def MCD_Score(train_a, test_a, test_b):
mcd = MinCovDet()
mcd.fit(train_a)
mcd_anoscore = mcd.mahalanobis(test_a)
mcd_normalscore = mcd.mahalanobis(test_b)
print("mcd ano score {} mcd normal score {}".format(
mcd_anoscore, mcd_normalscore))
def MCD_ano_score():
print("マハラノビス距離(each MCD) ano score")
mcd = MinCovDet()
mcd.fit(train_normal)
mcd_anoscore = mcd.mahalanobis(test_normal)
mcd_normalscore = mcd.mahalanobis(test_ano)
print("mcd ano score {} mcd normal score {}".format(
mcd_anoscore, mcd_normalscore))
def detect(train_data: np.ndarray, test_data: np.ndarray) -> list:
estimated_covarianvce = MinCovDet().fit(train_data)
train_dist = estimated_covarianvce.mahalanobis(train_data)
np_max = np.max(train_dist)
return [
0 if data <= np_max else 1
for data in estimated_covarianvce.mahalanobis(test_data)
]
def launch_mcd_on_dataset(n_samples, n_features, n_outliers, tol_loc, tol_cov,
tol_support):
rand_gen = np.random.RandomState(0)
data = rand_gen.randn(n_samples, n_features)
# add some outliers
outliers_index = rand_gen.permutation(n_samples)[:n_outliers]
outliers_offset = 10. * \
(rand_gen.randint(2, size=(n_outliers, n_features)) - 0.5)
data[outliers_index] += outliers_offset
inliers_mask = np.ones(n_samples).astype(bool)
inliers_mask[outliers_index] = False
pure_data = data[inliers_mask]
# compute MCD by fitting an object
mcd_fit = MinCovDet(random_state=rand_gen).fit(data)
T = mcd_fit.location_
S = mcd_fit.covariance_
H = mcd_fit.support_
# compare with the estimates learnt from the inliers
error_location = np.mean((pure_data.mean(0) - T) ** 2)
assert (error_location < tol_loc)
error_cov = np.mean((empirical_covariance(pure_data) - S) ** 2)
assert (error_cov < tol_cov)
assert (np.sum(H) >= tol_support)
assert_array_almost_equal(mcd_fit.mahalanobis(data), mcd_fit.dist_)
def outliers_finder(data_frame: pd.DataFrame) -> pd.DataFrame:
"""
Finding and removing outliers
:param data_frame:
:return:
"""
(df_X, df_y) = splitting_dataset(data_frame)
# Define the PCA object
pca = PCA()
# Run PCA on scaled data and obtain the scores array
T = pca.fit_transform(StandardScaler().fit_transform(df_X.values))
# fit a Minimum Covariance Determinant (MCD) robust estimator to data
robust_cov = MinCovDet().fit(T[:, :5])
# Get the Mahalanobis distance
m = robust_cov.mahalanobis(T[:, :5])
data_frame['mahalanobis'] = m
# calculate p-value for each mahalanobis distance
data_frame['p'] = 1 - chi2.cdf(data_frame['mahalanobis'], 3)
data_frame.sort_values('p', ascending=False)
Drops = (data_frame['p'] <= 0.001)
data_frame['Drops'] = (data_frame['p'] <= 0.001)
indexNames = data_frame[data_frame['Drops'] == True].index
print(indexNames.size)
data_frame.drop(indexNames, inplace=True)
return data_frame
def find_outliers_mahalanobis(featMatProjected,
extremeness=2.,
figsize=[8, 8],
saveto=None):
""" A function to determine to return a list of outlier indices using the
Mahalanobis distance.
Outlier threshold = std(Mahalanobis distance) * extremeness degree
[extreme_values=2, very_extreme_values=3 --> according to 68-95-99.7 rule]
"""
import numpy as np
import pandas as pd
import seaborn as sns
from pathlib import Path
from sklearn.covariance import MinCovDet
from matplotlib import pyplot as plt
# NB: Euclidean distance puts more weight than it should on correlated variables
# Chicken and egg situation, we can’t know they are outliers until we calculate
# the stats of the distribution, but the stats of the distribution are skewed by outliers!
# Mahalanobis gets around this by weighting by robust estimation of covariance matrix
# Fit a Minimum Covariance Determinant (MCD) robust estimator to data
robust_cov = MinCovDet().fit(
featMatProjected[:, :10]) # Use the first 10 principal components
# Get the Mahalanobis distance
MahalanobisDist = robust_cov.mahalanobis(featMatProjected[:, :10])
projectedTable = pd.DataFrame(featMatProjected[:,:10],\
columns=['PC' + str(n+1) for n in range(10)])
plt.ioff() if saveto else plt.ion()
plt.close('all')
plt.style.use(CUSTOM_STYLE)
sns.set_style('ticks')
fig, ax = plt.subplots(figsize=figsize)
ax.set_facecolor('#F7FFFF')
plt.scatter(np.array(projectedTable['PC1']),
np.array(projectedTable['PC2']),
c=MahalanobisDist) # colour PCA by Mahalanobis distance
plt.title('Mahalanobis Distance for Outlier Detection', fontsize=20)
plt.colorbar()
ax.grid(False)
if saveto:
saveto.parent.mkdir(exist_ok=True, parents=True)
suffix = Path(saveto).suffix.strip('.')
plt.savefig(saveto, format=suffix, dpi=300)
else:
plt.show()
k = np.std(MahalanobisDist) * extremeness
upper_t = np.mean(MahalanobisDist) + k
outliers = []
for i in range(len(MahalanobisDist)):
if (MahalanobisDist[i] >= upper_t):
outliers.append(i)
print("Outliers found: %d" % len(outliers))
return np.array(outliers)
def launch_mcd_on_dataset(n_samples, n_features, n_outliers, tol_loc, tol_cov,
tol_support):
rand_gen = np.random.RandomState(0)
data = rand_gen.randn(n_samples, n_features)
# add some outliers
outliers_index = rand_gen.permutation(n_samples)[:n_outliers]
outliers_offset = 10. * \
(rand_gen.randint(2, size=(n_outliers, n_features)) - 0.5)
data[outliers_index] += outliers_offset
inliers_mask = np.ones(n_samples).astype(bool)
inliers_mask[outliers_index] = False
pure_data = data[inliers_mask]
# compute MCD by fitting an object
mcd_fit = MinCovDet(random_state=rand_gen).fit(data)
T = mcd_fit.location_
S = mcd_fit.covariance_
H = mcd_fit.support_
# compare with the estimates learnt from the inliers
error_location = np.mean((pure_data.mean(0) - T) ** 2)
assert(error_location < tol_loc)
error_cov = np.mean((empirical_covariance(pure_data) - S) ** 2)
assert(error_cov < tol_cov)
assert(np.sum(H) >= tol_support)
assert_array_almost_equal(mcd_fit.mahalanobis(data), mcd_fit.dist_)
def getMahalanobisRobust(dat, critical_alpha = 0.01, good_rows = np.zeros(0)):
'''Calculate the Mahalanobis distance from the sample vector.'''
if good_rows.size == 0:
good_rows = np.any(~np.isnan(dat), axis=1);
#import pdb
#pdb.set_trace()
try:
robust_cov = MinCovDet().fit(dat[good_rows])
mahalanobis_dist = np.sqrt(robust_cov.mahalanobis(dat))
except ValueError:
#this step will fail if the covariance matrix is not singular. This happens if the data is not
#a unimodal symetric distribution. For example there is too many small noisy particles. Therefore
#I will take a safe option and return zeros in the mahalanobis distance if this is the case.
mahalanobis_dist = np.zeros(dat.shape[0])
#critial distance of the maholanobis distance using the chi-square distirbution
#https://en.wikiversity.org/wiki/Mahalanobis%27_distance
#http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chi2.html
maha_lim = chi2.ppf(1-critical_alpha, dat.shape[1])
outliers = mahalanobis_dist>maha_lim
return mahalanobis_dist, outliers, maha_lim
def _h_getMahalanobisRobust(dat, critical_alpha=0.01, good_rows=np.zeros(0)):
'''Calculate the Mahalanobis distance from the sample vector.'''
if good_rows.size == 0:
good_rows = np.any(~np.isnan(dat), axis=1)
try:
dat2fit = dat[good_rows]
assert not np.any(np.isnan(dat2fit))
robust_cov = MinCovDet().fit(dat2fit)
mahalanobis_dist = np.sqrt(robust_cov.mahalanobis(dat))
except ValueError:
# this step will fail if the covariance matrix is not singular. This happens if the data is not
# a unimodal symetric distribution. For example there is too many small noisy particles. Therefore
# I will take a safe option and return zeros in the mahalanobis
# distance if this is the case.
mahalanobis_dist = np.zeros(dat.shape[0])
# critial distance of the maholanobis distance using the chi-square distirbution
# https://en.wikiversity.org/wiki/Mahalanobis%27_distance
# http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chi2.html
maha_lim = chi2.ppf(1 - critical_alpha, dat.shape[1])
outliers = mahalanobis_dist > maha_lim
return mahalanobis_dist, outliers, maha_lim
class ActionDetector(object):
"""
Publish whether the robot is in action or not to rostopic, by MT method.
