ReadMNIST.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ReadMNIST.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": [
        "[View in Colaboratory](https://colab.research.google.com/github/bigkizd/FinalProject_MNIST_With_SVM/blob/master/ReadMNIST.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "-Bjty8XBmPSy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pickle\n",
        "import gzip\n",
        "from sklearn.svm import LinearSVC, SVC\n",
        "from time import time\n",
        "from sklearn import metrics\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TiAieXyLmPS3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def read_mnist(mnist_file):\n",
        "    \"\"\"\n",
        "    Reads MNIST data.\n",
        "    \n",
        "    Parameters\n",
        "    ----------\n",
        "    mnist_file : string\n",
        "        The name of the MNIST file (e.g., 'mnist.pkl.gz').\n",
        "    \n",
        "    Returns\n",
        "    -------\n",
        "    (train_X, train_Y, val_X, val_Y, test_X, test_Y) : tuple\n",
        "        train_X : numpy array, shape (N=50000, d=784)\n",
        "            Input vectors of the training set.\n",
        "        train_Y: numpy array, shape (N=50000)\n",
        "            Outputs of the training set.\n",
        "        val_X : numpy array, shape (N=10000, d=784)\n",
        "            Input vectors of the validation set.\n",
        "        val_Y: numpy array, shape (N=10000)\n",
        "            Outputs of the validation set.\n",
        "        test_X : numpy array, shape (N=10000, d=784)\n",
        "            Input vectors of the test set.\n",
        "        test_Y: numpy array, shape (N=10000)\n",
        "            Outputs of the test set.\n",
        "    \"\"\"\n",
        "    f = gzip.open(mnist_file, 'rb')\n",
        "    train_data, val_data, test_data = pickle.load(f, encoding='latin1')\n",
        "    f.close()\n",
        "    \n",
        "    train_X, train_Y = train_data\n",
        "    val_X, val_Y = val_data\n",
        "    test_X, test_Y = test_data    \n",
        "    \n",
        "    return train_X, train_Y, val_X, val_Y, test_X, test_Y\n",
        "\n",
        "# get datasets\n",
        "train_X, train_Y, val_X, val_Y, test_X, test_Y = read_mnist('mnist.pkl.gz')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Aw3b2z4YmPS7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Use SVM for Competitive on Kaggle"
      ]
    },
    {
      "metadata": {
        "id": "0adl9fZFmPS7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Use SVM for Competitive on Kaggle\n",
        "C=5.0\n",
        "\n",
        "linear_svm = SVC(C=C, gamma=0.05)\n",
        "clf = linear_svm.fit(train_X, train_Y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gxM8TNxPmPS-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Make predictions using the features from the test data set\n",
        "predictions = clf.predict(test[features])\n",
        "\n",
        "#Display our predictions - they are either 0 or 1 for each training instance \n",
        "#depending on whether our algorithm believes the person survived or not.\n",
        "predictions"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "t17S-KUImPTD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Create a  DataFrame with the passengers ids and our prediction regarding whether they survived or not\n",
        "submission = pd.DataFrame({'Id':test['Id'],'label1':predictions})\n",
        "\n",
        "#Visualize the first 5 rows\n",
        "submission.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JvtdHlucmPTH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "filenamefilename = 'Iris.csv'\n",
        "\n",
        "submission.to_csv(filename,index=False)\n",
        "\n",
        "print('Saved file: ' + filename)\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BvtFr9U5mPTK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Use SVM with Linear_Kernel\n"
      ]
    },
    {
      "metadata": {
        "id": "zVtkfhTimPTL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Use SVM with Linear_Kernel\n",
        "\n",
        "def SVM_Linear_Kernel(C):\n",
        "    linear_svm = LinearSVC(C=C)\n",
        "    \n",
        "    linear_svm_time = time()\n",
        "    linear_svm.fit(train_X, train_Y)\n",
        "    linear_svm_time = time() - linear_svm_time\n",
        "    \n",
        "    # Validation\n",
        "    linear_svm_time_for_validation = time()\n",
        "    linear_svm_score = linear_svm.score(val_X, val_Y)\n",
        "    linear_svm_time_for_validation = linear_svm_time + time() - linear_svm_time_for_validation\n",
        "    \n",
        "    print(\"Done with Validation in %f s\" % linear_svm_time_for_validation)\n",
        "\n",
        "    print(\"Accuracy of Validation: %0.3f\" % linear_svm_score)\n",
        "    \n",
        "    \n",
        "    # Test\n",
        "    linear_svm_time_for_test = time()\n",
        "    linear_svm_score = linear_svm.score(test_X, test_Y)\n",
        "    linear_svm_time_for_test = linear_svm_time + time() - linear_svm_time_for_test\n",
        "    \n",
        "    print(\"Done with Test in %f s\" % linear_svm_time_for_test)\n",
        "\n",
        "    print(\"Accuracy of Test: %0.3f\" % linear_svm_score)\n",
        "\n",
        "\n",
        "SVM_Linear_Kernel(1.0)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7ng-DkZomPTP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Use SVM With GaussianRBF Kernel"
      ]
    },
    {
      "metadata": {
        "id": "9RkrxcNpmPTQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Use SVM With GaussianRBF Kernel\n",
        "\n",
        "def SVM_GaussianRBF_Kernel(C, gamma):\n",
        "    kernel_svm = SVC(C=C, gamma=gamma)\n",
        "    \n",
        "    kernel_svm_time = time()\n",
        "    kernel_svm.fit(train_X, train_Y)\n",
        "    kernel_svm_time = time() - kernel_svm_time\n",
        "    \n",
        "    # Validation\n",
        "    kernel_svm_time_for_validation = time()\n",
