3_Predicting_high_score.ipynb

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   "source": [
    "# Using Spark ML Pipeline Predicting Score\n",
    "This notebook builds a Logistic Regression Model based on Spark pipeline to predict the score of Reddit Posts of Personal Finance Subreddit. "
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Import and Pre-Processed Data"
   ]
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     "text": [
      "Starting Spark application\n"
     ]
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       "<table>\n",
       "<tr><th>ID</th><th>YARN Application ID</th><th>Kind</th><th>State</th><th>Spark UI</th><th>Driver log</th><th>Current session?</th></tr><tr><td>1</td><td>application_1622769166794_0002</td><td>pyspark</td><td>idle</td><td><a target=\"_blank\" href=\"http://ip-172-31-94-75.ec2.internal:20888/proxy/application_1622769166794_0002/\" >Link</a></td><td><a target=\"_blank\" href=\"http://ip-172-31-91-150.ec2.internal:8042/node/containerlogs/container_1622769166794_0002_01_000001/livy\" >Link</a></td><td>✔</td></tr></table>"
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     "text": [
      "SparkSession available as 'spark'.\n"
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     "text": [
      "Package                    Version   \n",
      "-------------------------- ----------\n",
      "awscli                     1.19.87   \n",
      "beautifulsoup4             4.9.3     \n",
      "boto                       2.49.0    \n",
      "botocore                   1.20.87   \n",
      "click                      7.1.2     \n",
      "colorama                   0.4.3     \n",
      "docutils                   0.15.2    \n",
      "jmespath                   0.10.0    \n",
      "joblib                     0.17.0    \n",
      "lxml                       4.6.1     \n",
      "mysqlclient                1.4.2     \n",
      "nltk                       3.5       \n",
      "nose                       1.3.4     \n",
      "numpy                      1.16.5    \n",
      "pip                        9.0.1     \n",
      "py-dateutil                2.2       \n",
      "pyasn1                     0.4.8     \n",
      "python-dateutil            2.8.1     \n",
      "python37-sagemaker-pyspark 1.4.1     \n",
      "pytz                       2020.1    \n",
      "PyYAML                     5.3.1     \n",
      "regex                      2020.10.28\n",
      "rsa                        4.7.2     \n",
      "s3transfer                 0.4.2     \n",
      "setuptools                 28.8.0    \n",
      "six                        1.13.0    \n",
      "spark-nlp                  3.0.3     \n",
      "tqdm                       4.51.0    \n",
      "urllib3                    1.26.5    \n",
      "wheel                      0.29.0    \n",
      "windmill                   1.6"
     ]
    }
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   "source": [
    "sc.list_packages()"
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     "text": [
      "Collecting boto3\n",
      "  Using cached https://files.pythonhosted.org/packages/0e/02/d0e07f3782cc054269ae0649ab1e3a0205fee1168545d4e502e62c27ba7e/boto3-1.17.87-py2.py3-none-any.whl\n",
      "Requirement already satisfied: s3transfer<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/site-packages (from boto3)\n",
      "Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /usr/local/lib/python3.7/site-packages (from boto3)\n",
      "Requirement already satisfied: botocore<1.21.0,>=1.20.87 in /usr/local/lib/python3.7/site-packages (from boto3)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/local/lib/python3.7/site-packages (from botocore<1.21.0,>=1.20.87->boto3)\n",
      "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.7/site-packages (from botocore<1.21.0,>=1.20.87->boto3)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.21.0,>=1.20.87->boto3)\n",
      "Installing collected packages: boto3\n",
      "Successfully installed boto3-1.17.87\n",
      "\n",
      "Collecting seaborn\n",
      "  Using cached https://files.pythonhosted.org/packages/68/ad/6c2406ae175f59ec616714e408979b674fe27b9587f79d59a528ddfbcd5b/seaborn-0.11.1-py3-none-any.whl\n",
      "Requirement already satisfied: numpy>=1.15 in /usr/local/lib64/python3.7/site-packages (from seaborn)\n",
      "Collecting scipy>=1.0 (from seaborn)\n",
      "  Using cached https://files.pythonhosted.org/packages/7d/e8/43ffca541d2f208d516296950b25fe1084b35c2881f4d444c1346ca75815/scipy-1.6.3-cp37-cp37m-manylinux1_x86_64.whl\n",
      "Collecting matplotlib>=2.2 (from seaborn)\n",
      "  Using cached https://files.pythonhosted.org/packages/24/33/5568d443ba438d95d4db635dd69958056f087e57e1026bee56f959d53f9d/matplotlib-3.4.2-cp37-cp37m-manylinux1_x86_64.whl\n",
      "Collecting pandas>=0.23 (from seaborn)\n",
      "  Using cached https://files.pythonhosted.org/packages/51/51/48f3fc47c4e2144da2806dfb6629c4dd1fa3d5a143f9652b141e979a8ca9/pandas-1.2.4-cp37-cp37m-manylinux1_x86_64.whl\n",
      "Collecting pyparsing>=2.2.1 (from matplotlib>=2.2->seaborn)\n",
      "  Using cached https://files.pythonhosted.org/packages/8a/bb/488841f56197b13700afd5658fc279a2025a39e22449b7cf29864669b15d/pyparsing-2.4.7-py2.py3-none-any.whl\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn)\n",
      "Collecting pillow>=6.2.0 (from matplotlib>=2.2->seaborn)\n",
      "  Using cached https://files.pythonhosted.org/packages/33/34/542152297dcc6c47a9dcb0685eac6d652d878ed3cea83bf2b23cb988e857/Pillow-8.2.0-cp37-cp37m-manylinux1_x86_64.whl\n",
      "Collecting cycler>=0.10 (from matplotlib>=2.2->seaborn)\n",
      "  Using cached https://files.pythonhosted.org/packages/f7/d2/e07d3ebb2bd7af696440ce7e754c59dd546ffe1bbe732c8ab68b9c834e61/cycler-0.10.0-py2.py3-none-any.whl\n",
      "Collecting kiwisolver>=1.0.1 (from matplotlib>=2.2->seaborn)\n",
      "  Using cached https://files.pythonhosted.org/packages/d2/46/231de802ade4225b76b96cffe419cf3ce52bbe92e3b092cf12db7d11c207/kiwisolver-1.3.1-cp37-cp37m-manylinux1_x86_64.whl\n",
      "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/site-packages (from pandas>=0.23->seaborn)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/site-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn)\n",
      "Installing collected packages: scipy, pyparsing, pillow, cycler, kiwisolver, matplotlib, pandas, seaborn\n",
      "Successfully installed cycler-0.10.0 kiwisolver-1.3.1 matplotlib-3.4.2 pandas-1.2.4 pillow-8.2.0 pyparsing-2.4.7 scipy-1.6.3 seaborn-0.11.1\n",
      "\n",
      "Requirement already satisfied: pandas in /mnt/tmp/1622778125062-0/lib/python3.7/site-packages\n",
      "Requirement already satisfied: numpy>=1.16.5 in /usr/local/lib64/python3.7/site-packages (from pandas)\n",
      "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/site-packages (from pandas)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/site-packages (from pandas)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas)"
     ]
    }
   ],
   "source": [
    "sc.install_pypi_package(\"boto3\") # use boto3 to see book data in S3 bucket\n",
    "sc.install_pypi_package(\"seaborn\")\n",
    "sc.install_pypi_package(\"pandas\")"
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  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b2273e9b",
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       "VBox()"
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   ],
   "source": [
