def complete_wiki_processing(data, utility, train=True):
#convert to integers and padding
processed_data = []
num_bad_examples = 0
for example in data:
number_found = 0
if (example.is_bad_example):
num_bad_examples += 1
if (not (example.is_bad_example)):
example.string_question = example.question[:]
#entry match
example.processed_number_columns = example.processed_number_columns[:]
example.processed_word_columns = example.processed_word_columns[:]
example.word_exact_match, word_match, matched_indices = exact_match(
example.string_question, example.original_wc, number=False)
example.number_exact_match, number_match, _ = exact_match(
example.string_question, example.original_nc, number=True)
if (not (pick_one(example.word_exact_match))
and not (pick_one(example.number_exact_match))):
assert len(word_match) == 0
assert len(number_match) == 0
example.word_exact_match, word_match = partial_match(
example.string_question, example.original_wc, number=False)
#group by max
example.word_group_by_max = group_by_max(example.original_wc,
False)
example.number_group_by_max = group_by_max(example.original_nc,
True)
#column name match
example.word_column_exact_match, wcol_matched_indices = exact_column_match(
example.string_question,
example.original_wc_names,
number=False)
example.number_column_exact_match, ncol_matched_indices = exact_column_match(
example.string_question,
example.original_nc_names,
number=False)
if (not (1.0 in example.word_column_exact_match)
and not (1.0 in example.number_column_exact_match)):
example.word_column_exact_match = partial_column_match(
example.string_question,
example.original_wc_names,
number=False)
example.number_column_exact_match = partial_column_match(
example.string_question,
example.original_nc_names,
number=False)
if (len(word_match) > 0 or len(number_match) > 0):
example.question.append(utility.entry_match_token)
if (1.0 in example.word_column_exact_match
or 1.0 in example.number_column_exact_match):
example.question.append(utility.column_match_token)
example.string_question = example.question[:]
example.number_lookup_matrix = np.transpose(
example.number_lookup_matrix)[:]
example.word_lookup_matrix = np.transpose(
example.word_lookup_matrix)[:]
example.columns = example.number_columns[:]
example.word_columns = example.word_columns[:]
example.len_total_cols = len(example.word_column_names) + len(
example.number_column_names)
example.column_names = example.number_column_names[:]
example.word_column_names = example.word_column_names[:]
example.string_column_names = example.number_column_names[:]
example.string_word_column_names = example.word_column_names[:]
example.sorted_number_index = []
example.sorted_word_index = []
example.column_mask = []
example.word_column_mask = []
example.processed_column_mask = []
example.processed_word_column_mask = []
example.word_column_entry_mask = []
example.question_attention_mask = []
example.question_number = example.question_number_1 = -1
example.question_attention_mask = []
example.ordinal_question = []
example.ordinal_question_one = []
new_question = []
if (len(example.number_columns) > 0):
example.len_col = len(example.number_columns[0])
else:
example.len_col = len(example.word_columns[0])
for (start, length) in matched_indices:
for j in range(length):
example.question[start + j] = utility.unk_token
#print example.question
for word in example.question:
if (isinstance(word, numbers.Number)
or wiki_data.is_date(word)):
if (not (isinstance(word, numbers.Number))
and wiki_data.is_date(word)):
word = word.replace("X", "").replace("-", "")
number_found += 1
if (number_found == 1):
example.question_number = word
if (len(example.ordinal_question) > 0):
example.ordinal_question[
len(example.ordinal_question) - 1] = 1.0
else:
example.ordinal_question.append(1.0)
elif (number_found == 2):
example.question_number_1 = word
if (len(example.ordinal_question_one) > 0):
example.ordinal_question_one[
len(example.ordinal_question_one) - 1] = 1.0
else:
example.ordinal_question_one.append(1.0)
else:
new_question.append(word)
example.ordinal_question.append(0.0)
example.ordinal_question_one.append(0.0)
example.question = [
utility.word_ids[word_lookup(w, utility)] for w in new_question
]
example.question_attention_mask = [0.0] * len(example.question)
#when the first question number occurs before a word
example.ordinal_question = example.ordinal_question[
0:len(example.question)]
example.ordinal_question_one = example.ordinal_question_one[
0:len(example.question)]
#question-padding
example.question = [utility.word_ids[utility.dummy_token]
] * (utility.FLAGS.question_length -
len(example.question)) + example.question
example.question_attention_mask = [-10000.0] * (
utility.FLAGS.question_length -
len(example.question_attention_mask)
) + example.question_attention_mask
example.ordinal_question = [0.0] * (
utility.FLAGS.question_length -
len(example.ordinal_question)) + example.ordinal_question
example.ordinal_question_one = [
0.0
] * (utility.FLAGS.question_length - len(
example.ordinal_question_one)) + example.ordinal_question_one
if (True):
#number columns and related-padding
num_cols = len(example.columns)
start = 0
for column in example.number_columns:
if (check_processed_cols(
example.processed_number_columns[start], utility)):
example.processed_column_mask.append(0.0)
sorted_index = sorted(range(
len(example.processed_number_columns[start])),
key=lambda k: example.
