def test_output_dtype(output_type, dtype, out_dtype, shape):
inp = create_input('numpy', dtype, shape, order="F")
ary = CumlArray(inp)
if dtype in unsupported_cudf_dtypes and \
output_type in ['series', 'dataframe', 'cudf']:
with pytest.raises(ValueError):
res = ary.to_output(
output_type=output_type,
output_dtype=out_dtype
)
elif shape in [(10, 5), (1, 10)] and output_type == 'series':
with pytest.raises(ValueError):
res = ary.to_output(
output_type=output_type,
output_dtype=out_dtype
)
else:
res = ary.to_output(output_type=output_type, output_dtype=out_dtype)
if isinstance(res, cudf.DataFrame):
res.values.dtype == out_dtype
else:
res.dtype == out_dtype
def test_array_init_bad(input_type, dtype, shape, order):
"""
This test ensures that we assert on incorrect combinations of arguments
when creating CumlArray
"""
if input_type == 'series':
if dtype == np.float16:
pytest.skip("Skipping due to cuDF issue #9065")
inp = create_input(input_type, dtype, shape, 'C')
else:
inp = create_input(input_type, dtype, shape, order)
# Ensure the array is creatable
cuml_ary = CumlArray(inp)
with pytest.raises(AssertionError):
CumlArray(inp, dtype=cuml_ary.dtype)
with pytest.raises(AssertionError):
CumlArray(inp, shape=cuml_ary.shape)
with pytest.raises(AssertionError):
CumlArray(inp,
order=_strides_to_order(cuml_ary.strides, cuml_ary.dtype))
assert cp.all(cp.asarray(inp) == cp.asarray(cuml_ary))
def test_get_set_item(slice, order):
if order == 'F' and slice != 'both':
pytest.skip("See issue https://github.com/rapidsai/cuml/issues/2412")
inp = create_input('numpy', 'float32', (10, 10), order)
ary = CumlArray(data=inp)
if isinstance(slice, int):
assert np.array_equal(inp[slice], ary[slice].to_output('numpy'))
inp[slice] = 1.0
ary[slice] = 1.0
elif slice == 'left':
assert np.array_equal(inp[5:], ary[5:].to_output('numpy'))
inp[5:] = 1.0
ary[5:] = 1.0
elif slice == 'right':
assert np.array_equal(inp[:5], ary[:5].to_output('numpy'))
inp[:5] = 1.0
ary[:5] = 1.0
elif slice == 'both':
assert np.array_equal(inp[:], ary[:].to_output('numpy'))
inp[:] = 1.0
ary[:] = 1.0
else:
pytest.skip("not implemented logical indexing, unless we need it")
assert np.array_equal(inp, ary.to_output('numpy'))
def test_get_set_item(slice, order):
inp = create_input('numpy', 'float32', (10, 10), order)
ary = CumlArray(data=inp)
if isinstance(slice, int):
assert np.array_equal(inp[slice], ary[slice].to_output('numpy'))
inp[slice] = 1.0
ary[slice] = 1.0
elif slice == 'left':
assert np.array_equal(inp[5:], ary[5:].to_output('numpy'))
inp[5:] = 1.0
ary[5:] = 1.0
elif slice == 'right':
assert np.array_equal(inp[:5], ary[:5].to_output('numpy'))
inp[:5] = 1.0
ary[:5] = 1.0
elif slice == 'both':
assert np.array_equal(inp[:], ary[:].to_output('numpy'))
inp[:] = 1.0
ary[:] = 1.0
else:
pytest.skip("not implemented logical indexing, unless we need it")
assert np.array_equal(inp, ary.to_output('numpy'))
def test_serialize(input_type):
if input_type == 'series':
inp = create_input(input_type, np.float32, (10, 1), 'C')
else:
inp = create_input(input_type, np.float32, (10, 5), 'F')
ary = CumlArray(data=inp)
header, frames = ary.serialize()
ary2 = CumlArray.deserialize(header, frames)
assert pickle.loads(header['type-serialized']) is CumlArray
assert all(isinstance(f, Buffer) for f in frames)
