def test_binary_label_to_list_freq_as_period_merge_consecutive():
binary_series = pd.DataFrame(
np.array(
[
[0, 1, 0, 0, 1, 1, 0, 1, 1, 0],
[1, 0, 0, 0, 0, 0, 0, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
]
).T,
index=pd.date_range(start=0, periods=10, freq="1d"),
columns=["type1", "type2", "type3", "type4"],
)
anomaly_list = to_events(
binary_series, freq_as_period=True, merge_consecutive=True
)
anomaly_list_true = {
"type1": [
(
Timestamp("1970-01-02 00:00:00"),
Timestamp("1970-01-02 23:59:59.999999999"),
),
(
Timestamp("1970-01-05 00:00:00"),
Timestamp("1970-01-06 23:59:59.999999999"),
),
(
Timestamp("1970-01-08 00:00:00"),
Timestamp("1970-01-09 23:59:59.999999999"),
),
],
"type2": [
(
Timestamp("1970-01-01 00:00:00"),
Timestamp("1970-01-01 23:59:59.999999999"),
),
(
Timestamp("1970-01-08 00:00:00"),
Timestamp("1970-01-10 23:59:59.999999999"),
),
],
"type3": [],
"type4": [
(
Timestamp("1970-01-01 00:00:00"),
Timestamp("1970-01-10 23:59:59.999999999"),
)
],
}
assert anomaly_list == anomaly_list_true
for i in range(4):
assert (
to_events(binary_series["type{}".format(i + 1)])
== anomaly_list_true["type{}".format(i + 1)]
)
def arundo_adtk(data_path):
data = get_data(data_path)
data['date'] = data['timestamp'].apply(
lambda i: datetime.fromtimestamp(i)) # 时间转换
s_train = data[['date', 'value']]
# 设置索引项
s_train = s_train.set_index('date')
s_train = validate_series(s_train)
print(s_train)
# plot(s_train)
# STL分解+离群点检测
steps = [("deseasonal", STLDecomposition(freq=20)),
("quantile_ad", QuantileAD(high=0.9997, low=0.005))]
pipeline = Pipeline(steps)
anomalies = pipeline.fit_detect(s_train)
print(anomalies)
# plot(s_train, anomaly_pred=anomalies, ap_color='red', ap_marker_on_curve=True)
# 绘制检测结果]
known_anomalies = data.loc[data['label'] == 1]
known_anomalies = known_anomalies[['date', 'label']]
known_anomalies = known_anomalies.set_index('date')
known_anomalies = to_events(known_anomalies)
print(known_anomalies)
plot(s_train,
anomaly_true=known_anomalies,
anomaly_pred=anomalies,
ap_color='red',
ap_marker_on_curve=True,
at_color="orange")
plt.savefig(img_path + "arundo_adtk.png", dpi=1000)
plt.show()
def test_nan():
s = pd.Series(
[1, 1, 0, 0, 0, np.nan, 1, 1, np.nan, np.nan, 0, 1],
index=pd.date_range(start="2017-1-1", periods=12, freq="D"),
)
anomaly_list = to_events(s)
anomaly_list_true = [
(
Timestamp("2017-01-01 00:00:00"),
Timestamp("2017-01-02 23:59:59.999999999"),
),
(
Timestamp("2017-01-07 00:00:00"),
Timestamp("2017-01-08 23:59:59.999999999"),
),
(
Timestamp("2017-01-12 00:00:00"),
Timestamp("2017-01-12 23:59:59.999999999"),
),
]
assert anomaly_list == anomaly_list_true
df['datetime'] = pd.to_datetime(df['date'])
df = df.set_index('datetime')
df.drop(['date'], axis=1, inplace=True)
df.head()
s_train = df
# the same for the label
df = pd.DataFrame(dti, columns=['date'])
df[1] = (Y)
df['datetime'] = pd.to_datetime(df['date'])
df = df.set_index('datetime')
df.drop(['date'], axis=1, inplace=True)
df.head()
from adtk.data import to_events
known_anomalies = to_events(df)
from adtk.visualization import plot
plot(s_train, anomaly_true=known_anomalies)
plt.plot(Y)
from adtk.detector import SeasonalAD
seasonal_ad = SeasonalAD()
anomalies = seasonal_ad.fit_detect(s_train)
plot(s_train, anomaly_pred=anomalies, ap_color='red', ap_marker_on_curve=True)
from adtk.detector import LevelShiftAD
levelshift_ad = LevelShiftAD()
anomalies = levelshift_ad.fit_detect(s_train)
plot(s_train, anomaly_pred=anomalies, ap_color='red', ap_marker_on_curve=True)
