raise self._value NameError: name 'global_df' is not defined
raise self._value NameError: name 'global_df' is not defined
当我试图执行这段代码时。我收到与 global_df 相关的错误未定义。我是多处理的新手。主要问题在于 stock_sample，其中 global_df、目标、global_ems 和 global_ci 等变量未定义。
我不确定如何解决此错误。
我试图将 global_df 设置为全局 global_df 但它没有解决错误。
代码取自https://github.com/BUAA-WJR/PriceGraph/blob/master/code/dataset.py
#!/usr/bin/env python
# encoding: utf-8
import os
import sys
import math
import json
import pickle
import random
import numpy as np
import pandas as pd
from datetime import datetime
from datetime import timedelta
from multiprocessing import Pool
PWD = os.path.dirname(os.path.realpath(__file__))
def load_stock(s):
df = pd.read_csv(os.path.join('C:\Users\jyoti\Desktop\Blockchain\PriceGraph-master\PriceGraph-master\data\data', s), index_col=0)
df.set_index(df.index.astype('str'), inplace=True)
return df
def load_ci(f, xi='close'):
with open(os.path.join('C:\Users\jyoti\Desktop\Blockchain\PriceGraph-master\PriceGraph-master\code\CI', xi, '%s.json' % f[:-4])) as fp:
return json.load(fp)
def load_embedding(f, xi='close', ti=None):
with open(os.path.join('C:\Users\jyoti\Desktop\Blockchain\PriceGraph-master\PriceGraph-master\Struc2vec', xi, '%s.json' % f[:-4])) as fp:
j = json.load(fp)
if ti is not None:
return {d: j[d] for d in ti if d in j}
return j
def z_score(df):
return (df - df.mean()) / df.std()
def stock_sample(input_):
s, d = input_
T = 20
df = global_df[s]
if d not in df.index:
return
iloc = list(df.index).index(d) + 1
if iloc < T: # not enough history data
return
xss = {}
for xi in x_column:
# t
t = 1 if df.iloc[iloc+target-1,:][xi] > df.loc[d, xi] else 0
# y
y = df.iloc[iloc-T:iloc][xi].copy()
yz = np.array(z_score(y))
if np.isnan(yz).any():
return
# ems
ems = global_ems[s][xi]
if d not in ems:
return
keys = ['%s' % i for i in range(T)]
emd = np.array([ems[d][k] for k in keys])
if len(emd) < T:
return
# ci
cis = global_ci[s][xi]
if d not in cis:
return
cid = cis[d]
cid = [cid[str(i)] for i in range(T)]
ciz = np.array(z_score(np.array(cid)))
if np.isnan(ciz).any():
ciz = np.array(cid)
xss['%s_ems' % xi] = emd
xss['%s_ys' % xi] = yz
xss['%s_cis' % xi] = ciz
xss['%s_t' % xi] = t
return s, d, \
xss['close_t'], xss['close_ems'], xss['close_ys'], xss['close_cis'], \
xss['open_t'], xss['open_ems'], xss['open_ys'], xss['open_cis'], \
xss['high_t'], xss['high_ems'], xss['high_ys'], xss['high_cis'], \
xss['low_t'], xss['low_ems'], xss['low_ys'], xss['low_cis'], \
xss['vol_t'], xss['vol_ems'], xss['vol_ys'], xss['vol_cis'], \
xss['amount_t'], xss['amount_ems'], xss['amount_ys'], xss['amount_cis']
def sample_by_dates(dates):
files = os.listdir('C:\Users\jyoti\Desktop\Blockchain\PriceGraph-master\PriceGraph-master\data\data')
fds = [(f, d) for d in dates for f in files]
pool = Pool()
samples = pool.map(stock_sample, fds)
pool.close()
pool.join()
samples = filter(lambda s: s is not None, samples)
stocks, days, \
close_t, close_ems, close_ys, close_cis, \
open_t, open_ems, open_ys, open_cis, \
high_t, high_ems, high_ys, high_cis, \
low_t, low_ems, low_ys, low_cis, \
