spark_bill_ml.ipynb

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      "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>2</td><td>application_1639090373335_0003</td><td>pyspark</td><td>idle</td><td><a target=\"_blank\" href=\"http://ip-172-31-13-190.ec2.internal:20888/proxy/application_1639090373335_0003/\" class=\"emr-proxy-link\" emr-resource=\"j-3NY4Q49PKDE6G\n",
       "\" application-id=\"application_1639090373335_0003\">Link</a></td><td><a target=\"_blank\" href=\"http://ip-172-31-13-190.ec2.internal:8042/node/containerlogs/container_1639090373335_0003_01_000001/livy\" >Link</a></td><td>✔</td></tr></table>"
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     "text": [
      "SparkSession available as 'spark'.\n"
     ]
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   ],
   "source": [
    "# Link to bucket: https://s3.console.aws.amazon.com/s3/buckets/macs30123-bills?region=us-east-1&tab=objects"
   ]
  },
  {
   "cell_type": "code",
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      "Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /usr/local/lib/python3.7/site-packages (from boto3)\n",
      "Collecting botocore<1.24.0,>=1.23.23 (from boto3)\n",
      "  Using cached https://files.pythonhosted.org/packages/06/75/72b9236e2ff5ff7d24d1a04dae90cc5974f1cb830ddfe61ba8a18df4664d/botocore-1.23.23-py3-none-any.whl\n",
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      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.24.0,>=1.23.23->boto3)\n",
      "Installing collected packages: urllib3, python-dateutil, botocore, s3transfer, boto3\n",
      "Successfully installed boto3-1.20.23 botocore-1.23.23 python-dateutil-2.8.2 s3transfer-0.5.0 urllib3-1.26.7\n",
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      "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/site-packages (from pandas==1.0.3)\n",
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      "Installing collected packages: pandas\n",
      "Successfully installed pandas-1.0.3\n",
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      "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib64/python3.7/site-packages (from scipy==1.4.1)\n",
      "Installing collected packages: scipy\n",
      "Successfully installed scipy-1.4.1\n",
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      "  Using cached https://files.pythonhosted.org/packages/b2/c2/71fcf957710f3ba1f09088b35776a799ba7dd95f7c2b195ec800933b276b/matplotlib-3.2.1-cp37-cp37m-manylinux1_x86_64.whl\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /mnt/tmp/1639104851065-0/lib/python3.7/site-packages (from matplotlib==3.2.1)\n",
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      "Requirement already satisfied: numpy>=1.11 in /usr/local/lib64/python3.7/site-packages (from matplotlib==3.2.1)\n",
      "Collecting kiwisolver>=1.0.1 (from matplotlib==3.2.1)\n",
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      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/site-packages (from python-dateutil>=2.1->matplotlib==3.2.1)\n",
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      "Requirement already satisfied: pandas>=0.22.0 in /mnt/tmp/1639104851065-0/lib/python3.7/site-packages (from seaborn==0.10.1)\n",
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      "Requirement already satisfied: cycler>=0.10 in /mnt/tmp/1639104851065-0/lib/python3.7/site-packages (from matplotlib>=2.1.2->seaborn==0.10.1)\n",
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      "Installing collected packages: seaborn\n",
      "Successfully installed seaborn-0.10.1\n",
      "\n",
      "Collecting dask\n",
      "  Using cached https://files.pythonhosted.org/packages/e8/3e/161fc6b630603ab69266c90238068793e5cdd38b5e41b0fcba3d09636f1d/dask-2021.11.2-py3-none-any.whl\n",
      "Collecting packaging>=20.0 (from dask)\n",
      "  Using cached https://files.pythonhosted.org/packages/05/8e/8de486cbd03baba4deef4142bd643a3e7bbe954a784dc1bb17142572d127/packaging-21.3-py3-none-any.whl\n",
      "Collecting fsspec>=0.6.0 (from dask)\n",
      "  Using cached https://files.pythonhosted.org/packages/97/76/8f9921e10d9cbdf4790d4fb7a9883f3d636926e8b0564a4983ac15c167c9/fsspec-2021.11.1-py3-none-any.whl\n",
      "Requirement already satisfied: pyyaml in /usr/local/lib64/python3.7/site-packages (from dask)\n",
      "Collecting partd>=0.3.10 (from dask)\n",
      "  Using cached https://files.pythonhosted.org/packages/41/94/360258a68b55f47859d72b2d0b2b3cfe0ca4fbbcb81b78812bd00ae86b7c/partd-1.2.0-py3-none-any.whl\n",
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      "  Using cached https://files.pythonhosted.org/packages/b5/f1/3df506b493736e3ee11fc1a3c2de8014a55f025d830a71bb499acc049a2c/toolz-0.11.2-py3-none-any.whl\n",
