def grid_search(method = "NB"):
results = {}
#Getting the data from the training file
context_data = getTrainingContextData()
print("Grabbed the context data")
#Reading in the word to vector dataframe
word_vector_subset = readWordToVector()
print("Grabbed the word_vector_subset")
if method == "NB":
total = len(window_size_options) * len(n_components_options) * len(perplexity_options) * len(std)
print("Total steps: {0}".format(total))
for window_size in window_size_options:
for n_component in n_components_options:
for perplexity in perplexity_options:
for s in std:
results[str((window_size, n_component, perplexity, s))]= controller(method, context_data, word_vector_subset, window_size, n_component, perplexity, naive_bayes_window[0],"","","",s)
total -= 1
print("{0} to go".format(total))
else:
total = len(c_range) * len(gamma_range) *len(window_size_options)*len(svm_range)*len(std)
print("Total steps: {0}".format(total))
for window_size in window_size_options:
for c in c_range:
for g in gamma_range:
for srange in svm_range:
for s in std:
results[str((window_size, c, g, srange,s))]= controller(method, context_data, word_vector_subset, window_size, "", "", "", c, g, srange,s)
total -= 1
print("{0} to go".format(total))
pprint(results)
saveValidationData(results);
def main():
#Getting the data from the training file
context_data = getTrainingContextData()
print("Retrieved data from the training file")
#Adding data from the test file to the same context data
context_data = getTestContextData(context_data)
print("Retrieved data from the test file")
#Obtain training word counts to be used as (inverse) vector weights
if norm_word_counts == True:
word_freqs = getWordFreqs(context_data)
else:
word_freqs = Counter()
#Reading in the word to vector dataframe
word_vector_subset = readWordToVector()
print("Retrieved the word2vec dataset")
#Create the feature vector for each instance id in the above data structure and save it in JSON format
context_feature_data = makeFeatureVectorForWordInstance(context_data, word_vector_subset, word_freqs)
saveContextVectorData(context_feature_data)
print("Created the word vectors for all word types and their instances")
