def test_outlier_detection():
rnd = np.random.RandomState(0)
X = rnd.randn(100, 10)
clf = EllipticEnvelope(contamination=0.1)
clf.fit(X)
y_pred = clf.predict(X)
assert_array_almost_equal(
clf.decision_function(X, raw_values=True), clf.mahalanobis(X))
assert_array_almost_equal(clf.mahalanobis(X), clf.dist_)
assert_almost_equal(clf.score(X, np.ones(100)),
(100 - y_pred[y_pred == -1].size) / 100.)
def test_outlier_detection():
rnd = np.random.RandomState(0)
X = rnd.randn(100, 10)
clf = EllipticEnvelope(contamination=0.1)
assert_raises(NotFittedError, clf.predict, X)
assert_raises(NotFittedError, clf.decision_function, X)
clf.fit(X)
y_pred = clf.predict(X)
decision = clf.decision_function(X, raw_values=True)
decision_transformed = clf.decision_function(X, raw_values=False)
assert_array_almost_equal(decision, clf.mahalanobis(X))
assert_array_almost_equal(clf.mahalanobis(X), clf.dist_)
assert_almost_equal(clf.score(X, np.ones(100)), (100 - y_pred[y_pred == -1].size) / 100.0)
assert sum(y_pred == -1) == sum(decision_transformed < 0)
def test_outlier_detection():
rnd = np.random.RandomState(0)
X = rnd.randn(100, 10)
clf = EllipticEnvelope(contamination=0.1)
assert_raises(NotFittedError, clf.predict, X)
assert_raises(NotFittedError, clf.decision_function, X)
clf.fit(X)
y_pred = clf.predict(X)
decision = clf.decision_function(X, raw_values=True)
decision_transformed = clf.decision_function(X, raw_values=False)
assert_array_almost_equal(decision, clf.mahalanobis(X))
assert_array_almost_equal(clf.mahalanobis(X), clf.dist_)
assert_almost_equal(clf.score(X, np.ones(100)),
(100 - y_pred[y_pred == -1].size) / 100.)
assert (sum(y_pred == -1) == sum(decision_transformed < 0))
def test_elliptic_envelope():
rnd = np.random.RandomState(0)
X = rnd.randn(100, 10)
clf = EllipticEnvelope(contamination=0.1)
assert_raises(NotFittedError, clf.predict, X)
assert_raises(NotFittedError, clf.decision_function, X)
clf.fit(X)
y_pred = clf.predict(X)
scores = clf.score_samples(X)
decisions = clf.decision_function(X)
assert_array_almost_equal(scores, -clf.mahalanobis(X))
assert_array_almost_equal(clf.mahalanobis(X), clf.dist_)
assert_almost_equal(clf.score(X, np.ones(100)),
(100 - y_pred[y_pred == -1].size) / 100.)
assert (sum(y_pred == -1) == sum(decisions < 0))
def calc(self,outliers_fraction):
data, dqs, raw = self.get_data()
clf = EllipticEnvelope(contamination=outliers_fraction)
X = zip(data['Tbandwidth'],data['Tlatency'],data['Tframerate'])
clf.fit(X)
#data['y_pred'] = clf.decision_function(X).ravel()
#data['y_pred'] = clf.decision_function(X).ravel()
#threshold = np.percentile(data['y_pred'],100 * outliers_fraction)
data['MDist']=clf.mahalanobis(X)
#picking "bad" outliers, not good ones
outliers = chi2_outliers(data, [.8,.9,.95], 3)
#print outliers
outliers = [i[i['Tbandwidth']<i['Tlatency']] for i in outliers]
#outliers = data[data['y_pred']<threshold]
#data['y_pred'] = data['y_pred'] > threshold
#outliers = [x[['ticketid','MDist']].merge(raw, how='inner').drop_duplicates() for x in outliers]
#print raw
#outliers = [raw[raw['ticketid'].isin(j['ticketid'])] for j in outliers]
outliers = [k[k['Tframerate']<(k['Tframerate'].mean()+k['Tframerate'].std())] for k in outliers] #making sure we don't remove aberrantly good framrates
outliers = [t.sort_values(by='MDist', ascending=False).drop_duplicates().drop(['Tbandwidth','Tlatency','Tframerate'],axis=1) for t in outliers]
#dqs = raw[raw['ticketid'].isin(dqs['ticketid'])]
#data = data.sort_values('MDist', ascending=False).drop_duplicates()
return outliers, dqs, data.sort_values(by='MDist', ascending=False).drop_duplicates().drop(['Tbandwidth','Tlatency','Tframerate'],axis=1)
def test_elliptic_envelope():
rnd = np.random.RandomState(0)
X = rnd.randn(100, 10)
clf = EllipticEnvelope(contamination=0.1)
assert_raises(NotFittedError, clf.predict, X)
assert_raises(NotFittedError, clf.decision_function, X)
clf.fit(X)
y_pred = clf.predict(X)
scores = clf.score_samples(X)
decisions = clf.decision_function(X)
assert_array_almost_equal(
scores, -clf.mahalanobis(X))
assert_array_almost_equal(clf.mahalanobis(X), clf.dist_)
assert_almost_equal(clf.score(X, np.ones(100)),
(100 - y_pred[y_pred == -1].size) / 100.)
assert(sum(y_pred == -1) == sum(decisions < 0))
def test_outlier_detection():
rnd = np.random.RandomState(0)
X = rnd.randn(100, 10)
clf = EllipticEnvelope(contamination=0.1)
clf.fit(X)
y_pred = clf.predict(X)
assert_array_almost_equal(clf.decision_function(X, raw_mahalanobis=True),
clf.mahalanobis(X - clf.location_))
assert_almost_equal(clf.score(X, np.ones(100)),
(100 - y_pred[y_pred == -1].size) / 100.)
