aldos_madness_continued.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "a7eae5b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "import itertools\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import KNNImputer\n",
    "import numpy as np, pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import KNNImputer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import cross_validate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "d2ef19ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainData = pd.read_csv(\"Data/train.csv\")\n",
    "testData = pd.read_csv(\"Data/test.csv\")\n",
    "\n",
    "#print(trainData.head())\n",
    "\n",
    "# data = censusData.drop(columns=\"class\")\n",
    "# target = censusData[\"class\"]\n",
    "trainDataSmall = trainData[trainData.columns[9900:10002]]\n",
    "testDataSmall = testData[testData.columns[9900:10001]]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "dd162ac1",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainDataClass = trainData[\"Outcome\"]\n",
    "trainDataIds = trainData[\"Id\"]\n",
    "trainDataFeatures = trainData.drop(columns = [\"Outcome\", \"Id\"])\n",
    "\n",
    "testDataIds = testData[\"Id\"]\n",
    "testDataFeatures = testData.drop(columns = [\"Id\"])\n",
    "\n",
    "X_train, X_test, y_train, y_test, id_train, id_test = train_test_split(trainDataFeatures, trainDataClass, trainDataIds, test_size = .2, train_size = .8, random_state=24)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "91142936",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Gene 10000</th>\n",
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      ],
      "text/plain": [
       "      Gene 1  Gene 2  Gene 3  Gene 4   Gene 5   Gene 6  Gene 7  Gene 8  \\\n",
       "2643   261.0    49.0     4.0    77.0  38461.0  34064.0    62.0    20.0   \n",
       "1694   274.0    41.0     5.0   103.0  33056.0  32321.0    49.0    27.0   \n",
       "2236   290.0    45.0     2.0   118.0  22338.0  65234.0    46.0    40.0   \n",
       "3119   254.0    42.0     4.0   136.0  17102.0  71198.0    29.0    20.0   \n",
       "1731   244.0    32.0     4.0   142.0      NaN  89847.0    25.0    13.0   \n",
       "...      ...     ...     ...     ...      ...      ...     ...     ...   \n",
       "4079   257.0    45.0     NaN    31.0  39746.0      NaN    23.0    23.0   \n",
       "2193   250.0    34.0     NaN   127.0  38540.0  30823.0    49.0    40.0   \n",
       "3473   202.0    31.0     6.0   138.0  25683.0  59641.0    48.0    39.0   \n",
       "343    283.0    36.0     3.0   182.0  39376.0  53524.0     NaN     NaN   \n",
       "899    384.0    35.0     3.0   119.0  39241.0  67946.0    41.0     NaN   \n",
       "\n",
       "      Gene 9  Gene 10  ...  Gene 9991  Gene 9992  Gene 9993  Gene 9994  \\\n",
       "2643   286.0   5441.0  ...    24544.0    87579.0      187.0    65654.0   \n",
       "1694   303.0   5837.0  ...    16773.0    67534.0      207.0    66064.0   \n",
       "2236   207.0   6123.0  ...    23159.0    70180.0      202.0    41444.0   \n",
       "3119   325.0   3983.0  ...    18248.0    54323.0      174.0    69407.0   \n",
       "1731   218.0   6814.0  ...    26400.0    82193.0      179.0    87414.0   \n",
       "...      ...      ...  ...        ...        ...        ...        ...   \n",
       "4079   147.0   4236.0  ...    16589.0    77628.0      184.0    66771.0   \n",
       "2193     NaN   3544.0  ...    14881.0    47747.0      135.0    52028.0   \n",
       "3473   368.0   4490.0  ...        NaN    82429.0      185.0    80435.0   \n",
       "343    243.0      NaN  ...    25955.0    88776.0      248.0    47129.0   \n",
       "899    249.0   2588.0  ...    22178.0    86259.0      153.0    86262.0   \n",
       "\n",
       "      Gene 9995  Gene 9996  Gene 9997  Gene 9998  Gene 9999  Gene 10000  \n",
       "2643    28599.0       74.0        NaN    20502.0     4197.0         NaN  \n",
       "1694    16874.0       98.0      250.0     7957.0     4184.0      1175.0  \n",
       "2236    40740.0       60.0      171.0    21682.0     2421.0       755.0  \n",
       "3119    36130.0      125.0      125.0    26233.0     5023.0       111.0  \n",
       "1731    20390.0       92.0      121.0    20451.0     2732.0      1830.0  \n",
       "...         ...        ...        ...        ...        ...         ...  \n",
       "4079    18057.0        NaN      139.0        NaN     5781.0      1601.0  \n",
       "2193    37287.0      110.0      219.0    25509.0     3663.0      1840.0  \n",
       "3473    29112.0       76.0      310.0    14754.0     3604.0      1422.0  \n",
       "343     27392.0       94.0      142.0    22565.0        NaN       864.0  \n",
       "899     18280.0       96.0      182.0     7520.0     2645.0         NaN  \n",
       "\n",
       "[3360 rows x 10000 columns]"
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     },
     "metadata": {},
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   ],
   "source": [
    "display(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a8ec52ab",
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   "outputs": [
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       "      <th></th>\n",
       "      <th>Gene 1</th>\n",
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       "      <td>0</td>\n",
       "      <td>3377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>113.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>32761.0</td>\n",
       "      <td>47313.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>127.0</td>\n",
       "      <td>123217.0</td>\n",
