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Md_comparison_k.py
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Md_comparison_k.py
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"""
==============================================
Comparison of clustering optimization methods
with multi-dimensional data for partitional
algorithms (k-dependent)
FIV, Jan 2021
==============================================
"""
#!/usr/bin/env python3
print(__doc__)
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import pandas as pd
from sklearn import cluster, mixture
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import StandardScaler
from sklearn.metrics.cluster import adjusted_mutual_info_score
import clureal as cr
np.random.seed(100)
def get_cmap(n, name='hsv'):
return plt.cm.get_cmap(name, n)
def mkm(k):
model = cluster.MiniBatchKMeans(n_clusters=k, random_state=100)
return model
def ahc(k):
model = cluster.AgglomerativeClustering(linkage="average", affinity="cityblock",n_clusters=k)
return model
def gmm(k):
model = mixture.GaussianMixture(n_components=k, covariance_type='full',random_state=100)
return model
def bir(k):
model = cluster.Birch(n_clusters=k)
return model
def select_algorithm(argument,k):
switcher = {'mkm': mkm, 'ahc': ahc, 'gmm': gmm, 'bir': bir}
model = switcher.get(argument, lambda: "Invalid algorithm")
return model(k)
### LOG file
data_names = ['real_1','real_2','real_3','real_4','close_1', 'close_10', 'close_11', 'close_12', 'close_13', 'close_14', 'close_15', 'close_16', 'close_17', 'close_18', 'close_19', 'close_2', 'close_20', 'close_3', 'close_4', 'close_5', 'close_6', 'close_7', 'close_8', 'close_9', 'complex_1', 'complex_10', 'complex_11', 'complex_12', 'complex_13', 'complex_14', 'complex_15', 'complex_16', 'complex_17', 'complex_18', 'complex_19', 'complex_2', 'complex_20', 'complex_3', 'complex_4', 'complex_5', 'complex_6', 'complex_7', 'complex_8', 'complex_9', 'dens-diff_1', 'dens-diff_10', 'dens-diff_11', 'dens-diff_12', 'dens-diff_13', 'dens-diff_14', 'dens-diff_15', 'dens-diff_16', 'dens-diff_17', 'dens-diff_18', 'dens-diff_19', 'dens-diff_2', 'dens-diff_20', 'dens-diff_3', 'dens-diff_4', 'dens-diff_5', 'dens-diff_6', 'dens-diff_7', 'dens-diff_8', 'dens-diff_9', 'high-noise_1', 'high-noise_10', 'high-noise_11', 'high-noise_12', 'high-noise_13', 'high-noise_14', 'high-noise_15', 'high-noise_16', 'high-noise_17', 'high-noise_18', 'high-noise_19', 'high-noise_2', 'high-noise_20', 'high-noise_3', 'high-noise_4', 'high-noise_5', 'high-noise_6', 'high-noise_7', 'high-noise_8', 'high-noise_9', 'low-noise_1', 'low-noise_10', 'low-noise_11', 'low-noise_12', 'low-noise_13', 'low-noise_14', 'low-noise_15', 'low-noise_16', 'low-noise_17', 'low-noise_18', 'low-noise_19', 'low-noise_2', 'low-noise_20', 'low-noise_3', 'low-noise_4', 'low-noise_5', 'low-noise_6', 'low-noise_7', 'low-noise_8', 'low-noise_9', 'multidim_0002', 'multidim_0003', 'multidim_0005', 'multidim_0010', 'multidim_0015', 'multidim_0032', 'multidim_0064', 'multidim_0256', 'multidim_0512', 'multidim_1024', 'separated_1', 'separated_10', 'separated_11', 'separated_12', 'separated_13', 'separated_14', 'separated_15', 'separated_16', 'separated_17', 'separated_18', 'separated_19', 'separated_2', 'separated_20', 'separated_3', 'separated_4', 'separated_5', 'separated_6', 'separated_7', 'separated_8', 'separated_9']
sets_name = ['real','close','complex','dens-diff','high-noise','low-noise','multidim','separated']
algs = [ "ahc", "bir", "gmm", "mkm"]
