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* update automl.py - documentation update * update test_forecast.py * update model.py * update automl_time_series_forecast.ipynb * update time series forecast website examples Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com>
3529 lines
445 KiB
Plaintext
3529 lines
445 KiB
Plaintext
{
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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>=3.6. 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": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: idna<3,>=2.5 in c:\\users\\kevin chen\\anaconda3\\envs\\python38\\lib\\site-packages (from requests->openml==0.10.2->flaml[notebook,ts_forecast]) (2.10)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\kevin chen\\anaconda3\\envs\\python38\\lib\\site-packages (from requests->openml==0.10.2->flaml[notebook,ts_forecast]) (2021.5.30)\n"
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]
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}
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],
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"source": [
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"!pip install flaml[notebook,ts_forecast]"
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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()\n",
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"data = data.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.fillna(data.bfill()) # makes sure there are no missing values\n",
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"data = data.to_frame().reset_index()\n",
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"# data = data.rename(columns={'index': 'ds', 'co2': 'y'})"
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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": "markdown",
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"metadata": {},
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"source": [
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"### Run FLAML\n",
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"\n",
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"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."
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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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"source": [
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"''' import AutoML class from flaml package '''\n",
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"from flaml import AutoML\n",
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"automl = AutoML()"
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"settings = {\n",
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" \"time_budget\": 240, # total running time in seconds\n",
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" \"metric\": 'mape', # primary metric for validation: 'mape' is generally used for forecast tasks\n",
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" \"task\": 'ts_forecast', # task type\n",
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" \"log_file_name\": 'CO2_forecast.log', # flaml log file\n",
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" \"eval_method\": \"holdout\", # validation method can be chosen from ['auto', 'holdout', 'cv']\n",
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" \"seed\": 7654321, # random seed\n",
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"}"
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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": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[flaml.automl: 02-28 21:28:18] {2060} INFO - task = ts_forecast\n",
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"[flaml.automl: 02-28 21:28:18] {2062} INFO - Data split method: time\n",
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"[flaml.automl: 02-28 21:28:18] {2066} INFO - Evaluation method: holdout\n",
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"[flaml.automl: 02-28 21:28:18] {2147} INFO - Minimizing error metric: mape\n",
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"[flaml.automl: 02-28 21:28:18] {2205} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth', 'prophet', 'arima', 'sarimax']\n",
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"[flaml.automl: 02-28 21:28:19] {2573} INFO - Estimated sufficient time budget=2854s. Estimated necessary time budget=3s.\n",
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"[flaml.automl: 02-28 21:32:18] {2850} INFO - retrain sarimax for 0.7s\n",
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"[flaml.automl: 02-28 21:32:18] {2857} INFO - retrained model: <statsmodels.tsa.statespace.sarimax.SARIMAXResultsWrapper object at 0x000001B1D3387FA0>\n",
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"[flaml.automl: 02-28 21:32:18] {2234} INFO - fit succeeded\n",
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"[flaml.automl: 02-28 21:32:18] {2235} INFO - Time taken to find the best model: 188.97322726249695\n",
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"[flaml.automl: 02-28 21:32:18] {2246} WARNING - Time taken to find the best model is 79% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.\n"
