电影推荐的协同过滤

import pandas as pd
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
from zipfile import ZipFile

import keras
from keras import layers
from keras import ops

# 从 http://files.grouplens.org/datasets/movielens/ml-latest-small.zip 下载实际数据
# 使用 ratings.csv 文件
movielens_data_file_url = (
    "http://files.grouplens.org/datasets/movielens/ml-latest-small.zip"
)
movielens_zipped_file = keras.utils.get_file(
    "ml-latest-small.zip", movielens_data_file_url, extract=False
)
keras_datasets_path = Path(movielens_zipped_file).parents[0]
movielens_dir = keras_datasets_path / "ml-latest-small"

# 仅在脚本首次运行时提取数据。
if not movielens_dir.exists():
    with ZipFile(movielens_zipped_file, "r") as zip:
        # 提取文件
        print("正在提取所有文件...")
        zip.extractall(path=keras_datasets_path)
        print("完成！")

ratings_file = movielens_dir / "ratings.csv"
df = pd.read_csv(ratings_file)

user_ids = df["userId"].unique().tolist()
user2user_encoded = {x: i for i, x in enumerate(user_ids)}
userencoded2user = {i: x for i, x in enumerate(user_ids)}
movie_ids = df["movieId"].unique().tolist()
movie2movie_encoded = {x: i for i, x in enumerate(movie_ids)}
movie_encoded2movie = {i: x for i, x in enumerate(movie_ids)}
df["user"] = df["userId"].map(user2user_encoded)
df["movie"] = df["movieId"].map(movie2movie_encoded)

num_users = len(user2user_encoded)
num_movies = len(movie_encoded2movie)
df["rating"] = df["rating"].values.astype(np.float32)
# 最低和最高评级将在后面用于标准化评级
min_rating = min(df["rating"])
max_rating = max(df["rating"])

print(
    "用户数量: {}, 电影数量: {}, 最低评级: {}, 最高评级: {}".format(
        num_users, num_movies, min_rating, max_rating
    )
)

df = df.sample(frac=1, random_state=42)
x = df[["user", "movie"]].values
# 将目标标准化到0和1之间。易于训练。
y = df["rating"].apply(lambda x: (x - min_rating) / (max_rating - min_rating)).values
# 假设在90%的数据上进行训练，在10%的数据上进行验证。
train_indices = int(0.9 * df.shape[0])
x_train, x_val, y_train, y_val = (
    x[:train_indices],
    x[train_indices:],
    y[:train_indices],
    y[train_indices:],
)

EMBEDDING_SIZE = 50


class RecommenderNet(keras.Model):
    def __init__(self, num_users, num_movies, embedding_size, **kwargs):
        super().__init__(**kwargs)
        self.num_users = num_users
        self.num_movies = num_movies
        self.embedding_size = embedding_size
        self.user_embedding = layers.Embedding(
            num_users,
            embedding_size,
            embeddings_initializer="he_normal",
            embeddings_regularizer=keras.regularizers.l2(1e-6),
        )
        self.user_bias = layers.Embedding(num_users, 1)
        self.movie_embedding = layers.Embedding(
            num_movies,
            embedding_size,
            embeddings_initializer="he_normal",
            embeddings_regularizer=keras.regularizers.l2(1e-6),
        )
        self.movie_bias = layers.Embedding(num_movies, 1)

    def call(self, inputs):
        user_vector = self.user_embedding(inputs[:, 0])
        user_bias = self.user_bias(inputs[:, 0])
        movie_vector = self.movie_embedding(inputs[:, 1])
        movie_bias = self.movie_bias(inputs[:, 1])
        dot_user_movie = ops.tensordot(user_vector, movie_vector, 2)
        # 添加所有组成部分（包括偏差）
        x = dot_user_movie + user_bias + movie_bias
        # Sigmoid 激活将评分强制限制在 0 和 1 之间
        return ops.nn.sigmoid(x)


model = RecommenderNet(num_users, num_movies, EMBEDDING_SIZE)
model.compile(
    loss=keras.losses.BinaryCrossentropy(),
    optimizer=keras.optimizers.Adam(learning_rate=0.001),
)

history = model.fit(
    x=x_train,
    y=y_train,
    batch_size=64,
    epochs=5,
    verbose=1,
    validation_data=(x_val, y_val),
)

plt.plot(history.history["loss"])
plt.plot(history.history["val_loss"])
plt.title("模型损失")
plt.ylabel("损失")
plt.xlabel("轮次")
plt.legend(["训练", "测试"], loc="upper left")
plt.show()

movie_df = pd.read_csv(movielens_dir / "movies.csv")

# 让我们获取一位用户并查看前面的推荐。
user_id = df.userId.sample(1).iloc[0]
movies_watched_by_user = df[df.userId == user_id]
movies_not_watched = movie_df[
    ~movie_df["movieId"].isin(movies_watched_by_user.movieId.values)
]["movieId"]
movies_not_watched = list(
    set(movies_not_watched).intersection(set(movie2movie_encoded.keys()))
)
movies_not_watched = [[movie2movie_encoded.get(x)] for x in movies_not_watched]
user_encoder = user2user_encoded.get(user_id)
user_movie_array = np.hstack(
    ([[user_encoder]] * len(movies_not_watched), movies_not_watched)
)
ratings = model.predict(user_movie_array).flatten()
top_ratings_indices = ratings.argsort()[-10:][::-1]
recommended_movie_ids = [
    movie_encoded2movie.get(movies_not_watched[x][0]) for x in top_ratings_indices
]

print("显示用户的推荐：{}".format(user_id))
print("====" * 9)
print("用户的高评分电影")
print("----" * 8)
top_movies_user = (
    movies_watched_by_user.sort_values(by="rating", ascending=False)
    .head(5)
    .movieId.values
)
movie_df_rows = movie_df[movie_df["movieId"].isin(top_movies_user)]
for row in movie_df_rows.itertuples():
    print(row.title, ":", row.genres)

print("----" * 8)
print("前 10 个电影推荐")
print("----" * 8)
recommended_movies = movie_df[movie_df["movieId"].isin(recommended_movie_ids)]
for row in recommended_movies.itertuples():
    print(row.title, ":", row.genres)