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Popularité

La popularité est une évaluation communautaire de chaque livre calculée sur une échelle de 0 à 100.

Cette évaluation repose sur une agrégation de diverses quantités qui reflètent la vie de chaque livre au sein de la communauté des lecteurs. Cette vie s'exprime en fréquence de lecture, nombre d'étagères publiques et privées dans lesquelles le livre est présent.

Un calcul 'propriétaire' mélangeant ces quantités restitue sous la forme synthétique d'un score l'aura collective du livre.

# Viewing features feature_names = vectorizer.get_feature_names_out() print("Features:", feature_names) print("TF-IDF Features:", tfidf_features.toarray()) This example uses CountVectorizer and TfidfTransformer from scikit-learn to create basic features from your text. Adjustments would be needed based on your specific use case and data.

# TF-IDF transformer tfidf = TfidfTransformer() tfidf_features = tfidf.fit_transform(count_features)

# Tokenize (simple split) tokens = text.split(',')

# Vectorizer to convert text into a matrix of token counts vectorizer = CountVectorizer() count_features = vectorizer.fit_transform(data)

from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer

# Your data text = "in3x,net,watch,14zwhrd6,dildo,18"

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# Viewing features feature_names = vectorizer.get_feature_names_out() print("Features:", feature_names) print("TF-IDF Features:", tfidf_features.toarray()) This example uses CountVectorizer and TfidfTransformer from scikit-learn to create basic features from your text. Adjustments would be needed based on your specific use case and data.

# TF-IDF transformer tfidf = TfidfTransformer() tfidf_features = tfidf.fit_transform(count_features) in3x,net,watch,14zwhrd6,dildo,18

# Tokenize (simple split) tokens = text.split(',') # Viewing features feature_names = vectorizer

# Vectorizer to convert text into a matrix of token counts vectorizer = CountVectorizer() count_features = vectorizer.fit_transform(data) feature_names) print("TF-IDF Features:"

from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer

# Your data text = "in3x,net,watch,14zwhrd6,dildo,18"