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3Forests.py
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3Forests.py
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from sklearn.kernel_approximation import (RBFSampler,Nystroem)
from sklearn.ensemble import RandomForestClassifier
import pandas
import numpy as np
import random
from sklearn.svm import SVC
from sklearn.metrics.pairwise import rbf_kernel,laplacian_kernel,chi2_kernel,linear_kernel,polynomial_kernel,cosine_similarity
from sklearn import preprocessing
from sklearn.model_selection import GridSearchCV
import xlrd
import xlrd
import numpy as np
import pandas
import random
import time
from sklearn.metrics import accuracy_score
from sklearn import model_selection
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import RFE
from sklearn.svm import SVC
from sklearn.externals import joblib
from sklearn.pipeline import make_pipeline
from skrebate import ReliefF
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.metrics import roc_auc_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCV
from sklearn import preprocessing
from collections import Counter
import re
from math import floor
from joblib import Parallel, delayed
from sklearn.ensemble import ExtraTreesClassifier
from rr_forest import RRForestClassifier
from rr_extra_forest import RRExtraTreesClassifier
def floored_percentage(val, digits):
val *= 10 ** (digits + 2)
return '{1:.{0}f}\%\pm '.format(digits, floor(val) / 10 ** digits)
def splitdata(X,Y,ratio,seed):
'''This function is to split the data into train and test data randomly and preserve the pos/neg ratio'''
n_samples = X.shape[0]
y = Y.astype(int)
y_bin = np.bincount(y)
classes = np.nonzero(y_bin)[0]
#fint the indices for each class
indices = []
print()
for i in classes:
indice = []
for j in range(n_samples):
if y[j] == i:
indice.append(j)
#print(len(indice))
indices.append(indice)
train_indices = []
for i in indices:
k = int(len(i)*ratio)
train_indices += (random.Random(seed).sample(i,k=k))
#find the unused indices
s = np.bincount(train_indices,minlength=n_samples)
mask = s==0
test_indices = np.arange(n_samples)[mask]
return train_indices,test_indices
def rf_dis(n_trees, X,Y,train_indices,test_indices,seed):
clf = RandomForestClassifier(n_estimators=500,
random_state=seed, oob_score=True, n_jobs=-1)
clf = clf.fit(X[train_indices], Y[train_indices])
pred = clf.predict(X[test_indices])
weight = clf.score(X[test_indices], Y[test_indices])
#print(1 - clf.oob_score_)
n_samples = X.shape[0]
dis = np.zeros((n_samples,n_samples))
for i in range(n_samples):
dis[i][i] = 1
res = clf.apply(X)
for i in range(n_samples):
for j in range(i+1,n_samples):
a = np.ravel(res[i])
b = np.ravel(res[j])
score = a == b
d = float(score.sum())/n_trees
dis[i][j] =dis[j][i] = d
X_features1 = np.transpose(dis)
X_features2 = X_features1[train_indices]
X_features3 = np.transpose(X_features2)
return X_features3[train_indices],X_features3[test_indices],weight,pred
def Extreme_rf_dis(n_trees, X,Y,train_indices,test_indices,seed):
clf = ExtraTreesClassifier(n_estimators=500,
random_state=seed, oob_score=True, n_jobs=-1)
clf = clf.fit(X[train_indices], Y[train_indices])
pred = clf.predict(X[test_indices])
