def test_clustering_namingTestRequireUnique(self):
c = oe.cluster(self.data)
c.cluster('parent', 'kmeans', 'kmeans', K=2)
self.assertEqual(1, len(c.labels))
c.cluster('parent', 'kmeans', 'kmeans', Require_Unique=1, K=2)
self.assertEqual(1, len(c.labels))
def mixture_model(self, K=2, iterations=10):
"""
Finishing Technique to assemble a final, hard parition of the data according to maximizing the likelihood according to the
observed clustering solutions across the ensemble. This will operate on all clustering solutions contained in the container cluster class.
Operates on entire ensemble of clustering solutions in self, to create a mixture model
See finishing.mixture_model for more details.
Parameters
----------
K: int
number of clusters to create. Default K=2
iterations: int
number of iterations of EM algorithm to perform. Default iterations=10
Returns
-------
c: openensembles clustering object
a new clustering object with c.labels['mixture_model'] set to the final solution.
Raises
------
ValueError:
If there are not at least two clustering solutions
References
----------
Topchy, Jain, and Punch, "A mixture model for clustering ensembles Proc. SIAM Int. Conf. Data Mining (2004)"
Examples
--------
>>> cMM = c.mixture_model(4, 10)
>>> d.plot_data('parent', cluster_labels=cMM.labels['mixture_model'])
"""
params = {}
params['iterations'] = iterations
params['K'] = K
#check to make sure more than one solution exists in ensemble
if len(self.params) < 2:
raise ValueError("Mixture Model is a finsihing technique for an ensemble, the cluster object must contain more than one solution")
N = self.dataObj.D['parent'].shape[0]
parg = []
for solution in self.labels:
parg.append(self.labels[solution])
mixtureObj = finish.mixture_model(parg, N, nEnsCluster=K, iterations=iterations)
mixtureObj.emProcess()
c = oe.cluster(self.dataObj)
name = 'mixture_model'
c.labels[name] = mixtureObj.labels
c.data_source[name] = 'parent'
c.clusterNumbers[name] = np.unique(c.labels[name])
c.params[name] = params
c.algorithms[name] = 'mixture_model'
return c
def test_ReplicateValidation(self):
c = oe.cluster(self.data)
c.cluster('parent', 'kmeans', 'kmeans', K=2)
v = oe.validation(self.data, c)
len_expected = 0
self.assertEqual(len_expected, len(v.validation))
v.calculate('Ball_Hall', 'kmeans', 'parent')
len_expected = 1
self.assertEqual(len_expected, len(v.validation))
v.calculate('Ball_Hall', 'kmeans', 'parent')
self.assertEqual(len_expected, len(v.validation))
def test_validation_badSourceAndCluster(self):
c = oe.cluster(self.data)
c.cluster('parent', 'kmeans', 'kmeans', K=2)
v = oe.validation(self.data, c)
self.assertRaises(
ValueError,
lambda: v.calculate('Ball_Hall', 'kmeans', 'gobblygook'))
self.assertRaises(
ValueError,
lambda: v.calculate('Ball_Hall', 'gobblygook', 'parent'))
self.assertRaises(
ValueError, lambda: v.calculate('GobblyGook', 'kmeans', 'parent'))
def test_allValidationMetrics(self):
c = oe.cluster(self.data)
c.cluster('parent', 'kmeans', 'kmeans', K=2)
v = oe.validation(self.data, c)
FCN_DICT = v.validation_metrics_available()
len_expected = 1
for validation_name in FCN_DICT:
v.calculate(validation_name, 'kmeans', 'parent')
self.assertEqual(len_expected, len(v.validation))
self.assertEqual(len_expected, len(v.description))
self.assertEqual(len_expected, len(v.source_name))
self.assertEqual(len_expected, len(v.cluster_name))
len_expected += 1
def finish_co_occ_linkage(self, threshold, linkage='average'):
"""
The finishing technique that calculates a co-occurrence matrix on all cluster solutions in the ensemble and
then hierarchically clusters the co-occurrence, treating it as a similarity matrix. The clusters are defined by
the threshold of the distance used to cut.
