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buildmodel.py
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buildmodel.py
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import numpy as np
import parse_data as par
import load_data as ld
import matplotlib.pyplot as plt
from sklearn import linear_model
from sklearn import cross_validation
from sklearn.svm import SVR
from sklearn import tree
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import GradientBoostingRegressor
def get_data(data, data_name):
dfDict = par.parse_data(data)
X = dfDict[data_name][:,0:-1]
Y = dfDict[data_name][:,-1]
return X, Y
def lasso_reg(X,Y):
model = linear_model.Lasso(alpha = 0.1).fit(X,Y)
scores = cross_validation.cross_val_score(
model, X, Y, cv=10, scoring = 'mean_absolute_error')
mean_abs_err = np.abs(np.mean(scores))
print('Done Lasso. Mean absolute error: ', mean_abs_err)
return model, mean_abs_err
def svr_rbf(X,Y):
model = SVR(C=1.0,kernel='rbf').fit(X,Y)
scores = cross_validation.cross_val_score(
model, X, Y, cv=10, scoring = 'mean_absolute_error')
mean_abs_err = np.abs(np.mean(scores))
print('Done SVR-RBF. Mean absolute error: ', mean_abs_err)
return model, mean_abs_err
def svr_lin(X,Y):
model = SVR(C=1.0,kernel='linear').fit(X,Y)
scores = cross_validation.cross_val_score(
model, X, Y, cv=10, scoring = 'mean_absolute_error')
mean_abs_err = np.abs(np.mean(scores))
print('Done SVR-LIN. Mean absolute error: ', mean_abs_err)
return model, mean_abs_err
def reg_tree(X,Y):
model = tree.DecisionTreeRegressor().fit(X,Y)
scores = cross_validation.cross_val_score(
model, X, Y, cv=10, scoring = 'mean_absolute_error')
mean_abs_err = np.abs(np.mean(scores))
print('Done Regression Tree. Mean absolute error: ', mean_abs_err)
return model, mean_abs_err
def reg_rand_forest(X,Y):
model = RandomForestRegressor(n_estimators=100).fit(X,Y)
scores = cross_validation.cross_val_score(
model, X, Y, cv=10, scoring = 'mean_absolute_error')
mean_abs_err = np.abs(np.mean(scores))
print('Done Random Forest. Mean absolute error: ', mean_abs_err)
return model, mean_abs_err
def reg_boost(X,Y):
model = GradientBoostingRegressor(n_estimators=100).fit(X,Y)
scores = cross_validation.cross_val_score(
model, X, Y, cv=10, scoring = 'mean_absolute_error')
mean_abs_err = np.abs(np.mean(scores))
print('Done Boosting. Mean absolute error: ', mean_abs_err)
return model, mean_abs_err
if __name__ == "__main__":
sqlfilename = input("Type sql script filename: ")
data = ld.load_data(sqlfilename)
data_name = input("Which data set do you want? (CT, MD, DOSE, PRES, PHYS): ")
X,Y = get_data(data, data_name)
lasso_reg(X,Y)
svr_rbf(X,Y)
svr_lin(X,Y)
reg_tree(X,Y)
reg_rand_forest(X,Y)
reg_boost(X,Y)