def execute_ee(df, **kwargs):
"""
Elliptic Envelope を実行し、異常かどうかのラベルを返す。
Parameters
----------
df : pandas.DataFrame of shape (n_objects, n_features)
m_ap30 の表。
Other Parameters
----------------
以下、詳細は
https://scikit-learn.org/stable/modules/generated
/sklearn.covariance.EllipticEnvelope.html
を参照。
support_fraction : float, default 0.9
contamination : float, default 0.01
random_state : int or RandomState instance, default 42
Returns
-------
y_pred : numpy.ndarray
異常かどうかのラベル。
"""
support_fraction = kwargs.get('support_fraction', 0.9)
contamination = kwargs.get('contamination', 0.01)
random_state = kwargs.get('random_state', 42)
clf = EllipticEnvelope(support_fraction=support_fraction,
contamination=contamination,
random_state=random_state)
y_pred = clf.fit_predict(df)
return y_pred
def ee_outliers(col_name):
ee = EllipticEnvelope()
ee_preds = ee.fit_predict(np.array(df_processed[col_name]).reshape(-1,1))
ee_preds_class = ["ok" if i == 1 else "ee_outlier" for i in ee_preds]
df_processed["ee_outlier"] = ee_preds_class
def DetectOutliersUsingEnvelope(self):
data = self.__df[[self.__x, self.__y]].values
clf = EllipticEnvelope()
x_min_value, x_max_value = min(
self.__df[self.__x].values) - self.__factor, max(
self.__df[self.__x].values) + self.__factor
y_min_value, y_max_value = min(
self.__df[self.__y].values) - self.__factor, max(
self.__df[self.__y].values) + self.__factor
xx, yy = np.meshgrid(np.linspace(x_min_value, x_max_value, 500),
np.linspace(y_min_value, y_max_value, 500))
clf.fit(data)
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
pred = clf.fit_predict(data) #Outliers = -1, inliers = 1
if self.__drop_outliers: # Let's drop outliers from dataset!
for index, outlier in enumerate(pred):
if outlier == -1:
self.__df = self.__df.drop(index, axis=0)
return self.__df
else:
return xx, yy, Z
def outlier_detector(df, percentage=0.01):
model_detection = EllipticEnvelope(
# contamination=percentage # outlier percentage. If you want to add manually
random_state=0)
predict_x = model_detection.fit_predict(
df) # prediction [1 or -1] # -1 are outliers
outlier = np.where(predict_x == -1, 1,
0) # convert to 1 or 0 # work as boolean
# Add outlier column in DF
df.loc[df.index, "outlier"] = outlier
df["outlier"] = df["outlier"].astype("bool")
# print summary
df_check = df.copy()
df_check["count"] = 1
df_check = df_check.groupby(["outlier"])["count"].count()
df_check = df_check.to_frame()
sum_total = df_check.iloc[:, 0].sum() # add results in percentage of total
df_check["%_of_Total"] = df_check.iloc[:, 0].apply(
lambda x: x / sum_total) # .astype(str) + '%'
print(df_check)
df_no_outlier = df[df["outlier"] == False]
df_no_outlier = df_no_outlier.iloc[:, :-1]
print(
f"\nAmount of Outliers Removed: {len(df.index) - len(df_no_outlier.index)}"
)
return df_no_outlier
def main():
'''
The procedure contains two simple steps:
- Scale the data to the standard distribution with mean 0 and unit variance.
This might be too simplistic.
- Apply the elliptic envelope. The contamination level is set manually.
