lineapy-trial-prototype.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Requirement already satisfied: pyparsing>=2.0.2 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from packaging->bleach->nbconvert<7.0.0->lineapy) (2.4.7)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.0.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "! pip install lineapy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas==1.3.2 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (1.3.2)\n",
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      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.0.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "! python -m pip install pandas==1.3.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext lineapy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lineapy_config(home_dir=PosixPath('/Users/jenif/.lineapy'), database_url='sqlite:////Users/jenif/.lineapy/db.sqlite', artifact_storage_dir=PosixPath('/Users/jenif/.lineapy/linea_pickles'), customized_annotation_folder=PosixPath('/Users/jenif/.lineapy/custom-annotations'), do_not_track=False, logging_level='INFO', logging_file=PosixPath('/Users/jenif/.lineapy/lineapy.log'), storage_options=None, mlflow_registry_uri=None, mlflow_tracking_uri=None, default_ml_models_storage_backend=None)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lineapy.options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:17:31.460557Z",
     "start_time": "2019-06-16T21:17:29.395297Z"
    }
   },
   "outputs": [],
   "source": [
    "import lineapy\n",
    "import joblib\n",
    "import json\n",
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import confusion_matrix, f1_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:17:31.485189Z",
     "start_time": "2019-06-16T21:17:31.473720Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get data \n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "data = load_iris(as_frame=True)\n",
    "dataset = data.frame\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: setosa\n",
      "1: versicolor\n",
      "2: virginica\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None, None, None]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print labels for target values \n",
    "\n",
    "[print(f'{target}: {label}') for target, label in zip(data.target.unique(), data.target_names)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:17:32.328046Z",
     "start_time": "2019-06-16T21:17:32.323611Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal_length', 'sepal_width', 'petal_length', 'petal_width']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# feature names\n",
    "\n",
    "dataset.columns = [colname.strip(' (cm)').replace(' ', '_') for colname in dataset.columns.tolist()]\n",
    "\n",
    "feature_names = dataset.columns.tolist()[:4]\n",
    "feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save raw data as artifact\n",
    "dataset_csv = './data/raw/iris.csv'\n",
    "dataset.to_csv(dataset_csv, index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width  target\n",
       "0             5.1          3.5           1.4          0.2       0\n",
       "1             4.9          3.0           1.4          0.2       0\n",
       "2             4.7          3.2           1.3          0.2       0\n",
       "3             4.6          3.1           1.5          0.2       0\n",
       "4             5.0          3.6           1.4          0.2       0\n",
       "..            ...          ...           ...          ...     ...\n",
