# coding=utf-8
import pandas as pd
import numpy as np
import uuid
from hashlib import sha256
# batch_size of each time write rows to id_sha256.csv
batch_size = 200000
# total_samples
total_samples = 10000000
# path_id csv
path_id_csv = "./id_sha256.csv"
# gen numeric,if numeric gen int64 to id_sha256.csv,False gen sha256 object of pandas.
numeric = True
# set header "id"
no_header = True
def foo(band):
for index, v in enumerate(band):
a, b = v[0], v[1]
t = [k for k in range(a, b)]
yield t
def value_sha(a, b):
t = []
if numeric:
for k in range(a, b + 1):
t.append(k)
else:
for i in range(a, b + 1):
uid = str(uuid.uuid1()).replace("-", "")
id_value = sha256(uid.encode("utf-8")).hexdigest() # todo each time is same uid string,so need sha diff it
t.append(id_value)
# print(f"{index+1}次 length of sha_list is {len(t)},range is [{a},{b}]")
return t
def gen_id(batch_size, samples):
rangers = [[k, k + batch_size] for k in list(range(0, samples, batch_size))]
generator = foo(rangers) # <class.generator>
for index, value in enumerate(generator):
a, b = value[0], value[-1]
v = value_sha(a, b)
if numeric:
df = pd.DataFrame(np.array(v), columns=["id"], dtype=np.int64) # todo set dtype=np.int64
else:
df = pd.DataFrame(np.array(v), columns=["id"]) # todo set dtype=np.str
if index == 0:
print(df.dtypes)
df = pd.DataFrame(np.array(v), columns=["id"])
if no_header:
df.to_csv(path_id_csv, index=False, header=None)
else:
df.to_csv(path_id_csv, index=False)
else:
df.to_csv(path_id_csv, index=False, header=None, mode="a")
print(
f"finish {index + 1}x{batch_size} row time write,value index range is [{value[0]},{value[-1]}],length of sha256msg is {len(value)}")
def check_set():
df = pd.read_csv(path_id_csv)
array = df.values.tolist()
mp = list(map(lambda x: x[0], array))
print(f"set {path_id_csv} sha256 id columns去重后行数：", len(list(set(mp))))
if __name__ == '__main__':
import time
start = time.time()
gen_id(batch_size, total_samples)
print(time.time() - start)
print(f"<<<<<<<<<<finish gen {total_samples} rows sha256 id to {path_id_csv}<<<<<<<<<")
# check_set()
使用sha256或者id range生成id列
gendata out 根据上述产生csv的id 列进行交集大数据
import pandas as pd
import numpy as np
__author__ = 'Chenquan'
# todo before you run generate_output.py,please run shamsg_unique.py to gen id col to csv first for read.
""">>>>10wx1000columns cost 143.43s <<<<< 10wx10columns cost 2.02s"""
# 特征列
col = 10
# generate samples rows numbers,must be the same with id_sha256.csv id rows
totals_row = 100000
# 每次yield分批的写入save_data output数量样本,suggest 2000 or 5000 or 10000 ,
batch_size = 20000
# data_output path for guest or host data_set
target_path = "./breast_b.csv"
# id_csv path
id_csv_path = "./id_sha256.csv" # todo id col support numeric and sha256 object type
# with label,生成数据是否带有label
label_switch = True
# data_set id column dtype,$id_csv_path id type is numeric set dtype=np.int64,else dtype=np.object
numeric = True
if batch_size > totals_row:
raise ValueError(f"batch_size number can't more than samples")
def yield_id():
data_set = pd.read_csv(id_csv_path, chunksize=batch_size, iterator=True, header=None)
for it in data_set:
a = list(map(lambda x: x[0], it.values.tolist()))
yield a
def concat(with_label):
ids = yield_id()
for id_list in ids: # todo len(id_list)=batch_size
if numeric:
id_type = np.int64
else:
id_type = None
df_id = pd.DataFrame(id_list, columns=["id"], dtype=id_type)
value_a = np.around(np.random.normal(0, 1, (batch_size, col)), decimals=5, out=None)
df_feature = pd.DataFrame(value_a, columns=[f"x{i}" for i in range(col)])
if with_label:
df_y = pd.DataFrame(np.random.choice(2, batch_size), dtype=np.int64, columns=["y"])
one_iter_data = pd.concat([df_id, df_y, df_feature], axis=1, ignore_index=False)
else:
one_iter_data = pd.concat([df_id, df_feature], axis=1, ignore_index=False)
# print(one_iter_data)
yield one_iter_data
def save_data(path, with_label):
""" if with_label true then generate $target_path with label y column """
one_batch = concat(with_label)
for index, df_dt in enumerate(one_batch):
if index == 0:
print(df_dt.dtypes, "\n")
print(f"header of csv:\n{df_dt.columns.values.tolist()}")
df_dt.to_csv(path, index=False)
else:
df_dt.to_csv(path, index=False, mode="a", header=None)
if __name__ == '__main__':
import time
start = time.time()
idsha256 = pd.read_csv(id_csv_path, header=None)
id_sha256_rows = idsha256.shape[0]
if totals_row == id_sha256_rows:
pass
else:
raise ValueError(
f"Sample total rows is {totals_row} must be the same with id_sha256.csv id rows size:{id_sha256_rows}")
save_data(target_path, with_label=label_switch)
print(time.time() - start)
