AutoPrognosis - A system for automating the design of predictive modeling pipelines tailored for clinical prognosis.

:key: Features

• :rocket: Automatically learns ensembles of pipelines for classification, regression or survival analysis tasks.
• :cyclone: Easy to extend pluginable architecture.
• :fire: Interpretability and uncertainty quantification tools.
• :adhesive_bandage: Data imputation using HyperImpute.
• :zap: Build demonstrators using Streamlit.
• :notebook: Python and R tutorials available.
• :book: Read the docs

Note : The R bindings have been tested using R version 4.2 and Python 3.8.

:rocket: Installation

Using pip

The library can be installed from PyPI using

$ pip install autoprognosis

or from source, using

$ pip install .

Redis (Optional, but recommended)

AutoPrognosis can use Redis as a backend to improve the performance and quality of the searches.

For that, install the redis-server package following the steps described on the official site.

Environment variables

The library can be configured from a set of environment variables.

Example: export N_OPT_JOBS = 2 to use 2 cores for hyperparam search.

:boom: Sample Usage

Advanced Python tutorials can be found in the Python tutorials section.

R examples can be found in the R tutorials section.

List the available classifiers

from autoprognosis.plugins.prediction.classifiers import Classifiers
print(Classifiers().list_available())

Create a study for classifiers

from sklearn.datasets import load_breast_cancer

from autoprognosis.studies.classifiers import ClassifierStudy
from autoprognosis.utils.serialization import load_model_from_file
from autoprognosis.utils.tester import evaluate_estimator


X, Y = load_breast_cancer(return_X_y=True, as_frame=True)

df = X.copy()
df["target"] = Y

study_name = "example"

study = ClassifierStudy(
    study_name=study_name,
    dataset=df,  # pandas DataFrame
    target="target",  # the label column in the dataset
)
model = study.fit()

# Predict the probabilities of each class using the model
model.predict_proba(X)

(Advanced) Customize the study for classifiers

from pathlib import Path

from sklearn.datasets import load_breast_cancer

from autoprognosis.studies.classifiers import ClassifierStudy
from autoprognosis.utils.serialization import load_model_from_file
from autoprognosis.utils.tester import evaluate_estimator


X, Y = load_breast_cancer(return_X_y=True, as_frame=True)

df = X.copy()
df["target"] = Y

workspace = Path("workspace")
study_name = "example"

study = ClassifierStudy(
    study_name=study_name,
    dataset=df,  # pandas DataFrame
    target="target",  # the label column in the dataset
    num_iter=100,  # how many trials to do for each candidate
    timeout=60,  # seconds
    classifiers=["logistic_regression", "lda", "qda"],
    workspace=workspace,
)

study.run()

output = workspace / study_name / "model.p"
model = load_model_from_file(output)

# <model> contains the optimal architecture, but the model is not trained yet. You need to call fit() to use it.
# This way, we can further benchmark the selected model on the training set.
metrics = evaluate_estimator(model, X, Y)

print(f"model {model.name()} -> {metrics['str']}")

# Train the model
model.fit(X, Y)

# Predict the probabilities of each class using the model
model.predict_proba(X)

List the available regressors

from autoprognosis.plugins.prediction.regression import Regression
print(Regression().list_available())

Create a Regression study

# third party
import pandas as pd

# autoprognosis absolute
from autoprognosis.utils.serialization import load_model_from_file
from autoprognosis.utils.tester import evaluate_regression
from autoprognosis.studies.regression import RegressionStudy

# Load dataset
df = pd.read_csv(
    "https://archive.ics.uci.edu/ml/machine-learning-databases/00291/airfoil_self_noise.dat",
    header=None,
    sep="\\t",
)
last_col = df.columns[-1]
y = df[last_col]
X = df.drop(columns=[last_col])

df = X.copy()
df["target"] = y

# Search the model
study_name="regression_example"
study = RegressionStudy(
    study_name=study_name,
    dataset=df,  # pandas DataFrame
    target="target",  # the label column in the dataset
)
model = study.fit()

# Predict using the model
model.predict(X)

Advanced Customize the Regression study

# stdlib
from pathlib import Path

# third party
import pandas as pd

# autoprognosis absolute
from autoprognosis.utils.serialization import load_model_from_file
from autoprognosis.utils.tester import evaluate_regression
from autoprognosis.studies.regression import RegressionStudy

# Load dataset
df = pd.read_csv(
    "https://archive.ics.uci.edu/ml/machine-learning-databases/00291/airfoil_self_noise.dat",
    header=None,
    sep="\\t",
)
last_col = df.columns[-1]
y = df[last_col]
X = df.drop(columns=[last_col])

df = X.copy()
df["target"] = y

# Search the model
workspace = Path("workspace")
workspace.mkdir(parents=True, exist_ok=True)

study_name="regression_example"
study = RegressionStudy(
    study_name=study_name,
    dataset=df,  # pandas DataFrame
    target="target",  # the label column in the dataset
    num_iter=10,  # how many trials to do for each candidate. Default: 50
    num_study_iter=2,  # how many outer iterations to do. Default: 5
    timeout=50,  # timeout for optimization for each classfier. Default: 600 seconds
    regressors=["linear_regression", "xgboost_regressor"],
    workspace=workspace,
)

study.run()

