5. 회귀(Regression).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. 회귀(Regression)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression\n",
    "\n",
    "Classification이 이산형 데이터를 분류하는 것이라면 Regression은 키,온도와 같은 연속적인 값을 예측하는 문제이다. 예를 들어 한국 남자들의 키를 알고 싶을 때, 전수 조사는 어려우니까 샘플링을 하고 추측해본다. Training set을 통해 알고리즘을 적용하고 원가설을 추측할 수 있는 Hypothesis를 만든 다음, 우리가 갖고 있는 training data를 넣고 y값을 추측해본다.\n",
    "\n",
    "---\n",
    "### Linear Regression(선형 회귀) \n",
    "\n",
    "x와 y값 사이의 관계를 보기 위한 통계적인 프로세스이다. 모델의 적합도는 원본데이터와 비교해서 error로써 표현된다.\n",
    "\n",
    "- Simple Linear Regression : 하나의 변수(x)를 가지고 y값을 예측하는 것이다. 실제 상황에서는 하나의 변수만으로 예측하기 어려운 경우가 많기 때문에 거의 쓰이지 않는다.\n",
    "\n",
    "- Cost function : 예측 값과 실제 데이터의 차이를 이용해서 cost function 값을 계산하는데 이 값이 작을수록 예측이 정확한 것이다.\n",
    "\t\t\t\t\t\n",
    "- Multiple Linear Regression : 여러 개의 변수가 있는 경우이다. \n",
    "    - Feature Scaling : 모든 feature가 비슷한 범위에 있도록 만들어 준다.\n",
    "    - Feature들이 비슷한 범위에 있으면 수렴이 빠르다.\n",
    "    - Mean normalization\t\t\t\n",
    "---\n",
    "### 선형회귀와 관련된 사이킷런 함수/라이브러리\n",
    "\n",
    "- sklearn.linear_model.LinearRegression: 선형회귀 모델(클래스)을 불러옵니다.\n",
    "\n",
    "linear_model = LinearRegression()\n",
    "\n",
    "- LinearRegression.fit(x, y): (x, y) 데이터셋에 대해서 모델을 학습시킵니다.\n",
    "\n",
    "- LinearRegression.predict(x): x 데이터를 바탕으로 예측되는 값을 출력합니다.\n",
    "\n",
    "reference: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 8.87583891]\n",
      " [12.26129085]\n",
      " [ 3.65936838]\n",
      " [ 8.06887608]\n",
      " [ 9.41701309]\n",
      " [ 2.08915148]\n",
      " [ 9.92538464]\n",
      " [12.72738715]\n",
      " [ 5.72404071]\n",
      " [ 4.24986799]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\97030\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:445: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  % get_backend())\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1152x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#사이킷런을 활용한 선형 회귀(Linear Regression)\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "## 데이터 x와 y를 생성\n",
    "x = 5*np.random.rand(100,1)\n",
    "y = 2*x + 5*np.random.rand(100,1)\n",
    "\n",
    "lr = LinearRegression() # 회귀 모델을 만들기\n",
    "\n",
    "lr.fit(x,y) #모델을 학습\n",
    "\n",
    "predicted = lr.predict(x) # 회귀 결과를 출력\n",
    "print(predicted[:10])\n",
    "\n",
    "#시각화\n",
    "fig, ax = plt.subplots(1,2, figsize=(16, 7))\n",
    "\n",
    "ax[0].scatter(x,y)\n",
    "ax[1].scatter(x,y)\n",
    "ax[1].plot(x, predicted, color='r')\n",
    "\n",
    "ax[0].set_xlabel('x')\n",
    "ax[0].set_ylabel('y')\n",
    "ax[1].set_xlabel('x')\n",
    "ax[1].set_ylabel('y')\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 사이킷런을 활용한 다항 회귀\n",
    "\n",
    "### 유용한 함수\n",
    "\n",
    "- PolynomialFeatures(degree): Polynomial 객체를 생성합니다.\n",
    "    - degree: 만들어줄 다항식의 차수를 의미한다.\n",
    "    - interaction_only : True면 2차항에서 상호작용항만 출력\n",
    "    - include_bias : 상수항 생성 여부\n",
    "    \n",
