class IncrementalPCA(PCA):
"""
Based on sklearn.decomposition.IncrementalPCA from scikit-learn 0.23.1
Incremental principal components analysis (IPCA).
Linear dimensionality reduction using Singular Value Decomposition of
the data, keeping only the most significant singular vectors to
project the data to a lower dimensional space. The input data is
centered but not scaled for each feature before applying the SVD.
Depending on the size of the input data, this algorithm can be much
more memory efficient than a PCA, and allows sparse input.
This algorithm has constant memory complexity, on the order of
``batch_size * n_features``, enabling use of np.memmap files without
loading the entire file into memory. For sparse matrices, the input
is converted to dense in batches (in order to be able to subtract the
mean) which avoids storing the entire dense matrix at any one time.
The computational overhead of each SVD is
``O(batch_size * n_features ** 2)``, but only 2 * batch_size samples
remain in memory at a time. There will be ``n_samples / batch_size``
SVD computations to get the principal components, versus 1 large SVD
of complexity ``O(n_samples * n_features ** 2)`` for PCA.
Parameters
----------
handle : cuml.Handle
Specifies the cuml.handle that holds internal CUDA state for
computations in this model. Most importantly, this specifies the CUDA
stream that will be used for the model's computations, so users can
run different models concurrently in different streams by creating
handles in several streams.
If it is None, a new one is created.
n_components : int or None, (default=None)
Number of components to keep. If ``n_components`` is ``None``,
then ``n_components`` is set to ``min(n_samples, n_features)``.
whiten : bool, optional
If True, de-correlates the components. This is done by dividing them by
the corresponding singular values then multiplying by sqrt(n_samples).
Whitening allows each component to have unit variance and removes
multi-collinearity. It might be beneficial for downstream
tasks like LinearRegression where correlated features cause problems.
copy : bool, (default=True)
If False, X will be overwritten. ``copy=False`` can be used to
save memory but is unsafe for general use.
batch_size : int or None, (default=None)
The number of samples to use for each batch. Only used when calling
``fit``. If ``batch_size`` is ``None``, then ``batch_size``
is inferred from the data and set to ``5 * n_features``, to provide a
balance between approximation accuracy and memory consumption.
verbose : int or boolean, default=False
Sets logging level. It must be one of `cuml.common.logger.level_*`.
See :ref:`verbosity-levels` for more info.
output_type : {'input', 'cudf', 'cupy', 'numpy', 'numba'}, default=None
Variable to control output type of the results and attributes of
the estimator. If None, it'll inherit the output type set at the
module level, `cuml.global_output_type`.
See :ref:`output-data-type-configuration` for more info.
Attributes
----------
components_ : array, shape (n_components, n_features)
Components with maximum variance.
explained_variance_ : array, shape (n_components,)
Variance explained by each of the selected components.
explained_variance_ratio_ : array, shape (n_components,)
Percentage of variance explained by each of the selected components.
If all components are stored, the sum of explained variances is equal
to 1.0.
singular_values_ : array, shape (n_components,)
The singular values corresponding to each of the selected components.
The singular values are equal to the 2-norms of the ``n_components``
variables in the lower-dimensional space.
mean_ : array, shape (n_features,)
Per-feature empirical mean, aggregate over calls to ``partial_fit``.
var_ : array, shape (n_features,)
Per-feature empirical variance, aggregate over calls to
``partial_fit``.
noise_variance_ : float
The estimated noise covariance following the Probabilistic PCA model
from [4]_.
n_components_ : int
The estimated number of components. Relevant when
``n_components=None``.
n_samples_seen_ : int
The number of samples processed by the estimator. Will be reset on
new calls to fit, but increments across ``partial_fit`` calls.
batch_size_ : int
Inferred batch size from ``batch_size``.
Notes
-----
Implements the incremental PCA model from [1]_. This model is an extension
of the Sequential Karhunen-Loeve Transform from [2]_. We have specifically
abstained from an optimization used by authors of both papers, a QR
decomposition used in specific situations to reduce the algorithmic
complexity of the SVD. The source for this technique is [3]_. This
technique has been omitted because it is advantageous only when decomposing
a matrix with ``n_samples >= 5/3 * n_features`` where ``n_samples`` and
``n_features`` are the matrix rows and columns, respectively. In addition,
it hurts the readability of the implemented algorithm. This would be a good
opportunity for future optimization, if it is deemed necessary.
