01-introduction.ipynb

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  "metadata": {
    "colab": {
      "name": "01-introduction.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/maotian06/SysBioShortCourse/blob/master/01-introduction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "D230ayhf4GQL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Tutorial 1:  Introduction"
      ]
    },
    {
      "metadata": {
        "id": "6Meh6KDT4GQO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##### NumPy"
      ]
    },
    {
      "metadata": {
        "uuid": "e2b8e959-75f0-4fa9-a878-5ab024f89223",
        "id": "A1iL1mHe4GQP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "0543f1a3-9ff9-4524-a0e4-4bb8c69728be"
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "x = np.array([[1, 2, 3], [4, 5, 6]])\n",
        "print(\"x:\\n{}\".format(x))"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x:\n",
            "[[1 2 3]\n",
            " [4 5 6]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "hX1wD9tS5cLX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a559ef8e-1507-4312-b6ec-afa4ba0f94e4"
      },
      "cell_type": "code",
      "source": [
        "x[1,2]"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "metadata": {
        "id": "9FO4kJU15ilp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4e972f2b-538b-4d62-bee5-db709876c0fa"
      },
      "cell_type": "code",
      "source": [
        "x.shape"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "metadata": {
        "id": "W5kAkprH5vX_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "65932443-d33f-4b35-cc3a-b9025b8eae82"
      },
      "cell_type": "code",
      "source": [
        "5*x"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 5, 10, 15],\n",
              "       [20, 25, 30]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
        "id": "3a1gDwur4GQU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##### matplotlib"
      ]
    },
    {
      "metadata": {
        "uuid": "30faf136-0ef7-4762-bd82-3795eea323d0",
        "id": "glw19z-r4GQU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "9e77de53-4932-4d4f-a83b-d461f2ddfb45"
      },
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Generate a sequence numbers from -10 to 10 with 100 steps in between\n",
        "x = np.linspace(-10, 10, 100)\n",
        "# create a second array using sinus\n",
        "y = np.sin(x)\n",
        "# The plot function makes a line chart of one array against another\n",
        "plt.plot(x, y, marker=\"x\")"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f4bc4a8a2e8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
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pNv1j86TSGt3bbJiyVVZfuqUx/jvy6Usp78k5dCLn/LGcz4PAO3fyTEV+rCp9ayb+3jb9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\nJWV5SnZeSt+wzj+1xvEvZX1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wOwBCiHuEEDdkXdcopcxen9wL/IkQ4gngt4DP5vl82zi8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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "3XC1o-Y66Mg8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "3f19b1dc-b3db-476c-f627-8117062f5382"
      },
      "cell_type": "code",
      "source": [
        "x"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-10.        ,  -9.7979798 ,  -9.5959596 ,  -9.39393939,\n",
              "        -9.19191919,  -8.98989899,  -8.78787879,  -8.58585859,\n",
              "        -8.38383838,  -8.18181818,  -7.97979798,  -7.77777778,\n",
              "        -7.57575758,  -7.37373737,  -7.17171717,  -6.96969697,\n",
              "        -6.76767677,  -6.56565657,  -6.36363636,  -6.16161616,\n",
              "        -5.95959596,  -5.75757576,  -5.55555556,  -5.35353535,\n",
              "        -5.15151515,  -4.94949495,  -4.74747475,  -4.54545455,\n",
              "        -4.34343434,  -4.14141414,  -3.93939394,  -3.73737374,\n",
              "        -3.53535354,  -3.33333333,  -3.13131313,  -2.92929293,\n",
              "        -2.72727273,  -2.52525253,  -2.32323232,  -2.12121212,\n",
              "        -1.91919192,  -1.71717172,  -1.51515152,  -1.31313131,\n",
              "        -1.11111111,  -0.90909091,  -0.70707071,  -0.50505051,\n",
              "        -0.3030303 ,  -0.1010101 ,   0.1010101 ,   0.3030303 ,\n",
              "         0.50505051,   0.70707071,   0.90909091,   1.11111111,\n",
              "         1.31313131,   1.51515152,   1.71717172,   1.91919192,\n",
              "         2.12121212,   2.32323232,   2.52525253,   2.72727273,\n",
              "         2.92929293,   3.13131313,   3.33333333,   3.53535354,\n",
              "         3.73737374,   3.93939394,   4.14141414,   4.34343434,\n",
              "         4.54545455,   4.74747475,   4.94949495,   5.15151515,\n",
              "         5.35353535,   5.55555556,   5.75757576,   5.95959596,\n",
              "         6.16161616,   6.36363636,   6.56565657,   6.76767677,\n",
              "         6.96969697,   7.17171717,   7.37373737,   7.57575758,\n",
              "         7.77777778,   7.97979798,   8.18181818,   8.38383838,\n",
              "         8.58585859,   8.78787879,   8.98989899,   9.19191919,\n",
