Notebook/Parkinson's Oxford - SnapML(Random Forest vs Boosting Machine).ipynb

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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image-6.png](attachment:image-6.png)\n",
    "\n",
    "\n",
    "##  Data    \n",
    "<a href=\"https://archive.ics.uci.edu/dataset/174/parkinsons\">UCI Machine Learning</a>\n",
    "    \n",
    "\n",
    "## Objectives\n",
    "\n",
    "After completing this lab you will be able to:\n",
    "\n",
    "* Use <a href=\"https://snapml.readthedocs.io/en/latest/decision_trees.html\">IBM snapML</a> Decision Tree to classify yer or no\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Additional Information**\n",
    "\n",
    "This dataset is composed of a range of biomedical voice measurements from 31 people, 23 with Parkinson's disease (PD). Each column in the table is a particular voice measure, and each row corresponds one of 195 voice recording from these individuals (\"name\" column). The main aim of the data is to discriminate healthy people from those with PD, according to \"status\" column which is set to 0 for healthy and 1 for PD. \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Table of contents</h1>\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "    <ol>\n",
    "        <li><a href=\"#load_dataset\">Load the data</a></li>\n",
    "        <li><a href=\"#modeling\">Modeling</a></li>\n",
    "        <li><a href=\"#evaluation\">Evaluation</a></li>\n",
    "        <li><a href=\"#practice\">Practice</a></li>\n",
    "    </ol>\n",
    "</div>\n",
    "<br>\n",
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## import packages\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install --upgrade pip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install snapml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import scipy.optimize as opt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import normalize, StandardScaler\n",
    "from sklearn.utils.class_weight import compute_sample_weight\n",
    "from sklearn.metrics import roc_auc_score\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# dealing with imbalance classes\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from collections import Counter\n",
    "\n",
    "# suppress warnings\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Load Data From CSV File  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>MDVP:Fo(Hz)</th>\n",
       "      <th>MDVP:Fhi(Hz)</th>\n",
       "      <th>MDVP:Flo(Hz)</th>\n",
       "      <th>MDVP:Jitter(%)</th>\n",
       "      <th>MDVP:Jitter(Abs)</th>\n",
       "      <th>MDVP:RAP</th>\n",
       "      <th>MDVP:PPQ</th>\n",
       "      <th>Jitter:DDP</th>\n",
       "      <th>MDVP:Shimmer</th>\n",
       "      <th>...</th>\n",
       "      <th>Shimmer:DDA</th>\n",
       "      <th>NHR</th>\n",
       "      <th>HNR</th>\n",
       "      <th>status</th>\n",
       "      <th>RPDE</th>\n",
       "      <th>DFA</th>\n",
       "      <th>spread1</th>\n",
       "      <th>spread2</th>\n",
       "      <th>D2</th>\n",
       "      <th>PPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>phon_R01_S01_1</td>\n",
       "      <td>119.992</td>\n",
       "      <td>157.302</td>\n",
       "      <td>74.997</td>\n",
       "      <td>0.00784</td>\n",
       "      <td>0.00007</td>\n",
       "      <td>0.00370</td>\n",
       "      <td>0.00554</td>\n",
       "      <td>0.01109</td>\n",
       "      <td>0.04374</td>\n",
       "      <td>...</td>\n",
       "      <td>0.06545</td>\n",
       "      <td>0.02211</td>\n",
       "      <td>21.033</td>\n",
       "      <td>1</td>\n",
       "      <td>0.414783</td>\n",
       "      <td>0.815285</td>\n",
       "      <td>-4.813031</td>\n",
       "      <td>0.266482</td>\n",
       "      <td>2.301442</td>\n",
       "      <td>0.284654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>phon_R01_S01_2</td>\n",
       "      <td>122.400</td>\n",
       "      <td>148.650</td>\n",
       "      <td>113.819</td>\n",
       "      <td>0.00968</td>\n",
       "      <td>0.00008</td>\n",
       "      <td>0.00465</td>\n",
       "      <td>0.00696</td>\n",
