AHP.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
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          "text": [
            "In which field do you want to perform AHP: Phone\n",
            "On how many criterias should the decision be based? 3\n"
          ]
        }
      ],
      "source": [
        "field = input(\"In which field do you want to perform AHP: \")\n",
        "n = int(input(\"On how many criterias should the decision be based? \"))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
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          "text": [
            "Enter criteria 1 :Design\n",
            "Enter criteria 2 :Features\n",
            "Enter criteria 3 :Price\n"
          ]
        }
      ],
      "source": [
        "Factors = []\n",
        "for i in range (n):\n",
        "  criteria = input(f\"Enter criteria {i+1} :\")\n",
        "  Factors.append(criteria)"
      ]
    },
    {
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "How many options do you have for comparision?3\n",
            "Enter the option 1:S20\n",
            "Enter the option 2:S21\n",
            "Enter the option 3:S22\n"
          ]
        }
      ],
      "source": [
        "m = int(input(\"How many options do you have for comparision?\"))\n",
        "Options = []\n",
        "for i in range (m):\n",
        "  Options.append(input(f\"Enter the option {i+1}:\"))\n"
      ]
    },
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          "text": [
            "How much would you rate S20 with respect to S21 in Design if S21 = 1 3\n",
            "How much would you rate S20 with respect to S22 in Design if S22 = 1 9\n",
            "How much would you rate S21 with respect to S22 in Design if S22 = 1 6\n",
            "             S20   S21     S22  Priorities\n",
            "Design                                    \n",
            "S20     0.692308  0.72  0.5625    0.658269\n",
            "S21     0.230769  0.24  0.3750    0.281923\n",
            "S22     0.076923  0.04  0.0625    0.059808\n",
            "[2.04230769 0.86019231 0.1799359 ]\n",
            "Design\n",
            "S20    3.102542\n",
            "S21    3.051160\n",
            "S22    3.008574\n",
            "Name: Priorities, dtype: float64\n",
            "3.0540919130155935\n",
            "0.02704595650779673\n",
            "0.04663095949620127\n",
            "       Design\n",
            "S20  0.658269\n",
            "S21  0.281923\n",
            "S22  0.059808\n",
            "How much would you rate S20 with respect to S21 in Features if S21 = 1 0.5\n",
            "How much would you rate S20 with respect to S22 in Features if S22 = 1 0.13\n",
            "How much would you rate S21 with respect to S22 in Features if S22 = 1 0.2\n",
            "               S20       S21       S22  Priorities\n",
            "Features                                          \n",
            "S20       0.093525  0.076923  0.097744    0.089398\n",
            "S21       0.187050  0.153846  0.150376    0.163757\n",
            "S22       0.719424  0.769231  0.751880    0.746845\n",
            "[0.26836613 0.49192156 2.25330578]\n",
            "Features\n",
            "S20    3.001941\n",
            "S21    3.003964\n",
            "S22    3.017100\n",
            "Name: Priorities, dtype: float64\n",
            "3.0076681459144603\n",
            "0.00383407295723015\n",
            "0.006610470615914053\n",
            "       Design  Features\n",
            "S20  0.658269  0.089398\n",
            "S21  0.281923  0.163757\n",
            "S22  0.059808  0.746845\n",
            "How much would you rate S20 with respect to S21 in Price if S21 = 1 1\n",
            "How much would you rate S20 with respect to S22 in Price if S22 = 1 6\n",
            "How much would you rate S21 with respect to S22 in Price if S22 = 1 3\n",
            "            S20       S21  S22  Priorities\n",
            "Price                                     \n",
            "S20    0.461538  0.428571  0.6    0.496703\n",
            "S21    0.461538  0.428571  0.3    0.396703\n",
            "S22    0.076923  0.142857  0.1    0.106593\n",
            "[1.53296703 1.21318681 0.32161172]\n",
            "Price\n",
            "S20    3.086283\n",
            "S21    3.058172\n",
            "S22    3.017182\n",
            "Name: Priorities, dtype: float64\n",
            "3.0538790205257516\n",
            "0.026939510262875777\n",
            "0.04644743148771686\n",
            "       Design  Features     Price\n",
            "S20  0.658269  0.089398  0.496703\n",
            "S21  0.281923  0.163757  0.396703\n",
            "S22  0.059808  0.746845  0.106593\n"
          ]
        }
      ],
      "source": [
        "final_df = pd.DataFrame(data = [], index = Options )\n",
        "RI = [0,0,0,0.58,0.90,1.12,1.24,1.32,1.42]\n",
        "for i in range (n):\n",
        "  data = {Factors[i] : Options}\n",
        "  for j in range (m):\n",
        "    l = [1.]*m\n",
        "    data[Options[j]] = l\n",
        "  df = pd.DataFrame(data).set_index(Factors[i])\n",
        "  for j in range (m-1):\n",
        "    for k  in range (j+1,m):\n",
