def get_XY(problems, max_nb_inputs, max_nb_tokens):
y = []
rows_type = []
rows_val = []
# print(problems)
for problem in problems:
examples = [util.decode_example(x) for x in problem['examples']]
# print(examples[0])
row_type, row_val = get_row(examples, max_nb_inputs, L)
# print(row)
f_list = util.get_program_vec(problem['program'])
y.append(encode_program(f_list, max_nb_tokens))
rows_type.append(row_type)
rows_val.append(row_val)
y = np.array(y)
rows_type = np.array(rows_type)
rows_val = np.array(rows_val)
# preprocess
rows_val += np.ones_like(rows_val) * constants.INTMAX
# print(1111, X)
# print(rows_val)
# print(y)
return rows_type, rows_val, y
def get_XY(problems, max_nb_inputs):
y = []
rows = []
for problem in problems:
examples = [util.decode_example(x) for x in problem['examples']]
row = encoding.get_row(examples, max_nb_inputs, L)
y.append(problem['attribute'])
rows.append(row)
# convert
X = collections.defaultdict(list)
for row in rows:
for k, v in row.items():
X[k].append(v)
for k, v in X.items():
X[k] = np.array(v)
# preprocess
for k, v in X.items():
if not k.endswith('type'):
# keras embedding requires nonnegative ints so we inc
# by INTMAX. This is less than NULL which is 513
v[v < constants.NULL] += constants.INTMAX
return X, np.array(y)
def solve_problem(problem, T, mode='dfs', gas=np.inf):
examples = [util.decode_example(x) for x in problem['examples']]
predictions = problem.get('prediction') # (L2, len(f)+8)
start = time.time()
search_func = search.beam_search
solution, steps_used = search_func(examples, T, predictions, gas)
end = time.time()
if solution:
solution = solution.prefix
return solution, end - start, steps_used
def solve_problem(problem, T, mode='dfs', gas=np.inf):
examples = [util.decode_example(x) for x in problem['examples']]
predictions = problem.get('prediction', np.zeros(len(impl.FUNCTIONS)))
scores = dict(zip(impl.FUNCTIONS, predictions))
ctx = context.Context(scores)
start = time.time()
if mode == 'dfs':
search_func = search.dfs
elif mode == 'sort-and-add':
search_func = search.sort_and_add
else:
raise ValueError('invalid search mode {}'.format(mode))
solution, steps_used = search_func(examples, T, ctx, gas)
end = time.time()
if solution:
solution = solution.prefix
return solution, end - start, steps_used
def solve_problem(problem, T, mode='dfs', gas=np.inf):
examples = [util.decode_example(x) for x in problem['examples']]
predictions = problem.get('prediction', np.zeros(len(impl.FUNCTIONS)))
if mode != 'beam':
scores = dict(zip(impl.FUNCTIONS, predictions))
ctx = context.Context(scores)
start = time.time()
if mode == 'dfs':
search_func = search.dfs
solution, steps_used = search_func(examples, T, ctx, gas)
elif mode == 'sort-and-add':
search_func = search.sort_and_add
solution, steps_used = search_func(examples, T, ctx, gas)
else:
search_func = search.beam_search
solution, steps_used = search_func(examples, T, predictions, gas)
end = time.time()
if solution:
solution = solution.prefix
return solution, end - start, steps_used
def get_XY(problems, max_nb_inputs):
y = []
rows_type = []
rows_val = []
# print(problems)
for problem in problems:
examples = [util.decode_example(x) for x in problem['examples']]
# print(examples[0])
row_type, row_val = get_row(examples, max_nb_inputs, L)
# print(row)
y.append(problem['attribute'])
rows_type.append(row_type)
rows_val.append(row_val)
y = np.array(y)
rows_type = np.array(rows_type)
rows_val = np.array(rows_val)
# preprocess
rows_val += np.ones_like(rows_val) * constants.INTMAX
# print(1111, X)
return rows_type, rows_val, y
def get_XY(problems, max_nb_inputs, max_nb_tokens):
y = [[], []]
rows_type = []
rows_val = []
# print(problems)
for problem in problems:
examples = [util.decode_example(x) for x in problem['examples']]
# print(examples[0])
row_type, row_val = get_row(examples, max_nb_inputs, L)
# print(row)
next_y = encode_program(problem['program'])
y[0].append(next_y[0])
y[1].append(next_y[1])
rows_type.append(row_type)
rows_val.append(row_val)
rows_type = np.array(rows_type)
rows_val = np.array(rows_val)
# preprocess
rows_val += np.ones_like(rows_val) * constants.INTMAX
# print(1111, X)
return rows_type, rows_val, y #sketchy = [index] argy = [11100]