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Clustering.py
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Clustering.py
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import numpy as np
import math
import globals as gb
from sklearn.cluster import AffinityPropagation
from sklearn.cluster import MeanShift
from sklearn.cluster import KMeans
from sklearn.mixture import GMM
from sklearn.mixture import DPGMM
from sklearn.metrics import silhouette_score
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.feature_selection import VarianceThreshold
from Visualize import Visualize
from scipy.spatial import distance
import matplotlib.pyplot as plt
import scipy
from random import shuffle
class Clustering:
def __init__(self, X, scale=False, features=None):
self.random_seed = 12345 # set to None for random
self.X = X
self.k = None
self.centers = None
self.h = None
self.Y = None
self.scaler = None
self.ids = None
# Visualize().plot( zip(*self.X) ) # FOR DEBUG
# Reduce the number of features
if features is not None:
if isinstance( features, (int, float) ):
variances = VarianceThreshold().fit(self.X).variances_
self.ids = sorted(range(len(variances)), key=lambda i: variances[i])[-int(features):] # indexes of the top n_features values in variances
elif type(features) in [list,tuple]:
self.ids = features
self.X = self.reduceFeatures(self.X)
print("Selected features", self.ids, "on a total of", len(X[0])) # FOR DEBUG
# Visualize().plot( zip(*self.X) ) # FOR DEBUG
if scale:
self.scaler = StandardScaler() # MinMaxScaler() can also be used instead of StandardScaler()
self.X = self.scaler.fit_transform(self.X)
#---------------------------------------
def reduceFeatures(self, X):
if self.ids is None:
return X
else:
return [ [v for iv,v in enumerate(x) if iv in self.ids] for x in X ]
#---------------------------------------
def affinity(self, k=2): # K is not used here
self.h = AffinityPropagation(damping=0.75, preference=k, max_iter=200, convergence_iter=15, copy=True, affinity='euclidean').fit( self.X )
self.Y = self.h.labels_
self.k = k
self.centers = self.getCenters()
return self
#---------------------------------------
def meanshift(self, k=2): # K is not used here
self.h = MeanShift(bandwidth=None, seeds=None, bin_seeding=False, min_bin_freq=1, cluster_all=True).fit( self.X )
self.Y = self.h.labels_
self.k = k
self.centers = self.getCenters()
return self
#---------------------------------------
def kmeans(self, k=2):
self.h = KMeans(n_clusters = k, init = 'k-means++', n_init = 10, max_iter = 1000, tol = 0.00001, random_state = self.random_seed).fit( self.X )
self.Y = self.h.labels_
self.k = k
self.centers = self.getCenters()
return self
#---------------------------------------
def gmm(self, k=2):
self.h = GMM(n_components=k, random_state = self.random_seed).fit( self.X )
self.Y = self.h.predict( self.X )
self.k = k
self.centers = self.getCenters()
#TODO
# posterior = self.h.predict_proba( self.X[:5] )
# likelihood = self.h.score( self.X[:5] )
return self
#---------------------------------------
''' Dirichlet Process is as likely to start a new cluster for a point as it is to add that point to a cluster with alpha elements (0<alpha<inf).
A higher alpha means more clusters, as the expected number of clusters is alpha*log(N)'''
def dpgmm(self, k=10, alpha=1.0):
self.h = DPGMM(n_components=k, alpha=alpha, random_state = self.random_seed).fit( self.X )
self.Y = self.h.predict( self.X )
self.k = k # this is the max number of components in dpgmm
self.centers = self.getCenters()
#TODO
# posterior = self.h.predict_proba( self.X[:5] )
# likelihood = self.h.score( self.X[:5] )
return self
#---------------------------------------
def done(self):
if self.h is None:
print("Clustering is not yet done !")
return False
else:
return True
#---------------------------------------
def getCenters(self):
if not self.done(): return
try:
return self.h.cluster_centers_
# If the clustering has no centers, compute them based on clusters
except AttributeError:
unique_labels = np.unique(self.Y)
clusters = { ul:[] for ul in unique_labels }
for i in range( len(self.X) ):
clusters[ self.Y[i] ].append( self.X[i] )
centers = []
for label in clusters:
centers.append( [np.mean(col) for col in list(zip(* clusters[label] )) ] )
return centers
#---------------------------------------
def predict(self, x):
if not self.done(): return
x_processed = x
x_processed = self.reduceFeatures([x_processed])[0]
x_processed = x_processed if self.scaler is None else self.scaler.transform(x_processed)
return self.h.predict(x_processed)[0]
#---------------------------------------
def predictAll(self, X):
if not self.done(): return
X_processed = X
X_processed = self.reduceFeatures(X_processed)
X_processed = X_processed if self.scaler is None else self.scaler.transform(X_processed)
return list(self.h.predict(X_processed))
#---------------------------------------
def quality(self, X=None):
if not self.done(): return
if X is None: # if X not provided then use the training data and resulting labels
X = self.X
Y = self.Y
else: # if X is provided then use it with the predicted labels (clusters)
Y = self.predictAll(X)
indexs = range(len(X)); shuffle(indexs)
X = np.array([ X[i] for i in indexs[:5000] ])
Y = np.array([ Y[i] for i in indexs[:5000] ])
if len(set(Y)) < 2: return 0. # FIXME
return silhouette_score(X, Y, metric='euclidean')
#---------------------------------------
def plot(self, fig=None):
if not self.done(): return
viz = Visualize()
if len(self.X[0]) > 3:
X = viz.PCA_Transform( list(zip(*self.X)) )
else:
X = self.X
unique_labels = np.unique(self.Y)
clusters = { ul:[] for ul in unique_labels }
for i in range( len(X) ):
clusters[ self.Y[i] ].append( X[i] )
centers_for_plot = [] # Not the real centers because dimension was reduced using PCA
for label in clusters:
centers_for_plot.append( [np.mean(col) for col in list(zip(* clusters[label] )) ] )
viz.do_plot(list(zip(*centers_for_plot)), marker='o', color='m')
viz.plot_groups(clusters, fig)
#---------------------------------------