       "145           6.7          3.0           5.2          2.3       2\n",
       "146           6.3          2.5           5.0          1.9       2\n",
       "147           6.5          3.0           5.2          2.0       2\n",
       "148           6.2          3.4           5.4          2.3       2\n",
       "149           5.9          3.0           5.1          1.8       2\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.3.2\n"
     ]
    }
   ],
   "source": [
    "print(pd.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LineaArtifact(name='iris-raw', _version=4)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#save raw data as artifact to lineapy\n",
    "lineapy.save(dataset, \"iris-raw\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Features engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:21:02.150708Z",
     "start_time": "2019-06-16T21:21:02.144518Z"
    }
   },
   "outputs": [],
   "source": [
    "dataset['sepal_length_to_sepal_width'] = dataset['sepal_length'] / dataset['sepal_width']\n",
    "dataset['petal_length_to_petal_width'] = dataset['petal_length'] / dataset['petal_width']\n",
    "\n",
    "dataset = dataset[[\n",
    "    'sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n",
    "#     'sepal_length_in_square', 'sepal_width_in_square', 'petal_length_in_square', 'petal_width_in_square',\n",
    "    'sepal_length_to_sepal_width', 'petal_length_to_petal_width',\n",
    "    'target'\n",
    "]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:21:02.987144Z",
     "start_time": "2019-06-16T21:21:02.976092Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>sepal_length_to_sepal_width</th>\n",
       "      <th>petal_length_to_petal_width</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.457143</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.633333</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.468750</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.483871</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.388889</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width  \\\n",
       "0           5.1          3.5           1.4          0.2   \n",
       "1           4.9          3.0           1.4          0.2   \n",
       "2           4.7          3.2           1.3          0.2   \n",
       "3           4.6          3.1           1.5          0.2   \n",
       "4           5.0          3.6           1.4          0.2   \n",
       "\n",
       "   sepal_length_to_sepal_width  petal_length_to_petal_width  target  \n",
       "0                     1.457143                          7.0       0  \n",
       "1                     1.633333                          7.0       0  \n",
       "2                     1.468750                          6.5       0  \n",
       "3                     1.483871                          7.5       0  \n",
       "4                     1.388889                          7.0       0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save features\n",
    "features_path = './data/processed/featured_iris.csv'\n",
    "dataset.to_csv(features_path, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LineaArtifact(name='iris-preprocessed', _version=4)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#save features to lineapy\n",
    "lineapy.save(dataset, \"iris-preprocessed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:21:06.361378Z",
     "start_time": "2019-06-16T21:21:06.358647Z"
    }