methods = ['Best', 'CRAL']
df_columns = ["Grex", "Gstr", "Gmin", "Sil", "CH", "DB","AMI"]
iterables = [data_names,algs,methods]
df_index = pd.MultiIndex.from_product(iterables, names=['Data', 'Alg.','Method'])
df_val = pd.DataFrame(columns=df_columns,index=df_index)
iterables = [sets_name,algs,methods]
df_index = pd.MultiIndex.from_product(iterables, names=['Data', 'Alg.','Method'])
df_sum = pd.DataFrame(columns=df_columns,index=df_index)
### DATASETS
for d_ind, d_name in enumerate(data_names):
file_name = "dataMd/"+d_name
dataset = np.genfromtxt(file_name, delimiter=',')
print("\n------- DATASET: ", d_name, "-------")
X, ygt = dataset[:,0:-1], dataset[:,-1].astype(int)
p_outliers = False
if min(ygt)==-1:
p_outliers = True
p_n_clusters = max(ygt)
# normalize dataset
X = StandardScaler().fit_transform(X)
### CLUSTERING ALGORITHMS
range_k = np.arange(10)-5+p_n_clusters
if min(range_k)<2:
range_k+=2-min(range_k)
clustering_algorithms = ['ahc','bir','gmm','mkm']
for a_name in clustering_algorithms:
print("Silhouette Sweep:", a_name)
perf = np.ones(len(range_k))
for i in range_k:
algorithm = select_algorithm(a_name,i)
y = algorithm.fit_predict(X)
s,_,_ = cr.other_validations(X,y)
perf[i-min(range_k)] = s
best_k = np.argmax(perf)+min(range_k)
algorithm = select_algorithm(a_name,best_k)
y = algorithm.fit_predict(X)
cc = cr.cluster_context(X,y)
gv = cr.gval(cc)
if p_outliers==False:
y = cr.reassign_outliers(X,y,0,cc.centroids,gv.extR).astype(int)
S,CH,DB = cr.other_validations(X,y)
rc = cr.refinement_context(X,y,cc,gv)
AMI = adjusted_mutual_info_score(ygt, y)
df_val.loc[(d_name,a_name,'Best'), 'Grex'] = gv.Grex
df_val.loc[(d_name,a_name,'Best'), 'Gstr'] = gv.Gstr
df_val.loc[(d_name,a_name,'Best'), 'Gmin'] = gv.Gmin
df_val.loc[(d_name,a_name,'Best'), 'Sil'] = S
df_val.loc[(d_name,a_name,'Best'), 'CH'] = CH
df_val.loc[(d_name,a_name,'Best'), 'DB'] = DB
df_val.loc[(d_name,a_name,'Best'), 'AMI'] = AMI
print("CluReAL:", a_name)
k = 10 + p_n_clusters
algorithm = select_algorithm(a_name,k)
y = algorithm.fit_predict(X)
cc = cr.cluster_context(X,y)
gv = cr.gval(cc)
rc = cr.refinement_context(X,y,cc,gv)
if p_outliers:
y,cc = cr.refine(X,y,cc,gv,rc,0)
else:
y,cc = cr.refine(X,y,cc,gv,rc,0, min_rdens = -0.9, min_mass = 0.001, out_sens = 0 )
gv = cr.gval(cc)
S,CH,DB = cr.other_validations(X,y)
AMI = adjusted_mutual_info_score(ygt, y)
df_val.loc[(d_name,a_name,'CRAL'), 'Grex'] = gv.Grex
df_val.loc[(d_name,a_name,'CRAL'), 'Gstr'] = gv.Gstr
df_val.loc[(d_name,a_name,'CRAL'), 'Gmin'] = gv.Gmin
df_val.loc[(d_name,a_name,'CRAL'), 'Sil'] = S
df_val.loc[(d_name,a_name,'CRAL'), 'CH'] = CH
df_val.loc[(d_name,a_name,'CRAL'), 'DB'] = DB
df_val.loc[(d_name,a_name,'CRAL'), 'AMI'] = AMI
rc = cr.refinement_context(X,y,cc,gv)
for setj in sets_name:
df_aux = df_val.iloc[df_val.index.get_level_values(0).str.contains(setj)]
for a_name in clustering_algorithms:
df_aux2 = df_aux.iloc[df_aux.index.get_level_values(1).str.contains(a_name)]
df_auxB = df_aux2.iloc[df_aux2.index.get_level_values(2).str.contains('Best')]
df_auxC = df_aux2.iloc[df_aux2.index.get_level_values(2).str.contains('CRAL')]
df_sum.loc[(setj,a_name,'CRAL')] = df_auxC.mean()
df_sum.loc[(setj,a_name,'Best')] = df_auxB.mean()
df_val.to_csv('results/k_Md_results_complete.csv')
df_sum.to_csv('results/k_Md_results_sum.csv')
#out_table = df_sum.to_latex(caption="MultiD-experiments results")
#text_file = open('results/k_Md_results_sum.tex', "w")
#text_file.write(out_table)
#text_file.close()