              "         9.39393939,   9.5959596 ,   9.7979798 ,  10.        ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "FU7V5TyM6Q6J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "aee84293-1c41-4b02-d2e3-3be18f208a57"
      },
      "cell_type": "code",
      "source": [
        "y"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.54402111,  0.36459873,  0.17034683, -0.03083368, -0.23076008,\n",
              "       -0.42130064, -0.59470541, -0.74392141, -0.86287948, -0.94674118,\n",
              "       -0.99209556, -0.99709789, -0.96154471, -0.8868821 , -0.77614685,\n",
              "       -0.63384295, -0.46575841, -0.27872982, -0.0803643 ,  0.12126992,\n",
              "        0.31797166,  0.50174037,  0.66510151,  0.80141062,  0.90512352,\n",
              "        0.97202182,  0.99938456,  0.98609877,  0.93270486,  0.84137452,\n",
              "        0.7158225 ,  0.56115544,  0.38366419,  0.19056796, -0.01027934,\n",
              "       -0.21070855, -0.40256749, -0.57805259, -0.73002623, -0.85230712,\n",
              "       -0.93992165, -0.98930624, -0.99845223, -0.96698762, -0.8961922 ,\n",
              "       -0.78894546, -0.64960951, -0.48385164, -0.2984138 , -0.10083842,\n",
              "        0.10083842,  0.2984138 ,  0.48385164,  0.64960951,  0.78894546,\n",
              "        0.8961922 ,  0.96698762,  0.99845223,  0.98930624,  0.93992165,\n",
              "        0.85230712,  0.73002623,  0.57805259,  0.40256749,  0.21070855,\n",
              "        0.01027934, -0.19056796, -0.38366419, -0.56115544, -0.7158225 ,\n",
              "       -0.84137452, -0.93270486, -0.98609877, -0.99938456, -0.97202182,\n",
              "       -0.90512352, -0.80141062, -0.66510151, -0.50174037, -0.31797166,\n",
              "       -0.12126992,  0.0803643 ,  0.27872982,  0.46575841,  0.63384295,\n",
              "        0.77614685,  0.8868821 ,  0.96154471,  0.99709789,  0.99209556,\n",
              "        0.94674118,  0.86287948,  0.74392141,  0.59470541,  0.42130064,\n",
              "        0.23076008,  0.03083368, -0.17034683, -0.36459873, -0.54402111])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "PV0XudNr4GQY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##### pandas"
      ]
    },
    {
      "metadata": {
        "uuid": "ad1b06f7-e03a-4938-9d59-5bb40e848553",
        "id": "LfYN6Alh4GQZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "085b1b0c-c23b-4fdb-bfe6-75d1a22a2b4a"
      },
      "cell_type": "code",
      "source": [
        "#panda can be used to import .csv data\n",
        "import pandas as pd\n",
        "from IPython.display import display\n",
        "\n",
        "# create a simple dataset of people\n",
        "data = {'Name': [\"John\", \"Anna\", \"Peter\", \"Linda\"],\n",
        "        'Location' : [\"New York\", \"Paris\", \"Berlin\", \"London\"],\n",
        "        'Age' : [24, 13, 53, 33]\n",
        "       }\n",
        "\n",
        "data_pandas = pd.DataFrame(data)\n",
        "# IPython.display allows \"pretty printing\" of dataframes\n",
        "# in the Jupyter notebook\n",
        "display(data_pandas)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "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>Age</th>\n",
              "      <th>Location</th>\n",
              "      <th>Name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>24</td>\n",
              "      <td>New York</td>\n",
              "      <td>John</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13</td>\n",
              "      <td>Paris</td>\n",
              "      <td>Anna</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>53</td>\n",
              "      <td>Berlin</td>\n",
              "      <td>Peter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>London</td>\n",
              "      <td>Linda</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  Location   Name\n",
              "0   24  New York   John\n",
              "1   13     Paris   Anna\n",
              "2   53    Berlin  Peter\n",
              "3   33    London  Linda"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "clkNotTh4GQb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "9b6d8f3d-e84a-4e87-c4ad-2532e58b3c74"
      },
      "cell_type": "code",
      "source": [
        "# One of many possible ways to query the table:\n",
        "# selecting all rows that have an age column greate than 30\n",
        "display(data_pandas[data_pandas.Age > 30])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "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>Age</th>\n",
              "      <th>Location</th>\n",
              "      <th>Name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>53</td>\n",
              "      <td>Berlin</td>\n",
              "      <td>Peter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>London</td>\n",
              "      <td>Linda</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age Location   Name\n",
              "2   53   Berlin  Peter\n",
              "3   33   London  Linda"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "JeEaWhJC4GQe",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Versions "
      ]
    },
    {
      "metadata": {