NOTE
Before starting to detect action, some waiting time is required.
This is preparation time to calculate mahalanobis distance.
Reaction speed for action detection is a bit late
because spectrum is mean of spectrogram, not right edge of spectrogram
"""
def __init__(self):
# Config for loading no action spectrum (noise data)
rospack = rospkg.RosPack()
self.train_dir = osp.join(rospack.get_path(
'decopin_hand'), 'train_data')
if not osp.exists(self.train_dir):
makedirs(self.train_dir)
self.noise_data_path = osp.join(self.train_dir, 'noise.npy')
if not osp.exists(self.noise_data_path):
rospy.logerr('{} is not found. Exit.'.format(self.noise_data_path))
exit()
no_action_data = np.load(self.noise_data_path)
# extract about 100 data from no_action_data
divide = max(1, len(no_action_data) / 100)
no_action_data = no_action_data[::divide]
# Detect in action or not by mahalanobis distance
self.anormal_threshold = rospy.get_param('~anormal_threshold')
self.mcd = MinCovDet()
self.mcd.fit(no_action_data)
rospy.loginfo('Calc covariance matrix for Mahalanobis distance')
# ROS
self.bridge = CvBridge()
self.pub = rospy.Publisher('~in_action', Bool, queue_size=1)
self.sub = rospy.Subscriber('~raw_spectrogram', Image, self.cb)
def cb(self, msg):
"""
Main process of NoiseSaver class
Publish whether the robot is in action or not
"""
# spectrogram.shape is (height, width) = (spectrum, time)
spectrogram = self.bridge.imgmsg_to_cv2(msg)
self.current_spectrum = np.average(spectrogram, axis=1)
# Check whether current spectrogram is in action or not
spectrum = self.current_spectrum[None]
dist = self.mcd.mahalanobis(spectrum)[0]
info_message = '(mahalanobis distance, threshold) = ({}, {})'.format(
dist, self.anormal_threshold)
if dist < self.anormal_threshold:
self.in_action = False
rospy.loginfo('No action\n' + info_message + '\n')
else:
self.in_action = True
rospy.loginfo('### In action ###\n' + info_message + '\n')
pub_msg = Bool(data=self.in_action)
self.pub.publish(pub_msg)
def mahalanobis_calculate(data, num_pcs):
pca = PCA(num_pcs)
T = pca.fit_transform(data)
# fit a Minimum Covariance Determinant (MCD) robust estimator to data
robust_cov = MinCovDet().fit(T)
# Get the Mahalanobis distance
m = robust_cov.mahalanobis(T)
return m
def as7262_outliers(data, scatter_correction=None):
data_columns = data[as7262_wavelengths]
print(data_columns)
# data_columns.T.plot()
# plt.plot(data_columns.T)
plt.show()
if scatter_correction == "SNV":
data_columns = processing.snv(data_columns)
elif scatter_correction == "MSC":
data_columns, _ = processing.msc(data_columns)
# svm = OneClassSVM().fit_predict(snv_data)
# print(svm)
robust_cov = MinCovDet().fit(data_columns)
mahal_dist = robust_cov.mahalanobis(data_columns)
# mahal_dist = MahalanobisDist(np.array(data_columns), verbose=True)
print(mahal_dist)
zscore(data_columns)
print('+++++')
mean = np.mean(mahal_dist)
std = 3*np.std(mahal_dist)
print(mean, std)
print(mean - std, mean + std)
zscore_mahal = (mahal_dist - mean) / np.std(mahal_dist)
# print(zscore_mahal)
# print(zscore_mahal.max(), zscore_mahal.argmax(), data_columns.loc[zscore_mahal.argmax()])
print('pppp')
print(data_columns)
print(zscore_mahal.argmax())
outliers = data_columns.loc[zscore_mahal > 3].index
outliers = data_columns.iloc[zscore_mahal.argmax()].name
# print(data_columns.loc[zscore_mahal > 3].index)
rows = data_columns.loc[outliers]
# print(data_columns.loc[zscore_mahal.argmax()].name)
print(data_columns.shape)
print(rows)
# print((mahal_dist-mahal_dist.mean()).std())
# print(mahal_dist.std())
# print(mahal_dist.mean() + 3*mahal_dist.std())
# mahal_dist2 = MahalanobisDist(np.array(data_columns), verbose=True)
n, bins, _ = plt.hist(zscore_mahal, bins=40)
plt.show()
# x_hist = np.linspace(min(mahal_dist), max(mahal_dist), 100)
#
# popt, pcov = curve_fit(gauss_function, bins[:len(n)], n, maxfev=100000, p0=[300, 0, 20])
# new_fit = gauss_function(x_hist, *popt)
# plt.plot(x_hist, new_fit, 'r--')
# color = data_columns.shape[0] * ["#000000"]
# color[data_columns.loc[zscore_mahal.argmax()].name] = "#FF0000"
plt.plot(data_columns.T, c="black")
plt.plot(rows.T, c="red")
plt.plot(data_columns.mean(), c="blue", lw=4)
# snv_data.T.plot(color=color)
plt.show()
def mahalanobisDistances(dm):
reduced_data = PCA(n_components=2).fit_transform(dm)
robust_cov = MinCovDet().fit(reduced_data)
emp_cov = EmpiricalCovariance().fit(reduced_data)
fig = plt.figure()
plt.subplots_adjust(hspace=-.1, wspace=.4, top=.95, bottom=.05)
subfig1 = plt.subplot(3, 1, 1)
inlier_plot = subfig1.scatter(reduced_data[:, 0], reduced_data[:, 1], color='black', label='inliers')
subfig1.set_xlim(subfig1.get_xlim()[0], 11.)
