test_llp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "from pyspark.sql.functions import col\n",
    "\n",
    "\n",
    "# 创建SparkSession\n",
    "spark = SparkSession.builder.appName(\"MovieRecommend\")\\\n",
    "    .config(\"spark.executor.memory\", \"8G\")\\\n",
    "    .config(\"spark.executor.cores\", \"4\")\\\n",
    "    .config(\"spark.executor.instances\", \"4\")\\\n",
    "    .config(\"spark.driver.memory\", \"8G\")\\\n",
    "    .getOrCreate()\n",
    "\n",
    "\n",
    "# 加载数据\n",
    "ratings_df = spark.read.csv(\"dataset/ratings.csv\", inferSchema=True, header=True)\n",
    "\n",
    "# Read additional data\n",
    "tags_df = spark.read.csv(\"dataset/tags.csv\", header=True, inferSchema=True)\n",
    "movies_df = spark.read.csv(\"dataset/movies.csv\", header=True, inferSchema=True)\n",
    "genome_scores_df = spark.read.csv(\"dataset/genome-scores.csv\", header=True, inferSchema=True)\n",
    "genome_tags_df = spark.read.csv(\"dataset/genome-tags.csv\", header=True, inferSchema=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Join DataFrames\n",
    "# complete_data = movies_df.join(tags_df, \"movieId\") \\\n",
    "#                         .join(genome_scores_df, \"movieId\") \\\n",
    "#                         .join(genome_tags_df, \"tagId\") \\\n",
    "#                         .join(ratings_df, [\"userId\", \"movieId\"])\n",
    "\n",
    "# genome_data=genome_scores_df.join(genome_tags_df,\"tagId\")\n",
    "\n",
    "# complete_data=ratings_df.join(movies_df,\"movieId\")\\\n",
    "# .join(tags_df,\"movieId\")\\\n",
    "# .join(genome_data,[\"movieId\",\"tag\"])\n",
    "\n",
    "# complete_data.printSchema()\n",
    "# # complete_data.show(5)\n",
    "# complete_data.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import functions as F\n",
    "from pyspark.sql import DataFrame\n",
    "\n",
    "def add_sample_label(ratings_df):\n",
    "    # Show the first 5 rows and print the schema before modification\n",
    "    ratings_df.show(5, truncate=False)\n",
    "    ratings_df.printSchema()\n",
    "\n",
    "    # Add a new column 'label' based on the condition\n",
    "    ratings_df = ratings_df.withColumn('label', F.when(F.col('rating') >= 3.5, 2).otherwise(3))\n",
    "\n",
    "    return ratings_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "# Extract movie year\n",
    "def parse_year(title):\n",
    "    pattern = r\"\\((\\d{4})\\)\"  # 正则表达式模式，匹配括号内的四位数字\n",
    "    match = re.search(pattern, title)\n",
    "    if match:\n",
    "        year_str = match.group(1)\n",
    "        return int(year_str)\n",
    "    else:\n",
    "        return 1900"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------+-------+------+----------+\n",
      "|userId|movieId|rating|timestamp |\n",
      "+------+-------+------+----------+\n",
      "|1     |296    |5.0   |1147880044|\n",
      "|1     |306    |3.5   |1147868817|\n",
      "|1     |307    |5.0   |1147868828|\n",
      "|1     |665    |5.0   |1147878820|\n",
      "|1     |899    |3.5   |1147868510|\n",
      "+------+-------+------+----------+\n",
      "only showing top 5 rows\n",
      "\n",
      "root\n",
      " |-- userId: integer (nullable = true)\n",
      " |-- movieId: integer (nullable = true)\n",
      " |-- rating: double (nullable = true)\n",
      " |-- timestamp: integer (nullable = true)\n",
      "\n",
      "root\n",
      " |-- movieId: integer (nullable = true)\n",
      " |-- userId: integer (nullable = true)\n",
      " |-- rating: double (nullable = true)\n",
      " |-- timestamp: integer (nullable = true)\n",
      " |-- label: integer (nullable = false)\n",
      " |-- movie_year: integer (nullable = true)\n",
      " |-- genres_Action: integer (nullable = true)\n",
      " |-- genres_Horror: integer (nullable = true)\n",
      " |-- genres_Comedy: integer (nullable = true)\n",
      " |-- genres_Animation: integer (nullable = true)\n",
      " |-- genres_Musical: integer (nullable = true)\n",
      " |-- genres_Film-Noir: integer (nullable = true)\n",
      " |-- genres_Children: integer (nullable = true)\n",
      " |-- genres_Adventure: integer (nullable = true)\n",
      " |-- genres_Fantasy: integer (nullable = true)\n",
      " |-- genres_Western: integer (nullable = true)\n",