if input_type == 'numpy':
assert np.all(inp == ary2.to_output('numpy'))
elif input_type == 'series':
assert np.all(inp == ary2.to_output('series'))
else:
assert cp.all(inp == cp.asarray(ary2))
assert ary.__cuda_array_interface__['shape'] == \
ary2.__cuda_array_interface__['shape']
assert ary.__cuda_array_interface__['strides'] == \
ary2.__cuda_array_interface__['strides']
assert ary.__cuda_array_interface__['typestr'] == \
ary2.__cuda_array_interface__['typestr']
if input_type != 'series':
# skipping one dimensional ary order test
assert ary.order == ary2.order
def test_array_init(input_type, dtype, shape, order):
if input_type == 'series':
if dtype in unsupported_cudf_dtypes or \
shape in [(10, 5), (1, 10)]:
pytest.skip("Unsupported cuDF Series parameter")
if input_type is not None:
inp = create_input(input_type, dtype, shape, order)
ary = CumlArray(data=inp)
else:
inp = create_input('cupy', dtype, shape, order)
ptr = inp.__cuda_array_interface__['data'][0]
ary = CumlArray(data=ptr,
owner=inp,
dtype=inp.dtype,
shape=inp.shape,
order=order)
if shape == (10, 5):
assert ary.order == order
if shape == 10:
assert ary.shape == (10, )
len(ary) == 10
elif input_type == 'series':
# cudf Series make their shape (10,) from (10, 1)
if shape == (10, 1):
assert ary.shape == (10, )
else:
assert ary.shape == shape
assert ary.dtype == np.dtype(dtype)
if input_type == 'numpy':
assert isinstance(ary._owner, DeviceBuffer)
elif input_type in ['cupy', 'numba', 'series']:
assert ary._owner is inp
inp_copy = deepcopy(cp.asarray(inp))
# testing owner reference keeps data of ary alive
del inp
assert cp.all(cp.asarray(ary._owner) == cp.asarray(inp_copy))
else:
assert isinstance(ary._owner, cp.ndarray)
truth = cp.asnumpy(inp)
del inp
assert ary.ptr == ptr
data = ary.to_output('numpy')
assert np.array_equal(truth, data)
return True
def test_output(output_type, dtype, order, shape):
inp = create_input('numpy', dtype, shape, order)
ary = CumlArray(inp)
if dtype in unsupported_cudf_dtypes and \
output_type in ['series', 'dataframe', 'cudf']:
with pytest.raises(ValueError):
res = ary.to_output(output_type)
elif shape in [(10, 5), (1, 10)] and output_type == 'series':
with pytest.raises(ValueError):
res = ary.to_output(output_type)
else:
res = ary.to_output(output_type)
# using correct numba ndarray check
if output_type == 'numba':
assert cuda.devicearray.is_cuda_ndarray(res)
elif output_type == 'cudf':
if shape in [(10, 5), (1, 10)]:
assert isinstance(res, cudf.DataFrame)
else:
assert isinstance(res, cudf.Series)
else:
assert isinstance(res, test_output_types[output_type])
if output_type == 'numpy':
assert np.all(inp == ary.to_output('numpy'))
elif output_type == 'cupy':
assert cp.all(cp.asarray(inp) == ary.to_output('cupy'))
elif output_type == 'numba':
assert cp.all(cp.asarray(cuda.to_device(inp)) == cp.asarray(res))
elif output_type == 'series':
comp = cudf.Series(np.ravel(inp)) == res
assert np.all(comp.to_array())
elif output_type == 'dataframe':
mat = cuda.to_device(inp)
if len(mat.shape) == 1:
mat = mat.reshape(mat.shape[0], 1)
comp = cudf.DataFrame.from_gpu_matrix(mat)
comp = comp == res
assert np.all(comp.as_gpu_matrix().copy_to_host())
# check for e2e cartesian product:
if output_type not in ['dataframe', 'cudf']:
res2 = CumlArray(res)
res2 = res2.to_output('numpy')
if output_type == 'series' and shape == (10, 1):
assert np.all(inp.reshape((1, 10)) == res2)
else:
assert np.all(inp == res2)
def test_cumlary_binops(operation):
a = cp.arange(5)
b = cp.arange(5)
ary_a = CumlArray(a)
ary_b = CumlArray(b)
c = operation(a, b)
ary_c = operation(ary_a, ary_b)
assert (cp.all(ary_c.to_output('cupy') == c))
def create_ary_init_tests(ary_type, dtype, shape, order):
if ary_type is not None:
inp = create_input(ary_type, dtype, shape, order)
ary = CumlArray(data=inp)
ptr = ary.ptr
else:
inp = create_input('cupy', dtype, shape, order)
ptr = inp.__cuda_array_interface__['data'][0]
ary = CumlArray(data=ptr, owner=inp, dtype=inp.dtype, shape=inp.shape,
order=order)
return (inp, ary, ptr)
def test_deepcopy(input_type):
if input_type == 'series':
inp = create_input(input_type, np.float32, (10, 1), 'C')
else:
inp = create_input(input_type, np.float32, (10, 5), 'F')
ary = CumlArray(data=inp)
b = deepcopy(ary)
if input_type == 'numpy':
assert np.all(inp == b.to_output('numpy'))
elif input_type == 'series':
assert np.all(inp == b.to_output('series'))
else:
assert cp.all(inp == cp.asarray(b))
assert ary.ptr != b.ptr
assert ary.__cuda_array_interface__['shape'] == \
b.__cuda_array_interface__['shape']
assert ary.__cuda_array_interface__['strides'] == \
b.__cuda_array_interface__['strides']
assert ary.__cuda_array_interface__['typestr'] == \
b.__cuda_array_interface__['typestr']
if input_type != 'series':
# skipping one dimensional ary order test
assert ary.order == b.order
def convert_dtype(X, to_dtype=np.float32, legacy=True):
"""
Convert X to be of dtype `dtype`, raising a TypeError
if the conversion would lose information.
"""
would_lose_info = _typecast_will_lose_information(X, to_dtype)
if would_lose_info:
raise TypeError("Data type conversion would lose information.")
if isinstance(X, np.ndarray):
dtype = X.dtype
if dtype != to_dtype:
X_m = X.astype(to_dtype)
return X_m
elif isinstance(X, (cudf.Series, cudf.DataFrame, pd.Series, pd.DataFrame)):
return X.astype(to_dtype, copy=False)
elif numba.cuda.is_cuda_array(X):
X_m = cp.asarray(X)
X_m = X_m.astype(to_dtype, copy=False)
if legacy:
return numba.cuda.as_cuda_array(X_m)
else:
return CumlArray(data=X_m)
else:
raise TypeError("Received unsupported input type: %s" % type(X))
return X
def test_create_empty(shape, dtype, order):
ary = CumlArray.empty(shape=shape, dtype=dtype, order=order)
assert isinstance(ary.ptr, int)
if shape == 10:
assert ary.shape == (shape, )
else:
assert ary.shape == shape
assert ary.dtype == np.dtype(dtype)
def _set_doc_stats(self, X):
"""
We set the following document level statistics here:
n_samples
n_features
df(document frequency)
"""
# Should not have a cost if already sparse
output_dtype = _get_dtype(X)
X = self._convert_to_csr(X, output_dtype)
n_samples, n_features = X.shape
df = _sparse_document_frequency(X)
df = df.astype(output_dtype, copy=False)
self.__df = CumlArray(df)
self.__n_samples = n_samples
self.__n_features = n_features
return
def test_pickle(input_type, protocol):
if protocol > pickle.HIGHEST_PROTOCOL:
pytest.skip(
f"Trying to test with pickle protocol {protocol},"
f" but highest supported protocol is {pickle.HIGHEST_PROTOCOL}."
