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test_label_list_convert.py
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test_label_list_convert.py
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"""Test converter between binary labels and event lists
"""
import numpy as np
import pandas as pd
from pandas import Timestamp
from adtk.data import to_events, to_labels
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 test_binary_label_to_list_freq_as_period_not_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=False
)
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("1970-01-05 23:59:59.999999999"),
),
(
Timestamp("1970-01-06 00:00:00"),
Timestamp("1970-01-06 23:59:59.999999999"),
),
(
Timestamp("1970-01-08 00:00:00"),
Timestamp("1970-01-08 23:59:59.999999999"),
),
(
Timestamp("1970-01-09 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("1970-01-08 23:59:59.999999999"),
),
(
Timestamp("1970-01-09 00:00:00"),
Timestamp("1970-01-09 23:59:59.999999999"),
),
(
Timestamp("1970-01-10 00:00:00"),
Timestamp("1970-01-10 23:59:59.999999999"),
),
],
"type3": [],
"type4": [
(
Timestamp("1970-01-01 00:00:00"),
Timestamp("1970-01-01 23:59:59.999999999"),
),
(
Timestamp("1970-01-02 00:00:00"),
Timestamp("1970-01-02 23:59:59.999999999"),
),
(
Timestamp("1970-01-03 00:00:00"),
Timestamp("1970-01-03 23:59:59.999999999"),
),
(
Timestamp("1970-01-04 00:00:00"),
Timestamp("1970-01-04 23:59:59.999999999"),
),
(
Timestamp("1970-01-05 00:00:00"),
Timestamp("1970-01-05 23:59:59.999999999"),
),
(
Timestamp("1970-01-06 00:00:00"),
Timestamp("1970-01-06 23:59:59.999999999"),
),
(
Timestamp("1970-01-07 00:00:00"),
Timestamp("1970-01-07 23:59:59.999999999"),
),
(
Timestamp("1970-01-08 00:00:00"),
Timestamp("1970-01-08 23:59:59.999999999"),
),
(
Timestamp("1970-01-09 00:00:00"),
Timestamp("1970-01-09 23:59:59.999999999"),
),
(
Timestamp("1970-01-10 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)],
freq_as_period=True,
merge_consecutive=False,
)
== anomaly_list_true["type{}".format(i + 1)]
)
def test_binary_label_to_list_freq_not_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=False, merge_consecutive=True
)
anomaly_list_true = {
"type1": [
Timestamp("1970-01-02 00:00:00"),
(
Timestamp("1970-01-05 00:00:00"),
Timestamp("1970-01-06 00:00:00"),
),
(
Timestamp("1970-01-08 00:00:00"),
Timestamp("1970-01-09 00:00:00"),
),
],
"type2": [
Timestamp("1970-01-01 00:00:00"),
(
Timestamp("1970-01-08 00:00:00"),
Timestamp("1970-01-10 00:00:00"),
),
],
"type3": [],
"type4": [
(
Timestamp("1970-01-01 00:00:00"),
Timestamp("1970-01-10 00:00:00"),
)
],
}
assert anomaly_list == anomaly_list_true
for i in range(4):
assert (
to_events(
binary_series["type{}".format(i + 1)],
freq_as_period=False,
merge_consecutive=True,
)
== anomaly_list_true["type{}".format(i + 1)]
)
def test_binary_label_to_list_freq_not_as_period_not_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=False)
anomaly_list_true = {
"type1": [
Timestamp("1970-01-02 00:00:00"),
Timestamp("1970-01-05 00:00:00"),
Timestamp("1970-01-06 00:00:00"),
Timestamp("1970-01-08 00:00:00"),
Timestamp("1970-01-09 00:00:00"),
],
"type2": [
Timestamp("1970-01-01 00:00:00"),
Timestamp("1970-01-08 00:00:00"),
Timestamp("1970-01-09 00:00:00"),
Timestamp("1970-01-10 00:00:00"),
],
"type3": [],
"type4": [
Timestamp("1970-01-01 00:00:00"),
Timestamp("1970-01-02 00:00:00"),
Timestamp("1970-01-03 00:00:00"),
Timestamp("1970-01-04 00:00:00"),
