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674929344baa1ea38223e6767896110eb0ebc216
pytorch-ts/examples/Multivariate-Flow-Solar.ipynb
T
Dr. Kashif Rasul 674929344b added multivariate Flow example 
for issue #12
2020-05-25 12:25:55 +02:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import torch"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from pts.dataset import to_pandas, MultivariateGrouper, TrainDatasets\n",
"from pts.dataset.repository import get_dataset, dataset_recipes\n",
"from pts.model.tempflow import TempFlowEstimator\n",
"from pts.model.transformer_tempflow import TransformerTempFlowEstimator\n",
"from pts import Trainer\n",
"from pts.evaluation import make_evaluation_predictions\n",
"from pts.evaluation import MultivariateEvaluator"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"device = torch.device(\"cuda:3\" if torch.cuda.is_available() else \"cpu\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prepeare data set"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"dataset = get_dataset(\"solar_nips\", regenerate=False)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"MetaData(freq='H', target=None, feat_static_cat=[CategoricalFeatureInfo(name='feat_static_cat', cardinality='137')], feat_static_real=[], feat_dynamic_real=[], feat_dynamic_cat=[], prediction_length=24)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.metadata"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"train_grouper = MultivariateGrouper(max_target_dim=int(dataset.metadata.feat_static_cat[0].cardinality))\n",
"\n",
"test_grouper = MultivariateGrouper(num_test_dates=int(len(dataset.test)/len(dataset.train)), \n",
" max_target_dim=int(dataset.metadata.feat_static_cat[0].cardinality))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"dataset_train = train_grouper(dataset.train)\n",
"dataset_test = test_grouper(dataset.test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluator"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"evaluator = MultivariateEvaluator(quantiles=(np.arange(20)/20.0)[1:],\n",
" target_agg_funcs={'sum': np.sum})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## `GRU-Real-NVP`"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"estimator = TempFlowEstimator(\n",
" target_dim=int(dataset.metadata.feat_static_cat[0].cardinality),\n",
" prediction_length=dataset.metadata.prediction_length,\n",
" cell_type='GRU',\n",
" input_size=552,\n",
" freq=dataset.metadata.freq,\n",
" scaling=True,\n",
" dequantize=True,\n",
" n_blocks=4,\n",
" trainer=Trainer(device=device,\n",
" epochs=45,\n",
" learning_rate=1e-3,\n",
" num_batches_per_epoch=100,\n",
" batch_size=64)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
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]
}
],
"source": [
"predictor = estimator.train(dataset_train)\n",
"forecast_it, ts_it = make_evaluation_predictions(dataset=dataset_test,\n",
" predictor=predictor,\n",
" num_samples=100)\n",
"forecasts = list(forecast_it)\n",
"targets = list(ts_it)\n",
"\n",
"agg_metric, _ = evaluator(targets, forecasts, num_series=len(dataset_test))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Metrics"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CRPS: 0.36531966950112466\n",
"ND: 0.45434020382814283\n",
"NRMSE: 0.9820216603495642\n",
"MSE: 914.7868680304274\n"
]
}
],
"source": [
"print(\"CRPS: {}\".format(agg_metric['mean_wQuantileLoss']))\n",
"print(\"ND: {}\".format(agg_metric['ND']))\n",
"print(\"NRMSE: {}\".format(agg_metric['NRMSE']))\n",
"print(\"MSE: {}\".format(agg_metric['MSE']))"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CRPS-Sum: 0.2873863376280519\n",
"ND-Sum: 0.35970480888579265\n",
"NRMSE-Sum: 0.7184166842326591\n",
"MSE-Sum: 9189074.285714285\n"
]
}
],
"source": [
"print(\"CRPS-Sum: {}\".format(agg_metric['m_sum_mean_wQuantileLoss']))\n",
"print(\"ND-Sum: {}\".format(agg_metric['m_sum_ND']))\n",
"print(\"NRMSE-Sum: {}\".format(agg_metric['m_sum_NRMSE']))\n",
