import preprocess from sklearn.externals import joblib tags = {"UNKNOWN": 0, "O": 1, "B": 2, "I": 3, "START": 4} reverse_tags = {0: "UNKNOWN", 1: "O", 2: "B", 3: "I"} word_dict = None numFeatures = None if __name__ == "__main__": train = sys.argv[1] test = sys.argv[2] model_number = sys.argv[3] X = None y = None retagged_train, word_dict, totalNumWords = preprocess.iob(train) global numFeatures numDistinctWords = len(word_dict) numFeatures = numDistinctWords + 1 + 9 + 12 + 2 X = scipy.sparse.lil_matrix((totalNumWords, numFeatures)) y = np.empty([totalNumWords, ]) #training_seen = [0] * totalNumWords with open(retagged_train) as f: identifier_line = True split_line = None word = None tag = None length = None alpha = None allCaps = None hasNumeric = None
deltaO[index] = scoreI, "I", counts, phi, wordIndex if tag == "B": deltaB[index] = scoreI, "I", counts, phi, wordIndex if tag == "I": deltaI[index] = scoreI, "I", counts, phi, wordIndex return scoreI, "I", counts, phi, wordIndex if __name__ == "__main__": train = sys.argv[1] test = sys.argv[2] model_number = sys.argv[3] X = None y = None retagged_train, word_dict, totalNumWords = preprocess.iob(train) global numFeatures numDistinctWords = len(word_dict) numFeatures = numDistinctWords + 1 + 6 + 8 + 1 X = scipy.sparse.lil_matrix((totalNumWords, numFeatures)) y = np.empty([ totalNumWords, ]) with open(retagged_train) as f: identifier_line = True split_line = None word = None tag = None length = None alpha = None allCaps = None
import preprocess from sklearn.externals import joblib tags = {"UNKNOWN": 0, "O": 1, "B": 2, "I": 3} reverse_tags = {0: "UNKNOWN", 1: "O", 2: "B", 3: "I"} word_dict = None numFeatures = None if __name__ == "__main__": train = sys.argv[1] test = sys.argv[2] model_number = sys.argv[3] X = None y = None retagged_train, word_dict = preprocess.iob(train) global numFeatures numWords = len(word_dict) numFeatures = numWords + 3 + 4 X = scipy.sparse.lil_matrix((numWords, numFeatures)) y = np.empty([ numWords, ]) training_seen = [0] * numWords with open(retagged_train) as f: identifier_line = True split_line = None word = None tag = None length = None alpha = None
import preprocess from sklearn.externals import joblib tags = {"UNKNOWN": 0, "O": 1, "B": 2, "I": 3} reverse_tags = {0: "UNKNOWN", 1: "O", 2: "B", 3: "I"} word_dict = None numFeatures = None if __name__ == "__main__": train = sys.argv[1] test = sys.argv[2] model_number = sys.argv[3] X = None y = None retagged_train, word_dict = preprocess.iob(train) global numFeatures numWords = len(word_dict) numFeatures = numWords + 3 + 4 X = scipy.sparse.lil_matrix((numWords, numFeatures)) y = np.empty([numWords, ]) training_seen = [0] * numWords with open(retagged_train) as f: identifier_line = True split_line = None word = None tag = None length = None alpha = None allCaps = None hasNumeric = None