project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from image_entropy import *\n",
    "\n",
    "target,features = get_entropy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['s001', 's001', 's001', ..., 's559', 's559', 's559'], dtype='<U4'),\n",
       " array([[ 2.00385619,  2.40488945,  3.23508495, ...,  2.72888407,\n",
       "          3.47907957,  3.46346519],\n",
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       "          3.01868857,  2.88888407],\n",
       "        [ 1.45947057,  1.88410745,  2.01883007, ...,  3.62346519,\n",
       "          3.40307419,  4.05366069],\n",
       "        ...,\n",
       "        [-0.        ,  0.72192809,  0.72192809, ...,  4.10346519,\n",
       "          2.40229219,  2.99907957],\n",
       "        [-0.        , -0.        , -0.        , ...,  4.32385619,\n",
       "          3.81366069,  3.44385619],\n",
       "        [ 0.24229219,  1.10631371,  1.18631371, ...,  4.56385619,\n",
       "          3.89366069,  1.81701481]]))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target,features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "X =pd.DataFrame(features)\n",
    "y = target "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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       "      <td>3491.000000</td>\n",
       "      <td>3491.000000</td>\n",
       "      <td>3491.000000</td>\n",
       "      <td>3491.000000</td>\n",
       "      <td>3491.000000</td>\n",
       "      <td>3491.000000</td>\n",
       "      <td>3491.000000</td>\n",
       "      <td>3491.000000</td>\n",
       "      <td>3491.000000</td>\n",
       "      <td>3491.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.879244</td>\n",
       "      <td>2.632899</td>\n",
       "      <td>2.688836</td>\n",
       "      <td>2.746618</td>\n",
       "      <td>3.032485</td>\n",
       "      <td>2.978429</td>\n",
       "      <td>2.990671</td>\n",
       "      <td>3.004381</td>\n",
       "      <td>3.019828</td>\n",
       "      <td>3.031720</td>\n",
       "      <td>...</td>\n",
       "      <td>3.436301</td>\n",
       "      <td>3.397459</td>\n",
       "      <td>3.370587</td>\n",
       "      <td>3.353431</td>\n",
       "      <td>3.324532</td>\n",
       "      <td>3.201711</td>\n",
       "      <td>3.023218</td>\n",
       "      <td>3.070616</td>\n",
       "      <td>3.096695</td>\n",
       "      <td>3.003041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.858191</td>\n",
       "      <td>0.913890</td>\n",
       "      <td>0.875363</td>\n",
       "      <td>0.904727</td>\n",
       "      <td>0.948571</td>\n",
       "      <td>0.935563</td>\n",
       "      <td>0.919742</td>\n",
       "      <td>0.889518</td>\n",
       "      <td>0.848520</td>\n",
       "      <td>0.808241</td>\n",
       "      <td>...</td>\n",
       "      <td>0.750886</td>\n",
       "      <td>0.769098</td>\n",
       "      <td>0.798623</td>\n",
       "      <td>0.854485</td>\n",
       "      <td>0.935847</td>\n",
       "      <td>0.975127</td>\n",
       "      <td>1.031886</td>\n",
       "      <td>1.040618</td>\n",
       "      <td>1.043589</td>\n",
       "      <td>1.060247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.242292</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.443856</td>\n",
       "      <td>2.094694</td>\n",
       "      <td>2.260504</td>\n",
       "      <td>2.294694</td>\n",
       "      <td>2.647210</td>\n",
       "      <td>2.504498</td>\n",
       "      <td>2.609862</td>\n",
       "      <td>2.675085</td>\n",
       "      <td>2.742825</td>\n",
       "      <td>2.739471</td>\n",
       "      <td>...</td>\n",
       "      <td>2.978634</td>\n",
       "      <td>2.979471</td>\n",
       "      <td>2.854694</td>\n",
       "      <td>2.862683</td>\n",
       "      <td>2.771860</td>\n",
       "      <td>2.564889</td>\n",
       "      <td>2.307601</td>\n",
       "      <td>2.390714</td>\n",
       "      <td>2.384640</td>\n",
       "      <td>2.252558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.884107</td>\n",
       "      <td>2.851511</td>\n",
