import pandas as pd
import numpy as np
import pyaf.ForecastEngine as autof
import pyaf.Bench.TS_datasets as tsds
import pyaf.CodeGen.TS_CodeGenerator as tscodegen
#get_ipython().magic('matplotlib inline')
b1 = tsds.generate_random_TS(N = 3200 , FREQ = 'D', seed = 0, trendtype = "constant", cycle_length = 12, transform = "", sigma = 0.0, exog_count = 200);
df = b1.mPastData
H = b1.mHorizon;
N = df.shape[0];
for n in [N // 8 , N // 4 , N // 2 , N]:
df1 = df.head(n).copy();
lEngine = autof.cForecastEngine()
lEngine.mOptions.mEnableSeasonals = False;
# lEngine.mOptions.mDebugCycles = False;
lEngine
lEngine.train(df1 , b1.mTimeVar , b1.mSignalVar, H, None);
lEngine.getModelInfo();
# lEngine.standardPlots(name = "my_cycle_" + str(n));
lEngine.mSignalDecomposition.mBestModel.mTimeInfo.mResolution
lCodeGenerator = tscodegen.cTimeSeriesCodeGenerator();
lSQL = lCodeGenerator.testGeneration(lEngine);
def generateCode(self, iSignal, iHorizon):
lAutoF = self.mAutoForecastBySignal[iSignal + "_" + str(iHorizon)]
lCodeGenerator = tscodegen.cTimeSeriesCodeGenerator();
lSQL = lCodeGenerator.testGeneration(lAutoF);
del lCodeGenerator;