       "      <td>24424.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>11421.0</td>\n",
       "      <td>4510.0</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>854</th>\n",
       "      <td>275.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>28285.0</td>\n",
       "      <td>57066.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>5762.0</td>\n",
       "      <td>...</td>\n",
       "      <td>142.0</td>\n",
       "      <td>49195.0</td>\n",
       "      <td>26542.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>21766.0</td>\n",
       "      <td>3355.0</td>\n",
       "      <td>2336.0</td>\n",
       "      <td>0</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4029</th>\n",
       "      <td>184.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>22709.0</td>\n",
       "      <td>63028.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>3385.0</td>\n",
       "      <td>...</td>\n",
       "      <td>144.0</td>\n",
       "      <td>70193.0</td>\n",
       "      <td>35754.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>20647.0</td>\n",
       "      <td>4138.0</td>\n",
       "      <td>1545.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2447</th>\n",
       "      <td>145.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>39086.0</td>\n",
       "      <td>53715.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>5395.0</td>\n",
       "      <td>...</td>\n",
       "      <td>160.0</td>\n",
       "      <td>61990.0</td>\n",
       "      <td>39210.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>17987.0</td>\n",
       "      <td>2806.0</td>\n",
       "      <td>1478.0</td>\n",
       "      <td>0</td>\n",
       "      <td>564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000</th>\n",
       "      <td>240.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>20513.0</td>\n",
       "      <td>77389.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>207.0</td>\n",
       "      <td>3909.0</td>\n",
       "      <td>...</td>\n",
       "      <td>104.0</td>\n",
       "      <td>68926.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>21429.0</td>\n",
       "      <td>3158.0</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1565</th>\n",
       "      <td>177.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>24198.0</td>\n",
       "      <td>71003.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>2840.0</td>\n",
       "      <td>...</td>\n",
       "      <td>91.0</td>\n",
       "      <td>52905.0</td>\n",
       "      <td>36130.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2391.0</td>\n",
       "      <td>1771.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2292</th>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>32040.0</td>\n",
       "      <td>51461.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>4386.0</td>\n",
       "      <td>...</td>\n",
       "      <td>191.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44319.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23515.0</td>\n",
       "      <td>5313.0</td>\n",
       "      <td>1243.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2376</th>\n",
       "      <td>227.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>16421.0</td>\n",
       "      <td>64216.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>314.0</td>\n",
       "      <td>5272.0</td>\n",
       "      <td>...</td>\n",
       "      <td>194.0</td>\n",
       "      <td>73703.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>2598.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>196.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>29609.0</td>\n",
       "      <td>70354.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7613.0</td>\n",
       "      <td>...</td>\n",
       "      <td>238.0</td>\n",
       "      <td>102530.0</td>\n",
       "      <td>36217.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112.0</td>\n",
       "      <td>18287.0</td>\n",
       "      <td>4297.0</td>\n",
       "      <td>1234.0</td>\n",
       "      <td>3</td>\n",
       "      <td>831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2968 rows × 10002 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gene 1  Gene 2  Gene 3  Gene 4   Gene 5   Gene 6  Gene 7  Gene 8  \\\n",
       "772    174.0    31.0     3.0   149.0  29588.0  86826.0    59.0    46.0   \n",
       "870    113.0    31.0     5.0   115.0  32761.0  47313.0    59.0    41.0   \n",
       "854    275.0    31.0     5.0   130.0  28285.0  57066.0    35.0    45.0   \n",
       "4029   184.0    49.0     2.0   158.0  22709.0  63028.0    66.0    39.0   \n",
       "2447   145.0    36.0     2.0    73.0  39086.0  53715.0    49.0    14.0   \n",
       "...      ...     ...     ...     ...      ...      ...     ...     ...   \n",
       "3000   240.0    42.0     4.0   113.0  20513.0  77389.0    25.0     NaN   \n",
       "1565   177.0     NaN     4.0    46.0  24198.0  71003.0    36.0    26.0   \n",
       "2292     NaN    28.0     4.0   110.0  32040.0  51461.0    64.0    41.0   \n",
       "2376   227.0    52.0     2.0    91.0  16421.0  64216.0    33.0     NaN   \n",
       "1308   196.0    49.0     4.0    73.0  29609.0  70354.0    51.0    40.0   \n",
       "\n",
       "      Gene 9  Gene 10  ...  Gene 9993  Gene 9994  Gene 9995  Gene 9996  \\\n",
       "772    421.0   4459.0  ...      137.0    83903.0    16886.0      117.0   \n",
       "870    286.0      NaN  ...      127.0   123217.0    24424.0      106.0   \n",
       "854    282.0   5762.0  ...      142.0    49195.0    26542.0       89.0   \n",
