diff --git a/docs/readthedocs/source/doc/Chronos/QuickStart/chronos-anomaly-detector.md b/docs/readthedocs/source/doc/Chronos/QuickStart/chronos-anomaly-detector.md index c76f57ac..d28c39a4 100644 --- a/docs/readthedocs/source/doc/Chronos/QuickStart/chronos-anomaly-detector.md +++ b/docs/readthedocs/source/doc/Chronos/QuickStart/chronos-anomaly-detector.md @@ -1,8 +1,8 @@ -# Anomaly Detector +# Anomaly Detector Quickstart --- -![](../../../../image/colab_logo_32px.png)[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/analytics-zoo/blob/master/docs/docs/colab-notebook/chronos/chronos_minn_traffic_anomaly_detector.ipynb)  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/analytics-zoo/blob/master/docs/docs/colab-notebook/chronos/chronos_minn_traffic_anomaly_detector.ipynb) +![](../../../../image/colab_logo_32px.png)[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_minn_traffic_anomaly_detector.ipynb)  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_minn_traffic_anomaly_detector.ipynb) --- @@ -10,12 +10,12 @@ ### Step 0: Prepare Environment -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. +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](../Overview/chronos.html#install) for more details. ```bash -conda create -n zoo python=3.7 # "zoo" is conda environment name, you can use any name you like. -conda activate zoo -pip install analytics-zoo[automl] # install either version 0.10 or latest nightly build +conda create -n my_env python=3.7 # "my_env" is conda environment name, you can use any name you like. +conda activate my_env +pip install bigdl-chronos ``` ## Step 1: Prepare dataset @@ -27,7 +27,7 @@ For the machine_usage data, the pre-processing contains 2 parts:
2. Check missing values and handle missing data. ```python -from zoo.chronos.data import TSDataset +from bigdl.chronos.data import TSDataset tsdata = TSDataset.from_pandas(df, dt_col="timestamp", target_col="value") df = tsdata.resample("5min")\ @@ -36,10 +36,10 @@ df = tsdata.resample("5min")\ ``` ## Step 2: Use Chronos Anomaly Detector -Chronos provides many anomaly detector for anomaly detection, here we use DBScan as an example. More anomaly detector can be found [here](https://analytics-zoo.readthedocs.io/en/latest/doc/PythonAPI/Chronos/anomaly_detectors.html). +Chronos provides many anomaly detector for anomaly detection, here we use DBScan as an example. More anomaly detector can be found [here](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/anomaly_detectors.html). ```python -from zoo.chronos.detector.anomaly import DBScanDetector +from bigdl.chronos.detector.anomaly import DBScanDetector ad = DBScanDetector(eps=0.3, min_samples=6) ad.fit(df['value'].to_numpy()) diff --git a/docs/readthedocs/source/doc/Chronos/QuickStart/chronos-autotsest-quickstart.md b/docs/readthedocs/source/doc/Chronos/QuickStart/chronos-autotsest-quickstart.md index 9fafba11..a98acab3 100644 --- a/docs/readthedocs/source/doc/Chronos/QuickStart/chronos-autotsest-quickstart.md +++ b/docs/readthedocs/source/doc/Chronos/QuickStart/chronos-autotsest-quickstart.md @@ -1,8 +1,8 @@ -# AutoTSEstimator +# AutoTSEstimator Quickstart --- -![](../../../../image/colab_logo_32px.png)[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)  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/analytics-zoo/blob/master/docs/docs/colab-notebook/chronos/chronos_experimental_autots_nyc_taxi.ipynb) +![](../../../../image/colab_logo_32px.png)[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_autots_nyc_taxi.ipynb)  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_autots_nyc_taxi.ipynb) --- @@ -12,16 +12,16 @@ 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. -`AutoTSEstimator` is experimental and only support LSTM, TCN, and Seq2seq model for now. +`AutoTSEstimator` only support LSTM, TCN, and Seq2seq built-in models and 3rd party models for now. ### **Step 0: Prepare Environment** -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. +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](../Overview/chronos.html#install) for more details. ```bash -conda create -n zoo python=3.7 -conda activate zoo -pip install --pre --upgrade analytics-zoo[automl] +conda create -n my_env python=3.7 +conda activate my_env +pip install --pre --upgrade bigdl-chronos[all] ``` ### **Step 1: Init Orca Context** @@ -40,7 +40,7 @@ This is the only place where you need to specify local or distributed mode. View ### **Step 2: Prepare a TSDataset** Prepare a `TSDataset` and call necessary operations on it. ```python -from zoo.chronos.data import TSDataset +from bigdl.chronos.data import TSDataset from sklearn.preprocessing import StandardScaler tsdata_train, tsdata_val, tsdata_test\ @@ -56,13 +56,13 @@ There is no need to call `.roll()` or `.to_torch_data_loader()` in this step, wh 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. -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). +Detailed information please refer to [TSDataset API doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/tsdataset.html) and [Time series data basic concepts](https://bigdl.readthedocs.io/en/latest/doc/Chronos/Overview/data_processing_feature_engineering.html). ### **Step 3: Create an AutoTSEstimator** ```python -import zoo.orca.automl.hp as hp -from zoo.chronos.autots import AutoTSEstimator +import bigdl.orca.automl.hp as hp +from bigdl.chronos.autots import AutoTSEstimator auto_estimator = AutoTSEstimator(model='lstm', # the model name used for training search_space='normal', # a default hyper parameter search space past_seq_len=hp.randint(1, 10), # hp sampling function of past_seq_len for auto-tuning @@ -72,7 +72,7 @@ We prebuild three defualt search space for each build-in model, which you can us `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. -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). +Detailed