       "      <td>0.01394</td>\n",
       "      <td>0.06134</td>\n",
       "      <td>...</td>\n",
       "      <td>0.09403</td>\n",
       "      <td>0.01929</td>\n",
       "      <td>19.085</td>\n",
       "      <td>1</td>\n",
       "      <td>0.458359</td>\n",
       "      <td>0.819521</td>\n",
       "      <td>-4.075192</td>\n",
       "      <td>0.335590</td>\n",
       "      <td>2.486855</td>\n",
       "      <td>0.368674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>phon_R01_S01_3</td>\n",
       "      <td>116.682</td>\n",
       "      <td>131.111</td>\n",
       "      <td>111.555</td>\n",
       "      <td>0.01050</td>\n",
       "      <td>0.00009</td>\n",
       "      <td>0.00544</td>\n",
       "      <td>0.00781</td>\n",
       "      <td>0.01633</td>\n",
       "      <td>0.05233</td>\n",
       "      <td>...</td>\n",
       "      <td>0.08270</td>\n",
       "      <td>0.01309</td>\n",
       "      <td>20.651</td>\n",
       "      <td>1</td>\n",
       "      <td>0.429895</td>\n",
       "      <td>0.825288</td>\n",
       "      <td>-4.443179</td>\n",
       "      <td>0.311173</td>\n",
       "      <td>2.342259</td>\n",
       "      <td>0.332634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>phon_R01_S01_4</td>\n",
       "      <td>116.676</td>\n",
       "      <td>137.871</td>\n",
       "      <td>111.366</td>\n",
       "      <td>0.00997</td>\n",
       "      <td>0.00009</td>\n",
       "      <td>0.00502</td>\n",
       "      <td>0.00698</td>\n",
       "      <td>0.01505</td>\n",
       "      <td>0.05492</td>\n",
       "      <td>...</td>\n",
       "      <td>0.08771</td>\n",
       "      <td>0.01353</td>\n",
       "      <td>20.644</td>\n",
       "      <td>1</td>\n",
       "      <td>0.434969</td>\n",
       "      <td>0.819235</td>\n",
       "      <td>-4.117501</td>\n",
       "      <td>0.334147</td>\n",
       "      <td>2.405554</td>\n",
       "      <td>0.368975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>phon_R01_S01_5</td>\n",
       "      <td>116.014</td>\n",
       "      <td>141.781</td>\n",
       "      <td>110.655</td>\n",
       "      <td>0.01284</td>\n",
       "      <td>0.00011</td>\n",
       "      <td>0.00655</td>\n",
       "      <td>0.00908</td>\n",
       "      <td>0.01966</td>\n",
       "      <td>0.06425</td>\n",
       "      <td>...</td>\n",
       "      <td>0.10470</td>\n",
       "      <td>0.01767</td>\n",
       "      <td>19.649</td>\n",
       "      <td>1</td>\n",
       "      <td>0.417356</td>\n",
       "      <td>0.823484</td>\n",
       "      <td>-3.747787</td>\n",
       "      <td>0.234513</td>\n",
       "      <td>2.332180</td>\n",
       "      <td>0.410335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             name  MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
       "0  phon_R01_S01_1      119.992       157.302        74.997         0.00784   \n",
       "1  phon_R01_S01_2      122.400       148.650       113.819         0.00968   \n",
       "2  phon_R01_S01_3      116.682       131.111       111.555         0.01050   \n",
       "3  phon_R01_S01_4      116.676       137.871       111.366         0.00997   \n",
       "4  phon_R01_S01_5      116.014       141.781       110.655         0.01284   \n",
       "\n",
       "   MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  ...  \\\n",
       "0           0.00007   0.00370   0.00554     0.01109       0.04374  ...   \n",
       "1           0.00008   0.00465   0.00696     0.01394       0.06134  ...   \n",
       "2           0.00009   0.00544   0.00781     0.01633       0.05233  ...   \n",
       "3           0.00009   0.00502   0.00698     0.01505       0.05492  ...   \n",
       "4           0.00011   0.00655   0.00908     0.01966       0.06425  ...   \n",
       "\n",
       "   Shimmer:DDA      NHR     HNR  status      RPDE       DFA   spread1  \\\n",
       "0      0.06545  0.02211  21.033       1  0.414783  0.815285 -4.813031   \n",
       "1      0.09403  0.01929  19.085       1  0.458359  0.819521 -4.075192   \n",
       "2      0.08270  0.01309  20.651       1  0.429895  0.825288 -4.443179   \n",
