def test_ozone_debug_perf():
b1 = tsds.load_ozone()
df = b1.mPastData
# df.tail(10)
# df[:-10].tail()
# df[:-10:-1]
# df.describe()
lEngine = autof.cForecastEngine()
lEngine
H = b1.mHorizon
lEngine.mOptions.mDebugPerformance = True
lEngine.mOptions.mEnableCycles = False
lEngine.mOptions.mEnableTimeBasedTrends = False
lEngine.mOptions.mEnableARModels = False
lEngine.train(df, b1.mTimeVar, b1.mSignalVar, H)
lEngine.getModelInfo()
print(lEngine.mSignalDecomposition.mTrPerfDetails.head())
lEngine.mSignalDecomposition.mBestModel.mTimeInfo.mResolution
lEngine.standardPlots("outputs/my_ozone")
dfapp_in = df.copy()
dfapp_in.tail()
dfapp_out = lEngine.forecast(dfapp_in, H)
#dfapp_out.to_csv("outputs/ozone_apply_out.csv")
dfapp_out.tail(2 * H)
print("Forecast Columns ", dfapp_out.columns)
Forecast_DF = dfapp_out[[
b1.mTimeVar, b1.mSignalVar, b1.mSignalVar + '_Forecast'
]]
print(Forecast_DF.info())
print("Forecasts\n",
Forecast_DF.tail(H).values)
print("\n\n<ModelInfo>")
print(lEngine.to_json())
print("</ModelInfo>\n\n")
print("\n\n<Forecast>")
print(Forecast_DF.tail(2 * H).to_json(date_format='iso'))
print("</Forecast>\n\n")
def test_transformation(itransformation):
b1 = tsds.load_ozone()
df = b1.mPastData
lEngine = autof.cForecastEngine()
lEngine
H = b1.mHorizon
# lEngine.mOptions.enable_slow_mode();
lEngine.mOptions.mDebugPerformance = True
lEngine.mOptions.disable_all_transformations()
lEngine.mOptions.mActiveTransformations[itransformation] = True
lEngine.mOptions.mBoxCoxOrders = lEngine.mOptions.mExtensiveBoxCoxOrders
lEngine.train(df, b1.mTimeVar, b1.mSignalVar, H)
lEngine.getModelInfo()
print(lEngine.mSignalDecomposition.mTrPerfDetails.head())
lEngine.mSignalDecomposition.mBestModel.mTimeInfo.mResolution
lEngine.standrdPlots("outputs/my_ozone_" + itransformation)
dfapp_in = df.copy()
dfapp_in.tail()
dfapp_out = lEngine.forecast(dfapp_in, H)
#dfapp_out.to_csv("outputs/ozone_apply_out_" + itransformation + ".csv")
dfapp_out.tail(H)
print("Forecast Columns ", dfapp_out.columns)
Forecast_DF = dfapp_out[[
b1.mTimeVar, b1.mSignalVar, b1.mSignalVar + '_Forecast'
]]
print(Forecast_DF.info())
print("Forecasts\n", Forecast_DF.tail(H))
print("\n\n<ModelInfo>")
print(lEngine.to_json())
print("</ModelInfo>\n\n")
print("\n\n<Forecast>")
print(Forecast_DF.tail(2 * H).to_json(date_format='iso'))
print("</Forecast>\n\n")
def test_ozone_missing_data(iTimeMissingDataImputation,
iSignalMissingDataImputation):
b1 = tsds.load_ozone()
df = b1.mPastData
if (iTimeMissingDataImputation is not None):
df = add_some_missing_data_in_time(df, b1.mTimeVar)
if (iSignalMissingDataImputation is not None):
df = add_some_missing_data_in_signal(df, b1.mSignalVar)
lEngine = autof.cForecastEngine()
H = b1.mHorizon
lEngine.mOptions.mMissingDataOptions.mTimeMissingDataImputation = iTimeMissingDataImputation
lEngine.mOptions.mMissingDataOptions.mSignalMissingDataImputation = iSignalMissingDataImputation
lEngine.train(df, b1.mTimeVar, b1.mSignalVar, H)
lEngine.getModelInfo()
print(lEngine.mSignalDecomposition.mTrPerfDetails.head())
dfapp_in = df.copy()
dfapp_in.tail()
dfapp_out = lEngine.forecast(dfapp_in, H)
#dfapp_out.to_csv("outputs/ozone_apply_out.csv")
dfapp_out.tail(2 * H)
print("Forecast Columns ", dfapp_out.columns)
Forecast_DF = dfapp_out[[