      " |-- genres_Documentary: integer (nullable = true)\n",
      " |-- genres_Mystery: integer (nullable = true)\n",
      " |-- genres_Romance: integer (nullable = true)\n",
      " |-- genres_IMAX: integer (nullable = true)\n",
      " |-- genres_Crime: integer (nullable = true)\n",
      " |-- genres_Thriller: integer (nullable = true)\n",
      " |-- genres_War: integer (nullable = true)\n",
      " |-- genres_Sci-Fi: integer (nullable = true)\n",
      " |-- genres_(no genres listed): integer (nullable = true)\n",
      " |-- genres_Drama: integer (nullable = true)\n",
      "\n",
      "+-------+------+------+---------+-----+----------+-------------+-------------+-------------+----------------+--------------+----------------+---------------+----------------+--------------+--------------+------------------+--------------+--------------+-----------+------------+---------------+----------+-------------+-------------------------+------------+\n",
      "|movieId|userId|rating|timestamp|label|movie_year|genres_Action|genres_Horror|genres_Comedy|genres_Animation|genres_Musical|genres_Film-Noir|genres_Children|genres_Adventure|genres_Fantasy|genres_Western|genres_Documentary|genres_Mystery|genres_Romance|genres_IMAX|genres_Crime|genres_Thriller|genres_War|genres_Sci-Fi|genres_(no genres listed)|genres_Drama|\n",
      "+-------+------+------+---------+-----+----------+-------------+-------------+-------------+----------------+--------------+----------------+---------------+----------------+--------------+--------------+------------------+--------------+--------------+-----------+------------+---------------+----------+-------------+-------------------------+------------+\n",
      "|    296|     1|   5.0|       36|    2|      1994|            0|            0|            1|               0|             0|               0|              0|               0|             0|             0|                 0|             0|             0|          0|           1|              1|         0|            0|                        0|           1|\n",
      "|    306|     1|   3.5|       36|    2|      1994|            0|            0|            0|               0|             0|               0|              0|               0|             0|             0|                 0|             0|             0|          0|           0|              0|         0|            0|                        0|           1|\n",
      "|    307|     1|   5.0|       36|    2|      1993|            0|            0|            0|               0|             0|               0|              0|               0|             0|             0|                 0|             0|             0|          0|           0|              0|         0|            0|                        0|           1|\n",
      "|    665|     1|   5.0|       36|    2|      1995|            0|            0|            1|               0|             0|               0|              0|               0|             0|             0|                 0|             0|             0|          0|           0|              0|         1|            0|                        0|           1|\n",
      "|    899|     1|   3.5|       36|    2|      1952|            0|            0|            1|               0|             1|               0|              0|               0|             0|             0|                 0|             0|             1|          0|           0|              0|         0|            0|                        0|           0|\n",
      "|   1088|     1|   4.0|       36|    2|      1987|            0|            0|            0|               0|             1|               0|              0|               0|             0|             0|                 0|             0|             1|          0|           0|              0|         0|            0|                        0|           1|\n",
      "|   1175|     1|   3.5|       36|    2|      1991|            0|            0|            1|               0|             0|               0|              0|               0|             0|             0|                 0|             0|             1|          0|           0|              0|         0|            0|                        0|           1|\n",