processed_number_columns[start][k],
reverse=True)
sorted_index = sorted_index + [utility.FLAGS.pad_int] * (
utility.FLAGS.max_elements - len(sorted_index))
example.sorted_number_index.append(sorted_index)
example.columns[start] = column + [
utility.FLAGS.pad_int
] * (utility.FLAGS.max_elements - len(column))
example.processed_number_columns[start] += [
utility.FLAGS.pad_int
] * (utility.FLAGS.max_elements -
len(example.processed_number_columns[start]))
start += 1
example.column_mask.append(0.0)
for remaining in range(num_cols,
utility.FLAGS.max_number_cols):
example.sorted_number_index.append(
[utility.FLAGS.pad_int] * (utility.FLAGS.max_elements))
example.columns.append([utility.FLAGS.pad_int] *
(utility.FLAGS.max_elements))
example.processed_number_columns.append(
[utility.FLAGS.pad_int] * (utility.FLAGS.max_elements))
example.number_exact_match.append(
[0.0] * (utility.FLAGS.max_elements))
example.number_group_by_max.append(
[0.0] * (utility.FLAGS.max_elements))
example.column_mask.append(-100000000.0)
example.processed_column_mask.append(-100000000.0)
example.number_column_exact_match.append(0.0)
example.column_names.append([utility.dummy_token])
#word column and related-padding
start = 0
word_num_cols = len(example.word_columns)
for column in example.word_columns:
if (check_processed_cols(
example.processed_word_columns[start], utility)):
example.processed_word_column_mask.append(0.0)
sorted_index = sorted(
range(len(example.processed_word_columns[start])),
key=lambda k: example.processed_word_columns[start][k],
reverse=True)
sorted_index = sorted_index + [utility.FLAGS.pad_int] * (
utility.FLAGS.max_elements - len(sorted_index))
example.sorted_word_index.append(sorted_index)
column = convert_to_int_2d_and_pad(column, utility)
example.word_columns[start] = column + [
[utility.word_ids[utility.dummy_token]] *
utility.FLAGS.max_entry_length
] * (utility.FLAGS.max_elements - len(column))
example.processed_word_columns[start] += [
utility.FLAGS.pad_int
] * (utility.FLAGS.max_elements -
len(example.processed_word_columns[start]))
example.word_column_entry_mask.append(
[0] * len(column) +
[utility.word_ids[utility.dummy_token]] *
(utility.FLAGS.max_elements - len(column)))
start += 1
example.word_column_mask.append(0.0)
for remaining in range(word_num_cols,
utility.FLAGS.max_word_cols):
example.sorted_word_index.append(
[utility.FLAGS.pad_int] * (utility.FLAGS.max_elements))
example.word_columns.append(
[[utility.word_ids[utility.dummy_token]] *
utility.FLAGS.max_entry_length] *
(utility.FLAGS.max_elements))
example.word_column_entry_mask.append(
[utility.word_ids[utility.dummy_token]] *
(utility.FLAGS.max_elements))
example.word_exact_match.append(
[0.0] * (utility.FLAGS.max_elements))
example.word_group_by_max.append(
[0.0] * (utility.FLAGS.max_elements))
example.processed_word_columns.append(
[utility.FLAGS.pad_int] * (utility.FLAGS.max_elements))
example.word_column_mask.append(-100000000.0)
example.processed_word_column_mask.append(-100000000.0)
example.word_column_exact_match.append(0.0)
example.word_column_names.append(
[utility.dummy_token] * utility.FLAGS.max_entry_length)
seen_tables[example.table_key] = 1
#convert column and word column names to integers
example.column_ids = convert_to_int_2d_and_pad(
example.column_names, utility)
example.word_column_ids = convert_to_int_2d_and_pad(
example.word_column_names, utility)
for i_em in range(len(example.number_exact_match)):
example.number_exact_match[i_em] = example.number_exact_match[
i_em] + [0.0] * (utility.FLAGS.max_elements -
len(example.number_exact_match[i_em]))
