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location_vect or location_vectorizer #1

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location_vect or location_vectorizer #1

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5f4d407
change location_vect to location_vectorizer in model_fitting.py
shahmohamadi Jan 8, 2021
193b6f8
Update README.md
shahmohamadi Jan 8, 2021
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4 changes: 2 additions & 2 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -315,8 +315,8 @@ meta_location_doc = meta_df['user_described_location'].values

# fit_transform a tf-idf vectorizer using base_location_doc and use it to transform meta_location_doc
location_vectorizer = TfidfVectorizer(stop_words='english', tokenizer=tokenize, ngram_range=(1,2))
base_location_X = location_vect.fit_transform(base_location_doc.ravel())
meta_location_X = location_vect.transform(meta_location_doc)
base_location_X = location_vectorizer.fit_transform(base_location_doc.ravel())
meta_location_X = location_vectorizer.transform(meta_location_doc)

# Fit a Linear SVC Model with 'base_location_X' and 'base_y'. Note: it is important to use
# balanced class weights otherwise the model will overwhelmingly favor the majority class.
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4 changes: 2 additions & 2 deletions SRC/Training_Model/model_fitting.py
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Expand Up @@ -36,8 +36,8 @@ def tokenize(tweet):

# fit_transform a tf-idf vectorizer using base_location_doc and use it to transform meta_location_doc
location_vectorizer = TfidfVectorizer(stop_words='english', tokenizer=tokenize, ngram_range=(1,2))
base_location_X = location_vect.fit_transform(base_location_doc.ravel())
meta_location_X = location_vect.transform(meta_location_doc)
base_location_X = location_vectorizer.fit_transform(base_location_doc.ravel())
meta_location_X = location_vectorizer.transform(meta_location_doc)

# Fit a Linear SVC Model with 'base_location_X' and 'base_y'. Note: it is important to use
# balanced class weights otherwise the model will overwhelmingly favor the majority class.
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