ConvSAX_spearmint.py

# -*- coding: utf-8 -*-
"""
Created on Sun Jun 26 22:12:52 2016

@author: Stephen-Lu
"""

from sklearn.neighbors import KNeighborsClassifier as knn
from sklearn import svm
import bop
import numpy as np
import os
from sklearn import cross_validation

def read_output(direc):
    acc = []
    files = os.listdir(direc)
    for f in files[:-1]:
        lines = open(direc +f, 'r').readlines()
        if lines[-1] is not []:
            acc.append(float(lines[-1].split()[-1].split('}')[0]))
    return acc
    
def bop_vec(dataset, windowWidth = 10, skipSize = 1, numSymbols = 3, alphabetSize = 7):

    features = {}
    Bops = []
    # Find all unique features and class labels
    for datum in dataset:
        bops = bop.sax_words(bop.standardize(datum),
                             windowWidth, skipSize, numSymbols, None, alphabetSize, True)
        bops = bop.bop(bops)
        for key in bops.keys():
            features[key] = 1
        Bops.append(bops)
#    if len(features) > 20000:
#      return
    # Stores features and labels as sorted lists
    features = sorted(features.keys())
    
    # Write data instances
    mat = []
    for bops in Bops:
        vec = []
        for feature in features:
            if feature in bops:
                vec.append(bops[feature])
            else:
                vec.append(0)
        mat.append(vec)
    return np.asarray(mat, dtype = np.float32)

def bop_clf(windowWidth, numSymbols, alphabetSize, skipSize = 1):    
    npzfile = np.load('feature_train_ECG_MTS.npz')
    feature_train = npzfile['x']
    label_train = npzfile['y']
    
    npzfile = np.load('feature_test_ECG_MTS.npz')
    feature_test = npzfile['x']
    label_test = npzfile['y']
    train_num = feature_train.shape[0]
    
    feature = np.vstack((feature_train, feature_test))
    bops = bop_vec(feature, windowWidth = int(windowWidth * feature.shape[1]), skipSize = skipSize, numSymbols = numSymbols, alphabetSize = alphabetSize)
    bop_train = bops[:train_num]
    bop_test = bops[train_num:]
    #%%
    clf = svm.LinearSVC(C=10)
    clf.fit(bop_train, label_train)
    svctrain = cross_validation.cross_val_score(clf, bop_train, label_train, cv=len(label_train)/2)
    svctrain = np.mean(svctrain)
    svctest = clf.score(bop_test, label_test)
    #%%
#    clf = knn(n_neighbors=3)
#    clf.fit(bop_train, label_train)
#    knntrain = clf.score(bop_train, label_train)
#    knntest =  clf.score(bop_test, label_test)
    return {
#    'SVM train acc': svctrain,
    'SVM_cv_acc': svctest}
#    'KNN train acc': knntrain,
#    'KNN test acc': knntest}

def main(job_id, params):
    print 'Anything printed here will end up in the output directory for job #%d' % job_id
    print params
    return bop_clf(params['windowWidth'],params['numSymbols'], params['alphabetSize'] )