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* time series forecasting with panel datasets - integrate Temporal Fusion Transformer as a learner based on pytorchforecasting Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update setup.py Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update test_forecast.py Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update setup.py Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update setup.py Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update model.py and test_forecast.py - remove blank lines Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update model.py to prevent errors Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update automl.py and data.py - change forecast task name - update documentation for fit() method Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update test_forecast.py Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update test_forecast.py - add performance test - use 'fit_kwargs_by_estimator' Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * add time index function Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update test_forecast.py performance test Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update data.py Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update automl.py Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update data.py to prevent type error Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update setup.py Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update for pytorch forecasting tft on panel datasets Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update automl.py documentations Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * - rename estimator - add 'gpu_per_trial' for tft estimator Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update test_forecast.py Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * include ts panel forecasting as an example Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update model.py Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update documentations Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update automl_time_series_forecast.ipynb Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update documentations Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * "weights_summary" argument deprecated and removed for pl.Trainer() Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update model.py tft estimator prediction method Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update model.py Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update `fit_kwargs` documentation Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update automl.py Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> Co-authored-by: Chi Wang <wang.chi@microsoft.com>
4540 lines
454 KiB
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4540 lines
454 KiB
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Time Series Forecasting with FLAML Library"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. Introduction\n",
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"\n",
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"FLAML is a Python library (https://github.com/microsoft/FLAML) designed to automatically produce accurate machine learning models with low computational cost. It is fast and economical. The simple and lightweight design makes it easy to use and extend, such as adding new learners. FLAML can\n",
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"\n",
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" - serve as an economical AutoML engine,\n",
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" - be used as a fast hyperparameter tuning tool, or\n",
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" - be embedded in self-tuning software that requires low latency & resource in repetitive tuning tasks.\n",
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"\n",
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"In this notebook, we demonstrate how to use FLAML library for time series forecasting tasks: univariate time series forecasting (only time), multivariate time series forecasting (with exogneous variables) and forecasting discrete values.\n",
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"\n",
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"FLAML requires Python>=3.7. To run this notebook example, please install flaml with the notebook and forecast option:\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install flaml[notebook,ts_forecast]\n",
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"# avoid version 1.0.2 to 1.0.5 for this notebook due to a bug for arima and sarimax's init config"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2. Forecast Problem\n",
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"\n",
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"### Load data and preprocess\n",
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"\n",
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"Import co2 data from statsmodel. The dataset is from “Atmospheric CO2 from Continuous Air Samples at Mauna Loa Observatory, Hawaii, U.S.A.,” which collected CO2 samples from March 1958 to December 2001. The task is to predict monthly CO2 samples given only timestamps."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import statsmodels.api as sm\n",
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"data = sm.datasets.co2.load_pandas().data\n",
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"# data is given in weeks, but the task is to predict monthly, so use monthly averages instead\n",
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"data = data['co2'].resample('MS').mean()\n",
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"data = data.bfill().ffill() # makes sure there are no missing values\n",
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"data = data.to_frame().reset_index()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# split the data into a train dataframe and X_test and y_test dataframes, where the number of samples for test is equal to\n",
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"# the number of periods the user wants to predict\n",
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"num_samples = data.shape[0]\n",
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"time_horizon = 12\n",
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"split_idx = num_samples - time_horizon\n",
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"train_df = data[:split_idx] # train_df is a dataframe with two columns: timestamp and label\n",
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"X_test = data[split_idx:]['index'].to_frame() # X_test is a dataframe with dates for prediction\n",
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"y_test = data[split_idx:]['co2'] # y_test is a series of the values corresponding to the dates for prediction"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"train_df\n",
|
||
"\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"plt.plot(train_df['index'], train_df['co2'])\n",
|
||
"plt.xlabel('Date')\n",
|
||
"plt.ylabel('CO2 Levels')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Run FLAML\n",
|
||
"\n",
|
||
"In the FLAML automl run configuration, users can specify the task type, time budget, error metric, learner list, whether to subsample, resampling strategy type, and so on. All these arguments have default values which will be used if users do not provide them."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"''' import AutoML class from flaml package '''\n",
|
||
"from flaml import AutoML\n",
|
||
"automl = AutoML()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"settings = {\n",
|
||
" \"time_budget\": 240, # total running time in seconds\n",
|
||
" \"metric\": 'mape', # primary metric for validation: 'mape' is generally used for forecast tasks\n",
|
||
" \"task\": 'ts_forecast', # task type\n",
|
||
" \"log_file_name\": 'CO2_forecast.log', # flaml log file\n",
|
||
" \"eval_method\": \"holdout\", # validation method can be chosen from ['auto', 'holdout', 'cv']\n",
|
||
" \"seed\": 7654321, # random seed\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[flaml.automl: 07-28 21:10:44] {2478} INFO - task = ts_forecast\n",
|
||
"[flaml.automl: 07-28 21:10:44] {2480} INFO - Data split method: time\n",
|
||
"[flaml.automl: 07-28 21:10:44] {2483} INFO - Evaluation method: holdout\n",
|
||
"[flaml.automl: 07-28 21:10:44] {2552} INFO - Minimizing error metric: mape\n",
|
||
"[flaml.automl: 07-28 21:10:44] {2694} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth', 'prophet', 'arima', 'sarimax']\n",
|
||
"[flaml.automl: 07-28 21:10:44] {2986} INFO - iteration 0, current learner lgbm\n",
|
||
"[flaml.automl: 07-28 21:10:44] {3114} INFO - Estimated sufficient time budget=2005s. Estimated necessary time budget=2s.\n",
|
||
"[flaml.automl: 07-28 21:10:44] {3161} INFO - at 0.7s,\testimator lgbm's best error=0.0621,\tbest estimator lgbm's best error=0.0621\n",
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||
"[flaml.automl: 07-28 21:10:44] {2986} INFO - iteration 1, current learner lgbm\n",
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||
"[flaml.automl: 07-28 21:10:44] {3161} INFO - at 0.7s,\testimator lgbm's best error=0.0621,\tbest estimator lgbm's best error=0.0621\n",
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"[flaml.automl: 07-28 21:10:44] {2986} INFO - iteration 2, current learner lgbm\n",
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||
"[flaml.automl: 07-28 21:10:44] {3161} INFO - at 0.8s,\testimator lgbm's best error=0.0277,\tbest estimator lgbm's best error=0.0277\n",
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"[flaml.automl: 07-28 21:10:44] {2986} INFO - iteration 3, current learner lgbm\n",
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||
"[flaml.automl: 07-28 21:10:44] {3161} INFO - at 0.8s,\testimator lgbm's best error=0.0277,\tbest estimator lgbm's best error=0.0277\n",
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"[flaml.automl: 07-28 21:10:44] {2986} INFO - iteration 4, current learner lgbm\n",
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||
"[flaml.automl: 07-28 21:10:44] {3161} INFO - at 0.9s,\testimator lgbm's best error=0.0175,\tbest estimator lgbm's best error=0.0175\n",
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"[flaml.automl: 07-28 21:10:44] {2986} INFO - iteration 5, current learner lgbm\n",
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||
"[flaml.automl: 07-28 21:10:44] {3161} INFO - at 0.9s,\testimator lgbm's best error=0.0055,\tbest estimator lgbm's best error=0.0055\n",
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"[flaml.automl: 07-28 21:10:44] {2986} INFO - iteration 6, current learner lgbm\n",
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"[flaml.automl: 07-28 21:10:44] {3161} INFO - at 1.0s,\testimator lgbm's best error=0.0055,\tbest estimator lgbm's best error=0.0055\n",
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"[flaml.automl: 07-28 21:10:44] {2986} INFO - iteration 7, current learner lgbm\n",
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||
"[flaml.automl: 07-28 21:10:45] {3161} INFO - at 1.0s,\testimator lgbm's best error=0.0055,\tbest estimator lgbm's best error=0.0055\n",
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"[flaml.automl: 07-28 21:10:45] {2986} INFO - iteration 8, current learner lgbm\n",
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"[flaml.automl: 07-28 21:10:45] {3161} INFO - at 1.0s,\testimator lgbm's best error=0.0031,\tbest estimator lgbm's best error=0.0031\n",
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"[flaml.automl: 07-28 21:10:45] {2986} INFO - iteration 9, current learner lgbm\n",
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||
"[flaml.automl: 07-28 21:10:45] {3161} INFO - at 1.1s,\testimator lgbm's best error=0.0031,\tbest estimator lgbm's best error=0.0031\n",
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"[flaml.automl: 07-28 21:10:45] {2986} INFO - iteration 10, current learner lgbm\n",
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"[flaml.automl: 07-28 21:10:45] {3161} INFO - at 1.1s,\testimator lgbm's best error=0.0027,\tbest estimator lgbm's best error=0.0027\n",
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"[flaml.automl: 07-28 21:10:45] {2986} INFO - iteration 11, current learner lgbm\n",
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"[flaml.automl: 07-28 21:10:45] {3161} INFO - at 1.2s,\testimator lgbm's best error=0.0022,\tbest estimator lgbm's best error=0.0022\n",
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"[flaml.automl: 07-28 21:10:45] {2986} INFO - iteration 12, current learner lgbm\n",
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"[flaml.automl: 07-28 21:10:45] {3161} INFO - at 1.2s,\testimator lgbm's best error=0.0022,\tbest estimator lgbm's best error=0.0022\n",
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"[flaml.automl: 07-28 21:10:45] {2986} INFO - iteration 13, current learner lgbm\n",
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"[flaml.automl: 07-28 21:10:45] {3161} INFO - at 1.3s,\testimator lgbm's best error=0.0022,\tbest estimator lgbm's best error=0.0022\n",
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"[flaml.automl: 07-28 21:10:45] {2986} INFO - iteration 14, current learner lgbm\n",
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"[flaml.automl: 07-28 21:10:45] {3161} INFO - at 1.3s,\testimator lgbm's best error=0.0022,\tbest estimator lgbm's best error=0.0022\n",
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"[flaml.automl: 07-28 21:10:45] {2986} INFO - iteration 15, current learner lgbm\n",
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"[flaml.automl: 07-28 21:10:45] {3161} INFO - at 1.4s,\testimator lgbm's best error=0.0022,\tbest estimator lgbm's best error=0.0022\n",
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"[flaml.automl: 07-28 21:10:45] {2986} INFO - iteration 16, current learner rf\n",
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||
"[flaml.automl: 07-28 21:10:45] {3161} INFO - at 1.6s,\testimator rf's best error=0.0217,\tbest estimator lgbm's best error=0.0022\n",
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"[flaml.automl: 07-28 21:10:45] {2986} INFO - iteration 17, current learner xgboost\n",
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||
"[flaml.automl: 07-28 21:10:46] {3161} INFO - at 2.0s,\testimator xgboost's best error=0.6738,\tbest estimator lgbm's best error=0.0022\n",
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"[flaml.automl: 07-28 21:10:46] {2986} INFO - iteration 18, current learner extra_tree\n",
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||
"[flaml.automl: 07-28 21:10:46] {3161} INFO - at 2.1s,\testimator extra_tree's best error=0.0197,\tbest estimator lgbm's best error=0.0022\n",
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"[flaml.automl: 07-28 21:10:46] {2986} INFO - iteration 19, current learner extra_tree\n",
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"[flaml.automl: 07-28 21:14:44] {3425} INFO - retrain sarimax for 0.7s\n",
|
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"[flaml.automl: 07-28 21:14:44] {3432} INFO - retrained model: <statsmodels.tsa.statespace.sarimax.SARIMAXResultsWrapper object at 0x000001E2D9979400>\n",
|
||
"[flaml.automl: 07-28 21:14:44] {2725} INFO - fit succeeded\n",
|
||
"[flaml.automl: 07-28 21:14:44] {2726} INFO - Time taken to find the best model: 237.36335611343384\n",
|
||
"[flaml.automl: 07-28 21:14:44] {2737} WARNING - Time taken to find the best model is 99% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"'''The main flaml automl API'''\n",
|
||
"automl.fit(dataframe=train_df, # training data\n",
|
||
" label='co2', # label column\n",
|
||
" period=time_horizon, # key word argument 'period' must be included for forecast task)\n",
|
||
" **settings)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Best model and metric"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Best ML leaner: sarimax\n",
|
||
"Best hyperparmeter config: {'p': 8, 'd': 0, 'q': 8, 'P': 6, 'D': 3, 'Q': 1, 's': 6}\n",
|
||
"Best mape on validation data: 0.00043466573064228554\n",
|
||
"Training duration of best run: 0.7340686321258545s\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"''' retrieve best config and best learner'''\n",
|
||
"print('Best ML leaner:', automl.best_estimator)\n",
|
||
"print('Best hyperparmeter config:', automl.best_config)\n",
|
||
"print(f'Best mape on validation data: {automl.best_loss}')\n",
|
||
"print(f'Training duration of best run: {automl.best_config_train_time}s')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<statsmodels.tsa.statespace.sarimax.SARIMAXResultsWrapper at 0x1e2d9979400>"
|
||
]
|
||
},
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"automl.model.estimator"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"''' pickle and save the automl object '''\n",
|
||
"import pickle\n",
|
||
"with open('automl.pkl', 'wb') as f:\n",
|
||
" pickle.dump(automl, f, pickle.HIGHEST_PROTOCOL)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Predicted labels\n",
|
||
"2001-01-01 370.568362\n",
|
||
"2001-02-01 371.297747\n",
|
||
"2001-03-01 372.087653\n",
|
||
"2001-04-01 373.040897\n",
|
||
"2001-05-01 373.638221\n",
|
||
"2001-06-01 373.202665\n",
|
||
"2001-07-01 371.621574\n",
|
||
"2001-08-01 369.611740\n",
|
||
"2001-09-01 368.307775\n",
|
||
"2001-10-01 368.360786\n",
|
||
"2001-11-01 369.476460\n",
|
||
"2001-12-01 370.849193\n",
|
||
"Freq: MS, Name: predicted_mean, dtype: float64\n",
|
||
"True labels\n",
|
||
"514 370.175\n",
|
||
"515 371.325\n",
|
||
"516 372.060\n",
|
||
"517 372.775\n",
|
||
"518 373.800\n",
|
||
"519 373.060\n",
|
||
"520 371.300\n",
|
||
"521 369.425\n",
|
||
"522 367.880\n",
|
||
"523 368.050\n",
|
||
"524 369.375\n",
|
||
"525 371.020\n",
|
||
"Name: co2, dtype: float64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"''' compute predictions of testing dataset '''\n",
|
||
"flaml_y_pred = automl.predict(X_test)\n",
|
||
"print(f\"Predicted labels\\n{flaml_y_pred}\")\n",
