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autoprognosis-0.1.4
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مشاهده بیشتر
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مشاهده بیشترتوضیحات
A system for automating the design of predictive modeling pipelines tailored for clinical prognosis.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | autoprognosis-0.1.4 |
| نام | autoprognosis |
| نسخه کتابخانه | 0.1.4 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | vanderschaarlab |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://www.vanderschaar-lab.com/ |
| آدرس اینترنتی | https://pypi.org/project/autoprognosis/ |
| مجوز | MIT |
# AutoPrognosis - A system for automating the design of predictive modeling pipelines tailored for clinical prognosis.
<div align="center">
[](https://github.com/vanderschaarlab/autoprognosis/actions/workflows/test.yml)
[](https://github.com/vanderschaarlab/autoprognosis/actions/workflows/test_tutorials.yml)
[](https://colab.research.google.com/drive/1sFVnnxjRMCNVIn-Ikc--Ja44U0Ll4joY?usp=sharing)
[](https://colab.research.google.com/drive/1ZwjD9RkosCtboyblH4C8sQV1DuGY1H2X?usp=sharing)
[](https://arxiv.org/abs/2210.12090)
[](https://github.com/vanderschaarlab/autoprognosis/blob/main/LICENSE)
[](https://www.vanderschaar-lab.com/)
</div>
## :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](https://github.com/vanderschaarlab/hyperimpute).
- :zap: Build demonstrators using [Streamlit](https://streamlit.io/).
## :rocket:Installation
#### Using pip
The library can be installed from PyPI using
```bash
$ pip install autoprognosis
```
or from source, using
```bash
$ 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](https://redis.io/topics/quickstart).
## :boom: Sample Usage
More advanced use cases can be found on our [tutorials section](#tutorials).
List the available classifiers
```python
from autoprognosis.plugins.prediction.classifiers import Classifiers
print(Classifiers().list_available())
```
Create a study for classifiers
```python
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)
metrics = evaluate_estimator(model, X, Y)
print(f"model {model.name()} -> {metrics['clf']}")
```
List the available regressors
```python
from autoprognosis.plugins.prediction.regression import Regression
print(Regression().list_available())
```
Regression study
```python
# 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)
metrics = evaluate_regression(model, X, y)
print(f"Model {model.name()} score: {metrics['str']}")
```
List available survival analysis estimators
```python
from autoprognosis.plugins.prediction.risk_estimation import RiskEstimation
print(RiskEstimation().list_available())
```
Survival analysis study
```python
# 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)
metrics = evaluate_survival_estimator(model, X, T, Y, eval_time_horizons)
print(f"Model {model.name()} score: {metrics['clf']}")
```
## :high_brightness: Tutorials
### Plugins
- [](https://colab.research.google.com/drive/1QO7K3JqW8l4pgVSLxjVezTu5IfD9yHB-?usp=sharing) [ Imputation ](tutorials/plugins/tutorial_00_imputation_plugins.ipynb)
- [](https://colab.research.google.com/drive/1WQGZXQkQs0Wg5stB9fk-RvYey35ADIZu?usp=sharing)[ Preprocessing](tutorials/plugins/tutorial_01_preprocessing_plugins.ipynb)
- [](https://colab.research.google.com/drive/1WTzO_2hqaEOvvATHPSIcW220xc1WaJlC?usp=sharing)[ Classification](tutorials/plugins/tutorial_02_classification_plugins.ipynb)
- [](https://colab.research.google.com/drive/17bLtKUjN8ilHw4Cm7-53kiC0vCJO_pVb?usp=sharing)[ Pipelines](tutorials/plugins/tutorial_03_pipelines.ipynb)
- [](https://colab.research.google.com/drive/1K0yVwm4jQrXRbMKJ-em7tTYgHXWtoK5c?usp=sharing)[ Interpretability](tutorials/plugins/tutorial_04_interpretability.ipynb)
- [](https://colab.research.google.com/drive/1bY4CbiqMe2uoqeUu2d49aIdYRbtP156X?usp=sharing)[ Survival Analysis](tutorials/plugins/tutorial_05_survival_analysis_plugins.ipynb)
- [](https://colab.research.google.com/drive/1UK6WbsviT5nOQ_BAHSFYIjhpKtwppnUU?usp=sharing)[ Regression](tutorials/plugins/tutorial_06_regression_plugins.ipynb)
### AutoML
- [](https://colab.research.google.com/drive/1-lPuQAtjHESl32ahFQYsFl8ujAnDWxEJ?usp=sharing)[ Classification tasks](tutorials/automl/tutorial_00_classification_study.ipynb)
- [](https://colab.research.google.com/drive/16UDaA3F5JGw_YVY8XlYqWjfxcUV1OHJo?usp=sharing)[ Classification tasks with imputation](tutorials/automl/tutorial_01_automl_classification_with_imputation.ipynb)
