Sample/Linear-Regression/diabetes-azureml-sklearn-LR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 線形回帰 Linear Regression による要因探索とメトリック記録"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "糖尿病の被験者データに線形回帰を適用し、説明変数・因子がどのように糖尿病の進行に影響しているかを分析します。Scikit Learnを使用し線形回帰で学習します。Azure Machine Learning service のメトリック記録機能を使って、作業内容を記録することも示します。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Azure Machine Learning service\n",
    "Azure Machine Learning service の資産管理の仕組みを利用して、メトリック、データセット、モデルを保存します。\n",
    "### Python SDK インポート"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Azure ML SDK Version:  1.0.62\n"
     ]
    }
   ],
   "source": [
    "print(\"Azure ML SDK Version: \", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ワークスペースへの接続"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "azureml\teastus\tmlservice\teastus\n"
     ]
    }
   ],
   "source": [
    "ws = Workspace.from_config()\n",
    "print(ws.name, ws.location, ws.resource_group, ws.location, sep = '\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 実験の開始"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment=Experiment(ws, \"linear-regression-explore\")\n",
    "# 実験メトリックの記録開始\n",
    "run = experiment.start_logging()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 学習データの準備"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# sklearn の糖尿病のサンプルデータをロード\n",
    "diabetes = datasets.load_diabetes()\n",
    "X = pd.DataFrame(diabetes.data, columns=(\"age\", \"sex\", \"bmi\", \"map\", \"tc\", \"ldl\", \"hdl\", \"tch\", \"ltg\", \"glu\"))\n",
    "y = pd.DataFrame(diabetes.target, columns=[\"y\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>map</th>\n",
       "      <th>tc</th>\n",
       "      <th>ldl</th>\n",
       "      <th>hdl</th>\n",
       "      <th>tch</th>\n",
       "      <th>ltg</th>\n",
       "      <th>glu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.038076</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.061696</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>-0.044223</td>\n",
       "      <td>-0.034821</td>\n",
       "      <td>-0.043401</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.019908</td>\n",
       "      <td>-0.017646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.001882</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.051474</td>\n",
       "      <td>-0.026328</td>\n",
       "      <td>-0.008449</td>\n",
       "      <td>-0.019163</td>\n",
       "      <td>0.074412</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.068330</td>\n",
       "      <td>-0.092204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.085299</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.044451</td>\n",
       "      <td>-0.005671</td>\n",
       "      <td>-0.045599</td>\n",
       "      <td>-0.034194</td>\n",
       "      <td>-0.032356</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.002864</td>\n",
       "      <td>-0.025930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.089063</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.011595</td>\n",
       "      <td>-0.036656</td>\n",
       "      <td>0.012191</td>\n",
       "      <td>0.024991</td>\n",
       "      <td>-0.036038</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>0.022692</td>\n",
       "      <td>-0.009362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005383</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.036385</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>0.003935</td>\n",
       "      <td>0.015596</td>\n",
       "      <td>0.008142</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.031991</td>\n",
       "      <td>-0.046641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age       sex       bmi       map        tc       ldl       hdl  \\\n",
       "0  0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
       "1 -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
       "2  0.085299  0.050680  0.044451 -0.005671 -0.045599 -0.034194 -0.032356   \n",
       "3 -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
       "4  0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "\n",
       "        tch       ltg       glu  \n",
       "0 -0.002592  0.019908 -0.017646  \n",
       "1 -0.039493 -0.068330 -0.092204  \n",
       "2 -0.002592  0.002864 -0.025930  \n",
       "3  0.034309  0.022692 -0.009362  \n",
       "4 -0.002592 -0.031991 -0.046641  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       y\n",
       "0  151.0\n",
       "1   75.0\n",
       "2  141.0\n",
       "3  206.0\n",
       "4  135.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 学習データとテストデータに分割\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.1,random_state=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 線形回帰 Linear Regression による学習\n",
    "[Linear Regression](https://scikit-learn.org/0.20/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression) による機械学習モデリング"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/konabuta/miniconda3/envs/myenv/lib/python3.6/site-packages/sklearn/linear_model/base.py:485: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n",
      "  linalg.lstsq(X, y)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "lr = linear_model.LinearRegression()\n",
    "lr = lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### モデル精度の確認"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "平均絶対誤差 39.90285306407927\n",
      "平均二乗誤差 2525.7799143633047\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
    "\n",
    "pred = lr.predict(X_test)\n",
    "mae = mean_absolute_error(y_test, pred)\n",
    "mse = mean_squared_error(y_test,pred)\n",
    "print(\"平均絶対誤差\", mae)\n",
    "print(\"平均二乗誤差\", mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## モデルの解釈"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ -47.976923   -224.02487968  527.14980328  327.11373838 -688.61659641\n",
