We present in this repository code to train, evaluate and visualise (multi-class) Temporal Point Processes (TPPs), as described in our paper:
Joseph Enguehard, Dan Busbridge, Adam Bozson, Claire Woodcock, Nils Y. Hammerla, Neural Temporal Point Processes For Modelling Electronic Health Records
- Joseph Enguehard [email protected]
We want to allow Machine Learning (ML) to use Electronic Health Records (EHRs) in such a way that we can:
- Forecast, i.e. predict the future health interactions of a patient given their medical history,
- Impute/interpolate, i.e. highlight and determine missing data in a patient EHR,
- Represent, i.e. provide representations for EHRs so they can be searched/retrieved semantically/efficiently.
Our Neural TPP models are integrated into an encoder-decoder architecture framework.
Our encoders can be found in tpp/models/encoders, and our decoders in tpp/models/decoders.
Our encoders inherit from the abstract class defined in tpp/models/encoders/base/encoder.py.
Our decoders inherit from the abstract class defined in tpp/models/decoders/base/decoder.py.
These encoders and decoders are combined into a single model in tpp/model/enc_dec.py, where the intensity function and the likelihood are computed.
Our pre-trained models can be found here: https://drive.google.com/drive/folders/1LTkErAAEy7ZjX9HuRDMBb8PBzsqtHotR?usp=sharing.
Our preprocessed data can be found here: https://drive.google.com/drive/folders/1KZXUofQ6_jsUzRK4Oh049TVuUe5_ULGS?usp=sharing. We do not provide the data for Hawkes (dependent) and Hawkes (independent) as they are deterministically generated during training time.
Please cite our paper if you use this code in your own work:
@misc{enguehard2020neural,
title={Neural Temporal Point Processes For Modelling Electronic Health Records},
author={Joseph Enguehard and Dan Busbridge and Adam Bozson and Claire Woodcock and Nils Y. Hammerla},
year={2020},
eprint={2007.13794},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
- Recurrent Marked Temporal Point Processes: Embedding Event History to Vector - RNN encoder with simple exponential decay in time of intensity. Analytic likelihood, label classes not conditioned on time.
- The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process.
- Fully Neural Network based Model for General Temporal Point Processes.
- Intensity-Free Learning of Temporal Point Processes.
In order to use this repository, you need to create an environment:
conda env create
source activate env_tpp
pip install -e .You'll also need to install tick.
Depending on your machine there are number of different ways this might work.
See the commented out lines in the environment.yml for some examples.
We are aiming to remove the tick dependency in the future.
For any of the following (including starting MLflow) you will need to set up the python environment correctly.
source activate env_tpp
python scripts/train.py \
--no-mlflow --model {model_name} \
--param1 {value1} ...
In order to run experiments using MLflow, you need to follow these steps:
Set and create directories for the backend store and artifact root.
MLFLOW_BACKEND_STORE=~/mlflow/tpp/backend-store # For example
MLFLOW_DEFAULT_ARTIFACT_ROOT=~/mlflow/tpp/default-artifact-root # For example
mkdir -p $MLFLOW_BACKEND_STORE
mkdir -p $MLFLOW_DEFAULT_ARTIFACT_ROOT
Choose a host location.
MLFLOW_HOST=0.0.0.0
MLFLOW_PORT=5000
Start MLflow
mlflow server \
--backend-store-uri $MLFLOW_BACKEND_STORE \
--default-artifact-root $MLFLOW_DEFAULT_ARTIFACT_ROOT \
--host $MLFLOW_HOST \
--port $MLFLOW_PORT
Run the experiment.
REMOTE_SERVER_URI="http://${MLFLOW_HOST}:${MLFLOW_PORT}"
python scripts/train.py \
--remote-server-uri $REMOTE_SERVER_URI \
--encoder {encoder name} \
--encoder_param1 {value} \
--decoder {decoder name} \
--decoder_param1 {value} \
usage: scripts/train.py [-h] [--seed SEED] [--padding-id PADDING_ID] [--disable-cuda]
[--verbose] [--mu N [N ...]] [--alpha N [N ...]]