    "import boto3\n",
    "import numpy as np\n",
    "\n",
    "from pyspark.ml.classification import LogisticRegression\n",
    "import pyspark.sql.functions as F\n",
    "from pyspark.ml.feature import StringIndexer, OneHotEncoder, VectorAssembler\n",
    "from pyspark.ml import Pipeline\n",
    "from pyspark.ml.classification import LogisticRegression\n",
    "import pyspark.ml.evaluation as evals\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ceb37cf1",
   "metadata": {},
   "outputs": [
    {
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "data = spark.read.csv('s3://large-scale-computing-personal-finance/raw-data/personalfinance_2020-01-01_2021-01-01.csv', \n",
    "                      header=True, \n",
    "                      sep=',', \n",
    "                      escape=\"\\\"\", \n",
    "                      multiLine=True,\n",
    "                      inferSchema=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b88a14dd",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- all_awardings: string (nullable = true)\n",
      " |-- allow_live_comments: string (nullable = true)\n",
      " |-- author: string (nullable = true)\n",
      " |-- author_flair_css_class: string (nullable = true)\n",
      " |-- author_flair_richtext: string (nullable = true)\n",
      " |-- author_flair_text: string (nullable = true)\n",
      " |-- author_flair_type: string (nullable = true)\n",
      " |-- author_fullname: string (nullable = true)\n",
      " |-- author_patreon_flair: string (nullable = true)\n",
      " |-- author_premium: string (nullable = true)\n",
      " |-- awarders: string (nullable = true)\n",
      " |-- can_mod_post: string (nullable = true)\n",
      " |-- contest_mode: string (nullable = true)\n",
      " |-- created_utc: string (nullable = true)\n",
      " |-- domain: string (nullable = true)\n",
      " |-- full_link: string (nullable = true)\n",
      " |-- gildings: string (nullable = true)\n",
      " |-- id: string (nullable = true)\n",
      " |-- is_crosspostable: string (nullable = true)\n",
      " |-- is_meta: string (nullable = true)\n",
      " |-- is_original_content: string (nullable = true)\n",
      " |-- is_reddit_media_domain: string (nullable = true)\n",
      " |-- is_robot_indexable: string (nullable = true)\n",
      " |-- is_self: string (nullable = true)\n",
      " |-- is_video: string (nullable = true)\n",
      " |-- link_flair_background_color: string (nullable = true)\n",
      " |-- link_flair_css_class: string (nullable = true)\n",
      " |-- link_flair_richtext: string (nullable = true)\n",
      " |-- link_flair_template_id: string (nullable = true)\n",
      " |-- link_flair_text: string (nullable = true)\n",
      " |-- link_flair_text_color: string (nullable = true)\n",
      " |-- link_flair_type: string (nullable = true)\n",
      " |-- locked: string (nullable = true)\n",
      " |-- media_only: string (nullable = true)\n",
      " |-- no_follow: string (nullable = true)\n",
      " |-- num_comments: string (nullable = true)\n",
      " |-- num_crossposts: string (nullable = true)\n",
      " |-- over_18: string (nullable = true)\n",
      " |-- parent_whitelist_status: string (nullable = true)\n",
      " |-- permalink: string (nullable = true)\n",
      " |-- pinned: string (nullable = true)\n",
      " |-- pwls: string (nullable = true)\n",
      " |-- removed_by_category: string (nullable = true)\n",
      " |-- retrieved_on: string (nullable = true)\n",
      " |-- score: string (nullable = true)\n",
      " |-- selftext: string (nullable = true)\n",
      " |-- send_replies: string (nullable = true)\n",
      " |-- spoiler: string (nullable = true)\n",
      " |-- stickied: string (nullable = true)\n",
      " |-- subreddit: string (nullable = true)\n",
      " |-- subreddit_id: string (nullable = true)\n",
      " |-- subreddit_subscribers: string (nullable = true)\n",
      " |-- subreddit_type: string (nullable = true)\n",
      " |-- thumbnail: string (nullable = true)\n",
      " |-- title: string (nullable = true)\n",
      " |-- total_awards_received: string (nullable = true)\n",
      " |-- url: string (nullable = true)\n",
      " |-- whitelist_status: string (nullable = true)\n",
      " |-- wls: string (nullable = true)\n",
      " |-- author_flair_background_color: string (nullable = true)\n",
      " |-- author_flair_text_color: string (nullable = true)\n",
      " |-- crosspost_parent: string (nullable = true)\n",
      " |-- crosspost_parent_list: string (nullable = true)\n",
      " |-- post_hint: string (nullable = true)\n",
      " |-- preview: string (nullable = true)\n",
      " |-- steward_reports: string (nullable = true)\n",
      " |-- banned_by: string (nullable = true)\n",
      " |-- edited: string (nullable = true)\n",
      " |-- treatment_tags: string (nullable = true)\n",
      " |-- upvote_ratio: string (nullable = true)\n",
      " |-- distinguished: string (nullable = true)\n",
      " |-- suggested_sort: string (nullable = true)\n",
      " |-- author_cakeday: string (nullable = true)\n",
      " |-- url_overridden_by_dest: string (nullable = true)\n",
      " |-- thumbnail_height: string (nullable = true)\n",
      " |-- thumbnail_width: string (nullable = true)\n",
      " |-- gilded: string (nullable = true)\n",
      ": string (nullable = true)"
     ]
    }
   ],
   "source": [
    "data.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df9943c1",
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   "source": [
    "Variables:\n",
    "\n",
    "- link_flair_css_class\n",
    "- num_comments\n",
    "- length of selftext\n",
    "- length of title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2de6068c",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+--------------------+--------------------+------------+-----+\n",
      "|               title|            selftext|link_flair_css_class|num_comments|score|\n",
      "+--------------------+--------------------+--------------------+------------+-----+\n",
      "|Trade in car or k...|So we have a Jeep...|                Auto|           5|    1|\n",
      "|About to purchase...|I am about to pur...|             Housing|           9|    1|\n",
      "|How much should m...|I'm currently liv...|             Housing|          50|    1|\n",
      "|21 yrs old, 721 c...|I don’t know anyt...|              Credit|           5|    1|\n",
      "|Medical Bill issu...|Basically a month...|                Debt|           7|    1|\n",
      "+--------------------+--------------------+--------------------+------------+-----+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "data = (data[['title', 'selftext', 'link_flair_css_class','num_comments','score']]\n",
    "        .filter(\"selftext != '[removed]' and link_flair_css_class != 'Unset'\")\n",
    "        .dropna())\n",
    "data.show(5)"
   ]
  },
  {
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     "text": [
      "root\n",
      " |-- title: string (nullable = true)\n",
      " |-- selftext: string (nullable = true)\n",
      " |-- link_flair_css_class: string (nullable = true)\n",
      " |-- num_comments: integer (nullable = true)\n",
      " |-- score: integer (nullable = true)"
     ]
    }
   ],
   "source": [
    "data.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d66ea0e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "495c501f3ba34c288869766a1a204452",
       "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",
