("subject"). \
head(20).loc[:, "H.period":"UD.l.Return"]
self.training()
self.testing()
eers.append(evaluateEER(self.u_scores,
self.i_scores))
break
return np.mean(eers)
def SVM():
eers = []
false_negative = 0.0
false_positive = 0.0
for subject in subjects:
user_scores = []
imposter_scores = []
imposter_data = data.loc[data.subject != subject, :]
train = data.loc[data.subject == subject,
"H.period":"H.Return"][:200].values
test_genuine = data.loc[data.subject == subject,
"H.period":"H.Return"][200:].values
imposter = imposter_data.groupby("subject").head(
5).loc[:, "H.period":"H.Return"].values
clf = OneClassSVM(kernel='rbf', gamma=26)
clf.fit(train)
user_scores = -clf.decision_function(test_genuine)
imposter_scores = -clf.decision_function(imposter)
user_scores = list(user_scores)
imposter_scores = list(imposter_scores)
standard_deviation = np.std(user_scores)
mean_standard = np.mean(user_scores)
for score in user_scores:
if score > mean_standard + standard_deviation or score < mean_standard - standard_deviation:
false_positive = false_positive + 1
# checking for false positives
for score in imposter_scores:
if score < mean_standard + standard_deviation and score > mean_standard - standard_deviation:
false_negative = false_negative + 1
eers.append(Calc_equal_err_rate(user_scores, imposter_scores))
#print eers
return np.mean(eers), false_positive / (51 * 200), false_negative / (51 *
250)
def ocsvm(X, y, percentage=None, params={}, sh_params={}):
normalizer = Normalizer(norm="l1")
X = normalizer.fit_transform(X)
cf = OneClassSVM(**params)
cf.fit(X)
anomalyscores = -cf.decision_function(X)
ap = average_precision_score(y, anomalyscores)
auc = roc_auc_score(y, anomalyscores)
return ap, auc
def embed_dat_matrix_two_dimensions(low_dimension_data_matrix,
y=None,
labels=None,
density_colormap='Blues',
instance_colormap='YlOrRd'):
from sklearn.preprocessing import scale
low_dimension_data_matrix = scale(low_dimension_data_matrix)
# make mesh
x_min, x_max = low_dimension_data_matrix[:, 0].min(), low_dimension_data_matrix[:, 0].max()
y_min, y_max = low_dimension_data_matrix[:, 1].min(), low_dimension_data_matrix[:, 1].max()
step_num = 50
h = min((x_max - x_min) / step_num, (y_max - y_min) / step_num) # step size in the mesh
b = h * 10 # border size
x_min, x_max = low_dimension_data_matrix[:, 0].min() - b, low_dimension_data_matrix[:, 0].max() + b
y_min, y_max = low_dimension_data_matrix[:, 1].min() - b, low_dimension_data_matrix[:, 1].max() + b
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# induce a one class model to estimate densities
from sklearn.svm import OneClassSVM
gamma = max(x_max - x_min, y_max - y_min)
clf = OneClassSVM(gamma=gamma, nu=0.1)
clf.fit(low_dimension_data_matrix)
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, m_max] . [y_min, y_max].
if hasattr(clf, "decision_function"):
score_matrix = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
else:
score_matrix = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
# Put the result into a color plot
levels = np.linspace(min(score_matrix), max(score_matrix), 40)
score_matrix = score_matrix.reshape(xx.shape)
if y is None:
y = 'white'
plt.contourf(xx, yy, score_matrix, cmap=plt.get_cmap(density_colormap), alpha=0.9, levels=levels)
plt.scatter(low_dimension_data_matrix[:, 0], low_dimension_data_matrix[:, 1],
alpha=.5,
s=70,
edgecolors='gray',
c=y,
cmap=plt.get_cmap(instance_colormap))
# labels
if labels is not None:
for id in range(low_dimension_data_matrix.shape[0]):
label = labels[id]
x = low_dimension_data_matrix[id, 0]
y = low_dimension_data_matrix[id, 1]
plt.annotate(label, xy=(x, y), xytext=(0, 0), textcoords='offset points')
def embed_dat_matrix_two_dimensions(low_dimension_data_matrix,
y=None,
labels=None,
density_colormap='Blues',
instance_colormap='YlOrRd'):
from sklearn.preprocessing import scale
low_dimension_data_matrix = scale(low_dimension_data_matrix)
# make mesh
x_min, x_max = low_dimension_data_matrix[:, 0].min(), low_dimension_data_matrix[:, 0].max()
y_min, y_max = low_dimension_data_matrix[:, 1].min(), low_dimension_data_matrix[:, 1].max()
step_num = 50
h = min((x_max - x_min) / step_num, (y_max - y_min) / step_num) # step size in the mesh
b = h * 10 # border size
x_min, x_max = low_dimension_data_matrix[:, 0].min() - b, low_dimension_data_matrix[:, 0].max() + b
y_min, y_max = low_dimension_data_matrix[:, 1].min() - b, low_dimension_data_matrix[:, 1].max() + b
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# induce a one class model to estimate densities
from sklearn.svm import OneClassSVM
gamma = max(x_max - x_min, y_max - y_min)
clf = OneClassSVM(gamma=gamma, nu=0.1)
clf.fit(low_dimension_data_matrix)
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, m_max] . [y_min, y_max].
