AutoML-sklearn and torch

一、auto-sklearn

1.1 环境依赖

1. 额外安装swig 第三方库

2. linux 支持, mac，windows不支持

1.2 示例代码

time_left_for_this_task 设定任务最大时间

per_run_time_limit 每个子任务最大训练时间

include 可以限制任务训练的模型

import autosklearn.classification
import sklearn.model_selection
from sklearn import datasets
import sklearn.metricsif __name__ == "__main__":X, y = datasets.load_breast_cancer(return_X_y=True)X_train, X_test, y_train, y_test = \sklearn.model_selection.train_test_split(X, y, random_state=1)automl = autosklearn.classification.AutoSklearnClassifier(time_left_for_this_task=120,per_run_time_limit=30,tmp_folder="/tmp/autosklearn_classification_example_tmp",include={'classifier': ["random_forest"],'feature_preprocessor': ["no_preprocessing"]})automl.fit(X_train, y_train)y_hat = automl.predict(X_test)automl.get_models_with_weights()print("Accuracy score", sklearn.metrics.accuracy_score(y_test, y_hat))print(automl.leaderboard())models_with_weights = automl.get_models_with_weights()with open('../../preprocess/models_report.txt', 'w') as f:for model in models_with_weights:f.write(str(model) + '\n')

结果展示:

可以展示参数任务cost值排列顺序

以及训练参数配置:

1.3 模块扩展

在不支持的训练模块，可以扩展及自定义模型进行自动调参

代码示例:

继承AutoSklearnClassificationAlgorithm 并重写子方法

autosklearn.pipeline.components.classification.add_classifier(MLPClassifier) 将自定义模块注册至模块中

include 参数添加既可调用

"""
====================================================
Extending Auto-Sklearn with Classification Component
====================================================The following example demonstrates how to create a new classification
component for using in auto-sklearn.
"""
from typing import Optional
from pprint import pprintfrom ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (CategoricalHyperparameter,UniformIntegerHyperparameter,UniformFloatHyperparameter,
)import sklearn.metricsfrom autosklearn.askl_typing import FEAT_TYPE_TYPE
import autosklearn.classification
import autosklearn.pipeline.components.classification
from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm
from autosklearn.pipeline.constants import (DENSE,SIGNED_DATA,UNSIGNED_DATA,PREDICTIONS,
)from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split############################################################################
# Create MLP classifier component for auto-sklearn
# ================================================class MLPClassifier(AutoSklearnClassificationAlgorithm):def __init__(self,hidden_layer_depth,num_nodes_per_layer,activation,alpha,solver,random_state=None,):self.hidden_layer_depth = hidden_layer_depthself.num_nodes_per_layer = num_nodes_per_layerself.activation = activationself.alpha = alphaself.solver = solverself.random_state = random_statedef fit(self, X, y):self.num_nodes_per_layer = int(self.num_nodes_per_layer)self.hidden_layer_depth = int(self.hidden_layer_depth)self.alpha = float(self.alpha)from sklearn.neural_network import MLPClassifierhidden_layer_sizes = tuple(self.num_nodes_per_layer for i in range(self.hidden_layer_depth))self.estimator = MLPClassifier(hidden_layer_sizes=hidden_layer_sizes,activation=self.activation,alpha=self.alpha,solver=self.solver,random_state=self.random_state,)self.estimator.fit(X, y)return selfdef predict(self, X):if self.estimator is None:raise NotImplementedError()return self.estimator.predict(X)def predict_proba(self, X):if self.estimator is None:raise NotImplementedError()return self.estimator.predict_proba(X)@staticmethoddef get_properties(dataset_properties=None):return {"shortname": "MLP Classifier","name": "MLP CLassifier","handles_regression": False,"handles_classification": True,"handles_multiclass": True,"handles_multilabel": False,"handles_multioutput": False,"is_deterministic": False,# Both input and output must be tuple(iterable)"input": [DENSE, SIGNED_DATA, UNSIGNED_DATA],"output": [PREDICTIONS],}@staticmethoddef get_hyperparameter_search_space(feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None):cs = ConfigurationSpace()hidden_layer_depth = UniformIntegerHyperparameter(name="hidden_layer_depth", lower=1, upper=3, default_value=1)num_nodes_per_layer = UniformIntegerHyperparameter(name="num_nodes_per_layer", lower=16, upper=216, default_value=32)activation = CategoricalHyperparameter(name="activation",choices=["identity", "logistic", "tanh", "relu"],default_value="relu",)alpha = UniformFloatHyperparameter(name="alpha", lower=0.0001, upper=1.0, default_value=0.0001)solver = CategoricalHyperparameter(name="solver", choices=["lbfgs", "sgd", "adam"], default_value="adam")cs.add_hyperparameters([hidden_layer_depth,num_nodes_per_layer,activation,alpha,solver,])return cs# Add MLP classifier component to auto-sklearn.
autosklearn.pipeline.components.classification.add_classifier(MLPClassifier)
cs = MLPClassifier.get_hyperparameter_search_space()
print(cs)############################################################################
# Data Loading
# ============
def get_local_csv():import pandas as pdimport numpy as npdf = pd.read_csv("/data/projects/example/auto_ml/Radiomics-2D/features.csv")label = pd.read_csv("/data/projects/example/auto_ml/Radiomics-2D/labels.csv")["label"]label = np.array([1 if l == "Positive" else 0 for l in label])return df.to_numpy(), label# local
X, y = get_local_csv()# breast cancer
# X, y = load_breast_cancer(return_X_y=True)X_train, X_test, y_train, y_test = train_test_split(X, y)############################################################################
# Fit MLP classifier to the data
# ==============================clf = autosklearn.classification.AutoSklearnClassifier(time_left_for_this_task=60,per_run_time_limit=30,include={"classifier": ["gradient_boosting", "adaboost", "MLPClassifier"],'feature_preprocessor': ["no_preprocessing"]},
)
clf.fit(X_train, y_train)############################################################################
# Print test accuracy and statistics
# ==================================y_pred = clf.predict(X_test)
print("accuracy: ", sklearn.metrics.accuracy_score(y_pred, y_test))
print(clf.sprint_statistics())
print(clf.leaderboard(detailed=False,top_k=30))
pprint(clf.show_models(), indent=4)models_with_weights = clf.get_models_with_weights()
with open('./models_report.txt', 'w') as f:for model in models_with_weights:f.write(str(model) + '\n')