)
if input_type == 'series':
inp = create_input(input_type, np.float32, (10, 1), 'C')
else:
inp = create_input(input_type, np.float32, (10, 5), 'F')
ary = CumlArray(data=inp)
dumps_kwargs = {"protocol": protocol}
loads_kwargs = {}
f = []
len_f = 0
if protocol >= 5:
dumps_kwargs["buffer_callback"] = f.append
loads_kwargs["buffers"] = f
len_f = 1
a = pickle.dumps(ary, **dumps_kwargs)
b = pickle.loads(a, **loads_kwargs)
assert len(f) == len_f
if input_type == 'numpy':
assert np.all(inp == b.to_output('numpy'))
elif input_type == 'series':
assert np.all(inp == b.to_output('series'))
else:
assert cp.all(inp == cp.asarray(b))
assert ary.__cuda_array_interface__['shape'] == \
b.__cuda_array_interface__['shape']
assert ary.__cuda_array_interface__['strides'] == \
b.__cuda_array_interface__['strides']
assert ary.__cuda_array_interface__['typestr'] == \
b.__cuda_array_interface__['typestr']
if input_type != 'series':
# skipping one dimensional ary order test
assert ary.order == b.order
def from_np_or_pd(x):
if isinstance(x, (pd.DataFrame, pd.Series)):
return cudf.from_pandas(x)
elif isinstance(x, np.ndarray):
# print('-' * 10, x, x.dtype)
# return cupy.array(x)
sdtype = str(x.dtype)
if sdtype.find('int') >= 0 or sdtype.find('float') >= 0: # cupy does not support object
if enable_cuml_array:
return CumlArray(x)
else:
return cupy.array(x)
elif x.ndim > 1 and x.shape[1] > 1:
return cudf.DataFrame(x)
else:
return cudf.Series(x)
elif isinstance(x, (list, tuple)):
return [from_np_or_pd(i) for i in x]
elif isinstance(x, dict):
return {k: from_np_or_pd(v) for k, v in x.items()}
else:
return x
def test_array_init_from_bytes(data_type, dtype, shape, order):
dtype = np.dtype(dtype)
bts = bytes(_get_size_from_shape(shape, dtype)[0])
if data_type != bytes:
bts = data_type(bts)
ary = CumlArray(bts, dtype=dtype, shape=shape, order=order)
if shape == (10, 5):
assert ary.order == order
if shape == 10:
assert ary.shape == (10, )
else:
assert ary.shape == shape
assert ary.dtype == dtype
cp_ary = cp.zeros(shape, dtype=dtype)
assert cp.all(cp.asarray(cp_ary) == cp_ary)
def test_sliced_array_owner(order):
"""
When slicing a CumlArray, a new object can be created created which
previously had an incorrect owner. This was due to the requirement by
`cudf.core.Buffer` that all data be in "u1" form. CumlArray would satisfy
this requirement by calling
`cp.asarray(data).ravel(order='A').view('u1')`. If the slice is not
contiguous, this would create an intermediate object with no references
that would be cleaned up by GC causing an error when using the memory
"""
# Create 2 copies of a random array
random_cp = cp.array(cp.random.random((500, 4)),
dtype=np.float32,
order=order)
cupy_array = cp.array(random_cp, copy=True)
cuml_array = CumlArray(random_cp)
# Make sure we have 2 pieces of data
assert cupy_array.data.ptr != cuml_array.ptr
# Since these are C arrays, slice off the first column to ensure they are
# non-contiguous
cuml_slice = cuml_array[1:, 1:]
cupy_slice = cupy_array[1:, 1:]
# Delete the input object just to be sure
del random_cp
# Make sure to cleanup any objects. Forces deletion of intermediate owner
# object
gc.collect()
# Calling `to_output` forces use of the pointer. This can fail with a cuda
# error on `cupy.cuda.runtime.pointerGetAttributes(cuml_slice.ptr)` in CUDA
# < 11.0 or cudaErrorInvalidDevice in CUDA > 11.0 (unclear why it changed)
assert (cp.all(cuml_slice.to_output('cupy') == cupy_slice))
def test_cuda_array_interface(dtype, shape, order):
inp = create_input('numba', dtype, shape, 'F')
ary = CumlArray(inp)
if isinstance(shape, tuple):
assert ary.__cuda_array_interface__['shape'] == shape
else:
assert ary.__cuda_array_interface__['shape'] == (shape, )
assert ary.__cuda_array_interface__['strides'] == inp.strides
assert ary.__cuda_array_interface__['typestr'] == inp.dtype.str
assert ary.__cuda_array_interface__['data'] == \
(inp.device_ctypes_pointer.value, False)
assert ary.__cuda_array_interface__['version'] == 2
# since our test array is small, its faster to transfer it to numpy to
# square rather than a numba cuda kernel
truth = np.sqrt(inp.copy_to_host())
result = cp.sqrt(ary)
assert np.all(truth == cp.asnumpy(result))
return True
def input_to_cuml_array(X,
order='F',
deepcopy=False,
check_dtype=False,
convert_to_dtype=False,
check_cols=False,
check_rows=False,
fail_on_order=False,
force_contiguous=True):
"""
Convert input X to CumlArray.
