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Chronos: Fix the display problem of API doc (#3208)

Browse source 
* fix rst path

* recipe api doc error

* rename title

* Uniform name format
This commit is contained in:
liangs6212 2021-10-22 12:15:07 +08:00 committed by GitHub
parent b41ca7fb6e
commit 6137a08ddf
9 changed files with 52 additions and 50 deletions
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4
docs/readthedocs/source/conf.py
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@ -21,7 +21,9 @@ import urllib
# documentation root, use os.path.abspath to make it absolute, like shown here.
#sys.path.insert(0, '.')
sys.path.insert(0, os.path.abspath('.'))
sys.path.insert(0, os.path.abspath("../../../python/"))
sys.path.insert(0, os.path.abspath("../../../python/chronos/src/"))
sys.path.insert(0, os.path.abspath("../../../python/dllib/src/"))
sys.path.insert(0, os.path.abspath("../../../python/orca/src/"))
# -- Project information -----------------------------------------------------

6
docs/readthedocs/source/doc/PythonAPI/AutoML/automl.rst
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@ -6,7 +6,7 @@ orca.automl.auto_estimator
A general estimator supports automatic model tuning. It allows users to fit and search the best hyperparameter for their model.
.. automodule:: zoo.orca.automl.auto_estimator
.. automodule:: bigdl.orca.automl.auto_estimator
:members:
:show-inheritance:
@ -16,7 +16,7 @@ orca.automl.hp
Sampling specs to be used in search space configuration.
.. automodule:: zoo.orca.automl.hp
.. automodule:: bigdl.orca.automl.hp
:members:
:show-inheritance:
@ -25,6 +25,6 @@ automl.metrics
Evaluate unscaled metrics between y true value and y predicted value.
.. automodule:: zoo.orca.automl.metrics
.. automodule:: bigdl.orca.automl.metrics
:members:
:show-inheritance:

12
docs/readthedocs/source/doc/PythonAPI/Chronos/anomaly_detectors.rst
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@ -1,31 +1,31 @@
Anomaly Detectors
=====================
chronos.anomaly.ae_detector
AEDetector
----------------------------------------
AEDetector is unsupervised anomaly detector. It builds an autoencoder network, tries to fit the model to the input data, and calcuates the reconstruction error. The samples with larger reconstruction errors are more likely the anomalies.
.. automodule:: zoo.chronos.detector.anomaly.ae_detector
.. automodule:: bigdl.chronos.detector.anomaly.ae_detector
:members:
:show-inheritance:
chronos.anomaly.dbscan_detector
DBScanDetector
----------------------------------------
DBScanDetector uses DBSCAN clustering for anomaly detection. The DBSCAN algorithm tries to cluster the points and label the points that do not belong to any clusters as -1. It thus detects outliers in the input time series.
.. automodule:: zoo.chronos.detector.anomaly.dbscan_detector
.. automodule:: bigdl.chronos.detector.anomaly.dbscan_detector
:members:
:show-inheritance:
chronos.anomaly.th_detector
ThresholdDetector
----------------------------------------
ThresholdDetector is a simple anomaly detector that detectes anomalies based on threshold. The target value for anomaly testing can be either 1) the sample value itself or 2) the difference between the forecasted value and the actual value, if the forecasted values are provied. The thresold can be set by user or esitmated from the train data accoring to anomaly ratio and statistical distributions.
.. automodule:: zoo.chronos.detector.anomaly.th_detector
.. automodule:: bigdl.chronos.detector.anomaly.th_detector
:members: ThresholdDetector
:show-inheritance:

12
docs/readthedocs/source/doc/PythonAPI/Chronos/automodels.rst
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@ -7,7 +7,7 @@ AutoTCN
AutoTCN is a TCN forecasting model with Auto tuning.
Other API follows its base class(BasePytorchAutomodel).
.. automodule:: zoo.chronos.autots.model.auto_tcn
.. automodule:: bigdl.chronos.autots.model.auto_tcn
:members:
:undoc-members:
:show-inheritance:
@ -19,7 +19,7 @@ AutoLSTM
AutoLSTM is an LSTM forecasting model with Auto tuning.
Other API follows its base class(BasePytorchAutomodel).
.. automodule:: zoo.chronos.autots.model.auto_lstm
.. automodule:: bigdl.chronos.autots.model.auto_lstm
:members:
:undoc-members:
:show-inheritance:
@ -30,7 +30,7 @@ AutoSeq2Seq
AutoSeq2Seq is an Seq2Seq forecasting model with Auto tuning.
Other API follows its base class(BasePytorchAutomodel).
.. automodule:: zoo.chronos.autots.model.auto_seq2seq
.. automodule:: bigdl.chronos.autots.model.auto_seq2seq
:members:
:undoc-members:
:show-inheritance:
@ -40,7 +40,7 @@ AutoARIMA
AutoARIMA is an ARIMA forecasting model with Auto tuning.
.. automodule:: zoo.chronos.autots.model.auto_arima
.. automodule:: bigdl.chronos.autots.model.auto_arima
:members:
:undoc-members:
:show-inheritance:
@ -50,7 +50,7 @@ AutoProphet
AutoProphet is a Prophet forecasting model with Auto tuning.
.. automodule:: zoo.chronos.autots.model.auto_prophet
.. automodule:: bigdl.chronos.autots.model.auto_prophet
:members:
:undoc-members:
:show-inheritance:
@ -59,6 +59,6 @@ BasePytorchAutomodel
------------------------------------------------------------
AutoLSTM, AutoSeq2Seq and AutoTCN all follow the same API as stated below.
.. autoclass:: zoo.chronos.autots.model.base_automodel.BasePytorchAutomodel
.. autoclass:: bigdl.chronos.autots.model.base_automodel.BasePytorchAutomodel
:members:
:show-inheritance:

32
docs/readthedocs/source/doc/PythonAPI/Chronos/autots.rst
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@ -9,7 +9,7 @@ AutoTSTrainer
AutoTSTrainer trains a time series pipeline (including data processing, feature engineering, and model) with AutoML.
.. autoclass:: zoo.chronos.autots.deprecated.forecast.AutoTSTrainer
.. autoclass:: bigdl.chronos.autots.deprecated.forecast.AutoTSTrainer
:members:
:show-inheritance:
@ -19,7 +19,7 @@ TSPipeline
A pipeline for time series forecasting.
.. autoclass:: zoo.chronos.autots.deprecated.forecast.TSPipeline
.. autoclass:: bigdl.chronos.autots.deprecated.forecast.TSPipeline
:members:
:show-inheritance:
@ -29,58 +29,58 @@ Recipe
Recipe is used for search configuration for AutoTSTrainer.
.. autoclass:: zoo.chronos.autots.deprecated.config.recipe.SmokeRecipe
.. autoclass:: bigdl.chronos.autots.deprecated.config.recipe.SmokeRecipe
:members:
:show-inheritance:
.. autoclass:: zoo.chronos.autots.deprecated.config.recipe.MTNetSmokeRecipe
.. autoclass:: bigdl.chronos.autots.deprecated.config.recipe.MTNetSmokeRecipe
:members:
:show-inheritance:
.. autoclass:: zoo.chronos.autots.deprecated.config.recipe.TCNSmokeRecipe
.. autoclass:: bigdl.chronos.autots.deprecated.config.recipe.TCNSmokeRecipe
:members:
:show-inheritance:
.. autoclass:: zoo.chronos.autots.deprecated.config.recipe.PastSeqParamHandler
.. autoclass:: bigdl.chronos.autots.deprecated.config.recipe.PastSeqParamHandler
:members:
:show-inheritance:
.. autoclass:: zoo.chronos.autots.deprecated.config.recipe.GridRandomRecipe
.. autoclass:: bigdl.chronos.autots.deprecated.config.recipe.GridRandomRecipe
:members:
:show-inheritance:
.. autoclass:: zoo.chronos.autots.deprecated.config.recipe.LSTMSeq2SeqRandomRecipe
.. autoclass:: bigdl.chronos.autots.deprecated.config.recipe.LSTMSeq2SeqRandomRecipe
:members:
:show-inheritance:
.. autoclass:: zoo.chronos.autots.deprecated.config.recipe.LSTMGridRandomRecipe
.. autoclass:: bigdl.chronos.autots.deprecated.config.recipe.LSTMGridRandomRecipe
:members:
:show-inheritance:
.. autoclass:: zoo.chronos.autots.deprecated.config.recipe.Seq2SeqRandomRecipe
.. autoclass:: bigdl.chronos.autots.deprecated.config.recipe.Seq2SeqRandomRecipe
:members:
:show-inheritance:
.. autoclass:: zoo.chronos.autots.deprecated.config.recipe.MTNetGridRandomRecipe
.. autoclass:: bigdl.chronos.autots.deprecated.config.recipe.MTNetGridRandomRecipe
:members:
:show-inheritance:
.. autoclass:: zoo.chronos.autots.deprecated.config.recipe.TCNGridRandomRecipe
.. autoclass:: bigdl.chronos.autots.deprecated.config.recipe.TCNGridRandomRecipe
:members:
:show-inheritance:
.. autoclass:: zoo.chronos.autots.deprecated.config.recipe.RandomRecipe
.. autoclass:: bigdl.chronos.autots.deprecated.config.recipe.RandomRecipe
:members:
:show-inheritance:
.. autoclass:: zoo.chronos.autots.deprecated.config.recipe.BayesRecipe
.. autoclass:: bigdl.chronos.autots.deprecated.config.recipe.BayesRecipe
:members:
:show-inheritance:
.. autoclass:: zoo.chronos.autots.deprecated.config.recipe.XgbRegressorGridRandomRecipe
.. autoclass:: bigdl.chronos.autots.deprecated.config.recipe.XgbRegressorGridRandomRecipe
:members:
:show-inheritance:
.. autoclass:: zoo.chronos.autots.deprecated.config.recipe.XgbRegressorSkOptRecipe
.. autoclass:: bigdl.chronos.autots.deprecated.config.recipe.XgbRegressorSkOptRecipe
:members:
:show-inheritance:

4
docs/readthedocs/source/doc/PythonAPI/Chronos/autotsestimator.rst
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@ -7,7 +7,7 @@ AutoTSEstimator
Automated TimeSeries Estimator for time series forecasting task.
AutoTSEstimator will replace AutoTSTrainer in later version.
.. automodule:: zoo.chronos.autots.autotsestimator
.. automodule:: bigdl.chronos.autots.autotsestimator
:members:
:undoc-members:
:show-inheritance:
@ -19,7 +19,7 @@ TSPipeline
TSPipeline is an E2E solution for time series forecasting task.
AutoTSEstimator will replace original TSPipeline returned by AutoTSTrainer in later version.
.. automodule:: zoo.chronos.autots.tspipeline
.. automodule:: bigdl.chronos.autots.tspipeline
:members:
:undoc-members:
:show-inheritance:

22
docs/readthedocs/source/doc/PythonAPI/Chronos/forecasters.rst
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@ -10,7 +10,7 @@ Long short-term memory(LSTM) is a special type of recurrent neural network(RNN).
For the detailed algorithm description, please refer to `here <https://github.com/intel-analytics/analytics-zoo/blob/master/docs/docs/Chronos/Algorithm/LSTMAlgorithm.md>`__.
.. automodule:: zoo.chronos.forecaster.lstm_forecaster
.. automodule:: bigdl.chronos.forecaster.lstm_forecaster
:members:
:undoc-members:
:show-inheritance:
@ -23,7 +23,7 @@ Please refer to BasePytorchForecaster for other methods other than initializatio
Seq2SeqForecaster wraps a sequence to sequence model based on LSTM, and is suitable for multivariant & multistep time series forecasting.
.. automodule:: zoo.chronos.forecaster.seq2seq_forecaster
.. automodule:: bigdl.chronos.forecaster.seq2seq_forecaster
:members:
:undoc-members:
:show-inheritance:
@ -36,7 +36,7 @@ Please refer to BasePytorchForecaster for other methods other than initializatio
Temporal Convolutional Networks (TCN) is a neural network that use convolutional architecture rather than recurrent networks. It supports multi-step and multi-variant cases. Causal Convolutions enables large scale parallel computing which makes TCN has less inference time than RNN based model such as LSTM.
.. automodule:: zoo.chronos.forecaster.tcn_forecaster
.. automodule:: bigdl.chronos.forecaster.tcn_forecaster
:members:
:undoc-members:
:show-inheritance:
@ -57,7 +57,7 @@ TCMFForecaster supports distributed training and inference. It is based on Orca
* You can refer to `TCMFForecaster installation <https://github.com/intel-analytics/analytics-zoo/blob/master/docs/docs/Chronos/tutorials/TCMFForecaster.md/#step-0-prepare-environment>`__ to install required packages.
* Your operating system (OS) is required to be one of the following 64-bit systems: **Ubuntu 16.04 or later** and **macOS 10.12.6 or later**.
.. automodule:: zoo.chronos.forecaster.tcmf_forecaster
.. automodule:: bigdl.chronos.forecaster.tcmf_forecaster
:members:
:undoc-members:
:show-inheritance:
@ -72,7 +72,7 @@ MTNet is proposed by paper `A Memory-Network Based Solution for Multivariate Tim
For the detailed algorithm description, please refer to `here <https://github.com/intel-analytics/analytics-zoo/blob/master/docs/docs/Chronos/Algorithm/MTNetAlgorithm.md>`__.
.. automodule:: zoo.chronos.forecaster.mtnet_forecaster
.. automodule:: bigdl.chronos.forecaster.mtnet_forecaster
:members:
:undoc-members:
:show-inheritance:
@ -83,7 +83,7 @@ ARIMAForecaster