- [](https://colab.research.google.com/drive/1DtZCqebhaYdKB3ci5dr3hT0KvZPaTUOi?usp=sharing)[ Survival analysis tasks](tutorials/automl/tutorial_02_survival_analysis_study.ipynb)
- [](https://colab.research.google.com/drive/1sFVnnxjRMCNVIn-Ikc--Ja44U0Ll4joY?usp=sharing)[ Survival analysis tasks with imputation](tutorials/automl/tutorial_03_automl_survival_analysis_with_imputation.ipynb)
- [](https://colab.research.google.com/drive/1HLhWI-tRZn4e9ijQ6iEIuppuDszgWkCC?usp=sharing)[ Regression tasks](tutorials/automl/tutorial_04_regression.ipynb)
### Building a demonstrator
- [](https://colab.research.google.com/drive/1lqbElEVJa2Q0JDsXPgb8K_QUTDcZvUQq?usp=sharing)[ Classification demonstrator](tutorials/demonstrators/tutorial_00_build_a_demonstrator_classification.ipynb)
- [](https://colab.research.google.com/drive/1ZwjD9RkosCtboyblH4C8sQV1DuGY1H2X?usp=sharing)[ Survival analysis demonstrator](tutorials/demonstrators/tutorial_01_build_a_demonstrator_survival_analysis.ipynb)
## :zap: Plugins
### Imputation methods
```python
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`](https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html)|
|**median**|Replace the missing values using the median along each column with [`SimpleImputer`](https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html) |
|**most_frequent**|Replace the missing values using the most frequent value along each column with [`SimpleImputer`](https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html)|
|**missforest**|Iterative imputation method based on Random Forests using [`IterativeImputer`](https://scikit-learn.org/stable/modules/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer) and [`ExtraTreesRegressor`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html)|
|**ice**| Iterative imputation method based on regularized linear regression using [`IterativeImputer`](https://scikit-learn.org/stable/modules/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer) and [`BayesianRidge`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.BayesianRidge.html)|
|**mice**| Multiple imputations based on ICE using [`IterativeImputer`](https://scikit-learn.org/stable/modules/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer) and [`BayesianRidge`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.BayesianRidge.html)|
|**softimpute**| [`Low-rank matrix approximation via nuclear-norm regularization`](https://jmlr.org/papers/volume16/hastie15a/hastie15a.pdf)| [`plugin_softimpute.py`](src/hyperimpute/plugins/imputers/plugin_softimpute.py)|
|**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`](https://joon3216.github.io/research_materials/2019/em_imputation.html)|
|**gain**|[`GAIN: Missing Data Imputation using Generative Adversarial Nets`](https://arxiv.org/abs/1806.02920)|
### Preprocessing methods
```python
from autoprognosis.plugins.preprocessors import Preprocessors
preprocessor = Preprocessors().get(<NAME>)
```
| Name | Description |
|--- | --- |
| **maxabs_scaler** | Scale each feature by its maximum absolute value. [`MaxAbsScaler`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MaxAbsScaler.html)|
| **scaler** |Standardize features by removing the mean and scaling to unit variance. - [`StandardScaler`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler)|
|**feature_normalizer** | Normalize samples individually to unit norm. [`Normalizer`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Normalizer.html#sklearn.preprocessing.Normalizer)|
|**normal_transform** |Transform features using quantiles information.[`QuantileTransformer`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.QuantileTransformer.html#sklearn.preprocessing.QuantileTransformer)|
|**uniform_transform** |Transform features using quantiles information.[`QuantileTransformer`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.QuantileTransformer.html#sklearn.preprocessing.QuantileTransformer)|
|**minmax_scaler** |Transform features by scaling each feature to a given range.[`MinMaxScaler`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html#sklearn.preprocessing.MinMaxScaler)|
### Classification
```python
from autoprognosis.plugins.prediction.classifiers import Classifiers
classifier = Classifiers().get(<NAME>)
```
| Name | Description |
|--- | --- |
| **neural_nets** | PyTorch based neural net classifier.|
| **logistic_regression** | [`LogisticRegression`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html)|
