Demo - SciKit Learn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Demo - SciKit Learn\n",
    "* popular Python library providing efficient implementation of a large number of machine learning algorithms\n",
    "* purposely designed to be clean and uniform across tools\n",
    "* consistent data representation and common interface"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SciKit Learn Data Representation\n",
    "<img style=\"height: 400px;\" src=\"images/scikit-learn-data.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                  5.1               3.5                1.4               0.2   \n",
       "1                  4.9               3.0                1.4               0.2   \n",
       "2                  4.7               3.2                1.3               0.2   \n",
       "3                  4.6               3.1                1.5               0.2   \n",
       "4                  5.0               3.6                1.4               0.2   \n",
       "..                 ...               ...                ...               ...   \n",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "     target  \n",
       "0       0.0  \n",
       "1       0.0  \n",
       "2       0.0  \n",
       "3       0.0  \n",
       "4       0.0  \n",
       "..      ...  \n",
       "145     2.0  \n",
       "146     2.0  \n",
       "147     2.0  \n",
       "148     2.0  \n",
       "149     2.0  \n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "\n",
    "np.random.seed(5)\n",
    "\n",
    "# load the iris dataset that ships with scikit learn\n",
    "iris = datasets.load_iris()\n",
    "dat = pd.DataFrame(data= np.c_[iris['data'], iris['target']],\n",
    "                   columns= iris['feature_names'] + ['target'])\n",
    "dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set up the feature matrix\n",
    "X_iris = dat.drop('target', axis=1)\n",
    "X_iris.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set up the target vector\n",
    "y_iris = iris['target']\n",
    "y_iris.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# scikit-learn Objects\n",
    "* \"All objects within scikit-learn share a uniform common basic API consisting of three complementary interfaces: an estimator interface for building and fitting models, a predictor interface for making predictions and a transformer interface for converting data.\"\n",
    "<br>__*API design for machine learning software:\n",
    "experiences from the scikit-learn project*__ (https://arxiv.org/pdf/1309.0238.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimator API\n",
    "* Driven by a set of principles documented in the above paper:\n",
    "  * Consistency\n",
    "  * Allow Inspection\n",
    "  * Limited object hierarchies\n",
    "  * Composition\n",
    "  * Sensible defaults"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# General Workflow\n",
    "* Choose a model\n",
    "* Choose model hyperparameters\n",
    "* Arrange data into a features matrix and target vector\n",
    "* Fit the model to the data with the __`fit()`__ method\n",
    "* Apply the model to test data (__`predict()`__ or __`transform()`__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "# Linear Regression Example from Python Data Science Handbook\n",
    "# First, generate some sample data\n",
    "# We'll add some random noise so the points are scattered\n",
    "rng = np.random.RandomState(1)\n",
    "x = 10 * rng.rand(50)\n",
    "\n",
    "# generate some randomness in the y axis\n",
    "y = 2 * x - 5 + rng.randn(50)\n",
    "plt.scatter(x, y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model slope:     2.027208810360695\n",
      "Model intercept: -4.998577085553202\n"
     ]
    },
    {
     "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": [
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()\n",
    "# the data needs to be of the form [n_samples, n_features]\n",
    "# so we add a new axis to generate a 2-D array from x\n",
    "model.fit(x[:, np.newaxis], y)\n",
    "\n",
    "xfit = np.linspace(0, 10, 1000)\n",
    "# same for xfit...\n",
    "yfit = model.predict(xfit[:, np.newaxis])\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.plot(xfit, yfit)\n",
    "print(\"Model slope:    \", model.coef_[0])\n",
    "print(\"Model intercept:\", model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2]), array(['setosa', 'versicolor', 'virginica'], dtype='<U10'))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree_clf = DecisionTreeClassifier(max_depth=2)\n",
    "\n",
    "tree_clf.fit(X_iris, y_iris)\n",
    "tree_clf.predict([[5, 1.5, 2, 2.0]]), iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.datasets import make_blobs\n",
    "\n",
    "# create 100 random data points in 3 isotropic (uniform in all dimensions) blobs\n",
    "X, y = make_blobs(random_state=9)\n",
    "plt.scatter(X[:, 0], X[:, 1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 0 0 2 2 2 0 1 2 1 2 1 1 0 1 1 1 2 1 2 1 2 0 2 0 0 2 2 1 2 1 0 0 2 0 1\n",
      " 1 2 2 0 2 0 2 0 1 2 0 0 1 2 1 1 2 2 1 1 2 2 2 1 2 2 0 0 2 0 0 0 0 0 2 1 0\n",
      " 1 0 1 0 0 2 0 0 2 1 2 1 0 1 1 1 0 0 0 1 2 1 1 1 0 2]\n"
     ]
    },
    {
     "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": [
    "# Now let's do a K-means clustering...\n",
    "# Since we made 3 blobs, the algorithm should do well with 3 clusters\n",
    "kmeans = KMeans(n_clusters=3)\n",
    "kmeans.fit(X)\n",
    "print(kmeans.labels_)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=kmeans.labels_, cmap='Pastel2')\n",
    "centers = kmeans.cluster_centers_\n",
    "# mark the centers of each cluster, as determined by the K-means algorithm\n",
    "plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, marker='x');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 4 3 3 4 1 4 0 2 4 2 1 2 2 3 2 2 2 4 2 1 2 1 3 1 0 3 1 1 2 1 2 3 0 1 3 2\n",
      " 2 4 4 3 4 0 1 3 2 4 3 3 2 4 2 2 1 1 2 2 1 1 1 2 4 1 3 0 4 0 3 3 3 3 4 2 3\n",
      " 2 3 2 0 3 4 0 0 1 2 1 2 3 2 2 2 0 0 3 2 1 2 2 2 0 1]\n"
     ]
    },
    {
     "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": [
    "# let's try it with 5 clusters\n",
    "kmeans = KMeans(n_clusters=5)\n",
    "kmeans.fit(X)\n",
    "print(kmeans.labels_)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=kmeans.labels_, cmap='Pastel2')\n",
    "centers = kmeans.cluster_centers_\n",
    "plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, marker='x');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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