|
||
"print(f\"True labels\\n{y_test}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"mape = 0.0005710586398294955\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"''' compute different metric values on testing dataset'''\n",
|
||
"from flaml.ml import sklearn_metric_loss_score\n",
|
||
"print('mape', '=', sklearn_metric_loss_score('mape', y_true=y_test, y_predict=flaml_y_pred))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Log history"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"{'Current Learner': 'lgbm', 'Current Sample': 502, 'Current Hyper-parameters': {'n_estimators': 4, 'num_leaves': 4, 'min_child_samples': 20, 'learning_rate': 0.09999999999999995, 'log_max_bin': 8, 'colsample_bytree': 1.0, 'reg_alpha': 0.0009765625, 'reg_lambda': 1.0, 'optimize_for_horizon': False, 'lags': 3}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 4, 'num_leaves': 4, 'min_child_samples': 20, 'learning_rate': 0.09999999999999995, 'log_max_bin': 8, 'colsample_bytree': 1.0, 'reg_alpha': 0.0009765625, 'reg_lambda': 1.0, 'optimize_for_horizon': False, 'lags': 3}}\n",
|
||
"{'Current Learner': 'lgbm', 'Current Sample': 502, 'Current Hyper-parameters': {'n_estimators': 8, 'num_leaves': 4, 'min_child_samples': 19, 'learning_rate': 0.18686130359903158, 'log_max_bin': 9, 'colsample_bytree': 0.9311834484407709, 'reg_alpha': 0.0013872402855481538, 'reg_lambda': 0.43503398494225104, 'optimize_for_horizon': False, 'lags': 1}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 8, 'num_leaves': 4, 'min_child_samples': 19, 'learning_rate': 0.18686130359903158, 'log_max_bin': 9, 'colsample_bytree': 0.9311834484407709, 'reg_alpha': 0.0013872402855481538, 'reg_lambda': 0.43503398494225104, 'optimize_for_horizon': False, 'lags': 1}}\n",
|
||
"{'Current Learner': 'lgbm', 'Current Sample': 502, 'Current Hyper-parameters': {'n_estimators': 9, 'num_leaves': 4, 'min_child_samples': 14, 'learning_rate': 0.23100120527451992, 'log_max_bin': 8, 'colsample_bytree': 1.0, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.028424597762235913, 'optimize_for_horizon': False, 'lags': 1}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 9, 'num_leaves': 4, 'min_child_samples': 14, 'learning_rate': 0.23100120527451992, 'log_max_bin': 8, 'colsample_bytree': 1.0, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.028424597762235913, 'optimize_for_horizon': False, 'lags': 1}}\n",
|
||
"{'Current Learner': 'lgbm', 'Current Sample': 502, 'Current Hyper-parameters': {'n_estimators': 9, 'num_leaves': 9, 'min_child_samples': 9, 'learning_rate': 0.2917244979615619, 'log_max_bin': 7, 'colsample_bytree': 1.0, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.006048554644106909, 'optimize_for_horizon': False, 'lags': 4}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 9, 'num_leaves': 9, 'min_child_samples': 9, 'learning_rate': 0.2917244979615619, 'log_max_bin': 7, 'colsample_bytree': 1.0, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.006048554644106909, 'optimize_for_horizon': False, 'lags': 4}}\n",
|
||
"{'Current Learner': 'lgbm', 'Current Sample': 502, 'Current Hyper-parameters': {'n_estimators': 4, 'num_leaves': 8, 'min_child_samples': 11, 'learning_rate': 0.8116893577982964, 'log_max_bin': 8, 'colsample_bytree': 0.97502360023323, 'reg_alpha': 0.0012398377555843262, 'reg_lambda': 0.02776044509327881, 'optimize_for_horizon': False, 'lags': 4}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 4, 'num_leaves': 8, 'min_child_samples': 11, 'learning_rate': 0.8116893577982964, 'log_max_bin': 8, 'colsample_bytree': 0.97502360023323, 'reg_alpha': 0.0012398377555843262, 'reg_lambda': 0.02776044509327881, 'optimize_for_horizon': False, 'lags': 4}}\n",
|
||
"{'Current Learner': 'lgbm', 'Current Sample': 502, 'Current Hyper-parameters': {'n_estimators': 5, 'num_leaves': 16, 'min_child_samples': 7, 'learning_rate': 1.0, 'log_max_bin': 9, 'colsample_bytree': 0.9289697965752838, 'reg_alpha': 0.01291354098023607, 'reg_lambda': 0.012402833825431305, 'optimize_for_horizon': False, 'lags': 5}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 5, 'num_leaves': 16, 'min_child_samples': 7, 'learning_rate': 1.0, 'log_max_bin': 9, 'colsample_bytree': 0.9289697965752838, 'reg_alpha': 0.01291354098023607, 'reg_lambda': 0.012402833825431305, 'optimize_for_horizon': False, 'lags': 5}}\n",
|
||
"{'Current Learner': 'lgbm', 'Current Sample': 502, 'Current Hyper-parameters': {'n_estimators': 10, 'num_leaves': 13, 'min_child_samples': 8, 'learning_rate': 1.0, 'log_max_bin': 9, 'colsample_bytree': 0.915047969012756, 'reg_alpha': 0.1456985407754094, 'reg_lambda': 0.010186415963233664, 'optimize_for_horizon': False, 'lags': 9}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 10, 'num_leaves': 13, 'min_child_samples': 8, 'learning_rate': 1.0, 'log_max_bin': 9, 'colsample_bytree': 0.915047969012756, 'reg_alpha': 0.1456985407754094, 'reg_lambda': 0.010186415963233664, 'optimize_for_horizon': False, 'lags': 9}}\n",
|
||
"{'Current Learner': 'xgb', 'Current Sample': 502, 'Current Hyper-parameters': {'n_estimators': 17, 'max_depth': 6, 'min_child_weight': 1.1257301179325647, 'learning_rate': 0.3420575416463879, 'subsample': 1.0, 'colsample_bylevel': 0.8634518942394397, 'colsample_bytree': 0.8183410599521093, 'reg_alpha': 0.0031517221935712125, 'reg_lambda': 0.36563645650488746, 'optimize_for_horizon': False, 'lags': 1}, 'Best Learner': 'xgb', 'Best Hyper-parameters': {'n_estimators': 17, 'max_depth': 6, 'min_child_weight': 1.1257301179325647, 'learning_rate': 0.3420575416463879, 'subsample': 1.0, 'colsample_bylevel': 0.8634518942394397, 'colsample_bytree': 0.8183410599521093, 'reg_alpha': 0.0031517221935712125, 'reg_lambda': 0.36563645650488746, 'optimize_for_horizon': False, 'lags': 1}}\n",
|
||
"{'Current Learner': 'prophet', 'Current Sample': 502, 'Current Hyper-parameters': {'changepoint_prior_scale': 0.05, 'seasonality_prior_scale': 10.0, 'holidays_prior_scale': 10.0, 'seasonality_mode': 'multiplicative'}, 'Best Learner': 'prophet', 'Best Hyper-parameters': {'changepoint_prior_scale': 0.05, 'seasonality_prior_scale': 10.0, 'holidays_prior_scale': 10.0, 'seasonality_mode': 'multiplicative'}}\n",
|
||
"{'Current Learner': 'prophet', 'Current Sample': 502, 'Current Hyper-parameters': {'changepoint_prior_scale': 0.02574943279263944, 'seasonality_prior_scale': 10.0, 'holidays_prior_scale': 10.0, 'seasonality_mode': 'additive'}, 'Best Learner': 'prophet', 'Best Hyper-parameters': {'changepoint_prior_scale': 0.02574943279263944, 'seasonality_prior_scale': 10.0, 'holidays_prior_scale': 10.0, 'seasonality_mode': 'additive'}}\n",
|
||
"{'Current Learner': 'prophet', 'Current Sample': 502, 'Current Hyper-parameters': {'changepoint_prior_scale': 0.029044518309983725, 'seasonality_prior_scale': 10.0, 'holidays_prior_scale': 8.831739687246309, 'seasonality_mode': 'additive'}, 'Best Learner': 'prophet', 'Best Hyper-parameters': {'changepoint_prior_scale': 0.029044518309983725, 'seasonality_prior_scale': 10.0, 'holidays_prior_scale': 8.831739687246309, 'seasonality_mode': 'additive'}}\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from flaml.data import get_output_from_log\n",
|
||
"time_history, best_valid_loss_history, valid_loss_history, config_history, train_loss_history = \\\n",
|
||
" get_output_from_log(filename=settings['log_file_name'], time_budget=180)\n",
|
||
"\n",
|
||
"for config in config_history:\n",
|
||
" print(config)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"plt.title('Learning Curve')\n",
|
||
"plt.xlabel('Wall Clock Time (s)')\n",
|
||
"plt.ylabel('Validation Accuracy')\n",
|
||
"plt.scatter(time_history, 1 - np.array(valid_loss_history))\n",
|
||
"plt.step(time_history, 1 - np.array(best_valid_loss_history), where='post')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Visualize"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<matplotlib.legend.Legend at 0x1e2d8e31fa0>"
|
||
]
|
||
},
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"plt.plot(X_test, y_test, label='Actual level')\n",
|
||
"plt.plot(X_test, flaml_y_pred, label='FLAML forecast')\n",
|
||
"plt.xlabel('Date')\n",
|
||
"plt.ylabel('CO2 Levels')\n",
|
||
"plt.legend()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 3. Forecast Problems with Exogeneous Variables"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Load Data and Preprocess\n",
|
||
"\n",
|
||
"Load dataset on NYC energy consumption. The task is to predict the average hourly demand of enegry used in a day given information on time, temperature, and precipitation. Temperature and precipiation values are both continuous values. To demonstrate FLAML's ability to handle categorical values as well, create a column with categorical values, where 1 denotes daily tempurature is above monthly average and 0 is below."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"''' multivariate time series forecasting dataset'''\n",
|
||
"import pandas as pd\n",
|
||
"# pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
|
||
"multi_df = pd.read_csv(\n",
|
||
" \"https://raw.githubusercontent.com/srivatsan88/YouTubeLI/master/dataset/nyc_energy_consumption.csv\"\n",
|
||
")\n",
|
||
"# preprocessing data\n",
|
||
"multi_df[\"timeStamp\"] = pd.to_datetime(multi_df[\"timeStamp\"])\n",
|
||
"multi_df = multi_df.set_index(\"timeStamp\")\n",
|
||
"multi_df = multi_df.resample(\"D\").mean()\n",
|
||
"multi_df[\"temp\"] = multi_df[\"temp\"].fillna(method=\"ffill\")\n",
|
||
"multi_df[\"precip\"] = multi_df[\"precip\"].fillna(method=\"ffill\")\n",
|
||
"multi_df = multi_df[:-2] # last two rows are NaN for 'demand' column so remove them\n",
|
||
"multi_df = multi_df.reset_index()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"''' Use feature engineering to create a categorical value'''\n",
|
||
"# Using temperature values create categorical values \n",
|
||
"# where 1 denotes daily tempurature is above monthly average and 0 is below.\n",
|
||
"\n",
|
||
"def get_monthly_avg(data):\n",
|
||
" data[\"month\"] = data[\"timeStamp\"].dt.month\n",
|
||
" data = data[[\"month\", \"temp\"]].groupby(\"month\")\n",
|
||
" data = data.agg({\"temp\": \"mean\"})\n",
|
||
" return data\n",
|
||
"\n",
|
||
"monthly_avg = get_monthly_avg(multi_df).to_dict().get(\"temp\")\n",
|
||
"\n",
|
||
"def above_monthly_avg(date, temp):\n",
|
||
" month = date.month\n",
|
||
" if temp > monthly_avg.get(month):\n",
|
||
" return 1\n",
|
||
" else:\n",
|
||
" return 0\n",
|
||
"\n",
|
||
"multi_df[\"temp_above_monthly_avg\"] = multi_df.apply(\n",
|
||
" lambda x: above_monthly_avg(x[\"timeStamp\"], x[\"temp\"]), axis=1\n",
|
||
")\n",
|
||
"\n",
|
||
"del multi_df[\"temp\"], multi_df[\"month\"] # remove temperature column to reduce redundancy"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>timeStamp</th>\n",
|
||
" <th>demand</th>\n",
|
||
" <th>precip</th>\n",
|
||
" <th>temp_above_monthly_avg</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>2012-01-01</td>\n",
|
||
" <td>4954.833333</td>\n",
|
||
" <td>0.002487</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>2012-01-02</td>\n",
|
||
" <td>5302.954167</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>2012-01-03</td>\n",
|
||
" <td>6095.512500</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>2012-01-04</td>\n",
|
||
" <td>6336.266667</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>2012-01-05</td>\n",
|
||
" <td>6130.245833</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1864</th>\n",
|
||
" <td>2017-02-07</td>\n",
|
||
" <td>5861.319833</td>\n",
|
||
" <td>0.011938</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1865</th>\n",
|
||
" <td>2017-02-08</td>\n",
|
||
" <td>5667.644708</td>\n",
|
||
" <td>0.001258</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1866</th>\n",
|
||
" <td>2017-02-09</td>\n",
|
||
" <td>5947.661958</td>\n",
|
||
" <td>0.027029</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1867</th>\n",
|
||
" <td>2017-02-10</td>\n",
|
||
" <td>6195.122500</td>\n",
|
||
" <td>0.000179</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1868</th>\n",
|
||
" <td>2017-02-11</td>\n",
|
||
" <td>5461.026000</td>\n",
|
||
" <td>0.000492</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>1869 rows × 4 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" timeStamp demand precip temp_above_monthly_avg\n",
|
||
"0 2012-01-01 4954.833333 0.002487 1\n",
|
||
"1 2012-01-02 5302.954167 0.000000 1\n",
|
||
"2 2012-01-03 6095.512500 0.000000 0\n",
|
||
"3 2012-01-04 6336.266667 0.000000 0\n",
|
||
"4 2012-01-05 6130.245833 0.000000 1\n",
|
||
"... ... ... ... ...\n",
|
||
"1864 2017-02-07 5861.319833 0.011938 1\n",
|
||
"1865 2017-02-08 5667.644708 0.001258 1\n",
|
||
"1866 2017-02-09 5947.661958 0.027029 0\n",
|
||
"1867 2017-02-10 6195.122500 0.000179 0\n",
|
||
"1868 2017-02-11 5461.026000 0.000492 1\n",
|
||
"\n",
|
||
"[1869 rows x 4 columns]"
|
||
]
|
||
},
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# split data into train and test\n",
|
||
"num_samples = multi_df.shape[0]\n",
|
||
"multi_time_horizon = 180\n",
|
||
"split_idx = num_samples - multi_time_horizon\n",
|
||
"multi_train_df = multi_df[:split_idx]\n",
|
||
"multi_test_df = multi_df[split_idx:]\n",
|
||
"\n",
|
||
"multi_X_test = multi_test_df[\n",
|
||
" [\"timeStamp\", \"precip\", \"temp_above_monthly_avg\"]\n",
|
||
"] # test dataframe must contain values for the regressors / multivariate variables\n",
|
||
"multi_y_test = multi_test_df[\"demand\"]\n",
|
||
"\n",
|
||
"multi_train_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Run FLAML"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[flaml.automl: 07-28 21:14:47] {2478} INFO - task = ts_forecast\n",
|
||
"[flaml.automl: 07-28 21:14:47] {2480} INFO - Data split method: time\n",
|
||
"[flaml.automl: 07-28 21:14:47] {2483} INFO - Evaluation method: holdout\n",
|
||
"[flaml.automl: 07-28 21:14:47] {2552} INFO - Minimizing error metric: mape\n",
|
||
"[flaml.automl: 07-28 21:14:47] {2694} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth', 'prophet', 'arima', 'sarimax']\n",
|
||
"[flaml.automl: 07-28 21:14:47] {2986} INFO - iteration 0, current learner lgbm\n",
|
||
"[flaml.automl: 07-28 21:14:47] {3114} INFO - Estimated sufficient time budget=509s. Estimated necessary time budget=1s.\n",
|
||
"[flaml.automl: 07-28 21:14:47] {3161} INFO - at 0.1s,\testimator lgbm's best error=0.1103,\tbest estimator lgbm's best error=0.1103\n",
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||
"[flaml.automl: 07-28 21:14:47] {2986} INFO - iteration 1, current learner lgbm\n",
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||
"[flaml.automl: 07-28 21:14:47] {3161} INFO - at 0.1s,\testimator lgbm's best error=0.1103,\tbest estimator lgbm's best error=0.1103\n",
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"[flaml.automl: 07-28 21:14:47] {2986} INFO - iteration 2, current learner lgbm\n",
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"[flaml.automl: 07-28 21:14:47] {3161} INFO - at 0.2s,\testimator lgbm's best error=0.0983,\tbest estimator lgbm's best error=0.0983\n",
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"[flaml.automl: 07-28 21:14:47] {3161} INFO - at 0.2s,\testimator rf's best error=0.0968,\tbest estimator rf's best error=0.0968\n",
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"[flaml.automl: 07-28 21:14:48] {2986} INFO - iteration 15, current learner extra_tree\n",
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"[flaml.automl: 07-28 21:14:48] {3161} INFO - at 0.6s,\testimator extra_tree's best error=0.1059,\tbest estimator rf's best error=0.0836\n",
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"[flaml.automl: 07-28 21:14:48] {2986} INFO - iteration 16, current learner rf\n",
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"[flaml.automl: 07-28 21:14:48] {3161} INFO - at 0.7s,\testimator rf's best error=0.0743,\tbest estimator rf's best error=0.0743\n",
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"[flaml.automl: 07-28 21:14:48] {2986} INFO - iteration 17, current learner extra_tree\n",
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"[flaml.automl: 07-28 21:14:48] {3161} INFO - at 0.8s,\testimator extra_tree's best error=0.0962,\tbest estimator rf's best error=0.0743\n",
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"[flaml.automl: 07-28 21:14:48] {2986} INFO - iteration 18, current learner xgb_limitdepth\n",
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||
"[flaml.automl: 07-28 21:14:48] {3161} INFO - at 0.8s,\testimator xgb_limitdepth's best error=0.0820,\tbest estimator rf's best error=0.0743\n",
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"[flaml.automl: 07-28 21:14:48] {2986} INFO - iteration 29, current learner xgb_limitdepth\n",
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||
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||
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"[flaml.automl: 07-28 21:14:49] {3161} INFO - at 1.5s,\testimator rf's best error=0.0735,\tbest estimator rf's best error=0.0735\n",
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"[flaml.automl: 07-28 21:14:49] {2986} INFO - iteration 36, current learner xgboost\n",
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"[flaml.automl: 07-28 21:14:49] {3161} INFO - at 1.6s,\testimator xgboost's best error=0.0834,\tbest estimator rf's best error=0.0735\n",
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"[flaml.automl: 07-28 21:14:49] {2986} INFO - iteration 37, current learner prophet\n",
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"[flaml.automl: 07-28 21:14:53] {3161} INFO - at 6.0s,\testimator prophet's best error=0.0592,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 07-28 21:14:53] {2986} INFO - iteration 38, current learner arima\n",
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"[flaml.automl: 07-28 21:14:54] {3161} INFO - at 6.8s,\testimator arima's best error=0.6434,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 07-28 21:14:54] {2986} INFO - iteration 39, current learner sarimax\n",
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"[flaml.automl: 07-28 21:14:55] {3161} INFO - at 7.8s,\testimator sarimax's best error=0.6434,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 07-28 21:14:55] {2986} INFO - iteration 40, current learner sarimax\n",
