Python DictionaryLearning.get_params方法代码示例

# 需要导入模块: from sklearn.decomposition import DictionaryLearning [as 别名]
# 或者: from sklearn.decomposition.DictionaryLearning import get_params [as 别名]
class SC(object):
    """
    Wrapper for sklearn package.  Performs sparse coding

    Sparse Coding, or Dictionary Learning has 5 methods:
       - fit(waveforms)
       update class instance with Sparse Coding fit

       - fit_transform()
       do what fit() does, but additionally return the projection onto new basis space

       - inverse_transform(A)
       inverses the decomposition, returns waveforms for an input A, using Z^\dagger

       - get_basis()
       returns the basis vectors Z^\dagger

       - get_params()
       returns metadata used for fits.
    """
    def __init__(self, num_components=10,
                 catalog_name='unknown',
                 alpha = 0.001,
                 transform_alpha = 0.01,
                 max_iter = 2000,
                 tol = 1e-9,
                 n_jobs = 1,
                 verbose = True,
                 random_state = None):

        self._decomposition   = 'Sparse Coding'
        self._num_components  = num_components
        self._catalog_name    = catalog_name
        self._alpha           = alpha
        self._transform_alpha = 0.001
        self._n_jobs          = n_jobs
        self._random_state    = random_state

        self._DL = DictionaryLearning(n_components=self._num_components,
                              alpha           = self._alpha,
                              transform_alpha = self._transform_alpha,
                              n_jobs          = self._n_jobs,
                              verbose         = verbose,
                              random_state    = self._random_state)

    def fit(self,waveforms):
        # TODO make sure there are more columns than rows (transpose if not)
        # normalize waveforms
        self._waveforms = waveforms
        self._DL.fit(self._waveforms)

    def fit_transform(self,waveforms):
        # TODO make sure there are more columns than rows (transpose if not)
        # normalize waveforms
        self._waveforms = waveforms
        self._A = self._DL.fit_transform(self._waveforms)
        return self._A

    def inverse_transform(self,A):
        # convert basis back to waveforms using fit
        new_waveforms = self._DL.inverse_transform(A)
        return new_waveforms

    def get_params(self):
        # TODO know what catalog was used! (include waveform metadata)
        params = self._DL.get_params()
        params['num_components'] = params.pop('n_components')
        params['Decompositon'] = self._decomposition
        return params

    def get_basis(self):
        """ Return the SPCA basis vectors (Z^\dagger)"""
        return self._DL.components_

# 需要导入模块: from sklearn.decomposition import DictionaryLearning [as 别名]
# 或者: from sklearn.decomposition.DictionaryLearning import get_params [as 别名]
class SparseCoding:

    DEFAULT_MODEL_PARAMS = {
        'n_components' : 10,
        'n_features' : 64,
        'max_iter' : 5,
        'random_state' : 1,
        'dict_init' : None,
        'code_init' : None
    }

    def __init__(self, model_filename=None):
        if model_filename is not None:
            self.load_model(model_filename)
        else:
            # default model params
            self.n_components = SparseCoding.DEFAULT_MODEL_PARAMS['n_components']
            self.n_features = SparseCoding.DEFAULT_MODEL_PARAMS['n_features']
            self.max_iter = SparseCoding.DEFAULT_MODEL_PARAMS['max_iter']
            self.random_state = SparseCoding.DEFAULT_MODEL_PARAMS['random_state']
            self.dict_init = SparseCoding.DEFAULT_MODEL_PARAMS['dict_init']
            self.code_init = SparseCoding.DEFAULT_MODEL_PARAMS['code_init']

            # initialize Dictionary Learning object with default params and weights
            self.DL_obj = DictionaryLearning(n_components=self.n_components,
                                       alpha=1,
                                       max_iter=self.max_iter,
                                       tol=1e-08,
                                       fit_algorithm='lars',
                                       transform_algorithm='omp',
                                       transform_n_nonzero_coefs=None,
                                       transform_alpha=None,
                                       n_jobs=1,
                                       code_init=self.code_init,
                                       dict_init=self.dict_init,
                                       verbose=False,
                                       split_sign=False,
                                       random_state=self.random_state)


    def save_model(self, filename):
        # save DL object to file, compress is also to prevent multiple model files.
        joblib.dump(self.DL_obj, filename, compress=3)


    def load_model(self, filename):
        # load DL Object from file
        self.DL_obj = joblib.load(filename)

        # set certain model params as class attributes. Get values from DL Obj.get_params() or use default values.
        DL_params = self.DL_obj.get_params()
        for param in SparseCoding.DEFAULT_MODEL_PARAMS:
            if param in DL_params:
                setattr(self, param, DL_params[param])
            else:
                setattr(self, param, SparseCoding.DEFAULT_MODEL_PARAMS[param])


    def learn_dictionary(self, whitened_patches):
        # assert correct dimensionality of input data
        if whitened_patches.ndim == 3:
            whitened_patches = whitened_patches.reshape((whitened_patches.shape[0], -1))
        assert whitened_patches.ndim == 2, "Whitened patches ndim is %d instead of 2" %whitened_patches.ndim

        # learn dictionary
        self.DL_obj.fit(whitened_patches)


    def get_dictionary(self):
        try:
            return self.DL_obj.components_
        except AttributeError:
            raise AttributeError("Feature extraction dictionary has not yet been learnt for this model. " \
                                 + "Train the feature extraction model at least once to prevent this error.")


    def get_sparse_features(self, whitened_patches):
        # assert correct dimensionality of input data
        if whitened_patches.ndim == 3:
            whitened_patches = whitened_patches.reshape((whitened_patches.shape[0], -1))
        assert whitened_patches.ndim == 2, "Whitened patches ndim is %d instead of 2" %whitened_patches.ndim
        try:
            sparse_code = self.DL_obj.transform(whitened_patches)
        except NotFittedError:
            raise NotFittedError("Feature extraction dictionary has not yet been learnt for this model, " \
                                 + "therefore Sparse Codes cannot be extracted. Train the feature extraction model " \
                                 + "at least once to prevent this error.")
        return sparse_code


    def get_sign_split_features(self, sparse_features):
        n_samples, n_components = sparse_features.shape
        sign_split_features = np.empty((n_samples, 2 * n_components))
        sign_split_features[:, :n_components] = np.maximum(sparse_features, 0)
        sign_split_features[:, n_components:] = -np.minimum(sparse_features, 0)
        return sign_split_features


    def get_pooled_features(self, input_feature_map, filter_size=(19,19)):
        # assuming square filters and images
#.........这里部分代码省略.........