[--beta N [N ...]] [--marks MARKS] [--hawkes-seed HAWKES_SEED]
[--window WINDOW] [--train-size TRAIN_SIZE] [--val-size VAL_SIZE]
[--test-size TEST_SIZE]
[--include-poisson INCLUDE_POISSON] [--batch-size BATCH_SIZE]
[--train-epochs TRAIN_EPOCHS] [--use-coefficients USE_COEFFICIENTS]
[--multi-labels MULTI_LABELS] [--time-scale TIME_SCALE]
[--lr-rate-init LR_RATE_INIT] [--lr-poisson-rate-init LR_POISSON_RATE_INIT]
[--lr-scheduler {plateau,step,milestones,cos,findlr,noam,clr,calr}]
[--lr-scheduler-patience LR_SCHEDULER_PATIENCE]
[--lr-scheduler-step-size LR_SCHEDULER_STEP_SIZE]
[--lr-scheduler-gamma LR_SCHEDULER_GAMMA]
[--lr-scheduler-warmup LR_SCHEDULER_WARMUP]
[--patience PATIENCE]
[--loss-relative-tolerance LOSS_RELATIVE_TOLERANCE]
[--mu-cheat MU_CHEAT]
[--encoder {gru,identity,mlp-fixed,mlp-variable,stub,selfattention}]
[--encoder-history-size ENCODER_HISTORY_SIZE]
[--encoder-emb-dim ENCODER_EMB_DIM]
[--encoder-encoding {times_only,marks_only,concatenate,temporal,
learnable,temporal_with_labels,learnable_with_labels}]
[--encoder-time-encoding {absolute, relative}]
[--encoder-temporal-scaling ENCODER_TEMPORAL_SCALING]
[--encoder-embedding-constraint {None, nonneg, sigmoid, softplus}]
[--encoder-units-mlp N [N ...]]
[--encoder-activation-mlp ENCODER_ACTIVATION_MLP]
[--encoder-dropout-mlp ENCODER_DROPOUT_MLP]
[--encoder-constraint-mlp {None, nonneg, sigmoid, softplus}]
[--encoder-activation-final-mlp ENCODER_ACTIVATION_FINAL_MLP]
[--encoder-attn-activation {identity,softmax,sigmoid}]
[--encoder-dropout-rnn ENCODER_DROPOUT_RNN]
[--encoder-layers-rnn ENCODER_LAYERS_RNN]
[--encoder-units-rnn ENCODER_UNITS_RNN]
[--encoder-n-heads ENCODER_N_HEADS]
[--encoder-constraint-rnn {None, nonneg, sigmoid, softplus}]
[--decoder {conditional-poisson,conditional-poisson-cm,hawkes,log-normal-mixture,
mlp-cm,mlp-mc,poisson,rmtpp,rmtpp-cm,selfattention-cm,selfattention-mc}]
[--decoder-mc-prop-est DECODER_MC_PROP_EST]
[--decoder-model-log-cm DECODER_MODEL_LOG_CM]
[--decoder-do-zero-subtraction DECODER_DO_ZERO_SUBTRACTION]
[--decoder-emb-dim DECODER_EMB_DIM]
[--decoder-encoding {times_only,marks_only,concatenate,temporal,
learnable,temporal_with_labels,learnable_with_labels}]
[--decoder-time-encoding {absolute, relative}]
[--decoder-temporal-scaling DECODER_TEMPORAL_SCALING]
[--decoder-embedding-constraint {None, nonneg, sigmoid, softplus}]
[--decoder-units-mlp N [N ...]]
