def train():
# initiate the train
try:
# 1-. Take data and configuration
data = pd.read_csv(training_path, index_col=0)
# Read in any configuration stored
with open(param_path, 'r') as tc:
hyper_parameters = json.load(tc)
# 2-. Set up
# instantiate the Isolation Forest model
model = IForest(contamination=hyper_parameters['contamination'],
behaviour='new')
model.fit(data) # fit
# 3-. Save the model
model_name = 'great_model'
with open(os.path.join(model_path, '{}.pkl'.format(model_name)),
'wb') as out:
pickle.dump(model, out, protocol=0)
# consider that the train fails
except Exception as e:
# write the log
trc = traceback.format_exc()
with open(os.path.join(output_path, 'failure'), 'w') as s:
s.write('Exception during train: ' + str(e) + '\n' + trc)
sys.exit(255)
def fit(self, X_train, y_train=None):
"""Fit the model. y is ignored in unsupervised methods.
Parameters
----------
X_train : numpy array of shape (n_samples, n_features)
The input samples.
y_train : Ignored
Not used, present for API consistency by convention.
Returns
-------
self : object
The fitted estimator.
"""
self.model_ = IForest(
n_estimators=self.n_estimators,
max_samples=self.max_samples,
contamination=self.contamination,
max_features=1.,
bootstrap=False,
n_jobs=-1,
behaviour='deprecated', # no use any more in sklean 0.24.
random_state=self.random_state,
verbose=self.verbose)
self.model_.fit(X=X_train)
return self
def dorc(preprocessedData, random_state, outliers_fraction=0.1):
t0 = time.time()
clf = IForest(contamination=outliers_fraction,
random_state=random_state,
n_jobs=-1)
clf.fit(preprocessedData)
scores = clf.decision_function(preprocessedData)
# Apply IQR-based criteria to identify rare cells for further downstream analysis.
q3 = np.percentile(scores, 75)
iqr = stats.iqr(scores)
th = q3 + (1.5 * iqr)
# Select indexes that satisfy IQR-based thresholding criteria.
indIqr = np.where(scores >= th)[0]
print('shape of selected cells : {}'.format(indIqr.shape))
# Create a file with binary predictions
predictions = np.zeros(preprocessedData.shape[0])
predictions[indIqr] = 1 # Replace predictions for rare cells with '1'.
t1 = time.time()
duration = round(t1 - t0, ndigits=4)
print("Total running DoRC time is :" + str(duration) + " s")
return predictions, scores, duration
def densityBased(self):
'''
@brief Function that implements the dependency based component
@param self
@return It returns the vector with the scores of the instances
'''
# Initialize the scores
scores = np.array([0] * len(self.dataset)).astype(float)
for i in range(self.num_iter):
iforest = IForest(contamination=self.contamination,
behaviour="new")
# Number in the interval [50, 1000]
subsample_size = np.random.randint(50, 1001)
sample = []
if subsample_size >= len(self.dataset):
sample = list(range(len(self.dataset)))
else:
# Take the sample and train the model
sample = np.random.choice(len(self.dataset),
size=subsample_size,
replace=False)
iforest.fit(self.dataset[sample])
# Update the score to compute the mean
scores[sample] += iforest.decision_scores_
# Return the mean
scores = scores / self.num_iter
scores = scale(scores)
return scores
def anomaly_detection(data, label):
X = data[data.select_dtypes('number').columns.tolist()]
y = data[label]
y = y.values
X = X.drop([label], axis=1)
sc = StandardScaler()
X = pd.DataFrame(data=sc.fit_transform(X), columns=X.columns)
ifo = IForest(contamination=0.01,
behaviour='new',
n_estimators=1000,
