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kcell_r2.py
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# kcell.py
# python3
import pandas as pd
from sklearn import cross_validation, svm, metrics, cluster, tree, ensemble
import kkeras
param_d = {"n_cv_flt": 2, "n_cv_ln": 3, "cv_activation": "relu"}
def setparam( p_d):
"""
setup global parameters
Input
------
p_d: dictionary
This includes global parameters and values.
Parameters
-------------
n_cv_flt, int: number of CNN filters
n_cv_ln, int: length of a CNN filter
"""
global param_d
for k in p_d:
param_d[ k] = p_d[ k]
def clst( X_train, y_train, X_test, y_test, nb_classes):
model = tree.DecisionTreeClassifier()
model.fit( X_train, y_train)
dt_score = model.score( X_test, y_test)
print( "DT-C:", dt_score)
model = svm.SVC( kernel = 'linear')
model.fit( X_train, y_train)
sv_score = model.score( X_test, y_test)
print( "SVC:", sv_score)
model = kkeras.MLPC( [X_train.shape[1], 30, 10, nb_classes])
model.fit( X_train, y_train, X_test, y_test, nb_classes)
mlp_score = model.score( X_test, y_test)
print( "DNN:", mlp_score)
model = kkeras.CNNC( param_d["n_cv_flt"], param_d["n_cv_ln"], param_d["cv_activation"],
l=[X_train.shape[1], 30, 10, nb_classes])
model.fit( X_train, y_train, X_test, y_test, nb_classes)
cnn_score = model.score( X_test, y_test)
print( "DCNN:", cnn_score)
model = ensemble.RandomForestClassifier( n_estimators=10)
model.fit( X_train, y_train)
rf_score = model.score( X_test, y_test)
print( "RF:", rf_score)
return dt_score, sv_score, mlp_score, cnn_score, rf_score
def GET_clsf2_by_clst( nb_classes):
def clsf2_by_clst( Xpart_cf, Xpart_ct):
"""
Clustering is performed and then, classification performed by clustered indices.
"""
cl_model = cluster.KMeans(n_clusters=nb_classes)
cl_model.fit(Xpart_ct)
yint = cl_model.predict( Xpart_ct)
X_train, X_test, y_train, y_test = cross_validation.train_test_split( Xpart_cf, yint, test_size = 0.2)
return clst(X_train, y_train, X_test, y_test, nb_classes)
return clsf2_by_clst
def GET_clsf2_by_yint( nb_classes):
def clsf2_by_yint( X1part, yint):
"""
classification is performed by yint
"""
X_train, X_test, y_train, y_test = cross_validation.train_test_split( X1part, yint, test_size = 0.2)
return clst(X_train, y_train, X_test, y_test, nb_classes)
return clsf2_by_yint
def pd_df( ix, s_l, ms):
VI = {1:"Velocity", 2:"Intensity", 12:"Combined"}
ln = len( s_l)
df_i = pd.DataFrame()
df_i["Type"] = ["{}: ".format(ms) + str( ix)] * ln
df_i["Clustering"] = [ VI[ix[0]]] * ln
df_i["Classification"] = [ VI[ix[1]]] * ln
df_i["Clustering method"] = [ "KMeans"] * ln
df_i["Classification method"] = [ "DT", "SVC", "DNN", "DCNN", "RF"]
df_i["Pc"] = s_l
return df_i
def pd_clsf2_by_clst( ix, Xpart_ct, Xpart_cf, nb_classes):
print( "Type", ix, "- Clustering:", ix[1], "Classification:", ix[0])
s_l = GET_clsf2_by_clst(nb_classes)(Xpart_cf, Xpart_ct)
return pd_df( ix, s_l, "KMeans")
def pd_clsf2_by_yint( ix, yint, Xpart_cf, nb_classes):
print( "Type", ix, "- Clustering:", ix[1], "Classification:", ix[0])
s_l = GET_clsf2_by_yint(nb_classes)(Xpart_cf, yint)
return pd_df( ix, s_l, "Science")