       "4029   232.0   3385.0  ...      144.0    70193.0    35754.0       41.0   \n",
       "2447   226.0   5395.0  ...      160.0    61990.0    39210.0       42.0   \n",
       "...      ...      ...  ...        ...        ...        ...        ...   \n",
       "3000   207.0   3909.0  ...      104.0    68926.0        NaN       67.0   \n",
       "1565   198.0   2840.0  ...       91.0    52905.0    36130.0       83.0   \n",
       "2292   446.0   4386.0  ...      191.0        NaN    44319.0       79.0   \n",
       "2376   314.0   5272.0  ...      194.0    73703.0        NaN       51.0   \n",
       "1308   177.0   7613.0  ...      238.0   102530.0    36217.0        NaN   \n",
       "\n",
       "      Gene 9997  Gene 9998  Gene 9999  Gene 10000  Outcome    Id  \n",
       "772       226.0    30734.0     3090.0      1375.0        0  3377  \n",
       "870       138.0    11421.0     4510.0      2023.0        0  2235  \n",
       "854       222.0    21766.0     3355.0      2336.0        0   474  \n",
       "4029      187.0    20647.0     4138.0      1545.0        0  3031  \n",
       "2447      169.0    17987.0     2806.0      1478.0        0   564  \n",
       "...         ...        ...        ...         ...      ...   ...  \n",
       "3000      229.0    21429.0     3158.0      1930.0        3  4020  \n",
       "1565      223.0        NaN     2391.0      1771.0        3  3894  \n",
       "2292        NaN    23515.0     5313.0      1243.0        3  3396  \n",
       "2376      185.0     2598.0        NaN         NaN        3  2392  \n",
       "1308      112.0    18287.0     4297.0      1234.0        3   831  \n",
       "\n",
       "[2968 rows x 10002 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples = []\n",
    "for i in range(4):\n",
    "    indices = (trainData[trainData.Outcome == i]).index\n",
    "    samples += (np.random.choice(indices, 742)).tolist()\n",
    "\n",
    "    \n",
    "    \n",
    "    #downsampled = trainData[\"Outcome\"][np.random.choice(trainData.shape[0], 741, replace=False)]\n",
    "\n",
    "#trainData.shape[0]\n",
    "#i_class1_downsampled = np.random.choice(i_class1, size=n_class0, replace=False)\n",
    "\n",
    "#display(trainData.iloc[downsampledTrain,:])\n",
    "\n",
    "downsampledTrain = trainData.iloc[samples,:]\n",
    "\n",
    "display(downsampledTrain)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8361283a",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/t_/wz5wcv9x52jbwkh7hh8jcp7w0000gn/T/ipykernel_90947/353025872.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m   1342\u001b[0m             \u001b[0m_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1344\u001b[0;31m         X, y = self._validate_data(X, y, accept_sparse='csr', dtype=_dtype,\n\u001b[0m\u001b[1;32m   1345\u001b[0m                                    \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"C\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1346\u001b[0m                                    accept_large_sparse=solver != 'liblinear')\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    431\u001b[0m                 \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    432\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 433\u001b[0;31m                 \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    434\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    435\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;31m# extra_args > 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m    869\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"y cannot be None\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    870\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 871\u001b[0;31m     X = check_array(X, accept_sparse=accept_sparse,\n\u001b[0m\u001b[1;32m    872\u001b[0m                     \u001b[0maccept_large_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maccept_large_sparse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    873\u001b[0m                     \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;31m# extra_args > 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[1;32m    718\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    719\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 720\u001b[0;31m             _assert_all_finite(array,\n\u001b[0m\u001b[1;32m    721\u001b[0m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[1;32m    722\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m    101\u001b[0m                 not allow_nan and not np.isfinite(X).all()):\n\u001b[1;32m    102\u001b[0m             \u001b[0mtype_err\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'infinity'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mallow_nan\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'NaN, infinity'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m    104\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m                     (type_err,\n",
      "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
     ]
    }
   ],
   "source": [
    "trainDataClass = trainData[\"Outcome\"]\n",
    "trainDataFeatures = trainData.drop(columns = [\"Outcome\", \"Id\"])\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(trainDataFeatures, trainDataClass, test_size = 0.2, random_state = 24)\n",
    "\n",
    "# model = LogisticRegression()\n",
    "\n",
    "# model.fit(X_train, y_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4faee851",
   "metadata": {},
   "outputs": [],
   "source": [
    "downTrainFeat = downsampledTrain.drop(columns=[\"Outcome\", \"Id\"])\n",
    "downTrainClass = downsampledTrain[\"Outcome\"]\n",