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      "Collecting locket (from partd>=0.3.10->dask)\n",
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      "Installing collected packages: packaging, fsspec, toolz, locket, partd, cloudpickle, dask\n",
      "Successfully installed cloudpickle-2.0.0 dask-2021.11.2 fsspec-2021.11.1 locket-0.2.1 packaging-21.3 partd-1.2.0 toolz-0.11.2\n",
      "\n",
      "Collecting textblob\n",
      "  Using cached https://files.pythonhosted.org/packages/44/a4/595b66c1342a830f4ff476307c4367541cce3e361b9ee96d22a5eeeddd1d/textblob-0.17.1-py2.py3-none-any.whl\n",
      "Requirement already satisfied: nltk>=3.1; python_version >= \"3\" in /usr/local/lib/python3.7/site-packages (from textblob)\n",
      "Requirement already satisfied: click in /usr/local/lib64/python3.7/site-packages (from nltk>=3.1; python_version >= \"3\"->textblob)\n",
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      "Requirement already satisfied: tqdm in /usr/local/lib64/python3.7/site-packages (from nltk>=3.1; python_version >= \"3\"->textblob)\n",
      "Installing collected packages: textblob\n",
      "Successfully installed textblob-0.17.1\n",
      "\n",
      "You are using pip version 9.0.1, however version 21.3.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\n",
      "\n",
      "You are using pip version 9.0.1, however version 21.3.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\n",
      "\n",
      "You are using pip version 9.0.1, however version 21.3.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\n",
      "\n",
      "You are using pip version 9.0.1, however version 21.3.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\n",
      "\n",
      "You are using pip version 9.0.1, however version 21.3.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\n",
      "\n",
      "You are using pip version 9.0.1, however version 21.3.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\n",
      "\n",
      "You are using pip version 9.0.1, however version 21.3.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command."
     ]
    }
   ],
   "source": [
    "#install necessary packages\n",
    "sc.install_pypi_package(\"boto3\") # use boto3 to see book data in S3 bucket\n",
    "sc.install_pypi_package(\"pandas==1.0.3\")\n",
    "sc.install_pypi_package(\"scipy==1.4.1\")\n",
    "sc.install_pypi_package(\"matplotlib==3.2.1\")\n",
    "sc.install_pypi_package(\"seaborn==0.10.1\")\n",
    "sc.install_pypi_package(\"dask\") #install dask as well\n",
    "sc.install_pypi_package(\"textblob\") #this is used to conduct sentiment analysis for added feature columns"
   ]
  },
  {
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   ],
   "source": [
    "import boto3\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from dask import dataframe as dd\n",
    "from textblob import TextBlob\n",
    "from pyspark.ml.classification import LogisticRegression\n",
    "from pyspark.ml.evaluation import RegressionEvaluator\n",
    "from pyspark.sql.functions import udf\n",
    "from pyspark.sql.types import *\n",
    "from io import StringIO"
   ]
  },
  {
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   "execution_count": 4,
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   ],
   "source": [
    "s3 = boto3.client('s3', region_name='us-east-1',)\n",
    "\n",
    "response = s3.list_objects(Bucket='macs30123-bills')"
   ]
  },
  {
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   "source": [
    "data_congress = s3.get_object(Bucket=\"macs30123-bills\", Key=\"data_congress_breakdown.csv\")\n",
    "data_congress = data_congress['Body'].read()\n",
    "data_congress = data_congress.decode('utf-8')\n",
    "\n",
    "df_congress = pd.read_csv(StringIO(data_congress))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   ],
   "source": [
    "df_congress = df_congress[['Congress (years)', '# of House Seats', 'Democrats', 'Republicans',\n",
    "       'Other', '% Democrat', '% Republican']]"
   ]
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   "cell_type": "code",
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   ],
   "source": [
    "df_congress['% Republican'] = df_congress['% Republican'] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0d0b5d4c99d545ab901a6a9d785da770",
       "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": [
    "df_congress['Congress (years)'] = df_congress['Congress (years)'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b2ebf4c91b7949b483a51d027e06ef1c",
       "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": [
    "df_congress['congress'] = df_congress['Congress (years)'].apply(lambda txt: txt.split(' ', 1)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee6e7c5946d74fd4a02c721108dc6bb8",
