import os
from os.path import dirname
import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.cluster import KMeans
from ..impute import get_observed_mod_indicator
from ..utils import check_Xs
from ..preprocessing import remove_missing_samples_by_mod
matlabmodule_installed = False
oct2py_module_error = "Module 'matlab' needs to be installed."
try:
import oct2py
matlabmodule_installed = True
except ImportError:
pass
[docs]
class IMSCAGL(BaseEstimator, ClassifierMixin):
r"""
Incomplete Multiview Spectral Clustering With Adaptive Graph Learning (IMSCAGL). [#imscaglpaper1]_ [#imscaglpaper2]_ [#imscaglcode1]_ [#imscaglcode2]_
IMSCAGL utilizes graph learning and spectral clustering techniques to derive a unified representation for
incomplete multiview clustering.
Parameters
----------
n_clusters : int, default=8
The number of clusters to generate.
lambda1 : float, default=0.1
Penalty parameter for learning model of the multi-modal subspace clustering.
lambda2 : float, default=1000
Penalty parameter for learning model of the multi-modal subspace clustering.
lambda3 : float, default=100
Penalty parameter for learning the consensus representation from those cluster indicator matrices of all views.
k : int, default=5
Parameter k of KNN graph.
neighbor_mode : str, default='KNN'
Indicates how to construct the graph. Options are 'KNN' (default), and 'Supervised'.
weight_mode : str, default='Binary'
Indicates how to assign weights for each edge in the graph. Options
are 'Binary' (default), 'Cosine' and 'HeatKernel'.
max_iter : int, default=100
Maximum number of iterations.
miu : float, default=0.01
Constant for updating variables during the learning process.
rho : float, default=100
Constant for updating variables during the learning process.
random_state : int, default=None
Determines the randomness. Use an int to make the randomness deterministic.
engine : str, default=matlab
Engine to use for computing the model. Currently only 'matlab' is supported.
verbose : bool, default=False
Verbosity mode.
clean_space : bool, default=True
If engine is 'matlab' 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 representation matrix to be used as input for the KMeans clustering step.
References
----------
.. [#imscaglpaper1] J. Wen, Y. Xu and H. Liu, "Incomplete Multiview Spectral Clustering With Adaptive Graph
Learning," in IEEE Transactions on Cybernetics, vol. 50, no. 4, pp. 1418-1429, April 2020,
doi: 10.1109/TCYB.2018.2884715.
.. [#imscaglpaper2] Jie Wen, Zheng Zhang, Lunke Fei, Bob Zhang, Yong Xu, Zhao Zhang, Jinxing Li, A Survey on
Incomplete Multi-view Clustering, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS:
SYSTEMS, 2022.
.. [#imscaglcode1] https://github.com/DarrenZZhang/Survey_IMC
.. [#imscaglcode2] https://github.com/ckghostwj/Incomplete-Multiview-Spectral-Clustering-with-Adaptive-Graph-Learning
Example
--------
>>> import numpy as np
>>> import pandas as pd
>>> from imml.cluster import IMSCAGL
>>> Xs = [pd.DataFrame(np.random.default_rng(42).random((20, 10))) for i in range(3)]
>>> estimator = IMSCAGL(n_clusters = 2)
>>> labels = estimator.fit_predict(Xs)
"""
def __init__(self, n_clusters: int = 8, lambda1: float = 0.1, lambda2: float = 1000, lambda3: float = 100, k: int = 5,
neighbor_mode: str = 'KNN', weight_mode: str = 'Binary', max_iter: int = 100, miu: float = 0.01,
rho: float = 1.1, random_state: int = None, engine: str = "matlab", 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 = ["matlab"]
if engine not in engines_options:
raise ValueError(f"Invalid engine. Expected one of {engines_options}. {engine} was passed.")
if (engine == "matlab") and (not matlabmodule_installed):
raise ImportError(oct2py_module_error)
self.n_clusters = n_clusters
self.lambda1 = lambda1
self.lambda2 = lambda2
self.lambda3 = lambda3
self.miu = miu
self.rho = rho
self.beta = rho
self.k = k
self.neighbor_mode = neighbor_mode
self.weight_mode = weight_mode
self.max_iter = max_iter
self.random_state = random_state
self.engine = engine
self.verbose = verbose
self.clean_space = clean_space
if self.engine == "matlab":
matlab_folder = dirname(__file__)
matlab_folder = os.path.join(matlab_folder, "_" + (os.path.basename(__file__).split(".")[0]))
self._matlab_folder = matlab_folder
matlab_files = [x for x in os.listdir(matlab_folder) if x.endswith(".m")]
self._oc = oct2py.Oct2Py(temp_dir= matlab_folder)
for matlab_file in matlab_files:
with open(os.path.join(matlab_folder, matlab_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(Xs, ensure_all_finite='allow-nan')
if self.engine=="matlab":
if not isinstance(Xs[0], pd.DataFrame):
Xs = [pd.DataFrame(X) for X in Xs]
observed_mod_indicator = get_observed_mod_indicator(Xs=Xs)
transformed_Xs = remove_missing_samples_by_mod(Xs=Xs)
w = [pd.DataFrame(np.eye(len(X)), index=X.index, columns=X.index) for X in Xs]
w = [eye.loc[samples,:].values for eye, (_, samples) in zip(w, observed_mod_indicator.items())]
w = tuple(w)
transformed_Xs = tuple([X.T for X in transformed_Xs])
if self.random_state is not None:
self._oc.rand('seed', self.random_state)
F = self._oc.IMSAGL(transformed_Xs, w, self.n_clusters, self.lambda1, self.lambda2, self.lambda3,
self.miu, self.rho, self.max_iter,
{"NeighborMode": self.neighbor_mode, "WeightMode": self.weight_mode, "k": self.k})
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= F)
self.embedding_ = F
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._matlab_folder, x)) for x in ["reader.mat", "writer.mat"]]
self._oc.exit()
del self._oc
return None