example.number_group_by_max[
i_em] = example.number_group_by_max[i_em] + [0.0] * (
utility.FLAGS.max_elements -
len(example.number_group_by_max[i_em]))
for i_em in range(len(example.word_exact_match)):
example.word_exact_match[i_em] = example.word_exact_match[
i_em] + [0.0] * (utility.FLAGS.max_elements -
len(example.word_exact_match[i_em]))
example.word_group_by_max[i_em] = example.word_group_by_max[
i_em] + [0.0] * (utility.FLAGS.max_elements -
len(example.word_group_by_max[i_em]))
example.exact_match = example.number_exact_match + example.word_exact_match
example.group_by_max = example.number_group_by_max + example.word_group_by_max
example.exact_column_match = example.number_column_exact_match + example.word_column_exact_match
#answer and related mask, padding
if (example.is_lookup):
example.answer = example.calc_answer
example.number_print_answer = example.number_lookup_matrix.tolist(
)
example.word_print_answer = example.word_lookup_matrix.tolist()
for i_answer in range(len(example.number_print_answer)):
example.number_print_answer[
i_answer] = example.number_print_answer[i_answer] + [
0.0
] * (utility.FLAGS.max_elements -
len(example.number_print_answer[i_answer]))
for i_answer in range(len(example.word_print_answer)):
example.word_print_answer[
i_answer] = example.word_print_answer[i_answer] + [
0.0
] * (utility.FLAGS.max_elements -
len(example.word_print_answer[i_answer]))
example.number_lookup_matrix = convert_to_bool_and_pad(
example.number_lookup_matrix, utility)
example.word_lookup_matrix = convert_to_bool_and_pad(
example.word_lookup_matrix, utility)
for remaining in range(num_cols,
utility.FLAGS.max_number_cols):
example.number_lookup_matrix.append(
[False] * utility.FLAGS.max_elements)
example.number_print_answer.append(
[0.0] * utility.FLAGS.max_elements)
for remaining in range(word_num_cols,
utility.FLAGS.max_word_cols):
example.word_lookup_matrix.append(
[False] * utility.FLAGS.max_elements)
example.word_print_answer.append(
[0.0] * utility.FLAGS.max_elements)
example.print_answer = example.number_print_answer + example.word_print_answer
else:
example.answer = example.calc_answer
example.print_answer = [[0.0] * (utility.FLAGS.max_elements)
] * (utility.FLAGS.max_number_cols +
utility.FLAGS.max_word_cols)
#question_number masks
if (example.question_number == -1):
example.question_number_mask = np.zeros(
[utility.FLAGS.max_elements])
else:
example.question_number_mask = np.ones(
[utility.FLAGS.max_elements])
if (example.question_number_1 == -1):
example.question_number_one_mask = -10000.0
else:
example.question_number_one_mask = np.float64(0.0)
if (example.len_col > utility.FLAGS.max_elements):
continue
processed_data.append(example)
return processed_data
def complete_wiki_processing(data, utility, train=True):
#convert to integers and padding
processed_data = []
num_bad_examples = 0
for example in data:
number_found = 0
if (example.is_bad_example):
num_bad_examples += 1
if (not (example.is_bad_example)):
example.string_question = example.question[:]
#entry match
example.processed_number_columns = example.processed_number_columns[:]
example.processed_word_columns = example.processed_word_columns[:]
example.word_exact_match, word_match, matched_indices = exact_match(
example.string_question, example.original_wc, number=False)
example.number_exact_match, number_match, _ = exact_match(
example.string_question, example.original_nc, number=True)
if (not (pick_one(example.word_exact_match)) and not (
pick_one(example.number_exact_match))):
assert len(word_match) == 0
assert len(number_match) == 0
example.word_exact_match, word_match = partial_match(