       "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#View congress party makeup over time\n",
    "plt.close() # Close previous plots; otherwise, will just overwrite and display again\n",
    "\n",
    "sns.lineplot(x='congress', y='% Democrat', data=df_congress)\n",
    "sns.lineplot(x='congress', y='% Republican', data=df_congress)\n",
    "plt.title('Breakdown of Congress by Party Makeup over Time')\n",
    "plt.xlabel('Congress Session')\n",
    "plt.ylabel('Percent of Congress')\n",
    "plt.legend(['Republican', 'Democrat'])\n",
    "%matplot plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "#read in the bills dataset from our s3 bucket\n",
    "df_bills = s3.get_object(Bucket=\"macs30123-bills\", Key=\"data_bills.csv\")\n",
    "df_bills = df_bills['Body'].read()\n",
    "df_bills = df_bills.decode('utf-8')\n",
    "\n",
    "df_bills = pd.read_csv(StringIO(df_bills))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "df_congress = df_congress.dropna()\n",
    "df_congress['congress'] = df_congress['congress'].astype(int)\n",
    "df = df_bills.merge(df_congress, left_on='session', right_on='congress',)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "df = df[df['type'] != 'hres']\n",
    "df = df[df['type'] != 'sconres'] #remove hres and sconres type bills from our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array(['hr', 's'], dtype=object)"
     ]
    }
   ],
   "source": [
    "df['type'].unique() #verify that hres and sconres type bills have been removed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create our binary outcome variable based on whether or not bills have passed through Congress."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Unnamed: 0  session type  ... % Democrat  % Republican  congress\n",
      "0      BILLS-113hr1000ih      113   hr  ...  46.206897     53.793103       113\n",
      "1      BILLS-113hr1001ih      113   hr  ...  46.206897     53.793103       113\n",
      "2      BILLS-113hr1002ih      113   hr  ...  46.206897     53.793103       113\n",
      "3      BILLS-113hr1003ih      113   hr  ...  46.206897     53.793103       113\n",
      "4      BILLS-113hr1004ih      113   hr  ...  46.206897     53.793103       113\n",
      "...                  ...      ...  ...  ...        ...           ...       ...\n",
      "61716    BILLS-116s998rs      116    s  ...  54.022989     45.747126       116\n",
      "61717    BILLS-116s999is      116    s  ...  54.022989     45.747126       116\n",
      "61718    BILLS-116s999rs      116    s  ...  54.022989     45.747126       116\n",
      "61719     BILLS-116s99is      116    s  ...  54.022989     45.747126       116\n",
      "61720      BILLS-116s9is      116    s  ...  54.022989     45.747126       116\n",
      "\n",
      "[58011 rows x 17 columns]"
     ]
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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     },
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type  session\n",
      "hr    113         7831\n",
      "      114         9129\n",
      "      115        10721\n",
      "      116        11475\n",
      "s     113         3648\n",
      "      114         4412\n",
      "      115         4806\n",
      "      116         5977\n",
      "Name: outcome, dtype: int64"
     ]
    }
   ],
   "source": [
    "df.groupby(['type', 'session'])['outcome'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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   ],
   "source": [
    "df['pass'] = np.where(df['outcome'] == 'enr', 1, 0) #bills with an outcome of enr is considered a pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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   ],
   "source": [
    "df = df.drop(['Congress (years)', '# of House Seats', 'Democrats', 'Republicans', 'Other'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
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   ],
   "source": [
    "df['Unnamed: 0'] = df['Unnamed: 0'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
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   ],
   "source": [
    "df['outcome'] = df['outcome'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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   ],
   "source": [
    "#convert our merged dataset that contains all our data into a Spark Dataframe\n",
    "from pyspark.sql import SparkSession\n",
    "\n",
    "#create a spark session\n",
    "sparksesh = SparkSession.builder.master('local[1]').appName('SparkByExamples.com').getOrCreate()\n",
    "\n",
    "sdf = sparksesh.createDataFrame(df)"
   ]
  },
  {
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   "execution_count": 22,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------------+-------+----+-------+-------------+-------------+----------+-------------------+--------------------+-----------+------------+--------+----+\n",