vol_t, vol_ems, vol_ys, vol_cis, \
amount_t, amount_ems, amount_ys, amount_cis = zip(*samples)
return {'stock': np.array(stocks), 'day': np.array(days),
'close_t': np.array(close_t), 'close_ems': np.array(close_ems), 'close_ys': np.array(close_ys), 'close_cis': np.array(close_cis),
'open_t': np.array(open_t), 'open_ems': np.array(open_ems), 'open_ys': np.array(open_ys), 'open_cis': np.array(open_cis),
'high_t': np.array(high_t), 'high_ems': np.array(high_ems), 'high_ys': np.array(high_ys), 'high_cis': np.array(high_cis),
'low_t': np.array(low_t), 'low_ems': np.array(low_ems), 'low_ys': np.array(low_ys), 'low_cis': np.array(low_cis),
'vol_t': np.array(vol_t), 'vol_ems': np.array(vol_ems), 'vol_ys': np.array(vol_ys), 'vol_cis': np.array(vol_cis),
'amount_t': np.array(amount_t), 'amount_ems': np.array(amount_ems), 'amount_ys': np.array(amount_ys), 'amount_cis': np.array(amount_cis),
}
def generate_data_year(year):
global global_ems
start_date = datetime(year, 1, 1)
days = [(start_date+timedelta(days=i)).strftime('%Y%m%d') for i in range(366)]
days = [d for d in days if '%s0101' % year <= d <= '%s1231' % year]
global_ems = {f: {xc: load_embedding(f, xc, days) for xc in x_column} for f in files}
dataset = sample_by_dates(days)
with open(os.path.join('dataset', '%s.pickle' % year), 'wb') as fp:
pickle.dump(dataset, fp)
def generate_data_season(year, season):
global global_ems
start_date = datetime(year, sm, 1)
days = [(start_date+timedelta(days=i)).strftime('%Y%m%d') for i in range(366)]
sm, em = str((season - 1) * 3 + 1).zfill(2), str(season * 3).zfill(2)
days = [d for d in days if '%s%s01' % (year, sm) <= d <= '%s%s31' % (year, em)]
global_ems = {f: {xc: load_embedding(f, xc, days) for xc in x_column} for f in files}
dataset = sample_by_dates(days)
with open(os.path.join('dataset', '%s_S%s.pickle' % (year, season)), 'wb') as fp:
pickle.dump(dataset, fp)
if __name__ == '__main__':
files = os.listdir('C:\Users\jyoti\Desktop\Blockchain\PriceGraph-master\PriceGraph-master\data\data')
if not os.path.exists('C:\Users\jyoti\Desktop\Blockchain\PriceGraph-master\PriceGraph-master\dataset'):
os.makedirs('C:\Users\jyoti\Desktop\Blockchain\PriceGraph-master\PriceGraph-master\dataset')
x_column = ['close', 'open', 'high', 'low', 'vol', 'amount']
y_column = 'close'
target = 1
global_ems = None
global_df = {f: load_stock(f) for f in files}
global_ci = {f: {xc: load_ci(f, xc) for xc in x_column} for f in files}
for y in range(2018, 2009, -1):
print(y)
generate_data_year(y)
for m in range(1, 5):
print(m)
generate_data_season(2019, m)
如果您提供最小的、可重现的示例，Whosebug 通常会喜欢。我们不想下载您的所有 csvs 来重现您的部分代码。
它应该看起来像这样
from multiprocessing import Pool
def print_globals(text):
print(f"{text} {global_var}")
if __name__ == "__main__":
global_var = 1
Pool().map(print_globals, ["hi"])
这通常也会让您深入了解问题发生的原因。
在您的情况下，那是因为变量不在您 运行 的流程范围内。相反，您应该将所需的变量传递给需要它的函数。使用全局变量被认为是不好的做法，因为您很容易忘记 use/change 它的位置。
更好的版本是：
from multiprocessing import Pool
def print_globals(text, global_var):
print(f"{text} {global_var}")
if __name__ == "__main__":