max_samples=1024,
n_jobs=-1,
verbose=1)
ifo.fit(X)
ifo_pred = ifo.labels_
print('ROC score for Isolation forest: ', roc_auc_score(y, ifo_pred))
utilities.plot_outlier_scores(
y,
ifo.decision_scores_,
bw=0.1,
title='Fraud, Isolation forest. (n_estimators={})'.format(
ifo.n_estimators))
ae = AutoEncoder(hidden_neurons=[25, 20, 15, 20, 25],
hidden_activation='relu',
output_activation='sigmoid',
optimizer='adam',
epochs=20,
batch_size=128,
dropout_rate=0.2,
l2_regularizer=0.0,
validation_size=0.1,
preprocessing=False,
verbose=1,
random_state=1,
contamination=0.01)
ae.fit(X)
ae_pred = ae.labels_
print('ROC score for Autoencoder: ', roc_auc_score(y, ae_pred))
utilities.plot_outlier_scores(
y,
ae.decision_scores_,
bw=0.1,
title='Fraud, Autoencoder. (epochs={})'.format(ae.epochs))
# Too long to train, under-sample needed
lof = LOF(n_neighbors=int(y.sum() * 1.3), contamination=0.01, n_jobs=-1)
lof.fit(X)
lof_pred = lof.labels_
print('ROC score for LOF: ', roc_auc_score(y, lof_pred))
utilities.plot_outlier_scores(
y,
lof.decision_scores_,
bw=0.1,
title='Fraud, Local outliers factor. (n_neighbors={})'.format(
lof.n_neighbors))
return y, ifo_pred, ae_pred, lof_pred
class IForestSupervisedKNN(BaseDetector):
def __init__(self, get_top=0.8, if_params={}, knn_params={}):
super(IForestSupervisedKNN, self).__init__()
self.get_top = get_top
self.is_fitted = False
self.iforest = IForest(**if_params)
self.knn = KNN(**knn_params)
def fit(self, X, y=None):
X = check_array(X)
self._set_n_classes(y)
self.iforest.fit(X)
scores = self.iforest.predict_proba(X)[:, 1]
normal_instances = X[np.argsort(scores)[:int(len(X) * self.get_top)]]
self.knn.fit(normal_instances)
self.decision_scores_ = self.decision_function(X)
self._process_decision_scores()
self.is_fitted = True
return self
def decision_function(self, X):
check_is_fitted(self, ['is_fitted'])
return self.knn.decision_function(X)
def iforest(X_train, X_test, Y_train, Y_test):
from pyod.models.iforest import IForest
model = IForest(random_state=0)
model.fit(X_train)
pred = model.predict(X_test)
acc = np.sum(pred == Y_test) / X_test.shape[0]
print(acc)
return (acc * 100)
def __init__(self, get_top=0.8, if_params={}, knn_params={}):
super(IForestSupervisedKNN, self).__init__()
self.get_top = get_top
self.is_fitted = False
self.iforest = IForest(**if_params)
self.knn = KNN(**knn_params)
def do_iforest(x, n_estimators=100, max_samples=512):
clf = IForest(behaviour="new",
n_estimators=n_estimators,
max_samples=max_samples,
random_state=None)
y_pred = clf.fit_predict(x)
scores = clf.decision_function(x)
index = np.where(y_pred == 1)[0]
return clf, scores, index
def remove_outliars(dft, target_col):
ol_model = IForest() #### can be used as a hyperparameter
ol_model.fit(dft.drop(columns=target_col))
dft['is_outliar'] = ol_model.labels_
dft = dft[dft['is_outliar'] != 1]
dft = dft.drop(columns='is_outliar')
print("Completed Outliar Detection - ", datetime.datetime.now())
return dft
def outlier_iforest(data, **kwargs):
import pandas as pd
from pyod.models.iforest import IForest
contamination = float(kwargs.pop('contamination'))
clf = IForest(contamination=contamination)
clf.fit(data)
pred = clf.labels_
df = pd.DataFrame(pred, columns=['is_outlier'])
ret = pd.concat([data, df], axis=1)
return ret
def S2(self):
self.S1()
water_data = self.water_data
result = self.result
# 数据预处理及模型训练
clean_data = water_data[water_data['S1'] == 0]
Y = pd.DataFrame(index=clean_data.index, columns=['S2'])