'''
domains = []
raw = []
with open(sys.argv[1]) as fhandle:
for line in fhandle:
record = json.loads(line.strip())
for analyser in record['analysers']:
if analyser['analyser'] == 'FeaturesGenerator':
raw.extend(analyser['output'])
if analyser['analyser'] == 'WordSegmentation':
domains.extend(analyser['output'].keys())
if len(raw) != len(domains):
print(record)
sys.exit(0)
x_samples = scale(np.array(raw))
engine = EllipticEnvelope(contamination=0.015, support_fraction=1.0)
y_samples = engine.fit_predict(x_samples)
for index, y_sample in enumerate(y_samples):
if y_sample == -1:
print(domains[index])
def outliers_EllipticEnvelope(df, contamination):
dataset = df.copy()
clf = EllipticEnvelope(contamination=contamination)
df_with_ellip = dataset.join(pd.DataFrame(clf.fit_predict(dataset),
index=dataset.index, columns=['elliptic']), how='left')
return df_with_ellip.loc[df_with_ellip['elliptic'] != 1].index
def elliptic_envelope(df, modelDir, norm_confidence=0.95): from sklearn.covariance import EllipticEnvelope from scipy.stats import normaltest if "ds" in df.columns: del df["ds"] model = EllipticEnvelope() test_stats, p_vals = normaltest(df.values, axis=0) normal_cols = p_vals >= (1 - norm_confidence) df = df.loc[:, normal_cols] if df.shape[1] == 0: return None df.outlier = model.fit_predict(df.values) df.outlier = df.outlier < 0 # 1 if inlier, -1 if outlier return df
def elliptic_envelope(X_train, y_names):
cov = EllipticEnvelope(contamination=0.05)
y_pred = cov.fit_predict(X_train)
plot(X_train, y_pred, "Covariance")
outlier_list = []
for i in range(0, len(y_pred)):
if y_pred[i] == -1:
outlier_list.append(i)
print("Covariance")
for i in range(0, len(outlier_list)):
print(outlier_list[i], y_names[outlier_list[i]])
print("Number of outliers:", len(outlier_list))
def elliptic_envelope(df, modelDir, norm_confidence=0.95):
from sklearn.covariance import EllipticEnvelope
from scipy.stats import normaltest
if "ds" in df.columns:
del df["ds"]
model = EllipticEnvelope()
test_stats, p_vals = normaltest(df.values, axis=0)
normal_cols = p_vals >= (1 - norm_confidence)
df = df.loc[:, normal_cols]
if df.shape[1] == 0:
return None
df.outlier = model.fit_predict(df.values)
df.outlier = df.outlier < 0 # 1 if inlier, -1 if outlier
return df
def detect_anomoly():
firebase = firebase.FirebaseApplication(
'https://hackmit-df9ea.firebaseio.com', None)
result = firebase.get('/Test/Double', None)
items = result.items()
result = np.array(list(result.values())[-60:]).reshape(-1, 1)
# normalize result
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
result2 = scaler.fit_transform(result)
from sklearn.covariance import EllipticEnvelope
clf = EllipticEnvelope(contamination=.3)
y_Pred = clf.fit_predict(result2)
an_outlier = len(list(filter(lambda x: (x < 0), y_Pred))) > 0
if clf.precision_ > 100 and an_outlier:
return True
return False
def filter_outliers(points,
n_estimators=100,
contamination=0.05,
type='isolation_forest'):
if type == 'elliptic':
clf = EllipticEnvelope(contamination=contamination)
elif type == 'isolation_forest':
clf = IsolationForest(n_estimators=n_estimators,
contamination=contamination)
else:
raise ValueError("Unknown outlier filter type")
y = clf.fit_predict(points.numpy())
y = torch.tensor(y)
num_valid = (y > 0).sum()
num_filtered = (y <= 0).sum()
logger.info('filtered points',
num_filtered=num_filtered.item(),
num_valid=num_valid.item())
return y > 0
def get_outlyingness(data, contamination=0.1):
""" Outlier detection from covariance estimation in a Gaussian distributed dataset.
:param data: Data in which to detect outliers. Take care that n_samples > n_features ** 2 .
:type data: pandas.DataFrame
:param contamination: The amount of contamination of the data set, i.e. the proportion of outliers in the data set.
Range is (0, 0.5).
:type contamination: float
:returns: Decision on each row if it's an outlier. And contour array for drawing ellipse in graph.
