recipe final.ipynb

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   "metadata": {},
   "source": [
    "# Recipe Recommendation Assignment"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "#### by sindhu balaga , sivagiri , sowmava sarkar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "363ff818",
   "metadata": {},
   "source": [
    "## FEATURE EXTRACTION PART 1"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "## Initial Setup"
   ]
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   "source": [
    "#importing libraries"
   ]
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     "text": [
      "Starting Spark application\n"
     ]
    },
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       "<tr><th>ID</th><th>YARN Application ID</th><th>Kind</th><th>State</th><th>Spark UI</th><th>Driver log</th><th>User</th><th>Current session?</th></tr><tr><td>0</td><td>application_1712672816539_0001</td><td>pyspark</td><td>idle</td><td><a target=\"_blank\" href=\"http://ip-172-31-44-45.ec2.internal:20888/proxy/application_1712672816539_0001/\" class=\"emr-proxy-link\" emr-resource=\"j-4XW1W5ZXM23E\n",
       "\" application-id=\"application_1712672816539_0001\">Link</a></td><td><a target=\"_blank\" href=\"http://ip-172-31-36-26.ec2.internal:8042/node/containerlogs/container_1712672816539_0001_01_000001/livy\" >Link</a></td><td>None</td><td>✔</td></tr></table>"
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     "text": [
      "SparkSession available as 'spark'.\n"
     ]
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   "source": [
    "from pyspark.sql import SparkSession"
   ]
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   "source": [
    "from pyspark.sql import SparkSession\n",
    "spark = SparkSession.builder.appName(\"Basics\").getOrCreate()"
   ]
  },
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     "text": [
      "<pyspark.sql.session.SparkSession object at 0x7fc0f5406650>"
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   "source": [
    "spark"
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   "source": [
    "from pyspark.sql import functions as F\n",
    "\n",
    "# Import for typecasting columns\n",
    "from pyspark.sql.types import IntegerType,BooleanType,DateType,FloatType,StringType\n",
    "from pyspark.sql.types import ArrayType"
   ]
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   "source": [
    "### Task 01: Read the data"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "### Solution to Task 01"
   ]
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     "text": [
      "+--------------------+------+-------+--------------+-------------------+--------------------+--------------------+-------+--------------------+--------------------+--------------------+-------------+\n",
      "|                name|    id|minutes|contributor_id|          submitted|                tags|           nutrition|n_steps|               steps|         description|         ingredients|n_ingredients|\n",
      "+--------------------+------+-------+--------------+-------------------+--------------------+--------------------+-------+--------------------+--------------------+--------------------+-------------+\n",
      "|arriba   baked wi...|137739|     55|         47892|2005-09-16 00:00:00|['60-minutes-or-l...|[51.5, 0.0, 13.0,...|     11|['make a choice a...|autumn is my favo...|['winter squash',...|            7|\n",
      "|a bit different  ...| 31490|     30|         26278|2002-06-17 00:00:00|['30-minutes-or-l...|[173.4, 18.0, 0.0...|      9|['preheat oven to...|this recipe calls...|['prepared pizza ...|            6|\n",
      "|all in the kitche...|112140|    130|        196586|2005-02-25 00:00:00|['time-to-make', ...|[269.8, 22.0, 32....|      6|['brown ground be...|this modified ver...|['ground beef', '...|           13|\n",
      "|  alouette  potatoes| 59389|     45|         68585|2003-04-14 00:00:00|['60-minutes-or-l...|[368.1, 17.0, 10....|     11|['place potatoes ...|this is a super e...|['spreadable chee...|           11|\n",
      "|amish  tomato ket...| 44061|    190|         41706|2002-10-25 00:00:00|['weeknight', 'ti...|[352.9, 1.0, 337....|      5|['mix all ingredi...|my dh's amish mot...|['tomato juice', ...|            8|\n",
      "+--------------------+------+-------+--------------+-------------------+--------------------+--------------------+-------+--------------------+--------------------+--------------------+-------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "# Task 01 Cell 1 out of 1\n",
    "\n",
    "raw_recipes_df = spark.read.csv(\"s3://sdemo2024/data1/RAW_recipes_cleaned.csv\",inferSchema=True,header=True) \n",
    "                   # argument 1, Add an argument to communicate to the compiler that there is a header in the raw data.\n",
    "                   # argument 2, Add an argument to ask the complier to estimate the data types for all columns. \n",
    "raw_recipes_df.show(5)\n",
    "# Please forward the exact name of data frames and columns as suggested in the code. \n",
    "# It will ensure that the assert commands function correctly.  "
   ]
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      "root\n",
      " |-- name: string (nullable = true)\n",
      " |-- id: integer (nullable = true)\n",
      " |-- minutes: integer (nullable = true)\n",
      " |-- contributor_id: integer (nullable = true)\n",
      " |-- submitted: timestamp (nullable = true)\n",
      " |-- tags: string (nullable = true)\n",
      " |-- nutrition: string (nullable = true)\n",
      " |-- n_steps: integer (nullable = true)\n",
      " |-- steps: string (nullable = true)\n",
      " |-- description: string (nullable = true)\n",
      " |-- ingredients: string (nullable = true)\n",
      " |-- n_ingredients: integer (nullable = true)"
     ]
    }
   ],
   "source": [
    "raw_recipes_df.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cc19288",
   "metadata": {},
   "source": [
    "### Test cases for Task 01"
   ]
  },
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    "# Code check cells\n",
    "# Do not edit cells with assert commands\n",
    "# If an error is shown after running this cell, please recheck your code.  \n",
    "\n",
    "assert raw_recipes_df.count() == 231637\n",
    "assert len(raw_recipes_df.columns) == 12\n",
    "assert raw_recipes_df.schema[\"minutes\"].dataType == IntegerType()\n",
    "assert raw_recipes_df.schema[\"tags\"].dataType == StringType()\n",
    "assert raw_recipes_df.schema[\"n_ingredients\"].dataType == IntegerType()"
   ]
  },
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    "assert raw_recipes_df.count() == 231637"
   ]
  },
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   "execution_count": 10,
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    "assert len(raw_recipes_df.columns) == 12"
   ]
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    "assert raw_recipes_df.schema[\"minutes\"].dataType == IntegerType()"
   ]
  },
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   "execution_count": 12,
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   "source": [
    "assert raw_recipes_df.schema[\"tags\"].dataType == StringType()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
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   "source": [
    "assert raw_recipes_df.schema[\"n_ingredients\"].dataType == IntegerType()"
   ]
  },
  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "#### Task 1 Completed\n"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "## Extract nutrition values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
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   "source": [
    "# List of nutrition columns\n",
    "\n",
    "nutrition_column_names = ['calories',\n",
    "                          'total_fat_PDV',\n",
    "                          'sugar_PDV',\n",
    "                          'sodium_PDV',\n",
    "                          'protein_PDV',\n",
    "                          'saturated_fat_PDV',\n",
    "                          'carbohydrates_PDV']"
   ]
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   "source": [
    "## Task 02: Extract individual features from the nutrition column\n"
   ]
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   "source": [
    "As read by the spark compiler, the nutrition column is a string column when it should be an array of float values. Each row in the nutrition column contains seven values. Each value represents nutrition information. Your task is to separate the array into seven individual columns.\n",
    "\n",
    "Write a code that takes in the nutrition column from raw_recipes_df dataframe, and extracts individual values into seven different columns named calories, total fat (PDV), sugar (PDV), sodium (PDV), protein (PDV), saturated fat (PDV), and carbohydrates (PDV)."
   ]
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   "source": [
    "#### Solution to Task 02"
   ]
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      "+--------------------+\n",
      "|           nutrition|\n",
      "+--------------------+\n",
      "|[51.5, 0.0, 13.0,...|\n",
      "|[173.4, 18.0, 0.0...|\n",
      "|[269.8, 22.0, 32....|\n",
      "|[368.1, 17.0, 10....|\n",
      "|[352.9, 1.0, 337....|\n",
      "+--------------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "# Task 02 Cell 1 out of 2\n",
    "# 2.1 - string operations to remove square brakets\n",
    "\n",
    "raw_recipes_df.select('nutrition').show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "694a64d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5603a9d6964140caa9727f355543e505",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# STEP 2.1 string operations to remove square brakets in 'nutrition' column\n",
    "\n",
    "raw_recipes_df= (raw_recipes_df\n",
    "                  .withColumn('nutrition',(F.regexp_replace(\"nutrition\",\"[\\[\\]]\",\"\"))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fdda8689",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b74316d00dd24117925f6e4723cbf04c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Task 02 Cell 2 out of 3\n",
    "# STEP 2.2 - split the neutrition string into seven individial values. \n",
    "# Create an object to split the nutrition column\n",
    "import pyspark\n",
    "nutrition_cols_split = pyspark.sql.functions.split(raw_recipes_df['nutrition'],',')\n",
    "\n",
    "# Write a loop to extract individual values from the nutrition column\n",
    "\n",
    "for col_index, col_name in enumerate(nutrition_column_names):\n",
    "    # col_index holds the index number of each column, e.g., calories will be 0\n",
    "    # col_name holds the name of each column \n",
    "    \n",
    "    raw_recipes_df = (raw_recipes_df.withColumn(col_name, nutrition_cols_split.getItem(col_index).cast(\"float\") \n",
    "                                        # pyspark function to extract individual values from the nutrition_cols_split object\n",
    "                                        # You can also cast the extracted value to floats in the same code. \n",
    "                                               ))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "314d70a1",
   "metadata": {},
   "source": [
    "### Test cases for task 02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c0fcbf0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6b29d5bc6d7a4ed3952f1fb1188d9699",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code check cell\n",
    "# Do not edit cells with assert commands\n",
    "# If an error is shown after running this cell, please recheck your code.  \n",
    "\n",
    "assert raw_recipes_df.schema[\"carbohydrates_PDV\"].dataType == FloatType(), \"Recheck your typecasting\"\n",
    "assert raw_recipes_df.collect()[123432][14] == 62.0, \"The columns have not been split correctly.\"\n",
    "assert raw_recipes_df.collect()[10000][12] == 60.400001525878906, \"The columns have not been split correctly.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dcc41925",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2c7017a2416b4d1aad04d3a3d769361a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "assert raw_recipes_df.schema[\"carbohydrates_PDV\"].dataType == FloatType()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8a8ce28a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f0c2550424df488b8187962aa76d7d68",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "assert raw_recipes_df.collect()[123432][14] == 62.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b8c58af9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "524f010955864d3cb8029a7a663e31d7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "assert raw_recipes_df.collect()[10000][12] == 60.400001525878906"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44756b7b",
   "metadata": {},
   "source": [
    "### Task 2 Completed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04f9bd61",
   "metadata": {},
   "source": [
    "## Make nutrition-per-100 calorie columns\n",
    "\n",
    "By converting the nutrition values from absolute to relative terms, we ensure that portion size is not a factor in the analysis.\n",
    "\n",
    "Naming convention: Original column name total fat (PDV), column name after column total_fat_per_100_cal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb13b9ce",
   "metadata": {},
   "source": [
    "## Task 03: Standardize the nutrition values\n",
    "\n",
    "The current values for nutrition columns are not on the same scale. Your task is to standardize the nutrition columns using calories as the base of standardization.\n",
    "\n",
    "Convert the nutrition from absolute values to per 100 calorie values.\n",
    "\n",
    "We will use the sugar (PDV) column to demonstrate the calculations for standardization."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9d89fe7",
   "metadata": {},
   "source": [
    "#### Sample Calculation\n",
    "Before transformation: sugar (PDV) for recipe id 137739 = 13.0\n",
    "\n",
    "Calories in the recipe recipe id 137739 = 51.5\n",
    "\n",
    "Calculation:\n",
    "\n",
    "sugar_per_100_cal = 13.0 * 100 / 51.5\n",
    "\n",
    "After transformation sugar_per_100_cal = 25.24"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de87f13a",
   "metadata": {},
   "source": [
    "#### Solution to Task 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "00da7d9d",
   "metadata": {},
   "outputs": [
    {
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       "model_id": "a6028e82e1164ccda63621a4dd440e74",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "# Task 03 Cell 1 out of 1\n",
    "\n",
    "for nutrition_col in nutrition_column_names:# loop over each of the newly created nutrition columns \n",
    "    if nutrition_col != \"calories\":\n",
    "        # the calories column should not be a part of the transformation exercise\n",
    "        # following code will name the new columns \n",
    "        nutrition_per_100_cal_col = (nutrition_col\n",
    "                                 .replace('_PDV','')\n",
    "                                 +'_per_100_cal')\n",
    "        raw_recipes_df = raw_recipes_df.withColumn(nutrition_per_100_cal_col,\n",
    "                                               raw_recipes_df[nutrition_col]*100/raw_recipes_df[\"calories\"]\n",
    "                                                # pyspark code to recreate the intended transformation \n",
    "                                                  )\n",
    "        \n",
    "        # You might end up adding nulls to the data because of our intended transformation. \n",
    "        # Perform a fill na operation to fill all the nulls with 0s. \n",
    "        # You must limit the scope of the fill na to the current column only. \n",
    "        \n",
    "        raw_recipes_df = raw_recipes_df.fillna(value=0,subset=[nutrition_per_100_cal_col]) \n",
    "        # pyspark code to fill nulls with 0 in only the current nutrition_per_100_cal_col    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b04ebe5e",
   "metadata": {},
   "source": [
    "#### Test cases for Task 03"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "96e3d206",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e36257fb886f4165bd2efc96d33490ba",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
       "version_minor": 0
      },
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# total fat check for id 28881\n",
    "assert raw_recipes_df.filter(\"id == 28881\").select('total_fat_per_100_cal').first()[0] == 0, \"total_fat_per_100_cal for recipe 28881 should be 0\"\n",
    "\n",
    "# total fat check for id 112140\n",
    "assert round(raw_recipes_df.filter(\"id == 112140\").select('total_fat_per_100_cal').first()[0]) == 8, \"total_fat_per_100_cal for recipe 112140 should be 8\"\n",
    "\n",
    "# checking for nulls\n",
    "for c in ['total_fat_per_100_cal','sugar_per_100_cal','sodium_per_100_cal','protein_per_100_cal',\n",
    "                          'saturated_fat_per_100_cal','carbohydrates_per_100_cal']:\n",
    "    assert raw_recipes_df.select(F.count(F.when(F.isnan(c) | F.col(c).isNull(), c)).alias(c)).collect()[0][0] == 0, \"There are Nulls in the data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9d516c3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e414f57e2c6c4cdebb55d5b2101f1952",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True"
     ]
    }
   ],
   "source": [
    "raw_recipes_df.filter(\"id == 28881\").select('total_fat_per_100_cal').first()[0] == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5fa56d93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6e00593c344e443380477663038e9baa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True"
     ]
    }
   ],
   "source": [
    "round(raw_recipes_df.filter(\"id == 112140\").select('total_fat_per_100_cal').first()[0]) == 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0966c4c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7e2abca911eb44f08c6f9fc8ddf6d5db",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for c in ['total_fat_per_100_cal','sugar_per_100_cal','sodium_per_100_cal','protein_per_100_cal',\n",
    "                          'saturated_fat_per_100_cal','carbohydrates_per_100_cal']:\n",
    "    assert raw_recipes_df.select(F.count(F.when(F.isnan(c) | F.col(c).isNull(), c)).alias(c)).collect()[0][0] == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fbb41925",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6e20ffeef1844bffa115e6b16c951b80",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- name: string (nullable = true)\n",
      " |-- id: integer (nullable = true)\n",
      " |-- minutes: integer (nullable = true)\n",
      " |-- contributor_id: integer (nullable = true)\n",
      " |-- submitted: timestamp (nullable = true)\n",
      " |-- tags: string (nullable = true)\n",
      " |-- nutrition: string (nullable = true)\n",
      " |-- n_steps: integer (nullable = true)\n",
      " |-- steps: string (nullable = true)\n",
      " |-- description: string (nullable = true)\n",
      " |-- ingredients: string (nullable = true)\n",
      " |-- n_ingredients: integer (nullable = true)\n",
      " |-- calories: float (nullable = true)\n",
      " |-- total_fat_PDV: float (nullable = true)\n",
      " |-- sugar_PDV: float (nullable = true)\n",
      " |-- sodium_PDV: float (nullable = true)\n",
      " |-- protein_PDV: float (nullable = true)\n",
      " |-- saturated_fat_PDV: float (nullable = true)\n",
      " |-- carbohydrates_PDV: float (nullable = true)\n",
      " |-- total_fat_per_100_cal: double (nullable = false)\n",
      " |-- sugar_per_100_cal: double (nullable = false)\n",
      " |-- sodium_per_100_cal: double (nullable = false)\n",
      " |-- protein_per_100_cal: double (nullable = false)\n",
      " |-- saturated_fat_per_100_cal: double (nullable = false)\n",
      " |-- carbohydrates_per_100_cal: double (nullable = false)"
     ]
    }
   ],
   "source": [
    "raw_recipes_df.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79fb2182",
   "metadata": {},
   "source": [
    "#### Task 3 Completed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8909a29",
   "metadata": {},
   "source": [
    "### Task 04: Convert the tags column from a string to an array of strings\n",
    "\n",
    " 1. Currently, the tags column is a string column but holds an array of strings.\n",
    "\n",
    " 2. Your task is to convert the tags columns from a string to an array of strings.\n",
    "\n",
    " 3. Remove [ ] ' punctuation marks from the tags column. Split the tags column based on the comma delimiter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d9d3b4c",
   "metadata": {},
   "source": [
    "#### Solution to Task 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1799bf1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e07943d36dc447ec94ef3e77c0d4901a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyspark.sql.types import ArrayType, StringType\n",
    "\n",
    "\n",
    "raw_recipes_df= (raw_recipes_df\n",
    "                    .withColumn(\"tags\", F.concat_ws(\",\", \"tags\"))\n",
    "                    .withColumn(\"tags\", F.regexp_replace(\"tags\", \"[\\\\[\\\\]']\", \"\"))\n",
    "                    .withColumn(\"tags\", F.split(\"tags\", \", \").cast(ArrayType(StringType())))\n",
    "                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "30d1c885",
   "metadata": {},
   "outputs": [
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- name: string (nullable = true)\n",
      " |-- id: integer (nullable = true)\n",
      " |-- minutes: integer (nullable = true)\n",
      " |-- contributor_id: integer (nullable = true)\n",
      " |-- submitted: timestamp (nullable = true)\n",
      " |-- tags: array (nullable = false)\n",
      " |    |-- element: string (containsNull = true)\n",
      " |-- nutrition: string (nullable = true)\n",
      " |-- n_steps: integer (nullable = true)\n",
      " |-- steps: string (nullable = true)\n",
      " |-- description: string (nullable = true)\n",
      " |-- ingredients: string (nullable = true)\n",
      " |-- n_ingredients: integer (nullable = true)\n",
      " |-- calories: float (nullable = true)\n",
      " |-- total_fat_PDV: float (nullable = true)\n",
      " |-- sugar_PDV: float (nullable = true)\n",
      " |-- sodium_PDV: float (nullable = true)\n",
      " |-- protein_PDV: float (nullable = true)\n",
      " |-- saturated_fat_PDV: float (nullable = true)\n",
      " |-- carbohydrates_PDV: float (nullable = true)\n",
      " |-- total_fat_per_100_cal: double (nullable = false)\n",
      " |-- sugar_per_100_cal: double (nullable = false)\n",
      " |-- sodium_per_100_cal: double (nullable = false)\n",
      " |-- protein_per_100_cal: double (nullable = false)\n",
      " |-- saturated_fat_per_100_cal: double (nullable = false)\n",
      " |-- carbohydrates_per_100_cal: double (nullable = false)"
     ]
    }
   ],
   "source": [
    "raw_recipes_df.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2c2380b",
   "metadata": {},
   "source": [
    "#### Test cases for Task 04"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0fcd5832",
   "metadata": {},
   "outputs": [
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    },
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   ],
   "source": [
    "# Code check cell\n",
    "# Do not edit cells with assert commands\n",
    "# If an error is shown after running this cell, please recheck your code.  \n",
    "\n",
    "assert raw_recipes_df.schema[\"tags\"].dataType == ArrayType(StringType(), True), \"You have not split the string into an array.\"\n",
    "assert raw_recipes_df.collect()[2][5] == ['time-to-make','course', 'preparation', 'main-dish', 'chili', 'crock-pot-slow-cooker', 'dietary', 'equipment', '4-hours-or-less'], \"Recheck your string cleaning and splitting operations.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3b9c9c6e",
   "metadata": {},
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True"
     ]
    }
   ],
   "source": [
    "raw_recipes_df.schema[\"tags\"].dataType == ArrayType(StringType(), True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d156a392",
   "metadata": {},
   "outputs": [
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       "VBox()"
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     },
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    },
    {
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     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True"
     ]
    }
   ],
   "source": [
    "raw_recipes_df.collect()[2][5] == ['time-to-make','course', 'preparation', 'main-dish', 'chili', 'crock-pot-slow-cooker', 'dietary', 'equipment', '4-hours-or-less']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7adf0733",
   "metadata": {},
   "source": [
    "#### Task 4 Completed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60837438",
   "metadata": {},
   "source": [
    "### Join Recipe Data to Review Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b39fd13f",
   "metadata": {},
   "outputs": [
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     },
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    },
    {
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   ],
   "source": [
    "# Reading the second data set. \n",
    "# keep this cell unedited\n",
    "\n",
    "raw_ratings_df = (spark.read.csv(\"s3://sdemo2024/data/RAW_interactions_cleaned.csv\", \n",
    "                                 header=True, \n",
    "                                 inferSchema= True)\n",
    "                  .withColumn(\"review_date\",  F.col(\"date\"))\n",
    "                  .drop(F.col(\"date\"))\n",
    "                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "756de0f0",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- user_id: integer (nullable = true)\n",
      " |-- recipe_id: integer (nullable = true)\n",
      " |-- rating: integer (nullable = true)\n",
      " |-- review: string (nullable = true)\n",
      " |-- review_date: timestamp (nullable = true)"
     ]
    }
   ],
   "source": [
    "raw_ratings_df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7f15abfc",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "# Code check cell\n",
    "# Do not edit cells with assert commands\n",
    "# If an error is shown after running this cell, please recheck your code.  \n",
    "\n",
    "assert raw_ratings_df.count() == 1132367, \"There is a mistake in reading the data.\"\n",
    "assert len(raw_ratings_df.columns) == 5, \"There is a mistake in reading the data.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5a26511b",
   "metadata": {},
   "outputs": [
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+---------+------+--------------------+-------------------+\n",
      "|user_id|recipe_id|rating|              review|        review_date|\n",
      "+-------+---------+------+--------------------+-------------------+\n",
      "|  38094|    40893|     4|Great with a sala...|2003-02-17 00:00:00|\n",
      "|1293707|    40893|     5|So simple  so del...|2011-12-21 00:00:00|\n",
      "|   8937|    44394|     4|This worked very ...|2002-12-01 00:00:00|\n",
      "| 126440|    85009|     5|I made the Mexica...|2010-02-27 00:00:00|\n",
      "|  57222|    85009|     5|Made the cheddar ...|2011-10-01 00:00:00|\n",
      "+-------+---------+------+--------------------+-------------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "raw_ratings_df.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a52656c",
   "metadata": {},
   "source": [
    "### Task 05: Read the second data file\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e4c6d54",
   "metadata": {},
   "source": [
    "Along with raw recipes data, we also have raw ratings data.\n",
    "\n",
    "The code to read the data is already written above. Your task is to join the raw ratings and raw recipes data.\n",
    "\n",
    "The resulting dataframe must have the same number of rows as in the raw ratings data.\n",
    "\n",
    "Join both the dataframes using the recipie IDs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e14c864",
   "metadata": {},
   "source": [
    "###### Calculation explanation\n",
    "There are 25 columns in the raw_recipes_df and five in the raw_ratings_df. So total columns in the combined dataframe 25 + 5 = 30\n",
    "\n",
    "The number of rows in the combined dataframe must be the same as the rows in the raw_ratings_df. So total rows in combined dataframe 1132367\n",
    "\n",
    "We have included some test cases given below. You can use them to check if you have completed the task correctly."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "474e121d",
   "metadata": {},
   "source": [
    "### Solution to Task 05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b316d07e",
   "metadata": {},
   "outputs": [
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       "VBox()"
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    {
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   ],
   "source": [
    "# Task 05 Cell 1 out of 1\n",
    "\n",
    "interaction_level_df = raw_ratings_df.join(raw_recipes_df,raw_ratings_df.recipe_id==raw_recipes_df.id,\"inner\"\n",
    "                                           # add the key on which the join should happen\n",
    "                                           # mention the type of join expected. \n",
    "                                           )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "348b3000",
   "metadata": {},
   "source": [
    "### Test cases for Task 05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a1ffe1f5",
   "metadata": {},
   "outputs": [
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     },
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    },
    {
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   ],
   "source": [
    "# Code check cell\n",
    "# Do not edit cells with assert commands\n",
    "# If an error is shown after running this cell, please recheck your code.  \n",
    "\n",
    "assert (interaction_level_df.count() ,len(interaction_level_df.columns)) == (1132367, 30), \"The type of join is incorrect\"\n",
    "\n",
    "list1 = raw_ratings_df.select('recipe_id').collect()\n",
    "list2 = raw_recipes_df.select('id').collect()\n",
    "exclusive_set = set(list1)-set(list2)\n",
    "\n",
    "assert len(exclusive_set) == 0, \"There is a mistake in reading one of the two data files.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "522e5ad1",
   "metadata": {},
   "outputs": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True"
     ]
    }
   ],
   "source": [
    "(interaction_level_df.count() ,len(interaction_level_df.columns)) == (1132367, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "52a5905c",
   "metadata": {},
   "outputs": [
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       "version_major": 2,
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     },
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    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True"
     ]
    }
   ],
   "source": [
    "len(exclusive_set) == 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7d58683",
   "metadata": {},
   "source": [
    "### Task 5 Completed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d632e735",
   "metadata": {},
   "source": [
    "### Task 6: Create time-based features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "617f6895",
   "metadata": {},
   "source": [
    "Currently, both the date columns, the submitted date, and the review date are in string forms.\n",
    "\n",
    "First convert the submitted and review_date to DateType()\n",
    "\n",
    "Use review date and submission date to derive new features:\n",
    "\n",
    "days_since_submission_on_review_date Number of days between the recipe submission and the current review.\n",
    "months_since_submission_on_review_date Number of months between the recipe submission and the current review.\n",
    "years_since_submission_on_review_dateNumber of years between the recipe submission and the current review."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "717790b9",
   "metadata": {},
   "source": [
    "#### Sample Calculation\n",
    "Recipe 40893 was submitted on 2002-09-21 User 38094 reviewed recipe 40893 on 2003-02-17\n",
    "\n",
    "days_since_submission_on_review_date number of calender days between 2002-09-21 and 2003-02-17 that is 149\n",
    "\n",
    "months_since_submission_on_review_date number of calender months between 2002-09-21 and 2003-02-17 that is 4.87 (calculated by a pyspark function)\n",
    "\n",
    "years_since_submission_on_review_date number of calender months divided by 12 that is 0.40"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63a6cd6b",
   "metadata": {},
   "source": [
    "### Solution to Task 06"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b4d0198a",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "# Task 06 Cell 1 out of 2\n",
    "\n",
    "interaction_level_df = (interaction_level_df\n",
    "                        .withColumn('submitted',F.col(\"submitted\").cast(\"date\") # pyspark function to cast a column to DateType()\n",
    "                                   )\n",
    "                        .withColumn('review_date',F.col(\"review_date\").cast(\"date\") # pyspark function to cast a column to DateType()\n",
    "                                   )\n",
    "                                             \n",
    "                       )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d100df58",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "42c23956fc5b4aea9bcdd398e7b01b5f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
       "version_minor": 0
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interaction_level_df = (interaction_level_df\n",
    "                        .withColumn('days_since_submission_on_review_date',F.datediff(\"review_date\",\"submitted\")\n",
    "                                     # Pyspark function to find the number of days between two dates              \n",
    "                                   )\n",
    "                        .withColumn('months_since_submission_on_review_date',F.months_between(\"review_date\",\"submitted\")\n",
    "                                     # Pyspark function to find the number of months between two dates          \n",
    "                                   )\n",
    "                        .withColumn('years_since_submission_on_review_date',F.months_between(\"review_date\",\"submitted\")/12\n",
    "                                     # Pyspark function to find the number of months between two dates / 12          \n",
    "                                   )\n",
    "                         )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d633fb10",
   "metadata": {},
   "source": [
    "### Test cases for Task 06"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e1937c21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "41eb2d28e906491d8afd4ad00c8efc70",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
       "version_minor": 0
      },
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code check cell\n",
    "# Do not edit cells with assert commands\n",
    "# If an error is shown after running this cell, please recheck your code.  \n",
    "\n",
    "assert interaction_level_df.schema[\"days_since_submission_on_review_date\"].dataType == IntegerType()\n",
    "\n",
    "assert (interaction_level_df.filter((interaction_level_df.user_id == 428885) & (interaction_level_df.recipe_id == 335241))\n",
    "                            .select('days_since_submission_on_review_date').collect()[0][0]) == 77\n",
    "assert (interaction_level_df.filter((interaction_level_df.user_id == 2025676) & (interaction_level_df.recipe_id == 94265))\n",
    "                            .select('months_since_submission_on_review_date').collect()[0][0]) == 153.22580645\n",
    "assert (interaction_level_df.filter((interaction_level_df.user_id == 338588) & (interaction_level_df.recipe_id == 21859))\n",
    "                            .select('years_since_submission_on_review_date').collect()[0][0]) == 4.564516129166667"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "807d9fa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "728f6d59db7e473693723ec181547c08",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True"
     ]
    }
   ],
   "source": [
    "interaction_level_df.schema[\"days_since_submission_on_review_date\"].dataType == IntegerType()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "54948b46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ece70746b13d41d69efe8503292c44e2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True"
     ]
    }
   ],
   "source": [
    "(interaction_level_df.filter((interaction_level_df.user_id == 428885) & (interaction_level_df.recipe_id == 335241))\n",
    "                            .select('days_since_submission_on_review_date').collect()[0][0]) == 77"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "58de2c0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f68e4f15c6aa4539b1ea1319bb1f918d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True"
     ]
    }
   ],
   "source": [
    "(interaction_level_df.filter((interaction_level_df.user_id == 2025676) & (interaction_level_df.recipe_id == 94265))\n",
    "                            .select('months_since_submission_on_review_date').collect()[0][0]) == 153.22580645"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e41ce7c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0c320f3516b54d66a891199ae797a18b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True"
     ]
    }
   ],
   "source": [
    "(interaction_level_df.filter((interaction_level_df.user_id == 338588) & (interaction_level_df.recipe_id == 21859))\n",
    "                            .select('years_since_submission_on_review_date').collect()[0][0]) == 4.564516129166667"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50c8c6a4",
   "metadata": {},
   "source": [
    "### Task 6 Completed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75391d4b",
   "metadata": {},
   "source": [
    "### Save the data we have created so far in a parquet file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4d9bcf77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a311309ea5ea4ff8b3a8660df2f05cb9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- user_id: integer (nullable = true)\n",
      " |-- recipe_id: integer (nullable = true)\n",
      " |-- rating: integer (nullable = true)\n",
      " |-- review: string (nullable = true)\n",
      " |-- review_date: date (nullable = true)\n",
      " |-- name: string (nullable = true)\n",
      " |-- id: integer (nullable = true)\n",
      " |-- minutes: integer (nullable = true)\n",
      " |-- contributor_id: integer (nullable = true)\n",
      " |-- submitted: date (nullable = true)\n",
      " |-- tags: array (nullable = false)\n",
      " |    |-- element: string (containsNull = true)\n",
      " |-- nutrition: string (nullable = true)\n",
      " |-- n_steps: integer (nullable = true)\n",
      " |-- steps: string (nullable = true)\n",
      " |-- description: string (nullable = true)\n",
      " |-- ingredients: string (nullable = true)\n",
      " |-- n_ingredients: integer (nullable = true)\n",
      " |-- calories: float (nullable = true)\n",
      " |-- total_fat_PDV: float (nullable = true)\n",
      " |-- sugar_PDV: float (nullable = true)\n",
      " |-- sodium_PDV: float (nullable = true)\n",
      " |-- protein_PDV: float (nullable = true)\n",
      " |-- saturated_fat_PDV: float (nullable = true)\n",
      " |-- carbohydrates_PDV: float (nullable = true)\n",
      " |-- total_fat_per_100_cal: double (nullable = false)\n",
      " |-- sugar_per_100_cal: double (nullable = false)\n",
      " |-- sodium_per_100_cal: double (nullable = false)\n",
      " |-- protein_per_100_cal: double (nullable = false)\n",
      " |-- saturated_fat_per_100_cal: double (nullable = false)\n",
      " |-- carbohydrates_per_100_cal: double (nullable = false)\n",
      " |-- days_since_submission_on_review_date: integer (nullable = true)\n",
      " |-- months_since_submission_on_review_date: double (nullable = true)\n",
      " |-- years_since_submission_on_review_date: double (nullable = true)"
     ]
    }
   ],
   "source": [
    "interaction_level_df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8bf8f89e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "491a461e8d744ce3884fbc047c814679",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "assert (interaction_level_df.count() ,len(interaction_level_df.columns) ) == (1132367, 33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "66041dde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a2b79eea74cf4d729dbe17caca858f13",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True"
     ]
    }
   ],
   "source": [
    "(interaction_level_df.count() ,len(interaction_level_df.columns) ) == (1132367, 33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8895fe2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b68cb2d8b50742f4a410cc96910cb216",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Write the raw_recipes_df\n",
    "## create a folder named data in you current directry before running this. \n",
    "\n",
    "from pyspark.sql import SparkSession\n",
    "interaction_level_df.write.parquet(\"interaction_level_df\") # Modify the path as you need"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2652479d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8a2cfbaa3666440085e5e614173c0f63",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+---------+------+--------------------+-----------+--------------------+---+-------+--------------+----------+--------------------+--------------------+-------+--------------------+--------------------+--------------------+-------------+--------+-------------+---------+----------+-----------+-----------------+-----------------+---------------------+-------------------+------------------+-------------------+-------------------------+-------------------------+------------------------------------+--------------------------------------+-------------------------------------+\n",
      "|user_id|recipe_id|rating|              review|review_date|                name| id|minutes|contributor_id| submitted|                tags|           nutrition|n_steps|               steps|         description|         ingredients|n_ingredients|calories|total_fat_PDV|sugar_PDV|sodium_PDV|protein_PDV|saturated_fat_PDV|carbohydrates_PDV|total_fat_per_100_cal|  sugar_per_100_cal|sodium_per_100_cal|protein_per_100_cal|saturated_fat_per_100_cal|carbohydrates_per_100_cal|days_since_submission_on_review_date|months_since_submission_on_review_date|years_since_submission_on_review_date|\n",
      "+-------+---------+------+--------------------+-----------+--------------------+---+-------+--------------+----------+--------------------+--------------------+-------+--------------------+--------------------+--------------------+-------------+--------+-------------+---------+----------+-----------+-----------------+-----------------+---------------------+-------------------+------------------+-------------------+-------------------------+-------------------------+------------------------------------+--------------------------------------+-------------------------------------+\n",
      "| 269153|       81|     5|This is FANTASTIC...| 2006-03-09|all purpose quick...| 81|     40|        125579|1999-09-10|[60-minutes-or-le...|529.3, 37.0, 1.0,...|     36|['blend flours , ...|--adopted recipe-...|['whole wheat flo...|            5|   529.3|         37.0|      1.0|      36.0|       22.0|             73.0|             23.0|   6.9903647937494835|0.18892877820944548|6.8014360155400375|  4.156433120607801|       13.791800809289521|        4.345361898817246|                                2372|                           77.96774194|                    6.497311828333333|\n",
      "| 985729|       81|     5|I have made sever...| 2010-05-23|all purpose quick...| 81|     40|        125579|1999-09-10|[60-minutes-or-le...|529.3, 37.0, 1.0,...|     36|['blend flours , ...|--adopted recipe-...|['whole wheat flo...|            5|   529.3|         37.0|      1.0|      36.0|       22.0|             73.0|             23.0|   6.9903647937494835|0.18892877820944548|6.8014360155400375|  4.156433120607801|       13.791800809289521|        4.345361898817246|                                3908|                          128.41935484|                   10.701612903333334|\n",
      "| 985823|      122|     5|Awesome is what c...| 2010-05-25|commissary carrot...|122|    240|          1589|1999-09-09|[weeknight, time-...|1011.8, 103.0, 32...|     34|['in a heavy sauc...|best in the world...|['sugar', 'flour'...|           18|  1011.8|        103.0|    320.0|      16.0|       14.0|            141.0|             33.0|   10.179877568952445| 31.626804097716334|1.5813402048858167| 1.3836726792750895|        13.93556055555626|       3.2615141725769967|                                3911|                          128.51612903|                   10.709677419166667|\n",
      "|1294602|      122|     5|I use this recipe...| 2010-07-12|commissary carrot...|122|    240|          1589|1999-09-09|[weeknight, time-...|1011.8, 103.0, 32...|     34|['in a heavy sauc...|best in the world...|['sugar', 'flour'...|           18|  1011.8|        103.0|    320.0|      16.0|       14.0|            141.0|             33.0|   10.179877568952445| 31.626804097716334|1.5813402048858167| 1.3836726792750895|        13.93556055555626|       3.2615141725769967|                                3959|                          130.09677419|                   10.841397849166666|\n",
      "| 851190|      126|     5|Dinner was extra ...| 2010-05-25|carnation lean fe...|126|     40|          1556|1999-08-14|[60-minutes-or-le...|535.7, 24.0, 9.0,...|      9|['in a large pot ...|                    |['fettuccine', 'o...|           13|   535.7|         24.0|      9.0|      24.0|       57.0|             37.0|             23.0|    4.480119367763754| 1.6800447629114077| 4.480119367763754| 10.640283498438915|        6.906850691969121|        4.293447727440264|                                3937|                          129.35483871|                        10.7795698925|\n",
      "+-------+---------+------+--------------------+-----------+--------------------+---+-------+--------------+----------+--------------------+--------------------+-------+--------------------+--------------------+--------------------+-------------+--------+-------------+---------+----------+-----------+-----------------+-----------------+---------------------+-------------------+------------------+-------------------+-------------------------+-------------------------+------------------------------------+--------------------------------------+-------------------------------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "interaction_level_df.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3378f958",
   "metadata": {},
   "source": [
    "## 1_FeatureExtractionPart01 Completed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13fd4d26",
   "metadata": {},
   "source": [
    "## 2_EDA-Complete_Solution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "764ad4a3",
   "metadata": {},
   "source": [
    "### Initial Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d676f41e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1137702db26f4b8299764c5d68504935",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyspark.sql import SparkSession"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "197899d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aa325e95887f41648214eb53ec3e2a1b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "spark = SparkSession.builder.appName(\"Basics\").getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e339c0b1",
   "metadata": {},
   "outputs": [
    {
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      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5e246c9160e74d9ba0bc1becbf97f9db",
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       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_minor": 0
      },
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<pyspark.sql.session.SparkSession object at 0x7fc0f5406650>"
     ]
    }
   ],
   "source": [
    "spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ac150936",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6fc62b46debb406cb4d6acb89d623b56",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting plotly==5.5.0\n",
      "  Downloading plotly-5.5.0-py2.py3-none-any.whl (26.5 MB)\n",
      "Collecting tenacity>=6.2.0\n",
      "  Downloading tenacity-8.2.3-py3-none-any.whl (24 kB)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.7/site-packages (from plotly==5.5.0) (1.13.0)\n",
      "Installing collected packages: tenacity, plotly\n",
      "Successfully installed plotly-5.5.0 tenacity-8.2.3\n",
      "\n",
      "Collecting pandas==1.0.5\n",
      "  Downloading pandas-1.0.5-cp37-cp37m-manylinux1_x86_64.whl (10.1 MB)\n",
      "Collecting python-dateutil>=2.6.1\n",
      "  Downloading python_dateutil-2.9.0.post0-py2.py3-none-any.whl (229 kB)\n",
      "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/site-packages (from pandas==1.0.5) (2022.7)\n",
      "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib64/python3.7/site-packages (from pandas==1.0.5) (1.20.0)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/site-packages (from python-dateutil>=2.6.1->pandas==1.0.5) (1.13.0)\n",
      "Installing collected packages: python-dateutil, pandas\n",
      "Successfully installed pandas-1.0.5 python-dateutil-2.9.0.post0\n",
      "\n",
      "Collecting numpy==1.14.5\n",
      "  Downloading numpy-1.14.5-cp37-cp37m-manylinux1_x86_64.whl (12.2 MB)\n",
      "Installing collected packages: numpy\n",
      "Successfully installed numpy-1.14.5\n",
      "\n",
      "Collecting matplotlib==3.1.1\n",
      "  Downloading matplotlib-3.1.1-cp37-cp37m-manylinux1_x86_64.whl (13.1 MB)\n",
      "Collecting cycler>=0.10\n",
      "  Downloading cycler-0.11.0-py3-none-any.whl (6.4 kB)\n",
      "Collecting pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1\n",
      "  Downloading pyparsing-3.1.2-py3-none-any.whl (103 kB)\n",
      "Requirement already satisfied: numpy>=1.11 in ./tmp/1712673514080-0/lib/python3.7/site-packages (from matplotlib==3.1.1) (1.14.5)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in ./tmp/1712673514080-0/lib/python3.7/site-packages (from matplotlib==3.1.1) (2.9.0.post0)\n",
      "Collecting kiwisolver>=1.0.1\n",
      "  Downloading kiwisolver-1.4.5-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.1 MB)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/site-packages (from python-dateutil>=2.1->matplotlib==3.1.1) (1.13.0)\n",
      "Collecting typing-extensions; python_version < \"3.8\"\n",
      "  Downloading typing_extensions-4.7.1-py3-none-any.whl (33 kB)\n",
      "Installing collected packages: cycler, pyparsing, typing-extensions, kiwisolver, matplotlib\n",
      "Successfully installed cycler-0.11.0 kiwisolver-1.4.5 matplotlib-3.1.1 pyparsing-3.1.2 typing-extensions-4.7.1\n",
      "\n",
      "Collecting pyarrow==1.0.0\n",
      "  Downloading pyarrow-1.0.0-cp37-cp37m-manylinux2014_x86_64.whl (17.2 MB)\n",
      "Requirement already satisfied: numpy>=1.14 in ./tmp/1712673514080-0/lib/python3.7/site-packages (from pyarrow==1.0.0) (1.14.5)\n",
      "Installing collected packages: pyarrow\n",
      "Successfully installed pyarrow-1.0.0\n",
      "\n",
      "WARNING: The directory '/home/.cache/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.\n",
      "\n",
      "WARNING: The directory '/home/.cache/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.\n",
      "\n",
      "WARNING: The directory '/home/.cache/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.\n",
      "ERROR: After October 2020 you may experience errors when installing or updating packages. This is because pip will change the way that it resolves dependency conflicts.\n",
      "\n",
      "We recommend you use --use-feature=2020-resolver to test your packages with the new resolver before it becomes the default.\n",
      "\n",
      "python37-sagemaker-pyspark 1.4.2 requires pyspark==2.4.0, which is not installed.\n",
      "\n",
      "WARNING: The directory '/home/.cache/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.\n",
      "\n",
      "WARNING: The directory '/home/.cache/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag."
