我有一个在Kaggle.com服务器上开发的Python脚本。我想在本地运行它，但它会产生错误，并且在我尝试时永远不会完成。
我认为它与其中一种统计算法的并行性有关，但我无法弄清楚我需要在本地计算机上正确设置它。
这是脚本：
'''
An open source script from Kaggle, developed by a couple dozen people. Runs fine there.
'''
if __name__ == '__main__':
import time
start_time = time.time()
print("Starting imports")
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn import pipeline, grid_search
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import FeatureUnion
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import mean_squared_error, make_scorer
from nltk.stem.porter import *
stemmer = PorterStemmer()
import re
import random
random.seed(2016)
print("Reading in data")
df_train = pd.read_csv('train.csv', encoding="ISO-8859-1")
df_test = pd.read_csv('test.csv', encoding="ISO-8859-1")
df_pro_desc = pd.read_csv('product_descriptions.csv')
df_attr = pd.read_csv('attributes.csv')
print("pull brand")
df_brand = df_attr[df_attr.name == "MFG Brand Name"][["product_uid", "value"]].rename(columns={"value": "brand"})
print("dr_train.shape[0]")
num_train = df_train.shape[0]
df_all = pd.concat((df_train, df_test), axis=0, ignore_index=True)
df_all = pd.merge(df_all, df_pro_desc, how='left', on='product_uid')
df_all = pd.merge(df_all, df_brand, how='left', on='product_uid')
print("Functions")
def str_stem(s):
if isinstance(s, str):
s = s.lower()
s = s.replace("'","in.")
s = s.replace("inches","in.")
s = s.replace("inch","in.")
s = s.replace(" in ","in. ")
s = s.replace(" in.","in.")
s = s.replace("''","ft.")
s = s.replace(" feet ","ft. ")
s = s.replace("feet","ft.")
s = s.replace("foot","ft.")
s = s.replace(" ft ","ft. ")
s = s.replace(" ft.","ft.")
s = s.replace(" pounds ","lb. ")
s = s.replace(" pound ","lb. ")
s = s.replace("pound","lb.")
s = s.replace(" lb ","lb. ")
s = s.replace(" lb.","lb.")
s = s.replace(" lbs ","lb. ")
s = s.replace("lbs.","lb.")
s = s.replace(" x "," xby ")
s = s.replace("*"," xby ")
s = s.replace(" by "," xby")
s = s.replace("x0"," xby 0")
s = s.replace("x1"," xby 1")
s = s.replace("x2"," xby 2")
s = s.replace("x3"," xby 3")
s = s.replace("x4"," xby 4")
s = s.replace("x5"," xby 5")
s = s.replace("x6"," xby 6")
s = s.replace("x7"," xby 7")
s = s.replace("x8"," xby 8")
s = s.replace("x9"," xby 9")
s = s.replace("0x","0 xby ")
s = s.replace("1x","1 xby ")
s = s.replace("2x","2 xby ")
s = s.replace("3x","3 xby ")
s = s.replace("4x","4 xby ")
s = s.replace("5x","5 xby ")
s = s.replace("6x","6 xby ")
s = s.replace("7x","7 xby ")
s = s.replace("8x","8 xby ")
s = s.replace("9x","9 xby ")
s = s.replace(" sq ft","sq.ft. ")
s = s.replace("sq ft","sq.ft. ")
s = s.replace("sqft","sq.ft. ")
s = s.replace(" sqft ","sq.ft. ")
s = s.replace("sq. ft","sq.ft. ")
s = s.replace("sq ft.","sq.ft. ")
s = s.replace("sq feet","sq.ft. ")
s = s.replace("square feet","sq.ft. ")
s = s.replace(" gallons ","gal. ")
s = s.replace(" gallon ","gal. ")
s = s.replace("gallons","gal.")
s = s.replace("gallon","gal.")
s = s.replace(" gal ","gal. ")
s = s.replace(" gal","gal.")
s = s.replace("ounces","oz.")
s = s.replace("ounce","oz.")
s = s.replace(" oz.","oz. ")
s = s.replace(" oz ","oz. ")
s = s.replace("centimeters","cm.")
s = s.replace(" cm.","cm.")
s = s.replace(" cm ","cm. ")
s = s.replace("wayy", "way")
s = s.replace("milimeters","mm.")
s = s.replace(" mm.","mm.")