if hasattr(clf, "decision_function"):
score_matrix = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
else:
score_matrix = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
# Put the result into a color plot
levels = np.linspace(min(score_matrix), max(score_matrix), 40)
score_matrix = score_matrix.reshape(xx.shape)
if y is None:
y = 'white'
plt.contourf(xx, yy, score_matrix, cmap=plt.get_cmap(density_colormap), alpha=0.9, levels=levels)
plt.scatter(low_dimension_data_matrix[:, 0], low_dimension_data_matrix[:, 1],
alpha=.5,
s=70,
edgecolors='gray',
c=y,
cmap=plt.get_cmap(instance_colormap))
# labels
if labels is not None:
for id in range(low_dimension_data_matrix.shape[0]):
label = labels[id]
x = low_dimension_data_matrix[id, 0]
y = low_dimension_data_matrix[id, 1]
plt.annotate(label, xy=(x, y), xytext = (0, 0), textcoords = 'offset points')
def SVM_score(S):
X = np.array(S)
clf = OneClassSVM(kernel='linear')
clf.fit(X)
scores = clf.decision_function(X)
min = 999999
max = -99999
for i in range(len(scores)):
scores[i] = -1 * scores[i]
if scores[i] < min:
min = scores[i]
if scores[i] > max:
max = scores[i]
return scores
def find_anomaly(label1, label2, winsize):
print("Find anomaly in channel", label1 + '-' + label2 + '...', file=sys.stderr)
print("-"*80)
print("Channel [" + label1 + '-' + label2 + ']')
print("-"*80)
# find difference
electrode1 = eeg.chan_lab.index(label1)
electrode2 = eeg.chan_lab.index(label2)
wave = eeg.X[electrode1] - eeg.X[electrode2]
# # import random
# wave = [random.uniform(-20,20) for _ in range(400*30)] + [random.uniform(-2000,2000) for _ in range(5*30)]
# wave = np.array(wave)
print("Splitting into windows...", file=sys.stderr)
wave_windows = np.array_split(wave, len(wave)/eeg.sample_rate/winsize)
# wave_windows = np.array_split(wave, len(wave)/winsize)
print("Extracting features...", file=sys.stderr)
def extract_features(wave_window):
max_val = max(wave_window)
min_val = min(wave_window)
stdev = np.std(wave_window)
sum_val = sum(wave_window)
sum_pos_val = sum([x for x in wave_window if x > 0])
sum_abs_val = sum([abs(x) for x in wave_window])
return [max_val, min_val, stdev, sum_val, sum_pos_val, sum_abs_val]
Examples = np.array(map(extract_features, wave_windows))
print("Training model, assuming no more than", CONTAMINATION, "anomaly...", file=sys.stderr)
od = OneClassSVM(nu=CONTAMINATION, kernel='poly', gamma=0.05, max_iter=100000)
od.fit(Examples)
decisions = od.decision_function(Examples)
# print decisions
# print max(decisions), min(decisions)
print("Most likely windows with anomaly:")
# find most likely windows, in desc order
largest_indices = np.argsort((-np.absolute(decisions)).ravel())[:20]
for large_index in largest_indices:
print(large_index*winsize/60, "min (score:", decisions[large_index][0], ")")
sys.stdout.flush()
def remove_outliers_SVM(self): ## Remove outliers using a OneClassSVM method print "Running SVM to remove outliers..." svm = OneClassSVM(kernel='rbf', nu=0.1, degree=3, verbose=1) fit = svm.fit(self.DataArray) decision = svm.decision_function(self.DataArray) _indices = [] # If a value is below the decision hyperplane, eliminate it for i in range(len(decision)): if decision[i] < 0: _indices.append(i) print self.DataArray.shape self.DataArray = np.delete(self.DataArray, _indices, axis=0) self.TargetArray = np.delete(self.TargetArray, _indices, axis=0) print self.DataArray.shape
def predict_header_features(self, pkt_featurizer):
group_id = pkt_featurizer.pkt_type
features = pkt_featurizer.features
arrival_time = pkt_featurizer.arrival_time
try:
vectorizer = DictVectorizer()
vectorizer.fit(self.training_data[group_id])
training_data_vectorized = vectorizer.transform(self.training_data[group_id])
features_vectorized = vectorizer.transform(features)
scaler = preprocessing.StandardScaler(with_mean=False)
training_data_vectorized = scaler.fit_transform(training_data_vectorized)
features_vectorized = scaler.transform(features_vectorized)
classifier = OneClassSVM()
classifier.fit(training_data_vectorized)
result = classifier.predict(features_vectorized)
distance = classifier.decision_function(features_vectorized)
except KeyError:
result = 0
distance = 0
return result, distance
import numpy as np
import pandas as pd
from sklearn.svm import OneClassSVM
