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clinical.py
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clinical.py
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# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import numpy
import pandas
from pandas.api.types import is_categorical_dtype, is_numeric_dtype
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.utils.validation import check_is_fitted
from ._clinical_kernel import (
continuous_ordinal_kernel,
continuous_ordinal_kernel_with_ranges,
pairwise_continuous_ordinal_kernel,
pairwise_nominal_kernel,
)
__all__ = ['clinical_kernel', 'ClinicalKernelTransform']
def _nominal_kernel(x, y, out):
"""Number of features that match exactly"""
for i in range(x.shape[0]):
for j in range(y.shape[0]):
out[i, j] += (x[i, :] == y[j, :]).sum()
return out
def _get_continuous_and_ordinal_array(x):
"""Convert array from continuous and ordered categorical columns"""
nominal_columns = x.select_dtypes(include=['object', 'category']).columns
ordinal_columns = pandas.Index([v for v in nominal_columns if x[v].cat.ordered])
continuous_columns = x.select_dtypes(include=[numpy.number]).columns
x_num = x.loc[:, continuous_columns].astype(numpy.float64).values
if len(ordinal_columns) > 0:
x = _ordinal_as_numeric(x, ordinal_columns)
nominal_columns = nominal_columns.difference(ordinal_columns)
x_out = numpy.column_stack((x_num, x))
else:
x_out = x_num
return x_out, nominal_columns
def _ordinal_as_numeric(x, ordinal_columns):
x_numeric = numpy.empty((x.shape[0], len(ordinal_columns)), dtype=numpy.float64)
for i, c in enumerate(ordinal_columns):
x_numeric[:, i] = x[c].cat.codes
return x_numeric
def clinical_kernel(x, y=None):
"""Computes clinical kernel
The clinical kernel distinguishes between continuous
ordinal,and nominal variables.
See [1]_ for further description.
Parameters
----------
x : pandas.DataFrame, shape = (n_samples_x, n_features)
Training data
y : pandas.DataFrame, shape = (n_samples_y, n_features)
Testing data
Returns
-------
kernel : array, shape = (n_samples_x, n_samples_y)
Kernel matrix. Values are normalized to lie within [0, 1].
References
----------
.. [1] Daemen, A., De Moor, B.,
"Development of a kernel function for clinical data".
Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 5913-7, 2009
"""
if y is not None:
if x.shape[1] != y.shape[1]:
raise ValueError('x and y have different number of features')
if not x.columns.equals(y.columns):
raise ValueError('columns do not match')
else:
y = x
mat = numpy.zeros((x.shape[0], y.shape[0]), dtype=float)
x_numeric, nominal_columns = _get_continuous_and_ordinal_array(x)
if id(x) != id(y):
y_numeric, _ = _get_continuous_and_ordinal_array(y)
else:
y_numeric = x_numeric
continuous_ordinal_kernel(x_numeric, y_numeric, mat)
_nominal_kernel(x.loc[:, nominal_columns].values,
y.loc[:, nominal_columns].values,
mat)
mat /= x.shape[1]
return mat
class ClinicalKernelTransform(BaseEstimator, TransformerMixin):
"""Transform data using a clinical Kernel
The clinical kernel distinguishes between continuous
ordinal,and nominal variables.
See [1]_ for further description.
Parameters
----------
fit_once : bool, optional
If set to ``True``, fit() does only transform the training data, but not update
its internal state. You should call prepare() once before calling transform().
If set to ``False``, it behaves like a regular estimator, i.e., you need to
call fit() before transform().
Attributes
----------
n_features_in_ : int
Number of features seen during ``fit``.
feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during ``fit``. Defined only when `X`
has feature names that are all strings.
References
----------
.. [1] Daemen, A., De Moor, B.,
"Development of a kernel function for clinical data".
Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 5913-7, 2009
"""
def __init__(self, fit_once=False, _numeric_ranges=None, _numeric_columns=None, _nominal_columns=None):
self.fit_once = fit_once
self._numeric_ranges = _numeric_ranges
self._numeric_columns = _numeric_columns
self._nominal_columns = _nominal_columns
def prepare(self, X):
"""Determine transformation parameters from data in X.
Use if `fit_once` is `True`, in which case `fit()` does
not set the parameters of the clinical kernel.
Parameters
----------
X: pandas.DataFrame, shape = (n_samples, n_features)
Data to estimate parameters from.