:rtype: tuple[numpy.ndarray, numpy.ndarray]
"""
robust_cov = EllipticEnvelope(support_fraction=1., contamination=contamination)
outlyingness = robust_cov.fit_predict(data)
decision = (outlyingness-1).astype(bool)
# Visualisation.
xx, yy = np.meshgrid(np.linspace(0, 100, 101),
np.linspace(0, 100, 101))
z = robust_cov.predict(np.c_[xx.ravel(), yy.ravel()])
z = z.reshape(xx.shape)
return decision, z
code='Cluster_LOF_Labels',
title='Cluster Distribution')
LOF_G2_Bar = bar_cluster(df=df_X_train_std,
x='Cluster_LOF_Labels',
code='Cluster_LOF_Labels',
title='Cluster Distribution')
LOF_G3_Bar = bar_cluster(df=df_X_train_std,
x='Cluster_LOF_Labels',
code='Cluster_LOF_Labels',
title='Cluster Distribution')
## EE
EE = EllipticEnvelope(random_state=10, contamination=0.0058).fit(X_train_std)
EE_labels = EE.fit_predict(X_train_std)
## 3D Plot of Training Data
# Create and modify dataframe for the cluster column
df_X_train_std = pd.DataFrame(X_train_std)
df_X_train_std['Cluster_EE'] = pd.Series(EE_labels, index=df_X_train_std.index)
# Rename Cluster label names from EE
cluster_label_names = {1: "Human", -1: "Hacker"}
df_X_train_std['Cluster_EE_Labels'] = df_X_train_std['Cluster_EE'].map(
cluster_label_names)
df_X_train_std.columns = [
'Kill Death Ratio', "Headshot Kill Ratio", 'Win Ratio', "Top 10 Ratio",
'Cluster_EE', 'Cluster_EE_Labels'
]
def establish_model(self):
##更改按钮状态
self.new_model.setEnabled(False)
self.new_model.setText('训练中')
conn = pymysql.connect(host='localhost',
port=3306,
user='******',
password='',
database='resistance')
cs1 = conn.cursor()
all_character = pd.DataFrame()
cs1.execute('use resistance')
print(self.model_data_num.currentText().split('：'))
cs1.execute("select * from {}_{} order by id desc limit {}".format(
datetime.datetime.now().year,
datetime.datetime.now().month,
int(self.model_data_num.currentText().split('：')[1])))
columns_name = cs1.fetchall()
cs1.close()
character_data = pd.DataFrame(np.array(columns_name)).iloc[:, -12:]
print(character_data.shape)
now_time = datetime.datetime.now()
os.makedirs(os.getcwd() + '\\model\\{}'.format(
str(now_time.year) + '-' + str(now_time.month) + '-' +
str(now_time.day) + ' ' + str(now_time.hour) + '_' +
str(now_time.minute) + '_' + str(now_time.second)))
self.new_model_dir = str(now_time.year) + '-' + str(
now_time.month) + '-' + str(now_time.day) + ' ' + str(
now_time.hour) + '_' + str(now_time.minute) + '_' + str(
now_time.second)
##使用LOF进行预测
lof = LocalOutlierFactor(
novelty=True, n_neighbors=10,
contamination=0.028) # LOF模型实例化，0.028表示异常点在所有数据点中所占的比率
# error_lof_index = data_ana[clf.fit_predict(data_ana) == -1].index #将LOF模型判断为异常的点的索引列出
lof_result = lof.fit(character_data)
with open(
os.getcwd() + '\\model\\{}\\lof.model'.format(
str(now_time.year) + '-' + str(now_time.month) + '-' +
str(now_time.day) + ' ' + str(now_time.hour) + '_' +
str(now_time.minute) + '_' + str(now_time.second)),
'wb') as f:
pickle.dump(lof, f)
##使用孤立森林进行预测
ilf = IsolationForest(max_samples=100,
random_state=42,
n_estimators=200,
contamination=0.028) # 模型实例化
ilf_result = ilf.fit_predict(character_data)
with open(
os.getcwd() + '\\model\\{}\\ilf.model'.format(
str(now_time.year) + '-' + str(now_time.month) + '-' +
str(now_time.day) + ' ' + str(now_time.hour) + '_' +
str(now_time.minute) + '_' + str(now_time.second)),
'wb') as f:
pickle.dump(ilf, f)
##使用elf进行预测
