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TemporAI

βš—οΈ Status: This project is still in alpha, and the API may change without warning.

πŸ“ƒ Overview

TemporAI is a Machine Learning-centric time-series library for medicine. The tasks that are currently of focus in TemporAI are: time-to-event (survival) analysis with time-series data, treatment effects (causal inference) over time, and time-series prediction. Data preprocessing methods, including missing value imputation for static and temporal covariates, are provided. AutoML tools for hyperparameter tuning and pipeline selection are also available.

How is TemporAI unique?

  • πŸ₯ Medicine-first: Focused on use cases for medicine and healthcare, such as temporal treatment effects, survival analysis over time, imputation methods, models with built-in and post-hoc interpretability, ... See methods.
  • πŸ—οΈ Fast prototyping: A plugin design allowing for on-the-fly integration of new methods by the users.
  • πŸš€ From research to practice: Relevant novel models from research community adapted for practical use.
  • 🌍 A healthcare ecosystem vision: A range of interactive demonstration apps, new medical problem settings, interpretability tools, data-centric tools etc. are planned.

Key concepts

key concepts

πŸš€ Installation

Instal with pip

From the Python Package Index (PyPI):

$ pip install temporai

Or from source:

$ git clone https://github.com/vanderschaarlab/temporai.git
$ cd temporai
$ pip install .

Install in a conda environment

While have not yet published TemporAI on conda-forge, you can still install TemporAI in your conda environment using pip as follows:

Create and activate conda environment as normal:

$ conda create -n <my_environment>
$ conda activate <my_environment>

Then install inside your conda environment with pip:

$ pip install temporai

πŸ’₯ Sample Usage

(▢️ Expand to view the sections below.)

List the available plugins
from tempor import plugin_loader

print(plugin_loader.list())
Use a time-to-event (survival) analysis model
from tempor import plugin_loader

# Load a time-to-event dataset:
dataset = plugin_loader.get("time_to_event.pbc", plugin_type="datasource").load()

# Initialize the model:
model = plugin_loader.get("time_to_event.dynamic_deephit")

# Train:
model.fit(dataset)

# Make risk predictions:
prediction = model.predict(dataset, horizons=[0.25, 0.50, 0.75])
Use a temporal treatment effects model
import numpy as np

from tempor import plugin_loader

# Load a dataset with temporal treatments and outcomes:
dataset = plugin_loader.get(
    "treatments.temporal.dummy_treatments",
    plugin_type="datasource",
    temporal_covariates_missing_prob=0.0,
    temporal_treatments_n_features=1,
    temporal_treatments_n_categories=2,
).load()

# Initialize the model:
model = plugin_loader.get("treatments.temporal.regression.crn_regressor", epochs=20)

# Train:
model.fit(dataset)

# Define target variable horizons for each sample:
horizons = [
    tc.time_indexes()[0][len(tc.time_indexes()[0]) // 2 :] for tc in dataset.time_series
]

# Define treatment scenarios for each sample:
treatment_scenarios = [
    [np.asarray([1] * len(h)), np.asarray([0] * len(h))] for h in horizons
]

# Predict counterfactuals:
counterfactuals = model.predict_counterfactuals(
    dataset,
    horizons=horizons,
    treatment_scenarios=treatment_scenarios,
)
Use a missing data imputer
from tempor import plugin_loader

dataset = plugin_loader.get(
    "prediction.one_off.sine", plugin_type="datasource", with_missing=True
).load()
static_data_n_missing = dataset.static.dataframe().isna().sum().sum()
temporal_data_n_missing = dataset.time_series.dataframe().isna().sum().sum()

print(static_data_n_missing, temporal_data_n_missing)
assert static_data_n_missing > 0
assert temporal_data_n_missing > 0

# Initialize the model:
model = plugin_loader.get("preprocessing.imputation.temporal.bfill")

# Train:
model.fit(dataset)

# Impute:
imputed = model.transform(dataset)
temporal_data_n_missing = imputed.time_series.dataframe().isna().sum().sum()

print(static_data_n_missing, temporal_data_n_missing)
assert temporal_data_n_missing == 0
Use a one-off classifier (prediction)
from tempor import plugin_loader

dataset = plugin_loader.get("prediction.one_off.sine", plugin_type="datasource").load()

# Initialize the model:
model = plugin_loader.get("prediction.one_off.classification.nn_classifier", n_iter=50)

# Train:
model.fit(dataset)

# Predict:
prediction = model.predict(dataset)
Use a temporal regressor (forecasting)
from tempor import plugin_loader

# Load a dataset with temporal targets.
dataset = plugin_loader.get(
    "prediction.temporal.dummy_prediction",
    plugin_type="datasource",
    temporal_covariates_missing_prob=0.0,
).load()