X_train = np.array(clean_data.iloc[:, 1:12])
name = list(clean_data.iloc[:, 1:12].columns.values)
scaler = preprocessing.StandardScaler().fit(X_train)
X_train = scaler.transform(X_train)
clf1 = IForest(contamination=0.05, max_features=11, bootstrap=True)
clf2 = KNN(contamination=0.05, n_neighbors=100)
clf3 = HBOS(contamination=0.05, n_bins=10)
clf4 = PCA(contamination=0.05)
clf1.fit(X_train)
clf2.fit(X_train)
clf3.fit(X_train)
clf4.fit(X_train)
Y['S2'] = clf1.labels_ * clf2.labels_ * clf3.labels_ * clf4.labels_
water_data = pd.concat([water_data, Y], axis=1)
# water_data.loc[water_data['S2'].isna(),['S2']]=0，将S1中异常的，在S2中标注为0；
result['统计异常'] = water_data['S2'].values
# 寻找异常维度
from sklearn.neighbors import KernelDensity
clean_data = water_data[water_data['S1'] == 0]
dens = pd.DataFrame(index=clean_data.index,
columns=[
'temperature', 'pH', 'EC', 'ORP', 'DO',
'turbidity', 'transparency', 'COD', 'P',
'NH3N', 'flux'
])
for i in dens.columns:
kde = KernelDensity(kernel='gaussian', bandwidth=0.5).fit(
clean_data[i].values.reshape(-1, 1))
dens[i] = np.exp(
kde.score_samples(clean_data[i].values.reshape(-1, 1)))
dens = dens.iloc[:, 0:11].rank()
dens['S2_names'] = dens.idxmin(axis=1)
water_data = pd.concat([water_data, dens['S2_names']], axis=1)
self.water_data = water_data
result['统计异常维度'] = water_data['S2_names'].values
# 存储模型
joblib.dump(scaler, "./water_model/S2_scaler")
joblib.dump(clf1, "./water_model/S2_Iforest")
def setUp(self):
self.n_train = 200
self.n_test = 100
self.contamination = 0.1
self.roc_floor = 0.8
self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
n_train=self.n_train, n_test=self.n_test,
contamination=self.contamination, random_state=42)
self.clf = IForest(contamination=self.contamination, random_state=42)
self.clf.fit(self.X_train)
def setUp(self):
self.n_train = 100
self.n_test = 50
self.contamination = 0.1
self.roc_floor = 0.6
self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
n_train=self.n_train,
n_test=self.n_test,
contamination=self.contamination)
self.clf = IForest(contamination=self.contamination)
self.clf.fit(self.X_train)
def getOutlierIForest(dataset):
'''
@brief Function that executes IForest algorithm on the dataset and obtains the
labels of the dataset indicating which instance is an inlier (0) or outlier (1)
@param dataset Dataset on which to try the algorithm
@return It returns a list of labels 0 means inlier, 1 means outlier
'''
# Initializating the model without verbose
ifor = IForest(verbose=0)
# Fits the data and obtains labels
ifor.fit(dataset)
# Return labels
return ifor.labels_
def detect(self, X, y=None):
"""
:param X: Dataframe
:param y: np.array
:return: outlier scores
"""
rng = np.random.RandomState(42)
# 构造训练样本
n_estimators = 200 # 森林中树的棵数
outliers_fraction = 0.5 # 异常样本比例
clf = IForest(max_samples='auto', random_state=rng, contamination=outliers_fraction, n_estimators=n_estimators)
clf.fit(X)
scores = clf.decision_function(X)
return scores
class IForestPyOD(BaseAlgorithm):
name = "iForest_pyod"
def __init__(self, t=100, psi=256):
self.iforest = IForest(max_samples=psi, n_estimators=t, behaviour="new", contamination=0.1)
def fit(self, X):
self.iforest.fit(X)
def predict(self, X):
return self.iforest.decision_function(X)
def detect_outliers(stocks: list, all_stocks_cip: pd.DataFrame, rules=None):
"""
Returns a dataframe describing those outliers present in stocks based on the provided rules.
