a5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data.csv', header=None)\n",
    "X = np.array(data.iloc[:, [0, 1]])\n",
    "y = np.array(data.iloc[:, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rs(result, y):\n",
    "    print(result)\n",
    "    print(y)\n",
    "    print(accuracy_score(result, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GridSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model  = SVC(gamma='auto')\n",
    "parameters = {\n",
    "    'C' : [1, 2, 3, 4, 5],\n",
    "    'kernel' : ['linear', 'poly', 'rbf', 'sigmoid'],\n",
    "    'decision_function_shape' : ['ovo', 'ovr'],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1, 'decision_function_shape': 'ovo', 'kernel': 'linear'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr = GridSearchCV(model, parameters, cv=4)\n",
    "gr.fit(X_train, y_train)\n",
    "gr.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n",
      "[1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0]\n",
      "0.85\n"
     ]
    }
   ],
   "source": [
    "model = SVC(C=1, decision_function_shape='ovo', kernel='linear')\n",
    "model.fit(X_train, y_train)\n",
    "svm_result = model.predict(X_test)\n",
    "rs(svm_result, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GridSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'presort': True, 'splitter': 'best'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = DecisionTreeClassifier()\n",
    "parameters = {\n",
    "    'splitter' : ['random', 'best'],\n",
    "    'presort' : [True, False]\n",
    "}\n",
    "gr = GridSearchCV(model, parameters, cv=4, iid=True)\n",
    "gr.fit(X_train, y_train)\n",
    "gr.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0]\n",
      "[1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0]\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "model = DecisionTreeClassifier(presort=True, splitter='best')\n",
    "model.fit(X_train, y_train)\n",
    "tree_result = model.predict(X_test)\n",
    "rs(tree_result, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GridSearch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "model = MLPClassifier(hidden_layer_sizes=76)\n",
    "parameters = {\n",
    "    'activation' : ['identity', 'logistic', 'tanh', 'relu'],\n",
    "    'solver' : ['lbfgs', 'sgd', 'adam'],\n",
    "    'learning_rate' : ['constant', 'invscaling', 'adaptive'],\n",
    "    'shuffle' : [True, False],\n",
    "    'momentum' : [0.5, 0.6, 0.7, 0.8, 0.9, 1]\n",
    "}\n",
    "gr = GridSearchCV(model, parameters, cv=4)\n",
    "gr.fit(X_train, y_train)\n",
    "gr.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Google Colab:\n",
    "{'activation': 'tanh',\n",
    " 'learning_rate': 'adaptive',\n",
    " 'momentum': 1,\n",
    " 'shuffle': True,\n",
    " 'solver': 'lbfgs'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 0]\n",
      "[1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0]\n",
      "0.9\n"
     ]
    }
   ],
   "source": [
    "model = MLPClassifier(hidden_layer_sizes=76, activation='tanh', learning_rate='adaptive', momentum=1, shuffle=True, solver='lbfgs')\n",
    "model.fit(X_train, y_train)\n",
    "pla_result = model.predict(X_test)\n",
    "rs(pla_result, y_test)"
   ]
  }
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