def construct_binary_frame(data_sets, method, token, start, end, years_per_block, isamples):
"""
This method is used to construct a BinaryFrame object from a meta-data file which specifies what data sets we want
to download and what columns we are interested in from that data.
:param data_sets: the file containing the data sets we want
:param method: the method of conversion to binary
:param token: a Quandl authentication token
:param start: the start date
:param end: the end date
:param years_per_block: the time frame / dimension we want to look at
:return: a BinaryFrame object which can work with the RandomnessTester class
"""
downloader = QuandlInterface(token)
data_file = pandas.read_csv(data_sets)
data_sets = list(data_file["ID"])
drop_columns = list(data_file["DROP"])
data_prefix = ""
transform = "rdiff"
start_date = str(start) + "-01-01"
end_date = str(end) + "-01-01"
my_arguments = []
for i in range(len(data_sets)):
drop = drop_columns[i].split("#")
if drop == "":
drop = []
my_arguments.append(Argument(data_sets[i], start_date, end_date, data_prefix, drop, transform))
data_frame_full = downloader.get_data_sets(my_arguments)
binary_frame = BinaryFrame(data_frame_full, start, end, years_per_block)
binary_frame.convert(method, independent_samples=isamples)
return binary_frame
def construct_binary_frame(data_sets, method, token, start, end,
years_per_block, isamples):
"""
This method is used to construct a BinaryFrame object from a meta-data file which specifies what data sets we want
to download and what columns we are interested in from that data.
:param data_sets: the file containing the data sets we want
:param method: the method of conversion to binary
:param token: a Quandl authentication token
:param start: the start date
:param end: the end date
:param years_per_block: the time frame / dimension we want to look at
:return: a BinaryFrame object which can work with the RandomnessTester class
"""
downloader = QuandlInterface(token)
data_file = pandas.read_csv(data_sets)
data_sets = list(data_file["ID"])
drop_columns = list(data_file["DROP"])
data_prefix = ""
transform = "rdiff"
start_date = str(start) + "-01-01"
end_date = str(end) + "-01-01"
my_arguments = []
for i in range(len(data_sets)):
drop = drop_columns[i].split('#')
if drop == "":
drop = []
my_arguments.append(
Argument(data_sets[i], start_date, end_date, data_prefix, drop,
transform))
data_frame_full = downloader.get_data_sets(my_arguments)
binary_frame = BinaryFrame(data_frame_full, start, end, years_per_block)
binary_frame.convert(method, independent_samples=isamples)
return binary_frame
def construct_binary_frame(data_sets, method, start, end, years_per_block, isamples):
"""
此函数用于将原始价格数据转化为 BinaryFrame 对象
:param data_sets: 数据文件
:param method: 转二元向量的方法
:param start: 起始日期
:param end: 终止日期
:param years_per_block: 使用的时间窗口
:return: 可用于 RandomnessTester 类的BinaryFrame对象
"""
data_frame_full = pandas.read_excel(io=data_sets)
data_frame_full['date'] = pandas.to_datetime(data_frame_full['date'])
data_frame_full.set_index("date", inplace=True)
binary_frame = BinaryFrame(data_frame_full, start, end, years_per_block)
binary_frame.convert(method, independent_samples=isamples)
return binary_frame
def run_experiments(data_sets, block_sizes, q_sizes, method, start, end, years_per_block, isamples=False):
"""
:param data_sets: 数据文件
:param block_sizes: 块大小
:param q_sizes: 矩阵大小
:param start: 起始日期
:param end: 终止日期
:param methods: 转二元向量的方法
:param years_per_block: 使用的时间窗口
:return: 无
"""
print("\n")
print("METHOD =", method.upper())
length = 256 * (end - start)
gen = Generators(length)
prng = gen.numpy_integer()
all_passed = []
prng_data = pandas.DataFrame(numpy.array(prng))
prng_data.columns = ["Mersenne"]
prng_binary_frame = BinaryFrame(prng_data, start, end, years_per_block)
prng_binary_frame.convert(method, convert=False, independent_samples=isamples)
rng_tester = RandomnessTester(prng_binary_frame, False, 00, 00)
passed = rng_tester.run_test_suite(block_sizes, q_sizes)
for x in passed:
all_passed.append(x)
nrand = numpy.empty(length)
for i in range(length):
nrand[i] = (i % 10) / 10
nrand -= numpy.mean(nrand)
nrand_data = pandas.DataFrame(numpy.array(nrand))
nrand_data.columns = ["Deterministic"]
nrand_binary_frame = BinaryFrame(nrand_data, start, end, years_per_block)
nrand_binary_frame.convert(method, convert=True, independent_samples=isamples)
