Question

以下脚本用于用文本对给定的输入列进行词法修饰

%%time
import pandas as pd
from gensim.utils import lemmatize
from gensim.parsing.preprocessing import STOPWORDS
STOPWORDS = list(STOPWORDS)

data = pd.read_csv('https://pastebin.com/raw/0SEv1RMf')

def lemmatization(s):
    result = []
    # lowercase, tokenize, remove stopwords, len>3, lemmatize
    for token in lemmatize(s, stopwords=STOPWORDS, min_length=3):
        result.append(token.decode('utf-8').split('/')[0])
    # print(len(result)) <- This didn't work.
    return result

X_train = data.apply(lambda r: lemmatization(r['text']), axis=1)
print(X_train)

问题：

如何打印非原形化进度的进度？

Answer 1

您可以将一个变量传递给lemmatization函数，以跟踪该变量的调用次数-然后每隔1000次迭代打印一次。我将其包装在下面的列表中，以便可以通过引用而不是通过值传递int。

%%time
import pandas as pd
from gensim.utils import lemmatize
from gensim.parsing.preprocessing import STOPWORDS
STOPWORDS = list(STOPWORDS)

data = pd.read_csv('https://pastebin.com/raw/0SEv1RMf')

iteration_count = [0]

def lemmatization(s, iteration_count):
    result = []
    # lowercase, tokenize, remove stopwords, len>3, lemmatize
    for token in lemmatize(s, stopwords=STOPWORDS, min_length=3):
        result.append(token.decode('utf-8').split('/')[0])
    # print(len(result)) <- This didn't work.

    iteration_count[0] += 1

    if iteration_count[0] % 1000 == 0:
        print(iteration_count[0])

    return result

X_train = data.apply(lambda r: lemmatization(r['text'], iteration_count), axis=1)
print(X_train)

显示词素化的进展

1 个答案: