kdudzic/fuzzy-game-recommender
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move data to data dir

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Kacper 2023-01-27 18:43:32 +01:00
parent 6aed792d44
commit 04c9ce7f6e
5 changed files with 5 additions and 5 deletions
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0
games.csv → data/games.csv
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games_processed.csv → data/games_processed.csv
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games_processed_vectorized.csv → data/games_processed_vectorized.csv
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main.py
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@ -40,7 +40,7 @@ def compare_games(title_1: str, title_2: str, df: pd.DataFrame, show_graph: bool
if __name__ == '__main__': if __name__ == '__main__':
df = pd.read_pickle('games_processed_vectorized.csv') df = pd.read_pickle('data/games_processed_vectorized.csv')
while True: while True:
title_1 = input("Enter title 1: ") title_1 = input("Enter title 1: ")

8
process_dataset.py
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@ -34,7 +34,7 @@ def replace_with_vector(row, w2v):
if __name__ == '__main__': if __name__ == '__main__':
df = pd.read_csv('games.csv') df = pd.read_csv('data/games.csv')
df['positive_percentage'] = df.apply( df['positive_percentage'] = df.apply(
lambda row: calculate_positive_percentage(row.positive_ratings, row.negative_ratings), axis=1) lambda row: calculate_positive_percentage(row.positive_ratings, row.negative_ratings), axis=1)
@ -46,16 +46,16 @@ if __name__ == '__main__':
df['all_categorical'] = df['categories'] + ';' + df['genres'] + ';' + df['steamspy_tags'] df['all_categorical'] = df['categories'] + ';' + df['genres'] + ';' + df['steamspy_tags']
df['all_categorical'] = df['all_categorical'].map(lambda row: row.strip().replace(' ', ';').lower()) df['all_categorical'] = df['all_categorical'].map(lambda row: row.strip().replace(' ', ';').lower())
df['all_categorical'] = df['all_categorical'].apply(lambda row: replace(row)) df['all_categorical'] = df['all_categorical'].apply(lambda row: replace(row))
df.to_csv('games_processed.csv', index=False, encoding='utf-8') df.to_csv('data/games_processed.csv', index=False, encoding='utf-8')
try: try:
w2v = gensim.models.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', w2v = gensim.models.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin',
binary=True) binary=True)
df2 = pd.read_csv('games_processed.csv') df2 = pd.read_csv('data/games_processed.csv')
df2['temp'] = df2['categories'] + ';' + df2['genres'] + ';' + df2['steamspy_tags'] df2['temp'] = df2['categories'] + ';' + df2['genres'] + ';' + df2['steamspy_tags']
df2['temp'] = df2['temp'].map(lambda row: row.strip().replace(' ', ';').lower()) df2['temp'] = df2['temp'].map(lambda row: row.strip().replace(' ', ';').lower())
df2['all_categorical_vector'] = df2['temp'].apply(lambda row: replace_with_vector(row, w2v)) df2['all_categorical_vector'] = df2['temp'].apply(lambda row: replace_with_vector(row, w2v))
df2.drop('temp', inplace=True, axis=1) df2.drop('temp', inplace=True, axis=1)
df2.to_pickle('games_processed_vectorized.csv') df2.to_pickle('data/games_processed_vectorized.csv')
except: except:
print('A local copy of GoogleNews-vectors-negative300.bin was not found. The file can be downloaded from ' print('A local copy of GoogleNews-vectors-negative300.bin was not found. The file can be downloaded from '
'https://www.kaggle.com/datasets/leadbest/googlenewsvectorsnegative300. Finishing without vectorization') 'https://www.kaggle.com/datasets/leadbest/googlenewsvectorsnegative300. Finishing without vectorization')