Implement_CPT_MODEL_KJGRD_SDEE_v1.1.ipynb

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    "id": "53ewTw30PIoI"
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   "source": [
    "# Load the necessary python packages\n",
    "\n",
    "Artcle link for more information: \n",
    "\n",
    "https://doi.org/10.1016/j.soildyn.2022.107662"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "#Import libraries\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k3eQn_iASXrm"
   },
   "source": [
    "# Load Test Sites Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7747,
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     "timestamp": 1678856797547,
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    "outputId": "8a14619a-20fd-4949-c69d-6a25ef367af7"
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   "outputs": [],
   "source": [
    "# If test sites information csv format then load the CSV file into a Pandas dataframe\n",
    "#df = pd.read_csv('TestSites2.csv')\n",
    "# If test sites information excel format then load the excel file into a Pandas dataframe as follows\n",
    "df = pd.read_excel('TestSites.xlsx')\n",
    "\n",
    "# Extract the features\n",
    "#X = df.values\n",
    "X = df"
   ]
  },
  {
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   "outputs": [
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       "      <th>IP1</th>\n",
       "      <th>IP2</th>\n",
       "      <th>IP3</th>\n",
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       "      <th>0</th>\n",
       "      <td>7.62</td>\n",
       "      <td>0.250</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>79</td>\n",
       "      <td>2.47</td>\n",
       "      <td>61</td>\n",
       "      <td>78.3</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.99</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.770000e-07</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.60</td>\n",
       "      <td>0.370</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>35</td>\n",
       "      <td>1.90</td>\n",
       "      <td>35</td>\n",
       "      <td>162.6</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.500000e-05</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.60</td>\n",
       "      <td>0.640</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>49</td>\n",
       "      <td>1.76</td>\n",
       "      <td>4</td>\n",
       "      <td>159.3</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.000000e-05</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.93</td>\n",
       "      <td>0.220</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>72</td>\n",
       "      <td>1.77</td>\n",
       "      <td>4</td>\n",
       "      <td>93.3</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.05</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3.730000e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.54</td>\n",
       "      <td>0.174</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.7</td>\n",
       "      <td>57</td>\n",
       "      <td>2.55</td>\n",
       "      <td>85</td>\n",
       "      <td>90.1</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.08</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.580000e-07</td>\n",
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      "text/plain": [
       "    IP1    IP2  IP3  IP4  IP6   IP9  IP10   IP11  IP12  IP14  IP15  IP17  \\\n",
       "0  7.62  0.250  5.5  3.5   79  2.47    61   78.3  0.96  1.02  0.99   3.0   \n",
       "1  6.60  0.370  2.8  1.2   35  1.90    35  162.6  0.97  1.10  1.28   1.0   \n",
       "2  7.60  0.640  4.7  1.1   49  1.76     4  159.3  0.97  1.10  0.97   0.6   \n",
       "3  6.93  0.220  4.5  3.4   72  1.77     4   93.3  0.95  1.04  1.05   2.1   \n",
       "4  6.54  0.174  3.9  2.7   57  2.55    85   90.1  0.95  1.06  1.08   2.6   \n",
       "\n",
       "           IP18  \n",
       "0  2.770000e-07  \n",
       "1  1.500000e-05  \n",
       "2  4.000000e-05  \n",
       "3  3.730000e-05  \n",
       "4  1.580000e-07  "
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8 entries, 0 to 7\n",
      "Data columns (total 13 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   IP1     8 non-null      float64\n",
      " 1   IP2     8 non-null      float64\n",
      " 2   IP3     8 non-null      float64\n",
      " 3   IP4     8 non-null      float64\n",
      " 4   IP6     8 non-null      int64  \n",
      " 5   IP9     8 non-null      float64\n",
      " 6   IP10    8 non-null      int64  \n",
