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Copy pathmodel_futurs_anys_Nvars.py
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model_futurs_anys_Nvars.py
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import pandas
from pandas.plotting import scatter_matrix
import matplotlib.pyplot as plt
from sklearn import model_selection
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
import numpy as np
import random
seed = 123456
Ncross = 1
data = pandas.read_excel(open('./data/results_new.xlsx','rb'), sheet_name=0);
result = pandas.read_excel(open('./data/results_new.xlsx','rb'), sheet_name=1);
f = open('./data/errors_Nvar_model22.txt','w');
X0 = data.values
Y0 = result.values
N = 1000
Ncross = 5
Nfit = 1000
X = np.zeros([9000,12])
Y = np.zeros(9000)
k = 0
for ii in range(9):
for jj in range(N):
X[k,0:11] = X0[jj,:]
X[k,11] = Y0[jj,ii]
Y[k] = Y0[jj,ii+1]
k = k + 1
ordreDTC = [11, 6, 7, 3, 1, 2, 10, 4, 0, 5, 9, 8]
ordreRFC = [11, 6, 3, 7, 1, 4, 5, 2, 10, 0, 9, 8]
errorSVM = np.zeros([12,2])
errorLR = np.zeros([12,2])
errorLDA = np.zeros([12,2])
errorKNC = np.zeros([12,2])
errorDTC = np.zeros([12,2])
errorGNB = np.zeros([12,2])
errorRF = np.zeros([12,2])
for ss in range(Ncross):
for var in range(12):
for order in range(2):
print ss, var, order
if order == 0:
classificacio = ordreDTC
else:
classificacio = ordreRFC
indexs = range(9000)
indexs = random.sample(indexs,9000)
X = X[indexs,:]
Y = Y[indexs]
X_t = X[0:8000,classificacio[0:var+1]]
Y_t = Y[0:8000]
X_v = X[8000:9000,classificacio[0:var+1]]
Y_v = Y[8000:9000]
nn = len(X_v)
# Suport Vector Machine
clf = SVC()
clf.fit(X_t,Y_t)
Y_p = clf.predict(X_v)
for i in range(0,nn):
errorSVM[var,order] = errorSVM[var,order]+abs(float(Y_v[i]-Y_p[i]))/Y_v[i]
# Logistic Regression
logreg = LogisticRegression()
logreg.fit(X_t, Y_t)
Y_p = logreg.predict(X_v)
for i in range(0,nn):
errorLR[var,order] = errorLR[var,order]+abs(float(Y_v[i]-Y_p[i]))/Y_v[i]
# Linear Discriminant Analysis
lda = LinearDiscriminantAnalysis()
lda.fit(X_t, Y_t)
Y_p = lda.predict(X_v)
for i in range(0,nn):
errorLDA[var,order] = errorLDA[var,order]+abs(float(Y_v[i]-Y_p[i]))/Y_v[i]
# K Neighbors Classifier
KNC = KNeighborsClassifier()
KNC.fit(X_t, Y_t)
Y_p = KNC.predict(X_v)
for i in range(0,nn):
errorKNC[var,order] = errorKNC[var,order]+abs(float(Y_v[i]-Y_p[i]))/Y_v[i]
# Decision Tree Classifier
DTC = DecisionTreeClassifier()
DTC.fit(X_t, Y_t)
Y_p = DTC.predict(X_v)
for i in range(0,nn):
errorDTC[var,order] = errorDTC[var,order]+abs(float(Y_v[i]-Y_p[i]))/Y_v[i]
# Gaussian Naive Bayes
GNB = GaussianNB()
GNB.fit(X_t, Y_t)
Y_p = GNB.predict(X_v)
for i in range(0,nn):
errorGNB[var,order] = errorGNB[var,order]+abs(float(Y_v[i]-Y_p[i]))/Y_v[i]
# Random Forest
RFC = RandomForestClassifier()
RFC.fit(X_t, Y_t)
Y_p = RFC.predict(X_v)
for i in range(0,nn):
errorRF[var,order] = errorRF[var,order]+abs(float(Y_v[i]-Y_p[i]))/Y_v[i]
errorSVM = errorSVM/nn/Ncross
errorLR = errorLR/nn/Ncross
errorLDA = errorLDA/nn/Ncross
errorKNC = errorKNC/nn/Ncross
errorDTC = errorDTC/nn/Ncross
errorGNB = errorGNB/nn/Ncross
errorRF = errorRF/nn/Ncross
errr = errorSVM+errorLR+errorLDA+errorKNC+errorDTC+errorGNB+errorRF
print 'SVM error = ', errorSVM
print 'LR error = ', errorLR
print 'LDA error = ', errorLDA
print 'KNC error = ', errorKNC
print 'DTC error = ', errorDTC
print 'GNB error = ', errorGNB
print 'RF error = ', errorRF
for ii in range(2):
for err in errorSVM[:,ii]:
f.write('%.4f ' % err)
f.write('\n')
for err in errorLR[:,ii]:
f.write('%.4f ' % err)
f.write('\n')
for err in errorLDA[:,ii]:
f.write('%.4f ' % err)
f.write('\n')
for err in errorKNC[:,ii]:
f.write('%.4f ' % err)
f.write('\n')
for err in errorDTC[:,ii]:
f.write('%.4f ' % err)
f.write('\n')
for err in errorGNB[:,ii]:
f.write('%.4f ' % err)
f.write('\n')
for err in errorRF[:,ii]:
f.write('%.4f ' % err)
f.write('\n')
f.close()