temporai - TemporAI: v0.0.3

Release Notes

Changes: - Improved modularity. - There may be breaking API changes, see documentation for v0.0.3.

Other: - Improved documentation. - Improved tests (reorganized, coverage increased). - Some bug fixes and refactoring.

- Python
Published by DrShushen over 2 years ago

temporai - TemporAI v0.0.2

Release Notes

Additions: * AutoML in the form of hyperparameter tuning and pipeline search. * A data loader for UCI Diabetes dataset.

Changes: * Imputation methods reorganized: static_tabular_imputer and ts_tabular_imputer replace static_imputation.

Other: * Improved documentation. * Improved tests (reorganized, coverage increased). * Some bug fixes and refactoring.

- Python
Published by DrShushen about 3 years ago

temporai - TemporAI v0.0.1: First alpha release

Release Notes

The following methods are included in this release:

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Prediction

One-off

Prediction where targets are static.

• Classification (category: prediction.one_off.classification)

| Name | Description| Reference | | --- | --- | --- | | nn_classifier | Neural-net based classifier. Supports multiple recurrent models, like RNN, LSTM, Transformer etc. | --- | | ode_classifier | Classifier based on ordinary differential equation (ODE) solvers. | --- | | cde_classifier | Classifier based Neural Controlled Differential Equations for Irregular Time Series. | Paper | | laplace_ode_classifier | Classifier based Inverse Laplace Transform (ILT) algorithms implemented in PyTorch. | Paper |

• Regression (category: prediction.one_off.regression)

| Name | Description| Reference | | --- | --- | --- | | nn_regressor | Neural-net based regressor. Supports multiple recurrent models, like RNN, LSTM, Transformer etc. | --- | | ode_regressor | Regressor based on ordinary differential equation (ODE) solvers. | --- | | cde_regressor | Regressor based Neural Controlled Differential Equations for Irregular Time Series. | Paper | laplace_ode_regressor | Regressor based Inverse Laplace Transform (ILT) algorithms implemented in PyTorch. | Paper |

Temporal

Prediction where targets are temporal (time series).

• Classification (category: prediction.temporal.classification)

| Name | Description| Reference | | --- | --- | --- | | seq2seq_classifier | Seq2Seq prediction, classification | --- |

• Regression (category: prediction.temporal.regression)

| Name | Description| Reference | | --- | --- | --- | | seq2seq_regressor | Seq2Seq prediction, regression | --- |

Time-to-Event

Risk estimation given event data (category: time_to_event)

| Name | Description| Reference | | --- | --- | --- | | dynamic_deephit | Dynamic-DeepHit incorporates the available longitudinal data comprising various repeated measurements (rather than only the last available measurements) in order to issue dynamically updated survival predictions | Paper | | ts_coxph | Create embeddings from the time series and use a CoxPH model for predicting the survival function| --- | | ts_xgb | Create embeddings from the time series and use a SurvivalXGBoost model for predicting the survival function| --- |

Treatment effects

One-off

Treatment effects estimation where treatments are a one-off event.

• Regression on the outcomes (category: treatments.one_off.regression)

| Name | Description| Reference | | --- | --- | --- | | synctwin_regressor | SyncTwin is a treatment effect estimation method tailored for observational studies with longitudinal data, applied to the LIP setting: Longitudinal, Irregular and Point treatment. | Paper |

Temporal

Treatment effects estimation where treatments are temporal (time series).

• Classification on the outcomes (category: treatments.temporal.classification)

| Name | Description| Reference | | --- | --- | --- | | crn_classifier | The Counterfactual Recurrent Network (CRN), a sequence-to-sequence model that leverages the available patient observational data to estimate treatment effects over time. | Paper |

• Regression on the outcomes (category: treatments.temporal.regression)

| Name | Description| Reference | | --- | --- | --- | | crn_regressor | The Counterfactual Recurrent Network (CRN), a sequence-to-sequence model that leverages the available patient observational data to estimate treatment effects over time. | Paper |

Preprocessing

Imputation

• Static data (category: preprocessing.imputation.static)

| Name | Description| Reference | | --- | --- | --- | | static_imputation | Use HyperImpute to impute both the static and temporal data | Paper |

• Temporal data (category: preprocessing.imputation.temporal)

| Name | Description| Reference | | --- | --- | --- | | ffill | Propagate last valid observation forward to next valid | --- | | bfill | Use next valid observation to fill gap | --- |

Scaling

• Static data (category: preprocessing.scaling.static)

| Name | Description| Reference | | --- | --- | --- | | static_standard_scaler | Scale the static features using a StandardScaler | --- | | static_minmax_scaler | Scale the static features using a MinMaxScaler | --- |

• Temporal data (category: preprocessing.scaling.temporal)

| Name | Description| Reference | | --- | --- | --- | | ts_standard_scaler | Scale the temporal features using a StandardScaler | --- | | ts_minmax_scaler | Scale the temporal features using a MinMaxScaler | --- |

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Additional features: * Pipelines: tempor.plugins.pipeline. * Benchmarking: tempor.benchmarks (one off classification, regression, time-to-event).

- Python
Published by DrShushen about 3 years ago

temporai - TemporAI v0.0.1.dev0: First dev release

Release Notes

Initial dev release, not for production use.

- Python
Published by DrShushen over 3 years ago