Sensitivity Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from SALib.plotting.bar import plot as barplot\n",
    "from SALib.analyze import sobol\n",
    "from SALib.sample import saltelli\n",
    "import numpy as np\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mac_excel_path = r\"\\\\projectsportal.ghd.com@SSL\\DavWWWRoot\\sites\\pp15_01\\comdainwaternswrural\\ProjectDocs\\Wyangala Dam\\L11 - Suspended doors make safe\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(weights):\n",
    "    options = np.array([1,2,3,4,5,6,7])\n",
    "    mca = np.array([\n",
    "    [7, 7, 7, 3, 2, 1, 7],\n",
    "    [1, 2, 3, 6, 5, 7, 4],\n",
    "    [4, 1, 2, 6, 4, 7, 5],\n",
    "    [3, 7, 7, 3, 1, 7, 7],\n",
    "    [7, 3, 2, 5, 5, 1, 7],\n",
    "    [2, 1, 5, 7, 6, 4, 3],\n",
    "    [1, 4, 5, 7, 6, 3, 2],\n",
    "    ])\n",
    "\n",
    "#     mca = pd.read_excel(mac_excel_path)\n",
    "    weighted_mca =np.transpose(weights) * mca\n",
    "    evaluation = -weighted_mca.sum(axis=0)\n",
    "    rank = evaluation.argsort()\n",
    "    rank_order = rank.argsort()\n",
    "    return rank[0] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = [\"Operability\",\"Maintenance\",\"Reliability\",\"OH&S - Suspended Load\",\"OH&S - Additional Risks\",\"Capex\",\"Constructability\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "problem = {\n",
    "    'num_vars': 7,\n",
    "    'names':labels,\n",
    "    'bounds':[[0.05, 0.3],\n",
    "        [0.05, 0.3],\n",
    "        [0.05, 0.3],\n",
    "        [0.05, 0.3],\n",
    "        [0.05, 0.3],\n",
    "        [0.05, 0.3],\n",
    "        [0.05, 0.3],]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[0.10125807, 0.07157329, 0.17334967, ..., 0.11586486, 0.26711586,\n        0.05932243],\n       [0.22786706, 0.06149051, 0.14892928, ..., 0.09954257, 0.2294863 ,\n        0.05096547],\n       [0.09225948, 0.15408063, 0.15794445, ..., 0.1055682 , 0.24337784,\n        0.05405057],\n       ...,\n       [0.11508445, 0.08904815, 0.17931176, ..., 0.10100359, 0.1815036 ,\n        0.27648297],\n       [0.11123523, 0.08606976, 0.17331433, ..., 0.09762533, 0.16233654,\n        0.31377874],\n       [0.11666521, 0.09027128, 0.18177472, ..., 0.10239094, 0.17026104,\n        0.28028064]])"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_values = saltelli.sample(problem, 5000)\n",
    "row_sums = np.sum(param_values, axis=1)\n",
    "param_values = param_values/row_sums[:,np.newaxis]\n",
    "param_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "80000"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = np.zeros(param_values.shape[0])\n",
    "\n",
    "for i in range(param_values.shape[0]):\n",
    "    Y[i] = evaluate_model(param_values[i,:])\n",
    "Y.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "Si = sobol.analyze(problem, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "First order interactions\n[ 9.8348e-03, -9.0241e-05,  5.8147e-02,  4.8286e-02,  2.0750e-03,\n  4.4634e-02,  3.4910e-01]\n\n\nSecond order interactions\n"
    },
    {
     "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>Operability</th>\n      <th>Maintenance</th>\n      <th>Reliability</th>\n      <th>OH&amp;S - Suspended Load</th>\n      <th>OH&amp;S - Additional Risks</th>\n      <th>Capex</th>\n      <th>Constructability</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Operability</th>\n      <td></td>\n      <td>0.007</td>\n      <td>0.012</td>\n      <td>0.022</td>\n      <td>0.01</td>\n      <td>0.012</td>\n      <td>0.009</td>\n    </tr>\n    <tr>\n      <th>Maintenance</th>\n      <td></td>\n      <td></td>\n      <td>-0.003</td>\n      <td>-0</td>\n      <td>0.001</td>\n      <td>-0</td>\n      <td>0.006</td>\n    </tr>\n    <tr>\n      <th>Reliability</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>0.024</td>\n      <td>0.015</td>\n      <td>0.005</td>\n      <td>0.034</td>\n    </tr>\n    <tr>\n      <th>OH&amp;S - Suspended Load</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>0.003</td>\n      <td>0.017</td>\n      <td>0.109</td>\n    </tr>\n    <tr>\n      <th>OH&amp;S - Additional Risks</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>-0.001</td>\n      <td>0.012</td>\n    </tr>\n    <tr>\n      <th>Capex</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>0.026</td>\n    </tr>\n    <tr>\n      <th>Constructability</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "                        Operability Maintenance Reliability  \\\nOperability                               0.007       0.012   \nMaintenance                                          -0.003   \nReliability                                                   \nOH&S - Suspended Load                                         \nOH&S - Additional Risks                                       \nCapex                                                         \nConstructability                                              \n\n                        OH&S - Suspended Load OH&S - Additional Risks  Capex  \\\nOperability                             0.022                    0.01  0.012   \nMaintenance                                -0                   0.001     -0   \nReliability                             0.024                   0.015  0.005   \nOH&S - Suspended Load                                           0.003  0.017   \nOH&S - Additional Risks                                               -0.001   \nCapex                                                                          \nConstructability                                                               \n\n                        Constructability  \nOperability                        0.009  \nMaintenance                        0.006  \nReliability                        0.034  \nOH&S - Suspended Load              0.109  \nOH&S - Additional Risks            0.012  \nCapex                              0.026  \nConstructability                          "