elf = EllipticEnvelope(support_fraction=1, contamination=0.028)
elf_result = elf.fit_predict(character_data)
with open(
os.getcwd() + '\\model\\{}\\elf.model'.format(
str(now_time.year) + '-' + str(now_time.month) + '-' +
str(now_time.day) + ' ' + str(now_time.hour) + '_' +
str(now_time.minute) + '_' + str(now_time.second)),
'wb') as f:
pickle.dump(elf, f)
self.new_model_msg.emit('ok')
##更改按钮状态
self.new_model.setEnabled(True)
self.new_model.setText('开始训练')
self.st.stop_thread(self.sever_establish_model_th)
def plot_raw_overview(filename):
event_type = 'all'
if filename.name.startswith('sub-drouwen'):
CHANS = [f'IH0{x + 1}' for x in range(8)]
elif filename.name.startswith('sub-itens'):
CHANS = [f'C0{x + 1}' for x in range(8)]
elif filename.name.startswith('sub-lemmer'):
CHANS = [f'IH{x + 1}' for x in range(8)]
elif filename.name.startswith('sub-som705'):
CHANS = [f'GA0{x + 1}' for x in range(8)] # a bit random
elif filename.name.startswith('sub-ommen'):
CHANS = ['chan1',
'chan2'] # I dont 'understand why I cannot use 'chan64'
elif filename.name.startswith('sub-vledder') or filename.name.startswith(
'sub-ommen'):
CHANS = ['chan1', 'chan64']
elif '_acq-blackrock_' in filename.name:
CHANS = ['chan1', 'chan128']
else:
print('you need to specify reference channel for this test')
return None, None
d = Dataset(filename, bids=True)
event_names, event_onsets = select_events(d, event_type)
is_ecog = d.dataset.task.channels.tsv['type'] == 'ECOG'
is_seeg = d.dataset.task.channels.tsv['type'] == 'SEEG'
chans = array(d.header['chan_name'])[is_ecog | is_seeg]
data = d.read_data(begtime=event_onsets[0],
endtime=event_onsets[-1],
chan=list(chans))
data.data[0][isnan(data.data[0])] = 0 # ignore nan
data = montage(data, ref_chan=CHANS)
freq = frequency(data, taper='hann', duration=2, overlap=0.5)
hist = make_histogram(data, max=250, step=10)
divs = []
fig = plot_hist(hist)
divs.append(to_div(fig))
bad_chans = None
if AUTOMATIC:
from sklearn.covariance import EllipticEnvelope
algorithm = EllipticEnvelope(
contamination=P['data_quality']['histogram']['contamination'])
prediction = algorithm.fit(hist.data[0]).predict(hist.data[0])
new_bad_chans = data.chan[0][prediction == -1]
print('bad channels with histogram / elliptic envelope: ' +
', '.join(new_bad_chans))
bad_chans = set(new_bad_chans)
fig = plot_outliers(hist.chan[0],
algorithm.dist_,
prediction,
yaxis_title='distance',
yaxis_type='log')
divs.append(to_div(fig))
fig = plot_freq(freq)
divs.append(to_div(fig))
if AUTOMATIC:
from sklearn.neighbors import LocalOutlierFactor
algorithm = LocalOutlierFactor(
n_neighbors=P['data_quality']['spectrum']['n_neighbors'])
prediction = algorithm.fit_predict(freq.data[0])
new_bad_chans = data.chan[0][prediction == -1]
print('bad channels with spectrum / local outlier factor: ' +
', '.join(new_bad_chans))
bad_chans |= set(new_bad_chans)
fig = plot_outliers(freq.chan[0],
algorithm.negative_outlier_factor_,
prediction,
yaxis_title='distance',
yaxis_type='linear')
divs.append(to_div(fig))
# we use again the reference channel. Ref channel was handpicked but it might have a weird spectrum
bad_chans -= set(CHANS)
return bad_chans, divs
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import loadmat
from sklearn.covariance import EllipticEnvelope
from sklearn.ensemble import IsolationForest
from sklearn.neighbors import LocalOutlierFactor
from sklearn.decomposition import PCA