[--decoder-activation-mlp DECODER_ACTIVATION_MLP]
[--decoder-mlp-dropout DECODER_MLP_DROPOUT]
[--decoder-activation-mlp DECODER_ACTIVATION_FINAL_MLP]
[--decoder-constraint-mlp {None, nonneg, sigmoid, softplus}]
[--decoder-attn-activation {identity,softmax,sigmoid}]
[--decoder-activation-rnn DECODER_ACTIVATION_RNN]
[--decoder-dropout-rnn DECODER_DROPOUT_RNN]
[--decoder-layers-rnn DECODER_LAYERS_RNN]
[--decoder-rnn-units DECODER_RNN_UNITS]
[--decoder-n-heads DECODER_N_HEADS]
[--decoder-constraint-rnn {None, nonneg, sigmoid, softplus}]
[--decoder-n-mixture DECODER_N_MIXTURE] [--no-mlflow]
[--experiment-name EXPERIMENT_NAME] [--run-name RUN_NAME]
[--remote-server-uri REMOTE_SERVER_URI]
[--logging-frequency LOGGING_FREQUENCY]
[--save-model-freq SAVE_MODEL_FREQ]
[--eval-metrics EVAL_METRICS]
[--eval-metrics-per-class EVAL_METRICS_PER_CLASS]
[--load-from-dir LOAD_FROM_DIR]
[--data-dir DATA_DIR]
[--plots-dir PLOTS_DIR]
optional arguments:
-h, --help show this help message and exit
--seed SEED The random seed.
--padding-id PADDING_ID
The value used in the temporal sequences to indicate a
non-event.
--disable-cuda Disable CUDA
--verbose If `True`, prints all the things.
--mu N [N ...] The baseline intensity for the data generator.
--alpha N [N ...] The event parameter for the data generator. This will
be reshaped into a matrix the size of [mu,mu].
--beta N [N ...] The decay parameter for the data generator. This will
be reshaped into a matrix the size of [mu,mu].
--marks MARKS Generate a process with this many marks. Defaults to
`None`. If this is set to an integer, it will override
`alpha`, `beta` and `mu` with randomly generated
values corresponding to the number of requested marks.
--hawkes-seed HAWKES_SEED
The random seed for generating the `alpha, `beta` and
`mu` if `marks` is not `None`.
--window WINDOW The window of the simulated process.py. Also taken as
the window of any parametric Hawkes model if chosen.
--train-size TRAIN_SIZE
The number of unique sequences in each of the train
dataset.
--val-size VAL_SIZE The number of unique sequences in each of the
validation dataset.
--test-size TEST_SIZE The number of unique sequences in each of the test
dataset.
--include-poisson INCLUDE_POISSON
Include base intensity (where appropriate).
--batch-size BATCH_SIZE
The batch size to use for parametric model training
and evaluation.
--train-epochs TRAIN_EPOCHS
The number of training epochs.
--use-coefficients USE_COEFFICIENTS
If true, the modular process will be trained with
coefficients
--multi-labels MULTI_LABELS
Whether the likelihood is computed on multi-labels
events or not
--time-scale TIME_SCALE
Time scale used to prevent overflow
--enc-dec-hidden-size ENC_DEC_HIDDEN_SIZE
Number of features output by the encoder
--lr-rate-init LR_RATE_INIT
Initial learning rate for optimization
--lr-poisson-rate-init LR_POISSON_RATE_INIT
Initial poisson learning rate for optimization
--lr-scheduler {plateau,step,milestones,cos,findlr,noam,clr,calr}
method to adjust learning rate
--lr-scheduler-patience LR_SCHEDULER_PATIENCE
lr scheduler plateau: Number of epochs with no
improvement after which learning rate will be reduced
--lr-scheduler-step-size LR_SCHEDULER_STEP_SIZE
lr scheduler step: number of epochs of learning rate
decay.
--lr-scheduler-gamma LR_SCHEDULER_GAMMA
learning rate is multiplied by the gamma to decrease
it
--lr-scheduler-warmup LR_SCHEDULER_WARMUP
The number of epochs to linearly increase the learning
rate. (noam only)
--patience PATIENCE The patience for early stopping.
--loss-relative-tolerance LOSS_RELATIVE_TOLERANCE
The relative factor that the loss needs to decrease by
in order to not contribute to patience. If `None`,
will not use numerical convergence to control early
stopping. Defaults to `None`.
