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TemporAI v0.0.1: First alpha release
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@DrShushen DrShushen released this 10 Apr 17:20
· 31 commits to main since this release
v0.0.1
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Learn about vigilant mode.

Release Notes

The following methods are included in this release:

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 ---

Additional features:

  • Pipelines: tempor.plugins.pipeline.
  • Benchmarking: tempor.benchmarks (one off classification, regression, time-to-event).
Assets 2