      "|               title|            selftext|link_flair_css_class|num_comments|score|high_score|\n",
      "+--------------------+--------------------+--------------------+------------+-----+----------+\n",
      "|Trade in car or k...|So we have a Jeep...|                Auto|           5|    1|         0|\n",
      "|About to purchase...|I am about to pur...|             Housing|           9|    1|         0|\n",
      "|How much should m...|I'm currently liv...|             Housing|          50|    1|         0|\n",
      "|21 yrs old, 721 c...|I don’t know anyt...|              Credit|           5|    1|         0|\n",
      "|Medical Bill issu...|Basically a month...|                Debt|           7|    1|         0|\n",
      "+--------------------+--------------------+--------------------+------------+-----+----------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "data = data.withColumn('high_score', (data.score >= 2).cast(\"integer\"))\n",
    "data.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "baee94a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1173cedbdd524805a8873c6e01b4037a",
       "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",
      "|high_score| count|\n",
      "+----------+------+\n",
      "|         1|  7407|\n",
      "|         0|111465|\n",
      "+----------+------+"
     ]
    }
   ],
   "source": [
    "# count the number of high and low score\n",
    "(data.groupBy('high_score')\n",
    "     .count()\n",
    "     .show()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d3257494",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "032c141a47694b84b75756b128546592",
       "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": [
      "0.06644914914669349"
     ]
    }
   ],
   "source": [
    "ratio = 8126/122289\n",
    "ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6dc3c28c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "54662d3aa65b4e768c024865586d10a1",
       "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",
      "|high_score|count|\n",
      "+----------+-----+\n",
      "|         0| 7534|\n",
      "|         1| 7407|\n",
      "+----------+-----+"
     ]
    }
   ],
   "source": [
    "sampled = data.sampleBy(\"high_score\",fractions={1 : 1, 0 : ratio})\n",
    "sampled.groupBy(\"high_score\").count().orderBy(\"high_score\").show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "db68701f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a98233a96f8c435a9295b77eefec8012",
       "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": [
    "sampled = sampled.withColumn('text_tokens',F.split('selftext', ' ')) \\\n",
    "                 .withColumn('text_length',F.size('text_tokens')) \\\n",
    "                 .withColumn('title_tokens', F.split('title', ' ')) \\\n",
    "                 .withColumn('title_length', F.size('title_tokens'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e3aed3d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "714d5f7248f0496da7eade4ee9dfcf95",
       "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",
      "|               title|            selftext|link_flair_css_class|num_comments|score|high_score|         text_tokens|text_length|        title_tokens|title_length|\n",
      "+--------------------+--------------------+--------------------+------------+-----+----------+--------------------+-----------+--------------------+------------+\n",
      "|What are good res...|I know about Pays...|          Employment|           2|    1|         0|[I, know, about, ...|         97|[What, are, good,...|           7|\n",
      "+--------------------+--------------------+--------------------+------------+-----+----------+--------------------+-----------+--------------------+------------+\n",
      "only showing top 1 row"
     ]
    }
   ],
   "source": [
    "sampled.show(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0157b400",
   "metadata": {},
   "source": [
    "## Build ML Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "48a736cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c9fa9e8c580a4daca3c5504fd671cad8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
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       "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": [
    "# Create a StringIndexer\n",
    "flair_indexer = StringIndexer(inputCol=\"link_flair_css_class\",outputCol=\"flair_index\")\n",
    "\n",
    "# Create a OneHotEncoder\n",
    "flair_encoder = OneHotEncoder(inputCol=\"flair_index\",outputCol=\"flair_fact\")\n",
    "\n",
    "# Make a VectorAssembler\n",
    "vec_assembler = VectorAssembler(inputCols=[\"flair_fact\", \"num_comments\", \"title_length\", \"text_length\"], outputCol=\"features\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "deb271ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "be5b13f078af40c38c089d3e795c6254",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
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       "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": [
    "# Make the pipeline\n",
    "score_pipe = Pipeline(stages=[flair_indexer,flair_encoder,vec_assembler])\n",
    "\n",
    "# Fit and transform the data\n",
    "piped_data = score_pipe.fit(sampled).transform(sampled)\n",
    "\n",
    "# Split the data into training and test sets\n",
    "training, test = piped_data.randomSplit([0.6, 0.4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "da194ea7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "262fd85e9dde4ec486eb9388d851ccfc",
       "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": [
    "# Create a LogisticRegression Estimator\n",
    "lr = LogisticRegression(featuresCol = 'features', labelCol='high_score')\n",
    "\n",
    "# Create a BinaryClassificationEvaluator\n",
    "evaluator = evals.BinaryClassificationEvaluator(labelCol= 'high_score',metricName=\"areaUnderROC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a9370e67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "43e36e54ac984ab5aa1351d476a91467",
       "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": [
    "model = lr.fit(training)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75357f3b",
   "metadata": {},
   "source": [
    "## Evaluate Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a120199e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0ec1c98721d64e70ab61de9cc950bf58",
       "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
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Training Summary Data\n",
    "trainingSummary = model.summary\n",
    "evaluationSummary = model.evaluate(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "dc0cd022",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ba9e839085c44867a8f7fa346ca4eb77",
       "version_major": 2,
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      },
      "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%'),…"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training AUC: 0.6625576234384468\n",
      "Test AUC:  0.6546584904336328\n",
      "\n",
      "False positive rate by label (Training):\n",
      "label 0: 0.5697569756975698\n",
      "label 1: 0.21220100943603248\n",
      "\n",
      "True positive rate by label (Training):\n",
      "label 0: 0.7877989905639675\n",
      "label 1: 0.4302430243024302\n",
      "\n",
      "Training Accuracy: 0.611265414953894\n",
      "Test Accuracy:  0.6028619528619529"