   },
   "outputs": [],
   "source": [
    "test_size=0.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splittail train/test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:21:07.438133Z",
     "start_time": "2019-06-16T21:21:07.431649Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((120, 7), (30, 7))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset, test_dataset = train_test_split(dataset, test_size=test_size, random_state=42)\n",
    "train_dataset.shape, test_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save train and test sets\n",
    "trainset_path = './data/processed/train_iris.csv'\n",
    "testset_path = './data/processed/test_iris.csv'\n",
    "\n",
    "train_dataset.to_csv(trainset_path)\n",
    "test_dataset.to_csv(testset_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LineaArtifact(name='test-dataset', _version=4)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#save train and test sets to lineapy\n",
    "lineapy.save(train_dataset, \"train-dataset\")\n",
    "lineapy.save(test_dataset, \"test-dataset\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:21:10.932148Z",
     "start_time": "2019-06-16T21:21:10.927844Z"
    }
   },
   "outputs": [],
   "source": [
    "# Get X and Y\n",
    "\n",
    "y_train = train_dataset.loc[:, 'target'].values.astype('int32')\n",
    "X_train = train_dataset.drop('target', axis=1).values.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:21:55.427365Z",
     "start_time": "2019-06-16T21:21:55.416431Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.001, multi_class='multinomial')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an instance of Logistic Regression Classifier CV and fit the data\n",
    "\n",
    "logreg = LogisticRegression(C=0.001, solver='lbfgs', multi_class='multinomial', max_iter=100)\n",
    "logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./models/model.joblib']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_path= './models/model.joblib'\n",
    "joblib.dump(logreg, model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LineaArtifact(name='logreg-model', _version=3)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#save model to lineapy\n",
    "lineapy.save(model_path, \"logreg-model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:21:55.875303Z",
     "start_time": "2019-06-16T21:21:55.864724Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm,\n",
    "                          target_names,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=None,\n",
    "                          normalize=True):\n",
    "    \"\"\"\n",
    "    given a sklearn confusion matrix (cm), make a nice plot\n",
    "\n",
    "    Arguments\n",
    "    ---------\n",
    "    cm:           confusion matrix from sklearn.metrics.confusion_matrix\n",
    "\n",
    "    target_names: given classification classes such as [0, 1, 2]\n",
    "                  the class names, for example: ['high', 'medium', 'low']\n",
    "\n",
    "    title:        the text to display at the top of the matrix\n",
    "\n",
    "    cmap:         the gradient of the values displayed from matplotlib.pyplot.cm\n",
    "                  see http://matplotlib.org/examples/color/colormaps_reference.html\n",
    "                  plt.get_cmap('jet') or plt.cm.Blues\n",
    "\n",
    "    normalize:    If False, plot the raw numbers\n",
    "                  If True, plot the proportions\n",
    "\n",
    "    Usage\n",
    "    -----\n",