"""
if rules is None:
rules = default_point_score_rules()
str_rules = { str(r):r for r in rules }
rows = []
stocks_by_sector_df = stocks_by_sector() # NB: ETFs in watchlist will have no sector
stocks_by_sector_df.index = stocks_by_sector_df['asx_code']
for stock in stocks:
#print("Processing stock: ", stock)
try:
sector = stocks_by_sector_df.at[stock, 'sector_name']
sector_companies = list(stocks_by_sector_df.loc[stocks_by_sector_df['sector_name'] == sector].asx_code)
# day_low_high() may raise KeyError when data is currently being fetched, so it appears here...
day_low_high_df = day_low_high(stock, all_stocks_cip.columns)
except KeyError:
warning(None, "Unable to locate watchlist entry: {} - continuing without it".format(stock))
continue
state = {
'day_low_high_df': day_low_high_df, # never changes each day, so we init it here
'all_stocks_change_in_percent_df': all_stocks_cip,
'stock': stock,
'daily_range_threshold': 0.20, # 20% at either end of the daily range gets a point
}
points_by_rule = defaultdict(int)
for date in all_stocks_cip.columns:
market_avg = all_stocks_cip[date].mean()
sector_avg = all_stocks_cip[date].filter(items=sector_companies).mean()
stock_move = all_stocks_cip.at[stock, date]
state.update({ 'market_avg': market_avg, 'sector_avg': sector_avg,
'stock_move': stock_move, 'date': date })
for rule_name, rule in str_rules.items():
points_by_rule[rule_name] += rule(state)
d = { 'stock': stock }
d.update(points_by_rule)
rows.append(d)
df = pd.DataFrame.from_records(rows)
df = df.set_index('stock')
print(df)
from pyod.models.iforest import IForest
clf = IForest()
clf.fit(df)
scores = clf.predict(df)
results = [row[0] for row, value in zip(df.iterrows(), scores) if value > 0]
#print(results)
print("Found {} outlier stocks".format(len(results)))
return results
def add_other_class(num, size, pad):
res = pd.read_csv("data/train.txt", header=None).values
tif_data = []
for r in tqdm(range(res.shape[0])):
img = get_cell(res[r][1], res[r][2], size)
if img is None:
print("img NOT Exist.", res[r])
continue
img = img.reshape(-1).tolist()
tif_data.append([labels_key[res[r][0]]] + img)
tif_data = np.array(tif_data)
print(tif_data.shape)
np.random.shuffle(tif_data)
clf = IForest()
clf.fit(tif_data[:, 1:])
i = 0
pos = []
false_num = 0
while True:
ix = np.random.randint(pad, dataset.RasterXSize - pad)
iy = np.random.randint(pad, dataset.RasterYSize - pad)
t = get_cell(ix, iy, size)
if t is None:
continue
t = t.reshape(1, -1)
y_test_pred = clf.predict(t)[0] # outlier labels (0 or 1)
if y_test_pred == 1:
i += 1
pos.append(["其他"] + [ix, iy])
print("{}/{} added.".format(i, num))
else:
false_num += 1
print("{}/{} is not include {}.{}. false_num: {}".format(
i, num, ix, iy, false_num))
if i == num:
break
pos = np.concatenate((res, np.array(pos)), axis=0)
print(Counter(pos[:, 0]))
pd.DataFrame(pos).to_csv("data/train_enhance.txt", index=None, header=None)
pos[:, 2] = -1 * (pos[:, 2].astype(np.int))
pd.DataFrame(pos).to_csv("data/train_enhance_view.txt",
index=None,
header=None)
def transform(self, df2: pd.DataFrame) -> pd.DataFrame:
"""Apply the transforms to the dataframe."""