global_var = 1
Pool().starmap(print_globals, [("hi", global_var)])
当我试图执行这段代码时。我收到与 global_df 相关的错误未定义。我是多处理的新手。主要问题在于 stock_sample，其中 global_df、目标、global_ems 和 global_ci 等变量未定义。 我不确定如何解决此错误。 我试图将 global_df 设置为全局 global_df 但它没有解决错误。
代码取自https://github.com/BUAA-WJR/PriceGraph/blob/master/code/dataset.py
#!/usr/bin/env python
# encoding: utf-8
import os
import sys
import math
import json
import pickle
import random
import numpy as np
import pandas as pd
from datetime import datetime
from datetime import timedelta
from multiprocessing import Pool
PWD = os.path.dirname(os.path.realpath(__file__))
def load_stock(s):
df = pd.read_csv(os.path.join('C:\Users\jyoti\Desktop\Blockchain\PriceGraph-master\PriceGraph-master\data\data', s), index_col=0)
df.set_index(df.index.astype('str'), inplace=True)
return df
def load_ci(f, xi='close'):
with open(os.path.join('C:\Users\jyoti\Desktop\Blockchain\PriceGraph-master\PriceGraph-master\code\CI', xi, '%s.json' % f[:-4])) as fp:
return json.load(fp)
def load_embedding(f, xi='close', ti=None):
with open(os.path.join('C:\Users\jyoti\Desktop\Blockchain\PriceGraph-master\PriceGraph-master\Struc2vec', xi, '%s.json' % f[:-4])) as fp:
j = json.load(fp)
if ti is not None:
return {d: j[d] for d in ti if d in j}
return j
def z_score(df):
return (df - df.mean()) / df.std()
def stock_sample(input_):
s, d = input_
T = 20
df = global_df[s]
if d not in df.index:
return
iloc = list(df.index).index(d) + 1
if iloc < T: # not enough history data
return
xss = {}
for xi in x_column:
# t
t = 1 if df.iloc[iloc+target-1,:][xi] > df.loc[d, xi] else 0
# y
y = df.iloc[iloc-T:iloc][xi].copy()
yz = np.array(z_score(y))
if np.isnan(yz).any():
return
# ems
ems = global_ems[s][xi]
if d not in ems:
return
keys = ['%s' % i for i in range(T)]
emd = np.array([ems[d][k] for k in keys])
if len(emd) < T:
return
# ci
cis = global_ci[s][xi]
if d not in cis:
return
cid = cis[d]
cid = [cid[str(i)] for i in range(T)]
ciz = np.array(z_score(np.array(cid)))
if np.isnan(ciz).any():
ciz = np.array(cid)
xss['%s_ems' % xi] = emd
xss['%s_ys' % xi] = yz
xss['%s_cis' % xi] = ciz
xss['%s_t' % xi] = t
return s, d, \
xss['close_t'], xss['close_ems'], xss['close_ys'], xss['close_cis'], \
xss['open_t'], xss['open_ems'], xss['open_ys'], xss['open_cis'], \
xss['high_t'], xss['high_ems'], xss['high_ys'], xss['high_cis'], \
xss['low_t'], xss['low_ems'], xss['low_ys'], xss['low_cis'], \
xss['vol_t'], xss['vol_ems'], xss['vol_ys'], xss['vol_cis'], \
xss['amount_t'], xss['amount_ems'], xss['amount_ys'], xss['amount_cis']
def sample_by_dates(dates):
files = os.listdir('C:\Users\jyoti\Desktop\Blockchain\PriceGraph-master\PriceGraph-master\data\data')
fds = [(f, d) for d in dates for f in files]
pool = Pool()
samples = pool.map(stock_sample, fds)
pool.close()
pool.join()
samples = filter(lambda s: s is not None, samples)
stocks, days, \
close_t, close_ems, close_ys, close_cis, \
open_t, open_ems, open_ys, open_cis, \
high_t, high_ems, high_ys, high_cis, \
low_t, low_ems, low_ys, low_cis, \
vol_t, vol_ems, vol_ys, vol_cis, \