    "    plot_confusion_matrix(cm           = cm,                  # confusion matrix created by\n",
    "                                                              # sklearn.metrics.confusion_matrix\n",
    "                          normalize    = True,                # show proportions\n",
    "                          target_names = y_labels_vals,       # list of names of the classes\n",
    "                          title        = best_estimator_name) # title of graph\n",
    "\n",
    "    Citiation\n",
    "    ---------\n",
    "    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    accuracy = np.trace(cm) / float(np.sum(cm))\n",
    "    misclass = 1 - accuracy\n",
    "\n",
    "    if cmap is None:\n",
    "        cmap = plt.get_cmap('Blues')\n",
    "\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "\n",
    "    if target_names is not None:\n",
    "        tick_marks = np.arange(len(target_names))\n",
    "        plt.xticks(tick_marks, target_names, rotation=45)\n",
    "        plt.yticks(tick_marks, target_names)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "    thresh = cm.max() / 1.5 if normalize else cm.max() / 2\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        if normalize:\n",
    "            plt.text(j, i, \"{:0.4f}\".format(cm[i, j]),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "        else:\n",
    "            plt.text(j, i, \"{:,}\".format(cm[i, j]),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label\\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))\n",
    "    \n",
    "    return plt.gcf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:21:56.090756Z",
     "start_time": "2019-06-16T21:21:56.086966Z"
    }
   },
   "outputs": [],
   "source": [
    "# Get X and Y\n",
    "\n",
    "y_test = test_dataset.loc[:, 'target'].values.astype('int32')\n",
    "X_test = test_dataset.drop('target', axis=1).values.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:21:56.270245Z",
     "start_time": "2019-06-16T21:21:56.265054Z"
    }
   },
   "outputs": [],
   "source": [
    "prediction = logreg.predict(X_test)\n",
    "cm = confusion_matrix(prediction, y_test)\n",
    "f1 = f1_score(y_true = y_test, y_pred = prediction, average='macro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:21:56.493617Z",
     "start_time": "2019-06-16T21:21:56.489929Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9305555555555555"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# f1 score value\n",
    "f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save metrics\n",
    "metrics_file = './reports/metrics.json'\n",
    "\n",
    "metrics = {\n",
    "    'f1': f1\n",
    "}\n",
    "\n",
    "with open(metrics_file, 'w') as mf:\n",
    "    json.dump(\n",
    "        obj=metrics,\n",
    "        fp=mf,\n",
    "        indent=4\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bmVl1FTZ63H3aZmZmhXClbWZmlaXCLhjipG1mZpVWUM520jYzs2rrU1DWdp+2mZlZIVxpm5lZpRVUaDtpm5lZdUk+T9vMzKwYfcrJ2e7TNjMzK4UrbTMzqzQ3j5uZmRWioJztpG1mZtUl0qxopXCftpmZWSFcaZuZWaWVNHrcSdvMzKpLZV0wxM3jZmZmhXClbWZmlVZQoe2kbWZm1SXKusqXk7aZmVVaQTnbfdpmZmalcKVtZmaVVtLocSdtMzOrrHRpzkZH0XGtJm1J5wHR2vqIOLpbIjIzM+tBc8pAtFE9FoWZmZm1q9WkHRGX1z6WNF9ETO3+kMzMzHpOOXV2B0aPS9pY0pPAU/nxOpJ+2e2RmZmZ9QDlqUw7c+vAMRaWdIOkpyU9JWnjemLtyEC0c4BtgVsBIuIxSZvXczAzM7PeJE2u0iOHOhe4KyL2kNQfmLeenXRo9HhEvNTsl8SMeg5mZmZWNZIWBDYHDgCIiA+BD+vZV0cmV3lJ0iZASOov6XhyU7mZmVnRuqBpvAPN48sDrwOXSnpU0sWS5qsn3I4k7cOAI4EhwERg3fzYzMyseE3nanfmBgyUNKrmdkjNIfoBnwZ+FRHrAVOB79YTa