     ]
    }
   ],
   "source": [
    "print(\"Training AUC: \" + str(trainingSummary.areaUnderROC))\n",
    "print(\"Test AUC: \", str(evaluationSummary.areaUnderROC))\n",
    "\n",
    "print(\"\\nFalse positive rate by label (Training):\")\n",
    "for i, rate in enumerate(trainingSummary.falsePositiveRateByLabel):\n",
    "    print(\"label %d: %s\" % (i, rate))\n",
    "\n",
    "print(\"\\nTrue positive rate by label (Training):\")\n",
    "for i, rate in enumerate(trainingSummary.truePositiveRateByLabel):\n",
    "    print(\"label %d: %s\" % (i, rate))\n",
    "    \n",
    "print(\"\\nTraining Accuracy: \" + str(trainingSummary.accuracy))\n",
    "print(\"Test Accuracy: \", str(evaluationSummary.accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "822223fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c6d9be27c22b4d63a0c0e5dc0d950a2c",
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       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
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     },
     "metadata": {},
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    },
    {
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     },
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    {
     "data": {
      "image/png": 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XtCk884xp3Jwk/voLunWDgQNNkgTo3DmJnty9KQCKiIjIfV2+bC61mzoVfvvt7vlixaBDB9OzL8lC3x3bt5uLBk+dMnPMO3dqujcJKQCKiIjIPSwLfvrJLLj96iuIijLnH7pnX2JeePx4s0w4OhoKFDDJU+EvSSkAioiISKw//4QvvoDPP4cjR+6er1jRzL6GhDxEz77EvHjbtmYZMcCLL5rwFxCQTC/ovhQARURE3FxMjOnTN306fPNN3NG+li1N8CtXLpmLOHkSatSAM2fA1xcmTDAvrJG/ZKEAKCIi4qZOnDBbss2aBX/8cfd8uXKm3V7LlpAxYwoVExRk+sSkTWuWF5cunUIv7J4UAEVERNyEZcG+fWZ3jhUrzDV+d2TODC+/bK7tK1MmhQq6csUkTF9fs5R48WJIly4FU6f7UgAUERFxYefPww8/3P26ePHuzzw8oG5dE/qaNAE/vxQsbMMGeOklsxHwRx+Zc0m+lFgSogAoIiLiQkJDTbb68UezFduvv8b9ebp0UKuWCX5Nm0LevClcYEwMjBwJQ4eCw2FS6Y0bkD59Chfi3hQARUREUrFr12DTJhP41q2D3btNrrrDwwPKlzeBr149s5mGr69NxV68aOaZ16wxx23amJ09FP5SnAKgiIhIKnTwILz1FqxebQbV/q5IETPKV6sW1KkDWbPaU2Mca9dCq1YmBKZLB5MmmQAotlAAFBERSUXCwmDYMPjkE9MnGczi2dq174a+3LltLfFe4eGmp99ff0HJkmaVb/Hidlfl1hQARUREUgGHA+bMMRtk3FnI0aQJjBpltmRzav7+pqHzqlVmwUe6dHZX5PYUAEVERJxYRITZGGP8eNi2zZwrUsTkqAYN7K3tvr7/Hry8zBw0wPPPmy9xCgqAIiIiTiYqyiyO/fJLszPH9evmfIYMMHgwvPkm+PjYWmLCoqNhyBCz0jdbNti7F3LmtLsq+QcFQBEREScQHW3atyxYAEuXwtWrd3+WLx+0aAHdu0OuXLaV+O/++MP09tu0yRy/8ILpMC1ORwFQRETEJmFh5rK4//0PVq40ayTuyJEDmjc3eeqJJ1LBlrgrVphVvX/+efeav+bN7a5KEqAAKCIikoJ+/x2WLzdf69ffXckL8MgjpjnzSy9BzZrmEjqn53CYlSljx5rj8uVh4UIoWNDeuuS+FABFRERSwJUrMGAATJtm9uS9o1gxaNwYnn3WNGlOFaHv7zw94fJlc7tHDxg92sZO0/KgFABFRESSUXQ0TJ5sFm+Ehppz1aubwNe4sVnRmypFR4P3/8eIiRMhJAQaNrS3JnlgCoAiIiLJZMMGs3Bj3z5zXLq0aeBcrZq9df0nUVHw9ttw7JiZx/b0NFu5KfylKgqAIiIiSeTmTdOrb/16szfvxo3mfObM8N570LlzKpzi/bsTJ8xI3y+/mOOffjJbj0iqowAoIiLykP4e+NavN7ejou7+3MMDOnUy4c8p9uP9L5YsgQ4dzLZuWbLAF18o/KViCoAiIiKJdO2aWeswbpzZqePvcuUyK3hr1oTgYMif344Kk9CtW9C7N0yaZI6ffNJ0qA4Ksrcu+U8UAEVERB7Q7dtmFe8778ClS+ZcrlxmIKxGDRP6ChVKBT37EqNVK/jqK3O7Xz94911Ik8bemuQ/UwAUERH5F5ZltmTr1w+OHDHnCheGDz4wfftcKvD9U//+sH27Sb5OvfmwJIYCoIiISAIsC9atg6FDYfNmcy5rVjMC2KmTiw6E3bxpAl/Nmua4QgU4fly9/VyMp90FiIiIOBvLgrVrTb++4GAT/vz8zGDYsWPQtauLhr/Dh6FyZahfH/bsuXte4c/laARQRETk/90Jfu+8c3fEz9fXjPb17Qu5c9taXvKaPRtef92sasmRw6z2FZelACgiIm4vIgIWLYLPPoMdO8w5X1/Tt69vX7PQw2XduAHdusGsWea4Th2YOxcCA20tS5KXAqCIiLitAwdgyhQz+BUWZs65TfAD8wfQvDkcPGh29HjnHbNhcaruVi0PQgFQRETcyq1bpqfxlCmwadPd8/nymane9u3NDKhbWL7chL9cuWD+fNPLRtyCAqCIiLiFEydM6Js+Hf7805zz8oJnnzUjfnXrmkEwt9K3r0nE3bpBtmx2VyMpSAFQRERcVkwMrFplNrH47juzyAPMJhavvmp2NnP5ad6/27vXNHKeMwfSpTOJd9gwu6sSGygAioiIyzl71mxVO3UqnDp193y9etClCzzzDHi70yegZcHnn8Mbb0BkJBQsaPayE7flTv/5i4iIC4uKgv/9D2bMMKN+Doc5nzkztGsHr71mdu9wO+HhZrhz0SJz/MwzZupX3JoCoIiIpGoHDpjr+ubMgStX7p6vVs0s6Gje3Mx2uqVdu8wfwPHjZshz1Cjo2dMNL3aUf1IAFBGRVGnfPhgyxOzRe0fOnNC2rfkqUsSuypzE119DixZmaDRvXliwAJ54wu6qxEkoAIqISKpy6JBpV3dnRtPTE5o0MQs66td3s2v77qdiRciY0QyFzphh5sJF/p/+moiISKpw7JhZwDpv3t3r+0JCTBgsVszW0pzHmTNmiTNAnjzw88+mwaGHh61lifPRRQAiIuK0oqPNFO/TT5sp3TlzTPhr2tR0NFmwQOEPMKt8x483q3v/PieeP7/Cn8RLI4AiIuJ0zpyBadPM4o6zZ++ef/pp07auQgX7anM6V6+aix7/9z9zvGKFmRMXuQ8FQBERcQqWBT/+CBMmmAxzZ5o3a1bTxqVTJyhUyNYSnc+WLWahx5kzZhPj8eNNvxuRf6EAKCIitrIs+PZbeP992L797vkaNUyWee45k23kbxwOGDsWBgww250ULmxWxZQpY3dlkkooAIqIiC1iYmDxYhgxAn791Zzz9TWrebt317V997Vhw91mzi+9ZDY5zpjR3pokVVEAFBGRFHX7Nsyda4LfsWPmXIYMZou2nj0hMNDe+lKFmjWhVy+Tkjt21EIPSTQFQBERSRF3gt9778GJE+Zclixme9ru3dWm7r4cDnN9X8uWpts1wIcf2luTpGoKgCIikqziC37ZssFbb8Hrr5vRP7mPixfhlVfghx/M6pg1a7SVm/xnCoAiIpIsLMv06Rs0KG7we/ttE/zSp7e3vlRh3Tpo1QouXDAbGrdurfAnSUIBUEREkty+fWZad8MGc5w9uwl+r72m4PdAYmJg+HCz9YllwWOPmVW+JUrYXZm4CAVAERFJMlevwpAh8Nln5rK1tGmhf3+zXkHB7wFdvmz2uPvxR3PcsSN89JEZARRJIgqAIiLyn8XEmF07BgyAP/8051580bSqe/RRe2tLddKmhfPnzcWRU6aYhR8iSUwBUERE/pOtW810786d5vixx+Djj6F2bXvrSlWio821fZ6eJvgtXQre3mYDZJFkoCtJRUTkoVy4AG3aQNWqJvwFBJht3HbvVvhLlD/+gFq1TJuXO0qUUPiTZKUAKCIiiRIVZVrQFSkCs2ebc+3bw9GjpqdfmjT21peqrFxptm/btAlGjoTwcLsrEjfhUgFw4sSJ5MuXDz8/PypXrsyOHTvue/8JEyZQtGhR0qZNS1BQED179uTWrVspVK2ISOoSHQ2zZkHRotCnD1y7BhUrmv17p083K33lAd2+bZZFP/OMuWiyXDnzB+nvb3dl4iZcJgAuXLiQXr16MXToUHbt2kXp0qWpX78+ly5divf+8+fPp1+/fgwdOpRDhw4xffp0Fi5cyIABA1K4chER5xYTA/Pnm2v72rWDU6cgRw4T+rZtg0qV7K4wlfn9d6heHcaMMcfdu8OWLVCwoL11iVvxsCzLsruIpFC5cmUqVqzIp59+CoDD4SAoKIju3bvTr1+/e+7frVs3Dh06xNq1a2PP9e7dm+3bt7Np06YHes3w8HACAgIICwvDX/9qExEXY1nw9demrcuBA+bcI49A377Qtau6kjyUGzdM0Lt40Vw0OWMGNGtmd1VuR5/fLjICGBUVxc6dOwkODo495+npSXBwMFu3bo33MVWrVmXnzp2x08QnTpxg5cqVPP300ylSs4iIM/vtN6hTB55/3oS/TJnMVm4nT5ot3BT+HlL69NCvnxk23b1b4U9s4xJtYK5cuUJMTAw5cuSIcz5HjhwcPnw43se0bNmSK1eu8NRTT2FZFtHR0bz22mv3nQKOjIwkMjIy9jhcF+uKiIuJijIzk8OHQ2SkaUnXu7f5ypTJ7upSqRMnICICSpY0x2+8YYZQtVpGbOQSI4APY/369YwYMYJJkyaxa9cuvvrqK1asWMHw4cMTfMzIkSMJCAiI/QoKCkrBikVEkteWLWYtwqBBJvzVqwf795swqPD3kJYsgbJlzVDqtWvmnIeHwp/YziUCYNasWfHy8uLixYtxzl+8eJHAwMB4HzN48GBeeeUVOnbsSKlSpXjuuecYMWIEI0eOxOFwxPuY/v37ExYWFvt15syZJP9dRERS2u+/Q4cO8NRTZro3WzaYNw9WrYICBeyuLpW6dcuM8r34omntkjUrXL9ud1UisVwiAPr4+FC+fPk4CzocDgdr166lSpUq8T4mIiICT8+4v76XlxcACa2L8fX1xd/fP86XiEhqdfYsdOkChQubtQiWZVb5Hjpkdh/z8LC7wlTqt99Md+xJk8xxv36wfj3kzGlrWSJ/5xLXAAL06tWLNm3aUKFCBSpVqsSECRO4ceMG7dq1A6B169bkzp2bkSNHAtC4cWPGjRtH2bJlqVy5MseOHWPw4ME0btw4NgiKiLiiixdh1Cj47DMz1Qtmwcfw4ZDAv5nlQS1YAK++akb7smaFOXOgQQO7qxK5h8sEwJCQEC5fvsyQIUO4cOECZcqUYdWqVbELQ06fPh1nxG/QoEF4eHgwaNAgzp49S7Zs2WjcuDHvv/++Xb+CiEiy2rcPpkwxzZwjIsy5p54ywa9mTTsrcxGWZZojXr9u+vzNnw+5c9tdlUi8XKYPoB3UR0hEnF1EBCxaZILftm13z1eqZIJf3bqa6k1SFy+aEPj22+DtMmMsLkef3y40AigiIncdPmymeGfPhtBQc87bG5o2hc6dzZSvgl8SmDPH9PMbN84c58gB2lFKUgEFQBERF3L1qmnjMmUK3GlokC8fdOpkFngk0BhBEuvGDbOF28yZ5rhBA9M3RySVUAAUEXEBMTEwdSoMHGhCIEDjxqYTSd264OkSPR+cxIED0Lw5HDxo/mCHDjVDqiKpiAKgiEgqt3mzGYzavdsclywJn3yihR1JzrLMiF+3bnDzpmnrMn++/qAlVVIAFBFJhUJDYelS07D5xx/NuUyZ4N134fXXtf4gWbz5Jnz8sbldr565/i97dltLEnlYmhQQEUklbt2Cr74yu4oFBkLHjib8eXiY20ePmpFAhb9kUreu+cMdMQK++07hT1I1/W9CRMTJnToFI0fCwoUQFnb3/GOPQatWZteOvHltK891WRacPn33D7dRIzh2TH/Y4hIUAEVEnNRff8H775vr+aKizLk8eeCll0zwe/xxtXJJNuHhZun099/Dnj3w6KPmvMKfuAgFQBERJxMZCRMnwnvvmRAIZpHpwIFQo4ZW9Ca7XbvMKt/jx82U75YtdwOgiItQABQRcSJLl8Jbb8HJk+a4ZEkYPdq0mdNoXzKzLJO8e/c2Q66PPmrm3Z94wu7KRJKcAqCIiBO4cgW6dIHFi81xzpxmq7a2bcHLy9bS3ENoKHToYFbZADRpAjNmQJYstpYlklw0kSAiYrNly8yCjsWLTdgbOBB++83kEYW/FDJ2rAl/adLAhAnw9dcKf+LSNAIoImKTv/6CN94w7eTAhMAvvoDy5e2tyy0NHGh2+BgwACpWtLsakWSnEUARERt8/bW5vm/OHLOoo29f2LlT4S/FXL0K77xj9tADSJvWvCkKf+ImNAIoIpKC/vjD7CT2zTfmuEgRM+qndQYpaMsWaNECzpwxc+yDB9tdkUiK0wigiEgKiIkx/fxKlDDhz9sbBg2CvXsV/lKMw2GWVFevbsJfoUKmubOIG9IIoIhIMoqJgVWrzB69O3aYc1WqwOefmylgSSGXL0ObNmYLNzAjgFOmgL+/vXWJ2EQBUEQkGVy+bLqITJ5stnIDkzVGjYLOndXMOUVt22Y2UD53Dvz84OOPzebJaqwobkwBUEQkiViWyRqTJsGiRXe3b8uUyfTz