Acceptable input formats:
* cuDF Dataframe - returns a deep copy always.
* cuDF Series - returns by reference or a deep copy depending on
`deepcopy`.
* Numpy array - returns a copy in device always
* cuda array interface compliant array (like Cupy) - returns a
reference unless `deepcopy`=True.
* numba device array - returns a reference unless deepcopy=True
Parameters
----------
X : cuDF.DataFrame, cuDF.Series, NumPy array, Pandas DataFrame, Pandas
Series or any cuda_array_interface (CAI) compliant array like CuPy,
Numba or pytorch.
order: 'F', 'C' or 'K' (default: 'F')
Whether to return a F-major ('F'), C-major ('C') array or Keep ('K')
the order of X. Used to check the order of the input. If
fail_on_order=True, the method will raise ValueError,
otherwise it will convert X to be of order `order` if needed.
deepcopy: boolean (default: False)
Set to True to always return a deep copy of X.
check_dtype: np.dtype (default: False)
Set to a np.dtype to throw an error if X is not of dtype `check_dtype`.
convert_to_dtype: np.dtype (default: False)
Set to a dtype if you want X to be converted to that dtype if it is
not that dtype already.
check_cols: int (default: False)
Set to an int `i` to check that input X has `i` columns. Set to False
(default) to not check at all.
check_rows: boolean (default: False)
Set to an int `i` to check that input X has `i` columns. Set to False
(default) to not check at all.
fail_on_order: boolean (default: False)
Set to True if you want the method to raise a ValueError if X is not
of order `order`.
force_contiguous: boolean (default: True)
Set to True to force CumlArray produced to be contiguous. If `X` is
non contiguous then a contiguous copy will be done.
If False, and `X` doesn't need to be converted and is not contiguous,
the underlying memory underneath the CumlArray will be non contiguous.
Only affects CAI inputs. Only affects CuPy and Numba device array
views, all other input methods produce contiguous CumlArrays.
Returns
-------
`cuml_array`: namedtuple('cuml_array', 'array n_rows n_cols dtype')
A new CumlArray and associated data.
"""
def check_order(arr_order):
if order != 'K' and arr_order != order:
if fail_on_order:
raise ValueError("Expected " + order_to_str(order) +
" major order, but got the opposite.")
else:
debug("Expected " + order_to_str(order) + " major order, "
"but got the opposite. Converting data, this will "
"result in additional memory utilization.")
return True
return False
# dtype conversion
# force_contiguous set to True always for now
# upcoming CumlArray improvements will affect this
# https://github.com/rapidsai/cuml/issues/2412
force_contiguous = True
if convert_to_dtype:
X = convert_dtype(X, to_dtype=convert_to_dtype)
check_dtype = False
# format conversion
if (isinstance(X, cudf.Series)):
if X.null_count != 0:
raise ValueError("Error: cuDF Series has missing/null values, "
"which are not supported by cuML.")