      " 7   IP11    8 non-null      float64\n",
      " 8   IP12    8 non-null      float64\n",
      " 9   IP14    8 non-null      float64\n",
      " 10  IP15    8 non-null      float64\n",
      " 11  IP17    8 non-null      float64\n",
      " 12  IP18    8 non-null      float64\n",
      "dtypes: float64(11), int64(2)\n",
      "memory usage: 960.0 bytes\n"
     ]
    }
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   "source": [
    "df.info()"
   ]
  },
  {
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       "      <th>IP1</th>\n",
       "      <th>IP2</th>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000e+00</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.268750</td>\n",
       "      <td>0.301250</td>\n",
       "      <td>4.562500</td>\n",
       "      <td>2.425000</td>\n",
       "      <td>61.625000</td>\n",
       "      <td>2.048750</td>\n",
       "      <td>33.500000</td>\n",
       "      <td>113.850000</td>\n",
       "      <td>0.965</td>\n",
       "      <td>1.060000</td>\n",
       "      <td>1.068750</td>\n",
       "      <td>2.112500</td>\n",
       "      <td>1.134694e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.806482</td>\n",
       "      <td>0.148324</td>\n",
       "      <td>1.070297</td>\n",
       "      <td>1.005343</td>\n",
       "      <td>14.232132</td>\n",
       "      <td>0.440955</td>\n",
       "      <td>33.222196</td>\n",
       "      <td>49.642148</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.035051</td>\n",
       "      <td>0.128111</td>\n",
       "      <td>1.433714</td>\n",
       "      <td>2.821562e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.540000</td>\n",
       "      <td>0.174000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.330000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>54.800000</td>\n",
       "      <td>0.950</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.580000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.847500</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.767500</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>80.400000</td>\n",
       "      <td>0.950</td>\n",
       "      <td>1.030000</td>\n",
       "      <td>0.985000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.532500e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.930000</td>\n",
       "      <td>0.253000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>1.990000</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>91.700000</td>\n",
       "      <td>0.960</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>1.035000</td>\n",
       "      <td>1.850000</td>\n",
       "      <td>9.625000e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.605000</td>\n",
       "      <td>0.302500</td>\n",
       "      <td>5.050000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>71.250000</td>\n",
       "      <td>2.485000</td>\n",
       "      <td>62.250000</td>\n",
       "      <td>160.125000</td>\n",
       "      <td>0.970</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.120000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>3.797500e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>2.550000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>191.300000</td>\n",
       "      <td>1.010</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.105880e-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
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       "              title=\"Convert this dataframe to an interactive table.\"\n",
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       "        \n",
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       "  <style>\n",
       "    .colab-df-container {\n",
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       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
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       "      border-radius: 50%;\n",
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       "    .colab-df-convert:hover {\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
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       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-2be41d8d-d1e2-44eb-a723-754a3d5927d7 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-2be41d8d-d1e2-44eb-a723-754a3d5927d7');\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",
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       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "            IP1       IP2       IP3       IP4        IP6       IP9       IP10  \\\n",
