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skl_svc.py
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skl_svc.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Feb 3 11:45:46 2018
@author: noch
"""
import numpy as np
import pandas as pd
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
from data_prepared import read_data, prepare_data, split_data
isTr = 1
for i in range (3) :
X = read_data("Xtr"+str(i), isTr)
Y = read_data("Ytr"+str(i), isTr)
max_info = ""
max_predic = 0
Y['Bound'][Y['Bound'] == 0] = -1
f= open("/Users/noch/Documents/workspace/data_challenge/result/console_skl_svc_linear.txt","a+")
print("\n testing on Xtr" +str(i)+ ", Ytr" +str(i))
for k in range(2,6):
data_new = prepare_data(X, k+1)
data_new['Bound'] = Y['Bound']
data_train, data_test = split_data(data_new, 70)
X_tr = np.asarray(pd.DataFrame.as_matrix(data_train.iloc[:,:-1]), dtype=float)
Y_tr = pd.DataFrame.as_matrix(data_train['Bound']).astype(float).tolist()
X_te = np.asarray(pd.DataFrame.as_matrix(data_test.iloc[:,:-1]), dtype=float)
Y_te = pd.DataFrame.as_matrix(data_test['Bound'])
print("\n finished preparing number of char:" + str(k+1))
#C_arr = [10000, 1000, 100, 10, 5, 1, 0.01]
C_arr = [1, 0.1, 0.2]
for C in C_arr:
SVC(C=C, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
clf = SVC()
clf.fit(X_tr, Y_tr)
Y_predicted_tr = clf.predict(X_tr)
Y_predicted_te = clf.predict(X_te)
predicted_score_tr = accuracy_score(Y_predicted_tr, Y_tr, normalize=False)/len(Y_predicted_tr)
predicted_score_te = accuracy_score(Y_predicted_te, Y_te, normalize=False)/len(Y_predicted_te)
st_info = "\n test on Xtr" + str(i) + ", Ytr" + str(i)+\
"\n C: " +str(C) +\
"\n number of character: " + str(k+1)
if(predicted_score_te > max_predic):
max_predic = predicted_score_te
max_info = "\n max_result_tr: "+ str(predicted_score_tr) + st_info + "\n"
f.write("---------------------------------------")
f.write(st_info)
f.write("\n result_tr: "
+ str(accuracy_score(Y_predicted_tr, Y_tr, normalize=False)) +
"/" + str(len(Y_predicted_tr))
+ " = " + str(predicted_score_tr))
f.write("\n result_te: "
+ str(accuracy_score(Y_predicted_te, Y_te, normalize=False)) +
"/" + str(len(Y_predicted_te))
+ " = " + str(predicted_score_te) + "\n\n")
f.write("****************************************************************************************************************")
f.write("\n max_result_te: " + str(max_predic))
f.write(max_info + "\n\n")
f.close()
break