weight = clf.score(X[test_indices], Y[test_indices])
#print(1 - clf.oob_score_)
n_samples = X.shape[0]
dis = np.zeros((n_samples,n_samples))
for i in range(n_samples):
dis[i][i] = 1
res = clf.apply(X)
for i in range(n_samples):
for j in range(i+1,n_samples):
a = np.ravel(res[i])
b = np.ravel(res[j])
score = a == b
d = float(score.sum())/n_trees
dis[i][j] =dis[j][i] = d
X_features1 = np.transpose(dis)
X_features2 = X_features1[train_indices]
X_features3 = np.transpose(X_features2)
return X_features3[train_indices],X_features3[test_indices],weight,pred
def RR_rf_dis(n_trees, X,Y,train_indices,test_indices,seed):
clf = RRForestClassifier(n_estimators=500,
random_state=seed, oob_score=True, n_jobs=-1)
clf = clf.fit(X[train_indices], Y[train_indices])
pred = clf.predict(X[test_indices])
weight = clf.score(X[test_indices], Y[test_indices])
#print(1 - clf.oob_score_)
n_samples = X.shape[0]
dis = np.zeros((n_samples,n_samples))
for i in range(n_samples):
dis[i][i] = 1
res = clf.apply(X)
for i in range(n_samples):
for j in range(i+1,n_samples):
a = np.ravel(res[i])
b = np.ravel(res[j])
score = a == b
d = float(score.sum())/n_trees
dis[i][j] =dis[j][i] = d
X_features1 = np.transpose(dis)
X_features2 = X_features1[train_indices]
X_features3 = np.transpose(X_features2)
return X_features3[train_indices],X_features3[test_indices],weight,pred
def RRExtra_rf_dis(n_trees, X,Y,train_indices,test_indices,seed):
clf = RRExtraTreesClassifier(n_estimators=500,
random_state=seed, oob_score=True, n_jobs=-1)
clf = clf.fit(X[train_indices], Y[train_indices])
pred = clf.predict(X[test_indices])
weight = clf.score(X[test_indices], Y[test_indices])
#print(1 - clf.oob_score_)
n_samples = X.shape[0]
dis = np.zeros((n_samples,n_samples))
for i in range(n_samples):
dis[i][i] = 1
res = clf.apply(X)
for i in range(n_samples):
for j in range(i+1,n_samples):
a = np.ravel(res[i])
b = np.ravel(res[j])
score = a == b
d = float(score.sum())/n_trees
dis[i][j] =dis[j][i] = d
X_features1 = np.transpose(dis)
X_features2 = X_features1[train_indices]
X_features3 = np.transpose(X_features2)
return X_features3[train_indices],X_features3[test_indices],weight,pred
def nLsvm_patatune(train_x,train_y,test_x, test_y):
tuned_parameters = [
{'kernel': ['precomputed'], 'C': [0.01, 0.1, 1, 10, 100, 1000]}]
clf = GridSearchCV(SVC(C=1), tuned_parameters, cv=5, n_jobs=1
) # SVC(probability=True)#SVC(kernel="linear", probability=True)
clf.fit(train_x, train_y)
print(clf.score(test_x,test_y))
return clf.best_params_['C']
"""
url = 'nus_1.csv'
dataframe = pandas.read_csv(url) # , header=None)
array = dataframe.values
X = array[:, 1:]
for i in range(4):
url = 'nus_' + str(i + 2) + '.csv'
dataframe = pandas.read_csv(url) # , header=None)
array = dataframe.values
X1 = array[:, 1:]
X = np.concatenate((X, X1), axis=1)
Y = pandas.read_csv('nus_label.csv')
Y = Y.values
Y = Y[:, 1:]
# Y = Y.transpose()
Y = np.ravel(Y)
train_indices, test_indices = splitdata(X=X, Y=Y, ratio=0.1, seed=1000 )
n_trees = 500
n_feat = selected_f(639) # features selecleted
n_features = X.shape[1]
for i in range(n_features):
s = X[:,i]
mn = np.max(s)-np.min(s)
if mn == 0:
print(i)
"""
def mcode(ite):
R = 0.5
e11 = []
e12 = []
e21 = []
e22 = []
e31 = []
e32 = []
e41 = []
e42 = []