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]
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}
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],
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"source": [
|
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"'''The main flaml automl API'''\n",
|
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"automl.fit(dataframe=train_df, # training data\n",
|
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" label='co2', # label column\n",
|
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" period=time_horizon, # key word argument 'period' must be included for forecast task)\n",
|
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" **settings)"
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]
|
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},
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{
|
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"cell_type": "markdown",
|
|
"metadata": {},
|
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"source": [
|
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"### Best model and metric"
|
|
]
|
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},
|
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{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
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"text": [
|
|
"Best ML leaner: sarimax\n",
|
|
"Best hyperparmeter config: {'p': 8.0, 'd': 0.0, 'q': 8.0, 'P': 6.0, 'D': 3.0, 'Q': 1.0, 's': 6}\n",
|
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"Best mape on validation data: 0.00043466573064228554\n",
|
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"Training duration of best run: 0.6672513484954834s\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": 8,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
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"<statsmodels.tsa.statespace.sarimax.SARIMAXResultsWrapper at 0x1b1d3387fa0>"
|
|
]
|
|
},
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"automl.model.estimator"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"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": 10,
|
|
"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": 11,
|
|
"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": 12,
|
|
"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': 'rf', 'Current Sample': 502, 'Current Hyper-parameters': {'n_estimators': 4, 'max_features': 0.7336821866058406, 'max_leaves': 37, 'optimize_for_horizon': False, 'lags': 10}, 'Best Learner': 'rf', 'Best Hyper-parameters': {'n_estimators': 4, 'max_features': 0.7336821866058406, 'max_leaves': 37, 'optimize_for_horizon': False, 'lags': 10}}\n",
|
|
"{'Current Learner': 'rf', 'Current Sample': 502, 'Current Hyper-parameters': {'n_estimators': 4, 'max_features': 0.776140805521135, 'max_leaves': 71, 'optimize_for_horizon': False, 'lags': 10}, 'Best Learner': 'rf', 'Best Hyper-parameters': {'n_estimators': 4, 'max_features': 0.776140805521135, 'max_leaves': 71, 'optimize_for_horizon': False, 'lags': 10}}\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",
|
|
"{'Current Learner': 'prophet', 'Current Sample': 502, 'Current Hyper-parameters': {'changepoint_prior_scale': 0.02907295015483903, 'seasonality_prior_scale': 10.0, 'holidays_prior_scale': 10.0, 'seasonality_mode': 'additive'}, 'Best Learner': 'prophet', 'Best Hyper-parameters': {'changepoint_prior_scale': 0.02907295015483903, 'seasonality_prior_scale': 10.0, 'holidays_prior_scale': 10.0, '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": 13,
|
|
"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": 14,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.legend.Legend at 0x1b1d3e86df0>"
|
|
]
|
|
},
|
|
"execution_count": 14,
|
|
"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": 15,
|
|
"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": 16,
|
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"metadata": {},
|
|
"outputs": [],
|
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"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": 17,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"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\"]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Run FLAML"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[flaml.automl: 02-28 21:32:20] {2060} INFO - task = ts_forecast\n",
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"[flaml.automl: 02-28 21:32:20] {2062} INFO - Data split method: time\n",
|
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"[flaml.automl: 02-28 21:32:20] {2066} INFO - Evaluation method: holdout\n",
|
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"[flaml.automl: 02-28 21:32:20] {2147} INFO - Minimizing error metric: mape\n",
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"[flaml.automl: 02-28 21:32:20] {2205} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth', 'prophet', 'arima', 'sarimax']\n",
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"[flaml.automl: 02-28 21:32:20] {2458} INFO - iteration 0, current learner lgbm\n",