     ]
    }
   ],
   "source": [
    "spark.sparkContext.install_pypi_package(\"plotly==5.5.0\")\n",
    "spark.sparkContext.install_pypi_package(\"pandas==1.0.5\")\n",
    "spark.sparkContext.install_pypi_package(\"numpy==1.14.5\")\n",
    "spark.sparkContext.install_pypi_package(\"matplotlib==3.1.1\")\n",
    "spark.sparkContext.install_pypi_package(\"pyarrow==1.0.0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d6a217df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "53f803512d2f4772944f2273e5bf10ff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyspark.sql import functions as F\n",
    "from pyspark.ml.feature import Bucketizer\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Import for typecasting columns\n",
    "from pyspark.sql.types import IntegerType,BooleanType,DateType,FloatType,StringType\n",
    "from pyspark.sql.types import ArrayType"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfca4630",
   "metadata": {},
   "source": [
    "## Defining Custom Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e3fb3130",
   "metadata": {},
   "outputs": [
    {
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       "model_id": "2d44df21d003403ebd04a1801456bab0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "version_minor": 0
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_quantiles(df, col_name, quantiles_list = [0.01, 0.25, 0.5, 0.75, 0.99]):\n",
    "    \"\"\"\n",
    "    Takes a numerical column and returns column values at requested quantiles\n",
    "\n",
    "    Inputs \n",
    "    Argument 1: Dataframe\n",
    "    Argument 2: Name of the column\n",
    "    Argument 3: A list of quantiles you want to find. Default value [0.01, 0.25, 0.5, 0.75, 0.99]\n",
    "\n",
    "    Output \n",
    "    Returns a dictionary with quantiles as keys and column quantile values as values \n",
    "    \"\"\"\n",
    "    # Get min, max and quantile values for given column\n",
    "    min_val = df.agg(F.min(col_name)).first()[0]\n",
    "    max_val = df.agg(F.max(col_name)).first()[0]\n",
    "    quantiles_vals = df.approxQuantile(col_name,\n",
    "                                       quantiles_list,\n",
    "                                       0)\n",
    "  \n",
    "    # Store min, quantiles and max in output dict, sequentially\n",
    "    quantiles_dict = {0.0:min_val}\n",
    "    quantiles_dict.update(dict(zip(quantiles_list, quantiles_vals)))\n",
    "    quantiles_dict.update({1.0:max_val})\n",
    "    return(quantiles_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e0cdc477",
   "metadata": {},
   "outputs": [
    {
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       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_bucketwise_statistics (summary, bucketizer):\n",
    "    \"\"\"\n",
    "    Takes in a dataframe and a bucketizer object and plots the summary statistics for each bucket in the dataframe. \n",
    "  \n",
    "    Inputs\n",
    "    Argument 1: Pandas dataframe obtained from bucket_col_print_summary function \n",
    "    Argument 2: Bucketizer object obtained from bucket_col_print_summary function\n",
    "  \n",
    "    Output\n",
    "    Displays a plot of bucketwise average ratings nunber of ratings of a parameter.   \n",
    "    \"\"\"\n",
    "    # Creating bucket labels from splits\n",
    "    classlist = bucketizer.getSplits()\n",
    "    number_of_classes = len(classlist) - 1\n",
    "\n",
    "    class_labels = []\n",
    "    hover_labels = []\n",
    "    for i in range (number_of_classes):\n",
    "        hover_labels.append(str(classlist[i])+\"-\"+str(classlist[i+1]) +\" (Bucket name: \"+ str(int(i)) +\")\"  )\n",
    "        class_labels.append(str(classlist[i])+\"-\"+str(classlist[i+1]) )\n",
    "  \n",
    "    summary[\"Scaled_number\"] = (summary[\"n_ratings\"]-summary[\"n_ratings\"].min())/(summary[\"n_ratings\"].max()-summary[\"n_ratings\"].min()) + 1.5\n",
    "    summary['Bucket_Names'] = class_labels\n",
    "  \n",
    "    # making plot\n",
    "    x = summary[\"Bucket_Names\"]\n",
    "    y1 = summary[\"avg_rating\"]\n",
    "    y2 = summary[\"n_ratings\"]\n",
    "    err = summary[\"stddev_rating\"]  \n",
    "\n",
    "    # Plot scatter here\n",
    "    plt.rcParams[\"figure.figsize\"] = [summary.shape[0]+2, 6.0]\n",
    "    plt.rcParams[\"figure.autolayout\"] = True\n",
    "    fig, ax1 = plt.subplots()\n",
    "\n",
    "    bar = ax1.bar(x, y1, color = \"#262261\")\n",
    "    ax1.errorbar(x, y1, yerr=err, fmt=\"o\", color=\"#EE4036\")\n",
    "    ax1.set(ylim=(0, 7))\n",
    "  \n",
    "    #ax1.bar_label(bar , fmt='%.2f', label_type='edge')  \n",
    "    def barlabel(x_list,y_list):\n",
    "        for i in range(len(x_list)):\n",
    "            ax1.text(i,y_list[i] + 0.2,y_list[i], ha = 'center',\n",
    "  \t\t\t         fontdict=dict(size=10),\n",
    "  \t\t\t         bbox=dict(facecolor='#262261', alpha=0.2)         \n",
    "  \t\t\t        )\n",
    "    barlabel(summary[\"Bucket_Names\"].tolist() ,summary[\"avg_rating\"].round(2).tolist())\n",
    "  \n",
    "    ax2 = ax1.twinx()\n",
    "    ax2.scatter(x, y2, s=summary[\"Scaled_number\"]*500, c = '#FAAF40')  \n",
    "    ax2.set(ylim=(0, summary[\"n_ratings\"].max()*1.15))\n",
    "    def scatterlabel(x_list,y_list):\n",
    "  \t    for i in range(len(x_list)):\n",
    "  \t\t    ax2.text(i,y_list[i] + 15000,y_list[i], ha = 'center',\n",
    "  \t\t\t\t\t fontdict=dict(size=10),\n",
    "                     bbox=dict(facecolor='#FAAF40', alpha=0.5)\n",
    "  \t\t\t\t\t)\n",
    "    scatterlabel(summary[\"Bucket_Names\"].tolist() ,summary[\"n_ratings\"].tolist())\n",
    "  \n",
    "    # giving labels to the axises\n",
    "    ax1.set_xlabel(bucketizer.getOutputCol(), fontdict=dict(size=14)) \n",
    "    ax1.set_ylabel(\"Average Ratings\",fontdict=dict(size=14))\n",
    "  \n",
    "    # secondary y-axis label\n",
    "    ax2.set_ylabel('Number of Ratings',fontdict=dict(size=14))\n",
    "  \n",
    "    #plot Title\n",
    "    plt.title('Bucketwise average ratings and number of ratings for \\n'+bucketizer.getInputCol(), \n",
    "              fontdict=dict(size=14))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "487a40eb",
   "metadata": {},
   "outputs": [
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       "VBox()"
      ]
     },
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    },
    {
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "def bucket_col_print_summary(df, splits, inputCol, outputCol):\n",
    "    \"\"\"\n",
    "    Given a numerical column in a data frame, adds a bucketized version of the column to the data frame, according to splits provided.\n",
    "    Also prints a summary of ratings seen in each bucket made.\n",
    "\n",
    "    Inputs \n",
    "    Argument 1: Data Frame \n",
    "    Argument 2: Values at which the column will be split\n",
    "    Argument 3: Name of the input column (numerical column)\n",
    "    Argument 4: Name of the output column (bucketized numerical column)\n",
    "\n",
    "    Output: \n",
    "    1) New dataframe with the output column added\n",
    "    2) Bucketizer object trained from the input column \n",
    "    3) Pandas dataframe with summary statistics for ratings seen in buckets of the output column\n",
    "    Also plots summary statistics for ratings seen in buckets of the output column\n",
    "    \"\"\"\n",
    "\n",
    "    # Dropping bucket if it already exists\n",
    "    if outputCol in df.columns:\n",
    "        df = df.drop(outputCol)\n",
    "\n",
    "    # Training bucketizer\n",
    "    bucketizer = Bucketizer(splits = splits,\n",
    "                            inputCol  = inputCol,\n",
    "                            outputCol = outputCol)\n",
    "    \n",
    "    df = bucketizer.setHandleInvalid(\"keep\").transform(df)\n",
    "\n",
    "    # Printing meta information on buckets created\n",
    "    print(\"Added bucketized column {}\".format(outputCol))\n",
    "    print(\"\")\n",
    "    print(\"Bucketing done for split definition: {}\".format(splits))\n",
    "    print(\"\")  \n",
    "    print(\"Printing summary statistics for ratings in buckets below:\")\n",
    "\n",
    "    # Creating a summary statistics dataframe and passing it to the plotting function\n",
    "    summary =  (df\n",
    "                .groupBy(outputCol)\n",
    "                .agg(F.avg('rating').alias('avg_rating'),\n",
    "                     F.stddev('rating').alias('stddev_rating'),\n",
    "                     F.count('rating').alias('n_ratings'))\n",
    "                .sort(outputCol)\n",
    "                .toPandas())\n",
    "  \n",
    "    plot_bucketwise_statistics(summary,bucketizer)\n",
    "  \n",
    "    return df, bucketizer, summary"
   ]
  },
  {
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   "execution_count": 61,
   "id": "b64a9b47",
   "metadata": {},
   "outputs": [
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       "VBox()"
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    },
    {
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   ],
   "source": [
    "def get_column_distribution_summary(df, col_name):\n",
    "    \"\"\"\n",
    "    Takes a column in a data frame and prints the summary statistics (average, standard deviation, count and distinct count) for all unique values in that column.\n",
    "  \n",
    "    Inputs \n",
    "    Argument 1: Dataframe \n",
    "    Argument 2: Name of the column\n",
    "  \n",
    "    Output\n",
    "    Returns nothing \n",
    "    Prints a Dataframe with summary statistics\n",
    "    \"\"\"\n",
    "    print(df\n",
    "          .groupBy(col_name)\n",
    "          .agg(F.avg('rating').alias('avg_rating'),\n",
    "               F.stddev('rating').alias('stddev_rating'),\n",
    "               F.count('rating').alias('n_ratings'),\n",
    "               F.countDistinct('id').alias('n_recipes'))\n",
    "          .sort(F.col(col_name).asc())\n",
    "          .show(50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "612fb645",
   "metadata": {},
   "outputs": [
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      },
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       "VBox()"
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   ],
   "source": [
    "def get_n_items_satisfying_condition (df, condition, aggregation_level = \"recipe\"):\n",
    "    \"\"\"\n",
    "    Given a condition, find the number of recipes / reviews that match the condition.\n",
    "    Also calculates the percentage of such recipes / reviews as a percentage of all recipes / reviews.\n",
    "  \n",
    "    Inputs \n",
    "    Argument 1: Dataframe \n",
    "    Argument 2: Logical expression describing a condition, string type. eg: \"minutes == 0\"\n",
    "    Argument 3: Aggregation level for determining \"items\", either  \"recipe\" or \"review\". Default value == \"recipe\"\n",
    "  \n",
    "    Output: Returns no object.\n",
    "    Prints the following:\n",
    "    1) Number of recipes / reviews that satisfy the condition\n",
    "    2) Total number of recipes / reviews in the dataframe\n",
    "    3) Percentage of recipes / reviews that satisfy the condition\n",
    "    \"\"\"\n",
    "    # Find out num rows satisfying the condition\n",
    "    if aggregation_level == \"recipe\": \n",
    "        number_of_rows_satisfying_condition = (df\n",
    "                                             .filter(condition)\n",
    "                                             .agg(F.countDistinct(\"id\"))).first()[0]\n",
    "      \n",
    "        n_rows_total = (df.agg(F.countDistinct(\"id\"))).first()[0]\n",
    "    if aggregation_level == \"review\":\n",
    "        number_of_rows_satisfying_condition = (df\n",
    "                                             .filter(condition)\n",
    "                                             .agg(F.countDistinct(\"id\",\"user_id\"))).first()[0]\n",
    "        n_rows_total = (df.agg(F.countDistinct(\"id\",\"user_id\"))).first()[0]\n",
    "  \n",
    "    # Find out % rows satisfying the conditon and print a properly formatted output\n",
    "    perc_rows = round(number_of_rows_satisfying_condition * 100/ n_rows_total, 2)\n",
    "    print('Condition String                   : \"{}\"'.format(condition))\n",
    "    print(\"Num {}s Satisfying Condition   : {} [{}%]\".format(aggregation_level.title(), number_of_rows_satisfying_condition, perc_rows))\n",
    "    print(\"Total Num {}s                  : {}\".format(aggregation_level.title(), n_rows_total))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9e5b5a3",
   "metadata": {},
   "source": [
    "### Read the data\n",
    "\n",
    "    . Read interaction_level_df_processed"
   ]
  },
  {
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   ],
   "source": [
    "interaction_level_df_processed = spark.read.parquet(\"interaction_level_df\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "702f5cdf",
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   "outputs": [
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   ],
   "source": [
    "# Code check cell\n",
    "# Do not edit cells with assert commands\n",
    "# If an error is shown after running this cell, please recheck your code.  \n",
    "\n",
    "assert interaction_level_df.count() == 1132367, \"There is a mistake in reading the data.\"\n",
    "assert len(interaction_level_df.columns) == 33, \"There is a mistake in reading the data.\""
   ]
  },
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   "id": "0418ef5f",
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     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+---------+------+--------------------+-----------+--------------------+---+-------+--------------+----------+--------------------+--------------------+-------+--------------------+--------------------+--------------------+-------------+--------+-------------+---------+----------+-----------+-----------------+-----------------+---------------------+-------------------+------------------+-------------------+-------------------------+-------------------------+------------------------------------+--------------------------------------+-------------------------------------+\n",
      "|user_id|recipe_id|rating|              review|review_date|                name| id|minutes|contributor_id| submitted|                tags|           nutrition|n_steps|               steps|         description|         ingredients|n_ingredients|calories|total_fat_PDV|sugar_PDV|sodium_PDV|protein_PDV|saturated_fat_PDV|carbohydrates_PDV|total_fat_per_100_cal|  sugar_per_100_cal|sodium_per_100_cal|protein_per_100_cal|saturated_fat_per_100_cal|carbohydrates_per_100_cal|days_since_submission_on_review_date|months_since_submission_on_review_date|years_since_submission_on_review_date|\n",
      "+-------+---------+------+--------------------+-----------+--------------------+---+-------+--------------+----------+--------------------+--------------------+-------+--------------------+--------------------+--------------------+-------------+--------+-------------+---------+----------+-----------+-----------------+-----------------+---------------------+-------------------+------------------+-------------------+-------------------------+-------------------------+------------------------------------+--------------------------------------+-------------------------------------+\n",
      "| 269153|       81|     5|This is FANTASTIC...| 2006-03-09|all purpose quick...| 81|     40|        125579|1999-09-10|[60-minutes-or-le...|529.3, 37.0, 1.0,...|     36|['blend flours , ...|--adopted recipe-...|['whole wheat flo...|            5|   529.3|         37.0|      1.0|      36.0|       22.0|             73.0|             23.0|   6.9903647937494835|0.18892877820944548|6.8014360155400375|  4.156433120607801|       13.791800809289521|        4.345361898817246|                                2372|                           77.96774194|                    6.497311828333333|\n",
      "| 985729|       81|     5|I have made sever...| 2010-05-23|all purpose quick...| 81|     40|        125579|1999-09-10|[60-minutes-or-le...|529.3, 37.0, 1.0,...|     36|['blend flours , ...|--adopted recipe-...|['whole wheat flo...|            5|   529.3|         37.0|      1.0|      36.0|       22.0|             73.0|             23.0|   6.9903647937494835|0.18892877820944548|6.8014360155400375|  4.156433120607801|       13.791800809289521|        4.345361898817246|                                3908|                          128.41935484|                   10.701612903333334|\n",
      "| 985823|      122|     5|Awesome is what c...| 2010-05-25|commissary carrot...|122|    240|          1589|1999-09-09|[weeknight, time-...|1011.8, 103.0, 32...|     34|['in a heavy sauc...|best in the world...|['sugar', 'flour'...|           18|  1011.8|        103.0|    320.0|      16.0|       14.0|            141.0|             33.0|   10.179877568952445| 31.626804097716334|1.5813402048858167| 1.3836726792750895|        13.93556055555626|       3.2615141725769967|                                3911|                          128.51612903|                   10.709677419166667|\n",
      "|1294602|      122|     5|I use this recipe...| 2010-07-12|commissary carrot...|122|    240|          1589|1999-09-09|[weeknight, time-...|1011.8, 103.0, 32...|     34|['in a heavy sauc...|best in the world...|['sugar', 'flour'...|           18|  1011.8|        103.0|    320.0|      16.0|       14.0|            141.0|             33.0|   10.179877568952445| 31.626804097716334|1.5813402048858167| 1.3836726792750895|        13.93556055555626|       3.2615141725769967|                                3959|                          130.09677419|                   10.841397849166666|\n",
      "| 851190|      126|     5|Dinner was extra ...| 2010-05-25|carnation lean fe...|126|     40|          1556|1999-08-14|[60-minutes-or-le...|535.7, 24.0, 9.0,...|      9|['in a large pot ...|                    |['fettuccine', 'o...|           13|   535.7|         24.0|      9.0|      24.0|       57.0|             37.0|             23.0|    4.480119367763754| 1.6800447629114077| 4.480119367763754| 10.640283498438915|        6.906850691969121|        4.293447727440264|                                3937|                          129.35483871|                        10.7795698925|\n",
      "+-------+---------+------+--------------------+-----------+--------------------+---+-------+--------------+----------+--------------------+--------------------+-------+--------------------+--------------------+--------------------+-------------+--------+-------------+---------+----------+-----------+-----------------+-----------------+---------------------+-------------------+------------------+-------------------+-------------------------+-------------------------+------------------------------------+--------------------------------------+-------------------------------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "interaction_level_df.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f254e5ac",
   "metadata": {},
   "source": [
    "### Bucketing and Cleaning Numerical Features\n",
    "\n",
    "1. years_since_submission_on_review_date\n",
    "[Review Time Since Submission]\n",
    "\n",
    "Recipes more than 6 years old are rated low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "2e10d40a",
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     "text": [
      "{0.0: -4.833333333333333, 0.01: 0.005376344166666667, 0.25: 0.8602150541666668, 0.5: 2.9489247308333333, 0.75: 5.844086021666667, 0.99: 14.626344085833333, 1.0: 19.284946236666666}"
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   ],
   "source": [
    "get_quantiles(df = interaction_level_df,\n",
    "             col_name = \"years_since_submission_on_review_date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d292d4c9",
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Condition String                   : \"years_since_submission_on_review_date < 0\"\n",
      "Num Reviews Satisfying Condition   : 1246 [0.11%]\n",
      "Total Num Reviews                  : 1132367"
     ]
    }
   ],
   "source": [
    "get_n_items_satisfying_condition(df = interaction_level_df,\n",
    "                                 condition= 'years_since_submission_on_review_date < 0',\n",
    "                                 aggregation_level= \"review\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "de573351",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "# Only keep interactions with review dates >= recipe submission date\n",
    "\n",
    "interaction_level_df = (interaction_level_df\n",
    "                        .filter('years_since_submission_on_review_date >= 0'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e70dfd38",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added bucketized column years_since_submission_on_review_date_bucket\n",
      "\n",
      "Bucketing done for split definition: [0, 1, 3, 6, inf]\n",
      "\n",
      "Printing summary statistics for ratings in buckets below:"
     ]
    }
   ],
   "source": [
    "splits = [ 0, 1, 3, 6, float('Inf')]\n",
    "inputCol  = \"years_since_submission_on_review_date\"\n",
    "outputCol = \"years_since_submission_on_review_date_bucket\"\n",
    "\n",
    "(interaction_level_df, submission_time_bucketizer, submission_time_pandas_df) = bucket_col_print_summary(df = interaction_level_df,\n",
    "                                                                              splits = splits,\n",
    "                                                                              inputCol  = inputCol,\n",
    "                                                                              outputCol = outputCol)"
   ]