df = pd.read_csv('kddcup_for_elki_100000.csv', header=None, index_col=False)
labelix = df.shape[1]-1
labels = df[labelix]
df = df.drop(labelix, axis=1)
svm = OneClassSVM(kernel='rbf', gamma=1.0/df.shape[0], tol=0.001, nu=0.5, shrinking=True, cache_size=80)
svm = svm.fit(df.values)
scores = svm.decision_function(df.values).flatten()
maxvalue = np.max(scores)
scores = maxvalue - scores
output = pd.DataFrame()
# perform reverse sort
sort_ix = np.argsort(scores)[::-1]
output['labels'] = labels[sort_ix]
output['outlier_scores'] = scores[sort_ix]
output.to_csv('outlier_scores.csv', header=None, index=None)
unif = np.random.uniform(lim_inf, lim_sup,
size=(n_generated, n_features))
# fit:
print('IsolationForest processing...')
iforest = IsolationForest()
iforest.fit(X_train)
s_X_iforest = iforest.decision_function(X_test)
print('LocalOutlierFactor processing...')
lof = LocalOutlierFactor(n_neighbors=20)
lof.fit(X_train)
s_X_lof = lof.decision_function(X_test)
print('OneClassSVM processing...')
ocsvm = OneClassSVM()
ocsvm.fit(X_train[:min(ocsvm_max_train, n_samples_train - 1)])
s_X_ocsvm = ocsvm.decision_function(X_test).reshape(1, -1)[0]
s_unif_iforest = iforest.decision_function(unif)
s_unif_lof = lof.decision_function(unif)
s_unif_ocsvm = ocsvm.decision_function(unif).reshape(1, -1)[0]
plt.subplot(121)
auc_iforest, em_iforest, amax_iforest = em(t, t_max,
volume_support,
s_unif_iforest,
s_X_iforest, n_generated)
auc_lof, em_lof, amax_lof = em(t, t_max, volume_support,
s_unif_lof, s_X_lof, n_generated)
auc_ocsvm, em_ocsvm, amax_ocsvm = em(t, t_max, volume_support,
s_unif_ocsvm, s_X_ocsvm,
n_generated)
def select_candidates(X, h, objective_function, verbose=False,
cov_computation_method=empirical_covariance):
"""Finds the best pure subset of observations to compute MCD from it.
The purpose of this function is to find the best sets of h
observations with respect to a minimization of their covariance
matrix determinant. Equivalently, it removes n_samples-h
observations to construct what we call a pure data set (i.e. not
containing outliers). The list of the observations of the pure
data set is referred to as the `support`.
Starting from a support estimated with a Parzen density estimator,
the pure data set is found by the c_step procedure introduced by
Rousseeuw and Van Driessen in [1].
Parameters
----------
X: array-like, shape (n_samples, n_features)
Data (sub)set in which we look for the h purest observations
h: int, [(n + p + 1)/2] < h < n
The number of samples the pure data set must contain.
select: int, int > 0
Number of best candidates results to return.
See
---
`c_step` function
Returns
-------
best_locations: array-like, shape (select, n_features)
The `select` location estimates computed from the `select` best
supports found in the data set (`X`)
best_covariances: array-like, shape (select, n_features, n_features)
The `select` covariance estimates computed from the `select`
best supports found in the data set (`X`)
best_supports: array-like, shape (select, n_samples)
The `select` best supports found in the data set (`X`)
Notes
-----
References:
[1] A Fast Algorithm for the Minimum Covariance Determinant Estimator,
1999, American Statistical Association and the American Society
for Quality, TECHNOMETRICS
"""
n_samples, n_features = X.shape
from sklearn.metrics.pairwise import euclidean_distances
from sklearn.svm import OneClassSVM
pairwise_distances = np.ravel(euclidean_distances(X))
delta = sp.stats.scoreatpercentile(pairwise_distances, 10)
gamma = 0.01 / delta
clf = OneClassSVM(kernel='rbf', gamma=gamma)
clf.fit(X)
in_support = np.argsort(
-np.ravel(clf.decision_function(X)))[-(n_samples / 2):]
support = np.zeros(n_samples, dtype=bool)
support[in_support] = True
location = X[support].mean(0)
covariance = cov_computation_method(X[support])
initial_estimates = (location, covariance)
best_location, best_covariance, _, best_support = c_step(
X, h, objective_function, initial_estimates, verbose=verbose,
cov_computation_method=cov_computation_method)
return best_location, best_covariance, best_support
X_test = X[n_samples_train:, :]
y_train = y[:n_samples_train]
y_test = y[n_samples_train:]
# # training only on normal data:
# X_train = X_train[y_train == 0]
# y_train = y_train[y_train == 0]
print('OneClassSVM processing...')