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"[flaml.automl: 02-28 21:32:20] {2573} INFO - Estimated sufficient time budget=269s. Estimated necessary time budget=0s.\n",
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"[flaml.automl: 02-28 21:32:20] {2620} 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: 02-28 21:32:20] {2458} INFO - iteration 1, current learner lgbm\n",
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"[flaml.automl: 02-28 21:32:20] {2620} 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: 02-28 21:32:20] {2458} INFO - iteration 2, current learner lgbm\n",
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"[flaml.automl: 02-28 21:32:20] {2620} INFO - at 0.1s,\testimator lgbm's best error=0.0983,\tbest estimator lgbm's best error=0.0983\n",
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"[flaml.automl: 02-28 21:32:20] {2458} INFO - iteration 3, current learner rf\n",
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"[flaml.automl: 02-28 21:32:20] {2620} INFO - at 0.1s,\testimator rf's best error=0.0972,\tbest estimator rf's best error=0.0972\n",
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"[flaml.automl: 02-28 21:32:20] {2458} INFO - iteration 4, current learner xgboost\n",
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"[flaml.automl: 02-28 21:32:20] {2620} INFO - at 0.2s,\testimator xgboost's best error=0.6523,\tbest estimator rf's best error=0.0972\n",
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"[flaml.automl: 02-28 21:32:20] {2458} INFO - iteration 5, current learner extra_tree\n",
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"[flaml.automl: 02-28 21:32:20] {2620} INFO - at 0.2s,\testimator extra_tree's best error=0.1073,\tbest estimator rf's best error=0.0972\n",
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"[flaml.automl: 02-28 21:32:20] {2458} INFO - iteration 6, current learner xgb_limitdepth\n",
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"[flaml.automl: 02-28 21:32:20] {2620} INFO - at 0.2s,\testimator xgb_limitdepth's best error=0.0820,\tbest estimator xgb_limitdepth's best error=0.0820\n",
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"[flaml.automl: 02-28 21:32:20] {2458} INFO - iteration 7, current learner prophet\n",
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"[flaml.automl: 02-28 21:32:24] {2620} INFO - at 4.4s,\testimator prophet's best error=0.0592,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 02-28 21:32:24] {2458} INFO - iteration 8, current learner arima\n",
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"[flaml.automl: 02-28 21:32:25] {2620} INFO - at 5.1s,\testimator arima's best error=0.6434,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 02-28 21:32:25] {2458} INFO - iteration 9, current learner sarimax\n"
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]
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},
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{
|
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"name": "stdout",
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"output_type": "stream",
|
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"text": [
|
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"2016-08-16 00:00:00 2017-02-11 00:00:00 (180, 2)\n"
|
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]
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},
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{
|
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[flaml.automl: 02-28 21:32:26] {2620} INFO - at 6.0s,\testimator sarimax's best error=0.6434,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 10, current learner lgbm\n",
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"[flaml.automl: 02-28 21:32:26] {2620} INFO - at 6.0s,\testimator lgbm's best error=0.0983,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 11, current learner xgboost\n",
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"[flaml.automl: 02-28 21:32:26] {2620} INFO - at 6.0s,\testimator xgboost's best error=0.6523,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 12, current learner rf\n",
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"[flaml.automl: 02-28 21:32:26] {2620} INFO - at 6.1s,\testimator rf's best error=0.0862,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 13, current learner xgboost\n",
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"[flaml.automl: 02-28 21:32:26] {2620} INFO - at 6.1s,\testimator xgboost's best error=0.2637,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 14, current learner xgboost\n",
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"[flaml.automl: 02-28 21:32:26] {2620} INFO - at 6.1s,\testimator xgboost's best error=0.0959,\tbest estimator prophet's best error=0.0592\n"
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]
|
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},
|
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{
|
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"name": "stdout",
|
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"output_type": "stream",
|
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"text": [
|
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"2016-08-16 00:00:00 2017-02-11 00:00:00 (180, 2)\n"
|