       "3      0.08771  0.01353  20.644       1  0.434969  0.819235 -4.117501   \n",
       "4      0.10470  0.01767  19.649       1  0.417356  0.823484 -3.747787   \n",
       "\n",
       "    spread2        D2       PPE  \n",
       "0  0.266482  2.301442  0.284654  \n",
       "1  0.335590  2.486855  0.368674  \n",
       "2  0.311173  2.342259  0.332634  \n",
       "3  0.334147  2.405554  0.368975  \n",
       "4  0.234513  2.332180  0.410335  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parkinson_ox = pd.read_csv(\"parkinsons.csv\")\n",
    "parkinson_ox.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 24)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape\n",
    "parkinson_ox.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'MDVP:Fo(Hz)', 'MDVP:Fhi(Hz)', 'MDVP:Flo(Hz)', 'MDVP:Jitter(%)',\n",
       "       'MDVP:Jitter(Abs)', 'MDVP:RAP', 'MDVP:PPQ', 'Jitter:DDP',\n",
       "       'MDVP:Shimmer', 'MDVP:Shimmer(dB)', 'Shimmer:APQ3', 'Shimmer:APQ5',\n",
       "       'MDVP:APQ', 'Shimmer:DDA', 'NHR', 'HNR', 'status', 'RPDE', 'DFA',\n",
       "       'spread1', 'spread2', 'D2', 'PPE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the columns\n",
    "parkinson_ox.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                 object\n",
       "MDVP:Fo(Hz)         float64\n",
       "MDVP:Fhi(Hz)        float64\n",
       "MDVP:Flo(Hz)        float64\n",
       "MDVP:Jitter(%)      float64\n",
       "MDVP:Jitter(Abs)    float64\n",
       "MDVP:RAP            float64\n",
       "MDVP:PPQ            float64\n",
       "Jitter:DDP          float64\n",
       "MDVP:Shimmer        float64\n",
       "MDVP:Shimmer(dB)    float64\n",
       "Shimmer:APQ3        float64\n",
       "Shimmer:APQ5        float64\n",
       "MDVP:APQ            float64\n",
       "Shimmer:DDA         float64\n",
       "NHR                 float64\n",
       "HNR                 float64\n",
       "status                int64\n",
       "RPDE                float64\n",
       "DFA                 float64\n",
       "spread1             float64\n",
       "spread2             float64\n",
       "D2                  float64\n",
       "PPE                 float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data types\n",
    "parkinson_ox.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# missing values\n",
    "parkinson_ox.isnull().any().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.753846\n",
       "0    0.246154\n",
       "Name: status, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value count of the class to see if we are dealing with a balanced or unbalanced dataset\n",
    "parkinson_ox['status'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Outcome column** : class is unbalance with the presence of parkinson disease up making 75.39% of the data set and absent 24.62%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now let's visualize the outcome\n",
    "sns.countplot(data=parkinson_ox , x='status', hue='status').set(title = \"0 absent/healthy and 1 present/PD\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data pre-processing and selection\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's drop useless column like the id\n",
    "parkinson_ox.drop(['name'], axis = 1, inplace = True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assign y(target) and X(input/features):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# target value\n",
    "y =  parkinson_ox['status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's drop the 'class' column\n",
    "parkinson_ox.drop(['status'], axis = 1, inplace = True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train/Test dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We split our dataset into train and test set:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = parkinson_ox.copy()\n",
    "y = y.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (156, 22) (156,)\n",
      "Test set: (39, 22) (39,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4, stratify=y)\n",
    "print ('Train set:', X_train.shape,  y_train.shape)\n",
    "print ('Test set:', X_test.shape,  y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature scaling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler() # scaler objecy\n",