Parameters
----------
threshold: float
Linkage distance to use as a cutoff to create partitions
linkage: string
Linkage type. See `scipy.cluster.hierarchy <https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html#scipy.cluster.hierarchy.linkage>`_
Returns
-------
c: openensembles clustering object
a new clustering object with c.labels['co_occ_linkage'] set to the final solution.
Examples
--------
To determine where the cut is visually, at threshold=0.5:
>>> coMat = c.co_occurrence()
>>> coMat.plot(threshold=0.5, linkage='ward')
To create the cut at threshold=0.5
>>> cWard = c.co_occ_linkage(0.5, 'ward')
>>> d.plot_data('parent', cluster_labels=cWard.labels['co_occ_linkage'])
"""
params = {}
params['linkage'] = linkage
params['threshold'] = threshold
coMatObj = self.co_occurrence_matrix('parent')
coL = finish.co_occurrence_linkage(coMatObj,
threshold,
linkage=linkage)
coL.finish()
c = oe.cluster(self.dataObj)
name = 'co_occ_linkage'
c.labels[name] = coL.labels
c.params[name] = params
c.data_source[name] = 'parent'
c.clusterNumbers[name] = np.unique(c.labels[name])
c.algorithms[name] = 'co_occ_linkage'
return c
def test_cluster_merge(self):
c = oe.cluster(self.data)
c2 = oe.cluster(self.data)
c.cluster('parent', 'kmeans', 'kmeans', K=2)
c2.cluster('parent', 'kmeans', 'kmeans', K=2)
self.assertRaises(ValueError, lambda: c.merge(['string']))
self.assertEqual(1, len(c.labels))
dictTrans = c.merge([c2])
self.assertEqual(1, len(c2.labels))
self.assertEqual(2, len(c.labels))
#start again, to send in a list
c = oe.cluster(self.data)
c2 = oe.cluster(self.data)
c3 = oe.cluster(self.data)
c.cluster('parent', 'kmeans', 'kmeans', K=2)
c2.cluster('parent', 'kmeans', 'kmeans', K=2)
c2.cluster('parent', 'kmeans', 'kmeans_another', K=2)
c3.cluster('parent', 'kmeans', 'kmeans', K=2)
dictTrans = c.merge([c2, c3])
self.assertEqual(4, len(c.labels))
def test_cluster_slice(self):
c = oe.cluster(self.data)
c.cluster('parent', 'kmeans', 'kmeans_0', K=2)
c.cluster('parent', 'kmeans', 'kmeans_1', K=2)
c.cluster('parent', 'kmeans', 'kmeans_2', K=2)
self.assertEqual(3, len(c.labels))
names = ['kmeans_1', 'kmeans_2']
cNew = c.slice(names)
self.assertEqual(2, len(cNew.labels))
self.assertEqual(2, len(cNew.params))
self.assertEqual(2, len(cNew.clusterNumbers))
self.assertEqual(2, len(cNew.data_source))
names = ['kmeans_2', 'gooblygook']
self.assertRaises(ValueError, lambda: c.slice(names))
def test_all_algorithms(self):
"""
Test all algorithms with default parameters
"""
c = oe.cluster(self.data)
ALG_FCN_DICT = c.algorithms_available()
num = 0
#remove MeanShift, which cannot be used in this dataset
del ALG_FCN_DICT['MeanShift']
for algorithm in ALG_FCN_DICT:
name = algorithm + 'parent'
c.cluster('parent', algorithm, name, K=2)
num += 1
self.assertEqual(num, len(c.labels))
def slice(self, names):
"""
Returns a new cluster object containing a slice indicated by the list of names given (dictionary keys shared amongst labels, params, etc.)
