{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "from preamble import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 알고리즘 체인과 파이프라인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# 데이터 적재와 분할\n",
    "cancer = load_breast_cancer()\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    cancer.data, cancer.target, random_state=0)\n",
    "\n",
    "# 훈련 데이터의 최솟값, 최댓값을 계산합니다\n",
    "scaler = MinMaxScaler().fit(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "사이킷런 0.20 버전에서 `SVC` 클래스의 `gamma` 매개변수 옵션에 `auto`외에 `scale`이 추가되었습니다. `auto`는 `1/n_features`, 즉 특성 개수의 역수입니다. `scale`은 `1/(n_features * X.std())`로 스케일 조정이 되지 않은 특성에서 다 좋은 결과를 만듭니다. 사이킷런 0.22 버전부터는 `gamma` 매개변수의 기본값이 `auto`에서 `scale`로 변경됩니다. 서포트 벡터 머신을 사용하기 전에 특성을 표준화 전처리하면 `scale`과 `auto`는 차이가 없습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "테스트 점수: 0.97\n"
     ]
    }
   ],
   "source": [
    "# 훈련 데이터의 스케일을 조정합니다\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "\n",
    "svm = SVC()\n",
    "# 스케일 조정된 훈련데이터에 SVM을 학습시킵니다\n",
    "svm.fit(X_train_scaled, y_train)\n",
    "# 테스트 데이터의 스케일을 조정하고 점수를 계산합니다\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "print(\"테스트 점수: {:.2f}\".format(svm.score(X_test_scaled, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터 전처리와 매개변수 선택"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "최상의 교차 검증 정확도: 0.98\n",
      "테스트 점수: 0.97\n",
      "최적의 매개변수:  {'C': 1, 'gamma': 1}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "# 이 코드는 예를 위한 것입니다. 실제로 사용하지 마세요.\n",
    "param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "              'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}\n",
    "grid = GridSearchCV(SVC(), param_grid=param_grid, cv=5)\n",
    "grid.fit(X_train_scaled, y_train)\n",
    "print(\"최상의 교차 검증 정확도: {:.2f}\".format(grid.best_score_))\n",
    "print(\"테스트 점수: {:.2f}\".format(grid.score(X_test_scaled, y_test)))\n",
    "print(\"최적의 매개변수: \", grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 4500x2400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_improper_processing()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 파이프라인 구축하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "pipe = Pipeline([(\"scaler\", MinMaxScaler()), (\"svm\", SVC())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', MinMaxScaler()), ('svm', SVC())])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "테스트 점수: 0.97\n"
     ]
    }
   ],
   "source": [
    "print(\"테스트 점수: {:.2f}\".format(pipe.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 그리드 서치에 파이프라인 적용하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "param_grid = {'svm__C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "              'svm__gamma': [0.001, 0.01, 0.1, 1, 10, 100]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "최상의 교차 검증 정확도: 0.98\n",
      "테스트 세트 점수: 0.97\n",
      "최적의 매개변수: {'svm__C': 1, 'svm__gamma': 1}\n"
     ]
    }
   ],
   "source": [
    "grid = GridSearchCV(pipe, param_grid=param_grid, cv=5)\n",
    "grid.fit(X_train, y_train)\n",
    "print(\"최상의 교차 검증 정확도: {:.2f}\".format(grid.best_score_))\n",
    "print(\"테스트 세트 점수: {:.2f}\".format(grid.score(X_test, y_test)))\n",
    "print(\"최적의 매개변수:\", grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "application/pdf": 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      "text/plain": [
       "<Figure size 4500x2400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_proper_processing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnd = np.random.RandomState(seed=0)\n",
    "X = rnd.normal(size=(100, 10000))\n",
    "y = rnd.normal(size=(100,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_selected.shape: (100, 500)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectPercentile, f_regression\n",
    "\n",
    "select = SelectPercentile(score_func=f_regression, percentile=5).fit(X, y)\n",
    "X_selected = select.transform(X)\n",
    "print(\"X_selected.shape:\", X_selected.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "교차 검증 점수 (릿지): 0.91\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import Ridge\n",
    "print(\"교차 검증 점수 (릿지): {:.2f}\".format(\n",
    "      np.mean(cross_val_score(Ridge(), X_selected, y, cv=5))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "교차 검증 점수 (파이프라인): -0.25\n"