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]
|
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},
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
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"[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 15, current learner xgboost\n",
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"[flaml.automl: 02-28 21:32:26] {2620} INFO - at 6.2s,\testimator xgboost's best error=0.0959,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 16, current learner extra_tree\n",
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"[flaml.automl: 02-28 21:32:26] {2620} INFO - at 6.2s,\testimator extra_tree's best error=0.0961,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 17, current learner extra_tree\n",
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"[flaml.automl: 02-28 21:32:26] {2620} INFO - at 6.2s,\testimator extra_tree's best error=0.0961,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 18, current learner xgboost\n",
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"[flaml.automl: 02-28 21:32:26] {2620} INFO - at 6.2s,\testimator xgboost's best error=0.0959,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 19, current learner xgb_limitdepth\n",
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"[flaml.automl: 02-28 21:32:26] {2620} INFO - at 6.3s,\testimator xgb_limitdepth's best error=0.0820,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 20, current learner xgboost\n",
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"[flaml.automl: 02-28 21:32:26] {2620} INFO - at 6.3s,\testimator xgboost's best error=0.0834,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 21, current learner xgb_limitdepth\n",
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"[flaml.automl: 02-28 21:32:26] {2620} INFO - at 6.4s,\testimator xgb_limitdepth's best error=0.0820,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 22, current learner lgbm\n",
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"[flaml.automl: 02-28 21:32:26] {2620} INFO - at 6.4s,\testimator lgbm's best error=0.0925,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 23, current learner xgb_limitdepth\n",
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"[flaml.automl: 02-28 21:32:26] {2620} INFO - at 6.4s,\testimator xgb_limitdepth's best error=0.0820,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 24, current learner extra_tree\n",
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"[flaml.automl: 02-28 21:32:26] {2620} INFO - at 6.5s,\testimator extra_tree's best error=0.0922,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 25, current learner xgb_limitdepth\n",
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"[flaml.automl: 02-28 21:32:26] {2620} INFO - at 6.5s,\testimator xgb_limitdepth's best error=0.0820,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 26, current learner rf\n",
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"[flaml.automl: 02-28 21:32:26] {2620} INFO - at 6.5s,\testimator rf's best error=0.0862,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 27, current learner rf\n",
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"[flaml.automl: 02-28 21:32:26] {2620} INFO - at 6.6s,\testimator rf's best error=0.0856,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 28, current learner xgb_limitdepth\n",
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"[flaml.automl: 02-28 21:32:26] {2620} INFO - at 6.6s,\testimator xgb_limitdepth's best error=0.0820,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 02-28 21:32:27] {2458} INFO - iteration 29, current learner sarimax\n",
|
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"[flaml.automl: 02-28 21:32:28] {2620} INFO - at 7.9s,\testimator sarimax's best error=0.5313,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 02-28 21:32:28] {2458} INFO - iteration 30, current learner xgboost\n",
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"[flaml.automl: 02-28 21:32:28] {2620} INFO - at 8.0s,\testimator xgboost's best error=0.0834,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 02-28 21:32:28] {2458} INFO - iteration 31, current learner xgb_limitdepth\n",
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"[flaml.automl: 02-28 21:32:28] {2620} INFO - at 8.0s,\testimator xgb_limitdepth's best error=0.0791,\tbest estimator prophet's best error=0.0592\n",
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"[flaml.automl: 02-28 21:32:28] {2458} INFO - iteration 32, current learner arima\n"
|
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]
|
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},
|
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{
|
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"name": "stdout",
|
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"output_type": "stream",
|
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"text": [
|
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"2016-08-16 00:00:00 2017-02-11 00:00:00 (180, 2)\n"
|
|
]
|
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},
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
