def load_testing_data(): dp.establish_db_connection() sequence_list = [] padded_output = [] #sentence_numbers = [] testing_dataset = DBHelperMethod.load_dataset_by_type("testing") #testing_dataset = DBHelperMethod.load_dataset_by_type_and_sentence_number_for_testing_purpose("testing", 3228) #sentence_numbers.append(3228) sentence_numbers = DBHelperMethod.get_list_of_sentence_numbers_by( "testing") labels_and_equiv_encoding = dp.get_label_table() for each_sentence_number in sentence_numbers: selected_sentence = testing_dataset[numpy.where( testing_dataset[:, 3] == str(each_sentence_number))] x, y = pad_data(selected_sentence, selected_sentence[:, [0, 1]], labels_and_equiv_encoding) sequence_list.append(x) padded_output.append(y) padded_input, vocabulary, vocabulary_inv = convert_input_to_vocab( sequence_list) padded_output = numpy.array(list(chain(*padded_output))) testing_words = np.take(testing_dataset, 4, axis=1) input_testing_letters = np.take(testing_dataset, 0, axis=1) op_testing_letters = np.take(testing_dataset, 5, axis=1) sent_num = np.take(testing_dataset, 3, axis=1) letters_loc = np.take(testing_dataset, 6, axis=1) undiac_word = np.take(testing_dataset, 7, axis=1) return padded_input, padded_output, vocabulary, vocabulary_inv, testing_words, input_testing_letters, op_testing_letters,\ sent_num, letters_loc, undiac_word
def get_testing_data(): DBHelperMethod.connect_to_db() training_dataset = DBHelperMethod.load_dataset_by_type("testing") x = dp.load_nn_input_dataset_string(training_dataset[:, [0, 6]]) y = dp.load_nn_labels_dataset_string(training_dataset[:, [0, 1]]) return x, y
def load_testing_data(): dp.establish_db_connection() testing_dataset = DBHelperMethod.load_dataset_by_type("testing") # x = dp.load_nn_input_dataset_string(testing_dataset[:, [0, 6]]) x = dp.load_nn_input_dataset_string_space_only(testing_dataset[:, [0, 6]]) y = dp.load_nn_labels_dataset_string(testing_dataset[:, [0, 1]]) sent_num, sen_len = dp.load_nn_seq_lengths(testing_dataset[:, [3]]) sentences_padded, vocabulary, vocabulary_inv = dp.pad_sentences1(x, sen_len, req_char_index, window_size) return sentences_padded, y, vocabulary, vocabulary_inv
def load_testing_data(): dp.establish_db_connection() training_dataset = DBHelperMethod.load_dataset_by_type("testing") x = dp.load_nn_input_dataset_string_space_only(training_dataset[:, [0, 6]]) y = dp.load_nn_labels_dataset_string(training_dataset[:, [0, 1]]) sent_num, sen_len = dp.load_nn_seq_lengths(training_dataset[:, [3]]) sentences_padded, vocabulary, vocabulary_inv = dp.extract_sent_and_pad( x, sen_len, window_size) padded_output = dp.extract_sent_and_pad_output(y, sen_len, window_size) return sentences_padded, padded_output, vocabulary, vocabulary_inv
def load_training_data(): dp.establish_db_connection() training_dataset = DBHelperMethod.load_dataset_by_type("training") # x = dp.load_nn_input_dataset_string(training_dataset[:, [0, 6]]) x = dp.load_nn_input_dataset_string_space_only(training_dataset[:, [0, 6]]) y = dp.load_nn_labels_dataset_string(training_dataset[:, [0, 1]]) sent_num, sen_len = dp.load_nn_seq_lengths(training_dataset[:, [3]]) sentences_padded, vocabulary, vocabulary_inv = dp.build_one_to_one_input_data( x, sen_len, req_char_index, window_size) return numpy.array(sentences_padded), numpy.array( y), vocabulary, vocabulary_inv
def load_testing_data(): dp.establish_db_connection() training_dataset = DBHelperMethod.load_dataset_by_type("testing") x = dp.load_nn_input_dataset_string_space_only(training_dataset[:, [0, 6]]) y = dp.load_nn_labels_dataset_string(training_dataset[:, [0, 1]]) vocab, vocab_inv = dp.build_vocab(x) sent_num, sen_len = dp.load_nn_seq_lengths(training_dataset[:, [3]]) input_sentences = sqp.create_window_of_chars(list(x), window_size) input_sentences = dp.build_input_data_without_flattening( input_sentences, vocab) input_sentences = numpy.array(input_sentences[:-1]) output_labels = sqp.create_window_of_chars(list(y), window_size) output_labels = numpy.array(output_labels[:-1]) return input_sentences, output_labels, vocab, vocab_inv
def load_testing_data(): dp.establish_db_connection() testing_dataset = DBHelperMethod.load_dataset_by_type("testing") #testing_dataset = DBHelperMethod.load_dataset_by_type_and_sentence_number_for_testing_purpose("testing", 3062) x = dp.load_nn_input_dataset_string_space_only(testing_dataset[:, [0, 6]]) y = dp.load_nn_labels_dataset_string(testing_dataset[:, [0, 1]]) sent_num, sen_len = dp.load_nn_seq_lengths(testing_dataset[:, [3]]) sentences_padded, vocabulary, vocabulary_inv = dp.pad_sentences1(x, sen_len, req_char_index, window_size) testing_words = np.take(testing_dataset, 4, axis=1) input_testing_letters = np.take(testing_dataset, 0, axis=1) op_testing_letters = np.take(testing_dataset, 5, axis=1) sent_num = np.take(testing_dataset, 3, axis=1) letters_loc = np.take(testing_dataset, 6, axis=1) undiac_word = np.take(testing_dataset, 7, axis=1) return sentences_padded, y, vocabulary, vocabulary_inv, testing_words, input_testing_letters, op_testing_letters,\ sent_num, letters_loc, undiac_word
def load_testing_data(): dp.establish_db_connection() sequence_list = [] padded_output = [] training_dataset = DBHelperMethod.load_dataset_by_type("testing") sentence_numbers = DBHelperMethod.get_list_of_sentence_numbers_by( "testing") labels_and_equiv_encoding = dp.get_label_table() for each_sentence_number in sentence_numbers: selected_sentence = training_dataset[numpy.where( training_dataset[:, 3] == str(each_sentence_number))] x, y = pad_data(selected_sentence, selected_sentence[:, [0, 1]], labels_and_equiv_encoding) sequence_list.append(x) padded_output.append(y) padded_input, vocabulary, vocabulary_inv = convert_input_to_vocab( sequence_list) padded_output = numpy.array(list(chain(*padded_output))) return padded_input, padded_output, vocabulary, vocabulary_inv