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bKg0IqyMP1OPi4hTKq0+ApaWlxftRQvTqOBMKhdSuXTsurT7tpa+vT82bN6fi4uI6rWNtVNcOLVq0UPgBJZ/Wu3dvLi0+Pl7p/kr0an1VVVXrHGBJpdIqp02ePFmhPvUJsOLj43npFRUVpKampnTffPjwIQGggQMH8tLlAcbrgQIR0XfffUcAaOHChVyao6MjaWlpKQ1Grly5QgAoMjKSS5MHWJMmTVK6flX59ttvFX6cyp08ebLaL+66kK//5cuX6zTf+fPnCQDNmjVLobz6BljHjx9XmEc+LT8/n0tr3rw5iUQiSk9PV8j/ySefVBlg3b59u/Yr+Jqqzh2pqakEgNq0aaPwfVRRUUGtW7cmALwgub7n7w9RvW8RBgUF4dChQ9zfOTk5OHnyJCIiIuDl5YXjx4+jQ4cO9Sr71q1bKCgoQGBgYK0v3798+RIdO3bE2bNn8fvvv2PkyJEKeW7fvo3nz5/DzMwMs2fPVpielZXFLb82JBIJXFxckJiYiLKyMqioqCAhIQEDBgzg+qLFxsaidevWXP8BeXpVdHR00LVrVxw4cADt2rVD37594efnBzc3N95tLwBISkqClpaW0r4RAKChoVHrdbl//z7mzJmD48eP48mTJygpKeFNf/r0KaysrAAAtra2cHd3x6FDh5CdnQ1DQ0MAwIYNGwCAd3swOTkZFRUVKCkpUXoP/s6dOwBetXn37t1509zd3ZXWlYiwevVqrFmzBteuXUNeXh5kMhmvrnLy/jiv9wkDAC0tLTg7Oyv0M2vIdgUAJycn7pbg6zIzMzF37lwcPHgQqampePnyJW965XV5E7a2tmjSpAkvTX6rJDc3l0urT3uFhIRg2bJlsLe3R0hICPz9/eHp6QkNDY1a168+7eDs7AwVFX430mbNmgGA0nXy8fFRKMPKygoWFhZ1HobF1dVVIU3Zst/E690sVFRUYGxsjKKiIlhaWvKmyZ8cVdZOQqEQnp6eCuny9rh48SIAoKioCFevXoWZmRnmzZunkF/eJ0nZvl/VsVoV+TKV3V7z9PSEuro6Ll26VKcyq6Kurg4HBwel00pLS/Hrr79iy5YtuHXrFl68eAEi4qY31PEH1LzPaGtrIz8/HykpKWjTpg3vFrecl5cXDh8+zEsLCQnBzp074eHhgdDQUHTs2BE+Pj7ceflNyLeBr6+vQp83FRUVSKVS3Lp1C5cuXYKFhQVvel33iQ9Rg/XBMjAwwKeffgpNTU106tQJM2fOxJEjR+pVVl5eHoBXj73WVkFBAS5evAgDA4Mqg5hnz54BAK5fv47r169XWVZhYWGtl+vv749z584hOTkZampqyM/PR0BAANe5MjY2FqNHj0ZsbCw0NTVrtdNt374dP/74IzZt2oSvvvoKwKvAa/Dgwfjxxx+hqanJrU95ebnSYLEu63L37l24u7sjPz8f/v7+6NGjB3R0dKCiooK4uDjEx8crBFxhYWE4e/Ystm7dirFjx4KIsHHjRujp6aFbt25cPnmbnzx5EidPnqxTPZWdYABgwoQJ+PXXX2FhYYFPP/0UpqamEIvFAIDZs2fz6pqfnw8AMDY2VlqWsmU0VLvWtB7Pnj2Dm5sbHj58CC8vLwQGBkJXVxeqqqpcn4jX272+dHR0lKYLhUJep/36tNfixYvRokULrF69Gt9//z2+//57qKuro1+/fli0aFGNJ/r6toOydRIKX53SKq+T/HxS3TrVNcCq7bLfRFXLqG7ZlTtmyxkaGioEosD/bUt5+zx//hxEhCdPntR5369qH6+KfD9TNp9AIICJiQmePHlSpzKrYmxsXGWH+M8++wwxMTGwtbVF//79YWxsDDU1NeTm5mLx4sUNdvwBtdtn6nP89e3bF7t370Z0dDR+//13LF26FAKBAP7+/li0aNEb9YeubjsB/xfYy/PVVNf/mgbt5A6Au2qVnJzMSxcIBLwnQyrLy8vjdcyUdyavywFmbGyM5cuXo2fPnvDz80NsbCzs7Ox4eeQ7eJ8+ffDXX3/Vuuzq+Pv7cx08RSIR76kgf39/7Nu3Dy9evEBycjL8/PyqvJJRmaamJvdF9eDBA8TGxuL333/H4sWL8fLlSyxfvpxbH4FAoPDEZl399NNPeP78OdavX8+NbSY3atQo3lOPciEhIZg8eTI2bNiAsWPHIiEhAampqRg5ciQX7MjrCACRkZFKO8dWR9lJMTMzE0uXLoWjoyNOnz7NBZsAkJ6ervDFIF9+Zmam0mVkZGQopDVUu8pVdXL/888/8fDhQ3z33XeYOXMmb9rcuXOxZ8+eBll+XdSnvYRCIaZMmYIpU6bg6dOniI+Px+rVq7Fu3Tqkp6fj77//rnaZb7sd5OeWuqxTQ5EHNsrOffLA5m3Lzs6GTCZTCLLk6y1vH/m2d3V1xblz5+q0jLqOhC9fVkZGBndlXI6IkJGRUeWPgrqqqm7JycmIiYlBUFAQ9u/fz+uYn5SUhMWLFzfI8uuiPscfAAQHByM4OBgFBQU4efIk9yBQ586dcevWLe47tb71qWq56enpvHyVfahvR6iLBn9VjvwJusq3bABAT09PacCUkpKicEndzs4OOjo6SE5OrtVwDHJBQUHYu3cvcnNz4e/vj9u3b/Omf/TRR9DR0cG5c+eU/tJTRn7QVfWr1MfHB0KhEMePH0dsbCwcHBy4X+wBAQHIysrC8uXLUVZWVuunTSpr0aIFhgwZgvj4eDRp0oQ3lESHDh2Qk5PD3Warr3v37gEA96SgHBFVedXJ0NAQnTt3RlJSEu7evcvdHnw9QHNzc4NAIMDp06ffqI5y9+/fBxEhMDCQF1wBr4aQeJ38qbdTp04pTCsqKlL6SH9DtWtNqmp3QPm61LQvNoT6tFdlZmZm+Pzzz3Ho0CG0atUKR48eVbjd97q6tkNdyddJWVmpqal1HqqhLqr7sSi/Tfa2lZeXKz3+5O3h4uICANDW1sZHH32EmzdvNthtzqrIl6lsGI8zZ86guLj4rb+qRb7fdevWTeGpx6r2O1VV1bd6/Ono6KB58+a4e/eu0iBL2XFZmba2Njp37owVK1Zg0KBByMjIwJkzZ+pdH/k2SEhI4N06BV59PyQkJPDyMXwNHmBFR0cDAKRSKS/dzc0NKSkpvKshpaWlmDx5skIZQqEQI0eORF5eHiIiIhR26Ly8vCrHO+rUqRNiYmKQm5sLPz8/Xn8BoVCI0aNHIzU1FVOmTFEaZF27do23Y+vr6wNAlSfhJk2aoH379jh16hROnDjBG1xVfqtS3p+hpv5XwKt+YNeuXVNIf/78OUpKSniPf0+YMAEAMGTIEOTk5CjMk56ejps3b9a4TPkvyNfHpJo7d67SusjJ+1qtXLkS27dvR4sWLRT67jRt2hT9+vXDqVOnsGDBAoWDFHh1Qi0qKqqxnpXreurUKV4Q//jxY0yfPl1pfi8vL1y6dAlbt27lTVuwYAF3C7OyhmrXmlTV7ps2bcKBAwcU8uvp6UEgELzVgKCu7VVSUqL0pF9YWIgXL15ATU1N6e2p15cJ1L4d6srb2xstWrTAvn37eMsgIsyYMeOtf2Ha2dkhMTERd+/e5dILCgqU7q9vy4wZM1BaWsr9/fjxYyxevBhisRghISFc+oQJE1BUVIThw4crvRX44MGDBnltWGhoKIRCIaKjo3n9nEpLS/HFF18AwFt/JVRV+93169cxZ84cpfPo6+sjOzsbxcXFb61eAwYMQGlpqcJwFHFxcUqvBickJCjdh+XfY28yZIilpSX8/f1x/fp1hT6pK1aswM2bNxEQEKDQ/4p5pd63CO/evcvrtPzs2TOcPHkSFy5cgJ6enkInycmTJ+Pw4cPo2rUrPv/8c2hqauLIkSPQ1dVV+mqPb7/9FklJSVi/fj2SkpLQpUsXiMVi3L9/n3sVT1VRc8eOHbFv3z706NED/v7+OH78OD766CMAr/roXLhwAUuWLMH+/fshlUphbGyMJ0+e4OrVq7h8+TJOnz7N3QMPCAjAwoULMWLECPTp0wdaWlqwsrLideT29/dHUlIS9385c3Nz2NjY4M6dO2jSpAk3VlZ1njx5AhcXFzg5OcHR0RHm5ubIycnBnj17UFZWhilTpnB5O3fujK+//hrfffcdWrVqhc6dO8PKygo5OTm4e/cuTpw4ge+//55b96qMGjUKq1evRp8+fdCvXz8YGBggKSkJFy5cQLdu3bB//36l8/Xo0QMSiQTR0dEoKyvDhAkTlF4WXrZsGW7fvo1p06Zh/fr18PT0hK6uLh49eoRz587hzp07SEtLU7gipYypqSn69OmDHTt2oH379ujYsSMyMjKwb98+dOzYkftVWtkvv/wCqVSKAQMGYMeOHWjVqhUuXLiApKQkSKVSJCQk8IKAhmrXmoSFhWHevHkYP348YmNjYWVlhcuXL+PYsWPo3bs3du7cycsv34cSEhIQFhYGGxsbqKioICwsTOE2y5uoS3u9fPkSXl5esLW1haurKywtLfHixQvs27cP6enpmDJlCu+WcUO0Q12pqKhgxYoV6Nq1KwIDA7lxsI4fP460tDQ4OjriypUrb7SM6kRGRmLEiBHw9PRE3759IZPJcPDgwVqdDxqCqakpCgsL4ejoiB49enDjYOXk5GDJkiW8vq4jR45EUlIS1q5di5MnTyIwMBBmZmbIyMjArVu3cObMGWzatOmN38dnbW2NefPmITIyEo6OjujXrx+0tLQQExOD27dvIzg4WOFqeENzd3eHu7s7tm3bhrS0NHh4eODhw4fYu3cvunXrprQbSUBAAM6dO4cuXbrAx8cHIpEIUqlU4YLCm/jiiy+wY8cO/P7777h27Rp8fHzw+PFjbNu2DT169EBMTAzvfDVhwgQ8ffqUG6NQIBAgMTERZ8+ehYeHB7y9vd+oPr/99hu8vb0xfPhwxMTEoE2bNrh+/Tr27t0LIyMj/Pbbb2+6yh+uuj52WNUwDWKxmKytrWn06NFKH28nItq+fTs5ODiQSCSipk2b0vjx46mgoKDKR1+Li4tp4cKF5OzsTBoaGtSkSRNq06YNRUZG8saDqmr+2NhY0tLSIhMTE7p+/TqXXl5eTsuXLycvLy/S0dEhsVhMlpaW1LlzZ/rtt994Y88QEc2fP59sbGy4R6Rff6xaPm6Sqqoq5ebm8qaNGDGCG4dFmdfr/vz5c5o1axZJpVIyNTUlkUhEZmZm1LlzZzp48KDSMo4cOUI9evQgIyMjUlNTo6ZNm5Knpyd99913vMdnqxMbG0teXl6kra1Nurq61LVrVzp//jz3GHZsbKzS+YYNG8btA9U9JlxUVETz588nV1dX0tLSIg0NDWrRogX17NmT1q1bxxvTp6bHsQsKCigyMpKaN29OYrGYbGxs6LvvvqPS0tIqhwO5ePEiBQUFUZMmTUhbW5u6dOlCV69epe7duxMA3v4k96btWtMj+kREly5dok8++YT09PRIW1ubfH196ejRo1U+Nn379m3q2rUr6erqkkAg4G2b6oZpqMsYdES1b6/S0lKaN28effLJJ9SsWTMSiURkYmJCUqmUNm3aVONQI/Vph/oMfUBElJCQQFKplDQ0NEhfX5/69u1LqampdXr8Xz4UQ1RUlMK06uq1dOlS7hxiaWlJ33zzTZX7a3X1qW6YAGVlyfM/e/aMRowYQSYmJiQWi8nJyYk2bdpU5Xpu3bqVAgMDSU9Pj9TU1Mjc3Jz8/Pxo0aJFlJWVxeWr6fxQkz179pCvry9pa2uTWCwmBwcHWrRoEe98IFffYRqqai8ioszMTBoyZAiZmZmRuro6OTg40NKlS+n+/ftKt2VBQQENHz6cTE1NueE95PtCTcM0KFPVMECZmZk0dOhQMjQ0JHV1dXJ1daWdO3fSwoULCQDt2rWLy7tlyxbq168fWVtbk6amJkkkEnJycqJ58+YpDM1SlerGwSJ6NZzJ4MGDydTUlIRCIZmamtLgwYN5w/fUZn3/awRESu7ZMMx/QEVFBaytrfHy5cu32tH5Q8Hai2Ea1//+9z9s3LgRN27ceOMr6Mzb1+B9sBjm36a8vFzpE4Fz585Famqq0tf0/Jex9mKYxpWWlqaQFh8fjy1btsDOzo4FV+8JdgWL+eDl5ubCxMQEnTp1gq2tLcrKynDmzBkkJyfD1B/M+ZoAACAASURBVNQU58+fV9oP8L+KtRfDNC4XFxdoaGjA2dkZWlpauHHjBg4dOgRVVVXs378fnTp1auwqMrWgOuu/+Ipr5j9FRUUFmZmZOH/+POLj43HmzBkIBAKEhIRgw4YNMDMza+wq/quw9mKYxlVcXIxLly7hxIkTSEhIQE5ODgIDA7Fq1aoG7VDPvF3sChbDMAzDMEwDY32wGIZhGIZhGhgLsBiGYRiGYRoYC7CYd2bWrFkQCARKX4/BvCJ/SXhjeFfbx8/Pj72n7F9uzZo1EAgEWLNmTWNX5Y1Vfj8sw7xLLMBiGIZhmGo05g8f5v3FOrkz70x2djays7NhaWlZq9fi/BfJT+IN8b63unpX2+fhw4coKipC69at39oymDeTl5eHtLQ0mJqaQiKRNHZ13ohAIICvr+8bXZltzOOSeX/V+12EDFNXhoaGMDQ0bOxqMFV4V9vH0tLyrS+DeTMSieS9D6wYprGxW4QN4OjRoxAIBBgzZozS6ffu3YOKigqCgoK4tIKCAkRFRaFt27bQ0NCArq4ugoKCFN7sDgDnz5/HuHHjYG9vD4lEAg0NDTg4OGDu3LkoKytTyC+/nJ2bm4tx48bBwsICQqGQ60+RlpaGiIgI2NjYcMv+6KOPMGrUKOTl5dV5/WtbnrI+PikpKRAIBBg0aBDu3r2LXr16QU9PD1paWggMDMTly5eVLjMzMxORkZGws7ODhoYG9PX10aFDByxcuFAh75UrVxASEgJTU1OIRCJYWVlh/PjxyMnJqfO6yu3YsQO+vr4wNjaGuro6zMzMEBgYiB07dnB54uLiIBAIoGyoucrrrUxubi5GjhyJpk2bQl1dHS4uLti8ebNCvsptunr1ajg4OEBDQwMtWrTAkiVLAABEhEWLFsHOzg7q6uqwsbHBunXrqi2rstjYWHTp0gVmZmYQi8UwMTGBj48PVqxYwct34cIFfPbZZ7C0tIRYLIaRkRHc3Nzwww8/8PJV1QervLwc0dHRcHJygoaGBiQSCfz9/RETE6OQt3IfocOHD+Pjjz+GpqYmDAwMEB4e/kbbFgBWr16NDh06oEmTJmjSpAk6dOigtD9S5W187tw5dOrUCdra2pBIJOjVq1e9r3hU3j9u3ryJXr16wcDAAAKBgFfmgwcPMGzYMK7NTU1NMWjQIKSmpnJ5ioqKoK2tDWtr6yqX5+joCA0NDeTn5wOovg9WbZYJvBosUyKRoKKigkuTyWTQ19eHQCDAypUrefnl+198fHxdmoqzcuVK2NvbQ11dHRYWFpg2bRqKi4uV5q3tOVW+HVJTU5GamgqBQMB9Xj+uExIS0KNHDxgaGkIsFsPGxgYzZ85EUVFRvdaH+QA02lsQPyAymYysra1JIpFQYWGhwvQvv/ySAND27duJiCgnJ4fatm1LAMjLy4smTpxIQ4YMIQMDAxIKhbwXeRIRjRw5kszMzCgkJISmTp1KY8eO5ebv3bu3wvKsrKyoadOm5OLiQjY2NjRmzBiaMGECHThwgAoLC6lFixYkEAgoKCiIpk6dShEREfTpp5+SpqYm3blzp07rXpfylL0YVv6CVF9fXzIwMCCpVEqTJ0+m4OBgAkB6enqUnp7OW+atW7fI1NSUAJC3tzdNmzaNxo4dS35+fqSnp8fLu2fPHhKLxaShocG1X7du3QgA2djY0LNnz+q0vkREy5YtIwBkampKI0aMoOnTp9PgwYOpbdu2NGDAAC5ffV4MbGVlRaampuTq6kp2dnY0ZcoUGj16NBkYGBAAWrJkCS+/vE2Dg4NJIpHQwIEDacKECWRubk4A6I8//qAxY8aQiYkJDR06lEaPHk16enoEgOLj45WWVXn77Nu3jwQCAenp6dGgQYNo+vTpNGzYMHJzcyNvb28u38WLF0ksFpOmpiZ9/vnn9OWXX9KoUaNIKpWSpaUlbznKXgYrk8m4bW5ra0uRkZE0atQorq7R0dG8/PKX0/bq1YtEIhH16dOHIiMjyc3NjTuu6mv8+PEEgMzNzWnChAm89pwwYQIvr3wbd+3alTQ0NKhr164UGRlJAQEBBICsra3p5cuXda6DfP+Qv5Dey8uLJk+eTOHh4fTkyRMiIkpKSiKJREJCoZB69uxJU6dOpb59+5JQKCRjY2O6d+8eV578pcInT55UWNalS5cIAPXv359Lq+rlv3VZ5qRJkwgAnT17lku7cOEC93L40NBQXtlSqZTU1dWpuLi4zu317bffEgAyMTGhcePG0aRJk8jS0pJ7OfnrL8Gu7Tn1+fPnFBUVRRKJhCQSCUVFRXGfysfJsmXLuONk4MCBNGXKFPLz8yMA9PHHH1NJSUmd14l5/7EAq4HMmzePANCaNWt46WVlZWRqakrGxsZUWlpKREShoaHcl19lGRkZZGFhQUZGRryTcmpqKpWXl/PyymQyGjJkCAGgxMRE3jQrKysCQEFBQVRUVMSbtnfvXgJAEydOVFiHgoKCOp/c6lJedQEWAJo7dy5v/pkzZxIAmjNnDi+9ffv2BIBWrFihsMxHjx5x/8/OziYdHR0yNzdXeOv75s2bCQCNGzeuTutLRNSuXTsSiUSUkZGhMC07O5v7f30DLAAklUp5J+VHjx6RoaEhicVievz4MZcub1N9fX3el9vDhw9JJBKRRCIhW1tbyszM5KYlJSURAOrRowdv2cq2T+/evQkAXbp0qdp1nTx5MgGg3bt3V5uPSHmAtXbtWu6LsPJ6p6amkqGhIQmFQt76yQMAoVDI2//Ly8u5L7bTp08r1KUm8fHxBIA++ugjys3N5dKfPXtGtra2BIASEhK4dPk2BkBbtmzhlRUWFkYAaPPmzXWuR+Xj4ptvvlGYXlpaSs2bNydtbW26cOECb9qJEydIVVWVunfvzqUdPXqUANDo0aMVyoqMjCQAtG/fPi5NWYBV12XKzw3z5s3j0hYtWkQAqGPHjmRqasqlFxUVkUgkooCAgFq0Dt+dO3dIKBSSubk575jMy8sjOzs7pQFWfc6pVlZWSpd//fp1EgqF5OTkpLCvz5kzhwDQwoUL67xezPuP3SJsIIMHD4ZIJFK47L1//36kpaUhPDwcampqyM7OxtatWxEQEIBhw4bx8hobG2Pq1KnIysrC0aNHuXRLS0uoqqry8goEAowdOxYAeHkrmz9/PjQ0NJROU5bepEkTiMXimlf2LZTXokULTJ06lZc2dOhQAEBycjKXdvbsWZw7dw5SqRTDhw9XKKdZs2bc/9etW4f8/HzMmTMHVlZWvHwhISFo164dtmzZUqv6vU5NTQ1qamoK6QYGBvUq73U//vgjRCIR93ezZs0QERGBkpISpXWOiIhAy5Ytub8tLCzg7e2NvLw8fPXVVzAyMuKmdejQAS1btqzy9qsyyravsnWtbb7XrV27FsCrfbbyeltaWmLSpEkoLy/Hxo0bFeYLDQ2Fl5cX97eqqirCw8MB8Peb2pLXY9asWbw+SHp6eoiKigIApbfNpFIp+vfvz0sbMmRIvesh17RpU3z11VcK6fv27UNKSgqmTp0KFxcX3jRvb28EBwfjwIED3C0/f39/mJubY9u2bbxbYDKZDJs2bYKRkRGvC4MydV2mVCqFqqoqjh8/zuWLjY2FnZ0dBgwYgLS0NNy6dQsAcOrUKZSWltZrOIVNmzahvLwckydPhrGxMZeuo6ODmTNnKp2nvudUZZYvX47y8nL88ssvCvv6tGnTYGRkpPT2PvPhY53cG4iRkRF69+6NLVu24NatW9wTUvKASx5MJScno6KiAiUlJUr75ty5cwcAcOvWLXTv3h0AUFpail9//ZUr+8WLF6BKD38+ffpUoRx1dXU4ODgopEulUpiammLu3Lm4fPkyunfvDl9fX3z00Uf1GpuoocpzdnaGigo/3pcHS7m5uVza2bNnAQCffPJJjWUmJSUBAM6cOYN79+4pTC8uLuaenKtL5+6QkBBMmzYN9vb2CA0Nhb+/P7y9vaGjo1PrMqojFArh6empkO7j4wMAuHjxosI0Z2dnhTT5C5mrmnbmzJka6xISEoKdO3fCw8MDoaGh6NixI3x8fBTaq1+/fvj555/Rq1cv9O/fH506dYJUKoW5uXmNy5Cvk6amJtzd3RWm+fv7AwAuXbqkMM3V1VUhTdl+U1vytlX2Rf8u6yHn5OTECzjl5Pv27du3lZ5H0tPTIZPJ8M8//6B9+/ZQUVHBgAEDMH/+fBw4cADBwcEAgGPHjiEtLQ3jx4+HUFj910FdlymRSODi4oLExESUlZVBRUUFCQkJGDBgANeWsbGxaN26NWJjYwH8XxvXhfyHgvz4qExZGlC/c2pV5O3y999/49ixYwrT1dTUuECS+W9hAVYDGjlyJLZs2YKVK1di4cKFePr0KQ4ePAhfX1/Y2toCAJ49ewYAOHnyJE6ePFllWYWFhdz/P/vsM8TExMDW1hb9+/eHsbEx1NTUkJubi8WLF6OkpERhfmNjY6UBjkQiQVJSEr755hvExMTgwIEDAF5d8fjyyy+r7KhflYYqT1lwIj/hV+4kK+80X5svbnlbL126tNp8hYWFdQqwpkyZAgMDA/z2229YtGgRFi5cCKFQiG7duuGnn35CixYtal2WMoaGhgrBJgCYmJgAgNIHEaprv6qmlZeX11iXvn37Yvfu3YiOjsbvv/+OpUuXQiAQwN/fH4sWLeKCtw4dOiAuLg4//vgjNm3ahNWrVwMA3NzcMG/evBq/OPPz82FhYaF0mjxQlF8Zqay2+01t5efnQ0VFhXfFT87ExAQCgeCd1KPyMpWR79vKrupVVvk8EhYWhvnz52PDhg1cgLV+/XpuWk3qs0x/f3+cO3cOycnJUFNTQ35+PgICArgHcWJjYzF69GjExsZWGWDXRH48VL56JVdV+9XnnFoVebu8/jAHw7BbhA3Iz88PrVu3xrp161BaWorVq1ejoqKCdytLfiKOjIwEveoDp/Qjvx2RnJyMmJgYBAUF4caNG/jjjz/www8/YNasWQgJCamyLtVdPbK0tMSaNWuQlZWFixcvYt68eZDJZBg7dmy9LmU3dHnV0dXVBQA8efKkxrzytr569Wq1bf367cOaCAQCDBkyBMnJycjKysKuXbvQu3dv7NmzB927d+e+UOVBkrJAprqnNbOzsyGTyRTSMzIyAOCdPz4fHByM+Ph4PH/+HAcPHsSwYcMQFxeHzp07867O+Pj44ODBg3j+/DliY2MxefJkXL16Fd26dcP9+/erXYaOjg4yMzOVTktPT+fyvG06OjqQyWTIyspSmJaZmQkieif1kKvqOJbXISYmptp929fXl5vH3t4ezs7O2LdvH/Ly8lBUVIRdu3bBzs4Obm5uNdalPsusfKVK/sSl/Oqgv78/4uLi8OLFCyQnJ8PLy0vp1bqayI8HZfuP/JiprL7n1KrI2yU/P7/admH+e1iA1cBGjBiBrKws7N69G6tWrYKenh769OnDTXdzc4NAIMDp06drVZ781la3bt0U+gycOHHijeqqoqICZ2dnTJs2jQuE9u7d+68pTxn5L9zDhw/XmLdDhw4AUOu2rg8DAwP07NmT61d348YN3L17F8CrfjuA8mBQ2W0+ufLycqV1lm/v1/u/vCva2tro3LkzVqxYgUGDBiEjI0PpbUYNDQ34+flh0aJFmDFjBl6+fIkjR45UW7aLiwuKioq4W8CVyYeNUHars6HJ21bZoJTvsh41qe++HRYWhuLiYvz111/YtWsXXrx4gf/9739vbZk+Pj4QCoU4fvw4YmNj4eDgwF0tDggIQFZWFpYvX46ysrJ6v87GyckJgPLzobK0+pxTVVVVq7wSKW8X+a1ChpFjAVYDCw8Ph7q6OiZNmoT79+8jLCwM6urq3PSmTZuiX79+OHXqFBYsWKD0l82ZM2e4sVPkV1deHx/r+vXrmDNnTp3rd/36daW/6uRplevaGOXVxM3NDW5ubkhISMAff/yhML1yMDN48GBoa2vjq6++wvXr1xXyFhUV1eukGBcXp7DdysrKuFsF8nW2s7ODtrY29u7dy00DXrXN999/X+0yZsyYgdLSUu7vx48fY/HixRCLxfX6lV1fCQkJSr9Y5FcL5Ot6+vRppWMO1XY/kHdMnz59Oq8T9qNHjxAdHQ2hUIgBAwbUbyXqQF6P2bNn824F5uXlYfbs2bw8jSk4OBiWlpaIjo5GQkKCwvSysjKlY+qFhoZCVVUV69evx/r16yEQCGodYNVnmU2aNEH79u1x6tQpnDhxAgEBAdw0+dWtefPm8f6uK/k6RUdH865i5efnKz3O6nNO1dfXR3Z2ttJ9fMyYMRAKhRg/fjwePnyoMD03N7faH1TMh4v1wWpg+vr66Nu3L9e3QdmTbsuWLcPt27cxbdo0rF+/Hp6entDV1cWjR49w7tw53LlzB2lpaVyfBHd3d2zbtg1paWnw8PDAw4cPsXfvXnTr1g1//fVXnep35MgRTJ06FV5eXrC1tYWBgQHu37+PvXv3Ql1dnXuKprHKq42NGzfCz88PI0aM4NqvuLgY169fx8WLF7lBJuVP7/Tt2xdOTk7o3LkzWrdujZKSEqSkpCA+Ph4ff/wxDh06VKfl9+zZEzo6OvDw8ICVlRXKyspw5MgR3LhxA5999hl3AheJRBg/fjx+/PFHtGvXDsHBwSgoKEBMTAx8fX2VdrwHXvU5KiwshKOjI3r06IHCwkJs27YNOTk5WLJkSa07jjeECRMm4OnTp/D29kbz5s0hEAiQmJiIs2fPwsPDA97e3gBefUnGxsZCKpWiRYsWUFdXx4ULF3Ds2DG0bNkSvXr1qnY5YWFh2LlzJ/bs2QNHR0d0794dhYWF2Lp1K549e4ZFixbxnpJ8W6RSKcaPH49ffvkF9vb26NOnD4gIO3bswOPHjzFhwgRIpdK3Xo+aiMVi/PXXX+jSpQt8fX0REBAABwcHblDMEydOwMDAQKFzddOmTREYGIjDhw9DRUWF265vc5n+/v7cD5nKQZS5uTlsbGxw584dNGnSpFa3KZVp1aoVvvnmG0RFRcHR0RH9+vWDUCjEjh074OjoiNu3b/Py1+ecGhAQgHPnzqFLly7w8fGBSCSCVCqFVCqFvb09li1bhtGjR8POzg5du3aFtbU1CgoKcP/+fcTHx2PQoEH4/fff67V+zHvsrQ0A8R8mH3PGw8OjyjxFRUU0f/58cnV1JS0tLdLQ0KAWLVpQz549ad26dVRWVsblzczMpCFDhpCZmRmpq6uTg4MDLV26lO7fv1/lWEpVjdly48YNioiIIBcXFzIwMCCxWEwtW7ak8PBwun79ep3XtS7lVTcO1uvrIAclY9gQEaWnp1NERAS1bNmSRCIR6evrU4cOHRQGpCR6NTDp0KFDycrKikQiEenp6ZGDgwNNmDCBNwhibS1btow+/fRTsrKyInV1dTIwMCB3d3f67bffuLHO5CoqKmjWrFlkYWFBIpGIbG1tafHixTVuu2fPntGIESPIxMSExGIxOTk50aZNmxTqoqxN5eSDSz548EBhmrKxqJSVtWXLFurXrx9ZW1uTpqYmSSQScnJyonnz5lFBQQGX79ChQzRw4ECys7MjbW1tatKkCbVp04ZmzJhBWVlZNS6b6NWYcQsXLiQHBwcSi8Wkra1Nvr6+tGfPHoW8VQ2ESVT9+GO1tWrVKnJzcyNNTU3S1NQkNzc3WrVqVZ2WVdO+XZ3azvv48WOKiIggGxsbEovFpKOjQx999BENGzaMjh07pnSeDRs2cGNsLV++XGme6tq3rss8fPgwASBVVVXe2GJERCNGjODG7HtTf/zxB7Vp04ZEIhE1a9aMpkyZQkVFRUrPIXU9pxYUFNDw4cPJ1NSUVFVVlW7zs2fPUkhICJmZmZGamhoZGhpSu3bt6Msvv6SbN2++8fox7x/2sue3YOHChZg6dSr+/PNPbiwchmEYhmH+O1iA1cCKi4vRunVr5Ofn4/Hjx9DU1GzsKjEMwzAM846xPlgNJDExEfHx8fj777+RmpqKOXPmsOCKYRiGYf6jWIDVQI4ePYrZs2fD0NAQkyZNwpQpUxq7SvUWFxen9DH11zk7O6Nnz55vv0LvgLKRqZWZOHEiNxYX8374t2zbn3/+uVajug8aNKjWHc8/VKytmA/Be3GLsHnz5khNTVVIHzNmTI2jdDN1N2vWLO6R9OqEh4crfS/b+6i2r/V58OABO6G/Z/4t27aq89jrYmNj6z0m1IeCtRXzIXgvAqysrCzeWDzXrl1Dp06d2MHFMAzDMMy/0nsRYL1u4sSJ2LdvH+7cuVOvFxQzDMMwDMO8Te9dH6zS0lJs2LABkydPrjK4Kikp4b2ss7y8HDdv3oSFhYXSl+gyDMMwzPtIJpMhIyMDLi4u3AvGmX+JxhqAq762bt1Kqqqq9OTJkyrzyAdMZB/2YR/2YR/2+S98ajto8rJly8jBwYG0tbVJW1ubPDw86MCBA9x0+UDAlT8jR47klZGamkpdu3YlDQ0NMjIyoilTpvAGxyZ6NQivi4sLiUQisra2Vjpo7a+//kpWVlYkFovJ3d2dzpw5w5v+8uVLGjNmDOnr65OWlhb17t2b0tPTa7We/wbv3S3CoKAgiEQixMTEVJnn9StYjx49gr29Pc6ePQtTU9N3UU2GYRiGeevS0tLg7u6O1NRUWFpa1pg/JiYGqqqqsLGxARFh7dq1WLBgAS5evIi2bdvCz88Ptra2+Pbbb7l5NDU1oaOjAwCoqKiAs7MzmjZtigULFiAtLQ0DBw7E8OHD8eOPPwJ49cCIvb09Ro0ahWHDhuHYsWOYOHEi9u/fj6CgIADA1q1bMXDgQPz+++/o0KEDfv75Z2zfvh23b9+GsbExAGD06NHYv38/1qxZA4lEgnHjxkFFRQUnT55s6GZ8Oxo5wKuTlJQUUlFRod27d9dpvkePHhEAevTo0VuqGcMwDMO8ew3x/aanp0crV64koldXsCIiIqrMe+DAAVJRUeFdSfrtt99IR0eHSkpKiIho2rRp1LZtW958/fv3570Syd3dncaOHcv9XVFRQWZmZjRnzhwiIsrNzSU1NTXavn07l+fmzZsEgE6fPl3vdX2X3qsOSatXr4axsTG6devW2FVhGIZhmH+NgoIC5Ofnc5/Kd3GqUlFRgS1btqCwsBCenp5c+saNG2FoaAh7e3tMnz4dRUVF3LTTp0/DwcEBJiYmXFpQUBDy8/Nx/fp1Lk9gYCBvWUFBQTh9+jSAV32pz58/z8ujoqKCwMBALs/58+dRVlbGy9O6dWtYWlpyef7t3psecTKZDKtXr0Z4eDjryMcwDMMwlbRp04b3d1RUVJWD7F69ehWenp4oLi5GkyZNsGvXLm7+0NBQWFlZwczMDFeuXMEXX3yB27dvY+fOnQCA9PR0XnAFgPs7PT292jz5+fl4+fIlnj9/joqKCqV5bt26xZUhEokUBv81MTHhlvNv995EKkePHsXDhw/Zy5MZhmEY5jU3btyAubk597dYLK4yr52dHS5duoS8vDz89ddfCA8PR3x8PNq0aYMRI0Zw+RwcHGBqaoqOHTvi3r17sLa2fqvr8KF5bwKsTz75BPR+9cdnGIZhmHdCW1ub64heE5FIhFatWgEAXF1dkZycjMWLF2P58uUKeTt06AAAuHv3LqytrdG0aVOcPXuWlycjIwMA0LRpU+5feVrlPDo6OtDQ0ICqqipUVVWV5qlcRmlpKXJzc3lXsSrn+bd7r/pgMQzDMAzTsGQyWZV9ti5dugQA3BP4np6euHr1KjIzM7k8R44cgY6ODneb0dPTE8eOHeOVc+TIEa6fl0gkgqurKy+PTCbDsWPHuDyurq5QU1Pj5bl9+zYePnzI6y/2b/beXMFiGIZhGObNTJ8+HV26dIGlpSUKCgqwadMmxMXF4e+//8a9e/ewadMmdO3aFQYGBrhy5QomTZoEqVQKR0dHAK/uJrVp0wZhYWGYP38+0tPTMXPmTIwdO5a7LTlq1Cj8+uuvmDZtGoYMGYLjx49j27Zt2L9/P1ePyZMnIzw8HO3bt4e7uzt+/vlnFBYWYvDgwQAAiUSCoUOHYvLkydDX14eOjg7Gjx8PT09PeHh4vPuGq4/GfozxXWDDNDAMwzAforp+vw0ZMoSsrKxIJBKRkZERdezYkQ4fPkxERA8fPiSpVEr6+vokFoupVatWNHXqVMrLy+OVkZKSQl26dCENDQ0yNDSkyMhIpQONOjs7k0gkopYtWyodaPSXX34hS0tLEolE5O7uTklJSbzp8oFG9fT0SFNTk3r16kVpaWl1aJ3G9d4NNFofjx8/hoWFBR49eoRmzZo1dnUYhmEYpkGw77d/L9YHi2EYhmEYpoGxAIthGIZhGKaBsQCLYRiGYRimgbEAi2EYhmEYpoGxAIthGIZhGKaBsQCLYRiGYRimgbEAi2EYhmEYpoGxAIthGIZhGKaBsQCLYRiGYRimgbEAi2EYhmEYpoGxAIthGIZhGKaBsQCLYRiGYRimgbEAi2EYhmEYpoGxAIthGIZhGKaBCRu7Ah+6vLw8FBUVNXY1Go2mpiYkEkljV4NhGIZh3ikWYL1FeXl5+CV6DsqKchu7Ko1GTVMX4ydPZ0EWwzAM85/CAqy3qKioCGVFuejpZQ4jfe3Grs47l/WsALtPPkFRURELsBiGYZj/FBZgvQNG+towNdJt7GowDMMwDPOOsE7uDMMwDMMwDYwFWAzDMAzDMA2MBVgMwzAMwzANjPXBakS/bzmO5VtikfIkGwDQppU5Zo7+FF2kjgCA4pIyTJ2/BVsPnEFJaTk+8bbHr1+HwcSQ32F87a5E/Lz2b/yTkg6dJhr4LMgNv3wdxk3ffvAs5q7Yh39SM2Ckp40xoR0xZWgXbnri+X8wPXo7bt9PQ1FxKazMDDC8nx8mhgdxeawDpyD1aY7COoz+PIC3LIZhGIZhWIDVqMxN9PHDpM9gY2UCArBu90n0HrcE53bMRlsbc0TO3YwD8Zex5acxkGhrKvqpZgAAIABJREFUYsL3G/BZxK84sfErroyf1vyNn9Ycwrwp/eDuaI3ClyVI/f8BGwAcTLiCsC9WYPGMAejk1RY376dh1DdroKGuhrEDAgEAWhpijA3tCAdbC2hpinHy/D8YPXsttDTEGN7PDwCQtO0bVFQQV+61O4/RedhC9AlyeydtxTAMwzDvExZgNaIe/s68v7+f2AfLt8TizJV7aNZUD6t2JGDDgpEI8GgDAPjzh6Gw7z4DSZfvwcPJGs/zCvHNkp3YvTQCHT3bcOU42llw/98YcwrBAS4YGeIPAGhpYYwvhnfDgj8PYExoRwgEAri0sYJLGytunubmhth19DwSz//DBVhG+jq8us5fuR/WFsbwdbNr0DZhGIZhmA8B64P1L1FRIcPWA2dQ+LIEHk7WOH89BWXlFejo2ZbL07qlKSxNDZB06S4A4Oip65DJZHia+Rz23WfAyn8yQiYtw6O0/7uVV1JaDnWxGm9ZGupqeJz+XOktPwC4eCMVpy/ehbSK4Km0tBwbY05jUG8fCASCN111hmEYhvngsACrkV395xEkrqOg6TwcY2avxV9LxqFNK3NkZOdBpCaEro4mL7+xoQ7Ss/MAAPcfZ0EmI8xdsQ+LvvwcW38ei2d5heg8bCFKS8sBAJ942WPX0fM4dvoGZDIZ/klJx09r/gYApGXxR5i38p8MTafh6NBvNkaHdsTQz3yV1nnPsQvILShCeC+vhm4OhmEYhvkgsFuEjcyuuSnO75yNvBcvsePvZAyZsRLH135Zq3llMkJZeQV+mv45OrmZA8U5WP9Fe1j0WYfje/7AJy76GOIsw90u5ggevQhl5QQdLRHGf+6Fb1ekQeW1q09x66fjRVExzly+jxnR29HK0hgh3TwUlrtqZwI6+zjAzFivQdqAYRiGYT40LMBqZCKREK2sTAAArm2b49y1FPyy/gj6dnFHaVk5cvOLeFexMrPz0dRQAgKhqY4MANBakARKEQMAjCCAobYaHqXnAhWaEACYE2aF70MtkZ5bCiMdNRy/kgUAaF6RBFnOCwgkthAINdCimREAwMHWAhk5efh26R6FACv1STaOnb6BvxaPe9tNwzAMwzDvLXaL8F9GRjKUlJXDtW1zqAlVcTzpBjft9oM0PEzLQYcWAtC9HfA0eQIA+OdpEZfnWUEpsgvKYGmszitXVVUAcwMxRGoq2JqYCQ87HRhplQPZl0D3tkH2NA5UlA7CqycFZTJCSWmZQv3W7EqEsb4Ouvo6vYW1ZxiGYZgPA7uC1YhmRG9HZ6kjLE0NUFD4Epv3JSH+7G0c+CMSEm1NDOkjxZR5W6An0YJ2E3VMnL0SHnY66GCSBpQDtuaa6OFugMkr7+C3MXbQ1lDFzA0PYGeuCT/7V+8+zM4vxc5TWZDa66K4TIZ1x9Kx41QWjn33f08w/nbgCSyMcmBnfhNQN0TiQwmiVx/CuP8F8uork8mwdlciwnp6QShUfadtxTAMwzDvExZgNaKsZwUY/OUfSMvKg0RbAw62FjjwRyQ6ffzqycFFX34OFRUB+kX8ipKSEnRy1sMvI9vyylgd8RGmrLqL4P/H3n3HN1X1Dxz/3CRN96DQBQVaNgXZBSobER5FHxHcPoiAKDyADNkiU4aKAv5AEQe4GKIiAgIPG0GUoWwp2xa6WN07ub8/AoFAaUubNkn7fb9efem9OTnn24Y239x7zve8fRSNAu0a+LBuUiOcdLcuTn69PZ6xX55FVaF1XS+2TG9CeJ1bZReMqsrEr89yISETnVahRqArMwe159WXe1iMtWXvCaJir9K3Z7sS/KkIIYQQjk9RVVUtuJlju3jxIlWrViU6Oprg4OBSGzc2NpZFH0xlwOP1CPLzue/nq6ioiacgfh+g3vgqRa4BKEHtUJzci/T02MuJfLr2JANHTiYoKMjKwQkhhLDV+5somMzBslMqKmrCAYj/HTBS6skVQEYC6j/rULMTC24rhBBCCDNJsOyQiooatxeunyi4cQlHgiET9Z9fULOu2TgWIYQQwnFIgmVnTFeu9kPSaVuHcosxBzVqE2p2sq0jEUIIIRyCJFh2Rr1+Eq7/besw7nYzyTJm2zoSIYQQwu7JKkI7omYnQcJ+W4dxDyoY0lET9qMEyhY5ovQlJSWRnp5ecMMyys3NDW9vb1uHIYQoJEmw7ISKETV2t63DKFjSGVTPEBT3KraORJQjSUlJ/N8Hs8hJL78LLpzcfBg6crwkWUI4CEmw7IR67W/IvGLrMApFjd0NNZ5E0ehtHYooJ9LT08lJT6RHmyr4+XraOpxSd/laCj/tuUR6erokWEI4CEmwSsHlayn5Pq7mpqNe+A2blGIokizI3o3Gr1m+rQr6voW4X36+nkWqKSeEEKVNEqwS5ObmhpObDz/tuZRvOzXpNGpyDI6TYAHEowQ7oShO+bZycvPBzc0t3zZCCCFEWSMJVgny9vZm6Mjx+U7MVY255G4fANmOV+lcE9YGbfVH8m0jE3OFEEKUR5JglTBvb+98EwzjpR0YPLIA59ILyioUSN2BLrAviqLYOhghhBDCrkgdLBsznluNY74MKqRGo149bOtAhBBCCLvjiO/sZYaak4p69QimvQYdkKJFjf3N1lGIcm724nW0fmYqPi0GEdT2dXoO+ZDI87EWbc5GJdBr6P8R2GYoFcIH8dyIj4i/kmTR5lpiKr1Hf0KF8EFUbPVfBkz8gtS0TIs2RyKj6fCfmbg3GUBI55G89/kv94xr5S9/oAvrS88hH1qc14X1zfNrzucbivmTEELYE0mwbEhNPGPrEIpHNWC8ftLWUYhybteBSAY9/xB7lk9k42ejyMk18Mgr75OWngVAWnoWjwyYg6LA5iVj2PXtBLJzcnli8HyMxlsfbnqPWcyJM5fY+Nko1nw0nF8PRDJwylLz48mpGTzyyhyqV67IvlVTeGfUs0xbuIZPv9txV0wXLl1hzHsradu8zl2PXdw5z+Lrs7f7oSgKPbs2t/rPRghhOzIHy4bUxFOYclwHvYIFkHQKVTWiKJKrC9v4ZfEbFsdfzOxPUNthHDxxgfYt6rLnr9NcuHSFAz9MxcvDFYAls16hUushbPv9b7o82IC/z8awafdRfv9uEi0ahgIw783/8PjAubw7+lkq+1dg2bq9ZOcY+Ozt/uj1OhrUrsKhk1HM+3ITA57paB7fYDDSe8wnTB7Sg90HT5GYbLnIJdDPck7mz9v+omPLetSo6l8CPx0hhK3Iu6INqYll4OqPIQvS8i9DIURpSkrJAMDX2x2ArOxcFEXBWX/r86SLsxMajcKeP02bqv9+6Aw+Xm7m5AqgS0QYGo3CviPnbrQ5S7sWddDf1k/XNg2JPB/H9aQ087npH63B39eLfr3aFxhr/JUkftl1hH692hXjOxZC2CNJsGxIvf43Dn316gbTlTghbM9oNDJy9nIebFabhrWDAWjduAburs6Mf38V6RlZpKVnMebdlRgMRuIum7beibuSjL+vl0VfOp0WX2934m7M1Yq7kkRARcs2N49vttl98BRLfvyVT6a9XKh4v1qzB083F558uEWRv2chhH2SBMuWssrCvmoa1Kzrtg5CCACGTv+G46cvsmzOQPM5P18vVsz9L+t2HMK7xSB8W/2XxJR0moVVR6Ox3p/AlLQMXh73KYumvkylCoXbzmfpj7/ywmOtcXHOv2CvEMLxyBwsWzLm2DqC4lMUMGTbOgoheP3tr1m/8xDbvxpPcKCvxWNd2zTk1KZ3uXI9BZ1Wi4+XG1XaDeOZR/wACKzkRcK1ZIvn5OYauJaURkAFF9ScFAJ83Yi/ct1izmH81eQbz/fmbNRlLly6Qo/B8819GI2m3RmcH+jPifWzqFnt1jyrXw+cIvJ8HMveH2T9H4YQwuYkwbIl1fFvDwJgzLV1BKIcU1WVYTO+4actf7J16VhCg/3u2fbmlaVtv58g4VoKj3duAkDrJrVITE7nwIF9NAt1gYwrbN37N0ajkXCvY6jnTtO6SiKTvj1P9omvcHKvCK5+bNl+lLoh/lTwdsfVRc+hNdMtxps0/0dS0jKZO+EFqt6R9C35cRfNG4TQuF41K/9EhBD2QBIsW9LoysBVLBW0elsHIcqxodO/Zvn63/lxwet4ursSd9k0H8rb0xVXF9O/zaU//kq9mpXxq+DJ74fOMGLWMoa91JW6oUGo2UnU9bhIt2YVGTjlaxa+Voccg8qwRSd5pq0/lX1Nuyw81y6At1de4NWFJxn1ZDWOR53k/1aeZE6/Whj/WY9zhXo0qBWComjNsfl4mfbhvDkf7Kbk1Ay+37Sf90Y/Vxo/IiGEDThMgnXp0iXGjh3Lhg0bSE9Pp1atWixZsoQWLRx4cqjGyfETLFUFraNt8yPKkkUrtgPwUJ93LM5/PqM/fZ5sC0DkhTjenPs915LSCKlSifGvPc6wpxthjNoIGfEAfDm8HsM+PU23yYfRaODJ1n7MfaWWuT9vdx3rJzdm2OLTtB51gEpeTrz5TAivdK0MmVdQY3dD/D5Un7ooFR9A0dx7XtXKX/5AVeG57q2s/eMQQtgJh0iwrl+/Tps2bejUqRMbNmzAz8+P06dPU6FCBVuHVjzuVSDptK2jKCYVxb2yrYMQ5VjuiSUFtpk18mlmjXwaANWYg5pwEC7+z6KNr6cTX48My7efRiEebJ/Z9N4NjNlw7Shq8lkIascXM1/Js9mAZzpa1M4SQpQ9DpFgvfPOO1StWpUlS279IQ0NDc3nGY5B41sfY/I5UA22DqVYFJ+7q1ULYY/U9DjU2F8hN73gxsWRm44avcl0Ncuveb5Xs4QQZZNDlGn4+eefadGiBU8//TT+/v40bdqUTz/99J7ts7KySE5ONn+lpKSUYrSFp3jXcfjkCucKKM4OfiVRlAvGa8dRozeVfHJ1u8RI1AtrUXPTCm4rRCn4+OOPadSoEV5eXnh5eREREcGGDbf2wczMzGTw4MFUrFgRDw8PevXqRXx8vEUfUVFRdO/eHTc3N/z9/Rk9ejS5uZaLnXbs2EGzZs1wdnamVq1aLF269K5YFi5cSEhICC4uLrRq1Yp9+/ZZPF6YWOyZQyRY586d4+OPP6Z27dps2rSJQYMG8frrr/Pll1/m2X7WrFl4e3ubv8LC8r/sbyuKT11bh1BMGpQK9W0dhBAFMl45BJcP2GbwnFTUC7+g5tjnBz1RvgQHBzN79mwOHjzIgQMH6Ny5M0888QTHjx8HYMSIEaxdu5ZVq1axc+dOYmJi6Nmzp/n5BoOB7t27k52dzW+//caXX37J0qVLmTRpkrnN+fPn6d69O506deLQoUMMHz6cV155hU2bNpnbrFy5kpEjRzJ58mT+/PNPGjduTLdu3UhISDC3KSgWe6eoqqraOoiC6PV6WrRowW+//WY+9/rrr7N//3727t17V/usrCyysrLMx5cuXSIsLIzo6GiCg4Pvam8rqjGX3HXdTfM2HJGiQVOvL9q6/7F1JKKMi42NZdEHUxnweD2C/Hzu67nGq0fhyp8lFFlhKaBzRaneHUXndt/Pjr2cyKdrTzJw5GSCgoJKID7hqC5evEjVqlWL9f7m6+vLe++9x1NPPYWfnx/Lli3jqaeeAuDkyZPUr1+fvXv30rp1azZs2MBjjz1GTEwMAQEBACxatIixY8dy+fJl9Ho9Y8eOZf369Rw7dsw8xnPPPUdiYiIbN24EoFWrVoSHh7NgwQLAtAtD1apVGTp0KOPGjSMpKanAWOydQ1zBCgoKuusqVP369YmKisqzvbOzs/nyp5eXF56ehauqXNoUjQ4l+CG4bVm3Q1GNaKp2sXUUQtyTmnzeDpIrABVyM1Cj/4fq6NMCRJlhMBhYsWIFaWlpREREcPDgQXJycujS5dbf9Xr16lGtWjXzxYy9e/fywAMPmJMrgG7dupGcnGy+CrZ3716LPm62udlHdnY2Bw8etGij0Wjo0qWLuU1hYrF3DpFgtWnThsjISItzp06donr16jaKyHq0NZ5wzHlYihYloDWKW6CtIxEiT2puOmrcbwU3LDUqZCehXjlk60BEGZSSkmIx9/j2uzh3Onr0KB4eHjg7OzNw4EBWr15NWFgYcXFx6PV6fHwsrxIHBAQQFxcHQFxcnEVydfPxm4/l1yY5OZmMjAyuXLmCwWDIs83tfRQUi71ziFWEI0aM4MEHH2TmzJk888wz7Nu3j8WLF7N48WJbh1Zsik9d8K4NSWcAu79be4tqQFPjSVtHIcqZy9cKN49JRUWN2Q3pdji5/NpBlMwKKC6+Bbe9obDftyi/7rzLM3nyZKZMmZJn27p163Lo0CGSkpL4/vvv6dOnDzt37iyFKMsXh0iwwsPDWb16NePHj2fatGmEhoYyb948XnzxRVuHZhXamj0x/PlOwQ3tias/ir8DF3kVDsXNzQ0nNx9+2nOpUO3V9BjUq0dKOKqiUkD3A0rggxZV3wvi5OaDm9v9z98S5cOJEyeoUqWK+djZ+d4FoPV6PbVqmYroNm/enP379zN//nyeffZZsrOzSUxMtLhyFB8fT2Cg6W5FYGDgXav9bq7su73Nnav94uPj8fLywtXVFa1Wi1arzbPN7X0UFIu9c4gEC+Cxxx7jscces3UYJUKp0hn+XgoZlwHH2J9QW/cl84a3QpQ0b29vho4cT3p6wSUWVEM2uTtehWx7LoCroKnfCm1I90I/w83NDW9v7xKMqfiSkpIK9RqVVbZ8jTw9PfHy8irSc41GI1lZWTRv3hwnJye2bt1Kr169AIiMjCQqKoqIiAgAIiIimDFjBgkJCfj7mzYv37x5M15eXuaraBEREfzyyy8WY2zevNnch16vp3nz5mzdupUePXqYY9i6dStDhgwBKFQs9s5hEqyyTNHq0TafgGH3MFuHUjBFi1KpMUr1R20diShnbpZdKYjx4lYMHtmAPW/hpEDqdnSB/VEUxdbBWEVSUhL/98EsctITbR2KzTi5+TB05Hi7ToTHjx/PI488QrVq1UhJSWHZsmXs2LGDTZs24e3tTf/+/Rk5ciS+vr54eXkxdOhQIiIizKv2unbtSlhYGL179+bdd98lLi6OiRMnMnjwYPNVs4EDB7JgwQLGjBlDv3792LZtG9999x3r1683xzFy5Ej69OlDixYtaNmyJfPmzSMtLY2+ffsCFCoWeycJlp3QVGqEWqMXxnM/YtdzsTROaJuOKTNvCqLsMZz9ERQNqPZ8NViFtBjUK3+h+DWzdTBWkZ6eTk56Ij3aVMHP1z5Xbpeky9dS+GnPJdLT0+06wUpISOCll14iNjYWb29vGjVqxKZNm3j44YcBmDt3LhqNhl69epGVlUW3bt346KOPzM/XarWsW7eOQYMGERERgbu7O3369GHatGnmNqGhoaxfv54RI0Ywf/58goOD+eyzz+jWrZu5zbPPPsvly5eZNGkScXFxNGnShI0bN1pMfC8oFnvnEHWwissadUJKg5qbSe62vpCegL3eKtQ2GYXmPm5rCFGa1KRz5G7vb+swCkfRogS2Qddqqq0jsYri1CorC2xVq8xR3t/KI5lEY0cUnQvaFm+BRgvY2xUiBSWondwaFHbNGP0/x6krpxpQY3ej5qTaOhIhRAmQBMvOaHzD0LZ623SLw26SLA2KX1O0LSbKrUFh14zXjjtYXTkjauIZWwchhCgBkmDZIU1AS7Qtp974JG7rhEZBqdQYbau3UbR6G8cixL2pqhGSTts6jPukoCaesnUQQogSIAmWndIEtUH74Dug1dv0locS1AZtxGwUnavNYhCiUFIvguHe1avtkqKgJkUW3E4I4XBkFaEd0/g1Q+n8ObkH34FrR0tvYEULihZNw4FoQp8oF/WuynP9Hkeor1QYqsNdvQJUI+q1v20dRamYvXgdP205yMlzcbi6OBHRpBaz3niauqGmCeEXLl2h1sOj83zuig/+y1P/CgcgKuYqg6d9xY59J/Fwc6b3E22YOeIpdLpbH0R37DvJ6HeWc/xMDFUDfZkw8HH6PNnWos9L8dcZ//53bPz1KOmZ2dSq5s9nM/rTomGouc3fZ2MY/8Eqdu2PJNdgIKxmZb6bN4RqlSta+8cjyiBJsOyc4l4FXbt5GM+vwXhskWl+SUnPMakQhq75OBR3ey7UaD3lvX6PI9TuKQw1KxHTLXUHWxidnWTrCErFrgORDHr+IVo0DCXXYGDivB945JX3Obp2Bu5uzlQN9OXiznkWz/l01Q7e/2Ij/2r3AAAGg5F/D5pLQCVvfv32TWIvJ9J3/Kc46bTMGPEUAOcvXubfg+by6jOd+Ord19j2+wlenbSEQD9vurU19XM9KY32L86gY8v6rPtkJH6+npz+J54KXu7msc9GJdDhPzPp26s9kwf3wMvDlRNnLuHi7FRKPzHh6CTBcgCKokFb40k0AS0xHP4QNWGflev8aAAj6L3R1OtTbq5a3VSe6/c4Su2eQjFk3/i9cKRJ7oAxx9YRlIpfFr9hcfzFzP4EtR3GwRMXaN+iLlqthkA/y3+Da7b8ydP/CsfD3QWA/+05xomzMWz6fDQBlbxpUr8aU4f2ZPwHq5g8uAd6vY5PVm4ntIofc8Y+B0D9mpXZ8+dp5n/1P3OC9e7nvxAc6MvnM2+V9AgN9rMY+635P/BI+0a8M+oZ87ma1fyt9wMRZZ4kWA5Eca+C7sF3UNMuYTy/FuOFdZCbhjlBuu8OtaY3I98GaGv2RAlqi6Ipv/8k/Hw9y2X9nrLDwa5c3VT2SxHmKSklAwBfb/c8Hz94/AKHTkbx4Vv/MZ/7/fBZHqgdTEClW4lY17YNGTztK/6at4DGAR78/lcCD0VYbnzctU1DRs5ebj5et+0QD7dtyLPDF7LrQCRV/Csw8PnOvPJ0B8C0bcsvO48wqv8jPDJgDof+jiKkih/jBnTniS5lozCsKHnl5zJFGaK4V0HbcCC6R35A23QsSpUO4Hr7JysNKLo7vm6bKK91Bt8GaGo+ha7zFzi1/xBNlY7lOrkSZYBG75jJirb83XIyGo2MnL2cB5vVpmHtvItjLvlhF/VrVObBprXN5+KvJOFfyXK/vYCKpuP4VNOVwPirSfhXtGzjX9GL5NQMMjKzATh3MYFPVmyjVvUAfln8Bq8914nhM7/lq592A5BwNYXU9Eze/Ww93do+wIZPR9GjSzOeGraAnftPWueHIMo8eUd1YIrWGaX6v9BU/xcAanYqatIp1MTTkJ0MxmwwGkwrEbV6FI+qKD51wSO4XN0CFOWDovfAXndAyJcu7ys4ZdnQ6d9w/PRFdn4zIc/HMzKzWb7+d94c+O8SGd9oVGneMMQ8b6tpWHWOn77EJyt38FKPthhvTL/4d+emDO9j2t6lSf1q7D10hsUrd9AhvF6JxCXKFkmwyhBF72Ha16yM7G0mxP1QvGrZOoQi0Jg+9JQjr7/9Net3HmL7V+MJDvTNs80P/ztAekY2vZ940OJ8QCVv9h85Z3Eu/mqy6TEP05XAgIreJNw4d1PC1WS8PFxxdTHV8gvy8yGspuUinno1g/hx8wEAKvl4otNpqX9nmxpB7PnTAVerCpuQyxhCiLLBK8R0O9yRKKBUKB8JlqqqvP721/y05U82fzHmrknlt/vih1083rkpfr6Wt/paN67J0dMXLRKoLb8dx8vDhXoV3W60qcG2309YPG/L3uO0blLTfPxgs1pEno+zaHPqQry5/IJer6NFwxBO3dHm9IV4qkuJBlFIkmAJkY/Zi9fR+pmp+LQYRFDb1+k55EMiz8fe1W7voTN06fsOXs1fo0L4IDr2nmWe73G7rOwcmj85CV1YXw79HWXx2JHIaDr8ZybuTQYQ0nkk733+i8XjnfvMRhfW966vxwfONbdZvfkA/3plDv4RQ/IcoyxTNDrwCi24oT1RjSg+dWwdRakYOv1rvl27l6/few1Pd1fiLicRdznprt+TM//E8+uBU/Tr1f6uPrq2aUhYzcr0GbeYwyej2LT7KJM+/JFBz3bCWWd6O3v16Q6cu3iZsXO+4+S5WD5evo1VG/cz7KWu5n6GvdSVP46cY9Yn6zjzTzzL1+3ls1U7+O/zD5nbjOr3CN9t2Mdnq3Zy5p94Fn67hXU7DjHwuc4l9BMSZY2DfdwTonQVVLsHTMlV91c/YOyA7syf8B90Og1HTkaj0dy9zdG4Od8R5O/D4choi/PJqRk88socHooI46PJfTh2+iKvTPwCH083BjzTEYDv5w8hO+dWCYKriak06zmJp7qFm8+lZWTTplltnv5XOK9NWmr9H4id0/jWx5h8zqFKNZSXBGvRiu0APNTnHYvzn8/ob1EEdMmPvxIcUIGubRrc1YdWq2HNR8MZPO0r2r4wA3cXJ3o/Up/Jz1VDOW26ahXiEs2ad59k1IJf+b+vNxMcWIHF0/qaSzQAhD9Qg+8/HMLEud/z9sdrCA3244NxL/DC4xHmNj26NOejyS/xzqfrGT7zW+qGBLJq3mDaNi8fr5coPkmwhMhHQbV7AN6YvZwh/+nC2AHdze1uVqe+3YZdR9j823G+mzeYjb9aVuZftm4v2TkGPnu7P3q9jga1q3DoZBTzvtxkTrB8fTwsnrNywx+4uegtEqz//Ns0Z+XCpStF/6YdmFKlE5z/2dZhFJIGpWJDFOcKtg6kVOSeWFKodjNGPGWefH4nNSeVqvoofh4bAjm33apLPYUacOOGTNJJOgSr7J9dB6gDTp7g6Yaak4ridOt36LGOTXisY5N8Y+nbqz1987iSJkRhyC1CIe7DnbV7Eq4ms+/IOfx9vWj7wttUbjeMTi/NZvdByw18468kMXDyUpbOHoCbq/Nd/f5+6CztWtRBr7/1madrm4ZEno/jelJanrEs+WEXzz7aynwlTYBSsTF4BGP7TdILw4imRk9bB+EQ1LQYjBe3op77wbRtWE7KnS1QNAqKJo9K/jkpcO0o6rkfTH2kxZRW2KKckwRLiELKq3bPuYuXAZi28CdeeaoD6z8ZSdOw6nTt9x5XK25fAAAgAElEQVSnL5gmyKqqSr8Jn/Pqsx0t9jm7XdyVJHM9n5tuHsdduXsrlX1HznHs9KU856mUZ4qi3EhaHKAelt4HJaiNraOwa6ohE+OlHagXN0PapeJ3mHYJ9eJmU5+GzOL3J0Q+JMESopBu1u5ZNmeg+ZzRaKqXM+CZjrzcsx1Nw6rzwbjnqRsayJIffwVgwTdbSEnPZNyAx6wWy5IfdvFAnWBaNqphtT7LCk3Vrqaio3ZNgyb031LcNx9qygXUc6sh9Z+bZ6zRq+k/qf+gnluNmnLBCn0KkTf57RaiEO5Vu+fm1jp31dSpEUR07DUAtv/xN78fOoNbkwEWbVo9M5XnH23FojANfkkJxDtb/jrerO8TWMlyf7a09CxWbtjHlKE9rPPNlTGKkzuaur0x/v25rUO5BwX0nmhq9rJ1IHZJRUVNOAjXj5fsQMZs1JidqBWuoPg3R3GI28rCkUiCJUQ+VFVl2Ixv+GnLn2xdOvau2j0hVSpR2d+HyAt318vp1s60amnehBeZNuzWXJuYhEQeHfA+y98fRHhYNdj6My2reDLt99Pk5OTi5GT6tdzy23HqhgZS4Y692r7ftJ+s7BxefNyyCKO4RVP7eYyXdkLKOStuim4tKtqmo1H0XgU3LWdUVNS43yHpVMGNreX6cVRjDgS2liRLWJUkWGWUmpFB4qMdAfD5ZQeKq6ttA3JQQ6d/zfL1v/PjgtfNtXsAvD1NVaEVReGNfo8wdcFPNK5blcb1qvHVmj2cPB/LynmDAczFC2/ycHMBoEZVf4IDK5AFPBPmx+yDVxjw1hJG93+U42cu8n/fbOb9sc/fFdMXP+ziiYeaUfGOVYUA1xJTiYq9RkzCdQBOXTDV7Aqs5E2gn/dd7csqRaNF12ICudsHFNy4NCkalCqd0MjcqzypCQdLN7m6KekUqsYJxb9F6Y8tyixJsITIR2Fq9wx7qSuZWTm88c5yriWl0ahuVTZ+Noqa1fzv6u9evJ11/PLJcF6fuZyWT0+hUgVPJg76t7lEw02R52PZ8+dpNnw2Ks9+1m4/RP83b90ae+GNRQC89d8nmDykfN1SVLxC0dTvi/HEZ7YOxUTRgJMX2kav2zqSEnX52p0r/ApHTY1GjfvTytHch2t/oqQ5o3hULdLTi/p9i7JLEiwh8lHY2j1jB3S3qIN1L6pqpHolLTl730TNikdN+AdtTS2o8IBvAjsWPgMuFcHZGyWPNSh1Q4PyjanPk20tijaWd5raz6OmRKNGb7J1JKBxRvfge2X21qCbmxtObj78tOf+V/upxmzUmF9BzSmByO6D8jNK5XYoRVwk4eTmg5ubm5WDEo5KEiwhSpiKEVIvoV4/CelxwM05Qab5HppKimlxU1IkatLNlVIaVLdAlAr1wKNKnsmWKJiiaNA2HY3BkIkaswvblG/QgNYJ7YPvovg44obUhePt7c3QkeNJT0+/7+fm/jUHNT7A9vPlFA1KgC+6pm8U3DYPbm5ueHuXn1vxIn+SYAlRQlQ1F/Xa33D9bzBk5NUCMNVuMuVat7/5GyE9BjU9BrSuqBXqo/jWR3G0zYztgKLRog1/C8Nf76NGbSjlwbWgdUXXdg6KT9nf1Nnb2/u+Ewzj5YMYsv+ECk4lFNV9yj6IVheDxq+5rSMRDk4+FgtRAtSMBNRza+DKn/dIru6DIQOu/Il6bg1qRoJ1AixnFEWLtuloNI1eN9XIUrSlM65vA3SdFpeL5KqojGd/KLXXo1AULcazP9o6ClEGSIIlhBWpqgFjwn7TlZLcVOt2npuKGrXB1L8DbWZsLxRFQVvjSXQPLYEKYSU4kBY0ejSNXkfbdi6K+937UgoTNT3eVJbBnv49qwbUuL2o6fJhRhSPJFhCWIlqzEGN3gLXT5TsQNdPoF7cYqrdI+6b4l4ZXbt5aBoNA2efG2et8KdQMfWh+Ieje2gJ2hpPoijyJzY/xgvrsMt9IxUF44W1to5CODiZ0CGEFahqrim5yiylT73pcabxqj0s87KKQFE0aGv0QBPyGGrcbxjPrUa9cohbiVYhJ1srWtPVFycPNCH/RhPyOIp7YEmFXeYYY3ZS6J91aVKNGGN2oQ3rb+tIhAOTv8xC3FDk+j2oqDG7IT3WyhEVJBoSN6BUblvkCtTlvXaPotGhVG6PpnJ71JQojHF7URMjUa+dgIz4ez9R6wzetdFUqIfi2xAlMAJFa+/7H9oXNTcDUi/aOox7S72ImpuJonOxdSTCQUmCJcq94tTvAVBTo1BL+rbgPcWgVMhA8ahW5B6kdo+J4lkNreetn6OanWracDg9ibTJo0FVcZ/+ARrvKuARLLf/iklNOottymYUlhE1+SyKbwNbByIclCRYotwrTv0eNT2B3N3DwVC54MYlRZuBru0gFLfCV46/ndTuyZui90Cp2BDVLQPDedMkbE3FJrLtlJWoiacwzb+y1yRLMcUoCZYoIkmwhKBo9XtUVcWw511UHy2oziUUWSEoWpTYL9G2ed9UU0sIR5B55cYctlxbR5I3RQsZV2wdhXBgco1biCJSr58wTYy29RJz1YB65S/U63/bNg4h7och29YRFMyQZesIhAOTBEuIIjKe+8l+CiQqWlM8Qggh7IIkWEIUgZqViHppu+2vXt2kGlAvbUPNSrJ1JEIUjiOsutTa8Na/cHiSYAlRBMZL222/Me2dVKMpLiEcgUsl+/mAkhfVAK6VbB2FcGCSYAlRBOr1k+bK3XZD0ZjiEsIBKD51sN8VhACq7CEpisXO3iGEcAzqteP29+lbNZjiEsIBKN61sMttcsw0KN41bR2EcGCSYAlxn9TcDEiLsXUYeUu7ZIpPCDun6FzAI9jWYdybRzCKzMESxSAJlhD3KyMB+721od6ITwj7p6ncwf5utQMoGjSV29s6CuHg7PBfthB2zpBp6wjyJ7V7hIPQhDwGqh1+WFFVNCGP2zoK4eAkwRLiPtnj+4Eluw9QCAAUtwCUwNb2U08OQNGgBD5Y5K2nhLhJEiwh7pNi7/V7NDJvRDgOTc1e9rVgRDWiqdnT1lGIMkASLCHul4ufrSPIn4vU7hGOQ+PXHMVe5mIpGpTKHdH4NbN1JKIMsIN/0UI4FkXvAa52evvANcAUnxAORNt4OOjcsW3ZBgV0HqZYhLACSbCEKAKlQhj29+ujQfENs3UQQtw3xdkHbZM3sO38QRVt0zdQnL1tGIMoS+ztHUIIh6BUsM8Kz1J5WjgqTZUOaGo/Z7vxaz8npRmEVUmCJUQRaCp3BOxsL0KMaKp0sHUQQhSZJuxVlJB/l/q4SsgTaMJeLfVxRdkmCZYQRaC4B6IEtLKf5eWKBiWgNYpboK0jEaLIFEVB23j4bVeySnJOlqlvTe3n0DYehqLY87Y9whFJgiVEEWlq9LSf5eWqEU2NJ20dhRDFpigK2gavoQ2fAk4eJbO6UNGAkwfa8CloG7wmyZUoEZJgCVFEin8L8Ayx/fJyRQOeoaZ4hCgjNFU6oOvyNUrQjXlR1rhafKMPJag9ui5fyy11UaIkwRKiiBRFg675m7YOAwBd8wkotk70hLAyxdkbXcvJaNvMMd2SRynaBxpFAygoAa3RtpmDruVkWS0oSpzO1gEI4cgUn1po6vTGGPkVtllirqCp+xKKTy0bjC1E6dD4NUfj1xw1PQHjhbUYY3ZB6kVMC00UULSouTkAKDqnG7fuVUADHsFoKrdHE/K4bH8jSpVDJFhTpkxh6tSpFufq1q3LyZMnbRSRELdo6r6IMe43SD4LaimuLFQ04FUTTZ0XSm9MIWxIcfNHG9YfbVh/1NxM1OSzqImnUFNiyf5xGQDOPZ9B8QxC8amL4lUDRedi46hFeeUQCRZAgwYN2LJli/lYp3OY0EUZp2ic0D34Lrm7hkB6XOlMfFe04BaE7sH3UDROJT+eEHZG0bmg+DYA3waoGRlk7/oSALdxfVFcXW0cnRAOlGDpdDoCA2UJurBPirMPunbzyd09EtIuluyVLEUD7lXQtf1A5pEIIYSdcphZsadPn6Zy5crUqFGDF198kaioqHu2zcrKIjk52fyVkpJSipGK8kpxqYiu/f/dmIxbguMEtDKN41KxRMcRQpQ9s2bNIjw8HE9PT/z9/enRoweRkZEWbTp27IiiKBZfAwcOtGgTFRVF9+7dcXNzw9/fn9GjR5Obm2vRZseOHTRr1gxnZ2dq1arF0qVL74pn4cKFhISE4OLiQqtWrdi3b5/F45mZmQwePJiKFSvi4eFBr169iI+Pt84Po4Q5xBWsVq1asXTpUurWrUtsbCxTp06lXbt2HDt2DE9Pz7vaz5o16645W44gOzv7rn+gRaVmZJBuMN2q0qeno6jWm4Ct0+nQ6/VW668sUfReaFvNQL24BcPheWDIss4tQ0ULWme0jYejBHeRuj1CiCLZuXMngwcPJjw8nNzcXCZMmEDXrl05ceIE7u7u5nYDBgxg2rRp5mM3Nzfz/xsMBrp3705gYCC//fYbsbGxvPTSSzg5OTFz5kwAzp8/T/fu3Rk4cCDffvstW7du5ZVXXiEoKIhu3boBsHLlSkaOHMmiRYto1aoV8+bNo1u3bkRGRuLvb1qQMGLECNavX8+qVavw9vZmyJAh9OzZkz179pTGj6tYFFW14jtvKUlMTKR69ep88MEH9O/f/67Hs7KyyMrKMh9funSJsLAwoqOjCQ4OLs1QCy07O5v9+w+SlpZplf7U7GzS3hoDgPv0d1GsmBC5u7sQHt683CZZhU6Es66hPf01Suw286qm+0mLTL+YN1ZIBXXGULs3OPsW6rmSBFuPmpFB4qMdAfD5ZYfM77FD5fk1unjxIlWrVi3y+9vly5fx9/dn586dtG9vqjnWsWNHmjRpwrx58/J8zoYNG3jssceIiYkhICAAgEWLFjF27FguX76MXq9n7NixrF+/nmPHjpmf99xzz5GYmMjGjRsB08WT8PBwFixYAIDRaKRq1aoMHTqUcePGkZSUhJ+fH8uWLeOpp54C4OTJk9SvX5+9e/fSunXr+/5+S5NDXMG6k4+PD3Xq1OHMmTN5Pu7s7Iyzs7P5ODk5ubRCK7Lc3FzS0jJxd6uAXu9c8BMKoGZnoXc2Xd1z9wlAsUKfANnZWaSlXSc3N7dcvoHffyLcGp1LYwJzDxOcuw9XNREAIxoUjBYJlwqoaNDc2OMwU/Hhoq4lcbrG5Ca6wv7ThY6zvCfBQpQ3KSkpFu91d74P3ktSUhIAvr6WH96+/fZbvvnmGwIDA3n88cd56623zFex9u7dywMPPGBOrgC6devGoEGDOH78OE2bNmXv3r106dLFos9u3boxfPhwwPS39ODBg4wfP978uEajoUuXLuzduxeAgwcPkpOTY9FPvXr1qFatmiRYJSU1NZWzZ8/Su3dvW4didXq9M65W+PSlajQYtKaqxa4uriiF+EUrrLR0q3XlcIqaCKcRSqT6BE7GRNyy/8E1JxrXnGi0hjTU6LMYc4EaDclwrk6GU1XS9dXJ0fiAonD3TfD8lfckWIjyKCwszOJ48uTJTJkyJd/nGI1Ghg8fTps2bWjYsKH5/AsvvED16tWpXLkyR44cYezYsURGRvLjjz8CEBcXZ5FcAebjuLi4fNskJyeTkZHB9evXMRgMeba5WYIpLi4OvV6Pj4/PXW1ujmPPHCLBGjVqFI8//jjVq1cnJiaGyZMno9Vqef75520dmiinip4Iu5FNZbKJIAlQs7JInfUqAB7zXzcnwjqK98tZnpNgIcqjEydOUKVKFfNxYa5eDR48mGPHjrF7926L86+++qr5/x944AGCgoJ46KGHOHv2LDVr1rRe0GWcQyRYFy9e5Pnnn+fq1av4+fnRtm1bfv/9d/z8/GwdmhBCCGFznp6eeHl5Fbr9kCFDWLduHbt27Spw7larVqaV0WfOnKFmzZoEBgbetdrv5sq+m+WUAgMD71rtFx8fj5eXF66urmi1WrRabZ5tbu8jOzubxMREi6tYt7exZw5RpmHFihXExMSQlZXFxYsXWbFihWTRQgghxH1SVZUhQ4awevVqtm3bRmhoaIHPOXToEABBQUEAREREcPToURISEsxtNm/ejJeXl/lWZUREBFu3brXoZ/PmzURERACg1+tp3ry5RRuj0cjWrVvNbZo3b46Tk5NFm8jISKKiosxt7JlDXMESQoj7ISVPhMjb4MGDWbZsGWvWrMHT09M8l8nb2xtXV1fOnj3LsmXLePTRR6lYsSJHjhxhxIgRtG/fnkaNGgHQtWtXwsLC6N27N++++y5xcXFMnDiRwYMHm29NDhw4kAULFjBmzBj69evHtm3b+O6771i/fr05lpEjR9KnTx9atGhBy5YtmTdvHmlpafTt29ccU//+/Rk5ciS+vr54eXkxdOhQIiIi7H6CO0iC5bA++/xj5s9/l/+82JexYyfl2WZ9QhwzzpyC8Hrmc3q9noMHIvNsP236m6xatYwxo9+id+9+JRJ3eVKY12jN2h9567ddpoMbr9Odr9FHH81jw8a1xMfFonNyIizsAV4f+gaNGjUt8e/BEZVIyZMU00bC7nsPSMkT4dA+/vhjwFSK4XZLlizh5ZdfRq/Xs2XLFnOyU7VqVXr16sXEiRPNbbVaLevWrWPQoEFERETg7u5Onz59LOpmhYaGsn79ekaMGMH8+fMJDg7ms88+M9fAAnj22We5fPkykyZNIi4ujiZNmrBx40aLie9z585Fo9HQq1cvsrKy6NatGx999FEJ/XSsSxIsB3Ts2GG+X7WMOnXqFdjWXatl7bodt1YR3qMQ09atmzhy5C/8/QPybiDuy/2+RiuahuM+/T3T63THa1S9eigTJkwlOLgaWZmZfP3157w2sA/r123H11equd9JSp4IcW8Flb6sWrUqO3fuLLCf6tWr88svv+TbpmPHjvz111/5thkyZAhDhgy55+MuLi4sXLiQhQsXFhiTvZEEy8Gkp6cxbvxwJk+ZxeLFCwpsrwCVKvnlW6YhPj6OmbOm8MmiLxk8RK5cFVdRXqOKej0e93idund/wuJ49OiJ/Lj6O06dOknr1m2sFXaZIyVPhBC25BCT3MUtM2ZMol27zkS0bluo9hkGA90e70yXhx9k6OsDOHPmlMXjRqORCRNG0vflV6lVq05JhFzuFOU1evLAHzzcvWOer9HtcnKy+f775Xh6elK3bn1rhSyEEMLKJMFyIBs2rOXE38cZPmxModpXc3VjQq26zJ+zkFmzPkA1Gun90lPExcWa23zxxSK0Oi0vvvhyCUVdvtzvaxRSPZQJteryTv0GzJr2bp6vEcDOnVtp2aoBzVvU4+tvvmDxJ19ToULhts0RQojy6ujRo3zxxRcWVe4zMjIYNGgQVapUoVatWixatKhExpYEy0HExcUw+52pzJ49t1AF5AAe8PTiEf8A6tWtT3iL1sydu4gKFXxZ9f0yAI6fOMo33y7h7elzZPNgKyjKa9S4UVMe8Q+gjrsHLZq3vOs1uik8PILvV63n669+oE2bDowaNYSrV6+UxLchhBBlxttvv81bb72Fp+etPTEmTJjAJ598QkpKCtHR0QwePJjNmzdbfWxJsBzE8RPHuHbtKs8++zhNmtaiSdNaHDjwB98uW0qTprUw3FhGnh8nJyfq1QsjOuofAP48uJ9r167StVsbc58xMZeY8/4Muv2rcLe3xC0l8Rrd5ObmRrVqITRu3JRpU99Bq9OxevV3JfWtCCFEmbBv3z46depkvoiQm5vLkiVLaNmyJQkJCZw/fx4/Pz/mz59v9bFlkruDaN3qQX78YaPFubcmjSE0tAb9+g5Ee2MSbn4MBgOnT0fSrl0nAB5//Mm7JkkPHNSHxx57kh5PPGW94MuJkniN7sVoNJKdnV2seEXhSmncbsP/1jP2zTfo1OlhPpy/OM82Uu5ECPtx+fJlqlataj7ev38/ycnJDBw4EBcXFypXrswTTzxR4IrIopAEy0G4u3tQu3Zdi3Ourq74eFcwn58wYST+AYHm+T9fRP9DA09P6l6MJiUrk6VLFxMbe4lePZ8FwMenAj4+FSz61Ol0VKroR2ioVMq/X0V5jRZ9upDaidcIdnHFcPI4X3671OI1Sk9P59NPF9KxYxf8/Py4nnidFSu+JiEhjq5dHy3V76+suZ9SGgCxmZm8P/9dmjULv2cbKXcihH3R6XRkZWWZj3fs2IGiKHTqdOtDbMWKFblyxfpTLiTBKkNi42JQNLfu+qbk5jL7zGmuPfMoXl7ehIU9wNdf/UDNmrVtGGX5dudrlJySbHqNcrLxGvYaYQ0sXyOtVsv5C2f5+Y0fuH79Oj4+PjRo0Igvl34nqz6L4X5LaRhUlSmnT/LfYWP488hfpKQk39VGyp0IYX9CQkLYvn27+XjVqlWEhoZSvXp187lLly5RsaL1awpKguXAlnyxIt/jYaE1GRZaE4/5iwtdu2fTxt0FNxKFVtBrNGbkeP77z3mAPF8nZ2dn5s0tmRUu5dntpTQKk2Atif6HCk5O9HziKf48cnfhRCl3IoR96t27N6NHj6ZVq1Y4Oztz+PBh3nzzTYs2R44coXZt6194kARLCFGu3CylsWL5mkK1//PQQdYmxPFl4+b3bCPlToSwT0OGDGHfvn18//33qKrKo48+yoQJE8yPHz9+nMOHDzN16lSrjy0JlhCi3LhZSmPx4q8LVUojLS2VCZPHMK5mHXycnPJsc7PcyXcr10m5EyHsjLOzMytXriQ5ORlFUSzKNQAEBATw119/ERISYvWxJcESQpQbt5fSuMlgMHDw4D6Wr/iKgwciLVZ7RkdHERNziTExl0wnWjfAaDQC0KRpLdb+vNWi3Mntfc55fwbffPuF3HYXwg54eXnleb5SpUpUqlSpRMaUBEsIUW7cbymN0NCa/LD8Z9Jnm24fuI2bzILF/0d6Whpjx04iMDBIyp0IIfJktQTr0qVLnD17lhYtWuDm5gaYdu3+4IMP+Pnnn3F1dWXEiBF069bNWkMKIcR9ud9SGs7OztSuVYdUd3cAPGrVwdPT9En4ZnsfH72UOxHCTmk0mgJv3SuKgpeXF3Xr1uXJJ59k6NChVtko3moJ1sSJE/n555+Ji4szn5s1axYTJ040H2/bto3ffvuNFi1aWGtYIYSwqjtLaQghHFf79u1JSkri8OHDaLVaqlWrRkBAAPHx8URFRWEwGGjUqBEGg4EjR46wb98+vv32W3799dd73lYsLKslWHv27OGhhx7C6cZEUFVV+fDDD6lbty4bNmwgLi6Obt26MWfOHFasWFFAb+VXdnZWwY0KQc3OIvPG1izazAyUG/NGista8QlhLwoqpXGnGW/PKbBPmXclhH345ptvaNu2LS+99BJvv/02wcHB5scuXbrExIkT2bFjB7t378bb25tRo0axePFiZs6cyezZs4s1ttUSrPj4eItZ+IcPHyYhIYFJkyYREhJCSEgIPXr0YNeuXdYaskzR6XS4u7uQlnadtPTi96dmZJKWlQJAxl970NauZ7VP5e7uLuh05Xv6nj0nwpIECyGEyahRo6hcuTJLly6967EqVaqwZMkS2rRpw6hRo1i+fDkfffQRu3fvZvXq1faTYBkMBlRVNR/fLEffuXNn87ng4GCLW4jiFr1eT3h4c3Jzc4vdV/bunaQv/RDV88ay8lVLUCr54zZwKPq2HYrdv06nQ6/XF7sfR+QoibAkwUIIAVu2bOG1117Lt02HDh349NNPAdOcrXbt2uWZkN0vq/0FrlatGvv37zcfr1mzhsDAQOrVu7XPV1xcHN7e3tYasszR6/XFTlyyd20na9YUXAFuXxF1/SrqrCnops5G3z7/jYTFvTlKIlyek2AhhLgpMzOT2NjYfNvExsaSkZFhPvb09LTKB1SrJVg9e/Zk1qxZPPfcc7i4uLBr1y7++9//WrQ5efIkNWrUsNaQ4g6qwUD6gg/ybZO+YC5Obdqj3LEcXRSeJMJCFF92drZVPqgAqBkZpN+41a5PT0e57W5KccmHFcfWrFkzVqxYwYABA4iIiLjr8T/++IOVK1cSHn5rE/dz584REFD8DdutlmCNHj2aTZs28d133wHQoEEDi9LzUVFR/PHHH4wZM8ZaQ4o75B49hHo5Id826uV4co8ewqnJvbf9ECVLEmFR3mVnZ7N//0HS0jKt0p+anU1aSg4A7nsPoFgxIXJ3dyE8vLkkWQ5q+vTpPPzww7Rr145///vftGnTBn9/fxISEtizZw9r165Fo9Ewbdo0AFJTU9m0aRPPPPNMsce2WoLl7e3Nvn37OHz4MAANGza0uMRmMBhYuXIlLVu2tNaQNlW3Zj9bh3CXh92SmF6IgrQjes9kc7p93aqNPPuFrUMoNZIIi/IuNzeXtLRM3N0qoNcXbiP6/KjZWeidTVuguPsEoFihTzAtGElLu05ubq4kWA6qQ4cOrFu3jldffZWffvqJn376CUVRzHPGq1WrxqJFi+jQwTQtQ6PRsHv3bqpUqVLssa06C1ZRFJo0aZLnY6GhoYSGhlpzOHGHq4bCvZyFbefo7DEJBkmES4s9r/QEWe0JoNc7W6Wgo6rRYLhxtdfVxRWlEPtMFpY1FrMI2+ratSvnzp1j9+7dHD58mOTkZLy8vGjcuDFt27ZFc9vCIjc3Nxo3bmyVccvHO205cSjLjfhcHX7aXDR5FK41qpBg0HEoy630gxNmkgiXLEdZ6Qmy2lOI0qLRaGjfvj3t27cvtTGt9ptdp06dAttoNBqLcvQ9e/a01vACMKIw93oQsypFY1SxSLKMKijAvOtBGMl/2wBRsiQRLlmOstITZAK1EGWZ1RKs9PR0DAYD8fHxgCmZ8vX15dq1a+bd5wMCAoiOjubAgQMsW7aMrl278vPPP5urv4vi25HhxfgrVRlZIRZ/3a03mASDjnnXg9iRUbzS/6L4JBEuebLSUwhx0+XLl1myZAn79+8nMTERw41b/rdTFIWtW7dadVyrJVjHjx+nWys+T08AACAASURBVLdu1KtXj+nTpxMREYFWq8VgMLB3717eeustMjMz2bhxI7GxsYwcOZJNmzbxwQcfMHbsWGuFITAlWfsy3dlW9SQAwxOqsS/TQ96w7YgkwvZNVnraj88+/5j589/lPy/2ZezYSXm2OXP2NB+ePMHJtBTiwusxZvRb9O5tOQfzs88+YsvWTZw/fxYXZxcaN2nGiOFjZUPuMu7IkSN07tyZ69evWxRDv1NBG0IXhdV2NB0/fjypqals2bKFtm3bor3xR0er1dK2bVu2bNlCSkoK48ePp169evzwww9UrVqVZcuWWSsEcZvbk6lDWe6SXNmhHRlePBdby3w8PKEaPWPqSHJlB+5npacoOceOHeb7VcuoU6devu0yMzOp7OLCoOqhVKrol2ebAwf+4LnnevPtNz+yePFX5Obm8trAl0hPl1nsZdkbb7zBtWvXePPNNzl//jw5OTkYjca7vvK6qlVcVruCtXr1avr06WNOrO6k1Wp57LHH+Oqrr/joo49wdXWlS5cuLF++3FohCOFwJBG2z9WestLT9tLT0xg3fjiTp8xi8eIF+bZt2OABQkJMRaw/SUnOs82iRV9aHL89/T06dGzBiRNHadGilXWCFnZn79699OjRw1znqjRZ7QpWUlISKSkp+bZJTk4mMTHRfFypUiH+ggkhRCmTlZ62N2PGJNq160xE67Yl0n9qqun9ytvbp0T6F/ZBr9dTs6ZtbgNbLcGqX78+y5cv58KFC3k+fuHCBVasWEH9+vXN56KjoyXJEkLYnZsrPY33mLJhVCEuV1Z6lpQNG9Zy4u/jDB9WMjt/GI1G3nl3Ok2btqB27bolMoawDx06dODAgQM2GdtqCda4ceNITEykSZMmjBkzhjVr1rB3717WrFnDmDFjaNq0KUlJSYwbNw4wbZXwv//9jwcffNBaIQghhFXcXOmpwF1Jlqz0LFlxcTHMfmcqs2fPxdmKBUNvN2PGJM6cieTddz4skf6F/ZgzZw7Hjh1jzpw5pT621a5vP/3001y/fp033niDOXPm8P7775sfU1UVNzc3Fi5cyNNPPw1AWloaixYt4oEHHrBWCEIIYTWy0tM2jp84xrVrV3n22cfN5wwGAwcP7mP5iq84eCDynnN9C2PGzEns3LWNpUtWEhgYZI2QhR2bMWMGDRs2ZOzYsSxatIgmTZrg5XX3766iKHz++edWHduqEwheffVVnn76aVavXn1XOfoePXrg6+trbluhQgV69eplzeGFEMKqpORJ6Wvd6kF+/GGjxbm3Jo0hNLQG/foOLHJypaoqM2dNZtu2//HF58sJDq5qjXCFnVu6dKn5/8+dO8e5c+fybGf3CRaYEqd+/exvVZAQQhSFrPQsXe7uHnfNi3J1dcXHu4L5/IQJI/EPCDTP0crJyeZUWuqN/88hISGOkydP4ObmRrVqIYDptuAvG9Ywf/5i3N09uHLlMgAeHp64uLiU0ncnStv58+dtNrYsgRFCCOFQYuNiLPaDTLicwMuH/zQfL/3yU5Z++SktWrRiyRcrAFj53TcA9Ov3vEVf06e/R48nniqFqIUtVK9e3WZjWzXBysnJYe3atQWWo//kk0+sOawQQogy7GaSdK/jKpWD+e1B0ya+HvMXo+QxOf7oEdtdyRDlk9USrOjoaLp27cqpU6cKLEcvCZYQQgghrG3Xrl0AtGzZEhcXF/NxYbRv396qsVgtwRoxYgSRkZE8//zz9OvXj+DgYHQ6uQMphBBCiNLRsWNHFEXh77//pk6dOubjwrD2djlWy4C2bt1Kp06d+Pbbb63VpRBCCCFEoU2aNAlFUcxFzG8e24LVEiyDwUDz5s2t1Z0QQgghxH2ZMmVKvselyWqV3MPDwzl58qS1uhNCCCGEKJaoqCiSk/PeAPymlJQUoqKirD621RKs2bNns2XLFn766SdrdSmEEEIIUWShoaHMnz8/3zYffvghoaGhVh/barcIN2/eTJcuXejVqxcPPfQQzZo1u2c5+vHjx1trWCGEEEKIPKmqmm9lg5ttSoLVEqyJEyea/3/Lli1s2bIlz3aSYAkhhADIzs6ySj9qdhaZN1aAaTMzUIxGq/RrrfiEfbt48SKenp5W79eqV7CEEEKIguh0OtzdXUhLu05aevH7UzMySctKASDjrz1oa9ezqPReHO7uLlJyyMFMmzbN4njHjh15tjMYDERHR7NixQpat25t9Tis9q/moYceslZXQgghyjC9Xk94eHNyc3OL3Vf27p2kL/0Q1dPJdGLVEpRK/rgNHIq+bYdi96/T6dDr9cXuR5Se21cOKorCjh077plkAVSuXJl33nnH6nFIWi6EEKLU6fX6Yicu2bu2kzVrCq4AWu2tB65fRZ01Bd3U2ejbdyrWGMLxbN++HTDNrercuTMvv/wyffr0uaudVqvF19eXevXqobHSFc/bFTnBiomJASAwMBCNRmM+LozKlSsXdVghhBAC1WAgfcEH+bZJXzAXpzbtUW5PvkSZ16HDrSuXkydPplOnTlbfBqcwipxgBQcHo9FoOHHiBHXq1CE4OLhQ1VIVRbHKZWEhhBDlV+7RQ6iXE/Jto16OJ/foIZyaSBHs8mry5Mk2G7vICdYLL7yAoih4e3tbHAshhChb6tbsZ+sQ7vKwWxLTKxXcbkTvmWxO9y75gO5D5NkvbB1CuRQdHU1MTAxZWXmvDrWbzZ6/+eabfI+FEEKIknLVULi3r8K2E2XX2rVrGT16NKdPn863nbU3e7b+rC4hhBCihB3KciM+V4fxHjUijSrE5eo4lOVWuoEJu7Jjxw6efPJJUlNTGTJkCKqq0r59e1599VXCwsJQVZXu3bszadIkq49ttQRLr9czY8aMfNvMmjULZ2dnaw0phBCinDKiMPd6EArclWQZVVCAedeDMCJTV8qz2bNn4+HhwcGDB81b5nTq1ImPP/6Yo0ePMmPGDLZu3coTTzxh9bGtlmDl5uYWeHnNaDT+P3t3HldT/v8B/HXbk+5N0mZJZFD2kEiFvrIME41lxpB9+NbMkJ8ljMKMZox1Znw1DJoZDPoaW5lIJEtCo7FEY4kwbo2lIhR1fn94OF9Xi9InLV7Px+M+Hs457/P5fOq47ss5535OmW9w/+qrr6BQKDBp0qQytUNERFVbzCMlAm7Xx+2XLgOm5+kg4HZ9xDwq+Lg2erucOHECXl5esLCwkNflvzDTf0BAANq2bVu5z2CVxD///ANDQ8PX3v/EiRP44Ycf0KpVK4GjIiKiqirmkRJDb9nJy5PSG2Dg3+8wXBEA4OHDh6hbt668rK+vj6ysLI2aTp064ciRI8L7LlPA2rhxo/wCgNOnT2use/765ZdfsGDBAvzyyy9wcHB4rb4ePHiAYcOGYfXq1ahVq1ZZhk1ERNXIi5cBE3OMeFmwGMHBwejQoQOMjY1hbm4OLy8vJCcna9Q8fvwYvr6+qF27NmrWrAlvb2+kpaVp1KSmpqJv376oUaMGzM3NMXXq1AJXqGJiYtCuXTvo6+vDzs4OoaGhBcazYsUKNGzYEAYGBnBycsLx48dLPZbiWFpa4p9//pGX69ati3PnzmnU3LlzR/gN7kAZZ3L/6KOP5KkZFAoFtm3bhm3bthVaK0kS9PX1X/s0nK+vL/r27QsPDw988cUXxdbm5ORofA3z/v37r9UnERFRdXLw4EH4+vqiQ4cOePr0KWbOnImePXsiKSkJRkZGAIDJkycjIiICYWFhUKlU8PPzw8CBA+WzPHl5eejbty8sLS1x9OhR3Lp1CyNGjICuri4WLFgAAEhJSUHfvn0xYcIEbNiwAdHR0Rg7diysrKzg6ekJANi8eTP8/f0REhICJycnLFu2DJ6enkhOToa5uXmJxvIqrVu3xtmzZ+Xlbt264aeffsKvv/6K/v374/Dhw9iyZQscHcXPlVamgLV69WoAz8LT+PHj0b9/f/Tr169A3fPp6Dt37gwzsxJMXPKSTZs24Y8//sCJEydKVB8cHIy5c+eWuh8iIqLqLDIyUmM5NDQU5ubmSEhIgKurKzIzM7FmzRps3LgR3bt3BwCsW7cOzZs3x7Fjx9CpUyfs3bsXSUlJ2LdvHywsLNCmTRvMnz8f06dPR1BQEPT09BASEgJbW1ssXrwYANC8eXMcPnwYS5culQPWkiVLMG7cOIwaNQoAEBISgoiICKxduxYzZswo0VhepX///vDz88O1a9dgY2ODmTNnYuvWrfjoo4/kGh0dnVeeuHkdZQpYY8aMkf988OBBeHt7w8vLq8yDetH169fx2WefISoqCgYGBiXaJyAgAP7+/vLyzZs3YW9vL3RcRERElcX9+/c17i3S19cv0bf2MzMzAQCmpqYAgISEBDx58gQeHh5yTbNmzdCgQQPExcWhU6dOiIuLQ8uWLTVuHPf09MTEiRNx7tw5tG3bFnFxcRptPK95/gW13NxcJCQkICAgQN6upaUFDw8PxMXFlXgsrzJ69GiMHv2/iXJtbW1x4sQJLFmyBFeuXIGNjQ0mTJiANm3avLKt0hI2A9svv/wiqikNCQkJSE9PR7t27eR1eXl5iI2Nxffff4+cnBxov/ScqZf/Yr18QxsREVF18vJJhMDAQAQFBRW7T35+PiZNmoQuXbqgRYsWAAC1Wg09PT2YmJho1FpYWECtVss1L4ar59ufbyuuJisrC48ePcK9e/eQl5dXaM2FCxdKPJbX0bhxY6xYseK19y+pcpniVpIk3L17t8jp6EvzsOcePXrgzJkzGutGjRqFZs2aYfr06QXCFRER0dsmKSmpwLflXsXX1xdnz57F4cOHy3NoVUJMTAzc3d2Ftik0YCUmJmLWrFmIiYnB48ePC60p7cOejY2N5WT9nJGREWrXrl1gPRER0dvI2NgYSmXJp6bw8/NDeHg4YmNjUa9ePXm9paUlcnNzkZGRoXHmKC0tDZaWlnLNy9/2e/7NvhdrXv62X1paGpRKJQwNDaGtrQ1tbe1Ca15s41VjKasjR45gzpw5iImJqbyPyjl9+jRcXFxw8OBBuLu7Q5IktGjRAt27d4epqSkkSULXrl3xwQcfiOqSiIiISkGSJPj5+WHbtm3Yv38/bG1tNbY7OjpCV1cX0dHR8rrk5GSkpqbC2dkZAODs7IwzZ84gPT1dromKioJSqZQvVTo7O2u08bzmeRt6enpwdHTUqMnPz0d0dLRcU5KxFOXJkyf46aef8Omnn8Lf3x+//fabxvbExET06tULrq6uOHDgQOX7FuGL5s2bh7y8PJw8eRIODg7Q0tKCt7c35syZg+zsbEyePBk7d+7ETz/9VOa+YmJiyj5gIiKit4yvry82btyIHTt2wNjYWL6XSaVSwdDQECqVCmPGjIG/vz9MTU2hVCrxySefwNnZWb6pvGfPnrC3t8fw4cOxcOFCqNVqzJ49G76+vvKlyQkTJuD777/HtGnTMHr0aOzfvx9btmxBRESEPBZ/f3/4+Pigffv26NixI5YtW4bs7Gz5W4UlGUth7t+/D1dXV5w+fRqS9Ow5SsuXL8fAgQMRFhaGOXPmYMGCBcjPz0e7du0QFBSEd999V/jvWljAOnToEPr3768xkejzH8zIyAghISGIj4/HrFmzsH79elHdEhERUQmtXLkSAArcb7Ru3TqMHDkSALB06VL5JElOTg48PT3xn//8R67V1tZGeHg4Jk6cCGdnZxgZGcHHxwfz5s2Ta2xtbREREYHJkydj+fLlqFevHn788Ud5igYAGDJkCP755x/MmTMHarUabdq0QWRkpMaN768aS2G+/vpr/Pnnn2jdujWGDRsGAFi/fj1+++03DB06FFu2bIGdnR0WLVqE/v37v9bvsSSEBazMzEw0btxYXtbV1cWDBw/kZS0tLXTr1g2bNm0S1SURERGVwvMTH8UxMDDAihUriv2mnY2NDXbv3l1sO+7u7jh16lSxNX5+fvDz8yvTWF62Y8cO2NjYID4+Hnp6enI/zZo1Q1hYGHr37o3ffvutRF8EKAth92CZm5sjIyNDXrawsMClS5c0anJzc5GdnS2qSyIiIiINV65cQZ8+feRwBTwLan379gUALFq0qNzDFSAwYDVv3lzjeUadO3fG3r175dnX//rrL2zevBlNmzYV1SURERGRhkePHhWYXwuA/PidN5VDhAWsvn37IjY2Vv7K5bRp05CXl4dOnTrBysoKDg4OuHfvnsasrURERERvkpaWsOhTLGH3YE2YMAHe3t7yXBXt2rXD3r178eWXX+LKlStwcHDAJ598gvfee09Ul0REREQFnD17Flu2bCmwDgDCwsIKvRdt8ODBQscgLGDp6elpzCILAK6urnB1dRXVBREREdErbd26FVu3btVY9zxUDR06tMB6hUJReQNWSaWkpBSY2IyIiIhIhMDAwIoeAoA3GLCuX7+O+fPn4+effy7yMTpEREREZVGtAtaRI0eQkJAAHR0ddOnSBa1bt5a3paenY968eVizZg1ycnKEPT+IiIiIqLIqU8B68uQJvL29Naa+B4ApU6Zg4cKF+Pnnn+Hn54cHDx7AwsIC06dPx4QJE8o0YCIiIqLKrkwBa/ny5QgPD4elpaU83fyOHTuwePFi6Ojo4KuvvoJKpcKiRYvw73//GwYGBkIGTURERFSZlSlgbd68GbVr18aZM2dQu3ZtAMD8+fPRvHlzfP3113B0dERERIQ8uRcRERHR26BMs20lJydjwIABcrgCgDp16mDAgAEAnj1UkuGKiIiI3jZlClgPHjyAtbV1gfXP58N68WZ3IiIiordFmeeLL2zKeYVCAQDQ1dUta/NEREREJTJw4ECNGdxjY2ORmppaIWMp8zQNf//9N/74448C6wDg1KlThU5H365du7J2S0RERKRh+/btaNOmjbzcrVs3BAYGYs6cOW98LGUOWKtXr8bq1asLrJckCe3bty90n7y8vLJ2S0RERKTBxMQEWVlZ8nJhJ3nelDIFrA8//FC+HEhERERUkezt7fHrr7+iQ4cOsLKyAgBcvXoVsbGxr9xX9LOTyxSw1q9fL2ocRERERGUyZ84ceHl54cMPP5TX/fTTT/jpp59eua/oq2tv/GHPREREROWhZ8+eOH/+PPbt24ebN28iKCgIbm5ucHNze+NjYcAiIiKiasPGxgZjxowBAAQFBcHd3b1q3uROREREVBmlpKTAxMSkQvpmwCIiIqJqycbGRv7z06dPkZycjKysLCiVSjRt2hQ6OuUXg8o80SgRERFRZXX37l2MGzcOKpUKrVq1gouLC1q1agUTExOMHz8ed+7cKZd+eQaLiIiIqqW7d++iU6dOuHTpEkxNTdG1a1dYWVlBrVbj5MmT+PHHH3Hw4EHExcXB1NRUaN8MWNXUY0kLnVIdKnoYREREFWb+/Pm4dOkSpk6dijlz5sDIyEje9vDhQ8yfPx9ff/01vvzySyxevFho3+VyifDhw4c4ffo04uLiyqN5IiIiolfasWMH3N3d8fXXX2uEKwCoUaMGgoOD4e7ujm3btgnvW2jAun79Ory9vWFiYoK2bduia9eu8rYjR46gVatWOHjwoMguiYiIiAr1999/w9nZudgaZ2dn+RnKIgkLWDdu3EDHjh2xY8cO9OnTB05OThrPAHJycsKtW7ewadMmUV0SERERFUmlUuHatWvF1ly7dg0qlUp438ICVmBgIO7evYsDBw5g+/bt8PT01Niuo6MDV1dXHDp0SFSXRFXe83vlOqU64LHEL/USEYnk5uaGsLAw7Nu3r9Dt0dHRCAsLg7u7u/C+hd3kHhkZCS8vL43Lgi9r0KAB9u/fL6pLIiIioiIFBgYiIiICnp6e6NOnD9zc3GBhYYG0tDTExMTg999/R40aNcplpndhAev27duwtbV9ZV1OTo6oLomIiIiK5ODggD179mDkyJGIiIhAREQEFAqFfAtT48aNERoaCgcH8d+6FxawLCwscOnSpWJrzp49i/r164vqkoiIiKhYLi4uuHjxIo4cOYJTp07JM7m3bdsWXbp0gUKhKJd+hQWsf/3rX1i/fj3OnTtXaBI8cuQIoqOj8emnn4rqkoiIiOiVFAoFXFxc4OLi8sb6FHZX7axZs2BgYAAXFxd8/fXXuHLlCgAgKioKc+fOhaenJ0xNTTF16lRRXRIRERFVSsLOYDVq1AiRkZEYMmQIAgIC5GucvXr1giRJqFevHv773/+ibt26orokIiIiqpSEPirH2dkZly5dwo4dOxAfH4+7d+9CqVTCyckJAwcOhL6+vsjuiIiIiCol4c8i1NPTw6BBgzBo0CDRTRMRERFVCZzZkIiIiEgwYWewFixY8MoaLS0tKJVKNG3aFF27doWenp6o7omIiIgqDWEBa/bs2RpzSbz4HMKX1ysUCtSuXRvLli3Dhx9+KGoIRERERDJtbW0MHToUGzZseON9C7tEGBUVhXfffRe6uroYNWoU1qxZg/DwcKxZswajRo2Cnp4e+vXrh02bNmHq1Kl4/PgxRowYwUfnEBERUblQKpUVNsG5sDNY165dQ0xMDBISEgpMNDpq1ChMnjwZzs7O8PLywldffYWPPvoIjo6OWLRoEbp37y5qGEREREQAgI4dO+LPP/+skL6FncFatmwZhgwZUuTzfBwcHDB06FAsWbIEANCiRQu8++67OH78uKghEBEREcmCgoKwf/9+/Pzzz2+8b2FnsC5evIh333232JratWtrPK/Qzs4O4eHhooZAREREJIuKioK7uztGjRqF7777Dh06dICFhUWB5w8qFAp8/vnnQvsWFrDMzMwQGRlZ5LcJJUnCnj17YGpqKq/LzMyESqUSNQQiIuEeS1rolFr4mXkiqtyCgoLkPyckJCAhIaHQukodsIYMGYIlS5bAy8sLCxYsgL29vbwtKSkJs2bNwp9//olJkybJ648fP45mzZqJGgIRERGR7MCBAxXWt7CANW/ePBw/fhw7d+7Erl27oFQqYW5ujvT0dGRlZUGSJHTp0gXz5s0DAKjVatStWxdDhgwRNQQiIiIimZubW4X1Lewm9xo1aiAmJgYrV65E586dkZ+fj4sXLyI/Px9dunTBypUrERMTAyMjIwCApaUldu3ahY8++kjUEIiIiIgqBaGPytHS0sLHH3+MQ4cOITMzE/n5+cjMzERsbCw+/vhjaGtri+yOiIiIqFhPnz7F0qVL0bFjRyiVSujo/O/iXWJiIv7973/jr7/+Et6v8Ic9ExEREVUGjx49Qs+ePXH06FGYmZlBqVQiOztb3m5ra4t169bB1NQUX3zxhdC+y+Vhz5Ik4c6dO/j7778LfRERERGVtwULFuDIkSMIDg6GWq3G2LFjNbarVCq4ublhz549wvsWegYrMTERs2bNQkxMDB4/flxojUKhwNOnT0V2S0RERFTA5s2b0a1bN0ybNg0ACsx/BQCNGjXCqVOnhPctLGCdPn0aLi4uAAB3d3f8/vvvaNmyJczNzZGYmIg7d+7A1dW1wp4JRERERG+X1NRUDBgwoNgaY2NjZGZmCu9b2CXCefPmIS8vD/Hx8YiIiAAAeHt7IyoqClevXsXYsWNx4cIF4dc4iYiIiApjbGyM9PT0YmsuX76MOnXqCO9bWMA6dOgQ+vfvr/EsQkmSAABGRkYICQmBhYUFZs2aVeq2V65ciVatWkGpVEKpVMLZ2Rm///67qKETERFRNdSpUyfs2rULGRkZhW6/fv06du/eDVdXV+F9CwtYmZmZaNy4sbysq6uLBw8e/K8jLS1069YN+/btK3Xb9erVw1dffYWEhAScPHkS3bt3x3vvvYdz584JGTsRERFVP1OnTsW9e/fQo0cPHDlyRL4H/OHDh4iOjoanpyeePn0Kf39/4X0LC1jm5uYaCdHCwkLjwc4AkJubq/H1yJLq168f+vTpgyZNmuCdd97Bl19+iZo1a+LYsWNlHjcREdHbIjY2Fv369YO1tTUUCgW2b9+usX3kyJFQKBQar169emnU3L17F8OGDYNSqYSJiQnGjBmjcUIFeHZfdteuXWFgYID69etj4cKFBcYSFhaGZs2awcDAAC1btsTu3bs1tkuShDlz5sDKygqGhobw8PDAxYsXS/Xzurq64vvvv8eZM2fg6uoqPy/Z2NgYPXv2xKVLl/Cf//wHjo6OpWq3JIQFrObNmyM5OVle7ty5M/bu3YsTJ04AAP766y9s3rwZTZs2LVM/eXl52LRpE7Kzs+Hs7FymtoiIiN4m2dnZaN26NVasWFFkTa9evXDr1i359euvv2psHzZsGM6dO4eoqCiEh4cjNjYW48ePl7dnZWWhZ8+esLGxQUJCAr755hsEBQVh1apVcs3Ro0fxwQcfYMyYMTh16hS8vLzg5eWFs2fPyjULFy7Et99+i5CQEMTHx8PIyAienp5FzlJQlIkTJ+LPP/+En58fOnTogMaNG6Nt27aYMGECTp06VWDqBlGEfYuwb9++mDJlCtLS0mBhYYFp06Zh+/bt6NSpE8zNzXH79m3k5eVp/IJL48yZM3B2dsbjx49Rs2ZNbNu2TeOB0i/KyclBTk6OvHz//v3X6pOIiKg66d27N3r37l1sjb6+PiwtLQvddv78eURGRuLEiRNo3749AOC7775Dnz59sGjRIlhbW2PDhg3Izc3F2rVroaenBwcHByQmJmLJkiVyEFu+fDl69eqFqVOnAgDmz5+PqKgofP/99wgJCYEkSVi2bBlmz56N9957DwDw888/w8LCAtu3b8fQoUNL9XM3b94cy5cvL9U+ZSXsDNaECRNw9epVmJiYAADatWuHvXv3wsPDAzVr1oSbmxu2bdsGb2/v12q/adOmSExMRHx8PCZOnAgfHx8kJSUVWhscHAyVSiW/igpiREREpCkmJgbm5uZo2rQpJk6ciDt37sjb4uLiYGJiIocrAPDw8ICWlhbi4+PlGldXV+jp6ck1np6eSE5Oxr179+QaDw8PjX49PT0RFxcHAEhJSYFardaoUalUcHJyO0nHeQAAIABJREFUkmsqO2FnsPT09FC3bl2Nda6ursLuzNfT04OdnR0AwNHRESdOnMDy5cvxww8/FKgNCAjQuGHt5s2bDFlERFRt3b9/H1lZWfKyvr4+9PX1S91Or169MHDgQNja2uLy5cuYOXMmevfujbi4OGhra0OtVsPc3FxjHx0dHZiamkKtVgMA1Go1bG1tNWosLCzkbbVq1YJarZbXvVjzYhsv7ldYTWls27YNoaGhOHXqFDIzM6FSqdC2bVuMGjUKXl5epW6vJIQFrHfeeQe9evXCt99+K6rJYuXn52tcBnzRy3+xXvxLR0REVN28fBIhMDAQQUFBpW7nxUtvLVu2RKtWrdC4cWPExMSgR48eZR3mG/f06VN8+OGH2Lp1KyRJgo6ODmrXrg21Wo1du3YhPDwc3t7e2Lhxo8ZDoEUQdolQrVajZs2aoprTEBAQgNjYWFy9ehVnzpxBQEAAYmJiMGzYsHLpj4iIqCpJSkpCZmam/AoICBDSbqNGjWBmZibPCmBpaVlg4s6nT5/i7t278n1blpaWSEtL06h5vvyqmhe3v7hfYTUlERwcjP/+97/o2rUrDh06hMePH+PWrVt4/PgxYmNj4eLigq1bt+Krr74qcZslJSxgtWzZEn/99Zeo5jSkp6djxIgRaNq0KXr06IETJ05gz549+Ne//lUu/REREVUlxsbG8mTcSqXytS4PFubGjRu4c+cOrKysAADOzs7IyMhAQkKCXLN//37k5+fDyclJromNjcWTJ0/kmqioKDRt2hS1atWSa6KjozX6ioqKkmcHsLW1haWlpUZNVlYW4uPjSzWDwLp169CsWTPs27cPXbp0gZbWs9ijpaUFFxcX7Nu3D++88w7Wrl1bml9LiQgLWNOmTcOuXbtw6NAhUU3K1qxZg6tXryInJwfp6enYt28fwxUREVEpPXjwAImJiUhMTATw7GbyxMREpKam4sGDB5g6dSqOHTuGq1evIjo6Gu+99x7s7Ozg6ekJ4Nm38Xr16oVx48bh+PHjOHLkCPz8/DB06FBYW1sDAD788EPo6elhzJgxOHfuHDZv3ozly5dr3Bv92WefITIyEosXL8aFCxcQFBSEkydPws/PD8CzhzJPmjQJX3zxBXbu3IkzZ85gxIgRsLa2LtU9U7du3UK/fv2KvPynq6uLfv364datW6/1+yyOsAuO2dnZ8PDwQPfu3fH++++jQ4cOsLCwKPTJ1R9++KGobomIiKiETp48iW7dusnLz0OPj48PVq5cidOnT+Onn35CRkYGrK2t0bNnT8yfP1/jjNiGDRvg5+eHHj16QEtLC97e3hr3X6tUKuzduxe+vr5wdHSEmZkZ5syZozFXVufOnbFx40bMnj0bM2fORJMmTbB9+3a0aNFCrpk2bRqys7Mxfvx4ZGRkwMXFBZGRkTAwMCjxz1u/fv0Ck6C+LDs7Gw0aNChxmyWlkJ4/MLCMtLS0oFAo8GJzL4crSZKgUCiQl5cnossSu3HjBurXr4/r16+jXr16Qtps2ni0kHbomeTL4k/P8hiJx+NU+b2Nx8hAkY+Y+ucBAO7Xm+OxJOziTLkQeYzK4/OtOlm4cCG++eYbnD59Wr7M+aKbN2+idevWmD59ujwnlyjCzmCtXr1aVFNEREREpZaamqqxPHjwYBw5cgRt27bFpEmT4OLiAgsLC6SlpeHQoUNYvnw5XFxcMGjQIOFjERawxowZI6opIiIiolJr2LBhobcmSZKEWbNmFbp+586dCA8Plx8ELYrYSR+IiIiIKsiIESMKDVgVQXjA2rVrF3799VdcuHABDx8+xIULFwAAFy5cwO7duzW+aUBEREQkSmhoaEUPQSYsYOXn52P48OHYtGkTJEmCgYGBxkzrJiYmmD59OnJzczFjxgxR3RIRERFVOsK+arF8+XL8+uuvGDt2LO7cuYNp06ZpbLe0tISLiwsiIiJEdUlERERUKQkLWOvWrYOjoyN++OEH1KpVq9BroE2aNMGVK1dEdUlERERUrMOHD8PLywu2trbQ19eHtrZ2gZfo5xACAi8RXrx4Eb6+vsXW1K5dG3fu3BHVJREREVGRfvnlF4wcORKSJKFRo0bo2LFjuYSpwgjrxcDAAFlZWcXWpKamwsTERFSXREREREWaP38+atWqhd27d6Njx45vtG9hlwjbtm2LPXv2aNzY/qJ79+4hMjJSfhgkERERUXm6fv06hg4d+sbDFSAwYPn5+eH69esYNGgQ1Gq1xrZr167B29sbGRkZ+OSTT0R1SURERFQkGxsb5ObmVkjfwi4RDhw4EFOmTMHixYtRt25dGBsbAwCsra2RlpYGSZIQEBAADw8PUV0SERERFWncuHFYvHgx7t69C1NT0zfat9A7vb755ht0794d33//PeLj4wEADx8+hIeHBz799FP07dtXZHdERERERZoyZQquXLmCLl26YPbs2WjdujWUSmWhtQ0aNBDat/Bb6Xv37o3evXuLbpaIiIio1Nq1a4eNGzdixIgRRdYoFIrK+yzCq1evomHDhqKaIyIiIiqT7777DpMmTYKuri66desGKyurqjdNQ6NGjdC1a1cMHz4cgwYNgkqlEtU0ERERUaktXboUdevWxdGjR1GvXr032rewbxH26tULR48exccffwxLS0sMGjQIO3fuFH7KjYiIiKgk1Go1vL2933i4AgQGrN27d+PmzZtYvHgx7O3tsXXrVgwYMABWVlbw9fVFXFycqK6IiIiIXsnOzg4ZGRkV0rewgAUA5ubmmDRpEhISEpCUlITp06fDyMgIK1euhIuLC5o0aYK5c+eK7JKIiIioUJMnT8aOHTtw7dq1N953ud3p1axZMyxYsAALFixAbGwsfv75Z/zyyy+YN28eAgMDy6tbIiIiIgBA48aN4ebmhvbt22PSpEnFTtPg6uoqtO9yv5U+JSUFBw8exKFDh/DkyRMoFIry7pKIiIgI7u7uUCgUkCQJn3/+ebEZJC8vT2jf5RKw7t27h02bNmH9+vU4duwYJEmCSqXCmDFjMHz48PLokoiIiEjDnDlzKuzEjrCAlZubi507d2L9+vWIjIzEkydPoK2tjb59+2L48OHo378/9PX1RXVHREREVKygoKAK61tYwLKwsEBWVhYkSUKHDh0wfPhwDB06FGZmZqK6ICIiIqoShAUslUoFPz8/DB8+HO+8846oZomIiIiqHKGPyimJnJwcXiokIiKicqelpVWie7Aq9bMIX+WPP/7AmjVrsGnTJty5c+dNdUtERERvKVdX10IDVmZmJi5evIjs7Gy0bt0aJiYmwvsu14CVkZGB9evXY82aNTh9+jQkSYKhoWF5dklERG+Zx5IWOqU6VPQwqBKKiYkpctvDhw8xY8YMREZGIioqSnjfQmdyf27fvn344IMPYG1tjc8++wx//vknOnXqhFWrVkGtVpdHl0REREQlVqNGDXz77bdQqVSYOnWq8PaFncG6fv061q1bh3Xr1iE1NRWSJKFu3bq4efMmRo4cibVr14rqioiIiEiIrl27Yv369cLbLVPAevLkCbZv3441a9YgOjoaeXl5MDIywrBhwzBixAh0794dOjo60NF5Y7d6EREREZXYP//8gwcPHghvt0zJx9raGnfv3oVCoUC3bt0wYsQIDBw4EEZGRqLGR0RERCRcfn4+NmzYgM2bN6N9+/bC2y9TwLpz5w60tLQwefJkTJs2DXXq1BE1LiIiIqIyadSoUaHrnz59ivT0dDx58gS6uroIDg4W3neZbnIfOXIkDA0NsWTJEtSrVw/9+/dHWFgYcnNzRY2PiIiI6LXk5+dDkqQCL11dXbRo0QLjx49HQkIC3NzchPddpjNYa9euxbfffotNmzZhzZo1CA8PR0REBJRKJQYPHswHOxMREVGFKekk6OWhzNM01KxZE2PHjkVcXBzOnTuHSZMmQU9PD6tXr4abmxsUCgWSk5Nx7do1EeMlIiIiqvSEzoPVvHlzLF68GDdv3sSWLVvQs2dPKBQKHDp0CI0bN0aPHj3wyy+/iOySiIiIqNIpl/kTdHR08P777+P999/HjRs3sG7dOoSGhuLAgQOIiYnhpUMiIiIqF6NHjy71PgqFAmvWrBE6jnKfoKpevXr4/PPP8fnnnyM6OpoTjhIREVG5CQ0NLXGtQqGAJElVM2C9qEePHujRo8eb7JKIiIjeInFxcSWqu3TpEoKCgnD58uVyGQenWCciIqJqw8nJqdjtt2/fxty5c7F69Wrk5ubCxcUFX3/9tfBxMGARERFRtffw4UMsWrQIixcvxv379+Hg4IAFCxagX79+5dIfAxYRERFVW3l5efjhhx8wf/58pKWloV69eli2bBl8fHygpSV0MgUNDFhERERULYWFhWH27Nm4dOkSVCoVvvrqK3z66acwMDAo974ZsIiIiKhaiYmJwfTp03Hy5Eno6elhypQpmDlzJkxMTN7YGBiwiIiIqNro3bs39u7dCy0tLfj4+GDevHmoV6/eGx8HAxYRERFVG3v27IFCoUCDBg2gVqsxfvz4V+6jUCgQEREhdBwMWERERFStSJKElJQUpKSklKheoVAIHwMDFhEREVUbJQ1V5Y0Bi4iIiKoNGxubih4CAKD8JoAgIiIieksxYBEREREJxoBFREREJBgDFhEREZFgDFhEREREglWJgBUcHIwOHTrA2NgY5ubm8PLyQnJyckUPi4iIiKhQVSJgHTx4EL6+vjh27BiioqLw5MkT9OzZE9nZ2RU9NCIiIqICqkTAioyMxMiRI+Hg4IDWrVsjNDQUqampSEhIqOihERERVRmxsbHo168frK2toVAosH37do3tkiRhzpw5sLKygqGhITw8PHDx4kWNmrt372LYsGFQKpUwMTHBmDFj8ODBA42a06dPo2vXrjAwMED9+vWxcOHCAmMJCwtDs2bNYGBggJYtW2L37t2lHktlViUC1ssyMzMBAKamphU8EiIioqojOzsbrVu3xooVKwrdvnDhQnz77bcICQlBfHw8jIyM4OnpicePH8s1w4YNw7lz5xAVFYXw8HDExsZqPO8vKysLPXv2hI2NDRISEvDNN98gKCgIq1atkmuOHj2KDz74AGPGjMGpU6fg5eUFLy8vnD17tlRjqcwUkiRJFT2I0sjPz0f//v2RkZGBw4cPF1qTk5ODnJwcefnmzZuwt7fH9evXhT1Ru2nj0ULaoWeSL68V3iaPkXg8TpUfj1HlJ/IY3bhxA/Xr13+tzzeFQoFt27bBy8sLwLMzRtbW1pgyZQr+7//+D8CzExoWFhYIDQ3F0KFDcf78edjb2+PEiRNo3749gGdXmfr06YMbN27A2toaK1euxKxZs6BWq6GnpwcAmDFjBrZv344LFy4AAIYMGYLs7GyEh4fL4+nUqRPatGmDkJCQEo2lsqtyZ7B8fX1x9uxZbNq0qcia4OBgqFQq+WVvb/8GR0hERPRm3b9/H1lZWfLrxZMMJZWSkgK1Wg0PDw95nUqlgpOTE+Li4gAAcXFxMDExkcMVAHh4eEBLSwvx8fFyjaurqxyuAMDT0xPJycm4d++eXPNiP89rnvdTkrFUdlUqYPn5+SE8PBwHDhwoNqkHBAQgMzNTfiUlJb3BURIREb1Z9vb2GicWgoODS92GWq0GAFhYWGist7CwkLep1WqYm5trbNfR0YGpqalGTWFtvNhHUTUvbn/VWCq7KvGwZ0mS8Mknn2Dbtm2IiYmBra1tsfX6+vrQ19eXl7Oyssp7iERERBUmKSkJdevWlZdf/AykilElzmD5+vpi/fr12LhxI4yNjaFWq6FWq/Ho0aOKHhoREVGFMzY2hlKplF+vE7AsLS0BAGlpaRrr09LS5G2WlpZIT0/X2P706VPcvXtXo6awNl7so6iaF7e/aiyVXZUIWCtXrkRmZibc3d1hZWUlvzZv3lzRQyMiIqoWbG1tYWlpiejoaHldVlYW4uPj4ezsDABwdnZGRkaGxjRJ+/fvR35+PpycnOSa2NhYPHnyRK6JiopC06ZNUatWLbnmxX6e1zzvpyRjqeyqzCVCIiIiKpsHDx7g0qVL8nJKSgoSExNhamqKBg0aYNKkSfjiiy/QpEkT2Nra4vPPP4e1tbX8TcPmzZujV69eGDduHEJCQvDkyRP4+flh6NChsLa2BgB8+OGHmDt3LsaMGYPp06fj7NmzWL58OZYuXSr3+9lnn8HNzQ2LFy9G3759sWnTJpw8eVKeykGhULxyLJVdlQhYREREVHYnT55Et27d5GV/f38AgI+PD0JDQzFt2jRkZ2dj/PjxyMjIgIuLCyIjI2FgYCDvs2HDBvj5+aFHjx7Q0tKCt7c3vv32W3m7SqXC3r174evrC0dHR5iZmWHOnDkac2V17twZGzduxOzZszFz5kw0adIE27dvR4sWLeSakoylMqty82C9jrLME1IUzgsjFufuqRp4nCo/HqPKr7LMg0Xlq0rcg0VERERUlTBgEREREQnGgEVEREQkGAMWERERkWAMWERERESCMWARERERCcaARURERCQYAxYRERGRYAxYRERERIIxYBEREREJxoBFREREJBgDFhEREZFgDFhEREREgjFgEREREQnGgEVEREQkGAMWERERkWAMWERERESCMWARERERCcaARURERCQYAxYRERGRYAxYRERERIIxYBEREREJxoBFREREJBgDFhEREZFgDFhEREREgjFgEREREQnGgEVEREQkGAMWERERkWAMWERERESCMWARERERCcaARURERCQYAxYRERGRYAxYRERERIIxYBEREREJxoBFREREJBgDFhEREZFgDFhEREREgjFgEREREQnGgEVEREQkGAMWERERkWAMWERERESCMWARERERCcaARURERCQYAxYRERGRYAxYRERERIIxYBEREREJxoBFREREJBgDFhEREZFgDFhEREREgjFgEREREQnGgEVEREQkGAMWERERkWAMWERERESCMWARERERCcaARURERCRYlQhYsbGx6NevH6ytraFQKLB9+/aKHhIRERFRkapEwMrOzkbr1q2xYsWKih4KERFRlRUUFASFQqHxatasmbz98ePH8PX1Re3atVGzZk14e3sjLS1No43U1FT07dsXNWrUgLm5OaZOnYqnT59q1MTExKBdu3bQ19eHnZ0dQkNDC4xlxYoVaNiwIQwMDODk5ITjx4+Xy89cUXQqegAl0bt3b/Tu3buih0FERFTlOTg4YN++ffKyjs7/osDkyZMRERGBsLAwqFQq+Pn5YeDAgThy5AgAIC8vD3379oWlpSWOHj2KW7duYcSIEdDV1cWCBQsAACkpKejbty8mTJiADRs2IDo6GmPHjoWVlRU8PT0BAJs3b4a/vz9CQkLg5OSEZcuWwdPTE8nJyTA3N3+Dv43yUyXOYBEREZEYOjo6sLS0lF9mZmYAgMzMTKxZswZLlixB9+7d4ejoiHXr1uHo0aM4duwYAGDv3r1ISkrC+vXr0aZNG/Tu3Rvz58/HihUrkJubCwAICQmBra0tFi9ejObNm8PPzw/vv/8+li5dKo9hyZIlGDduHEaNGgV7e3uEhISgRo0aWLt27Zv/hZSTahmwcnJykJWVJb/u379f0UMiIiIqN/fv39f43MvJySmy9uLFi7C2tkajRo0wbNgwpKamAgASEhLw5MkTeHh4yLXNmjVDgwYNEBcXBwCIi4tDy5YtYWFhIdd4enoiKysL586dk2tebON5zfM2cnNzkZCQoFGjpaUFDw8PuaY6qJYBKzg4GCqVSn7Z29tX9JCIiIjKjb29vcbnXnBwcKF1Tk5OCA0NRWRkJFauXImUlBR07doV9+/fh1qthp6eHkxMTDT2sbCwgFqtBgCo1WqNcPV8+/NtxdVkZWXh0aNHuH37NvLy8gqted5GdVAl7sEqrYCAAPj7+8vLN2/eZMgiIqJqKykpCXXr1pWX9fX1C6178X7mVq1awcnJCTY2NtiyZQsMDQ3LfZxvk2p5BktfXx9KpVJ+GRsbV/SQiIiIyo2xsbHG515RAetlJiYmeOedd3Dp0iVYWloiNzcXGRkZGjVpaWmwtLQEAFhaWhb4VuHz5VfVKJVKGBoawszMDNra2oXWPG+jOqgSAevBgwdITExEYmIigGffUEhMTJSvGxMREVHpPXjwAJcvX4aVlRUcHR2hq6uL6OhoeXtycjJSU1Ph7OwMAHB2dsaZM2eQnp4u10RFRUGpVMpXipydnTXaeF7zvA09PT04Ojpq1OTn5yM6OlquqQ6qxCXCkydPolu3bvLy88t/Pj4+hc6tQURERAX93//9H/r16wcbGxv8/fffCAwMhLa2Nj744AOoVCqMGTMG/v7+MDU1hVKpxCeffAJnZ2d06tQJANCzZ0/Y29tj+PDhWLhwIdRqNWbPng1fX1/5rNmECRPw/fffY9q0aRg9ejT279+PLVu2ICIiQh6Hv78/fHx80L59e3Ts2BHLli1DdnY2Ro0aVSG/l/JQJQKWu7s7JEmq6GEQERFVaTdu3MAHH3yAO3fuoE6dOnBxccGxY8dQp04dAMDSpUuhpaUFb29v5OTkwNPTE//5z3/k/bW1tREeHo6JEyfC2dkZRkZG8PHxwbx58+QaW1tbREREYPLkyVi+fDnq1auHH3/8UZ4DCwCGDBmCf/75B3PmzIFarUabNm0QGRlZ4Mb3qkwhvQXJ5caNG6hfvz6uX7+OevXqCWmzaePRQtqhZ5Ivi5/7hMdIPB6nyo/HqPITeYzK4/ONxKgS92ARERERVSUMWERERESCMWARERERCcaARURERCQYAxYRERGRYAxYRERERIIxYBEREREJxoBFREREJBgDFhEREZFgDFhEREREgjFgEREREQnGgEVEREQkGAMWERERkWAMWERERESCMWARERERCcaARURERCQYAxYRERGRYAxYRERERIIxYBEREREJxoBFREREJBgDFhEREZFgDFhEREREgjFgEREREQnGgEVEREQkGAMWERERkWAMWERERESCMWARERERCcaARURERCQYAxYRERGRYAxYRERERIIxYBEREREJxoBFREREJBgDFhEREZFgDFhEREREgjFgEREREQnGgEVEREQkGAMWERERkWAMWERERESCMWARERERCcaARURERCQYAxYRERGRYAxYRERERIIxYBEREREJxoBFREREJBgDFhEREZFgDFhEREREgjFgEREREQnGgEVEREQkGAMWERERkWAMWERERESCMWARERERCcaARURERCQYAxYRERGRYAxYRERERIIxYBEREREJxoBFREREJFiVClgrVqxAw4YNYWBgACcnJxw/fryih0RERFTl8PO0/FWZgLV582b4+/sjMDAQf/zxB1q3bg1PT0+kp6dX9NCIiIiqDH6evhlVJmAtWbIE48aNw6hRo2Bvb4+QkBDUqFEDa9eureihERERVRn8PH0zqkTAys3NRUJCAjw8POR1Wlpa8PDwQFxcXAWOjIiIqOrg5+mbo1PRAyiJ27dvIy8vDxYWFhrrLSwscOHChQL1OTk5yMnJkZczMzMBALdu3RI2pidPs4W1RcCNGzeEt8ljJB6PU+XHY1T5iTxGzz/XMjMzoVQq5fX6+vrQ19cvUF/az1N6fVUiYJVWcHAw5s6dW2B9x44dK2A0VBL162+p6CFQCfA4VX48RpVfeRyjFi1aaCwHBgYiKChIeD9UclUiYJmZmUFbWxtpaWka69PS0mBpaVmgPiAgAP7+/vLy06dPcf78edSvXx9aWlXiqqgQ9+/fh729PZKSkmBsbFzRw6Ei8DhVfjxGld/beozy8/ORmpoKe3t76Oj87yO9sLNXQOk/T+n1VYmApaenB0dHR0RHR8PLywvAs79U0dHR8PPzK1Bf2KnRLl26vJGxViZZWVkAgLp162qcOqbKhcep8uMxqvze5mPUoEGDEteW9vOUXl+VCFgA4O/vDx8fH7Rv3x4dO3bEsmXLkJ2djVGjRlX00IiIiKoMfp6+GVUmYA0ZMgT//PMP5syZA7VajTZt2iAyMrLAjXpERERUNH6evhlVJmABgJ+fH09hloK+vj4CAwOLvBZPlQOPU+XHY1T58RiVDj9Py59CkiSpogdBREREVJ28PV+pIyIiInpDGLCIiIiIBGPAIiKiaq1hw4ZYtmxZqfY5cuQIWrZsCV1dXXk6A6LSYMCqRFasWIGGDRvCwMAATk5OOH78eLH1YWFhaNasGQwMDNCyZUvs3r37lX2sWrUK7u7uUCqVUCgUyMjIKJexVUexsbHo168frK2toVAosH379lfuExMTg3bt2kFfXx92dnYIDQ195T5BQUFo1qwZjIyMUKtWLXh4eCA+Pv6V+73tx2jlypVo1aoVlEollEolnJ2d8fvvvxe7z+u8hwAgLi4O3bt3h5GREZRKJVxdXfHo0aNi93nbj09hbt68iY8++gi1a9eGoaEhWrZsiZMnTxa7z+u8p06cOIHx48eXamz+/v5o06YNUlJSStQHUQESVQqbNm2S9PT0pLVr10rnzp2Txo0bJ5mYmEhpaWmF1h85ckTS1taWFi5cKCUlJUmzZ8+WdHV1pTNnzhTbz9KlS6Xg4GApODhYAiDdu3dP+Niqq927d0uzZs2SfvvtNwmAtG3btmLrr1y5ItWoUUPy9/eXkpKSpO+++07S1taWIiMji91vw4YNUlRUlHT58mXp7Nmz0pgxYySlUimlp6cXuQ+PkSTt3LlTioiIkP766y8pOTlZmjlzpqSrqyudPXu20PrXfQ8dPXpUUiqVUnBwsHT27FnpwoUL0ubNm6XHjx8XuQ+PT0F3796VbGxspJEjR0rx8fHSlStXpD179kiXLl0qcp/XfU+9jtq1a0tr164V3i69PRiwKomOHTtKvr6+8nJeXp5kbW0tBQcHF1o/ePBgqW/fvhrrnJycpI8//rhE/R04cKDEAau0Y3sblCRgTZs2TXJwcNBYN2TG9YqOAAAgAElEQVTIEMnT07NUfWVmZkoApH379hVZw2NUuFq1akk//vhjodte9z3k5OQkzZ49u1Tj4PEpaPr06ZKLi0up9nnd95SNjY20dOlSeRmAtHr1asnLy0syNDSU7OzspB07dkiSJEkpKSkSAI3XunXrSjVOIkmSJF4irARyc3ORkJAADw8PeZ2WlhY8PDwQFxdX6D5xcXEa9QDg6elZZP2bHBs9I+IY5ebmYtWqVVCpVGjdunWRNTxGmvLy8rBp0yZkZ2fD2dm50JrXOT7p6emIj4+Hubk5OnfuDAsLC7i5ueHw4cNF7sPjU7idO3eiffv2GDRoEMzNzdG2bVusXr262H1E/rs3d+5cDB48GKdPn0afPn0wbNgw3L17F/Xr18etW7egVCqxbNky3Lp1C0OGDCl1+0QMWJXA7du3kZeXV2AWXQsLC6jV6kL3UavVpap/k2OjZ4o6RllZWa+8Xyc8PBw1a9aEgYEBli5diqioKJiZmRVay2P0P2fOnEHNmjWhr6+PCRMmYNu2bbC3ty+09nXeQ1euXAHw7D65cePGITIyEu3atUOPHj1w8eLFQvfh8SnclStXsHLlSjRp0gR79uzBxIkT8emnn+Knn34qcp+yvKdeNnLkSHzwwQews7PDggUL8ODBAxw/fhza2tqwtLSEQqGASqWCpaUlDA0NX+tnpLcbA1Y1tWDBAtSsWVN+paamVvSQ6CUbNmzQOEaHDh2St3Xr1g2JiYk4evQoevXqhcGDByM9Pb0CR1s1NG3aFImJiYiPj8fEiRPh4+ODpKSk12qrsPdQfn4+AODjjz/GqFGj0LZtWyxduhRNmzbF2rVrRf4o1V5+fj7atWuHBQsWoG3bthg/fjzGjRuHkJCQ126zuPfUy1q1aiX/+fmXFfgeI5Gq1KNyqiszMzNoa2sjLS1NY31aWhosLS0L3cfS0rLY+gkTJmDw4MHyNmtr6zc2NnqmqGOkVCphaGiI/v37w8nJSd5Wt25d+c9GRkaws7ODnZ0dOnXqhCZNmmDNmjUICAgo0A+P0f/o6enBzs4OAODo6IgTJ05g+fLl+OGHHwrUvs57KC8vDwAKnBVr3rx5kf+J4fEpnJWVVaG/x61btxa5T1neUy/T1dXVWFYoFHKAJhKBZ7AqAT09PTg6OiI6Olpel5+fj+jo6CLvH3F2dtaoB4CoqCi53tTUVP6AtrOzg47O62Xp1xkbPfOqY2RsbKxxjIq7DJGfn4+cnJxCt/EYFa2439vrvIcaNmwIa2trJCcna+z3119/wcbGptB+eHwK16VLl1L9HgGx7ymiclfRd9nTM5s2bZL09fWl0NBQKSkpSRo/frxkYmIiqdVqSZIkafjw4dKMGTPk+iNHjkg6OjrSokWLpPPnz0uBgYEl+or5rVu3pFOnTkmrV6+WAEixsbHSqVOnpDt37sg13bt3l7777rsSj+1tcf/+fenUqVPSqVOnJADSkiVLpFOnTknXrl2TJEmSZsyYIQ0fPlyuf/6V8qlTp0rnz5+XVqxY8cqvlD948EAKCAiQ4uLipKtXr0onT56URo0aJenr62tMN8BjVNCMGTOkgwcPSikpKdLp06elGTNmSAqFQtq7d68kSeLeQ0uXLpWUSqUUFhYmXbx4UZo9e7ZkYGCgMb0Aj8+rHT9+XNLR0ZG+/PJL6eLFi9KGDRukGjVqSOvXr5drRLynJKnwbxG+/C1glUql8W3Bl5eJSosBqxL57rvvpAYNGkh6enpSx44dpWPHjsnb3NzcJB8fH436LVu2SO+8846kp6cnOTg4SBEREa/sIzAwsMBXkPHS15BtbGykwMDAEo/tbfF8aouXX8+Pi4+Pj+Tm5lZgnzZt2kh6enpSo0aNXvkP9qNHj6QBAwZI1tbWkp6enmRlZSX1799fOn78uEYdj1FBo0ePlmxsbCQ9PT2pTp06Uo8ePeRwJUni3kOSJEnBwcFSvXr1pBo1akjOzs7SoUOHNLbz+JTMrl27pBYtWkj6+vpSs2bNpFWrVmlsF/GekiQGLKoYCkmSpIo4c0ZERERUXfEeLCIiIiLBGLCIiIiIBGPAIiIiIhKMAYuIiIhIMAYsIiIiIsEYsIiIiIgEY8AiIiIiEowBi4iIiEgwBqxqKCgoCAqFAjExMRU9lEqrYcOGaNiwYYX0/aaOj7u7OxQKRbn2QWUTGhoKhUKB0NDQih5KmSkUCri7u1f0MEqtsh2Dqvp7pIIYsIiIqFKoyP/4vA34n643S6eiB0Di+fn5YejQoWjQoEFFD4UK8aaOz88//4yHDx+Wax9UNgMGDECnTp1gZWVV0UMhIsEYsKohMzMzmJmZVfQwqAhv6vgwYFd+KpUKKpWqoodBROWgSl0i3LdvHxQKBf79738Xuv3y5cvQ0tKCp6envO7+/fsIDAyEg4MDDA0NYWJiAk9PTxw+fLjA/gkJCfDz80OLFi2gUqlgaGiIli1b4quvvsKTJ08K1D8/nZ2RkQE/Pz/Ur18fOjo68rX8W7du4bPPPkOTJk3kvps3b44JEyYgMzOz1D9/Sdsr7B6fq1evQqFQYOTIkbh06RIGDBiAWrVqwcjICB4eHvjzzz8L7TM9PR1TpkxB06ZNYWhoCFNTUzg5OWHRokUFak+fPo2hQ4fCysoKenp6sLGxwSeffII7d+6U+md9buvWrXBzc4O5uTkMDAxgbW0NDw8PbN26Va6JiYmBQqFAUFBQgf1f/LkLk5GRgY8//hiWlpYwMDBA27Zt8euvvxaoe/F3um7dOrRs2RKGhoawtbXFt99+CwCQJAmLFy9G06ZNYWBggCZNmuDnn38utq0XHThwAL1794a1tTX09fVhYWGBrl27YtWqVRp1f/zxB95//300aNAA+vr6qFOnDjp06IAvv/xSo66oywFPnz7FkiVL0Lp1axgaGkKlUqFbt27YtWtXgdoX70/Zu3cvOnfujBo1aqB27drw8fEp07EFgHXr1sHJyQk1a9ZEzZo14eTkVOi9MC8e45MnT+Jf//oXjI2NoVKpMGDAAFy9evW1+n/x78f58+cxYMAA1K5dGwqFQqPNlJQUjB07Vv6dW1lZYeTIkbh27Zpc8/DhQxgbG6Nx48ZF9teqVSsYGhoiKysLQPH3/5SkTwBo27YtVCoV8vLy5HX5+fkwNTWFQqHAjz/+qFH//O/fwYMHS/Orkv34449o0aIFDAwMUL9+fUybNg2PHz8utLak/6Y+Pw7Xrl3DtWvXoFAo5NfL7+vY2Fj069cPZmZm0NfXR5MmTTB79mwhZ2t37NiBjh07okaNGqhTpw5Gjx6NtLQ0jZpX/ZtS1D1U9+/fx9y5c9GqVSvUqFEDKpUKbdu2xeeff17o58uLJEnC5MmToVAoMGzYMI36kn7GvXjMX/z9FvVzkABSFZKfny81btxYUqlUUnZ2doHtM2bMkABIYWFhkiRJ0p07dyQHB4f/b+/Mo6I6sj/+hW66kUgjNohIBHEBzIjg0ooBmiWZuIABxYXgsEgMiRpQ2Y44MyJmjMEBRp2RLEzUSNxyjBsed6dBMa6j5mRwJIy4QESWQRYhCML9/eHvvaHt1013B03Mqc85nAN1a71V79Z99aoKAkDe3t60dOlSio2NJblcTmKxmPbt26eW/t1336VBgwZReHg4paSk0OLFi/n0M2fO1CjPycmJBg4cSGPGjKERI0bQokWLKCEhgQ4fPkwtLS3k7OxMJiYmNHnyZEpJSaElS5bQm2++SRYWFlRWVmZQ2w3JLz09nQCQSqXiw27dukUAyM/Pj+RyOSmVSkpMTKSQkBACQNbW1nT//n21Mm/cuEH29vYEgHx8fCg1NZUWL15M/v7+ZG1trRb3wIEDJJVKqU+fPrz+goKCCACNGDGC6uvrDWovEVFubi4BIHt7e4qLi6O0tDSaP38+/eY3v6F58+bx8VQqFQGg9PR0jTy4dkdHR6uFOzk5kb29PY0bN45cXV0pOTmZFi5cSHK5nADQxo0b1eJzOg0JCSErKyuKioqihIQEcnBwIACUl5dHixYtIjs7O3r77bdp4cKFZG1tTQCoqKhIMK/u/XPo0CEyMTEha2triomJobS0NFqwYAEpFAry8fHh4129epWkUilZWFjQW2+9RcuXL6f33nuPlEolOTo6qpXj5+dHTz/iXV1dfJ+7uLhQUlISvffee3xdc3Jy1OJv2bKFANCMGTNIIpFQWFgYJSUlkUKh4J8rY4mPjycA5ODgQAkJCWr6TEhIUIvL9fG0adOoT58+NG3aNEpKSqLAwEACQMOGDaMff/zR4Dpw48Pb25tkMhl5e3tTYmIiRUdH0w8//EBEROfPnycrKysSi8UUGhpKKSkpNHv2bBKLxTRgwAC6efMmn190dDQBoLNnz2qUde3aNQJAc+fO5cM4/W7ZskUtriFlLlu2jADQxYsX+bArV64QAAJAERERankrlUoyNzentrY2g/W1evVqAkB2dnb0/vvv07Jly8jR0ZGCg4N5+9IdfW3qgwcPKD09naysrMjKyorS09P5n+7PSW5uLv+cREVFUXJyMvn7+xMAevXVV+nRo0cGt4nrg+DgYDIzM6O33nqL0tLSKCAggADQ8OHD1eyXNpvCIaSH6upqcnNzIwDk6elJiYmJtHTpUpoyZQqZmZnRgwcPtKZvb2+niIgIAkBLly6lrq4uXmbIHJeenk5OTk68reR+np4HGb3HC+VgERFlZmYSANq6dataeEdHB9nb29OAAQOovb2diIgflHl5eWpxq6urafDgwWRra6tmlO/cuUOPHz9Wi9vV1UWxsbEEgIqLi9Vk3GCdPHkytba2qskOHjzIPxBP09zcbLBxMyQ/XQ4WAProo4/U0v/hD38gALR27Vq18PHjxxMA+uyzzzTKrKio4H+vq6sjmUxGDg4OdPv2bbV4O3fuJAD0/vvvG9ReIqKxY8eSRCKh6upqDVldXR3/u7EOFgBSKpVqRrmiooJsbGxIKpVSZWUlH87ptH///mqT2927d0kikZCVlRW5uLhQTU0NLzt//jwBoOnTp6uVLdQ/M2fOJAB07do1nW1NTEwkALR//36d8YiEHawvvviCN+Dd233nzh2ysbEhsVis1j5u8hGLxWrj//Hjx/zEdu7cOY269ERRUREBoJEjR1JDQwMfXl9fTy4uLgSATp8+zYdzfQyAdu3apZZXZGQkAaCdO3caXI/uz8XKlSs15O3t7TRkyBCytLSkK1euqMnOnDlDIpGIgoOD+bCTJ08SAFq4cKFGXklJSQSADh06xIcJOViGlsnZhszMTD4sOzubANBrr71G9vb2fHhraytJJBIKDAzUQzvqlJWVkVgsJgcHB7VnsrGxkVxdXQUdC2NsqpOTk2D5JSUlJBaLycPDQ2Osr127lgBQVlaWwe3i+gAAHT16VE3GvbR3t1/GOFhhYWEEgFasWKER//79+9TR0SGYvrm5md544w1B+0xk+BwnZBMYz44XTtM1NTUkkUjU3uqJiPbv308AKCUlhYiIamtrSSQSaTUkGzduJABUUFDQY5n//Oc/CQCtWrVKLZybpL/99luNNJzRS0tL07dpOjEkP10OlrOzM3V2dqrF52Td3ygvXLjAOyA9kZOTQwBo27ZtgvKxY8eSjY1Nj/kIpXvppZd6XP36KQ7W0waeiOiDDz7QMNacTjMyMjTic6soX3zxhYZs6NChGitLuhys0tJSnW3lHKxjx47pjEckbEy5ul64cEEj/po1awgArV69mg/jJp+oqCiN+Jzs6dU+feAm2N27d2vItm/fTgAoNjaWD+P6WGg8crLExESD68GNj4EDBwqufuzdu1dDJ92ZOXMmmZqaUmNjIxERdXZ2koODA8nlcv5Fjwu3t7cnW1tbtclUyMEytMyGhgYSiUQ0efJkPk5wcDC5urrS5s2bCQD9+9//JqL/OYDa8tZFRkYGAaDs7GwNWX5+vqBjoQ1dNlWbg5WQkKDheHN0dnaSra0tjRs3Tq/yu8P1weuvv64ha25upn79+pFMJuPtpqEOVlVVFZmYmNCwYcPUxoQ2uPS1tbWkUChIJBLR5s2bNeIZM8cxB+v58sJtcre1tcXMmTOxa9cu3LhxA25ubgDA7zNYsGABAODSpUvo7OzEo0ePBPfmlJWVAQBu3LiB4OBgAEB7ezv+9re/8Xk/fPgQRMSnuXfvnkY+5ubmcHd31whXKpWwt7fHRx99hG+//RbBwcHw8/PDyJEjjTom21v5eXp6wtRUfevdyy+/DODJfiSOixcvAgDeeOONHvM8f/48AODChQu4efOmhrytrQ11dXWoq6szaHN3eHg4UlNTMWrUKERERCAgIAA+Pj6QyWR656ELsViMSZMmaYT7+voCAK5evaoh8/T01AjjToBpk124cKHHuoSHh2Pv3r3w8vJCREQEXnvtNfj6+mroa86cOVi/fj1mzJiBuXPn4re//S2USiUcHBx6LINrk4WFBSZMmKAhCwgIAABcu3ZNQzZu3DiNMKFxoy+cboX2qjzPenB4eHhAIpFohHNju7S0VNCO3L9/H11dXfj+++8xfvx4mJqaYt68eVi3bh0OHz6MkJAQAMCpU6dQVVWF+Ph4iMW6za6hZXJ7eYqLi9HR0QFTU1OcPn0a8+bN43WpUqng5uYGlUoF4H86NgRunyb3fHRHKAwwzqZqg9PLsWPHcOrUKQ25mZkZbty4oXd+TyPUhr59+8LT0xOFhYUoLy/H8OHDDc738uXLICIEBATAzMxMrzTV1dXw9vZGRUUF9u3bh+nTp2vEMWaOYzxfXjgHCwDeffdd7Nq1C3//+9+RlZWFe/fu4ciRI/Dz84OLiwsAoL6+HgBw9uxZnD17VmteLS0t/O+zZs1CQUEBXFxcMHfuXAwYMABmZmZoaGjAhg0b8OjRI430AwYMEHRwrKyscP78eaxcuRIFBQU4fPgwAGDw4MFYvny51o362uit/IScE87gd98ky22a12fi5nS9adMmnfFaWloMcrCSk5Mhl8vx8ccfIzs7G1lZWRCLxQgKCsJf/vIXODs7652XEDY2NhrOJgDY2dkBgOBBBF360yZ7/Phxj3WZPXs29u/fj5ycHHzyySfYtGkTTExMEBAQgOzsbN55mzhxIgoLC/Hhhx9ix44d2LJlCwBAoVAgMzOzx4mzqakJgwcPFpRxjiK3Abs7+o4bfWlqaoKpqSlsbW01ZHZ2djAxMXku9ehephDc2N6+fbvO9N3tSGRkJNatW4cvv/ySd7Dy8/N5WU8YU2ZAQAAuX76MS5cuwczMDE1NTQgMDOQP4qhUKixcuBAqlUqrg90T3PMwYMAADZk2/RljU7XB6eXpwxy9hbY26LIH+mCILeWoqqpCU1MThg8fjokTJwrGMWaOYzxfXqhThBz+/v5wc3PDtm3b0N7eji1btqCzsxPvvPMOH4czxElJSaAnn0IFf9LT0wE8eRsoKCjA5MmTcf36deTl5WHNmjVYtWoVwsPDtdZF1+qRo6Mjtm7ditraWly9ehWZmZno6urC4sWLBU+q9URv56eLfv36AQB++OGHHuNyuv7uu+906trJycmgOpiYmCA2NhaXLl1CbW0t9u3bh5kzZ+LAgQMIDg7mJ1TOSRJyZHQZxbq6OnR1dWmEc6eGnvfx+ZCQEBQVFeHBgwc4cuQIFixYgMLCQkyZMkVtdcbX1xdHjhzBgwcPoFKpkJiYiO+++w5BQUEoLy/XWYZMJkNNTY2g7P79+3ycZ41MJkNXVxdqa2s1ZDU1NSCi51IPDm3PMVeHgoICnWPbz8+PTzNq1Ch4enri0KFDaGxsRGtrK/bt2wdXV1coFIoe62JMmd1XqrgTl9zqYEBAAAoLC/Hw4UNcunQJ3t7egqt1PcE9D0Lj5+mTdoDxNlUbnF6ampp06sVYhNrQPZxrv6H2xhBbyuHp6YnPP/8cN2/eREBAgGDdDJ3jGM+fF9LBAoC4uDjU1tZi//792Lx5M6ytrREWFsbLFQoFTExMcO7cOb3y4z5tBQUFQSQSqcnOnDnzk+pqamoKT09PpKam8o7QwYMHfzH5CcG94R4/frzHuNwblr66Nga5XI7Q0FDs3r0bgYGBuH79Ov7zn/8AAKytrQEIGzChz3wcjx8/Fqwz199jxozpjaobjKWlJaZMmYLPPvsMMTExqK6uFvzM2KdPH/j7+yM7OxsrVqzAjz/+iBMnTujMe8yYMWhtbeU/AXeHuzZC6FNnb8PpVujfBT3PevSEsWM7MjISbW1t2LNnD/bt24eHDx/id7/73TMr09fXF2KxGP/4xz+gUqng7u7OrxYHBgaitrYWn376KTo6Ooz+NyweHh4AhO2hUJgxNlUkEmldieT0wn0q7G2E6vTw4UNcu3YNMpkMQ4cOBaDbYRKyN9ynY5VK1eN1DN2ZP38+tmzZghs3bgg6WYbOcQD4fvgpq70M/XlhHazo6GiYm5tj2bJlKC8vR2RkJMzNzXn5wIEDMWfOHHzzzTf485//LPhmc+HCBf7uFG515em7Q0pKSrB27VqD61dSUiL41sGFda/rz5FfTygUCigUCpw+fRp5eXka8u7GZf78+bC0tMTvf/97lJSUaMRtbW01yigWFhZq9FtHRwe/NM612dXVFZaWljh48CAvA57o5k9/+pPOMlasWIH29nb+78rKSmzYsAFSqdSot2xjOX36tKDR41YLuLaeO3dO8M4hfcdBdHQ0ACAtLU3N2FdUVCAnJwdisRjz5s0zrhEGwNUjIyND7VNgY2MjMjIy1OL8nISEhMDR0RE5OTk4ffq0hryjo0PwTr2IiAiIRCLk5+cjPz8fJiYmejtYxpTZt29fjB8/Ht988w3OnDmDwMBAXsatbmVmZqr9bShcm3JyctRWsZqamgSfM2Nsav/+/VFXVyc4xhctWgSxWIz4+HjcvXtXQ97Q0KDzhaonTp48iWPHjqmFrVmzBg0NDYiKiuJXrmQyGVxdXVFcXMy/5AFP7qNKS0vTyNfOzg5hYWG4efMmP7a7U1NTo3UbQVRUFLZu3YrS0lL4+/vzq8yA4XMc8ES/wJPnnfHseSH3YAFPBsrs2bP5vQ3dPw9y5ObmorS0FKmpqcjPz8ekSZPQr18/VFRU4PLlyygrK0NVVRW/J2HChAn46quvUFVVBS8vL9y9excHDx5EUFAQ9uzZY1D9Tpw4gZSUFHh7e8PFxQVyuRzl5eU4ePAgzM3NsXjx4p81P33Yvn07/P39ERcXx+uvra0NJSUluHr1Kn/JpK2tLXbu3InZs2fDw8MDU6ZMgZubGx49eoTbt2+jqKgIr776Ko4ePWpQ+aGhoZDJZPDy8oKTkxM6Ojpw4sQJXL9+HbNmzeINuEQiQXx8PD788EOMHTsWISEhaG5uRkFBAfz8/AQ33gNP9hy1tLRg9OjRmD59OlpaWvDVV1/hv//9LzZu3GjQnomfSkJCAu7duwcfHx8MGTIEJiYmKC4uxsWLF+Hl5QUfHx8ATyZJlUoFpVIJZ2dnmJub48qVKzh16hSGDh2KGTNm6CwnMjISe/fuxYEDBzB69GgEBwejpaUFu3fvRn19PbKzs/k39WeJUqlEfHw8/vrXv2LUqFEICwsDEeHrr79GZWUlEhISoFQqn3k9ekIqlWLPnj2YOnUq/Pz8EBgYCHd3d/5SzDNnzkAul2tsrh44cCBef/11HD9+HKampny/PssyAwIC+BeZ7k6Ug4MDRowYgbKyMvTt21evz5RCDB8+HCtXrkR6ejpGjx6NOXPmQCwW4+uvv8bo0aNRWlqqFt8YmxoYGIjLly9j6tSp8PX1hUQigVKphFKpxKhRo5Cbm4uFCxfC1dUV06ZNw7Bhw9Dc3Izy8nIUFRUhJiYGn3zyiVHtCw4OxvTp0zFr1iwMGTIE58+fh0qlwrBhw7B69Wq1uElJSYiLi8OkSZMwe/ZsdHV14ciRI1p1m5ubi3/9619Ys2YNDh8+jMDAQBARvv/+exw/fhzV1dX8ytjTREZGwtTUFNHR0fD394dKpeL3Sxoyx3H63bNnD8LCwjB16lSYm5vDw8NDcBM9oxfo5VOJzxXuyLGXl5fWOK2trbRu3ToaN24cvfTSS9SnTx9ydnam0NBQ2rZtm9qR6ZqaGoqNjaVBgwaRubk5ubu706ZNm6i8vFzrUX9tR4qvX79OS5YsoTFjxpBcLiepVEpDhw6l6OhoKikpMbithuSn65oGQ+5uIXpyR8uSJUto6NChJJFIqH///jRx4kSNCymJnlxM+vbbb5OTkxNJJBKytrYmd3d3SkhIULsEUV9yc3PpzTffJCcnJzI3Nye5XE4TJkygjz/+WOO4c2dnJ61atYoGDx5MEomEXFxcaMOGDT32XX19PcXFxZGdnR1JpVLy8PCgHTt2aNRFSKcc3OWSt27d0pAJHYsWymvXrl00Z84cGjZsGFlYWJCVlRV5eHhQZmYmNTc38/GOHj1KUVFR5OrqSpaWltS3b1965ZVXaMWKFVRbW9tj2URP7ozLysoid3d3kkqlZGlpSX5+fnTgwAGNuNouwiTSfT2GvmzevJkUCgVZWFiQhYUFKRQKwSPpxlzFoQ/6pq2srKQlS5bQiBEjSCqVkkwmo5EjR9KCBQvo1KlTgmm+/PJL/n6lTz/9VDCOLv0aWubx48cJAIlEIrW7xYiI4uLiCP9/Z99PJS8vj1555RWSSCT08ssvU3JyMrW2tgraEENtanNzM73zzjtkb29PIpFIsM8vXrxI4eHhNGjQIDIzMyMbGxsaO3YsLV++nL+OwhC698H+/ftJoVBQnz59SC6XU0xMDFVVVQmm27RpE40YMYLMzMzI0dGRVq5cSe3t7VptaWNjI/3xj38kNzc3kkqlZGVlRZ6ennw6Dm3pd+zYQSKRiFxdXflLcIkMm+M6OjooNTWVHB0dSSwWG/3cMPTDhOgn7Ar8mcnKykJKSgo+//xzxMbG/tzVYTAYDAaDwQAAvLAOVltbG2RDeaAAAAGPSURBVNzc3NDU1ITKykp+CZTBYDAYDAbj5+aF24NVXFyMoqIiHDt2DHfu3MHatWuZc8VgMBgMBuMXxQvnYJ08eRIZGRmwsbHBsmXLkJyc/HNXyWgKCwsFj6k/jaenJ0JDQ599hZ4DQjcOC7F06VKtmz4Zv0x+KX27fv16vW51j4mJ0Xvj+a+VX6OuGhoasH79er3i6jtmGQxjeGE/Ef4aWLVqleCx3aeJjo7G1q1bn32FngP6/lufW7duvTAGnfGEX0rfDhkyBHfu3OkxnkqlMvpOqF8Lv0Zd3b59W+//8sCmP8azhDlYDAaDwWAwGL3MC3vRKIPBYDAYDMYvFeZgMRgMBoPBYPQyzMFiMBgMBoPB6GWYg8VgMBgMBoPRyzAHi8FgMBgMBqOXYQ4Wg8FgMBgMRi/DHCwGg8FgMBiMXoY5WAwGg8FgMBi9DHOwGAwGg8FgMHoZ5mAxGAwGg8Fg9DLMwWIwGAwGg8HoZZiDxWAwGAwGg9HLMAeLwWAwGAwGo5dhDhaDwWAwGAxGL/N/996x86JiCB0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0beaf296",
   "metadata": {},
   "source": [
    "### 2. minutes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2e0cd2b",
   "metadata": {},
   "source": [
    "[prep time]\n",
    "\n",
    " . Somewhat relevant\n",
    " . Low prep time is preferred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4638ca35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f37f95d308f846f7a6de35ee607df6f7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0.0: 0, 0.01: 2.0, 0.05: 5.0, 0.25: 20.0, 0.5: 40.0, 0.75: 70.0, 0.95: 310.0, 0.99: 930.0, 1.0: 2147483647}"
     ]
    }
   ],
   "source": [
    "get_quantiles(df = interaction_level_df,\n",
    "              col_name = \"minutes\",\n",
    "              quantiles_list=[0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e204658a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c689d47cfce648cb91bcacdfe4d67c75",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Capping prep time at 930 minutes\n",
    "\n",
    "interaction_level_df = (interaction_level_df\n",
    "                        .withColumn(\"minutes\",\n",
    "                                    F.when(interaction_level_df[\"minutes\"] > 930, 930)\n",
    "                                     .otherwise(interaction_level_df[\"minutes\"])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c4b8bdf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fd8c5f47f3474fe1a2d8ffc87d5c50ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------------+------------------+------------------+---------+---------+\n",
      "|n_steps_modified|        avg_rating|     stddev_rating|n_ratings|n_recipes|\n",
      "+----------------+------------------+------------------+---------+---------+\n",
      "|              01|              4.24|1.0908712114635715|       25|       12|\n",
      "|              02|4.4423076923076925|  1.08678668673504|      104|       28|\n",
      "|              03| 3.989130434782609|1.5750414356065066|      184|       44|\n",
      "|              04|  4.30635838150289|1.3867374413641127|      173|       57|\n",
      "|              05| 4.231788079470198|1.3563861924663059|      302|       90|\n",
      "|              06|       4.470703125|1.1463893346523668|      512|      102|\n",
      "|              07|4.3447432762836184|1.2875641979464003|      409|       92|\n",
      "|              08| 4.381995133819951| 1.277408546667124|      411|       92|\n",
      "|              09| 4.076190476190476|1.5270088873176948|      315|       86|\n",
      "|           >= 10| 4.240963855421687| 1.376815549387175|     2075|      491|\n",
      "+----------------+------------------+------------------+---------+---------+\n",
      "\n",
      "None"
     ]
    }
   ],
   "source": [
    "# investigating recipes with minutes = 0 -> Look at n_steps for such recipes.\n",
    "\n",
    "get_column_distribution_summary(df = (interaction_level_df\n",
    "                                      .filter('minutes == 0')\n",
    "                                      .withColumn('n_steps_modified', (F.when(interaction_level_df['n_steps'] >= 10, \">= 10\")\n",
    "                                                                        .otherwise(F.lpad(interaction_level_df['n_steps'],2,\"0\"))))),\n",
    "                                col_name = 'n_steps_modified')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "5b49d2c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "00b29760bdcd443eb549ac2946f6876f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+---------+------+--------------------+-----------+--------------------+----+-------+--------------+----------+--------------------+--------------------+-------+--------------------+-----------+--------------------+-------------+--------+-------------+---------+----------+-----------+-----------------+-----------------+---------------------+-----------------+--------------------+-------------------+-------------------------+-------------------------+------------------------------------+--------------------------------------+-------------------------------------+--------------------------------------------+\n",
      "|user_id|recipe_id|rating|              review|review_date|                name|  id|minutes|contributor_id| submitted|                tags|           nutrition|n_steps|               steps|description|         ingredients|n_ingredients|calories|total_fat_PDV|sugar_PDV|sodium_PDV|protein_PDV|saturated_fat_PDV|carbohydrates_PDV|total_fat_per_100_cal|sugar_per_100_cal|  sodium_per_100_cal|protein_per_100_cal|saturated_fat_per_100_cal|carbohydrates_per_100_cal|days_since_submission_on_review_date|months_since_submission_on_review_date|years_since_submission_on_review_date|years_since_submission_on_review_date_bucket|\n",
      "+-------+---------+------+--------------------+-----------+--------------------+----+-------+--------------+----------+--------------------+--------------------+-------+--------------------+-----------+--------------------+-------------+--------+-------------+---------+----------+-----------+-----------------+-----------------+---------------------+-----------------+--------------------+-------------------+-------------------------+-------------------------+------------------------------------+--------------------------------------+-------------------------------------+--------------------------------------------+\n",
      "|  11285|     5281|     5|Easy to make  swe...| 2001-06-18| creamsicle cocktail|5281|      0|          1549|1999-12-05|[15-minutes-or-le...|659.9, 25.0, 243....|      1|['blenderize all ...|           |['triple sec', 'v...|            4|   659.9|         25.0|    243.0|       4.0|       15.0|             49.0|             23.0|   3.7884526557182605|36.82375981358149|  0.6061524249149217|  2.273071593430956|        7.425367205207791|       3.4853764432607997|                                 561|                           18.41935484|                   1.5349462366666666|                                         1.0|\n",
      "|  38185|     5281|     4|This may not tast...| 2002-06-10| creamsicle cocktail|5281|      0|          1549|1999-12-05|[15-minutes-or-le...|659.9, 25.0, 243....|      1|['blenderize all ...|           |['triple sec', 'v...|            4|   659.9|         25.0|    243.0|       4.0|       15.0|             49.0|             23.0|   3.7884526557182605|36.82375981358149|  0.6061524249149217|  2.273071593430956|        7.425367205207791|       3.4853764432607997|                                 918|                           30.16129032|                   2.5134408599999998|                                         1.0|\n",