        "    kernel_svm_score = kernel_svm.score(val_X, val_Y)\n",
        "    kernel_svm_time_for_validation = kernel_svm_time + time() - kernel_svm_time_for_validation\n",
        "    \n",
        "    print(\"Done with Validation in %f s\" % kernel_svm_time_for_validation)\n",
        "\n",
        "    print(\"Accuracy of Validation: %0.3f\" % kernel_svm_score)\n",
        "    \n",
        "\n",
        "    # Test\n",
        "    kernel_svm_time_for_test = time()\n",
        "    kernel_svm_score = kernel_svm.score(test_X, test_Y)\n",
        "    kernel_svm_time_for_test = kernel_svm_time + time() - kernel_svm_time_for_test\n",
        "    \n",
        "    print(\"Done with Test in %f s\" % kernel_svm_time_for_test)\n",
        "\n",
        "    print(\"Accuracy of Test: %0.3f\" % kernel_svm_score)\n",
        "\n",
        "\n",
        "SVM_GaussianRBF_Kernel(C=C, gamma=gamma)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pzr_sjBfmRph",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# What' Cooling?\n"
      ]
    },
    {
      "metadata": {
        "id": "53mkxqOlmYYq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Import Library"
      ]
    },
    {
      "metadata": {
        "id": "yFNhE1gFmbkz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "outputId": "f415be84-197f-4657-a0cd-64faad3ef6b7"
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "import sklearn\n",
        "import matplotlib.pylob as plt\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-4e83c0e23e46>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpylob\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib.pylob'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "cbQDNnKMmmQG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Upload on Colab include Train and Test Datasets"
      ]
    },
    {
      "metadata": {
        "id": "v1oIugn0msdY",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "76a9184c-2aa9-4af2-9788-572d1180d49e"
      },
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-330e2200-e86d-4bed-adb5-cce7a178cce5\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-330e2200-e86d-4bed-adb5-cce7a178cce5\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving test.json to test (1).json\n",
            "Saving train.json to train (1).json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ce_wWhVtnA-N",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Input datasets"
      ]
    },
    {
      "metadata": {
        "id": "oG6if1aPnEuL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        },
        "outputId": "f764b775-5a6b-4445-c54b-4e771b456bb4"
      },
      "cell_type": "code",
      "source": [
        "train = pd.read_json('train.json')\n",
        "test = pd.read_json('test.json')\n",
        "\n",
        "\n",
        "train.groupby('cuisine').count()\n",
        "\n"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>id</th>\n",
              "      <th>ingredients</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cuisine</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>brazilian</th>\n",
              "      <td>467</td>\n",
              "      <td>467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>british</th>\n",
              "      <td>804</td>\n",
              "      <td>804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cajun_creole</th>\n",
              "      <td>1546</td>\n",
              "      <td>1546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chinese</th>\n",
              "      <td>2673</td>\n",
              "      <td>2673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>filipino</th>\n",
              "      <td>755</td>\n",
              "      <td>755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>french</th>\n",
              "      <td>2646</td>\n",
              "      <td>2646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>greek</th>\n",
              "      <td>1175</td>\n",
              "      <td>1175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>indian</th>\n",
              "      <td>3003</td>\n",
              "      <td>3003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>irish</th>\n",
              "      <td>667</td>\n",
              "      <td>667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>italian</th>\n",
              "      <td>7838</td>\n",
              "      <td>7838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>jamaican</th>\n",
              "      <td>526</td>\n",
              "      <td>526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>japanese</th>\n",
              "      <td>1423</td>\n",
              "      <td>1423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>korean</th>\n",
              "      <td>830</td>\n",
              "      <td>830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mexican</th>\n",
              "      <td>6438</td>\n",
              "      <td>6438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>moroccan</th>\n",
              "      <td>821</td>\n",
              "      <td>821</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>russian</th>\n",
              "      <td>489</td>\n",
              "      <td>489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>southern_us</th>\n",
              "      <td>4320</td>\n",
              "      <td>4320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spanish</th>\n",
              "      <td>989</td>\n",
              "      <td>989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thai</th>\n",
              "      <td>1539</td>\n",
              "      <td>1539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vietnamese</th>\n",
              "      <td>825</td>\n",
              "      <td>825</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                id  ingredients\n",
              "cuisine                        \n",
              "brazilian      467          467\n",
              "british        804          804\n",
              "cajun_creole  1546         1546\n",
