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import pandas as pd |
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import numpy as np |
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from sklearn.model_selection import train_test_split |
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from sklearn.preprocessing import LabelEncoder |
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from sklearn.metrics.pairwise import cosine_similarity |
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import matplotlib.pyplot as plt |
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from tensorflow.keras.models import Model |
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from tensorflow.keras.layers import Input, Embedding, Flatten, concatenate, Dense |
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from tensorflow.keras.optimizers import Adam |
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books = pd.read_csv("../data/dataset/books.csv") |
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ratings = pd.read_csv("../data/dataset/ratings.csv") |
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user_encoder = LabelEncoder() |
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book_encoder = LabelEncoder() |
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ratings["user_id"] = user_encoder.fit_transform(ratings["user_id"]) |
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ratings["book_id"] = book_encoder.fit_transform(ratings["book_id"]) |
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train, test = train_test_split(ratings, test_size=0.2, random_state=42) |
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def build_model(num_users, num_books, embedding_size=50): |
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""" |
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Build a recommendation model. |
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Args: |
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num_users (int): The number of users in the dataset. |
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num_books (int): The number of books in the dataset. |
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embedding_size (int, optional): The size of the embedding vectors. Defaults to 50. |
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Returns: |
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keras.Model: The compiled recommendation model. |
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""" |
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user_input = Input(shape=(1,)) |
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book_input = Input(shape=(1,)) |
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user_embedding = Embedding(input_dim=num_users, output_dim=embedding_size)( |
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user_input |
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) |
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book_embedding = Embedding(input_dim=num_books, output_dim=embedding_size)( |
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book_input |
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) |
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user_flat = Flatten()(user_embedding) |
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book_flat = Flatten()(book_embedding) |
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merged = concatenate([user_flat, book_flat]) |
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dense1 = Dense(128, activation="relu")(merged) |
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output = Dense(1)(dense1) |
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model = Model(inputs=[user_input, book_input], outputs=output) |
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model.compile(loss="mean_squared_error", optimizer=Adam(learning_rate=0.001)) |
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return model |
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model = build_model( |
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num_users=len(ratings["user_id"].unique()), |
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num_books=len(ratings["book_id"].unique()), |
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) |
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history = model.fit( |
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[train["user_id"], train["book_id"]], |
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train["rating"], |
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epochs=5, |
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batch_size=128, |
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validation_split=0.1, |
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) |
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plt.figure(figsize=(12, 6)) |
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plt.plot(history.history["loss"], label="Training Loss") |
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plt.plot(history.history["val_loss"], label="Validation Loss") |
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plt.xlabel("Epoch") |
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plt.ylabel("Loss") |
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plt.legend() |
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plt.show() |
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model.save("recommendation_model.h5") |
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test_loss = model.evaluate([test["user_id"], test["book_id"]], test["rating"]) |
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print(f"Test Loss: {test_loss}") |
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