def fixLeftovers():
from NormalGlove import Vocab
path = DatasetManager.getVisualEmbeddingsFullSizeFolderPath()
vocab = Vocab.readVocabFromCSVFile()
lostEmbeddings = Leftovers.findWordsThatLackEmbedings(vocab, path)
print("Leftovers found", len(lostEmbeddings))
ImageEmbedder.createAverageEmbeddingsForVocab(lostEmbeddings, 3, 10,
[224, 224], path, 3)
def performPCA(mainFilename,
gloveOutputFolder,
dimensions,
includeSize,
skipSize=0,
version=1):
skipDimensions = [d + skipSize for d in dimensions]
PCAOutputFolder = DatasetManager.getVisualEmbeddingsFullSizeFolderPath(
version) + "PCA-{}".format(mainFilename)
if (os.path.isdir(PCAOutputFolder) == False):
os.mkdir(PCAOutputFolder)
embeddingFilePath = DatasetManager.getVisualEmbeddingsFullSizeFolderPath(
version) + "/Keyed-VisualGlove-Full"
model = Model.loadKeyedVectors(embeddingFilePath)
pureEmbeddings = [model.wv[k] for k in model.vocab]
PCAReduction.createPCATransformers(pureEmbeddings[:includeSize],
skipDimensions, PCAOutputFolder)
PCAReduction.createPCAEmbeddingFiles(model, pureEmbeddings,
PCAOutputFolder, gloveOutputFolder,
mainFilename, skipDimensions,
skipSize)
def main():
from Datasets import DatasetManager
embeddingPath = DatasetManager.getVisualEmbeddingsFullSizeFolderPath() + "VisualGlove-Full.txt"
embeddings = DatasetManager.getWordsAndEmbeddingsFromFile(embeddingPath)
FileProcessing.saveToFile(embeddings, 'tempfullEmbeddings')
pureEmbeddings = [embeddings[w] for w in embeddings.keys()]
print("Getting TSNE embeddings")
reducedEmbeddings = getTsneEmbeddings(pureEmbeddings)
print("Saving to file...")
FileProcessing.saveToFile(reducedEmbeddings, 'tempEmbeddings')
labels = [k for k in embeddings.keys()]
visualizeEmbeddings(reducedEmbeddings, labels)
wordsFoundInFolders[w] = 1
counter += 1
wordsNotFound = []
for w in fullVocab:
if (w not in wordsFoundInFolders):
wordsNotFound.append(w)
return wordsNotFound
if (__name__ == '__main__'):
from NormalGlove import Model
# removeListCharsFromFile(DatasetManager.getVisualEmbeddingsFullSizeFolderPath() + "/VisualGlove-Full.txt", "NewFile")
file = DatasetManager.getVisualEmbeddingsFullSizeFolderPath(
) + "/ProperFormat.txt"
# currentVocab = DatasetManager._getWordsFromEmbeddingFile(file)
# concatenateEmbeddingsFiles(DatasetManager.getVisualEmbeddingsFullSizeFolderPath(), "ProperFormat.txt")
temp = Model.loadGloveVectors(file)
'''
fullVocab = Vocab.readVocabFromCSVFile()
print("Full Vocab loaded")
print("Current vocab loaded")
currentVocabLookup = {}
for w in currentVocab:
currentVocabLookup[w] = 1
del currentVocab
fullVocabSize = len(fullVocab)
missingVocab = []
def concatEmbeddingFiles():
path = DatasetManager.getVisualEmbeddingsFullSizeFolderPath()
GloveFormatter.concatenateEmbeddingsFiles(path, "VisualGlove-Full.txt")