--mu-cheat MU_CHEAT If True, the starting mu value will be the actual mu
value. Defaults to False.
--encoder {gru,identity,mlp-fixed,mlp-variable,stub,selfattention}
The type of encoder to use.
--encoder-history-size ENCODER_HISTORY_SIZE
The (fixed) history length to use for fixed history
size parametric models.
--encoder-emb-dim ENCODER_EMB_DIM
Size of the embeddings. This is the size of the
temporal encoding and/or the label embedding if either
is used.
--encoder-encoding {times_only,marks_only,concatenate,temporal,learnable,
temporal_with_labels,learnable_with_labels}
Type of the event encoding.
--encoder-time-encoding {absolute, relative}
--encoder-temporal-scaling ENCODER_TEMPORAL_SCALING
Rescale of times when using temporal encoding
--encoder-embedding-constraint {None, nonneg, sigmoid, softplus}
--encoder-units-mlp N [N ...]
Size of hidden layers in the encoder MLP. This will
have the decoder input size appended to it during
model build.
--encoder-activation-mlp ENCODER_ACTIVATION_MLP
Activation function of the MLP
--encoder-dropout-mlp ENCODER_DROPOUT_MLP
Dropout rate of the MLP.
--encoder-constraint-mlp {None, nonneg, sigmoid, softplus}
--encoder-activation-final-mlp ENCODER_ACTIVATION_FINAL_MLP
Final activation function of the MLP.
--encoder-attn-activation {identity,softmax,sigmoid}
Activation function of the attention coefficients
--encoder-dropout-rnn ENCODER_DROPOUT_RNN
Dropout rate of the RNN.
--encoder-layers-rnn ENCODER_LAYERS_RNN
Number of layers for RNN and self-attention encoder.
--encoder-units-rnn ENCODER_UNITS_RNN
Hidden size for RNN and self attention encoder.
--encoder-n-heads ENCODER_N_HEADS
Number of heads for the transformer
--encoder-constraint-rnn {None, nonneg, sigmoid, softplus}
--decoder {conditional-poisson,conditional-poisson-cm,hawkes,log-normal-mixture,
mlp-cm,mlp-mc,poisson,rmtpp,rmtpp-cm,selfattention-cm,selfattention-mc}
The type of decoder to use.
--decoder-mc-prop-est DECODER_MC_PROP_EST
Proportion of MC samples, compared to dataset size
--decoder-model-log-cm DECODER_MODEL_LOG_CM
Whether the cumulative models the log integral or the
integral
--decoder-do-zero-subtraction DECODER_DO_ZERO_SUBTRACTION
For cumulative estimation. If `True` the class computes
Lambda(tau) = f(tau) - f(0) in order to enforce
Lambda(0) = 0. Defaults to `true`, where instead
Lambda(tau) = f(tau).
--decoder-emb-dim DECODER_EMB_DIM
Size of the embeddings. This is the size of the
temporal encoding and/or the label embedding if either
is used.
--decoder-encoding {times_only,marks_only,concatenate,temporal,
learnable,temporal_with_labels,learnable_with_labels}
Type of the event decoding.
--decoder-time-encoding {absolute, relative}
--decoder-temporal-scaling DECODER_TEMPORAL_SCALING
Rescale of times when using temporal encoding
--decoder-embedding-constraint {None, nonneg, sigmoid, softplus}
--decoder-units-mlp N [N ...]
Size of hidden layers in the decoder MLP. This will
have the number of marks appended to it during model
build.
--decoder-activation-mlp DECODER_ACTIVATION_MLP
Activation function of the MLP
--decoder-mlp-dropout DECODER_MLP_DROPOUT
Dropout rate of the MLP
--decoder-activation-mlp DECODER_ACTIVATION_FINAL_MLP
Final activation function of the MLP.