        "id": "LRk6RG0B4GQf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "4bb46292-bc72-44d4-e58c-29a3f06cfcd3"
      },
      "cell_type": "code",
      "source": [
        "import sys\n",
        "print(\"Python version: {}\".format(sys.version))\n",
        "\n",
        "import pandas as pd\n",
        "print(\"pandas version: {}\".format(pd.__version__))\n",
        "\n",
        "import matplotlib\n",
        "print(\"matplotlib version: {}\".format(matplotlib.__version__))\n",
        "\n",
        "import numpy as np\n",
        "print(\"NumPy version: {}\".format(np.__version__))\n",
        "\n",
        "import IPython\n",
        "print(\"IPython version: {}\".format(IPython.__version__))\n",
        "\n",
        "import sklearn\n",
        "print(\"scikit-learn version: {}\".format(sklearn.__version__))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Python version: 3.6.7 (default, Oct 22 2018, 11:32:17) \n",
            "[GCC 8.2.0]\n",
            "pandas version: 0.22.0\n",
            "matplotlib version: 3.0.2\n",
            "NumPy version: 1.14.6\n",
            "IPython version: 5.5.0\n",
            "scikit-learn version: 0.20.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "5U_z7JGs4GQi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### A First Application: Classifying iris species\n",
        "<img src=\"https://github.com/maotian06/SysBioShortCourse/blob/master/images/iris_petal_sepal.png?raw=1\" width=\"300\" height=\"300\" />\n",
        "#### Meet the data"
      ]
    },
    {
      "metadata": {
        "scrolled": true,
        "id": "0aRMQsjI4GQi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris\n",
        "iris_dataset = load_iris()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "66-KIw8B4GQk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3d659b68-d9cf-46a9-d46d-0421ba046a3f"
      },
      "cell_type": "code",
      "source": [
        "print(\"Keys of iris_dataset: {}\".format(iris_dataset.keys()))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Keys of iris_dataset: dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "scrolled": true,
        "id": "--WRGvmX4GQn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "afa631f1-40c9-4f12-d7fd-664b361d99de"
      },
      "cell_type": "code",
      "source": [
        "print(iris_dataset['DESCR'][:193] + \"\\n...\")"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ".. _iris_dataset:\n",
            "\n",
            "Iris plants dataset\n",
            "--------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 150 (50 in each of three classes)\n",
            "    :Number of Attributes: 4 numeric, pre\n",
            "...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "LzHs1ytm4GQq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2761ffef-cd9b-4397-cbfd-af3d7a8e6300"
      },
      "cell_type": "code",
      "source": [
        "print(\"Target names: {}\".format(iris_dataset['target_names']))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Target names: ['setosa' 'versicolor' 'virginica']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "PIzPCpSI4GQt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7ec5a33a-f408-45d3-f263-f7c9f943af20"
      },
      "cell_type": "code",
      "source": [
        "print(\"Feature names: {}\".format(iris_dataset['feature_names']))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Feature names: ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Uwocfg3S4GQx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "057473ff-eabd-4dec-aa77-9dd946bc3594"
      },
      "cell_type": "code",
      "source": [
        "print(\"Type of data: {}\".format(type(iris_dataset['data'])))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Type of data: <class 'numpy.ndarray'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "0cpWMQkn4GQ0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "aa17af8f-ceb5-49ed-a938-8bd000457b00"
      },
      "cell_type": "code",
      "source": [
        "print(\"Shape of data: {}\".format(iris_dataset['data'].shape))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of data: (150, 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "CyS1J_Jf4GQ4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "bbcaaa3b-e4b8-4ff2-f0ef-86f892b3f573"
      },
      "cell_type": "code",
      "source": [
        "print(\"First five rows of data:\\n{}\".format(iris_dataset['data'][:5]))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "First five rows of data:\n",
            "[[5.1 3.5 1.4 0.2]\n",
            " [4.9 3.  1.4 0.2]\n",
            " [4.7 3.2 1.3 0.2]\n",
            " [4.6 3.1 1.5 0.2]\n",
            " [5.  3.6 1.4 0.2]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "zw8bmDJn4GQ7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cf47a8ad-5629-4e06-cf16-7372ea239974"
      },
      "cell_type": "code",
      "source": [
        "print(\"Type of target: {}\".format(type(iris_dataset['target'])))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Type of target: <class 'numpy.ndarray'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Q5R9e7Rc4GRA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f3cfc802-afaa-4fcc-a354-d735f71866f6"
      },