      "|   1217|     1|   3.5|       36|    2|      1985|            0|            0|            0|               0|             0|               0|              0|               0|             0|             0|                 0|             0|             0|          0|           0|              0|         1|            0|                        0|           1|\n",
      "|   1237|     1|   5.0|       36|    2|      1957|            0|            0|            0|               0|             0|               0|              0|               0|             0|             0|                 0|             0|             0|          0|           0|              0|         0|            0|                        0|           1|\n",
      "|   1250|     1|   4.0|       36|    2|      1957|            0|            0|            0|               0|             0|               0|              0|               1|             0|             0|                 0|             0|             0|          0|           0|              0|         1|            0|                        0|           1|\n",
      "|   1260|     1|   3.5|       36|    2|      1931|            0|            0|            0|               0|             0|               1|              0|               0|             0|             0|                 0|             0|             0|          0|           1|              1|         0|            0|                        0|           0|\n",
      "|   1653|     1|   4.0|       36|    2|      1997|            0|            0|            0|               0|             0|               0|              0|               0|             0|             0|                 0|             0|             0|          0|           0|              1|         0|            1|                        0|           1|\n",
      "|   2011|     1|   2.5|       36|    3|      1989|            0|            0|            1|               0|             0|               0|              0|               1|             0|             0|                 0|             0|             0|          0|           0|              0|         0|            1|                        0|           0|\n",
      "|   2012|     1|   2.5|       36|    3|      1990|            0|            0|            1|               0|             0|               0|              0|               1|             0|             1|                 0|             0|             0|          0|           0|              0|         0|            1|                        0|           0|\n",
      "|   2068|     1|   2.5|       36|    3|      1982|            0|            0|            0|               0|             0|               0|              0|               0|             1|             0|                 0|             1|             0|          0|           0|              0|         0|            0|                        0|           1|\n",
      "|   2161|     1|   3.5|       36|    2|      1984|            0|            0|            0|               0|             0|               0|              1|               1|             1|             0|                 0|             0|             0|          0|           0|              0|         0|            0|                        0|           0|\n",
      "|   2351|     1|   4.5|       36|    2|      1957|            0|            0|            0|               0|             0|               0|              0|               0|             0|             0|                 0|             0|             0|          0|           0|              0|         0|            0|                        0|           1|\n",
      "|   2573|     1|   4.0|       36|    2|      1998|            0|            0|            0|               0|             1|               0|              0|               0|             0|             0|                 0|             0|             0|          0|           0|              0|         0|            0|                        0|           1|\n",
      "|   2632|     1|   5.0|       36|    2|      1965|            0|            0|            0|               0|             0|               0|              0|               1|             0|             0|                 0|             1|             0|          0|           0|              0|         0|            0|                        0|           1|\n",