information please refer to [AutoTSEstimator API doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/autotsestimator.html#autotsestimator) and basic concepts [here](https://bigdl.readthedocs.io/en/latest/doc/Chronos/Overview/forecasting.html#use-autots-pipeline). ### **Step 4: Fit with AutoTSEstimator** ```python @@ -81,7 +81,7 @@ ts_pipeline = auto_estimator.fit(data=tsdata_train, # train dataset validation_data=tsdata_val, # validation dataset epochs=5) # number of epochs to train in each trial ``` -Detailed information please refer to [AutoTSEstimator API doc](https://analytics-zoo.readthedocs.io/en/latest/doc/PythonAPI/Chronos/autotsestimator.html#id1). +Detailed information please refer to [AutoTSEstimator API doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/autotsestimator.html#autotsestimator). ### **Step 5: Further deployment with TSPipeline** 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. ```python @@ -101,10 +101,10 @@ print("Evaluate: the smape value is", smape) my_ppl_file_path = "/tmp/saved_pipeline" ts_pipeline.save(my_ppl_file_path) # restore the pipeline for further deployment -from zoo.chronos.autots import TSPipeline +from bigdl.chronos.autots import TSPipeline loaded_ppl = TSPipeline.load(my_ppl_file_path) ``` -Detailed information please refer to [TSPipeline API doc](https://analytics-zoo.readthedocs.io/en/latest/doc/PythonAPI/Chronos/autotsestimator.html#tspipeline-experimental). +Detailed information please refer to [TSPipeline API doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/tsdataset.html). ### **Optional: Examine the leaderboard visualization** 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. @@ -113,4 +113,4 @@ To view the evaluation result of "not chosen" trails and find some insight or ev %load_ext tensorboard %tensorboard --logdir /tmp/autots_estimator/autots_estimator_leaderboard/ ``` -Detailed information please refer to [Visualization](https://analytics-zoo.readthedocs.io/en/latest/doc/Chronos/Overview/chronos.html#Visualization). +Detailed information please refer to [Visualization](https://bigdl.readthedocs.io/en/latest/doc/Chronos/Overview/useful_functionalities.html#automl-visualization). diff --git a/docs/readthedocs/source/doc/Chronos/QuickStart/chronos-tsdataset-forecaster-quickstart.md b/docs/readthedocs/source/doc/Chronos/QuickStart/chronos-tsdataset-forecaster-quickstart.md index 696655a5..f0a59d88 100644 --- a/docs/readthedocs/source/doc/Chronos/QuickStart/chronos-tsdataset-forecaster-quickstart.md +++ b/docs/readthedocs/source/doc/Chronos/QuickStart/chronos-tsdataset-forecaster-quickstart.md @@ -1,8 +1,8 @@ -# TSDataset and Forecaster +# TSDataset and Forecaster Quickstarts --- -![](../../../../image/colab_logo_32px.png)[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/analytics-zoo/blob/master/docs/docs/colab-notebook/chronos/chronos_nyc_taxi_tsdataset_forecaster.ipynb)  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/analytics-zoo/blob/master/docs/docs/colab-notebook/chronos/chronos_nyc_taxi_tsdataset_forecaster.ipynb) +![](../../../../image/colab_logo_32px.png)[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_nyc_taxi_tsdataset_forecaster.ipynb)  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_nyc_taxi_tsdataset_forecaster.ipynb) --- @@ -10,22 +10,22 @@ ### **Step 0: Prepare Environment** -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. +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](../Overview/chronos.html#install) for more details. ```bash -conda create -n zoo python=3.7 # "zoo" is conda environment name, you can use any name you like. -conda activate zoo -pip install analytics-zoo[automl] # install latest nightly build +conda create -n my_env python=3.7 # "my_env" is conda environment name, you can use any name you like. +conda activate my_env +pip install bigdl-chronos[all] ``` ### Step 1: Data transformation and feature engineering using Chronos TSDataset -[TSDataset](https://analytics-zoo.readthedocs.io/en/latest/doc/PythonAPI/Chronos/tsdataset.html) is our abstract of time series dataset for data transformation and feature engineering. Here we use it to preprocess the data. +[TSDataset](https://bigdl.readthedocs.io/en/latest/doc/Chronos/Overview/data_processing_feature_engineering.html) is our abstract of time series dataset for data transformation and feature engineering. Here we use it to preprocess the data. Initialize train, valid and test tsdataset from raw pandas dataframe. ```python -from zoo.chronos.data import TSDataset +from bigdl.chronos.data import TSDataset from sklearn.preprocessing import StandardScaler tsdata_train, tsdata_valid, tsdata_test = TSDataset.from_pandas(df, dt_col="timestamp", target_col="value", @@ -54,7 +54,7 @@ for tsdata in [tsdata_train, tsdata_valid, tsdata_test]: ### Step 2: Time series forecasting using Chronos Forecaster -After preprocessing the datasets. We can use [Chronos Forecaster](https://analytics-zoo.readthedocs.io/en/latest/doc/PythonAPI/Chronos/forecasters.html) to handle the forecasting tasks. +After preprocessing the datasets. We can use [Chronos Forecaster](https://bigdl.readthedocs.io/en/latest/doc/Chronos/Overview/forecasting.html#use-standalone-forecaster-pipeline) to handle the forecasting tasks. Transform TSDataset to sampled numpy ndarray and feed them to forecaster. @@ -68,7 +68,7 @@ forecaster = TCNForecaster(past_seq_len=lookback, # number of steps to look bac future_seq_len=horizon, # number of steps to predict input_feature_num=x.shape[-1], # number of feature to use output_feature_num=y.shape[-1]) # number of feature to predict -res = forecaster.fit((x, y), validation_data=(x_val, y_val), epochs=3) +res = forecaster.fit(data=(x, y), epochs=3) ``` ### Step 3: Further deployment with fitted forecaster