变点检测算法 Python 实现

"""
Reference:

https://techrando.com/2019/08/14/a-brief-introduction-to-change-point-detection-using-python/

https://github.com/kperry2215/change_point_detection

"""


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# import eia
import changefinder
# import ruptures as rpt

#
# def retrieve_time_series(api, series_ID):
#     """
#     Return the time series dataframe, based on API and unique Series ID
#     api: API that we're connected to
#     series_ID: string. Name of the series that we want to pull from the EIA API
#     """
#     # Retrieve Data By Series ID
#     series_search = api.data_by_series(series=series_ID)
#     ##Create a pandas dataframe from the retrieved time series
#     df = pd.DataFrame(series_search)
#     return df


if __name__ == "__main__":
    # # Create EIA API using your specific API key
    # api_key = 'YOUR API KEY HERE'
    # api = eia.API(api_key)
    #
    # # Pull the oil WTI price data
    # series_ID = 'PET.RWTC.D'
    # price_df = retrieve_time_series(api, series_ID)
    # price_df.reset_index(level=0, inplace=True)
    # # Rename the columns for easer analysis
    # price_df.rename(columns={'index': 'Date',
    #                          price_df.columns[1]: 'WTI_Price'},
    #                 inplace=True)
    # # Format the 'Date' column
    # price_df['Date'] = price_df['Date'].astype(str).str[:-3]
    # # Convert the Date column into a date object
    # price_df['Date'] = pd.to_datetime(price_df['Date'], format='%Y %m%d')
    # # Subset to only include data going back to 2014
    # price_df = price_df[(price_df['Date'] >= '2014-01-01')]
    #
    # # Convert the time series values to a numpy 1D array
    # points = np.array(price_df['WTI_Price'])
    #
    # # RUPTURES PACKAGE
    # # Changepoint detection with the Pelt search method
    # model = "rbf"
    # algo = rpt.Pelt(model=model).fit(points)
    # result = algo.predict(pen=10)
    # rpt.display(points, result, figsize=(10, 6))
    # plt.title('Change Point Detection: Pelt Search Method')
    # plt.show()
    #
    # # Changepoint detection with the Binary Segmentation search method
    # model = "l2"
    # algo = rpt.Binseg(model=model).fit(points)
    # my_bkps = algo.predict(n_bkps=10)
    # # show results
    # rpt.show.display(points, my_bkps, figsize=(10, 6))
    # plt.title('Change Point Detection: Binary Segmentation Search Method')
    # plt.show()
    #
    # # Changepoint detection with window-based search method
    # model = "l2"
    # algo = rpt.Window(width=40, model=model).fit(points)
    # my_bkps = algo.predict(n_bkps=10)
    # rpt.show.display(points, my_bkps, figsize=(10, 6))
    # plt.title('Change Point Detection: Window-Based Search Method')
    # plt.show()
    #
    # # Changepoint detection with dynamic programming search method
    # model = "l1"
    # algo = rpt.Dynp(model=model, min_size=3, jump=5).fit(points)
    # my_bkps = algo.predict(n_bkps=10)
    # rpt.show.display(points, my_bkps, figsize=(10, 6))
    # plt.title('Change Point Detection: Dynamic Programming Search Method')
    # plt.show()

    # Create a synthetic data set to test against
    # points = np.concatenate([np.random.rand(100) + 5,
    #                          np.random.rand(100) + 10,
    #                          np.random.rand(100) + 5])

    points = np.concatenate([np.random.rand(100) + 5,
                             np.random.rand(5) + 10])

    # CHANGEFINDER PACKAGE
    f, (ax1, ax2) = plt.subplots(2, 1)
    f.subplots_adjust(hspace=0.4)
    ax1.plot(points)
    ax1.set_title("data point")
    # Initiate changefinder function
    cf = changefinder.ChangeFinder()
    scores = [cf.update(p) for p in points]
    ax2.plot(scores)
    ax2.set_title("anomaly score")
    plt.show()