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https://github.com/jasper-koops/easy-gscv

This library allows you to quickly train machine learning classifiers by automatically splitting the data set and using both grid search and cross validation in the training process.
https://github.com/jasper-koops/easy-gscv

classification machine-learning python3 scikit-learn

Last synced: 12 days ago
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This library allows you to quickly train machine learning classifiers by automatically splitting the data set and using both grid search and cross validation in the training process.

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## Easy Grid Search / Cross Validation



*From data to score in 4 lines of code.*



This library allows you to quickly train machine learning classifiers by

automatically splitting the dataset and using both

[grid search](https://en.wikipedia.org/wiki/Hyperparameter_optimization) and [cross validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics)) in the training process. Users can either pass define the parameters themselves or let the **GSCV** object

choose them automatically (based on the classifier).



This library is an extension of the [scikit-learn](http://scikit-learn.org/stable/index.html) project.



[View on pypi](https://pypi.org/project/easy-gscv/)



### Example:



```

from sklearn.neural_network import MLPClassifier

from sklearn import datasets

from easy_gscv.classifiers import GSCV



# Create test dataset

iris = datasets.load_iris()

X = iris.data

y = iris.target

clf = MLPClassifier()



# Create model instance

gscv_model = GSCV(clf(), X, y)



# Get score

gscv_model.score()



```



## install



*requires python 3.7+*



```

pip install easy-gscv

```



## create



```

from easy_gscv.models import GSCV

clf = LogisticRegression()

gscv_model = GSCV(

    clf(), X, y, cv=15, n_jobs=-1, params={

        'C': [10, 100],

        'penalty': ['l2']

    }

)

```



No need to create separate train / test datasets, the model does this

automatically on initialization.

If no parameters are provided the grid search is performed on a default set.

But these can be overridden.



The number of folds to be used for cross validation can be specified

by using the `cv` keyword.

To speed up the training process you can use the `n_jobs` parameter to

set the number of cpu cores to use (or set it to `-1` to use all available.)



The model accepts either sklearn classifiers or string values.

You can get a list of valid classifiers by calling the 'classifiers' property. Passing string arguments to the GSCV object in turn saves

you from having to import sklearn classifiers yourself.



```

gscv_model = GSCV('RandomForestClassifier',, X, y)

gscv_model.classifiers



'KNeighborsClassifier',

'RandomForestClassifier',

'GradientBoostingClassifier',

'MLPClassifier',

'LogisticRegression',

```



## score



```

gscv_model.score()

```



The grid search is performed on the training data. Use the `score` method to evaluate

how well the model can be generalized by scoring it against the test dataset.



## get_best_estimator



```

gscv_model.get_best_estimator()

```



Returns the best scoring sklearn classifier (based on training data).

As its a valid scikit-learn classifier, you can use it do anything that

you could do with sklearn classifier.



The following classifiers are currently supported. With the eventual goal of

supporting all scikit-learn classifiers in the future.



* KNeighborsClassifier

* RandomForestClassifier

* GradientBoostingClassifier

* MLPClassifier

* LogisticRegression

* SVM (Support Vector Machines)



## get_fit_details



As cross validation returns an average, it can be helpful to

get a more detailed overview of the best scoring classifier.



This method returns a table like the one displayed below, which

then can be used to further refine the choice or parameters for

subsequent runs.



```

clf = KNeighborsClassifier()

gscv_model = GSCV(clf(), X, y)

gscv_model.get_fit_details()



0.965 (+/-0.026) for {'weights': 'uniform', 'n_neighbors': 3}

0.977 (+/-0.013) for {'weights': 'distance', 'n_neighbors': 3}

0.979 (+/-0.011) for {'weights': 'uniform', 'n_neighbors': 5}

0.979 (+/-0.011) for {'weights': 'distance', 'n_neighbors': 5}

0.976 (+/-0.018) for {'weights': 'uniform', 'n_neighbors': 8}

0.975 (+/-0.018) for {'weights': 'distance', 'n_neighbors': 8}

0.971 (+/-0.022) for {'weights': 'uniform', 'n_neighbors': 12}

0.973 (+/-0.024) for {'weights': 'distance', 'n_neighbors': 12}

0.973 (+/-0.025) for {'weights': 'uniform', 'n_neighbors': 15}



```