       "      <td>2.884889</td>\n",
       "      <td>2.915085</td>\n",
       "      <td>3.284889</td>\n",
       "      <td>3.239080</td>\n",
       "      <td>3.239080</td>\n",
       "      <td>3.239080</td>\n",
       "      <td>3.204889</td>\n",
       "      <td>3.178689</td>\n",
       "      <td>...</td>\n",
       "      <td>3.593270</td>\n",
       "      <td>3.559080</td>\n",
       "      <td>3.563074</td>\n",
       "      <td>3.543465</td>\n",
       "      <td>3.589275</td>\n",
       "      <td>3.413661</td>\n",
       "      <td>3.163074</td>\n",
       "      <td>3.235085</td>\n",
       "      <td>3.319080</td>\n",
       "      <td>3.212879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.512488</td>\n",
       "      <td>3.235085</td>\n",
       "      <td>3.235085</td>\n",
       "      <td>3.253661</td>\n",
       "      <td>3.697797</td>\n",
       "      <td>3.669275</td>\n",
       "      <td>3.669275</td>\n",
       "      <td>3.614177</td>\n",
       "      <td>3.589275</td>\n",
       "      <td>3.589275</td>\n",
       "      <td>...</td>\n",
       "      <td>4.003856</td>\n",
       "      <td>3.973661</td>\n",
       "      <td>3.973661</td>\n",
       "      <td>4.003856</td>\n",
       "      <td>4.053661</td>\n",
       "      <td>4.003856</td>\n",
       "      <td>3.893661</td>\n",
       "      <td>3.943465</td>\n",
       "      <td>3.973661</td>\n",
       "      <td>3.863465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.019471</td>\n",
       "      <td>4.453661</td>\n",
       "      <td>4.563856</td>\n",
       "      <td>4.643856</td>\n",
       "      <td>4.643856</td>\n",
       "      <td>4.563856</td>\n",
       "      <td>4.403856</td>\n",
       "      <td>4.179471</td>\n",
       "      <td>4.563856</td>\n",
       "      <td>4.213661</td>\n",
       "      <td>...</td>\n",
       "      <td>4.643856</td>\n",
       "      <td>4.643856</td>\n",
       "      <td>4.643856</td>\n",
       "      <td>4.643856</td>\n",
       "      <td>4.643856</td>\n",
       "      <td>4.643856</td>\n",
       "      <td>4.643856</td>\n",
       "      <td>4.643856</td>\n",
       "      <td>4.643856</td>\n",
       "      <td>4.643856</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 625 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0            1            2            3            4    \\\n",
       "count  3491.000000  3491.000000  3491.000000  3491.000000  3491.000000   \n",
       "mean      1.879244     2.632899     2.688836     2.746618     3.032485   \n",
       "std       0.858191     0.913890     0.875363     0.904727     0.948571   \n",
       "min      -0.000000    -0.000000    -0.000000    -0.000000    -0.000000   \n",
       "25%       1.443856     2.094694     2.260504     2.294694     2.647210   \n",
       "50%       1.884107     2.851511     2.884889     2.915085     3.284889   \n",
       "75%       2.512488     3.235085     3.235085     3.253661     3.697797   \n",
       "max       4.019471     4.453661     4.563856     4.643856     4.643856   \n",
       "\n",
       "               5            6            7            8            9    ...  \\\n",
       "count  3491.000000  3491.000000  3491.000000  3491.000000  3491.000000  ...   \n",
       "mean      2.978429     2.990671     3.004381     3.019828     3.031720  ...   \n",
       "std       0.935563     0.919742     0.889518     0.848520     0.808241  ...   \n",
       "min      -0.000000    -0.000000    -0.000000    -0.000000    -0.000000  ...   \n",
       "25%       2.504498     2.609862     2.675085     2.742825     2.739471  ...   \n",
       "50%       3.239080     3.239080     3.239080     3.204889     3.178689  ...   \n",
       "75%       3.669275     3.669275     3.614177     3.589275     3.589275  ...   \n",
       "max       4.563856     4.403856     4.179471     4.563856     4.213661  ...   \n",
       "\n",
       "               615          616          617          618          619  \\\n",
       "count  3491.000000  3491.000000  3491.000000  3491.000000  3491.000000   \n",