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S2_df = pd.DataFrame(Si['S2'], columns=labels)\n",
    "\n",
    "print('First order interactions')\n",
    "print(np.array2string(Si['S1'], precision=4, separator=', '))\n",
    "print('\\n')\n",
    "print('Second order interactions')\n",
    "S2_df.set_index(pd.Index(labels), inplace=True)\n",
    "rounded_df = S2_df.round(decimals=3)\n",
    "rounded_df = rounded_df.replace(np.nan, '', regex=True)\n",
    "rounded_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "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>S1</th>\n      <th>ST</th>\n    </tr>\n    <tr>\n      <th>Criteria</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Operability</th>\n      <td>0.009835</td>\n      <td>0.121008</td>\n    </tr>\n    <tr>\n      <th>Maintenance</th>\n      <td>-0.000090</td>\n      <td>0.071752</td>\n    </tr>\n    <tr>\n      <th>Reliability</th>\n      <td>0.058147</td>\n      <td>0.238193</td>\n    </tr>\n    <tr>\n      <th>OH&amp;S - Suspended Load</th>\n      <td>0.048286</td>\n      <td>0.355745</td>\n    </tr>\n    <tr>\n      <th>OH&amp;S - Additional Risks</th>\n      <td>0.002075</td>\n      <td>0.118618</td>\n    </tr>\n    <tr>\n      <th>Capex</th>\n      <td>0.044634</td>\n      <td>0.206654</td>\n    </tr>\n    <tr>\n      <th>Constructability</th>\n      <td>0.349102</td>\n      <td>0.684362</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "                               S1        ST\nCriteria                                   \nOperability              0.009835  0.121008\nMaintenance             -0.000090  0.071752\nReliability              0.058147  0.238193\nOH&S - Suspended Load    0.048286  0.355745\nOH&S - Additional Risks  0.002075  0.118618\nCapex                    0.044634  0.206654\nConstructability         0.349102  0.684362"
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "Si_copy = Si\n",
    "keys_to_drop = ['ST_conf', 'S2', 'S2_conf', 'S1_conf']\n",
    "for key in keys_to_drop:\n",
    "    Si_copy.pop(key, None)\n",
    "Si_df = pd.DataFrame.from_dict(Si_copy)\n",
    "Si_df['Criteria'] = labels\n",
    "Si_df.set_index('Criteria', inplace=True)\n",
    "Si_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
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     },
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   ],
   "source": [
    "fig = go.Figure(\n",
    "    data=[\n",
    "        go.Bar(name='First Order', x=labels, y=Si_df.S1),\n",
    "        go.Bar(name='Total Order', x=labels, y=Si_df.ST)\n",
    "    ],\n",
    ")\n",
    "# Change the bar mode\n",
    "fig.update_layout(barmode='group', template=\"plotly_white\", plot_bgcolor=\"#FFFFFF\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
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    "fig.write_image(\"Sensitivity of Criteria.png\", scale = 10)"
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  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique, counts = np.unique(Y, return_counts=True)"
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  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
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  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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            "gridcolor": "#EBF0F8",
            "linecolor": "#EBF0F8",
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          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "white",
            "gridcolor": "#DFE8F3",
            "gridwidth": 2,
            "linecolor": "#EBF0F8",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "#EBF0F8"
           },
           "yaxis": {
            "backgroundcolor": "white",
            "gridcolor": "#DFE8F3",
            "gridwidth": 2,
            "linecolor": "#EBF0F8",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "#EBF0F8"
           },
           "zaxis": {
            "backgroundcolor": "white",
            "gridcolor": "#DFE8F3",
            "gridwidth": 2,
            "linecolor": "#EBF0F8",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "#EBF0F8"
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          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "#DFE8F3",
            "linecolor": "#A2B1C6",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "#DFE8F3",
            "linecolor": "#A2B1C6",
            "ticks": ""
           },
           "bgcolor": "white",
           "caxis": {
            "gridcolor": "#DFE8F3",
            "linecolor": "#A2B1C6",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "#EBF0F8",
           "linecolor": "#EBF0F8",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "#EBF0F8",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "#EBF0F8",
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           "ticks": "",
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           },
           "zerolinecolor": "#EBF0F8",
           "zerolinewidth": 2
          }
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = go.Figure(data=[\n",
    "    go.Bar(name='Frquency Variance', x=option_labels, y=counts/sum(counts)*100),\n",
    "])\n",
    "fig.update_layout(barmode='group', template=\"plotly_white\",)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.write_image(\"Success frequency at varying weightings.png\", scale = 10)"
   ]
  }
 ],
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