References
----------
.. [1] `D. Ross, J. Lim, R. Lin, M. Yang. Incremental Learning for Robust
Visual Tracking, International Journal of Computer Vision, Volume 77,
Issue 1-3, pp. 125-141, May 2008.
<https://www.cs.toronto.edu/~dross/ivt/RossLimLinYang_ijcv.pdf>`_
.. [2] `A. Levy and M. Lindenbaum, Sequential Karhunen-Loeve Basis
Extraction and its Application to Images, IEEE Transactions on Image
Processing, Volume 9, Number 8, pp. 1371-1374, August 2000.
<https://www.cs.technion.ac.il/~mic/doc/skl-ip.pdf>`_
.. [3] G. Golub and C. Van Loan. Matrix Computations, Third Edition,
Chapter 5, Section 5.4.4, pp. 252-253.
.. [4] `C. Bishop, 1999. "Pattern Recognition and Machine Learning",
Section 12.2.1, pp. 574
<http://www.miketipping.com/papers/met-mppca.pdf>`_
Examples
---------
.. code-block:: python
>>> from cuml.experimental.decomposition import IncrementalPCA
>>> import cupy as cp
>>> import cupyx
>>>
>>> X = cupyx.scipy.sparse.random(1000, 4, format='csr', density=0.07)
>>> ipca = IncrementalPCA(n_components=2, batch_size=200)
>>> ipca.fit(X)
>>>
>>> # Components:
>>> ipca.components_
array([[-0.02362926, 0.87328851, -0.15971988, 0.45967206],
[-0.14643883, 0.11414225, 0.97589354, 0.11471273]])
>>>
>>> # Singular Values:
>>> ipca.singular_values_
array([4.90298662, 4.54498226])
>>>
>>> # Explained Variance:
>>> ipca.explained_variance_
array([0.02406334, 0.02067754])
>>>
>>> # Explained Variance Ratio:
>>> ipca.explained_variance_ratio_
array([0.28018011, 0.24075775])
>>>
>>> # Mean:
>>> ipca.mean_
array([0.03249896, 0.03629852, 0.03268694, 0.03216601])
>>>
>>> # Noise Variance:
>>> ipca.noise_variance_.item()
0.003474966583315544
"""
def __init__(self,
handle=None,
n_components=None,
*,
whiten=False,
copy=True,
batch_size=None,
verbose=False,
output_type=None):
super(IncrementalPCA, self).__init__(handle=handle,
n_components=n_components,
whiten=whiten,
copy=copy,
verbose=verbose,
output_type=output_type)
self.batch_size = batch_size
self._hyperparams = ["n_components", "whiten", "copy", "batch_size"]
self._cupy_attributes = True
self._sparse_model = True
@with_cupy_rmm
def fit(self, X, y=None):
"""
Fit the model with X, using minibatches of size batch_size.
Parameters
----------
X : array-like or sparse matrix, shape (n_samples, n_features)
Training data, where n_samples is the number of samples and
n_features is the number of features.
y : Ignored
Returns
-------
self : object
Returns the instance itself.
"""
self._set_base_attributes(output_type=X)
self.n_samples_seen_ = 0
self._mean_ = .0
self.var_ = .0
if scipy.sparse.issparse(X) or cupyx.scipy.sparse.issparse(X):
X = _validate_sparse_input(X)
else:
X, n_samples, n_features, self.dtype = \
input_to_cuml_array(X, order='K',
check_dtype=[cp.float32, cp.float64])
# NOTE: While we cast the input to a cupy array here, we still
# respect the `output_type` parameter in the constructor. This
# is done by PCA, which IncrementalPCA inherits from. PCA's
# transform and inverse transform convert the output to the
# required type.
X = X.to_output(output_type='cupy')
n_samples, n_features = X.shape
if self.batch_size is None:
self.batch_size_ = 5 * n_features
else:
self.batch_size_ = self.batch_size
for batch in _gen_batches(n_samples,
self.batch_size_,
min_batch_size=self.n_components or 0):
X_batch = X[batch]
if cupyx.scipy.sparse.issparse(X_batch):
X_batch = X_batch.toarray()
self.partial_fit(X_batch, check_input=False)
return self
@with_cupy_rmm
def partial_fit(self, X, y=None, check_input=True):
"""
Incremental fit with X. All of X is processed as a single batch.