      "|   8728|     3831|     4|This is a great r...| 2002-01-02|choco banana wond...|3831|      0|          1533|1999-11-01|[15-minutes-or-le...|349.9, 11.0, 153....|      1|['combine in a bl...|           |['plain nonfat yo...|            4|   349.9|         11.0|    153.0|      11.0|       37.0|             10.0|             18.0|   3.1437554135123187|43.72677984248952|  3.1437554135123187| 10.574450027268709|       2.8579594668293806|        5.144327040292885|                                 793|                           26.03225806|                    2.169354838333333|                                         1.0|\n",
      "| 962690|     5411|     3|There is no amoun...| 2009-01-16|      russian tea ii|5411|      0|          1799|1999-12-13|[15-minutes-or-le...|1825.5, 2.0, 1738...|      1|['steep tea , rem...|           |['tea bags', 'pin...|            7|  1825.5|          2.0|   1738.0|       1.0|       14.0|              1.0|            154.0|  0.10955902492467817|95.20679265954533|0.054779512462339086| 0.7669131744727472|     0.054779512462339086|        8.436044919200219|                                3322|                          109.09677419|                    9.091397849166666|                                         3.0|\n",
      "|  41487|     3507|     4|This is a great s...| 2002-07-25|   cucumber cocktail|3507|      0|          1533|1999-10-19|[15-minutes-or-le...|76.6, 0.0, 40.0, ...|      1|['mix in blender ...|           |['kiwi', 'cucumbe...|            6|    76.6|          0.0|     40.0|       7.0|        4.0|              0.0|              6.0|                  0.0|52.21932218903867|   9.138381383081768|  5.221932218903867|                      0.0|       7.8328983283558005|                                1010|                           33.19354839|                         2.7661290325|                                         1.0|\n",
      "+-------+---------+------+--------------------+-----------+--------------------+----+-------+--------------+----------+--------------------+--------------------+-------+--------------------+-----------+--------------------+-------------+--------+-------------+---------+----------+-----------+-----------------+-----------------+---------------------+-----------------+--------------------+-------------------+-------------------------+-------------------------+------------------------------------+--------------------------------------+-------------------------------------+--------------------------------------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "# let's look at some examples with 1 step only to see if this makes sense\n",
    "\n",
    "interaction_level_df.filter('minutes == 0 and n_steps == 1').show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "1405397f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "baa2113364dd4f66a477fae0bd8e0798",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Condition String                   : \"minutes == 0\"\n",
      "Num Recipes Satisfying Condition   : 1094 [0.47%]\n",
      "Total Num Recipes                  : 231501"
     ]
    }
   ],
   "source": [
    "get_n_items_satisfying_condition(df = interaction_level_df,\n",
    "                                 condition = 'minutes == 0',\n",
    "                                 aggregation_level = \"recipe\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "fdd7094f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7d445a815e2c45a0bfe67cb3419eca4b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove recipes with cook time zero\n",
    "\n",
    "interaction_level_df = interaction_level_df.filter(\"minutes > 0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "9419f362",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cb75ab34fe4d487e8de3b8677a00ace2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added bucketized column prep_time_bucket\n",
      "\n",
      "Bucketing done for split definition: [0, 5, 15, 30, 60, 300, 900, inf]\n",
      "\n",
      "Printing summary statistics for ratings in buckets below:"
     ]
    }
   ],
   "source": [
    "splits = [0, 5, 15, 30, 60, 300, 900, float('Inf')]\n",
    "inputCol  = \"minutes\"\n",
    "outputCol = \"prep_time_bucket\"\n",
    "\n",
    "(interaction_level_df, prep_time_bucketizer, prep_time_summary_pandas_df) = bucket_col_print_summary(df = interaction_level_df,\n",
    "                                                                              splits = splits,\n",
    "                                                                              inputCol  = inputCol,\n",
    "                                                                              outputCol = outputCol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "91e4a1c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ca1befa",
   "metadata": {},
   "source": [
    "### 3. n_steps\n",
    "\n",
    "         . Clearly relevant\n",
    "         . Recipes with less than 2 steps are rated high\n",
    "         . Recipes with more than 29 steps are rated very low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "6ab740c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cab6a4f19eda42fc9dd323e7e5b42966",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0.0: 0, 0.01: 2.0, 0.25: 6.0, 0.5: 8.0, 0.75: 12.0, 0.99: 29.0, 1.0: 145}"
     ]
    }
   ],
   "source": [
    "get_quantiles(df = interaction_level_df,\n",
    "              col_name = \"n_steps\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "6210da30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b7216f791757465784063173ed2e487a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+---------+------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------+----------------+------+-------+--------------+----------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------+-------+-----+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+-------------+--------+-------------+---------+----------+-----------+-----------------+-----------------+---------------------+-----------------+------------------+-------------------+-------------------------+-------------------------+------------------------------------+--------------------------------------+-------------------------------------+--------------------------------------------+----------------+\n",
      "|user_id|recipe_id|rating|review                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |review_date|name            |id    |minutes|contributor_id|submitted |tags                                                                                                                                                                                                                                                                                                                                          |nutrition                              |n_steps|steps|description                                                                                                                                                                                                                                               |ingredients                                                                                                                                               |n_ingredients|calories|total_fat_PDV|sugar_PDV|sodium_PDV|protein_PDV|saturated_fat_PDV|carbohydrates_PDV|total_fat_per_100_cal|sugar_per_100_cal|sodium_per_100_cal|protein_per_100_cal|saturated_fat_per_100_cal|carbohydrates_per_100_cal|days_since_submission_on_review_date|months_since_submission_on_review_date|years_since_submission_on_review_date|years_since_submission_on_review_date_bucket|prep_time_bucket|\n",
      "+-------+---------+------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------+----------------+------+-------+--------------+----------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------+-------+-----+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+-------------+--------+-------------+---------+----------+-----------+-----------------+-----------------+---------------------+-----------------+------------------+-------------------+-------------------------+-------------------------+------------------------------------+--------------------------------------+-------------------------------------+--------------------------------------------+----------------+\n",
      "|38218  |176767   |5     |This is a little bit different from my usual recipie (which I've lost). but it makes a lovely  fragrant loaf.  Sublime with a smear of cream cheese.  I've only tried the carrot so far  but that was such a success I can't wait to try the others.  I made as directed with rasins and walnuts.                                                                                                                                                                                                                                                                                                                                                                                                         |2006-08-26 |all season bread|176767|90     |331268        |2006-07-10|[time-to-make, course, main-ingredient, cuisine, preparation, occasion, north-american, for-large-groups, breads, fruit, vegetables, easy, fall, spring, winter, muffins, coffee-cakes, seasonal, comfort-food, inexpensive, quick-breads, tropical-fruit, bananas, carrots, taste-mood, number-of-servings, 3-steps-or-less, 4-hours-or-less]|198.8, 11.0, 70.0, 18.0, 5.0, 5.0, 10.0|0      |[]   |just change the fruit/vegetable in this recipe and make the (tender  moist  heavy dark) bread your heart desires!  try zucchini in the summer  pumpkin in the fall   carrot-raisin in the winter  & banana-walnut in the spring.     use your imagination.|['flour', 'baking soda', 'salt', 'baking powder', 'cinnamon', 'eggs', 'white sugar', 'vegetable oil', 'real vanilla', 'raw carrots', 'raisins', 'walnuts']|12           |198.8   |11.0         |70.0     |18.0      |5.0        |5.0              |10.0             |5.533199110231471    |35.21126706510936|9.054325816742407 |2.5150905046506686 |2.5150905046506686       |5.030181009301337        |47                                  |1.51612903                            |0.12634408583333334                  |0.0                                         |4.0             |\n",
      "|233583 |176767   |4     |Made for PAC 2010  What can you say about a bread that is so versatile and readily available with ingredients on hand but YUMMO!  Made this yesterday for a snack and a healthier breakfast on the run.  I used bananas and shredded carrots and mmmm mmmm it came out very tastey.  So 1/2 disappeared before pictures could be taken but did get a shot this morning of the 1 loaf and a few muffins left over.  It came out so good I made a 2nd batch to take to the cottage for the weekend.  Quite a moist bread/muffin.  I believe I will make the zucchini variety next week as I still have some frozen from last summer harvest so out fo the freezer it comes to defrost and drain.  Thank you.|2010-04-23 |all season bread|176767|90     |331268        |2006-07-10|[time-to-make, course, main-ingredient, cuisine, preparation, occasion, north-american, for-large-groups, breads, fruit, vegetables, easy, fall, spring, winter, muffins, coffee-cakes, seasonal, comfort-food, inexpensive, quick-breads, tropical-fruit, bananas, carrots, taste-mood, number-of-servings, 3-steps-or-less, 4-hours-or-less]|198.8, 11.0, 70.0, 18.0, 5.0, 5.0, 10.0|0      |[]   |just change the fruit/vegetable in this recipe and make the (tender  moist  heavy dark) bread your heart desires!  try zucchini in the summer  pumpkin in the fall   carrot-raisin in the winter  & banana-walnut in the spring.     use your imagination.|['flour', 'baking soda', 'salt', 'baking powder', 'cinnamon', 'eggs', 'white sugar', 'vegetable oil', 'real vanilla', 'raw carrots', 'raisins', 'walnuts']|12           |198.8   |11.0         |70.0     |18.0      |5.0        |5.0              |10.0             |5.533199110231471    |35.21126706510936|9.054325816742407 |2.5150905046506686 |2.5150905046506686       |5.030181009301337        |1383                                |45.41935484                           |3.7849462366666664                   |2.0                                         |4.0             |\n",
      "+-------+---------+------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------+----------------+------+-------+--------------+----------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------+-------+-----+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+-------------+--------+-------------+---------+----------+-----------+-----------------+-----------------+---------------------+-----------------+------------------+-------------------+-------------------------+-------------------------+------------------------------------+--------------------------------------+-------------------------------------+--------------------------------------------+----------------+"
     ]
    }
   ],
   "source": [
    "interaction_level_df.filter('n_steps == 0').show(5, truncate = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "f096e413",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "48ccf68092d74ed9850a577c2f02c17c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Condition String                   : \"n_steps == 0\"\n",
      "Num Recipes Satisfying Condition   : 1 [0.0%]\n",
      "Total Num Recipes                  : 230407"
     ]
    }
   ],
   "source": [
    "get_n_items_satisfying_condition(df = interaction_level_df,\n",
    "                                 condition = 'n_steps == 0',\n",
    "                                 aggregation_level = \"recipe\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "f44c332f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "937c6a86f44546e2b485d99a809a943b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove recipes with n_steps zero\n",
    "\n",
    "interaction_level_df = interaction_level_df.filter(\"n_steps > 0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "133f8790",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c34a004345c74cf6b6715f908bfb5dc8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added bucketized column n_steps_bucket\n",
      "\n",
      "Bucketing done for split definition: [0, 2, 6, 8, 12, 29, inf]\n",
      "\n",
      "Printing summary statistics for ratings in buckets below:"
     ]
    }
   ],
   "source": [
    "splits = [0, 2, 6, 8, 12, 29, float('Inf')]\n",
    "inputCol  = \"n_steps\"\n",
    "outputCol = \"n_steps_bucket\"\n",
    "\n",
    "(interaction_level_df, n_steps_bucketizer, n_steps_pandas_df) = bucket_col_print_summary(df = interaction_level_df,\n",
    "                                                                              splits = splits,\n",
    "                                                                              inputCol  = inputCol,\n",
    "                                                                              outputCol = outputCol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "265f8f93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2735f474",
   "metadata": {},
   "source": [
    "### 4. n_ingredients\n",
    "\n",
    "        . Not relevant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "bb2a0373",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "419a9b365069440786c59bd81eca0b00",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0.0: 1, 0.01: 2.0, 0.25: 6.0, 0.5: 9.0, 0.75: 11.0, 0.99: 20.0, 1.0: 43}"
     ]
    }
   ],
   "source": [
    "get_quantiles(df = interaction_level_df,\n",
    "              col_name = \"n_ingredients\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "7ca8324f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "25ae221c15254d4a93d15f65a3cd81ce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added bucketized column n_ingredients_bucket\n",
      "\n",
      "Bucketing done for split definition: [0, 6, 9, 11, inf]\n",
      "\n",
      "Printing summary statistics for ratings in buckets below:"
     ]
    }
   ],
   "source": [
    "splits = [0, 6, 9, 11, float('Inf')]\n",
    "inputCol  = \"n_ingredients\"\n",
    "outputCol = \"n_ingredients_bucket\"\n",
    "\n",
    "(interaction_level_df, n_ingredients_bucketizer, n_ingredients_pandas_df) = bucket_col_print_summary(df = interaction_level_df,\n",
    "                                                                              splits = splits,\n",
    "                                                                              inputCol  = inputCol,\n",
    "                                                                              outputCol = outputCol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "0c2078ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "235faebe",
   "metadata": {},
   "source": [
    "### 5. nutrition columns\n",
    "        \n",
    "        . calories - Calories per serving seems irrelevant\n",
    "        . fat (per 100 cal) - Calories per serving seems irrelevant\n",
    "        . sugar (per 100 cal) - Calories per serving seems irrelevant\n",
    "        . sodium (per 100 cal) - Calories per serving seems irrelevant\n",
    "        . protein (per 100 cal) - Calories per serving seems irrelevant\n",
    "        . sat. fat (per 100 cal) - Calories per serving seems irrelevant\n",
    "        . carbs (per 100 cal) - Calories per serving seems irrelevant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b2bbc17c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fa9f8343d2784c119ce791a4dea63b4f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['user_id', 'recipe_id', 'rating', 'review', 'review_date', 'name', 'id', 'minutes', 'contributor_id', 'submitted', 'tags', 'nutrition', 'n_steps', 'steps', 'description', 'ingredients', 'n_ingredients', 'calories', 'total_fat_PDV', 'sugar_PDV', 'sodium_PDV', 'protein_PDV', 'saturated_fat_PDV', 'carbohydrates_PDV', 'total_fat_per_100_cal', 'sugar_per_100_cal', 'sodium_per_100_cal', 'protein_per_100_cal', 'saturated_fat_per_100_cal', 'carbohydrates_per_100_cal', 'days_since_submission_on_review_date', 'months_since_submission_on_review_date', 'years_since_submission_on_review_date', 'years_since_submission_on_review_date_bucket', 'prep_time_bucket', 'n_steps_bucket', 'n_ingredients_bucket']"
     ]
    }
   ],
   "source": [
    "interaction_level_df.columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "1870294c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0531b1d05e504927bf697774e5dd41eb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nutrition_cols = ['calories', \n",
    "                  'total_fat_PDV', \n",
    "                  'sugar_PDV', \n",
    "                  'sodium_PDV', \n",
    "                  'protein_PDV', \n",
    "                  'saturated_fat_PDV', \n",
    "                  'carbohydrates_PDV', \n",
    "                  'total_fat_per_100_cal', \n",
    "                  'sugar_per_100_cal', \n",
    "                  'sodium_per_100_cal', \n",
    "                  'protein_per_100_cal', \n",
    "                  'saturated_fat_per_100_cal', \n",
    "                  'carbohydrates_per_100_cal']\n",
    "\n",
    "quantiles_list = [0.00, 0.05, 0.25, 0.5, 0.75, 0.95, 1.00]\n",
    "nutrition_col_quantiles = pd.DataFrame(index = quantiles_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "506ab71f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7876f0c0f73c49ea9160133ce10d9ac2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in nutrition_cols:\n",
    "    nutrition_col_quantiles[col] = (get_quantiles(df = interaction_level_df,\n",
    "                                                col_name = col,\n",
    "                                                quantiles_list=quantiles_list)\n",
    "                                  .values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "c2c7dfbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e9ed022d533e4366bac72a44d842e6cd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nutrition_col_quantile_summary = pd.DataFrame(index = [\"0.00-0.25\", \"0.25-0.50\", \"0.50-0.75\", \"0.75-0.95\", \"0.95 - 1.00\"])\n",
    "\n",
    "for col in nutrition_cols:\n",
    "    splits = ([0]\n",
    "            + list(nutrition_col_quantiles.loc[[0.25, 0.5, 0.75, 0.95], col].round())\n",
    "            + [float('Inf')])\n",
    "    inputCol  = col\n",
    "    outputCol = col+\"_bucket\"\n",
    "\n",
    "    if outputCol in interaction_level_df.columns:\n",
    "        interaction_level_df = interaction_level_df.drop(outputCol)\n",
    "\n",
    "  # Training bucketizer\n",
    "    bucketizer = Bucketizer(splits = splits,\n",
    "                          inputCol  = inputCol,\n",
    "                          outputCol = outputCol)\n",
    "  \n",
    "    interaction_level_df = bucketizer.setHandleInvalid(\"keep\").transform(interaction_level_df)\n",
    "  \n",
    "    nutrition_col_quantile_summary.loc[:, col] = (interaction_level_df\n",
    "                                                .groupBy(outputCol)\n",
    "                                                .agg(F.avg('rating').alias('avg_rating'))\n",
    "                                                .sort(outputCol)\n",
    "                                                .select('avg_rating').toPandas().values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "462a390f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b035dedd4885442d9c68819349eed052",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set the max columns to none\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "f7aa18a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a8d65c1351e94214875d1b2748224c47",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             calories  total_fat_PDV  sugar_PDV  sodium_PDV  protein_PDV  \\\n",
      "0.00-0.25    4.416167       4.393560   4.416368    4.423843     4.422679   \n",
      "0.25-0.50    4.428239       4.420946   4.434610    4.408631     4.419234   \n",
      "0.50-0.75    4.418471       4.427758   4.403005    4.422298     4.410517   \n",
      "0.75-0.95    4.393681       4.411867   4.406985    4.397199     4.399930   \n",
      "0.95 - 1.00  4.342026       4.371152   4.332979    4.373164     4.372771   \n",
      "\n",
      "             saturated_fat_PDV  carbohydrates_PDV  total_fat_per_100_cal  \\\n",
      "0.00-0.25             4.396849           4.439453               4.385130   \n",
      "0.25-0.50             4.422079           4.421594               4.396544   \n",
      "0.50-0.75             4.423550           4.417625               4.415027   \n",
      "0.75-0.95             4.414637           4.382006               4.438629   \n",
      "0.95 - 1.00           4.351969           4.324922               4.476848   \n",
      "\n",
      "             sugar_per_100_cal  sodium_per_100_cal  protein_per_100_cal  \\\n",
      "0.00-0.25             4.412924            4.417770             4.412914   \n",
      "0.25-0.50             4.427622            4.398280             4.413150   \n",
      "0.50-0.75             4.410983            4.423546             4.416714   \n",
      "0.75-0.95             4.392021            4.414099             4.402535   \n",
      "0.95 - 1.00           4.413383            4.389711             4.404998   \n",
      "\n",
      "             saturated_fat_per_100_cal  carbohydrates_per_100_cal  \n",
      "0.00-0.25                     4.394655                   4.438210  \n",
      "0.25-0.50                     4.405957                   4.419843  \n",
      "0.50-0.75                     4.412177                   4.400808  \n",
      "0.75-0.95                     4.429877                   4.379254  \n",
      "0.95 - 1.00                   4.446134                   4.399911"
     ]
    }
   ],
   "source": [
    "nutrition_col_quantile_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "41ebe85c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dcacde093e234b408cbb6ac18431d9ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Writing the modified data to S3 \n",
    "interaction_level_df.write.parquet(\"interaction_level_df_processed_data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3140e25",
   "metadata": {},
   "source": [
    "## 02_EDA-CompleteSolution Completed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5083514c",
   "metadata": {},
   "source": [
    "# 03_FEATURE_EXTRACTION_PART_02"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37ba0a50",
   "metadata": {},
   "source": [
    "### Initial Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "883fbf8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8f951c9775047dc9d1607d8dee94789",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyspark.sql import SparkSession"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "da8730c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9f34b3a2d697404f9c0fbb94e5591aa3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "spark = SparkSession.builder.appName(\"Basics\").getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "357147f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6887e48b47314f9790edbc2753de9e87",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<pyspark.sql.session.SparkSession object at 0x7fc0f5406650>"
     ]
    }
   ],