subfig1.set_title("Mahalanobis distances of a contaminated data set:")
# Show contours of the distance functions
xx, yy = np.meshgrid(np.linspace(plt.xlim()[0], plt.xlim()[1], 100),
np.linspace(plt.ylim()[0], plt.ylim()[1], 100))
zz = np.c_[xx.ravel(), yy.ravel()]
mahal_emp_cov = emp_cov.mahalanobis(zz)
mahal_emp_cov = mahal_emp_cov.reshape(xx.shape)
emp_cov_contour = subfig1.contour(xx, yy, np.sqrt(mahal_emp_cov),
cmap=plt.cm.PuBu_r,
linestyles='dashed')
mahal_robust_cov = robust_cov.mahalanobis(zz)
mahal_robust_cov = mahal_robust_cov.reshape(xx.shape)
robust_contour = subfig1.contour(xx, yy, np.sqrt(mahal_robust_cov),
cmap=plt.cm.YlOrBr_r, linestyles='dotted')
plt.xticks(())
plt.yticks(())
# Plot the scores for each point
emp_mahal = emp_cov.mahalanobis(reduced_data - np.mean(reduced_data, 0)) ** (0.33)
subfig2 = plt.subplot(2, 2, 3)
plt.yticks(())
robust_mahal = robust_cov.mahalanobis(reduced_data - robust_cov.location_) ** (0.33)
subfig3 = plt.subplot(2, 2, 4)
plt.yticks(())
plt.show()
def mahal_plot(e):
first_half = e[1:len(e) - 1]
second_half = e[2:len(e)]
X = np.array([first_half, second_half])
X = np.transpose(X)
# fit a Minimum Covariance Determinant (MCD) robust estimator to data
robust_cov = MinCovDet().fit(X)
# compare estimators learnt from the full data set with true parameters
emp_cov = EmpiricalCovariance().fit(X)
fig = plt.figure()
# Show data set
subfig1 = plt.subplot(1, 1, 1)
inlier_plot = subfig1.scatter(first_half,
second_half,
color='black',
label='daily diff in homes passed')
subfig1.set_title("Mahalanobis distances of the iid invariants:")
# Show contours of the distance functions
xx, yy = np.meshgrid(np.linspace(plt.xlim()[0],
plt.xlim()[1], 800),
np.linspace(plt.ylim()[0],
plt.ylim()[1], 100))
zz = np.c_[xx.ravel(), yy.ravel()]
mahal_emp_cov = emp_cov.mahalanobis(zz)
mahal_emp_cov = mahal_emp_cov.reshape(xx.shape)
emp_cov_contour = subfig1.contour(xx,
yy,
np.sqrt(mahal_emp_cov),
cmap=plt.cm.PuBu_r,
linestyles='dashed')
mahal_robust_cov = robust_cov.mahalanobis(zz)
mahal_robust_cov = mahal_robust_cov.reshape(xx.shape)
robust_contour = subfig1.contour(xx,
yy,
np.sqrt(mahal_robust_cov),
cmap=plt.cm.YlOrBr_r,
color='red',
linewidth="3")
subfig1.legend([
emp_cov_contour.collections[1], robust_contour.collections[1],
inlier_plot
], ['MLE dist', 'robust dist', 'kpis'],
loc="upper right",
borderaxespad=0)
print(np.corrcoef(first_half, second_half))
return (robust_cov, emp_cov)
def get_outliers(X, chi2thr=0.975, plot=False, figurename=None):
""" detect outliers by Mahalanobis distance
"""
robust_cov = MinCovDet(random_state=100).fit(X)
MD = robust_cov.mahalanobis(X)
n_samples = len(MD)
chi2 = stats.chi2
degrees_of_freedom = X.shape[1]
threshold = chi2.ppf(chi2thr, degrees_of_freedom)
y_pred = MD > threshold
outlierpercent = sum(y_pred) / float(n_samples)
return outlierpercent, y_pred, MD
def l_ratio(X, labels):
''' This is a meassure of how far a cluster is from neighbouring clusters
computing the mahalanobis distance to the closest point that does not
belong to the cluster
ATENTION: the covariance matrix is estimated with the robust
covariance (outliers not taken into account)
Parameters
----------
X : ndarray
Data (assumed to be multivariate normal distributed)
labels : ndarray
Labels
Returns
-------
lr : list, size(number of clusters)
L-ratio for each cluster
'''
lr = list()
# unique labels
unique_l = set(labels).difference([-1])
# if the set is empty, return 0
if len(unique_l)==0:
return -1
# degrees of freedom
df = len(X[0])
# for each cluster
for label in unique_l:
# compute points in cluster
Xi = X[(labels==label)]
# number of spikes in cluster
n = len(Xi)
# compute points out of the cluster
outliers = X[(labels!=label)]
# estimate robust covariance
mcd = MinCovDet().fit(Xi)
# compute mahalanobis distance for outliers
Dmcd = mcd.mahalanobis(outliers)
# compute L-ratio
lr.append(np.sum(1-chi2.cdf(Dmcd,df))/n)
return lr
def RejectOutliers(data, threshold=3):
"""
Rejects nodal outliers based on :threshold: away from the mean based on the
mahalanobis distance
"""
from sklearn.covariance import MinCovDet
clf = MinCovDet()
clf.fit(data)
distances = clf.mahalanobis(data)
outliers = np.where(distances >= threshold)[0]
inliers = np.where(distances < threshold)[0]
return inliers, outliers
def __init__(self, lab_coords_x, lab_coords_y, data, i_panel, delta_scalar, params, verbose=False):
training_data = []
mean_x = flex.mean(lab_coords_x)
mean_y = flex.mean(lab_coords_y)
limit=delta_scalar * 10
for ix in range(len(data)):
if abs(lab_coords_x[ix] - mean_x) > limit: continue
if abs(lab_coords_y[ix] - mean_y) > limit: continue
if abs(data[ix])>1: continue
training_data.append((lab_coords_x[ix],lab_coords_y[ix],data[ix]))
if verbose: print("Training data is less",len(lab_coords_x) - len(training_data),end=" ")
colorcode_set = []
for ix in range(len(data)):
colorcode_set.append((lab_coords_x[ix],lab_coords_y[ix],data[ix]))
from sklearn.covariance import EmpiricalCovariance, MinCovDet
# compare estimators learnt from the full data set with true parameters
emp_cov = EmpiricalCovariance(assume_centered=False, store_precision=True).fit(X=training_data)
# fit a Minimum Covariance Determinant (MCD) robust estimator to data
robust_cov = MinCovDet(assume_centered=False, store_precision=True).fit(X=training_data)
features = ["Δx","Δy","ΔΨ(deg)"]
if verbose:
print("%3d"%i_panel,end=" ")
print("%4d items "%(len(training_data),),end=" ")
for idx_report in range(len(features)):
feature = features[idx_report]
diag_elem = math.sqrt(emp_cov.covariance_[idx_report,idx_report])
if verbose: print( "%s=%7.2f±%6.2f"%(feature, emp_cov.location_[idx_report], diag_elem),end=" ")
if verbose: print("%4d items:"%(flex.bool(robust_cov.support_).count(True)),end=" ")
for idx_report in range(len(features)):
feature = features[idx_report]
diag_elem = math.sqrt(robust_cov.covariance_[idx_report,idx_report])
if verbose: print( "%s=%7.2f±%6.2f"%(feature, robust_cov.location_[idx_report], diag_elem),end=" ")
disc = flex.double(robust_cov.mahalanobis(X=colorcode_set)) # this metric represents malahanobis ** 2
disc_select = disc < (params.residuals.mcd_filter.mahalanobis_distance)**2
if params.residuals.mcd_filter.keep == "outliers":
disc_select = (disc_select==False)
if verbose: print("OK %4.1f%%"%(100*(disc_select.count(True))/len(training_data)))
self.lab_coords_x = lab_coords_x.select(disc_select)
self.lab_coords_y = lab_coords_y.select(disc_select)
self.data = data.select(disc_select)
self.n_input = len(lab_coords_x)
self.n_output = len(self.lab_coords_x)
self.emp_cov = emp_cov
self.rob_cov = robust_cov
def mahalanobis_plot(ctry=None, df=None, weighted=True, inliers=False):
"""
See http://scikit-learn.org/0.13/modules/outlier_detection.html#\
fitting-an-elliptic-envelop
for details.
"""
if df is None and ctry is None:
raise ValueError('Either the country or a dataframe must be supplied')
elif df is None:
df = load_res(ctry, weighted=weighted)
if inliers:
df = get_inliers(df=df)
X = df.values
robust_cov = MinCovDet().fit(X)
#-----------------------------------------------------------------------------
# compare estimators learnt from the full data set with true parameters
emp_cov = EmpiricalCovariance().fit(X)
#-----------------------------------------------------------------------------
# Display results
fig = plt.figure()
fig.subplots_adjust(hspace=-.1, wspace=.4, top=.95, bottom=.05)
#-----------------------------------------------------------------------------
# Show data set
ax1 = fig.add_subplot(1, 1, 1)
ax1.scatter(X[:, 0], X[:, 1], alpha=.5, color='k', marker='.')
ax1.set_title(country_code[ctry])
#-----------------------------------------------------------------------------
# Show contours of the distance functions
xx, yy = np.meshgrid(np.linspace(ax1.get_xlim()[0], ax1.get_xlim()[1],
100),
np.linspace(ax1.get_ylim()[0], ax1.get_ylim()[1],
100))
zz = np.c_[xx.ravel(), yy.ravel()]
#-----------------------------------------------------------------------------
mahal_emp_cov = emp_cov.mahalanobis(zz)
mahal_emp_cov = mahal_emp_cov.reshape(xx.shape)
emp_cov_contour = ax1.contour(xx, yy, np.sqrt(mahal_emp_cov),
cmap=plt.cm.PuBu_r,
linestyles='dashed')
#-----------------------------------------------------------------------------
mahal_robust_cov = robust_cov.mahalanobis(zz)
mahal_robust_cov = mahal_robust_cov.reshape(xx.shape)
robust_contour = ax1.contour(xx, yy, np.sqrt(mahal_robust_cov),
cmap=plt.cm.YlOrBr_r, linestyles='dotted')
ax1.legend([emp_cov_contour.collections[1], robust_contour.collections[1]],
['MLE dist', 'robust dist'],
loc="upper right", borderaxespad=0)
ax1.grid()
return (fig, ax1, ctry)
def calcurate_mahalabinos_distance(TCKname):
length_stats = TCKname+"-stat.txt"
tdi_stats = TCKname+"-tdi.txt"
cur_stats = TCKname+"-cur.txt"
length =np.loadtxt(length_stats, delimiter = '\t')
tdi = np.loadtxt(tdi_stats, delimiter = '\t')
tdi = np.reciprocal(tdi)
cur = np.loadtxt(cur_stats, delimiter = '\t')
n_samples = length.shape[0]
tdi = np.reshape(tdi, (n_samples,1))
cur = np.reshape(cur, (n_samples,1))
length = np.reshape(length, (n_samples,1))
X = np.hstack((length, tdi, cur))
robust_cov = MinCovDet().fit(X)
robust_mahal = robust_cov.mahalanobis(X - robust_cov.location_)
return robust_mahal
def ComputeMahalanobisDistance(data):
"""Compute MahalanobisDistance and return as DataFrame
Parameters:
data (DataFrame): Pandas DataFrame
Returns:
DataFrame: contains mahalanobis distances with indices from data
"""
rob_cov = MinCovDet().fit(data)
distances = rob_cov.mahalanobis(data)
distances = np.sqrt(distances)
df = pd.DataFrame(data=distances,
columns={'distance'},
index=data.index.values)
return df
def pre_screen(self, var, disp, thresh=10):
"""Uses Minimum Covariance Determinand / Mahalanobis distance ideas to detect outliers, loosely based on :cite:`chawla_k-means:_2013`.