      "|       Unnamed: 0|session|type|outcome|sponsor_count|section_count|word_count|       subjectivity|            polarity| % Democrat|% Republican|congress|pass|\n",
      "+-----------------+-------+----+-------+-------------+-------------+----------+-------------------+--------------------+-----------+------------+--------+----+\n",
      "|BILLS-113hr1000ih|    113|  hr|     ih|            1|           10|      5828| 0.3655130309870722| 0.06525710181466382|46.20689655|  53.7931034|     113|   0|\n",
      "|BILLS-113hr1001ih|    113|  hr|     ih|            3|            1|       482| 0.4160595238095238| 0.08928273809523811|46.20689655|  53.7931034|     113|   0|\n",
      "|BILLS-113hr1002ih|    113|  hr|     ih|            8|            4|       914| 0.7384660421545669|  0.3741412958626073|46.20689655|  53.7931034|     113|   0|\n",
      "|BILLS-113hr1003ih|    113|  hr|     ih|            5|            1|       304|  0.448529411764706|-0.03186274509803...|46.20689655|  53.7931034|     113|   0|\n",
      "|BILLS-113hr1004ih|    113|  hr|     ih|            5|            5|      2407|0.28045944940476186| 0.08803013392857144|46.20689655|  53.7931034|     113|   0|\n",
      "|BILLS-113hr1005ih|    113|  hr|     ih|            4|            3|       156|0.24940476190476196| 0.03273809523809524|46.20689655|  53.7931034|     113|   0|\n",
      "|BILLS-113hr1006ih|    113|  hr|     ih|            2|            2|       359|             0.3925| 0.16583333333333333|46.20689655|  53.7931034|     113|   0|\n",
      "|BILLS-113hr1007ih|    113|  hr|     ih|            2|            3|       624| 0.4836950146627567| 0.05337732160312806|46.20689655|  53.7931034|     113|   0|\n",
      "|BILLS-113hr1008ih|    113|  hr|     ih|           44|           10|      2051| 0.3813060385941737|  0.2408455621591215|46.20689655|  53.7931034|     113|   0|\n",
      "|BILLS-113hr1009ih|    113|  hr|     ih|           13|            3|       813| 0.3778756957328386|0.014955679241393524|46.20689655|  53.7931034|     113|   0|\n",
      "| BILLS-113hr100ih|    113|  hr|     ih|            8|           23|      5638| 0.4959002086890532| 0.11747249098842727|46.20689655|  53.7931034|     113|   0|\n",
      "|BILLS-113hr1010ih|    113|  hr|     ih|          132|            2|       745| 0.3617676767676768| 0.13858585858585856|46.20689655|  53.7931034|     113|   0|\n",
      "|BILLS-113hr1011ih|    113|  hr|     ih|            4|            1|       117| 0.3525252525252525| 0.09612794612794613|46.20689655|  53.7931034|     113|   0|\n",
      "|BILLS-113hr1012ih|    113|  hr|     ih|            6|            7|      2750| 0.2723322905804658| 0.03734358706986445|46.20689655|  53.7931034|     113|   0|\n",
      "|BILLS-113hr1013ih|    113|  hr|     ih|            2|            2|       303| 0.2858333333333333|-0.01083333333333333|46.20689655|  53.7931034|     113|   0|\n",
      "|BILLS-113hr1014ih|    113|  hr|     ih|            1|            1|       145|0.14265734265734267|-0.04463869463869463|46.20689655|  53.7931034|     113|   0|\n",
      "|BILLS-113hr1015ih|    113|  hr|     ih|            2|            4|       907|0.28556033168709216|-0.01603713188220...|46.20689655|  53.7931034|     113|   0|\n",
      "|BILLS-113hr1016ih|    113|  hr|     ih|            3|            3|       639| 0.4267813852813851| 0.08960822510822512|46.20689655|  53.7931034|     113|   0|\n",
      "|BILLS-113hr1017ih|    113|  hr|     ih|            1|            3|       306| 0.4024844720496894|0.036594202898550726|46.20689655|  53.7931034|     113|   0|\n",
      "|BILLS-113hr1018ih|    113|  hr|     ih|            6|            1|       465| 0.4905714285714286| 0.20904761904761904|46.20689655|  53.7931034|     113|   0|\n",
      "+-----------------+-------+----+-------+-------------+-------------+----------+-------------------+--------------------+-----------+------------+--------+----+\n",
      "only showing top 20 rows"
     ]
    }
   ],
   "source": [
    "#view our newly created Spark Dataframe\n",
    "sdf.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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     },
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   ],
   "source": [
    "from pyspark.ml.feature import MinMaxScaler\n",
    "from pyspark.ml.feature import VectorAssembler\n",
    "from pyspark.ml import Pipeline\n",
    "from pyspark.sql.functions import udf\n",
    "from pyspark.sql.types import DoubleType\n",
    "\n",
    "# UDF used to convert the column type from a vector to a double type\n",
    "unlist = udf(lambda x: round(float(list(x)[0]),3), DoubleType())\n",
    "\n",