Parameters
----------
X : array-like or sparse matrix, shape (n_samples, n_features)
Training data, where n_samples is the number of samples and
n_features is the number of features.
check_input : bool
Run check_array on X.
y : Ignored
Returns
-------
self : object
Returns the instance itself.
"""
if check_input:
if scipy.sparse.issparse(X) or cupyx.scipy.sparse.issparse(X):
raise TypeError(
"IncrementalPCA.partial_fit does not support "
"sparse input. Either convert data to dense "
"or use IncrementalPCA.fit to do so in batches.")
self._set_output_type(X)
X, n_samples, n_features, self.dtype = \
input_to_cuml_array(X, order='K',
check_dtype=[cp.float32, cp.float64])
X = X.to_output(output_type='cupy')
else:
n_samples, n_features = X.shape
if not hasattr(self, '_components_'):
self._components_ = None
if self.n_components is None:
if self._components_ is None:
self.n_components_ = min(n_samples, n_features)
else:
self.n_components_ = self._components_.shape[0]
elif not 1 <= self.n_components <= n_features:
raise ValueError("n_components=%r invalid for n_features=%d, need "
"more rows than columns for IncrementalPCA "
"processing" % (self.n_components, n_features))
elif not self.n_components <= n_samples:
raise ValueError("n_components=%r must be less or equal to "
"the batch number of samples "
"%d." % (self.n_components, n_samples))
else:
self.n_components_ = self.n_components
if (self._components_ is not None) and (self._components_.shape[0] !=
self.n_components_):
raise ValueError("Number of input features has changed from %i "
"to %i between calls to partial_fit! Try "
"setting n_components to a fixed value." %
(self._components_.shape[0], self.n_components_))
if not self._cupy_attributes:
self._cumlarray_to_cupy_attrs()
self._cupy_attributes = True
# This is the first partial_fit
if not hasattr(self, 'n_samples_seen_'):
self.n_samples_seen_ = 0
self._mean_ = .0
self.var_ = .0
# Update stats - they are 0 if this is the first step
col_mean, col_var, n_total_samples = \
_incremental_mean_and_var(
X, last_mean=self._mean_, last_variance=self.var_,
last_sample_count=cp.repeat(cp.asarray([self.n_samples_seen_]),
X.shape[1]))
n_total_samples = n_total_samples[0]
# Whitening
if self.n_samples_seen_ == 0:
# If it is the first step, simply whiten X
X = X - col_mean
else:
col_batch_mean = cp.mean(X, axis=0)
X = X - col_batch_mean
# Build matrix of combined previous basis and new data
mean_correction = \
cp.sqrt((self.n_samples_seen_ * n_samples) /
n_total_samples) * (self._mean_ - col_batch_mean)
X = cp.vstack((self._singular_values_.reshape(
(-1, 1)) * self._components_, X, mean_correction))
U, S, V = cp.linalg.svd(X, full_matrices=False)
U, V = _svd_flip(U, V, u_based_decision=False)
explained_variance = S**2 / (n_total_samples - 1)
explained_variance_ratio = S**2 / cp.sum(col_var * n_total_samples)
self.n_samples_seen_ = n_total_samples
self._components_ = V[:self.n_components_]
self._singular_values_ = S[:self.n_components_]
self._mean_ = col_mean
self.var_ = col_var
self._explained_variance_ = explained_variance[:self.n_components_]
self._explained_variance_ratio_ = \
explained_variance_ratio[:self.n_components_]
if self.n_components_ < n_features:
self._noise_variance_ = \
explained_variance[self.n_components_:].mean()
else:
self._noise_variance_ = 0.
if self._cupy_attributes:
self._cupy_to_cumlarray_attrs()
self._cupy_attributes = False
return self
@with_cupy_rmm
def transform(self, X, convert_dtype=False):
"""
Apply dimensionality reduction to X.
X is projected on the first principal components previously extracted
from a training set, using minibatches of size batch_size if X is
sparse.
Parameters
----------
X : array-like or sparse matrix, shape (n_samples, n_features)
New data, where n_samples is the number of samples
and n_features is the number of features.
convert_dtype : bool, optional (default = False)
When set to True, the transform method will automatically
convert the input to the data type which was used to train the
model. This will increase memory used for the method.
Returns
-------
X_new : array-like, shape (n_samples, n_components)
"""
if scipy.sparse.issparse(X) or cupyx.scipy.sparse.issparse(X):
out_type = self._get_output_type(X)
X = _validate_sparse_input(X)
n_samples = X.shape[0]
output = []
for batch in _gen_batches(n_samples,
self.batch_size_,
min_batch_size=self.n_components or 0):
output.append(super().transform(X[batch]))
output, _, _, _ = \
input_to_cuml_array(cp.vstack(output), order='K')
return output.to_output(out_type)
else:
return super().transform(X)
def get_param_names(self):
# Skip super() since we dont pass any extra parameters in __init__
return Base.get_param_names(self) + self._hyperparams
def _cupy_to_cumlarray_attrs(self):
self._components_ = CumlArray(self._components_.copy())
self._mean_ = CumlArray(self._mean_)
self._noise_variance_ = CumlArray(self._noise_variance_)
self._singular_values_ = CumlArray(self._singular_values_)
self._explained_variance_ = CumlArray(self._explained_variance_.copy())
self._explained_variance_ratio_ = \
CumlArray(self._explained_variance_ratio_)
def _cumlarray_to_cupy_attrs(self):
self._components_ = self._components_.to_output("cupy")
self._mean_ = self._mean_.to_output("cupy")
self._noise_variance_ = self._noise_variance_.to_output("cupy")
self._singular_values_ = self._singular_values_.to_output("cupy")
self._explained_variance_ = self._explained_variance_.to_output("cupy")
self._explained_variance_ratio_ = \
self._explained_variance_ratio_.to_output("cupy")
class IncrementalPCA(PCA):
"""
Based on sklearn.decomposition.IncrementalPCA from scikit-learn 0.23.1
Incremental principal components analysis (IPCA).
Linear dimensionality reduction using Singular Value Decomposition of
the data, keeping only the most significant singular vectors to
project the data to a lower dimensional space. The input data is
centered but not scaled for each feature before applying the SVD.
Depending on the size of the input data, this algorithm can be much
more memory efficient than a PCA, and allows sparse input.
This algorithm has constant memory complexity, on the order of
``batch_size * n_features``, enabling use of np.memmap files without
loading the entire file into memory. For sparse matrices, the input
is converted to dense in batches (in order to be able to subtract the
mean) which avoids storing the entire dense matrix at any one time.
The computational overhead of each SVD is
``O(batch_size * n_features ** 2)``, but only 2 * batch_size samples
remain in memory at a time. There will be ``n_samples / batch_size``
SVD computations to get the principal components, versus 1 large SVD
of complexity ``O(n_samples * n_features ** 2)`` for PCA.
Examples
---------
.. code-block:: python
from cuml.decomposition import IncrementalPCA
import cupy as cp
import cupyx
X = cupyx.scipy.sparse.random(1000, 5, format='csr', density=0.07)
ipca = IncrementalPCA(n_components=2, batch_size=200)
ipca.fit(X)
print("Components: \n", ipca.components_)
print("Singular Values: ", ipca.singular_values_)
print("Explained Variance: ", ipca.explained_variance_)
print("Explained Variance Ratio: ",
ipca.explained_variance_ratio_)
print("Mean: ", ipca.mean_)
print("Noise Variance: ", ipca.noise_variance_)
Output:
.. code-block:: python
Components:
[[ 0.40465797 0.70924681 -0.46980153 -0.32028596 -0.09962083]
[ 0.3072285 -0.31337166 -0.21010504 -0.25727659 0.83490926]]
Singular Values: [4.67710479 4.0249979 ]
Explained Variance: [0.02189721 0.01621682]
Explained Variance Ratio: [0.2084041 0.15434174]
Mean: [0.03341744 0.03796517 0.03316038 0.03825872 0.0253353 ]
Noise Variance: 0.0049539530909571425
Parameters
----------
handle : cuml.Handle
If it is None, a new one is created just for this class
n_components : int or None, (default=None)
Number of components to keep. If ``n_components `` is ``None``,
then ``n_components`` is set to ``min(n_samples, n_features)``.
