62 lines
2.8 KiB
Python
62 lines
2.8 KiB
Python
import pandas as pd
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from fuzzy_controllers import fuzzy_controler_popularity
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import gensim
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import numpy as np
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def calculate_positive_percentage(positive_ratings: int, negative_ratings: int) -> float:
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return round((100*positive_ratings)/(positive_ratings+negative_ratings), 2)
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def owners_average_max_min(owners: int) -> int:
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return int(owners.split("-")[-1]) - int(owners.split("-")[0])
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def replace(row):
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words = list(set(row.split(';')))
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words.sort()
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return words
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def vectorize(embeddings, word):
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try:
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vector = embeddings[word]
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except:
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vector = np.zeros(300, )
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return vector
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def replace_with_vector(row, w2v):
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words = set(row.split(';'))
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vectors = [vectorize(w2v, word) for word in words]
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vector_sum = np.array(vectors).sum(axis=0)
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return vector_sum
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if __name__ == '__main__':
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df = pd.read_csv('data/games.csv')
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df = df.drop_duplicates(subset=['name'])
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df['positive_percentage'] = df.apply(
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lambda row: calculate_positive_percentage(row.positive_ratings, row.negative_ratings), axis=1)
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df['owners'] = df.apply(lambda row: owners_average_max_min(row.owners), axis=1)
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df['fuzzy_popularity'] = df.apply(lambda row: fuzzy_controler_popularity(price=row.price,
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game_length=row.average_playtime,
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rating=row.positive_percentage,
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number_of_owners=row.owners), axis=1)
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df['all_categorical'] = df['categories'] + ';' + df['genres'] + ';' + df['steamspy_tags']
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df['all_categorical'] = df['all_categorical'].map(lambda row: row.strip().replace(' ', ';').lower())
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df['all_categorical'] = df['all_categorical'].apply(lambda row: replace(row))
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df.to_csv('data/games_processed.csv', index=False, encoding='utf-8')
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try:
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w2v = gensim.models.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin',
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binary=True)
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df2 = pd.read_csv('data/games_processed.csv')
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df2['temp'] = df2['categories'] + ';' + df2['genres'] + ';' + df2['steamspy_tags']
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df2['temp'] = df2['temp'].map(lambda row: row.strip().replace(' ', ';').lower())
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df2['all_categorical_vector'] = df2['temp'].apply(lambda row: replace_with_vector(row, w2v))
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df2.drop('temp', inplace=True, axis=1)
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df2.to_pickle('data/games_processed_vectorized.csv')
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except:
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print('A local copy of GoogleNews-vectors-negative300.bin was not found. The file can be downloaded from '
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'https://www.kaggle.com/datasets/leadbest/googlenewsvectorsnegative300. Finishing without vectorization')
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