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"[flaml.automl: 07-28 21:14:57] {3161} INFO - at 9.8s,\testimator sarimax's best error=0.5313,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 07-28 21:14:57] {2986} INFO - iteration 41, current learner xgboost\n",
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"[flaml.automl: 07-28 21:14:57] {3161} INFO - at 9.9s,\testimator xgboost's best error=0.0834,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 07-28 21:14:57] {2986} INFO - iteration 42, current learner extra_tree\n",
|
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"[flaml.automl: 07-28 21:14:57] {3161} INFO - at 10.0s,\testimator extra_tree's best error=0.0962,\tbest estimator prophet's best error=0.0592\n",
|
||
"[flaml.automl: 07-28 21:14:57] {2986} INFO - iteration 43, current learner lgbm\n",
|
||
"[flaml.automl: 07-28 21:14:57] {3161} INFO - at 10.1s,\testimator lgbm's best error=0.0861,\tbest estimator prophet's best error=0.0592\n",
|
||
"[flaml.automl: 07-28 21:15:01] {3425} INFO - retrain prophet for 3.8s\n",
|
||
"[flaml.automl: 07-28 21:15:01] {3432} INFO - retrained model: <prophet.forecaster.Prophet object at 0x000001E2D9A005E0>\n",
|
||
"[flaml.automl: 07-28 21:15:01] {2725} INFO - fit succeeded\n",
|
||
"[flaml.automl: 07-28 21:15:01] {2726} INFO - Time taken to find the best model: 5.99089241027832\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"automl = AutoML()\n",
|
||
"settings = {\n",
|
||
" \"time_budget\": 10, # total running time in seconds\n",
|
||
" \"metric\": \"mape\", # primary metric\n",
|
||
" \"task\": \"ts_forecast\", # task type\n",
|
||
" \"log_file_name\": \"energy_forecast_categorical.log\", # flaml log file\n",
|
||
" \"eval_method\": \"holdout\",\n",
|
||
" \"log_type\": \"all\",\n",
|
||
" \"label\": \"demand\",\n",
|
||
"}\n",
|
||
"'''The main flaml automl API'''\n",
|
||
"try:\n",
|
||
" import prophet\n",
|
||
"\n",
|
||
" automl.fit(dataframe=multi_train_df, **settings, period=multi_time_horizon)\n",
|
||
"except ImportError:\n",
|
||
" print(\"not using prophet due to ImportError\")\n",
|
||
" automl.fit(\n",
|
||
" dataframe=multi_train_df,\n",
|
||
" **settings,\n",
|
||
" estimator_list=[\"arima\", \"sarimax\"],\n",
|
||
" period=multi_time_horizon,\n",
|
||
" )"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Prediction and Metrics"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Predicted labels 0 5352.985670\n",
|
||
"1 6013.062371\n",
|
||
"2 6106.856497\n",
|
||
"3 6368.692993\n",
|
||
"4 6081.394081\n",
|
||
" ... \n",
|
||
"175 6841.950842\n",
|
||
"176 7584.557653\n",
|
||
"177 7614.970448\n",
|
||
"178 7729.474679\n",
|
||
"179 7585.110004\n",
|
||
"Name: yhat, Length: 180, dtype: float64\n",
|
||
"True labels 1869 5486.409375\n",
|
||
"1870 6015.156208\n",
|
||
"1871 5972.218042\n",
|
||
"1872 5838.364167\n",
|
||
"1873 5961.476375\n",
|
||
" ... \n",
|
||
"2044 5702.361542\n",
|
||
"2045 6398.154167\n",
|
||
"2046 6471.626042\n",
|
||
"2047 6811.112167\n",
|
||
"2048 5582.297000\n",
|
||
"Name: demand, Length: 180, dtype: float64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"''' compute predictions of testing dataset '''\n",
|
||
"multi_y_pred = automl.predict(multi_X_test)\n",
|
||
"print(\"Predicted labels\", multi_y_pred)\n",
|
||
"print(\"True labels\", multi_y_test)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"mape = 0.08347031511602677\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"''' compute different metric values on testing dataset'''\n",
|
||
"from flaml.ml import sklearn_metric_loss_score\n",
|
||
"print('mape', '=', sklearn_metric_loss_score('mape', y_true=multi_y_test, y_predict=multi_y_pred))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Visualize"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"plt.figure()\n",
|
||
"plt.plot(multi_X_test[\"timeStamp\"], multi_y_test, label=\"Actual Demand\")\n",
|
||
"plt.plot(multi_X_test[\"timeStamp\"], multi_y_pred, label=\"FLAML Forecast\")\n",
|
||
"plt.xlabel(\"Date\")\n",
|
||
"plt.ylabel(\"Energy Demand\")\n",
|
||
"plt.legend()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 4. Forecasting Discrete Values"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Load Dataset and Preprocess\n",
|
||
"\n",
|
||
"Import [sales data](https://hcrystalball.readthedocs.io/en/v0.1.7/api/hcrystalball.utils.get_sales_data.html) from hcrystalball. The task is to predict whether daily sales will be above mean sales for thirty days into the future."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from hcrystalball.utils import get_sales_data\n",
|
||
"time_horizon = 30\n",
|
||
"df = get_sales_data(n_dates=180, n_assortments=1, n_states=1, n_stores=1)\n",
|
||
"df = df[[\"Sales\", \"Open\", \"Promo\", \"Promo2\"]]\n",
|
||
"# feature engineering - create a discrete value column\n",
|
||
"# 1 denotes above mean and 0 denotes below mean\n",
|
||
"import numpy as np\n",
|
||
"df[\"above_mean_sales\"] = np.where(df[\"Sales\"] > df[\"Sales\"].mean(), 1, 0)\n",
|
||
"df.reset_index(inplace=True)\n",
|
||
"# train-test split\n",
|
||
"discrete_train_df = df[:-time_horizon]\n",
|
||
"discrete_test_df = df[-time_horizon:]\n",
|
||
"discrete_X_train, discrete_X_test = (\n",
|
||
" discrete_train_df[[\"Date\", \"Open\", \"Promo\", \"Promo2\"]],\n",
|
||
" discrete_test_df[[\"Date\", \"Open\", \"Promo\", \"Promo2\"]],\n",
|
||
")\n",
|
||
"discrete_y_train, discrete_y_test = discrete_train_df[\"above_mean_sales\"], discrete_test_df[\"above_mean_sales\"]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Date</th>\n",
|
||
" <th>Sales</th>\n",
|
||
" <th>Open</th>\n",
|
||
" <th>Promo</th>\n",
|
||
" <th>Promo2</th>\n",
|
||
" <th>above_mean_sales</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>2015-02-02</td>\n",
|
||
" <td>24894</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>2015-02-03</td>\n",
|
||
" <td>22139</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>2015-02-04</td>\n",
|
||
" <td>20452</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>2015-02-05</td>\n",
|
||
" <td>20977</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>2015-02-06</td>\n",
|
||
" <td>19151</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>145</th>\n",
|
||
" <td>2015-06-27</td>\n",
|
||
" <td>13108</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>146</th>\n",
|
||
" <td>2015-06-28</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>147</th>\n",
|
||
" <td>2015-06-29</td>\n",
|
||
" <td>28456</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>148</th>\n",
|
||
" <td>2015-06-30</td>\n",
|
||
" <td>27140</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>149</th>\n",
|
||
" <td>2015-07-01</td>\n",
|
||
" <td>24957</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>150 rows × 6 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Date Sales Open Promo Promo2 above_mean_sales\n",
|
||
"0 2015-02-02 24894 True True False 1\n",
|
||
"1 2015-02-03 22139 True True False 1\n",
|
||
"2 2015-02-04 20452 True True False 1\n",
|
||
"3 2015-02-05 20977 True True False 1\n",
|
||
"4 2015-02-06 19151 True True False 1\n",
|
||
".. ... ... ... ... ... ...\n",
|
||
"145 2015-06-27 13108 True False False 0\n",
|
||
"146 2015-06-28 0 False False False 0\n",
|
||
"147 2015-06-29 28456 True True False 1\n",
|
||
"148 2015-06-30 27140 True True False 1\n",
|
||
"149 2015-07-01 24957 True True False 1\n",
|
||
"\n",
|
||
"[150 rows x 6 columns]"
|
||
]
|
||
},
|
||
"execution_count": 3,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"discrete_train_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Run FLAML"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from flaml import AutoML\n",
|
||
"automl = AutoML()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"settings = {\n",
|
||
" \"time_budget\": 15, # total running time in seconds\n",
|
||
" \"metric\": \"accuracy\", # primary metric\n",
|
||
" \"task\": \"ts_forecast_classification\", # task type\n",
|
||
" \"log_file_name\": \"sales_classification_forecast.log\", # flaml log file\n",
|
||
" \"eval_method\": \"holdout\",\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[flaml.automl: 08-03 20:33:26] {2520} INFO - task = ts_forecast_classification\n",
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||
"[flaml.automl: 08-03 20:33:26] {2522} INFO - Data split method: time\n",
|
||
"[flaml.automl: 08-03 20:33:26] {2525} INFO - Evaluation method: holdout\n",
|
||
"[flaml.automl: 08-03 20:33:26] {2644} INFO - Minimizing error metric: 1-accuracy\n",
|
||
"[flaml.automl: 08-03 20:33:27] {2786} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth']\n",
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"[flaml.automl: 08-03 20:33:27] {3088} INFO - iteration 0, current learner lgbm\n",
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"[flaml.automl: 08-03 20:33:29] {3221} INFO - Estimated sufficient time budget=11912s. Estimated necessary time budget=12s.\n",
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"[flaml.automl: 08-03 20:33:29] {3268} INFO - at 2.2s,\testimator lgbm's best error=0.2667,\tbest estimator lgbm's best error=0.2667\n",
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"[flaml.automl: 08-03 20:33:41] {3268} INFO - at 15.0s,\testimator extra_tree's best error=0.0333,\tbest estimator lgbm's best error=0.0333\n",
|
||
"[flaml.automl: 08-03 20:33:41] {3532} INFO - retrain lgbm for 0.0s\n",
|
||
"[flaml.automl: 08-03 20:33:41] {3539} INFO - retrained model: LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
|
||
" importance_type='split', learning_rate=0.7333523408279569,\n",
|
||
" max_bin=31, max_depth=-1, min_child_samples=8,\n",
|
||
" min_child_weight=0.001, min_split_gain=0.0, n_estimators=4,\n",
|
||
" n_jobs=-1, num_leaves=5, objective=None, random_state=None,\n",
|
||
" reg_alpha=0.0009765625, reg_lambda=7.593190995489472,\n",
|
||
" silent=True, subsample=1.0, subsample_for_bin=200000,\n",
|
||
" subsample_freq=0, verbose=-1)\n",
|
||
"[flaml.automl: 08-03 20:33:41] {2817} INFO - fit succeeded\n",
|
||
"[flaml.automl: 08-03 20:33:41] {2818} INFO - Time taken to find the best model: 2.6732513904571533\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"\"\"\"The main flaml automl API\"\"\"\n",
|
||
"automl.fit(X_train=discrete_X_train,\n",
|
||
" y_train=discrete_y_train,\n",
|
||
" **settings,\n",
|
||
" period=time_horizon)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Best Model and Metric"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Best ML leaner: lgbm\n",
|
||
"Best hyperparmeter config: {'n_estimators': 4, 'num_leaves': 5, 'min_child_samples': 8, 'learning_rate': 0.7333523408279569, 'log_max_bin': 5, 'colsample_bytree': 1.0, 'reg_alpha': 0.0009765625, 'reg_lambda': 7.593190995489472, 'optimize_for_horizon': False, 'lags': 5}\n",
|
||
"Best mape on validation data: 0.033333333333333326\n",
|
||
"Training duration of best run: 0.017951011657714844s\n",
|
||
"LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
|
||
" importance_type='split', learning_rate=0.7333523408279569,\n",
|
||
" max_bin=31, max_depth=-1, min_child_samples=8,\n",
|
||
" min_child_weight=0.001, min_split_gain=0.0, n_estimators=4,\n",
|
||
" n_jobs=-1, num_leaves=5, objective=None, random_state=None,\n",
|
||
" reg_alpha=0.0009765625, reg_lambda=7.593190995489472,\n",
|
||
" silent=True, subsample=1.0, subsample_for_bin=200000,\n",
|
||
" subsample_freq=0, verbose=-1)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"\"\"\" retrieve best config and best learner\"\"\"\n",
|
||
"print(\"Best ML leaner:\", automl.best_estimator)\n",
|
||
"print(\"Best hyperparmeter config:\", automl.best_config)\n",
|
||
"print(f\"Best mape on validation data: {automl.best_loss}\")\n",
|
||
"print(f\"Training duration of best run: {automl.best_config_train_time}s\")\n",
|
||
"print(automl.model.estimator)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Predicted label [1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1]\n",
|
||
"True label 150 1\n",
|
||
"151 1\n",
|
||
"152 0\n",
|
||
"153 0\n",
|
||
"154 1\n",
|
||
"155 1\n",
|
||
"156 1\n",
|
||
"157 1\n",
|
||
"158 1\n",
|
||
"159 0\n",
|
||
"160 0\n",
|
||
"161 1\n",
|
||
"162 1\n",
|
||
"163 1\n",
|
||
"164 1\n",
|
||
"165 1\n",
|
||
"166 0\n",
|
||
"167 0\n",
|
||
"168 1\n",
|
||
"169 1\n",
|
||
"170 1\n",
|
||
"171 1\n",
|
||
"172 1\n",
|
||
"173 0\n",
|
||
"174 0\n",
|
||
"175 1\n",
|
||
"176 1\n",
|
||
"177 1\n",
|
||
"178 1\n",
|
||
"179 1\n",
|
||
"Name: above_mean_sales, dtype: int32\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"\"\"\" compute predictions of testing dataset \"\"\"\n",
|
||
"discrete_y_pred = automl.predict(discrete_X_test)\n",
|
||
"print(\"Predicted label\", discrete_y_pred)\n",
|
||
"print(\"True label\", discrete_y_test)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"accuracy = 1.0\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from flaml.ml import sklearn_metric_loss_score\n",
|
||
"print(\"accuracy\", \"=\", 1 - sklearn_metric_loss_score(\"accuracy\", discrete_y_test, discrete_y_pred))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 5. Forecast Problems with Panel Datasets (Multiple Time Series)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Load data and preprocess\n",
|
||
"\n",
|
||
"Import Stallion & Co.'s beverage sales data from pytorch-forecasting, orginally from Kaggle. The dataset contains about 21,000 monthly historic sales record as well as additional information about the sales price, the location of the agency, special days such as holidays, and volume sold in the entire industry. There are thousands of unique wholesaler-SKU/products combinations, each representing an individual time series. The task is to provide a six month forecast of demand at SKU level for each wholesaler."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def get_stalliion_data():\n",
|
||
" from pytorch_forecasting.data.examples import get_stallion_data\n",
|
||
"\n",
|
||
" data = get_stallion_data()\n",
|
||
" # add time index\n",
|
||
" data[\"time_idx\"] = data[\"date\"].dt.year * 12 + data[\"date\"].dt.month\n",
|
||
" data[\"time_idx\"] -= data[\"time_idx\"].min()\n",
|
||
" # add additional features\n",
|
||
" data[\"month\"] = data.date.dt.month.astype(str).astype(\n",
|
||
" \"category\"\n",
|
||
" ) # categories have be strings\n",
|
||
" data[\"log_volume\"] = np.log(data.volume + 1e-8)\n",
|
||
" data[\"avg_volume_by_sku\"] = data.groupby(\n",
|
||
" [\"time_idx\", \"sku\"], observed=True\n",
|
||
" ).volume.transform(\"mean\")\n",
|
||
" data[\"avg_volume_by_agency\"] = data.groupby(\n",
|
||
" [\"time_idx\", \"agency\"], observed=True\n",
|
||
" ).volume.transform(\"mean\")\n",
|
||
" # we want to encode special days as one variable and thus need to first reverse one-hot encoding\n",
|
||
" special_days = [\n",
|
||
" \"easter_day\",\n",
|
||
" \"good_friday\",\n",
|
||
" \"new_year\",\n",
|
||
" \"christmas\",\n",
|
||
" \"labor_day\",\n",
|
||
" \"independence_day\",\n",
|
||
" \"revolution_day_memorial\",\n",
|
||
" \"regional_games\",\n",
|
||
" \"beer_capital\",\n",
|
||
" \"music_fest\",\n",
|
||
" ]\n",
|
||
" data[special_days] = (\n",
|
||
" data[special_days]\n",
|
||
" .apply(lambda x: x.map({0: \"-\", 1: x.name}))\n",
|
||
" .astype(\"category\")\n",
|
||
" )\n",
|
||
" return data, special_days"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"data, special_days = get_stalliion_data()\n",
|
||
"time_horizon = 6 # predict six months\n",
|
||
"# make time steps first column\n",
|
||
"data[\"time_idx\"] = data[\"date\"].dt.year * 12 + data[\"date\"].dt.month\n",
|
||
"data[\"time_idx\"] -= data[\"time_idx\"].min()\n",
|
||
"training_cutoff = data[\"time_idx\"].max() - time_horizon\n",
|
||
"ts_col = data.pop(\"date\")\n",
|
||
"data.insert(0, \"date\", ts_col)\n",
|
||
"# FLAML assumes input is not sorted, but we sort here for comparison purposes with y_test\n",
|
||
"data = data.sort_values([\"agency\", \"sku\", \"date\"])\n",
|
||
"X_train = data[lambda x: x.time_idx <= training_cutoff]\n",
|
||
"X_test = data[lambda x: x.time_idx > training_cutoff]\n",
|
||
"y_train = X_train.pop(\"volume\")\n",
|
||
"y_test = X_test.pop(\"volume\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>date</th>\n",
|
||
" <th>agency</th>\n",
|
||
" <th>sku</th>\n",
|
||
" <th>industry_volume</th>\n",
|
||
" <th>soda_volume</th>\n",
|
||
" <th>avg_max_temp</th>\n",
|
||
" <th>price_regular</th>\n",
|
||
" <th>price_actual</th>\n",
|
||
" <th>discount</th>\n",
|
||
" <th>avg_population_2017</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>football_gold_cup</th>\n",
|
||
" <th>beer_capital</th>\n",
|
||
" <th>music_fest</th>\n",
|
||
" <th>discount_in_percent</th>\n",
|
||
" <th>timeseries</th>\n",
|
||
" <th>time_idx</th>\n",
|
||
" <th>month</th>\n",
|
||
" <th>log_volume</th>\n",
|
||
" <th>avg_volume_by_sku</th>\n",
|
||
" <th>avg_volume_by_agency</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>25</th>\n",
|
||
" <td>2013-01-01</td>\n",
|
||
" <td>Agency_01</td>\n",
|
||
" <td>SKU_01</td>\n",
|
||
" <td>492612703</td>\n",
|
||
" <td>718394219</td>\n",
|
||
" <td>17.072000</td>\n",
|
||
" <td>1141.500000</td>\n",
|
||
" <td>1033.432731</td>\n",
|
||
" <td>108.067269</td>\n",
|
||
" <td>153733</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-</td>\n",
|
||
" <td>-</td>\n",
|
||
" <td>9.467128</td>\n",
|
||
" <td>249</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>4.390441</td>\n",
|
||
" <td>2613.377501</td>\n",
|
||
" <td>74.829600</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7183</th>\n",
|
||
" <td>2013-02-01</td>\n",
|
||
" <td>Agency_01</td>\n",
|
||
" <td>SKU_01</td>\n",
|
||
" <td>431937346</td>\n",
|
||
" <td>753938444</td>\n",
|
||
" <td>19.984000</td>\n",
|
||
" <td>1141.500000</td>\n",
|
||
" <td>1065.417195</td>\n",
|
||
" <td>76.082805</td>\n",
|
||
" <td>153733</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-</td>\n",
|
||
" <td>-</td>\n",
|
||
" <td>6.665160</td>\n",
|
||
" <td>249</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>4.585620</td>\n",
|
||
" <td>2916.978087</td>\n",
|
||
" <td>90.036700</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8928</th>\n",
|
||
" <td>2013-03-01</td>\n",
|
||
" <td>Agency_01</td>\n",
|
||
" <td>SKU_01</td>\n",
|
||
" <td>509281531</td>\n",
|
||
" <td>892192092</td>\n",
|
||
" <td>24.600000</td>\n",
|
||
" <td>1179.345820</td>\n",
|
||
" <td>1101.133633</td>\n",
|
||
" <td>78.212187</td>\n",
|
||
" <td>153733</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-</td>\n",
|
||
" <td>music_fest</td>\n",
|
||
" <td>6.631828</td>\n",
|
||
" <td>249</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>4.895628</td>\n",
|
||
" <td>3215.061952</td>\n",
|
||
" <td>130.487150</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10588</th>\n",
|
||
" <td>2013-04-01</td>\n",
|
||
" <td>Agency_01</td>\n",
|
||
" <td>SKU_01</td>\n",
|
||
" <td>532390389</td>\n",
|
||
" <td>838099501</td>\n",
|
||
" <td>27.532000</td>\n",
|
||
" <td>1226.687500</td>\n",
|
||
" <td>1138.283357</td>\n",
|
||
" <td>88.404143</td>\n",
|
||
" <td>153733</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-</td>\n",
|
||
" <td>-</td>\n",
|
||
" <td>7.206737</td>\n",
|
||
" <td>249</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>4.992553</td>\n",
|
||
" <td>3515.822697</td>\n",
|
||
" <td>130.246150</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12260</th>\n",
|
||
" <td>2013-05-01</td>\n",
|
||
" <td>Agency_01</td>\n",
|
||
" <td>SKU_01</td>\n",
|
||
" <td>551755254</td>\n",
|
||
" <td>864420003</td>\n",
|
||
" <td>29.396000</td>\n",
|
||
" <td>1230.331104</td>\n",
|
||
" <td>1148.969634</td>\n",
|
||
" <td>81.361470</td>\n",
|
||
" <td>153733</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-</td>\n",
|
||
" <td>-</td>\n",
|
||
" <td>6.612974</td>\n",
|
||
" <td>249</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>5.168254</td>\n",
|
||
" <td>3688.107793</td>\n",
|
||
" <td>159.051550</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8403</th>\n",
|
||
" <td>2017-02-01</td>\n",
|
||
" <td>Agency_60</td>\n",
|
||
" <td>SKU_23</td>\n",
|
||
" <td>530252010</td>\n",
|
||
" <td>850913048</td>\n",
|
||
" <td>25.242657</td>\n",
|
||
" <td>4261.294565</td>\n",
|
||
" <td>4087.082609</td>\n",
|
||
" <td>174.211956</td>\n",
|
||
" <td>2180611</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-</td>\n",
|
||
" <td>-</td>\n",
|
||
" <td>4.088240</td>\n",
|
||
" <td>190</td>\n",
|
||
" <td>49</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0.924259</td>\n",
|
||
" <td>2.418750</td>\n",
|
||
" <td>2664.670179</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10359</th>\n",
|
||
" <td>2017-03-01</td>\n",
|
||
" <td>Agency_60</td>\n",
|
||
" <td>SKU_23</td>\n",
|
||
" <td>613143990</td>\n",
|
||
" <td>886129111</td>\n",
|
||
" <td>25.374816</td>\n",
|
||
" <td>4259.769000</td>\n",
|
||
" <td>4126.776000</td>\n",
|
||
" <td>132.993000</td>\n",
|
||
" <td>2180611</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-</td>\n",
|
||
" <td>music_fest</td>\n",
|
||
" <td>3.122071</td>\n",
|
||
" <td>190</td>\n",
|
||
" <td>50</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>0.536493</td>\n",
|
||
" <td>4.353750</td>\n",
|
||
" <td>2965.472829</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12114</th>\n",
|
||
" <td>2017-04-01</td>\n",
|
||
" <td>Agency_60</td>\n",
|
||
" <td>SKU_23</td>\n",
|
||
" <td>589969396</td>\n",
|
||
" <td>940912941</td>\n",
|
||
" <td>27.109204</td>\n",
|
||
" <td>4261.896428</td>\n",
|
||
" <td>4115.753572</td>\n",
|
||
" <td>146.142856</td>\n",
|
||
" <td>2180611</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-</td>\n",
|
||
" <td>-</td>\n",
|
||
" <td>3.429057</td>\n",
|
||
" <td>190</td>\n",
|
||
" <td>51</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>0.231112</td>\n",
|
||
" <td>2.396250</td>\n",
|
||
" <td>2861.802300</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13884</th>\n",
|
||
" <td>2017-05-01</td>\n",
|
||
" <td>Agency_60</td>\n",
|
||
" <td>SKU_23</td>\n",
|
||
" <td>628759461</td>\n",
|
||
" <td>917412482</td>\n",
|
||
" <td>28.479272</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>2180611</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-</td>\n",
|
||
" <td>-</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>190</td>\n",
|
||
" <td>52</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>-18.420681</td>\n",
|
||
" <td>2.182500</td>\n",
|
||
" <td>3489.190286</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15669</th>\n",
|
||
" <td>2017-06-01</td>\n",
|
||
" <td>Agency_60</td>\n",
|
||
" <td>SKU_23</td>\n",
|
||
" <td>636846973</td>\n",
|
||
" <td>928366256</td>\n",
|
||
" <td>29.609259</td>\n",
|
||
" <td>4256.675000</td>\n",
|
||
" <td>4246.018750</td>\n",
|
||
" <td>10.656250</td>\n",
|
||
" <td>2180611</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-</td>\n",
|
||
" <td>-</td>\n",
|
||
" <td>0.250342</td>\n",
|
||
" <td>190</td>\n",
|
||
" <td>53</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>0.924259</td>\n",
|
||
" <td>2.362500</td>\n",
|
||
" <td>3423.810793</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>18900 rows × 30 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" date agency sku industry_volume soda_volume \\\n",
|
||
"25 2013-01-01 Agency_01 SKU_01 492612703 718394219 \n",
|
||
"7183 2013-02-01 Agency_01 SKU_01 431937346 753938444 \n",
|
||
"8928 2013-03-01 Agency_01 SKU_01 509281531 892192092 \n",
|
||
"10588 2013-04-01 Agency_01 SKU_01 532390389 838099501 \n",
|
||
"12260 2013-05-01 Agency_01 SKU_01 551755254 864420003 \n",
|
||
"... ... ... ... ... ... \n",
|
||
"8403 2017-02-01 Agency_60 SKU_23 530252010 850913048 \n",
|
||
"10359 2017-03-01 Agency_60 SKU_23 613143990 886129111 \n",
|
||
"12114 2017-04-01 Agency_60 SKU_23 589969396 940912941 \n",
|
||
"13884 2017-05-01 Agency_60 SKU_23 628759461 917412482 \n",
|
||
"15669 2017-06-01 Agency_60 SKU_23 636846973 928366256 \n",
|
||
"\n",
|
||
" avg_max_temp price_regular price_actual discount \\\n",
|
||
"25 17.072000 1141.500000 1033.432731 108.067269 \n",
|
||
"7183 19.984000 1141.500000 1065.417195 76.082805 \n",
|
||
"8928 24.600000 1179.345820 1101.133633 78.212187 \n",
|
||
"10588 27.532000 1226.687500 1138.283357 88.404143 \n",
|
||
"12260 29.396000 1230.331104 1148.969634 81.361470 \n",
|
||
"... ... ... ... ... \n",
|
||
"8403 25.242657 4261.294565 4087.082609 174.211956 \n",
|
||
"10359 25.374816 4259.769000 4126.776000 132.993000 \n",
|
||
"12114 27.109204 4261.896428 4115.753572 146.142856 \n",
|
||
"13884 28.479272 0.000000 0.000000 0.000000 \n",
|
||
"15669 29.609259 4256.675000 4246.018750 10.656250 \n",
|
||
"\n",
|
||
" avg_population_2017 ... football_gold_cup beer_capital music_fest \\\n",
|
||
"25 153733 ... 0 - - \n",
|
||
"7183 153733 ... 0 - - \n",
|
||
"8928 153733 ... 0 - music_fest \n",
|
||
"10588 153733 ... 0 - - \n",
|
||
"12260 153733 ... 0 - - \n",
|
||
"... ... ... ... ... ... \n",
|
||
"8403 2180611 ... 0 - - \n",
|
||
"10359 2180611 ... 0 - music_fest \n",
|
||
"12114 2180611 ... 0 - - \n",
|
||
"13884 2180611 ... 0 - - \n",
|
||
"15669 2180611 ... 0 - - \n",
|
||
"\n",
|
||
" discount_in_percent timeseries time_idx month log_volume \\\n",
|
||
"25 9.467128 249 0 1 4.390441 \n",
|
||
"7183 6.665160 249 1 2 4.585620 \n",
|
||
"8928 6.631828 249 2 3 4.895628 \n",
|
||
"10588 7.206737 249 3 4 4.992553 \n",
|
||
"12260 6.612974 249 4 5 5.168254 \n",
|
||
"... ... ... ... ... ... \n",
|
||
"8403 4.088240 190 49 2 0.924259 \n",
|
||
"10359 3.122071 190 50 3 0.536493 \n",
|
||
"12114 3.429057 190 51 4 0.231112 \n",
|
||
"13884 0.000000 190 52 5 -18.420681 \n",
|
||
"15669 0.250342 190 53 6 0.924259 \n",
|
||
"\n",
|
||
" avg_volume_by_sku avg_volume_by_agency \n",
|
||
"25 2613.377501 74.829600 \n",
|
||
"7183 2916.978087 90.036700 \n",
|
||
"8928 3215.061952 130.487150 \n",
|
||
"10588 3515.822697 130.246150 \n",
|
||
"12260 3688.107793 159.051550 \n",
|
||
"... ... ... \n",
|
||
"8403 2.418750 2664.670179 \n",
|
||
"10359 4.353750 2965.472829 \n",
|
||
"12114 2.396250 2861.802300 \n",
|
||
"13884 2.182500 3489.190286 \n",
|
||
"15669 2.362500 3423.810793 \n",
|
||
"\n",
|
||
"[18900 rows x 30 columns]"
|
||
]
|
||
},
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"X_train"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Run FLAML"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Missing timestamps detected. To avoid error with estimators, set estimator list to ['prophet']. \n",
|
||
"[flaml.automl: 07-28 21:26:03] {2478} INFO - task = ts_forecast_panel\n",
|
||
"[flaml.automl: 07-28 21:26:03] {2480} INFO - Data split method: time\n",
|
||
"[flaml.automl: 07-28 21:26:03] {2483} INFO - Evaluation method: holdout\n",
|
||
"[flaml.automl: 07-28 21:26:03] {2552} INFO - Minimizing error metric: mape\n",
|
||
"[flaml.automl: 07-28 21:26:03] {2694} INFO - List of ML learners in AutoML Run: ['tft']\n",
|
||
"[flaml.automl: 07-28 21:26:03] {2986} INFO - iteration 0, current learner tft\n",
|
||
"GPU available: False, used: False\n",
|
||
"TPU available: False, using: 0 TPU cores\n",
|
||
"IPU available: False, using: 0 IPUs\n",
|
||
"\n",
|
||
" | Name | Type | Params\n",
|
||
"----------------------------------------------------------------------------------------\n",
|
||
"0 | loss | QuantileLoss | 0 \n",
|
||
"1 | logging_metrics | ModuleList | 0 \n",
|
||
"2 | input_embeddings | MultiEmbedding | 1.3 K \n",
|
||
"3 | prescalers | ModuleDict | 256 \n",
|
||
"4 | static_variable_selection | VariableSelectionNetwork | 3.4 K \n",
|
||
"5 | encoder_variable_selection | VariableSelectionNetwork | 8.0 K \n",
|
||
"6 | decoder_variable_selection | VariableSelectionNetwork | 2.7 K \n",
|
||
"7 | static_context_variable_selection | GatedResidualNetwork | 1.1 K \n",
|
||
"8 | static_context_initial_hidden_lstm | GatedResidualNetwork | 1.1 K \n",
|
||
"9 | static_context_initial_cell_lstm | GatedResidualNetwork | 1.1 K \n",
|
||
"10 | static_context_enrichment | GatedResidualNetwork | 1.1 K \n",
|
||
"11 | lstm_encoder | LSTM | 4.4 K \n",
|
||
"12 | lstm_decoder | LSTM | 4.4 K \n",
|
||
"13 | post_lstm_gate_encoder | GatedLinearUnit | 544 \n",
|
||
"14 | post_lstm_add_norm_encoder | AddNorm | 32 \n",
|
||
"15 | static_enrichment | GatedResidualNetwork | 1.4 K \n",
|
||
"16 | multihead_attn | InterpretableMultiHeadAttention | 676 \n",
|
||
"17 | post_attn_gate_norm | GateAddNorm | 576 \n",
|
||
"18 | pos_wise_ff | GatedResidualNetwork | 1.1 K \n",
|
||
"19 | pre_output_gate_norm | GateAddNorm | 576 \n",
|
||
"20 | output_layer | Linear | 119 \n",
|
||
"----------------------------------------------------------------------------------------\n",
|
||
"33.6 K Trainable params\n",
|
||
"0 Non-trainable params\n",
|
||
"33.6 K Total params\n",
|
||
"0.135 Total estimated model params size (MB)\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Epoch 19: 100%|██████████| 129/129 [00:56<00:00, 2.27it/s, loss=45.9, v_num=2, train_loss_step=43.00, val_loss=65.20, train_loss_epoch=46.50]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[flaml.automl: 07-28 21:46:46] {3114} INFO - Estimated sufficient time budget=12424212s. Estimated necessary time budget=12424s.\n",
|
||
"[flaml.automl: 07-28 21:46:46] {3161} INFO - at 1242.6s,\testimator tft's best error=1324290483134574.7500,\tbest estimator tft's best error=1324290483134574.7500\n",
|
||
"GPU available: False, used: False\n",
|
||
"TPU available: False, using: 0 TPU cores\n",
|
||
"IPU available: False, using: 0 IPUs\n",
|
||
"\n",
|
||
" | Name | Type | Params\n",
|
||
"----------------------------------------------------------------------------------------\n",
|
||
"0 | loss | QuantileLoss | 0 \n",
|
||
"1 | logging_metrics | ModuleList | 0 \n",
|
||
"2 | input_embeddings | MultiEmbedding | 1.3 K \n",
|
||
"3 | prescalers | ModuleDict | 256 \n",
|
||
"4 | static_variable_selection | VariableSelectionNetwork | 3.4 K \n",
|
||
"5 | encoder_variable_selection | VariableSelectionNetwork | 8.0 K \n",
|
||
"6 | decoder_variable_selection | VariableSelectionNetwork | 2.7 K \n",
|
||
"7 | static_context_variable_selection | GatedResidualNetwork | 1.1 K \n",
|
||
"8 | static_context_initial_hidden_lstm | GatedResidualNetwork | 1.1 K \n",
|
||
"9 | static_context_initial_cell_lstm | GatedResidualNetwork | 1.1 K \n",
|
||
"10 | static_context_enrichment | GatedResidualNetwork | 1.1 K \n",
|
||
"11 | lstm_encoder | LSTM | 4.4 K \n",
|
||
"12 | lstm_decoder | LSTM | 4.4 K \n",
|
||
"13 | post_lstm_gate_encoder | GatedLinearUnit | 544 \n",
|
||
"14 | post_lstm_add_norm_encoder | AddNorm | 32 \n",
|
||
"15 | static_enrichment | GatedResidualNetwork | 1.4 K \n",
|
||
"16 | multihead_attn | InterpretableMultiHeadAttention | 676 \n",
|
||
"17 | post_attn_gate_norm | GateAddNorm | 576 \n",
|
||
"18 | pos_wise_ff | GatedResidualNetwork | 1.1 K \n",
|
||
"19 | pre_output_gate_norm | GateAddNorm | 576 \n",
|
||
"20 | output_layer | Linear | 119 \n",
|
||
"----------------------------------------------------------------------------------------\n",
|
||
"33.6 K Trainable params\n",
|
||
"0 Non-trainable params\n",
|
||
"33.6 K Total params\n",
|
||
"0.135 Total estimated model params size (MB)\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Epoch 19: 100%|██████████| 145/145 [01:03<00:00, 2.28it/s, loss=45.2, v_num=3, train_loss_step=46.30, val_loss=67.60, train_loss_epoch=48.10]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[flaml.automl: 07-28 22:08:05] {3425} INFO - retrain tft for 1279.6s\n",
|
||
"[flaml.automl: 07-28 22:08:05] {3432} INFO - retrained model: TemporalFusionTransformer(\n",
|
||
" (loss): QuantileLoss()\n",
|
||
" (logging_metrics): ModuleList(\n",
|
||
" (0): SMAPE()\n",
|
||
" (1): MAE()\n",
|
||
" (2): RMSE()\n",
|
||
" (3): MAPE()\n",
|
||
" )\n",
|
||
" (input_embeddings): MultiEmbedding(\n",
|
||
" (embeddings): ModuleDict(\n",
|
||
" (agency): Embedding(58, 16)\n",
|
||
" (sku): Embedding(25, 10)\n",
|
||
" (special_days): TimeDistributedEmbeddingBag(11, 6, mode=sum)\n",
|
||
" (month): Embedding(12, 6)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (prescalers): ModuleDict(\n",
|
||
" (avg_population_2017): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (avg_yearly_household_income_2017): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (encoder_length): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (y_center): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (y_scale): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (time_idx): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (price_regular): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (discount_in_percent): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (relative_time_idx): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (y): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (log_volume): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (industry_volume): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (soda_volume): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (avg_max_temp): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (avg_volume_by_agency): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (avg_volume_by_sku): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" )\n",
|
||
" (static_variable_selection): VariableSelectionNetwork(\n",
|
||
" (flattened_grn): GatedResidualNetwork(\n",
|
||
" (resample_norm): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((7,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (fc1): Linear(in_features=66, out_features=7, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=7, out_features=7, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=7, out_features=14, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((7,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (single_variable_grns): ModuleDict(\n",
|
||
" (agency): ResampleNorm(\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (sku): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (avg_population_2017): GatedResidualNetwork(\n",
|
||
" (resample_norm): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (fc1): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=8, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (avg_yearly_household_income_2017): GatedResidualNetwork(\n",
|
||
" (resample_norm): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (fc1): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=8, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (encoder_length): GatedResidualNetwork(\n",
|
||
" (resample_norm): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (fc1): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=8, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (y_center): GatedResidualNetwork(\n",
|
||
" (resample_norm): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (fc1): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=8, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (y_scale): GatedResidualNetwork(\n",
|
||
" (resample_norm): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (fc1): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=8, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (prescalers): ModuleDict(\n",
|
||
" (avg_population_2017): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (avg_yearly_household_income_2017): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (encoder_length): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (y_center): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (y_scale): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" )\n",
|
||
" (softmax): Softmax(dim=-1)\n",
|
||
" )\n",
|
||
" (encoder_variable_selection): VariableSelectionNetwork(\n",
|
||
" (flattened_grn): GatedResidualNetwork(\n",
|
||
" (resample_norm): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((13,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (fc1): Linear(in_features=100, out_features=13, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (context): Linear(in_features=16, out_features=13, bias=False)\n",
|
||
" (fc2): Linear(in_features=13, out_features=13, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=13, out_features=26, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((13,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (single_variable_grns): ModuleDict(\n",
|
||
" (special_days): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (month): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (time_idx): GatedResidualNetwork(\n",
|
||