whiten : bool, optional
If True, de-correlates the components. This is done by dividing them by
the corresponding singular values then multiplying by sqrt(n_samples).
Whitening allows each component to have unit variance and removes
multi-collinearity. It might be beneficial for downstream
tasks like LinearRegression where correlated features cause problems.
copy : bool, (default=True)
If False, X will be overwritten. ``copy=False`` can be used to
save memory but is unsafe for general use.
batch_size : int or None, (default=None)
The number of samples to use for each batch. Only used when calling
``fit``. If ``batch_size`` is ``None``, then ``batch_size``
is inferred from the data and set to ``5 * n_features``, to provide a
balance between approximation accuracy and memory consumption.
verbose : int or boolean (default = False)
Logging level
Attributes
----------
components_ : array, shape (n_components, n_features)
Components with maximum variance.
explained_variance_ : array, shape (n_components,)
Variance explained by each of the selected components.
explained_variance_ratio_ : array, shape (n_components,)
Percentage of variance explained by each of the selected components.
If all components are stored, the sum of explained variances is equal
to 1.0.
singular_values_ : array, shape (n_components,)
The singular values corresponding to each of the selected components.
The singular values are equal to the 2-norms of the ``n_components``
variables in the lower-dimensional space.
mean_ : array, shape (n_features,)
Per-feature empirical mean, aggregate over calls to ``partial_fit``.
var_ : array, shape (n_features,)
Per-feature empirical variance, aggregate over calls to
``partial_fit``.
noise_variance_ : float
The estimated noise covariance following the Probabilistic PCA model
from Tipping and Bishop 1999. See "Pattern Recognition and
Machine Learning" by C. Bishop, 12.2.1 p. 574 or
http://www.miketipping.com/papers/met-mppca.pdf.
n_components_ : int
The estimated number of components. Relevant when
``n_components=None``.
n_samples_seen_ : int
The number of samples processed by the estimator. Will be reset on
new calls to fit, but increments across ``partial_fit`` calls.
batch_size_ : int
Inferred batch size from ``batch_size``.
Notes
-----
Implements the incremental PCA model from:
*D. Ross, J. Lim, R. Lin, M. Yang, Incremental Learning for Robust Visual
Tracking, International Journal of Computer Vision, Volume 77, Issue 1-3,
pp. 125-141, May 2008.*
See https://www.cs.toronto.edu/~dross/ivt/RossLimLinYang_ijcv.pdf
This model is an extension of the Sequential Karhunen-Loeve Transform from:
*A. Levy and M. Lindenbaum, Sequential Karhunen-Loeve Basis Extraction and
its Application to Images, IEEE Transactions on Image Processing, Volume 9,
Number 8, pp. 1371-1374, August 2000.*
See https://www.cs.technion.ac.il/~mic/doc/skl-ip.pdf
We have specifically abstained from an optimization used by authors of both
papers, a QR decomposition used in specific situations to reduce the
algorithmic complexity of the SVD. The source for this technique is
*Matrix Computations, Third Edition, G. Holub and C. Van Loan, Chapter 5,
section 5.4.4, pp 252-253.*. This technique has been omitted because it is
advantageous only when decomposing a matrix with ``n_samples`` (rows)
>= 5/3 * ``n_features`` (columns), and hurts the readability of the
implemented algorithm. This would be a good opportunity for future
optimization, if it is deemed necessary.
