111 lines
3.1 KiB
Python
111 lines
3.1 KiB
Python
# encoding:utf-8
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# pylint: disable=c0103
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"""forecast helper methods"""
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import numpy as np
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import pandas as pd
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from matplotlib import pyplot as plt
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from taosanalytics.conf import app_logger, conf
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from taosanalytics.servicemgmt import loader
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def do_forecast(input_list, ts_list, algo_name, params):
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""" data fc handler """
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s = loader.get_service(algo_name)
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if s is None:
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s = loader.get_service("holtwinters")
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if s is None:
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raise ValueError(f"failed to load {algo_name} or holtwinters analysis service")
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s.set_input_list(input_list, ts_list)
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s.set_params(params)
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app_logger.log_inst.debug("start to do forecast")
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res = s.execute()
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app_logger.log_inst.debug("forecast done")
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res["period"] = s.period
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res["algo"] = algo_name
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check_fc_results(res)
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fc = res["res"]
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# draw_fc_results(input_list, len(fc) > 2, fc, len(fc[0]), algo_name)
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return res
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def do_add_fc_params(params, json_obj):
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""" add params into parameters """
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if "forecast_rows" in json_obj:
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params["rows"] = int(json_obj["forecast_rows"])
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if "start" in json_obj:
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params["start_ts"] = int(json_obj["start"])
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if "every" in json_obj:
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params["time_step"] = int(json_obj["every"])
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if "conf" in json_obj:
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params["conf"] = int(json_obj["conf"])
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if "return_conf" in json_obj:
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params["return_conf"] = int(json_obj["return_conf"])
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def insert_ts_list(res, start_ts, time_step, fc_rows):
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""" insert the ts list before return results """
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ts_list = [start_ts + i * time_step for i in range(fc_rows)]
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res.insert(0, ts_list)
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return res
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def draw_fc_results(input_list, return_conf, fc, n_rows, fig_name):
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"""Visualize the forecast results """
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# controlled by option, do not visualize the anomaly detection result
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if not conf.get_draw_result_option():
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return
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app_logger.log_inst.debug('draw forecast result in debug model')
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plt.clf()
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x = np.arange(len(input_list), len(input_list) + n_rows, 1)
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# draw the range of conf
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if return_conf:
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lower_series = pd.Series(fc[2], index=x)
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upper_series = pd.Series(fc[3], index=x)
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plt.fill_between(lower_series.index, lower_series, upper_series, color='k', alpha=.15)
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plt.plot(input_list)
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plt.plot(x, fc[1], c='blue')
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plt.savefig(fig_name)
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app_logger.log_inst.debug("draw results completed in debug model")
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def check_fc_results(res):
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app_logger.log_inst.debug("start to check forecast result")
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if "res" not in res:
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raise ValueError("forecast result is empty")
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fc = res["res"]
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if len(fc) < 2:
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raise ValueError("result length should greater than or equal to 2")
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n_rows = len(fc[0])
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if n_rows != len(fc[1]):
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raise ValueError("result length is not identical, ts rows:%d res rows:%d" % (
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n_rows, len(fc[1])))
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if len(fc) > 2 and (len(fc[2]) != n_rows or len(fc[3]) != n_rows):
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raise ValueError(
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"result length is not identical in confidence, ts rows:%d, lower confidence rows:%d, "
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"upper confidence rows%d" %
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(n_rows, len(fc[2]), len(fc[3])))
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