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from __future__ import absolute_import | ||
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import pandas as pd | ||
import numpy as np | ||
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import pyaf.ForecastEngine as autof | ||
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def draw(iEngine, iFormula, iSignal): | ||
print("DRAWING_PREDICTION_INTERVALS", iFormula) | ||
b64_string = iEngine.mSignalDecomposition.mBestModels[iSignal].getPredictionIntervalPlot() | ||
import io, base64, imageio | ||
sourceString = io.BytesIO(base64.b64decode(b64_string)) | ||
lArray = imageio.imread(sourceString, pilmode='RGB') | ||
return lArray | ||
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def plot_model(arg): | ||
(lDataset , lSpec) = arg | ||
lSignal = lDataset.mSignalVar | ||
print("PLOT_MODEL_START" , lSpec) | ||
lEngine = autof.cForecastEngine() | ||
lEngine | ||
H = lDataset.mHorizon; | ||
# lEngine.mOptions.enable_slow_mode(); | ||
# lEngine.mOptions.mDebugPerformance = True; | ||
lEngine.mOptions.mNbCores = 1 | ||
lEngine.mOptions.set_active_transformations(lSpec["Transformation"]); | ||
lEngine.mOptions.set_active_trends(lSpec["Trend"]); | ||
lEngine.mOptions.set_active_periodics(lSpec["Periodics"]); | ||
lEngine.mOptions.set_active_autoregressions(lSpec["AutoRegression"]); | ||
lEngine.mOptions.set_active_decomposition_types(lSpec["Decomposition"]); | ||
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lPlots = {} | ||
lDict = {} | ||
try: | ||
lEngine.train(lDataset.mPastData , lDataset.mTimeVar , lDataset.mSignalVar, H); | ||
lEngine.getModelInfo(); | ||
lDict = lEngine.to_dict() | ||
lMAPE = lDict[lSignal]["Model_Performance"]["MAPE"] | ||
lFormula = lDict[lSignal]["Model"]["Best_Decomposition"] | ||
print("PLOT_MODEL_END" , lSpec , | ||
lFormula, lMAPE) | ||
img = draw(lEngine, lFormula, lSignal) | ||
except Exception as e: | ||
print("PLOT_MODEL_END_FAILED" , lSpec, str(e)) | ||
return (lSpec, None, None, None) | ||
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return (lSpec, img, lFormula, lMAPE) | ||
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class cModelEstheticsVisualizer: | ||
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def __init__(self): | ||
self.mDataset = None | ||
self.mNbThreads = 12 | ||
pass | ||
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def gen_all(self): | ||
lEngine = autof.cForecastEngine() | ||
lSpecs = [] | ||
lSpec = {} | ||
lKnownAutoRegressions = [x for x in lEngine.mOptions.mKnownAutoRegressions if not x.endswith('X')] | ||
lKnownAutoRegressions = [x for x in lKnownAutoRegressions if (x != 'CROSTON')] | ||
lKnownPeriodics = ['NoCycle', 'BestCycle', 'Seasonal_MonthOfYear']; | ||
for tr in lEngine.mOptions.mKnownTransformations: | ||
lSpec["Transformation"] = tr | ||
for tr1 in lEngine.mOptions.mKnownTrends: | ||
lSpec["Trend"] = tr1 | ||
for per in lKnownPeriodics: | ||
lSpec["Periodics"] = per | ||
for ar in lKnownAutoRegressions: | ||
lSpec["AutoRegression"] = ar | ||
for dec in lEngine.mOptions.mKnownDecompositionTypes: | ||
lSpec["Decomposition"] = dec | ||
lSpecs = lSpecs + [(self.mDataset, lSpec.copy())] | ||
print("TESTED_MODELS" , len(lSpecs)) | ||
lPlots = {} | ||
from multiprocessing import Pool | ||
pool = Pool(self.mNbThreads) | ||
for res in pool.imap(plot_model, lSpecs): | ||
(lSpec, img, lFormula, lMAPE) = res | ||
lPlots[str(lSpec)] = (lSpec, img, lFormula, lMAPE) | ||
pool.close() | ||
pool.join() | ||
return lPlots | ||
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def generate_video(self, iDataset): | ||
self.mDataset = iDataset | ||
lSignal = self.mDataset.mSignalVar | ||
plots = self.gen_all() | ||
images = [] | ||
for(lSpec , lValue) in plots.items(): | ||
(lSpec1, img, lFormula, lMAPE) = lValue | ||
images.append((img , lMAPE)) | ||
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images = sorted(images, key = lambda x : -x[1]) | ||
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import imageio as iio | ||
writer = iio.get_writer(lSignal + "_models.mp4", fps=20) | ||
for im in images: | ||
writer.append_data(im[0]) | ||
writer.close() |
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import pyaf.Bench.TS_datasets as tsds | ||
import model_esthetics_visualizer as viz | ||
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lDataset = tsds.load_airline_passengers() | ||
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lVisualizer = viz.cModelEstheticsVisualizer() | ||
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lVisualizer.generate_video(lDataset) |
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import pyaf.Bench.TS_datasets as tsds | ||
import model_esthetics_visualizer as viz | ||
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lDataset = tsds.load_ozone() | ||
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lVisualizer = viz.cModelEstheticsVisualizer() | ||
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lVisualizer.generate_video(lDataset) |