data = loadmat('ex8data2.mat')
X = data['X']
e1 = EllipticEnvelope()
labels1 = e1.fit_predict(X)
e2 = LocalOutlierFactor()
labels2 = e2.fit_predict(X)
n_components = 3
pca1 = PCA(n_components=n_components)
Xproj = pca1.fit_transform(X)
plt.figure()
plt.clf()
ax = plt.axes(projection='3d')
# ax.scatter(image_array[:, 0], image_array[:, 1], image_array[:, 2], c=labels, cmap='coolwarm', marker=',')
ax.scatter(Xproj[:, 0], Xproj[:, 1], Xproj[:, 2], marker='o', c=labels1)
SVM = svm.OneClassSVM(gamma='auto')
detected_results_SVM = SVM.fit_predict(featureData)
outliers_SVM = []
for i in range(len(detected_results_SVM)):
if detected_results_SVM[i] < 0:
outliers_SVM.append(i)
SVM_data = np.delete(featureData, (outliers_SVM), axis=0)
output_SVM = np.delete(outputLabels, (outliers_SVM), axis=0)
# Elliptic envelope
EE = EllipticEnvelope()
detected_results_EE = EE.fit_predict(featureData)
outliers_EE = []
for i in range(len(detected_results_EE)):
if detected_results_EE[i] < 0:
outliers_EE.append(i)
EE_data = np.delete(featureData, (outliers_EE), axis=0)
output_EE = np.delete(outputLabels, (outliers_EE), axis=0)
# Compare the outlier results
accuracy_scores = {}
model0 = svm.SVC()
model0.fit(IS_data[0:int(len(IS_data) / 2)],
output_IS[0:int(len(IS_data) / 2)])
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
def findAnomalies(self, saveChart=False, saveEvaluation=False):
outliers_fraction = 0.15
clf = EllipticEnvelope(contamination=outliers_fraction)
predicted_outlier = []
list_of_df = self.dataCollector.getWithAnomaly()
for df in list_of_df:
if df.shape[0] > 0:
data = df.drop(['anomaly', 'changepoint'], axis=1)
self.st_tr_time.append(datetime.datetime.now().timestamp())
prediction = pd.Series(clf.fit_predict(data) * -1, index=df.index) \
.rolling(5) \
.median() \
.fillna(0).replace(-1, 0)
self.en_tr_time.append(datetime.datetime.now().timestamp())
# predicted outliers saving
predicted_outlier.append(prediction)
df['rocov_anomaly'] = prediction
true_outlier = [df.anomaly for df in list_of_df]
if saveChart:
for i in range(len(predicted_outlier)):
plt.figure()
plt.rcParams["font.family"] = "Times New Roman"
csfont = {'fontname': 'Times New Roman'}
plt.xlabel('Time', **csfont)
plt.ylabel('Value', **csfont)
plt.title('Robust covariance On File [{}]'.format(i + 1),
**csfont)
predicted_outlier[i].plot(figsize=(12, 6),
label='predictions',
marker='o',
markersize=5)
true_outlier[i].plot(marker='o', markersize=2)
# data = list_of_df[i]
# plt.scatter(x=data[data['rocov_anomaly'] == data['anomaly']].index,
# y=data[data['rocov_anomaly'] == data['anomaly']]['anomaly'], label='True Prediction'
# , c='g', zorder=4)
# plt.scatter(x=data[data['rocov_anomaly'] != data['anomaly']].index,
# y=data[data['rocov_anomaly'] != data['anomaly']]['anomaly'], label='False Prediction'
# , c='r', zorder=5)
plt.legend(loc='upper right')
plt.savefig(self.path_to_plt +
'anom/rocov-pre-{}.png'.format(i + 1),
format='png')
print('Chart {} is Generated'.format(i + 1))
plt.clf()
plt.close('all')
if saveChart:
ts = 1
for df in list_of_df:
data = df.drop(['anomaly', 'changepoint'], axis=1)
pc = PCA(n_components=2).fit_transform(data)
df[['X', 'Y']] = pc
plt.figure()
sb.set(font='Times New Roman')
sns = sb.scatterplot(data=df,
x='X',
y='Y',
hue='rocov_anomaly',
palette='bright')
sns.set_title(
'The Anomaly Detected By Robust covariance, File {}'.