e51 = []
e52 = []
e8 = []
elaterf = []
elaterfdis = []
#data reading
if ite ==0:
ss = "lowGrade"
url = '../lowGrade/text_lg_1.csv'
dataframe = pandas.read_csv(url, header=None)
array = dataframe.values
X = array
Y = pandas.read_csv('../lowGrade/label_lowGrade.csv', header=None)
Y = Y.values
Y = np.ravel(Y)
print(Y.shape)
for i in range(4):
url = '../lowGrade/text_lg_' + str(i + 2) + '.csv'
dataframe = pandas.read_csv(url, header=None)
array = dataframe.values
X1 = array
print(X1.shape)
X = np.concatenate((X, X1), axis=1)
Xnew1 = X[:, 0:1680]
Xnew2 = X[:, 1680:3360]
Xnew3 = X[:, 3360:5040]
Xnew4 = X[:, 5040:6720]
Xnew5 = X[:, 6720:6745]
elif ite ==1:
ss = "IDHCodel"
url = '../IDHCodel/text_pr_1.csv'
dataframe = pandas.read_csv(url, header=None)
array = dataframe.values
X = array
Y = pandas.read_csv('../IDHCodel/label_IDHCodel.csv', header=None)
Y = Y.values
Y = np.ravel(Y)
print(Y.shape)
for i in range(4):
url = '../IDHCodel/text_pr_' + str(i + 2) + '.csv'
dataframe = pandas.read_csv(url, header=None)
array = dataframe.values
X1 = array
print(X1.shape)
X = np.concatenate((X, X1), axis=1)
Xnew1 = X[:, 0:1680]
Xnew2 = X[:, 1680:3360]
Xnew3 = X[:, 3360:5040]
Xnew4 = X[:, 5040:6720]
Xnew5 = X[:, 6720:6745]
elif ite == 2:
ss = "nonIDH1"
url = '../nonIDH1/text_nonIDH1_1.csv'
dataframe = pandas.read_csv(url, header=None)
array = dataframe.values
X = array
Y = pandas.read_csv('../nonIDH1/label_nonIDH1.csv', header=None)
Y = Y.values
Y = np.ravel(Y)
print(Y.shape)
for i in range(4):
url = '../nonIDH1/text_nonIDH1_' + str(i + 2) + '.csv'
dataframe = pandas.read_csv(url, header=None)
array = dataframe.values
X1 = array
print(X1.shape)
X = np.concatenate((X, X1), axis=1)
Xnew1 = X[:, 0:1680]
Xnew2 = X[:, 1680:3360]
Xnew3 = X[:, 3360:5040]
Xnew4 = X[:, 5040:6720]
Xnew5 = X[:, 6720:6745]
else:
ss = "progression"
url = '../progression/text_pr_1.csv'
dataframe = pandas.read_csv(url, header=None)
array = dataframe.values
X = array
Y = pandas.read_csv('../progression/label_progression.csv', header=None)
Y = Y.values
Y = np.ravel(Y)
print(Y.shape)
for i in range(4):
url = '../progression/text_pr_' + str(i + 2) + '.csv'
dataframe = pandas.read_csv(url, header=None)
array = dataframe.values
X1 = array
print(X1.shape)
X = np.concatenate((X, X1), axis=1)
Xnew1 = X[:, 0:1680]
Xnew2 = X[:, 1680:3360]
Xnew3 = X[:, 3360:5040]
Xnew4 = X[:, 5040:6720]
Xnew5 = X[:, 6720:6745]
testfile = open(("RF"+ss+"%f_%f.txt" % (R, ite)), 'w')
for ii in range(10):
erfsvm = []
seed = 1000+ii
train_indices, test_indices = splitdata(X=X, Y=Y, ratio=R, seed=seed)
print("Start rest")
# view1
X_features_train1, X_features_test1, w1, pred1 = rf_dis(n_trees=500, X=Xnew1, Y=Y, train_indices=train_indices,
test_indices=test_indices, seed=seed)
m12 = RandomForestClassifier(n_estimators=500, random_state=seed, oob_score=True, n_jobs=1).fit(
X_features_train1, Y[train_indices])
pre1 = m12.predict(X_features_test1)
#e12.append(m12.score(X_features_test1, Y[test_indices]))
#e11.append(w1)
# view 2
X_features_train2, X_features_test2, w2, pred2 = rf_dis(n_trees=500, X=Xnew2, Y=Y, train_indices=train_indices,
test_indices=test_indices, seed=seed)
m22 = RandomForestClassifier(n_estimators=500, random_state=seed, oob_score=True, n_jobs=1).fit(
