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Regressor.py
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Regressor.py
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import pylab as pl
from sklearn import linear_model
from sklearn import tree
from sklearn.metrics import confusion_matrix
from sklearn import tree
from sklearn import linear_model
from sklearn import gaussian_process
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import explained_variance_score
from sklearn.metrics import explained_variance_score
from sklearn import datasets, linear_model
import numpy as np
from sklearn.preprocessing import PolynomialFeatures
def runRegressor( clf,featureMat,targets,no_of_training_example ):
try:
clf.fit(featureMat[:no_of_training_example,:], targets[:no_of_training_example])
y_pred = clf.predict(featureMat[no_of_training_example:,:])
print 'Variance Score'
print explained_variance_score(targets[no_of_training_example:], y_pred)
print 'Mean absolute error'
print mean_absolute_error(targets[no_of_training_example:], y_pred)
print 'Explained variance score'
print explained_variance_score(targets[no_of_training_example:], y_pred)
except Exception, e:
print e
return;
def callAllRegressor(featureMat,targets,counter):
no_of_training_example = 0.75*counter
print 'Result for Linear Regression:- '
clf = linear_model.LinearRegression()
runRegressor(clf,featureMat,targets,no_of_training_example);
print 'Result for Decision Tree Regression:- '
clf = tree.DecisionTreeRegressor()
runRegressor(clf,featureMat,targets,no_of_training_example);
print 'Result for Bayesian Linear Regression:- '
clf = linear_model.BayesianRidge()
runRegressor(clf,featureMat,targets,no_of_training_example);
print 'Result for Exponential Regression:- '
poly = PolynomialFeatures(degree=2)
featureMat = poly.fit_transform(featureMat)
runRegressor(clf,featureMat,targets,no_of_training_example);