      "|   2692|     1|   5.0|       36|    2|      1998|            1|            0|            0|               0|             0|               0|              0|               0|             0|             0|                 0|             0|             0|          0|           1|              0|         0|            0|                        0|           0|\n",
      "+-------+------+------+---------+-----+----------+-------------+-------------+-------------+----------------+--------------+----------------+---------------+----------------+--------------+--------------+------------------+--------------+--------------+-----------+------------+---------------+----------+-------------+-------------------------+------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "from pyspark.ml.feature import OneHotEncoder, VectorAssembler\n",
    "from pyspark.ml.regression import RandomForestRegressor\n",
    "from pyspark.ml.evaluation import RegressionEvaluator\n",
    "import os\n",
    "import pickle\n",
    "from pyspark.sql.functions import udf\n",
    "from pyspark.sql.types import StringType\n",
    "from pyspark.sql.types import IntegerType\n",
    "from pyspark.ml import Pipeline\n",
    "\n",
    "# # spark = SparkSession.builder.appName(\"PrepareData\").getOrCreate()\n",
    "# # 创建SparkSession\n",
    "# spark = SparkSession.builder.appName(\"MovieRecommend\")\\\n",
    "#     .config(\"spark.executor.memory\", \"8G\")\\\n",
    "#     .config(\"spark.executor.cores\", \"4\")\\\n",
    "#     .config(\"spark.executor.instances\", \"4\")\\\n",
    "#     .config(\"spark.driver.memory\", \"8G\")\\\n",
    "#     .getOrCreate()\n",
    "\n",
    "\n",
    "# Read movie data\n",
    "data_path = \"dataset/\"\n",
    "df_movie = spark.read.csv(\n",
    "    \"dataset/movies.csv\", header=True,inferSchema=True)\n",
    "\n",
    "# Extract movie year\n",
    "parse_year_udf = udf(parse_year, IntegerType())\n",
    "df_movie = df_movie.withColumn(\"movie_year\", parse_year_udf(df_movie['title']))\n",
    "df_movie=df_movie.drop('title')\n",
    "\n",
    "# # Join genome data to df\n",
    "# df = df.join(genome_scores_df, \"movieId\")\n",
    "# df = df.join(genome_tags_df, \"tagId\")\n",
    "\n",
    "# One-hot encoding genres\n",
    "genres = df_movie.select(\"genres\").distinct().rdd.flatMap(lambda x: x).collect()\n",
    "genres_unique = list(set([i for sublist in genres for i in sublist.split(\"|\")]))\n",
    "\n",
    "for genres_name in genres_unique:\n",
    "    col = \"genres_\" + genres_name\n",
    "    df_movie = df_movie.withColumn(col, \n",
    "                                   (df_movie.genres.contains(genres_name)).cast(IntegerType()))\n",
    "\n",
    "df_movie = df_movie.drop('genres')\n",
    "genres_col_names = [\"genres_\" + x for x in genres_unique]\n",
    "\n",
    "# Read rating data\n",
    "\n",
    "df_rating = spark.read.csv(\n",
    "    \"dataset/ratings.csv\",\n",
    "    header=True,\n",
    "    inferSchema=True\n",
    ")\n",
    "\n",
    "df_rating = add_sample_label(df_rating)\n",
    "df_rating = df_rating.withColumn('timestamp', (df_rating['timestamp'] / (365 * 24 * 3600)).cast(IntegerType()))\n",
    "\n",
    "# Data merge\n",
    "df = df_rating.join(df_movie, 'movieId')\n",
    "\n",
    "\n",
    "# df_X = df.select([\"userId\", \"movieId\"] + genres_col_names + [\"timestamp\"])\n",
    "# df_y = df.select([\"rating\"])\n",
    "# print(\"Data read completed\")\n",
    "\n",
    "df.printSchema()\n",
    "df.show(20)\n",
    "# df_X.show(10)\n",
    "# df_y.show(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data read completed\n"
     ]
    }
   ],
   "source": [
    "from pyspark.ml.feature import VectorAssembler\n",
    "\n",
    "# 使用 VectorAssembler 进行特征组合\n",
    "assembler = VectorAssembler(\n",
    "    inputCols=[\"userId\", \"movieId\", \"timestamp\",\"movie_year\",\"label\"] + genres_col_names ,\n",
    "    outputCol=\"features\"\n",
    ")\n",
    "\n",
    "# 对数据框进行转换\n",
    "df = assembler.transform(df)\n",
    "\n",
    "# 打印数据读取完成的消息\n",
    "print(\"Data read completed\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "23/11/29 09:43:03 WARN MemoryStore: Not enough space to cache rdd_294_5 in memory! (computed 104.0 MiB so far)\n",
