116 lines
6.8 KiB
Markdown
116 lines
6.8 KiB
Markdown
# Use AutoTSEstimator for Time-Series Forecasting
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---
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[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/analytics-zoo/blob/master/docs/docs/colab-notebook/chronos/chronos_experimental_autots_nyc_taxi.ipynb) [View source on GitHub](https://github.com/intel-analytics/analytics-zoo/blob/master/docs/docs/colab-notebook/chronos/chronos_experimental_autots_nyc_taxi.ipynb)
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---
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**In this guide we will demonstrate how to use _Chronos AutoTSEstimator_ and _Chronos TSPipeline_ to auto tune a time seires forecasting task and handle the whole model development process easily.**
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### **Introduction**
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Chronos provides `AutoTSEstimator` as a highly integrated solution for time series forecasting task with hyperparameter autotuning, auto feature selection and auto preprocessing. Users can prepare a `TSDataset`(recommended, used in this notebook) or their own data creator as input data. By constructing a `AutoTSEstimator` and calling `fit` on the data, a `TSPipeline` contains the best model and pre/post data processing will be returned for further development of deployment.
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`AutoTSEstimator` is experimental and only support LSTM, TCN, and Seq2seq model for now.
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### **Step 0: Prepare Environment**
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We recommend using [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/) to prepare the environment. Please refer to the [install guide](../../UserGuide/python.md) for more details.
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```bash
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conda create -n zoo python=3.7
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conda activate zoo
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pip install --pre --upgrade analytics-zoo[automl]
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```
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### **Step 1: Init Orca Context**
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```python
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if args.cluster_mode == "local":
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init_orca_context(cluster_mode="local", cores=4) # run in local mode
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elif args.cluster_mode == "k8s":
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init_orca_context(cluster_mode="k8s", num_nodes=2, cores=2) # run on K8s cluster
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elif args.cluster_mode == "yarn":
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init_orca_context(cluster_mode="yarn-client", num_nodes=2, cores=2) # run on Hadoop YARN cluster
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```
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This is the only place where you need to specify local or distributed mode. View [Orca Context](../../Orca/Overview/orca-context.md) for more details.
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**Note:** You should `export HADOOP_CONF_DIR=/path/to/hadoop/conf/dir` when running on Hadoop YARN cluster. View [Hadoop User Guide](../../UserGuide/hadoop.md) for more details.
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### **Step 2: Prepare a TSDataset**
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Prepare a `TSDataset` and call necessary operations on it.
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```python
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from zoo.chronos.data import TSDataset
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from sklearn.preprocessing import StandardScaler
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tsdata_train, tsdata_val, tsdata_test\
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= TSDataset.from_pandas(df, dt_col="timestamp", target_col="value", with_split=True, val_ratio=0.1, test_ratio=0.1)
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standard_scaler = StandardScaler()
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for tsdata in [tsdata_train, tsdata_val, tsdata_test]:
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tsdata.gen_dt_feature()\
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.impute(mode="last")\
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.scale(standard_scaler, fit=(tsdata is tsdata_train))
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```
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There is no need to call `.roll()` or `.to_torch_data_loader()` in this step, which is the largest difference between the usage of `AutoTSEstimator` and _Chronos Forecaster_. `AutoTSEstimator` will do that automatically and tune the parameters as well.
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Please call `.gen_dt_feature()`(recommended), `.gen_rolling_feature()`, and `gen_global_feature()` to generate all candidate features to be selected by `AutoTSEstimator` as well as your input extra feature.
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Detailed information please refer to [TSDataset API doc](https://analytics-zoo.readthedocs.io/en/latest/doc/PythonAPI/Chronos/tsdataset.html#tsdataset) and [Time series data basic concepts](https://analytics-zoo.readthedocs.io/en/latest/doc/Chronos/Overview/chronos.html#data-processing-and-feature-engineering).
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### **Step 3: Create an AutoTSEstimator**
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```python
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import zoo.orca.automl.hp as hp
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from zoo.chronos.autots import AutoTSEstimator
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auto_estimator = AutoTSEstimator(model='lstm', # the model name used for training
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search_space='normal', # a default hyper parameter search space
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past_seq_len=hp.randint(1, 10), # hp sampling function of past_seq_len for auto-tuning
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)
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```
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We prebuild three defualt search space for each build-in model, which you can use the by setting `search_space` to "minimal"，"normal", or "large" or define your own search space in a dictionary. The larger the search space, the better accuracy you will get and the more time will be cost.
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`past_seq_len` can be set as a hp sample function, the proper range is highly related to your data. A range between 0.5 cycle and 3 cycle is reasonable.
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Detailed information please refer to [AutoTSEstimator API doc](https://analytics-zoo.readthedocs.io/en/latest/doc/PythonAPI/Chronos/autotsestimator.html#id1) and some basic concepts [here](https://analytics-zoo.readthedocs.io/en/latest/doc/Orca/Overview/distributed-tuning.html#search-space-and-search-algorithms).
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### **Step 4: Fit with AutoTSEstimator**
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```python
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# fit with AutoTSEstimator for a returned TSPipeline
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ts_pipeline = auto_estimator.fit(data=tsdata_train, # train dataset
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validation_data=tsdata_val, # validation dataset
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epochs=5) # number of epochs to train in each trial
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```
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Detailed information please refer to [AutoTSEstimator API doc](https://analytics-zoo.readthedocs.io/en/latest/doc/PythonAPI/Chronos/autotsestimator.html#id1).
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### **Step 5: Further deployment with TSPipeline**
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The `TSPipeline` will reply the same preprcessing and corresponding postprocessing operations on the test data. You may carry out predict, evaluate or save/load for further development.
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```python
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# predict with the best trial
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y_pred = ts_pipeline.predict(tsdata_test)
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```
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```python
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# evaluate the result pipeline
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mse, smape = ts_pipeline.evaluate(tsdata_test, metrics=["mse", "smape"])
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print("Evaluate: the mean square error is", mse)
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print("Evaluate: the smape value is", smape)
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```
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```python
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# save the pipeline
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my_ppl_file_path = "/tmp/saved_pipeline"
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ts_pipeline.save(my_ppl_file_path)
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# restore the pipeline for further deployment
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from zoo.chronos.autots import TSPipeline
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loaded_ppl = TSPipeline.load(my_ppl_file_path)
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```
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Detailed information please refer to [TSPipeline API doc](https://analytics-zoo.readthedocs.io/en/latest/doc/PythonAPI/Chronos/autotsestimator.html#tspipeline-experimental).
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### **Optional: Examine the leaderboard visualization**
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To view the evaluation result of "not chosen" trails and find some insight or even possibly improve you search space for a new autotuning task. We provide a leaderboard through tensorboard.
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```python
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# show a tensorboard view
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%load_ext tensorboard
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%tensorboard --logdir /tmp/autots_estimator/autots_estimator_leaderboard/
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```
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Detailed information please refer to [Visualization](https://analytics-zoo.readthedocs.io/en/latest/doc/Chronos/Overview/chronos.html#Visualization).
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