       "count  8.000000  8.000000  8.000000  8.000000   8.000000  8.000000   8.000000   \n",
       "mean   7.268750  0.301250  4.562500  2.425000  61.625000  2.048750  33.500000   \n",
       "std    0.806482  0.148324  1.070297  1.005343  14.232132  0.440955  33.222196   \n",
       "min    6.540000  0.174000  2.800000  1.100000  35.000000  1.330000   1.000000   \n",
       "25%    6.847500  0.220000  3.900000  1.500000  55.000000  1.767500   4.000000   \n",
       "50%    6.930000  0.253000  4.600000  2.600000  65.000000  1.990000  23.500000   \n",
       "75%    7.605000  0.302500  5.050000  3.400000  71.250000  2.485000  62.250000   \n",
       "max    9.000000  0.640000  6.300000  3.500000  79.000000  2.550000  85.000000   \n",
       "\n",
       "             IP11   IP12      IP14      IP15      IP17          IP18  \n",
       "count    8.000000  8.000  8.000000  8.000000  8.000000  8.000000e+00  \n",
       "mean   113.850000  0.965  1.060000  1.068750  2.112500  1.134694e-04  \n",
       "std     49.642148  0.020  0.035051  0.128111  1.433714  2.821562e-04  \n",
       "min     54.800000  0.950  1.020000  0.920000  0.600000  1.580000e-07  \n",
       "25%     80.400000  0.950  1.030000  0.985000  1.000000  2.532500e-07  \n",
       "50%     91.700000  0.960  1.050000  1.035000  1.850000  9.625000e-06  \n",
       "75%    160.125000  0.970  1.100000  1.120000  2.700000  3.797500e-05  \n",
       "max    191.300000  1.010  1.100000  1.280000  5.000000  8.105880e-04  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cD2kU8NcpttE"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bq3JfbR7SdrI"
   },
   "source": [
    "# Load the developed CPT Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[09:25:47] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:1098: Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "model = xgb.XGBClassifier()\n",
    "model.load_model('MODEL_D_Liq_Jas_Dodagoudar.txt') #This .txt file is working fine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "executionInfo": {
     "elapsed": 819,
     "status": "ok",
     "timestamp": 1678857538547,
     "user": {
      "displayName": "Kaushik Jas ce19d202",
      "userId": "01693998378593910946"
     },
     "user_tz": -330
    },
    "id": "lNevBFpxTm1M"
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1678857541141,
     "user": {
      "displayName": "Kaushik Jas ce19d202",
      "userId": "01693998378593910946"
     },
     "user_tz": -330
    },
    "id": "03ashfUoUX62"
   },
   "outputs": [],
   "source": [
    "y_ProbPred = model.predict_proba(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 0 1 0 1 1 0]\n"
     ]
    }
   ],
   "source": [
    "print (y_pred) #Deterministic class labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.22919524 0.77080476]\n",
      " [0.32009494 0.67990506]\n",
      " [0.7045293  0.29547074]\n",
      " [0.31951046 0.68048954]\n",
      " [0.7698336  0.23016638]\n",
      " [0.11221308 0.8877869 ]\n",
      " [0.22919524 0.77080476]\n",
      " [0.9361835  0.06381651]]\n"
     ]
    }
   ],
   "source": [
    "print (y_ProbPred) #Probabilistic class labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "GPl1m7daVmSz"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cJX3rLKtVkfd"
   },
   "source": [
    "# Load the SHAP package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6028,
     "status": "ok",
     "timestamp": 1678857560647,
     "user": {
      "displayName": "Kaushik Jas ce19d202",
      "userId": "01693998378593910946"
     },
     "user_tz": -330
    },
    "id": "1pd4U62CViTx",
    "outputId": "8189e89e-7d57-4b89-fe81-35e56e9e6621"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Collecting shap\n",
      "  Downloading shap-0.41.0-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (572 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m572.4/572.4 KB\u001b[0m \u001b[31m15.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: cloudpickle in /usr/local/lib/python3.9/dist-packages (from shap) (2.2.1)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.9/dist-packages (from shap) (1.10.1)\n",
      "Requirement already satisfied: numba in /usr/local/lib/python3.9/dist-packages (from shap) (0.56.4)\n",
      "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.9/dist-packages (from shap) (1.2.2)\n",
      "Requirement already satisfied: tqdm>4.25.0 in /usr/local/lib/python3.9/dist-packages (from shap) (4.65.0)\n",
