Manning_Intro2Pytorch.ipynb

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  "metadata": {
    "colab": {
      "name": "Manning_Intro2Pytorch.ipynb",
      "provenance": [],
      "mount_file_id": "183Cc5LYg425w4Vcljo8nGTNmunJnAzzv",
      "authorship_tag": "ABX9TyN8/MuosaidVkvTv6XVJl3a",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cjsutton77/ML_toy_examples/blob/master/Manning_Intro2Pytorch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "GWG-R9qVPU-h"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('/content/drive/MyDrive/SMSSpamCollection.txt') as f:\n",
        "  lines = f.readlines()"
      ],
      "metadata": {
        "id": "c8E_tiohO57m"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels = []\n",
        "for line in lines:\n",
        "  temp = line.split('\\t')\n",
        "  labels.append([temp[0],temp[1].strip()])"
      ],
      "metadata": {
        "id": "alEzdG0OPdsw"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nlabels = np.array(labels)"
      ],
      "metadata": {
        "id": "HNmL9r7AhZ0U"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import string"
      ],
      "metadata": {
        "id": "IlCg_-VKUanI"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "string.punctuation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "kmYdhLqmUcG8",
        "outputId": "15905592-7444-41dd-86bb-3b998e862a79"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = []\n",
        "for i in nlabels[:,1]:\n",
        "  loweri = i.lower()\n",
        "  out = ''.join([j for j in loweri if j not in string.punctuation])\n",
        "  text.append(out)\n",
        "  "
      ],
      "metadata": {
        "id": "6vGg9GJyTeIs"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v7WFIAAhTt90",
        "outputId": "36bb162e-de58-4720-d440-8a64454992e7"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5574"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nlabels.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VI3-fuMEWB_K",
        "outputId": "9f2f313f-c5fb-4eeb-b0e2-4781e7c643be"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5574, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = nlabels[:,0]"
      ],
      "metadata": {
        "id": "2Y_os3x6WK7y"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-InGz011WPYB",
        "outputId": "90b747c6-5c6b-43fa-aafb-cadb4886b91b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['ham', 'ham', 'spam', ..., 'ham', 'ham', 'ham'], dtype='<U910')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F5J6QqNveyf8",
        "outputId": "d2f394f1-e969-4220-bf61-ea8b80678042"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5574,)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "RwS4LYalWSQv"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer"
      ],
      "metadata": {
        "id": "wk0HTLs_XtM5"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vectorizer = CountVectorizer()"
      ],
      "metadata": {
        "id": "OkUcLALCYoWp"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = vectorizer.fit_transform(text)"
      ],
      "metadata": {
        "id": "X1rHDUsPY9HE"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vectorizer.get_feature_names_out()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0UL0b9g0ZDuF",
        "outputId": "6b1337f2-db56-4572-abbc-72238c151db6"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['008704050406', '0089my', '0121', ..., 'zyada', 'üll', '〨ud'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.33, random_state=42)"
      ],
      "metadata": {
        "id": "zt8AreHoan26"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = X_train.toarray()\n",
        "x_test = X_test.toarray()"
      ],
      "metadata": {
        "id": "ecuT6ncbfG1U"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB"
      ],
      "metadata": {
        "id": "MUm0lmHxcGzG"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "sQHm8CuPfGXg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gnb = GaussianNB()"
      ],
      "metadata": {
        "id": "bficBU2Fc2cX"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_t = gnb.fit(x_train, y_train)"
      ],
      "metadata": {
        "id": "kbs3ryIndAVI"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = y_t.predict(x_test)"
      ],
      "metadata": {
        "id": "8yukScF-dtOQ"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(y_test == y_pred).sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gMA8V3Tgd9cI",
        "outputId": "4e749630-bd82-43c1-a73a-84d3a7ad1cfd"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1653"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'{(y_test == y_pred).sum()/len(y_test) * 100:0.2f}% correct')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CgY1ppuxfmPo",
        "outputId": "6ac0de44-1a39-4c3b-ba35-11c3c3b71d2e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "89.84% correct\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report"
      ],
      "metadata": {
        "id": "1_vVx28hgiEX"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4vMl6RZsgjam",
        "outputId": "3aba1db0-6bd5-42e5-d94e-1a09064ea138"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         ham       0.98      0.90      0.94      1585\n",
            "        spam       0.59      0.91      0.71       255\n",
            "\n",
            "    accuracy                           0.90      1840\n",
            "   macro avg       0.78      0.90      0.83      1840\n",
            "weighted avg       0.93      0.90      0.91      1840\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "esK4zyimgwBD"
      },
      "execution_count": 27,
      "outputs": []
    }
  ]
}