471 lines
13 KiB
Python
471 lines
13 KiB
Python
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# Authors: Manoj Kumar
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# Thomas Unterthiner
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# Giorgio Patrini
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#
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# License: BSD 3 clause
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import scipy.sparse as sp
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import numpy as np
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from .fixes import sparse_min_max
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from .sparsefuncs_fast import (
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csr_mean_variance_axis0 as _csr_mean_var_axis0,
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csc_mean_variance_axis0 as _csc_mean_var_axis0,
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incr_mean_variance_axis0 as _incr_mean_var_axis0)
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def _raise_typeerror(X):
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"""Raises a TypeError if X is not a CSR or CSC matrix"""
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input_type = X.format if sp.issparse(X) else type(X)
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err = "Expected a CSR or CSC sparse matrix, got %s." % input_type
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raise TypeError(err)
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def _raise_error_wrong_axis(axis):
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if axis not in (0, 1):
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raise ValueError(
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"Unknown axis value: %d. Use 0 for rows, or 1 for columns" % axis)
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def inplace_csr_column_scale(X, scale):
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"""Inplace column scaling of a CSR matrix.
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Scale each feature of the data matrix by multiplying with specific scale
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provided by the caller assuming a (n_samples, n_features) shape.
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Parameters
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----------
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X : CSR matrix with shape (n_samples, n_features)
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Matrix to normalize using the variance of the features.
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scale : float array with shape (n_features,)
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Array of precomputed feature-wise values to use for scaling.
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"""
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assert scale.shape[0] == X.shape[1]
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X.data *= scale.take(X.indices, mode='clip')
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def inplace_csr_row_scale(X, scale):
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""" Inplace row scaling of a CSR matrix.
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Scale each sample of the data matrix by multiplying with specific scale
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provided by the caller assuming a (n_samples, n_features) shape.
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Parameters
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----------
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X : CSR sparse matrix, shape (n_samples, n_features)
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Matrix to be scaled.
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scale : float array with shape (n_samples,)
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Array of precomputed sample-wise values to use for scaling.
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"""
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assert scale.shape[0] == X.shape[0]
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X.data *= np.repeat(scale, np.diff(X.indptr))
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def mean_variance_axis(X, axis):
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"""Compute mean and variance along an axix on a CSR or CSC matrix
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Parameters
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----------
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X : CSR or CSC sparse matrix, shape (n_samples, n_features)
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Input data.
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axis : int (either 0 or 1)
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Axis along which the axis should be computed.
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Returns
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-------
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means : float array with shape (n_features,)
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Feature-wise means
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variances : float array with shape (n_features,)
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Feature-wise variances
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"""
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_raise_error_wrong_axis(axis)
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if isinstance(X, sp.csr_matrix):
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if axis == 0:
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return _csr_mean_var_axis0(X)
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else:
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return _csc_mean_var_axis0(X.T)
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elif isinstance(X, sp.csc_matrix):
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if axis == 0:
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return _csc_mean_var_axis0(X)
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else:
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return _csr_mean_var_axis0(X.T)
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else:
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_raise_typeerror(X)
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def incr_mean_variance_axis(X, axis, last_mean, last_var, last_n):
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"""Compute incremental mean and variance along an axix on a CSR or
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CSC matrix.
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last_mean, last_var are the statistics computed at the last step by this
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function. Both must be initilized to 0-arrays of the proper size, i.e.
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the number of features in X. last_n is the number of samples encountered
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until now.
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Parameters
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----------
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X : CSR or CSC sparse matrix, shape (n_samples, n_features)
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Input data.
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axis : int (either 0 or 1)
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Axis along which the axis should be computed.
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last_mean : float array with shape (n_features,)
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Array of feature-wise means to update with the new data X.
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last_var : float array with shape (n_features,)
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Array of feature-wise var to update with the new data X.
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last_n : int
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Number of samples seen so far, excluded X.
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Returns
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-------
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means : float array with shape (n_features,)
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Updated feature-wise means.
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variances : float array with shape (n_features,)
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Updated feature-wise variances.
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n : int
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Updated number of seen samples.
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"""
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_raise_error_wrong_axis(axis)
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if isinstance(X, sp.csr_matrix):
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if axis == 0:
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return _incr_mean_var_axis0(X, last_mean=last_mean,
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last_var=last_var, last_n=last_n)
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else:
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return _incr_mean_var_axis0(X.T, last_mean=last_mean,
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last_var=last_var, last_n=last_n)
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elif isinstance(X, sp.csc_matrix):
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if axis == 0:
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return _incr_mean_var_axis0(X, last_mean=last_mean,
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last_var=last_var, last_n=last_n)
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else:
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return _incr_mean_var_axis0(X.T, last_mean=last_mean,
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last_var=last_var, last_n=last_n)
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else:
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_raise_typeerror(X)
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def inplace_column_scale(X, scale):
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"""Inplace column scaling of a CSC/CSR matrix.
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Scale each feature of the data matrix by multiplying with specific scale
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provided by the caller assuming a (n_samples, n_features) shape.