"""
fx = var.columns.names.index('file')
feat = pd.concat((var.mean(), var.std()), 1)
mcd = MinCovDet().fit(feat)
md = mcd.mahalanobis(feat)
s = set(np.where(md > thresh)[0])
k = s.intersection(disp).union(s.intersection({0, var.shape[1]}))
self.dispensable = list(set(disp) - k)
if len(k) > 0:
print(
'\n\nThe following files have been removed from the concatenation as unnecessary outliers:\n'
)
for i in k:
print(var.columns[i][fx])
return var.drop(var.columns[list(k)], axis=1)
def MahalanobisOutliers(featMatProjected, extremeness=2., showplot=True):
""" A function to determine to return a list of outlier indices using the
Mahalanobis distance.
Outlier threshold = std(Mahalanobis distance) * extremeness degree
[extreme_values=2, very_extreme_values=3 --> according to 68-95-99.7 rule]
"""
# NB: Euclidean distance puts more weight than it should on correlated variables
# Chicken and egg situation, we can’t know they are outliers until we calculate
# the stats of the distribution, but the stats of the distribution are skewed outliers!
# Mahalanobis gets around this by weighting by robust estimation of covariance matrix
# Fit a Minimum Covariance Determinant (MCD) robust estimator to data
robust_cov = MinCovDet().fit(
featMatProjected[:, :10]) # Use the first 10 principal components
# TODO: Make PCs to use = min(PCsToUse, FeatMatProjected.shape[1])
# TODO: Check tutorial on Mahalanobis -> check whether distance is always positive - might be throwing away points close to the centre
# Get the Mahalanobis distance
MahalanobisDist = robust_cov.mahalanobis(featMatProjected[:, :10])
# Colour PCA by Mahalanobis distance
if showplot:
plt.close('all')
plt.rc('xtick', labelsize=15)
plt.rc('ytick', labelsize=15)
fig, ax = plt.subplots(figsize=[10, 10])
ax.set_facecolor('white')
plt.scatter(np.array(projectedTable['PC1']),
np.array(projectedTable['PC2']),
c=MahalanobisDist)
plt.title('Mahalanobis Distance for Outlier Detection', fontsize=20)
plt.colorbar()
k = np.std(MahalanobisDist) * extremeness
upper_t = np.mean(MahalanobisDist) + k
outliers = []
for i in range(len(MahalanobisDist)):
# TODO: Vectorise
if (MahalanobisDist[i] >= upper_t):
outliers.append(i)
print("Outliers found: %d" % len(outliers))
return np.array(outliers)
def MahalanobisOutliers(featMatProjected, extremeness=2., showplot=False):
""" A function to determine to return a list of outlier indices using the
Mahalanobis distance.
Outlier threshold = std(Mahalanobis distance) * extremeness degree
[extreme_values=2, very_extreme_values=3 --> according to 68-95-99.7 rule]
"""
# NB: Euclidean distance puts more weight than it should on correlated variables
# Chicken and egg situation, we can’t know they are outliers until we calculate
# the stats of the distribution, but the stats of the distribution are skewed by outliers!
# Mahalanobis gets around this by weighting by robust estimation of covariance matrix
# Fit a Minimum Covariance Determinant (MCD) robust estimator to data
robust_cov = MinCovDet().fit(
featMatProjected[:, :10]) # Use the first 10 principal components
# Get the Mahalanobis distance
MahalanobisDist = robust_cov.mahalanobis(featMatProjected[:, :10])
# Colour PCA by Mahalanobis distance
if showplot:
projectedTable = pd.DataFrame(featMatProjected[:,:10],\
columns=['PC' + str(n+1) for n in range(10)])
plt.close('all')
plt.rc('xtick', labelsize=15)
plt.rc('ytick', labelsize=15)
fig, ax = plt.subplots(figsize=[10, 10])
ax.set_facecolor('#F7FFFF')
plt.scatter(np.array(projectedTable['PC1']),
np.array(projectedTable['PC2']),
c=MahalanobisDist)
plt.title('Mahalanobis Distance for Outlier Detection', fontsize=20)
plt.colorbar()
k = np.std(MahalanobisDist) * extremeness
upper_t = np.mean(MahalanobisDist) + k
outliers = []
for i in range(len(MahalanobisDist)):
if (MahalanobisDist[i] >= upper_t):
outliers.append(i)
print("Outliers found: %d" % len(outliers))
return np.array(outliers)
def outlier_measure(X, method="robust_covar"):
"""
outlier_prediction
"""
if method == "robust_covar":
robust_cov = MinCovDet().fit(X)
measure = np.sqrt(robust_cov.mahalanobis(X))
offset = 3
elif method == "isolation_forest":
clf = IsolationForest(behaviour="new", contamination="auto")
y_pred = clf.fit(X).predict(X)
measure = -clf.score_samples(X)
offset = -clf.offset_
elif method == "local_outlier_detection":
clf = LOF(contamination="auto")
y_pred = clf.fit_predict(X)
measure = -clf.negative_outlier_factor_
offset = -clf.offset_
assignment = np.where(measure < offset, 1, 0)
return measure, offset, assignment
def estimateGaussian(nb_objects_init, nb_objects_final, thr, who, genes, siRNA,
loadingFolder = '../resultData/thrivisions/predictions',
threshold=0.05,):
arr=np.vstack((thr, nb_objects_init, nb_objects_final)).T
#deleting siRNAs that have only one experiment
print len(siRNA)
all_=Counter(siRNA);siRNA = np.array(siRNA)
toDelsi=filter(lambda x: all_[x]==1, all_)
toDelInd=[]
for si in toDelsi:
toDelInd.extend(np.where(siRNA==si)[0])
print len(toDelInd)
dd=dict(zip(range(4), [arr, who, genes, siRNA]))
for array_ in dd:
dd[array_]=np.delete(dd[array_],toDelInd,0 )
arr, who, genes, siRNA = [dd[el] for el in range(4)]
print arr.shape
arr_ctrl=arr[np.where(np.array(genes)=='ctrl')]
ctrlcov=MinCovDet().fit(arr_ctrl)
robdist= ctrlcov.mahalanobis(arr)*np.sign(arr[:,0]-np.mean(arr[:,0]))
new_siRNA=np.array(siRNA)[np.where((genes!='ctrl')&(robdist>0))]
pval,qval =empiricalPvalues(np.absolute(robdist[np.where(genes=='ctrl')])[:, np.newaxis],\
robdist[np.where((genes!='ctrl')&(robdist>0))][:, np.newaxis],\
folder=loadingFolder, name="thrivision", sup=True, also_pval=True)
assert new_siRNA.shape==qval.shape
hits=Counter(new_siRNA[np.where(qval<threshold)[0]])
hits=filter(lambda x: float(hits[x])/all_[x]>=0.5, hits)
gene_hits = [genes[list(siRNA).index(el)] for el in hits]
gene_hits=Counter(gene_hits)
return robdist, pval,qval, hits, gene_hits
color='red', label='outliers')
subfig1.set_xlim(subfig1.get_xlim()[0], 11.)