   "source": [
    "spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "80fee1ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0312fb22ee8d42fda543c8ecab8223aa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyspark.sql import functions as F\n",
    "from pyspark.ml.feature import Bucketizer\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from pyspark.sql.window import Window\n",
    "\n",
    "# Import for typecasting columns\n",
    "from pyspark.sql.types import IntegerType,BooleanType,DateType,FloatType,StringType\n",
    "from pyspark.sql.types import ArrayType"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e5f4b3c",
   "metadata": {},
   "source": [
    "## Defining Custom Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "28ffa733",
   "metadata": {},
   "outputs": [
    {
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       "model_id": "154cbf604daa45a38bfce8dcc6103e55",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_minor": 0
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_quantiles(df, col_name, quantiles_list = [0.01, 0.25, 0.5, 0.75, 0.99]):\n",
    "    \"\"\"\n",
    "    Takes a numerical column and returns column values at requested quantiles\n",
    "\n",
    "    Inputs \n",
    "    Argument 1: Dataframe\n",
    "    Argument 2: Name of the column\n",
    "    Argument 3: A list of quantiles you want to find. Default value [0.01, 0.25, 0.5, 0.75, 0.99]\n",
    "\n",
    "    Output \n",
    "    Returns a dictionary with quantiles as keys and column quantile values as values \n",
    "    \"\"\"\n",
    "    # Get min, max and quantile values for given column\n",
    "    min_val = df.agg(F.min(col_name)).first()[0]\n",
    "    max_val = df.agg(F.max(col_name)).first()[0]\n",
    "    quantiles_vals = df.approxQuantile(col_name,\n",
    "                                       quantiles_list,\n",
    "                                       0)\n",
    "  \n",
    "    # Store min, quantiles and max in output dict, sequentially\n",
    "    quantiles_dict = {0.0:min_val}\n",
    "    quantiles_dict.update(dict(zip(quantiles_list, quantiles_vals)))\n",
    "    quantiles_dict.update({1.0:max_val})\n",
    "    return(quantiles_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "38a2ff87",
   "metadata": {},
   "outputs": [
    {
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       "model_id": "810cd0c5045b47118cac30adeee258c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "def plot_bucketwise_statistics (summary, bucketizer):\n",
    "    \"\"\"\n",
    "    Takes in a dataframe and a bucketizer object and plots the summary statistics for each bucket in the dataframe. \n",
    "  \n",
    "    Inputs\n",
    "    Argument 1: Pandas dataframe obtained from bucket_col_print_summary function \n",
    "    Argument 2: Bucketizer object obtained from bucket_col_print_summary function\n",
    "  \n",
    "    Output\n",
    "    Displays a plot of bucketwise average ratings nunber of ratings of a parameter.   \n",
    "    \"\"\"\n",
    "    # Creating bucket labels from splits\n",
    "    classlist = bucketizer.getSplits()\n",
    "    number_of_classes = len(classlist) - 1\n",
    "\n",
    "    class_labels = []\n",
    "    hover_labels = []\n",
    "    for i in range (number_of_classes):\n",
    "        hover_labels.append(str(classlist[i])+\"-\"+str(classlist[i+1]) +\" (Bucket name: \"+ str(int(i)) +\")\"  )\n",
    "        class_labels.append(str(classlist[i])+\"-\"+str(classlist[i+1]) )\n",
    "  \n",
    "    summary[\"Scaled_number\"] = (summary[\"n_ratings\"]-summary[\"n_ratings\"].min())/(summary[\"n_ratings\"].max()-summary[\"n_ratings\"].min()) + 1.5\n",
    "    summary['Bucket_Names'] = class_labels\n",
    "  \n",
    "    # making plot\n",
    "    x = summary[\"Bucket_Names\"]\n",
    "    y1 = summary[\"avg_rating\"]\n",
    "    y2 = summary[\"n_ratings\"]\n",
    "    err = summary[\"stddev_rating\"]  \n",
    "\n",
    "    # Plot scatter here\n",
    "    plt.rcParams[\"figure.figsize\"] = [summary.shape[0]+2, 6.0]\n",
    "    plt.rcParams[\"figure.autolayout\"] = True\n",
    "    fig, ax1 = plt.subplots()\n",
    "\n",
    "    bar = ax1.bar(x, y1, color = \"#262261\")\n",
    "    ax1.errorbar(x, y1, yerr=err, fmt=\"o\", color=\"#EE4036\")\n",
    "    ax1.set(ylim=(0, 7))\n",
    "  \n",
    "    #ax1.bar_label(bar , fmt='%.2f', label_type='edge')  \n",
    "    def barlabel(x_list,y_list):\n",
    "        for i in range(len(x_list)):\n",
    "            ax1.text(i,y_list[i] + 0.2,y_list[i], ha = 'center',\n",
    "  \t\t\t         fontdict=dict(size=10),\n",
    "  \t\t\t         bbox=dict(facecolor='#262261', alpha=0.2)         \n",
    "  \t\t\t        )\n",
    "    barlabel(summary[\"Bucket_Names\"].tolist() ,summary[\"avg_rating\"].round(2).tolist())\n",
    "  \n",
    "    ax2 = ax1.twinx()\n",
    "    ax2.scatter(x, y2, s=summary[\"Scaled_number\"]*500, c = '#FAAF40')  \n",
    "    ax2.set(ylim=(0, summary[\"n_ratings\"].max()*1.15))\n",
    "    def scatterlabel(x_list,y_list):\n",
    "  \t    for i in range(len(x_list)):\n",
    "  \t\t    ax2.text(i,y_list[i] + 15000,y_list[i], ha = 'center',\n",
    "  \t\t\t\t\t fontdict=dict(size=10),\n",
    "                     bbox=dict(facecolor='#FAAF40', alpha=0.5)\n",
    "  \t\t\t\t\t)\n",
    "    scatterlabel(summary[\"Bucket_Names\"].tolist() ,summary[\"n_ratings\"].tolist())\n",
    "  \n",
    "    # giving labels to the axises\n",
    "    ax1.set_xlabel(bucketizer.getOutputCol(), fontdict=dict(size=14)) \n",
    "    ax1.set_ylabel(\"Average Ratings\",fontdict=dict(size=14))\n",
    "  \n",
    "    # secondary y-axis label\n",
    "    ax2.set_ylabel('Number of Ratings',fontdict=dict(size=14))\n",
    "  \n",
    "    #plot Title\n",
    "    plt.title('Bucketwise average ratings and number of ratings for \\n'+bucketizer.getInputCol(), \n",
    "              fontdict=dict(size=14)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "01558b1f",
   "metadata": {},
   "outputs": [
    {
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       "version_minor": 0
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       "VBox()"
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     },
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    },
    {
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     },
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   ],
   "source": [
    "def bucket_col_print_summary(df, splits, inputCol, outputCol):\n",
    "    \"\"\"\n",
    "    Given a numerical column in a data frame, adds a bucketized version of the column to the data frame, according to splits provided.\n",
    "    Also prints a summary of ratings seen in each bucket made.\n",
    "\n",
    "    Inputs \n",
    "    Argument 1: Data Frame \n",
    "    Argument 2: Values at which the column will be split\n",
    "    Argument 3: Name of the input column (numerical column)\n",
    "    Argument 4: Name of the output column (bucketized numerical column)\n",
    "\n",
    "    Output: \n",
    "    1) New dataframe with the output column added\n",
    "    2) Bucketizer object trained from the input column \n",
    "    3) Pandas dataframe with summary statistics for ratings seen in buckets of the output column\n",
    "    Also plots summary statistics for ratings seen in buckets of the output column\n",
    "    \"\"\"\n",
    "\n",
    "    # Dropping bucket if it already exists\n",
    "    if outputCol in df.columns:\n",
    "        df = df.drop(outputCol)\n",
    "\n",
    "    # Training bucketizer\n",
    "    bucketizer = Bucketizer(splits = splits,\n",
    "                            inputCol  = inputCol,\n",
    "                            outputCol = outputCol)\n",
    "    \n",
    "    df = bucketizer.setHandleInvalid(\"keep\").transform(df)\n",
    "\n",
    "    # Printing meta information on buckets created\n",
    "    print(\"Added bucketized column {}\".format(outputCol))\n",
    "    print(\"\")\n",
    "    print(\"Bucketing done for split definition: {}\".format(splits))\n",
    "    print(\"\")  \n",
    "    print(\"Printing summary statistics for ratings in buckets below:\")\n",
    "\n",
    "    # Creating a summary statistics dataframe and passing it to the plotting function\n",
    "    summary =  (df\n",
    "                .groupBy(outputCol)\n",
    "                .agg(F.avg('rating').alias('avg_rating'),\n",
    "                     F.stddev('rating').alias('stddev_rating'),\n",
    "                     F.count('rating').alias('n_ratings'))\n",
    "                .sort(outputCol)\n",
    "                .toPandas())\n",
    "  \n",
    "    plot_bucketwise_statistics(summary,bucketizer)\n",
    "  \n",
    "    return df, bucketizer, summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "1bd456f6",
   "metadata": {},
   "outputs": [
    {
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       "model_id": "e7ade0c57a2d4b8384283fa3e9df79f3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_column_distribution_summary(df, col_name):\n",
    "    \"\"\"\n",
    "    Takes a column in a data frame and prints the summary statistics (average, standard deviation, count and distinct count) for all unique values in that column.\n",
    "  \n",
    "    Inputs \n",
    "    Argument 1: Dataframe \n",
    "    Argument 2: Name of the column\n",
    "  \n",
    "    Output\n",
    "    Returns nothing \n",
    "    Prints a Dataframe with summary statistics\n",
    "    \"\"\"\n",
    "    print(df\n",
    "          .groupBy(col_name)\n",
    "          .agg(F.avg('rating').alias('avg_rating'),\n",
    "               F.stddev('rating').alias('stddev_rating'),\n",
    "               F.count('rating').alias('n_ratings'),\n",
    "               F.countDistinct('id').alias('n_recipes'))\n",
    "          .sort(F.col(col_name).asc())\n",
    "          .show(50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "05fac085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a7270e86c8be44e7bca7d58b4f9ca770",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_n_items_satisfying_condition (df, condition, aggregation_level = \"recipe\"):\n",
    "    \"\"\"\n",
    "    Given a condition, find the number of recipes / reviews that match the condition.\n",
    "    Also calculates the percentage of such recipes / reviews as a percentage of all recipes / reviews.\n",
    "  \n",
    "    Inputs \n",
    "    Argument 1: Dataframe \n",
    "    Argument 2: Logical expression describing a condition, string type. eg: \"minutes == 0\"\n",
    "    Argument 3: Aggregation level for determining \"items\", either  \"recipe\" or \"review\". Default value == \"recipe\"\n",
    "  \n",
    "    Output: Returns no object.\n",
    "    Prints the following:\n",
    "    1) Number of recipes / reviews that satisfy the condition\n",
    "    2) Total number of recipes / reviews in the dataframe\n",
    "    3) Percentage of recipes / reviews that satisfy the condition\n",
    "    \"\"\"\n",
    "    # Find out num rows satisfying the condition\n",
    "    if aggregation_level == \"recipe\": \n",
    "        number_of_rows_satisfying_condition = (df\n",
    "                                             .filter(condition)\n",
    "                                             .agg(F.countDistinct(\"id\"))).first()[0]\n",
    "      \n",
    "        n_rows_total = (df.agg(F.countDistinct(\"id\"))).first()[0]\n",
    "    if aggregation_level == \"review\":\n",
    "        number_of_rows_satisfying_condition = (df\n",
    "                                             .filter(condition)\n",
    "                                             .agg(F.countDistinct(\"id\",\"user_id\"))).first()[0]\n",
    "        n_rows_total = (df.agg(F.countDistinct(\"id\",\"user_id\"))).first()[0]\n",
    "  \n",
    "    # Find out % rows satisfying the conditon and print a properly formatted output\n",
    "    perc_rows = round(number_of_rows_satisfying_condition * 100/ n_rows_total, 2)\n",
    "    print('Condition String                   : \"{}\"'.format(condition))\n",
    "    print(\"Num {}s Satisfying Condition   : {} [{}%]\".format(aggregation_level.title(), number_of_rows_satisfying_condition, perc_rows))\n",
    "    print(\"Total Num {}s                  : {}\".format(aggregation_level.title(), n_rows_total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "acf25332",
   "metadata": {},
   "outputs": [
    {
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       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def add_OHE_columns (df, n_name_list):\n",
    "    \"\"\"\n",
    "    Given a list of tags, creates one hot encoded columns for each tag. \n",
    "  \n",
    "    Input\n",
    "    Argument 1: Dataframe in which the function will add the new columns\n",
    "    Argument 2: list of tags\n",
    "  \n",
    "    Output\n",
    "    Prints the names of columns that have been added \n",
    "    Returns the modified dataframe \n",
    "    \"\"\"\n",
    "    for name in n_name_list:\n",
    "        df = (df.withColumn(\"has_tag_\"+name, F.when(F.array_contains(df.tags, name), 1).otherwise(0)))\n",
    "        print (\"added column: has_tag_\"+name)\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29566270",
   "metadata": {},
   "source": [
    "## Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "3c9ede89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a93578f016394fdd81e048cce4a97ba0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interaction_level_df = spark.read.parquet(\"interaction_level_df_processed_data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7e15daa",
   "metadata": {},
   "source": [
    "## Adding user level average features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "be109eb9",
   "metadata": {},
   "outputs": [
    {
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      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f991b7cd2214c18b493a70d5d939e26",
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       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "partition = Window.partitionBy(\"user_id\")\n",
    "\n",
    "interaction_level_df = (interaction_level_df\n",
    "                        .withColumn(\"user_avg_rating\",\n",
    "                                    F.avg(F.col(\"rating\")).over(partition))\n",
    "                        .withColumn(\"user_n_ratings\",\n",
    "                                    F.count(F.col(\"rating\")).over(partition))\n",
    "                        .withColumn(\"user_avg_years_betwn_review_and_submission\",\n",
    "                                    F.avg(F.col(\"years_since_submission_on_review_date\")).over(partition))\n",
    "                        .withColumn(\"user_avg_prep_time_recipes_reviewed\",\n",
    "                                    F.avg(F.col(\"minutes\")).over(partition))\n",
    "                        .withColumn(\"user_avg_n_steps_recipes_reviewed\",\n",
    "                                    F.avg(F.col(\"n_steps\")).over(partition))\n",
    "                        .withColumn(\"user_avg_n_ingredients_recipes_reviewed\",\n",
    "                                    F.avg(F.col(\"n_ingredients\")).over(partition)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "1b26c3b0",
   "metadata": {},
   "outputs": [
    {
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       "model_id": "f0376521e6274315af9f5a3cadc9805c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nutrition_cols = ['calories',\n",
    "                  'total_fat_per_100_cal',\n",
    "                  'sugar_per_100_cal',\n",
    "                  'sodium_per_100_cal',\n",
    "                  'protein_per_100_cal',\n",
    "                  'saturated_fat_per_100_cal',\n",
    "                  'carbohydrates_per_100_cal']\n",
    "\n",
    "for nutri_col in nutrition_cols:\n",
    "    interaction_level_df = (interaction_level_df\n",
    "                            .withColumn(\"user_avg_{}_recipes_reviewed\".format(nutri_col),\n",
    "                                        F.avg(F.col(nutri_col)).over(partition)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "b0146a3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "85c51c9ead264c40a9afcd149fc40708",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_minor": 0
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "# Code check cell\n",
    "# Do not edit cells with assert commands\n",
    "# If an error is shown after running this cell, please recheck your code. \n",
    "\n",
    "assert(round(interaction_level_df.filter('user_id == 601529').select('user_avg_rating').first()[0], 2) == 4.22)\n",
    "assert(interaction_level_df.filter('user_id == 601529').select('user_n_ratings').first()[0] == 27)\n",
    "assert(round(interaction_level_df.filter('user_id == 601529').select('user_avg_years_betwn_review_and_submission').first()[0], 2) == 3.51)\n",
    "assert(interaction_level_df.filter('user_id == 233044').select('user_avg_prep_time_recipes_reviewed').first()[0] == 50.3)\n",
    "assert(interaction_level_df.filter('user_id == 233044').select('user_avg_n_steps_recipes_reviewed').first()[0] == 8.8)\n",
    "assert(interaction_level_df.filter('user_id == 233044').select('user_avg_n_ingredients_recipes_reviewed').first()[0] == 8.2)\n",
    "assert(round(interaction_level_df.filter('user_id == 233044').select('user_avg_total_fat_per_100_cal_recipes_reviewed').first()[0]) == 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdee022e",
   "metadata": {},
   "source": [
    "## More Features:\n",
    "       \n",
    "       1. high_ratings = 5 rating\n",
    "\n",
    "       2. user_avg_years_betwn_review_and_submission_high_ratings\n",
    "\n",
    "       3. user_avg_prep_time_recipes_reviewed_high_ratings\n",
    "\n",
    "       4. user_avg_n_steps_recipes_reviewed_high_ratings\n",
    "\n",
    "       5. user_avg_n_ingredients_recipes_reviewed_high_ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "90e3c052",
   "metadata": {},
   "outputs": [
    {
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       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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     },
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    }
   ],
   "source": [
    "interaction_level_df = (interaction_level_df\n",
    "                        .withColumn(\"ind_5_rating\",\n",
    "                                    F.when(interaction_level_df[\"rating\"] != 5, None)\n",
    "                                     .otherwise(1))\n",
    "                        .withColumn(\"years_since_submission_on_review_date_5_ratings\",\n",
    "                                    F.when(interaction_level_df[\"rating\"] != 5, None)\n",
    "                                     .otherwise(F.col(\"years_since_submission_on_review_date\")))\n",
    "                        .withColumn(\"minutes_5_ratings\",\n",
    "                                    F.when(interaction_level_df[\"rating\"] != 5, None)\n",
    "                                     .otherwise(F.col(\"minutes\")))\n",
    "                        .withColumn(\"n_steps_5_ratings\",\n",
    "                                    F.when(interaction_level_df[\"rating\"] != 5, None)\n",
    "                                     .otherwise(F.col(\"n_steps\")))\n",
    "                        .withColumn(\"n_ingredients_5_ratings\",\n",
    "                                    F.when(interaction_level_df[\"rating\"] != 5, None)\n",
    "                                     .otherwise(F.col(\"n_ingredients\"))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "e0d8bd26",
   "metadata": {},
   "outputs": [
    {
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      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "partition = Window.partitionBy(\"user_id\")\n",
    "\n",
    "interaction_level_df = (interaction_level_df\n",
    "                        .withColumn(\"user_n_5_ratings\",\n",
    "                                    F.sum(F.col(\"ind_5_rating\")).over(partition))\n",
    "                        .withColumn(\"user_avg_years_betwn_review_and_submission_5_ratings\",\n",
    "                                    F.avg(F.col(\"years_since_submission_on_review_date_5_ratings\")).over(partition))\n",
    "                        .withColumn(\"user_avg_prep_time_recipes_reviewed_5_ratings\",\n",
    "                                    F.avg(F.col(\"minutes_5_ratings\")).over(partition))\n",
    "                        .withColumn(\"user_avg_n_steps_recipes_reviewed_5_ratings\",\n",
    "                                    F.avg(F.col(\"n_steps_5_ratings\")).over(partition))\n",
    "                        .withColumn(\"user_avg_n_ingredients_recipes_reviewed_5_ratings\",\n",
    "                                    F.avg(F.col(\"n_ingredients_5_ratings\")).over(partition)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "8d5d6e1e",
   "metadata": {},
   "outputs": [
    {
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       "version_major": 2,
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      },
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       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "for nutri_col in nutrition_cols:\n",
    "    interaction_level_df = (interaction_level_df\n",
    "                            .withColumn(\"{}_5_ratings\".format(nutri_col),\n",
    "                                        F.when(interaction_level_df[\"rating\"] != 5, None)\n",
    "                                         .otherwise(F.col(nutri_col))))\n",
    "    interaction_level_df = (interaction_level_df\n",
    "                            .withColumn(\"user_avg_{}_recipes_reviewed_5_ratings\".format(nutri_col),\n",
    "                                        F.avg(F.col(\"{}_5_ratings\".format(nutri_col))).over(partition)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "14bf3a15",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "0b2b2a390d1b45059d9e0f8f3fd05b07",
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       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "# Check - All rows with ratings should have non-null values in corresponding user_avg_5_ratings columns\n",
    "\n",
    "assert(interaction_level_df\n",
    "       .filter(\"rating == 5\")\n",
    "       .filter(interaction_level_df.user_n_5_ratings.isNull() |\n",
    "               interaction_level_df.user_avg_years_betwn_review_and_submission_5_ratings.isNull() |\n",
    "               interaction_level_df.user_avg_prep_time_recipes_reviewed_5_ratings.isNull() |\n",
    "               interaction_level_df.user_avg_n_steps_recipes_reviewed_5_ratings.isNull() |\n",
    "               interaction_level_df.user_avg_n_ingredients_recipes_reviewed_5_ratings.isNull())\n",
    "       .count() == 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "736e72d2",
   "metadata": {},
   "outputs": [
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     },
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check values for a given user id\n",
    "\n",
    "assert(interaction_level_df.filter('user_id == 233044').select('user_n_5_ratings').first()[0] == 7)\n",
    "assert(round(interaction_level_df.filter('user_id == 233044').select('user_avg_years_betwn_review_and_submission_5_ratings').first()[0], 2) == 2.24)\n",
    "assert(round(interaction_level_df.filter('user_id == 233044').select('user_avg_prep_time_recipes_reviewed_5_ratings').first()[0]) == 46)\n",
    "assert(round(interaction_level_df.filter('user_id == 233044').select('user_avg_n_steps_recipes_reviewed_5_ratings').first()[0], 2) == 7.29)\n",
    "assert(round(interaction_level_df.filter('user_id == 233044').select('user_avg_n_ingredients_recipes_reviewed_5_ratings').first()[0], 2) == 6.86)"
   ]
  },
  {
   "cell_type": "code",
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   "id": "30ed3b88",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- user_id: integer (nullable = true)\n",
      " |-- recipe_id: integer (nullable = true)\n",
      " |-- rating: integer (nullable = true)\n",
      " |-- review: string (nullable = true)\n",
      " |-- review_date: date (nullable = true)\n",
      " |-- name: string (nullable = true)\n",
      " |-- id: integer (nullable = true)\n",
      " |-- minutes: integer (nullable = true)\n",
      " |-- contributor_id: integer (nullable = true)\n",
      " |-- submitted: date (nullable = true)\n",
      " |-- tags: array (nullable = true)\n",
      " |    |-- element: string (containsNull = true)\n",
      " |-- nutrition: string (nullable = true)\n",
      " |-- n_steps: integer (nullable = true)\n",
      " |-- steps: string (nullable = true)\n",
      " |-- description: string (nullable = true)\n",
      " |-- ingredients: string (nullable = true)\n",
      " |-- n_ingredients: integer (nullable = true)\n",
      " |-- calories: float (nullable = true)\n",
      " |-- total_fat_PDV: float (nullable = true)\n",
      " |-- sugar_PDV: float (nullable = true)\n",
      " |-- sodium_PDV: float (nullable = true)\n",
