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learn.py
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learn.py
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# -*- coding: utf-8 -*-
"""
Created on Sat Feb 27 21:52:39 2016
@author: dudu
"""
import numpy as np
import pandas as pd
from sklearn.svm import SVR
from sklearn.grid_search import GridSearchCV
from sklearn.kernel_ridge import KernelRidge
from sklearn.ensemble import RandomForestRegressor,BaggingRegressor
from sklearn.ensemble import AdaBoostRegressor
def load_data():
df_x_train = pd.read_csv('data/features_train.csv')
df_y_train = pd.read_csv('data/relevance.csv')
df_x_test = pd.read_csv('data/features_test.csv')
X = df_x_train.as_matrix()
Y = df_y_train.as_matrix()
Xt = df_x_test.as_matrix()
return X[:,1:], Y[:,1:], Xt[:,1:]
def clamp_1_3(x):
if x < 1.0:
return 1.0
elif x > 3.0:
return 3.0
else:
return x
def save_submission(Yp):
df = pd.read_csv('data/sample_submission.csv')
df['relevance'] = Yp
df.to_csv('data/my_submission.csv', index=False)
def learn_svr(X,Y,Xt):
print ('learn')
clf = SVR()
clf.fit(X,Y.squeeze())
print ('predict')
Yp = clf.predict(Xt)
Yp_clamped = np.array([clamp_1_3(x) for x in Yp])
return Yp_clamped
def random_forest(X,Y,Xt):
print('learn')
rf = RandomForestRegressor(n_estimators=15, max_depth=6, random_state=0)
clf = BaggingRegressor(rf, n_estimators=45, max_samples=0.1, random_state=25)
clf.fit(X, Y)
print('predict')
Yp_clamped = clf.predict(Xt)
return Yp_clamped
def adaboost_regressor(X,Y,Xt):
print('learn')
ada=GridSearchCV(AdaBoostRegressor(loss='square', random_state=0),cv=5,param_grid={'learning_rate':[0.001,0.01,0.1,1.0],'n_estimators':[40,50]})
ada.fit(X,Y)
print('predict')
Yp_clamped=ada.predict(Xt)
return Yp_clamped
def learn_svr_grid_search(X,Y,Xt):
print ('learn')
svr = GridSearchCV(SVR(kernel='rbf', gamma=0.1), cv=5,param_grid={"C": [1e0, 1e1, 1e2, 1e3],
"gamma": np.logspace(-2, 2, 5)})
svr.fit(X[1:1000],Y.squeeze()[1:1000])
print(svr.best_score_)
print(svr.best_estimator_.gamma)
print(svr.best_estimator_.C)
print ('predict')
Yp = svr.predict(Xt)
Yp_clamped = np.array([clamp_1_3(x) for x in Yp])
return Yp_clamped
def learn_kernel_ridge(X,Y,Xt):
print ('learn')
kr = GridSearchCV(KernelRidge(kernel='rbf', gamma=0.1), cv=5,
param_grid={"alpha": [1e0, 0.1, 1e-2, 1e-3],
"gamma": np.logspace(-2, 2, 5)})
kr.fit(X[1:20000,:],Y[1:20000,:])
print ('predict')
Yp = kr.predict(Xt)
Yp_clamped = np.array([clamp_1_3(x) for x in Yp])
return Yp_clamped
if __name__ == '__main__':
X,Y,Xt = load_data()
Yp = random_forest(X,Y.squeeze(),Xt)
save_submission(Yp)