# Initialize the model:
model = plugin_loader.get("prediction.temporal.regression.seq2seq_regressor", epochs=10)

# Train:
model.fit(dataset)

# Predict:
prediction = model.predict(dataset, n_future_steps=5)
Benchmark models, time-to-event task
from tempor.benchmarks import benchmark_models
from tempor import plugin_loader
from tempor.methods.pipeline import pipeline

testcases = [
    (
        "pipeline1",
        pipeline(
            [
                "preprocessing.scaling.temporal.ts_minmax_scaler",
                "time_to_event.dynamic_deephit",
            ]
        )({"ts_coxph": {"n_iter": 100}}),
    ),
    (
        "plugin1",
        plugin_loader.get("time_to_event.dynamic_deephit", n_iter=100),
    ),
    (
        "plugin2",
        plugin_loader.get("time_to_event.ts_coxph", n_iter=100),
    ),
]
dataset = plugin_loader.get("time_to_event.pbc", plugin_type="datasource").load()

aggr_score, per_test_score = benchmark_models(
    task_type="time_to_event",
    tests=testcases,
    data=dataset,
    n_splits=2,
    random_state=0,
    horizons=[2.0, 4.0, 6.0],
)

print(aggr_score)
Serialization
from tempor.utils.serialization import load, save
from tempor import plugin_loader

# Initialize the model:
model = plugin_loader.get("prediction.one_off.classification.nn_classifier", n_iter=50)

buff = save(model)  # Save model to bytes.
reloaded = load(buff)  # Reload model.

# `save_to_file`, `load_from_file` also available in the serialization module.
AutoML - search for the best pipeline for your task
from tempor.automl.seeker import PipelineSeeker

dataset = plugin_loader.get("prediction.one_off.sine", plugin_type="datasource").load()

# Specify the AutoML pipeline seeker for the task of your choice, providing candidate methods,
# metric, preprocessing steps etc.
seeker = PipelineSeeker(
    study_name="my_automl_study",
    task_type="prediction.one_off.classification",
    estimator_names=[
        "cde_classifier",
        "ode_classifier",
        "nn_classifier",
    ],
    metric="aucroc",
    dataset=dataset,
    return_top_k=3,
    num_iter=100,
    tuner_type="bayesian",
    static_imputers=["static_tabular_imputer"],
    static_scalers=[],
    temporal_imputers=["ffill", "bfill"],
    temporal_scalers=["ts_minmax_scaler"],
)

# The search will return the best pipelines.
best_pipelines, best_scores = seeker.search()  # doctest: +SKIP

πŸ“– Tutorials

Data

User Guide

Extending TemporAI

πŸ“˜ Documentation

See the full project documentation here.

Note on documentation versions:

  • If you have installed TemporAI from PyPI, you should refer to the stable documentation.
  • If you have installed TemporAI from source, you should refer to the latest documentation.

See the Instal with pip section for reference.

🌍 TemporAI Ecosystem (Experimental)

We provide additional tools in the TemporAI ecosystem, which are in active development, and are currently (very) experimental. Suggestions and contributions are welcome!

These include:

πŸ”‘ Methods

(▢️ Expand to view the sections below.)

Time-to-Event (survival) analysis over time

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

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 ---

Preprocessing

Feature Encoding

  • Static data (category: preprocessing.encoding.static)
Name Description Reference
static_onehot_encoder One-hot encode categorical static features ---
  • Temporal data (category: preprocessing.encoding.temporal)
Name Description Reference
ts_onehot_encoder One-hot encode categorical time series features ---

Imputation

  • Static data (category: preprocessing.imputation.static)
Name Description Reference
static_tabular_imputer Use any method from HyperImpute (HyperImpute, Mean, Median, Most-frequent, MissForest, ICE, MICE, SoftImpute, EM, Sinkhorn, GAIN, MIRACLE, MIWAE) to impute the static 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 ---
ts_tabular_imputer Use any method from HyperImpute (HyperImpute, Mean, Median, Most-frequent, MissForest, ICE, MICE, SoftImpute, EM, Sinkhorn, GAIN, MIRACLE, MIWAE) to impute the time series data Paper

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 ---

πŸ”¨ Tests and Development

Install the testing dependencies using:

pip install .[testing]

The tests can be executed using:

pytest -vsx

For local development, we recommend that you should install the [dev] extra, which includes [testing] and some additional dependencies:

pip install .[dev]

For development and contribution to TemporAI, see:

✍️ Citing

If you use this code, please cite the associated paper:

@article{saveliev2023temporai,
  title={TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicine},
  author={Saveliev, Evgeny S and van der Schaar, Mihaela},
  journal={arXiv preprint arXiv:2301.12260},
  year={2023}
}