69MHcue2s0I3lS6dWfRRrJh5Y0qVsrsiEdspAIqIJIG9e6FHD9iw4e658uVNc+cWLUwGkRQUFQU+Pub244/DypVmhW+GDPbWJeIkNAkhIvIfXL0KXbtCuXIm/Pn6mtG+HTvgl1+gfXuFvxS3bp1Z4LF9+91ztWop/In8jQKgiMhDiIkxCzmKFDFTvg4HvPgiHD0KM2eqnZwtYmJg6FAIDjarfIcPt7siEaelKWARkUSwLPj+e7NhxK5d5txjj5l1BbVr21ubWzt3Dlq1gvXrzXH79qbvjojESyOAIiIPaP16qFYNGjQw4S8gwGwbu3u3wp+tvv8eypQxb1D69GZ7lenTNfcuch8aARQR+RdbtpjNItatM8e+vmZxR79+kD27vbW5vc2bTSK3LLPYY9EiKFrU7qpEnJ4CoIhIAnbuhCFDzAJSgDRp4NVXzfSv2rk4iapVzW4euXLB+PFmT18R+VcKgCIi//Drryb4LVtmjr28zMrewYMhb147KxMA1qyBypUhY0bTzHnpUpPOReSB6RpAEZH/d/iw6dlXurQJfx4e8PLL5vy0aQp/trt9G95+G+rWhddfN9O+oPAn8hA0Aigibu/KFXM938yZpp0LmJYu77wDJUrYWprccfq0Sedbt5rjzJnNm+XlZW9dIqmUAqCIuC3LgnnzoGdPEwIBmjSBYcPMKKA4ieXLzRz8X3+ZpdfTp5u9fUXkoSkAiohbOn7czCL+8IM5fuwxmDIFnnzS3rrkb6KioG9f02sHTHfthQshf35byxJxBboGUETcisMBo0dDyZIm/Pn6wvvvm75+Cn9OJjQUvvzS3O7ZEzZtUvgTSSIaARQRtxEdDR06wOzZ5rh2bZg8GQoXtrcuSUD27CYAXrsGzz5rdzUiLkUBUETcQlQUtGxpOoZ4ecFnn0HHjmalrziJW7egTx+z3UpIiDlXq5a9NYm4KE0Bi4jLi4gwizuWLgUfH/P91VcV/pzKb7+Zps4TJ0KnTmbBh4gkGwVAEXFpoaHw9NOwapXZGvbbb00YFCeyYAGUL282VX7kETPtmzmz3VWJuDQFQBFxSVeumJ078uWDn34Cf39Yvdr0EBYncfMmdO4ML71krvOrVg327DGJXUSSla4BFBGXcvYsfPihaekSEWHOFSsGc+eaQSZxEjdvQpUqsHevmYsfMMB03vbWx5JIStDfNBFxCQcOwMcfw6xZZsEHQLlyJlc89xx4ar7DuaRNC3XqwPnzJp1raFYkRXlY1p3NFCWxwsPDCQgIICwsDH9/f7vLEXE7YWGmL/CMGbB9+93z1arBwIFQr54WejiViAgID4fAQHMcFQVXr949Fkkh+vzWCKCIpDKWBRs2mNC3eLGZSQQzc9ioEfTqZQKgOJkDB6B5c8iUCdavhzRpzJJshT8RWygAikiqsX27aeR84MDdc8WLm3Mvvww5cthXmyTAssy8fNeuJq0HBsLJk1CkiN2Vibg1BUARcXqRkWZ9wOjRZiu3jBmhRQto3x4qV9Y0r9O6ft1suDx3rjmuW9fczp7d3rpExHkC4K1bt/Dz87O7DBFxMrt2QZs2sH+/OX75ZbPYQ23inNy+ffDii3D0qFmBM3w49Oun1TgiTsLWv4kOh4Phw4eTO3duMmTIwIkTJwAYPHgw06dPt7M0EbFZVJQZ9atc2YS/7Nnhq69gzhyFP6dnWWY3j6NHIXduc83fgAEKfyJOxNa/je+99x6zZs1i9OjR+Pj4xJ4vWbIk06ZNs7EyEbHTli1QtiwMGwbR0fDCCyYEPvec3ZXJA/HwgC++MG/cnj1alSPihGwNgLNnz+bzzz+nVatWeHl5xZ4vXbo0hw8ftrEyEbFDaKi5ZOzJJ+HgQciWzewKtmiRuS1ObPdu0337jqJFzTLtrFntq0lEEmTrNYBnz56lUKFC95x3OBzcvn3bhopExA6WZaZ3u3c3fYHBLPAYMwayZLG3NvkXlgWTJpn+O9HRUKKERvxEUgFbA2CJEiXYuHEjefPmjXN+yZIllC1b1qaqRCSlXL9uRvimTIGdO825IkXMcc2atpYmDyI0FDp2hKVLzfGzz8Jjj9lakog8GFsD4JAhQ2jTpg1nz57F4XDw1VdfceTIEWbPns23335rZ2kikoz27DEhb948uHbNnPP1hbffNmsF1BAgFfj5ZwgJMT390qQxPXreeEM9eURSCdu3gtu4cSPvvvsue/fu5fr165QrV44hQ4ZQr149O8t6INpKRuTBWRYsWAATJsCOHXfPFy4MnTubVi+6XCyVmDgRevaE27chf36zH1/FinZXJfLA9PntBH0Aq1Wrxg8//GB3GSKSjK5eNTOFX39tjtOkMSt6O3eGWrU0aJTqeHub8Pf88zBtmtneTURSFVtXARcoUIA///zznvOhoaEUKFDAhopEJKn99BOULm3CX5o0prffH3+YQaPatRX+Uo3IyLu3O3WClSvNKl+FP5FUydYAeOrUKWJiYu45HxkZydmzZ22oSESSSnQ0DBliRvj++MMs7ti2DYYO1U5gqYrDYZZjlyplFn2ASe0NGyq9i6RitkwBL1++PPb26tWrCQgIiD2OiYlh7dq15MuXz4bKRCQphIdDo0awcaM5btfObN+WIYO9dUkiXbliLs5cudIcf/GFWeghIqmeLQGwadOmAHh4eNCmTZs4P0uTJg358uXjww8/tKEyEfmvbt0y1/dt3Aj+/ma1b4sWdlclibZxI7z0Epw9a5Zof/wxvPqq3VWJSBKxJQA6HA4A8ufPz88//0xWLf0TcQkxMfDyy7BunRntW7cOype3uypJFIcDRo0y8/cxMWZHj0WL4PHH7a5MRJKQrauAT548aefLi0gSsizo0sX0BPbxgW++UfhLld57z1yoCSbNf/aZ5u5FXJDtbWBu3LjBTz/9xOnTp4mKiorzsx49ethUlYgkhmXBoEHw+edmXcD8+WaFr6RCXbuaN7BvX2jbVgs9RFyUrQFw9+7dPP3000RERHDjxg2yZMnClStXSJcuHdmzZ1cAFEkFrl83l4YtWGCOP/vMtIeTVCImxgzXPvecCXuPPAL795tefyLismxtA9OzZ08aN27MX3/9Rdq0adm2bRu///475cuXZ+zYsXaWJiIP4PBhqFTJhD9vb/j0U9PcWVKJ8+ehXj2T2GfNunte4U/E5dkaAPfs2UPv3r3x9PTEy8uLyMhIgoKCGD16NAMGDLCzNBH5F4sXm92/Dh2CXLlg/XozeyipxA8/QJkyZqVO+vRmpa+IuA1bA2CaNGnw9DQlZM+endOnTwMQEBDAmTNn7CxNRBIQEwN9+kDz5mb6t2ZN2LULnnzS7srkgURHmws269eHS5dMg+dffoGWLe2uTERSkK0BsGzZsvz8888A1KhRgyFDhjBv3jzefPNNSpYsmejnmzhxIvny5cPPz4/KlSuz4+87zscjNDSUrl27kjNnTnx9fSlSpAgr7zQ8FZF73LplevrdadPZt68ZSMqRw9665AGdPWtW57z/vlm507kzbN8OxYrZXZmIpDBbA+CIESPImTMnAO+//z6ZM2fm9ddf5/Lly0yZMiVRz7Vw4UJ69erF0KFD2bVrF6VLl6Z+/fpcunQp3vtHRUVRt25dTp06xZIlSzhy5AhTp04ld+7c//n3EnFFV6+ay8WWLDF7+s6fb9rF6XKxVOTwYdi0CTJmhC+/hMmTIW1au6sSERt4WJZl2V1EUqhcuTIVK1bk008/BUyz6aCgILp3706/fv3uuf/kyZMZM2YMhw8fJk2aNA/1muHh4QQEBBAWFoa/v/9/ql/EmZ0+DQ0amOv9/P1h2TKzx6+kQp9/bt68woXtrkTENvr8tnkEMCG7du2iUaNGD3z/qKgodu7cSXBwcOw5T09PgoOD2bp1a7yPWb58OVWqVKFr167kyJGDkiVLMmLECGJiYhJ8ncjISMLDw+N8ibi6Q4egShXzPXduM4Ck8JdKnD4NDRvC8eN3z3XqpPAnIvYFwNWrV9OnTx8GDBjAiRMnADh8+DBNmzalYsWKsdvFPYgrV64QExNDjn9ciJQjRw4uXLgQ72NOnDjBkiVLiImJYeXKlQwePJgPP/yQ9957L8HXGTlyJAEBAbFfQUFBD1yjSGp0+jTUrQvnzsFjj8HWrWbNgKQCy5ebVb6rVpnQJyLyN7YEwOnTp9OwYUNmzZrFBx98wBNPPMHcuXOpUqUKgYGB7N+/P9kXYzgcDrJnz87nn39O+fLlCQkJYeDAgUyePDnBx/Tv35+wsLDYL61UFld25Yq55u/sWSheHH76CfRvnlQgKgp69YImTeCvv0yvnqlT7a5KRJyMLZdvf/TRR3zwwQe89dZbLF26lBdffJFJkybx66+/kidPnkQ/X9asWfHy8uLixYtxzl+8eJHAwMB4H5MzZ07SpEmDl5dX7LnixYtz4cIFoqKi8PHxuecxvr6++KpXlriBa9fg6afhyBET+lavNhtEiJM7eRJCQuD/uyvw5pvwwQdmc2YRkb+xZQTw+PHjvPjiiwA0a9YMb29vxowZ81DhD8DHx4fy5cuzdu3a2HMOh4O1a9dSpUqVeB/z5JNPcuzYsThTzUePHiVnzpzxhj8RdxEZCc2amQzxyCPw/fca+UsV9uyBsmXNG5cpk1mpM368wp+IxMuWAHjz5k3SpUsHgIeHB76+vrHtYB5Wr169mDp1Kl988QWHDh3i9ddf58aNG7Rr1w6A1q1b079//9j7v/7661y9epU33niDo0ePsmLFCkaMGEFXbWUgbuz0aaheHdasMZtDrFypFnGpRokSUKQIPPGECYNNmthdkYg4Mds6eE2bNo0MGTIAEB0dzaxZs8iaNWuc+/To0eOBny8kJITLly8zZMgQLly4QJkyZVi1alXswpDTp0/H7joCEBQUxOrVq+nZsyePP/44uXPn5o033qBv375J8NuJpD7ff282g/jzT8icGZYuNfv8ihM7dcoszU6Txoz0ffutefMesrWViLgPW/oA5suXDw8Pj/vex8PDI3Z1sLNSHyFxBQ4HjBgBQ4aYzSHKlTPNnvPnt7syua9Fi6BjR+jWzbyBIvLA9Plt0wjgqVOn7HhZEfmH69fN1m4rVpjjV1+Fjz8GPz9765L7uHkTevaEO7slbd4Mt29r1E9EEsUpG0GLSPK7cQOeecaEPz8/mDHDbBKh8OfEjhwx1/hNmQIeHjBwIKxdq/AnIommXTxF3FBEBDz7LGzYYLZ2W73a5ApxYnPnwmuvmeSeLRvMm2e6dIuIPAQFQBE3c+sWNG0K69ZBhgxmowiFPyd3/jx07mySe61aJvz9x84JIuLeFABF3MidHn8//GDavHz3ndnnV5xczpwwebLZ03fwYPhbA3sRkYehACjiJqKi4IUXTOhLm9Zc+/fUU3ZXJQmaNQsKFDCNGQFeecXWckTEtdi+COT48eMMGjSIl156iUuXLgHw3XffceDAAZsrE3Edt2+bHcK+/dYs8vjf/6BGDburknhdvw5t2kC7dvDSS3D1qt0ViYgLsjUA/vTTT5QqVYrt27fz1Vdfcf36dQD27t3L0KFD7SxNxGVER5sGz8uWga8vfPMN1Kljd1USr337oGJFmD0bPD2hSxezrZuISBKzNQD269eP9957jx9++CHO/ru1a9dm27ZtNlYm4hqio83M4ZIlZqOIr76CevXsrkruYVmmB0/lynD4MOTKBT/+aNq8eNo+USMiLsjW/7P8+uuvPPfcc/ecz549O1euXLGhIhHXcfgw1K4NCxaYNnFLlsDTT9tdldwjMtIM0XbubJZoN2hg9vK9c+2fiEgysDUAZsqUifPnz99zfvfu3eTOnduGikRSv8hIGDYMSpeGjRshXTqza1jjxnZXJvHy8YGYGLOy94MPzOqcbNnsrkpEXJytAbBFixb07duXCxcu4OHhgcPhYPPmzfTp04fWrVvbWZpIqrRxI5QpA++8Y1b9Pv00HDhg+v6JE7EsM9oHZkePqVNh0yZ4+21N+YpIirD1/zQjRoygWLFiBAUFcf36dUqUKEH16tWpWrUqgwYNsrM0kVTl2jWzSUT16mbqN0cOWLjQrPrNl8/u6iSOsDBo3hxeftkEQYCAAHXjFpEU5WFZd/4PZJ/Tp0+zf/9+rl+/TtmyZSlcuLDdJT2Q8PBwAgICCAsLw9/f3+5yxE39+CO0bw+nTpnjV181M4mZM9talsTn559NP56TJ82FmT//bObqRSRF6fPb5kbQmzZt4qmnnuLRRx/l0UcftbMUkVTnxg3o3x8++cQc58sHM2dCzZp2ViXxsiz4+GN46y3TlDFfPjNEq/AnIjaxdQq4du3a5M+fnwEDBnDw4EE7SxFJVbZsMdf63Ql/nTubFnIKf07o6lV47jl4800T/po1g927oVIluysTETdmawA8d+4cvXv35qeffqJkyZKUKVOGMWPG8Mcff9hZlohTW7nSBL1jxyBPHli92mwTmzGj3ZXJPSwLnn3WdN/28TGJfckSNXcWEds5xTWAACdPnmT+/Pl8+eWXHD58mOrVq7Nu3Tq7y7ovXUMgKW3dOrOyNzLSDCrNmKEs4fQ2bYKOHWHePChf3u5qRAR9foMTBUCAmJgYvvvuOwYPHsy+ffuIiYmxu6T70n9AkpK2bDG7eNy4YQaVliwx6wjEyfz5p5niDQ6+ey46GrxtveRaRP5Gn982TwHfsXnzZrp06ULOnDlp2bIlJUuWZMWKFXaXJeI0du0yI383bkDdumb9gMKfE9q0yVyc2aQJ/P26ZoU/EXEytv5fqX///ixYsIBz585Rt25dPvroI5o0aUK6dOnsLEvEqaxeDa1amfZx1arBsmXg52d3VRKHw2F67wwebHb1KFLEfBcRcVK2BsANGzbw1ltv0bx5c7JmzWpnKSJO5+ZNszHEp5+a44oVTWNn/fvIyVy6BK+8At9/b45btYLPPtOqHBFxarYGwM2bN9v58iJOa9cus1HEoUPmuFs3M8Ck8Odk1q+Hli3h/HlIm9ak9XbtzPZuIiJOLMUD4PLly2nYsCFp0qRh+fLl973vs88+m0JViTgHhwNGj4YhQ0zLuMBA09y5QQO7K5N4rVljwl/x4rB4MTz2mN0ViYg8kBRfBezp6cmFCxfInj07nvfZ9NzDw0OrgMWtREZC69awaJE5btYMpkwBXR3hxKKjYexY6N4d0qe3uxoReUD6/LZhFbDD4SB79uyxtxP6cvbwJ5KUrl2DRo1M+EuTBqZNM21eFP6czJo15o2KjDTH3t7Qr5/Cn4ikOra2gZk9ezaRd/5H+jdRUVHMnj3bhopEUt7ly1C7tskW6dPDihXQoYMuI3Mq0dEwaJBpxLhiBYwfb3dFIiL/ia2NoL28vDh//nzsiOAdf/75J9mzZ3f6UUANIct/9fvvJlMcPQqPPALffWdW+4oTOXvWLPTYsMEcv/oqfPSRWfQhIqmSPr9tHgG0LAuPeIY5/vjjDwICAmyoSCTl3Al7R4/Co4+aHsIKf05m1SrT2HnDBsiQAebPh88/V/gTkVTPljYwZcuWxcPDAw8PD+rUqYP337rkx8TEcPLkSRpo2aO4qMhIc9nYhAnm+PHHzaxinjy2liX/NHkyvP66uV2mjLlAs3BhW0sSEUkqtgTApk2bArBnzx7q169PhgwZYn/m4+NDvnz5eP755+0oTSRZHTkCLVrAnj3muFs3GDNGO3s4pXr1ICDANHb+8EO9SSLiUmwJgEOHDgUgX758hISE4Kf/sYqLsyyYMQN69ICICHO938yZ0Lix3ZVJHEePmm3cAAoUMJ24c+a0tyYRkWRg6zWAbdq0UfgTl3fliunp17GjCX+1a8O+fQp/TiUqCnr3Ng2d72zpBgp/IuKyUnwEMEuWLBw9epSsWbOSOXPmeBeB3HH16tUUrEwk6X33HbRvDxcumP5+w4dDnz7g5WV3ZRLr1CkICYEdO8zx1q1m+ldExIWleAAcP348Gf9/k/Tx48ffNwCKpFYREfDWWzBpkjkuUQLmzoWyZe2tS/7h669NQg8NhUyZzLz8/1+jLCLiymztA5jaqY+QxGfzZtPI+cgRc/zGGzBypDqHOJXISJPQP/nEHD/xBCxYAHnz2luXiKQIfX7bfA3grl27+PXXX2OPv/nmG5o2bcqAAQOIioqysTKRxLtxw4S9atVM+MuVy1xONmGCwp/T+e67u+GvTx/T50/hT0TciK0BsHPnzhw9ehSAEydOEBISQrp06Vi8eDFvv/22naWJJMratVCqFHz8sVnx27497N8PdevaXZnEq2lTePNN+PZb04cnTRq7KxIRSVG2BsCjR49SpkwZABYvXkyNGjWYP38+s2bNYunSpXaWJvJAbtyATp0gOBhOnjQ7eqxeDdOnQ+bMdlcnsW7dgv79zZLsO8aPh2eesa8mEREb2dIH8A7LsnA4HACsWbOGRo0aARAUFMSVv/+PWsQJhYWZ/LB5sznu0gVGjYL/X+MkzuLIEWje3PTe2b8fli8HLT4TETdnawCsUKEC7733HsHBwfz000989tlnAJw8eZIcOXLYWZrIfV25AvXrw65dZvHo0qWmv584mXnzoHNnM1SbLZvZekXhT0TE3ingCRMmsGvXLrp168bAgQMpVKgQAEuWLKFq1ap2liaSoPPnoUYNE/6yZYMff1T4czoREabz9ssvm/BXs6bZf69+fbsrExFxCk7ZBubWrVt4eXmRxskvzNYycvfz++9Qpw4cPw65c8OaNVCsmN1VSRwnT8Kzz5rpXg8PGDwYhgxR920RiaXPb5ungO/YuXMnhw4dAqBEiRKUK1fO5opE7nX+vBnpO3EC8uc3K3/z57e7KrlHlixm1C9HDjMFXKeO3RWJiDgdWwPgpUuXCAkJ4aeffiJTpkwAhIaGUqtWLRYsWEC2bNnsLE8kVmgoNGxowl+BAqZtXO7cdlclsW7eBD8/M+IXEGAWemTNCoGBdlcmIuKUbL0GsHv37ly/fp0DBw5w9epVrl69yv79+wkPD6dHjx52liYS6+ZNaNIE9u41g0rff6/w51R+/RXKlYPJk++eK1lS4U9E5D5svQYwICCANWvWULFixTjnd+zYQb169QgNDbWnsAekawhcX3Q0vPACfPMN+PvDTz/B/7euFLtZlmm42L276fNXoAAcOgQ+PnZXJiJOTp/fNo8AOhyOeBd6pEmTJrY/oIhdoqKgTRsT/nx9zayiwp+TuHYNWrWCV1814a9BA9i2TeFPROQB2RoAa9euzRtvvMG5c+diz509e5aePXtSRxdui43Cw02T5/nzwdsbFi40rV/ECezZA+XLw5dfmpW9o0bBihWmJ4+IiDwQWxeBfPrppzz77LPky5ePoKAgAM6cOUPJkiWZO3eunaWJGzt3ziz42LcP0qc3TZ7VPs5JXL4MTz5p+vzlyQMLFphjERFJFFsDYFBQELt27WLt2rWxbWCKFy9OcHCwnWWJGzt40MwmnjljFnysXGnWF4iTyJYNBgww072zZsEjj9hdkYhIqmRbAFy4cCHLly8nKiqKOnXq0L17d7tKEQHgu++gZUvT8qVIEVi1Sn3+nMIvv0CGDHc7bvfvb9q9aEs3EZGHZss1gJ999hkvvfQSv/zyC7/99htdu3blrbfesqMUEWJiYOhQc81faChUqQKbNyv82c6y4OOPoWpVePFF048HwNNT4U9E5D+yJQB++umnDB06lCNHjrBnzx6++OILJk2aZEcp4uauXIGnn4Z33zV54/XXzd6+WbPaXZmb++svaNYM3ngDbt+GQoXMsmwREUkStgTAEydO0KZNm9jjli1bEh0dzfnz5+0oR9zUzz+bxaTffw9p08Ls2TBpkmn5Ijbavh3KloVly0xbl48/hq++Mjt8iIhIkrAlAEZGRpI+ffq7RXh64uPjw807UzwiycjhgHHjzOLR06fN4NL27fDKK3ZX5uYsCz78EJ56Cn7/3TR23rLFNHrWlK+ISJKybRHI4MGDSZcuXexxVFQU77//PgF/+1f+uHHj7ChNXNjFi6a58+rV5rhZM5gxQ4NLTiEmxoz6RUdD8+bw+ed6Y0REkoktAbB69eocOXIkzrmqVaty4sSJ2GMP/YtfktiqVSb8XboEfn4wYQJ06qTBJafh7W06b69aBR076o0REUlGtu4FnNppL8HUweGAfv1gzBhzXKqU2UTiscfsrcvtORwwejRcvWq+i4ikEH1+29wIWiS5WRZ06QJTppjjbt1MEPTzs7cut3fpErRufXcuvnlzqFDB3ppERNyIAqC4LMsy6wemTDGziV98oYUeTuGnn+Cll+D8eZPEP/3ULMcWEZEUY8sqYJHkZlnQsydMnGjC38yZCn+2i4kxDRdr1zbhr3hx04unQwdd7yciksI0Aigux7Lg7bfho4/M8dSpZvGH2Kx5c9PPD8wbMnEi/K0dlIiIpByXGgGcOHEi+fLlw8/Pj8qVK7Njx44HetyCBQvw8PCgadOmyVugpIhJk2DsWHN78mQzwCROoEULSJcOZs0yXwp/IiK2sT0Abty4kZdffpkqVapw9uxZAObMmcOmTZsS9TwLFy6kV69eDB06lF27dlG6dGnq16/PpUuX7vu4U6dO0adPH6pVq/bQv4M4j40b4c03ze0PPoDOnW0tx73FxMBvv909fvFFOHFCw7EiIk7A1gC4dOlS6tevT9q0adm9ezeRkZEAhIWFMWLEiEQ917hx43j11Vdp164dJUqUYPLkyaRLl44ZM2Yk+JiYmBhatWrFsGHDKFCgwH/6XcR+Z8+ajBEdbQab3nrL7orc2LlzUKeO2dXj71s85shhX00iIhLL1gD43nvvMXnyZKZOnUqaNGlizz/55JPs2rXrgZ8nKiqKnTt3EhwcHHvO09OT4OBgtm7dmuDj3n33XbJnz06HB5wjjIyMJDw8PM6XOIfISHj+ebPTx+OPw7RpWldgm9WroXRps9o3IgL277e7IhER+QdbA+CRI0eoXr36PecDAgIIDQ194Oe5cuUKMTEx5PjH6EKOHDm4cOFCvI/ZtGkT06dPZ+rUqQ/8OiNHjiQgICD2Kygo6IEfK8nrjTfMfr6ZMpl1Brq8zAbR0dC/PzRoAFeumBC4cyfUrWt3ZSIi8g+2BsDAwECOHTt2z/lNmzYl65TstWvXeOWVV5g6dSpZs2Z94Mf179+fsLCw2K8zZ84kW43y4BYsuNvr78svoWBBuytyQ2fOQM2aMGqUOe7SBbZtgyJFbC1LRETiZ2sbmFdffZU33niDGTNm4OHhwblz59i6dSt9+vRh8ODBD/w8WbNmxcvLi4sXL8Y5f/HiRQIDA++5//Hjxzl16hSNGzeOPedwOADw9vbmyJEjFIwnRfj6+uLr6/vAdUnyO3bM7OcLMHCgGXwSG3zwAWzeDP7+Zv79xRftrkhERO7D1gDYr18/HA4HderUISIigurVq+Pr60ufPn3o3r37Az+Pj48P5cuXZ+3atbGtXBwOB2vXrqVbt2733L9YsWL8+uuvcc4NGjSIa9eu8dFHH2lqN5WIjDSLPa5dg2rVYOhQuytyY6NGmT19hw/XEKyISCrgYVmWZXcRUVFRHDt2jOvXr1OiRAkyZMiQ6OdYuHAhbdq0YcqUKVSqVIkJEyawaNEiDh8+TI4cOWjdujW5c+dm5MiR8T6+bdu2hIaGsmzZsgd+TW0mba+ePWHCBMiSBfbuhTx57K7IjZw6ZebdR4zQahsRSXX0+e0kO4H4+PhQokSJ//QcISEhXL58mSFDhnDhwgXKlCnDqlWrYheGnD59Gk9P29seShKwLBg3zoQ/MD2FFf5S0LJl0K4dhIZCYKBZgSMiIqmKrSOAtWrVwuM+owfr1q1LwWoST/+CSHnh4WZnjyVLzPFbb8Ho0fbW5DYiI80eex9/bI4rVzYrcPLls7UsEZHE0ue3zSOAZcqUiXN8+/Zt9uzZw/79+2mj3QLkHw4cML3+jhyBNGnMCODrr9tdlZs4fhxCQkxbF4Devc30r4+PvXWJiMhDsTUAjh8/Pt7z77zzDtevX0/hasSZLVhgRv4iIsx07+LF8MQTdlflJr79Flq1MsOvWbLAF19Ao0Z2VyUiIv+BU14U9/LLL993CzdxL598Ai+9ZMJfcDDs2qXwl6Jy5ICbN+HJJ2HPHoU/EREX4JQBcOvWrfj5+dldhjiBzz6DHj3M7d69YdUqyJbN3prcQkTE3dsVK8K6dfDjj6AWSSIiLsHWKeBmzZrFObYsi/Pnz/PLL78kqhG0uKapU82GEgB9+8LIkeo4kiK+/NKs7P3+e7hzne5TT9lakoiIJC1bA2BAQECcY09PT4oWLcq7775LvXr1bKpKnMHMmXd3+OjVS+EvRUREmOA3bZo5/uQTmD7d3ppERCRZ2BYAY2JiaNeuHaVKlSJz5sx2lSFO5vffzWYSdy4B7dEDxo5V+Et2hw5B8+awf7/5wx48GIYMsbsqERFJJrZdA+jl5UW9evUIDQ21qwRxImfPQteuULiwGXSyLBP+JkxQ+Et2X3wBFSqY8JcjB/zwAwwbBl5edlcmIiLJxNZFICVLluTEiRN2liA2u3TJTPEWLAiTJsHt21CnDmzZAh99pPCX7L75Btq2NdO/deqYVb516thdlYiIJDNbrwF877336NOnD8OHD6d8+fKkT58+zs/dtTu3O7AsmDPHjPrdafn41FNm+rdmTVtLcy+NGkH9+qbFy4ABGvUTEXETtmwF9+6779K7d28yZsx4t5C/DfVYloWHhwcxMTEpXVqiaCuZhxMebnbwmD/fHFeoAO+/D3XrasQv2VmW6aLduDGkTWvOORygfbJFxI3o89umAOjl5cX58+c5dOjQfe9Xo0aNFKro4eg/oMTbtg1atoSTJ81g0zvvQP/+GnhKEdeumeQ9b55ZYj1lit0ViYjYQp/fNk0B38mczh7wJOk4HPDBB2ZxaUwM5MtnRgCrVLG7Mjexd69Z5Xv0qEnb+fOb0UANuYqIuCXbrgH00AeP27hxA1q3hq++MsctWsDkyfCPNpCSHCzLjPS9+SZERpqNlBcsMNf8iYiI27ItABYpUuRfQ+DVq1dTqBpJLqdPw7PPmgEoHx+YOBE6dNDAU4oICzNTvYsWmeNGjWDWLHjkEVvLEhER+9kWAIcNG3bPTiDiWrZuheeeg4sXIXt2MwKogacUFB4Oa9aAt7eZf+/ZU8lbREQAGwNgixYtyJ49u10vL8nsiy/M4FNUFJQubdrN5c1rd1VuJijI7OsbEACVK9tdjYiIOBFbej/o+j/XFRlpFpq2bWvC33PPwaZNCn8p4q+/4PnnYfnyu+fq1VP4ExGRe9gSAG3oPCMp4PffTTPnyZPNTOPQobBkCWTIYHdlbmD7dihXzsyzd+4Mt27ZXZGIiDgxW6aAHQ6HHS8ryWj1atPf7+pVyJLFtJpr0MDuqtyAZcH48dC3L0RHQ4ECZtGHn5/dlYmIiBNT+3/5TywLRo2Chg1N+CtfHnbuVPhLEX/+aZZY9+5twt+LL8KuXeZNEBERuQ9b9wKW1C0mBrp1M1O+YBZ9fPSRBp9SxNWrULYsnDkDvr4wYYKZ+tX1tSIi8gAUAOWhRETASy+Z9QYeHib4de9ud1VuJEsWePppWLfOTPmWKWN3RSIikoooAEqiXbkCjRubfX19fc31fs8/b3dVbuDyZbOnXo4c5nj8eDP1mzGjvXWJiEiqo2sAJVH+/NM0c962DTJnNn2GFf5SwIYNZpSvZUsz9w6QNq3Cn4iIPBQFQHlglmV6/B09anoMb95s2r5IMoqJgffeg1q14Nw5OH8eLl2yuyoREUnlFADlgS1YAIsXm53Fvv4aihe3uyIXd/GiWU49eLCZ+m3TBn7+GXLmtLsyERFJ5XQNoDyQs2eha1dze9AgdRpJduvWmeneixchXTqYNMkEQBERkSSgACj/yrKgY0ez01j58jBggN0VubjoaLOk+uJFKFnSrPLVcKuIiCQhTQHLvxo1ClatMit+58yBNGnsrsjFeXvDl1+aCy537FD4ExGRJKcAKAmyLHjnnbsjfqNHK4skm9Wr4fPP7x4//riZ9k2b1r6aRETEZWkKWOJlWdCnD4wbZ45HjIAePeytySVFR5tFHqNGmZG/ChWgXDm7qxIRERenACj3iImBLl3uDkh9/LF2+UgWZ86Y7VQ2bzbHnTpBiRL21iQiIm5BAVDiiIqCtm3NJWienjB1KrRvb3dVLmjFCmjd2uzp6+8P06bBiy/aXZWIiLgJBUCJFRYGzZqZDiTe3maLt+bN7a7KBQ0caObUwSyrXrgQCha0tyYREXErWgQigOnzV726CX8ZMpgBKoW/ZPLII+Z7jx5m+lfhT0REUphGAIUDB6BhQ3NJWmAgrFwJZcvaXZWLuXED0qc3t3v2hMqVzabKIiIiNtAIoJv79Vezn++ZM1C0KGzdqvCXpKKi4M03zere69fNOQ8PhT8REbGVAqAbi4yEVq0gNBSqVDGzkfny2V2VCzlxwgS9jz6Cw4fh22/trkhERARQAHRrQ4eaEcBs2WDZsruXpkkSWLLEDKX+8gtkyQL/+x+0aGF3VSIiIoACoNvavBnGjDG3P/8csme3tx6XcesWdO1qWrqEh5sRwD17oFEjuysTERGJpQDohq5fhzZtwOEwreiaNrW7IhfSp4/Zwg2gf3/48UcICrK3JhERkX9QAHRDgwbB8eMml3z0kd3VuJhBg6BUKVi1yvT6S5PG7opERETuoQDoZv74Az77zNyeOhUyZbK1nNTv5k2YP//ucWCgmfKtX9+2kkRERP6N+gC6mVGjTGeSGjWUUf6zw4dNt+xffwUvLwgJMec99e8qERFxbvqkciNnz5pRPzArgOU/mD3bbOP266+QIwdkzWp3RSIiIg9MAdCN3Bn9q1YNata0u5pU6sYNaNfOrKKJiIA6dcyUb506dlcmIiLywBQA3cQ/R/88POytJ1U6cAAqVYJZs8w077vvwurV5ro/ERGRVETXALoByzKt6SIjTVu62rXtriiVOnECDh6EnDnhyy/NhZQiIiKpkAKgG5g9G775xnQkmThRo3+JYll3/8AaN4bp