subfig1.set_title("Mahalanobis distances of a contaminated data set:")
# Show contours of the distance functions
xx, yy = np.meshgrid(np.linspace(pl.xlim()[0], pl.xlim()[1], 100),
np.linspace(pl.ylim()[0], pl.ylim()[1], 100))
zz = np.c_[xx.ravel(), yy.ravel()]
mahal_emp_cov = emp_cov.mahalanobis(zz)
mahal_emp_cov = mahal_emp_cov.reshape(xx.shape)
emp_cov_contour = subfig1.contour(xx, yy, np.sqrt(mahal_emp_cov),
cmap=pl.cm.PuBu_r,
linestyles='dashed')
mahal_robust_cov = robust_cov.mahalanobis(zz)
mahal_robust_cov = mahal_robust_cov.reshape(xx.shape)
robust_contour = subfig1.contour(xx, yy, np.sqrt(mahal_robust_cov),
cmap=pl.cm.YlOrBr_r, linestyles='dotted')
subfig1.legend([emp_cov_contour.collections[1], robust_contour.collections[1],
inlier_plot, outlier_plot],
['MLE dist', 'robust dist', 'inliers', 'outliers'],
loc="upper right", borderaxespad=0)
pl.xticks(())
pl.yticks(())
# Plot the scores for each point
emp_mahal = emp_cov.mahalanobis(X - np.mean(X, 0)) ** (0.33)
subfig2 = pl.subplot(2, 2, 3)
subfig2.boxplot([emp_mahal[:-n_outliers], emp_mahal[-n_outliers:]], widths=.25)
color='red', label='outliers')
subfig1.set_xlim(subfig1.get_xlim()[0], 11.)
subfig1.set_title("Mahalanobis distances of a contaminated data set:")
# Show contours of the distance functions
xx, yy = np.meshgrid(np.linspace(plt.xlim()[0], plt.xlim()[1], 100),
np.linspace(plt.ylim()[0], plt.ylim()[1], 100))
zz = np.c_[xx.ravel(), yy.ravel()]
mahal_emp_cov = emp_cov.mahalanobis(zz)
mahal_emp_cov = mahal_emp_cov.reshape(xx.shape)
emp_cov_contour = subfig1.contour(xx, yy, np.sqrt(mahal_emp_cov),
cmap=plt.cm.PuBu_r,
linestyles='dashed')
mahal_robust_cov = robust_cov.mahalanobis(zz)
mahal_robust_cov = mahal_robust_cov.reshape(xx.shape)
robust_contour = subfig1.contour(xx, yy, np.sqrt(mahal_robust_cov),
cmap=plt.cm.YlOrBr_r, linestyles='dotted')
subfig1.legend([emp_cov_contour.collections[1], robust_contour.collections[1],
inlier_plot, outlier_plot],
['MLE dist', 'robust dist', 'inliers', 'outliers'],
loc="upper right", borderaxespad=0)
plt.xticks(())
plt.yticks(())
# Plot the scores for each point
# emp_mahal = emp_cov.mahalanobis(X - np.mean(X, 0)) ** (0.33)
# subfig2 = plt.subplot(2, 2, 3)
# subfig2.boxplot([emp_mahal[:-n_outliers], emp_mahal[-n_outliers:]], widths=.25)
def main():
parser = argparse.ArgumentParser(
description='Plot outlier-like distances for a 2-dimensional dataset')
parser.add_argument('dataset',
type=argparse.FileType('r'),
help='a CSV file containing the dataset')
parser.add_argument(
'--plot',
type=str,
choices=['train', 'grid'],
default='grid',
help='plot the dataset or a grid evenly distributed over its span')
parser.add_argument('--plotdims',
type=int,
choices=[2, 3],
default=2,
help='the number of dimensions to plot')
args = parser.parse_args()
X = np.loadtxt(args.dataset, delimiter=',')
fig = plt.figure()
xformer = NullTransformer()
if X.shape[1] > 2:
xformer = PCA(n_components=2)
X = xformer.fit_transform(X)
if args.plotdims == 2:
plt.scatter(X[:, 0], X[:, 1], s=60, linewidth='0')
else:
plt.scatter(X[:, 0], X[:, 1])
plt.show(block=False)
path_to_script = os.path.realpath(__file__)
dir_of_script = os.path.dirname(path_to_script)
dataset_path = dir_of_script + '/outliers.npy'
np.save(dataset_path, X)
###########################################################################
# Train autoencoder with the n samples until convergence. Run
# evenly distributed samples through the autoencoder and compute
# their reconstruction error.
###########################################################################
maxseq_orig = np.max(X)
minseq_orig = np.min(X)
seqrange = np.abs(maxseq_orig - minseq_orig)
maxseq = maxseq_orig + 0.5 * seqrange
minseq = minseq_orig - 0.5 * seqrange
print("minseq", minseq, "maxseq", maxseq)
if args.plot == 'grid':
seq = np.linspace(minseq, maxseq, num=50, endpoint=True)
Xplot = np.array([_ for _ in product(seq, seq)])
else:
Xplot = X
robust_cov = MinCovDet().fit(X)
robust_md = robust_cov.mahalanobis(Xplot)
empirical_cov = EmpiricalCovariance().fit(X)
empirical_md = empirical_cov.mahalanobis(Xplot)
# Assume Xplot is at least 2-dimensional.
if Xplot.shape[1] > 2:
Xplot2d = bh_sne(Xplot)
else:
Xplot2d = Xplot
robust_md01 = robust_md - np.nanmin(robust_md)
robust_md01 = robust_md01 / np.nanmax(robust_md01)
empirical_md01 = empirical_md - np.nanmin(empirical_md)
empirical_md01 = empirical_md01 / np.nanmax(empirical_md01)
fig = plt.figure()
if args.plotdims == 2:
ax = fig.add_subplot(1, 1, 1)
ax.scatter(Xplot2d[:, 0],
Xplot2d[:, 1],
cmap=plt.cm.jet,
c=robust_md01,
s=60,
linewidth='0')
else:
ax = fig.add_subplot(1, 1, 1, projection='3d')
ax.plot_trisurf(Xplot2d[:, 0],
Xplot2d[:, 1],
robust_md01,
cmap=plt.cm.jet,
color=robust_md01)
ax.set_zlabel('Mahalanobis distance')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title('Mahalanobis distance (robust covariance)')
fig = plt.figure()
if args.plotdims == 2:
ax = fig.add_subplot(1, 1, 1)
ax.scatter(Xplot2d[:, 0],
Xplot2d[:, 1],
cmap=plt.cm.jet,
c=empirical_md01,
s=60,
linewidth='0')
else:
ax = fig.add_subplot(1, 1, 1, projection='3d')
ax.plot_trisurf(Xplot2d[:, 0],
Xplot2d[:, 1],
empirical_md01,
cmap=plt.cm.jet,
color=empirical_md01)
ax.set_zlabel('Mahalanobis distance')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title('Mahalanobis distance (empirical covariance)')
