# License: BSD-3-Clause
import os
from os.path import dirname
import pandas as pd
from sklearn.base import BaseEstimator, ClusterMixin
from sklearn.cluster import KMeans
from ..impute import get_observed_mod_indicator
from ..utils import check_Xs_y
from .. import octavemodule_installed, oct2py_module_error
if octavemodule_installed:
import oct2py
[docs]
class PIMVC(BaseEstimator, ClusterMixin):
r"""
Projective Incomplete Multi-View Clustering (PIMVC). [#pimvcpaper]_ [#pimvccode]_
The objective of PIMVC is to simultaneously discover the projection matrix for each modality and establish a unified
feature representation shared across incomplete multiple views, facilitating clustering. Essentially, PIMVC
transforms the traditional multi-modality matrix factorization model into a multi-modality projection learning model. By
consolidating various modality-specific objective losses into a cohesive subspace of equal dimensions, it adeptly
handles the challenge where a single modality might overly influence consensus representation learning due to
imbalanced information across views stemming from diverse dimensions. Furthermore, to capture the data geometric
structure, PIMVC incorporates a penalty term for graph regularization.
Parameters
----------
n_clusters : int, default=8
The number of clusters to generate.
dele : float, default=0.1
nonnegative.
lamb : float, default=100000
Penalty parameters. Should be greather than 0.
beta : float, default=1
Trade-off parameter.
k : int, default=3
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.
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. Currently only 'octave' is supported.
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.
loss_ : array-like of shape (n_iter\_,)
Values of the loss function.
n_iter_ : int
Number of iterations.
References
----------
.. [#pimvcpaper] S. Deng, J. Wen, C. Liu, K. Yan, G. Xu and Y. Xu, "Projective Incomplete Multi-View Clustering,"
in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2023.3242473.
.. [#pimvccode] https://github.com/Dshijie/PIMVC
Example
--------
>>> import numpy as np
>>> import pandas as pd
>>> from imml.cluster import PIMVC
>>> Xs = [pd.DataFrame(np.random.default_rng(42).random((20, 10))) for i in range(3)]
>>> estimator = PIMVC(n_clusters = 2)
>>> labels = estimator.fit_predict(Xs)
"""
def __init__(self, n_clusters: int = 8, dele: float = 0.1, lamb: int = 100000, beta: int = 1, k: int = 3,
neighbor_mode: str = 'KNN', weight_mode: str = 'Binary', max_iter: int = 100,
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)
if lamb <= 0:
raise ValueError(f"Invalid lamb. It must be a positive value. {lamb} was passed.")
if k <= 0:
raise ValueError(f"Invalid k. It must be a positive value. {k} was passed.")
self.n_clusters = n_clusters
self.dele = dele
self.lamb = lamb
self.beta = beta
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 == "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')
try:
assert self.n_clusters <= min([X.shape[1] for X in Xs])
except AssertionError:
raise ValueError(f"n_clusters ({self.n_clusters}) should be smaller or equal to " +
f"the smallest n_features_i ({min([X.shape[1] for X in Xs])}).")
if self.engine=="octave":
observed_mod_indicator = get_observed_mod_indicator(Xs)
if isinstance(observed_mod_indicator, pd.DataFrame):
observed_mod_indicator = observed_mod_indicator.values
transformed_Xs = tuple([X.T for X in Xs])
if self.random_state is not None:
self._oc.rand('seed', self.random_state)
v, loss = self._oc.PIMVC(transformed_Xs, self.n_clusters, observed_mod_indicator, self.lamb, self.beta,
self.max_iter, {"NeighborMode": self.neighbor_mode,
"WeightMode": self.weight_mode,
"k": self.k}, nout=2)
if self.clean_space:
self._clean_space()
model = KMeans(n_clusters= self.n_clusters, n_init= "auto", random_state= self.random_state)
v = v.T
self.labels_ = model.fit_predict(X= v)
self.embedding_ = v
self.loss_ = loss[:, 0]
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