def main():
ds = DatasetSplit("data/datasetSplit.txt")
print("Train size: " + str(len(ds.train)))
print("Test size: " + str(len(ds.test)))
trainTest = TrainTestSplit(ds, "data/datasetSentences.txt")
config = ConfigParser.ConfigParser()
config.read("conf.ini")
modelParameters = ModelParameters(
HyperParameters(config.getint('HyperParams', 'iterations'),
config.getint('HyperParams', 'minibatchsize'),
config.getint('HyperParams', 'C'),
config.getint('HyperParams', 'D'),
config.getint('HyperParams', 'K'),
config.getint('HyperParams', 'annealingRate'),
config.getfloat('HyperParams', 'eta'),
config.getint('HyperParams', 'seed'),
config.getfloat('HyperParams', 'alpha'),
config.getint('HyperParams', 'X')))
# modelParameters.Init(trainTest)
if config.getboolean('Debug', 'bTrain'):
start = time.time()
sgd = SGD()
finalTestLogLiklihood = sgd.LearnParamsUsingSGD(
trainTest, modelParameters)
total_time = time.time() - start
pickle_save('model.pkl', modelParameters)
with open("output/results.txt", 'a') as output:
output.write("Hyperparameters: " +
str(config.items('HyperParams')) + "\n")
output.write("Train time: " + str(total_time / 3600) + "H " +
str(total_time % 3600 / 60) + "M" +
str(total_time % 3600 % 60) + "S" + "\n")
output.write("Final Test Logliklihood: " +
str(finalTestLogLiklihood))
else:
#modelParameters = pickle_load('model.pkl')
modelParameters = pickle_load('eta_2.0_model.pkl')
#plot_logLiklihood(modelParameters.hyperParams)
if config.getboolean('Debug', 'bVariantD'):
total_time = []
finalTestLogLiklihood = []
finalTrainLogLiklihood = []
d_vals = np.linspace(10.0, 300.0, 5, dtype=int)
# for d in d_vals:
# start = time.time()
# modelParameters = ModelParameters(HyperParameters(config.getint('HyperParams', 'iterations'),
# config.getint('HyperParams', 'minibatchsize'),
# config.getint('HyperParams', 'C'),
# d,
# config.getint('HyperParams', 'K'),
# config.getint('HyperParams', 'annealingRate'),
# config.getfloat('HyperParams', 'eta'),
# config.getint('HyperParams', 'seed'),
# config.getfloat('HyperParams', 'alpha'),
# config.getint('HyperParams', 'X')))
# modelParameters.Init(trainTest)
# sgd = SGD()
# finalTestLogLiklihood.append(sgd.LearnParamsUsingSGD(trainTest, modelParameters))
# finalTrainLogLiklihood.append(loglikelihood(trainTest.train, modelParameters))
# total_time.append(time.time() - start)
# pickle_save(str(d) + "_finalTrainLogLiklihood.pkl", finalTrainLogLiklihood)
# pickle_save(str(d) + "_finalTestLogLiklihood.pkl", finalTestLogLiklihood)
# pickle_save(str(d) + "_d_total_time.pkl", total_time)
#pickle_save("d_finalTrainLogLiklihood.pkl", finalTrainLogLiklihood)
#pickle_save("d_finalTestLogLiklihood.pkl", finalTestLogLiklihood)
#pickle_save("d_total_time.pkl", total_time)
finalTrainLogLiklihood = pickle_load("d_finalTrainLogLiklihood.pkl")
finalTestLogLiklihood = pickle_load("d_finalTestLogLiklihood.pkl")
total_time = pickle_load("d_total_time.pkl")
plot_varientParam(modelParameters.hyperParams, d_vals,
finalTrainLogLiklihood, finalTestLogLiklihood,
total_time, "size of the word embedding-D. ",
'size of the word embedding - D')
if config.getboolean('Debug', 'bVariantLearnRate'):
total_time = []
finalTestLogLiklihood = []
finalTrainLogLiklihood = []
sgd = SGD()
eta_vals = np.linspace(0.1, 2, 5)
# for eta in eta_vals:
# start = time.time()
# modelParameters = ModelParameters(HyperParameters(config.getint('HyperParams', 'iterations'),
# config.getint('HyperParams', 'minibatchsize'),
# config.getint('HyperParams', 'C'),
# config.getint('HyperParams', 'D'),
# config.getint('HyperParams', 'K'),
# config.getint('HyperParams', 'annealingRate'),
# eta,
# config.getint('HyperParams', 'seed'),
# config.getfloat('HyperParams', 'alpha'),
# config.getint('HyperParams', 'X')))
# modelParameters.Init(trainTest)
# finalTestLogLiklihood.append(sgd.LearnParamsUsingSGD(trainTest, modelParameters))
# finalTrainLogLiklihood.append(loglikelihood(trainTest.train, modelParameters))
# total_time.append(time.time() - start)
# pickle_save("eta_" + str(eta) + "_model.pkl", modelParameters)
#pickle_save("eta_finalTrainLogLiklihood.pkl", finalTrainLogLiklihood)
#pickle_save("eta_finalTestLogLiklihood.pkl", finalTestLogLiklihood)
#pickle_save("eta_total_time.pkl", total_time)
finalTrainLogLiklihood = pickle_load("eta_finalTrainLogLiklihood.pkl")
finalTestLogLiklihood = pickle_load("eta_finalTestLogLiklihood.pkl")
total_time = pickle_load("eta_total_time.pkl")
plot_varientParam(modelParameters.hyperParams, eta_vals,
finalTrainLogLiklihood, finalTestLogLiklihood,
total_time, "eta", "eta")
if config.getboolean('Debug', 'bEvaluate'):
print "Best Context words:"
for word in ["good", "bad", "lame", "cool", "exciting"]:
print "\t Target:" + word
print "\t " + str(PredictContext(modelParameters, word))
modelParameters = ModelParameters(
HyperParameters(config.getint('HyperParams', 'iterations'),
config.getint('HyperParams', 'minibatchsize'),
config.getint('HyperParams', 'C'),
config.getint('HyperParams', 'D'), 2,
config.getint('HyperParams', 'annealingRate'),
config.getfloat('HyperParams', 'eta'),
config.getint('HyperParams', 'seed'),
config.getfloat('HyperParams', 'alpha'),
config.getint('HyperParams', 'X')))
modelParameters.Init(trainTest)
sgd = SGD()
sgd.LearnParamsUsingSGD(trainTest, modelParameters)
ScatterMatrix(modelParameters,
["good", "bad", "lame", "cool", "exciting"])
#modelParameters = pickle_load('model.pkl')
modelParameters = pickle_load('eta_2.0_model.pkl')
print "model hyperparams:" + str(modelParameters.hyperParams)
print "Predict input for - The movie was surprisingly __:"
print "\t" + str(
PredictInput(modelParameters,
["The", "movie", "was", "surprisingly"]))
print "Predict input for - __ was really disappointing:"
print "\t" + str(
PredictInput(modelParameters, ["was", "really", "disappointing"]))
print "Predict input for -Knowing that she __ was the best part:"
print "\t" + str(
PredictInput(
modelParameters,
["Knowing", "that", "she", "was", "the", "best", "part"]))
print "Solving analogy for - man is to woman as men is to:"
print "\t" + str(AnalogySolver(modelParameters, "man", "woman", "men"))
print "Solving analogy for - good is to great as bad is to:"
print "\t" + str(AnalogySolver(modelParameters, "good", "great",
"bad"))
print "Solving analogy for - warm is to cold as summer is to:"
print "\t" + str(
AnalogySolver(modelParameters, "warm", "cold", "summer"))