Parameters
----------
names: list
A list of strings matching the names to keep in the new slice
Returns
--------
c: an openensembles clustering object
A oe.cluster object that contains only those names passed in
Examples
--------
Get only the solutions made by agglomerative clustering
>>> names = c.search_field('algorithm', 'agglomerative') #return all solutions with agglomerative
>>> cNew = c.slice(names)
Get only the solutions that were made with K=2 calls
>>> names = c.search_field('K', 2) #return all solution names that used K=2
>>> cNew = c.slice(names)
Raises
------
ValueError
If a name in the list of names does not exist in cluster object
"""
c = oe.cluster(self.dataObj)
names_existing = list(self.labels.keys())
for name in names:
if name not in names_existing:
raise ValueError(
"ERROR: the source you requested for slicing does not exist in cluster object %s"
% (name))
c.labels[name] = self.labels[name]
c.data_source[name] = self.data_source[name]
c.params[name] = self.params[name]
c.clusterNumbers[name] = self.clusterNumbers[name]
c.algorithms[name] = self.algorithms[name]
return c
def test_cluster_search_field(self):
self.data.transform('parent', 'zscore', 'zscore', axis=0)
c = oe.cluster(self.data)
c.cluster('parent', 'kmeans', 'parent_kmeans_2', K=2)
c.cluster('zscore', 'kmeans', 'kmeans_3', K=3)
c.cluster('zscore',
'agglomerative',
'zscore_agglom_ward',
K=2,
linkage='ward')
c.cluster('zscore',
'agglomerative',
'zscore_agglom_complete',
K=2,
linkage='complete')
self.assertEqual(4, len(c.labels))
#test for algorithm
names = c.search_field('algorithm', 'kmeans')
self.assertEqual(2, len(names))
#test for data_source
names = c.search_field('data_source', 'parent')
self.assertEqual(1, len(names))
self.assertEqual('parent_kmeans_2', names[0])
#test for cluster number
names = c.search_field('clusterNumber', 3)
self.assertEqual(1, len(names))
self.assertEqual('kmeans_3', names[0])
#test for K
names = c.search_field('K', 2)
self.assertEqual(3, len(names))
#test for linkage
names = c.search_field('linkage', 'ward')
self.assertEqual(1, len(names))
self.assertEqual('zscore_agglom_ward', names[0])
#test for no parameter of that type found
self.assertRaises(ValueError, lambda: c.search_field('gobbly', 'gook'))
def run_mv_oe(X, y=None):
"""Deprecated"""
print("a")
n_features = X.shape[1]
columns = [f"x{i}" for i in range(n_features)]
df = pd.DataFrame(X, columns=columns)
dataObj = oe.data(df, list(range(n_features)))
c = oe.cluster(dataObj)
c_MV_arr = []
for i in range(30):
name = f'kmeans_{i}'
c.cluster('parent', 'kmeans', name, K=15, init='random', n_init=1)
c_MV_arr.append(c.finish_majority_vote(threshold=0.5))
final_labels = c_MV_arr[-1].labels['majority_vote'] - 1
print(len(np.unique(final_labels)))
return X, final_labels, y if len(np.unique(final_labels)) > 1 else run_mv_oe(X, y)
def finish_graph_closure(self, threshold, clique_size=3):
"""
The finishing technique that treats the co-occurrence matrix as a graph, that is binarized by the threshold (>=threshold
becomes an unweighted, undirected edge in an adjacency matrix). This graph object is then subjected to clique formation
according to clique_size (such as triangles if clique_size=3). The cliques are then combined in the graph to create unique
cluster formations.