001TZ3XOFhGRVExTwC7u9Gno0cPcHjYMSpe2t55UZe9eeOopOHPm7rn27RX+REQk1VMAdGEOh8kr4eFQpQq89ZbdFaUSlgVTpkDlyrBlC/TubXdFIiIiSUpTwC5s4kRYuxbSpYMvvgBvvdv/LjwcXn0VFi0yx40a3e2cLSIi4iI0Auii9uy5O+I3ejQULmxrOanDrl1QrpwJf97eMHYsLF8Ojzxid2UiIiJJSmNCLuj6dbMpRWSkWbfQpYvdFaUC69ZBw4amUWLevLBgATzxhN1ViYiIJAsFQBfUtSscPQp58sDMmVr1+0CqVIGiRaFgQZgxAzJntrsiERGRZKMA6GJmzzZfnp4wf75mL+/rwAEoVszs45s2Lfz4I2TJosQsIiIuT9cAupAjR+5O9w4bZrZ8k3hYFowfD2XLwsiRd88/8ojCn4iIuAWNALqIW7fMdX83bpidPvr3t7siJ3X1KrRtC//7nzk+cCBus2cRERE3oBFAF9G7t+lbnC0bzJ1rZjXlH7ZsgTJlTPjz9YVJk8w8ucKfiIi4GQVAF7B0qckyAHPmmK1q5W8cDtMLp3p1s6tH4cKwbRu8/rrCn4iIuCUFwFTu5Eno0MHc7tsX6te3tx6ndPw4DBkCMTHQsiXs3GlGAkVERNyUrgFMxW7dgubNISzMdDEZPtzuipxU4cJmWxTLMmlZo34iIuLmXGoEcOLEieTLlw8/Pz8qV67Mjh07Erzv1KlTqVatGpkzZyZz5swEBwff9/7OxrLMit9ffjGLV7/8EtKksbsqJxETA++/Dz//fPdchw7QsaPCn4iICC4UABcuXEivXr0YOnQou3btonTp0tSvX59Lly7Fe//169fz0ksv8eOPP7J161aCgoKoV68eZ8+eTeHKH87kyabJs6en2bQib167K3ISFy9CgwYwaJAZHr1xw+6KREREnI6HZVmW3UUkhcqVK1OxYkU+/fRTABwOB0FBQXTv3p1+/fr96+NjYmLInDkzn376Ka1bt36g1wwPDycgIICwsDD8/f3/U/2JsXkz1KoFt2+btQ139vx1e+vWQatWcOECpEtnVsa0aWN3VSIi4mTs+vx2Ji4xAhgVFcXOnTsJDg6OPefp6UlwcDBbt259oOeIiIjg9u3bZMmSJcH7REZGEh4eHucrpV24AC+8YMJf8+bQp0+Kl+B8YmJg6FAIDjZ/QI89ZqZ/Ff5ERETi5RIB8MqVK8TExJAjR44453PkyMGFCxce6Dn69u1Lrly54oTIfxo5ciQBAQGxX0FBQf+p7ocxePDdjDN9ui5pIzzcBL933zUXRnbsCDt2QIkSdlcmIiLitFwiAP5Xo0aNYsGCBXz99df4+fkleL/+/fsTFhYW+3XmzJkUrBIOHoQZM8ztzz+HDBlS9OWdU4YMkD69+T5vHkydaqZ/RUREJEEu0QYma9aseHl5cfHixTjnL168SGBg4H0fO3bsWEaNGsWaNWt4/PHH73tfX19ffH19/3O9D2vAANPT+LnnoGpV28qwX3S0mQNPm9asgvniC7hyBYoWtbsyERGRVMElRgB9fHwoX748a9eujT3ncDhYu3YtVapUSfBxo0ePZvjw4axatYoKFSqkRKkPbfNm+OYbs8XbiBF2V2OjP/4wmx137mymfMH0wVH4ExEReWAuMQII0KtXL9q0aUOFChWoVKkSEyZM4MaNG7Rr1w6A1q1bkzt3bkaOHAnABx98wJAhQ5g/fz758uWLvVYwQ4YMZHCyuVXLgrffNrc7dIBixeytxzYrV0Lr1vDnn7BnD/z+O+TLZ3dVIiIiqY7LBMCQkBAuX77MkCFDuHDhAmXKlGHVqlWxC0NOnz6Np+fdAc/PPvuMqKgoXnjhhTjPM3ToUN55552ULP1fffklbNliZjyHDrW7Ghvcvg0DB8KYMea4XDlYuFDhT0RE5CG5TB9AO6REH6E//4TixeHyZbPV26BByfIyzuv0aWjRAu608+ne3QRBG6/FFBGR1E19AF1oBNBV9eljwl/Jknengd2Gw2F29Th0CDJlMkugn3vO7qpERERSPZdYBOKq1q6FWbNMr7/PPwcfH7srSmGenvDxx/DEE7B7t8KfiIhIElEAdFI3b5qFrgBdusB9FjO7lhMn4Pvv7x4HB5sLIHW9n4iISJJRAHRSU6bA8eOQO7cbtX1ZuhTKljV73R0/fve82293IiIikrQUAJ2QwwGTJpnbgwaBy1+feusWdOtmgl94OJQq5Ybz3SIiIilHAdAJrV0Lv/0GGTPCyy/bXU0y++03s63JxInmuG9fWL8ebNhnWURExF1oFbATujP616aNi+/3u2ABdOoE165B1qwwZ45Z9SsiIiLJSgHQyZw5A8uXm9tduthbS7Lbvt2Ev+rVYf58c8GjiIiIJDsFQCfz+efmGsBatUwDaJdjWXcXdXzwARQubEYBvfWfooiISErRNYBOxLJg9mxz+7XX7K0lWcyeDQ0bQnS0OfbxMcOcCn8iIiIpSgHQiWzbZnY+y5ABGje2u5okdOMGtG9vLmpcvRpmzrS7IhEREbemoRcnsnCh+d6kCaRNa28tSebAAWjeHA4eNDt7DB1qwqCIiIjYRgHQScTEwKJF5nZIiL21JAnLMiN93bqZbU1y5jQLPWrWtLsyERERt6cpYCexaROcPw+ZMkG9enZXkwSGDYMOHUz4q1cP9uxR+BMREXESCoBOYt488/2558DX195akkRIiNnCZMQI+O47yJ7d7opERETk/2kK2AkcPHh3XUSbNvbW8tAsy4zylS1rjosXh5MnIUsWW8sSERGRe2kE0GaWBW+8YTqjNGkCNWrYXdFDCA+HFi2gQgXYsOHueYU/ERERp6QAaLNvvoE1a8y077hxdlfzEHbtgnLlzAoWT084fNjuikRERORfKADa6OZN6NnT3O7TBwoUsLeeRLEs+PRTqFIFjh+HRx+FjRvNrh4iIiLi1HQNoI3GjYNTp8wWuP37211NIoSGQseOsHSpOW7SBGbM0JSviIhIKqERQJuEhsKYMeb26NGQPr2t5STOsmUm/KVJAxMmwNdfK/yJiIikIhoBtMlHH0FYGJQsadZPpCpt2sC+ffDSS1Cxot3ViIiISCJpBNAGoaEwfry5PXiwWTvh1K5ehddfN4UDeHiY+WuFPxERkVRJI4A2+PhjM/pXogS88ILd1fyLrVtNU+czZ0y7lzsdq0VERCTVcvaxJ5cTFnZ39G/IECce/XM4zMWJ1aqZ8FeokFmqLCIiIqmeRgBT2NSpZia1eHEnHv27cgVatzZbuIG51m/KFMiY0d66REREJEkoAKYghwM++8zc7t0bvLzsrSdee/ZAo0Zw9iz4+Zn56o4dzXV/IiIi4hIUAFPQ6tVw4gQEBJhBNaeUJ4/5XrSo2d3j8cftrUdERESSnAJgCpo0yXxv1w7SpbO3ljjCwkwqBcia1STVvHkhQwZ76xIREZFk4axLEFzOyZOwYoW5/frr9tYSx7p1UKwYfPHF3XOPPabwJyIi4sIUAFPIlClm+9y6daFIEburAWJi4J13IDgYLlwww5MOh91ViYiISApQAEwBlgULF5rbnTrZWwsA58+bJDpsmCmufXv48Ucn7kkjIiIiSUnXAKaA/fvh1CmzqLZhQ5uL+eEHePlluHTJbEA8ebI5FhEREbehAJgCvvnGfA8ONpnLNidOmAQaE2NW9y5aZFb7ioiIiFtRAEwBy5eb788+a28dFCgAffuavX3HjYO0aW0uSEQk+TgcDqKiouwuQ2yQJk0avJyy2a7z8LAsy7K7iNQqPDycgIAAwsLC8Pf3j/c+585B7tzm9vnzEBiYggUCrFxpVvkWKGCOLUtNnUXE5UVFRXHy5EkcWtzmtjJlykRgYCAe8XzmPcjnt6vTCGAy+/Zb871y5RQOf7dvw8CBMGYMVKgAmzaBr6/Cn4i4PMuyOH/+PF5eXgQFBeGpBW5uxbIsIiIiuHTpEgA5c+a0uSLnpACYzH76yXxP0cUfv/8OLVrAtm3muEqVFHxxERF7RUdHExERQa5cuUjnVF33JaWk/f9LnC5dukT27Nk1HRwPBcBktmOH+f7EEyn0gt98Y7Ya+esvs7vHjBnQrFkKvbiIiP1iYmIA8PHxsbkSsdOd8H/79m0FwHhoXDwZXb0Kx46Z2xUrJvOLRUVBz57QtKkJfxUrwu7dCn8i4rbiu/ZL3Ife//tTAExGv/xivhcqBFmypMALbtpkvvfsaW7nz58CLyoiIiKpjaaAk9Gd6d9kHf27s6rXx8dsN7J/vxP0mxERERFnphHAZHQnAFaqlAxPfusWdOsGgwffPVeggMKfiEgqt3XrVry8vHjmmWfu+dn69evx8PAgNDT0np/ly5ePCRMmxDn3448/8vTTT/PII4+QLl06SpQoQe/evTl79mwyVQ+3bt2ia9euPPLII2TIkIHnn3+eixcv/uvjDh06xLPPPktAQADp06enYsWKnD59OvbnNWvWxMPDI87Xa6+9lmy/h6tTAEwmkZF3Z2STPAAeOwZVq8LEiTBy5N0LDUVEJNWbPn063bt3Z8OGDZw7d+6hn2fKlCkEBwcTGBjI0qVLOXjwIJMnTyYsLIwPP/wwCSuOq2fPnvzvf/9j8eLF/PTTT5w7d45m/3I9+vHjx3nqqacoVqwY69evZ9++fQwePBg/P78493v11Vc5f/587Nfo0aOT7fdwdZoCTibLl5u1GHnymB6ASWbBAujUCa5dg6xZYfZsc5GhiIiketevX2fhwoX88ssvXLhwgVmzZjFgwIBEP88ff/xBjx496NGjB+PHj489ny9fPqpXrx7vCGJSCAsLY/r06cyfP5/atWsDMHPmTIoXL862bdt4IoGWGAMHDuTpp5+OE+gKFix4z/3SpUtHYIrvqOCaNAKYTGbNMt9bt4YkWX1+8yZ07gwvvWTCX7VqsGdPCjcYFBFJfSwLbtyw5yuxe20tWrSIYsWKUbRoUV5++WVmzJjBw2zYtXjxYqKionj77bfj/XmmTJkSfGzDhg3JkCFDgl+PPfZYgo/duXMnt2/fJjg4OPZcsWLFePTRR9m6dWu8j3E4HKxYsYIiRYpQv359smfPTuXKlVm2bNk99503bx5Zs2alZMmS9O/fn4iIiARrkfvTCGAyOHcOVq0yt9u0SYIndDigTh3YutUs+Bg4EIYOBW+9fSIi/yYiAjJksOe1r1+H9Okf/P7Tp0/n5ZdfBqBBgwaEhYXx008/UbNmzUS97m+//Ya/v/9D7YIxbdo0bt68meDP06RJk+DPLly4gI+Pzz0BM0eOHFy4cCHex1y6dInr168zatQo3nvvPT744ANWrVpFs2bN+PHHH6lRowYALVu2JG/evOTKlYt9+/bRt29fjhw5wldffZXo31EUAJPF3Lkmsz35JBQpkgRP6Olppn2PHzdPXrduEjypiIg4kyNHjrBjxw6+/vprALy9vQkJCWH69OmJDoCWZT10H7zcdzawTyF39mtu0qQJPXv2BKBMmTJs2bKFyZMnxwbATp06xT6mVKlS5MyZkzp16nD8+PF4p4vl/hQAk5hl3Z3+bdfuPzzRjRtmS7cSJcxx27amyfN9hu1FRORe6dKZkTi7XvtBTZ8+nejoaHLlyhV7zrIsfH19+fTTTwkICMDf3x8w19r9c5QtNDSUgIAAAIoUKUJYWBjnz59P9Chgw4YN2bhxY4I/z5s3LwcOHIj3Z4GBgURFRREaGhqnvosXLyZ47V7WrFnx9vamxJ3Pu/9XvHhxNt1ZTRmPyv9/gf2xY8cUAB+CAmASO34cDh0ybflefPEhn+TAAWjeHMLCzHV+WbOa8wp/IiKJ5uGRuGlYO0RHRzN79mw+/PBD6tWrF+dnTZs25csvv+S1116jcOHCeHp6snPnTvLmzRt7nxMnThAWFkaR/592euGFF+jXrx+jR4+Oswjkjn8GtL/7L1PA5cuXJ02aNKxdu5bnn38eMCObp0+fpkoC+9L7+PhQsWJFjhw5Euf80aNH4/yO/7Rnzx6Ah5rmFgXAJHfnH00VK8L//0Ptwd0ZPuza1Sz6CAw0o4B3AqCIiLikb7/9lr/++osOHTrEjuLd8fzzzzN9+nRee+01MmbMSMeOHenduzfe3t6UKlWKM2fO0LdvX5544gmqVq0KQFBQEOPHj6dbt26Eh4fTunVr8uXLxx9//MHs2bPJkCFDgq1g/ssUcEBAAB06dKBXr15kyZIFf39/unfvTpUqVeKsAC5WrBgjR47kueeeA+Ctt94iJCSE6tWrU6tWLVatWsX//vc/1q9fD5g2MfPnz4/tabhv3z569uxJ9erVefzxxx+6XrdmyUMLCwuzACssLCz2XNu2lgWW1b9/Ip/s2jXLevll82CwrLp1LevChaQtWETEDdy8edM6ePCgdfPmTbtLeWCNGjWynn766Xh/tn37dguw9u7da1mW+f2GDh1qFStWzEqbNq2VP39+q1OnTtbly5fveewPP/xg1a9f38qcObPl5+dnFStWzOrTp4917ty5ZPtdbt68aXXp0sXKnDmzlS5dOuu5556zzp8/H+c+gDVz5sw456ZPn24VKlTI8vPzs0qXLm0tW7Ys9menT5+2qlevbmXJksXy9fW1ChUqZL311ltxPn/jqyOh/w7i+/x2Nx6W9RDrywWA8PBwAgICCAsLi70uo2BBOHECVq5MRIeWffvMlO+RI2bBx/Dh0K+fuS0iIoly69YtTp48Sf78+e9pJCzu437/HcT3+e1uNAWchM6eNeHP09Ns1PHARo824S93bvjyS9PjT0RERCSZKAAmoTvX/5UuDf+4hOP+Jk40S8VGjND1fiIiIpLsNMeYhBYuNN//v2VRwnbvhj597raIDwiAzz9X+BMREZEUoRHAJLJ3LyxbZtoN/K1XZVyWBZMmQa9eEBUFjz32H5sFioiIiCSeAmASefdd8z0kBIoXj+cOoaHQsSMsXWqOn30WmjRJqfJERNyO1ji6N73/96cp4CSwfz989ZUZ/Rs8OJ47/PwzlCtnwl+aNDB+vBkuzJIlpUsVEXF5Xl5eAERFRdlcidgpIiICuH/janemEcAk8MEH5vuLL97duS3WjBnw2mtw+zbkz28uFKxYMcVrFBFxF97e3qRLl47Lly+TJk0aPNVSy61YlkVERASXLl0iU6ZMsf8gkLgUAJPA8uXme7yjf4UKQUwMPP88TJum7dxERJKZh4cHOXPm5OTJk/z+++92lyM2yZQpU4L7D4sCYJKpUwdKlvz/g9DQu0GvenXYscNMAXt42FSdiIh78fHxoXDhwpoGdlNp0qTRyN+/UABMIo0aAQ4HfPghvP8+bN16dzVI+fK21iYi4o48PT21E4hIAlzqwoiJEyeSL18+/Pz8qFy5Mjt27Ljv/RcvXkyxYsXw8/OjVKlSrFy58qFfu9ETV6BxY3j7bQgLg7lzH/q5RERERJKTywTAhQsX0qtXL4YOHcquXbsoXbo09evX59KlS/Hef8uWLbz00kt06NCB3bt307RpU5o2bcr+/fsT/drNArdQ8IUyZgNgX1+YMgXee+8//kYiIiIiycPDcpFGOZUrV6ZixYp8+umnADgcDoKCgujevTv9+vW75/4hISHcuHGDb7/9NvbcE088QZkyZZg8efIDveadzaSv4klmHFC0KCxaBI8/njS/lIiIiCS5O5/fYWFh+Pv7212OLVziGsCoqCh27txJ//79Y895enoSHBzM1q1b433M1q1b6dWrV5xz9evXZ9myZQm+TmRkJJGRkbHHYWFhANzAgVfz5qa/X4YMEB7+H34bERERSU7h//857SJjYA/FJQLglStXiImJIUeOHHHO58iRg8OHD8f7mAsXLsR7/wsXLiT4OiNHjmTYsGH3nA8CM/K3aFGiaxcRERF7/PnnnwQEBNhdhi1cIgCmlP79+8cZNQwNDSVv3rycPn3abf8Dchbh4eEEBQVx5swZtx3OdxZ6L5yL3g/noffCeYSFhfHoo4+SxY135HKJAJg1a1a8vLy4ePFinPMXL15MsAlkYGBgou4P4Ovri6+v7z3nAwIC9JfZSfj7++u9cBJ6L5yL3g/noffCebjzLjEu8Zv7+PhQvnx51q5dG3vO4XCwdu1aqlSpEu9jqlSpEuf+AD/88EOC9xcRERFxFS4xAgjQq1cv2rRpQ4UKFahUqRITJkzgxo0btGvXDoDWrVuTO3duRo4cCcAbb7xBjRo1+PDDD3nmmWdYsGABv/zyC59//rmdv4aIiIhIsnOZABgSEsLly5cZMmQIFy5coEyZMqxatSp2ocfp06fjDPVWrVqV+fPnM2jQIAYMGEDhwoVZtmwZJWP3c/t3vr6+DB06NN5pYUlZei+ch94L56L3w3novXAeei9cqA+giIiIiDwYl7gGUEREREQenAKgiIiIiJtRABQRERFxMwqAIiIiIm5GAfBfTJw4kXz58uHn50flypXZsWPHfe+/ePFiihUrhp+fH6VKlWLlypUpVKnrS8x7MXXqVKpVq0bmzJnJnDkzwcHB//reyYNL7N+LOxYsWICHhwdNmzZN3gLdSGLfi9DQULp27UrOnDnx9fWlSJEi+v9UEkrs+zFhwgSKFi1K2rRpCQoKomfPnty6dSuFqnVdGzZsoHHjxuTKlQsPDw+WLVv2r49Zv3495cqVw9fXl0KFCjFr1qxkr9NWliRowYIFlo+PjzVjxgzrwIED1quvvmplypTJunjxYrz337x5s+Xl5WWNHj3aOnjwoDVo0CArTZo01q+//prClbuexL4XLVu2tCZOnGjt3r3bOnTokNW2bVsrICDA+uOPP1K4cteT2PfijpMnT1q5c+e2qlWrZjVp0iRlinVxiX0vIiMjrQoVKlhPP/20tWnTJuvkyZPW+vXrrT179qRw5a4pse/HvHnzLF9fX2vevHnWyZMnrdWrV1s5c+a0evbsmcKVu56VK1daAwcOtL766isLsL7++uv73v/EiRNWunTprF69elkHDx60PvnkE8vLy8tatWpVyhRsAwXA+6hUqZLVtWvX2OOYmBgrV65c1siRI+O9f/Pmza1nnnkmzrnKlStbnTt3TtY63UFi34t/io6OtjJmzGh98cUXyVWi23iY9yI6OtqqWrWqNW3aNKtNmzYKgEkkse/FZ599ZhUoUMCKiopKqRLdSmLfj65du1q1a9eOc65Xr17Wk08+max1upsHCYBvv/229dhjj8U5FxISYtWvXz8ZK7OXpoATEBUVxc6dOwkODo495+npSXBwMFu3bo33MVu3bo1zf4D69esneH95MA/zXvxTREQEt2/fduuNv5PCw74X7777LtmzZ6dDhw4pUaZbeJj3Yvny5VSpUoWuXbuSI0cOSpYsyYgRI4iJiUmpsl3Ww7wfVatWZefOnbHTxCdOnGDlypU8/fTTKVKz3OWOn98usxNIUrty5QoxMTGxO4nckSNHDg4fPhzvYy5cuBDv/S9cuJBsdbqDh3kv/qlv377kypXrnr/gkjgP815s2rSJ6dOns2fPnhSo0H08zHtx4sQJ1q1bR6tWrVi5ciXHjh2jS5cu3L59m6FDh6ZE2S7rYd6Pli1bcuXKFZ566iksyyI6OprXXnuNAQMGpETJ8jcJfX6Hh4dz8+ZN0qZNa1NlyUcjgOLyRo0axYIFC/j666/x8/Ozuxy3cu3aNV555RWmTp1K1qxZ7S7H7TkcDrJnz87nn39O+fLlCQkJYeDAgUyePNnu0tzS+vXrGTFiBJMmTWLXrl189dVXrFixguHDh9tdmrgBjQAmIGvWrHh5eXHx4sU45y9evEhgYGC8jwkMDEzU/eXBPMx7ccfYsWMZNWoUa9as4fHHH0/OMt1CYt+L48ePc+rUKRo3bhx7zuFwAODt7c2RI0coWLBg8hbtoh7m70XOnDlJkyYNXl5eseeKFy/OhQsXiIqKwsfHJ1lrdmUP834MHjyYV155hY4dOwJQqlQpbty4QadOnRg4cGCc/esleSX0+e3v7++So3+gEcAE+fj4UL58edauXRt7zuFwsHbtWqpUqRLvY6pUqRLn/gA//PBDgveXB/Mw7wXA6NGjGT58OKtWraJChQopUarLS+x7UaxYMX799Vf27NkT+/Xss89Sq1Yt9uzZQ1BQUEqW71Ie5u/Fk08+ybFjx2JDOMDRo0fJmTOnwt9/9DDvR0RExD0h7044tywr+YqVe7jl57fdq1Cc2YIFCyxfX19r1qxZ1sGDB61OnTpZmTJlsi5cuGBZlmW98sorVr9+/WLvv3nzZsvb29saO3asdejQIWvo0KFqA5NEEvtejBo1yvLx8bGWLFlinT9/Pvbr2rVrdv0KLiOx78U/aRVw0knse3H69GkrY8aMVrdu3awjR45Y3377rZU9e3brvffes+tXcCmJfT+GDh1qZcyY0fryyy+tEydOWN9//71VsGBBq3nz5nb9Ci7j2rVr1u7du63du3dbgDVu3Dhr9+7d1u+//25ZlmX169fPeuWVV2Lvf6cNzFtvvWUdOnTImjhxotrAuLtPPvnEevTRRy0fHx+rUqVK1rZt22J/VqNGDatNmzZx7r9o0SKrSJEilo+Pj/XYY49ZK1asSOGKXVdi3ou8efNawD1fQ4cOTfnCXVBi/178nQJg0krse7FlyxarcuXKlq+vr1WgQAHr/ffft6Kjo1O4ateVmPfj9u3b1jvvvGMVLFjQ8vPzs4KCgqwuXbpYf/31V8oX7mJ+/PHHeD8D7vz5t2nTxqpRo8Y9jylTpozl4+NjFShQwJo5c2aK152SPCxL48wiIiIi7kTXAIqIiIi4GQVAERERETejACgiIiLiZhQARURERNyMAqCIiIiIm1EAFBEREXEzCoAiIiIibkYBUESSxaxZs8iUKZPdZTw0Dw8Pli1bdt/7tG3blqZNm6ZIPSIiSUkBUEQS1LZtWzw8PO75OnbsmN2lMWvWrNh6PD09yZMnD+3atePSpUtJ8vznz5+nYcOGAJw6dQoPDw/27NkT5z4fffQRs2bNSpLXS8g777wT+3t6eXkRFBREp06duHr1aqKeR2FVRP7O2+4CRMS5NWjQgJkzZ8Y5ly1bNpuqicvf358jR47gcDjYu3cv7dq149y5c6xevfo/P3dgYOC/3icgIOA/v86DeOyxx1izZg0xMTEcOnSI9u3bExYWxsKFC1Pk9UXE9WgEUETuy9fXl8DAwDhfXl5ejBs3jlKlSpE+fXqCgoLo0qUL169fT/B59u7dS61atciYMSP+/v6UL1+eX375JfbnmzZtolq1aqRNm5agoCB69OjBjRs37lubh4cHgYGB5MqVi4YNG9KjRw/WrFnDzZs3cTgcvPvuu+TJkwdfX1/KlCnDqlWrYh8bFRVFt27dyJkzJ35+fuTNm5eRI0fGee47U8D58+cHoGzZsnh4eFCzZk0g7qja559/Tq5cuXA4HHFqbNKkCe3bt489/uabbyhXrhx+fn4UKFCAYcOGER0dfd/f09vbm8DAQHLnzk1wcDAvvvgiP/zwQ+zPY2Ji6NChA/nz5ydt2rQULVqUjz76KPbn77zzDl988QXffPNN7Gji+vXrAThz5gzNmzcnU6ZMZMmShSZNmnDq1Kn71iMiqZ8CoIg8FE9PTz7++GMOHDjAF198wbp163j77bcTvH+rVq3IkycPP//8Mzt37qRfv36kSZMGgOPHj9OgQQOef/559u3bx8KFC9m0aRPdunVLVE1p06bF4XAQHR3NRx99xIcffsjYsWPZt28f9evX59lnn+W3334D4OOPP2b58uUsWrSII0eOMG/ePPLlyxfv8+7YsQOANWvWcP78eb766qt77vPiiy/y559/8uOPP8aeu3r1KqtWraJVq1YAbNy4kdatW/PGG29w8OBBpkyZwqxZs3j//fcf+Hc8deoUq1evxsfHJ/acw+EgT548LF68mIMHDzJkyBAGDBjAokWLAOjTpw/NmzenQYMGnD9/nvPnz1O1alVu375N/fr1yZgxIxs3bmTz5s1kyJCBBg0aEBUV9cA1iUgqZImIJKBNmzaWl5eXlT59+tivF154Id77Ll682HrkkUdij2fOnGkFBATEHmfMmNGaNWtWvI/t0KGD1alTpzjnNm7caHl6elo3b96M9zH/fP6jR49aRYoUsSpUqGBZlmXlypXLev/99+M8pmLFilaXLl0sy7Ks7t27W7Vr17YcDke8zw9YX3/9tWVZlnXy5EkLsHbv3h3nPm3atLGaNGkSe9ykSROrffv2scdTpkyxcuXKZcXExFiWZVl16tSxRowYEec55syZY+XMmTPeGizLsoYOHWp5enpa6dOnt/z8/CzAAqxx48Yl+BjLsqyuXbtazz//fIK13nntokWLxvkziIyMtNKmTWutXr36vs8vIqmbrgEUkfuqVasWn332Wexx+vTpATMaNnLkSA4fPkx4eDjR0dHcunWLiIgI0qVLd8/z9OrVi44dOzJnzpzYacyCBQsCZnp43759zJs3L/b+lmXhcDg4efIkxYsXj7e2sLAwMmTIgMPh4NatWzz11FNMmzaN8PBwzp07x5NPPhnn/k8++SR79+4FzPRt3bp1KVq0KA0aNKBRo0bUq1fvP/1ZtWrVildffZVJkybh6+vLvHnzaNGiBZ6enrG/5+bNm+OM+MXExNz3zw2gaNGiLF++nFu3bjF37lz27NlD9+7d49xn4sSJzJgxg9OnT3Pz5k2ioqIoU6bMfevdu3cvx44dI2PGjHHO37p1i+PHjz/En4CIpBYKgCJyX+nTp6dQoUJxzp06dYpGjRrx+uuv8/7775MlSxY2bdpEhw4diIqKijfIvPPOO7Rs2ZIVK1bw3XffMXToUBYsWMBzzz3H9evX6dy5Mz169LjncY8++miCtWXMmJFdu3bh6elJzpw5SZs2LQDh4eH/+nuVK1eOkydP8t1337FmzRqaN29OcHAwS5Ys+dfHJqRx48ZYlsWKFSuoWLEiGzduZPz48bE/v379OsOGDaNZs2b3PNbPzy/B5/Xx8Yl9D0aNGsUzzzzDsGHDGD58OAALFiygT58+fPjhh1SpUoWMGTMyZswYtm/fft96r1+/Tvny5eME7zucZaGPiCQPBUARSbSdO3ficDj48MMPY0e37lxvdj9FihShSJEi9OzZk5deeomZM2fy3HPPUa5cOQ4ePHhP0Pw3np6e8T7G39+fXLlysXnzZmrUqBF7fvPmzVSqVCnO/UJCQggJCeGFF16gQYMGXL16lSxZssR5vjvX28XExNy3Hj8/P5o1a8a8efM4duwYRYsWpVy5crE/L1euHEeOHEn07/lPgwYNonbt2rz++uuxv2fVqlXp0qVL7H3+OYLn4+NzT/3lypVj4cKFZM+eHX9///9Uk4ikLloEIiKJVqhQIW7fvs0nn3zCiRMnmDNnDpMnT07w/jdv3qRbt26sX7+e33//nc2bN/Pzzz/HTu327duXLVu20K1bN/bs2cNvv/3GN998k+hFIH/31ltv8cEHH7Bw4UKOHDlCv3792LNnD2+88QYA48aN48svv+Tw4cMcPXqUxYsXExgYGG/z6uzZs5M2bVpWrVrFxYsXCQsLS/B1W7VqxYoVK5gxY0bs4o87hgwZwuzZsxk2bBgHDhzg0KFDLFiwgEGDBiXqd6tSpQqPP/44I0aMAKBw4cL88ssvrF69mqNHjzJ48GB+/vnnOI/Jly8f+/bt48iRI1y5coXbt2/TqlUrsmbNSpMmTdi4cSMnT55k/fr19OjRgz/++CNRNYlI6qIAKCKJVrp0acaNG8cHH3xAyZIlmTdvXpwWKv/k5eXFn3/+SevWrSlSpAjNmzenYcOGDBs2DIDHH3+cn376iaNHj1KtWjXKli3LkCFDyJUr10PX2KNHD3r16kXv3r0pVaoUq1atYvny5RQuXBgw08ejR4+mQoUKVKxYkVOnTrFy5crYEc2/8/b25uOPP2bKlCnkypWLJk2aJPi6tWvXJkuWLBw5coSWLVvG+Vn9+vX59ttv+f7776lYsSJPPPEE48ePJ2/evIn+/Xr27Mm0adM4c+YMnTt3plmzZoSEhFC5cmX+/PPPOKOBAK+++ipFixalQoUKZMuWjc2bN5MuXTo2bNjAo48+SrNmzShevDgdOnTg1q1bGhEUcXEelmVZdhchIiIiIilHI4AiIiIibkYBUERERMTNKACKiIiIuBkFQBERERE3owAoIiIi4mYUAEVERETcjAKgiIiIiJtRABQRERFxMwqAIiIiIm5GAVBERETEzSgAioiIiLgZBUARERERN/N/+ddYRZGF10AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get ROC curve and send it to Pandas so that we can plot it\n",
    "roc_df = evaluationSummary.roc.toPandas()\n",
    "\n",
    "plt.plot(roc_df.FPR, roc_df.TPR, 'b', label = 'AUC = %0.2f' % evaluationSummary.areaUnderROC)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.show()\n",
    "\n",
    "%matplot plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5a310ff",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PySpark",
   "language": "",
   "name": "pysparkkernel"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "python",
    "version": 3
   },
   "mimetype": "text/x-python",
   "name": "pyspark",
   "pygments_lexer": "python3"
  }
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