    "- PolynomialFeatures.fit_transform(x):x와 x의 degree제곱을 한 다항 회귀 변수를 반환한다.\n",
    "\n",
    "reference: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1]\n",
      " [2 3]\n",
      " [4 5]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  1.,  0.,  0.,  1.],\n",
       "       [ 1.,  2.,  3.,  4.,  6.,  9.],\n",
       "       [ 1.,  4.,  5., 16., 20., 25.]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "x = np.arange(6).reshape(3,2)\n",
    "print(x)\n",
    "\n",
    "# Polynomial 객체를 생성, degree:만들어줄 다항식의 차수\n",
    "poly = PolynomialFeatures(2)\n",
    "poly.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  1.,  0.],\n",
       "       [ 1.,  2.,  3.,  6.],\n",
       "       [ 1.,  4.,  5., 20.]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly = PolynomialFeatures(interaction_only = True)\n",
    "poly.fit_transform(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Years of Experience - Salary 예제로 살펴보는 Polynomial Regression\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "dataset = pd.read_csv('position_salaries.csv')\n",
    "X = dataset.iloc[:, 1:2].values\n",
    "y = dataset.iloc[:, 2].values\n",
    "\n",
    "from sklearn.model_selection import train_test_split \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "# Fitting Linear Regression to the dataset\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X, y)\n",
    "\n",
    "# Visualizing the Linear Regression results\n",
    "def viz_linear():\n",
    "    plt.scatter(X, y, color='red')\n",
    "    plt.plot(X, lin_reg.predict(X), color='blue')\n",
    "    plt.title('Truth or Bluff (Linear Regression)')\n",
    "    plt.xlabel('Position level')\n",
    "    plt.ylabel('Salary')\n",
    "    plt.show()\n",
    "    return\n",
    "viz_linear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fitting Polynomial Regression to the dataset\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly_reg = PolynomialFeatures(degree=4)\n",
    "X_poly = poly_reg.fit_transform(X)\n",
    "pol_reg = LinearRegression()\n",
    "pol_reg.fit(X_poly, y)\n",
    "\n",
    "# Visualizing the Polymonial Regression results\n",
    "def viz_polymonial():\n",
    "    plt.scatter(X, y, color='red')\n",
    "    plt.plot(X, pol_reg.predict(poly_reg.fit_transform(X)), color='blue')\n",
    "    plt.title('Truth or Bluff (Linear Regression)')\n",
    "    plt.xlabel('Position level')\n",
    "    plt.ylabel('Salary')\n",
    "    plt.show()\n",
    "    return\n",
    "viz_polymonial()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[249500.]\n",
      "[132148.43750003]\n"
     ]
    }
   ],
   "source": [
    "# 5.5년차 사원의 Salary예측 \n",
    "\n",
    "# Predicting a new result with Linear Regression\n",
    "print(lin_reg.predict([[5.5]]))\n",
    "#output should be 249500\n",
    "\n",
    "# Predicting a new result with Polymonial Regression\n",
    "print(pol_reg.predict(poly_reg.fit_transform([[5.5]])))\n",
    "#output should be 132148.43750003\n",
    "\n",