      "cell_type": "code",
      "source": [
        "print(\"Shape of target: {}\".format(iris_dataset['target'].shape))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of target: (150,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "-dr8AjOG4GRE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "4656b866-8eca-4de0-ee13-a184bdc6bb91"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "print(\"Target:\\n{}\".format(iris_dataset['target']))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Target:\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "QOzRRI5J4GRH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Measuring Success: Training and testing data"
      ]
    },
    {
      "metadata": {
        "id": "6NX-uZC_4GRH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    iris_dataset['data'], iris_dataset['target'], random_state=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xlwN0DX04GRJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "89365cf3-7cca-4acc-96f8-8042660a6b0c"
      },
      "cell_type": "code",
      "source": [
        "print(\"X_train shape: {}\".format(X_train.shape))\n",
        "print(\"y_train shape: {}\".format(y_train.shape))"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train shape: (112, 4)\n",
            "y_train shape: (112,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WpVTozBu4GRL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "9e526f8b-75b9-45a2-cca1-f0ca4dd9ef55"
      },
      "cell_type": "code",
      "source": [
        "print(\"X_test shape: {}\".format(X_test.shape))\n",
        "print(\"y_test shape: {}\".format(y_test.shape))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_test shape: (38, 4)\n",
            "y_test shape: (38,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "B8I_uv814GRN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### First things first: Look at your data"
      ]
    },
    {
      "metadata": {
        "id": "ayLO0Cqd4GRO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1222
        },
        "outputId": "44b4e775-36f0-4acc-e6d0-e074c35f6dc1"
      },
      "cell_type": "code",
      "source": [
        "# create dataframe from data in X_train\n",
        "# label the columns using the strings in iris_dataset.feature_names\n",
        "iris_dataframe = pd.DataFrame(X_train, columns=iris_dataset.feature_names)\n",
        "# create a scatter matrix from the dataframe, color by y_train\n",
        "pd.tools.plotting.scatter_matrix(iris_dataframe, c=y_train, figsize=(15, 15), marker='o',\n",
        "                           hist_kwds={'bins': 20}, s=60, alpha=.8, cmap='hot')"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: FutureWarning: 'pandas.tools.plotting.scatter_matrix' is deprecated, import 'pandas.plotting.scatter_matrix' instead.\n",
            "  after removing the cwd from sys.path.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f4bc0b98898>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4bc090aef0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4bc08b7588>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4bc08ddbe0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f4bc088f4e0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4bc0833e48>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4bc08617f0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4bc08101d0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f4bc0810208>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4bc07e44a8>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4bc0789e10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4bc07377b8>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f4bc0767160>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4bc070bac8>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4bc06bc470>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4bc06e0dd8>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IlSIvreu+U4F2h2d8Tl1a1/ntxeS+rmXpcr+bS/2WmZViPuWl9V339frbnole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4BoPBYDB6H/437oP8/kPpz9zh3RC3vIXYubdCV87oQcsYjN4B17oX4QevA4kdSpfxTVshbn4D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\nhAhDMpgTQgghhBBCiDD0fyMSa6WfkVwoAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x1080 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "adIyXqi94GRR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Building your first model: k nearest neighbors"
      ]
    },
    {
      "metadata": {
        "id": "qPcl1xEl4GRS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier(n_neighbors=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AaazrdHX4GRT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "13508409-b2be-4ced-cac9-d6bb96861e58"
      },
      "cell_type": "code",
      "source": [
        "knn.fit(X_train, y_train)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "           metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
              "           weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "id": "rRugKHMF4GRW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Making predictions"
      ]
    },
    {
      "metadata": {
        "id": "xpLjTF4U4GRW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "deb4ab27-214d-4ced-dc3b-344b2b00fdea"