X_features_train2, Y[train_indices])
pre2 = m22.predict(X_features_test2)
#e22.append(m22.score(X_features_test2, Y[test_indices]))
#e21.append(w2)
# view 3
X_features_train3, X_features_test3, w3, pred3 = rf_dis(n_trees=500, X=Xnew3, Y=Y, train_indices=train_indices,
test_indices=test_indices, seed=seed)
m32 = RandomForestClassifier(n_estimators=500, random_state=seed, oob_score=True, n_jobs=1).fit(
X_features_train3, Y[train_indices])
pre3 = m32.predict(X_features_test3)
#e32.append(m32.score(X_features_test3, Y[test_indices]))
#e31.append(w3)
# view 4
X_features_train4, X_features_test4, w4, pred4 = rf_dis(n_trees=500, X=Xnew4, Y=Y, train_indices=train_indices,
test_indices=test_indices, seed=seed)
m42 = RandomForestClassifier(n_estimators=500, random_state=seed, oob_score=True, n_jobs=1).fit(
X_features_train4, Y[train_indices])
pre4 = m42.predict(X_features_test4)
#e42.append(m42.score(X_features_test4, Y[test_indices]))
#e41.append(w4)
# view 5
X_features_train5, X_features_test5, w5, pred5 = rf_dis(n_trees=500, X=Xnew5, Y=Y, train_indices=train_indices,
test_indices=test_indices, seed=seed)
m52 = RandomForestClassifier(n_estimators=500, random_state=seed, oob_score=True, n_jobs=1).fit(
X_features_train5, Y[train_indices])
pre5 = m52.predict(X_features_test5)
#e52.append(m52.score(X_features_test5, Y[test_indices]))
#e51.append(w5)
# Late RF
resall1 = np.column_stack((pred1, pred2, pred3, pred4, pred5))
Laterf = list(range(len(test_indices)))
for i in range(len(test_indices)):
Laterf[i], empty = Counter(resall1[i]).most_common()[0]
LRF = accuracy_score(Y[test_indices], Laterf)
elaterf.append(LRF)
# Late RF dis
resall = np.column_stack((pre1, pre2, pre3, pre4, pre5))
LSVTres = list(range(len(test_indices)))
for i in range(len(test_indices)):
LSVTres[i], empty = Counter(resall[i]).most_common()[0]
LSVTscore = accuracy_score(Y[test_indices], LSVTres)
elaterfdis.append(LSVTscore)
# multi view
X_features_trainm = (
X_features_train1 + X_features_train2 + X_features_train3 + X_features_train4 + X_features_train5) / 5
X_features_testm = (
X_features_test1 + X_features_test2 + X_features_test3 + X_features_test4 + X_features_test5) / 5
mv = RandomForestClassifier(n_estimators=500, random_state=seed, oob_score=True, n_jobs=1).fit(
X_features_trainm, Y[train_indices])
e8.append(mv.score(X_features_testm, Y[test_indices]))
# RFSVM
c = nLsvm_patatune(train_x=X_features_trainm, train_y=Y[train_indices], test_x=X_features_testm,
test_y=Y[test_indices])
clf = SVC(C=c, kernel='precomputed')
clf.fit(X_features_trainm, Y[train_indices])
erfsvm.append(clf.score(X_features_testm, Y[test_indices]))
testfile.write(
"RFSVM&%s pm%s & " % (floored_percentage(np.mean(erfsvm), 2), floored_percentage(np.std(erfsvm), 2)) + '\n')
testfile.write("RFDIS &%s pm%s & " % (floored_percentage(np.mean(e8), 2), floored_percentage(np.std(e8), 2)) + '\n')
testfile.write(
" LATERF&%s pm%s &" % (floored_percentage(np.mean(elaterf), 2), floored_percentage(np.std(elaterf), 2)) + '\n')
testfile.write(" LATERFDIS&%s pm%s & " % (
floored_percentage(np.mean(elaterfdis), 2), floored_percentage(np.std(elaterfdis), 2)) + '\n')
if __name__ == '__main__':
Parallel(n_jobs=4)(delayed(mcode)(ite=i) for i in range(4))