example.string_question, example.original_wc, number=False)
#group by max
example.word_group_by_max = group_by_max(example.original_wc, False)
example.number_group_by_max = group_by_max(example.original_nc, True)
#column name match
example.word_column_exact_match, wcol_matched_indices = exact_column_match(
example.string_question, example.original_wc_names, number=False)
example.number_column_exact_match, ncol_matched_indices = exact_column_match(
example.string_question, example.original_nc_names, number=False)
if (not (1.0 in example.word_column_exact_match) and not (
1.0 in example.number_column_exact_match)):
example.word_column_exact_match = partial_column_match(
example.string_question, example.original_wc_names, number=False)
example.number_column_exact_match = partial_column_match(
example.string_question, example.original_nc_names, number=False)
if (len(word_match) > 0 or len(number_match) > 0):
example.question.append(utility.entry_match_token)
if (1.0 in example.word_column_exact_match or
1.0 in example.number_column_exact_match):
example.question.append(utility.column_match_token)
example.string_question = example.question[:]
example.number_lookup_matrix = np.transpose(
example.number_lookup_matrix)[:]
example.word_lookup_matrix = np.transpose(example.word_lookup_matrix)[:]
example.columns = example.number_columns[:]
example.word_columns = example.word_columns[:]
example.len_total_cols = len(example.word_column_names) + len(
example.number_column_names)
example.column_names = example.number_column_names[:]
example.word_column_names = example.word_column_names[:]
example.string_column_names = example.number_column_names[:]
example.string_word_column_names = example.word_column_names[:]
example.sorted_number_index = []
example.sorted_word_index = []
example.column_mask = []
example.word_column_mask = []
example.processed_column_mask = []
example.processed_word_column_mask = []
example.word_column_entry_mask = []
example.question_attention_mask = []
example.question_number = example.question_number_1 = -1
example.question_attention_mask = []
example.ordinal_question = []
example.ordinal_question_one = []
new_question = []
if (len(example.number_columns) > 0):
example.len_col = len(example.number_columns[0])
else:
example.len_col = len(example.word_columns[0])
for (start, length) in matched_indices:
for j in range(length):
example.question[start + j] = utility.unk_token
#print example.question
for word in example.question:
if (isinstance(word, numbers.Number) or wiki_data.is_date(word)):
if (not (isinstance(word, numbers.Number)) and
wiki_data.is_date(word)):
word = word.replace("X", "").replace("-", "")
number_found += 1
if (number_found == 1):
example.question_number = word
if (len(example.ordinal_question) > 0):
example.ordinal_question[len(example.ordinal_question) - 1] = 1.0
else:
example.ordinal_question.append(1.0)
elif (number_found == 2):
example.question_number_1 = word
if (len(example.ordinal_question_one) > 0):
example.ordinal_question_one[len(example.ordinal_question_one) -
1] = 1.0
else:
example.ordinal_question_one.append(1.0)
else:
new_question.append(word)
example.ordinal_question.append(0.0)
example.ordinal_question_one.append(0.0)
example.question = [
utility.word_ids[word_lookup(w, utility)] for w in new_question
]
example.question_attention_mask = [0.0] * len(example.question)
#when the first question number occurs before a word
example.ordinal_question = example.ordinal_question[0:len(
example.question)]
example.ordinal_question_one = example.ordinal_question_one[0:len(
example.question)]
