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model_futurs_anys.py
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model_futurs_anys.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 = 100
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_All.txt','w');
X0 = data.values
Y0 = result.values
N = 1000
Ncross = 100
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
errorSVM = 0
errorLR = 0
errorLDA = 0
errorKNC = 0
errorDTC = 0
errorGNB = 0
errorRF = 0
classificationDTC = np.zeros(12)
classificationRF = np.zeros(12)
for ss in range(Ncross):
print ss
indexs = range(9000)
indexs = random.sample(indexs,9000)
X_t, X_v, Y_t, Y_v = model_selection.train_test_split(X[indexs,:], Y[indexs], test_size=0.15)
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 = errorSVM+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 = errorLR+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 = errorLDA+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 = errorKNC+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 = errorDTC+abs(float(Y_v[i]-Y_p[i]))/Y_v[i]
classificationDTC = classificationDTC + DTC.feature_importances_
# Gaussian Naive Bayes
#GNB = GaussianNB()
#GNB.fit(X_t, Y_t)
#Y_p = GNB.predict(X_v)
#for i in range(0,nn):
# errorGNB = errorGNB+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 = errorRF+abs(float(Y_v[i]-Y_p[i]))/Y_v[i]
classificationRF = classificationRF + RFC.feature_importances_
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
print classificationDTC/Ncross
print classificationRF/Ncross