" (resample_norm): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (fc1): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=8, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (price_regular): GatedResidualNetwork(\n",
|
||
" (resample_norm): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (fc1): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=8, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (discount_in_percent): GatedResidualNetwork(\n",
|
||
" (resample_norm): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (fc1): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=8, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (relative_time_idx): GatedResidualNetwork(\n",
|
||
" (resample_norm): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (fc1): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=8, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (y): GatedResidualNetwork(\n",
|
||
" (resample_norm): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (fc1): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=8, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (log_volume): GatedResidualNetwork(\n",
|
||
" (resample_norm): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (fc1): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=8, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (industry_volume): GatedResidualNetwork(\n",
|
||
" (resample_norm): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (fc1): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=8, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (soda_volume): GatedResidualNetwork(\n",
|
||
" (resample_norm): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (fc1): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=8, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (avg_max_temp): GatedResidualNetwork(\n",
|
||
" (resample_norm): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (fc1): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=8, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (avg_volume_by_agency): GatedResidualNetwork(\n",
|
||
" (resample_norm): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (fc1): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=8, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (avg_volume_by_sku): GatedResidualNetwork(\n",
|
||
" (resample_norm): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (fc1): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=8, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (prescalers): ModuleDict(\n",
|
||
" (time_idx): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (price_regular): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (discount_in_percent): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (relative_time_idx): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (y): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (log_volume): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (industry_volume): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (soda_volume): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (avg_max_temp): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (avg_volume_by_agency): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (avg_volume_by_sku): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" )\n",
|
||
" (softmax): Softmax(dim=-1)\n",
|
||
" )\n",
|
||
" (decoder_variable_selection): VariableSelectionNetwork(\n",
|
||
" (flattened_grn): GatedResidualNetwork(\n",
|
||
" (resample_norm): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((6,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (fc1): Linear(in_features=44, out_features=6, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (context): Linear(in_features=16, out_features=6, bias=False)\n",
|
||
" (fc2): Linear(in_features=6, out_features=6, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=6, out_features=12, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((6,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (single_variable_grns): ModuleDict(\n",
|
||
" (special_days): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (month): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (time_idx): GatedResidualNetwork(\n",
|
||
" (resample_norm): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (fc1): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=8, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (price_regular): GatedResidualNetwork(\n",
|
||
" (resample_norm): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (fc1): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=8, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (discount_in_percent): GatedResidualNetwork(\n",
|
||
" (resample_norm): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (fc1): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=8, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (relative_time_idx): GatedResidualNetwork(\n",
|
||
" (resample_norm): ResampleNorm(\n",
|
||
" (resample): TimeDistributedInterpolation()\n",
|
||
" (gate): Sigmoid()\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (fc1): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=8, out_features=8, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=8, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (prescalers): ModuleDict(\n",
|
||
" (time_idx): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (price_regular): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (discount_in_percent): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" (relative_time_idx): Linear(in_features=1, out_features=8, bias=True)\n",
|
||
" )\n",
|
||
" (softmax): Softmax(dim=-1)\n",
|
||
" )\n",
|
||
" (static_context_variable_selection): GatedResidualNetwork(\n",
|
||
" (fc1): Linear(in_features=16, out_features=16, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=16, out_features=16, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=16, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (static_context_initial_hidden_lstm): GatedResidualNetwork(\n",
|
||
" (fc1): Linear(in_features=16, out_features=16, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=16, out_features=16, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=16, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (static_context_initial_cell_lstm): GatedResidualNetwork(\n",
|
||
" (fc1): Linear(in_features=16, out_features=16, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=16, out_features=16, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=16, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (static_context_enrichment): GatedResidualNetwork(\n",
|
||
" (fc1): Linear(in_features=16, out_features=16, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=16, out_features=16, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=16, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (lstm_encoder): LSTM(16, 16, num_layers=2, batch_first=True, dropout=0.1)\n",
|
||
" (lstm_decoder): LSTM(16, 16, num_layers=2, batch_first=True, dropout=0.1)\n",
|
||
" (post_lstm_gate_encoder): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=16, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (post_lstm_gate_decoder): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=16, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (post_lstm_add_norm_encoder): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (post_lstm_add_norm_decoder): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" (static_enrichment): GatedResidualNetwork(\n",
|
||
" (fc1): Linear(in_features=16, out_features=16, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (context): Linear(in_features=16, out_features=16, bias=False)\n",
|
||
" (fc2): Linear(in_features=16, out_features=16, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=16, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (multihead_attn): InterpretableMultiHeadAttention(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (v_layer): Linear(in_features=16, out_features=4, bias=True)\n",
|
||
" (q_layers): ModuleList(\n",
|
||
" (0): Linear(in_features=16, out_features=4, bias=True)\n",
|
||
" (1): Linear(in_features=16, out_features=4, bias=True)\n",
|
||
" (2): Linear(in_features=16, out_features=4, bias=True)\n",
|
||
" (3): Linear(in_features=16, out_features=4, bias=True)\n",
|
||
" )\n",
|
||
" (k_layers): ModuleList(\n",
|
||
" (0): Linear(in_features=16, out_features=4, bias=True)\n",
|
||
" (1): Linear(in_features=16, out_features=4, bias=True)\n",
|
||
" (2): Linear(in_features=16, out_features=4, bias=True)\n",
|
||
" (3): Linear(in_features=16, out_features=4, bias=True)\n",
|
||
" )\n",
|
||
" (attention): ScaledDotProductAttention(\n",
|
||
" (softmax): Softmax(dim=2)\n",
|
||
" )\n",
|
||
" (w_h): Linear(in_features=4, out_features=16, bias=False)\n",
|
||
" )\n",
|
||
" (post_attn_gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=16, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (pos_wise_ff): GatedResidualNetwork(\n",
|
||
" (fc1): Linear(in_features=16, out_features=16, bias=True)\n",
|
||
" (elu): ELU(alpha=1.0)\n",
|
||
" (fc2): Linear(in_features=16, out_features=16, bias=True)\n",
|
||
" (gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=16, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (pre_output_gate_norm): GateAddNorm(\n",
|
||
" (glu): GatedLinearUnit(\n",
|
||
" (fc): Linear(in_features=16, out_features=32, bias=True)\n",
|
||
" )\n",
|
||
" (add_norm): AddNorm(\n",
|
||
" (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (output_layer): Linear(in_features=16, out_features=7, bias=True)\n",
|
||
")\n",
|
||
"[flaml.automl: 07-28 22:08:05] {2725} INFO - fit succeeded\n",
|
||
"[flaml.automl: 07-28 22:08:05] {2726} INFO - Time taken to find the best model: 1242.6435902118683\n",
|
||
"[flaml.automl: 07-28 22:08:05] {2737} WARNING - Time taken to find the best model is 414% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from flaml import AutoML\n",
|
||
"automl = AutoML()\n",
|
||
"settings = {\n",
|
||
" \"time_budget\": 300, # total running time in seconds\n",
|
||
" \"metric\": \"mape\", # primary metric\n",
|
||
" \"task\": \"ts_forecast_panel\", # task type\n",
|
||
" \"log_file_name\": \"stallion_forecast.log\", # flaml log file\n",
|
||
" \"eval_method\": \"holdout\",\n",
|
||
"}\n",
|
||
"fit_kwargs_by_estimator = {\n",
|
||
" \"tft\": {\n",
|
||
" \"max_encoder_length\": 24,\n",
|
||
" \"static_categoricals\": [\"agency\", \"sku\"],\n",
|
||
" \"static_reals\": [\"avg_population_2017\", \"avg_yearly_household_income_2017\"],\n",
|
||
" \"time_varying_known_categoricals\": [\"special_days\", \"month\"],\n",
|
||
" \"variable_groups\": {\n",
|
||
" \"special_days\": special_days\n",
|
||
" }, # group of categorical variables can be treated as one variable\n",
|
||
" \"time_varying_known_reals\": [\n",
|
||
" \"time_idx\",\n",
|
||
" \"price_regular\",\n",
|
||
" \"discount_in_percent\",\n",
|
||
" ],\n",
|
||
" \"time_varying_unknown_categoricals\": [],\n",
|
||
" \"time_varying_unknown_reals\": [\n",
|
||
" \"y\", # always need a 'y' column for the target column\n",
|
||
" \"log_volume\",\n",
|
||
" \"industry_volume\",\n",
|
||
" \"soda_volume\",\n",
|
||
" \"avg_max_temp\",\n",
|
||
" \"avg_volume_by_agency\",\n",
|
||
" \"avg_volume_by_sku\",\n",
|
||
" ],\n",
|
||
" \"batch_size\": 128,\n",
|
||
" \"gpu_per_trial\": -1,\n",
|
||
" }\n",
|
||
"}\n",
|
||
"\"\"\"The main flaml automl API\"\"\"\n",
|
||
"automl.fit(\n",
|
||
" X_train=X_train,\n",
|
||
" y_train=y_train,\n",
|
||
" **settings,\n",
|
||
" period=time_horizon,\n",
|
||
" group_ids=[\"agency\", \"sku\"],\n",
|
||
" fit_kwargs_by_estimator=fit_kwargs_by_estimator,\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Prediction and Metrics"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"17156 59.292\n",
|
||
"18946 66.420\n",
|
||
"20680 95.904\n",
|
||
"3189 52.812\n",
|
||
"4954 37.908\n",
|
||
" ... \n",
|
||
"19207 1.980\n",
|
||
"20996 1.260\n",
|
||
"3499 0.990\n",
|
||
"5248 0.090\n",
|
||
"6793 2.250\n",
|
||
"Name: volume, Length: 2100, dtype: float64\n",
|
||
"Agency_01 SKU_01 2017-07-01 2017-07-01 77.331932\n",
|
||
" 2017-08-01 2017-08-01 71.502121\n",
|
||
" 2017-09-01 2017-09-01 88.353912\n",
|
||
" 2017-10-01 2017-10-01 60.969868\n",
|
||
" 2017-11-01 2017-11-01 60.205246\n",
|
||
" ... \n",
|
||
"Agency_60 SKU_23 2017-08-01 2017-08-01 1.713270\n",
|
||
" 2017-09-01 2017-09-01 1.513947\n",
|
||
" 2017-10-01 2017-10-01 0.993663\n",
|
||
" 2017-11-01 2017-11-01 1.144696\n",
|
||
" 2017-12-01 2017-12-01 1.989883\n",
|
||
"Length: 2100, dtype: float32\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"\"\"\" compute predictions of testing dataset \"\"\"\n",
|
||
"y_pred = automl.predict(X_test)\n",
|
||
"print(y_test)\n",
|
||
"print(y_pred)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"mape = 2743417592614313.0\n",
|
||
"smape = 52.37\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"\"\"\" compute different metric values on testing dataset\"\"\"\n",
|
||
"from flaml.ml import sklearn_metric_loss_score\n",
|
||
"print(\"mape\", \"=\", sklearn_metric_loss_score(\"mape\", y_pred, y_test))\n",
|
||
"\n",
|
||
"def smape(y_pred, y_test):\n",
|
||
" import numpy as np\n",
|
||
"\n",
|
||
" y_test, y_pred = np.array(y_test), np.array(y_pred)\n",
|
||
" return round(\n",
|
||
" np.mean(\n",
|
||
" np.abs(y_pred - y_test) /\n",
|
||
" ((np.abs(y_pred) + np.abs(y_test)) / 2)\n",
|
||
" ) * 100, 2\n",
|
||
" )\n",
|
||
"\n",
|
||
"print(\"smape\", \"=\", smape(y_pred, y_test))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 6. Comparison with Alternatives (CO2 Dataset)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"FLAML's MAPE"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"flaml mape = 0.0005710586398294955\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from flaml.ml import sklearn_metric_loss_score\n",
|
||
"print('flaml mape', '=', sklearn_metric_loss_score('mape', flaml_y_pred, y_test))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Default Prophet"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from prophet import Prophet\n",
|
||
"prophet_model = Prophet()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<prophet.forecaster.Prophet at 0x1e2d990d7c0>"
|
||
]
|
||
},
|
||
"execution_count": 35,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"X_train_prophet = train_df.copy()\n",
|
||
"X_train_prophet = X_train_prophet.rename(columns={'index': 'ds', 'co2': 'y'})\n",
|
||
"prophet_model.fit(X_train_prophet)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Predicted labels 0 370.450675\n",
|
||
"1 371.177764\n",
|
||
"2 372.229577\n",
|
||
"3 373.419835\n",
|
||
"4 373.914917\n",
|
||
"5 373.406484\n",
|
||
"6 372.053428\n",
|
||
"7 370.149037\n",
|
||
"8 368.566631\n",
|
||
"9 368.646853\n",
|
||
"10 369.863891\n",
|
||
"11 371.135959\n",
|
||
"Name: yhat, dtype: float64\n",
|
||
"True labels 514 370.175\n",
|
||
"515 371.325\n",
|
||
"516 372.060\n",
|
||
"517 372.775\n",
|
||
"518 373.800\n",
|
||
"519 373.060\n",
|
||
"520 371.300\n",
|
||
"521 369.425\n",
|
||
"522 367.880\n",
|
||
"523 368.050\n",
|
||
"524 369.375\n",
|
||
"525 371.020\n",
|
||
"Name: co2, dtype: float64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"X_test_prophet = X_test.copy()\n",
|
||
"X_test_prophet = X_test_prophet.rename(columns={'index': 'ds'})\n",
|
||
"prophet_y_pred = prophet_model.predict(X_test_prophet)['yhat']\n",
|
||
"print('Predicted labels', prophet_y_pred)\n",
|
||
"print('True labels', y_test)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Default Prophet MAPE"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 37,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"default prophet mape = 0.0011396920680673015\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from flaml.ml import sklearn_metric_loss_score\n",
|
||
"print('default prophet mape', '=', sklearn_metric_loss_score('mape', prophet_y_pred, y_test))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Auto ARIMA Models"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 38,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from pmdarima.arima import auto_arima\n",
|
||
"import pandas as pd\n",
|
||
"import time\n",
|
||
"\n",
|
||
"X_train_arima = train_df.copy()\n",
|
||
"X_train_arima.index = pd.to_datetime(X_train_arima['index'])\n",
|
||
"X_train_arima = X_train_arima.drop('index', axis=1)\n",
|
||
"X_train_arima = X_train_arima.rename(columns={'co2': 'y'})"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 39,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" ARIMA(0,1,0)(0,0,0)[0] intercept : AIC=1638.009, Time=0.02 sec\n",
|
||
" ARIMA(0,1,1)(0,0,0)[0] intercept : AIC=1344.207, Time=0.09 sec\n",
|
||
" ARIMA(0,1,2)(0,0,0)[0] intercept : AIC=1222.286, Time=0.14 sec\n",
|
||
" ARIMA(0,1,3)(0,0,0)[0] intercept : AIC=1174.928, Time=0.20 sec\n",
|
||
" ARIMA(0,1,4)(0,0,0)[0] intercept : AIC=1188.947, Time=0.43 sec\n",
|
||
" ARIMA(0,1,5)(0,0,0)[0] intercept : AIC=1091.452, Time=0.55 sec\n",
|
||
" ARIMA(1,1,0)(0,0,0)[0] intercept : AIC=1298.693, Time=0.08 sec\n",
|
||
" ARIMA(1,1,1)(0,0,0)[0] intercept : AIC=1240.963, Time=0.12 sec\n",
|
||
" ARIMA(1,1,2)(0,0,0)[0] intercept : AIC=1196.535, Time=0.19 sec\n",
|
||
" ARIMA(1,1,3)(0,0,0)[0] intercept : AIC=1176.484, Time=0.34 sec\n",
|
||
" ARIMA(1,1,4)(0,0,0)[0] intercept : AIC=inf, Time=1.18 sec\n",
|
||
" ARIMA(2,1,0)(0,0,0)[0] intercept : AIC=1180.404, Time=0.08 sec\n",
|
||
" ARIMA(2,1,1)(0,0,0)[0] intercept : AIC=990.719, Time=0.26 sec\n",
|
||
" ARIMA(2,1,2)(0,0,0)[0] intercept : AIC=988.094, Time=0.53 sec\n",
|
||
" ARIMA(2,1,3)(0,0,0)[0] intercept : AIC=1140.469, Time=0.53 sec\n",
|
||
" ARIMA(3,1,0)(0,0,0)[0] intercept : AIC=1126.139, Time=0.21 sec\n",
|
||
" ARIMA(3,1,1)(0,0,0)[0] intercept : AIC=989.496, Time=0.51 sec\n",
|
||
" ARIMA(3,1,2)(0,0,0)[0] intercept : AIC=991.558, Time=1.17 sec\n",
|
||
" ARIMA(4,1,0)(0,0,0)[0] intercept : AIC=1125.025, Time=0.19 sec\n",
|
||
" ARIMA(4,1,1)(0,0,0)[0] intercept : AIC=988.660, Time=0.98 sec\n",
|
||
" ARIMA(5,1,0)(0,0,0)[0] intercept : AIC=1113.673, Time=0.22 sec\n",
|
||
"\n",
|
||
"Best model: ARIMA(2,1,2)(0,0,0)[0] intercept\n",
|
||
"Total fit time: 8.039 seconds\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# use same search space as FLAML\n",