#question-padding
example.question = [utility.word_ids[utility.dummy_token]] * (
utility.FLAGS.question_length - len(example.question)
) + example.question
example.question_attention_mask = [-10000.0] * (
utility.FLAGS.question_length - len(example.question_attention_mask)
) + example.question_attention_mask
example.ordinal_question = [0.0] * (utility.FLAGS.question_length -
len(example.ordinal_question)
) + example.ordinal_question
example.ordinal_question_one = [0.0] * (utility.FLAGS.question_length -
len(example.ordinal_question_one)
) + example.ordinal_question_one
if (True):
#number columns and related-padding
num_cols = len(example.columns)
start = 0
for column in example.number_columns:
if (check_processed_cols(example.processed_number_columns[start],
utility)):
example.processed_column_mask.append(0.0)
sorted_index = sorted(
range(len(example.processed_number_columns[start])),
key=lambda k: example.processed_number_columns[start][k],
reverse=True)
sorted_index = sorted_index + [utility.FLAGS.pad_int] * (
utility.FLAGS.max_elements - len(sorted_index))
example.sorted_number_index.append(sorted_index)
example.columns[start] = column + [utility.FLAGS.pad_int] * (
utility.FLAGS.max_elements - len(column))
example.processed_number_columns[start] += [utility.FLAGS.pad_int] * (
utility.FLAGS.max_elements -
len(example.processed_number_columns[start]))
start += 1
example.column_mask.append(0.0)
for remaining in range(num_cols, utility.FLAGS.max_number_cols):
example.sorted_number_index.append([utility.FLAGS.pad_int] *
(utility.FLAGS.max_elements))
example.columns.append([utility.FLAGS.pad_int] *
(utility.FLAGS.max_elements))
example.processed_number_columns.append([utility.FLAGS.pad_int] *
(utility.FLAGS.max_elements))
example.number_exact_match.append([0.0] *
(utility.FLAGS.max_elements))
example.number_group_by_max.append([0.0] *
(utility.FLAGS.max_elements))
example.column_mask.append(-100000000.0)
example.processed_column_mask.append(-100000000.0)
example.number_column_exact_match.append(0.0)
example.column_names.append([utility.dummy_token])
#word column and related-padding
start = 0
word_num_cols = len(example.word_columns)
for column in example.word_columns:
if (check_processed_cols(example.processed_word_columns[start],
utility)):
example.processed_word_column_mask.append(0.0)
sorted_index = sorted(
range(len(example.processed_word_columns[start])),
key=lambda k: example.processed_word_columns[start][k],
reverse=True)
sorted_index = sorted_index + [utility.FLAGS.pad_int] * (
utility.FLAGS.max_elements - len(sorted_index))
example.sorted_word_index.append(sorted_index)
column = convert_to_int_2d_and_pad(column, utility)
example.word_columns[start] = column + [[
utility.word_ids[utility.dummy_token]
] * utility.FLAGS.max_entry_length] * (utility.FLAGS.max_elements -
len(column))
example.processed_word_columns[start] += [utility.FLAGS.pad_int] * (
utility.FLAGS.max_elements -
len(example.processed_word_columns[start]))
example.word_column_entry_mask.append([0] * len(column) + [
utility.word_ids[utility.dummy_token]
] * (utility.FLAGS.max_elements - len(column)))
start += 1
example.word_column_mask.append(0.0)
for remaining in range(word_num_cols, utility.FLAGS.max_word_cols):
example.sorted_word_index.append([utility.FLAGS.pad_int] *
(utility.FLAGS.max_elements))
example.word_columns.append([[utility.word_ids[utility.dummy_token]] *
utility.FLAGS.max_entry_length] *
(utility.FLAGS.max_elements))
example.word_column_entry_mask.append(
[utility.word_ids[utility.dummy_token]] *
(utility.FLAGS.max_elements))