# converting pandas to numpy before sending it to CumlArray
if isinstance(X, pd.DataFrame) or isinstance(X, pd.Series):
# pandas doesn't support custom order in to_numpy
X = cp.asarray(X.to_numpy(copy=False), order=order)
if isinstance(X, cudf.DataFrame):
if order == 'K':
X_m = CumlArray(data=X.as_gpu_matrix(order='F'))
else:
X_m = CumlArray(data=X.as_gpu_matrix(order=order))
elif isinstance(X, CumlArray):
X_m = X
elif hasattr(X, "__array_interface__") or \
hasattr(X, "__cuda_array_interface__"):
# Since we create the array with the correct order here, do the order
# check now if necessary
interface = getattr(X, "__array_interface__", None) or getattr(
X, "__cuda_array_interface__", None)
arr_info = ArrayInfo.from_interface(interface)
check_order(arr_info.order)
make_copy = False
if force_contiguous or hasattr(X, "__array_interface__"):
if not _check_array_contiguity(X):
debug("Non contiguous array or view detected, a "
"contiguous copy of the data will be done.")
# X = cp.array(X, order=order, copy=True)
make_copy = True
cp_arr = cp.array(X, copy=make_copy, order=order)
X_m = CumlArray(data=cp_arr)
if deepcopy:
X_m = copy.deepcopy(X_m)
else:
msg = "X matrix format " + str(X.__class__) + " not supported"
raise TypeError(msg)
if check_dtype:
if not isinstance(check_dtype, list):
check_dtype = [check_dtype]
check_dtype = [np.dtype(dtype) for dtype in check_dtype]
if X_m.dtype not in check_dtype:
type_str = X_m.dtype
del X_m
raise TypeError("Expected input to be of type in " +
str(check_dtype) + " but got " + str(type_str))
# Checks based on parameters
n_rows = X_m.shape[0]
if len(X_m.shape) > 1:
n_cols = X_m.shape[1]
else:
n_cols = 1
if n_cols == 1 or n_rows == 1:
order = 'K'
if check_cols:
if n_cols != check_cols:
raise ValueError("Expected " + str(check_cols) +
" columns but got " + str(n_cols) + " columns.")
if check_rows:
if n_rows != check_rows:
raise ValueError("Expected " + str(check_rows) + " rows but got " +
str(n_rows) + " rows.")
if (check_order(X_m.order)):
X_m = cp.array(X_m, copy=False, order=order)
X_m = CumlArray(data=X_m)
return cuml_array(array=X_m, n_rows=n_rows, n_cols=n_cols, dtype=X_m.dtype)
def create_input(input_type, input_dtype, input_shape, input_order):
rand_ary = cp.ones(input_shape, dtype=input_dtype, order=input_order)
cuml_ary = CumlArray(rand_ary)
return cuml_ary.to_output(input_type)
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 test_array_init(input_type, dtype, shape, order):
if input_type == 'series':
if dtype in unsupported_cudf_dtypes or \
shape in [(10, 5), (1, 10)]:
pytest.skip("Unsupported cuDF Series parameter")
if input_type is not None:
inp = create_input(input_type, dtype, shape, order)
ary = CumlArray(data=inp)
ptr = ary.ptr
else:
inp = create_input('cupy', dtype, shape, order)
ptr = inp.__cuda_array_interface__['data'][0]
ary = CumlArray(data=ptr,
owner=inp,
dtype=inp.dtype,
shape=inp.shape,
order=order)
if shape == (10, 5):
assert ary.order == order
if shape == 10:
assert ary.shape == (10, )
assert len(ary) == 10
elif input_type == 'series':
# cudf Series make their shape (10,) from (10, 1)
if shape == (10, 1):
assert ary.shape == (10, )
else:
assert ary.shape == shape
assert ary.dtype == np.dtype(dtype)
if (input_type == "numpy"):
assert isinstance(ary._owner, cp.ndarray)
truth = cp.asnumpy(inp)
del inp
assert ary.ptr == ptr
data = ary.to_output('numpy')
assert np.array_equal(truth, data)
else:
found_owner = False
def get_owner(curr):
if (isinstance(curr, CumlArray)):
return curr._owner
elif (isinstance(curr, cp.ndarray)):
return curr.data.mem._owner
else:
return None
# Make sure the input array is in the ownership chain
curr_owner = ary
while (curr_owner is not None):
if (curr_owner is inp):
found_owner = True
break
curr_owner = get_owner(curr_owner)
assert found_owner, "GPU input arrays must be in the owner chain"
inp_copy = deepcopy(cp.asarray(inp))
# testing owner reference keeps data of ary alive
del inp
# Force GC just in case it lingers
gc.collect()
assert cp.all(cp.asarray(ary._owner) == cp.asarray(inp_copy))
return True
def test_create_full(shape, dtype, order):
value = cp.array([cp.random.randint(100)]).astype(dtype)
ary = CumlArray.full(value=value[0], shape=shape, dtype=dtype, order=order)
test = cp.zeros(shape).astype(dtype) + value[0]
assert cp.all(test == cp.asarray(ary))
def test_create_ones(shape, dtype, order):
ary = CumlArray.ones(shape=shape, dtype=dtype, order=order)
test = cp.ones(shape).astype(dtype)
assert cp.all(test == cp.asarray(ary))
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 TfidfTransformer(Base):
"""
Transform a count matrix to a normalized tf or tf-idf representation
Tf means term-frequency while tf-idf means term-frequency times inverse
document-frequency. This is a common term weighting scheme in information
retrieval, that has also found good use in document classification.