     ]
    }
   ],
   "source": [
    "pipe = Pipeline([(\"select\", SelectPercentile(score_func=f_regression,\n",
    "                                             percentile=5)),\n",
    "                 (\"ridge\", Ridge())])\n",
    "print(\"교차 검증 점수 (파이프라인): {:.2f}\".format(\n",
    "      np.mean(cross_val_score(pipe, X, y, cv=5))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 파이프라인 인터페이스"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit(self, X, y):\n",
    "    X_transformed = X\n",
    "    for name, estimator in self.steps[:-1]:\n",
    "        # 마지막 단계를 빼고 fit과 transform을 반복합니다\n",
    "        X_transformed = estimator.fit_transform(X_transformed, y)\n",
    "    # 마지막 단계 fit을 호출합니다\n",
    "    self.steps[-1][1].fit(X_transformed, y)\n",
    "    return self"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(self, X):\n",
    "    X_transformed = X\n",
    "    for step in self.steps[:-1]:\n",
    "        # 마지막 단계를 빼고 transform을 반복합니다\n",
    "        X_transformed = step[1].transform(X_transformed)\n",
    "    # 마지막 단계 predict을 호출합니다\n",
    "    return self.steps[-1][1].predict(X_transformed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `make_pipleline`을 사용한 파이프라인 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "# 표준적인 방법\n",
    "pipe_long = Pipeline([(\"scaler\", MinMaxScaler()), (\"svm\", SVC(C=100))])\n",
    "# 간소화된 방법\n",
    "pipe_short = make_pipeline(MinMaxScaler(), SVC(C=100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "파이프라인 단계:\n",
      " [('minmaxscaler', MinMaxScaler()), ('svc', SVC(C=100))]\n"
     ]
    }
   ],
   "source": [
    "print(\"파이프라인 단계:\\n\", pipe_short.steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "파이프라인 단계:\n",
      " [('standardscaler-1', StandardScaler()), ('pca', PCA(n_components=2)), ('standardscaler-2', StandardScaler())]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pipe = make_pipeline(StandardScaler(), PCA(n_components=2), StandardScaler())\n",
    "print(\"파이프라인 단계:\\n\", pipe.steps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 단계 속성에 접근하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "components.shape: (2, 30)\n"
     ]
    }
   ],
   "source": [
    "# cancer 데이터셋에 앞서 만든 파이프라인을 적용합니다\n",
    "pipe.fit(cancer.data)\n",
    "# \"pca\" 단계의 두 개 주성분을 추출합니다\n",
    "components = pipe.named_steps[\"pca\"].components_\n",
    "print(\"components.shape:\", components.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 그리드 서치 안의 파이프라인의 속성에 접근하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "pipe = make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'logisticregression__C': [0.01, 0.1, 1, 10, 100]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                                       ('logisticregression',\n",
       "                                        LogisticRegression(max_iter=1000))]),\n",
       "             param_grid={'logisticregression__C': [0.01, 0.1, 1, 10, 100]})"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    cancer.data, cancer.target, random_state=4)\n",
    "grid = GridSearchCV(pipe, param_grid, cv=5)\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "최상의 모델:\n",
      " Pipeline(steps=[('standardscaler', StandardScaler()),\n",
      "                ('logisticregression', LogisticRegression(C=1, max_iter=1000))])\n"
     ]
    }
   ],
   "source": [
    "print(\"최상의 모델:\\n\", grid.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "로지스틱 회귀 단계:\n",
      " LogisticRegression(C=1, max_iter=1000)\n"
     ]
    }
   ],
   "source": [
    "print(\"로지스틱 회귀 단계:\\n\",\n",
    "      grid.best_estimator_.named_steps[\"logisticregression\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "로지스틱 회귀 계수:\n",
      " [[-0.436 -0.343 -0.408 -0.534 -0.15   0.61  -0.726 -0.785  0.039  0.275\n",
      "  -1.298  0.049 -0.673 -0.934 -0.139  0.45  -0.13  -0.101  0.434  0.716\n",
      "  -1.091 -1.095 -0.852 -1.064 -0.743  0.073 -0.823 -0.653 -0.644 -0.42 ]]\n"
     ]
    }
   ],
   "source": [
    "print(\"로지스틱 회귀 계수:\\n\",\n",
