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 sklearn.gaussian_process import kernels
from ..impute import get_observed_mod_indicator
from ..utils import check_Xs
matlabmodule_installed = False
oct2py_module_error = "Module 'matlab' needs to be installed."
try:
import oct2py
matlabmodule_installed = True
except ImportError:
pass
[docs]
class OSLFIMVC(BaseEstimator, ClassifierMixin):
r"""
One-Stage Incomplete Multi-View Clustering via Late Fusion (OS-LF-IMVC). [#oslfimvcpaper]_ [#oslfimvccode]_
OS-LF-IMVC integrates the processes of imputing incomplete views and clustering into a cohesive optimization
procedure. This approach enables the direct utilization of the learned consensus partition matrix to enhance
the final clustering task.
Parameters
----------
n_clusters : int, default=8
The number of clusters to generate.
kernel : callable, default=kernels.Sum(kernels.DotProduct(), kernels.WhiteKernel())
Specifies the kernel type to be used in the algorithm.
lambda_reg : float, default=1.
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=matlab
Engine to use for computing the model. Current options are 'matlab'.
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_ : np.array
Consensus clustering matrix to be used as input for the KMeans clustering step.
WP_ : array-like of shape (n_clusters, n_clusters, n_mods)
p-th permutation matrix.
C_ : array-like of shape (n_clusters, n_clusters)
Centroids.
beta_ : array-like of shape (n_mods,)
Adaptive weights of clustering matrices.
loss_ : array-like of shape (n_iter\_,)
Values of the loss function.
n_iter_ : int
Number of iterations.
References
----------
.. [#oslfimvcpaper] Yi Zhang, Xinwang Liu, Siwei Wang, Jiyuan Liu, Sisi Dai, and En Zhu. 2021. One-Stage Incomplete
Multi-view Clustering via Late Fusion. In Proceedings of the 29th ACM International Conference
on Multimedia (MM '21). Association for Computing Machinery, New York, NY, USA, 2717–2725.
https://doi.org/10.1145/3474085.3475204.
.. [#oslfimvccode] https://github.com/ethan-yizhang/OSLF-IMVC
Example
--------
>>> import numpy as np
>>> import pandas as pd
>>> from imml.cluster import OSLFIMVC
>>> Xs = [pd.DataFrame(np.random.default_rng(42).random((20, 10))) for i in range(3)]
>>> estimator = OSLFIMVC(n_clusters = 2)
>>> labels = estimator.fit_predict(Xs)
"""
def __init__(self, n_clusters: int = 8, kernel: callable = kernels.Sum(kernels.DotProduct(), kernels.WhiteKernel()),
lambda_reg: float = 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.kernel = kernel
self.lambda_reg = lambda_reg
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())
self._oc.eval("pkg load statistics")
[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":
observed_mod_indicator = get_observed_mod_indicator(Xs)
if isinstance(observed_mod_indicator, pd.DataFrame):
observed_mod_indicator = observed_mod_indicator.reset_index(drop=True)
elif isinstance(observed_mod_indicator[0], np.ndarray):
observed_mod_indicator = pd.DataFrame(observed_mod_indicator)
s = [modality[modality == 0].index.values for _,modality in observed_mod_indicator.items()]
transformed_Xs = [self.kernel(X) for X in Xs]
transformed_Xs = np.array(transformed_Xs).swapaxes(0, -1)
s = tuple([{"indx": i +1} for i in s])
if self.random_state is not None:
self._oc.rand('seed', self.random_state)
U, C, WP, beta, obj = self._oc.OS_LF_IMVC_alg(transformed_Xs, s, self.n_clusters, self.lambda_reg, nout=5)
beta = beta[:,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= U)
self.embedding_, self.WP_, self.C_, self.beta_, self.loss_ = U, WP, C, beta, 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._matlab_folder, x)) for x in ["reader.mat", "writer.mat"]]
self._oc.exit()
del self._oc
return None