    "# Converting our column to a vector using VectorAssembler\n",
    "assembler = VectorAssembler(inputCols=['word_count'],outputCol='word_count_vect')\n",
    "\n",
    "# Convert vector to scalar using MinMaxScaler\n",
    "scaler = MinMaxScaler(inputCol='word_count_vect', outputCol='word_count_scaled')\n",
    "\n",
    "# Create a pipeline from of VectorAssembler and MinMaxScaler\n",
    "pipeline = Pipeline(stages=[assembler, scaler])\n",
    "\n",
    "# Fitting pipeline the column word_count to normalise it due to the large degree of variation\n",
    "sdf = pipeline.fit(sdf).transform(sdf).withColumn('word_count_scaled', unlist('word_count_scaled')).drop('word_count_vect')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
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     "output_type": "stream",
     "text": [
      "+-----------------+-------+----+-------+-------------+-------------+----------+-------------------+--------------------+-----------+------------+--------+----+-----------------+\n",
      "|       Unnamed: 0|session|type|outcome|sponsor_count|section_count|word_count|       subjectivity|            polarity| % Democrat|% Republican|congress|pass|word_count_scaled|\n",
      "+-----------------+-------+----+-------+-------------+-------------+----------+-------------------+--------------------+-----------+------------+--------+----+-----------------+\n",
      "|BILLS-113hr1000ih|    113|  hr|     ih|            1|           10|      5828| 0.3655130309870722| 0.06525710181466382|46.20689655|  53.7931034|     113|   0|            0.006|\n",
      "|BILLS-113hr1001ih|    113|  hr|     ih|            3|            1|       482| 0.4160595238095238| 0.08928273809523811|46.20689655|  53.7931034|     113|   0|            0.001|\n",
      "|BILLS-113hr1002ih|    113|  hr|     ih|            8|            4|       914| 0.7384660421545669|  0.3741412958626073|46.20689655|  53.7931034|     113|   0|            0.001|\n",
      "|BILLS-113hr1003ih|    113|  hr|     ih|            5|            1|       304|  0.448529411764706|-0.03186274509803...|46.20689655|  53.7931034|     113|   0|              0.0|\n",
      "|BILLS-113hr1004ih|    113|  hr|     ih|            5|            5|      2407|0.28045944940476186| 0.08803013392857144|46.20689655|  53.7931034|     113|   0|            0.003|\n",
      "|BILLS-113hr1005ih|    113|  hr|     ih|            4|            3|       156|0.24940476190476196| 0.03273809523809524|46.20689655|  53.7931034|     113|   0|              0.0|\n",
      "|BILLS-113hr1006ih|    113|  hr|     ih|            2|            2|       359|             0.3925| 0.16583333333333333|46.20689655|  53.7931034|     113|   0|              0.0|\n",
      "|BILLS-113hr1007ih|    113|  hr|     ih|            2|            3|       624| 0.4836950146627567| 0.05337732160312806|46.20689655|  53.7931034|     113|   0|            0.001|\n",
      "|BILLS-113hr1008ih|    113|  hr|     ih|           44|           10|      2051| 0.3813060385941737|  0.2408455621591215|46.20689655|  53.7931034|     113|   0|            0.002|\n",
      "|BILLS-113hr1009ih|    113|  hr|     ih|           13|            3|       813| 0.3778756957328386|0.014955679241393524|46.20689655|  53.7931034|     113|   0|            0.001|\n",
      "| BILLS-113hr100ih|    113|  hr|     ih|            8|           23|      5638| 0.4959002086890532| 0.11747249098842727|46.20689655|  53.7931034|     113|   0|            0.006|\n",
      "|BILLS-113hr1010ih|    113|  hr|     ih|          132|            2|       745| 0.3617676767676768| 0.13858585858585856|46.20689655|  53.7931034|     113|   0|            0.001|\n",
      "|BILLS-113hr1011ih|    113|  hr|     ih|            4|            1|       117| 0.3525252525252525| 0.09612794612794613|46.20689655|  53.7931034|     113|   0|              0.0|\n",
      "|BILLS-113hr1012ih|    113|  hr|     ih|            6|            7|      2750| 0.2723322905804658| 0.03734358706986445|46.20689655|  53.7931034|     113|   0|            0.003|\n",
      "|BILLS-113hr1013ih|    113|  hr|     ih|            2|            2|       303| 0.2858333333333333|-0.01083333333333333|46.20689655|  53.7931034|     113|   0|              0.0|\n",
      "|BILLS-113hr1014ih|    113|  hr|     ih|            1|            1|       145|0.14265734265734267|-0.04463869463869463|46.20689655|  53.7931034|     113|   0|              0.0|\n",
      "|BILLS-113hr1015ih|    113|  hr|     ih|            2|            4|       907|0.28556033168709216|-0.01603713188220...|46.20689655|  53.7931034|     113|   0|            0.001|\n",
      "|BILLS-113hr1016ih|    113|  hr|     ih|            3|            3|       639| 0.4267813852813851| 0.08960822510822512|46.20689655|  53.7931034|     113|   0|            0.001|\n",
      "|BILLS-113hr1017ih|    113|  hr|     ih|            1|            3|       306| 0.4024844720496894|0.036594202898550726|46.20689655|  53.7931034|     113|   0|              0.0|\n",
      "|BILLS-113hr1018ih|    113|  hr|     ih|            6|            1|       465| 0.4905714285714286| 0.20904761904761904|46.20689655|  53.7931034|     113|   0|              0.0|\n",
      "+-----------------+-------+----+-------+-------------+-------------+----------+-------------------+--------------------+-----------+------------+--------+----+-----------------+\n",
      "only showing top 20 rows"
     ]
    }
   ],
   "source": [
    "#view new spark dataframe with the scaled word_count column\n",
    "sdf.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "10fc91c5cd214dd18a7175e4af75bfbf",
       "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": [
      "+-----------------+-------+----+-------+-------------+-------------+----------+-------------------+--------------------+-----------+------------+--------+----+-----------------+--------------------+\n",
      "|       Unnamed: 0|session|type|outcome|sponsor_count|section_count|word_count|       subjectivity|            polarity| % Democrat|% Republican|congress|pass|word_count_scaled|            features|\n",
      "+-----------------+-------+----+-------+-------------+-------------+----------+-------------------+--------------------+-----------+------------+--------+----+-----------------+--------------------+\n",
      "|BILLS-113hr1000ih|    113|  hr|     ih|            1|           10|      5828| 0.3655130309870722| 0.06525710181466382|46.20689655|  53.7931034|     113|   0|            0.006|[1.0,10.0,0.006,0...|\n",
      "|BILLS-113hr1001ih|    113|  hr|     ih|            3|            1|       482| 0.4160595238095238| 0.08928273809523811|46.20689655|  53.7931034|     113|   0|            0.001|[3.0,1.0,0.001,0....|\n",
      "|BILLS-113hr1002ih|    113|  hr|     ih|            8|            4|       914| 0.7384660421545669|  0.3741412958626073|46.20689655|  53.7931034|     113|   0|            0.001|[8.0,4.0,0.001,0....|\n",
      "|BILLS-113hr1003ih|    113|  hr|     ih|            5|            1|       304|  0.448529411764706|-0.03186274509803...|46.20689655|  53.7931034|     113|   0|              0.0|[5.0,1.0,0.0,0.44...|\n",
      "|BILLS-113hr1004ih|    113|  hr|     ih|            5|            5|      2407|0.28045944940476186| 0.08803013392857144|46.20689655|  53.7931034|     113|   0|            0.003|[5.0,5.0,0.003,0....|\n",
      "|BILLS-113hr1005ih|    113|  hr|     ih|            4|            3|       156|0.24940476190476196| 0.03273809523809524|46.20689655|  53.7931034|     113|   0|              0.0|[4.0,3.0,0.0,0.24...|\n",
      "|BILLS-113hr1006ih|    113|  hr|     ih|            2|            2|       359|             0.3925| 0.16583333333333333|46.20689655|  53.7931034|     113|   0|              0.0|[2.0,2.0,0.0,0.39...|\n",
      "|BILLS-113hr1007ih|    113|  hr|     ih|            2|            3|       624| 0.4836950146627567| 0.05337732160312806|46.20689655|  53.7931034|     113|   0|            0.001|[2.0,3.0,0.001,0....|\n",
      "|BILLS-113hr1008ih|    113|  hr|     ih|           44|           10|      2051| 0.3813060385941737|  0.2408455621591215|46.20689655|  53.7931034|     113|   0|            0.002|[44.0,10.0,0.002,...|\n",
      "|BILLS-113hr1009ih|    113|  hr|     ih|           13|            3|       813| 0.3778756957328386|0.014955679241393524|46.20689655|  53.7931034|     113|   0|            0.001|[13.0,3.0,0.001,0...|\n",
      "| BILLS-113hr100ih|    113|  hr|     ih|            8|           23|      5638| 0.4959002086890532| 0.11747249098842727|46.20689655|  53.7931034|     113|   0|            0.006|[8.0,23.0,0.006,0...|\n",
      "|BILLS-113hr1010ih|    113|  hr|     ih|          132|            2|       745| 0.3617676767676768| 0.13858585858585856|46.20689655|  53.7931034|     113|   0|            0.001|[132.0,2.0,0.001,...|\n",
      "|BILLS-113hr1011ih|    113|  hr|     ih|            4|            1|       117| 0.3525252525252525| 0.09612794612794613|46.20689655|  53.7931034|     113|   0|              0.0|[4.0,1.0,0.0,0.35...|\n",
      "|BILLS-113hr1012ih|    113|  hr|     ih|            6|            7|      2750| 0.2723322905804658| 0.03734358706986445|46.20689655|  53.7931034|     113|   0|            0.003|[6.0,7.0,0.003,0....|\n",
      "|BILLS-113hr1013ih|    113|  hr|     ih|            2|            2|       303| 0.2858333333333333|-0.01083333333333333|46.20689655|  53.7931034|     113|   0|              0.0|[2.0,2.0,0.0,0.28...|\n",
      "|BILLS-113hr1014ih|    113|  hr|     ih|            1|            1|       145|0.14265734265734267|-0.04463869463869463|46.20689655|  53.7931034|     113|   0|              0.0|[1.0,1.0,0.0,0.14...|\n",
