multi_channel_contacts.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "multi-channel-contacts.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qtqsb92tLWY0",
        "outputId": "2062296d-f0e6-4c0b-8ac8-e491bf434be6"
      },
      "source": [
        "# Mount google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y87vTdSrLwfz"
      },
      "source": [
        "# Read unzipped dataset from mounted drive\n",
        "import os\n",
        "\n",
        "path = \"/content/drive/My Drive/data/Dataset for Multi-Channel Contacts Problem (1).zip (Unzipped Files)\"\n",
        "os.chdir(path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jPekyCvgMTUJ"
      },
      "source": [
        "# Read dataset from JSON file\n",
        "import json\n",
        "\n",
        "with open('contacts.json') as json_file:\n",
        "    itemData = json.load(json_file)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zlz7fqd5R6A6"
      },
      "source": [
        "class Graph:\n",
        "  def __init__(self, V):\n",
        "    self.V = V\n",
        "    self.adj = [[] for i in range(V)]\n",
        "\n",
        "  def addEdge(self, u, v):\n",
        "    if u == v:\n",
        "      return\n",
        "    self.adj[u].append(v)\n",
        "    self.adj[v].append(u)\n",
        "    \n",
        "  def DFSUtil(self, temp, v, visited):\n",
        "    visited[v] = True\n",
        "    temp.append(v)\n",
        "    for i in self.adj[v]:\n",
        "      if visited[i] == False:\n",
        "        temp = self.DFSUtil(temp, i, visited)\n",
        "    return temp\n",
        "\n",
        "  def connectedComponents(self):\n",
        "    visited = []\n",
        "    cc = []\n",
        "    for i in range(self.V):\n",
        "      visited.append(False)\n",
        "    \n",
        "    for v in range(self.V):\n",
        "      if visited[v] == False:\n",
        "        temp = []\n",
        "        cc.append(self.DFSUtil(temp, v ,visited))\n",
        "    return cc"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t3OjDK5noR5p",
        "outputId": "659a9b25-df93-4f81-999b-71a4568e8e57"
      },
      "source": [
        "itemData[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Contacts': 1, 'Email': 'gkzAbIy@qq.com', 'Id': 0, 'OrderId': '', 'Phone': ''}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZJZfBZ8eQHIU"
      },
      "source": [
        "emails = dict()\n",
        "orderIds = dict()\n",
        "phoneNumbers = dict()\n",
        "\n",
        "for item in itemData:\n",
        "  if item['Email'] != '':\n",
        "    try:\n",
        "        emails[item['Email']] += list(item['Id'])\n",
        "    except:\n",
        "        emails[item['Email']] = list()\n",
        "        emails[item['Email']].append(item['Id'])\n",
        "  if item['OrderId'] != '':\n",
        "    try:\n",
        "        orderIds[item['OrderId']] += list(item['Id'])\n",
        "    except:\n",
        "        orderIds[item['OrderId']] = list()\n",
        "        orderIds[item['OrderId']].append(item['Id'])\n",
        "  if item['Phone'] != '':\n",
        "    try:\n",
        "        phoneNumbers[item['Phone']] += list(item['Id'])\n",
        "    except:\n",
        "        phoneNumbers[item['Phone']] = list()\n",
        "        phoneNumbers[item['Phone']].append(item['Id'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gd-thNkQMrxQ"
      },
      "source": [
        "# Create graph representation of dataset\n",
        "g = Graph(len(itemData))\n",
        "for item in itemData:\n",
        "  if (item['Email'] != ''):\n",
        "    g.addEdge(emails[item['Email']][0], item['Id'])\n",
        "  if (item['OrderId'] != ''):\n",
        "    g.addEdge(orderIds[item['OrderId']][0], item['Id'])\n",
        "  if (item['Phone'] != ''):\n",
        "    g.addEdge(phoneNumbers[item['Phone']][0], item['Id'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1PvQeVFU47q"
      },
      "source": [
        "cc = g.connectedComponents()\n",
        "\n",
        "ans = dict()\n",
        "for L in cc:\n",
        "  for i in L:\n",
        "    ans[i] = L\n",
        "\n",
        "result = []\n",
        "\n",
        "for i in range(500000):\n",
        "  ans[i] = sorted(ans[i])\n",
        "  sum = 0\n",
        "  text = ''\n",
        "\n",
        "  for j in ans[i]:\n",
        "    sum += itemData[j]['Contacts']\n",
        "  text = '-'.join([str(k) for k in ans[i]])\n",
        "  text += ', '\n",
        "  text += str(sum)\n",
        "  result.append(text)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ACfMg8XAOXLB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8224d87-56d9-46a1-ee34-31384b6147df"
      },
      "source": [
        "result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              " '994, 2',\n",
              " '995-316304-341113, 8',\n",
              " '996-3500, 5',\n",
              " '997, 2',\n",
              " '998-78821-387554, 6',\n",
              " '999, 2',\n",
              " ...]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GnrlYLGCeFMw"
      },
      "source": [
        "# Create csv file\n",
        "import pandas as pd\n",
        "\n",
        "solution = pd.DataFrame({'ticket_id': [i for i in range(len(itemData))], 'ticket_trace/contact': result}, columns=['ticket_id', 'ticket_trace/contact'])\n",
        "solution.to_csv(\"solution.csv\",index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "0o0ksofGCMPj",
        "outputId": "4f8c2914-d2a0-4563-9041-135fdbf9b504"
      },
      "source": [
        "# Download solution file\n",
        "\n",
        "from google.colab import files\n",
        "files.download('solution.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_915b4881-8ca3-4cd8-834e-c3ca075d681a\", \"solution.csv\", 17310307)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}