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svm.py
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svm.py
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from __future__ import division
import logging
import numpy as np
from sklearn import svm
from learning_algo import LearningAlgorithm
from error import ErrorMeasure
import pickle as pkl
import math
class Dataset(object):
def __init__(self, is_binary=False):
self.is_binary = is_binary
#Examples
self.Xtrain = None
self.Xtest = None
#Labels
self.Ytrain = None
self.Ytest = None
self.sparsity = 0.0
self.n_examples = 0
def _get_data(self, data_path):
if data_path.endswith("pkl") or data_path.endswith("pickle"):
data = pkl.load(open(data_path, "rb"))
else:
data = np.load(data_path)
return data
def binarize_labels(self, labels=None):
last_lbl = np.max(labels)
binarized_lbls = []
if self.is_binary:
for label in labels:
if label == last_lbl:
binarized_lbls.append(0)
else:
binarized_lbls.append(1)
return binarized_lbls
def setup_dataset(self, data_path=None, train_split_scale = 0.8):
data = self._get_data(data_path)
self.n_examples = data[0].shape[0]
ntrain = math.floor(self.n_examples * train_split_scale)
self.Xtrain = data[0][:ntrain]
self.Xtest = data[0][ntrain:]
self.Ytrain = np.array(self.binarize_labels(data[1][:ntrain].flatten()) \
if self.is_binary else data[1][:ntrain].flatten())
self.Ytest = np.array(self.binarize_labels(data[1][ntrain:].flatten()) \
if self.is_binary else data[1][ntrain:].flatten())
def comp_sparsity(self):
num_sparse_els = 0
for el in self.Xtrain.flatten():
if el == 0:
num_sparse_els+=1
for el in self.Xtest.flatten():
if el == 0:
num_sparse_els+=1
self.sparsity = (num_sparse_els/self.n_examples)
return self.sparsity
class CSVM(LearningAlgorithm):
def __init__(self):
self.clf = None
def train(self, Xtrain, Ytrain, **kwargs):
print "Training on data has started"
kern = kwargs["kern"]
gamma = kwargs["gamma"]
C = kwargs["C"]
kern = kern.encode("ascii", "ignore")
self.clf = svm.SVC(kernel=kern, gamma=gamma, C=C)
self.clf.fit(list(Xtrain), list(Ytrain))
def test(self, Xtest, Ytest, **kwargs):
print "Testing on data has started"
is_binary_data = kwargs["binary_data"]
error_comp = ErrorMeasure(binary_measure=is_binary_data)
predictions = self.clf.predict(list(Xtest))
for i, ex in enumerate(Xtest):
error_comp.add_new_measure(Ytest[i], predictions[i])
return error_comp
def get_logger(self):
logger = logging.getLogger('Crossvalidation')
logger.setLevel(logging.INFO)
# create file handler which logs even debug messages
fh = logging.FileHandler('svm_tetromino_crossvalidation.log')
fh.setLevel(logging.DEBUG)
# create formatter
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
logger.addHandler(fh)
return logger
if __name__=="__main__":
from kcv import KfoldCrossvalidation
DS = Dataset(is_binary=True)
DS.setup_dataset(data_path="/home/gulcehre/dataset/pentomino/pieces/pento64x64_4k_task4_seed_98981222.npy")
kfoldCrossValidation = KfoldCrossvalidation(no_of_folds=2)
cs_args = {
"train_args":{
"kern":"rbf",
"gamma": 0.01,
"C": 10
},
"test_args":{
"binary_data":True
}
}
csvm = CSVM()
valid_errs, test_errs = kfoldCrossValidation.crossvalidate(DS.Xtrain,
DS.Ytrain, DS.Xtest, DS.Ytest, csvm, **cs_args)