preprocessing as separated module

main.py

@@ -1,73 +1,12 @@
 import pandas as pd
-import string
-import re
-import nltk
-from nltk.tokenize import word_tokenize
-from nltk.corpus import stopwords
-from nltk.stem import WordNetLemmatizer
+from prefect import task, Flow, context
+from pandas import DataFrame
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.linear_model import LogisticRegression
 from sklearn.model_selection import train_test_split
-from prefect import task, Flow, context
-from pandas import DataFrame
-
-nltk.download('stopwords')
-nltk.download('wordnet')
-nltk.download('punkt')
 
 
-# remove urls, handles, and the hashtag from hashtags (taken from https://stackoverflow.com/questions/8376691/how-to-remove-hashtag-user-link-of-a-tweet-using-regular-expression)
-def remove_urls(text):
-    new_text = ' '.join(re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)"," ",text).split())
-    return new_text
-
-
-# make all text lowercase
-def text_lowercase(text):
-    return text.lower()
-
-
-# remove numbers
-def remove_numbers(text):
-    result = re.sub(r'\d+', '', text)
-    return result
-
-
-# remove punctuation
-def remove_punctuation(text):
-    translator = str.maketrans('', '', string.punctuation)
-    return text.translate(translator)
-
-
-# tokenize
-def tokenize(text):
-    text = word_tokenize(text)
-    return text
-
-
-# remove stopwords
-stop_words = set(stopwords.words('english'))
-def remove_stopwords(text):
-    text = [i for i in text if not i in stop_words]
-    return text
-
-# lemmatize
-lemmatizer = WordNetLemmatizer()
-def lemmatize(text):
-    text = [lemmatizer.lemmatize(token) for token in text]
-    return text
-
-
-def preprocessing(text):
-    text = text_lowercase(text)
-    text = remove_urls(text)
-    text = remove_numbers(text)
-    text = remove_punctuation(text)
-    text = tokenize(text)
-    text = remove_stopwords(text)
-    text = lemmatize(text)
-    text = ' '.join(text)
-    return text
+from preprocessing import preprocess_text
 
 
 @task
@@ -91,7 +30,7 @@ def get_test_set() -> DataFrame:
 def preprocess_train(train: DataFrame) -> DataFrame:
     pp_text_train = []
     for text_data in train['text']:
-        pp_text_data = preprocessing(text_data)
+        pp_text_data = preprocess_text(text_data)
         pp_text_train.append(pp_text_data)
     train['pp_text'] = pp_text_train
     return train
@@ -101,7 +40,7 @@ def preprocess_train(train: DataFrame) -> DataFrame:
 def preprocess_test(test: DataFrame) -> DataFrame:
     pp_text_test = []
     for text_data in test['text']:
-        pp_text_data = preprocessing(text_data)
+        pp_text_data = preprocess_text(text_data)
         pp_text_test.append(pp_text_data)
     test['pp_text'] = pp_text_test
     return test

preprocessing.py

@@ -0,0 +1,64 @@
+import string
+import re
+import nltk
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+from nltk.stem import WordNetLemmatizer
+
+nltk.download('stopwords')
+nltk.download('wordnet')
+nltk.download('punkt')
+
+
+# remove urls, handles, and the hashtag from hashtags (taken from https://stackoverflow.com/questions/8376691/how-to-remove-hashtag-user-link-of-a-tweet-using-regular-expression)
+def remove_urls(text):
+    new_text = ' '.join(re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)"," ",text).split())
+    return new_text
+
+
+# make all text lowercase
+def text_lowercase(text):
+    return text.lower()
+
+
+# remove numbers
+def remove_numbers(text):
+    result = re.sub(r'\d+', '', text)
+    return result
+
+
+# remove punctuation
+def remove_punctuation(text):
+    translator = str.maketrans('', '', string.punctuation)
+    return text.translate(translator)
+
+
+# tokenize
+def tokenize(text):
+    text = word_tokenize(text)
+    return text
+
+
+# remove stopwords
+stop_words = set(stopwords.words('english'))
+def remove_stopwords(text):
+    text = [i for i in text if not i in stop_words]
+    return text
+
+# lemmatize
+lemmatizer = WordNetLemmatizer()
+def lemmatize(text):
+    text = [lemmatizer.lemmatize(token) for token in text]
+    return text
+
+
+def preprocess_text(text):
+    text = text_lowercase(text)
+    text = remove_urls(text)
+    text = remove_numbers(text)
+    text = remove_punctuation(text)
+    text = tokenize(text)
+    text = remove_stopwords(text)
+    text = lemmatize(text)
+    text = ' '.join(text)
+    return text