      "23/11/29 09:43:03 WARN BlockManager: Persisting block rdd_294_5 to disk instead.\n",
      "23/11/29 09:43:03 WARN MemoryStore: Not enough space to cache rdd_294_4 in memory! (computed 104.0 MiB so far)\n",
      "23/11/29 09:43:03 WARN BlockManager: Persisting block rdd_294_4 to disk instead.\n",
      "23/11/29 09:43:04 WARN MemoryStore: Not enough space to cache rdd_294_1 in memory! (computed 104.0 MiB so far)\n",
      "23/11/29 09:43:04 WARN BlockManager: Persisting block rdd_294_1 to disk instead.\n",
      "23/11/29 09:43:04 WARN MemoryStore: Not enough space to cache rdd_294_8 in memory! (computed 104.0 MiB so far)\n",
      "23/11/29 09:43:04 WARN BlockManager: Persisting block rdd_294_8 to disk instead.\n",
      "23/11/29 09:43:04 WARN MemoryStore: Not enough space to cache rdd_294_10 in memory! (computed 104.0 MiB so far)\n",
      "23/11/29 09:43:04 WARN BlockManager: Persisting block rdd_294_10 to disk instead.\n",
      "23/11/29 09:43:04 WARN MemoryStore: Not enough space to cache rdd_294_2 in memory! (computed 104.0 MiB so far)\n",
      "23/11/29 09:43:04 WARN BlockManager: Persisting block rdd_294_2 to disk instead.\n",
      "23/11/29 09:43:04 WARN MemoryStore: Not enough space to cache rdd_294_15 in memory! (computed 156.0 MiB so far)\n",
      "23/11/29 09:43:04 WARN BlockManager: Persisting block rdd_294_15 to disk instead.\n",
      "23/11/29 09:43:04 WARN MemoryStore: Not enough space to cache rdd_294_12 in memory! (computed 156.0 MiB so far)\n",
      "23/11/29 09:43:04 WARN BlockManager: Persisting block rdd_294_12 to disk instead.\n",
      "23/11/29 09:43:04 WARN MemoryStore: Not enough space to cache rdd_294_13 in memory! (computed 156.0 MiB so far)\n",
      "23/11/29 09:43:04 WARN BlockManager: Persisting block rdd_294_13 to disk instead.\n",
      "23/11/29 09:43:04 WARN MemoryStore: Not enough space to cache rdd_294_0 in memory! (computed 104.0 MiB so far)\n",
      "23/11/29 09:43:04 WARN BlockManager: Persisting block rdd_294_0 to disk instead.\n",
      "23/11/29 09:43:04 WARN MemoryStore: Not enough space to cache rdd_294_6 in memory! (computed 156.0 MiB so far)\n",
      "23/11/29 09:43:04 WARN BlockManager: Persisting block rdd_294_6 to disk instead.\n",
      "23/11/29 09:43:04 WARN MemoryStore: Not enough space to cache rdd_294_9 in memory! (computed 156.0 MiB so far)\n",
      "23/11/29 09:43:04 WARN BlockManager: Persisting block rdd_294_9 to disk instead.\n",
      "23/11/29 09:43:04 WARN MemoryStore: Not enough space to cache rdd_294_14 in memory! (computed 156.0 MiB so far)\n",
      "23/11/29 09:43:04 WARN BlockManager: Persisting block rdd_294_14 to disk instead.\n",
      "23/11/29 09:43:04 WARN MemoryStore: Not enough space to cache rdd_294_7 in memory! (computed 156.0 MiB so far)\n",
      "23/11/29 09:43:04 WARN BlockManager: Persisting block rdd_294_7 to disk instead.\n",
      "23/11/29 09:43:04 WARN MemoryStore: Not enough space to cache rdd_294_3 in memory! (computed 156.0 MiB so far)\n",
      "23/11/29 09:43:04 WARN BlockManager: Persisting block rdd_294_3 to disk instead.\n",
      "23/11/29 09:43:04 WARN MemoryStore: Not enough space to cache rdd_294_11 in memory! (computed 156.0 MiB so far)\n",
      "23/11/29 09:43:04 WARN BlockManager: Persisting block rdd_294_11 to disk instead.\n",
      "23/11/29 09:43:17 WARN MemoryStore: Not enough space to cache rdd_294_5 in memory! (computed 234.0 MiB so far)\n",
      "23/11/29 09:43:17 WARN MemoryStore: Not enough space to cache rdd_294_0 in memory! (computed 156.0 MiB so far)\n",
      "23/11/29 09:43:18 WARN MemoryStore: Not enough space to cache rdd_294_2 in memory! (computed 234.0 MiB so far)\n",
      "23/11/29 09:43:34 WARN MemoryStore: Not enough space to cache rdd_294_2 in memory! (computed 234.0 MiB so far)\n",
      "[Stage 88:=================================================>      (14 + 2) / 16]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Square Error (RMSE): 0.6054813981609843\n",
      "Mean Absolute Error (MAE): 0.5013850522995075\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "from pyspark.ml.feature import StringIndexer, VectorAssembler\n",
    "from pyspark.ml.regression import RandomForestRegressor\n",