s = s.replace(" mm ","mm. ")
s = s.replace("°","deg. ")
s = s.replace("degrees","deg. ")
s = s.replace("degree","deg. ")
s = s.replace("volts","volt. ")
s = s.replace("volt","volt. ")
s = s.replace("watts","watt. ")
s = s.replace("watt","watt. ")
s = s.replace("ampere","amp. ")
s = s.replace("amps","amp. ")
s = s.replace(" amp ","amp. ")
s = s.replace("whirpool","whirlpool")
s = s.replace("whirlpoolga", "whirlpool")
s = s.replace("whirlpoolstainless","whirlpool stainless")
s = s.replace(" "," ")
s = (" ").join([stemmer.stem(z) for z in s.split(" ")])
return s.lower()
else:
return "null"
def str_common_word(str1, str2):
words, cnt = str1.split(), 0
for word in words:
if str2.find(word)>=0:
cnt+=1
return cnt
def str_whole_word(str1, str2, i_):
cnt = 0
while i_ < len(str2):
i_ = str2.find(str1, i_)
if i_ == -1:
return cnt
else:
cnt += 1
i_ += len(str1)
return cnt
def fmean_squared_error(ground_truth, predictions):
fmean_squared_error_ = mean_squared_error(ground_truth, predictions)**0.5
return fmean_squared_error_
RMSE = make_scorer(fmean_squared_error, greater_is_better=False)
class cust_regression_vals(BaseEstimator, TransformerMixin):
def fit(self, x, y=None):
return self
def transform(self, hd_searches):
d_col_drops=['id','relevance','search_term','product_title','product_description','product_info','attr','brand']
hd_searches = hd_searches.drop(d_col_drops,axis=1).values
return hd_searches
class cust_txt_col(BaseEstimator, TransformerMixin):
def __init__(self, key):
self.key = key
def fit(self, x, y=None):
return self
def transform(self, data_dict):
return data_dict[self.key].apply(str)
def fmean_squared_error(ground_truth, predictions):
fmean_squared_error_ = mean_squared_error(ground_truth, predictions)**0.5
return fmean_squared_error_
RMSE = make_scorer(fmean_squared_error, greater_is_better=False)
#if adding features consider any drops on the 'cust_regression_vals' class
df_all['search_term'] = df_all['search_term'].map(lambda x:str_stem(x))
df_all['product_title'] = df_all['product_title'].map(lambda x:str_stem(x))
df_all['product_description'] = df_all['product_description'].map(lambda x:str_stem(x))
df_all['brand'] = df_all['brand'].map(lambda x:str_stem(x))
df_all['len_of_query'] = df_all['search_term'].map(lambda x:len(x.split())).astype(np.int64)
df_all['len_of_title'] = df_all['product_title'].map(lambda x:len(x.split())).astype(np.int64)
df_all['len_of_description'] = df_all['product_description'].map(lambda x:len(x.split())).astype(np.int64)
df_all['len_of_brand'] = df_all['brand'].map(lambda x:len(x.split())).astype(np.int64)
df_all['product_info'] = df_all['search_term']+"\t"+df_all['product_title'] +"\t"+df_all['product_description']
df_all['query_in_title'] = df_all['product_info'].map(lambda x:str_whole_word(x.split('\t')[0],x.split('\t')[1],0))
df_all['query_in_description'] = df_all['product_info'].map(lambda x:str_whole_word(x.split('\t')[0],x.split('\t')[2],0))
df_all['word_in_title'] = df_all['product_info'].map(lambda x:str_common_word(x.split('\t')[0],x.split('\t')[1]))
df_all['word_in_description'] = df_all['product_info'].map(lambda x:str_common_word(x.split('\t')[0],x.split('\t')[2]))
df_all['ratio_title'] = df_all['word_in_title']/df_all['len_of_query']
df_all['ratio_description'] = df_all['word_in_description']/df_all['len_of_query'] #hack-r.com
df_all['attr'] = df_all['search_term']+"\t"+df_all['brand']
df_all['word_in_brand'] = df_all['attr'].map(lambda x:str_common_word(x.split('\t')[0],x.split('\t')[1])) #linkedin.com/in/datasci
df_all['ratio_brand'] = df_all['word_in_brand']/df_all['len_of_brand']
df_brand = pd.unique(df_all.brand.ravel())
d={}
i = 1
for s in df_brand:
d[s]=i
i+=1
df_all['brand_feature'] = df_all['brand'].map(lambda x:d[x])
df_all['search_term_feature'] = df_all['search_term'].map(lambda x:len(x))
df_train = df_all.iloc[:num_train]
df_test = df_all.iloc[num_train:]