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Parameters
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----------
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X : CSC or CSR matrix with shape (n_samples, n_features)
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Matrix to normalize using the variance of the features.
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scale : float array with shape (n_features,)
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Array of precomputed feature-wise values to use for scaling.
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"""
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if isinstance(X, sp.csc_matrix):
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inplace_csr_row_scale(X.T, scale)
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elif isinstance(X, sp.csr_matrix):
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inplace_csr_column_scale(X, scale)
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else:
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_raise_typeerror(X)
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def inplace_row_scale(X, scale):
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""" Inplace row scaling of a CSR or CSC matrix.
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Scale each row of the data matrix by multiplying with specific scale
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provided by the caller assuming a (n_samples, n_features) shape.
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Parameters
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----------
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X : CSR or CSC sparse matrix, shape (n_samples, n_features)
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Matrix to be scaled.
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scale : float array with shape (n_features,)
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Array of precomputed sample-wise values to use for scaling.
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"""
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if isinstance(X, sp.csc_matrix):
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inplace_csr_column_scale(X.T, scale)
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elif isinstance(X, sp.csr_matrix):
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inplace_csr_row_scale(X, scale)
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else:
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_raise_typeerror(X)
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def inplace_swap_row_csc(X, m, n):
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"""
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Swaps two rows of a CSC matrix in-place.
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Parameters
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----------
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X : scipy.sparse.csc_matrix, shape=(n_samples, n_features)
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Matrix whose two rows are to be swapped.
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m : int
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Index of the row of X to be swapped.
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n : int
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Index of the row of X to be swapped.
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"""
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for t in [m, n]:
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if isinstance(t, np.ndarray):
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raise TypeError("m and n should be valid integers")
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if m < 0:
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m += X.shape[0]
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if n < 0:
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n += X.shape[0]
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m_mask = X.indices == m
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X.indices[X.indices == n] = m
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X.indices[m_mask] = n
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def inplace_swap_row_csr(X, m, n):
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"""
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Swaps two rows of a CSR matrix in-place.
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Parameters
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----------
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X : scipy.sparse.csr_matrix, shape=(n_samples, n_features)
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Matrix whose two rows are to be swapped.
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m : int
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Index of the row of X to be swapped.
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n : int
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Index of the row of X to be swapped.
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"""
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for t in [m, n]:
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if isinstance(t, np.ndarray):
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raise TypeError("m and n should be valid integers")
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if m < 0:
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m += X.shape[0]
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if n < 0:
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n += X.shape[0]
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# The following swapping makes life easier since m is assumed to be the
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# smaller integer below.
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if m > n:
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m, n = n, m
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indptr = X.indptr
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m_start = indptr[m]
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m_stop = indptr[m + 1]
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n_start = indptr[n]
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n_stop = indptr[n + 1]
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nz_m = m_stop - m_start
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nz_n = n_stop - n_start
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if nz_m != nz_n:
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# Modify indptr first
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X.indptr[m + 2:n] += nz_n - nz_m
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X.indptr[m + 1] = m_start + nz_n
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X.indptr[n] = n_stop - nz_m
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X.indices = np.concatenate([X.indices[:m_start],
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X.indices[n_start:n_stop],
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X.indices[m_stop:n_start],
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X.indices[m_start:m_stop],
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X.indices[n_stop:]])
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X.data = np.concatenate([X.data[:m_start],
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X.data[n_start:n_stop],
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X.data[m_stop:n_start],
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X.data[m_start:m_stop],
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X.data[n_stop:]])
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def inplace_swap_row(X, m, n):
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"""
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Swaps two rows of a CSC/CSR matrix in-place.
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Parameters
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----------
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X : CSR or CSC sparse matrix, shape=(n_samples, n_features)
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Matrix whose two rows are to be swapped.
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m : int
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Index of the row of X to be swapped.
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n : int
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Index of the row of X to be swapped.
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"""
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if isinstance(X, sp.csc_matrix):
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inplace_swap_row_csc(X, m, n)
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elif isinstance(X, sp.csr_matrix):
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inplace_swap_row_csr(X, m, n)
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else:
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_raise_typeerror(X)
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def inplace_swap_column(X, m, n):
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"""
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Swaps two columns of a CSC/CSR matrix in-place.
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Parameters
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----------
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X : CSR or CSC sparse matrix, shape=(n_samples, n_features)
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Matrix whose two columns are to be swapped.
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m : int
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Index of the column of X to be swapped.
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n : int
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Index of the column of X to be swapped.
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"""
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if m < 0:
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m += X.shape[1]
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if n < 0:
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n += X.shape[1]
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if isinstance(X, sp.csc_matrix):
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inplace_swap_row_csr(X, m, n)
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elif isinstance(X, sp.csr_matrix):
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inplace_swap_row_csc(X, m, n)
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else:
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_raise_typeerror(X)
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def min_max_axis(X, axis):
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"""Compute minimum and maximum along an axis on a CSR or CSC matrix
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Parameters
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----------
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X : CSR or CSC sparse matrix, shape (n_samples, n_features)
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Input data.