    "from pyspark.ml.evaluation import RegressionEvaluator\n",
    "\n",
    "(training_data, test_data) = df.randomSplit([0.7, 0.3],seed=123)\n",
    "\n",
    "rf = RandomForestRegressor(\n",
    "    featuresCol=\"features\",\n",
    "    labelCol=\"rating\",\n",
    "    numTrees=10,\n",
    "    maxDepth=7,\n",
    "    maxBins=32,\n",
    "    minInstancesPerNode=1\n",
    ")\n",
    "\n",
    "# 训练模型\n",
    "model_rf = rf.fit(training_data)\n",
    "\n",
    "# Predicting on test data\n",
    "predictions_rf = model_rf.transform(test_data)\n",
    "\n",
    "\n",
    "\n",
    "# Define multiple evaluators\n",
    "rmse_evaluator = RegressionEvaluator(metricName=\"rmse\", labelCol=\"rating\", predictionCol=\"prediction\")\n",
    "mae_evaluator = RegressionEvaluator(metricName=\"mae\", labelCol=\"rating\", predictionCol=\"prediction\")\n",
    "\n",
    "rmse = rmse_evaluator.evaluate(predictions_rf)\n",
    "mae = mae_evaluator.evaluate(predictions_rf)\n",
    "print(f\"Root Mean Square Error (RMSE): {rmse}\")\n",
    "print(f\"Mean Absolute Error (MAE): {mae}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame[title: string, prediction: double]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    " # Get top5 movie recommendations\n",
    "top_predictions = predictions_rf .orderBy('prediction', ascending=False).limit(5)\n",
    "\n",
    "    # Join with the movies dataframe to get movie titles\n",
    "movies_df = spark.read.csv(\"dataset/movies.csv\", header=True, inferSchema=True)  # Load movies data\n",
    "top_movies_with_titles = top_predictions.join(movies_df, 'movieId').select('title', 'prediction')\n",
    "print(top_movies_with_titles)\n",
    "\n",
    "    # Convert to pandas dataframe for visualization\n",
    "top_movies_pd = top_movies_with_titles.toPandas()\n",
    "top_movies_pd.set_index('title', inplace=True)\n",
    "\n",
    "    # Plot\n",
    "plt.figure(figsize=(12, 8))\n",
    "top_movies_pd['prediction'].plot(kind='bar')\n",
    "plt.xlabel('Predicted Rating')\n",
    "plt.ylabel('Movie Title')\n",
    "plt.title('Top 5 Movie Recommendations')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from pyspark.sql import SparkSession\n",
    "# from pyspark.ml.feature import VectorAssembler\n",
    "# from pyspark.ml.regression import RandomForestRegressor\n",
    "# from pyspark.sql.functions import col, lit, explode\n",
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# # Create a DataFrame with all unique user IDs\n",
    "# all_users = df.select(\"userId\").distinct()\n",
    "\n",
    "# # Cross join with the features of all distinct movies\n",
    "# all_users_recs = all_users.crossJoin(df.select(\"movieId\", \"timestamp\",\"movie_year\",\"label\").distinct())\n",
    "\n",
    "# # Vectorize features for the recommendation DataFrame\n",
    "# all_users_recs = assembler.transform(all_users_recs)\n",
    "\n",
    "\n",
    "# # Predict using the trained model\n",
    "# all_users_predictions = model_rf.transform(all_users_recs)\n",
    "\n",
    "\n",
    "#  # Get top5 movie recommendations\n",
    "# top_predictions = all_users_predictions .orderBy('prediction', ascending=False).limit(5)\n",
    "\n",
    "#     # Join with the movies dataframe to get movie titles\n",
    "# movies_df = spark.read.csv(\"dataset/movies.csv\", header=True, inferSchema=True)  # Load movies data\n",
    "# top_movies_with_titles = top_predictions.join(movies_df, 'movieId').select('title', 'prediction')\n",
    "# print(top_movies_with_titles)\n",
    "\n",
    "#     # Convert to pandas dataframe for visualization\n",
    "# top_movies_pd = top_movies_with_titles.toPandas()\n",
    "# top_movies_pd.set_index('title', inplace=True)\n",
    "\n",
    "#     # Plot\n",
    "# plt.figure(figsize=(12, 8))\n",
    "# top_movies_pd['prediction'].plot(kind='bar')\n",
    "# plt.xlabel('Predicted Rating')\n",
    "# plt.ylabel('Movie Title')\n",
    "# plt.title('Top 5 Movie Recommendations')\n",
    "# plt.tight_layout()\n",
    "# plt.show()\n",
    "# plt.close()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from pyspark.ml.tuning import CrossValidator, ParamGridBuilder\n",
    "# from pyspark.ml.evaluation import RegressionEvaluator\n",
    "# from datetime import datetime\n",
