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# takes in parameters and trains a svm model with those parameters
import sklearn
from sklearn import svm
from sklearn import grid_search, datasets
import csv
import random
import numpy as np
import pickle
import re
print("testwordNB.csv is opening")
with open("testwordNB.csv", 'rU') as f: #opens PW file
reader = csv.reader(f)
testmatrix = list(list(rec) for rec in csv.reader(f, delimiter=','))
with open("goodSVM_radial_7_7_80_word", "rb") as f: #"goodSVM"
goodmodel = pickle.load(f)
print("Splitting labels and predictors")
file_names =[]
X = [] # X = matrix of train
z = 0
z2 = 0
for row in testmatrix:
if z == 0:
z = 1
X += [row[:-1]]
file_names += [row[-1]]
z2 += 1
print("Changing strings into numbers")
z2 = 0
for row in X:
X[z2] = map(float, row)
z2 += 1
print("Predicting labels")
predicted = goodmodel.predict(X)
def numonly(x):
return int(re.sub("[^0-9]", "", x))
print("Cleaning up text file names.")
file_names = map(numonly, file_names)
print("Resorting file names and labels")
final = zip(file_names, list(predicted))
x = map(list, final)
x = [["id", "category"]] + x
print("Writing out testsvmpred_radial_7_7_80.csv")
with open("testsvmpred_radial_7_7_80.csv", "wb") as f:
writer = csv.writer(f)
print("Script complete.")