{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scikit-learnのバーションは 0.19の必要があります\n",
    "!pip install scikit-learn==0.19"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# scikit-learn modelをWatson MLに登録"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 認証情報の設定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wml_credentials={\n",
    "    # 事前準備した認証情報を張り付けて下さい\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分類モデルの生成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 必要ライブラリのロード\n",
    "from IPython.display import display\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 学習データの準備\n",
    "iris = load_iris()\n",
    "columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\n",
    "df_iris = pd.DataFrame(iris.data, columns=columns)\n",
    "target = iris.target\n",
    "display(df_iris.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modelの生成\n",
    "# 注意　モデルは確率値を返すものである必要があります。SVMではうまくいきません。\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()\n",
    "lr.fit(df_iris, target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# テスト用データの準備\n",
    "values = df_iris.iloc[[0,1,50,51,100,101]].values.tolist()\n",
    "print(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# テストの実施 \n",
    "# 予測値は [0, 0, 1, 1, 2, 2]になっているはずです\n",
    "lr.predict(values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Watson ML Model / Webサービスの登録"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Watson ML Clientの生成\n",
    "from watson_machine_learning_client import WatsonMachineLearningAPIClient\n",
    "client = WatsonMachineLearningAPIClient(wml_credentials)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modelの登録\n",
    "model_details = client.repository.store_model( lr, \"scikit-learn lr model\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Webサービスの登録\n",
    "model_id = model_details[\"metadata\"][\"guid\"]\n",
    "deployment_details = client.deployments.create( artifact_uid=model_id, name=\"scikit-learn lr deployment\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Webサービスのテスト\n",
    "model_endpoint_url = client.deployments.get_scoring_url( deployment_details )\n",
    "print(model_endpoint_url)\n",
    "client.deployments.score( model_endpoint_url, { \"values\" : values } )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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