le = LabelEncoder()
df2['mm'] = df2['make'] + ' ' + df2['model']
g_mm_count = df2.groupby(['mm']).count().reset_index()
mm_more_than_100 = g_mm_count[g_mm_count['make'] > 100]['mm']
df2 = df2[df2['mm'].isin(mm_more_than_100)]
dfn3 = df2.copy()
g1 = dfn3.groupby('mm')
clf1 = IForest(contamination=0.01)
flag = [1]
if 1 in flag:
dff1 = pd.DataFrame(columns=[
'idv_id', 'kms_run', 'owners', 'age', 'Popularity Index',
'quoted_price', 'outlier', 'dep_percentage'
])
for idv_id, idv_id_df in g1:
idv_id_df1 = idv_id_df[[
'kms_run', 'owners', 'age', 'quoted_price',
'dep_percentage'
]]
clf1.fit(idv_id_df1)
y_pred = clf1.predict(idv_id_df1)
idv_id_df['outlier'] = y_pred.tolist()
dff1 = pd.concat([dff1, idv_id_df])
outlier_idv_if_dff1 = set(dff1[dff1['outlier'] == 1].index)
df2 = df2.drop(outlier_idv_if_dff1)
df = df2.copy()
X = df[[
'make', 'model', 'city', 'variant', 'owners', 'kms_run', 'age',
'Popularity Index', 'ex_showroom_price', 'fuel_type',
'transmission', 'color'
]]
categorical_feature_mask = X.dtypes == object
categorical_cols = X.columns[categorical_feature_mask].tolist()
self.dic = {}
for i in categorical_cols:
X[i] = le.fit_transform(X[i])
self.dic[i] = dict(zip(le.classes_, le.transform(le.classes_)))
y = df[['dep_percentage']]
aa = pd.concat([X, y], axis=1)
return aa
def define_classifiers(random_state, outliers_fraction):
classifiers = {
'Angle-based Outlier Detector (ABOD)':
ABOD(contamination=outliers_fraction),
'Cluster-based Local Outlier Factor':
CBLOF(contamination=outliers_fraction,
check_estimator=False,
random_state=random_state),
'Feature Bagging':
FeatureBagging(contamination=outliers_fraction,
random_state=random_state),
'Histogram-base Outlier Detection (HBOS)':
HBOS(contamination=outliers_fraction),
'Isolation Forest':
IForest(contamination=outliers_fraction, random_state=random_state),
'K Nearest Neighbors (KNN)':
KNN(contamination=outliers_fraction),
'Local Outlier Factor (LOF)':
LOF(contamination=outliers_fraction),
'Minimum Covariance Determinant (MCD)':
MCD(contamination=outliers_fraction, random_state=random_state),
'One-class SVM (OCSVM)':
OCSVM(contamination=outliers_fraction),
'Principal Component Analysis (PCA)':
PCA(contamination=outliers_fraction, random_state=random_state)
}
return classifiers
def __load_classifiers(self):
outliers_fraction = 0.05
random_state = np.random.RandomState(0)
classifiers = {
'Cluster-based Local Outlier Factor (CBLOF)':
CBLOF(contamination=outliers_fraction,
check_estimator=False,
random_state=random_state),
'Feature Bagging':
FeatureBagging(LOF(n_neighbors=35),
contamination=outliers_fraction,
random_state=random_state),
'Histogram-base Outlier Detection (HBOS)':
HBOS(contamination=outliers_fraction),
'Isolation Forest':
IForest(contamination=outliers_fraction,
random_state=random_state),
'K Nearest Neighbors (KNN)':
KNN(contamination=outliers_fraction),
'Average KNN':
KNN(method='mean', contamination=outliers_fraction),
'Local Outlier Factor (LOF)':
LOF(n_neighbors=35, contamination=outliers_fraction),
'Minimum Covariance Determinant (MCD)':
MCD(contamination=outliers_fraction, random_state=random_state),
'One-class SVM (OCSVM)':
OCSVM(contamination=outliers_fraction),
}
return classifiers
def train_model(X_train, contamination, model):
"""
Train the model based on the user's choice (KNN, IForest, OCSVM).
