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A system for automating the design of predictive modeling pipelines tailored for clinical prognosis.
The library can be installed from PyPI using
$ pip install autoprognosis
or from source, using
$ pip install .
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.
The library can be configured from a set of environment variables.
Variable | Description |
---|---|
N_OPT_JOBS | Number of cores to use for hyperparameter search. Default : 1 |
N_LEARNER_JOBS | Number of cores to use by inidividual learners. Default: all cpus |
REDIS_HOST | IP address for the Redis database. Default 127.0.0.1 |
REDIS_PORT | Redis port. Default: 6379 |
Example: export N_OPT_JOBS = 2
to use 2 cores for hyperparam search.
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)
from autoprognosis.plugins.imputers import Imputers
imputer = Imputers().get(<NAME>)
Name | Description |
---|---|
hyperimpute | Iterative imputer using both regression and classification methods based on linear models, trees, XGBoost, CatBoost and neural nets |
mean | Replace the missing values using the mean along each column with SimpleImputer |
median | Replace the missing values using the median along each column with SimpleImputer |
most_frequent | Replace the missing values using the most frequent value along each column with SimpleImputer |
missforest | Iterative imputation method based on Random Forests using IterativeImputer and ExtraTreesRegressor |
ice | Iterative imputation method based on regularized linear regression using IterativeImputer and BayesianRidge |
mice | Multiple imputations based on ICE using IterativeImputer and BayesianRidge |
softimpute | Low-rank matrix approximation via nuclear-norm regularization |
EM | Iterative procedure which uses other variables to impute a value (Expectation), then checks whether that is the value most likely (Maximization) - EM imputation algorithm |
gain | GAIN: Missing Data Imputation using Generative Adversarial Nets |
from autoprognosis.plugins.preprocessors import Preprocessors
preprocessor = Preprocessors().get(<NAME>)
Name | Description |
---|---|
maxabs_scaler | Scale each feature by its maximum absolute value. MaxAbsScaler |
scaler | Standardize features by removing the mean and scaling to unit variance. - StandardScaler |
feature_normalizer | Normalize samples individually to unit norm. Normalizer |
normal_transform | Transform features using quantiles information.QuantileTransformer |
uniform_transform | Transform features using quantiles information.QuantileTransformer |
minmax_scaler | Transform features by scaling each feature to a given range.MinMaxScaler |
from autoprognosis.plugins.prediction.classifiers import Classifiers
classifier = Classifiers().get(<NAME>)
Name | Description |
---|---|
neural_nets | PyTorch based neural net classifier. |
logistic_regression | LogisticRegression |
catboost | Gradient boosting on decision trees - CatBoost |
random_forest | A random forest classifier. RandomForestClassifier |
tabnet | TabNet : Attentive Interpretable Tabular Learning |
xgboost | XGBoostClassifier |
from autoprognosis.plugins.prediction.risk_estimation import RiskEstimation
predictor = RiskEstimation().get(<NAME>)
Name | Description |
---|---|
survival_xgboost | XGBoost Survival Embeddings |
loglogistic_aft | Log-Logistic AFT model |
deephit | DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks |
cox_ph | Cox’s proportional hazard model |
weibull_aft | Weibull AFT model. |
lognormal_aft | Log-Normal AFT model |
coxnet | CoxNet is a Cox proportional hazards model also referred to as DeepSurv |
from autoprognosis.plugins.prediction.regression import Regression
regressor = Regression().get(<NAME>)
Name | Description |
---|---|
tabnet_regressor | TabNet : Attentive Interpretable Tabular Learning |
catboost_regressor | Gradient boosting on decision trees - CatBoost |
random_forest_regressor | RandomForestRegressor |
xgboost_regressor | XGBoostClassifier |
neural_nets_regression | PyTorch-based neural net regressor. |
linear_regression | LinearRegression |
from autoprognosis.plugins.explainers import Explainers
explainer = Explainers().get(<NAME>)
Name | Description |
---|---|
risk_effect_size | Feature importance using Cohen's distance between probabilities |
lime | Lime: Explaining the predictions of any machine learning classifier |
symbolic_pursuit | [Symbolic Pursuit ](Learning outside the black-box: at the pursuit of interpretable models) |
shap_permutation_sampler | SHAP Permutation Sampler |
kernel_shap | SHAP KernelExplainer |
invase | INVASE: Instance-wise Variable Selection |
from autoprognosis.plugins.uncertainty import UncertaintyQuantification
model = UncertaintyQuantification().get(<NAME>)
Name | Description |
---|---|
cohort_explainer | |
conformal_prediction | |
jackknife |
After installing the library, the tests can be executed using pytest
$ pip install .[testing]
$ pytest -vxs -m "not slow"
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}
}
FAQs
A system for automating the design of predictive modeling pipelines tailored for clinical prognosis.
We found that autoprognosis demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
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