enc_dec = [
# tanh encoder, linear decoder
['tanh', 'linear'],
# sigmoid encoder, linear decoder
['sigmoid', 'linear'],
#######################################################################
# The reconstruction error of the autoencoders trained with the
# remaining commented-out pairs don't seem to match Mahalanobis
# distance very well. Feel free to uncomment them to see for
# yourself.
# linear encoder, linear decoder
# ['linear', 'linear'],
# tanh encoder, tanh decoder
# ['tanh', 'tanh'],
# tanh encoder, sigmoid decoder
# ['tanh', 'sigmoid'],
# sigmoid encoder, tanh decoder
# ['sigmoid', 'tanh'],
# sigmoid encoder, sigmoid decoder
# ['sigmoid', 'sigmoid']
#######################################################################
]
for i, act in enumerate(enc_dec):
enc, dec = act
if dec == 'linear':
dec = None
model = train_autoencoder(dataset_path,
act_enc=enc,
act_dec=dec,
nvis=X.shape[1],
nhid=16)
Xshared = theano.shared(np.asarray(Xplot, dtype=theano.config.floatX),
borrow=True)
f = theano.function([], outputs=model.reconstruct(Xshared))
fit = f()
error = reconstruction_error(Xplot, fit)
error01 = error - np.nanmin(error)
error01 = error01 / np.nanmax(error01)
fig = plt.figure()
if args.plotdims == 2:
ax = fig.add_subplot(1, 1, 1)
ax.scatter(Xplot2d[:, 0],
Xplot2d[:, 1],
cmap=plt.cm.jet,
c=error,
s=60,
linewidth='0')
else:
ax = fig.add_subplot(1, 1, 1, projection='3d')
ax.plot_trisurf(Xplot2d[:, 0],
Xplot2d[:, 1],
error,
cmap=plt.cm.jet,
color=error01)
ax.set_zlabel('Reconstruction error')
ax.set_xlabel('x')
ax.set_ylabel('y')
encdec_type = ', '.join(act)
ax.set_title('Reconstruction error (' + encdec_type + ')')
print("Correlation of robust MD and reconstruction error (" +
str(encdec_type) + ") " + str(pearsonr(robust_md, error)))
print("Correlation of empirical MD and reconstruction error (" +
str(encdec_type) + ") " + str(pearsonr(empirical_md, error)))
print("Correlation of robust MD and empirical MD " +
str(pearsonr(robust_md, empirical_md)))
os.remove(dataset_path)
os.remove('outliers.pkl')
plt.show(block=True)
# Show contours of the distance functions
xx, yy = np.meshgrid(np.linspace(plt.xlim()[0],
plt.xlim()[1], 100),
np.linspace(plt.ylim()[0],
plt.ylim()[1], 100))
zz = np.c_[xx.ravel(), yy.ravel()]
mahal_emp_cov = emp_cov.mahalanobis(zz)
mahal_emp_cov = mahal_emp_cov.reshape(xx.shape)
emp_cov_contour = subfig1.contour(xx,
yy,
np.sqrt(mahal_emp_cov),
cmap=plt.cm.PuBu_r,
linestyles='dashed')
mahal_robust_cov = robust_cov.mahalanobis(zz)
mahal_robust_cov = mahal_robust_cov.reshape(xx.shape)
robust_contour = subfig1.contour(xx,
yy,
np.sqrt(mahal_robust_cov),
cmap=plt.cm.YlOrBr_r,
linestyles='dotted')
subfig1.legend([
emp_cov_contour.collections[1], robust_contour.collections[1], inlier_plot,
outlier_plot
], ['MLE dist', 'robust dist', 'inliers', 'outliers'],
loc="upper right",
borderaxespad=0)
plt.xticks(())
plt.yticks(())
class Grid:
def __init__(self, dim=10, noise=0.1, outliers=0):
self.points = create_grid(dim, noise, outliers)
self.polardata = np.zeros((len(self.points), 2))
self.polar_cov = 0
self.inlier_points = np.zeros((len(self.points), 2))
self.inlier_indicies = np.zeros((len(self.points), 1))
self.normalized_points = np.zeros((len(self.points), 2))
self.theta = 0
self.step_size = 1
self.linedata = np.zeros((3 * len(self.points), 2))
self.normalized_point_ids = []
self.bounds = [0, 0, 0, 0]
def step(self, rotation=0):
self.points = rotate(self.points, rotation)
def analyze(self, mahalanobis_tolerance=2):
self.inlier_points = np.zeros((len(self.points), 2))
for id1 in range(len(self.points)):
id2 = closest_point(self.points, self.points[id1], id1)[0]
#keep lines fro plotting purposes
self.linedata[3 * id1] = self.points[id1]
self.linedata[3 * id1 + 1] = self.points[id2]
self.linedata[3 * id1 + 2] = [None, None]
# we are repeating every pi/2, so we compress the angle space by 4x
a = 4 * math.atan2((self.points[id1, 1] - self.points[id2, 1]),
(self.points[id1, 0] - self.points[id2, 0]))
r = np.linalg.norm(self.points[id1] - self.points[id2])
self.polardata[id1] = [r * math.cos(a), r * math.sin(a)]
#find the minimal covariance inlier cluster
self.polar_cov = MinCovDet().fit(self.polardata)
# extract the grid angle and size. angle is divided by 4 because
# we previously scaled it up to repeat every 90 deg
self.theta = math.atan2(-self.polar_cov.location_[1],
self.polar_cov.location_[0]) / 4
self.step_size = np.linalg.norm(self.polar_cov.location_)
# extract inlier points
polar_mahal = self.polar_cov.mahalanobis(self.polardata)**(0.33)
inlier_count = 0
for i in range(len(polar_mahal)):
if polar_mahal[
i] < mahalanobis_tolerance: # stdev tolerance to outliers
self.inlier_points[inlier_count] = self.points[i]
self.inlier_indicies[inlier_count] = i
inlier_count += 1
self.normalized_points = rotate(self.inlier_points[:inlier_count],
-self.theta) / self.step_size
#enumerate grid IDs
origin_id = closest_point(self.normalized_points,
np.mean(self.normalized_points))[0]
self.normalized_points = self.normalized_points - self.normalized_points[
origin_id]
inlier_count = 0
self.bounds = [sys.maxint, sys.maxint, -sys.maxint, -sys.maxint]
for p in self.normalized_points:
x = round(p[0])
y = round(p[1])
d = np.linalg.norm(p - [x, y])
if d < 0.4: #tolerance from unit position
self.normalized_points[inlier_count] = [x, y]
if (x < self.bounds[0]):
self.bounds[0] = x
if (x > self.bounds[2]):
self.bounds[2] = x
if (y < self.bounds[1]):
self.bounds[1] = y
if (y > self.bounds[3]):
self.bounds[3] = y
inlier_count += 1
self.normalized_points = self.normalized_points[:inlier_count]
subfig1.set_title("Mahalanobis distances of a contaminated data set:")
# Show contours of the distance functions
xx, yy = np.meshgrid(np.linspace(plt.xlim()[0], plt.xlim()[1], 800),
np.linspace(plt.ylim()[0], plt.ylim()[1], 100))
zz = np.c_[xx.ravel(), yy.ravel()]
mahal_emp_cov = emp_cov.mahalanobis(zz)
mahal_emp_cov = mahal_emp_cov.reshape(xx.shape)
emp_cov_contour = subfig1.contour(xx, yy, np.sqrt(mahal_emp_cov),
cmap=plt.cm.PuBu_r,
linestyles='dashed')
mahal_robust_cov = robust_cov.mahalanobis(zz)
mahal_robust_cov = mahal_robust_cov.reshape(xx.shape)
robust_contour = subfig1.contour(xx, yy, np.sqrt(mahal_robust_cov),
cmap=plt.cm.YlOrBr_r, linestyles='dotted')
subfig1.legend([emp_cov_contour.collections[1], robust_contour.collections[1],
inlier_plot],
['MLE dist', 'robust dist', 'kpis'],
loc="upper right", borderaxespad=0)
print(np.corrcoef(first_half,second_half))
#%%
full_data = full_data.drop(['Year', 'Month', 'Day', 'Data Quality','Max Temp (°C)',
'Max Temp Flag', 'Min Temp (°C)', 'Min Temp Flag',
'Mean Temp Flag',
'Heat Deg Days Flag',
subfig1.scatter(X[:, 0][-n_outliers:], X[:, 1][-n_outliers:],
color='red', label='outliers')
subfig1.set_xlim(subfig1.get_xlim()[0], 11.)
subfig1.set_title("Mahalanobis distances of a contaminated data set:")
subfig1.legend(loc="upper right")
emp_mahal = emp_cov.mahalanobis(X) ** (0.33)
subfig2 = pl.subplot(2, 2, 3)
subfig2.boxplot([emp_mahal[:-n_outliers], emp_mahal[-n_outliers:]], widths=.25)
subfig2.plot(1.26 * np.ones(n_samples - n_outliers),
emp_mahal[:-n_outliers], '+k', markeredgewidth=1)
subfig2.plot(2.26 * np.ones(n_outliers),
emp_mahal[-n_outliers:], '+k', markeredgewidth=1)
subfig2.axes.set_xticklabels(('inliers', 'outliers'), size=11)
subfig2.set_ylabel(r"$\sqrt[3]{\rm{(Mahal. dist.)}}$")
subfig2.set_title("1. from non-robust estimates\n(Maximum Likelihood)")
robust_mahal = robust_cov.mahalanobis(X) ** (0.33)
subfig3 = pl.subplot(2, 2, 4)
subfig3.boxplot([robust_mahal[:-n_outliers], robust_mahal[-n_outliers:]],
widths=.25)
subfig3.plot(1.26 * np.ones(n_samples - n_outliers),
robust_mahal[:-n_outliers], '+k', markeredgewidth=1)
subfig3.plot(2.26 * np.ones(n_outliers),
robust_mahal[-n_outliers:], '+k', markeredgewidth=1)
subfig3.axes.set_xticklabels(('inliers', 'outliers'), size=11)
subfig3.set_ylabel(r"$\sqrt[3]{\rm{(Mahal. dist.)}}$")
subfig3.set_title("2. from robust estimates\n(Minimum Covariance Determinant)")
pl.show()
def main():
parser = argparse.ArgumentParser(
description='Plot outlier-like distances for a 2-dimensional dataset')
parser.add_argument(
'dataset', type=argparse.FileType('r'),
help='a CSV file containing the dataset')
parser.add_argument(
'--plot', type=str, choices=['train', 'grid'], default='grid',
help='plot the dataset or a grid evenly distributed over its span')
parser.add_argument(
'--plotdims', type=int, choices=[2, 3], default=2,
help='the number of dimensions to plot')
args = parser.parse_args()
X = np.loadtxt(args.dataset, delimiter=',')
fig = plt.figure()
xformer = NullTransformer()
if X.shape[1] > 2:
xformer = PCA(n_components=2)
X = xformer.fit_transform(X)
if args.plotdims == 2:
plt.scatter(X[:, 0], X[:, 1], s=60, linewidth='0')
else:
plt.scatter(X[:, 0], X[:, 1])
plt.show(block=False)
path_to_script = os.path.realpath(__file__)
dir_of_script = os.path.dirname(path_to_script)
dataset_path = dir_of_script + '/outliers.npy'
np.save(dataset_path, X)