      },
      "cell_type": "code",
      "source": [
        "X_new = np.array([[5, 2.9, 1, 0.2]])\n",
        "print(\"X_new.shape: {}\".format(X_new.shape))"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_new.shape: (1, 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "dCgfWDjk4GRZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "33658486-c70c-4c02-883b-1f5e81f272ec"
      },
      "cell_type": "code",
      "source": [
        "prediction = knn.predict(X_new)\n",
        "print(\"Prediction: {}\".format(prediction))\n",
        "print(\"Predicted target name: {}\".format(\n",
        "       iris_dataset['target_names'][prediction]))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Prediction: [0]\n",
            "Predicted target name: ['setosa']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "epNka-lK4GRc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Evaluating the model"
      ]
    },
    {
      "metadata": {
        "id": "QATSXhti4GRe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "b14d64b0-4a7d-4584-d544-5b4b501d4cb4"
      },
      "cell_type": "code",
      "source": [
        "y_pred = knn.predict(X_test)\n",
        "print(\"Test set predictions:\\n {}\".format(y_pred))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test set predictions:\n",
            " [2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0\n",
            " 2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "2Z0Jd4_94GRh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7edff30b-0588-4c07-e08d-8457e4348bf0"
      },
      "cell_type": "code",
      "source": [
        "print(\"Test set score: {:.2f}\".format(np.mean(y_pred == y_test)))"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test set score: 0.97\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "HGA6V60K4GRl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3d5ab5b7-50b1-454c-f765-c754e96f5b61"
      },
      "cell_type": "code",
      "source": [
        "print(\"Test set score: {:.2f}\".format(knn.score(X_test, y_test)))"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test set score: 0.97\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Nh593NTa4GRn",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Summary and Outlook"
      ]
    },
    {
      "metadata": {
        "id": "_PJvDSIY4GRo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7a95548e-0100-4b04-a2da-eeb615f6df2d"
      },
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    iris_dataset['data'], iris_dataset['target'], random_state=0)\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=1)\n",
        "knn.fit(X_train, y_train)\n",
        "\n",
        "print(\"Test set score: {:.2f}\".format(knn.score(X_train, y_train)))"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test set score: 1.00\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "OA20pBv64GRp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Exercise 1\n",
        "\n",
        "#### 1. Calculate the score on testing data\n",
        "\n",
        "#### 2. Rerun the predictor with difference choices of n_neighbors.  Compare test scores. Which number is the best?"
      ]
    },
    {
      "metadata": {
        "id": "Ckp79jSz4GRq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "9b870c42-e8fc-4529-b18c-6b1e4a6d5053"
      },
      "cell_type": "code",
      "source": [
        "y_pred = knn.predict(X_test)\n",
        "print(\"Test set predictions:\\n {}\".format(y_pred))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test set predictions:\n",
            " [2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0\n",
            " 2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3WDNHi9IBLAg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f2dd9f6d-c2b0-4dec-efea-d04dd29d5b49"
      },
      "cell_type": "code",
      "source": [
        "print(\"Test set score: {:.2f}\".format(np.mean(y_pred == y_test)))"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test set score: 0.97\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "5twQ4lDyBLew",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "97624451-8667-4bbb-f507-940b4717537a"
      },
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    iris_dataset['data'], iris_dataset['target'], random_state=0)\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=2)\n",
        "knn.fit(X_train, y_train)\n",
        "\n",
        "print(\"Test set score: {:.2f}\".format(knn.score(X_train, y_train)))"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test set score: 0.96\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "mdekGNwIBTUq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7fb08406-b78a-46ad-bf6c-6e3ba05281b4"
      },
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    iris_dataset['data'], iris_dataset['target'], random_state=0)\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "knn.fit(X_train, y_train)\n",
        "\n",
        "print(\"Test set score: {:.2f}\".format(knn.score(X_train, y_train)))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test set score: 0.97\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "DKyyRh23BVL-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}