mirror of
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a229a6112a
* non hashable value out of signature * parallel trials * add random in _search_parallel * fix bug in retraining * check memory constraint before training * retrain_full * log custom metric * retraining budget check * sample size check before retrain * remove 'time2eval' from result * report 'total_search_time' in result * rename total_search_time to wall_clock_time * rename train_loss boolean to log_training_metric * set default train_loss to None * exclude oom result * log retrained model * no subsample * doc str * notebook * predicted value is NaN for sarimax * version Co-authored-by: Chi Wang <wang.chi@microsoft.com> Co-authored-by: Qingyun Wu <qxw5138@psu.edu>
1442 lines
165 KiB
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1442 lines
165 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"# Time Series Forecasting with FLAML Library"
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],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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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 cheap. 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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" - In this notebook, we demonstrate how to use FLAML library to tune hyperparameters of XGBoost with a regression example.\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 option:\n"
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],
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"metadata": {}
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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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"source": [
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"!pip install flaml[notebook,forecast]"
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],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## 2. Forecast Problem\r\n",
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"\r\n",
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"### Load data and preprocess\r\n",
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"\r\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."
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],
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"metadata": {}
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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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"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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"outputs": [],
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"metadata": {}
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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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"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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"X_train = data[:split_idx] # X_train is a dataframe with two columns: time and value\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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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Run FLAML\r\n",
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"\r\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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"metadata": {}
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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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"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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"outputs": [],
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"metadata": {}
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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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"source": [
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"settings = {\n",
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" \"time_budget\": 300, # 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\": '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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" # \"estimator_list\": [\"sarimax\"],\n",
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" # \"verbose\": 3,\n",
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" \"split_type\": 'time' # for foretask task, 'split_type' has to be 'time'\n",
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"}"
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],
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"outputs": [],
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"metadata": {}
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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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"source": [
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"'''The main flaml automl API'''\n",
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"automl.fit(dataframe=X_train, # training data\n",
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" label=('index', 'co2'), # For 'forecast' task, label should be a tuple of strings for timestamp and value columns\n",
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" **settings, \n",
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" period=time_horizon, # key word argument 'period' must be included for forecast task\n",
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" freq='M')"
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],
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"outputs": [
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{
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"output_type": "stream",
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"name": "stderr",
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"text": [
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"[flaml.automl: 08-23 15:56:25] {1219} INFO - Evaluation method: holdout\n",
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"[flaml.automl: 08-23 15:56:25] {691} INFO - Using TimeSeriesSplit\n",