b1.mTimeVar, b1.mSignalVar, b1.mSignalVar + '_Forecast'
]]
print(Forecast_DF.info())
print("Forecasts\n", Forecast_DF.tail(H))
print("\n\n<ModelInfo>")
print(lEngine.to_json())
print("</ModelInfo>\n\n")
print("\n\n<Forecast>")
print(Forecast_DF.tail(2 * H).to_json(date_format='iso'))
print("</Forecast>\n\n")
def build_model(iPerf):
b1 = tsds.load_ozone()
df = b1.mPastData
lEngine = autof.cForecastEngine()
lEngine
H = b1.mHorizon;
# lEngine.mOptions.enable_slow_mode();
lEngine.mOptions.mDebugPerformance = True;
lEngine.mOptions.mModelSelection_Criterion = iPerf;
lEngine.train(df , b1.mTimeVar , b1.mSignalVar, H);
lEngine.getModelInfo();
print(lEngine.mSignalDecomposition.mTrPerfDetails.head());
lEngine.mSignalDecomposition.mBestModel.mTimeInfo.mResolution
lEngine.standardPlots("outputs/my_ozone_perfs_" + iPerf);
dfapp_in = df.copy();
dfapp_in.tail()
# H = 12
dfapp_out = lEngine.forecast(dfapp_in, H);
# dfapp_out.to_csv("outputs/ozone_apply_out.csv")
dfapp_out.tail(2 * H)
print("Forecast Columns " , dfapp_out.columns);
Forecast_DF = dfapp_out[[b1.mTimeVar , b1.mSignalVar, b1.mSignalVar + '_Forecast']]
print(Forecast_DF.info())
print("Forecasts\n" , Forecast_DF.tail(H));
print("\n\n<ModelInfo>")
print(lEngine.to_json());
print("</ModelInfo>\n\n")
print("\n\n<Forecast>")
print(Forecast_DF.tail(2*H).to_json(date_format='iso'))
print("</Forecast>\n\n")
import pyaf.ForecastEngine as autof import pyaf.Bench.TS_datasets as tsds b1 = tsds.load_ozone() df = b1.mPastData lEngine = autof.cForecastEngine() lEngine H = b1.mHorizon; lEngine.mOptions.mFilterSeasonals = False; lEngine.mOptions.mParallelMode = False; lEngine.mOptions.mDebugPerformance = True; lEngine.train(df , b1.mTimeVar , b1.mSignalVar, H); lEngine.getModelInfo(); print(lEngine.mSignalDecomposition.mTrPerfDetails.head());
def buildModel(iParallel=True):
import pandas as pd
import numpy as np
import pyaf.ForecastEngine as autof
import pyaf.Bench.TS_datasets as tsds
import logging
import logging.config
# logging.config.fileConfig('logging.conf')
logging.basicConfig(level=logging.INFO)
# get_ipython().magic('matplotlib inline')
b1 = tsds.load_ozone()
df = b1.mPastData
# df.tail(10)
# df[:-10].tail()
# df[:-10:-1]
# df.describe()
lEngine = autof.cForecastEngine()
lEngine
H = b1.mHorizon
# lEngine.mOptions.enable_slow_mode();
lEngine.mOptions.mDebugPerformance = True
lEngine.mOptions.mParallelMode = iParallel
lEngine.mOptions.set_active_autoregressions(['MLP', 'LSTM'])
lEngine.train(df, b1.mTimeVar, b1.mSignalVar, H)
lEngine2 = pickleModel(lEngine)
lEngine2.getModelInfo()
print(lEngine2.mSignalDecomposition.mTrPerfDetails.head())
lEngine2.mSignalDecomposition.mBestModel.mTimeInfo.mResolution
lEngine2.standardPlots("outputs/my_rnn_ozone")
dfapp_in = df.copy()
dfapp_in.tail()
# H = 12
dfapp_out = lEngine2.forecast(dfapp_in, H)
# dfapp_out.to_csv("outputs/rnn_ozone_apply_out.csv")
dfapp_out.tail(2 * H)
print("Forecast Columns ", dfapp_out.columns)
Forecast_DF = dfapp_out[[
b1.mTimeVar, b1.mSignalVar, b1.mSignalVar + '_Forecast'
]]
print(Forecast_DF.info())
print("Forecasts\n", Forecast_DF.tail(H))
print("\n\n<ModelInfo>")
print(lEngine2.to_json())
print("</ModelInfo>\n\n")
print("\n\n<Forecast>")
print(Forecast_DF.tail(2 * H).to_json(date_format='iso'))
print("</Forecast>\n\n")
