DenseTransformer：将稀疏矩阵转换为稠密的NumPy数组，例如，在scikit-learn管道中使用

一个简单的变换器，将稀疏矩阵转换为密集的numpy数组，例如，在使用CountVectorizers与不兼容稀疏矩阵的估计器组合时，scikit-learn的Pipeline所需的。

> from mlxtend.preprocessing import DenseTransformer

示例 1

from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import CountVectorizer
from mlxtend.preprocessing import DenseTransformer
import re
import numpy as np

X_train = np.array(['abc def ghi', 'this is a test',
                    'this is a test', 'this is a test'])
y_train = np.array([0, 0, 1, 1])

pipe_1 = Pipeline([
    ('vect', CountVectorizer()),
    ('to_dense', DenseTransformer()),
    ('clf', RandomForestClassifier())
])

parameters_1 = dict(
    clf__n_estimators=[50, 100, 200],
    clf__max_features=['sqrt', 'log2', None],)

grid_search_1 = GridSearchCV(pipe_1, 
                             parameters_1, 
                             n_jobs=1, 
                             verbose=1,
                             scoring='accuracy',
                             cv=2)


print("Performing grid search...")
print("pipeline:", [name for name, _ in pipe_1.steps])
print("parameters:")
grid_search_1.fit(X_train, y_train)
print("Best score: %0.3f" % grid_search_1.best_score_)
print("Best parameters set:")
best_parameters_1 = grid_search_1.best_estimator_.get_params()
for param_name in sorted(parameters_1.keys()):
    print("\t%s: %r" % (param_name, best_parameters_1[param_name]))


Performing grid search...
pipeline: ['vect', 'to_dense', 'clf']
parameters:
Fitting 2 folds for each of 9 candidates, totalling 18 fits
Best score: 0.500
Best parameters set:
    clf__max_features: 'sqrt'
    clf__n_estimators: 50


[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    3.9s finished

API

DenseTransformer(return_copy=True)

Convert a sparse array into a dense array.

For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/preprocessing/DenseTransformer/

Methods

fit(X, y=None)

Mock method. Does nothing.

Parameters

Returns

self

fit_transform(X, y=None)

Return a dense version of the input array.

Parameters

Returns

get_params(deep=True)

Get parameters for this estimator.

Parameters

Returns

set_params(params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it's possible to update each component of a nested object.

Returns

self

transform(X, y=None)

Return a dense version of the input array.

Parameters

Returns