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"[flaml.automl: 02-28 21:32:30] {2620} INFO - at 10.3s,\testimator arima's best error=0.5998,\tbest estimator prophet's best error=0.0592\n"
|
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]
|
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},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2016-08-16 00:00:00 2017-02-11 00:00:00 (180, 2)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[flaml.automl: 02-28 21:32:32] {2850} INFO - retrain prophet for 2.2s\n",
|
|
"[flaml.automl: 02-28 21:32:32] {2857} INFO - retrained model: <prophet.forecaster.Prophet object at 0x000001B1D3EE2B80>\n",
|
|
"[flaml.automl: 02-28 21:32:32] {2234} INFO - fit succeeded\n",
|
|
"[flaml.automl: 02-28 21:32:32] {2235} INFO - Time taken to find the best model: 4.351356506347656\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": 19,
|
|
"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": 20,
|
|
"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": 21,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"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": 50,
|
|
"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": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Run FLAML"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 51,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from flaml import AutoML\n",
|
|
"automl = AutoML()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 52,
|
|
"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": 53,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[flaml.automl: 02-28 21:54:50] {2060} INFO - task = ts_forecast_classification\n",
|
|
"[flaml.automl: 02-28 21:54:50] {2062} INFO - Data split method: time\n",
|
|
"[flaml.automl: 02-28 21:54:50] {2066} INFO - Evaluation method: holdout\n",
|
|
"[flaml.automl: 02-28 21:54:50] {2147} INFO - Minimizing error metric: 1-accuracy\n",
|
|
"[flaml.automl: 02-28 21:54:50] {2205} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth']\n",
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"[flaml.automl: 02-28 21:54:50] {2458} INFO - iteration 0, current learner lgbm\n",
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"[flaml.automl: 02-28 21:54:50] {2573} INFO - Estimated sufficient time budget=249s. Estimated necessary time budget=0s.\n",
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|
"[flaml.automl: 02-28 21:54:50] {2620} INFO - at 0.0s,\testimator lgbm's best error=0.2667,\tbest estimator lgbm's best error=0.2667\n",
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"[flaml.automl: 02-28 21:54:50] {2458} INFO - iteration 1, current learner lgbm\n",
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"[flaml.automl: 02-28 21:54:50] {2620} INFO - at 0.1s,\testimator lgbm's best error=0.2667,\tbest estimator lgbm's best error=0.2667\n",
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"[flaml.automl: 02-28 21:54:50] {2458} INFO - iteration 2, current learner lgbm\n",
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"[flaml.automl: 02-28 21:54:50] {2620} INFO - at 0.1s,\testimator lgbm's best error=0.1333,\tbest estimator lgbm's best error=0.1333\n",
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"[flaml.automl: 02-28 21:54:50] {2458} INFO - iteration 3, current learner rf\n",
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"[flaml.automl: 02-28 21:54:50] {2620} INFO - at 0.1s,\testimator rf's best error=0.1333,\tbest estimator lgbm's best error=0.1333\n",
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"[flaml.automl: 02-28 21:54:50] {2458} INFO - iteration 4, current learner xgboost\n",
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"[flaml.automl: 02-28 21:54:50] {2620} INFO - at 0.2s,\testimator xgboost's best error=0.1333,\tbest estimator lgbm's best error=0.1333\n",
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"[flaml.automl: 02-28 21:54:50] {2458} INFO - iteration 5, current learner lgbm\n",
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"[flaml.automl: 02-28 21:54:50] {2620} INFO - at 0.2s,\testimator lgbm's best error=0.1333,\tbest estimator lgbm's best error=0.1333\n",
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"[flaml.automl: 02-28 21:54:50] {2458} INFO - iteration 6, current learner rf\n",
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"[flaml.automl: 02-28 21:54:50] {2620} INFO - at 0.2s,\testimator rf's best error=0.0667,\tbest estimator rf's best error=0.0667\n",
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"[flaml.automl: 02-28 21:54:50] {2458} INFO - iteration 7, current learner lgbm\n",
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"[flaml.automl: 02-28 21:54:50] {2620} INFO - at 0.3s,\testimator lgbm's best error=0.0667,\tbest estimator rf's best error=0.0667\n",
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"[flaml.automl: 02-28 21:55:05] {2850} INFO - retrain extra_tree for 0.0s\n",
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"[flaml.automl: 02-28 21:55:05] {2857} INFO - retrained model: ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,\n",
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" criterion='gini', max_depth=None, max_features=0.1,\n",
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" max_leaf_nodes=8, max_samples=None,\n",
