demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.datasets import load_wine\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "%matplotlib inline\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "from auto_learning.models import CLASSIFICATION_MODELS, REGRESSION_MODELS\n",
    "from auto_learning.hyp_param_search import HypParamSearch\n",
    "\n",
    "data = load_wine()\n",
    "x_train, x_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.3)"
   ]
  },
  {
   "source": [
    "## 分類"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### モデルを自分で定義"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Logisitic回帰をimport\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Logistic回帰のハイパラ候補を辞書形式で格納\n",
    "params = {'C': [10**i for i in range(0, 5)]}\n",
    "\n",
    "# ハイパラ探索時の条件を指定\n",
    "# 変数選択をしない\n",
    "feature_selection = 'None'\n",
    "# クロスバリデーションはkfoldで行う\n",
    "crossval_type = 'kfold'\n",
    "# ハイパラ探索はベイズ最適化で行う\n",
    "search_type = 'bayes_Gpyopt'\n",
    "# メトリクスはaccuracy\n",
    "metrics = 'accuracy'\n",
    "# 分類問題\n",
    "problem_type = 'classification'\n",
    "\n",
    "hyp = HypParamSearch(\n",
    "    x_train,\n",
    "    y_train,\n",
    "    x_test,\n",
    "    y_test,\n",
    "    est=LogisticRegression,\n",
    "    problem_type=problem_type,\n",
    "    feature_selection=feature_selection,\n",
    "    params_dict=params,\n",
    "    crossval_type=crossval_type,\n",
    "    search_type=search_type,\n",
    "    metrics=metrics\n",
    ")\n",
    "y_test_list, y_test_predicted_list, val_score, test_score, est = hyp.hyp_param_search()\n",
    "\n",
    "cm = confusion_matrix(y_test_list, y_test_predicted_list)\n",
    "cm_df = pd.DataFrame(cm, index=data.target_names, columns=data.target_names)\n",
    "plt.figure()\n",
    "sns.heatmap(cm_df, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "### モデルを選択"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "`CLASSIFICATION_MODELS`中に代表的なモデルとハイパラ候補が格納されているので、それを使う\n",
    "`CLASSIFICATION_MODELS.keys()`で一覧が確認できる"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "dict_keys(['logistic', 'tree', 'rfc', 'svc', 'knc', 'gbc', 'rfc_cc', 'svrsvc'])"
      ]
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "CLASSIFICATION_MODELS.keys()"
   ]
  },
  {
   "source": [
    "svcを選んでみる"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'sklearn.svm._classes.SVC'>\n{'C': [0.03125, 0.0625, 0.125, 0.25, 0.5, 1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'gamma': [0.0009765625, 0.001953125, 0.00390625, 0.0078125, 0.015625, 0.03125, 0.0625, 0.125, 0.25, 0.5, 1, 2, 4, 8, 16]}\n"
     ]
    }
   ],
   "source": [
    "func = CLASSIFICATION_MODELS['svc']\n",
    "est, params = func()\n",
    "print(est)\n",
    "print(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# ハイパラ探索時の条件を指定\n",
    "# 変数選択をしない\n",
    "feature_selection = 'None'\n",
    "# クロスバリデーションはkfoldで行う\n",
    "crossval_type = 'kfold'\n",
    "# ハイパラ探索はベイズ最適化で行う\n",
    "search_type = 'bayes_Gpyopt'\n",
    "# メトリクスはaccuracy\n",
    "metrics = 'accuracy'\n",
    "# 分類問題\n",
    "problem_type = 'classification'\n",
    "\n",
    "hyp = HypParamSearch(\n",
    "    x_train,\n",
    "    y_train,\n",
    "    x_test,\n",
    "    y_test,\n",
    "    est=est,\n",
    "    problem_type=problem_type,\n",
    "    feature_selection=feature_selection,\n",
    "    params_dict=params,\n",
    "    crossval_type=crossval_type,\n",
    "    search_type=search_type,\n",
    "    metrics=metrics\n",
    ")\n",
    "y_test_list, y_test_predicted_list, val_score, test_score, est = hyp.hyp_param_search()\n",
    "\n",
    "cm = confusion_matrix(y_test_list, y_test_predicted_list)\n",
    "cm_df = pd.DataFrame(cm, index=data.target_names, columns=data.target_names)\n",
    "plt.figure()\n",
    "sns.heatmap(cm_df, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "## 回帰"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_diabetes\n",
    "\n",
    "data = load_diabetes()\n",
    "x_train, x_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.3)"
   ]
  },
  {
   "source": [
    "`REGRESSION_MODELS`中に代表的なモデルとハイパラ候補が格納されているので、それを使う\n",
    "`REGRESSION_MODELS.keys()`で一覧が確認できる"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "dict_keys(['ridge', 'lasso', 'svr', 'rfr', 'pca_ridge', 'pca_lasso'])"
      ]
     },
     "metadata": {},
     "execution_count": 28
    }
   ],
   "source": [
    "REGRESSION_MODELS.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'sklearn.svm._classes.SVR'>\n('C', [0.03125, 0.0625, 0.125, 0.25, 0.5, 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024])\n('epsilon', [0.0009765625, 0.001953125, 0.00390625, 0.0078125, 0.015625, 0.03125, 0.0625, 0.125, 0.25, 0.5, 1])\n('gamma', [9.5367431640625e-07, 1.9073486328125e-06, 3.814697265625e-06, 7.62939453125e-06, 1.52587890625e-05, 3.0517578125e-05, 6.103515625e-05, 0.0001220703125, 0.000244140625, 0.00048828125, 0.0009765625, 0.001953125, 0.00390625, 0.0078125, 0.015625, 0.03125, 0.0625, 0.125, 0.25, 0.5, 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024])\n"
     ]
    }
   ],
   "source": [
    "# SVRを選択\n",
    "func = REGRESSION_MODELS['svr']\n",
    "est, params = func()\n",
    "\n",
    "print(est)\n",
    "\n",
    "for param in params.items():\n",
    "    print(param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "r2 score =  0.5646058471323482\n"
     ]
    }
   ],
   "source": [
    "# ハイパラ探索時の条件を指定\n",
    "# 変数選択をしない\n",
    "feature_selection = 'None'\n",
    "# クロスバリデーションはkfoldで行う\n",
    "crossval_type = 'kfold'\n",
    "# ハイパラ探索はベイズ最適化で行う\n",
    "search_type = 'bayes_Gpyopt'\n",
    "# メトリクスはr2 score\n",
    "metrics = 'r2'\n",
    "# 回帰問題\n",
    "problem_type = 'regression'\n",
    "\n",
    "hyp = HypParamSearch(\n",
    "    x_train,\n",
    "    y_train,\n",
    "    x_test,\n",
    "    y_test,\n",
    "    est=est,\n",
    "    problem_type=problem_type,\n",
    "    feature_selection=feature_selection,\n",
    "    params_dict=params,\n",
    "    crossval_type=crossval_type,\n",
    "    search_type=search_type,\n",
    "    metrics=metrics\n",
    ")\n",
    "y_test_list, y_test_predicted_list, val_score, test_score, est = hyp.hyp_param_search()\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(y_test_list, y_test_predicted_list)\n",
    "plt.show()\n",
    "\n",
    "print('r2 score = ', test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "auto_learning_kernel",
   "language": "python",
   "name": "auto_learning_kernel"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}