How to use TPOT with Dask?
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How to use TPOT with Dask?

How to use TPOT with Dask?

This recipe helps you use TPOT with Dask

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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(digits.data,digits.target,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.

TP.fit(xtrain, ytrain)

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

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