See also
--------
finishing.py
Returns
-------
c: openenembles clustering object
New cluster object with final solution and name 'graph_closure'
Examples
--------
>>> cGraph = c.finish_graph_closure(0.5, 3)
>>> d.plot_data('parent', cluster_labels=cGraph.labels['graph_closure'])
"""
params = {}
params['threshold'] = threshold
params['clique_size'] = clique_size
coMatObj = self.co_occurrence_matrix('parent')
c_G = finish.graph_closure(coMatObj.co_matrix,
threshold,
clique_size=clique_size)
c_G.finish()
c = oe.cluster(self.dataObj)
name = 'graph_closure'
c.labels[name] = c_G.labels
c.params[name] = params
c.data_source[name] = 'parent'
c.clusterNumbers[name] = np.unique(c.labels[name])
c.algorithms[name] = 'graph_closure'
return c
def test_distance_requirements_clustering(self):
c = oe.cluster(self.data)
self.assertRaises(
ValueError, lambda: c.cluster('parent',
'agglomerative',
'agglomerative',
K=2,
linkage='complete',
distance='precomputed'))
self.assertRaises(
ValueError, lambda: c.cluster(
'parent', 'spectral', 'spectral', K=2, affinity='precomputed'))
D = ca.returnDistanceMatrix(self.data.D['parent'], 'euclidean')
S = ca.convertDistanceToSimilarity(D)
self.assertRaises(
ValueError, lambda: c.cluster('parent',
'spectral',
'spectral',
K=2,
distance='precomputed',
M=D))
c.cluster('parent',
'spectral',
'spectral',
K=2,
affinity='precomputed',
M=S)
self.assertEqual(1, len(c.labels))
c.cluster('parent',
'DBSCAN',
'DBSCAN',
K=2,
affinity='precomputed',
M=D)
self.assertEqual(2, len(c.labels))
def finish_majority_vote(self, threshold=0.5):
"""
Based on Ana Fred's 2001 paper: Fred, Ana. Finding Consistent Clusters in Data Partitions. In Multiple Classifier Systems,
edited by Josef Kittler and Fabio Roli, LNCS 2096, 309-18. Springer, 2001.
This algorithm assingns clusters to the same class if they co-cluster at least 50 of the time. It
greedily joins clusters with the evidence that at least one pair of items from two different clusters co-cluster
a majority of the time. Outliers will get their own cluster.
Parameters
----------
threshold: float
the threshold, or fraction of times objects co-cluster to consider a 'majority'. Default is 0.5 (50% of the time)
Returns
-------
c: openensembles cluster object
New cluster object with final solution and name 'majority_vote'
Examples
--------
>>> c_MV = c.majority_vote(threshold=0.7)
>>> labels = c_MV.labels['majority_vote']
"""
params = {}
coMatObj = self.co_occurrence_matrix('parent')
c_MV = finish.majority_vote(coMatObj.co_matrix, threshold)
c_MV.finish()
c = oe.cluster(self.dataObj)
name = 'majority_vote'
c.labels[name] = c_MV.labels
c.params[name] = params
c.data_source[name] = 'parent'
c.clusterNumbers[name] = np.unique(c.labels[name])
c.algorithms[name] = 'majority_vote'
return c
def test_validation_merge(self):
c = oe.cluster(self.data)
c.cluster('parent',
'kmeans',
'kmeans_1',
K=2,
random_seed=0,
init='random',
n_init=1)
c.cluster('parent',
'kmeans',
'kmeans_2',
K=2,
random_seed=0,
init='random',
n_init=1)
c.cluster('parent',
'kmeans',
'kmeans_3',
K=2,
random_seed=0,
init='random',
n_init=1)
v = oe.validation(self.data, c)
v2 = oe.validation(self.data, c)
v3 = oe.validation(self.data, c)
v.calculate('silhouette', 'kmeans_1', 'parent')
v2.calculate('silhouette', 'kmeans_2', 'parent')
v3.calculate('silhouette', 'kmeans_3', 'parent')
self.assertEqual(1, len(v.validation.keys()))
self.assertRaises(ValueError, lambda: v.merge(['string']))
v.merge([v2, v3])
self.assertEqual(3, len(v.validation.keys()))
# --- SECTION 1 ---
# Libraries and data loading
import openensembles as oe
import numpy as np
import pandas as pd
import sklearn.metrics
from sklearn.datasets import load_breast_cancer
from sklearn.manifold import TSNE
bc = load_breast_cancer()
t = TSNE()
# --- SECTION 2 ---
# Create the data object
cluster_data = oe.data(pd.DataFrame(t.fit_transform(bc.data)), [0, 1])
np.random.seed(123456)
# --- SECTION 3 ---
# Create the ensembles and calculate the homogeneity score
for K in [2, 3, 4, 5, 6, 7]:
for ensemble_size in [3, 4, 5]:
ensemble = oe.cluster(cluster_data)
for i in range(ensemble_size):
name = f'kmeans_{ensemble_size}_{i}'
ensemble.cluster('parent', 'kmeans', name, K)
preds = ensemble.finish_majority_vote(threshold=0.5)
print(f'K: {K}, size {ensemble_size}:', end=' ')
print('%.2f' % sklearn.metrics.homogeneity_score(
bc.target, preds.labels['majority_vote']))
def test_clustering_setup(self):
c = oe.cluster(self.data)
self.assertEqual(1, len(c.dataObj.D))
import pandas as pd
from sklearn import datasets
import openensembles as oe
import matplotlib.pyplot as plt
import seaborn as sns
#Set up a dataset and put in pandas DataFrame.