    "X_train = scaler.fit_transform(X_train.to_numpy()) # allow the scaler object to learn the features and transform the training data\n",
    "X_test =  scaler.transform(X_test.to_numpy()) # apply the learned features on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(156, 22)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scaled x_train\n",
    "X_train = np.asarray(X_train)\n",
    "X_train.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39, 22)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scaled x_test\n",
    "X_test = np.asarray(X_test)\n",
    "X_test.shape "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"svm_snap\">\n",
    "    <h2>Build a RandomForest model with Snap ML</h2>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 0 ns\n",
      "Wall time: 3 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# import the RandomForest from Snap ML\n",
    "from snapml import RandomForestClassifier as SnapForest\n",
    "\n",
    "# in contrast to scikit-learn's LinearSVC, Snap ML offers multi-threaded CPU/GPU training of RandomForest\n",
    "# to use the GPU, set the use_gpu parameter to True\n",
    "# rf = SnapForest(class_weight='balanced', random_state=25, use_gpu=True, fit_intercept=False)\n",
    "\n",
    "# to set the number of threads used at training time, one needs to set the n_jobs parameter\n",
    "rf = SnapForest(n_estimators=10, criterion='gini', \n",
    "                            max_depth=4, min_samples_leaf=1, \n",
    "                            max_features='auto', bootstrap=True, \n",
    "                            n_jobs=1, random_state=None, verbose=False, \n",
    "                            use_histograms=False, hist_nbins=256, \n",
    "                            use_gpu=False, gpu_ids=[0], \n",
    "                            compress_trees=False)\n",
    "\n",
    "# train an SVM model using Snap ML\n",
    "clf = rf.fit(X_train, np.asarray(y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After being fitted, the model can then be used to predict new values:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat = clf.predict(X_test)\n",
    "yhat [0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"evaluation\">Evaluation</h2>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.50      0.67        10\n",
      "           1       0.85      1.00      0.92        29\n",
      "\n",
      "    accuracy                           0.87        39\n",
      "   macro avg       0.93      0.75      0.79        39\n",
      "weighted avg       0.89      0.87      0.86        39\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[ 5  5]\n",
      " [ 0 29]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test, yhat)\n",
    "#np.set_printoptions(precision=2)\n",
    "\n",
    "print (classification_report(y_test, yhat,labels=np.unique(yhat)))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure(figsize=(11,5))\n",
    "plot_confusion_matrix(cnf_matrix, ['absent','present'],normalize= False,  title='Confusion matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also easily use the __sklearn metrics__ to evaluate our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score, precision_score, jaccard_score, accuracy_score, recall_score "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8718\n",
      "Jaccard score: 0.8529\n",
      "Precision: 0.8529\n",
      "Recall: 1.0000\n",
      "Avg F1-score: 0.8555\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy: %.4f\" % accuracy_score(y_test, yhat))\n",
    "print(\"Jaccard score: %.4f\" % jaccard_score(y_test, yhat))\n",
    "print(\"Precision: %.4f\" % precision_score(y_test, yhat))\n",
    "print(\"Recall: %.4f\" % recall_score(y_test, yhat))\n",
    "print(\"Avg F1-score: %.4f\" % f1_score(y_test, yhat, average='weighted'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pickle the model:\n",
    "\n",
    "We'll pickle the model so we don't have to refit it every time we run this notebook. Remember, there are three steps:\n",
    "\n",
    "   1. Define the path to the location where it will save\n",