      "   425.04508039   96.98090996  162.53776611  736.83537797   55.59044149]] [151.48940506]\n"
     ]
    }
   ],
   "source": [
    "# 回帰係数と切片の大きさ\n",
    "print(lr.coef_, lr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ltg</th>\n",
       "      <td>736.835378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tc</th>\n",
       "      <td>688.616596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi</th>\n",
       "      <td>527.149803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ldl</th>\n",
       "      <td>425.045080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>map</th>\n",
       "      <td>327.113738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>224.024880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tch</th>\n",
       "      <td>162.537766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hdl</th>\n",
       "      <td>96.980910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glu</th>\n",
       "      <td>55.590441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>47.976923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Feature Importance\n",
       "ltg          736.835378\n",
       "tc           688.616596\n",
       "bmi          527.149803\n",
       "ldl          425.045080\n",
       "map          327.113738\n",
       "sex          224.024880\n",
       "tch          162.537766\n",
       "hdl           96.980910\n",
       "glu           55.590441\n",
       "age           47.976923"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = pd.DataFrame(lr.coef_[0], (\"age\", \"sex\", \"bmi\", \"map\", \"tc\", \"ldl\", \"hdl\", \"tch\", \"ltg\", \"glu\"), columns=[\"Feature Importance\"]).abs()\n",
    "coeff_df.sort_values(by=[\"Feature Importance\"], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
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     "metadata": {},
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     "data": {
      "text/plain": [
       "<Figure size 432x432 with 0 Axes>"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.clf()\n",
    "plt.figure(figsize=(6,6))\n",
    "coeff_df.plot.bar()\n",
    "plt.title(\"feature importance\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.savefig('lr-im.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## メトリックの記録"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Experiment に紐づけて、利用したハイパーパラメータやモデル精度を記録します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 精度\n",
    "run.log(\"平均絶対誤差\", mae)\n",
    "run.log(\"平均二乗誤差\", mse)\n",
    "\n",
    "# 画像記録\n",
    "run.log_image(\"feature importance\", \"lr-im.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### モデル登録"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "filename = 'lr-model.pkl'\n",
    "pickle.dump(lr, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<azureml._restclient.models.batch_artifact_content_information_dto.BatchArtifactContentInformationDto at 0x123581320>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run.upload_file(\"outputs/\"+filename, filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# モデル登録"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Azure Machine Learning service 上にモデルを保存します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.model import Model\n",
    "\n",
    "model = run.register_model(model_name='lr-sklearn', \n",
    "                           model_path = 'outputs/' + filename,\n",
    "                           tags = {'area': \"sklearn による線形回帰\", 'type': \"回帰 Regression\", 'run_id' : run.id}\n",
    "                          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr-sklearn 8 {'area': 'sklearn による線形回帰', 'type': '回帰 Regression', 'run_id': '9ce173bf-0d7d-4edd-8635-d5babfbf394a'}\n"
     ]
    }
   ],
   "source": [
    "print(model.name, model.version, model.tags)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 実験完了"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "start_logging() で始めた Experiment を終了します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "run.complete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'runId': '9ce173bf-0d7d-4edd-8635-d5babfbf394a',\n",
       " 'target': 'sdk',\n",
       " 'status': 'Completed',\n",
       " 'startTimeUtc': '2019-09-22T05:21:39.674366Z',\n",
       " 'endTimeUtc': '2019-09-22T05:22:00.314263Z',\n",
       " 'properties': {'azureml.git.repository_uri': 'https://github.com/konabuta/DataExplore-Workshop.git',\n",
       "  'mlflow.source.git.repoURL': 'https://github.com/konabuta/DataExplore-Workshop.git',\n",
       "  'azureml.git.branch': 'master',\n",
       "  'mlflow.source.git.branch': 'master',\n",
       "  'azureml.git.commit': '279341ec30a72695aeb9220af510d5a30520bbd6',\n",
       "  'mlflow.source.git.commit': '279341ec30a72695aeb9220af510d5a30520bbd6',\n",
       "  'azureml.git.dirty': 'True',\n",
       "  'ContentSnapshotId': '715bd63d-e3dd-4c37-9452-f3a1af548647'},\n",
       " 'logFiles': {}}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Git情報などの確認\n",
    "run.get_details()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"width:100%\"><tr><th>Experiment</th><th>Id</th><th>Type</th><th>Status</th><th>Details Page</th><th>Docs Page</th></tr><tr><td>linear-regression-explore</td><td>9ce173bf-0d7d-4edd-8635-d5babfbf394a</td><td></td><td>Completed</td><td><a href=\"https://mlworkspace.azure.ai/portal/subscriptions/9c0f91b8-eb2f-484c-979c-15848c098a6b/resourceGroups/mlservice/providers/Microsoft.MachineLearningServices/workspaces/azureml/experiments/linear-regression-explore/runs/9ce173bf-0d7d-4edd-8635-d5babfbf394a\" target=\"_blank\" rel=\"noopener\">Link to Azure Portal</a></td><td><a href=\"https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run.Run?view=azure-ml-py\" target=\"_blank\" rel=\"noopener\">Link to Documentation</a></td></tr></table>"
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