Parameters
----------
X_train : list of shape (n_train, n_features) containing the training set with only features.
contamination : float representing the expected proportion of anomalies in the training set.
model : string, claiming the model to use for evaluating the confidence. It can be one of: KNN, IForest, OCSVM.
Returns
----------
clf : obj with the model trained on the training set.
"""
np.random.seed(331)
n = np.shape(X_train)[0]
if model == 'KNN':
clf = KNN(n_neighbors=max(np.int(n*contamination),1), contamination = contamination).fit(X_train)
elif model == 'IForest':
clf = IForest(contamination = contamination, random_state = 331).fit(X_train)
elif model == 'OCSVM':
clf = OCSVM(contamination = contamination).fit(X_train)
return clf
def construct_raw_base_estimators():
from pyod.models.knn import KNN
from pyod.models.lof import LOF
from pyod.models.cblof import CBLOF
from pyod.models.hbos import HBOS
from pyod.models.iforest import IForest
from pyod.models.abod import ABOD
from pyod.models.ocsvm import OCSVM
estimator_list = []
# predefined range of n_neighbors for KNN, AvgKNN, and LOF
k_range = [3, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
for k in k_range:
estimator_list.append(
KNN(n_neighbors=k, method="largest", contamination=0.05))
estimator_list.append(
KNN(n_neighbors=k, method="mean", contamination=0.05))
estimator_list.append(LOF(n_neighbors=k, contamination=0.05))
# predefined range of nu for one-class svm
nu_range = [0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.99]
for nu in nu_range:
estimator_list.append(OCSVM(nu=nu, contamination=0.05))
# predefined range for number of estimators in isolation forests
n_range = [10, 20, 50, 70, 100, 150, 200, 250]
for n in n_range:
estimator_list.append(
IForest(n_estimators=n, random_state=42, contamination=0.05))
return estimator_list
def train_model(X, Y, contamination, name, from_scratch=True):
model_dir = './model'
if not os.path.exists(model_dir):
os.mkdir(model_dir)
file_name = name + '.pkl'
if from_scratch:
if name == 'ocsvm':
model = OCSVM(contamination=contamination)
model.fit(X)
elif name == 'iforest':
model = IForest(contamination=contamination)
model.fit(X)
elif name == 'lof':
model = LOF(contamination=contamination)
model.fit(X)
elif name == 'knn':
model = KNN(contamination=contamination)
model.fit(X)
elif name == 'xgbod':
model = XGBOD(contamination=contamination)
model.fit(X, Y)
save(model, model_dir, file_name)
else:
model = load(model_dir, file_name)
return model
def calculate(method, total_roc, total_prn, x_train, x_test, y_train, y_test):
if method == 'KNN':
clf = KNN()
elif method == 'CBLOF':
clf = CBLOF()
elif method == 'PCA':
clf = PCA()
else:
clf = IForest()
clf.fit(x_train) # 使用x_train训练检测器clf
# 返回训练数据x_train上的异常标签和异常分值
y_train_pred = clf.labels_ # 返回训练数据上的分类标签 (0: 正常值, 1: 异常值)
y_train_scores = clf.decision_scores_ # 返回训练数据上的异常值 (分值越大越异常)
print("On train Data:")
evaluate_print(method, y_train, y_train_scores)
# 用训练好的clf来预测未知数据中的异常值
y_test_pred = clf.predict(x_test) # 返回未知数据上的分类标签 (0: 正常值, 1: 异常值)
y_test_scores = clf.decision_function(x_test) # 返回未知数据上的异常值 (分值越大越异常)
print("On Test Data:")
evaluate_print(method, y_test, y_test_scores)
y_true = column_or_1d(y_test)
y_pred = column_or_1d(y_test_scores)
check_consistent_length(y_true, y_pred)
roc = np.round(roc_auc_score(y_true, y_pred), decimals=4),
prn = np.round(precision_n_scores(y_true, y_pred), decimals=4)
total_roc.append(roc)
total_prn.append(prn)
class IForestWrapper:
def __init__(self, **kwargs):
self._model = IForest(**kwargs)
def fit(self, X, T):
# unsupervised learning Targets not used
self._model.fit(X)
return self
def predict(self, X):
Y = self._model.predict(X)