# Test the model
output = workspace / study_name / "model.p"

model = load_model_from_file(output)
# <model> contains the optimal architecture, but the model is not trained yet. You need to call fit() to use it.
# This way, we can further benchmark the selected model on the training set.

metrics = evaluate_regression(model, X, y)

print(f"Model {model.name()} score: {metrics['str']}")

# Train the model
model.fit(X, y)


# Predict using the model
model.predict(X)

List available survival analysis estimators

from autoprognosis.plugins.prediction.risk_estimation import RiskEstimation
print(RiskEstimation().list_available())

Create a Survival analysis study

# third party
import numpy as np
from pycox import datasets

# autoprognosis absolute
from autoprognosis.studies.risk_estimation import RiskEstimationStudy
from autoprognosis.utils.serialization import load_model_from_file
from autoprognosis.utils.tester import evaluate_survival_estimator

df = datasets.gbsg.read_df()
df = df[df["duration"] > 0]

X = df.drop(columns = ["duration"])
T = df["duration"]
Y = df["event"]

eval_time_horizons = np.linspace(T.min(), T.max(), 5)[1:-1]

study_name = "example_risks"

study = RiskEstimationStudy(
    study_name=study_name,
    dataset=df,
    target="event",
    time_to_event="duration",
    time_horizons=eval_time_horizons,
)

model = study.fit()

# Predict using the model
model.predict(X, eval_time_horizons)

Advanced Customize the Survival analysis study

# stdlib
import os
from pathlib import Path

# third party
import numpy as np
from pycox import datasets

# autoprognosis absolute
from autoprognosis.studies.risk_estimation import RiskEstimationStudy
from autoprognosis.utils.serialization import load_model_from_file
from autoprognosis.utils.tester import evaluate_survival_estimator

df = datasets.gbsg.read_df()
df = df[df["duration"] > 0]

X = df.drop(columns = ["duration"])
T = df["duration"]
Y = df["event"]

eval_time_horizons = np.linspace(T.min(), T.max(), 5)[1:-1]

workspace = Path("workspace")
study_name = "example_risks"

study = RiskEstimationStudy(
    study_name=study_name,
    dataset=df,
    target="event",
    time_to_event="duration",
    time_horizons=eval_time_horizons,
    num_iter=10,
    num_study_iter=1,
    timeout=10,
    risk_estimators=["cox_ph", "survival_xgboost"],
    score_threshold=0.5,
    workspace=workspace,
)

study.run()

output = workspace / study_name / "model.p"

model = load_model_from_file(output)
# <model> contains the optimal architecture, but the model is not trained yet. You need to call fit() to use it.
# This way, we can further benchmark the selected model on the training set.

metrics = evaluate_survival_estimator(model, X, T, Y, eval_time_horizons)

print(f"Model {model.name()} score: {metrics['str']}")

# Train the model
model.fit(X, T, Y)

# Predict using the model
model.predict(X, eval_time_horizons)

:high_brightness: Tutorials

Plugins

• Imputation
• Preprocessing
• Classification
• Pipelines
• Interpretability
• Survival Analysis
• Regression

AutoML

• Classification tasks
• Classification tasks with imputation
• Survival analysis tasks
• Survival analysis tasks with imputation
• Regression tasks
• Classifiers with explainers
• Multiple imputation example

Building a demonstrator

• Classification demonstrator
• Survival analysis demonstrator

AutoPrognosis 101 Tutorial Series

• 00. Run a classification study
• 01. Run a regression study
• 02. Run a survival analysis study
• 03. Run a study and interpret the model
• 04. What’s a plugin? Survival analysis example
• 05. Pipelines
• 06. [Advanced] Creating your own plugin: preprocessing example

:zap: Plugins

Imputation methods

from autoprognosis.plugins.imputers import  Imputers

imputer = Imputers().get(<NAME>)

Preprocessing methods

from autoprognosis.plugins.preprocessors import Preprocessors

preprocessor = Preprocessors().get(<NAME>)

Classification

from autoprognosis.plugins.prediction.classifiers import Classifiers

classifier = Classifiers().get(<NAME>)

Survival Analysis

from autoprognosis.plugins.prediction.risk_estimation import RiskEstimation

predictor = RiskEstimation().get(<NAME>)

Regression

from autoprognosis.plugins.prediction.regression import Regression

regressor = Regression().get(<NAME>)

Explainers

from autoprognosis.plugins.explainers import Explainers

explainer = Explainers().get(<NAME>)

Uncertainty

from autoprognosis.plugins.uncertainty import UncertaintyQuantification
model = UncertaintyQuantification().get(<NAME>)

:hammer: Test

After installing the library, the tests can be executed using pytest

$ pip install .[dev]
$ pytest -vxs -m "not slow"

Citing

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

@misc{https://doi.org/10.48550/arxiv.2210.12090,
  doi = {10.48550/ARXIV.2210.12090},
  url = {https://arxiv.org/abs/2210.12090},
  author = {Imrie, Fergus and Cebere, Bogdan and McKinney, Eoin F. and van der Schaar, Mihaela},
  keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}

References

1. AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning
2. Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning
3. Cardiovascular Disease Risk Prediction using Automated Machine Learning: A Prospective Study of 423,604 UK Biobank Participants