The goal of using tf-idf instead of the raw frequencies of occurrence of a
token in a given document is to scale down the impact of tokens that occur
very frequently in a given corpus and that are hence empirically less
informative than features that occur in a small fraction of the training
corpus.
The formula that is used to compute the tf-idf for a term t of a document d
in a document set is tf-idf(t, d) = tf(t, d) * idf(t), and the idf is
computed as idf(t) = log [ n / df(t) ] + 1 (if ``smooth_idf=False``), where
n is the total number of documents in the document set and df(t) is the
document frequency of t; the document frequency is the number of documents
in the document set that contain the term t. The effect of adding "1" to
the idf in the equation above is that terms with zero idf, i.e., terms
that occur in all documents in a training set, will not be entirely
ignored.
(Note that the idf formula above differs from the standard textbook
notation that defines the idf as
idf(t) = log [ n / (df(t) + 1) ]).
If ``smooth_idf=True`` (the default), the constant "1" is added to the
numerator and denominator of the idf as if an extra document was seen
containing every term in the collection exactly once, which prevents
zero divisions: idf(t) = log [ (1 + n) / (1 + df(t)) ] + 1.
Furthermore, the formulas used to compute tf and idf depend
on parameter settings that correspond to the SMART notation used in IR
as follows:
Tf is "n" (natural) by default, "l" (logarithmic) when
``sublinear_tf=True``.
Idf is "t" when use_idf is given, "n" (none) otherwise.
Normalization is "c" (cosine) when ``norm='l2'``, "n" (none)
when ``norm=None``.
Parameters
----------
norm : {'l1', 'l2'}, default='l2'
Each output row will have unit norm, either:
* 'l2': Sum of squares of vector elements is 1. The cosine similarity
between two vectors is their dot product when l2 norm has been
applied.
* 'l1': Sum of absolute values of vector elements is 1.
use_idf : bool, default=True
Enable inverse-document-frequency reweighting.
smooth_idf : bool, default=True
Smooth idf weights by adding one to document frequencies, as if an
extra document was seen containing every term in the collection
exactly once. Prevents zero divisions.
sublinear_tf : bool, default=False
Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).
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.
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_settings.output_type`.
See :ref:`output-data-type-configuration` for more info.
Attributes
----------
idf_ : array of shape (n_features)
The inverse document frequency (IDF) vector; only defined
if ``use_idf`` is True.
"""
def __init__(self,
*,
norm='l2',
use_idf=True,
smooth_idf=True,
sublinear_tf=False,
handle=None,
verbose=False,
output_type=None):
super().__init__(handle=handle,
verbose=verbose,
output_type=output_type)
self.norm = norm
self.use_idf = use_idf
self.smooth_idf = smooth_idf
self.sublinear_tf = sublinear_tf
def _set_doc_stats(self, X):
"""
We set the following document level statistics here:
n_samples
n_features
df(document frequency)
"""
# Should not have a cost if already sparse
output_dtype = _get_dtype(X)
X = self._convert_to_csr(X, output_dtype)
n_samples, n_features = X.shape
df = _sparse_document_frequency(X)
df = df.astype(output_dtype, copy=False)
self.__df = CumlArray(df)
self.__n_samples = n_samples
self.__n_features = n_features
return
def _set_idf_diag(self):
"""
Sets idf_diagonal sparse array
"""
# perform idf smoothing if required
df = self.__df.to_output('cupy') + int(self.smooth_idf)
n_samples = self.__n_samples + int(self.smooth_idf)