    "      grid.best_estimator_.named_steps[\"logisticregression\"].coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 전처리와 모델의 매개변수를 위한 그리드 서치"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 보스턴 주택 데이터셋이 1.0 버전에 deprecated 되었고 1.2 버전에서 삭제됩니다.\n",
    "# 경고 메시지를 피하기 위해 다음 코드를 추가합니다.\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "\n",
    "from sklearn.datasets import load_boston\n",
    "boston = load_boston()\n",
    "X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target,\n",
    "                                                    random_state=0)\n",
    "\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "pipe = make_pipeline(\n",
    "    StandardScaler(),\n",
    "    PolynomialFeatures(),\n",
    "    Ridge())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "param_grid = {'polynomialfeatures__degree': [1, 2, 3],\n",
    "              'ridge__alpha': [0.001, 0.01, 0.1, 1, 10, 100]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                                       ('polynomialfeatures',\n",
       "                                        PolynomialFeatures()),\n",
       "                                       ('ridge', Ridge())]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'polynomialfeatures__degree': [1, 2, 3],\n",
       "                         'ridge__alpha': [0.001, 0.01, 0.1, 1, 10, 100]})"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid = GridSearchCV(pipe, param_grid=param_grid, cv=5, n_jobs=-1)\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/pdf": 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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.tools.heatmap(grid.cv_results_['mean_test_score'].reshape(3, -1),\n",
    "                      xlabel=\"ridge__alpha\", ylabel=\"polynomialfeatures__degree\",\n",
    "                      xticklabels=param_grid['ridge__alpha'],\n",
    "                      yticklabels=param_grid['polynomialfeatures__degree'], vmin=0)\n",
    "plt.show() # 책에는 없음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "최적의 매개변수: {'polynomialfeatures__degree': 2, 'ridge__alpha': 10}\n"
     ]
    }
   ],
   "source": [
    "print(\"최적의 매개변수:\", grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "테스트 세트 점수: 0.77\n"
     ]
    }
   ],
   "source": [
    "print(\"테스트 세트 점수: {:.2f}\".format(grid.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "다항 특성이 없을 때 점수: 0.63\n"
     ]
    }
   ],
   "source": [
    "param_grid = {'ridge__alpha': [0.001, 0.01, 0.1, 1, 10, 100]}\n",
    "pipe = make_pipeline(StandardScaler(), Ridge())\n",
    "grid = GridSearchCV(pipe, param_grid, cv=5)\n",
    "grid.fit(X_train, y_train)\n",
    "print(\"다항 특성이 없을 때 점수: {:.2f}\".format(grid.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 모델 선택을 위한 그리드 서치"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline([('preprocessing', StandardScaler()), ('classifier', SVC())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "param_grid = [\n",
    "    {'classifier': [SVC()], 'preprocessing': [StandardScaler()],\n",
    "     'classifier__gamma': [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "     'classifier__C': [0.001, 0.01, 0.1, 1, 10, 100]},\n",
    "    {'classifier': [RandomForestClassifier(n_estimators=100)],\n",
    "     'preprocessing': [None], 'classifier__max_features': [1, 2, 3]}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "최적의 매개변수:\n",
      "{'classifier': SVC(C=10, gamma=0.01), 'classifier__C': 10, 'classifier__gamma': 0.01, 'preprocessing': StandardScaler()}\n",
      "\n",
      "최상의 교차 검증 점수: 0.99\n",
      "테스트 세트 점수: 0.98\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    cancer.data, cancer.target, random_state=0)\n",
    "\n",
    "grid = GridSearchCV(pipe, param_grid, cv=5)\n",
    "grid.fit(X_train, y_train)\n",
    "\n",
    "print(\"최적의 매개변수:\\n{}\\n\".format(grid.best_params_))\n",
    "print(\"최상의 교차 검증 점수: {:.2f}\".format(grid.best_score_))\n",
    "print(\"테스트 세트 점수: {:.2f}\".format(grid.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 중복 계산 피하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline([('preprocessing', StandardScaler()), ('classifier', SVC())],\n",
    "                memory=\"cache_folder\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 요약 및 정리"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}