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axis : int (either 0 or 1)
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Axis along which the axis should be computed.
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Returns
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-------
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mins : float array with shape (n_features,)
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Feature-wise minima
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maxs : float array with shape (n_features,)
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Feature-wise maxima
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"""
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if isinstance(X, sp.csr_matrix) or isinstance(X, sp.csc_matrix):
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return sparse_min_max(X, axis=axis)
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else:
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_raise_typeerror(X)
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def count_nonzero(X, axis=None, sample_weight=None):
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"""A variant of X.getnnz() with extension to weighting on axis 0
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Useful in efficiently calculating multilabel metrics.
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Parameters
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----------
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X : CSR sparse matrix, shape = (n_samples, n_labels)
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Input data.
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axis : None, 0 or 1
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The axis on which the data is aggregated.
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sample_weight : array, shape = (n_samples,), optional
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Weight for each row of X.
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"""
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if axis == -1:
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axis = 1
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elif axis == -2:
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axis = 0
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elif X.format != 'csr':
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raise TypeError('Expected CSR sparse format, got {0}'.format(X.format))
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# We rely here on the fact that np.diff(Y.indptr) for a CSR
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# will return the number of nonzero entries in each row.
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# A bincount over Y.indices will return the number of nonzeros
|
||
|
|
# in each column. See ``csr_matrix.getnnz`` in scipy >= 0.14.
|
||
|
|
if axis is None:
|
||
|
|
if sample_weight is None:
|
||
|
|
return X.nnz
|
||
|
|
else:
|
||
|
|
return np.dot(np.diff(X.indptr), sample_weight)
|
||
|
|
elif axis == 1:
|
||
|
|
out = np.diff(X.indptr)
|
||
|
|
if sample_weight is None:
|
||
|
|
return out
|
||
|
|
return out * sample_weight
|
||
|
|
elif axis == 0:
|
||
|
|
if sample_weight is None:
|
||
|
|
return np.bincount(X.indices, minlength=X.shape[1])
|
||
|
|
else:
|
||
|
|
weights = np.repeat(sample_weight, np.diff(X.indptr))
|
||
|
|
return np.bincount(X.indices, minlength=X.shape[1],
|
||
|
|
weights=weights)
|
||
|
|
else:
|
||
|
|
raise ValueError('Unsupported axis: {0}'.format(axis))
|
||
|
|
|
||
|
|
|
||
|
|
def _get_median(data, n_zeros):
|
||
|
|
"""Compute the median of data with n_zeros additional zeros.
|
||
|
|
|
||
|
|
This function is used to support sparse matrices; it modifies data in-place
|
||
|
|
"""
|
||
|
|
n_elems = len(data) + n_zeros
|
||
|
|
if not n_elems:
|
||
|
|
return np.nan
|
||
|
|
n_negative = np.count_nonzero(data < 0)
|
||
|
|
middle, is_odd = divmod(n_elems, 2)
|
||
|
|
data.sort()
|
||
|
|
|
||
|
|
if is_odd:
|
||
|
|
return _get_elem_at_rank(middle, data, n_negative, n_zeros)
|
||
|
|
|
||
|
|
return (_get_elem_at_rank(middle - 1, data, n_negative, n_zeros) +
|
||
|
|
_get_elem_at_rank(middle, data, n_negative, n_zeros)) / 2.
|
||
|
|
|
||
|
|
|
||
|
|
def _get_elem_at_rank(rank, data, n_negative, n_zeros):
|
||
|
|
"""Find the value in data augmented with n_zeros for the given rank"""
|
||
|
|
if rank < n_negative:
|
||
|
|
return data[rank]
|
||
|
|
if rank - n_negative < n_zeros:
|
||
|
|
return 0
|
||
|
|
return data[rank - n_zeros]
|
||
|
|
|
||
|
|
|
||
|
|
def csc_median_axis_0(X):
|
||
|
|
"""Find the median across axis 0 of a CSC matrix.
|
||
|
|
It is equivalent to doing np.median(X, axis=0).
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : CSC sparse matrix, shape (n_samples, n_features)
|
||
|
|
Input data.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
median : ndarray, shape (n_features,)
|
||
|
|
Median.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if not isinstance(X, sp.csc_matrix):
|
||
|
|
raise TypeError("Expected matrix of CSC format, got %s" % X.format)
|
||
|
|
|
||
|
|
indptr = X.indptr
|
||
|
|
n_samples, n_features = X.shape
|
||
|
|
median = np.zeros(n_features)
|
||
|
|
|
||
|
|
for f_ind, (start, end) in enumerate(zip(indptr[:-1], indptr[1:])):
|
||
|
|
|
||
|
|
# Prevent modifying X in place
|
||
|
|
data = np.copy(X.data[start: end])
|
||
|
|
nz = n_samples - data.size
|
||
|
|
median[f_ind] = _get_median(data, nz)
|
||
|
|
|
||
|
|
return median
|