def test_get_subslice():
a = slice(10, 100)
b = slice(20, 30)
assert_array_equal(get_sub_slice(a, b), np.arange(30, 40))
assert_array_equal(get_sub_slice(None, b), np.arange(20, 30))
a = np.arange(10, 100)
assert_array_equal(get_sub_slice(a, b), np.arange(30, 40))
def partial_fit(self, X, sample_indices=None):
"""
Update the factorization using rows from X
Parameters
----------
X: ndarray, shape (n_samples, n_features)
Input data
sample_indices:
Indices for each row of X. If None, consider that row i index is i
(useful when providing the whole data to the function)
Returns
-------
self
"""
X = check_array(X, dtype=[np.float32, np.float64], order='C')
n_samples, n_features = X.shape
batches = gen_batches(n_samples, self.batch_size)
for batch in batches:
this_X = X[batch]
these_sample_indices = get_sub_slice(sample_indices, batch)
self._single_batch_fit(this_X, these_sample_indices)
return self
def transform(self, X):
"""
Compute the codes associated to input matrix X, decomposing it onto
the dictionary
Parameters
----------
X: ndarray, shape = (n_samples, n_features)
Returns
-------
code: ndarray, shape = (n_samples, n_components)
"""
check_is_fitted(self, 'components_')
dtype = self.components_.dtype
X = check_array(X, order='C', dtype=dtype.type)
if X.flags['WRITEABLE'] is False:
X = X.copy()
n_samples, n_features = X.shape
if not hasattr(self, 'G_agg') or self.G_agg != 'full':
G = self.components_.dot(self.components_.T)
else:
G = self.G_
Dx = X.dot(self.components_.T)
code = np.ones((n_samples, self.n_components), dtype=dtype)
sample_indices = np.arange(n_samples)
size_job = ceil(n_samples / self.n_threads)
batches = list(gen_batches(n_samples, size_job))
par_func = lambda batch: _enet_regression_single_gram(
G, Dx[batch], X[batch], code, get_sub_slice(
sample_indices, batch), self.code_l1_ratio, self.code_alpha,
self.code_pos, self.tol, self.max_iter)
if self.n_threads > 1:
res = self._pool.map(par_func, batches)
_ = list(res)
else:
_enet_regression_single_gram(G, Dx, X, code, sample_indices,
self.code_l1_ratio, self.code_alpha,
self.code_pos, self.tol,
self.max_iter)
return code
def _compute_code(self, X, sample_indices, w_sample, subset):
"""Update regression statistics if
necessary and compute code from X[:, subset]"""
batch_size, n_features = X.shape
reduction = self.reduction
if self.n_threads > 1:
size_job = ceil(batch_size / self.n_threads)
batches = list(gen_batches(batch_size, size_job))
if self.Dx_agg != 'full' or self.G_agg != 'full':
components_subset = self.components_[:, subset]
if self.Dx_agg == 'full':
Dx = X.dot(self.components_.T)
else:
X_subset = X[:, subset]
Dx = X_subset.dot(components_subset.T) * reduction
if self.Dx_agg == 'average':
self.Dx_average_[sample_indices] \
*= 1 - w_sample[:, np.newaxis]
self.Dx_average_[sample_indices] \
+= Dx * w_sample[:, np.newaxis]
Dx = self.Dx_average_[sample_indices]
if self.G_agg != 'full':
G = components_subset.dot(components_subset.T) * reduction
if self.G_agg == 'average':
G_average = np.array(self.G_average_[sample_indices],
copy=True)
if self.n_threads > 1:
par_func = lambda batch: _update_G_average(
G_average[batch],
G,
w_sample[batch],
)
res = self._pool.map(par_func, batches)
_ = list(res)
else:
_update_G_average(G_average, G, w_sample)
self.G_average_[sample_indices] = G_average
else:
G = self.G_
if self.n_threads > 1:
if self.G_agg == 'average':
par_func = lambda batch: _enet_regression_multi_gram(
G_average[batch], Dx[batch], X[batch], self.code_,
get_sub_slice(sample_indices, batch), self.code_l1_ratio,
self.code_alpha, self.code_pos, self.tol, self.max_iter)
else:
par_func = lambda batch: _enet_regression_single_gram(
G, Dx[batch], X[batch], self.code_,
get_sub_slice(sample_indices, batch), self.code_l1_ratio,
self.code_alpha, self.code_pos, self.tol, self.max_iter)
res = self._pool.map(par_func, batches)
_ = list(res)
else:
if self.G_agg == 'average':
_enet_regression_multi_gram(G_average, Dx, X, self.code_,
sample_indices, self.code_l1_ratio,
self.code_alpha, self.code_pos,
self.tol, self.max_iter)
else:
_enet_regression_single_gram(G, Dx, X, self.code_,
sample_indices,
self.code_l1_ratio,
self.code_alpha, self.code_pos,
self.tol, self.max_iter)