example.word_exact_match.append([0.0] * (utility.FLAGS.max_elements))
example.word_group_by_max.append([0.0] * (utility.FLAGS.max_elements))
example.processed_word_columns.append([utility.FLAGS.pad_int] *
(utility.FLAGS.max_elements))
example.word_column_mask.append(-100000000.0)
example.processed_word_column_mask.append(-100000000.0)
example.word_column_exact_match.append(0.0)
example.word_column_names.append([utility.dummy_token] *
utility.FLAGS.max_entry_length)
seen_tables[example.table_key] = 1
#convert column and word column names to integers
example.column_ids = convert_to_int_2d_and_pad(example.column_names,
utility)
example.word_column_ids = convert_to_int_2d_and_pad(
example.word_column_names, utility)
for i_em in range(len(example.number_exact_match)):
example.number_exact_match[i_em] = example.number_exact_match[
i_em] + [0.0] * (utility.FLAGS.max_elements -
len(example.number_exact_match[i_em]))
example.number_group_by_max[i_em] = example.number_group_by_max[
i_em] + [0.0] * (utility.FLAGS.max_elements -
len(example.number_group_by_max[i_em]))
for i_em in range(len(example.word_exact_match)):
example.word_exact_match[i_em] = example.word_exact_match[
i_em] + [0.0] * (utility.FLAGS.max_elements -
len(example.word_exact_match[i_em]))
example.word_group_by_max[i_em] = example.word_group_by_max[
i_em] + [0.0] * (utility.FLAGS.max_elements -
len(example.word_group_by_max[i_em]))
example.exact_match = example.number_exact_match + example.word_exact_match
example.group_by_max = example.number_group_by_max + example.word_group_by_max
example.exact_column_match = example.number_column_exact_match + example.word_column_exact_match
#answer and related mask, padding
if (example.is_lookup):
example.answer = example.calc_answer
example.number_print_answer = example.number_lookup_matrix.tolist()
example.word_print_answer = example.word_lookup_matrix.tolist()
for i_answer in range(len(example.number_print_answer)):
example.number_print_answer[i_answer] = example.number_print_answer[
i_answer] + [0.0] * (utility.FLAGS.max_elements -
len(example.number_print_answer[i_answer]))
for i_answer in range(len(example.word_print_answer)):
example.word_print_answer[i_answer] = example.word_print_answer[
i_answer] + [0.0] * (utility.FLAGS.max_elements -
len(example.word_print_answer[i_answer]))
example.number_lookup_matrix = convert_to_bool_and_pad(
example.number_lookup_matrix, utility)
example.word_lookup_matrix = convert_to_bool_and_pad(
example.word_lookup_matrix, utility)
for remaining in range(num_cols, utility.FLAGS.max_number_cols):
example.number_lookup_matrix.append([False] *
utility.FLAGS.max_elements)
example.number_print_answer.append([0.0] * utility.FLAGS.max_elements)
for remaining in range(word_num_cols, utility.FLAGS.max_word_cols):
example.word_lookup_matrix.append([False] *
utility.FLAGS.max_elements)
example.word_print_answer.append([0.0] * utility.FLAGS.max_elements)
example.print_answer = example.number_print_answer + example.word_print_answer
else:
example.answer = example.calc_answer
example.print_answer = [[0.0] * (utility.FLAGS.max_elements)] * (
utility.FLAGS.max_number_cols + utility.FLAGS.max_word_cols)
#question_number masks
if (example.question_number == -1):
example.question_number_mask = np.zeros([utility.FLAGS.max_elements])
else:
example.question_number_mask = np.ones([utility.FLAGS.max_elements])
if (example.question_number_1 == -1):
example.question_number_one_mask = -10000.0
else:
example.question_number_one_mask = np.float64(0.0)
if (example.len_col > utility.FLAGS.max_elements):
continue
processed_data.append(example)
return processed_data
