How to use TPOT with Dask?

How to use TPOT with Dask?

How to use TPOT with Dask?

This recipe helps you use TPOT with Dask


Recipe Objective.

How to use TPOT with Dask.

TPOT stands for tree-based pipeline optimization tool. It is an automated machine learning library. It uses a tree-based structure to create a model pipeline.

#!pip install tpot --upgrade #!pip install dask_ml #!pip install dask distributed --upgrade

Step 1- Importing Libraries.

We will import tpot, tpot classifier along with all the Libraries.

import tpot from tpot import TPOTClassifier from sklearn.datasets import load_digits from sklearn.model_selection import train_test_split import dask_ml.model_selection

Step 2- Creating Client

from dask.distributed import Client client = Client() client

Step 3- Splitting the dataset

We will load the data and then split them into training and testing data while keeping the training size as 0.8.

digits = load_digits() xtrain, xtest, ytrain, ytest = train_test_split(,,train_size=0.8,test_size=0.2)

Step 4- Initializing TPOT Classifier.

We will define Tpot Classifier with all of the hyperparameters and We will declare True the use of Dask in the hyperparameters.

TP = TPOTClassifier(generations=3,population_size=10,cv=2,n_jobs=-1,config_dict=tpot.config.classifier_config_dict_light,use_dask=True)

Step 5- Fitting the model., ytrain)

We can see the final fitted model and the defined parameters.

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