    "   2. Write the file (i.e., save the model)\n",
    "   3. Read the model back in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This module lets us save our models once we fit them.\n",
    "import pickle\n",
    "\n",
    "# Pickle the model\n",
    "with open('Parkinson_ox(RF-snapML).pickle', 'wb') as to_write:\n",
    "    pickle.dump(rf, to_write) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open pickled model\n",
    "with open('Parkinson_ox(RF-snapML).pickle', 'rb') as to_read:\n",
    "    rf_snapML = pickle.load(to_read)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"practice\">Practice</h2>\n",
    "Now use a <a href=\"https://snapml.readthedocs.io/en/latest/boosting_machines.html\">Boosting Machine Classifier</a> to see how the decision change."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boosting Machine Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 31.2 ms\n",
      "Wall time: 79.5 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# import the BoostingMachineClassifier from Snap ML\n",
    "from snapml import BoostingMachineClassifier as BoostMachine\n",
    "\n",
    "Booting_clf = BoostMachine(n_jobs=1, num_round=100, max_depth=4, \n",
    "                                 min_max_depth=1, max_max_depth=5, early_stopping_rounds=10, \n",
    "                                 random_state=0, base_score=None, learning_rate=0.1, verbose=False, \n",
    "                                 compress_trees=False, class_weight=None, use_histograms=True, \n",
    "                                 hist_nbins=256, use_gpu=False, gpu_ids=[0], colsample_bytree=1.0,\n",
    "                                 subsample=1.0, lambda_l2=0.0, \n",
    "                                 tree_select_probability=1.0, regularizer=1.0,\n",
    "                                 fit_intercept=False, gamma=1.0, n_components=10)\n",
    "\n",
    "\n",
    "Booting_clf.fit(X_train, y_train) \n",
    "yhat2 = Booting_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also easily use the __sklearn metrics__ to evaluate our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8974\n",
      "Jaccard score: 0.8788\n",
      "Precision: 0.8788\n",
      "Recall: 1.0000\n",
      "Avg F1-score: 0.8879\n"
     ]
    }
   ],
   "source": [
    "# print the F1-score and jaccard_score\n",
    "print(\"Accuracy: %.4f\" % accuracy_score(y_test, yhat2))\n",
    "print(\"Jaccard score: %.4f\" % jaccard_score(y_test, yhat2))\n",
    "print(\"Precision: %.4f\" % precision_score(y_test, yhat2))\n",
    "print(\"Recall: %.4f\" % recall_score(y_test, yhat2))\n",
    "print(\"Avg F1-score: %.4f\" % f1_score(y_test, yhat2, average='weighted'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.60      0.75        10\n",
      "           1       0.88      1.00      0.94        29\n",
      "\n",
      "    accuracy                           0.90        39\n",
      "   macro avg       0.94      0.80      0.84        39\n",
      "weighted avg       0.91      0.90      0.89        39\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[ 6  4]\n",
      " [ 0 29]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test, yhat2)\n",
    "#np.set_printoptions(precision=2)\n",
    "\n",
    "print (classification_report(y_test, yhat2,labels=np.unique(yhat2)))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure(figsize=(11,5))\n",
    "plot_confusion_matrix(cnf_matrix, classes=['absent','present'],normalize= False,  title='Confusion matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the boosting machine model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This module lets us save our models once we fit them.\n",
    "import pickle\n",
    "\n",
    "# Pickle the model\n",
    "with open('Parkinson_ox(Boost-snapML).pickle', 'wb') as to_write:\n",
    "    pickle.dump(Booting_clf, to_write) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open pickled model\n",
    "with open('Parkinson_ox(Boost-snapML).pickle', 'rb') as to_read:\n",
    "    boosting_snapML = pickle.load(to_read)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thank you for completing this lab!\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}