      "|BILLS-113hr1015ih|    113|  hr|     ih|            2|            4|       907|0.28556033168709216|-0.01603713188220...|46.20689655|  53.7931034|     113|   0|            0.001|[2.0,4.0,0.001,0....|\n",
      "|BILLS-113hr1016ih|    113|  hr|     ih|            3|            3|       639| 0.4267813852813851| 0.08960822510822512|46.20689655|  53.7931034|     113|   0|            0.001|[3.0,3.0,0.001,0....|\n",
      "|BILLS-113hr1017ih|    113|  hr|     ih|            1|            3|       306| 0.4024844720496894|0.036594202898550726|46.20689655|  53.7931034|     113|   0|              0.0|[1.0,3.0,0.0,0.40...|\n",
      "|BILLS-113hr1018ih|    113|  hr|     ih|            6|            1|       465| 0.4905714285714286| 0.20904761904761904|46.20689655|  53.7931034|     113|   0|              0.0|[6.0,1.0,0.0,0.49...|\n",
      "+-----------------+-------+----+-------+-------------+-------------+----------+-------------------+--------------------+-----------+------------+--------+----+-----------------+--------------------+\n",
      "only showing top 20 rows"
     ]
    }
   ],
   "source": [
    "#use vectorassembler to get our features into the form expected by pyspark.ml\n",
    "from pyspark.ml.feature import VectorAssembler\n",
    "\n",
    "features = ['sponsor_count', 'section_count', 'word_count_scaled', 'subjectivity', 'polarity', '% Democrat', '% Republican']\n",
    "assembler = VectorAssembler(inputCols = features, outputCol = 'features')\n",
    "\n",
    "s_df = assembler.transform(sdf)\n",
    "s_df.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "650ee4cd27f1407db86db05d13cbb06b",
       "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": [
    "#split into training and test datasets\n",
    "train, test = s_df.randomSplit([0.7, 0.3])\n",
    "\n",
    "#create and run model\n",
    "from pyspark.ml.classification import LogisticRegression\n",
    "lr = LogisticRegression(featuresCol='features', labelCol='pass')\n",
    "model = lr.fit(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0d82eb7377de4ba79a34a87638080b91",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Summary of Training Data\n",
    "trainingSummary = model.summary\n",
    "evaluationSummary = model.evaluate(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ed6c27b1130e493ba10991bac3c9b494",
       "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": [
      "Training AUC: 0.937940382960352\n",
      "Test AUC:  0.9362854477995789\n",
      "\n",
      "False positive rate by label (Training):\n",
      "label 0: 0.9903329752953813\n",
      "label 1: 0.000277112986522232\n",
      "\n",
      "True positive rate by label (Training):\n",
      "label 0: 0.9997228870134778\n",
      "label 1: 0.00966702470461869\n",
      "\n",
      "Training Accuracy: 0.977034411460641\n",
      "Test Accuracy:  0.975150992234685"
     ]
    }
   ],
   "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c570543103b44abdbc490e704c48b696",
       "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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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Obtain ROC curve and send to Pandas for plot\n",
    "roc_df = evaluationSummary.roc.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6bdd8dcec20a4f8f867cb307d04fbac4",
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       "version_minor": 0
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      "text/plain": [
       "VBox()"
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     },
     "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Close previous plots; otherwise, will just overwrite and display again\n",
    "plt.close()\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": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4df5e8a334cf48aca5870037a7f76faf",
       "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": [
      "DenseVector([-2.6085, -0.0012, 14.4678, -5.8688, 4.57, 0.0337, 0.0378])"
     ]
    }
   ],
   "source": [
    "#view feature importances\n",
    "model.coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f5eb7dfcf3234c2787c17f4dfbe070a4",
       "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"
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot our model features by importance\n",
    "\n",
    "plt.close() # Close previous plots; otherwise, will just overwrite and display again\n",
    "sns.set(rc={'figure.figsize':(20, 7)})\n",
    "\n",
    "sns.barplot(x=features, y= list(model.coefficients))\n",
    "plt.title('Feature Coefficients from Logistic Regression Model')\n",
    "plt.xlabel('Feature')\n",
    "plt.ylabel('Feature Coefficient')\n",
    "%matplot plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "25789be7ef79439ea8add8e1569c2632",