###########################################################################
# Train autoencoder with the n samples until convergence. Run
# evenly distributed samples through the autoencoder and compute
# their reconstruction error.
###########################################################################
maxseq_orig = np.max(X)
minseq_orig = np.min(X)
seqrange = np.abs(maxseq_orig - minseq_orig)
maxseq = maxseq_orig + 0.5 * seqrange
minseq = minseq_orig - 0.5 * seqrange
print("minseq", minseq, "maxseq", maxseq)
if args.plot == 'grid':
seq = np.linspace(minseq, maxseq, num=50, endpoint=True)
Xplot = np.array([_ for _ in product(seq, seq)])
else:
Xplot = X
robust_cov = MinCovDet().fit(X)
robust_md = robust_cov.mahalanobis(Xplot)
empirical_cov = EmpiricalCovariance().fit(X)
empirical_md = empirical_cov.mahalanobis(Xplot)
# Assume Xplot is at least 2-dimensional.
if Xplot.shape[1] > 2:
Xplot2d = bh_sne(Xplot)
else:
Xplot2d = Xplot
robust_md01 = robust_md - np.nanmin(robust_md)
robust_md01 = robust_md01 / np.nanmax(robust_md01)
empirical_md01 = empirical_md - np.nanmin(empirical_md)
empirical_md01 = empirical_md01 / np.nanmax(empirical_md01)
fig = plt.figure()
if args.plotdims == 2:
ax = fig.add_subplot(1, 1, 1)
ax.scatter(Xplot2d[:, 0], Xplot2d[:, 1],
cmap=plt.cm.jet, c=robust_md01, s=60, linewidth='0')
else:
ax = fig.add_subplot(1, 1, 1, projection='3d')
ax.plot_trisurf(Xplot2d[:, 0], Xplot2d[:, 1], robust_md01,
cmap=plt.cm.jet, color=robust_md01)
ax.set_zlabel('Mahalanobis distance')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title('Mahalanobis distance (robust covariance)')
fig = plt.figure()
if args.plotdims == 2:
ax = fig.add_subplot(1, 1, 1)
ax.scatter(Xplot2d[:, 0], Xplot2d[:, 1],
cmap=plt.cm.jet, c=empirical_md01, s=60, linewidth='0')
else:
ax = fig.add_subplot(1, 1, 1, projection='3d')
ax.plot_trisurf(Xplot2d[:, 0], Xplot2d[:, 1], empirical_md01,
cmap=plt.cm.jet, color=empirical_md01)
ax.set_zlabel('Mahalanobis distance')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title('Mahalanobis distance (empirical covariance)')
enc_dec = [
# tanh encoder, linear decoder
['tanh', 'linear'],
# sigmoid encoder, linear decoder
['sigmoid', 'linear'],
#######################################################################
# The reconstruction error of the autoencoders trained with the
# remaining commented-out pairs don't seem to match Mahalanobis
# distance very well. Feel free to uncomment them to see for
# yourself.
# linear encoder, linear decoder
# ['linear', 'linear'],
# tanh encoder, tanh decoder
# ['tanh', 'tanh'],
# tanh encoder, sigmoid decoder
# ['tanh', 'sigmoid'],
# sigmoid encoder, tanh decoder
# ['sigmoid', 'tanh'],
# sigmoid encoder, sigmoid decoder
# ['sigmoid', 'sigmoid']
#######################################################################
]
for i, act in enumerate(enc_dec):
enc, dec = act
if dec == 'linear':
dec = None
model = train_autoencoder(dataset_path,
act_enc=enc, act_dec=dec, nvis=X.shape[1], nhid=16)
Xshared = theano.shared(
np.asarray(Xplot, dtype=theano.config.floatX), borrow=True)
f = theano.function([], outputs=model.reconstruct(Xshared))
fit = f()
error = reconstruction_error(Xplot, fit)
error01 = error - np.nanmin(error)
error01 = error01 / np.nanmax(error01)
fig = plt.figure()
if args.plotdims == 2:
ax = fig.add_subplot(1, 1, 1)
ax.scatter(Xplot2d[:, 0], Xplot2d[:, 1],
cmap=plt.cm.jet, c=error, s=60, linewidth='0')
else:
ax = fig.add_subplot(1, 1, 1, projection='3d')
ax.plot_trisurf(Xplot2d[:, 0], Xplot2d[:, 1], error,
cmap=plt.cm.jet, color=error01)
ax.set_zlabel('Reconstruction error')
ax.set_xlabel('x')
ax.set_ylabel('y')
encdec_type = ', '.join(act)
ax.set_title('Reconstruction error (' + encdec_type + ')')
print("Correlation of robust MD and reconstruction error (" +
str(encdec_type) + ") " + str(pearsonr(robust_md, error)))
print("Correlation of empirical MD and reconstruction error (" +
str(encdec_type) + ") " + str(pearsonr(empirical_md, error)))
print("Correlation of robust MD and empirical MD " +
str(pearsonr(robust_md, empirical_md)))
os.remove(dataset_path)
os.remove('outliers.pkl')
plt.show(block=True)
class Grid:
def __init__(self, dim=10, noise=0.1, outliers=0):
self.points = create_grid(dim, noise, outliers)
self.polardata = np.zeros((len(self.points), 2))
self.polar_cov = 0
self.inlier_points = np.zeros((len(self.points), 2))
self.inlier_indicies = np.zeros((len(self.points), 1))
self.normalized_points = np.zeros((len(self.points), 2))
self.theta = 0
self.step_size = 1
self.linedata = np.zeros((3*len(self.points), 2))
self.normalized_point_ids = []
self.bounds = [0, 0, 0, 0]
def step(self, rotation=0):
self.points = rotate(self.points, rotation)
def analyze(self, mahalanobis_tolerance=2):
self.inlier_points = np.zeros((len(self.points), 2))
for id1 in range(len(self.points)):
id2 = closest_point(self.points, self.points[id1], id1)[0]
#keep lines fro plotting purposes
self.linedata[3*id1] = self.points[id1]
self.linedata[3*id1+1] = self.points[id2]
self.linedata[3*id1+2] = [None, None]
# we are repeating every pi/2, so we compress the angle space by 4x
a = 4*math.atan2((self.points[id1, 1] - self.points[id2, 1]), (self.points[id1, 0] - self.points[id2, 0]))
r = np.linalg.norm(self.points[id1] - self.points[id2])
self.polardata[id1] = [r*math.cos(a), r*math.sin(a)]
#find the minimal covariance inlier cluster
self.polar_cov = MinCovDet().fit(self.polardata)
# extract the grid angle and size. angle is divided by 4 because
# we previously scaled it up to repeat every 90 deg
self.theta = math.atan2(-self.polar_cov.location_[1], self.polar_cov.location_[0])/4
self.step_size = np.linalg.norm(self.polar_cov.location_)
# extract inlier points
polar_mahal = self.polar_cov.mahalanobis(self.polardata)**(0.33)
inlier_count = 0
for i in range(len(polar_mahal)):
if polar_mahal[i] < mahalanobis_tolerance: # stdev tolerance to outliers
self.inlier_points[inlier_count] = self.points[i]
self.inlier_indicies[inlier_count] = i
inlier_count += 1
self.normalized_points = rotate(self.inlier_points[:inlier_count], -self.theta)/self.step_size
#enumerate grid IDs
origin_id = closest_point(self.normalized_points, np.mean(self.normalized_points))[0]
self.normalized_points = self.normalized_points - self.normalized_points[origin_id]
inlier_count = 0
self.bounds = [sys.maxint, sys.maxint, -sys.maxint, -sys.maxint]
for p in self.normalized_points:
x = round(p[0])
y = round(p[1])
d = np.linalg.norm(p-[x, y])
if d < 0.4: #tolerance from unit position
self.normalized_points[inlier_count] = [x, y]
if (x < self.bounds[0]):
self.bounds[0] = x
if (x > self.bounds[2]):
self.bounds[2] = x
if (y < self.bounds[1]):
self.bounds[1] = y
if (y > self.bounds[3]):
self.bounds[3] = y
inlier_count += 1
self.normalized_points = self.normalized_points[:inlier_count]
lm2 = ols('word_diff ~ Age + C(Centre_ID)',
data=clean_st,subset=subset).fit()
print(lm2.summary())
# <markdowncell>
# # Snippets. Might come back to this later:
# <codecell>
from scipy.stats import pearsonr
from sklearn.covariance import MinCovDet
# just look at what's interesting for now, and drop the NAs involved
clean = st_v_merged.loc[:,['norm_diff','Interview_Suggested_Ranking_numerical_']]
clean = clean.dropna(axis=0)
# calculate robust covariance estimate, calculate what's too far away
mcd = MinCovDet()
mcd.fit(clean)
pearsonr(clean.iloc[:,0],clean.iloc[:,1])
# <codecell>
d = mcd.mahalanobis(clean)
d.sort()
d