    "\n",
    "pipe = Pipeline([('std', preprocessing.StandardScaler()), (\"KNN\", KNNImputer()), \n",
    "                 ('logisticRegression', LogisticRegression())], verbose = True)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(downTrainFeat, downTrainClass, test_size = 0.2, \n",
    "                                                    random_state = 24)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "99d287bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Pipeline] ............... (step 1 of 3) Processing std, total=   1.2s\n",
      "[Pipeline] ............... (step 2 of 3) Processing KNN, total= 8.1min\n",
      "[Pipeline]  (step 3 of 3) Processing logisticRegression, total=   2.0s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('std', StandardScaler()), ('KNN', KNNImputer()),\n",
       "                ('logisticRegression', LogisticRegression())],\n",
       "         verbose=True)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "037edd80",
   "metadata": {},
   "outputs": [],
   "source": [
    "MLAssThree = pipe.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "6fa2d5ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "        Id\n",
       "4072  3918\n",
       "2854   250\n",
       "1839  1879\n",
       "3204    51\n",
       "2305  3381\n",
       "...    ...\n",
       "199   4179\n",
       "415    509\n",
       "1787  2280\n",
       "110    485\n",
       "633   3888\n",
       "\n",
       "[594 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Outcome</th>\n",
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       "</table>\n",
       "<p>594 rows × 1 columns</p>\n",
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      ],
      "text/plain": [
       "     Outcome\n",
       "0          0\n",
       "1          3\n",
       "2          0\n",
       "3          0\n",
       "4          1\n",
       "..       ...\n",
       "589        0\n",
       "590        2\n",
       "591        3\n",
       "592        1\n",
       "593        0\n",
       "\n",
       "[594 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "ValueError",
     "evalue": "cannot reindex from a duplicate axis",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/t_/wz5wcv9x52jbwkh7hh8jcp7w0000gn/T/ipykernel_90947/611197034.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMLAssThreeThree\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mfinal_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Id\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMLAssThreeThree\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Outcome\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal_out\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[1;32m    305\u001b[0m     )\n\u001b[1;32m    306\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    506\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    507\u001b[0m                 \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_axes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 508\u001b[0;31m                 \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcons\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    509\u001b[0m                 \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"concat\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    612\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    613\u001b[0m             \u001b[0;31m# GH#38939 de facto copy defaults to False only in non-dict cases\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 614\u001b[0;31m             \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict_to_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmanager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    615\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    616\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36mdict_to_mgr\u001b[0;34m(data, index, columns, dtype, typ, copy)\u001b[0m\n\u001b[1;32m    462\u001b[0m         \u001b[0;31m# TODO: can we get rid of the dt64tz special case above?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    463\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 464\u001b[0;31m     return arrays_to_mgr(\n\u001b[0m\u001b[1;32m    465\u001b[0m         \u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtyp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsolidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    466\u001b[0m     )\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36marrays_to_mgr\u001b[0;34m(arrays, arr_names, index, columns, dtype, verify_integrity, typ, consolidate)\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m         \u001b[0;31m# don't force copy because getting jammed in an ndarray anyway\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m         \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_homogenize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36m_homogenize\u001b[0;34m(data, index, dtype)\u001b[0m\n\u001b[1;32m    569\u001b[0m                 \u001b[0;31m# Forces alignment. No need to copy data since we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    570\u001b[0m                 \u001b[0;31m# are putting it into an ndarray later\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 571\u001b[0;31m                 \u001b[0mval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    572\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    573\u001b[0m             \u001b[0mval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mreindex\u001b[0;34m(self, index, **kwargs)\u001b[0m\n\u001b[1;32m   4578\u001b[0m     )\n\u001b[1;32m   4579\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4580\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4582\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mdeprecate_nonkeyword_arguments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mversion\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallowed_args\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"self\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"labels\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mreindex\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   4816\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4817\u001b[0m         \u001b[0;31m# perform the reindex on the axes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4818\u001b[0;31m         return self._reindex_axes(\n\u001b[0m\u001b[1;32m   4819\u001b[0m             \u001b[0maxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4820\u001b[0m         ).__finalize__(self, method=\"reindex\")\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_reindex_axes\u001b[0;34m(self, axes, level, limit, tolerance, method, fill_value, copy)\u001b[0m\n\u001b[1;32m   4837\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4838\u001b[0m             \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4839\u001b[0;31m             obj = obj._reindex_with_indexers(\n\u001b[0m\u001b[1;32m   4840\u001b[0m                 \u001b[0;34m{\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnew_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4841\u001b[0m                 \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_reindex_with_indexers\u001b[0;34m(self, reindexers, fill_value, copy, allow_dups)\u001b[0m\n\u001b[1;32m   4881\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4882\u001b[0m             \u001b[0;31m# TODO: speed up on homogeneous DataFrame objects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4883\u001b[0;31m             new_data = new_data.reindex_indexer(\n\u001b[0m\u001b[1;32m   4884\u001b[0m                 \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4885\u001b[0m                 \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mreindex_indexer\u001b[0;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy, consolidate, only_slice)\u001b[0m\n\u001b[1;32m    668\u001b[0m         \u001b[0;31m# some axes don't allow reindexing with dups\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    669\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 670\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_can_reindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    671\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    672\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_validate_can_reindex\u001b[0;34m(self, indexer)\u001b[0m\n\u001b[1;32m   3783\u001b[0m         \u001b[0;31m# trying to reindex on an axis with duplicates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3784\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_index_as_unique\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3785\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cannot reindex from a duplicate axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3786\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3787\u001b[0m     def reindex(\n",
      "\u001b[0;31mValueError\u001b[0m: cannot reindex from a duplicate axis"
     ]
    }
   ],
   "source": [
    "#MLAssThreeTwo = list(MLAssThree)\n",
    "MLAssThreeTwo = MLAssThree.copy()\n",
    "MLAssThreeTwo = pd.DataFrame(MLAssThreeTwo, columns=[\"Outcome\"])\n",
    "indices = pd.DataFrame(trainData.iloc[list(X_test.index)][\"Id\"])\n",
    "display(indices)\n",
    "MLAssThreeThree = MLAssThreeTwo\n",
    "display(MLAssThreeThree)\n",
    "\n",
    "final_out = pd.concat([indices[\"Id\"], MLAssThreeThree[\"Outcome\"]], axis=1)\n",
    "display(final_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "e9588c5e",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/t_/wz5wcv9x52jbwkh7hh8jcp7w0000gn/T/ipykernel_90947/3660487671.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel_to_tune\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_distributions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparam_dist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m )\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0msearch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"The best parameters found are: {search.best_params_}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;31m# extra_args > 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    839\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    840\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 841\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    842\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    843\u001b[0m             \u001b[0;31m# multimetric is determined here because in the case of a callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1631\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1632\u001b[0m         \u001b[0;34m\"\"\"Search n_iter candidates from param_distributions\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1633\u001b[0;31m         evaluate_candidates(ParameterSampler(\n\u001b[0m\u001b[1;32m   1634\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_distributions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_iter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1635\u001b[0m             random_state=self.random_state))\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[1;32m    793\u001b[0m                               