       "mean      3.436301     3.397459     3.370587     3.353431     3.324532   \n",
       "std       0.750886     0.769098     0.798623     0.854485     0.935847   \n",
       "min      -0.000000     0.242292    -0.000000    -0.000000    -0.000000   \n",
       "25%       2.978634     2.979471     2.854694     2.862683     2.771860   \n",
       "50%       3.593270     3.559080     3.563074     3.543465     3.589275   \n",
       "75%       4.003856     3.973661     3.973661     4.003856     4.053661   \n",
       "max       4.643856     4.643856     4.643856     4.643856     4.643856   \n",
       "\n",
       "               620          621          622          623          624  \n",
       "count  3491.000000  3491.000000  3491.000000  3491.000000  3491.000000  \n",
       "mean      3.201711     3.023218     3.070616     3.096695     3.003041  \n",
       "std       0.975127     1.031886     1.040618     1.043589     1.060247  \n",
       "min      -0.000000    -0.000000    -0.000000    -0.000000    -0.000000  \n",
       "25%       2.564889     2.307601     2.390714     2.384640     2.252558  \n",
       "50%       3.413661     3.163074     3.235085     3.319080     3.212879  \n",
       "75%       4.003856     3.893661     3.943465     3.973661     3.863465  \n",
       "max       4.643856     4.643856     4.643856     4.643856     4.643856  \n",
       "\n",
       "[8 rows x 625 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA()\n",
    "\n",
    "X_pca = pca.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.85794444, -4.46169249, -4.16911677, ...,  0.08927608,\n",
       "        -0.19864286, -0.04219044],\n",
       "       [ 0.97766904, -4.9836049 , -4.88134804, ..., -0.39266027,\n",
       "        -0.03256753, -0.16062686],\n",
       "       [ 0.44005411, -3.44027712, -6.25970523, ..., -0.19781633,\n",
       "         0.11562976,  0.26319107],\n",
       "       ...,\n",
       "       [ 2.85640541,  0.3452776 ,  0.70472883, ...,  0.10710453,\n",
       "        -0.02470018, -0.17215551],\n",
       "       [ 2.77629378, -3.21675657, -1.11907886, ...,  0.05009788,\n",
       "        -0.11157521,  0.03848595],\n",
       "       [ 1.85173699, -2.96015854, -1.04038803, ...,  0.02692512,\n",
       "         0.07101965, -0.06777201]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import  train_test_split\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X_pca,y,test_size=0.25,random_state=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2618, 625)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9117983963344788"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16,  0,  0, ...,  0,  0,  0],\n",
       "       [ 0,  8,  0, ...,  0,  0,  0],\n",
       "       [ 0,  0, 13, ...,  0,  0,  0],\n",
       "       ...,\n",
       "       [ 0,  0,  0, ..., 14,  0,  0],\n",
       "       [ 0,  0,  0, ...,  0,  0,  0],\n",
       "       [ 0,  0,  0, ...,  0,  0, 16]], dtype=int64)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "y_pred = knn.predict(X_test)\n",
    "con_mat = confusion_matrix(y_pred=y_pred,y_true=y_test)\n",
    "con_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svc = SVC(kernel='linear')\n",
    "svc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9404352806414662"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "svc.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16,  0,  0, ...,  0,  0,  0],\n",
       "       [ 0,  9,  0, ...,  0,  0,  0],\n",
       "       [ 0,  0, 13, ...,  0,  0,  0],\n",
       "       ...,\n",
       "       [ 1,  0,  1, ..., 13,  0,  0],\n",
       "       [ 0,  0,  0, ...,  0,  1,  0],\n",
       "       [ 0,  0,  0, ...,  0,  0, 17]], dtype=int64)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = svc.predict(X_test)\n",
    "con_mat_01 = confusion_matrix(y_pred=y_pred,y_true=y_test)\n",
    "con_mat_01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
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       "<p>3491 rows × 625 columns</p>\n",
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       "3486 -0.000000 -0.000000 -0.000000  ...  3.493661  4.213661  3.643074   \n",