return Y
def predict_proba(self, X):
probs = self._model.predict_proba(X)
return probs
def load_classifiers(outliers_fraction):
outliers_fraction = min(0.5, outliers_fraction)
random_state = np.random.RandomState(42)
# Define nine outlier detection tools to be compared
classifiers = {
'Angle-based Outlier Detector (ABOD)':
ABOD(contamination=outliers_fraction),
'Cluster-based Local Outlier Factor (CBLOF)':
CBLOF(contamination=outliers_fraction,
check_estimator=False,
random_state=random_state),
'Feature Bagging':
FeatureBagging(LOF(n_neighbors=35),
contamination=outliers_fraction,
random_state=random_state),
'Histogram-base Outlier Detection (HBOS)':
HBOS(contamination=outliers_fraction),
'Isolation Forest':
IForest(contamination=outliers_fraction,
random_state=random_state,
behaviour="new"),
'K Nearest Neighbors (KNN)':
KNN(contamination=outliers_fraction),
'Average KNN':
KNN(method='mean', contamination=outliers_fraction),
'Local Outlier Factor (LOF)':
LOF(n_neighbors=35, contamination=outliers_fraction),
'Minimum Covariance Determinant (MCD)':
MCD(contamination=outliers_fraction, random_state=random_state),
'One-class SVM (OCSVM)':
OCSVM(contamination=outliers_fraction),
'Principal Component Analysis (PCA)':
PCA(contamination=outliers_fraction, random_state=random_state)
}
return classifiers
def model_init(self, model):
"""Model initialisation of a single model.
"""
if self.model == 'pca':
self.models[model] = PCA(contamination=self.contamination)
elif self.model == 'loda':
self.models[model] = LODA(contamination=self.contamination)
elif self.model == 'iforest':
self.models[model] = IForest(n_estimators=50,
bootstrap=True,
behaviour='new',
contamination=self.contamination)
elif self.model == 'cblof':
self.models[model] = CBLOF(n_clusters=3,
contamination=self.contamination)
elif self.model == 'feature_bagging':
self.models[model] = FeatureBagging(
base_estimator=PCA(contamination=self.contamination),
contamination=self.contamination)
elif self.model == 'copod':
self.models[model] = COPOD(contamination=self.contamination)
elif self.model == 'hbos':
self.models[model] = HBOS(contamination=self.contamination)
else:
self.models[model] = HBOS(contamination=self.contamination)
self.custom_model_scalers[model] = MinMaxScaler()
def setUp(self):
# Define data file and read X and y
# Generate some data if the source data is missing
this_directory = path.abspath(path.dirname(__file__))
mat_file = 'cardio.mat'
try:
mat = loadmat(path.join(*[this_directory, 'data', mat_file]))
except TypeError:
print('{data_file} does not exist. Use generated data'.format(
data_file=mat_file))
X, y = generate_data(train_only=True) # load data
except IOError:
print('{data_file} does not exist. Use generated data'.format(
data_file=mat_file))
X, y = generate_data(train_only=True) # load data
else:
X = mat['X']
y = mat['y'].ravel()
X, y = check_X_y(X, y)
self.X_train, self.X_test, self.y_train, self.y_test = \
train_test_split(X, y, test_size=0.4, random_state=42)
self.base_estimators = [LOF(), LOF(), IForest(), COPOD()]
self.clf = SUOD(base_estimators=self.base_estimators)
self.clf.fit(self.X_train)
self.roc_floor = 0.7
def setUp(self):
self.n_train = 100
self.n_test = 50
self.contamination = 0.1
self.roc_floor = 0.6
self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
n_train=self.n_train, n_test=self.n_test,
contamination=self.contamination, random_state=42)
self.clf = IForest(contamination=self.contamination, random_state=42)
self.clf.fit(self.X_train)
class TestIForest(unittest.TestCase):
def setUp(self):
self.n_train = 100
self.n_test = 50
self.contamination = 0.1
self.roc_floor = 0.6
self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