rng_tester = RandomnessTester(nrand_binary_frame, False, 00, 00)
passed = rng_tester.run_test_suite(block_sizes, q_sizes)
for x in passed:
all_passed.append(x)
my_binary_frame = construct_binary_frame(data_sets, method, start, end, years_per_block, isamples)
rng_tester = RandomnessTester(my_binary_frame, True, start, end)
passed = rng_tester.run_test_suite(block_sizes, q_sizes)
for x in passed:
all_passed.append(x)
print("\n")
return all_passed
def run_experiments(data_sets, block_sizes, q_sizes, method, start, end, years_per_block, isamples=False):
"""
This method just runs the experiments which were used to write the blog post
:param data_sets: the file containing a list of data sets we want
:param block_sizes: a list of block sizes
:param q_sizes: a list of matrix sizes
:param start: the start date
:param end: the end date
:param methods: the methods of conversion to binary we want to test
:param years_per_block: the time frame / dimension we want to look at
:return: nothing just prints out stuff
"""
print("\n")
print("METHOD =", method.upper())
length = 256 * (end - start)
gen = Generators(length)
prng = gen.numpy_integer()
all_passed = []
prng_data = pandas.DataFrame(numpy.array(prng))
prng_data.columns = ["Mersenne"]
prng_binary_frame = BinaryFrame(prng_data, start, end, years_per_block)
prng_binary_frame.convert(method, convert=False, independent_samples=isamples)
# method, real_data, start_year, end_year, block_size
rng_tester = RandomnessTester(prng_binary_frame, False, 00, 00)
passed = rng_tester.run_test_suite(block_sizes, q_sizes)
for x in passed:
all_passed.append(x)
nrand = numpy.empty(length)
for i in range(length):
nrand[i] = (i % 10) / 10
nrand -= numpy.mean(nrand)
nrand_data = pandas.DataFrame(numpy.array(nrand))
nrand_data.columns = ["Deterministic"]
nrand_binary_frame = BinaryFrame(nrand_data, start, end, years_per_block)
nrand_binary_frame.convert(method, convert=True, independent_samples=isamples)
rng_tester = RandomnessTester(nrand_binary_frame, False, 00, 00)
passed = rng_tester.run_test_suite(block_sizes, q_sizes)
for x in passed:
all_passed.append(x)
t = setup_environment()
my_binary_frame = construct_binary_frame(data_sets, method, t, start, end, years_per_block, isamples)
rng_tester = RandomnessTester(my_binary_frame, True, start, end)
passed = rng_tester.run_test_suite(block_sizes, q_sizes)
for x in passed:
all_passed.append(x)
print("\n")
return all_passed
def run_experiments(data_sets,
block_sizes,
q_sizes,
method,
start,
end,
years_per_block,
isamples=False):
"""
This method just runs the experiments which were used to write the blog post
:param data_sets: the file containing a list of data sets we want
:param block_sizes: a list of block sizes
:param q_sizes: a list of matrix sizes
:param start: the start date
:param end: the end date
:param methods: the methods of conversion to binary we want to test
:param years_per_block: the time frame / dimension we want to look at
:return: nothing just prints out stuff
"""
print("\n")
print("METHOD =", method.upper())
length = 256 * (end - start)
gen = Generators(length)
prng = gen.numpy_integer()
all_passed = []
prng_data = pandas.DataFrame(numpy.array(prng))
prng_data.columns = ["Mersenne"]
prng_binary_frame = BinaryFrame(prng_data, start, end, years_per_block)
prng_binary_frame.convert(method,
convert=False,
independent_samples=isamples)
# method, real_data, start_year, end_year, block_size
rng_tester = RandomnessTester(prng_binary_frame, False, 00, 00)
passed = rng_tester.run_test_suite(block_sizes, q_sizes)
for x in passed:
all_passed.append(x)
nrand = numpy.empty(length)
for i in range(length):
nrand[i] = (i % 10) / 10
nrand -= numpy.mean(nrand)
nrand_data = pandas.DataFrame(numpy.array(nrand))
nrand_data.columns = ["Deterministic"]
nrand_binary_frame = BinaryFrame(nrand_data, start, end, years_per_block)
nrand_binary_frame.convert(method,
convert=True,
independent_samples=isamples)
rng_tester = RandomnessTester(nrand_binary_frame, False, 00, 00)
passed = rng_tester.run_test_suite(block_sizes, q_sizes)
for x in passed:
all_passed.append(x)
t = setup_environment()
my_binary_frame = construct_binary_frame(data_sets, method, t, start, end,
years_per_block, isamples)
rng_tester = RandomnessTester(my_binary_frame, True, start, end)
passed = rng_tester.run_test_suite(block_sizes, q_sizes)
for x in passed:
all_passed.append(x)
print("\n")
return all_passed