"""
if not self.fit_once:
raise ValueError('prepare can only be used if fit_once parameter is set to True')
self._prepare_by_column_dtype(X)
def _prepare_by_column_dtype(self, X):
"""Get distance functions for each column's dtype"""
if not isinstance(X, pandas.DataFrame):
raise TypeError('X must be a pandas DataFrame')
numeric_columns = []
nominal_columns = []
numeric_ranges = []
fit_data = numpy.empty_like(X)
for i, dt in enumerate(X.dtypes):
col = X.iloc[:, i]
if is_categorical_dtype(dt):
if col.cat.ordered:
numeric_ranges.append(col.cat.codes.max() - col.cat.codes.min())
numeric_columns.append(i)
else:
nominal_columns.append(i)
col = col.cat.codes
elif is_numeric_dtype(dt):
numeric_ranges.append(col.max() - col.min())
numeric_columns.append(i)
else:
raise TypeError('unsupported dtype: %r' % dt)
fit_data[:, i] = col.values
self._numeric_columns = numpy.asarray(numeric_columns)
self._nominal_columns = numpy.asarray(nominal_columns)
self._numeric_ranges = numpy.asarray(numeric_ranges, dtype=float)
self.X_fit_ = fit_data
def fit(self, X, y=None, **kwargs): # pylint: disable=unused-argument
"""Determine transformation parameters from data in X.
Subsequent calls to `transform(Y)` compute the pairwise
distance to `X`.
Parameters of the clinical kernel are only updated
if `fit_once` is `False`, otherwise you have to
explicitly call `prepare()` once.
Parameters
----------
X: pandas.DataFrame, shape = (n_samples, n_features)
Data to estimate parameters from.
y : None
Argument is ignored (included for compatibility reasons).
kwargs : dict
Argument is ignored (included for compatibility reasons).
Returns
-------
self : object
Returns the instance itself.
"""
if X.ndim != 2:
raise ValueError("expected 2d array, but got %d" % X.ndim)
self._check_feature_names(X, reset=True)
self._check_n_features(X, reset=True)
if self.fit_once:
self.X_fit_ = X
else:
self._prepare_by_column_dtype(X)
return self
def transform(self, Y):
r"""Compute all pairwise distances between `self.X_fit_` and `Y`.
Parameters
----------
Y : array-like, shape = (n_samples_y, n_features)
Returns
-------
kernel : ndarray, shape = (n_samples_y, n_samples_X_fit\_)
Kernel matrix. Values are normalized to lie within [0, 1].
"""
check_is_fitted(self, 'X_fit_')
self._check_feature_names(Y, reset=False)
self._check_n_features(Y, reset=False)
n_samples_x = self.X_fit_.shape[0]
Y = numpy.asarray(Y)
n_samples_y = Y.shape[0]
mat = numpy.zeros((n_samples_y, n_samples_x), dtype=float)
continuous_ordinal_kernel_with_ranges(Y[:, self._numeric_columns].astype(numpy.float64),
self.X_fit_[:, self._numeric_columns].astype(numpy.float64),
self._numeric_ranges, mat)
if len(self._nominal_columns) > 0:
_nominal_kernel(Y[:, self._nominal_columns],
self.X_fit_[:, self._nominal_columns],
mat)
mat /= self.n_features_in_
return mat
def __call__(self, X, Y):
"""Compute Kernel matrix between `X` and `Y`.
Parameters
----------
x : array-like, shape = (n_samples_x, n_features)
Training data
y : array-like, shape = (n_samples_y, n_features)
Testing data
Returns
-------
kernel : ndarray, shape = (n_samples_x, n_samples_y)
Kernel matrix. Values are normalized to lie within [0, 1].
"""
return self.fit(X).transform(Y).T
def pairwise_kernel(self, X, Y):
"""Function to use with :func:`sklearn.metrics.pairwise.pairwise_kernels`
Parameters
----------
X : array, shape = (n_features,)
Y : array, shape = (n_features,)
Returns
-------
similarity : float
Similarities are normalized to be within [0, 1]
"""
check_is_fitted(self, 'X_fit_')
if X.shape[0] != Y.shape[0]:
raise ValueError('X and Y have different number of features')
val = pairwise_continuous_ordinal_kernel(X[self._numeric_columns], Y[self._numeric_columns],
self._numeric_ranges)
if len(self._nominal_columns) > 0:
val += pairwise_nominal_kernel(X[self._nominal_columns].astype(numpy.int8),
Y[self._nominal_columns].astype(numpy.int8))
val /= X.shape[0]
return val