n_splits, n_candidates, n_candidates * n_splits))\n\u001b[1;32m    794\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 795\u001b[0;31m                 out = parallel(delayed(_fit_and_score)(clone(base_estimator),\n\u001b[0m\u001b[1;32m    796\u001b[0m                                                        \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    797\u001b[0m                                                        \u001b[0mtrain\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1054\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1055\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1056\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1057\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1058\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    933\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    934\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 935\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    936\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    937\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    540\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m    541\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    543\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    438\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mCANCELLED\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCANCELLED_AND_NOTIFIED\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    310\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    311\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 312\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    313\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    314\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#5 Nested Cross Validation\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import cross_val_score, KFold\n",
    "\n",
    "#data, target = load_breast_cancer(return_X_y=True)\n",
    "\n",
    "#param_grid = {\"C\": [0.1, 1, 10], \"gamma\": [.01, .1]}\n",
    "param_dist = {\"PCA__n_components\" : [10, 50, 100, 200, 400, 800], \"Imputing__n_neighbors\" : [5, 10, 20, 40, 80, 150]}\n",
    "model_to_tune = Pipeline([\n",
    "    (\"Scaling\", StandardScaler()), \n",
    "    (\"Imputing\", KNNImputer()),\n",
    "    (\"PCA\", PCA()),\n",
    "    (\"Regression\", LogisticRegression())], \n",
    "    verbose = True)\n",
    "\n",
    "search = RandomizedSearchCV(\n",
    "    estimator=model_to_tune, param_distributions=param_dist, n_jobs=2\n",
    ")\n",
    "search.fit(X_train, y_train)\n",
    "\n",
    "print(f\"The best parameters found are: {search.best_params_}\")\n",
    "print(f\"The mean CV score of the best model is: {search.best_score_:.3f}\")\n",
    "\n",
    "# Declare the inner and outer cross-validation strategies\n",
    "inner_cv = KFold(n_splits=5, shuffle=True, random_state=0)\n",
    "outer_cv = KFold(n_splits=3, shuffle=True, random_state=0)\n",
    "\n",
    "# Inner cross-validation for parameter search\n",
    "model = RandomizedSearchCV(\n",
    "    estimator=model_to_tune, param_distributions=param_dist, cv=inner_cv, n_jobs=2\n",
    ")\n",
    "\n",
    "# Outer cross-validation to compute the testing score\n",
    "test_score = cross_val_score(model, X_train, y_train, cv=outer_cv, n_jobs=2)\n",
    "print(f\"The mean score using nested cross-validation is: \"\n",
    "      f\"{test_score.mean():.3f} +/- {test_score.std():.3f}\")\n",
    "\n",
    "test_score_not_nested = []\n",
    "test_score_nested = []\n",
    "\n",
    "N_TRIALS = 5\n",
    "for i in range(N_TRIALS):\n",
    "    # For each trial, we use cross-validation splits on independently\n",
    "    # randomly shuffled data by passing distinct values to the random_state\n",
    "    # parameter.\n",
    "    inner_cv = KFold(n_splits=5, shuffle=True, random_state=i)\n",
    "    outer_cv = KFold(n_splits=3, shuffle=True, random_state=i)\n",
    "\n",
    "    # Non_nested parameter search and scoring\n",
    "    model = RandomizedSearchCV(estimator=model_to_tune, param_distributions=param_dist,\n",
    "                         cv=inner_cv, n_jobs=2)\n",
    "    model.fit(X_train, y_train)\n",
    "    test_score_not_nested.append(model.best_score_)\n",
    "\n",
    "    # Nested CV with parameter optimization\n",
    "    test_score = cross_val_score(model, X_train, y_train, cv=outer_cv, n_jobs=2)\n",
    "    test_score_nested.append(test_score.mean())\n",
    "    \n",
    "all_scores = {\n",
    "    \"Not nested CV\": test_score_not_nested,\n",
    "    \"Nested CV\": test_score_nested,\n",
    "}\n",
    "all_scores = pd.DataFrame(all_scores)\n",
    "\n",
    "color = {\"whiskers\": \"black\", \"medians\": \"black\", \"caps\": \"black\"}\n",
    "all_scores.plot.box(color=color, vert=False)\n",
    "plt.xlabel(\"Accuracy\")\n",
    "_ = plt.title(\"Comparison of mean accuracy obtained on the test sets with\\n\"\n",
    "              \"and without nested cross-validation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "870cca85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.3532738095238095,\n",
       " 0.34732142857142856,\n",
       " 0.3639880952380952,\n",
       " 0.36309523809523814]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[0.36607142857142855,\n",
       " 0.3657738095238095,\n",
       " 0.36488095238095236,\n",
       " 0.3389880952380952,\n",
       " 0.36607142857142855]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(test_score_nested)\n",
    "display(test_score_not_nested)"
   ]
  }
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