        "      df.iloc[j][k] = float(input(f\"How much would you rate {Options[j]} with respect to {Options[k]} in {Factors[i]} if {Options[k]} = 1 \"))\n",
        "      df.iloc[k][j] = 1 / df.iloc[j][k]\n",
        "  df_org = df.copy()\n",
        "  df = df.div(df.sum())\n",
        "  df['Priorities'] = np.mean(df,axis=1)\n",
        "  print(df)\n",
        "  dot_product = np.dot(df_org,df['Priorities'])\n",
        "  print(dot_product)\n",
        "  consistency_vector = dot_product / df ['Priorities']\n",
        "  print(consistency_vector) \n",
        "  Lambda = consistency_vector.mean()\n",
        "  print(Lambda)\n",
        "  consistency_index = (Lambda - m)/(m-1)\n",
        "  print(consistency_index)\n",
        "  if(consistency_index > 0.1):\n",
        "    print(\"Your answers are highly consistent with different choices.So a better option can not be selected with these priorities\")\n",
        "    exit(0)\n",
        "  consistency_ratio = consistency_index / RI[m]\n",
        "  print(consistency_ratio)\n",
        "  \n",
        "\n",
        "\n",
        "  final_df[Factors[i]] = df ['Priorities']\n",
        "  print(final_df)\n",
        "\n"
      ]
    },
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          "text": [
            "            0    1    2\n",
            "Design    1.0  1.0  1.0\n",
            "Features  1.0  1.0  1.0\n",
            "Price     1.0  1.0  1.0\n",
            "How much is Design more important for you with respect to Features  if Features = 1 0.13\n",
            "How much is Design more important for you with respect to Price  if Price = 1 0.33\n",
            "How much is Features more important for you with respect to Price  if Price = 1 3\n"
          ]
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              "      <th>Priorities</th>\n",
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              "      <th>Design</th>\n",
              "      <td>0.085305</td>\n",
              "      <td>0.088838</td>\n",
              "      <td>0.076212</td>\n",
              "      <td>0.083452</td>\n",
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              "      <th>Features</th>\n",
              "      <td>0.656194</td>\n",
              "      <td>0.683371</td>\n",
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              "                 0         1         2  Priorities\n",
              "Design    0.085305  0.088838  0.076212    0.083452\n",
              "Features  0.656194  0.683371  0.692841    0.677469\n",
              "Price     0.258501  0.227790  0.230947    0.239079"
            ]
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      ],
      "source": [
        "# FP (Factors Priority)\n",
        "FP = pd.DataFrame(data = np.ones((n,n)),index = Factors)\n",
        "print(FP)\n",
        "for i in range (n-1):\n",
        "    for j in range (i+1,n):\n",
        "      FP.iloc[i][j] = float(input(f\"How much is {Factors[i]} more important for you with respect to {Factors[j]}  if {Factors[j]} = 1 \"))\n",
        "      FP.iloc[j][i] = (1)/(FP.iloc[i][j])\n",
        "\n",
        "\n",
        "FP_org = FP.copy()\n",
        "FP = FP.div(FP.sum())\n",
        "FP['Priorities'] = np.mean(FP,axis=1)\n",
        "FP"
      ]
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              "array([0.2342494 , 0.22931117, 0.53643943])"
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      "source": [
        "dot_product = np.dot(final_df,FP['Priorities'])\n",
        "dot_product"
      ]
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              "      <th>S20</th>\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d5973864-09da-48ba-ab5c-d47c7c10a0f8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d5973864-09da-48ba-ab5c-d47c7c10a0f8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       Design  Features     Price  Priorities\n",
              "S20  0.658269  0.089398  0.496703    0.234249\n",
              "S21  0.281923  0.163757  0.396703    0.229311\n",
              "S22  0.059808  0.746845  0.106593    0.536439"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "final_df['Priorities'] = dot_product\n",
        "final_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u1bCb8YKZsiL",
        "outputId": "c09bc5f2-c691-4b1c-e81c-a073efe5ae8a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.5364394270475124\n"
          ]
        }
      ],
      "source": [
        "print(final_df['Priorities'].max())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QGD6PQd_ediN",
        "outputId": "71acf1f9-9015-4728-e0ab-ea3b88937162"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "S22. Acoording to your answers this is the best selection for you bu AHP\n"
          ]
        }
      ],
      "source": [
        "print(final_df.loc[final_df['Priorities'] == final_df['Priorities'].max()].index[0], end = \". \")\n",
        "print(\"Acoording to your answers this is the best selection for you bu AHP\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LSs2Ho3BfaKY"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.10.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}