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"[flaml.automl: 08-23 15:56:25] {1250} INFO - Minimizing error metric: mape\n",
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"[flaml.automl: 08-23 15:56:25] {1274} INFO - List of ML learners in AutoML Run: ['fbprophet', 'arima', 'sarimax']\n",
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"[flaml.automl: 08-23 15:56:25] {1457} INFO - iteration 0, current learner fbprophet\n",
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"INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
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"INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
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"[flaml.automl: 08-23 15:56:30] {1614} INFO - at 4.7s,\tbest fbprophet's error=0.0007,\tbest fbprophet's error=0.0007\n",
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"INFO:flaml.automl: at 4.7s,\tbest fbprophet's error=0.0007,\tbest fbprophet's error=0.0007\n",
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"[flaml.automl: 08-23 15:56:30] {1457} INFO - iteration 1, current learner fbprophet\n",
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"INFO:flaml.automl:iteration 1, current learner fbprophet\n",
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"INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
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"INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
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"[flaml.automl: 08-23 15:56:31] {1614} INFO - at 6.1s,\tbest fbprophet's error=0.0006,\tbest fbprophet's error=0.0006\n",
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"INFO:flaml.automl: at 6.1s,\tbest fbprophet's error=0.0006,\tbest fbprophet's error=0.0006\n",
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"[flaml.automl: 08-23 15:56:31] {1457} INFO - iteration 2, current learner fbprophet\n",
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"INFO:flaml.automl:iteration 2, current learner fbprophet\n",
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"INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
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"INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
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"[flaml.automl: 08-23 15:56:32] {1614} INFO - at 7.5s,\tbest fbprophet's error=0.0006,\tbest fbprophet's error=0.0006\n",
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"INFO:flaml.automl: at 7.5s,\tbest fbprophet's error=0.0006,\tbest fbprophet's error=0.0006\n",
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"[flaml.automl: 08-23 15:56:32] {1457} INFO - iteration 3, current learner fbprophet\n",
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"INFO:flaml.automl:iteration 3, current learner fbprophet\n",
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"INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
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"INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
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"[flaml.automl: 08-23 15:56:34] {1614} INFO - at 8.9s,\tbest fbprophet's error=0.0006,\tbest fbprophet's error=0.0006\n",
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"INFO:flaml.automl: at 8.9s,\tbest fbprophet's error=0.0006,\tbest fbprophet's error=0.0006\n",
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"INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
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"INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
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"[flaml.automl: 08-23 15:56:37] {1614} INFO - at 11.6s,\tbest arima's error=0.0120,\tbest fbprophet's error=0.0005\n",
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"[flaml.automl: 08-23 16:01:24] {1614} INFO - at 299.5s,\tbest fbprophet's error=0.0005,\tbest fbprophet's error=0.0005\n",
|
|
"INFO:flaml.automl: at 299.5s,\tbest fbprophet's error=0.0005,\tbest fbprophet's error=0.0005\n",
|
|
"[flaml.automl: 08-23 16:01:24] {1691} INFO - selected model: <prophet.forecaster.Prophet object at 0x7fa3b2ed31f0>\n",
|
|
"INFO:flaml.automl:selected model: <prophet.forecaster.Prophet object at 0x7fa3b2ed31f0>\n",
|
|
"INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
|
|
"INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
|
|
"[flaml.automl: 08-23 16:01:26] {1732} INFO - retrain fbprophet for 1.0s\n",
|
|
"INFO:flaml.automl:retrain fbprophet for 1.0s\n",
|
|
"[flaml.automl: 08-23 16:01:26] {1735} INFO - retrained model: <prophet.forecaster.Prophet object at 0x7fa3b2f85a60>\n",
|
|
"INFO:flaml.automl:retrained model: <prophet.forecaster.Prophet object at 0x7fa3b2f85a60>\n",
|
|
"[flaml.automl: 08-23 16:01:26] {1298} INFO - fit succeeded\n",
|
|
"INFO:flaml.automl:fit succeeded\n",
|
|
"[flaml.automl: 08-23 16:01:26] {1299} INFO - Time taken to find the best model: 278.8971173763275\n",
|
|
"INFO:flaml.automl:Time taken to find the best model: 278.8971173763275\n",
|
|
"[flaml.automl: 08-23 16:01:26] {1304} WARNING - Time taken to find the best model is 93% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.\n",
|
|
"WARNING:flaml.automl:Time taken to find the best model is 93% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"### Best model and metric"
|
|
],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"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')"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stdout",
|
|
"text": [
|
|
"Best ML leaner: fbprophet\n",
|
|
"Best hyperparmeter config: {'changepoint_prior_scale': 0.03498447027670827, 'seasonality_prior_scale': 2.616244037716704, 'holidays_prior_scale': 5.713876592939503, 'seasonality_mode': 'additive'}\n",
|
|
"Best mape on validation data: 0.00047658614467724217\n",
|
|
"Training duration of best run: 1.8398802280426025s\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"source": [
|
|
"print(automl.model.estimator)"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stdout",
|
|
"text": [
|
|
"<prophet.forecaster.Prophet object at 0x7fa3b2f85a60>\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"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)"