n_train=self.n_train, n_test=self.n_test,
contamination=self.contamination, random_state=42)
self.clf = IForest(contamination=self.contamination, random_state=42)
self.clf.fit(self.X_train)
def test_sklearn_estimator(self):
check_estimator(self.clf)
def test_parameters(self):
assert_true(hasattr(self.clf, 'decision_scores_') and
self.clf.decision_scores_ is not None)
assert_true(hasattr(self.clf, 'labels_') and
self.clf.labels_ is not None)
assert_true(hasattr(self.clf, 'threshold_') and
self.clf.threshold_ is not None)
assert_true(hasattr(self.clf, '_mu') and
self.clf._mu is not None)
assert_true(hasattr(self.clf, '_sigma') and
self.clf._sigma is not None)
assert_true(hasattr(self.clf, 'estimators_') and
self.clf.estimators_ is not None)
assert_true(hasattr(self.clf, 'estimators_samples_') and
self.clf.estimators_samples_ is not None)
assert_true(hasattr(self.clf, 'max_samples_') and
self.clf.max_samples_ is not None)
def test_train_scores(self):
assert_equal(len(self.clf.decision_scores_), self.X_train.shape[0])
def test_prediction_scores(self):
pred_scores = self.clf.decision_function(self.X_test)
# check score shapes
assert_equal(pred_scores.shape[0], self.X_test.shape[0])
# check performance
assert_greater(roc_auc_score(self.y_test, pred_scores), self.roc_floor)
def test_prediction_labels(self):
pred_labels = self.clf.predict(self.X_test)
assert_equal(pred_labels.shape, self.y_test.shape)
def test_prediction_proba(self):
pred_proba = self.clf.predict_proba(self.X_test)
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_linear(self):
pred_proba = self.clf.predict_proba(self.X_test, method='linear')
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_unify(self):
pred_proba = self.clf.predict_proba(self.X_test, method='unify')
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_parameter(self):
with assert_raises(ValueError):
self.clf.predict_proba(self.X_test, method='something')
def test_fit_predict(self):
pred_labels = self.clf.fit_predict(self.X_train)
assert_equal(pred_labels.shape, self.y_train.shape)
def test_fit_predict_score(self):
self.clf.fit_predict_score(self.X_test, self.y_test)
self.clf.fit_predict_score(self.X_test, self.y_test,
scoring='roc_auc_score')
self.clf.fit_predict_score(self.X_test, self.y_test,
scoring='prc_n_score')
with assert_raises(NotImplementedError):
self.clf.fit_predict_score(self.X_test, self.y_test,
scoring='something')
def test_predict_rank(self):
pred_socres = self.clf.decision_function(self.X_test)
pred_ranks = self.clf._predict_rank(self.X_test)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=3)
assert_array_less(pred_ranks, self.X_train.shape[0] + 1)
assert_array_less(-0.1, pred_ranks)
def test_predict_rank_normalized(self):
pred_socres = self.clf.decision_function(self.X_test)
pred_ranks = self.clf._predict_rank(self.X_test, normalized=True)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=3)
assert_array_less(pred_ranks, 1.01)
assert_array_less(-0.1, pred_ranks)
def tearDown(self):
pass
if __name__ == "__main__":
contamination = 0.1 # percentage of outliers
n_train = 200 # number of training points
n_test = 100 # number of testing points
# Generate sample data
X_train, y_train, X_test, y_test = \
generate_data(n_train=n_train,
n_test=n_test,
n_features=2,
contamination=contamination,
random_state=42)
# train IForest detector
clf_name = 'IForest'
clf = IForest()
clf.fit(X_train)
# get the prediction labels and outlier scores of the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
# evaluate and print the results
print("\nOn Training Data:")
evaluate_print(clf_name, y_train, y_train_scores)
print("\nOn Test Data:")
evaluate_print(clf_name, y_test, y_test_scores)