# log+1 instead of log makes sure terms with zero idf don't get
# suppressed entirely.
idf = cp.log(n_samples / df) + 1
self._idf_diag = cp.sparse.dia_matrix(
(idf, 0),
shape=(self.__n_features, self.__n_features),
dtype=df.dtype)
# Free up memory occupied by below
del self.__df
@cuml.internals.api_base_return_any_skipall
def fit(self, X) -> "TfidfTransformer":
"""Learn the idf vector (global term weights).
Parameters
----------
X : array-like of shape n_samples, n_features
A matrix of term/token counts.
"""
output_dtype = _get_dtype(X)
X = self._convert_to_csr(X, output_dtype)
if self.use_idf:
self._set_doc_stats(X)
self._set_idf_diag()
return self
@cuml.internals.api_base_return_any_skipall
def transform(self, X, copy=True):
"""Transform a count matrix to a tf or tf-idf representation
Parameters
----------
X : array-like of (n_samples, n_features)
A matrix of term/token counts
copy : bool, default=True
Whether to copy X and operate on the copy or perform in-place
operations.
Returns
-------
vectors : array-like of shape (n_samples, n_features)
"""
if copy:
X = X.copy()
dtype = _get_dtype(X)
X = self._convert_to_csr(X, dtype)
if X.dtype != dtype:
X = X.astype(dtype)
n_samples, n_features = X.shape
if self.sublinear_tf:
cp.log(X.data, X.data)
X.data += 1
if self.use_idf:
self._check_is_idf_fitted()
expected_n_features = self._idf_diag.shape[0]
if n_features != expected_n_features:
raise ValueError("Input has n_features=%d while the model"
" has been trained with n_features=%d" %
(n_features, expected_n_features))
csr_diag_mul(X, self._idf_diag, inplace=True)
if self.norm:
if self.norm == 'l1':
csr_row_normalize_l1(X, inplace=True)
elif self.norm == 'l2':
csr_row_normalize_l2(X, inplace=True)
return X
@cuml.internals.api_base_return_any_skipall
def fit_transform(self, X, copy=True):
"""
Fit TfidfTransformer to X, then transform X.
Equivalent to fit(X).transform(X).
Parameters
----------
X : array-like of (n_samples, n_features)
A matrix of term/token counts
copy : bool, default=True
Whether to copy X and operate on the copy or perform in-place
operations.
Returns
-------
vectors : array-like of shape (n_samples, n_features)
"""
return self.fit(X).transform(X, copy=copy)
def _check_is_idf_fitted(self):
if not hasattr(self, 'idf_'):
msg = ("This TfidfTransformer instance is not fitted or the "
"value of use_idf is not consistant between "
".fit() and .transform().")
raise NotFittedError(msg)
def _convert_to_csr(self, X, dtype):
"""Convert array to CSR format if it not sparse nor CSR."""
if not cupyx.scipy.sparse.isspmatrix_csr(X):
if not cupyx.scipy.sparse.issparse(X):
X = cupyx.scipy.sparse.csr_matrix(X.astype(dtype))
else:
X = X.tocsr()
return X
@property
def idf_(self):
# if _idf_diag is not set, this will raise an attribute error,
# which means hasattr(self, "idf_") is False
return self._idf_diag.data
@idf_.setter
def idf_(self, value):
value = cp.asarray(value, dtype=cp.float32)
n_features = value.shape[0]
self._idf_diag = cupyx.scipy.sparse.dia_matrix(
(value, 0), shape=(n_features, n_features), dtype=cp.float32)
def get_param_names(self):
return super().get_param_names() + \
["norm", "use_idf", "smooth_idf", "sublinear_tf"]
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")