| **catboost** |Gradient boosting on decision trees - [`CatBoost`](https://catboost.ai/)|
| **random_forest** | A random forest classifier. [`RandomForestClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)|
| **tabnet** |[`TabNet : Attentive Interpretable Tabular Learning`](https://github.com/dreamquark-ai/tabnet)|
| **xgboost** |[`XGBoostClassifier`](https://xgboost.readthedocs.io/en/stable/)|
### Survival Analysis
```python
from autoprognosis.plugins.prediction.risk_estimation import RiskEstimation
predictor = RiskEstimation().get(<NAME>)
```
| Name | Description |
|--- | --- |
| **survival_xgboost** | [`XGBoost Survival Embeddings`](https://github.com/loft-br/xgboost-survival-embeddings)|
| **loglogistic_aft** | [` Log-Logistic AFT model`](https://lifelines.readthedocs.io/en/latest/fitters/regression/LogLogisticAFTFitter.html)|
| **deephit** | [`DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks`](https://github.com/chl8856/DeepHit)|
| **cox_ph** | [`Cox’s proportional hazard model`](https://lifelines.readthedocs.io/en/latest/fitters/regression/CoxPHFitter.html)|
| **weibull_aft** | [`Weibull AFT model.`](https://lifelines.readthedocs.io/en/latest/fitters/regression/WeibullAFTFitter.html)|
| **lognormal_aft** | [`Log-Normal AFT model`](https://lifelines.readthedocs.io/en/latest/fitters/regression/LogNormalAFTFitter.html)|
| **coxnet** | [`CoxNet is a Cox proportional hazards model also referred to as DeepSurv`](https://github.com/havakv/pycox)|
### Regression
```python
from autoprognosis.plugins.prediction.regression import Regression
regressor = Regression().get(<NAME>)
```
| Name | Description |
|--- | --- |
| **tabnet_regressor** |[`TabNet : Attentive Interpretable Tabular Learning`](https://github.com/dreamquark-ai/tabnet)|
| **catboost_regressor** |Gradient boosting on decision trees - [`CatBoost`](https://catboost.ai/)|
| **random_forest_regressor** |[`RandomForestRegressor`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html)|
| **xgboost_regressor** |[`XGBoostClassifier`](https://xgboost.readthedocs.io/en/stable/)|
| **neural_nets_regression** |PyTorch-based neural net regressor.|
| **linear_regression** |[`LinearRegression`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html)|
### Explainers
```python
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`](https://github.com/marcotcr/lime)|
| **symbolic_pursuit** |[`Symbolic Pursuit`](Learning outside the black-box: at the pursuit of interpretable models)|
| **shap_permutation_sampler** |[`SHAP Permutation Sampler`](https://shap.readthedocs.io/en/latest/generated/shap.explainers.Permutation.html)|
| **kernel_shap** |[`SHAP KernelExplainer`](https://shap-lrjball.readthedocs.io/en/latest/generated/shap.KernelExplainer.html)|
| **invase** |[`INVASE: Instance-wise Variable Selection`](https://github.com/vanderschaarlab/invase)|
### Uncertainty
```python
from autoprognosis.plugins.uncertainty import UncertaintyQuantification
model = UncertaintyQuantification().get(<NAME>)
```
| Name | Description |
|--- | --- |
| **cohort_explainer** ||
| **conformal_prediction** ||
| **jackknife** ||
## :hammer: Test
After installing the library, the tests can be executed using `pytest`
```bash
$ pip install .[testing]
$ 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](https://arxiv.org/abs/1802.07207)
2. [Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning](https://www.nature.com/articles/s41598-018-29523-2)
3. [Cardiovascular Disease Risk Prediction using Automated Machine Learning: A Prospective Study of 423,604 UK Biobank Participants](https://www.ncbi.nlm.nih.gov/pubmed/31091238)
نیازمندی
| مقدار | نام |
|---|---|
| >=1.20 | numpy |
| >=1.3 | pandas |
| >=1.10 | torch |
| >=1.0 | scikit-learn |
| >=1.3.2 | scipy |
| - | pydantic |
| - | seaborn |
| - | matplotlib |
| - | cloudpickle |
| - | lifelines |
| - | loguru |
| - | redis |
| - | optuna |
| - | joblib |
| - | importlib-metadata |
| - | setuptools |
| - | bandit |
| - | black |
| - | black-nb |
| - | darglint |
| - | doc8 |
| - | flake8 |
| - | isort |
| - | nbconvert |
| - | nbformat |
| - | pytest |
| - | pytest-benchmark |
| - | pytest-cov |
| - | pytest-xdist[psutil] |
| - | pytest-xprocess |
| - | py |
| - | pre-commit |
| - | tabulate |
| - | click |
| - | optuna |
| - | pycox |
نحوه نصب
نصب پکیج whl autoprognosis-0.1.4:
pip install autoprognosis-0.1.4.whl
نصب پکیج tar.gz autoprognosis-0.1.4:
pip install autoprognosis-0.1.4.tar.gz