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fourier_train.py
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fourier_train.py
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import pickle
import numpy as np
from numpy import fft
import pandas as pd
import warnings
import matplotlib.pyplot as plt
warnings.filterwarnings("ignore")
import collections
import argparse
class FourierForecast:
def __init__(self, train, test):
self.train = np.array(train["values"])
self.ds_train = np.array(train["timestamps"])
self.test = np.array(test["values"])
self.ds_test = np.array(test["timestamps"])
def fourierExtrapolation(self, n_predict, n_harm):
n = self.train.size # number of harmonics in model
t = np.arange(0, n)
p = np.polyfit(t, self.train, 1) # find linear trend in x
train_notrend = self.train - p[0] * t # detrended x
train_freqdom = fft.fft(train_notrend) # detrended x in frequency domain
f = fft.fftfreq(n) # frequencies
indexes = np.arange(n).tolist()
# sort indexes by frequency, lower -> higher
indexes.sort(key = lambda i:np.absolute(f[i]))
t = np.arange(0, n + n_predict)
restored_sig = np.zeros(t.size)
for i in indexes[:1 + n_harm * 2]:
ampli = np.absolute(train_freqdom[i]) / n # amplitude
phase = np.angle(train_freqdom[i]) # phase
restored_sig += ampli * np.cos(2 * np.pi * f[i] * t + phase)
return restored_sig + p[0] * t
def fit_model(self, n_predict):
minimum = np.min(self.train)
stddev = np.std(self.train)
upper = np.max(self.train) + stddev
lower = minimum - stddev
if minimum > 0:
lower = max(0, lower)
# n_harm = 1/3 of number of data points was chosen by visual inspection
n_harm = int(len(self.train)/3)
forecast = self.fourierExtrapolation(n_predict, n_harm)
ds = np.append(self.ds_train, self.ds_test)
self.forecast = pd.DataFrame({"ds": ds, "yhat": forecast, "yhat_upper": upper,"yhat_lower": lower})
return self.forecast
def graph(self):
plt.figure(figsize=(40,10))
# ds = np.arange(0, len(np.array(self.forecast["ds"])))
# ds_train = np.arange(0,len(self.ds_train))
# ds_test = np.arange(len(self.ds_train),len(self.ds_train) + len(self.ds_test))
# plt.plot(ds_train, self.train, 'b', label = 'train', linewidth = 3)
# plt.plot(ds_test, self.test, 'g', label = 'test', linewidth = 3)
# plt.plot(ds, np.array(self.forecast["yhat"]), 'y', label = 'yhat')
ds_forecast = np.array(self.forecast["ds"])
forecast = np.array(self.forecast["yhat"])
ds_forecast = ds_forecast[len(self.ds_train):]
forecast = forecast[len(self.ds_train):]
plt.plot(self.ds_train, self.train, 'b', label = 'train', linewidth = 3)
plt.plot(self.ds_test, self.test, 'g', label = 'test', linewidth = 3)
plt.plot(ds_forecast,forecast, 'y', label = 'yhat')
# plt.plot(np.array(self.forecast["ds"]), np.array(self.forecast["yhat_upper"]), 'y', label = 'yhat_upper')
# plt.plot(np.array(self.forecast["ds"]), np.array(self.forecast["yhat_lower"]), 'y', label = 'yhat_lower')
plt.legend()
def calc_delta(vals):
diff = vals - np.roll(vals, 1)
diff[0] = 0
return diff
def monotonically_inc(vals):
# check corner case
if len(vals) == 1:
return True
diff = calc_delta(vals)
diff[np.where(vals == 0)] = 0
if ((diff < 0).sum() == 0):
return True
else:
return False
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="frun Prophet training on time series")
parser.add_argument("--metric", type=str, help='metric name', required=True)
parser.add_argument("--key", type=int, help='key number')
args = parser.parse_args()
metric_name = args.metric
pkl_file = open("../pkl_data/" + metric_name + "_dataframes.pkl", "rb")
#pkl_file = open("../data/real_data_test.pkl", "rb")
dfs = pickle.load(pkl_file)
pkl_file.close()
key_vals = list(dfs.keys())
selected = [args.key]
for ind in selected:
key = key_vals[ind]
#df = dfs["{'__name__': 'http_request_duration_microseconds', 'beta_kubernetes_io_arch': 'amd64', 'beta_kubernetes_io_instance_type': 'm4.xlarge', 'beta_kubernetes_io_os': 'linux', 'failure_domain_beta_kubernetes_io_region': 'us-east-2', 'failure_domain_beta_kubernetes_io_zone': 'us-east-2a', 'handler': 'prometheus', 'hostname': 'free-stg-node-compute-e0756', 'instance': 'ip-172-31-76-144.us-east-2.compute.internal', 'job': 'kubernetes-nodes-exporter', 'kubernetes_io_hostname': 'ip-172-31-76-144.us-east-2.compute.internal', 'logging_infra_fluentd': 'true', 'node_role_kubernetes_io_compute': 'true', 'quantile': '0.99', 'region': 'us-east-2', 'type': 'compute'}"]
df = dfs[key]
# df["timestamps"] = df["ds"]
# df["values"] = df["y"]
df = df.sort_values(by=['timestamps'])
print(key)
df["values"] = df["values"].apply(pd.to_numeric)
vals = np.array(df["values"].tolist())
# check if metric is a counter, if so, run AD on difference
if monotonically_inc(vals):
print("monotonically_inc")
vals = calc_delta(vals)
df["values"] = vals
train = df[0:int(0.7*len(vals))]
test = df[int(0.7*len(vals)):]
# graph(vals)
ff = FourierForecast(train, test)
forecast = ff.fit_model(test.shape[0])
f = open("../fourier_forecasts/forecast_" + metric_name + "_" + str(args.key) + ".pkl", "wb")
pickle.dump(forecast, f)
pickle.dump(train, f)
pickle.dump(test,f)
f.close()
ff.graph()
plt.savefig("../presentation/graphs/" + str(args.key) + "_" + args.metric + ".png", transparent=True)
plt.close()