AutoRegressive Integrated Moving Average (ARIMA) is a class of statistical models for analyzing and forecasting time series data. It consists of 3 components: AR (AutoRegressive), I (Integrated) and MA (Moving Average). In ARIMAForecaster we use the SARIMA model (Seasonal ARIMA), which is an extension of ARIMA that additionally supports the direct modeling of the seasonal component of the time series.
.. automodule:: zoo.chronos.forecaster.arima_forecaster
.. automodule:: bigdl.chronos.forecaster.arima_forecaster
:members:
:undoc-members:
:show-inheritance:
@ -96,23 +96,23 @@ Prophet is a procedure for forecasting time series data based on an additive mod
For the detailed algorithm description, please refer to `here <https://github.com/facebook/prophet>`__.
.. automodule:: zoo.chronos.forecaster.prophet_forecaster
.. automodule:: bigdl.chronos.forecaster.prophet_forecaster
:members:
:undoc-members:
:show-inheritance:
chronos.forecast.tfpark_forecaster
TFParkForecaster
----------------------------------------
.. automodule:: zoo.chronos.forecaster.tfpark_forecaster
.. automodule:: bigdl.chronos.forecaster.tfpark_forecaster
:members:
:undoc-members:
:show-inheritance:
chronos.forecast.base_forecaster.BasePytorchForecaster
BasePytorchForecaster
----------------------------------------
.. autoclass:: zoo.chronos.forecaster.base_forecaster.BasePytorchForecaster
.. autoclass:: bigdl.chronos.forecaster.base_forecaster.BasePytorchForecaster
:members:
:show-inheritance:

2
docs/readthedocs/source/doc/PythonAPI/Chronos/simulator.rst
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@ -4,7 +4,7 @@ Simulator
DPGANSimulator
------------------------------------
.. automodule:: zoo.chronos.simulator.doppelganger_simulator
.. automodule:: bigdl.chronos.simulator.doppelganger_simulator
:members:
:undoc-members:
:show-inheritance:

8
docs/readthedocs/source/doc/PythonAPI/Chronos/tsdataset.rst
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@ -1,24 +1,24 @@
TSDataset
===========
chronos.data.tsdataset
TSDataset
----------------------------------------
Time series data is a special data formulation with specific operations. TSDataset is an abstract of time series dataset, which provides various data processing operations (e.g. impute, deduplicate, resample, scale/unscale, roll) and feature engineering methods (e.g. datetime feature, aggregation feature). Cascade call is supported for most of the methods.
TSDataset can be initialized from a pandas dataframe and be converted to a pandas dataframe or numpy ndarray.
.. automodule:: zoo.chronos.data.tsdataset
.. automodule:: bigdl.chronos.data.tsdataset
:members:
:undoc-members:
:show-inheritance:
chronos.data.experimental.xshards_tsdataset
XShardsTSDataset
----------------------------------------
Time series data is a special data formulation with specific operations. XShardsTSDataset is an abstract of time series dataset, which provides various data processing operations (e.g. impute, deduplicate, resample, scale/unscale, roll) and feature engineering methods (e.g. datetime feature, aggregation feature). Cascade call is supported for most of the methods.
XShardsTSDataset can be initialized from xshards of pandas dataframe and be converted to xshards of numpy in an distributed and parallized fashion.
.. automodule:: zoo.chronos.data.experimental.xshards_tsdataset
.. automodule:: bigdl.chronos.data.experimental.xshards_tsdataset
:members:
:undoc-members:
:show-inheritance: