1010_E2Quiz.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quiz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_wine\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X, y = load_wine().data, load_wine().target\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "models = {\n",
    "    'DecisionTreeClassifier': DecisionTreeClassifier,\n",
    "    'RandomForestClassifier': RandomForestClassifier,\n",
    "    'SVC': SVC,\n",
    "    'SGDClassifier': SGDClassifier,\n",
    "    'LogisticRegression': LogisticRegression\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pha\\anaconda3\\envs\\1pha\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "\n",
    "wine_models, wine_result = dict(), dict()\n",
    "for name, model in models.items():\n",
    "    \n",
    "    classifier = model()\n",
    "    classifier.fit(X_train, y_train)\n",
    "    y_pred = classifier.predict(X_test)\n",
    "    wine_models[name] = classifier\n",
    "    wine_result[name] = {\n",
    "        'accuracy_score': accuracy_score(y_test, y_pred),\n",
    "        'classification_report': classification_report(y_test, y_pred),\n",
    "        'confusion_matrix': confusion_matrix(y_test, y_pred)\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SGDClassifier\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "insight = 'confusion_matrix'\n",
    "for name, result in wine_result.items():\n",
    "    \n",
    "    print(name)\n",
    "    sns.heatmap(result[insight], annot=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2. With Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_wine, load_breast_cancer, load_digits\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run(dataset='wine'):\n",
    "    \n",
    "    data_list = {\n",
    "        'wine': load_wine,\n",
    "        'breast_cancer': load_breast_cancer,\n",
    "        'digits': load_digits,\n",
    "        }\n",
    "    \n",
    "    models = {\n",
    "        'DecisionTreeClassifier': DecisionTreeClassifier,\n",
    "        'RandomForestClassifier': RandomForestClassifier,\n",
    "        'SVC': SVC,\n",
    "        'SGDClassifier': SGDClassifier,\n",
    "        'LogisticRegression': LogisticRegression\n",
    "        }\n",
    "    \n",
    "    results = dict()\n",
    "    for name, model in models.items():\n",
    "    \n",
    "        classifier = model()\n",
    "        classifier.fit(X_train, y_train)\n",
    "        y_pred = classifier.predict(X_test)\n",
    "        models[name] = classifier\n",
    "        results[name] = {\n",
    "            'accuracy_score': accuracy_score(y_test, y_pred),\n",
    "            'classification_report': classification_report(y_test, y_pred),\n",
    "            'confusion_matrix': confusion_matrix(y_test, y_pred)\n",
    "        }\n",
    "        print(name)\n",
    "        sns.heatmap(results[name]['confusion_matrix'], annot=True)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SGDClassifier\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pha\\anaconda3\\envs\\1pha\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pha\\anaconda3\\envs\\1pha\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run(dataset='wine')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3. Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AutoML:\n",
    "\n",
    "    \n",
    "    def __init__(self, data='wine', test_size=0.2):\n",
    "            \n",
    "        data_list = {\n",
    "            'wine': load_wine,\n",
    "            'breast_cancer': load_breast_cancer,\n",
    "            'digits': load_digits,\n",
    "            }\n",
    "\n",
    "        models = {\n",
    "            'DecisionTreeClassifier': DecisionTreeClassifier,\n",
    "            'RandomForestClassifier': RandomForestClassifier,\n",
    "            'SVC': SVC,\n",
    "            'SGDClassifier': SGDClassifier,\n",
    "            'LogisticRegression': LogisticRegression\n",
    "            }\n",
    "    \n",
    "        self.dataset = data_list[data]()\n",
    "        X, y = self.dataset.data, self.dataset.target\n",
    "        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "        \n",
    "        self.models, self.results = dict(), dict()\n",
    "        \n",
    "        \n",
    "    def fit(self):\n",
    "        \n",
    "        for name, model in models.items():\n",
    "    \n",
    "            classifier = model()\n",
    "            classifier.fit(self.X_train, self.y_train)\n",
    "            self.models[name] = classifier\n",
    "            \n",
    "            \n",
    "    def predict(self):\n",
    "        \n",
    "        for name, model in self.models.items():\n",
    "            \n",
    "            y_pred = model.predict(self.X_test)\n",
    "            self.results[name] = {\n",
    "                'accuracy_score': accuracy_score(self.y_test, y_pred),\n",
    "                'classification_report': classification_report(self.y_test, y_pred),\n",
    "                'confusion_matrix': confusion_matrix(self.y_test, y_pred)\n",
    "            }\n",
    "            \n",
    "    def show(self, insight='classification_report'):\n",
    "        \n",
    "        for name, model in self.results.items():\n",
    "\n",
    "            print(name)\n",
    "            if insight == 'confusion_matrix':\n",
    "                sns.heatmap(result[insight], annot=True)\n",
    "                plt.show()\n",
    "\n",
    "            else:\n",
    "                print(result[insight])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl = AutoML(data='breast_cancer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pha\\anaconda3\\envs\\1pha\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SGDClassifier\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "automl.fit()\n",
    "automl.predict()\n",
    "automl.show(insight='confusion_matrix')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}