      " |-- protein_PDV: float (nullable = true)\n",
      " |-- saturated_fat_PDV: float (nullable = true)\n",
      " |-- carbohydrates_PDV: float (nullable = true)\n",
      " |-- total_fat_per_100_cal: double (nullable = true)\n",
      " |-- sugar_per_100_cal: double (nullable = true)\n",
      " |-- sodium_per_100_cal: double (nullable = true)\n",
      " |-- protein_per_100_cal: double (nullable = true)\n",
      " |-- saturated_fat_per_100_cal: double (nullable = true)\n",
      " |-- carbohydrates_per_100_cal: double (nullable = true)\n",
      " |-- days_since_submission_on_review_date: integer (nullable = true)\n",
      " |-- months_since_submission_on_review_date: double (nullable = true)\n",
      " |-- years_since_submission_on_review_date: double (nullable = true)\n",
      " |-- years_since_submission_on_review_date_bucket: double (nullable = true)\n",
      " |-- prep_time_bucket: double (nullable = true)\n",
      " |-- n_steps_bucket: double (nullable = true)\n",
      " |-- n_ingredients_bucket: double (nullable = true)\n",
      " |-- calories_bucket: double (nullable = true)\n",
      " |-- total_fat_PDV_bucket: double (nullable = true)\n",
      " |-- sugar_PDV_bucket: double (nullable = true)\n",
      " |-- sodium_PDV_bucket: double (nullable = true)\n",
      " |-- protein_PDV_bucket: double (nullable = true)\n",
      " |-- saturated_fat_PDV_bucket: double (nullable = true)\n",
      " |-- carbohydrates_PDV_bucket: double (nullable = true)\n",
      " |-- total_fat_per_100_cal_bucket: double (nullable = true)\n",
      " |-- sugar_per_100_cal_bucket: double (nullable = true)\n",
      " |-- sodium_per_100_cal_bucket: double (nullable = true)\n",
      " |-- protein_per_100_cal_bucket: double (nullable = true)\n",
      " |-- saturated_fat_per_100_cal_bucket: double (nullable = true)\n",
      " |-- carbohydrates_per_100_cal_bucket: double (nullable = true)\n",
      " |-- user_avg_rating: double (nullable = true)\n",
      " |-- user_n_ratings: long (nullable = false)\n",
      " |-- user_avg_years_betwn_review_and_submission: double (nullable = true)\n",
      " |-- user_avg_prep_time_recipes_reviewed: double (nullable = true)\n",
      " |-- user_avg_n_steps_recipes_reviewed: double (nullable = true)\n",
      " |-- user_avg_n_ingredients_recipes_reviewed: double (nullable = true)\n",
      " |-- user_avg_calories_recipes_reviewed: double (nullable = true)\n",
      " |-- user_avg_total_fat_per_100_cal_recipes_reviewed: double (nullable = true)\n",
      " |-- user_avg_sugar_per_100_cal_recipes_reviewed: double (nullable = true)\n",
      " |-- user_avg_sodium_per_100_cal_recipes_reviewed: double (nullable = true)\n",
      " |-- user_avg_protein_per_100_cal_recipes_reviewed: double (nullable = true)\n",
      " |-- user_avg_saturated_fat_per_100_cal_recipes_reviewed: double (nullable = true)\n",
      " |-- user_avg_carbohydrates_per_100_cal_recipes_reviewed: double (nullable = true)\n",
      " |-- ind_5_rating: integer (nullable = true)\n",
      " |-- years_since_submission_on_review_date_5_ratings: double (nullable = true)\n",
      " |-- minutes_5_ratings: integer (nullable = true)\n",
      " |-- n_steps_5_ratings: integer (nullable = true)\n",
      " |-- n_ingredients_5_ratings: integer (nullable = true)\n",
      " |-- user_n_5_ratings: long (nullable = true)\n",
      " |-- user_avg_years_betwn_review_and_submission_5_ratings: double (nullable = true)\n",
      " |-- user_avg_prep_time_recipes_reviewed_5_ratings: double (nullable = true)\n",
      " |-- user_avg_n_steps_recipes_reviewed_5_ratings: double (nullable = true)\n",
      " |-- user_avg_n_ingredients_recipes_reviewed_5_ratings: double (nullable = true)\n",
      " |-- calories_5_ratings: float (nullable = true)\n",
      " |-- user_avg_calories_recipes_reviewed_5_ratings: double (nullable = true)\n",
      " |-- total_fat_per_100_cal_5_ratings: double (nullable = true)\n",
      " |-- user_avg_total_fat_per_100_cal_recipes_reviewed_5_ratings: double (nullable = true)\n",
      " |-- sugar_per_100_cal_5_ratings: double (nullable = true)\n",
      " |-- user_avg_sugar_per_100_cal_recipes_reviewed_5_ratings: double (nullable = true)\n",
      " |-- sodium_per_100_cal_5_ratings: double (nullable = true)\n",
      " |-- user_avg_sodium_per_100_cal_recipes_reviewed_5_ratings: double (nullable = true)\n",
      " |-- protein_per_100_cal_5_ratings: double (nullable = true)\n",
      " |-- user_avg_protein_per_100_cal_recipes_reviewed_5_ratings: double (nullable = true)\n",
      " |-- saturated_fat_per_100_cal_5_ratings: double (nullable = true)\n",
      " |-- user_avg_saturated_fat_per_100_cal_recipes_reviewed_5_ratings: double (nullable = true)\n",
      " |-- carbohydrates_per_100_cal_5_ratings: double (nullable = true)\n",
      " |-- user_avg_carbohydrates_per_100_cal_recipes_reviewed_5_ratings: double (nullable = true)"
     ]
    }
   ],
   "source": [
    "interaction_level_df.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd2769a8",
   "metadata": {},
   "source": [
    "## Tags level EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "9a8e7ef4",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "interaction_tag_level_df = interaction_level_df.withColumn('individual_tag',F.explode('tags'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "15414839",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "tags_ratings_summary = (interaction_tag_level_df\n",
    "                        .groupBy('individual_tag').agg(F.avg('rating').alias('avg_user_rating'),\n",
    "#                                                      F.max('rating').alias('max_user_rating'),\n",
    "#                                                      F.min('rating').alias('min_user_rating'),\n",
    "                                                       F.count('rating').alias('n_user_ratings'),\n",
    "                                                       F.countDistinct('id').alias('n_recipes')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "35687f44",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "interactions, recipes  =  interaction_level_df.count(), interaction_level_df.agg(F.countDistinct('id')).first()[0]\n",
    "\n",
    "tags_ratings_summary = (tags_ratings_summary.withColumn(\"in_percent_recipies\", F.col (\"n_recipes\")/F.lit(recipes))\n",
    "                                            .withColumn(\"in_percent_interactions\", F.col (\"n_user_ratings\")/F.lit(interactions)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b48350e",
   "metadata": {},
   "source": [
    "### 1. Top n most rated tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "1ece369a",
   "metadata": {},
   "outputs": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+------------------+--------------+---------+-------------------+-----------------------+\n",
      "|    individual_tag|   avg_user_rating|n_user_ratings|n_recipes|in_percent_recipies|in_percent_interactions|\n",
      "+------------------+------------------+--------------+---------+-------------------+-----------------------+\n",
      "|       preparation|4.4119124813277715|       1123326|   229318| 0.9952779007491125|     0.9970859455232471|\n",
      "|      time-to-make| 4.414416558383976|       1105132|   224098| 0.9726222407402585|       0.98093659823417|\n",
      "|            course| 4.412402044928726|       1071920|   217130| 0.9423799727437654|     0.9514569828574067|\n",
      "|           dietary| 4.412032038984685|        901277|   163918| 0.7114311259255401|     0.7999909462821618|\n",
      "|   main-ingredient| 4.424040070642098|        864074|   169549| 0.7358705936477349|     0.7669688418963456|\n",
      "|              easy|4.4183637556952755|        630786|   125789| 0.5459449840715953|     0.5598978882646952|\n",
      "|          occasion|4.4144829634028655|        619666|   113433| 0.4923179083878024|     0.5500275605822428|\n",
      "|         equipment| 4.415547752950291|        496985|    69892| 0.3033427948924941|     0.4411335254733452|\n",
      "|           cuisine| 4.416942151349161|        478853|    90639|0.39338819301580685|    0.42503921058681404|\n",
      "|  low-in-something| 4.414730950603082|        445959|    85258|0.37003376648177566|    0.39584185817794815|\n",
      "|         main-dish| 4.395996656937766|        384079|    71531|  0.310456324922094|    0.34091596995940915|\n",
      "|60-minutes-or-less| 4.405568569863525|        343212|    69929|0.30350338098834234|    0.30464162810700074|\n",
      "|number-of-servings| 4.407139294746751|        338857|    58410| 0.2535090232025208|     0.3007760456378389|\n",
      "|              meat| 4.408259712746521|        319091|    55769| 0.2420466480907615|    0.28323136065840054|\n",
      "|        taste-mood| 4.412428615527087|        310992|    52060| 0.2259489770231678|    0.27604253117097416|\n",
      "|    north-american| 4.413212293557913|        283433|    48182|0.20911781811237554|    0.25158062823925603|\n",
      "|30-minutes-or-less|4.4268528818028265|        267003|    55059|0.23896513111637718|    0.23699704156455345|\n",
      "|        vegetables| 4.454577657305231|        259718|    53562|0.23246790448165414|    0.23053073426539286|\n",
      "|              oven| 4.417805174050443|        249669|    30777| 0.1335772505924325|    0.22161104695595366|\n",
      "|   4-hours-or-less| 4.383299863701983|        247986|    49450|0.21462114701874083|    0.22011718351264725|\n",
      "+------------------+------------------+--------------+---------+-------------------+-----------------------+\n",
      "only showing top 20 rows"
     ]
    }
   ],
   "source": [
    "tags_ratings_summary.sort(F.col(\"n_user_ratings\").desc()).show(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "928a8eee",
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   "outputs": [
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   ],
   "source": [
    "tags_ratings_summary = tags_ratings_summary.filter(tags_ratings_summary.in_percent_interactions < 0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "b12f462a",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "top_most_frequent_tags = tags_ratings_summary.sort(F.col(\"n_user_ratings\").desc())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "b0257bc3",
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     "text": [
      "{0.0: 8.876193959039915e-07, 0.01: 8.876193959039915e-07, 0.25: 0.00035948585534111656, 0.5: 0.0034235480100016954, 0.75: 0.018886765506045133, 0.8: 0.02746826982564492, 0.85: 0.04430729738533955, 0.9: 0.08068815356525645, 0.95: 0.16414301678754564, 0.99: 0.34091596995940915, 1.0: 0.5598978882646952}"
     ]
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   ],
   "source": [
    "get_quantiles(df = top_most_frequent_tags , \n",
    "              col_name = 'in_percent_interactions', \n",
    "              quantiles_list = [0.01,0.25,0.5, 0.75,0.8,0.85,0.9,0.95, 0.99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "32b292ee",
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   "outputs": [
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    {
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     "text": [
      "29"
     ]
    }
   ],
   "source": [
    "# keep tags appearing in the top 5 percentile \n",
    "top_most_frequent_tags = top_most_frequent_tags.filter(\"in_percent_interactions > 0.16\")\n",
    "\n",
    "top_most_frequent_tags.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
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     "output_type": "display_data"
    }
   ],
   "source": [
    "top_frequent_tags_list = [data[0] for data in top_most_frequent_tags.select('individual_tag').collect()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "da2207df",
   "metadata": {},
   "outputs": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "added column: has_tag_easy\n",
      "added column: has_tag_occasion\n",
      "added column: has_tag_equipment\n",
      "added column: has_tag_cuisine\n",
      "added column: has_tag_low-in-something\n",
      "added column: has_tag_main-dish\n",
      "added column: has_tag_60-minutes-or-less\n",
      "added column: has_tag_number-of-servings\n",
      "added column: has_tag_meat\n",
      "added column: has_tag_taste-mood\n",
      "added column: has_tag_north-american\n",
      "added column: has_tag_30-minutes-or-less\n",
      "added column: has_tag_vegetables\n",
      "added column: has_tag_oven\n",
      "added column: has_tag_4-hours-or-less\n",
      "added column: has_tag_low-carb\n",
      "added column: has_tag_holiday-event\n",
      "added column: has_tag_desserts\n",
      "added column: has_tag_healthy\n",
      "added column: has_tag_dinner-party\n",
      "added column: has_tag_15-minutes-or-less\n",
      "added column: has_tag_low-sodium\n",
      "added column: has_tag_american\n",
      "added column: has_tag_beginner-cook\n",
      "added column: has_tag_low-cholesterol\n",
      "added column: has_tag_low-calorie\n",
      "added column: has_tag_inexpensive\n",
      "added column: has_tag_comfort-food\n",
      "added column: has_tag_kid-friendly"
     ]
    }
   ],
   "source": [
    "interaction_level_df = add_OHE_columns (interaction_level_df, top_frequent_tags_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69706276",
   "metadata": {},
   "source": [
    "### 2. Bottom n least rated tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "2475f88d",
   "metadata": {},
   "outputs": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+---------------+--------------+---------+--------------------+-----------------------+\n",
      "|      individual_tag|avg_user_rating|n_user_ratings|n_recipes| in_percent_recipies|in_percent_interactions|\n",
      "+--------------------+---------------+--------------+---------+--------------------+-----------------------+\n",
      "|     cranberry-sauce|            5.0|             1|        1|4.340164752654011E-6|   8.876193959039915E-7|\n",
      "|           pot-roast|            0.0|             1|        1|4.340164752654011E-6|   8.876193959039915E-7|\n",
      "|   main-dish-seafood|            0.0|             1|        1|4.340164752654011E-6|   8.876193959039915E-7|\n",
      "|   ham-and-bean-soup|            4.0|             1|        1|4.340164752654011E-6|   8.876193959039915E-7|\n",
      "|lamb-sheep-main-dish|            0.0|             1|        1|4.340164752654011E-6|   8.876193959039915E-7|\n",
      "+--------------------+---------------+--------------+---------+--------------------+-----------------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "tags_ratings_summary.sort(F.col(\"n_user_ratings\").asc()).show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceb20cec",
   "metadata": {},
   "source": [
    "The above tags are present in 1 recipe in over two hundred thousand. \n",
    "The features we create based on these tags will not teach the model new information.\n",
    "If these tags were one hot encoded, the entire column would be filled with zeros, and only a few rows will have 1s. \n",
    "One hot encoding of these tags is not a good idea. \n",
    "If you come up with an encoding that captures the rarity of these tags, only then can you add these tags to the analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebfae0f7",
   "metadata": {},
   "source": [
    "### 3. Top n rated tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "df91af18",
   "metadata": {},
   "outputs": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+---------------+--------------+---------+--------------------+-----------------------+\n",
      "|      individual_tag|avg_user_rating|n_user_ratings|n_recipes| in_percent_recipies|in_percent_interactions|\n",
      "+--------------------+---------------+--------------+---------+--------------------+-----------------------+\n",
      "|   side-dishes-beans|            5.0|             2|        2|8.680329505308021E-6|   1.775238791807983E-6|\n",
      "|             cabbage|            5.0|             1|        1|4.340164752654011E-6|   8.876193959039915E-7|\n",
      "|heirloom-historic...|            5.0|             3|        2|8.680329505308021E-6|   2.662858187711975E-6|\n",
      "|middle-eastern-ma...|            5.0|             2|        1|4.340164752654011E-6|   1.775238791807983E-6|\n",
      "|  breakfast-potatoes|            5.0|             1|        1|4.340164752654011E-6|   8.876193959039915E-7|\n",
      "+--------------------+---------------+--------------+---------+--------------------+-----------------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "tags_ratings_summary.sort(F.col(\"avg_user_rating\").desc()).show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "6c5be718",
   "metadata": {},
   "outputs": [
    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0.0: 1, 0.01: 1.0, 0.05: 1.0, 0.1: 12.0, 0.15: 99.0, 0.2: 187.0, 0.25: 405.0, 0.5: 3857.0, 0.75: 21278.0, 0.99: 384079.0, 1.0: 630786}"
     ]
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   ],
   "source": [
    "get_quantiles (tags_ratings_summary, \"n_user_ratings\", quantiles_list = [0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.5, 0.75, 0.99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "37b7b3cb",
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   "outputs": [
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   ],
   "source": [
    "tags_ratings_summary = tags_ratings_summary.filter(tags_ratings_summary.n_user_ratings > 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "60c1b79c",
   "metadata": {},
   "outputs": [
    {
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   ],
   "source": [
    "top_rated_tags_df = tags_ratings_summary.sort(F.col(\"avg_user_rating\").desc())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "9fd7d5df",
   "metadata": {},
   "outputs": [
    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0.0: 3.6742424242424243, 0.01: 3.962536023054755, 0.25: 4.344224006718102, 0.5: 4.403587357364895, 0.75: 4.451890903550206, 0.8: 4.461777662606893, 0.85: 4.48284199758207, 0.9: 4.503597122302159, 0.95: 4.536699941280094, 0.99: 4.609756097560975, 1.0: 4.822727272727272}"
     ]
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   ],
   "source": [
    "get_quantiles(df = top_rated_tags_df , \n",
    "              col_name = 'avg_user_rating', \n",
    "              quantiles_list = [0.01,0.25,0.5, 0.75,0.8,0.85,0.9,0.95, 0.99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "f0486f75",
   "metadata": {},
   "outputs": [
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26"
     ]
    }
   ],
   "source": [
    "# keep tags above 95 percentile\n",
    "top_rated_tags_df = top_rated_tags_df.filter(\"avg_user_rating > 4.53\")\n",
    "\n",
    "top_rated_tags_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "b578f0f5",
   "metadata": {},
   "outputs": [
    {
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   ],
   "source": [
    "top_rated_tags_list = [data[0] for data in top_rated_tags_df.select('individual_tag').collect()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "4ea2ae02",
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    {
     "name": "stdout",
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     "text": [
      "set()"
     ]
    }
   ],
   "source": [
    "set(top_frequent_tags_list) & set(top_rated_tags_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "1c8fa18d",
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   ],
   "source": [
    "all_added_columns_set = set(top_frequent_tags_list).union(set(top_rated_tags_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "e49a78a6",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "added column: has_tag_ragu-recipe-contest\n",
      "added column: has_tag_simply-potatoes2\n",
      "added column: has_tag_non-alcoholic\n",
      "added column: has_tag_a1-sauce\n",
      "added column: has_tag_labor-day\n",
      "added column: has_tag_punch\n",
      "added column: has_tag_lettuces\n",
      "added column: has_tag_cocktails\n",
      "added column: has_tag_mashed-potatoes\n",
      "added column: has_tag_smoothies\n",
      "added column: has_tag_turkey-burgers\n",
      "added column: has_tag_avocado\n",
      "added column: has_tag_beverages\n",
      "added column: has_tag_mango\n",
      "added column: has_tag_asparagus\n",
      "added column: has_tag_memorial-day\n",
      "added column: has_tag_shakes\n",
      "added column: has_tag_strawberries\n",
      "added column: has_tag_omelets-and-frittatas\n",
      "added column: has_tag_salsas\n",
      "added column: has_tag_greek\n",
      "added column: has_tag_salads\n",
      "added column: has_tag_barbecue\n",
      "added column: has_tag_australian\n",
      "added column: has_tag_grilling\n",
      "added column: has_tag_polynesian"
     ]
    }
   ],
   "source": [
    "interaction_level_df = add_OHE_columns (interaction_level_df, top_rated_tags_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14bcfdd5",
   "metadata": {},
   "source": [
    "### 4. Bottom n rated tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "210c85d9",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "bottom_rated_tags_df = tags_ratings_summary.sort(F.col(\"avg_user_rating\").asc())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "190737ba",
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     "text": [
      "{0.0: 3.6742424242424243, 0.01: 3.962536023054755, 0.02: 4.054406964091403, 0.03: 4.096385542168675, 0.04: 4.126760563380282, 0.05: 4.171974522292993, 0.1: 4.238095238095238, 0.15: 4.303225806451613, 0.2: 4.325726141078838, 0.25: 4.344224006718102, 0.5: 4.403587357364895, 0.75: 4.451890903550206, 0.99: 4.609756097560975, 1.0: 4.822727272727272}"
     ]
    }
   ],
   "source": [
    "get_quantiles (bottom_rated_tags_df, \"avg_user_rating\", quantiles_list = [0.01, 0.02, 0.03, 0.04, 0.05, 0.1, 0.15, 0.2, 0.25, 0.5, 0.75, 0.99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "a13b8fde",
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   "outputs": [
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      "text/plain": [
       "VBox()"
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    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6"
     ]
    }
   ],
   "source": [
    "bottom_rated_tags_df = bottom_rated_tags_df.filter(\"avg_user_rating < 4.00\")\n",
    "\n",
    "bottom_rated_tags_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "0488c143",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7e76beee0b824054918f34c06c7a272c",
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       "VBox()"
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    {
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   ],
   "source": [
    "bottom_rated_tags_list = [data[0] for data in bottom_rated_tags_df.select('individual_tag').collect()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "0ccb2204",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc59c09e80034a26ade914f8f74335a7",
       "version_major": 2,
       "version_minor": 0
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       "VBox()"
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     },
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    },
    {
     "data": {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set()"
     ]
    }
   ],
   "source": [
    "all_added_columns_set & set(bottom_rated_tags_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "ad092097",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "99666ceb2b564dad9e274f9836a8184c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "added column: has_tag_pressure-canning\n",
      "added column: has_tag_honduran\n",
      "added column: has_tag_unprocessed-freezer\n",
      "added column: has_tag_birthday\n",
      "added column: has_tag_jellies\n",
      "added column: has_tag_water-bath"
     ]
    }
   ],
   "source": [
    "interaction_level_df =  add_OHE_columns(interaction_level_df, bottom_rated_tags_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8857e73d",
   "metadata": {},
   "source": [
    "## Final DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "dbf63b5b",
   "metadata": {},
   "outputs": [
    {
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      "application/vnd.jupyter.widget-view+json": {
       "model_id": "744ffb0d24104f62bcdab90f46aa8d44",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "148"
     ]
    }
   ],
   "source": [
    "len(interaction_level_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "eb1328f7",
   "metadata": {},
   "outputs": [
    {
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      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
       "version_minor": 0
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       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "interaction_level_df.write.mode(\"overwrite\").parquet(\"interaction_level_df_BDA\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c665db7",
   "metadata": {},
   "source": [
    "## 03_FeatureExtractionPart02 Completed"
   ]
  }
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