二、auto-pytorch

1. 1 环境依赖

额外安装brew install cmake

lightgbm 库依赖第三方库 pip install lightgbm

brew install libomp

pip install autoPyTorch

mac 允许不限制memory, M1 芯片对内容限制的操作目前还有bug

1.2 支持用法

支持大量的表格型数据，图片数据支持少，且不支持扩展

代码示例:

用法比较固定，没有更多的文档来作为参考，且无法扩展。

import numpy as npimport sklearn.model_selectionimport torchvision.datasetsfrom autoPyTorch.pipeline.image_classification import ImageClassificationPipeline# Get the training data for tabular classification
trainset = torchvision.datasets.FashionMNIST(root='../datasets/', train=True, download=True)
data = trainset.data.numpy()
data = np.expand_dims(data, axis=3)
# Create a proof of concept pipeline!
dataset_properties = dict()
pipeline = ImageClassificationPipeline(dataset_properties=dataset_properties)# Train and test split
train_indices, val_indices = sklearn.model_selection.train_test_split(list(range(data.shape[0])),random_state=1,test_size=0.25,
)# Configuration space
pipeline_cs = pipeline.get_hyperparameter_search_space()
print("Pipeline CS:\n", '_' * 40, f"\n{pipeline_cs}")
config = pipeline_cs.sample_configuration()
print("Pipeline Random Config:\n", '_' * 40, f"\n{config}")
pipeline.set_hyperparameters(config)# Fit the pipeline
print("Fitting the pipeline...")pipeline.fit(X=dict(X_train=data,is_small_preprocess=True,dataset_properties=dict(mean=np.array([np.mean(data[:, :, :, i]) for i in range(1)]),std=np.array([np.std(data[:, :, :, i]) for i in range(1)]),num_classes=10,num_features=data.shape[1] * data.shape[2],image_height=data.shape[1],image_width=data.shape[2],is_small_preprocess=True),train_indices=train_indices,val_indices=val_indices,))# Showcase some components of the pipeline
print(pipeline)