model = OneClassSVM(cache_size=500)
tstart = time()
model.fit(X_train)
fit_time += time() - tstart
tstart = time()
scoring = -model.decision_function(X_test) # the lower,the more normal
predict_time += time() - tstart
fpr_, tpr_, thresholds_ = roc_curve(y_test, scoring)
if fit_time + predict_time > max_time:
raise TimeoutError
f = interp1d(fpr_, tpr_)
tpr += f(x_axis)
tpr[0] = 0.
precision_, recall_ = precision_recall_curve(y_test, scoring)[:2]
# cluster: old version of scipy -> interpol1d needs sorted x_input
arg_sorted = recall_.argsort()
recall_ = recall_[arg_sorted]
def decision_function(self, data):
return -OneClassSVM.decision_function(self, data)
def main():
usage="refine2d using simmx information "
parser = EMArgumentParser(usage=usage,version=EMANVERSION)
parser.add_argument("--ptcls", type=str,help="particle file", default=None)
parser.add_argument("--simmx", type=str,help="simmx", default=None)
parser.add_argument("--npca", type=int,help="number of pca factors", default=10)
parser.add_argument("--niter", type=int,help="number of iterations", default=5)
parser.add_argument("--outlier", type=float,help="outlier fraction", default=0.1)
parser.add_argument("--ncls", type=int,help="number of centers", default=128)
parser.add_argument("--nref", type=int,help="number of references", default=32)
(options, args) = parser.parse_args()
logid=E2init(sys.argv)
simmxfile=options.simmx
for itr in range(options.niter):
### start from the simmx
print "Pre-processing simmx"
e=EMData(simmxfile)
pts=e.numpy().T.copy()
for i in range(len(pts)):
pts[i]-=np.mean(pts[i])
pts[i]/=np.std(pts[i])
pts=pts.astype(np.float).copy();
#e=from_numpy(pts.T.copy())
#e.write_image("simmx_tmp.hdf")
#exit()
print "Doing PCA"
(nptcl, ncls) = pts.shape;
#nfac=options.npca
pca=PCA(options.npca)
pts_pca=pca.fit_transform(pts)
bs=pts_pca
bs/=np.std(bs)
print bs.shape,pts.shape
np.savetxt("test_pca_{:02d}".format(itr),pts_pca)
print "Removing outliers"
outliers_fraction=options.outlier
svm=OneClassSVM(nu=0.95 * outliers_fraction + 0.05,kernel="rbf", gamma=0.1)
svm.fit(bs)
y_pred = svm.decision_function(bs).ravel()
nkeep=int(len(bs)*(1-outliers_fraction))
st=np.argsort(y_pred)[::-1]
st=st[:nkeep]
print "Clustering"
ncnt=options.ncls
centroids,_ = kmeans(bs[st],ncnt)
l,_ = vq(bs[st],centroids)
labels=np.zeros(len(bs))-1
labels[st]=l
print "Class averaging"
e=EMData(1,len(labels))
for i in range(len(labels)):
e.set_value_at(0,i,labels[i])
clsmxfile="clsmx_{:02d}.hdf".format(itr)
e.write_image(clsmxfile)
clsout="classes_{:02d}.hdf".format(itr)
run("e2classaverage.py --input={} --classmx={} --output={} --force --center xform.center --iter=5 --align=rotate_translate_flip:maxshift=32 --averager=mean --keep=.6 --cmp=ccc --aligncmp=ccc --normproc=normalize --parallel=thread:12".format(options.ptcls,clsmxfile,clsout))
simmxfile="simmx_{:02d}.hdf".format(itr)
run("e2simmx.py {} {} {} --align rotate_translate_flip --aligncmp ccc --cmp ccc --saveali --parallel thread:12".format(options.ptcls, clsout, simmxfile))
E2end(logid)