def elliptEnvMethod(data, uniqueTrains, **kwargs):
elp = EllipticEnvelope(support_fraction=1)
elp.fit_predict(data)
# Squared Mahalanobis distances of the points of data
# Note this is the same as using the "elp.dist_" parameter
m_d = elp.mahalanobis(data)
# Get the regular Mahalanobis distances
elp_d = np.sqrt(m_d)
# IMPLEMENT THE AUTOMATED CUT-OFF
SCORE_INCREASE_RATIO = 1.3
sortD = np.sort(elp_d)
sortD = sortD[math.floor(
len(sortD) / 2):] # Get the end half of the sorted list of scores
ratioD = np.array([sortD[i] / sortD[i - 1] for i in range(1, len(sortD))])
# print(f'\nSorted distances: {sortD}\n\n Ratios: {ratioD}')
ind = np.where(ratioD > SCORE_INCREASE_RATIO)
if len(ind[0]) >= 1:
ind = ind[0][0] + 1
SIGMA = sortD[
ind] # Get the score which increases by the score_ratio compared to the previous score
else:
SIGMA = 100.0 # use an arbritary high score as there are no big score jumps
SIGMA = max(SIGMA, 4.0) # Limit the SIGMA function from being too low
# Segment
labels = (elp_d >= SIGMA).astype(int)
# labelOLD = (elp_d > 4.0).astype(int)
if False:
print('.dist_ = {m1}\t .m(data)={}'.format(m2)) #\n {m_d}')
print("Sigma labels: {}".format(labels))
print("\nCovariance = {}".format(elp.covariance_))
if False:
labelColours = 'white'
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(20, 6))
colours = np.array(['blue', 'red'])
ax[0].set_title('Elliptical envelope - Mahalanobis distance',
color='white')
ax[0].plot(elp_d, 'bo', alpha=0.4) #, color=colours[labels])
ax[1].scatter(data[:, 0],
data[:, 1],
s=20,
color=colours[labels],
alpha=0.4)
ax[1].set_title(
'Elliptical envelope (adjusted cutoff={})'.format(SIGMA),
color='white')
# ax[2].scatter(data[:, 0], data[:, 1], s=20, color=colours[labelOLD], alpha=0.4)
# ax[2].set_title('Elliptical envelope (adjusted cutoff={})'.format(4.0), color='white')
for i, a in enumerate(ax.flat):
ax[i].set_xlabel('Mean normalised') #, fontsize = 15.0)
ax[i].set_ylabel('Standard deviation normalised')
# fig1 = plt.figure(figsize=(5,5))
plt.show()
anomalies = [
train for ind, train in enumerate(uniqueTrains) if labels[ind] == 1
]
anomalyDates = []
if kwargs.get('dates', None) is not None:
dates = kwargs.get('dates')
anomalyDates = [dates[i] for i, val in enumerate(labels) if val == 1]
return anomalies, labels, anomalyDates
rot = pcac.components_
pd.DataFrame(rot[0, :], index=cfat.columns).round(3).T
#
pd.DataFrame(rot[1, :], index=cfat.columns).round(3).T
#
from sklearn.covariance import EllipticEnvelope
ee = EllipticEnvelope()
ee.fit(cfat)
#
md = np.sqrt(ee.mahalanobis(cfat))
n = len(md)
ix = np.arange(1, n + 1)
halfq = sp.stats.norm.ppf((n + ix) / (2 * n + 1)),
plt.scatter(halfq, np.sort(md))
plt.xlabel(r'$\chi^2$ quantiles')
plt.ylabel('Mahalanobis distances')
#
xmat = sm.add_constant(cfat)
lmod = sm.OLS(fat.brozek, xmat).fit()
lmod.sumary()
#
import numpy as np
from scipy.io import loadmat
from sklearn.covariance import EmpiricalCovariance, EllipticEnvelope
from sklearn.metrics import accuracy_score, classification_report
cardio_data = loadmat('cardio.mat')
estimator = EmpiricalCovariance()
cov = estimator.fit(cardio_data['X'])
mahal_cov = cov.mahalanobis(cardio_data['X'])
# sort values and extract n maximum values
# number of outliers in cardio data = 176
indexes = np.argpartition(mahal_cov, 176)[-176:]
y_pred = np.zeros(cardio_data['y'].shape)
y_pred[indexes] = 1
print(classification_report(cardio_data['y'], y_pred))
print(accuracy_score(cardio_data['y'], y_pred))
cov = EllipticEnvelope().fit(np.dot(cardio_data['X'].T, cardio_data['X']))
mahal_cov = cov.mahalanobis(cardio_data['X'])
indexes = np.argpartition(mahal_cov, 176)[-176:]
y_pred = np.zeros(cardio_data['y'].shape)
y_pred[indexes] = 1
print(classification_report(cardio_data['y'], y_pred))
print(accuracy_score(cardio_data['y'], y_pred))
fig = plt.figure()
ax = fig.add_subplot(111)
arr[:, 0] /= arr[:, 2]
arr[:, 1] /= arr[:, 2]
a = arr[:, 0:2]
ee = EllipticEnvelope()
try:
ee.fit(a)
except:
continue
dsts = ee.decision_function(a).ravel()
m1, m2 = min(dsts), max(dsts)
maaa = ee.mahalanobis(a)
min_x, max_x = min(arr[:, 0]), max(arr[:, 0])
min_y, max_y = min(arr[:, 1]), max(arr[:, 1])
min_x -= 10
max_x += 10
min_y -= 10
max_y += 10
xx, yy = np.meshgrid(np.linspace(min_x, max_x, 500),
np.linspace(min_y, max_y, 500))
Z = ee.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
ax.contour(xx,