    "# import pandas as pd\n",
    "\n",
    "# # # Create a Spark session\n",
    "# # spark = SparkSession.builder.appName(\"RandomForestRegressorExample\").getOrCreate()\n",
    "\n",
    "\n",
    "# # Select features and target column\n",
    "# df = df.select(\"features\", \"rating\")\n",
    "\n",
    "# # Split the data into training and test sets\n",
    "# (training_data, test_data) = df.randomSplit([0.7, 0.3], seed=123)\n",
    "\n",
    "# # Create a RandomForestRegressor\n",
    "# rf = RandomForestRegressor(featuresCol=\"features\", labelCol=\"rating\")\n",
    "\n",
    "# # Define the parameter grid to search over\n",
    "# param_grid = ParamGridBuilder() \\\n",
    "#     .addGrid(rf.numTrees, [10, 20, 30]) \\\n",
    "#     .addGrid(rf.maxDepth, [5, 10, 15]) \\\n",
    "#     .addGrid(rf.maxBins, [16, 32, 64]) \\\n",
    "#     .addGrid(rf.minInstancesPerNode, [1, 5, 10]) \\\n",
    "#     .build()\n",
    "\n",
    "\n",
    "# # Define multiple evaluators\n",
    "# rmse_evaluator = RegressionEvaluator(metricName=\"rmse\", labelCol=\"rating\", predictionCol=\"prediction\")\n",
    "# mae_evaluator = RegressionEvaluator(metricName=\"mae\", labelCol=\"rating\", predictionCol=\"prediction\")\n",
    "\n",
    "# # Create the CrossValidator\n",
    "# crossval = CrossValidator(estimator=rf,\n",
    "#                           estimatorParamMaps=param_grid,\n",
    "#                           evaluator=rmse_evaluator,\n",
    "#                           numFolds=3)  # Number of folds for cross-validation\n",
    "\n",
    "# # Record the start time\n",
    "# start_time = datetime.now()\n",
    "\n",
    "# # Run cross-validation, and choose the best set of parameters\n",
    "# cv_model = crossval.fit(training_data)\n",
    "\n",
    "# # Record the end time\n",
    "# end_time = datetime.now()\n",
    "\n",
    "# # Calculate the training time\n",
    "# training_time = end_time - start_time\n",
    "# print(f\"Training Time: {training_time}\")\n",
    "\n",
    "# # Make predictions on test data\n",
    "# predictions_rf_cv = cv_model.transform(test_data)\n",
    "\n",
    "# # Evaluate the model\n",
    "# rmse_cv = rmse_evaluator.evaluate(predictions_rf_cv)\n",
    "# mae_cv = mae_evaluator.evaluate(predictions_rf_cv)\n",
    "# print(f\"Cross-validated Root Mean Square Error (RMSE): {rmse_cv}\")\n",
    "# print(f\"Cross-validated Mean Absolute Error (MAE): {mae_cv}\")\n",
    "\n",
    "# # Print the best parameters found during cross-validation\n",
    "# best_params = cv_model.bestModel.extractParamMap()\n",
    "# print(\"Best Parameters:\")\n",
    "# for param, value in best_params.items():\n",
    "#     print(f\"{param.name}: {value}\")\n",
    "\n",
    "# # Create a pandas DataFrame with the results\n",
    "# results_dict = {\n",
    "#     \"Training Time\": [str(training_time)],\n",
    "#     \"RMSE\": [rmse_cv],\n",
    "#     \"MAE\": [mae_cv],\n",
    "#     \"Best Parameters\": [best_params]\n",
    "# }\n",
    "\n",
    "# results_df = pd.DataFrame(results_dict)\n",
    "\n",
    "# # Save the DataFrame to a CSV file\n",
    "# csv_path = \"/root/CineSpark-Insights/results/rf_model_performance.csv\"\n",
    "# results_df.to_csv(csv_path, index=False)\n",
    "\n",
    "# print(f\"Results saved to: {csv_path}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from pyspark.ml.feature import StringIndexer, VectorAssembler\n",
    "# from pyspark.ml.regression import RandomForestRegressor\n",
    "# from pyspark.ml.evaluation import RegressionEvaluator\n",
    "# import time\n",
    "# import pandas as pd\n",
    "\n",
    "\n",
    "\n",
    "# # 选择特征和目标列\n",
    "# df = df.select(\"features\", \"rating\")\n",
    "\n",
    "# # 划分数据集为训练集和测试集\n",
    "# (training_data, test_data) = df.randomSplit([0.7, 0.3],seed=123)\n",
    "\n",
    "# start_time = time.time()\n",
    "\n",
    "# rf = RandomForestRegressor(\n",
    "#     featuresCol=\"features\",\n",
    "#     labelCol=\"rating\",\n",
    "#     numTrees=10,\n",
    "#     maxDepth=5,\n",
    "#     maxBins=64,\n",
    "#     minInstancesPerNode=1\n",
    "# )\n",
    "\n",
    "# # 训练模型\n",
    "# model_rf = rf.fit(training_data)\n",
    "\n",
    "# # Predicting on test data\n",
    "# predictions_rf = model_rf.transform(test_data)\n",
