# License: BSD-3-Clause
import os
from os.path import dirname
import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator, ClusterMixin
from sklearn.cluster import KMeans
from sklearn.gaussian_process import kernels
from ..utils import check_Xs_y
from ..explore import get_missing_samples_by_mod
from .. import octavemodule_installed, oct2py_module_error
if octavemodule_installed:
import oct2py
[docs]
class MKKMIK(BaseEstimator, ClusterMixin):
r"""
Multiple Kernel K-Means with Incomplete Kernels (MKKM-IK). [#mkkmikpaper]_ [#mkkmikcode]_
MKKM-IK integrates imputation and clustering into a single optimization procedure. Thus, the clustering result
guides the missing kernel imputation, and the latter is used to conduct the subsequent clustering. Both procedures
will be performed until convergence.
Parameters
----------
n_clusters : int, default=8
The number of clusters to generate.
kernel : callable, default=None
Specifies the kernel type to be used in the algorithm. It uses dot product kernel by default.
kernel_initialization : str, default="zeros"
Specifies the algorithm to initialize the kernel. It should be one of ['zeros', 'mean', 'knn', 'em', 'laplacian'].
lambda_reg : float, default=1.
Regularization parameter. The algorithm demonstrated stable performance across a wide range of
this hyperparameter.
qnorm : float, default=2.
Regularization parameter. The algorithm demonstrated stable performance across a wide range of
this hyperparameter.
random_state : int, default=None
Determines the randomness. Use an int to make the randomness deterministic.
engine : str, default='octave'
Engine to use for computing the model. Current options are 'octave'.
verbose : bool, default=False
Verbosity mode.
clean_space : bool, default=True
If engine is 'octave' and clean_space is True, the session will be closed after fitting the model.
Attributes
----------
labels_ : array-like of shape (n_samples,)
Labels of each point in training data.
embedding_ : array-like of shape (n_samples, n_clusters)
Consensus clustering matrix to be used as input for the KMeans clustering step.
gamma_ : array-like of shape (n_mods,)
Kernel weights.
KA_ : array-like of shape (n_samples, n_mods)
Kernel sub-matrix.
loss_ : array-like of shape (n_iter\_,)
Values of the loss function.
n_iter_ : int
Number of iterations.
References
----------
.. [#mkkmikpaper] X. Liu et al., "Multiple Kernel k-Means with Incomplete Kernels," in IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol. 42, no. 5, pp. 1191-1204, 1 May 2020,
doi: 10.1109/TPAMI.2019.2892416.
.. [#mkkmikcode] https://github.com/wangsiwei2010/multiple_kernel_clustering_with_absent_kernel
Example
--------
>>> import numpy as np
>>> import pandas as pd
>>> from imml.cluster import MKKMIK
>>> Xs = [pd.DataFrame(np.random.default_rng(42).random((20, 10))) for i in range(3)]
>>> estimator = MKKMIK(n_clusters = 2)
>>> labels = estimator.fit_predict(Xs)
"""
def __init__(self, n_clusters: int = 8, kernel_initialization: str = "zeros",
kernel: callable = None,
qnorm: float = 2., random_state: int = None, engine: str = "octave",
verbose=False, clean_space: bool = True):
if not isinstance(n_clusters, int):
raise ValueError(f"Invalid n_clusters. It must be an int. A {type(n_clusters)} was passed.")
if n_clusters < 2:
raise ValueError(f"Invalid n_clusters. It must be an greater than 1. {n_clusters} was passed.")
engines_options = ["octave"]
if engine not in engines_options:
raise ValueError(f"Invalid engine. Expected one of {engines_options}. {engine} was passed.")
