def test_correct_data_is_marked_as_change_point(self):
input_data = [5, 5, 5, 5, 5, 5, 5, 5]
input_data.extend([7, 7, 7, 7, 7, 7, 7, 7])
X_train = pd.Series(input_data, name="ts")
cpd = IidChangePointDetector(confidence=95,
change_history_length=4) << {
'result': 'ts'
}
data = cpd.fit_transform(X_train)
self.assertEqual(data.loc[8, 'result.Alert'], 1.0)
data = data.loc[data['result.Alert'] == 1.0]
self.assertEqual(len(data), 1)
###############################################################################
# IidChangePointDetector
import pandas as pd
from nimbusml.timeseries import IidChangePointDetector
# Create a sample series with a change
input_data = [5, 5, 5, 5, 5, 5, 5, 5]
input_data.extend([7, 7, 7, 7, 7, 7, 7, 7])
X_train = pd.Series(input_data, name="ts")
cpd = IidChangePointDetector(confidence=95, change_history_length=4) << {
'result': 'ts'
}
data = cpd.fit_transform(X_train)
print(data)
# ts result.Alert result.Raw Score result.P-Value Score result.Martingale Score
# 0 5 0.0 5.0 5.000000e-01 0.001213
# 1 5 0.0 5.0 5.000000e-01 0.001213
# 2 5 0.0 5.0 5.000000e-01 0.001213
# 3 5 0.0 5.0 5.000000e-01 0.001213
# 4 5 0.0 5.0 5.000000e-01 0.001213
# 5 5 0.0 5.0 5.000000e-01 0.001213
# 6 5 0.0 5.0 5.000000e-01 0.001213
# 7 5 0.0 5.0 5.000000e-01 0.001213
# 8 7 1.0 7.0 1.000000e-08 10298.666376 <-- alert is on, predicted changepoint
# 9 7 0.0 7.0 1.328455e-01 33950.164799
# 10 7 0.0 7.0 2.613750e-01 60866.342063
# 11 7 0.0 7.0 3.776152e-01 78362.038772