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" min_impurity_decrease=0.0, min_samples_leaf=1,\n",
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" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
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" n_estimators=6, n_jobs=-1, oob_score=False,\n",
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" random_state=None, verbose=0, warm_start=False)\n",
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"[flaml.automl: 02-28 21:55:05] {2234} INFO - fit succeeded\n",
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"[flaml.automl: 02-28 21:55:05] {2235} INFO - Time taken to find the best model: 12.538578033447266\n",
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"[flaml.automl: 02-28 21:55:05] {2246} WARNING - Time taken to find the best model is 84% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.\n"
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]
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}
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],
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"source": [
|
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"\"\"\"The main flaml automl API\"\"\"\n",
|
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"automl.fit(X_train=discrete_X_train,\n",
|
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" y_train=discrete_y_train,\n",
|
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" **settings,\n",
|
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" period=time_horizon)"
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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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"### Best Model and Metric"
|
|
]
|
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},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 54,
|
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"metadata": {},
|
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"outputs": [
|
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{
|
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"name": "stdout",
|
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"output_type": "stream",
|
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"text": [
|
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"Best ML leaner: extra_tree\n",
|
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"Best hyperparmeter config: {'n_estimators': 6, 'max_leaves': 8, 'optimize_for_horizon': False, 'max_features': 0.1, 'lags': 8}\n",
|
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"Best mape on validation data: 0.0\n",
|
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"Training duration of best run: 0.022936344146728516s\n",
|
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"ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,\n",
|
|
" criterion='gini', max_depth=None, max_features=0.1,\n",
|
|
" max_leaf_nodes=8, max_samples=None,\n",
|
|
" min_impurity_decrease=0.0, min_samples_leaf=1,\n",
|
|
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
|
|
" n_estimators=6, n_jobs=-1, oob_score=False,\n",
|
|
" random_state=None, verbose=0, warm_start=False)\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": 55,
|
|
"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": 56,
|
|
"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. Comparison with Alternatives (CO2 Dataset)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"FLAML's MAPE"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"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": 30,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from prophet import Prophet\n",
|
|
"prophet_model = Prophet()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<prophet.forecaster.Prophet at 0x1b1d3efed00>"
|
|
]
|
|
},
|
|
"execution_count": 31,
|
|
"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": 32,
|
|
"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": 33,
|
|
"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": 34,
|
|
"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": 35,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" ARIMA(0,1,0)(0,0,0)[0] intercept : AIC=1638.009, Time=0.04 sec\n",
|
|
" ARIMA(0,1,1)(0,0,0)[0] intercept : AIC=1344.207, Time=0.11 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=0.18 sec\n",
|
|
" ARIMA(0,1,4)(0,0,0)[0] intercept : AIC=1188.947, Time=0.38 sec\n",
|
|
" ARIMA(0,1,5)(0,0,0)[0] intercept : AIC=1091.452, Time=0.52 sec\n",
|
|
" ARIMA(1,1,0)(0,0,0)[0] intercept : AIC=1298.693, Time=0.06 sec\n",
|
|
" ARIMA(1,1,1)(0,0,0)[0] intercept : AIC=1240.963, Time=0.10 sec\n",
|
|
" ARIMA(1,1,2)(0,0,0)[0] intercept : AIC=1196.535, Time=0.15 sec\n",
|
|
" ARIMA(1,1,3)(0,0,0)[0] intercept : AIC=1176.484, Time=0.28 sec\n",
|
|
" ARIMA(1,1,4)(0,0,0)[0] intercept : AIC=inf, Time=1.19 sec\n",
|
|
" ARIMA(2,1,0)(0,0,0)[0] intercept : AIC=1180.404, Time=0.10 sec\n",
|
|
" ARIMA(2,1,1)(0,0,0)[0] intercept : AIC=990.719, Time=0.28 sec\n",
|
|
" ARIMA(2,1,2)(0,0,0)[0] intercept : AIC=988.094, Time=0.55 sec\n",
|
|
" ARIMA(2,1,3)(0,0,0)[0] intercept : AIC=1140.469, Time=0.57 sec\n",
|
|
" ARIMA(3,1,0)(0,0,0)[0] intercept : AIC=1126.139, Time=0.27 sec\n",
|
|