if (engine == "octave") and (not octavemodule_installed):
raise ImportError(oct2py_module_error)
kernel_initializations = ['zeros', 'mean', 'knn', 'em', 'laplacian']
if kernel_initialization not in kernel_initializations:
raise ValueError(f"Invalid kernel_initialization. Expected one of: {kernel_initializations}")
if kernel is None:
kernel = kernels.Sum(kernels.DotProduct(), kernels.WhiteKernel())
self.n_clusters = n_clusters
self.kernel_initialization = kernel_initialization
self.qnorm = qnorm
self.kernel = kernel
self.random_state = random_state
self.engine = engine
self.verbose = verbose
self.kernel_initializations = {"zeros": "algorithm2", "mean": "algorithm3", "knn": "algorithm0",
"em": "algorithm6", "laplacian": "algorithm4"}
self.clean_space = clean_space
if self.engine == "octave":
octave_folder = dirname(__file__)
octave_folder = os.path.join(octave_folder, "_" + (os.path.basename(__file__).split(".")[0]))
self._octave_folder = octave_folder
octave_files = [x for x in os.listdir(octave_folder) if x.endswith(".m")]
self._oc = oct2py.Oct2Py(temp_dir= octave_folder)
for octave_file in octave_files:
with open(os.path.join(octave_folder, octave_file)) as f:
self._oc.eval(f.read())
[docs]
def fit(self, Xs, y=None):
r"""
Fit the transformer to the input data.
Parameters
----------
Xs : list of array-likes objects
- Xs length: n_mods
- Xs[i] shape: (n_samples, n_features_i)
A list of different modalities.
y : Ignored
Not used, present here for API consistency by convention.
Returns
-------
self : Fitted estimator.
"""
Xs = check_Xs_y(Xs, ensure_all_finite='allow-nan')
if self.engine == "octave":
if isinstance(Xs[0], pd.DataFrame):
transformed_Xs = [X.values for X in Xs]
elif isinstance(Xs[0], np.ndarray):
transformed_Xs = Xs
s = get_missing_samples_by_mod(Xs=transformed_Xs, return_as_list=True)
s = tuple([{"indx": pd.Series(i).add(1).to_list()} for i in s])
transformed_Xs = [self.kernel(X) for X in transformed_Xs]
transformed_Xs = np.array(transformed_Xs).swapaxes(0, -1)
kernel = self.kernel_initializations[self.kernel_initialization]
if self.random_state is not None:
self._oc.rand('seed', self.random_state)
H_normalized,gamma,obj,KA = self._oc.myabsentmultikernelclustering(transformed_Xs, s, self.n_clusters,
self.qnorm, kernel, nout=4)
KA = KA[:, 0]
obj = obj[0]
if self.clean_space:
self._clean_space()
model = KMeans(n_clusters=self.n_clusters, n_init="auto", random_state=self.random_state)
self.labels_ = model.fit_predict(X=H_normalized)
self.embedding_, self.gamma_, self.KA_, self.loss_ = H_normalized, gamma, KA, obj
self.n_iter_ = len(self.loss_)
return self
def _predict(self, Xs):
r"""
Return clustering results for samples.
Parameters
----------
Xs : list of array-likes objects
- Xs length: n_mods
- Xs[i] shape: (n_samples, n_features_i)
A list of different modalities.
Returns
-------
labels : ndarray of shape (n_samples,)
Index of the cluster each sample belongs to.
"""
return self.labels_
[docs]
def fit_predict(self, Xs, y=None):
r"""
Fit the model and return clustering results.
Convenience method; equivalent to calling fit(X) followed by predict(X).
Parameters
----------
Xs : list of array-likes objects
- Xs length: n_mods
- Xs[i] shape: (n_samples, n_features_i)
A list of different modalities.
Returns
-------
labels : ndarray of shape (n_samples,)
Index of the cluster each sample belongs to.
"""
labels = self.fit(Xs)._predict(Xs)
return labels
def _clean_space(self):
[os.remove(os.path.join(self._octave_folder, x)) for x in ["reader.mat", "writer.mat"]]
self._oc.exit()
del self._oc
return None