format(ts))
sns.figure.savefig(self.path_to_plt +
'chart/chart-{}.png'.format(ts))
plt.close('all')
print('The Chart of File {} is Generated.'.format(ts))
ts += 1
if saveEvaluation:
evaluator = Evaluator(true_outlier,
predicted_outlier,
metric='binary',
numenta_time='30 sec')
metrics = evaluator.getConfusionMetrics()
TP = metrics['TP']
TN = metrics['TN']
FP = metrics['FP']
FN = metrics['FN']
print('\n-----------------------------------------------------')
print('Robust covariance Outputs: ')
print(f'\t False Alarm Rate: {round(FP / (FP + TN) * 100, 2)} %')
print(f'\t Missing Alarm Rate: {round(FN / (FN + TP) * 100, 2)} %')
print(
f'\t Accuracy Rate: {round((TP + TN) / (TP + TN + FN + FP) * 100, 2)} %'
)
trainTime = np.array(self.en_tr_time).sum() - np.array(
self.st_tr_time).sum()
print(f'\t Train & Train Time {round(trainTime, 2)}s')
data = {
'far': round(FP / (FP + TN) * 100, 2),
'mar': round(FN / (FN + TP) * 100, 2),
'acc': round((TP + TN) / (TP + TN + FN + FP) * 100, 2),
'tr': trainTime,
'te': 0,
'tp': TP,
'tn': TN,
'fp': FP,
'fn': FN
}
output = OutputWriter(self.path_to_plt, 'RobustCov', data)
output.write()
model4 = LocalOutlierFactor(n_neighbors=200, algorithm="brute",
leaf_size=200, contamination=0.1) # fix
# model5 = DBSCAN()
model6 = EllipticEnvelope(
contamination=0.10, random_state=100, support_fraction=0.1) # fix
# model fitting outlier detection
# print("====== OUTLIER DETECTION =======")
X_train_pred2, X_test_pred2 = model2.fit_predict(
df_X_train), model2.fit_predict(df_X_test)
X_train_pred3, X_test_pred3 = model3.fit_predict(
df_X_train), model3.fit_predict(df_X_test)
X_train_pred4, X_test_pred4 = model4.fit_predict(
df_X_train), model4.fit_predict(df_X_test)
# y_pred5 = model5.fit_predict(df)
X_train_pred6, X_test_pred6 = model6.fit_predict(
df_X_train), model6.fit_predict(df_X_test)
# print("====== NOVELTY DETECTION =======")
# model2.fit(df_X_train), model2.fit(df_X_test)
# novelty_X_train_pred2, novelty_X_test_pred2 = model2.predict(
# df_X_train), model2.predict(df_X_test)
model2.fit(df_X_train)
novelty_X_train_pred2 = model2.predict(df_X_test)
# print("X_train_pred2 bbbbb: ", X_train_pred2)
# print("novelty_X_train_pred2 bbbbb: ", novelty_X_train_pred2)
# with np.printoptions(threshold=np.inf):
# print("X_train_pred2 shape: ", X_train_pred2.size)
from sklearn.covariance import EllipticEnvelope
from sklearn.metrics import mean_absolute_error
# load the dataset
url = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.csv'
df = read_csv(url, header=None)
# retrieve the array
data = df.values
# split into input and output elements
X, y = data[:, :-1], data[:, -1]
# split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)
# summarize the shape of the training dataset
print(X_train.shape, y_train.shape)
# identify outliers in the training dataset
ee = EllipticEnvelope(contamination=0.01)
yhat = ee.fit_predict(X_train)
