DenseTransformer: Transforms a sparse into a dense NumPy array, e.g., in a scikit-learn pipeline
A simple transformer that converts a sparse into a dense numpy array, e.g., required for scikit-learn's Pipeline when, for example, CountVectorizers are used in combination with estimators that are not compatible with sparse matrices.
from mlxtend.preprocessing import DenseTransformer
Example 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
-
X: {array-like, sparse matrix}, shape = [n_samples, n_features]Training vectors, where n_samples is the number of samples and n_features is the number of features.
-
y: array-like, shape = [n_samples] (default: None)
Returns
self
fit_transform(X, y=None)
Return a dense version of the input array.
Parameters
-
X: {array-like, sparse matrix}, shape = [n_samples, n_features]Training vectors, where n_samples is the number of samples and n_features is the number of features.
-
y: array-like, shape = [n_samples] (default: None)
Returns
X_dense: dense version of the input X array.
get_params(deep=True)
Get parameters for this estimator.
Parameters
-
deep: boolean, optionalIf True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns
-
params: mapping of string to anyParameter names mapped to their values.
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
-
X: {array-like, sparse matrix}, shape = [n_samples, n_features]Training vectors, where n_samples is the number of samples and n_features is the number of features.
-
y: array-like, shape = [n_samples] (default: None)
Returns
X_dense: dense version of the input X array.