Timestamp("1970-01-05 00:00:00"),
Timestamp("1970-01-06 00:00:00"),
Timestamp("1970-01-07 00:00:00"),
Timestamp("1970-01-08 00:00:00"),
Timestamp("1970-01-09 00:00:00"),
Timestamp("1970-01-10 00:00:00"),
],
}
assert anomaly_list == anomaly_list_true
for i in range(4):
assert (
to_events(
binary_series["type{}".format(i + 1)],
freq_as_period=False,
merge_consecutive=False,
)
== anomaly_list_true["type{}".format(i + 1)]
)
def test_list_to_label_freq_as_period():
anomaly_list = {
"type1": [
(
Timestamp("1970-01-02 00:00:00"),
Timestamp("1970-01-02 00:00:00"),
),
(
Timestamp("1970-01-05 00:00:00"),
Timestamp("1970-01-06 00:00:00"),
),
(
Timestamp("1970-01-08 00:00:00"),
Timestamp("1970-01-09 00:00:00"),
),
],
"type2": [
(
Timestamp("1970-01-01 00:00:00"),
Timestamp("1970-01-01 00:00:00"),
),
(
Timestamp("1970-01-08 00:00:00"),
Timestamp("1970-01-10 00:00:00"),
),
],
"type3": [],
"type4": [
(
Timestamp("1970-01-01 00:00:00"),
Timestamp("1970-01-10 00:00:00"),
)
],
"type5": [
(
Timestamp("1969-12-12 00:00:05"),
Timestamp("1969-12-25 00:00:05"),
),
(
Timestamp("1969-12-27 00:00:05"),
Timestamp("1970-01-01 00:00:05"),
),
(
Timestamp("1970-01-02 00:00:05"),
Timestamp("1970-01-04 00:00:05"),
),
(
Timestamp("1970-01-07 00:00:05"),
Timestamp("1970-01-15 00:00:05"),
),
],
}
labels_true = 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],
[1, 1, 1, 1, 0, 0, 1, 1, 1, 1],
]
).T,
columns=["type{}".format(i + 1) for i in range(5)],
index=pd.date_range(start=0, periods=10, freq="1d"),
).astype(bool)
for i in range(5):
labels = to_labels(
anomaly_list["type{}".format(i + 1)],
pd.date_range(start=0, periods=10, freq="1d"),
freq_as_period=True,
)
pd.testing.assert_series_equal(
labels, labels_true["type{}".format(i + 1)], check_names=False
)
labels = to_labels(
anomaly_list,
pd.date_range(start=0, periods=10, freq="1d"),
freq_as_period=True,
)
pd.testing.assert_frame_equal(
labels.sort_index(axis=1), labels_true.sort_index(axis=1)
)
def test_list_to_label_freq_not_as_period():
anomaly_list = {
"type1": [
Timestamp("1970-01-02 00:00:00"),
(
Timestamp("1970-01-05 00:00:00"),
Timestamp("1970-01-06 00:00:00"),
),
(
Timestamp("1970-01-08 00:00:00"),
Timestamp("1970-01-09 00:00:00"),
),
],
"type2": [
Timestamp("1970-01-01 00:00:00"),
(
Timestamp("1970-01-08 00:00:00"),
Timestamp("1970-01-10 00:00:00"),
),
],
"type3": [],
"type4": [
(
Timestamp("1970-01-01 00:00:00"),
Timestamp("1970-01-10 00:00:00"),
)
],
"type5": [
(
Timestamp("1969-12-12 00:00:05"),
Timestamp("1969-12-25 00:00:05"),
),
(
Timestamp("1969-12-27 00:00:05"),
Timestamp("1970-01-01 00:00:05"),
),
(
Timestamp("1970-01-02 00:00:05"),
Timestamp("1970-01-04 00:00:05"),
),
(
Timestamp("1970-01-07 00:00:05"),
Timestamp("1970-01-15 00:00:05"),
),
],
}
labels_true = 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],
[1, 0, 1, 1, 0, 0, 0, 1, 1, 1],
]
).T,
columns=["type{}".format(i + 1) for i in range(5)],
index=pd.date_range(start=0, periods=10, freq="1d"),
).astype(bool)
for i in range(5):
labels = to_labels(
anomaly_list["type{}".format(i + 1)],
pd.date_range(start=0, periods=10, freq="1d"),
freq_as_period=False,
)
pd.testing.assert_series_equal(
labels, labels_true["type{}".format(i + 1)], check_names=False
)
labels = to_labels(
anomaly_list,
pd.date_range(start=0, periods=10, freq="1d"),
freq_as_period=False,
)
pd.testing.assert_frame_equal(
labels.sort_index(axis=1), labels_true.sort_index(axis=1)
)
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