SparseCoder
¶
-
class
ibex.sklearn.decomposition.
SparseCoder
(dictionary, transform_algorithm='omp', transform_n_nonzero_coefs=None, transform_alpha=None, split_sign=False, n_jobs=1)¶ Bases:
sklearn.decomposition.dict_learning.SparseCoder
,ibex._base.FrameMixin
Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Sparse coding
Finds a sparse representation of data against a fixed, precomputed dictionary.
Each row of the result is the solution to a sparse coding problem. The goal is to find a sparse array code such that:
X ~= code * dictionary
Read more in the User Guide.
- dictionary : array, [n_components, n_features]
- The dictionary atoms used for sparse coding. Lines are assumed to be normalized to unit norm.
- transform_algorithm : {‘lasso_lars’, ‘lasso_cd’, ‘lars’, ‘omp’, ‘threshold’}
- Algorithm used to transform the data:
lars: uses the least angle regression method (linear_model.lars_path)
lasso_lars: uses Lars to compute the Lasso solution
lasso_cd: uses the coordinate descent method to compute the
Lasso solution (linear_model.Lasso). lasso_lars will be faster if
the estimated components are sparse.
omp: uses orthogonal matching pursuit to estimate the sparse solution
threshold: squashes to zero all coefficients less than alpha from
the projection
dictionary * X'
- transform_n_nonzero_coefs : int,
0.1 * n_features
by default - Number of nonzero coefficients to target in each column of the solution. This is only used by algorithm=’lars’ and algorithm=’omp’ and is overridden by alpha in the omp case.
- transform_alpha : float, 1. by default
- If algorithm=’lasso_lars’ or algorithm=’lasso_cd’, alpha is the penalty applied to the L1 norm. If algorithm=’threshold’, alpha is the absolute value of the threshold below which coefficients will be squashed to zero. If algorithm=’omp’, alpha is the tolerance parameter: the value of the reconstruction error targeted. In this case, it overrides n_nonzero_coefs.
- split_sign : bool, False by default
- Whether to split the sparse feature vector into the concatenation of its negative part and its positive part. This can improve the performance of downstream classifiers.
- n_jobs : int,
- number of parallel jobs to run
- components_ : array, [n_components, n_features]
- The unchanged dictionary atoms
DictionaryLearning MiniBatchDictionaryLearning SparsePCA MiniBatchSparsePCA sparse_encode
-
fit
(X, y=None)[source]¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Do nothing and return the estimator unchanged
This method is just there to implement the usual API and hence work in pipelines.
X : Ignored.
y : Ignored.
- self : object
- Returns the object itself
- A parameter
-
fit_transform
(X, y=None, **fit_params)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- X : numpy array of shape [n_samples, n_features]
- Training set.
- y : numpy array of shape [n_samples]
- Target values.
- X_new : numpy array of shape [n_samples, n_features_new]
- Transformed array.
- A parameter
-
transform
(X)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Encode the data as a sparse combination of the dictionary atoms.
Coding method is determined by the object parameter transform_algorithm.
- X : array of shape (n_samples, n_features)
- Test data to be transformed, must have the same number of features as the data used to train the model.
- X_new : array, shape (n_samples, n_components)
- Transformed data
- A parameter
- A parameter