|
||
"start_time = time.time()\n",
|
||
"arima_model = auto_arima(X_train_arima,\n",
|
||
" start_p=2, d=None, start_q=1, max_p=10, max_d=10, max_q=10,\n",
|
||
" suppress_warnings=True, stepwise=False, seasonal=False,\n",
|
||
" error_action='ignore', trace=True, n_fits=650)\n",
|
||
"autoarima_y_pred = arima_model.predict(n_periods=12)\n",
|
||
"arima_time = time.time() - start_time"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 40,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" ARIMA(0,1,0)(0,0,0)[12] intercept : AIC=1638.009, Time=0.02 sec\n",
|
||
" ARIMA(0,1,0)(0,0,1)[12] intercept : AIC=1238.943, Time=0.23 sec\n",
|
||
" ARIMA(0,1,0)(0,0,2)[12] intercept : AIC=1040.890, Time=0.53 sec\n",
|
||
" ARIMA(0,1,0)(0,0,3)[12] intercept : AIC=911.545, Time=1.76 sec\n",
|
||
" ARIMA(0,1,0)(0,0,4)[12] intercept : AIC=823.103, Time=3.18 sec\n",
|
||
" ARIMA(0,1,0)(0,0,5)[12] intercept : AIC=792.850, Time=5.99 sec\n",
|
||
" ARIMA(0,1,0)(1,0,0)[12] intercept : AIC=inf, Time=0.26 sec\n",
|
||
" ARIMA(0,1,0)(1,0,1)[12] intercept : AIC=inf, Time=1.37 sec\n",
|
||
" ARIMA(0,1,0)(1,0,2)[12] intercept : AIC=inf, Time=2.60 sec\n",
|
||
" ARIMA(0,1,0)(1,0,3)[12] intercept : AIC=447.302, Time=5.94 sec\n",
|
||
" ARIMA(0,1,0)(1,0,4)[12] intercept : AIC=inf, Time=11.23 sec\n",
|
||
" ARIMA(0,1,0)(2,0,0)[12] intercept : AIC=inf, Time=1.10 sec\n",
|
||
" ARIMA(0,1,0)(2,0,1)[12] intercept : AIC=inf, Time=2.37 sec\n",
|
||
" ARIMA(0,1,0)(2,0,2)[12] intercept : AIC=inf, Time=2.75 sec\n",
|
||
" ARIMA(0,1,0)(2,0,3)[12] intercept : AIC=427.135, Time=7.49 sec\n",
|
||
" ARIMA(0,1,0)(3,0,0)[12] intercept : AIC=inf, Time=3.56 sec\n",
|
||
" ARIMA(0,1,0)(3,0,1)[12] intercept : AIC=424.286, Time=6.44 sec\n",
|
||
" ARIMA(0,1,0)(3,0,2)[12] intercept : AIC=431.435, Time=6.86 sec\n",
|
||
" ARIMA(0,1,0)(4,0,0)[12] intercept : AIC=inf, Time=8.12 sec\n",
|
||
" ARIMA(0,1,0)(4,0,1)[12] intercept : AIC=430.321, Time=11.65 sec\n",
|
||
" ARIMA(0,1,0)(5,0,0)[12] intercept : AIC=inf, Time=17.56 sec\n",
|
||
" ARIMA(0,1,1)(0,0,0)[12] intercept : AIC=1344.207, Time=0.08 sec\n",
|
||
" ARIMA(0,1,1)(0,0,1)[12] intercept : AIC=1112.274, Time=0.37 sec\n",
|
||
" ARIMA(0,1,1)(0,0,2)[12] intercept : AIC=993.565, Time=0.76 sec\n",
|
||
" ARIMA(0,1,1)(0,0,3)[12] intercept : AIC=891.683, Time=3.11 sec\n",
|
||
" ARIMA(0,1,1)(0,0,4)[12] intercept : AIC=820.025, Time=5.52 sec\n",
|
||
" ARIMA(0,1,1)(1,0,0)[12] intercept : AIC=612.811, Time=0.60 sec\n",
|
||
" ARIMA(0,1,1)(1,0,1)[12] intercept : AIC=393.876, Time=1.61 sec\n",
|
||
" ARIMA(0,1,1)(1,0,2)[12] intercept : AIC=416.358, Time=3.64 sec\n",
|
||
" ARIMA(0,1,1)(1,0,3)[12] intercept : AIC=424.837, Time=8.45 sec\n",
|
||
" ARIMA(0,1,1)(2,0,0)[12] intercept : AIC=510.637, Time=1.63 sec\n",
|
||
" ARIMA(0,1,1)(2,0,1)[12] intercept : AIC=398.093, Time=3.18 sec\n",
|
||
" ARIMA(0,1,1)(2,0,2)[12] intercept : AIC=401.837, Time=4.14 sec\n",
|
||
" ARIMA(0,1,1)(3,0,0)[12] intercept : AIC=467.985, Time=8.25 sec\n",
|
||
" ARIMA(0,1,1)(3,0,1)[12] intercept : AIC=412.757, Time=10.34 sec\n",
|
||
" ARIMA(0,1,1)(4,0,0)[12] intercept : AIC=448.948, Time=7.42 sec\n",
|
||
" ARIMA(0,1,2)(0,0,0)[12] intercept : AIC=1222.286, Time=0.14 sec\n",
|
||
" ARIMA(0,1,2)(0,0,1)[12] intercept : AIC=1046.922, Time=0.32 sec\n",
|
||
" ARIMA(0,1,2)(0,0,2)[12] intercept : AIC=947.532, Time=0.92 sec\n",
|
||
" ARIMA(0,1,2)(0,0,3)[12] intercept : AIC=867.310, Time=2.67 sec\n",
|
||
" ARIMA(0,1,2)(1,0,0)[12] intercept : AIC=608.450, Time=0.65 sec\n",
|
||
" ARIMA(0,1,2)(1,0,1)[12] intercept : AIC=389.029, Time=1.72 sec\n",
|
||
" ARIMA(0,1,2)(1,0,2)[12] intercept : AIC=421.446, Time=3.85 sec\n",
|
||
" ARIMA(0,1,2)(2,0,0)[12] intercept : AIC=507.685, Time=2.02 sec\n",
|
||
" ARIMA(0,1,2)(2,0,1)[12] intercept : AIC=408.463, Time=3.61 sec\n",
|
||
" ARIMA(0,1,2)(3,0,0)[12] intercept : AIC=460.596, Time=5.28 sec\n",
|
||
" ARIMA(0,1,3)(0,0,0)[12] intercept : AIC=1174.928, Time=0.18 sec\n",
|
||
" ARIMA(0,1,3)(0,0,1)[12] intercept : AIC=1037.324, Time=0.56 sec\n",
|
||
" ARIMA(0,1,3)(0,0,2)[12] intercept : AIC=947.471, Time=1.46 sec\n",
|
||
" ARIMA(0,1,3)(1,0,0)[12] intercept : AIC=602.141, Time=0.82 sec\n",
|
||
" ARIMA(0,1,3)(1,0,1)[12] intercept : AIC=399.084, Time=2.40 sec\n",
|
||
" ARIMA(0,1,3)(2,0,0)[12] intercept : AIC=500.296, Time=2.60 sec\n",
|
||
" ARIMA(0,1,4)(0,0,0)[12] intercept : AIC=1188.947, Time=0.42 sec\n",
|
||
" ARIMA(0,1,4)(0,0,1)[12] intercept : AIC=999.240, Time=0.87 sec\n",
|
||
" ARIMA(0,1,4)(1,0,0)[12] intercept : AIC=604.133, Time=0.99 sec\n",
|
||
" ARIMA(0,1,5)(0,0,0)[12] intercept : AIC=1091.452, Time=0.53 sec\n",
|
||
" ARIMA(1,1,0)(0,0,0)[12] intercept : AIC=1298.693, Time=0.05 sec\n",
|
||
" ARIMA(1,1,0)(0,0,1)[12] intercept : AIC=1075.553, Time=0.25 sec\n",
|
||
" ARIMA(1,1,0)(0,0,2)[12] intercept : AIC=971.074, Time=0.69 sec\n",
|
||
" ARIMA(1,1,0)(0,0,3)[12] intercept : AIC=882.846, Time=2.63 sec\n",
|
||
" ARIMA(1,1,0)(0,0,4)[12] intercept : AIC=818.711, Time=4.91 sec\n",
|
||
" ARIMA(1,1,0)(1,0,0)[12] intercept : AIC=inf, Time=0.59 sec\n",
|
||
" ARIMA(1,1,0)(1,0,1)[12] intercept : AIC=414.969, Time=1.19 sec\n",
|
||
" ARIMA(1,1,0)(1,0,2)[12] intercept : AIC=402.836, Time=3.25 sec\n",
|
||
" ARIMA(1,1,0)(1,0,3)[12] intercept : AIC=429.921, Time=6.47 sec\n",
|
||
" ARIMA(1,1,0)(2,0,0)[12] intercept : AIC=inf, Time=1.76 sec\n",
|
||
" ARIMA(1,1,0)(2,0,1)[12] intercept : AIC=419.397, Time=2.89 sec\n",
|
||
" ARIMA(1,1,0)(2,0,2)[12] intercept : AIC=409.246, Time=4.10 sec\n",
|
||
" ARIMA(1,1,0)(3,0,0)[12] intercept : AIC=inf, Time=4.96 sec\n",
|
||
" ARIMA(1,1,0)(3,0,1)[12] intercept : AIC=419.507, Time=7.41 sec\n",
|
||
" ARIMA(1,1,0)(4,0,0)[12] intercept : AIC=inf, Time=11.83 sec\n",
|
||
" ARIMA(1,1,1)(0,0,0)[12] intercept : AIC=1240.963, Time=0.11 sec\n",
|
||
" ARIMA(1,1,1)(0,0,1)[12] intercept : AIC=1069.162, Time=0.45 sec\n",
|
||
" ARIMA(1,1,1)(0,0,2)[12] intercept : AIC=973.065, Time=1.21 sec\n",
|
||
" ARIMA(1,1,1)(0,0,3)[12] intercept : AIC=884.323, Time=4.46 sec\n",
|
||
" ARIMA(1,1,1)(1,0,0)[12] intercept : AIC=588.156, Time=1.52 sec\n",
|
||
" ARIMA(1,1,1)(1,0,1)[12] intercept : AIC=399.035, Time=1.88 sec\n",
|
||
" ARIMA(1,1,1)(1,0,2)[12] intercept : AIC=409.509, Time=4.49 sec\n",
|
||
" ARIMA(1,1,1)(2,0,0)[12] intercept : AIC=503.551, Time=1.88 sec\n",
|
||
" ARIMA(1,1,1)(2,0,1)[12] intercept : AIC=399.929, Time=3.30 sec\n",
|
||
" ARIMA(1,1,1)(3,0,0)[12] intercept : AIC=457.277, Time=7.70 sec\n",
|
||
" ARIMA(1,1,2)(0,0,0)[12] intercept : AIC=1196.535, Time=0.18 sec\n",
|
||
" ARIMA(1,1,2)(0,0,1)[12] intercept : AIC=1042.432, Time=0.50 sec\n",
|
||
" ARIMA(1,1,2)(0,0,2)[12] intercept : AIC=948.444, Time=1.55 sec\n",
|
||
" ARIMA(1,1,2)(1,0,0)[12] intercept : AIC=587.318, Time=1.60 sec\n",
|
||
" ARIMA(1,1,2)(1,0,1)[12] intercept : AIC=403.282, Time=1.93 sec\n",
|
||
" ARIMA(1,1,2)(2,0,0)[12] intercept : AIC=498.922, Time=3.90 sec\n",
|
||
" ARIMA(1,1,3)(0,0,0)[12] intercept : AIC=1176.484, Time=0.29 sec\n",
|
||
" ARIMA(1,1,3)(0,0,1)[12] intercept : AIC=1039.309, Time=0.94 sec\n",
|
||
" ARIMA(1,1,3)(1,0,0)[12] intercept : AIC=604.131, Time=1.21 sec\n",
|
||
" ARIMA(1,1,4)(0,0,0)[12] intercept : AIC=inf, Time=1.19 sec\n",
|
||
" ARIMA(2,1,0)(0,0,0)[12] intercept : AIC=1180.404, Time=0.09 sec\n",
|
||
" ARIMA(2,1,0)(0,0,1)[12] intercept : AIC=1058.115, Time=0.33 sec\n",
|
||
" ARIMA(2,1,0)(0,0,2)[12] intercept : AIC=973.051, Time=0.92 sec\n",
|
||
" ARIMA(2,1,0)(0,0,3)[12] intercept : AIC=883.377, Time=2.84 sec\n",
|
||
" ARIMA(2,1,0)(1,0,0)[12] intercept : AIC=inf, Time=0.60 sec\n",
|
||
" ARIMA(2,1,0)(1,0,1)[12] intercept : AIC=416.548, Time=1.59 sec\n",
|
||
" ARIMA(2,1,0)(1,0,2)[12] intercept : AIC=420.663, Time=3.27 sec\n",
|
||
" ARIMA(2,1,0)(2,0,0)[12] intercept : AIC=inf, Time=2.23 sec\n",
|
||
" ARIMA(2,1,0)(2,0,1)[12] intercept : AIC=402.478, Time=4.16 sec\n",
|
||
" ARIMA(2,1,0)(3,0,0)[12] intercept : AIC=inf, Time=6.51 sec\n",
|
||
" ARIMA(2,1,1)(0,0,0)[12] intercept : AIC=990.719, Time=0.26 sec\n",
|
||
" ARIMA(2,1,1)(0,0,1)[12] intercept : AIC=881.526, Time=1.10 sec\n",
|
||
" ARIMA(2,1,1)(0,0,2)[12] intercept : AIC=837.402, Time=3.23 sec\n",
|
||
" ARIMA(2,1,1)(1,0,0)[12] intercept : AIC=584.045, Time=2.20 sec\n",
|
||
" ARIMA(2,1,1)(1,0,1)[12] intercept : AIC=443.982, Time=2.03 sec\n",
|
||
" ARIMA(2,1,1)(2,0,0)[12] intercept : AIC=501.152, Time=2.59 sec\n",
|
||
" ARIMA(2,1,2)(0,0,0)[12] intercept : AIC=988.094, Time=0.50 sec\n",
|
||
" ARIMA(2,1,2)(0,0,1)[12] intercept : AIC=757.710, Time=2.77 sec\n",
|
||
" ARIMA(2,1,2)(1,0,0)[12] intercept : AIC=595.703, Time=3.85 sec\n",
|
||
" ARIMA(2,1,3)(0,0,0)[12] intercept : AIC=1140.469, Time=0.95 sec\n",
|
||
" ARIMA(3,1,0)(0,0,0)[12] intercept : AIC=1126.139, Time=0.39 sec\n",
|
||
" ARIMA(3,1,0)(0,0,1)[12] intercept : AIC=996.923, Time=0.66 sec\n",
|
||
" ARIMA(3,1,0)(0,0,2)[12] intercept : AIC=918.438, Time=1.53 sec\n",
|
||
" ARIMA(3,1,0)(1,0,0)[12] intercept : AIC=inf, Time=0.88 sec\n",
|
||
" ARIMA(3,1,0)(1,0,1)[12] intercept : AIC=406.495, Time=2.17 sec\n",
|
||
" ARIMA(3,1,0)(2,0,0)[12] intercept : AIC=inf, Time=3.32 sec\n",
|
||
" ARIMA(3,1,1)(0,0,0)[12] intercept : AIC=989.496, Time=0.51 sec\n",
|
||
" ARIMA(3,1,1)(0,0,1)[12] intercept : AIC=856.486, Time=1.64 sec\n",
|
||
" ARIMA(3,1,1)(1,0,0)[12] intercept : AIC=604.951, Time=0.94 sec\n",
|
||
" ARIMA(3,1,2)(0,0,0)[12] intercept : AIC=991.558, Time=1.11 sec\n",
|
||
" ARIMA(4,1,0)(0,0,0)[12] intercept : AIC=1125.025, Time=0.18 sec\n",
|
||
" ARIMA(4,1,0)(0,0,1)[12] intercept : AIC=987.621, Time=0.50 sec\n",
|
||
" ARIMA(4,1,0)(1,0,0)[12] intercept : AIC=inf, Time=1.05 sec\n",
|
||
" ARIMA(4,1,1)(0,0,0)[12] intercept : AIC=988.660, Time=1.00 sec\n",
|
||
" ARIMA(5,1,0)(0,0,0)[12] intercept : AIC=1113.673, Time=0.22 sec\n",
|
||
"\n",
|
||
"Best model: ARIMA(0,1,2)(1,0,1)[12] intercept\n",
|
||
"Total fit time: 343.809 seconds\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"start_time = time.time()\n",
|
||
"sarima_model = auto_arima(X_train_arima,\n",
|
||
" start_p=2, d=None, start_q=1, max_p=10, max_d=10, max_q=10,\n",
|
||
" start_P=2, D=None, start_Q=1, max_P=10, max_D=10, max_Q=10, m=12,\n",
|
||
" suppress_warnings=True, stepwise=False, seasonal=True,\n",
|
||
" error_action='ignore', trace=True, n_fits=50)\n",
|
||
"sarima_time = time.time() - start_time\n",
|
||
"autosarima_y_pred = sarima_model.predict(n_periods=12)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Auto ARIMA Models MAPE"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 41,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"auto arima mape = 0.0032060326207122916\n",
|
||
"auto sarima mape = 0.0007347495325972257\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from flaml.ml import sklearn_metric_loss_score\n",
|
||
"print('auto arima mape', '=', sklearn_metric_loss_score('mape', y_test, autoarima_y_pred))\n",
|
||
"print('auto sarima mape', '=', sklearn_metric_loss_score('mape', y_test, autosarima_y_pred))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Compare All"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 42,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"flaml mape = 0.0005706814258795216\n",
|
||
"default prophet mape = 0.0011396920680673015\n",
|
||
"auto arima mape = 0.0032060326207122916\n",
|
||
"auto sarima mape = 0.0007347495325972257\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from flaml.ml import sklearn_metric_loss_score\n",
|
||
"print('flaml mape', '=', sklearn_metric_loss_score('mape', y_test, flaml_y_pred))\n",
|
||
"print('default prophet mape', '=', sklearn_metric_loss_score('mape', prophet_y_pred, y_test))\n",
|
||
"print('auto arima mape', '=', sklearn_metric_loss_score('mape', y_test, autoarima_y_pred))\n",
|
||
"print('auto sarima mape', '=', sklearn_metric_loss_score('mape', y_test, autosarima_y_pred))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 43,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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epFOnTtja2mJpacnJkyeZOXMm+fn52NraMnz48Of1I6x0Xo+tnTIvjJJiBYd8wylQz+VKq4P4uW56etO5iD2IPVMQBQV8LvkwduIsaus8fmjgWVCprU+HCUvxPzWW2wcW4R3xO2pXdyFZeUHnGaDfAi3tGnR724JW7RtyfPM1di8OpY2LM06651kSsgT3pu7oa+pXmsZn5o+ZcOvK822zoRX0XPDYkPsfegAmJibl/IsextatWzl06BDXrl1j2bJl5YaRWrZsye7du7l79y5+fn6MGDGCP/74o0JS69evz+rVq1m0aBG///47BQUFuLm5ceTIEczMzBg1ahS//PJLmaOrhoYGp06dIjU1FU9PTw4fPkytWrX45ptvWLx4MTNnzmTw4MFs3boVBwcHsrKy0NTUpH79+hw6dAgNDQ2ioqIYOnQowcHBbN68me7duzNr1iwUCgV5eXm4urqybNmyV85LSe4pyDwV5/Zc5+7NPA6bbOBTt1kVWmpaRlEu7JkC20YSLxrQu+hrug1/HyODyksID9LfxRaLUd/TnWVslHqjDN8Ny9rBrsmQXmrLbWShz9DPnLDt2oyrgTdxOTecwqIiFocsfiEaXzUeHD56UkI4f/489erVw8jICA8PDy5cuMDdu3fLxXh6erJlyxbOnj2Lq+t/7zleu3YNExMTzMzMAPD29ubEiRNl5feHl86cOUNERATOzs7Y2tqyfv164uPjuXbtGoaGhjg4lJ4Hrq2tjZqaGsXFxYwfPx4rKyu8vLzKLLcdHBzw9fVlzpw5XLlyhTp1Xl3rdrmnIFNhbkZncPHQDSLqB9K5Y1s6NelU8crJoaU7k9NiONfYm+ExHnzc2xpn06czO3tWXFoasHpyT8asM2BFTi82mp/BOHwLXN5auvy10weo65ngPNCUxmY67PvpMt7Np7EyZj79WvR7ee0xnvBE/zLg5+dHZGRk2bxDVlYWO3fuZNy4cWUxQ4YMwd7eHm9vb1RU/vsz65MWCNSqVassrmvXrvj5lT8d+PLlyw/ds7JkyRIaNGjApUuXUCqVaGhoANCpUydOnDjBvn37GDlyJB9++CGjRo36z/qrErmnIFMhigpKOOAbRo5GOml2V5nWdlrFKiqVpbYUq7tAUR5nXX15K6Y7/doaM8bZuFI1P4qWDeqwy8cZ/YbNcA/rxqb2exCO4+HK9tKew54pcDceYysDjK30qXm5Mc1rmPHV2a8oVhRXieZXHaVSyfbt27l8+TJxcXHExcWxe/fuf30YN2vWjHnz5uHj4/NM72dubk5cXBzR0dEAbNiwgc6dO/8rrn379gQGBpbF5eXl8ddff2Fubk5ycjLnz58HIDs7m5KSEjIzMzE0NERFRYUNGzaUTVTHx8dTv359xo8fz9tvv82FCxcAUFdXp7j41fqdkZOCTIUI3BlNTlohp8y2M/+NedRQrfHkSlnJsKE/HPoMWvXgmuefjA7QxK6ZDvMGWFbp7uF6dWqyZXx7elg0ZNbhVD4tHEHJ/y5Au7FwaQv8aA97p9Kxex0URUqGZk/heuZ11oWvqzLNrxJHjhyhSZMmZa9vvvmGxo0bl1tN1KlTJyIiIrh582a5uhMnTqRFi38vSc7LyyvX5uLFjx7S09DQwNfXFy8vL6ysrFBRUWHSpEn/iqtXrx7r1q1j6NChWFtb0759eyIjI6lRowZbt25lypQp2NjY0LVrVwoKCvDx8WH9+vW0b9+ev/76q6zHERAQgK2tLXZ2duzcuZOpU6cCpVbh1tbWr9REs7x5TeaJ3IhIY+/SS1wyPEbPYfYMaDngyZUi98Hu/0FJAfRYQHqrIfT9KZBihZK9/3OhvrZG5QuvAEqlYOGBayw/HkNns3osG2ZHncLbcHIxXPgVtPQ52eg3rpy8zY3uJzic8zu7+u2iaZ2mVS1d3rwmUyHkzWsyz5WC3GIOrLvCXc1b1HUtpL9p/8dXKMqDve/BlmGg0xQmnqDYdiQ+my9wJ7uQFSPbvTQJAUBFRWJmT3Pme1pxKjoVr+VBJAt96L0YvPdCzi0cDA5QQ1ONtjE9UZPUmHd2nrypTea1RU4KMo/lmF8EBdklXLY6wKcusx8/5HPzEqzsDCG+4DwV3j4MBi356vcIzsSms8DTCtumT7G57QUy1LEZ68Y4kHQ3n/4/BXIlMROMOkCrXmiELMGhWwPuROUySecDApMCORR/qKoly8hUCnJSkHkk0RfuEBucxsXGh/ik9zS0a2g/PFCphNPLYJVHqXPpqN3QdS6o1WDLuRusD4pnvKsJnvaPPlTnZcC1ZT12+nREXVWFt1YEcTD8FrzxKRRmY8kmdBpooX6uMW10LPjm3DfkFOVUtWQZmedOpSUFSZI0JEk6J0nSJUmSwiVJ+uLe9a2SJIXee8VJkhR677rjA9cvSZJUgYFrmcoiL6uIQxsuc6fWDRx7Nce2vu3DA7NvwaaBcHAWmHWHyaehuRsAwXHpfLo7DNeWBszoYf7CtD8LZg3qsOudjpg1qM3EjSH8GqsF1oNRDV6Bc089Mm7nM0ZlOin5KfwU+lNVy5WRee5UZk+hEHhDCGED2AI9JElqL4QYLISwFULYAjuB+x4CYUC7e9d7ACskSZL3UVQBQgj2r79AcYGCO06hjLcd9/DAW1fgl44QHwS9v4fBG0FLD4DkjHwmbbxAYx1Nlg21R0311emU1q+jwZYJHXBvVZ+5eyO41XYaKBUY3fmZJua6JB4rYLDxMDZHbiYiLaKq5crIPFcq7S9VlHK/f61+71U2OyeVDk6/Bfjdi88TQpTcK9Z4MFbmxRIRlMTt8Dwumxzl8zdnPtzx9G4cbBwIahow8Ti0G1N27kFBsYKJG0IoKFawalQ76mo9o8FdFaBZQ5Uv+1sCsPySAtqORgrdgEtXDYryS2if3Bvdmrp8GfQlCmXlmqrJyLxIKvXxTZIk1XvDQ3eAQ0KIsw8UuwK3hRBRD8Q7SZIUDlwBJj2QJB5sc4IkScGSJAWnpKRUpvxqSXZ6Acf8rpJcJ5ohb3WjYa2G/w7KTYUNnlBSCCN+g3qtyoqEEMzYeZmw5Ey+H2xLywav7nb/xjqa9LdrzJbzN0hr9y6o1kD/6iJauzTir5MpvNd8BmFpYWz/a3tVS60y7ltnW1pa4uXlRV5e3jO3GRcXh6Wl5VPV8ff3L7Oc+CcpKSk4OTlhZ2fHyZMnn1nfs5KRkcHPP/9c1TIeSaUmBSGE4t5wUBPAUZKkB/+nh3Kvl/BA/FkhhAXgAHwsSdK/1i4KIVYKIdoJIdrVq1evMuVXO4RS8NuqIEoUJdTqlkkXY49/BxXmwKZBpRvThm2D+uXnClaciGV3aDIfdGtFlzYNXpDyymNS5xYUlihZG5oHTpMgbAdOjgWo1lBB/Vwj2hu254cLP5Can1rVUquE+95HYWFh1KhRg+XLl5crr2xr6vs8LikcOXIEc3NzLl68WGE/pcrUXa2Twn2EEBlAAKVzBdybK/AEHnoShhDiKpALPN3jgswzcebINXKuC6JbBzLdfcq/A0qKYNtIuHkZvHyhmVO54mORd/jmz0jetDbEx+0/HJLzEmJavzY9LBrya1A8We18QKMuWufm0a6nMXFX0pikP50iRRELzy+saqlVjqurK9HR0QQEBODu7s6wYcOwsrKioKCAMWPGYGVlhZ2dHceOHQNg3bp19OvXjx49etCqVSu++OKLsrYUCgXjx4/HwsKCbt26kZ+fD0BMTAw9evSgbdu2uLq6EhkZyenTp9mzZw8ffvhhmW32fUJDQ/noo4/Yv38/tra25Ofn4+fnh5WVFZaWlsyYMaMstnbt2nz22Wc4OTkRFBTExo0bcXR0xNbWlokTJ5Ylij///BN7e3tsbGzw8Ch9cDp37hwdO3bEzs6Ojh07cu3aNQDCw8PL2rC2tiYqKoqZM2cSExODra0tH374YeX+p/wHKm0iV5KkekCxECJDkiRNoAvwzb3iLkCkECLxgXgTIEEIUSJJkhHQCoirLH0y5Um/nUOw/w2SdaN5d/goNNT+0UlTKmH3OxBzFPr9BK16liuOScnh3S0Xad1Qm28HWVephcXzxsfNlD/CbrExNBMf5/fgyBdYt08iTF+DqP3ZvP3mOH658jP9TfvTsVHHKtH4zblviEyPfK5tmuuZM8NxxpMDgZKSEv744w969OgBlH5IhoWFYWJiwnfffQfAlStXiIyMpFu3bvz111/l4rS0tHBwcODNN9/EwMCAqKgo/Pz8WLVqFW+99RY7d+5kxIgRTJgwgeXLl9OyZUvOnj2Lj48PR48epW/fvvTu3ZtBgwaV02Vra8vcuXMJDg5m2bJlJCcnM2PGDEJCQtDV1aVbt274+/vTv39/cnNzsbS0ZO7cuVy9epVvvvmGwMBA1NXV8fHxYdOmTfTs2ZPx48dz4sQJTExMSE9PL/1ZmZtz4sQJ1NTUOHz4MJ988gk7d+5k+fLlTJ06leHDh1NUVIRCoWDBggWEhYW9tJbalbm6xxBYL0mSKqU9km1CiN/vlQ3hH0NHgAswU5KkYkAJ+Aghqmef/AWjVAq2Lj9JMQosBxpgpmdWPkAIODgbrmwDj8/AbkS54sz8YsavD6aGqgorR7VFq8brtWjMqkldXFsasPbUdcZOH4/G2RWoHZ9LxwHrObA6HJecXuzX3sfXZ79mZ9+d1FStWdWSXxgPnqfg6urK22+/zenTp3F0dMTExASAU6dOMWVKac/T3NwcIyOjsqTQtWtX9PVLz6nw9PTk1KlT9O/fHxMTk7J227ZtS1xcHDk5OZw+fRovL6+y979/cE5FOX/+PG5ubtwfeh4+fDgnTpygf//+qKqqMnDgQKB0yCkkJKTMOjs/P5/69etz5swZOnXqVHZvenqlq+0yMzPx9vYmKioKSZLKTPA6dOjAvHnzSExMxNPTk5YtWz6V3qqg0v56hRCXAbtHlI1+yLUNwIbK0iPzaA7sPo/yZk3SnIKZ1vYh3dnTS+HMT+A4EVymlytSKAXvbbnIjfQ8No1zoomu1gtS/WLxcTNl6KozbL+UxsjOH8K+92nhfAlDU31C9t5gps8nTD4xkbVX1jLZdvIL11fRJ/rnzf05hX9y3ygOHm9j/c8e5f3va9b8O7GqqqqSn5+PUqlER0fnmZ6wH6dFQ0MDVVXVsjhvb2/mz59fLmbPnj0P7QV/+umnuLu7s2vXLuLi4nBzcwNg2LBhODk5sW/fPrp3787q1atp3rz5f9b/Inh1Fo/LVAqJ8alEH8wgsf5VPhgy/t+/8KF+pS6nFgOgx4KyZaf3+fbANY5dS2FOXwucmr/EJ5M9I+2b62HfTIflx2MpthkBusZIR+fi7NmC/OxiVEPr08ukF6uurCI+K76q5b5UdOrUiU2bNgHw119/cePGDVq1Kl2xdujQIdLT08nPz8ff3x9nZ+dHtqOtrY2JiQnbt5eu9hJCcOnSJQDq1KlDdnb2E7U4OTlx/PhxUlNTUSgU+Pn5PdRS28PDgx07dnDnzh0A0tPTiY+Pp0OHDhw/fpzr16+XXYfSnsJ9B9h169aVtRMbG0vz5s1599136du3L5cvX66w1qpCTgrVGEWJkh3LAylQy6ePdzt0NXXLB0QdKp1HMOkMA1bAA4eeKJWCbw9Esvx4DMOdmjGivdELVv9ikSQJHzdTkjLy2RuWCu6z4PYVGuQeplX7hoQeSWBy86loqGrw1ZmvZMO8B/Dx8UGhUGBlZcXgwYNZt25dWU/AxcWFkSNHYmtry8CBA2nX7qHGnWVs2rSJNWvWYGNjg4WFBbt37wZKD+f59ttvsbOzKzfR/E8MDQ2ZP38+7u7u2NjYYG9vT79+/f4V16ZNG7766iu6deuGtbU1Xbt25ebNm9SrV4+VK1fi6emJjY1N2QluH330ER9//DHOzs7lVi5t3boVS0tLbG1tiYyMZNSoUejr6+Ps7IylpeVLOdEsW2dXYzatP0hGkBqKbvG86zmmfGFiMKzvA/qmMHofaPzte5RbWMK0raEcjLjNEIemfNnfEvVXaMfyf0WpFPRaepISpeDgVBdUVrhCST45w0+y6YtgjCwNyOwUzldnv+Ib12/o1bxXpep51a2z161bVzYBLFN5yNbZMhXicngU6UEq3Gn6F5P7l/mMw6wAACAASURBVJ84JuUv2OQFtRvAiJ3lEkLi3TwG/nKaw1dv83mfNsz3tKoWCQFKbbYnu7Ug+k4OhyJTwONTSI+l9vVt2HUzIubCHZxVumKpb8nC8wvJKsqqaskyMk9N9fhrlilHfkEBB9deIa9mJmMm9EJd5QEbiqxk2OgJKqow8jeoXb+sKDgunX7LAknKyGfdGEfGOJu8VktPK8KbVoY009Pi54AYRMvu