              "chinese       2673         2673\n",
              "filipino       755          755\n",
              "french        2646         2646\n",
              "greek         1175         1175\n",
              "indian        3003         3003\n",
              "irish          667          667\n",
              "italian       7838         7838\n",
              "jamaican       526          526\n",
              "japanese      1423         1423\n",
              "korean         830          830\n",
              "mexican       6438         6438\n",
              "moroccan       821          821\n",
              "russian        489          489\n",
              "southern_us   4320         4320\n",
              "spanish        989          989\n",
              "thai          1539         1539\n",
              "vietnamese     825          825"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "metadata": {
        "id": "3wziaqe9nzow",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "1553f04d-62a0-4f78-bcb2-99c9eafc6558"
      },
      "cell_type": "code",
      "source": [
        "test.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>id</th>\n",
              "      <th>ingredients</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>18009</td>\n",
              "      <td>[baking powder, eggs, all-purpose flour, raisi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>28583</td>\n",
              "      <td>[sugar, egg yolks, corn starch, cream of tarta...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41580</td>\n",
              "      <td>[sausage links, fennel bulb, fronds, olive oil...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>29752</td>\n",
              "      <td>[meat cuts, file powder, smoked sausage, okra,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>35687</td>\n",
              "      <td>[ground black pepper, salt, sausage casings, l...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      id                                        ingredients\n",
              "0  18009  [baking powder, eggs, all-purpose flour, raisi...\n",
              "1  28583  [sugar, egg yolks, corn starch, cream of tarta...\n",
              "2  41580  [sausage links, fennel bulb, fronds, olive oil...\n",
              "3  29752  [meat cuts, file powder, smoked sausage, okra,...\n",
              "4  35687  [ground black pepper, salt, sausage casings, l..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "vRqmQvuQnSk9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Trainning Models With Support Vector Machine"
      ]
    },
    {
      "metadata": {
        "id": "Rm70B2_LnWrv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 710
        },
        "outputId": "1d80394c-eb6b-4de2-e577-690e7eecfb5e"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "train_X = train['ingredients']\n",
        "train_y = train['cuisine']\n",
        "\n",
        "print(train_X.values)\n",
        "\n",
        "C = 5.0\n",
        "clf = SVC(C=C)\n",
        "clf.fit(train_X.values, train_y.values)\n",
        "\n"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[list(['romaine lettuce', 'black olives', 'grape tomatoes', 'garlic', 'pepper', 'purple onion', 'seasoning', 'garbanzo beans', 'feta cheese crumbles'])\n",
            " list(['plain flour', 'ground pepper', 'salt', 'tomatoes', 'ground black pepper', 'thyme', 'eggs', 'green tomatoes', 'yellow corn meal', 'milk', 'vegetable oil'])\n",
            " list(['eggs', 'pepper', 'salt', 'mayonaise', 'cooking oil', 'green chilies', 'grilled chicken breasts', 'garlic powder', 'yellow onion', 'soy sauce', 'butter', 'chicken livers'])\n",
            " ...\n",
            " list(['eggs', 'citrus fruit', 'raisins', 'sourdough starter', 'flour', 'hot tea', 'sugar', 'ground nutmeg', 'salt', 'ground cinnamon', 'milk', 'butter'])\n",
            " list(['boneless chicken skinless thigh', 'minced garlic', 'steamed white rice', 'baking powder', 'corn starch', 'dark soy sauce', 'kosher salt', 'peanuts', 'flour', 'scallions', 'Chinese rice vinegar', 'vodka', 'fresh ginger', 'egg whites', 'broccoli', 'toasted sesame seeds', 'sugar', 'store bought low sodium chicken stock', 'baking soda', 'Shaoxing wine', 'oil'])\n",
            " list(['green chile', 'jalapeno chilies', 'onions', 'ground black pepper', 'salt', 'chopped cilantro fresh', 'green bell pepper', 'garlic', 'white sugar', 'roma tomatoes', 'celery', 'dried oregano'])]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-42-6991580e82a8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mC\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSVC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mC\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_X\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_y\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    147\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sparse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msparse\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'C'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    150\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    571\u001b[0m     X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,\n\u001b[1;32m    572\u001b[0m                     \u001b[0mensure_2d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_nd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_min_samples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 573\u001b[0;31m                     ensure_min_features, warn_on_dtype, estimator)\n\u001b[0m\u001b[1;32m    574\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    575\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    431\u001b[0m                                       force_all_finite)\n\u001b[1;32m    432\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 433\u001b[0;31m         \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    434\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    435\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence."
          ]
        }
      ]
    }
  ]
}