|
|
],
|
|
"outputs": [],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"source": [
|
|
"''' compute predictions of testing dataset '''\n",
|
|
"y_pred = automl.predict(X_test)\n",
|
|
"print('Predicted labels', y_pred)\n",
|
|
"print('True labels', y_test)"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stdout",
|
|
"text": [
|
|
"Predicted labels 0 370.443113\n",
|
|
"1 371.170226\n",
|
|
"2 372.222488\n",
|
|
"3 373.412902\n",
|
|
"4 373.907859\n",
|
|
"5 373.399315\n",
|
|
"6 372.046066\n",
|
|
"7 370.141179\n",
|
|
"8 368.558253\n",
|
|
"9 368.637791\n",
|
|
"10 369.854576\n",
|
|
"11 371.126664\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"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"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))"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stdout",
|
|
"text": [
|
|
"mape = 0.0011218052996337928\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"### Log history"
|
|
],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"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=300)\n",
|
|
"\n",
|
|
"for config in config_history:\n",
|
|
" print(config)"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stdout",
|
|
"text": [
|
|
"{'Current Learner': 'fbprophet', 'Current Sample': 502, 'Current Hyper-parameters': {'changepoint_prior_scale': 0.010000000000000002, 'seasonality_prior_scale': 1.0, 'holidays_prior_scale': 1.0, 'seasonality_mode': 'multiplicative'}, 'Best Learner': 'fbprophet', 'Best Hyper-parameters': {'changepoint_prior_scale': 0.010000000000000002, 'seasonality_prior_scale': 1.0, 'holidays_prior_scale': 1.0, 'seasonality_mode': 'multiplicative'}}\n",
|
|
"{'Current Learner': 'fbprophet', 'Current Sample': 502, 'Current Hyper-parameters': {'changepoint_prior_scale': 0.0091602623296037, 'seasonality_prior_scale': 0.8823866403788657, 'holidays_prior_scale': 3.2294014074557995, 'seasonality_mode': 'additive'}, 'Best Learner': 'fbprophet', 'Best Hyper-parameters': {'changepoint_prior_scale': 0.0091602623296037, 'seasonality_prior_scale': 0.8823866403788657, 'holidays_prior_scale': 3.2294014074557995, 'seasonality_mode': 'additive'}}\n",
|
|
"{'Current Learner': 'fbprophet', 'Current Sample': 502, 'Current Hyper-parameters': {'changepoint_prior_scale': 0.04806697427414373, 'seasonality_prior_scale': 0.8715399932617315, 'holidays_prior_scale': 1.771092340237384, 'seasonality_mode': 'additive'}, 'Best Learner': 'fbprophet', 'Best Hyper-parameters': {'changepoint_prior_scale': 0.04806697427414373, 'seasonality_prior_scale': 0.8715399932617315, 'holidays_prior_scale': 1.771092340237384, 'seasonality_mode': 'additive'}}\n",
|
|
"{'Current Learner': 'fbprophet', 'Current Sample': 502, 'Current Hyper-parameters': {'changepoint_prior_scale': 0.026349035969911335, 'seasonality_prior_scale': 1.5914763468191142, 'holidays_prior_scale': 6.860537461967591, 'seasonality_mode': 'additive'}, 'Best Learner': 'fbprophet', 'Best Hyper-parameters': {'changepoint_prior_scale': 0.026349035969911335, 'seasonality_prior_scale': 1.5914763468191142, 'holidays_prior_scale': 6.860537461967591, 'seasonality_mode': 'additive'}}\n",
|
|
"{'Current Learner': 'fbprophet', 'Current Sample': 502, 'Current Hyper-parameters': {'changepoint_prior_scale': 0.03767818581325282, 'seasonality_prior_scale': 3.031296651199767, 'holidays_prior_scale': 6.715821078742762, 'seasonality_mode': 'additive'}, 'Best Learner': 'fbprophet', 'Best Hyper-parameters': {'changepoint_prior_scale': 0.03767818581325282, 'seasonality_prior_scale': 3.031296651199767, 'holidays_prior_scale': 6.715821078742762, 'seasonality_mode': 'additive'}}\n",
|
|
"{'Current Learner': 'fbprophet', 'Current Sample': 502, 'Current Hyper-parameters': {'changepoint_prior_scale': 0.03498447027670827, 'seasonality_prior_scale': 2.616244037716704, 'holidays_prior_scale': 5.713876592939503, 'seasonality_mode': 'additive'}, 'Best Learner': 'fbprophet', 'Best Hyper-parameters': {'changepoint_prior_scale': 0.03498447027670827, 'seasonality_prior_scale': 2.616244037716704, 'holidays_prior_scale': 5.713876592939503, 'seasonality_mode': 'additive'}}\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"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()"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"output_type": "display_data",
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
],
|
|
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"
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},
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"metadata": {
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"needs_background": "light"
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}
|
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}
|
|
],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"source": [],
|
|
"outputs": [],
|
|
"metadata": {}
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"name": "python3",
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"display_name": "Python 3.8.0 64-bit ('blend': conda)"
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},
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"language_info": {
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"name": "python",
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"version": "3.8.0",
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"mimetype": "text/x-python",
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"pygments_lexer": "ipython3",
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"nbconvert_exporter": "python",
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"file_extension": ".py"
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},
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"interpreter": {
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|
"hash": "0cfea3304185a9579d09e0953576b57c8581e46e6ebc6dfeb681bc5a511f7544"
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}
|
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},
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"nbformat": 4,
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"nbformat_minor": 2
|
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} |