--decoder-constraint-mlp {None, nonneg, sigmoid, softplus}
--decoder-attn-activation {identity,softmax,sigmoid}
Activation function of the attention coefficients
--decoder-activation-rnn DECODER_ACTIVATION_RNN
Activation for the rnn.
--decoder-dropout-rnn DECODER_DROPOUT_RNN
Dropout rate of the RNN
--decoder-layers-rnn DECODER_LAYERS_RNN
Number of layers for self attention decoder.
--decoder-rnn-units DECODER_RNN_UNITS
Hidden size for self attention decoder.
--decoder-n-heads DECODER_N_HEADS
Number of heads for the transformer
--decoder-constraint-rnn {None, nonneg, sigmoid, softplus}
--decoder-n-mixture DECODER_N_MIXTURE
Number of mixtures for the log normal mixture model
--no-mlflow Do not use MLflow (default=False)
--experiment-name EXPERIMENT_NAME
Name of MLflow experiment
--run-name RUN_NAME Name of MLflow run
--remote-server-uri REMOTE_SERVER_URI
Remote MLflow server URI
--logging-frequency LOGGING_FREQUENCY
The frequency to log values to MLFlow.
--save-model-freq SAVE_MODEL_FREQ
The best model is saved every nth epoch
--eval-metrics EVAL_METRICS
The model is evaluated using several metrics
--eval-metrics-per-class EVAL_METRICS_PER_CLASS
The model is evaluated using several metrics per class
--load-from-dir LOAD_FROM_DIR
If not None, load data from a directory
--data-dir DATA_DIR Directory to save the preprocessed data
--plots-dir PLOTS_DIR
Directory to save the plots
usage: scripts/evaluate.py [-h] [--model-dir MODEL_DIR] [--seed SEED]
[--padding-id PADDING_ID] [--mu N [N ...]] [--alpha N [N ...]]
[--beta N [N ...]] [--marks MARKS] [--window WINDOW]
[--val-size VAL_SIZE] [--test-size TEST_SIZE]
[--batch-size BATCH_SIZE]
[--time-scale TIME_SCALE] [--multi-labels MULTI_LABELS]
[--eval-metrics EVAL_METRICS]
[--eval-metrics-per-class EVAL_METRICS_PER_CLASS]
[--load-from-dir LOAD_FROM_DIR]
[--data-dir DATA_DIR]
[--plots-dir PLOTS_DIR]
required arguments:
--model-dir MODEL_DIR
Directory of the saved model
optional arguments:
-h, --help Show this help message and exit
--seed SEED The random seed.
--padding-id PADDING_ID
The value used in the temporal sequences to indicate a
non-event.
--mu N [N ...] The baseline intensity for the data generator.
--alpha N [N ...] The event parameter for the data generator. This will
be reshaped into a matrix the size of [mu,mu].
--beta N [N ...] The decay parameter for the data generator. This will
be reshaped into a matrix the size of [mu,mu].
--marks MARKS Generate a process with this many marks. Defaults to
`None`. If this is set to an integer, it will override
`alpha`, `beta` and `mu` with randomly generated
values corresponding to the number of requested marks.
--window WINDOW The window of the simulated process.py. Also taken as
the window of any parametric Hawkes model if chosen.
--val-size VAL_SIZE The number of unique sequences in each of the
validation dataset.
--test-size TEST_SIZE The number of unique sequences in each of the test
dataset.
--batch-size BATCH_SIZE
The batch size to use for parametric model training
and evaluation.
--time-scale TIME_SCALE
Time scale used to prevent overflow
--multi-labels MULTI_LABELS
Whether the likelihood is computed on multi-labels
events or not
--eval-metrics EVAL_METRICS
The model is evaluated using several metrics
--eval-metrics-per-class EVAL_METRICS_PER_CLASS
The model is evaluated using several metrics per class
--load-from-dir LOAD_FROM_DIR
If not None, load data from a directory
--data-dir DATA_DIR Directory to save the preprocessed data
--plots-dir PLOTS_DIR
Directory to save the plots
This repository was originally forked from https://github.com/woshiyyya/ERPP-RMTPP