# select all rows that are not outliers
mask = yhat != -1
X_train, y_train = X_train[mask, :], y_train[mask]
# summarize the shape of the updated training dataset
print(X_train.shape, y_train.shape)
# fit the model
model = LinearRegression()
model.fit(X_train, y_train)
# evaluate the model
yhat = model.predict(X_test)
# evaluate predictions
mae = mean_absolute_error(y_test, yhat)
print('MAE: %.3f' % mae)
def getRobustCovairance(_df): clf = EllipticEnvelope(contamination=OUTLIER_FRACTION) return clf.fit_predict(_df)
_temp = np.ceil(data['Fare'].quantile(0.75) + (IQR * 3))
data_processed.loc[data_processed.Fare > _temp, 'Fare'] = _temp
X_train_processed, X_test_processed, y_train_processed, y_test_processed = train_test_split(
data_processed[['Age', 'Fare']].fillna(0),
data_processed['Survived'],
test_size=0.2)
from sklearn.covariance import EllipticEnvelope
df_outliers = data.copy()
df_outliers = df_outliers.fillna(0)
column_name = 'Fare'
obj = EllipticEnvelope()
_temp = obj.fit_predict(df_outliers[[column_name]])
print(np.unique(_temp, return_counts=True))
central = df_outliers[_temp==1][column_name].mean()
max_val = df_outliers[_temp==1][column_name].max()
min_val = df_outliers[_temp==1][column_name].min()
df_outliers.loc[_temp==-1,[column_name]] = df_outliers.loc[_temp==-1,[column_name]].apply(lambda x: [max_val if y > central else y for y in x])
df_outliers.loc[_temp==-1,[column_name]] = df_outliers.loc[_temp==-1,[column_name]].apply(lambda x: [min_val if y < central else y for y in x])
print(data.shape)
print(df_outliers.shape)
column_name = 'Age'
obj = EllipticEnvelope()
_temp = obj.fit_predict(df_outliers[[column_name]])
print(np.unique(_temp, return_counts=True))
central = df_outliers[_temp==1][column_name].mean()
max_val = df_outliers[_temp==1][column_name].max()
def minimum_covariance_determinant(X_train):
# identify outliers in the training dataset
ee = EllipticEnvelope(contamination=0.01)
yhat = ee.fit_predict(X_train)
return yhat
def get_gauss(db: pd.DataFrame) -> list:
ee = EllipticEnvelope(contamination=0.01)
yhat_gaus = ee.fit_predict(db)
return yhat_gaus == -1
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import loadmat
from sklearn.covariance import EllipticEnvelope
from sklearn.ensemble import IsolationForest
from sklearn.neighbors import LocalOutlierFactor
data = loadmat('ex8data1.mat')
X = data['X']
plt.figure()
estimator = EllipticEnvelope(contamination=.015)
labels = estimator.fit_predict(X)
e1 = IsolationForest()
labels1 = e1.fit_predict(X)
xx, yy = np.meshgrid(np.linspace(min(X[:, 0]), max(X[:, 0]), 150),
np.linspace(min(X[:, 1]), max(X[:, 1]), 150))
# Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()])
# Z = Z.reshape(xx.shape)
# plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors='black', alpha=0.5)
#
# Z1 = e1.predict(np.c_[xx.ravel(), yy.ravel()])
# Z1 = Z1.reshape(xx.shape)
# plt.contour(xx, yy, Z1, levels=[0], linewidths=2, colors='red', alpha=0.2)