0MQRji/Ezq0etXRqErQzhtlOs7lbeJelF5ZWtdyXmtGjR8u9hJcQOSlUQ1av3oNmrg4tB9TGuF6zvwvyM0r9jPLvwvAdoPf3KontwQkMXXWGOhpq7PJxppNZ9dxNrqaqwsTOzbmUkMHp2HToMgeyb6J+aQ0d+jfnTnw2ajF6DDMfxrZr27iScqWqJcvIPBVyUqhmHAkKQiXMgNxWiXi6df+7oDgf/IZCahQM2QSNSteIK5SCefsi+HDHZRxN9PB/xxnT+rWrSP3LwUD7JtSrU5OfA6LB2BlMu8CpJZhZa1LfqA5B/rFMaD2Jepr1+PLMl5Qo/2XhJSPz0iInhWpESkYaIVtvkqt1l0kTHjiuQqmAnePgRhB4rig7DyG7oJhx68+z6uR1vDsYsW6MIzpaNapE+8uEhroq411NCIxOIzQho3RDX/5dpDPLcPZqSW5GIVHH05nhOIOr6VfZErmlqiXLyFQYOSlUE4QQrFyxB80CbVxHtkC7Vp37BbBvOkT+Dj2/AcvSHZ3xabl4/nyak1GpfNXfki/6VY8VRhVlmJMRdTXV+flYNBjalO7jCPqZRg2LaGFfn4sH4+mg7YpLYxd+vPgjt3NvV7VkGZkKIf+VVxM2/LELnetGaLTNoYPdA6eoBSyAkHXg+j44TQQgKCaNfj8FkpJTyK9vO772exD+C7VrquHd0ZiDEbeJup0N7rOhpABOfkdHzxYolYKzu6/zidMnKISCb85/8+RGX1F27dqFJElERlbMe+n777+vsMX2xYsXkSSJAwcOPDauV69eZGRkVKjNp+HDDz/EwsLipdlPsG7dOpKTkyv1PeSkUA0IS7zK7T9Uya+bwWjv3n8XnF8NxxeUehm98SkAm8/eYOSasxjUrom/jzMdWxhUkeqXnzEdjdFUV+WXgBgwMAW74RC8Fm3VO9h6NOXa2VvUSKvLROuJHIo/xMnEqvfyrwz8/PxwcXFhy5aKDZM9TVK437af3z+t0koRQqBUKtm/fz86OjoV1lxRVqxYwYULF/j2228rFF9SUrnzR3JSkHlmCkoK2Lr6BBolWvSf4ID6fbO6iN2w7wMw6wG9f6BEKZizJ5xPdl3BpaUBv/l0xNig1uMbr+bo1qrBUMdm7L6UTEJ6HnSeCUgQsIC2PYzRrKNO4PYovNt407xuc+adnUdBSUFVy36u5OTkEBgYyJo1a8olhYCAAHr3/vsB5H//+x/r1q1j6dKlJCcn4+7ujru7O8AjrayFEOzYsYN169Zx8OBBCgpKf3ZxcXG0bt0aHx8f7O3tSUhIwNjYmNTUVOLi4jA3N2fcuHFYWloyfPhwDh8+jLOzMy1btuTcuXPAo62uH6Rv377k5ubi5OTE1q1biY+Px8PDA2trazw8PLhx4wZQurR2+vTpuLu7M2PGjIfaewPcvn2bAQMGYGNjg42NDadPnwagf//+tG3bFgsLC1auXAmUWoePHj0aS0tLrKysWLJkCTt27CA4OJjhw4eX2YBXBq+XnaXMv/hhmy8Nb7XC0F0N0xZNSy9eP1k6sdzUEQb5klkoeGfzeU5FpzLe1YSZPVujqlK99h/8V8Z3MmHDmThWnogtPb7TcTyc+ZkazlNx6tucgE3XuHEpg9ntZzP2wFhWXl7Ju/bvPncdt77+msKrz9c6u2Zrcxp+8sljY/z9/enRowdmZmbo6elx4cIF7O3tHxn/7rvvsnjxYo4dO4aBgcFjrawDAwMxMTGhRYsWuLm5sX//fjw9PQG4du0avr6+Dz2sJjo6mu3bt7Ny5UocHBzYvHkzp06dYs+ePXz99df4+/s/0ur6Qfbs2UPt2rXLDPj69OnDqFGj8Pb2Zu3atbz77rv4+/sDpZ5Ohw8fRlVVFQ8Pj4fae7/77rt07tyZXbt2oVAoyMkpPa147dq16OnpkZ+fj4ODAwMHDiQuLo6kpCTCwsKA0oN5dHR0WLZsGYsWLXqiHcizIPcUXmP+DDuCWmATlPVz6T/IpfTirSuwZVjpHoShW4jJVNL/50DOXk9j4SBrZr3ZRk4IT4FhXU087ZqwLTiBlOzCUhdZ9Vpw9EtaOzdCv3FtTv8WjZ2+PX1b9MU33JfYjNiqlv3c8PPzY8iQIUCp/9CjhnkexYNW1mpqamVW1k9q28jIiPbt2z+0TRMTE6ysrFBRUcHCwgIPDw8kScLKyoq4uDig1MDOy8sLS0tLpk2bRnh4+BO1BgUFMWzYMABGjhzJqVOnysq8vLxQVVUtZ+99/3CemzdvAnD06FEmTy510FVVVaVu3boALF26FBsbG9q3b09CQgJRUVE0b96c2NhYpkyZwp9//om2tjYvCrmn8JpyM+cmJzdH0UCYMHRyB1RUVeBuXOnmtJp1YMROTiYpeGdTIOqqKmwe3x4HY72qlv1KMsmtBdtDElgbeJ0ZPcyh4xQI+BqV5As4e5my5/tQLh9NZHqn6QQkBPDlmS9Z233tc90N/qQn+sogLS2No0ePEhYWhiRJKBQKJEli4cKFqKmpoVQqy2LvD/38k0d5rykUCnbu3MmePXuYN28eQgjS0tLK3EUftOb+Jw/abquoqJR9r6KiUjbm/yir66fhwf+/+3qe1t47ICCAw4cPExQUhJaWFm5ubhQUFKCrq8ulS5c4cOAAP/30E9u2bWPt2rVPrfG/IPcUXkMUSgWLN66lUboZ1n0MMTDUhtxU2OAJJYWIETtZF1bMaN/zNNLRxP8dZzkhPAMmBrXoaWXIhqB4MvOLoYMPaBnAkS9oaq6HsbUBwX/EoVlch2ltpxF8O5i9sXurWvYzs2PHDkaNGkV8fDxxcXEkJCRgYmLCqVOnMDIyIiIigsLCQjIzMzly5EhZvQetox9lZX348GFsbGxISEggLi6O+Ph4Bg4cWDZc86w8yur6cXTs2LFs3mTTpk24uLj8K+Zx9t4eHh788ssvQGnSy8rKIjMzE11dXbS0tIiMjOTMmTMApKamolQqGThwIF9++SUXLlwAKm4R/izISeE1ZFXgOhpdtqNmsxI6d7eCwpxSg7usZIoHb+GTUyXM2RuBe6v67JjckaZ6r+fBOC+SyZ1bkFNYwsYz8aU9Mdf34fpxiA3AeaApiiIlZ/fG4tnSE5t6Niw6v4jMwsyqlv1M+Pn5MWDAgHLXBg4cyObNm2natClvvfUW1tbWDB8+HDu7v8/bmjBhAj179sTd3f2RVtaPa/t58Cir68exdOlSfH19sba2ZsOGDfzwww8PjXuUvfcPP/zAsWPHsLKyom3btoSHh9OjRw9KSkqwtrbm008/LRsSS0pKws3NDVtbW0aPHl122M/o0aOZNGlSpU40y9bZrxkXb11k5+JgGuQ3w3tOJ7TrqoLfYIg9Tnb/9Yw7Y8DZ6+n4uLXgg26tUJHnD54bo33PcTkxk8AZb6ApFcOPbaFOAxh3hJPbo7hyLJG3ZjmSppXE4N8H09+0P3M6zvnP7/eqW2fLvBhk6+xqTFZRFis37cAwqwWub5mhrVuz9JCcmKPcclvImwdqczEhg+8H2/JRD3M5ITxnfNxMSc8tYuv5G6CuAW4zISkEIn/H4U0TamiqEbgjCjNdM0a2GcnOqJ2E3nk5D2+Xqb7ISeE1QQjBvD8WYR7dCT1zdWxcjOHQp3BlGzHW0+lytCn5xQq2TmhPf7vGVS33tcTRRA8HY11WnoilqEQJNkPBwAyOfImGpgoOvU1IjLxL3JU0JttMpmGthsw9M5diZXFVS5eRKUNOCq8JWy/vQPtYG9Q0VOg31gnp9I8QtIywJkPocr4txgZa7PmfM3bNdJ/cmMx/xsfNlOTMAnaHJoGqGrwxG1KvwaUtWHZujE4DLU7vjKampMHHjh8TdTeKTRGbqlq2jEwZclJ4DbiaEsnlzWnULdTnzf6N0IzZAYc+JVTbnT7Rvell2YjtEztiWFezqqW+9ri1qkdrQ21+OR6DQimgdV9oZAcB81EVxTgPNCXjdh5hx5N4o9kbuDV14+dLP3Mz52ZVS5eRAeSk8MqTV5zH+uW/0zjTDIuE3eRPHMRf3l8QHmTC6nNtmGlblx+H2qFZQ7WqpVYLJEnCx60FsSm5HAy/BZJUaq2dmQDBvhhZ6dPEXJfz+65TkFvMx44fAzD/3PwqVi4jU4qcFF5hFJmZbPtwPk2T7GmacITmtW/SwDEXZWN17t6pyTuhu+g0ZyKx3btz87PPyfrzAIpKcJKUKU8vK0OM9e8d2SkENHcHY1c48S1SUS4uXi0pyi/h/O/XaVS7EZNtJnMs4RjHbhyraukyMpWXFCRJ0pAk6ZwkSZckSQqXJOmLe9e3SpIUeu8VJ0lS6L3rXSVJCpEk6cq9f9+oLG2vOiUpKdxZtIigPmPIy+tEnZLreLxrTWOrM2SbaPK+yxfo7D1A8/37aDB7NjVbtiRr3z6S3nuPvzp05PogL+4sXkLumTMoi4qq+nZeO1RVJCZ1bsGVpExORafe6y18DnmpcOYX9BvXprVLI8KOJ3H3Vi4j2ozAVMeU+efmk1dcMffQl4nKss5eu3YtVlZWWFtbY2lpWbbev6Ls2bOHBQsWPFWdihAZGYmtrS12dnbExMQ89/aflri4uOe2fwMoXbVSGS9AAmrf+1odOAu0/0fMd8Bn9762Axrd+9oSSHrSe7Rt21ZUJ4oSE8XNL+aKq9Y2IsTOVSwbv1ssmbpD5EafF0VfNRbxn5kK7yW7xO3M/H/VVRYVidyQC+LOj8vE9WHDRYSFpYhoZS6u2tiK+LfHidTVa0T+1atCqVBUwZ29fhQUlwjHeYfE4BWn/764eagQXzcRIjdN5GYWihVTA8Tvy0KFEEKcv3leWK6zFBsjNlb4PSIiIp637P+El5eXcHFxEZ9//nmF4o2MjERKSspjYxISEkTz5s1FRkaGEEKI7OxsERsbW2FNxcXFFY59WubPny8+++yzCscrlUqhqMS/q2PHjok333zzkeUP+z0BgsWjPrsfVfA8X4AWcAFweuCaBCQALR8SLwFpQM3HtVtdkkJBTIxImjGz9IPc0krEzJwtvn1vq/jund9FTMhRUfBVE3HjsxZi0jJ/kZVfVKE2S7JzRNbRo+LmV/NEdK83RUQrcxHRylxc69BRJE5/X9zdsUMUJSdX8p293qw6ESOMZvwuguPSSy/cChfi87pCHJglhBAi5M84sWziEXEjIk0IIcSo/aNE9x3dRbGiYh9oL0NSyM7OFo0aNRLXrl0TrVq1Krv+zw+qd955R/j6+ooffvhBqKurC0tLS+Hm5iaEEGLz5s3C0tJSWFhYiI8++kgIIURISIiwsbERJSUl/3rPlStXinbt2glra2vh6ekpcnNzhRBCeHt7i2nTpgk3Nzcxffp04evrK955552yskmTJgk3NzdhYmIiAgICxJgxY4S5ubnw9vYua3vSpEmibdu2ok2bNg/94N+3b59o0KCBaNSoUZn+7777TlhYWAgLCwuxZMkSIYQQ169fF+bm5mLy5MnC1tZWxMXFiYULF4p27doJKyurcm2vX79eWFlZCWtrazFixAghhBB79uwRjo6OwtbWVnh4eIhbt24JIYQICAgQNjY2wsbGRtja2oqsrCzh5OQktLW1hY2NjVi8ePG/ND9tUqhUQzxJklSBEMAU+EkIcfaBYlfgthAi6iFVBwIXhRCFD2lzAjABoFmzZs9f9EtEfng4aStXkX3wIFLNmugOG4qu92h+9j1Nzbt1Me13l0b7J5NSXIPvGy9m8eg3KzyhrFq7FnXc3alzz9O++PZtck8HkXv6NLlBQWTt2wdADRMTanXoQC3njmg5OqJap06l3e/rxlDHZiw7Fs0vAdGs9naABm3AZgicWwXtfbB5oynhJ5M4tT2KwbMc8LbwZuqxqRy+cZgexj2e6r1ObvuL1ISc56rfoGltXN8ye2xMZVln9+nThwYNGmBiYoKHhweenp706dMHAE9PT8aPHw/A7NmzWbNmDVOmTAHKW1j/09Po7t27HD16lD179tCnTx8CAwNZvXo1Dg4OhIaGYmtry7x589DT00OhUODh4cHly5extrYua6NXr15MmjSJ2rVr88EHHxASEoKvry9nz55FCIGTkxOdO3dGV1e3nL33wYMHiYqK4ty5cwgh6Nu3LydOnEBfX5958+YRGBiIgYEB6enpALi4uHDmzBkkSWL16tUsXLiQ7777jkWLFvHTTz/h7OxMTk4OGhoaLFiwgEWLFvH7778/9f/xw6jUiWYhhEIIYQs0ARwlSbJ8oHgo8C+fXUmSLIBvgImPaHOlEKKdEKJdvXr1KkN2lZMXEsKN8ROIGziI3MBA9CdMwPToERp+8gk7DoZTI1EPRbso3EI+IK1YjRUm3zN/bO9nWmGk3qABOgP60/jbhbQ8eQKT3bupP3MG6k2bkLFrF4nv/I+/2ncgbshQUpYuJS84GFEsb7p6HLVqqjG6ozGHr97h2q17JmZuH4NSAce/QVVdhQ4DTElPziUi8CZuTd0w0jZifdj6R7qHvmxUlnW2qqoqf/75Jzt27MDMzIxp06YxZ84cAMLCwnB1dcXKyopNmzaVs72+b2H9MPr06VNmod2gQYNy9tr3LbW3bduGvb09dnZ2hIeHExER8Vj9p06dYsCAAdSqVYvatWvj6enJyZOlJ+w9aO998OBBDh48iJ2dHfb29kRGRhIVFcXRo0cZNGgQBgalJxzq6ZUaUyYmJtK9e3esrKz49ttvy+7R2dmZ6dOns3TpUjIyMlBTe/7P9S/EOlsIkSFJUgDQAwiTJEkN8ATaPhgnSVITYBcwSghR9TM4LxAhBLmnTpG6YgX5wSGo6ulRb9o0dIcNLXs6Px0QTsZZVW41vcIHt5aQUazKry1/5POhPVFTfX75XZIkNFqZodHKDP3RoxFFReSFhpb1IlKXryD1519Q0dJCy8mJ+tOnUbNly+f2/q8Tozsas/JELL8ERPP9EDvQNYJ2Y+D8Guj4Li3sm2NoWpdze2Np6dCAUW1G8eWZLwm5HUK7hhU/SOVJT/SVQWVaZ0Pp76GjoyOOjo507dqVMWPGMGfOHEaPHo2/vz82NjasW7eOgICAsjoVsdR+0E77/vclJSVcv36dRYsWcf78eXR1dRk9evQjdVdE/4NahBB8/PHHTJxY/ll36dKlD7VQnzJlCtOnT6dv374EBASUJcSZM2fy5ptvsn//ftq3b8/hw4cfq++/UJmrj+pJkqRz72tNoAtwf3lCFyBSCJH4QLwOsA/4WAgRWFm6XjaEUknWgYPEDRxEwvgJFCcm0eCTTzA9chiDiRPKEkJidBoh25K5VTeG4dKPFBQLtlv8woxhvZ5rQngYUo0a1HJ0pP5772GydStmQadp/ONStPv1JT80lOuDh5D15+MPVq+u6GjVYLhTM/ZcSuZG2r0VN50+BLWacGwekiTh4tWS/OxiQv6Io2+LvujW1GV9+PqqFV4BKtM6Ozk5ucwuGiA0NBQjIyMAsrOzMTQ0pLi4mE2bnt9u8KysLGrVqkXdunW5ffs2f/zxxxPrdOrUCX9/f/Ly8sjNzWXXrl24urr+K6579+6sXbu27LS1pKQk7ty5g4eHB9u2bSMtLQ2gbPjoQWvv9ev//l2IiYnBysqKGTNm0K5dOyIjI5+7nXZlfpoYAsckSboMnAcOCSHuD3oN4d9DR/+jdO7h0weWrNavRH1ViiguJsPfn9jefUiaOhVFbg6GX32J6cED6I0aiYrm37uPs9ML2PVTMNnq6djrLUO/sIj99quY4tW9SkztVOvWRbtrVww//xwT/13UbGlK0nvvcee77xAVtCGuToxzbY6aigorTtzr/NauD+0nQ9hOuHmZ+kbatGrfkEtHEyjOgqHmQwlIDCA28+U+oa0yrbOLi4v54IMPMDc3x9bWlq1bt5ZZVX/55Zc4OTnRtWtXzM3Nn9v92NjYYGdnh4WFBWPHjsXZ2fmJdezt7Rk9ejSOjo44OTkxbty4cvd6n27dujFs2DA6dOiAlZUVgwYNIjs7GwsLC2bNmkXnzp2xsbFh+vTpAMyZMwcvLy9cXV3LhpagdDmvpaUlNjY2aGpq0rNnT6ytrVFTU8PGxoYlS5Y8889Bts5+wSgLC8nYuZP01WsoTk6mZqtWGEycQJ3u3ZEeMhZaXKjg16+Pk5VaQLbJQiZnx3DUaS2De3WtAvUPR1lUxO2v5pGxbRu1nJ1ptOhb1HRlj6UH+fi3K+wMSeTUDHfqa2tAfgb8YFN6Tvbw7WSnF7BhdhC2XZpi3kuPbju60bt578daa8vW2TIVQbbOfklR5OSStmYN0R5duD33S9Tq1aPJLz9j4r8L7V69HpoQhFKwb80F8m8riTD2ZVxONGddfV+qhACgUqMGhnO/oOGXc8k7d464QV4UXL1a1bJeKiZ1bk6JUsmaU9dLL2jqgMt7EHUQ4k9TR0+D5jYGRAQmo61Sl34t+rE3Zi+p+alVK1ym2iEnhRdAcVISsb17c+fbRWiYtaTZunUYbfGjjrv7Y8/pPbsvlqTL2QQ3282UgjNcc/+V3l26vEDlT4eulxdGGzcgSkqIGzqMzL3PZ4nc64CRfi16Wzdi45l4MvPurdpynAi1G8LhL0AIrNyaUJhbQlTwbUa2GUmxspgtkVuqVrhMtUNOCpVMyd273Bg3HmVeHkabNtJs7VpqtXd64qHt0SF3CNkXT2S9s/RU/Z3cN37FvfPL7/yhaWODyc4daFhakPzhh9yevwBx77D06s5ktxbkFilYHxRXeqGGFnT+CBLOQNRBGpnpoNeoFpePJWKkbYR7U3e2XNtCfsmjj118lYd/ZSqf//L7ISeFSkRZUECizzsUJyXR9Oef0Grb9smVgJQb2Rz0vcKt2tepbfArrdx86eDsXslqnx9qBgYY+fqiO2IE6evXc2Ps25TcW11RnWltqI2HeX18A6+TV3QvUdqPAl0TOPIl0r3eQmpCDrdis/C28CazMJPd0Q/3/NHQ0CAtLU1ODDIPRQhBWloaGhoaT1XvqfYpSJKkCzQVQlx+qnephgiFgqQPPiA/NJTGS5ag1a5ia85zMwvxXxpMjspdIpqv4FOHpdg6uFWu2EpAUlen4exZaFpZcvOzz7k+cBBNflyKppVVVUurUnzcWzDwlyD8ziXwtosJqKqD+yz4bRyE/4aZY3+CdsVwJSCRrmPtsDaw5teIX/Ey80JVpfy8U5MmTUhMTCQlJaWK7kbmZUdDQ4MmTZo8VZ0nJoV7m8763osNBVIkSTouhJj+X0RWB4QQ3J43j5zDR2gwaxbaPbpXqF5JsYLfvj9Hfl4Bh9usYpb9dGwdXq5J5aelbr9+1DA1JXHKFOKHj6Dh55+jM9CzqmVVGW2N9HA00WP1yVhGtjeihpoKWA6EwO/h6FfUaNOP1h0NuXIsEedBpnhbePP+8fcJSAjAw8ijXFvq6uqYmJhU0Z3IvK5UZPiorhAii9IdyL5CiLaUbj6TeQRpK1dxd7Mf+uPeRm/kiArVEULw20/nyLpZzGHTjQy2cKNz+yGVrPTFoGlhgcnOnWi2tefmrFncmjsXUY0tu99xN+VmZgH+F5NKL6iolB7befc6hP2GZefGKJWC8JPJeDTzoHHtxqwLX1elmmWqDxVJCmqSJBkCbwHycpInkLHLn5QlS9Du04d60yvemdrzazApkfmcb7IPE1N1xrrNrESVLx41XV2arVqF3tix3N3sR/zoMRTfuVPVsqqETi0NsGj0wJGdAC27g35LOLcCnfpaNLPQJ/xEEiglRrUZRWhKKKF3QqtWuEy1oCJJYS5wAIgWQpyXJKk58DBn02pPzsmT3Jw9m1odO9Bo3ldIKhWbx9+76wKJQZkk6IVwq8VF5vVY9sTVSa8ikpoaDT76kMaLv6Pg6lXiBg4i78LFqpb1wpEkiXfcTbmemsufYbdKL6qogNNESAqBxGCs3BqTl1VEbGgK/U37o11D+5WwvpB59Xnip5YQYrsQwloI4XPv+1ghxMDKl/ZqkR8WTuLU96hpZkbjpUuRatSoUL3df4aSdPAW+ZoJHGm5jcXdllG7Ru1KVlu1aPfqhfGWLUgaGsR7e3N3y5Zqt4Kmu0VDmhvU4qdj0X/fu80QqKkNZ5djZKGPdj1NrhxLREtdi8GtBnPkxhFuZN2oWuEyLwU5J05U2gbRRyYFSZJ+lCRp6aNelaLmFaUoIYGEiRNR09Gh6YrlqNau2If61kP/Z++8o6Oquj78nEmb9N4TEhJIgNAhdAIoSgcL0puF9ooKlg/1pasICqivoGChSy8WEBSVXgKhhtAJ6b33ZCZzvj8mBhCEAEkmwH3WuouZc9tv4vLue/beZ++TpP18iVJVEZvqf8/ENhNp4NigitXWDNSBAdTetBHLtm1ImjGTxClT0BXf0j7jkeXvlp3nEnPYe6kse8jMGpoNg4itiPxkGnXyJPFqNqkxuQypPwRjlTErz600rHAFg5O1aROx4/9D6udfVMn17zRTCEPfIOffNgVAm5FB7CujQavF+7tvMXGpWA2/Zb8fR/PzMYp1NvzUYDmt67RgSL0hVay2ZmFka4v311/jOG4s2Zu3ED1sOJrEREPLqjaeaeaJu62ar/bcUCU++BV9v4WwZdRv546xqYrwvXE4mTvRx78PP135icyiTMOJVjAYUkpSFy4iccpULNu2xWP+/Cq5z78aBSnlihs3YNM/vj/26AoKiB03Hk1SEl5ff42Zn99dz5FS8uW2o9j8upNsTR2O198GrkXMaj/rkYwj3A1hZITLhMfPFQAAIABJREFUxIl4LfySkshIrj3fn/yjRw0tq1owNVYxuqMfR69lEBalL5mMoz/UfRrClmJmqiOwtRuXjiZTlKdhRIMRFJUWsf7iesMKV6h2pFZL0rRppC1ciO0zz+D99VcYWf1774gH4a4xBSFEWyHEOeB82fcmQoivqkTNQ4TUaol/8y2Kzp7Fc/48LJrfWi73n+h0kjk/HqXOnpUkFbUl0/8Ux+0P8EnIJ9ia2VaD6pqLddeu+G5Yj5GtLTEvvkTGylWPRZxhUCtvHCxNb54ttB4L+SkQ8SONOntRqtFx7lAC/nb+hHiFsPbCWopLHx9X2+OOrqCAuFcnkLVxE47jx+H+8WyEiUmV3a8i6TGfA92AdAAp5WkgpMoUPQRIKUmaOYu8PXtwmzYV6woUqdOW6pi64TCtQz/jWl4/TD3TWO+8nNebv05Tl6bVoLrmY+bvj+/GDVh17kzy7NkkTJ6MrvDf6/48CliYGvNiO1/+upDCuYQc/aD/E+AUAKFf4+hhiUddO87ujUenk4wKGkVGUQa/XP3FsMIVqgVtejrRI0eRt38/bjNm4PLGG1XuUahQzqSUMvYfQ491J5W0RV+RtXEjjuPGYj/o7gvMdDrJ/609RPczH3A1ewgW9lq+8/qc9p7tGBU0quoFP0QYWVnh9eX/cH7jdXJ+2UbUkKGUxMUbWlaVMqKtL5amRny9t2y2IAS0GgMJJ8vSU73ITS8iOjyNlq4taeDYgBURK9BJ3Z0vrPBQUxIdTdTgIRRfvozXwi+xHzSwWu5bEaMQK4RoB0ghhKkQ4m3KXEmPI5kbN+r9es8+i/Mbb1TonOV/nuCFC+8RnT0EYWbBrw1XY2mp5qMOH6ESSk3CfyJUKpzGj8d78ddo4uKIev558g4+uh1abS1MGNbWh+1nEohKy9cPNhlcnp7q19QJK3szwvfEIYRgVNAoonKi2Be3z7DCFaqMwjNniBo8BF1ODrWWLcX6ieqrkFyRJ9I44FXAE4gDmpZ9f+zI3b2bpBkzsezYEfdZMys0jTtzOoyQ/aNIznmeXOlBfMeTXNCGM6fjHBzNHate9EOMVadO1N60EWMXF2JHjyH9u+8e2TjDyx1qY6xS8d2BshacZlbQbDic+xFVfjJBHT2JPZ9JZlI+T/k8hbulu1L64hEld88eokeOQmVhgc/aNVjcpr1nVVIRoyCklEOllK5SShcp5TAp5WNXB7nw9GniJ72Jul49vD7/rEKBnpzzf+GyaQTHMt4gQdMYu27FbMpbybgm42jl3qoaVD/8mPr44LtuLdZPP03KvPkkvvvuI9mfwcVazXPNPdkYFkdaXlkQuVVZeurxZTTo4IHKWBC+Nx5jlTHD6g/jePJxwlPDDStcoVLJ3LiRuFcnYObnh++6tZgZoOBhRYzCISHE70KIl4UQdlWuqAZSEhVF7LjxGDs7471kMSrLu6eC6cJWULR6ItvTZ5GFH81GuPBZ3gyC3YIZ23hsNah+dFBZWuL52QKc33id7J9+Jm7iRHSPYEG90SF+lJTqWHkoSj/g4AcB3SBsKRYWkrotXLlwOJGSIi3PBzyPtYk1K84p2eGPAlJKUr9cSNLUaVi2a4fPyhUYOzkZREtFylzUBaYAQcAJIcQ2IUTFSn8+AmjT0ogZPQaAWt9+c/f/ULpS+H0K8Zu/Y3PGHIrNnOjymh9zk6egNlIzp+OcW+riK9wdIQRO48fjOmUKeX/8Sdy48egKCgwtq1Lxd7biqfqurDwSfb0JT+uxkJ8KEVtp1NkLTVEpF48kYWliSf/A/uyK3kV83qMdiH/UkVotiVOnkrZoEbbPPov3V4sq9OJZVVQ0++hoWf+EVkAG8Fi8nujy84kdOw5tWhreSxZj6ut75xOK82D9MM79cY6fM6dRYG5B61cdePX0yyTmJ/Jpp09xsajYimeF2+MwbCjus2eTf+QIMa+MpjQ319CSKpWxnfzIKtCw4VhZwp9fF3AKhCNf4+prjYuPNeF74pBSMrTeUFSoWH1utWFFK9w3uoICYl99lexNm3H6z3jcZ39UIdf0hosbOJZ0rEo0VWTxmo0QYqQQYgdwCEhEbxweaaRGQ9wbEym6cAHPzxZg3rjxnU/Ijkcu7cHhMAd250wgydyIui8XMz70FYxURqzssZLW7q2rR/wjjt1zz+K5YAGF4eHEjByFNiPD0JIqjRY+DrT0sefb/dfQlur06amtx0DiKYg7RuMuXmQmFRB3IRNXS1d6+vVk8+XNZBdnG1q6wj2iTU8nesRI8vcfwG3GDJxff71CySuXMy/z8dGP2XhpY5XoqshM4TT6jKNZUsoAKeVkKeUjXftISkni1GnkHziA24zpWHfufOcT4k+gXfI0v1/uzon85wk302L+3Hn+G/Y2de3qsqbXGgIdAqtF++OCTfdueC9aSPHVq0QPH4Em+dHpzTC2kz/xWYVsDy+rA9V4EJjZQuhi/Fu4YG5tQvieOABGNBhBobawyh4QClVD+RqEK1fuaQ2CVqdl2sFp2Jja8F6r96pEW0WMgp+UchJwT32ZhRBqIcRRIcRpIUSEEGJm2fh6IcSpsi1KCHGqbNxRCLFbCJEnhFh47z+l8kj94guyf/wRpwkTsH/hhTsffO4nCr8byM+Jk7hS0Ja96mKyO+1gbeRievj24Ptu3+NkbpiA0aOOVUgI3t9+gzYxkehhwyiJizO0pErhyXou+Dtb8s2+SH0KrpkVNB8O537CuDCFBu09iDqTRk5aIYEOgbTzaMea82soKX30gu+PIoVnzhA1aDC63Fx8li+7pzUIq86t4mz6WSZ5v495adWU2K+IUWhzn7WPioEnpJRN0M80ugsh2kgpB0opm0opmwKbgS1lxxcBU4G37/lXVCKZa9eSvngJdi+8gNOr//n3A6WE/fPJWjOZzRkfk1Tixy82uSQ0/56zuX/wnyb/YW7IXNTG6uoT/xhi2aoVtZYvozQnh+ihwyiOjDS0pAdGpRKMCfEjIiGHg1fKsr/Lq6cuJSjEE4Tg7D59gHlk0EhSC1P59dqvBlStUBFyd+/Wr0GwssJ37RrMm1a8xM217GssPLmQp9y7EbPejGXzw6pEY5XVPpJ68sq+mpRt5SuPhN55NgBYW3Z8vpTyAHrjYBBy//iDpA8+xKpzZ9ymT/t3/562BH56lYQdG9mU/RlFxi5sd00jsd5nFIprzO04l/FNxz+WVU8NgXnjxvisXIksLSV66DCKzp0ztKQH5plmnjhbm7FkX1npC4faENAdwpZibSPwa+LEuYMJaEtKaevelgD7AFZErHhkF/c9CmRu2HB9DcLaNXdPXLkBndQx/dB01MZq2sYOQlVcSl5dA1VJhfuvfSSEMCpzD6UAu6SUoTfs7ggkSynvqbWnEGKMECJMCBGWmpp6L6fekYITJ4l/623UjRriuWA+wtj49gfmp8OqZ7h0OJqfsj5EbW/H7jrRRHt9ipW5jqXdl9LTr2el6VKoGOrAAHxXryrr5jaKgpMPd5tPM2MjXmpfm/2X0zgbXxZEbj0WCtLg7BYadfaiOF/L5bDk8tIXV7KucDDh0S0H8rAipST1f1+SNG06lh3a39cahLUX1nIy5SSTfN8j+VgO59Q6Xulbr0r0VmntIyllaZmbyAtoJYRoeMPuwZTNEu4FKeU3UsqWUsqWzs7O93r6bSmOjCRu/HhM3Nzw/vprVBYWtz8w7TLy2ycJO+/Fruy3cPN3ILTJKU6bz8PF3I2NfdbRxLlJpWhSuHdMfX3x/WE1Rg72xLz8CvmHDxta0gMxpHUtrMyM+XZ/mUvMr7M+PTV0MR51bXHwsOTMbn16anff7rhYuCilL2oYUqMhccoU0r76CtvnnsN70b2vQYjLjeOLE1/QwaMDhX86Uygk/l29sLesWMvfe+V+ax/dwdl+K1LKLGAP0B1ACGEMPAcYvFuIJiVF3znN2Bjv777F2MHh9gdG7qH026fZHf8coTmDqBPswr6Gv7An7yscVY34+fm1eFh5VK94hVsw8fDAd/VqTL28iB0zlty//jK0pPvG1tyEwa282XYmkdiMgrL01LGQeAoRd4xGnb1Ii80jKTIHEyMThtUfRmhiKOfTH9t6lTUKXX6+fg3C5i36NQgffXjPfRCklMw4NAOVUDHS+A1y4vIJtS7llSfqVJHqiq1oTvtn7SPg/budJ4Rw/rsshhDCHOgKXCjb3RW4IKU0aLpIaV4esWPGos3KwnvJEky9vW9/4PHlFK8czra09zif24Em3T3Y6LmQP5I2YVbQmZ/6f4eVadVkAijcO8bOzvisXIFZ/frEvfY62du2G1rSffNSh9oI4PsD1/QDTa6npwa0csXU3Lg8PfX5gOexMLZQSl/UALRpafo1CAcO4jZzZoXXIPyTzZc3E5oUyqSgtznzSwpxRqV0eMoH26JYfWyzCrjfus0DKnCMO7BbCHEGOIY+prCtbN8gbuM6EkJEAQuAUUKIOCFElXWxlyUlxL32mj5P+IsvMG8YdOtBulL47b/k/Pghm3M+I6GoLs0HujFfvEdo0hE0yc+xvO+H2FkoGUY1DSM7O2otXYpF8+YkvPMOmRs2GFrSfeFua06/pp6sPxZLZn4JmFqWp6ealqRQv607V4+nkJ9djI2pDc8HPM/OaztJyk8ytPTHlpKoKP0ahKtX8Vq0EPuBFXlc3kpSfhLzw+bT2q01LmcbUlKg5bCdjpfaesLq/rBhRCUr13O/RuGuJk9KeUZK2UxK2VhK2VBKOeuGfaOklItvc46vlNJBSmklpfSSUlZJGonU6Uj47xQKDh/B/cMPsOrY4daDivNg3VCS9+1kU86X5Etn6g+34t2U8cTmJFIQ8yL/DXmJhp6PdxvNmoyRlSXe3yzBsmMHkqZNJ33ZckNLui/GhPhRqCll9ZFo/UDwKyB1ELaUhp080ekkEfsTABhWX1+WTCl9YRgKT5/W90HIy8NnxXKsu3S5r+tIKZl1eBalspTXvSZzbn8Cx8209H/SD5vjX0PGVWg1upLV6/lXoyCEcPiXzZEKGIWaTMGRI+T88gvOkyZh98wztx6QHQdLuxMZnsGPWXMxtrLFaUg+b10eh7EwJ+vKeLrX6ciw1rWqX7zCPaEyN8d74UKsu3UjZe5cUhcueujSNgPdrOkS6MzyQ1EUaUr16amBPSBsGXYOKmoFORKxL55SrQ4PKw+e9n2aTZc3kVvyaNWFquncsgahyf0nnWyL3Mb++P283vQNLvyURYmJ4Kw9vBikgv3zoMEzUOfJSlR/nTvNFI4DYWX/3riFAQ/10knLdu3wWfMDjmNuY2njjyO/eYLT0YHsyJyMg7ctOT1PM+3cZBo4NCIv8j94WtViznONlHUIDwnC1BTP+fOwffZZ0hYuJGXuJw+dYRjbyZ/0/BI2HS8Lw/2dnhqxhUadPSnIKSHylD5Fe2TQSPI1+Wy+tNmAih8fpJRkrFqtX4NQp849r0H4J2mFacw5OodmLs1onBJCakwuO02KebFTbSz/eA+EEXSbXXk/4B/8q1GQUtaWUvqV/fvPza/KFFUTFs2b3/pQj/gR3dLe7E8fwoGsofg2ceRo8EaWXPmKZ/yfRaSMITPXhEVDmmOtvrcsAgXDIoyNcf/oQ+yHDiVj+XKSpk1Hlj48rcZb13agibcd3+6PpFQnoXYncK4HR77Gp4EDNs7mhO/WG4wgxyBaubVi9fnVaHQaAyt/tJEaDUmzZpH80UdYde6Mz4rlD9wHYXbobIq0RbzfaBqhP18jy0ZFqp0RLzmdh8u/QZf3wNazkn7BrSgNgkFfsmLfPErWj2VH3gzCs0II7OLED96f8FvcTt5s8SbumhHsu5jJ1D4NlDjCQ4pQqXCd8l8cx44la+NGEv5vMlLzcDw0hRCMC/EjOr2A3yKSrqenJp1BxB+lUSdPEq9mkxqjdxmNDBpJckEyv0X9ZmDljy6lOTnEjh1L1tp1OLz8El5f/u+B+yD8HvU7u6J38WqzV4nbVYKmuJRNsoAJHd1R//E+ONeH1uMq6RfcHsUoaIvhx/Hk71rI1sJFROfUpV5fW+YavcWV7Ct83uVzGlr1Y/6uS/Rq7K7EER5yhBC4TJqI85tvkrN9O3FvTERXXGxoWRXi6SA3fB0tWLL3qt791XggqPXpqfXaumNsqiJ8r3620MGzA/62/krpiyqiJDqaqEGDyT8WhvtHH+L6zjsIowdrnpVZlMlHoR8R5BhEV+O+XAxNIsbFCCNbU4ZrNkF2LPReAEZV66V4vI1Cfjqs7Ed62EE25S0mu8QZ7wGC9zLHI6VkRfcVNHXswGtrTuJlb67EER4hnMaMxnXqFPL++ovYcePQ5ecbWtJdMVIJXunox+m4bEKvZZSlp46Acz+j1qYQ0NqNS0eTKcrX6Bc7BY3kQsYFQpNC735xhQqTf/QoUQMGUpqeTq3vv8Pu+ecr5bpzj80lpySHGW1mcmD9VUxtTNhUmMu7wSpMjiyEJkPAp12l3OtOPL5GIfUifPcEMVe1bM75HGlmjfGz8fw35g38bP1Y22stgfb1eHPDKTLyS5Q4wiOIw9ChuH/8MQWhR/Vd3HJyDC3prvRv4YWjpSlL9pYVyrshPbVxZy9KNTrOHdSnp/by64Wj2lEpfVGJZG3eTMzLr2Dk6Ijvxg1YtqqcfmN7YvewPXI7YxqNoeCEmszEfE44CRysTXk28TMwtYCnZt39QpXAnVJSGwkhjgghYoUQ3wgh7G/Yd7Ra1FUVccfhu6eISGvOtoz3sXGxIubJvXwW/TFdfbqyrPsynC2cWbIvkj0XU5U4wiOM3bPP4PnZZxSePUv0Q9DFTW1ixKh2vuy+mMrFpFyw94XAnnB8GY4uxnjUtePs3nh0OompkSlD6w/lYPxBLmfeU91JhX8gS0tJ/uRTEv87BcvgYHzXrcW0VuW4knNKcvjg8AfUta/LIK/hHNt2DVt/G3ZkZDOv/hWMovbBk9PAqnJqvd2NO80UvgZmAI2AS8ABIYR/2b6H+pVZ2vtxSL7FntThuNez4c9mS9mYsJYxjccwr9M8zI3NORaVwbzfLypxhMcAm25P4/3VIkoiI4keNhxNcrKhJd2RYW18MDcx4pt9ZYXyWo+FgnQ4u5lGnb3ITS8i+qy+D8OAwAGYG5uzIkIpfXG/6PLziXvtdTKWLsV+yGC8v1mCkY1NpV1/fth80ovS+aD9B4RuvgYSdpoWUcemlI6Rn4FHM2jxYqXd727cyShYSSl3SimzpJTzgAnATiFEG27oi/AwEhcjORnTiFptbFjqNZPjmceY3WE2rzV7DZVQkZFfosQRHjOsOnak1nffok1OJnroMEpi/1ktvuZgb2nKwGBvfjoVT2J2IdQO0WelhC6mdhNHLO3MCN+t129rZsuzdZ5l+7XtpBQ8Oi1LqwtNQgJRQ4eRt2cPrlOm4DZt2r+X1b8PDiUcYsvlLYwKGoVVkhtXT6bi2MqZA4nZfOm+E5GfAr0WgOrBgtj3wp2MghBClPtMpJS7geeBVYBPVQurSrzrO1DvRXPmmL5Btiab77t9Tx//PgDodFKJIzymWAQHU2v5MnS5uUQPGUrxlSuGlvSvvNyhNhJYeuDaTempRvFHaRjiSez5TDKT9MHzYQ2GoZM61pxfY1jRDxmFp09zbcBANHFxeC9ZgsOwoZV6/XxNPjMPzcTXxpfRQWPZt+4Sdi7m/JCVSWebJOrFrIWWL4Fn80q97924k1GYC9S/cUBKeQZ4kustNB9KjiUd4+2L/8HZwpk1vdbQzKVZ+T4ljvB4Y96oEbVWrURKSfTwERRGRBha0m3xdrCgd2N31oTGkF2ogcYDQG0HoYtp0MEDlbEgfK++Xae3tTdP1nqSDZc2kK+p+VlWNYHs7duJHj4ClVqN77q1t6+P9oB8fvxzEvMT+aD9B0T8kUROaiE2HVw5FZ/JPIvlCHMHeHJqpd/3btxpRfMaKeURACGElRDCsmw8RkpZNZWYqommzk0Z1XAUq3quwsvaq3xciSMoAKgDyrq4mauJGT6C3D//NLSk2zImxI/8klLWhMZcT089/wsWMpU6LVy4cDiRkiItAKOCRpFbksvWy1sNrLpmI6Uk9cuFJLz1NupGjfDdsB6zOpXfuyAsKYx1F9cxtP5QaosATuyMpk4LF765mMBYm8M4ZZ2Bpz8Ac/u7X6ySuWNKqhBivBAiBohG34EtWghxTw12aiImRia80fwNrE2ty8eUOILCjZj6+uK7di2mfn7EvTqB1K++qnGLwII8bOlY14mlB69RrC3Vp6ciIex7Gnf2RlNUysUj+hLajZ0b09ylOavOrUKr0xpWeA1FV1REwltvk7ZoEbbPPEOtZUv/venWA1CoLWT6oel4WXkxoekE9q27jMpYoGtiR1x8LJPkaqjVDpoMrvR7V4Q7paROAfoAnaWUjlJKB6AL0KNs3yODEkdQuB0mrq74rF6FTd8+pP3vS+InTqpxi9zGhviTmlvMjyfjwd5Hn54atgxXLxNcfKwJ3xNXbsxGBo0kIT+BP6L/MLDqmoc2NZXokSPJ2bED57fexP3j2ahMq6bd5aKTi4jJjWFmu5kkRxQQE5FOq961+fLwNT6w2oypNg96zdfHigzAnWYKw4HnpJSRfw+UfR4AVE13BwOhxBEU/g2VWo3H3Lm4vPMOubt2ETVkKCVx8YaWVU77Oo4EediwZF8kOp3UB5wLM/TpqV28yEwqIO5CJgCdvTvjY+PD8ojlNW7WY0iKLlzg2oCBFF+6jNeX/8Np9Ogq8xScTj3NqvOrGBAwgKb2zdm/4RKOnlYkuphglnSc3tpdiDbjwbXK+ovdlTu6j6SURbcZKwR0VaaomlHiCAp3QwiB48sv4b1ksT5FsX9/8kNrxvpNIQRjQvyITM3nzwsp4NsRXBpA6GLqNHfG3NqkvF2nSqgY0WAEEekRHE8+bmDlNYPcv/4iashQ0Onw/WE11l27Vtm9SkpLmHZwGi4WLkxqMYmwX6PIyyym46C6fPnnBT4xX4609oDO71aZhopwJ6MQJ4S4pYuDEOIJILHqJFUfShxB4V6w6tgR3w3rMXJwIOall8j44Yca8cbdq5E7nnbm+tIX5emp4RgnHqVBew+izqSRk1YIQF//vtib2T/2i9mklKR//72+B4KfH74bNqBuULVv54tPLyYyO5LpbadTkiY4/Ucs9dq5c6qwiFZpW6iru4boPhvMrO9+sSrkTkbhdWCJEGK5EOI1IcQEIcQK4Bv0C9keapQ4gsL9YFa7Nr7r12HVoQPJH3yo78tQYtieU8ZGKkZ3rE1YdCZhURnQ6Hp6alCIvu5+xH69y0ttrGZwvcHsidtDZHbknS77yCJLSkicMoWUT+dh3a0bPqtWYuLqUqX3PJ9+nqVnl9LPvx/tPdqzd+1FTNRGtO7nx8rfj/COySak3xP6jmoG5k4pqRFAQ2Af4Av4lX1uWLbvoUaJIyjcL0bW1nh9tQjHMWPI2riR6FEvok1LM6imAcHe2FmYsGRfpL54WouRcH4b1kZp1G7qTMSBBLQl+qZCA+sNxMzIjJURKw2q2RBoMzOJefkVsjdvwek/4/FcMB+VuXmV3lOj0zD14FTs1fa8E/wOl44mk3A5izbP+PPntTQGZ3+DWqVB9JpnsODyjdwp+6gO0EJKuVRK+ZaU8k0p5fdA8A01kB5KjkdnKnEEhQdCGBnh8uYkPBfMp+jcOa71f4HCs4Z7V7IwNWZEGx/+OJ/MlZS86+mpx76ncWcvivO1XA7T13RyUDvQz78fv1z9hbRCwxqz6qQ4MpKogYMoPH0aj08/xfn11xGqqi8UvTR8KRczLzK1zVTUpRYc3HwFF18bAtq6sW/nZp4xOoSq/URwrBmP1Tv9RT4Hbtf5u7Bs30NLoJs1L7bzVeIICg+MTc+e+K75AYQgeuhQsrdtN5iWEe18MTVS8d3+SLCrVVY9dTketc1w8LDkzO7r6anDGwxHo9Ow8tzjMVvIO3iQqIGD0OXnU2vFcmz79K6W+17JvMLiM4vp4duDJ2o9Qegv1yjKLaHT4AC2n45mfMHXFFh6IULeqhY9FeFORsG3rKzFTUgpw9C7kx5arMyMmdK7gRJHUKgU1A0aUHvTRtSNGpLw9tukzJ9vkP7PTlZmvNDSiy0n4knJKdK3bSzMQJRVT02LzSMpUt8zwtfWl95+vfnh3A8k5SdVu9bqJHPtWmLHjMXE3Z3aG9Zj0azZ3U+qBLQ6LVMPTsXaxJp3W79LakwuZ/fE0TDEEwcvK1J/W0AdVQLqvgvApGpdWPfCnYyC+g777voLhBBqIcRRIcRpIUSEEGJm2fh6IcSpsi1KCHHqhnPeE0JcEUJcFEJ0q/jPUFAwLMaOjvgsXYrdwIGkf/sdsePHG6Rpzysd/NDodCw7FAW+HcAlCEKXEBDsgqm5cXl6KsCEZhOQSBaeXFjtOqsDqdWS9OFHJM2chVWHDvisWYOJZ9U1vP8nq8+t5mz6Wd5v/T72pvbsXXsRtZUJrfv58dvBYwwvWU+KZ1dUgTXrUXcno3BMCHFLjSMhxMtARZKci4EnpJRNgKZAdyFEGynlQCllUyllU2AzZcX1hBANgEFAENAd+EoIUX31YhUUHhBhaor7zBm4zZhO/qHDRA0cRHHktWrV4OtkSY+Gbqw+Ek1eSak+PTU5HNPko9Rv687V4ynkZ+t7UntYeTC0/lB+vvozFzMuVqvOqqY0O5vYcePJXL0ah1Gj8PpqEUZWltV2/6jsKBaeWsgT3k/Qzbcb5w4mkHwth/bP10FlZoT13qkIIXDuv6DaNFWUOxmFicCLQog9Qoj5Zdte4BXgjbtdWOrJK/tqUraVJ3ULvTN/ALC2bKgfsE5KWSylvAZcASqn152CQjViP2gQPsuWUpqVRdSAAeTt3Vut9x8b4k9ukZZ1R2Og0Qv6omqhi2nYyROdThKxP6H82FcavYKVqRWfn3iow4TlSJ2OrM2budoCQGTQAAAgAElEQVS9B/lHjuA2ayau705GGFXf+6VO6ph+aDqmRqZMaTOFonwNh3+8ikddOwJau3FoxxpCSkOJa/Iawr7mdSG4U0pqspSyHTATiCrbZkop20opK+SEFEIYlbmHUoBdUsobO4h3BJKllH/3CfQEbuxsElc29s9rjhFChAkhwlJTUysiQ0Gh2rEIDqb2po2YeHkRO2486d99V20L3Zp429HGz4HvD1yjRKWG5iPhwnbszNKpFeRAxL54SrX6ogS2ZraMaTSGA/EHCE0MvcuVazaFERFEDx5C4n+nYOrnR+1NG7EfMKDaday7sI4TKSeYHDwZZwtnDm+9iqawlJDBAWiKCqhzfBaxRt749/m/atdWEe6ajyWl3C2l/LJs++teLi6lLC1zE3kBrYQQDW/YPZjrswSA26UB3fJ/kZTyGyllSyllS2fn6ulZqqBwP5h4euK75gesu3cjZd58Et5+B11hYbXce2yIP4nZRfxyOgGCX+bv6qmNOntRkFNC5KnrL1SD6w/G3dKdBccXoJMPXwWb0qwsEmfOJKr/C5TEx+Mxdw4+q1ehrlev2rXE5cbx+YnPae/Znr7+fUm8ms35g4k0edIbRw8rLmyaiadMJqPzbISxWbXrqwhVn6QLSCmzgD3oYwUIIYyB54D1NxwWB3jf8N0LSEBB4SFGZWGB54IFOE+cSM6vvxI9dBiaxKqvEtM50JlAV2u+2ReJtPWGer3g+HJ8AiywcVLfFHA2MzLjtWavcS79HDuv7axybZWF1OnI3LiRqz16krVhI/bDh+G/41ds+/UzSKq5lJIZh2egEiqmt5mO1En2rr2Ilb0ZLXv5Upx8iXpXv2efujONO/Spdn0VpcqMghDCWQhhV/bZHOgKXCjb3RW4IKWMu+GUn4FBQggzIURtoC5QM6qOKSg8AEIInMaNxWvRIkqio7nW/wUKTpyo8nuOCfHjYnIuey6llqWnZiIiNtGosxeJV7JJjb2+DKmXXy8C7QP538n/UVJq2LIdFaHwbARRgweTNHWa3lW0ZTNu77+PkbXh6gZtubyF0MRQ3mzxJu5W7oTviSc9Lo8OL9TF1MyItA2vUyRNUPf8uEavj6rKmYI7sFsIcQY4hj6msK1s3yBudh39XVZjA3AO2Am8KqWs/mRvBYUqwvqJLviuX4fKypLokaPI3LChSu/Xp4kH7rZqfaE8n/bg2hBCl1CvjRvGpqqbZgsqoeLNFm8SnxfP+ovr73BVw6LNzCRx+gyiXngBTXwCHp/M1buKAgMNqispP4l5YfNo5daK/gH9yc8uJvSXSGoFOeDXzJmS8C14ph9mk+0oghvVv/sFDUiVGQUp5RkpZTMpZWMpZUMp5awb9o2SUi6+zTkfSSn9pZSBUsodVaVNQcFQmNWpQ+0NG7Bs1YqkadNJmvUBUqOpknuZGqt4qX1tjkRmcDouuyw99SzqtKMEtHbj0tFkivKv37udZzvaurflmzPfkFNS/Wss7oTU6cjcsIHIHj3J2rQJhxHD9a6ivn0N/tZdqivl/QPvo5M6ZrTTu48ObrqCTivpODAAUZKHZvtkInQ+NOg7yeB670a1xBQUFBSuY2Rri/eSxTi8+CKZa9YQ8/IraDMyquReg1p5Y6025pt9kTelpzbu7EWpRldePfVvJrWYRFZxFkvDl1aJnvuhMDycqIGDSJo2XW9Ut2zB9b33DOoqupEV51ZwLOkY77V+D29rb2IvZHD5WDLNu9XCzsUCzV8fY1mcynrXSbSp42pouXdFMQoKCgZAGBvjOvn/8Jg7h8JTp4jq/wJFFy7c/cR7xFptwrA2Puw4m0hUtg5ajIIL23G0zKRWkAOn/oilpOh6z+b6jvXp7deb1edXG7z8hTYzk8Rp04kaMBBNUiIen35CrVUrUQcGGFTXjZxLP8eXJ7/kKZ+n6Offj1Ktjn1rL2HjpKZ5Nx9IjsDo6GLWaLvQp1c/Q8utEIpRUFAwILb9+uGzehVSqyVq0GASp06j4OTJSl3T8GI7X4xVKr47EAktX9YPHvuO4F61KcrTcHbvzbOFCc0moJM6Fp1aVGka7gVZWkrm+g1Edu9B1ubNOIwYgf+OHdj26VOjXC+F2kIm75uMg9qB6W2nI4Tg1B8xZCUXEDIoEGMTFaW/vEmOtGB/rVcJ9nUwtOQKoRgFBQUDY964Mb6bNmLTvTvZ27YRPXgIkT17kfbNt2iSUx74+i42ap5t5snGsDjSjF2gXm84sQI3LxNqBTlwclfMTbMFTytPhtQbws9Xf+ZS5qUHvv+9UHjmjN5VNH06ZgEB1N66Bdf33sXIyqpadVSEecfmEZ0TzewOs7E1syUnvZCw7VH4NXPGp6EjnF6LUdwRZmsG8Uq3loaWW2EUo6CgUAMwcXHBY87H1N2/D/cPP8DI3p7UBQu40qULMWPGkLNzJ7oH6PA2OsSPYq2OlYejy9NTCd/4r7OF0Y1HY2liyefHq6f8hTYzk8Sp04gaOAhtcjIen35KrZUrUAfUHFfRjeyJ3cOGSxsYFTSK1u6tATiw4TII6PBCXSjIQPf7VE4TQKp/f1r42BtYccVRjIKCQg3CyMoKu/798V3zA/47d+A4ejTFFy8RP3ESlzuGkDTrAwrPRtyze6mOixVPNXBl5eEoCtxbgWsjCF2CW20bajW4dbZga2bL6Eaj2R+/n6OJVbdcSJaWkrlund5VtGULDiNH4rfjV2z79K5RrqIbSStMY9rBadRzqMeEZvrOxJGnUrl2Oo3gXrWxdlDDXx9AQQbvFY9i4lPVv7L6QVCMgoJCDcXU1xeXSROp89efeH/7LVbt25O1aRNR/ftzrd8zpC9ffk9ZS+M6+ZFVoGFDWJw+PTUlAqL2E9z79rOFIfWHVGn5i8LTp4kaMJCkGTMxCwzE78etuL47uUa6iv5GSsmUg1Mo0BYwt+NcTI1MKSnUsm/dJRw9LWnS1RvijyPDlrGG7njUa0UTbztDy74nFKOgoFDDEUZGWHXsgOeC+dTdvw+36dMQZmakzJnL5ZBOxE6YQO5ff911vUMLHwda+Njz3YFraBs8B1ZusPN93GqZ33a2YGZkxoRmE4hIj+C3qN8q7fdoMzJImDJF7ypKScFj/jxqrViOWd26lXaPqmLthbUcjD/I2y3fxs/OD4DDP14lP7uYzsPqYSQkbHuTfFNH5hQ9x8SuNdP9dScUo6Cg8BBhZGuL/eDB1N64Ab9ffsZhxAgKT50m7j+vcrlzF5LnzKXo0r8Hh8eG+BGXWcivF7Oh92eQHA4HFvzrbKFXbX35iy9OfPHA5S9kaSmZa9dytUdPsn/8CYeXXsJvxw5se/Wqsa6iG7mSeYX5YfMJ8QphYOBAABKvZnN2XzyNO3vhVtsWwpZC4ilmFQ+hfVBtGnraGlj1vSOqq5xvVdCyZUsZFhZmaBkKCgZFajTk7T9A9tYt5O7eA1ot6oYNsX3uWWx79sTI7rr7QqeTdP1sL+YmRmx7rQNiy2iI2Apj9vLLBi0pMbkM/7Atpmrj8nMOxh9k3B/jmBw8mWENht1Zi1aLJikZTVwsJbGxaGLj0MTFURIXhyY6mtLsbCxat8Zt6hTM6tSpqj9JpVNSWsLg7YNJK0xjc9/NOJk7UarRsX72MTTFWgZPa42pNgO+bEmMui4hyZPY8UYI9d1tDC39tgghjkspb5sSZXy7QQUFhYcHYWKC9RNdsH6iC9qMDHK2bSNry1aSZ31AysdzsOr6JHbPPYdlu3aojIwYG+LH5M3hHLySTocen0DkHvjpPwT32Mrm+ac5uy+e5k9fb/7SzqMdbdzbsOTMEvr698WyUEdJbFzZg1//0C//nJgI2usuKIyNMfHwwNTLC3X37li2a4f10089FDODG/nixBdcyrzEoicX4WTuBMCJ36PJTMyn16uN9UZ06zSkpoBXi4bSs5F7jTUId0MxCgoKjxDGDg44jBiBw4gRFJ07R9aWreT88gu5O3Zi7OqKbb9+9Ozbl3nWZizZd5UOL7eGXgtgw3DcEpdSq8FTnPwtmgAfLaQk6N/24+KZdFVD1IU0ouZ2xKTwZjeSkYMDJt5emDdujE2vnph6eWHi5Y2ptxfGrq4I44f7MXM44TArz61kYOBAQrxCAMhIzCdsRxR1W7rg28gJLv0Gp9dyxGMkZ6+5Mu/Jhy+W8DeK+0hB4RFHV1JC3u49ZG/ZQt7+/aDTkeVfn5XWQUzoHoRLQQaa/WvRxCeSog7mqN9o/K9uxSf2DwCEmRkmXl5ctcjjvFk6/ULG4OjfQP/g9/JEZVl9vY+rm6yiLJ7/+XmsTK1Y13sd5sbmSJ1k64ITZCTkM2RGGyxKE2BJCFobb4KTJtOhvjdfDm5maOl3RHEfKSg8xqhMTbHp9jQ23Z5Gk5JCzs8/Y7x5C6+f2gSnNpEGGLs4YyKM8LS6gKtlHnH1+tH2w1FY+Hpj7OyEUKlQ58Xz7tY+pPulMqv9k4b+WVWOlJKZh2eSUZzBoq6LMDc2ByDiQAKJV7J5YkQ9LMx18P1wAL51m0l2bDFvPFnzs6juhJJ9pKDwGGHi4oLjK69Q59ftHJu6kNFP/h+ZW/6g7r59+C6ajUfjSNp3SKRYo+JqthMmri4Ilf4x4WnlyeB6g/np6k9czrx8lzs9/Px45Uf+iPmDN5q9QT0H/QK0/KxiDm+5gmegPfXausOOdyDpDJndvuSLkxr6NfWkjkvNXWdRERSjoKDwGCKEYMCAzkhvHz7cFUmpTkLQc1C/D+5np+Jdx4yTv8egKb65z9XoRqOxNLbk8xPVU/7CUETnRPPx0Y9p7daaEUEjysf3rb9Eaamk89BAxMnVcGIlssNb/F+4J1LCxK4P9ywBFKOgoPDYojYx4v+6B3IuMYctJ+JACH3Q2dSCYONvKMrTEL437qZz7NR2vNL4FfbF7eNY0jEDKa9aNDoN7+1/DxOVCR92+BCV0D8mI0+lEnkyleBevtiVXoFf34bandjm9CK7ziXz1tMB+Dg+/PEVxSgoKDzG9GnsQRMvW+b9fpHCklKwcoEen+Ce9SPe7nmc2nXrbGFIvSG4WriyIGxBpZb4riksOb2E8LRwpredjpulGwDFhVr2rb2Io5cVTTvYwobhYO5AZo+vmfHLBZp42fJS+9oGVl45KEZBQeExRqUSTOndgOScYr7dH6kfbPQCBPYkWPsJhbm3rnJWG6t5rdlrnE0/y2/RlVf+oiZwIvkE34Z/Sz//fjzt+3T5+OGtVynIKaHLkACMfh4P2XEwYAUz/0ohp0jD3P6NMTZ6NB6nj8avUFBQuG+CfR3oHuTG4r1XSckp0ruRen+Gu2UM3jbXOPl79C2zhd5+vQmwD+CL41+gKa2aHtPVTW5JLu8feB8PSw/ea/1e+XjClSwi9sXTuIs3rvFL4dIOePoj/sr34cdTCfyncx3quT2cC9Vuh2IUFBQUeLdHPTSlOhbsKqubZO0G3ecQbLyEwtvURDJSGTGpxSTi8uLYcGmDARRXPrNDZ5OUn8SckDlYmuhjA6UaHXtWX8DaQU2rxvH6kthBz5Hb5CXe33KWAFcrXu3y8JTrqAiKUVBQUMDXyZLhbXzZEBbLhaQc/WCTwbgH+eBtdoaTv0XeMlto79Ge1u6tWXJ6CbkluQZQXXnsuLaDbZHbGNtkLE2cm5SPH98ZRWZSAZ2eccL055fBsS70/ZKPd14kJbeIT/o3wdT40XqMPlq/RkFB4b55/ck6WKtN+Gj7ef2AEND7c4LtfqYwX8fZPbE3HS+EYFKLSWQWZ7Ls7DIDKK4cEvMS+eDwBzR1bsroRqPLxzMS8jm+M5q6LZzxOfMqaAph4CoOxxWzJjSGlzvUpulD1iuhIihGQUFBAQA7C1Nee6IO+y+nsediWW9oW0/c+43C2/QUJ3dcvmW2EOQYRM/aPVl1bhXJ+ckGUP1glOpKee/Ae+jQMbvjbIxV+iIPUifZvfoCJmojOjhvhLij0O9LCm3r8O6WM/g4WvDmU4EGVl81VJlREEKohRBHhRCnhRARQoiZN+x7TQhxsWz8k7IxUyHEMiFEeNk5natKm4KCwu0Z0dYXH0cLZv96Hm1pWbe1ZsMJDrxCYZERZ3eG33LOa81eo1SW8tXpr6pZ7YOzLGIZx5OP837r9/G29i4fj9gfT1JkNh3aZGNx6gtoPR4aPs+CXReJTi9gznONMTc1MqDyqqMqZwrFwBNSyiZAU6C7EKKNEKIL0A9oLKUMAuaVHT8aQErZCHgKmC+EUGYyCgrViKmxine71+NScp6+bSeAELgPfQ9vdTgnd8WhuaE7G4CXtReD6g3ixys/ciXzigFU3x8RaREsOrmIbr7d6OPXp3w8L7OYQ1uv4uVvRuDFseDdGp6axanYLL4/cI0hrWvR1t/RgMqrlip76Eo9eWVfTco2CYwH5kgpi8uOK5un0gD484axLOC2VfwUFBSqju4N3Qj2tWfBrkvkFZcZADtvgrs6Uai14Oyan245Z0yjMQ9V+YsCTQHv7n8XR3NHpraZelN/h33rLiJLJZ1VHyBMzeGF5ZRgzORNZ3CxVvNuj3oGVF71VOmbuBDCSAhxCkgBdkkpQ4EAoKMQIlQIsVcIEVx2+GmgnxDCWAhRG2gBeN/mmmOEEGFCiLDU1NSqlK+g8FgihOC/vRqQllfMkr1Xy8fdew3B2zaak2EqNCnRN51jp7bj5UYvszdu70NR/mJe2Dx9faOOH2Nrdr1l5tWTKVw7nUawTxi2eaHQfynYeLBo9xUuJucy+7mG2KhNDKi86qlSoyClLJVSNgW8gFZCiIboy3XbA22Ad4ANQm+mlwJxQBjwOXAI0N7mmt9IKVtKKVs6OztXpXwFhceWpt529G3iwbf7I0nMLtQPCkHwoLYU6mw5u3QF/KPExdD6Q3G1cOWz45/V6PIXf8X8xcZLG3mx4YsEuwWXjxcXaNi37hJODkU0zf0InpgCfp24kJTDV3uu0K+pB0/UczWg8uqhWnz2UsosYA/QHf2Df0uZe+kooAOcpJRaKeUkKWVTKWU/wA549OvzKijUUN7pFohOwqe/XSwfc29WDy+PAk7GNERz9Iebjlcbq5nQbALhaeH8Hv17dcutEKkFqcw4NIP6DvWZ0HTCTfsObb1KYU4JXVTTUQV2g/aT0JbqmLzpDDZqE6b3CTKQ6uqlKrOPnIUQdmWfzYGuwAXgR+CJsvEAwBRIE0JYCCEsy8afArRSynNVpU9BQeHOeDtY8GJ7X7aciOdsfHb5ePCgdhTq7Dj7417Ivnmlcx+/PtS1r8sXJ2pe+Qud1DH14FQKtYXMCZmDidF1N1DC5UzO7U+gse2fuDiXwLNfg0rF0oPXOB2XzYy+QThYmhpQffVRlTMFd2C3EOIMcAx9TGEbejeRnxDiLLAOGCn1c00X4IQQ4jwwGRhehdoUFBQqwKtd6uBgacqH28+Vu4Q8AhzwqqPmZHZPND+9fZMbyUhlxKTmk4jNjWXjpY2Gkn1b1l5Yy8GEg7wT/A5+tn7l41pNKbtXX8DaLJvWFqtgwCowtycqLZ/5v1+ia31Xejd2N6Dy6qUqs4/OSCmbSSkbSykbSilnlY2XSCmHlY01l1L+VTYeJaUMlFLWl1J2lVJG3/kOCgoKVY2N2oSJXetyJDKDP86nlI8HP9NAH1sIN4bT6246p4NnB1q7tWbx6cXkleT985IG4XLmZRaELaCzV2deCHjhpn3Hd0STlVxIZ4vPMekzG9wbo9NJJm8+g6mxio+ebXhTdtKjjrIOQEFB4Y4MblULP2dLPv71PJqyBW0edezwqmfPycIBaH6dCjmJ5ccLIZjUsqz8RYThy18UlxYzef9krE2tmdFuxk0P+PSEPE7svEaAei+12jaGZsMAWHsshtBrGUzpVR9XG7WhpBsExSgoKCjcERMjFe/3qE9kWj5rQmPKx4N716ZQa8nZ7BDY/uZNbqQgxyB61O7ByoiVpBSk3O6y1cYXJ77gcuZlPmj/AY7m1xedSZ1kz/LTmJJHhzqh0ONTABKyCvn41wu0r+PIgJa3ZMU/8ihGQUFB4a48Wd+Ftn6OfP7HJXKK9AHk8tlC8SA0F/6A8E03nfN6s9fRSi1fnTJc+YtD8YdYdW4Vg+sNpqNXx5v2nd0dRVJMMe0d1mI+dDGYqJFS8t+t4ZTqJB8/2/ixchv9jWIUFBQU7op+QVt9sgo1LNp9vZRFcO/aFBYZE2EyGna8A7nXi+J5WXsxKHAQW69s5WrW1dtdtkrJLMpkysEp+Nv682aLN2/al5dZxOEtl/A2PUXgsFFg7wvAT6cS2H0xlbe7BVLL0aLaNdcEFKOgoKBQIRp62vJcMy+WHYgiNqMAuD5bOJH5tL4m0q9v3eRGGtN4DBbGFnx+vHrLX0gpmXFoBlnFWcwNmYvaWH3Tvr2L/0SW6uj0pBZRrzsAaXnFzPwlgua17BjVzrda9dYkjA0tQEFB4eHhnW6BbA9P4JPfLvLl4GYABPeqzdb5J4gI/Jim5ydAxFZo+BwA9mp7Xm70Ml+c+IKwpDBauj14OTONTkN6YTpphWmkFaaRWpiq/1yg/5xemE5KYQpJ+Um83fJtAh1uLnF99a9jREWb085nH7Z9ppaPT/85gvziUuY+3xgj1ePnNvobxSgoKChUGDdbNWM6+vG/v67wUntfmtWyx6Nu2WzhsglB/q0w+fVtqB0Clk4ADKs/jLUX1vLZ8c9Y3XP1bf30UkoKtAWkFqSWP9jLH/aFaaQWpJJWpH/wZxZn3labvZk9ThZOOJs742vrS6B9IMMaDLvpmKL0VPZvicfZLJsmr74KKn35698ikth+JpG3ngqgrqt1Jf/VHi5ETa5Rcjdatmwpw8LCDC1DQeGxIq9YS+dP9+DjaMGmcW0RQpBwOYut80/Qvps1TcN7QP3e8MLy8nO2Xt7KtEPTeKnhS1iZWN38hl+2FWoLb7mXicoEJ3P9g97R3BFnc2ecLJzKx/4edzR3xER1l0J1Oh27Z33N+aQAXnjJGudWbQDILtTw1IK9OFqZ8fOE9pgYPfpedSHEcSnlbadtykxBQUHhnrAyM+atpwN4b0s4O84m0bOR+/XZwqE8gnq8i8k+fYN7GvQFoK9/X344/wNLzy4FwNrEuvytvqFTw5se8M4WzjipnXC2cMbG1KbSMoDiN3/DuaT6NG2YiXOrp8rHZ28/T3p+CUtHBT8WBuFuKEZBQUHhnhnQ0pvlB6OYs+MCT9Z3wczY6HpsQfs8Td1+0q9d8O0AFg4YqYxY0WMFmUWZOJk73RT4rQ60F/9izx5LbMxzaTW6X/n4gctprA+LZVwnfxp62t7hCo8PillUUFC4Z4xUgvd71Scmo4BVh/UVacpnC7vi0PRcBIWZsOP/ys+xNLHEy9qr2g0C2fGELdtGVqknnUe1xMRM/y6cX6zl3S1n8HOyZGLXutWrqQajGAUFBYX7olOAMyEBzvzvz8tkFZQA+kykwlwNEZfsIOQdCN8IF341nEhtCekr3uFkVncCm1jg3cSzfNe83y8Sl1nInOcbozZ5NPst3w+KUVBQULhv/tuzPnnFWv73p35Bm0ddOzwD7TnxewyaVm+Aa0PYNhEKMgyiT/f7VHZf7oipWkX74c3Lx49HZ7D8UBQj2vrQqraDQbTVVBSjoKCgcN8EulkzMNibVUeiiErLB6BVb18Kc0qIOJQK/RZBfhr89n71iwvfxNndMSRrAukwuCHmVvp+CEWaUv5v0xk8bM35v+6Pdr/l+0EJNCsoKDwQk54K4OdTCczZcYHFw1vgUde+fLYQFNIWk45vwr5PIehZCOhWOTfVFkNR9vWtMAuKsm4YyyL30BaO5H9Krfr2BLS63kZz4V9XuJqaz4qXWmFlpjwC/4nyF1FQUHggXKzVjOvkz/xdlzh6LYNWtR1o1duXrfNPErEvnqad34Hz2+CXifCfw2BuB6VaKM65+UFemHXzg74o6zYP/rLPt1nTcCPy/9u78+AoyzuA499fQkISAgkhRONuSERAAklADB5Yj0EsxaOKwGjVjnbGsfWqPaxHj9GqVLSORUdsPUpbrWNHqbRjrUexWjxR5AiGgAgUE0IFjbnQQI5f/3if7CwhB5vdDbub32dmZ3ef93mPHy95f/s+z/u+T1IqK/f+Ek1O4/RLJwYua62sbeC3/9nKvGl+Tp9gY7x3x5KCMSZsV546lqdWfcLCFzay/JpTupwt+Ei5YAk8PgsemAId7bC/qfcFSjKkZR34ypvYpSzbvbzvmpZFQ3M61ds72FHVyI7az5kx7xhG5KYD0NbewU3LKhiZkcovzi0egH+V+GRJwRgTtvTUZH4y+1h+/Ox6nq+o5fypvgPPFmYdDxc+Bh+/6p0pBB3MSctyZUHfUzPhEG5aa2lupXpTHTWr6qiuqqOprgWA4aPSmDqrgCkz/YG6j76xjcraRn576TSyMwbHeMv9YUnBGBMRc4/zsfSt7dz70mZmTz7y4LOF0vlQOj+sdbS3drBrWwPVVXVUb6xjT3UTKKSmJeM7diTTZo/BX5xD1uj0A+6E3rqnmcUrtjCn5EjmlA6e8Zb7w5KCMSYikpK8MRcueWwVS9/azjVnjOtytjAm5GWqKnW1e70kUFVH7ZZ62vZ3kJQkHDF2BCecezQFxTnkFQ4nqYdHVHR0KDcvqyA9JZlfnj853DATniUFY0zEzDgml1nFeTz82lYuKi9wZwvZrH3lE0pO8zEkte+bxPY27KOmqo7qqi+o3lTHlw3ejXHZR2RQPOMoCibl4BufTWr6oR2+nnx3B6t3fMF9C6aQN3xwjbfcH5YUjDERdcucYmYvXsniFVu484ISpp9zNH+7fy2Vb9Qy5cyDxzxu3d9O7ZZ6qqvqqKmq4/Od3v0OaZkpFEwcib84h4LiHIbnhH5Ar677knte2sRpE0Yzb5qv7xmMJQVjTGSNy8vk0hPH8NSqT7h8RiHjJnhnC2te3sHkU48ieUgSe6qbXJPQF+Tx2WQAAAmESURBVOzaWk9Hm5I8JIn8cVmcPPdICopzyPVnImEMdqOq/HT5BgT41dySQTnecn9YUjDGRNwNZ45n+Zqd3P3PTfz+iumBs4Xl96+lcc9XtOxtBWCUL5OyM/wUTMohf1w2KYfQvHSoln1QwxtbPuOO8yfjHzk4x1vuj6glBRFJA1YCQ916lqnqbW7a9cB1QBvwgqreJCIpwOPANFf/CVW9O1rbZ4yJnlGZQ7l25jgWvbiJtz/+jBkTcjl6Si67dzRRVDoq0CSUMSLyl4Y2trRSUd3Anf/YyPSikVx2YmHE15HIonmmsA+YqarN7oD/poi8CKQD5wNlqrpPRPJc/QXAUFUtFZEMYKOIPK2q/43iNhpjouSKGUU8+c4O7nqhiuev/xpnX10W8XV8ub+NytpGKmoa2FBTT8XOBrbt8fokMocOYdG8MpIG8XjL/RG1pKDeOJ/N7muKeylwNbBIVfe5ers7ZwGGicgQvMSxH2iM1vYZY6IrLSWZm+dM5PtPr+W5NTUsKD+4kzkULa3tbNzVyIaaBi8J7Kzn493NdLgRhfOz0ij1ZXHhcT5K/dlM9WeTldHHEJ3mIFHtUxCRZOADYBywRFVXicgE4FQRWQi0ADeq6vvAMrwziF1ABvBDVT3oebsichVwFcCYMaFf92yMGTjnleWz9M3t3PfKZs4pyycj9dAOOfvbOtj8vyYqdtYHksBHnzbR5jJAbmYqZf5s5pTkU+bPotSXRd4Iu9w0EqKaFFS1HZgqItnAchEpcescCZwETAeeEZGxwAlAO3CUm/6GiKxQ1W1dlvko8ChAeXm5RnP7jTHhERF+fk4x83/3Do+t3M4N3Yxw1tbewZbdzWyoaWB9TT0bdjawaVcT+9s7AMjOSKHUl8V3J46l1JdNmT+L/Kw0u5ooSgbk6iNVrReR14FvADXAc6556T0R6QBygUuAl1S1FdgtIm8B5cC2HhZrjIkD5UU5zCk5kkdWbuWi6QU0tbS65p8GKmrqqaxtZF+blwCGDx1CiS+L75xSRKk/iyn+bPwj0y0BDKBoXn00Gmh1CSEdmAXcg9fPMBN43TUlpQKfAZ8AM0Xkz3jNRycBi6O1fcaYgXPLnImsqPqUGYteDfQBpKckU+IbwWUnFQaagIpGDbOO4cMsmmcK+cCfXL9CEvCMqv5DRFKBpSLyIV5n8uWqqiKyBPgD8CEgwB9UtSKK22eMGSCFo4axcG4plTsbKPV7TUDHjM4k2RJAzBGvFSc+lZeX6+rVqw/3ZhhjTFwRkQ9Utby7aTZGszHGmABLCsYYYwIsKRhjjAmwpGCMMSbAkoIxxpgASwrGGGMCLCkYY4wJsKRgjDEmIK5vXhORPcCOMBaRi/eIjUSSiDEFS+T4Ejm2TokeY7zEV6iqo7ubENdJIVwisrqnu/riVSLGFCyR40vk2DoleoyJEJ81HxljjAmwpGCMMSZgsCeFRw/3BkRBIsYULJHjS+TYOiV6jHEf36DuUzDGGHOgwX6mYIwxJoglBWOMMQFxkxREpEBEXhORKhGpFJEbXHmOiPxLRLa495FB89wqIh+LyGYRmR1UvlBEqkWkuY91Hi8iG9wyHhQ3UKyInCYia0SkTUTmJ1Bc33Pl60TkTRGZFE5sMRjfFSKyx8W3TkSuTKDYfhMU10ciUh9ObDEaY6GIvCoiFSLyuoj44zC2butJBI8pYVPVuHjhDe85zX0eDnwETALuBW5x5bcA97jPk4D1wFDgaGArkOymneSW19zHOt8DTsYbHvRFYI4rLwLKgCeA+QkU14igOt8EXkqw/XYF8FAi/p/sUud6YGmixQg8izd8L3jjvD8Zh7F1W48IHlPC3ueHc+Vh7tC/A2cBm4H8oJ282X2+Fbg1qP7LwMldltHjDnTL2hT0/VvAI13q/DHSOzAW4goqfzGR9hsRTgqxFFuXem8DZyVajEAl4HefBWiMp9gOpV40jimhvuKm+SiYiBQBxwGrgCNUdReAe89z1XxAddBsNa7sUPncPP2dP2SxEJeIXCsiW/F+LX0/tAh6FwvxAfNc88MyESkIKYBexEhsiEgh3q/Yf4ew3EMSAzGuB+a5z3OB4SIyKoRl92iAYosLcZcURCQT+CvwA1Vt7K1qN2WhXH8b7vwhiZW4VHWJqh4D3Az8PITl9r7S2IjveaBIVcuAFcCfQlhuzyuMjdg6XQwsU9X2EJbb94pjI8YbgdNFZC1wOrATaAth2d2vcOBiiwtxlRREJAVv5z2lqs+54k9FJN9Nzwd2u/IaIPiXoB+o7WXZyUEddXe4+YM7snqdPxwxGtdfgAv6E0832xAT8anq56q6z5U/BhwfXmSxE1uQi4Gn+xtPD9sREzGqaq2qXqiqxwE/c2UNcRRbfDicbVchtvcJXifM4i7lv+bATqF73efJHNgptA3XKdRXu17Q9PfxOoY6O7zOjnT7XyzFBYwPqnMesDqR9huundh9ngu8myixuWnHAv/F3ZQaiVcsxYj3BNIk93khcEe8xdZXPWKgT+GwrbgfO/BreKdqFcA69zobGAW8Cmxx7zlB8/wM7wqBzQRdpYHXXl4DdLj323tYZznwoVvGQ51/bMB0N99e4HOgMkHiegCvM28d8BowOcH2290uvvUuvomJEpubdjuwKIH/7ua79X0EPA4MjcPYuq1HBI8p4b7sMRfGGGMC4qpPwRhjTHRZUjDGGBNgScEYY0yAJQVjjDEBlhSMMcYEWFIwJgQi0u5uRqoUkfUi8iMR6fXvSESKROSSgdpGY8JhScGY0HylqlNVdTLew9POBm7rY54iwJKCiQt2n4IxIRCRZlXNDPo+Fu8O3FygEHgSGOYmX6eqb4vIu0AxsB3veUsPAouAM/Dujl2iqo8MWBDG9MKSgjEh6JoUXNkXwESgCehQ1RYRGQ88rarlInIGcKOqnuvqXwXkqepdIjIUeAtYoKrbBzQYY7ox5HBvgDEJoPPpmSnAQyIyFWgHJvRQ/+tAWdAIW1nAeLwzCWMOK0sKxoTBNR+14z1J8zbgU2AKXn9dS0+zAder6ssDspHGhMA6mo3pJxEZDfwObzQ3xfvFv0tVO4BvA8muahPecI+dXgaudo9tRkQmiMgwjIkBdqZgTGjSRWQdXlNRG17H8v1u2sPAX0VkAd5TWPe68gqgTUTW4z0a+QG8K5LWuEHp9xChsSuMCZd1NBtjjAmw5iNjjDEBlhSMMcYEWFIwxhgTYEnBGGNMgCUFY4wxAZYUjDHGBFhSMMYYE/B/2speOwpDTWgAAAAASUVORK5CYII=",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"plt.plot(X_test, y_test, label='Actual level')\n",
|
||
"plt.plot(X_test, flaml_y_pred, label='FLAML forecast')\n",
|
||
"plt.plot(X_test, prophet_y_pred, label='Prophet forecast')\n",
|
||
"plt.plot(X_test, autoarima_y_pred, label='AutoArima forecast')\n",
|
||
"plt.plot(X_test, autosarima_y_pred, label='AutoSarima forecast')\n",
|
||
"plt.xlabel('Date')\n",
|
||
"plt.ylabel('CO2 Levels')\n",
|
||
"plt.legend()\n",
|
||
"plt.show()"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python ('pytorch_forecasting')",
|
||
"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": "3.8.1"
|
||
},
|
||
"vscode": {
|
||
"interpreter": {
|
||
"hash": "25a19fbe0a9132dfb9279d48d161753c6352f8f9478c2e74383d340069b907c3"
|
||
}
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|