Random Forest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn import metrics\n",
    "\n",
    "%matplotlib inline \n",
    "\n",
    "# define project path\n",
    "projectpath = 'projectpath/'\n",
    "train_data_csv = projectpath + 'ready_train.csv'\n",
    "test_data_csv = projectpath + 'ready_test.csv'\n",
    "\n",
    "df_train = pd.read_csv(train_data_csv,header=None)\n",
    "\n",
    "# save target values with index into new dataframe 'labels'\n",
    "df_labels = pd.read_csv(train_data_csv, names=['target'], header=None, usecols=[1])\n",
    "\n",
    "# save as array\n",
    "df_labels = df_labels['target'].values\n",
    "\n",
    "# save data into new dataframe 'training_data'\n",
    "df_training_data_raw = df_train.drop(columns=[0,1])\n",
    "\n",
    "# split dataset into train (80%) and test data (20%); random_state=1 makes it reproducible (could be any number); stratify ensures that the proportion stays in test and training data sets\n",
    "data_train, data_test, labels_train, labels_test = train_test_split(df_training_data_raw, df_labels, test_size=0.2, random_state=1, shuffle=True, stratify=df_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['G', 'F', 'G', 'J', 'G'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_test[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features=9, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=4,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestClassifier(bootstrap=False, criterion=\"gini\", max_depth=None, max_features=9, min_samples_split=4)\n",
    "clf.fit(data_train,labels_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## use gridsearchcv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a RF model and use it with GridSearchCV\n",
    "rf_model = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# create a dictionary of all values we want to test for parameter n_estimators (= number of trees)\n",
    "param_grid = {'n_estimators': np.arange(1, 150), 'max_depth': np.arange(2, 5)}\n",
    "\n",
    "# use gridsearch to test all values for n_estimators in range defined above\n",
    "optimal_model = GridSearchCV(rf_model, param_grid, cv=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fit model on training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit model to data\n",
    "optimal_model.fit(data_train, labels_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## see how model performs on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A' 'F' 'E' 'J' 'B' 'J' 'B' 'D' 'G' 'J' 'J' 'C' 'G' 'C' 'G' 'C' 'H' 'E'\n",
      " 'D' 'G' 'I' 'H' 'C' 'G' 'A' 'B' 'H' 'B' 'E' 'H' 'H' 'I' 'A' 'D' 'I' 'F'\n",
      " 'J' 'B' 'A' 'A' 'E' 'H' 'I' 'I' 'I' 'D' 'A' 'H' 'E' 'G' 'J' 'B' 'A' 'J'\n",
      " 'B' 'D' 'B' 'D' 'B' 'E' 'H' 'B' 'A' 'A' 'B' 'C' 'E' 'I' 'B' 'J' 'H' 'H'\n",
      " 'E' 'I' 'H' 'H' 'F' 'J' 'D' 'H' 'G' 'B' 'G' 'G' 'I' 'C' 'F' 'B' 'A' 'B'\n",
      " 'A' 'E' 'F' 'G' 'A' 'H' 'J' 'I' 'J' 'D']\n"
     ]
    }
   ],
   "source": [
    "# predict the labels for the test slice\n",
    "labels_pred = clf.predict(data_test)\n",
    "print(labels_pred[:100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "       2    3    4    5    6    7    8    9    10   11  ...   776  777  778  \\\n",
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       "[5 rows x 784 columns]"
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     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8871527777777778\n",
      "Precision: [0.87239583 0.85809906 0.91081081 0.88082902 0.8719346  0.89164491\n",
      " 0.8902439  0.88266667 0.91643454 0.89801325]\n",
      "Recall: [0.89452603 0.85580774 0.90106952 0.90787717 0.85447263 0.91188251\n",
      " 0.87716956 0.88384513 0.87850467 0.90641711]\n"
     ]
    }
   ],
   "source": [
    "# check the accuracy for the test data\n",
    "print(\"Accuracy:\", metrics.accuracy_score(labels_test, labels_pred))\n",
    "# check the precision for the test data\n",
    "print(\"Precision:\",metrics.precision_score(labels_test, labels_pred, average=None))\n",
    "# check the recall for the test data\n",
    "print(\"Recall:\",metrics.recall_score(labels_test, labels_pred, average=None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A' 'B' 'A' 'A' 'B' 'G' 'F' 'G' 'A' 'H' 'A' 'A' 'A' 'D' 'A' 'A' 'A' 'A'\n",
      " 'A' 'A' 'A' 'A' 'A' 'F' 'A' 'A' 'A' 'A' 'A' 'A' 'H' 'A' 'A' 'F' 'A' 'A'\n",
      " 'H' 'A' 'A' 'A' 'A' 'A' 'A' 'A' 'A' 'A' 'A' 'A' 'A' 'A' 'A' 'A' 'A' 'A'\n",
      " 'A' 'A' 'A' 'A' 'A' 'A' 'A' 'A' 'J' 'A' 'A' 'A' 'A' 'A' 'A' 'A' 'A' 'A'\n",
      " 'A' 'A' 'B' 'A' 'A' 'A' 'A' 'A' 'A' 'D' 'C' 'A' 'A' 'A' 'A' 'A' 'A' 'D'\n",
      " 'A' 'A' 'G' 'A' 'A' 'A' 'A' 'A' 'A' 'A']\n"
     ]
    }
   ],
   "source": [
    "df_test=pd.read_csv(test_data_csv, sep=',', header=None)\n",
    "test_data_new = df_test.drop(columns=[0])\n",
    "# predict values for new data\n",
    "predicted = clf.predict(test_data_new)\n",
    "print(predicted[0:100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, target]\n",
       "Index: []"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_submission = pd.DataFrame(columns=['id','target'])\n",
    "predicted = pd.Series(predicted)\n",
    "df_submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>target</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>B</td>\n",
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      ],
      "text/plain": [
       "   id target\n",
       "0   0      A\n",
       "1   1      B\n",
       "2   2      A\n",
       "3   3      A\n",
       "4   4      B"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add calculated target values to csv and format for submission\n",
    "df_submission['target'] = predicted\n",
    "df_submission['id'] = df_submission.index\n",
    "submission_file = projectpath + 'submission_rf.csv'\n",
    "df_submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_submission.to_csv(submission_file, index=False, columns=['id','target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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