"print(\"MSE-Sum: {}\".format(agg_metric['m_sum_MSE']))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## `GRU-MAF`"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"estimator = TempFlowEstimator(\n",
" target_dim=int(dataset.metadata.feat_static_cat[0].cardinality),\n",
" prediction_length=dataset.metadata.prediction_length,\n",
" cell_type='GRU',\n",
" input_size=552,\n",
" freq=dataset.metadata.freq,\n",
" scaling=True,\n",
" dequantize=True,\n",
" flow_type='MAF',\n",
" trainer=Trainer(device=device,\n",
" epochs=25,\n",
" learning_rate=1e-3,\n",
" num_batches_per_epoch=100,\n",
" batch_size=64)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
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]
}
],
"source": [
"predictor = estimator.train(dataset_train)\n",
"forecast_it, ts_it = make_evaluation_predictions(dataset=dataset_test,\n",
" predictor=predictor,\n",
" num_samples=100)\n",
"forecasts = list(forecast_it)\n",
"targets = list(ts_it)\n",
"\n",
"agg_metric, _ = evaluator(targets, forecasts, num_series=len(dataset_test))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Metrics"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CRPS: 0.3855313301520275\n",
"ND: 0.48820539490099113\n",
"NRMSE: 1.018839692673421\n",
"MSE: 984.6672641166102\n"
]
}
],
"source": [
"print(\"CRPS: {}\".format(agg_metric['mean_wQuantileLoss']))\n",
"print(\"ND: {}\".format(agg_metric['ND']))\n",
"print(\"NRMSE: {}\".format(agg_metric['NRMSE']))\n",
"print(\"MSE: {}\".format(agg_metric['MSE']))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CRPS-Sum: 0.3268739166960563\n",
"ND-Sum: 0.40321702146475014\n",
"NRMSE-Sum: 0.75586334994103\n",
"MSE-Sum: 10171980.5\n"
]
}
],
"source": [
"print(\"CRPS-Sum: {}\".format(agg_metric['m_sum_mean_wQuantileLoss']))\n",
"print(\"ND-Sum: {}\".format(agg_metric['m_sum_ND']))\n",
"print(\"NRMSE-Sum: {}\".format(agg_metric['m_sum_NRMSE']))\n",
"print(\"MSE-Sum: {}\".format(agg_metric['m_sum_MSE']))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## `Transformer-MAF`"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"estimator = TransformerTempFlowEstimator(\n",
" d_model=16,\n",
" num_heads=4,\n",
" input_size=552,\n",
" target_dim=int(dataset.metadata.feat_static_cat[0].cardinality),\n",
" prediction_length=dataset.metadata.prediction_length,\n",
" context_length=dataset.metadata.prediction_length*4,\n",
" flow_type='MAF',\n",
" dequantize=True,\n",
" freq=dataset.metadata.freq,\n",
" trainer=Trainer(\n",
" device=device,\n",
" epochs=14,\n",
" learning_rate=1e-3,\n",
" num_batches_per_epoch=100,\n",
" batch_size=64,\n",
" )\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
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},
{
"name": "stderr",
"output_type": "stream",
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]
}
],
"source": [
"predictor = estimator.train(dataset_train)\n",
"forecast_it, ts_it = make_evaluation_predictions(dataset=dataset_test,\n",
" predictor=predictor,\n",
" num_samples=100)\n",
"forecasts = list(forecast_it)\n",
"targets = list(ts_it)\n",
"\n",
"agg_metric, _ = evaluator(targets, forecasts, num_series=len(dataset_test))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Metrics"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CRPS: 0.37264046134993567\n",
"ND: 0.5043621354947913\n",
"NRMSE: 0.9928759300158241\n",
"MSE: 935.1208752979203\n"
]
}
],
"source": [
"print(\"CRPS: {}\".format(agg_metric['mean_wQuantileLoss']))\n",
"print(\"ND: {}\".format(agg_metric['ND']))\n",
"print(\"NRMSE: {}\".format(agg_metric['NRMSE']))\n",
"print(\"MSE: {}\".format(agg_metric['MSE']))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CRPS-Sum: 0.30787625107438427\n",
"ND-Sum: 0.4188356756894787\n",
"NRMSE-Sum: 0.7504274205713227\n",
"MSE-Sum: 10026199.285714285\n"
]
}
],
"source": [
"print(\"CRPS-Sum: {}\".format(agg_metric['m_sum_mean_wQuantileLoss']))\n",
"print(\"ND-Sum: {}\".format(agg_metric['m_sum_ND']))\n",
"print(\"NRMSE-Sum: {}\".format(agg_metric['m_sum_NRMSE']))\n",
"print(\"MSE-Sum: {}\".format(agg_metric['m_sum_MSE']))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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