" ARIMA(3,1,1)(0,0,0)[0] intercept : AIC=989.496, Time=0.57 sec\n",
|
|
" ARIMA(3,1,2)(0,0,0)[0] intercept : AIC=991.555, Time=1.02 sec\n",
|
|
" ARIMA(4,1,0)(0,0,0)[0] intercept : AIC=1125.025, Time=0.17 sec\n",
|
|
" ARIMA(4,1,1)(0,0,0)[0] intercept : AIC=988.660, Time=1.12 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: 8.065 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": 36,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" ARIMA(0,1,0)(0,0,0)[12] intercept : AIC=1638.009, Time=0.01 sec\n",
|
|
" ARIMA(0,1,0)(0,0,1)[12] intercept : AIC=1238.943, Time=0.21 sec\n",
|
|
" ARIMA(0,1,0)(0,0,2)[12] intercept : AIC=1040.890, Time=0.57 sec\n",
|
|
" ARIMA(0,1,0)(0,0,3)[12] intercept : AIC=911.545, Time=1.81 sec\n",
|
|
" ARIMA(0,1,0)(0,0,4)[12] intercept : AIC=823.103, Time=3.23 sec\n",
|
|
" ARIMA(0,1,0)(0,0,5)[12] intercept : AIC=792.850, Time=6.07 sec\n",
|
|
" ARIMA(0,1,0)(1,0,0)[12] intercept : AIC=inf, Time=0.24 sec\n",
|
|
" ARIMA(0,1,0)(1,0,1)[12] intercept : AIC=inf, Time=1.14 sec\n",
|
|
" ARIMA(0,1,0)(1,0,2)[12] intercept : AIC=inf, Time=2.78 sec\n",
|
|
" ARIMA(0,1,0)(1,0,3)[12] intercept : AIC=447.738, Time=6.32 sec\n",
|
|
" ARIMA(0,1,0)(1,0,4)[12] intercept : AIC=inf, Time=11.02 sec\n",
|
|
" ARIMA(0,1,0)(2,0,0)[12] intercept : AIC=inf, Time=1.11 sec\n",
|
|
" ARIMA(0,1,0)(2,0,1)[12] intercept : AIC=inf, Time=3.27 sec\n",
|
|
" ARIMA(0,1,0)(2,0,2)[12] intercept : AIC=inf, Time=3.04 sec\n",
|
|
" ARIMA(0,1,0)(2,0,3)[12] intercept : AIC=427.344, Time=8.22 sec\n",
|
|
" ARIMA(0,1,0)(3,0,0)[12] intercept : AIC=inf, Time=3.70 sec\n",
|
|
" ARIMA(0,1,0)(3,0,1)[12] intercept : AIC=425.322, Time=6.95 sec\n",
|
|
" ARIMA(0,1,0)(3,0,2)[12] intercept : AIC=431.465, Time=7.77 sec\n",
|
|
" ARIMA(0,1,0)(4,0,0)[12] intercept : AIC=inf, Time=10.95 sec\n",
|
|
" ARIMA(0,1,0)(4,0,1)[12] intercept : AIC=430.340, Time=11.56 sec\n",
|
|
" ARIMA(0,1,0)(5,0,0)[12] intercept : AIC=inf, Time=18.31 sec\n",
|
|
" ARIMA(0,1,1)(0,0,0)[12] intercept : AIC=1344.207, Time=0.07 sec\n",
|
|
" ARIMA(0,1,1)(0,0,1)[12] intercept : AIC=1112.274, Time=0.38 sec\n",
|
|
" ARIMA(0,1,1)(0,0,2)[12] intercept : AIC=993.565, Time=0.87 sec\n",
|
|
" ARIMA(0,1,1)(0,0,3)[12] intercept : AIC=891.683, Time=3.02 sec\n",
|
|
" ARIMA(0,1,1)(0,0,4)[12] intercept : AIC=820.025, Time=5.93 sec\n",
|
|
" ARIMA(0,1,1)(1,0,0)[12] intercept : AIC=612.811, Time=0.55 sec\n",
|
|
" ARIMA(0,1,1)(1,0,1)[12] intercept : AIC=392.446, Time=1.55 sec\n",
|
|
" ARIMA(0,1,1)(1,0,2)[12] intercept : AIC=398.980, Time=4.08 sec\n",
|
|
" ARIMA(0,1,1)(1,0,3)[12] intercept : AIC=424.632, Time=8.78 sec\n",
|
|
" ARIMA(0,1,1)(2,0,0)[12] intercept : AIC=510.637, Time=1.92 sec\n",
|
|
" ARIMA(0,1,1)(2,0,1)[12] intercept : AIC=396.708, Time=3.45 sec\n",
|
|
" ARIMA(0,1,1)(2,0,2)[12] intercept : AIC=396.399, Time=4.38 sec\n",
|
|
" ARIMA(0,1,1)(3,0,0)[12] intercept : AIC=467.985, Time=5.55 sec\n",
|
|
" ARIMA(0,1,1)(3,0,1)[12] intercept : AIC=412.398, Time=8.44 sec\n",
|
|
" ARIMA(0,1,1)(4,0,0)[12] intercept : AIC=448.948, Time=7.91 sec\n",
|
|
" ARIMA(0,1,2)(0,0,0)[12] intercept : AIC=1222.286, Time=0.13 sec\n",
|
|
" ARIMA(0,1,2)(0,0,1)[12] intercept : AIC=1046.922, Time=0.33 sec\n",
|
|
" ARIMA(0,1,2)(0,0,2)[12] intercept : AIC=947.532, Time=1.05 sec\n",
|
|
" ARIMA(0,1,2)(0,0,3)[12] intercept : AIC=867.310, Time=2.79 sec\n",
|
|
" ARIMA(0,1,2)(1,0,0)[12] intercept : AIC=608.450, Time=0.70 sec\n",
|
|
" ARIMA(0,1,2)(1,0,1)[12] intercept : AIC=386.324, Time=1.79 sec\n",
|
|
" ARIMA(0,1,2)(1,0,2)[12] intercept : AIC=421.305, Time=4.21 sec\n",
|
|
" ARIMA(0,1,2)(2,0,0)[12] intercept : AIC=507.685, Time=2.19 sec\n",
|
|
" ARIMA(0,1,2)(2,0,1)[12] intercept : AIC=408.351, Time=3.86 sec\n",
|
|
" ARIMA(0,1,2)(3,0,0)[12] intercept : AIC=460.596, Time=7.99 sec\n",
|
|
" ARIMA(0,1,3)(0,0,0)[12] intercept : AIC=1174.928, Time=0.17 sec\n",
|
|
" ARIMA(0,1,3)(0,0,1)[12] intercept : AIC=1037.324, Time=0.50 sec\n",
|
|
" ARIMA(0,1,3)(0,0,2)[12] intercept : AIC=947.471, Time=1.55 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=397.131, Time=2.42 sec\n",
|
|
" ARIMA(0,1,3)(2,0,0)[12] intercept : AIC=500.296, Time=2.70 sec\n",
|
|
" ARIMA(0,1,4)(0,0,0)[12] intercept : AIC=1188.947, Time=0.37 sec\n",
|
|
" ARIMA(0,1,4)(0,0,1)[12] intercept : AIC=999.240, Time=0.86 sec\n",
|
|
" ARIMA(0,1,4)(1,0,0)[12] intercept : AIC=604.133, Time=1.00 sec\n",
|
|
" ARIMA(0,1,5)(0,0,0)[12] intercept : AIC=1091.452, Time=0.51 sec\n",
|
|
" ARIMA(1,1,0)(0,0,0)[12] intercept : AIC=1298.693, Time=0.06 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=0.73 sec\n",
|
|
" ARIMA(1,1,0)(0,0,3)[12] intercept : AIC=882.846, Time=2.86 sec\n",
|
|
" ARIMA(1,1,0)(0,0,4)[12] intercept : AIC=818.711, Time=5.36 sec\n",
|
|
" ARIMA(1,1,0)(1,0,0)[12] intercept : AIC=inf, Time=0.64 sec\n",
|
|
" ARIMA(1,1,0)(1,0,1)[12] intercept : AIC=401.107, Time=1.22 sec\n",
|
|
" ARIMA(1,1,0)(1,0,2)[12] intercept : AIC=408.857, Time=3.70 sec\n",
|
|
" ARIMA(1,1,0)(1,0,3)[12] intercept : AIC=429.002, Time=7.05 sec\n",
|
|
" ARIMA(1,1,0)(2,0,0)[12] intercept : AIC=inf, Time=1.83 sec\n",
|
|
" ARIMA(1,1,0)(2,0,1)[12] intercept : AIC=419.393, Time=2.12 sec\n",
|
|
" ARIMA(1,1,0)(2,0,2)[12] intercept : AIC=409.260, Time=4.23 sec\n",
|
|
" ARIMA(1,1,0)(3,0,0)[12] intercept : AIC=inf, Time=5.46 sec\n",