References
----------
D. Ross, J. Lim, R. Lin, M. Yang. Incremental Learning for Robust Visual
Tracking, International Journal of Computer Vision, Volume 77,
Issue 1-3, pp. 125-141, May 2008.
G. Golub and C. Van Loan. Matrix Computations, Third Edition, Chapter 5,
Section 5.4.4, pp. 252-253.
"""
def __init__(self,
handle=None,
n_components=None,
*,
whiten=False,
copy=True,
batch_size=None,
verbose=None,
output_type=None):
super(IncrementalPCA, self).__init__(handle=handle,
n_components=n_components,
whiten=whiten,
copy=copy,
verbose=verbose,
output_type=output_type)
self.batch_size = batch_size
self._hyperparams = ["n_components", "whiten", "copy", "batch_size"]
self._cupy_attributes = True
self._sparse_model = True
@with_cupy_rmm
def fit(self, X, y=None):
"""Fit the model with X, using minibatches of size batch_size.
Parameters
----------
X : array-like or sparse matrix, shape (n_samples, n_features)
Training data, where n_samples is the number of samples and
n_features is the number of features.
y : Ignored
Returns
-------
self : object
Returns the instance itself.
"""
self._set_base_attributes(output_type=X)
self.n_samples_seen_ = 0
self._mean_ = .0
self.var_ = .0
if scipy.sparse.issparse(X) or cupyx.scipy.sparse.issparse(X):
X = _validate_sparse_input(X)
else:
X, n_samples, n_features, self.dtype = \
input_to_cuml_array(X, order='K',
check_dtype=[cp.float32, cp.float64])
# NOTE: While we cast the input to a cupy array here, we still
# respect the `output_type` parameter in the constructor. This
# is done by PCA, which IncrementalPCA inherits from. PCA's
# transform and inverse transform convert the output to the
# required type.
X = X.to_output(output_type='cupy')
n_samples, n_features = X.shape
if self.batch_size is None:
self.batch_size_ = 5 * n_features
else:
self.batch_size_ = self.batch_size
for batch in _gen_batches(n_samples,
self.batch_size_,
min_batch_size=self.n_components or 0):
X_batch = X[batch]
if cupyx.scipy.sparse.issparse(X_batch):
X_batch = X_batch.toarray()
self.partial_fit(X_batch, check_input=False)
return self
@with_cupy_rmm
def partial_fit(self, X, y=None, check_input=True):
"""Incremental fit with X. All of X is processed as a single batch.
Parameters
----------
X : array-like or sparse matrix, shape (n_samples, n_features)
Training data, where n_samples is the number of samples and
n_features is the number of features.
check_input : bool
Run check_array on X.
y : Ignored
Returns
-------
self : object
Returns the instance itself.
"""
if check_input:
if scipy.sparse.issparse(X) or cupyx.scipy.sparse.issparse(X):
raise TypeError(
"IncrementalPCA.partial_fit does not support "
"sparse input. Either convert data to dense "
"or use IncrementalPCA.fit to do so in batches.")