    "#결과가 매우 다르게 나왔으며, linear 모델보다 Polymonial 모델이 예측을 더 잘했다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### 사이킷런으로 구현하는 릿지/라쏘/엘라스틱넷\n",
    "\n",
    "#### Lasso Regression\n",
    "- Cost function 에 **L1 regularization 항**을 추가\n",
    "- Regularization? : overfitting 을 막기 위해 사용하는 기법. 중요하지 않은 계수 𝜃를 0에 가깝게 만들어 모델의 복잡성을 줄인다.\n",
    "\n",
    "\n",
    "- sklearn.linear_model.Lasso: 라쏘 회귀 (클래스)를 불러옵니다.\n",
    "\n",
    "    - alpha: 설정해야 하는 변수(스칼라 값)\n",
    "    \n",
    "#### Ridge Regression\n",
    "- Cost function에 **L2 regularization 항**을 추가 \n",
    "\n",
    "\n",
    "- sklearn.linear_model.Ridge: 릿지 회귀 (클래스)를 불러옵니다.\n",
    "\n",
    "    - alpha: 설정해야 하는 변수(스칼라 값), learning rate (너무 작으면 수렴이 느림, 너무 크면 수렴 안될 수도 있음),작은 값부터 천천히 늘려나가보기\n",
    "\n",
    "#### Elastic Net Regression\n",
    "- Lasso 회귀는 너무 많은 계수를 0으로 만들어 모델의 정확성이 떨어질 수 있음\n",
    "- Ridge 회귀는 계수값을 0에 가깝게 만들지만 0은 아니므로 모델이 여전히 복잡할 수 있음\n",
    "- 그러면 **두 개를 같이** 써보자!!\n",
    "- 𝝀𝟏과 𝝀𝟐의 비율을 조정해 가며 모델을 만든다\n",
    "- 상황에 맞게 계수를 정하는 것이 중요\n",
    "\n",
    "    \n",
    "- sklearn.linear_model.ElasticNet: 엘라스틱넷 (클래스)를 불러옵니다.(Ridge와 Lasso 합친것! l2와 l1의 비율을 조정해가며 모델을 만든다)\n",
    "\n",
    "    - alpha: 설정해야 하는 변수(스칼라 값)\n",
    "    - l1_ratio: l1 norm 을 반영할 비율, 상황에 맞게 계수를 정하는 것이 중요"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9248706298856207\n",
      "0.7546386894816657\n",
      "0.6540862884187704\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#릿지(Ridge) / 라쏘(Lasso) / 엘라스틱넷(ElasticNet)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import ElasticNet\n",
    "import numpy as np\n",
    "\n",
    "x = 2*np.random.rand(50,1) # 50개씩 생성하겠다.\n",
    "y = 3*x + 2*np.random.rand(50,1)\n",
    "\n",
    "#1.릿지 회귀를 구현하고 x,y 데이터를 바탕으로 학습\n",
    "ridge_reg = Ridge(alpha = 0.45) # alpha 값을 다르게 넣어가며 조정\n",
    "ridge_reg.fit(x,y)\n",
    "\n",
    "#2.라쏘 회귀를 구현하고 x,y 데이터를 바탕으로 학습\n",
    "lasso_reg = Lasso(alpha = 0.45)\n",
    "lasso_reg.fit(x,y)\n",
    "\n",
    "#3.엘라스틱넷을 구현하고 x,y 데이터를 바탕으로 학습\n",
    "elastic_net = ElasticNet(alpha = 0.45, l1_ratio = 0.4) \n",
    "# l1_ratio : 추가적인 파라미터 (ridge와 lasso를 합친거라 파라미터가 하나 더 필요함)\n",
    "elastic_net.fit(x,y)\n",
    "\n",
    "#4.각 모델들을 이용하여 데이터 x에 대한 예측값을 변수에 따로 저장\n",
    "ridge_y_pred = ridge_reg.predict(x)\n",
    "lasso_y_pred = lasso_reg.predict(x)\n",
    "elastic_y_pred = elastic_net.predict(x)\n",
    "\n",
    "print(ridge_reg.score(x,y))\n",
    "print(lasso_reg.score(x,y))\n",
    "print(elastic_net.score(x,y))\n",
    "\n",
    "## 시각화\n",
    "plt.scatter(x, y)\n",
    "\n",
    "## 릿지 회귀 결과를 그래프로 나타냅니다.\n",
    "plt.plot(x,ridge_y_pred, color='yellow')\n",
    "\n",
    "## 라쏘 회귀 결과를 그래프로 나타냅니다.\n",
    "plt.plot(x,lasso_y_pred, color='green')\n",
    "\n",
    "## 엘라스틱넷 회귀 결과를 그래프로 나타냅니다.\n",
    "plt.plot(x,elastic_y_pred, color='red')\n",
    "\n",
    "#그래프를 확인합니다.\n",
    "plt.show()"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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}