x, y = datasets.make_moons(n_samples=200, shuffle=True, noise=0.02, random_state=None)
df = pd.DataFrame(x)
#instantiate the oe data object
dataObj = oe.data(df, [1,2])
#instantiate an oe clustering object
c = oe.cluster(dataObj)
c_MV_arr = []
val_arr = []
for i in range(0,39):
# add a new clustering solution, with a unique name
name = 'kmeans_' + str(i)
c.cluster('parent', 'kmeans', name, K=16, init = 'random', n_init = 1)
# calculate a new majority vote solution, where c has one more solution on each iteration
c_MV_arr.append(c.finish_majority_vote(threshold=0.5))
#calculate the determinant ratio metric for each majority vote solution
v = oe.validation(dataObj, c_MV_arr[i])
val_name = v.calculate('det_ratio', 'majority_vote', 'parent')
val_arr.append(v.validation[val_name])
#calculate the co-occurrence matrix
coMat = c.co_occurrence_matrix()
coMat.plot(labels=False)
def test_clustering_NoAlgorithm(self):
c = oe.cluster(self.data)
self.assertRaises(ValueError,
lambda: c.cluster('parent', 'gobblygook', 'bad'))
d = oe.data(df, [i for i in range(1, len(df.columns) + 1)])
'''
WHAT NEEDS TO BE ADDED FOR FUTURE USAGE FROM OTHERS:
a) After loading data
Ensure everything is either normalized or handle in this cell before creating the ensemble
b) Ensure categorical features are encoded
Code cell accidently deleted for encoding but pandas has useful tool for easy encoding
https://scikit-learn.org/stable/modules/generated/sklearn.compose.ColumnTransformer.html
'''
# pass oe dataobj to cluster class
c = oe.cluster(
d) #instantiate an object so we can get all available algorithms
# Call this to list all algorithms currently available in algorithms.py
# These are the algorithms available
# [kmeans,spectral,agglomerative, DBSCAN,HDBSCAN,AffinityPropagation,Birch,MeanShift,GaussianMixture,]
a = c.algorithms_available()
# returns Keys equal to parameters {K, linkages, distances} and values as lists of algorithms that use that key as a variable
# Example :
paramsC = c.clustering_algorithm_parameters(
) #here we will rely on walking through
print(paramsC)
# remove DBSCAN -- this does very well on unstructured data, we want to ask if we can use poorly performing algorithms
# to identify if there isn't structure.
# 'DBSCAN', 'Birch', 'GaussianMixture', 'HDBSCAN', 'MeanShift', 'AffinityPropagation','kmeans', 'agglomerative', 'spectral'
algorithmsToRemove = [
def test_clustering_NoSource(self):
c = oe.cluster(self.data)
self.assertRaises(ValueError,
lambda: c.cluster('parentZ', 'kmeans', 'bad'))