amount_t, amount_ems, amount_ys, amount_cis = zip(*samples)
return {'stock': np.array(stocks), 'day': np.array(days),
'close_t': np.array(close_t), 'close_ems': np.array(close_ems), 'close_ys': np.array(close_ys), 'close_cis': np.array(close_cis),
'open_t': np.array(open_t), 'open_ems': np.array(open_ems), 'open_ys': np.array(open_ys), 'open_cis': np.array(open_cis),
'high_t': np.array(high_t), 'high_ems': np.array(high_ems), 'high_ys': np.array(high_ys), 'high_cis': np.array(high_cis),
'low_t': np.array(low_t), 'low_ems': np.array(low_ems), 'low_ys': np.array(low_ys), 'low_cis': np.array(low_cis),
'vol_t': np.array(vol_t), 'vol_ems': np.array(vol_ems), 'vol_ys': np.array(vol_ys), 'vol_cis': np.array(vol_cis),
'amount_t': np.array(amount_t), 'amount_ems': np.array(amount_ems), 'amount_ys': np.array(amount_ys), 'amount_cis': np.array(amount_cis),
}
def generate_data_year(year):
global global_ems
start_date = datetime(year, 1, 1)
days = [(start_date+timedelta(days=i)).strftime('%Y%m%d') for i in range(366)]
days = [d for d in days if '%s0101' % year <= d <= '%s1231' % year]
global_ems = {f: {xc: load_embedding(f, xc, days) for xc in x_column} for f in files}
dataset = sample_by_dates(days)
with open(os.path.join('dataset', '%s.pickle' % year), 'wb') as fp:
pickle.dump(dataset, fp)
def generate_data_season(year, season):
global global_ems
start_date = datetime(year, sm, 1)
days = [(start_date+timedelta(days=i)).strftime('%Y%m%d') for i in range(366)]
sm, em = str((season - 1) * 3 + 1).zfill(2), str(season * 3).zfill(2)
days = [d for d in days if '%s%s01' % (year, sm) <= d <= '%s%s31' % (year, em)]
global_ems = {f: {xc: load_embedding(f, xc, days) for xc in x_column} for f in files}
dataset = sample_by_dates(days)
with open(os.path.join('dataset', '%s_S%s.pickle' % (year, season)), 'wb') as fp:
pickle.dump(dataset, fp)
if __name__ == '__main__':
files = os.listdir('C:\Users\jyoti\Desktop\Blockchain\PriceGraph-master\PriceGraph-master\data\data')
if not os.path.exists('C:\Users\jyoti\Desktop\Blockchain\PriceGraph-master\PriceGraph-master\dataset'):
os.makedirs('C:\Users\jyoti\Desktop\Blockchain\PriceGraph-master\PriceGraph-master\dataset')
x_column = ['close', 'open', 'high', 'low', 'vol', 'amount']
y_column = 'close'
target = 1
global_ems = None
global_df = {f: load_stock(f) for f in files}
global_ci = {f: {xc: load_ci(f, xc) for xc in x_column} for f in files}
for y in range(2018, 2009, -1):
print(y)
generate_data_year(y)
for m in range(1, 5):
print(m)
generate_data_season(2019, m)
如果您提供最小的、可重现的示例，Whosebug 通常会喜欢。我们不想下载您的所有 csvs 来重现您的部分代码。
它应该看起来像这样
from multiprocessing import Pool
def print_globals(text):
print(f"{text} {global_var}")
if __name__ == "__main__":
global_var = 1
Pool().map(print_globals, ["hi"])
这通常也会让您深入了解问题发生的原因。
在您的情况下，那是因为变量不在您 运行 的流程范围内。相反，您应该将所需的变量传递给需要它的函数。使用全局变量被认为是不好的做法，因为您很容易忘记 use/change 它的位置。
更好的版本是：
from multiprocessing import Pool
def print_globals(text, global_var):
print(f"{text} {global_var}")
if __name__ == "__main__":
global_var = 1
Pool().starmap(print_globals, [("hi", global_var)])