import numpy as np from sklearn.covariance import MinCovDet from heapq import nlargest from load_data import data, norm_data, reformat NUMBER_OF_ANOMALIES = 10 robust_cov = MinCovDet().fit(norm_data) mahal_robust_cov = enumerate(robust_cov.mahalanobis(norm_data)) anomalies = nlargest(NUMBER_OF_ANOMALIES, mahal_robust_cov, key=lambda _: _[1]) print(anomalies)
for k1 in choosen:
a1, a2, a3 = extractStats(k1[2], fr)
a1.extend(a2)
a1.extend(a3)
ab.append(a1)
rr = np.array(ab)
#print(dataNameList_f)
if dataNameList_f:
print('fitting ' + str(len(dataNameList_f)) + ' new data')
mcd.fit(rr[:-1 * nrAnalysis - 1, :])
else:
print('no new data')
arn = mcd.mahalanobis(rr[-1 * nrAnalysis - 1:-1, :] -
mcd.location_)**(0.33)
aro = mcd.mahalanobis(rr[:-1 * nrAnalysis - 1, :] - mcd.location_)**(0.33)
print(np.median(aro[mcd.support_]))
ax1.clear()
ax1.scatter(rr[:-1 * nrAnalysis - 1, [0]],
rr[:-1 * nrAnalysis - 1, [3]],
marker='+')
ax1.scatter(rr[-1 * nrAnalysis - 1:-1, [0]],
rr[-1 * nrAnalysis - 1:-1, [3]],
marker='o',
c='r')
#ax1.scatter(*mcd.location_,c='r',s=4)
class MCD(BaseDetector):
"""Detecting outliers in a Gaussian distributed dataset using
Minimum Covariance Determinant (MCD): robust estimator of covariance.
The Minimum Covariance Determinant covariance estimator is to be applied
on Gaussian-distributed data, but could still be relevant on data
drawn from a unimodal, symmetric distribution. It is not meant to be used
with multi-modal data (the algorithm used to fit a MinCovDet object is
likely to fail in such a case).
One should consider projection pursuit methods to deal with multi-modal
datasets.
First fit a minimum covariance determinant model and then compute the
Mahalanobis distance as the outlier degree of the data
See :cite:`rousseeuw1999fast,hardin2004outlier` for details.
Parameters
----------
contamination : float in (0., 0.5), optional (default=0.1)
The amount of contamination of the data set,
i.e. the proportion of outliers in the data set. Used when fitting to
define the threshold on the decision function.
store_precision : bool
Specify if the estimated precision is stored.
assume_centered : Boolean
If True, the support of the robust location and the covariance
estimates is computed, and a covariance estimate is recomputed from
it, without centering the data.
Useful to work with data whose mean is significantly equal to
zero but is not exactly zero.
If False, the robust location and covariance are directly computed
with the FastMCD algorithm without additional treatment.
support_fraction : float, 0 < support_fraction < 1
The proportion of points to be included in the support of the raw
MCD estimate. Default is None, which implies that the minimum
value of support_fraction will be used within the algorithm:
[n_sample + n_features + 1] / 2
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Attributes
----------
raw_location_ : array-like, shape (n_features,)
The raw robust estimated location before correction and re-weighting.
raw_covariance_ : array-like, shape (n_features, n_features)
The raw robust estimated covariance before correction and re-weighting.
raw_support_ : array-like, shape (n_samples,)
A mask of the observations that have been used to compute
the raw robust estimates of location and shape, before correction
and re-weighting.
location_ : array-like, shape (n_features,)
Estimated robust location
covariance_ : array-like, shape (n_features, n_features)
Estimated robust covariance matrix
precision_ : array-like, shape (n_features, n_features)
Estimated pseudo inverse matrix.
(stored only if store_precision is True)
support_ : array-like, shape (n_samples,)
A mask of the observations that have been used to compute
the robust estimates of location and shape.
decision_scores_ : numpy array of shape (n_samples,)
The outlier scores of the training data.
The higher, the more abnormal. Outliers tend to have higher
scores. This value is available once the detector is
fitted. Mahalanobis distances of the training set (on which
`:meth:`fit` is called) observations.
threshold_ : float
The threshold is based on ``contamination``. It is the
``n_samples * contamination`` most abnormal samples in
``decision_scores_``. The threshold is calculated for generating
binary outlier labels.
labels_ : int, either 0 or 1
The binary labels of the training data. 0 stands for inliers
and 1 for outliers/anomalies. It is generated by applying
``threshold_`` on ``decision_scores_``.
"""
def __init__(self, contamination=0.1, store_precision=True,
assume_centered=False, support_fraction=None,
random_state=None):
super(MCD, self).__init__(contamination=contamination)
self.store_precision = store_precision
self.assume_centered = assume_centered
self.support_fraction = support_fraction
self.random_state = random_state
# noinspection PyIncorrectDocstring
def fit(self, X, y=None):
"""Fit detector. y is ignored in unsupervised methods.
Parameters
----------
X : numpy array of shape (n_samples, n_features)
The input samples.
y : Ignored
Not used, present for API consistency by convention.
Returns
-------
self : object
Fitted estimator.
"""
# Validate inputs X and y (optional)
X = check_array(X)
self._set_n_classes(y)
self.detector_ = MinCovDet(store_precision=self.store_precision,
assume_centered=self.assume_centered,
support_fraction=self.support_fraction,
random_state=self.random_state)
self.detector_.fit(X=X, y=y)
# Use mahalanabis distance as the outlier score
self.decision_scores_ = self.detector_.dist_
self._process_decision_scores()
return self
def decision_function(self, X):
"""Predict raw anomaly score of X using the fitted detector.
The anomaly score of an input sample is computed based on different
detector algorithms. For consistency, outliers are assigned with
larger anomaly scores.
Parameters
----------
X : numpy array of shape (n_samples, n_features)
The training input samples. Sparse matrices are accepted only
if they are supported by the base estimator.
Returns
-------
anomaly_scores : numpy array of shape (n_samples,)
The anomaly score of the input samples.
"""
check_is_fitted(self, ['decision_scores_', 'threshold_', 'labels_'])
X = check_array(X)
# Computer mahalanobis distance of the samples
return self.detector_.mahalanobis(X)
@property
def raw_location_(self):
"""The raw robust estimated location before correction and
re-weighting.
Decorator for scikit-learn MinCovDet attributes.
"""
return self.detector_.raw_location_
@property
def raw_covariance_(self):
"""The raw robust estimated location before correction and
re-weighting.
Decorator for scikit-learn MinCovDet attributes.
"""
return self.detector_.raw_covariance_
@property
def raw_support_(self):
"""A mask of the observations that have been used to compute
the raw robust estimates of location and shape, before correction
and re-weighting.
Decorator for scikit-learn MinCovDet attributes.
"""
return self.detector_.raw_support_
@property
def location_(self):
"""Estimated robust location.
Decorator for scikit-learn MinCovDet attributes.
"""
return self.detector_.location_
@property
def covariance_(self):
"""Estimated robust covariance matrix.
Decorator for scikit-learn MinCovDet attributes.
"""
return self.detector_.covariance_
@property
def precision_(self):
""" Estimated pseudo inverse matrix.
(stored only if store_precision is True)
Decorator for scikit-learn MinCovDet attributes.
"""
return self.detector_.precision_
@property
def support_(self):
"""A mask of the observations that have been used to compute
the robust estimates of location and shape.
Decorator for scikit-learn MinCovDet attributes.
"""
return self.detector_.support_