    "\n",
    "\n",
    "\n",
    "# # Define multiple evaluators\n",
    "# rmse_evaluator = RegressionEvaluator(metricName=\"rmse\", labelCol=\"rating\", predictionCol=\"prediction\")\n",
    "# mae_evaluator = RegressionEvaluator(metricName=\"mae\", labelCol=\"rating\", predictionCol=\"prediction\")\n",
    "\n",
    "# rmse = rmse_evaluator.evaluate(predictions_rf)\n",
    "# mae = mae_evaluator.evaluate(predictions_rf)\n",
    "\n",
    "# end_time = time.time()\n",
    "# execution_time = end_time - start_time\n",
    "# print(f\"Execution Time: {execution_time} seconds\")\n",
    "# print(f\"Root Mean Square Error (RMSE): {rmse}\")\n",
    "# print(f\"Mean Absolute Error (MAE): {mae}\")\n",
    "\n",
    "# # 将结果保存到DataFrame\n",
    "# results_dict = {\n",
    "#     \"NumTrees\": [rf.getNumTrees()],\n",
    "#     \"MaxDepth\": [rf.getMaxDepth()],\n",
    "#     \"MaxBins\": [rf.getMaxBins()],\n",
    "#     \"MinInstancesPerNode\": [rf.getMinInstancesPerNode()],\n",
    "#     \"RMSE\": [rmse],\n",
    "#     \"MAE\": [mae],\n",
    "#     \"ExecutionTime\": [execution_time]\n",
    "# }\n",
    "\n",
    "# results_df = pd.DataFrame(results_dict)\n",
    "\n",
    "# # 写入CSV文件\n",
    "# results_df.to_csv('/root/CineSpark-Insights/results/rf_model_performance.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pandas as pd\n",
    "# from pyspark.ml.evaluation import RegressionEvaluator\n",
    "# from pyspark.ml.regression import RandomForestRegressor\n",
    "# import time\n",
    "# from itertools import product\n",
    "\n",
    "# def train_and_evaluate_rf(numTrees, maxDepth, maxBins, minInstancesPerNode):\n",
    "#     # ... (SparkSession initialization and data preprocessing steps)\n",
    "\n",
    "#     # Split the dataset into training and test sets\n",
    "#     (training_data, test_data) = df.randomSplit([0.7, 0.3], seed=123)\n",
    "    \n",
    "#     start_time = time.time()\n",
    "\n",
    "#     rf = RandomForestRegressor(\n",
    "#         featuresCol=\"features\",\n",
    "#         labelCol=\"rating\",\n",
    "#         numTrees=numTrees,\n",
    "#         maxDepth=maxDepth,\n",
    "#         maxBins=maxBins,\n",
    "#         minInstancesPerNode=minInstancesPerNode\n",
    "#     )\n",
    "\n",
    "#     # Train the model\n",
    "#     model_rf = rf.fit(training_data)\n",
    "\n",
    "#     # Predicting on test data\n",
    "#     predictions_rf = model_rf.transform(test_data)\n",
    "\n",
    "#     # Define evaluators\n",
    "#     rmse_evaluator = RegressionEvaluator(metricName=\"rmse\", labelCol=\"rating\", predictionCol=\"prediction\")\n",
    "#     mae_evaluator = RegressionEvaluator(metricName=\"mae\", labelCol=\"rating\", predictionCol=\"prediction\")\n",
    "\n",
    "#     # Evaluate model\n",
    "#     rmse = rmse_evaluator.evaluate(predictions_rf)\n",
    "#     mae = mae_evaluator.evaluate(predictions_rf)\n",
    "    \n",
    "#     # Record execution time\n",
    "   \n",
    "#     end_time = time.time()\n",
    "#     execution_time = end_time - start_time\n",
    "\n",
    "#     # ... (Code for calculating execution time and saving results)\n",
    "\n",
    "#     # Return results\n",
    "#     return {\n",
    "#         \"NumTrees\": numTrees,\n",
    "#         \"MaxDepth\": maxDepth,\n",
    "#         \"MaxBins\": maxBins,\n",
    "#         \"MinInstancesPerNode\": minInstancesPerNode,\n",
    "#         \"RMSE\": rmse,\n",
    "#         \"MAE\": mae,\n",
    "#         \"ExecutionTime\": execution_time\n",
    "#     }\n",
    "\n",
    "# # Call the function with different parameter combinations\n",
    "# num_trees = [10]\n",
    "# max_depth = [5]\n",
    "# max_bins = [32]\n",
    "# min_instances_per_node = [1]\n",
    "\n",
    "# param_combinations = list(product(num_trees, max_depth, max_bins, min_instances_per_node))\n",
    "\n",
    "\n",
    "# for params in param_combinations:\n",
    "#     result = train_and_evaluate_rf(*params)\n",
    "#     results_df = pd.DataFrame(result, index=[0])\n",
    "\n",
    "#     # Write to CSV file (append mode)\n",
    "# results_df.to_csv('/root/CineSpark-Insights/results/rf_model_performance.csv', mode='a', index=False, header=False)\n"
   ]
  }
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