       "3487  0.866314  0.916118  0.954310  ...  3.093661  2.641542  3.733661   \n",
       "3488  0.721928  0.866314  0.721928  ...  3.433270  2.563216  2.984498   \n",
       "3489 -0.000000 -0.000000 -0.000000  ...  3.207210  3.102683  3.463465   \n",
       "3490  0.721928  0.866314  0.866314  ...  3.943465  3.783465  2.949275   \n",
       "\n",
       "           618       619       620       621       622       623       624  \n",
       "0     4.103465  4.163856  3.893661  3.193270  2.728884  3.479080  3.463465  \n",
       "1     2.915085  2.949275  3.093661  3.779471  4.243856  3.018689  2.888884  \n",
       "2     3.749275  4.003856  4.293661  3.943465  3.623465  3.403074  4.053661  \n",
       "3     2.129026  3.239080  3.128884  4.323856  4.083856  3.273270  3.703465  \n",
       "4     2.065451  2.236624  3.513270  4.323856  3.623465  3.573661  4.213661  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "3486  2.753411  2.938689  3.433270  3.643074  4.133661  3.943465  3.913270  \n",
       "3487  3.673270  4.243856  4.563856  4.403856  4.483856  4.293661  2.992488  \n",
       "3488  3.893661  4.103465  4.293661  3.463465  4.103465  2.402292  2.999080  \n",
       "3489  3.243074  2.549275  3.493661  4.243856  4.323856  3.813661  3.443856  \n",
       "3490  4.003856  3.593270  4.563856  4.483856  4.563856  3.893661  1.817015  \n",
       "\n",
       "[3491 rows x 625 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA \n",
    "\n",
    "lda = LDA(solver='svd')\n",
    "X_lda = lda.fit_transform(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.00641585,   0.26095092,  -0.80185371, ...,  -0.20551681,\n",
       "          1.97975769,   0.17973675],\n",
       "       [ -0.0598625 ,  -0.2014438 ,  -1.31800704, ...,  -0.36394085,\n",
       "          2.04654149,  -1.75003074],\n",
       "       [  0.02644127,   1.31259996,   0.9858325 , ...,  -0.21077156,\n",
       "          1.14203967,  -0.32831174],\n",
       "       ...,\n",
       "       [-16.34432292,   1.28692336,   0.05309955, ...,   1.76773687,\n",
       "          1.83015353,   0.0660279 ],\n",
       "       [-17.44212266,   2.98504332,  -0.34650141, ...,  -0.74317681,\n",
       "         -0.64058515,  -0.12642854],\n",
       "       [-14.31383932,   0.86259399,  -0.41969315, ...,  -0.0201999 ,\n",
       "         -1.95771231,   0.64124387]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_lda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.model_selection import  train_test_split\n",
    "\n",
    "Xl_train,Xl_test,yl_train,yl_test = train_test_split(X_lda,y,test_size=0.3,random_state=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['s001', 's001', 's001', ..., 's559', 's559', 's559'], dtype='<U4')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "svm_2 = SVC(kernel='linear')\n",
    "svm_2.fit(Xl_train,yl_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9904580152671756"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_2.score(Xl_test,yl_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22,  0,  0, ...,  0,  0,  0],\n",
       "       [ 0, 13,  0, ...,  0,  0,  0],\n",
       "       [ 0,  0, 17, ...,  0,  0,  0],\n",
       "       ...,\n",
       "       [ 0,  0,  0, ..., 17,  0,  0],\n",
       "       [ 0,  0,  0, ...,  0,  7,  0],\n",
       "       [ 0,  0,  0, ...,  0,  0, 18]], dtype=int64)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from sklearn.metrics import confusion_matrix\n",
    "\n",
    "yl_pred = svm_2.predict(Xl_test)\n",
    "con_mat = confusion_matrix(y_pred=yl_pred,y_true=yl_test)\n",
    "con_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3491, 56), (3491,))"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_lda.shape , y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_2 = KNeighborsClassifier(n_neighbors=3)\n",
    "knn_2.fit(Xl_train,yl_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9818702290076335"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_2.score(Xl_test,yl_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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