self._set_output_type(X)
X, n_samples, n_features, self.dtype = \
input_to_cuml_array(X, order='K',
check_dtype=[cp.float32, cp.float64])
X = X.to_output(output_type='cupy')
else:
n_samples, n_features = X.shape
if not hasattr(self, '_components_'):
self._components_ = None
if self.n_components is None:
if self._components_ is None:
self.n_components_ = min(n_samples, n_features)
else:
self.n_components_ = self._components_.shape[0]
elif not 1 <= self.n_components <= n_features:
raise ValueError("n_components=%r invalid for n_features=%d, need "
"more rows than columns for IncrementalPCA "
"processing" % (self.n_components, n_features))
elif not self.n_components <= n_samples:
raise ValueError("n_components=%r must be less or equal to "
"the batch number of samples "
"%d." % (self.n_components, n_samples))
else:
self.n_components_ = self.n_components
if (self._components_ is not None) and (self._components_.shape[0] !=
self.n_components_):
raise ValueError("Number of input features has changed from %i "
"to %i between calls to partial_fit! Try "
"setting n_components to a fixed value." %
(self._components_.shape[0], self.n_components_))
if not self._cupy_attributes:
self._cumlarray_to_cupy_attrs()
self._cupy_attributes = True
# This is the first partial_fit
if not hasattr(self, 'n_samples_seen_'):
self.n_samples_seen_ = 0
self._mean_ = .0
self.var_ = .0
# Update stats - they are 0 if this is the first step
col_mean, col_var, n_total_samples = \
_incremental_mean_and_var(
X, last_mean=self._mean_, last_variance=self.var_,
last_sample_count=cp.repeat(cp.asarray([self.n_samples_seen_]),
X.shape[1]))
n_total_samples = n_total_samples[0]
# Whitening
if self.n_samples_seen_ == 0:
# If it is the first step, simply whiten X
X = X - col_mean
else:
col_batch_mean = cp.mean(X, axis=0)
X = X - col_batch_mean
# Build matrix of combined previous basis and new data
mean_correction = \
cp.sqrt((self.n_samples_seen_ * n_samples) /
n_total_samples) * (self._mean_ - col_batch_mean)
X = cp.vstack((self._singular_values_.reshape(
(-1, 1)) * self._components_, X, mean_correction))
U, S, V = cp.linalg.svd(X, full_matrices=False)
U, V = _svd_flip(U, V, u_based_decision=False)
explained_variance = S**2 / (n_total_samples - 1)
explained_variance_ratio = S**2 / cp.sum(col_var * n_total_samples)
self.n_samples_seen_ = n_total_samples
self._components_ = V[:self.n_components_]
self._singular_values_ = S[:self.n_components_]
self._mean_ = col_mean
self.var_ = col_var
self._explained_variance_ = explained_variance[:self.n_components_]
self._explained_variance_ratio_ = \
explained_variance_ratio[:self.n_components_]
if self.n_components_ < n_features:
self._noise_variance_ = \
explained_variance[self.n_components_:].mean()
else:
self._noise_variance_ = 0.
if self._cupy_attributes:
self._cupy_to_cumlarray_attrs()
self._cupy_attributes = False
return self
@with_cupy_rmm
def transform(self, X, convert_dtype=False):
"""Apply dimensionality reduction to X.
X is projected on the first principal components previously extracted
from a training set, using minibatches of size batch_size if X is
sparse.
Parameters
----------
X : array-like or sparse matrix, shape (n_samples, n_features)
New data, where n_samples is the number of samples
and n_features is the number of features.
convert_dtype : bool, optional (default = False)
When set to True, the transform method will automatically
convert the input to the data type which was used to train the
model. This will increase memory used for the method.
Returns
-------
X_new : array-like, shape (n_samples, n_components)
"""
if scipy.sparse.issparse(X) or cupyx.scipy.sparse.issparse(X):
out_type = self._get_output_type(X)
X = _validate_sparse_input(X)
n_samples = X.shape[0]
output = []
for batch in _gen_batches(n_samples,
self.batch_size_,
min_batch_size=self.n_components or 0):
output.append(super().transform(X[batch]))
output, _, _, _ = \
input_to_cuml_array(cp.vstack(output), order='K')
return output.to_output(out_type)
else:
return super().transform(X)
def get_param_names(self):
return self._hyperparams
def _cupy_to_cumlarray_attrs(self):
self._components_ = CumlArray(self._components_.copy())
self._mean_ = CumlArray(self._mean_)
self._noise_variance_ = CumlArray(self._noise_variance_)
self._singular_values_ = CumlArray(self._singular_values_)
self._explained_variance_ = CumlArray(self._explained_variance_.copy())
self._explained_variance_ratio_ = \
CumlArray(self._explained_variance_ratio_)
def _cumlarray_to_cupy_attrs(self):
self._components_ = self._components_.to_output("cupy")
self._mean_ = self._mean_.to_output("cupy")
self._noise_variance_ = self._noise_variance_.to_output("cupy")
self._singular_values_ = self._singular_values_.to_output("cupy")
self._explained_variance_ = self._explained_variance_.to_output("cupy")
self._explained_variance_ratio_ = \
self._explained_variance_ratio_.to_output("cupy")