id_test = df_test['id']
y_train = df_train['relevance'].values
X_train =df_train[:]
X_test = df_test[:]
print("--- Features Set: %s minutes ---" % round(((time.time() - start_time)/60),2))
rfr = RandomForestRegressor(n_estimators = 175, n_jobs = 1, random_state = 2016, verbose = 0)
tfidf = TfidfVectorizer(ngram_range=(1, 1), stop_words='english')
tsvd = TruncatedSVD(n_components=25, random_state = 2016)
clf = pipeline.Pipeline([
('union', FeatureUnion(
transformer_list = [
('cst', cust_regression_vals()),
('txt1', pipeline.Pipeline([('s1', cust_txt_col(key='search_term')), ('tfidf1', tfidf), ('tsvd1', tsvd)])),
('txt2', pipeline.Pipeline([('s2', cust_txt_col(key='product_title')), ('tfidf2', tfidf), ('tsvd2', tsvd)])),
('txt3', pipeline.Pipeline([('s3', cust_txt_col(key='product_description')), ('tfidf3', tfidf), ('tsvd3', tsvd)])),
('txt4', pipeline.Pipeline([('s4', cust_txt_col(key='brand')), ('tfidf4', tfidf), ('tsvd4', tsvd)]))
],
transformer_weights = {
'cst': 1.0,
'txt1': 0.5,
'txt2': 0.25,
'txt3': 0.0,
'txt4': 0.5
},
n_jobs = -1
)),
('rfr', rfr)])
param_grid = {'rfr__max_features': [24], 'rfr__max_depth': [29]}
model = grid_search.GridSearchCV(estimator = clf, param_grid = param_grid, n_jobs = -1, cv = 2, verbose = 20, scoring=RMSE)
model.fit(X_train, y_train)
print("Best parameters found by grid search:")
print(model.best_params_)
print("Best CV score:")
print(model.best_score_)
y_pred = model.predict(X_test)
y_train_pred = model.predict(X_train)
print("--- Running Predictions for Training Data --")
pd.DataFrame({"id": id_test, "relevance": y_pred}).to_csv('test_python_predictions.csv', index=False)
print("--- Training & Testing: %s minutes ---" % ((time.time() - start_time)/60))
print("--- Running Predictions for Training Data --")
pd.DataFrame({"id": id_test, "relevance": y_train_pred}).to_csv('train_python_predictions.csv', index=False)
print("-- Now Printing Training Data to File --")
pd.DataFrame({"id": id_test, "Data": X_train}).to_csv('X_train.csv', index=False)
print("-- Now Printing Testing Data to File --")
pd.DataFrame({"id": id_test, "Data": X_train}).to_csv('X_test.csv', index=False)
以下是错误：
C:\Users\hackr\Desktop\hd>python wc.py
Starting imports
Reading in data
pull brand
dr_train.shape[0]
Functions
--- Features Set: 7.18 minutes ---
Fitting 2 folds for each of 1 candidates, totalling 2 fits
Process SpawnPoolWorker-3:
Traceback (most recent call last):
File "C:\python35\lib\multiprocessing\process.py", line 254, in _bootstrap
self.run()
File "C:\python35\lib\multiprocessing\process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "C:\python35\lib\multiprocessing\pool.py", line 108, in worker
task = get()
File "C:\python35\lib\site-packages\sklearn\externals\joblib\pool.py", line 360, in get
return recv()
File "C:\python35\lib\multiprocessing\connection.py", line 251, in recv
return ForkingPickler.loads(buf.getbuffer())
AttributeError: Can't get attribute 'cust_regression_vals' on <module '__mp_main__' from 'C:\\Users\\hackr\\Desktop\\hd\\wc.py'>
Process SpawnPoolWorker-2:
Traceback (most recent call last):
File "C:\python35\lib\multiprocessing\process.py", line 254, in _bootstrap
self.run()
File "C:\python35\lib\multiprocessing\process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "C:\python35\lib\multiprocessing\pool.py", line 108, in worker
task = get()
File "C:\python35\lib\site-packages\sklearn\externals\joblib\pool.py", line 360, in get
return recv()
File "C:\python35\lib\multiprocessing\connection.py", line 251, in recv
return ForkingPickler.loads(buf.getbuffer())
AttributeError: Can't get attribute 'cust_regression_vals' on <module '__mp_main__' from 'C:\\Users\\hackr\\Desktop\\hd\\wc.py'>
我甚至不能通过任务管理器杀死Python进程来杀死这个脚本。如果我在ctrl+C运行时使用它会发出更多错误，并在屏幕上显示我的键盘中断，但不会停止。
答案 0 :(得分：0)
最可能的原因是您的类（特别是cust_regression_vals）仅在if __name__ == '__main__'内定义，这限制了它仅在脚本直接作为主程序执行时才被定义，而不是作为主程序处理模块。定义通常不具备这种条件。