7vN4xExGdinnp2bmZn1dl00I9rkiBjWyroJwISI+Ed+fAN1Ju2OjB5fXtJtkl6X9JqkWyQtX8/BzMzMqiYiXiF1Na+SF20FPFnPvjoyEO3/gAuA3fPjvYFrgM/Uc0AzM7PeogdHj38DuDqPHP8XcGA9O+lI0lZEXFnz+CpJR9VzMDMzs96mJy4YEhFjgNaazzusrbnHF813/yzpu8AI0lzkw4E7OntgMzOz3qCkGdHaqrQfISXpptdzaM26AE7vrqDMzMxsVm3NPb5cTwZiZmbW06SyrvLVkfO0kbSmpL0kfbXp1t2BWePcc/ddrL3GKqyx6or89CdnNjoc64SlFxnAxfus88ntjsM/wx7rLdXosKxO/tvsHl10nnaPaHcgmqSTgS2A1YE7ge2B+4ErujUya4gZM2bwraOP5I7f/4EhQ4ey2UYbsNNOu7Da6qs3OjSrw0tTpnHw1Y8BaYTsDQdvwN/Gv9ngqKwe/tvsPj0xEK2rdKTS3oN0TtkrEXEgsA4wd7dGZQ0z8uGHWWGFFVlu+eXp378/ew7fm9tvu6XRYVkX+PTSCzPxrfd59Z0PGh2K1cF/mwYdS9rTIuJjYHqe9Pw10jyqNgeaNGkiQ4cu/cnjIUOGMnHixAZGZF1ly1UG8qdnXm90GFYn/212n5KaxzuStEdJWhj4DWlE+Wjg4e4MqiWSTpO0dR3P20LS7d0R05woImZZVlLTkbWsXx+x6fKLct9zbzQ6FKuT/za7hxB91PlbT+nI3ONH5LsXSroLWDAixnZHMEr/ApUr++ZxnNQdx2whhn4RMb0njtUbDRkylAkTXvrk8cSJExg8eHADI7Ku8JllF+HZ195lynsfNToUq5P/Ng3aqLQlfbr5DVgU6Jfvt0rSjyUdUfP4FEnHSfq2pJGSxko6Na9bVtJTkn5JquKXlnSZpCckPS7pmLzdZZL2yPc3kPR3SY9JeljSApLmkXRpfs6jkj7fQlyLSvpdPv5Dktauie8iSfdQ8QF2wzbYgPHjn+OF55/nww8/5PprR7DjTrs0OizrpK1WGci9z0xudBjWCf7b7CZd0DTeW0aP/6yNdQFs2cb6EcA5wC/z472AM4HNgA1JE7bcKmlz4N/AKsCBEXGEpPWBIRGxJkBumv9Enrf1WmB4RIzM/ezTgG8CRMRaklYF7pG0crO4TgUejYjdJG1JStDr5nXrA5tFxLQ2Xtccr1+/fvz83PPZecdtmTFjBvsfcBCrr7FGo8OyTpi7Xx/WX2ZhfnbvPxsdinWC/za7T0ndDG1NrjJLpdpREfGopMUlDQYGAVOAtYEvAI/mzeYHViIl7Rcj4qG8/F/A8pLOI02Xek+z3a8CvBwRI/Ox3gaQtBlwXl72tKQXgeZJezPgS3mbP0laTNJCed2tbSXsfG3UQwCWXmaZDr8XJdpu+x3YbvsdGh2GdZEPpn/Mrr/u8WEo1g38t9k9OjRhSS/RnbHeQDpdbDip8hbwo4hYN99WjIhL8rZTm54UEVNIp5XdBxwJXNxsvyJV+s115KdSS9s07WtqC+v+u1HERRExLCKGDRo4qAOHMjMz61rdmbRHkC7juQcpgd8NHCRpfgBJQyQt3vxJkgYCfSLiRuB/geb9508DgyVtkLdfQFI/4K/APnnZysAywDPNnlu7zRaki5a/3elXamZmRRKpebyzt57SkUtz1iUixklaAJgYES8DL0taDXgwv8B3gX2BGc2eOgS4VFLTD4oTmu33Q0nDgfMkDSD1Z29N6j+/UNLjwHTggIj4oNmbeUre91jgPWD/LnvBZmZWpB66nnaX6Mg0piJVp8tHxGmSlgGWjIh2O8kiYq1mj88Fzm1h0zVrtnmMWatrIuKAmvsjgY1a2M8BzRdExH2kpnYi4k1g1xa2OaWl+M3MbM5XUtLuSPP4L4GNgS/nx+8AF3RbRGZmZtaijjSPfyYiPi3pUUgDxfJpV2ZmZkVL51mXU2p3JGl/JKkveZS1pEHALDOWmZmZlaik5vGOJO1fADcDi0v6IWk0+IndGpWZmVkPKajQ7tDc41dLeoR0eU4Bu0XEU90emZmZmc2kI6PHlyGdHnVb7bKI+Hd3BmZmZtbdBD16la7O6kjz+B2k/mwB8wDLkSYt8aS3ZmZWvJKmMe1I8/hM51rnK3wd2m0RmZmZ9aCCCu3Z/4EREaOBDbohFjMzM2tDR/q0j6152Ic0W9nr3RaRmZlZD5E0x/VpL1Bzfzqpj/vG7gnHzMysZxWUs9tO2nlSlfkj4ts9FI+ZmVmPKmlylVb7tCX1i4gZtHDxDjMzM+t5bVXaD5MS9hhJtwLXA1ObVkbETd0cm5mZWbeaE8/TXhR4A9iS/56vHYCTtpmZFa+gnN1m0l48jxx/gv8m6ybRrVGZmZn1BJXVp91W0u4LzM/MybqJk7aZmVkPaytpvxwRp/VYJGZmZg2gFmvT3qmtpF3OqzAzM6tDGojW6Cg6rq1pTLfqsSjMzMysXa1W2hHxZk8GYmZm1gglVdodOeXLzMxsjqWCzvly0jYzs8qak/q0zczMrBdxpW1mZtWlOWdGNDMzsznenDb3uJmZ2RzJfdpmZmbWLVxpm5lZpRXUOu6kbWZmVSb6FDRrt5O2mZlVliir0naftpmZWSFcaZuZWXWprNHjTtpmZlZpPk/bzMysAO7TNjMzs27hStvMzCrNzeNmZmaFKChnO2mbmVl1iZ7rJ5bUFxgFTIyInerZh/u0zczMesY3gac6swMnbTMzqy6BpE7f2j2MNBTYEbi4M+G6edzMzCqth7q0zwH+B1igMztxpW1mZtZ5AyWNqrkd0rRC0k7AaxHxSGcP4krbzMwqS3TZKV+TI2JYK+s2BXaRtAMwD7CgpKsiYt/ZPYgrbTMzqzR1wa0tEXFCRAyNiGWBvYE/1ZOwwZW2mZlVnM/TNjMzs1lExH3AffU+30nbzMwqrGOnbPUWTtpmZlZZPTkjWldw0jYzs0orqdIu6QeGmZlZpbnSNjOzSiunznbStoo7f491Gh2CdaFFNjiq0SFYaVRW87iTtpmZVVZpA9FKitXMzKzSXGmbmVmluXnczMysEOWkbCdtMzOruIIKbfdpm5mZlcKVtpmZVVYaPV5Oqe2kbWZmlVZS87iTtpmZVZhQQZW2+7TNzMwK4UrbzMwqzc3jZmZmBfBANDMzs1KorErbfdpmZmaFcKVtZmaVVlKl7aRtZmaV5lO+zMzMrMu50jYzs8oS0KecQttJ28zMqq2k5nEnbTMzq7SSBqK5T9vMzKwQrrTNzKzS3DxuZmZWAA9EMzMzK4YvzWlmZmbdwJW2mZlVV2EXDHHSNjOzSisoZztpm5lZdaWBaOWkbfdpm5mZFcKVtpmZVVo5dbaTtpmZVV1BWdtJ28zMKs3naZuZmVmXc6VtZmaVVtDgcSdtMzOrtoJytpO2mZlVXEFZ233aZmZmhXClbWZmlSXKGj3upG1mZtXlC4aYmZmVo6Cc7T5tMzOzUrjSNjOzaiuo1HbSNjOzClNRA9HcPG5mZlYIV9pmZlZpHj1uZmZWAFFUl7aTtpmZVVxBWdt92mZmZoVw0jYzs0pTF/zX5v6lpSX9WdJTksZJ+ma9sTpp2yzuufsu1l5jFdZYdUV++pMzGx2O1emVSRM4aK8d2OXz67PbVhtw1SW/bHRINpsuPHkfXrz3R4y6/nufLPvi1uvxyA3fZ+ojv+DTqy/TwOjmHFLnb+2YDhwXEasBGwFHSlq9nlidtG0mM2bM4FtHH8ktt/2eR8c+yfUjruGpJ59sdFhWh759+3H8/57BrX9+hKtv+RMjLr+Ifz77dKPDstlw5W0PseuRF8y0bNw/J7H3cb/h/tH/bFBUcx51wa0tEfFyRIzO998BngKG1BOrk7bNZOTDD7PCCiuy3PLL079/f/Ycvje333ZLo8OyOgxaYklWX2tdAOabfwGWW3EVXn1lUmODstnywOh/8uZb78207JnnX+W5F19rUETWWZKWBdYD/lHP8520bSaTJk1k6NClP3k8ZMhQJk6c2MCIrCtMfOlFnh43lrXXG9boUMx6l64os1OpPVDSqJrbIbMcSpofuBH4VkS8XU+4DU/akgZLuqGO590paeF2tjlN0tZ1B1dBETHLMpU084DN4r2p73LMofvynVPOZP4FFmx0OGa9ThcNRJscEcNqbhfNdAxpLlLCvjoibqo31oafpx0Rk4A9mi+X1C8iprfxvB06sO+TOhle5QwZMpQJE1765PHEiRMYPHhwAyOyzvjoo4845pB92XG3vdh6+10bHY5ZryO6f0Y0pcrnEuCpiDi7M/vq0Upb0o8lHVHz+BRJx0l6Ij8+QNL1km4D7pE0r6TrJI2VdK2kf0galrd9QdJAScvmYfS/yUPp75E0IG9zmaQ98v0NJP1d0mOSHpa0QH7u3ySNzrdNevL96I2GbbAB48c/xwvPP8+HH37I9deOYMeddml0WFaHiODkbx/J8iutwv6HfKPR4ZhV2abAfsCWksbkW7uFZ0t6utIeAZwDNJ17shdwGHBgzTYbA2tHxJuSjgemRMTaktYExrSy35WAL0fE1yVdB3wJuKpppaT+wLXA8IgYKWlBYBrwGrBNRLwvaSXgGqDSnX79+vXj5+eez847bsuMGTPY/4CDWH2NNRodltXh0ZEPctuN17DSqmuwx7bp9+jR3zmZzbfctsGRWUdd/qMD+Oz6KzFw4fkZf9fpnH7hnUx5aypnf2dPBi4yPzf94jDGPjORXZqNMLfZ090dgBFxf1cdpkeTdkQ8KmlxSYOBQcAU4N/NNvtDRLyZ728GnJuf+4Sksa3s+vmIGJPvPwIs22z9KsDLETEy7+ttAEnzAedLWheYAazcWux5UMEhAEsvM2efG7nd9juw3fZ1/Qi0XuTTG27C4y+90+gwrBP2P+GyFpff+ufWvgqtLgUN22lEn/YNpD7sJUmVd3NTa+539K38oOb+DGBAs/UCZh1hBccArwLrkLoK3m/tAHlQwUUA668/rKV9mZlZgXw97baNAPYmJe72Ro3fT2pCJ88es1adx3waGCxpg7yvBST1AxYiVeAfk/ob+ta5fzMzs27X40k7IsYBCwATI+Lldjb/JTAoN4t/BxgLvFXHMT8EhgPnSXoM+AMwT97//pIeIjWNT219L2ZmNifqgWlMu0xDTvmKiLVq7r8ArJnvXwZcVrPp+8C+eaDYCsC9wIt522XzNpObnp+Xn1Vz/4Ca+yNJc77Weg5Yu+bxCXW9IDMzK1Y5jeO94DztdswL/DmflC7g8Fw1m5mZdY2CsnavTtp5YvVKn4JlZmbWpFcnbTMzs+6Upg4vp9R20jYzs+rq4YFkndXwC4aYmZlZx7jSNjOzSiuo0HbSNjOziisoaztpm5lZhamogWju0zYzMyuEK20zM6u0kkaPO2mbmVlliaK6tJ20zcys4grK2u7TNjMzK4QrbTMzq7SSRo87aZuZWaV5IJqZmVkhCsrZ7tM2MzMrhSttMzOrrsKu8uWkbWZmFVdO1nbSNjOzyhJlVdru0zYzMyuEK20zM6u0ggptJ20zM6u2kprHnbTNzKzSSpoRzX3aZmZmhXClbWZm1VZOoe2kbWZm1VZQznbSNjOz6lJhM6K5T9vMzKwQrrTNzKzSSho97qRtZmbVVk7OdvO4mZlZKVxpm5lZpRVUaDtpm5lZtZU0etxJ28zMKkxFDURzn7aZmVkhXGmbmVllibKax11pm5mZFcKVtpmZVZorbTMzM+tyrrTNzKzSSho97qRtZmbVVdhVvpy0zcysskRZM6K5T9vMzKwQrrTNzKzaCiq1nbTNzKzSPBDNzMysECUNRHOftpmZWSFcaZuZWaUVVGi70jYzs4pTF9zaO4S0naRnJI2X9N16Q3WlbWZmldbdA9Ek9QUuALYBJgAjJd0aEU/O7r5caZuZmXWvDYHxEfGviPgQGAHsWs+OXGmbmVll9dD1tIcAL9U8ngB8pp4dOWnXYfToRyYPmEsvNjqOHjAQmNzoIKxL+LOcc1Tls/xUTxxk9OhH7h4wlwZ2wa7mkTSq5vFFEXFRvt/Sz4Ko5yBO2nWIiEGNjqEnSBoVEcMaHYd1nj/LOYc/y64VEdv1wGEmAEvXPB4KTKpnR+7TNjMz614jgZUkLSepP7A3cGs9O3KlbWZm1o0iYrqko4C7gb7AbyNiXD37ctK2tlzU/iZWCH+Wcw5/lgWKiDuBOzu7H0XU1RduZmZmPcx92mZmZoVw0jYzMyuEk7aZzUQq6UKFZtXipG1mn5CkyANdJO0nabNGx2Szr6UfXv4xNmdw0ra6SPKZB3OgmoS9Helc0mcaG5HNrqYfXpI+L2m4pC9D+myduMvnpG2zTdIRwCWSTpX02UbHY11L0obAQcBjEfF6XuYv+0Lk5LwjcB7wAXCupO/XrPNnWTAnbZstko4E9gTOJ1255gxJOzc2KuuMFr7E3wD+DawtaVPwl31JJC0FHA98CZgBvAgcI+mn8N/WFCuTmzitwyQtCCwC7EKqxAAuB74t6eOIuKNhwVldmvVh70S6iMEbwMnACcDO+bN90F/2vVdNk/i8EfGypP2AQcBpEbFebj15SNK0iDipweFaJ7jStg6RtG5EvE1qchtMStxfJM2f2xc4UtJ8rsaKIwBJhwFnAMOAm4DdgXNJzatfyV/61gvVJOydgBGSFoiICcACwEN5s36k1rH7GxWndQ0nbWuXpG8Cp0kaGhFvkf7dTCNVZVsAo4ADImKqq7EySFo1f9l/LGkwadDZVyLiVGA74HRgU+BXwMvA842L1trSNOgM+CFwXkS8k1d9BCwi6RfAlcDVEXGPf1iXzdOYWpsk7Qp8D9g2Iv4jacmIeEXSr4GlgNWB3SLiiYYGah0maX7gHOBj4ND8pX8ZqRJ7NCJmSPoisENEHCypf0R82LiIrTlJSwBLRsRj+fGhwIyIuFjS3BHxQV7+WdLf6RsRcW/jIrau4krbWiSp6d/Gp4DRwIqSTgNul/T3iDgUOAzYyAm7OO+REvQMUvIGmAgcByyUHy8GzJ3/HXzU0wFau/YCpkmaN1fOiwBNp3Y1JeyNgX9HxHVO2HMOV9rWIkmLRMQUSYsA15G+4C8H7gAuBs6IiDENDNFmU7NBZ32A1YBvAxMj4vuSLgSWBN4BVgUO9A+y3ikn6iWAE0lN308AvwCmAscCnwEuA74WEX9tUJjWDZy0bRaSDgF2BV4AxkTEb2rW7Qr8CNgqIl5uTIQ2u5ol7OVIXaEvSFqd9CX/SkScKGkNYAjwbES80LiIrSXNPsd5Sf3YfUk/rCcDpwEL59tpEXF7YyK17uKkbTOR9CXgFNIpXSuTBpq9QfpF/0XgVGBPV2BlknQMMJzUNTYO+AEwL/AtYDpwmAcT9m55JP98pAGg00in5y0IXBERj0haCJg7Il6rTfI2Z3CfdsW1MJJ0QeDiiBhJOvXnV6RTvJYG/gJs54RdptzHORzYBtiElKSPjYjHgZ8D75OaXK2Xafo7lbQ5MII0OPRMYDPSj+wpwFGSNo2ItyLiNfBEKnMiJ+0KkzQXsHW+f5SkLUh//EdKWi0ipkXEaNIgl4ER8WpEvNiwgG22tPCD7F3SgLO5ImI6aSDhZyR9Lf8Q+3ZEvNLTcVrrmj7DPMJ/E+DrwLak7qtnSLOebU46RW8i8J/GRGo9xUm72voCu0t6ADgUeCEifgdcCJwnaQtJewKLA5MaF6bNrmZ9n/tLWpvUlPoBaXrShSJiBqk15X0An9bVu+TpSC+U1Dcv2hzYB1goIt4DbgSezcu2iIgTI2JcY6K1nuJpTCssIt6XNAL4Aqnp+yWlq3f9mtR0ejzpS/7rETGxcZHa7KpJ2EcChwDDI2K8pD8BRwPPSfqAdOrQro2L1FqTpyM9B/iUpCkRcaakgaREvkceSHgz6XvcLSQV4YFoFZa/AOYiJegfk5pPz8iTp8wbEe9JmisifJ5uISQtBrwVEdNzpTYC+Gptt4akL5BGiK8MXBoRzzYmWmuNpL65JQRJlwDrkc7YmCLpFNK4hP3zD7FPJlOxOZ+TdkXlCmxHYDzwFHAF6Tzs8aTJNHYnDVZ6x4NZyiBpRVLlfDbwIWmClNuAL0TE200zm0kaGBGTGxmrtS9/nm9FxOv5HPrVSLMPTpH0I1IL2WbABxHxcSNjtZ7jPu0KkrQ36fKahwCLAp+LiKnAwaSJNRYlzUP9thN2OSJiPGm0/2rANpGuhf0Y8HNJ/XLCPgi4UtI8noO696kZJb4h6dzr/5O0YEQcRuq/vkHSohFxArB3HizqhF0hrrQrJs87/QXSNXbXB/YgzTE9XdJyEfF8/oKf3tBArcNqRxjnx6cCywKXkC728Q3gs6Sqe2dgP5+213vl7osTSK1fRwNjgOMi4k1JV5OmFt7Cf6PV5KRdIZKOAOYmjRb+MfBwRDSd8vV1YEXgJPePlaPZKPHdgVcj4u+STiSdX38j8GdSy8p7wNMR8VzDArZ25Yu3PBwRv5Q0N3AVaQKcvSPiHaXL5I5pZIzWOB49XhH5KkAHALtHxERJywKrS1oG2Il0ytdXnLDLUpOwjyVdMOKrefkPJB1PuuSmgJs8oLB3k7QD0J90gZ4FlK6L/U7+sf0ocBLpXPoxnumsutynXQGSBgDbA/8LfCDpcNJgs3VJ01huQUrYPsezELX90ZLWJHVzbEI6lWtrSftHxFmk+eN3Ip0lYL2UpHWBo0jdVs8AGwLrS1qANLnRn4AdcouYZzqrMFfaFRAR0yTdSbrQxwTSl8KLwDWkeYs/cv9YOZo1ie9IGv0/iXR61yukqUgXk7RYRPwwjxZ/r3ERW3P5oi3rRsTN+dS8bwEfR8Sjef3KpIGh3yRdcW0XYCvSvAlWYe7TrghJ8wBrAf/MA1r2IX0p7Ogv9DJJ2gb4Pmk+8cWBrwG/joinJB0IDI2I0xsZo7VM0vqkls6ncxP4QaQuqosi4pK8zUqkFpJ3gDWAs0gX63mqQWFbL+CkXTFK11E+kPTL/sseRVwmSRsBNwPfiohrm637GnAkaZS4uzx6qVxt3wX8OCJ+K2k/0lSlf4uIK2q2WwK4Fjg6IsY2JlrrLdw8Xj3zAB8De/kXezlaGHg0mjT17MmSfhcRH+SxC8sA25Fmy3LC7qUkLQ58hXQWx8GSPo6IyyR9TOq7VkRcDhARr0raPiKmNTJm6x1caVeQR56WpVkf9rakaymPASYDZwArkc4KeE9Sf6Cvv+B7t3wRkGuAl0gzEV4KnB0RV0v6KvBopEumms3ESdusEPkUrp2BUcDGpAk4/kGq1jYiTbjhZN2LSRoMDIiIf+a5/y8FfgIEcBlwWm3TuFlzPuXLrACSVgfWjIjPka6b/DZwP+nUvROAvwGDGhehtUfSfMD3gJ/lgYJNn+HSEXE/aSDa8w0M0QrgStusl5O0AbApaWrSxUnn7e4SER9J2gv4Y0S82cAQrYMkLQisTbqoy83A50jdGztExDN5G3dfWatcaZv1YnkSlc+RkvZE0jnYx+aEfQBpwpx5GhehzY58EZ77gd2AkaSLgCxBGqfQtI0TtrXKlbZZL1VzTfN+wO+BKaTJcZYHXiMl8r08Srxsklb2Nc2to5y0zXohSVuSKuyREXF7nkhlTdJ5vYuTLp86OiJebGCY1gmS+tReVtPN4tYRPk/brHd6gVRR/yTPjDUd2BV4ICL+0sjArGs0vw62E7Z1hCtts14sz0G9N+mSqicA1wP7AtP9JW9WPU7aZr1cvqaygOOB69z/aVZdTtpmvZz7Os2siZO2mZlZIXyetpmZWSGctM3MzArhpG1mZlYIJ20zM7NCOGmbmZkVwknbrBtImiFpjKQnJF0vad5O7OsySXvk+xfny3S2tu0Wkjap4xgv5Os7d2h5s23enc1jnZKvDW5ms8lJ26x7TIuIdSNiTeBD4LDalZL61rPTiDg4Ip5sY5MtgNlO2mZWBidts+73N2DFXAX/WdL/AY9L6ivpp5JGShor6VBIk6lIOl/Sk5LuIF0ghLzuPknD8v3tJI2W9JikeyUtS/pxcEyu8j8raZCkG/MxRkraND93MUn3SHpU0q9JM661SdLvJD0iaZykQ5qt+1mO5V5Jg/KyFSTdlZ/zN0mrdsm7aVZhvmCIWTfKl9XcnnR1LoANgTUj4vmc+N6KiA3yVKUPSLoHWA9YBViLdK3lJ4HfNtvvIOA3wOZ5X4tGxJuSLgTejYiz8nb/B/w8Iu6XtAxwN7AacDJwf0ScJmlHYKYk3IqD8jEGACMl3RgRb5CuBT06Io6TdFLe91HARcBhEfGcpM8AvwS2rONtNLPMSdusewyQNCbf/xtwCanZ+uGIeD4v/wKwdlN/NbAQsBKwOXBNRMwAJkn6Uwv73wj4a9O+IuLNVuLYGlhd+qSQXlDSAvkYX8zPvUPSlA68pqMl7Z7vL51jfQP4GLg2L78KuEnS/Pn1Xl9z7Lk7cAwza4OTtln3mBYR69YuyMlrau0i4BsRcXez7XYA2ptfWB3YBlIX2MYRMa2FWDo8h7GkLUg/ADaOiPck3QfM08rmkY/7n+bvgZl1jvu0zRrnbuBwSXNBugynpPmAvwJ75z7vpYDPt/DcB4HPSVouP3fRvPwdYIGa7e4hNVWTt1s33/0rsE9etj2wSDuxLgRMyQl7VVKl36QP0NRa8BVSs/vbwPOS9szHkKR12jmGmbXDSduscS4m9VePlvQE8GtS69fNwHPA48CvgL80f2JEvE7qh75J0mP8t3n6NmD3poFowNHAsDzQ7Un+O4r9VGBzSaNJzfT/bifWu4B+ksYCpwMP1aybCqwh6RFSn/Vpefk+wNdyfOOAXTvwnphZG3yVLzMzs0K40jYzMyuEk7aZmVkhnLTNupikuSVdK2m8pH/kSU9a2m547mseJ+knNcsPk/R47pe+v2naUkmfyhOVjMnPOazmOZfkSVbGSrohn3LVFa9lF0nfreN5n0wC0xMkrZ/fs/GSfqGa88yabXdC3uYZSdvWLO8v6SJJz0p6WtKXatbtpTTRzbh83juSPp8/h6bb+5J26/YXapXnPm2rBEn9ImJ6Dx3rCGDtiDhM0t7A7hExvNk2iwGPAutHxOuSLgeuiIh7JS2YR18jaRfgiIjYTlJ/0t/sBzkpPwFsEhGTmj3nbOC1iDizJ15vS/IpYcdHxKgeOt7DwDdJA+TuBH4REb9vts3qwDWkCW4GA38EVo6IGZJOBfpGxImS+gCLRsRkSSsB1wFbRsQUSYtHxGvN9rsoMB4YGhHvdfNLtYpzpW0NpVamxlSzKTrzsvklXZorqrFN1ZBqLlghaQ9Jl+X7l0k6W9KfgR9L2lDS35Wm7vy7pFXydn0lnVWz329I2krSzTX73UbSTR18WbsCl+f7NwBbtVD5LQ88m0eBQ0ogXwJoSr7ZfOTzqSPiw4j4IC+fm5q/35qELWBA03MkDZN0cfMAJS2bK8qLlS5qcrWkrSU9IOk5SRvm7Q6QdH6+v2fe9jFJf23tvWvhWL+SNCp/xqfWLD8zV7BjJZ3V2jHao3Ra3IIR8WCkKuQKYLcWNt0VGBERH+RJacaTEjjAQcCP8nv5cURMzsu/DlwQEVPyuteY1R7A752wrSd4chVrtFmmxiQlo5mm6Mzb/i9p2s+1ACS1d24xwMrA1rmaWjDvc7qkrYEzSInyEGA5YL28blFgCnCBpEE5sR4IXJqPey1pmtHmzo6IK4AhwEsAeX9vAYsBk2u2HQ+sqtR0PoGUZPo3rZR0JHBsXrZlzfKlgTuAFYFvR8SkmnWXAjuQTiM7Lh9/FHBwK+/NisCe+fWPJJ1jvRmwC/A9Zk18JwHbRsRESQvnZS29d819P3/GfYF7Ja2dX/PuwKoRETX7m+UY+cfVtS3sF9IFUobk/TWZkJc1N4SZT1WbAAypOfbpSpPI/BM4KiJeJf37QdIDQF/glIi4i5ntDZzdSnxmXcpJ2xqtpakxB9HyFJ1bk74gycs7MvXm9Xk6UEgThFyemzwDmKtmvxc2NZ83HU/SlcC+ORluDHw1r5+pqbsFLfWnztQPlZtaDyclo4+Bv5Oq76b1F5B+NHwFOBHYPy9/iTT16WDgd5JuyMmFiDgwJ8bzgOHkHxlteD4iHs+vdRxwb06gjwPLtrD9A8Blkq4DmlodWnzvmtkrt6L0A5YCVif9sHgfuFjpoii3t3aMiHgGWLe1F9FCKwa0PNtba9v1A4YCD0TEsZKOBc4C9svrViL9OBgK/E3SmhHxn3zspUhzxN89667Nup6bx61hNPPUmOuQ+njnofUpOltbXrus+dSatdOGng78OV8uc+eabVvb76XAvsCXScl/eo77Ws08CKnp9tX8vAmkHyBNFwxZCJglmUXEbRHxmYjYGHiGNKFKcyNooak3V9jjgM82Wz6D9EPgS82f04IPau5/XPP4Y1r4QR8Rh5F+QCwNjFHql29zOlWlGduOB7aKiLVJrQTz5PdyQ+DG/Pruau0YklZp5f0ek6vkCaSE2mQoMIlZffK5NNvuDeA90qQ2ANcDn655zi0R8VH+EfkMKYk32Qu4OSI+au09MOtKTtrWSK1NjdnaFJ3Np+Rsah5/VdJqSgOImqr21o43Md8/oGb5PcBhOcF+crycGCeRkshlTRtHxPB8rezmtyvyJreSK2NSf+efooURn5IWr3kdR5BmSCO3BDTZkZzMJQ3N3QhNz9kUeEbJinm5SD9Ins6PN5R0BV1A0goR8Y+IOInU1L80rbx3NRYk/XB6S9ISpCueoTSQbqGIuBP4FrmSbukYEfFMK+/3uhHxn4h4GXhH0kb59X8VuKWFl3AraXrYufO/rZVIF3AJ0kxyW+TttiK1BAD8jjyNrKSBpObyf9Xs88ukwW1mPcLN49ZId5G+8MeSKpiHIE3RmZtTb8qJ+DVgG+AHpCbjJ4AZpKk4bwK+S2pefYk0orq1051+QmoePxaovXLWxaQv47GSPiL1p5+f110NDIqIJ+m4S4ArJY0nVdifNOlLGhP/vYjGufrvfNynRcSz+f5Ruc/9I1LfetMPgNWAn0kKUoV7VkQ8nt+jy3OfvYDHgMPzc5YBZrpYSCf8NP+gEHBvPs4TtP7eERGPSXqU1CrwL1LzN6T50W+R1NSyckwbx+iIw0k/rAYAv8+3ptH3wyLipIgYl5vdnwSmA0fWdJ18h/SZnQM0jWGA1Oz9BaUpYGeQxhG8kfe9LOmHyyzTzJp1F5/yZdYGpZHTj0bEJY2OpR6SfgpcGRFjGx2LmXWek7ZZK5QugDEV2KbmVCszs4Zx0jYzMyuEB6KZmZkVwknbzMysEE7aZmZmhXDSNjMzK4STtpmZWSGctM3MzArx/8JpoYjLihYHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_plot = plot_confusion_matrix(cm, data.target_names, normalize=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save confusion matrix image\n",
    "confusion_matrix_image = './reports/confusion_matrix.png'\n",
    "cm_plot.savefig(confusion_matrix_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LineaArtifact(name='plot-confusion-matrix', _version=1)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#save confusion matrix to lineapy\n",
    "lineapy.save(plot_confusion_matrix, \"plot-confusion-matrix\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#commenting for change\n"
   ]
  }
 ],
 "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"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}