AutoPrognosis - 一个针对临床预后自动设计预测建模流程的系统。

:key: 特点

• :rocket: 自动学习用于分类、回归或生存分析任务的管道集成。
• :cyclone: 易于扩展的可插拔架构。
• :fire: 可解释性和不确定性量化工具。
• :adhesive_bandage: 使用HyperImpute进行数据插补。
• :zap: 使用Streamlit构建演示。
• :notebook: 提供Python和R教程。
• :book: 阅读文档

:rocket: 安装

使用pip

可以使用以下方式从PyPI安装库

$ pip install autoprognosis

或从源安装

$ pip install .

Redis（可选，但推荐）

AutoPrognosis可以使用Redis作为后端，以改进搜索的性能和质量。

为此，请按照官方网站上描述的步骤安装redis-server包。

环境变量

库可以通过一组环境变量进行配置。

示例：将 export N_OPT_JOBS = 2 设置为使用2个核心进行超参数搜索。

:boom: 示例用法

高级Python教程可以在Python教程部分找到。

R示例可以在R教程部分找到。

列出可用的分类器

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

为分类器创建研究

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)

(高级)自定义分类器研究

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)

列出可用的回归器

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

创建回归研究

# 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)

高级自定义回归研究

# 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)

列出可用的生存分析估计器

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

创建生存分析研究

# 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)

高级自定义生存分析研究

# 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: 教程

插件

• 填充
• 预处理
• 分类
• 管道
• 可解释性
• 生存分析
• 回归

AutoML

• 分类任务
• 带有填充的分类任务
• 生存分析任务
• 带有插补的生存分析任务
• 回归任务
• 具有解释器的分类器
• 多重插补示例

构建演示器

• 分类演示器
• 生存分析演示器

:zap: 插件

插补方法

from autoprognosis.plugins.imputers import  Imputers

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

预处理方法

from autoprognosis.plugins.preprocessors import Preprocessors

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

分类

from autoprognosis.plugins.prediction.classifiers import Classifiers

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

生存分析

from autoprognosis.plugins.prediction.risk_estimation import RiskEstimation

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

回归

from autoprognosis.plugins.prediction.regression import Regression

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

解释器

from autoprognosis.plugins.explainers import Explainers

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

不确定性

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

:hammer: 测试

安装库后，可以使用 pytest 执行测试

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

引用

如果您使用此代码，请引用相关论文

@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}
}

参考文献

1. AutoPrognosis：通过结构核学习进行贝叶斯优化的自动临床预后建模
2. 通过自动机器学习进行囊性纤维化预后和风险因素的预测
3. 使用自动机器学习进行心血管疾病风险预测：对423,604名英国生物样本库参与者的前瞻性研究