       "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": [
      "[[1.6951e+04 4.0000e+00]\n",
      " [4.2800e+02 2.0000e+00]]"
     ]
    }
   ],
   "source": [
    "import pyspark.sql.functions as F\n",
    "from pyspark.mllib.evaluation import MulticlassMetrics\n",
    "from pyspark.sql.types import FloatType\n",
    "\n",
    "predictions = model.transform(test)\n",
    "\n",
    "#Create a Confusion Matrix to further evaluate model performance\n",
    "\n",
    "#First create a prediction column and convert to float type, then order by prediction\n",
    "pred_labels = predictions.select(['prediction','pass']).withColumn('label', F.col('pass').cast(FloatType())).orderBy('prediction')\n",
    "\n",
    "pred_labels = pred_labels.select(['prediction','label'])\n",
    "\n",
    "metrics = MulticlassMetrics(pred_labels.rdd.map(tuple))\n",
    "\n",
    "print(metrics.confusionMatrix().toArray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "tp = 2 #true positives\n",
    "fp = 4 #false positives\n",
    "fn = 428 #false negatives\n",
    "tn = 16951 #true negatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score for our model is 0.975150992234685\n",
      "Precision score for our model is 0.3333333333333333\n",
      "Recall score for our model is 0.004651162790697674\n",
      "The F-score for our model is 0.009174311926605505\n"
     ]
    }
   ],
   "source": [
    "#Calculate precision, recall, and accuracy\n",
    "accuracy = (tp+tn)/(tp+tn+fp+fn)\n",
    "precision = tp/(tp+fp)\n",
    "recall = tp/(tp+fn)\n",
    "\n",
    "print('Accuracy score for our model is {}'.format(accuracy))\n",
    "print('Precision score for our model is {}'.format(precision))\n",
    "print('Recall score for our model is {}'.format(recall))\n",
    "print('The F-score for our model is {}'.format(2*recall*precision/(recall+precision)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f58656c0c0fb4b4abfee635cbc8a179a",
       "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": [
    "p_pred_labels = pred_labels.toPandas() #convert our precition dataframe from spark to pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "10d498bce11b456288197d89a1c3f670",
       "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 sklearn\n",
      "Collecting scikit-learn (from sklearn)\n",
      "  Using cached https://files.pythonhosted.org/packages/6a/7e/d41bc4dba43998890dadc6c7893f2d9b6a17bf7c9bb924458d04252ad0e6/scikit_learn-1.0.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl\n",
      "Requirement already satisfied: joblib>=0.11 in /usr/local/lib64/python3.7/site-packages (from scikit-learn->sklearn)\n",
      "Collecting threadpoolctl>=2.0.0 (from scikit-learn->sklearn)\n",
      "  Using cached https://files.pythonhosted.org/packages/ff/fe/8aaca2a0db7fd80f0b2cf8a16a034d3eea8102d58ff9331d2aaf1f06766a/threadpoolctl-3.0.0-py3-none-any.whl\n",
      "Requirement already satisfied: numpy>=1.14.6 in /usr/local/lib64/python3.7/site-packages (from scikit-learn->sklearn)\n",
      "Requirement already satisfied: scipy>=1.1.0 in /mnt/tmp/1639104851065-0/lib/python3.7/site-packages (from scikit-learn->sklearn)\n",
      "Installing collected packages: threadpoolctl, scikit-learn, sklearn\n",
      "Successfully installed scikit-learn-1.0.1 sklearn-0.0 threadpoolctl-3.0.0\n",
      "\n",
      "You are using pip version 9.0.1, however version 21.3.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command."
     ]
    }
   ],
   "source": [
    "sc.install_pypi_package(\"sklearn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9e53ad83b8a94821a61393232956027f",
       "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": [
      "[[16951     4]\n",
      " [  428     2]]"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "#Confusion matrix from pandas df\n",
    "p_cf = confusion_matrix(p_pred_labels['label'], p_pred_labels['prediction'])\n",
    "\n",
    "print(p_cf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e7b3ef7f2a964b34843a6125f219c714",
       "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"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "plt.close()\n",
    "\n",
    "ax = sns.heatmap(p_cf, annot=True, cmap='Blues')\n",
    "\n",
    "ax.set_title('Confusion Matrix of Logistic Regression Model\\n');\n",
    "ax.set_xlabel('\\nPredicted Values')\n",
    "ax.set_ylabel('Actual Values ');\n",
    "\n",
    "## Ticket labels - List must be in alphabetical order\n",
    "ax.xaxis.set_ticklabels(['False','True'])\n",
    "ax.yaxis.set_ticklabels(['False','True'])\n",
    "\n",
    "## Display the visualization of the Confusion Matrix.\n",
    "plt.show()\n",
    "\n",
    "%matplot plt"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}