|
|
" ARIMA(1,1,0)(3,0,1)[12] intercept : AIC=419.508, Time=7.69 sec\n",
|
|
" ARIMA(1,1,0)(4,0,0)[12] intercept : AIC=inf, Time=10.61 sec\n",
|
|
" ARIMA(1,1,1)(0,0,0)[12] intercept : AIC=1240.963, Time=0.09 sec\n",
|
|
" ARIMA(1,1,1)(0,0,1)[12] intercept : AIC=1069.162, Time=0.41 sec\n",
|
|
" ARIMA(1,1,1)(0,0,2)[12] intercept : AIC=973.065, Time=1.28 sec\n",
|
|
" ARIMA(1,1,1)(0,0,3)[12] intercept : AIC=884.323, Time=4.08 sec\n",
|
|
" ARIMA(1,1,1)(1,0,0)[12] intercept : AIC=588.156, Time=1.35 sec\n",
|
|
" ARIMA(1,1,1)(1,0,1)[12] intercept : AIC=399.034, Time=1.60 sec\n",
|
|
" ARIMA(1,1,1)(1,0,2)[12] intercept : AIC=409.556, Time=4.85 sec\n",
|
|
" ARIMA(1,1,1)(2,0,0)[12] intercept : AIC=503.551, Time=2.00 sec\n",
|
|
" ARIMA(1,1,1)(2,0,1)[12] intercept : AIC=399.923, Time=3.45 sec\n",
|
|
" ARIMA(1,1,1)(3,0,0)[12] intercept : AIC=457.277, Time=7.95 sec\n",
|
|
" ARIMA(1,1,2)(0,0,0)[12] intercept : AIC=1196.535, Time=0.16 sec\n",
|
|
" ARIMA(1,1,2)(0,0,1)[12] intercept : AIC=1042.432, Time=0.45 sec\n",
|
|
" ARIMA(1,1,2)(0,0,2)[12] intercept : AIC=948.444, Time=1.39 sec\n",
|
|
" ARIMA(1,1,2)(1,0,0)[12] intercept : AIC=589.937, Time=1.47 sec\n",
|
|
" ARIMA(1,1,2)(1,0,1)[12] intercept : AIC=399.533, Time=1.78 sec\n",
|
|
" ARIMA(1,1,2)(2,0,0)[12] intercept : AIC=502.534, Time=4.66 sec\n",
|
|
" ARIMA(1,1,3)(0,0,0)[12] intercept : AIC=1176.484, Time=0.31 sec\n",
|
|
" ARIMA(1,1,3)(0,0,1)[12] intercept : AIC=1039.309, Time=0.97 sec\n",
|
|
" ARIMA(1,1,3)(1,0,0)[12] intercept : AIC=604.131, Time=1.65 sec\n",
|
|
" ARIMA(1,1,4)(0,0,0)[12] intercept : AIC=inf, Time=1.16 sec\n",
|
|
" ARIMA(2,1,0)(0,0,0)[12] intercept : AIC=1180.404, Time=0.10 sec\n",
|
|
" ARIMA(2,1,0)(0,0,1)[12] intercept : AIC=1058.115, Time=0.34 sec\n",
|
|
" ARIMA(2,1,0)(0,0,2)[12] intercept : AIC=973.051, Time=0.95 sec\n",
|
|
" ARIMA(2,1,0)(0,0,3)[12] intercept : AIC=883.377, Time=2.91 sec\n",
|
|
" ARIMA(2,1,0)(1,0,0)[12] intercept : AIC=inf, Time=0.59 sec\n",
|
|
" ARIMA(2,1,0)(1,0,1)[12] intercept : AIC=400.994, Time=1.63 sec\n",
|
|
" ARIMA(2,1,0)(1,0,2)[12] intercept : AIC=407.847, Time=3.51 sec\n",
|
|
" ARIMA(2,1,0)(2,0,0)[12] intercept : AIC=inf, Time=2.49 sec\n",
|
|
" ARIMA(2,1,0)(2,0,1)[12] intercept : AIC=403.427, Time=4.40 sec\n",
|
|
" ARIMA(2,1,0)(3,0,0)[12] intercept : AIC=inf, Time=6.75 sec\n",
|
|
" ARIMA(2,1,1)(0,0,0)[12] intercept : AIC=990.719, Time=0.24 sec\n",
|
|
" ARIMA(2,1,1)(0,0,1)[12] intercept : AIC=881.526, Time=1.03 sec\n",
|
|
" ARIMA(2,1,1)(0,0,2)[12] intercept : AIC=837.402, Time=3.12 sec\n",
|
|
" ARIMA(2,1,1)(1,0,0)[12] intercept : AIC=584.703, Time=1.86 sec\n",
|
|
" ARIMA(2,1,1)(1,0,1)[12] intercept : AIC=438.400, Time=1.78 sec\n",
|
|
" ARIMA(2,1,1)(2,0,0)[12] intercept : AIC=494.774, Time=4.37 sec\n",
|
|
" ARIMA(2,1,2)(0,0,0)[12] intercept : AIC=988.094, Time=0.51 sec\n",
|
|
" ARIMA(2,1,2)(0,0,1)[12] intercept : AIC=inf, Time=1.98 sec\n",
|
|
" ARIMA(2,1,2)(1,0,0)[12] intercept : AIC=590.680, Time=2.26 sec\n",
|
|
" ARIMA(2,1,3)(0,0,0)[12] intercept : AIC=1140.469, Time=0.54 sec\n",
|
|
" ARIMA(3,1,0)(0,0,0)[12] intercept : AIC=1126.139, Time=0.23 sec\n",
|
|
" ARIMA(3,1,0)(0,0,1)[12] intercept : AIC=996.923, Time=0.41 sec\n",
|
|
" ARIMA(3,1,0)(0,0,2)[12] intercept : AIC=918.438, Time=1.17 sec\n",
|
|
" ARIMA(3,1,0)(1,0,0)[12] intercept : AIC=inf, Time=0.78 sec\n",
|
|
" ARIMA(3,1,0)(1,0,1)[12] intercept : AIC=407.208, Time=1.74 sec\n",
|
|
" ARIMA(3,1,0)(2,0,0)[12] intercept : AIC=inf, Time=3.23 sec\n",
|
|
" ARIMA(3,1,1)(0,0,0)[12] intercept : AIC=989.496, Time=0.54 sec\n",
|
|
" ARIMA(3,1,1)(0,0,1)[12] intercept : AIC=856.486, Time=1.86 sec\n",
|
|
" ARIMA(3,1,1)(1,0,0)[12] intercept : AIC=604.951, Time=0.84 sec\n",
|
|
" ARIMA(3,1,2)(0,0,0)[12] intercept : AIC=991.555, Time=0.93 sec\n",
|
|
" ARIMA(4,1,0)(0,0,0)[12] intercept : AIC=1125.025, Time=0.16 sec\n",
|
|
" ARIMA(4,1,0)(0,0,1)[12] intercept : AIC=987.621, Time=0.44 sec\n",
|
|
" ARIMA(4,1,0)(1,0,0)[12] intercept : AIC=inf, Time=1.06 sec\n",
|
|
" ARIMA(4,1,1)(0,0,0)[12] intercept : AIC=988.660, Time=0.98 sec\n",
|
|
" ARIMA(5,1,0)(0,0,0)[12] intercept : AIC=1113.673, Time=0.20 sec\n",
|
|
"\n",
|
|
"Best model: ARIMA(0,1,2)(1,0,1)[12] intercept\n",
|
|
"Total fit time: 352.159 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": 37,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"auto arima mape = 0.0032060283828607705\n",
|
|
"auto sarima mape = 0.0007319806481537022\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": 38,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"flaml mape = 0.0005706814258795216\n",
|
|
"default prophet mape = 0.0011396920680673015\n",
|
|
"auto arima mape = 0.0032060283828607705\n",
|
|
"auto sarima mape = 0.0007319806481537022\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": 39,
|
|
"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",
|
|
"\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": {
|
|
"interpreter": {
|
|
"hash": "8b6c8c3ba4bafbc4530f534c605c8412f25bf61ef13254e4f377ccd42b838aa4"
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python 3.8.10 64-bit ('python38': conda)",
|
|
"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": "3.8.10"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|