      "Requirement already satisfied: packaging>20.9 in /usr/local/lib/python3.9/dist-packages (from shap) (23.0)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from shap) (1.22.4)\n",
      "Requirement already satisfied: pandas in /usr/local/lib/python3.9/dist-packages (from shap) (1.4.4)\n",
      "Collecting slicer==0.0.7\n",
      "  Downloading slicer-0.0.7-py3-none-any.whl (14 kB)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.9/dist-packages (from numba->shap) (63.4.3)\n",
      "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.9/dist-packages (from numba->shap) (0.39.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas->shap) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas->shap) (2022.7.1)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.9/dist-packages (from scikit-learn->shap) (1.1.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from scikit-learn->shap) (3.1.0)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.8.1->pandas->shap) (1.15.0)\n",
      "Installing collected packages: slicer, shap\n",
      "Successfully installed shap-0.41.0 slicer-0.0.7\n"
     ]
    }
   ],
   "source": [
    "%pip install shap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the SHAP explainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "executionInfo": {
     "elapsed": 9849,
     "status": "ok",
     "timestamp": 1678857585969,
     "user": {
      "displayName": "Kaushik Jas ce19d202",
      "userId": "01693998378593910946"
     },
     "user_tz": -330
    },
    "id": "xPJF8k3mbMvA"
   },
   "outputs": [],
   "source": [
    "import shap\n",
    "explainer = shap.TreeExplainer(model) #Explainer corresponding to best model\n",
    "shap_values = explainer(X) # Shap value for the whole dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ARmlC-sGj62W"
   },
   "source": [
    "See SHAP values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1678857585970,
     "user": {
      "displayName": "Kaushik Jas ce19d202",
      "userId": "01693998378593910946"
     },
     "user_tz": -330
    },
    "id": "Gr-bO1blj7AC",
    "outputId": "8bb658a3-1363-4a33-c246-ec60c41d7e64"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       ".values =\n",
       "array([[-0.01505005,  0.08165662,  0.        , -0.4671053 ,  0.        ,\n",
       "         0.        ,  0.2418636 ,  1.1230073 ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.11272408],\n",
       "       [ 0.6255532 ,  0.319398  ,  0.        ,  0.45045164,  0.        ,\n",
       "         0.        ,  0.572726  , -1.4754827 ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.12492439],\n",
       "       [-0.0645622 ,  0.319398  ,  0.        ,  0.73358905,  0.        ,\n",
       "         0.        , -0.03436356, -2.0837107 ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.12492439],\n",
       "       [-0.01505005,  0.24286096,  0.        , -0.4711704 ,  0.        ,\n",
       "         0.        , -0.01451182,  0.76540464,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.11272408],\n",
       "       [ 0.14725101, -1.3687873 ,  0.        , -0.3589    ,  0.        ,\n",
       "         0.        ,  0.2418636 , -0.11728813,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.11272408],\n",
       "       [-0.01505005,  0.17330487,  0.        ,  0.18658422,  0.        ,\n",
       "         0.        ,  0.2418636 ,  1.1719815 ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.17388341],\n",
       "       [-0.01505005,  0.08165662,  0.        , -0.4711704 ,  0.        ,\n",
       "         0.        , -0.01451182,  1.3834478 ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.11272408],\n",
       "       [-0.0645622 ,  0.21037592,  0.        , -0.2564405 ,  0.        ,\n",
       "         0.        , -0.05178384, -2.18357   ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        , -0.4755838 ]], dtype=float32)\n",
       "\n",
       ".base_values =\n",
       "array([0.135765, 0.135765, 0.135765, 0.135765, 0.135765, 0.135765,\n",
       "       0.135765, 0.135765], dtype=float32)\n",
       "\n",
       ".data =\n",
       "array([[7.62000e+00, 2.50000e-01, 5.50000e+00, 3.50000e+00, 7.90000e+01,\n",
       "        2.47000e+00, 6.10000e+01, 7.83000e+01, 9.60000e-01, 1.02000e+00,\n",
       "        9.90000e-01, 3.00000e+00, 2.77000e-07],\n",
       "       [6.60000e+00, 3.70000e-01, 2.80000e+00, 1.20000e+00, 3.50000e+01,\n",
       "        1.90000e+00, 3.50000e+01, 1.62600e+02, 9.70000e-01, 1.10000e+00,\n",
       "        1.28000e+00, 1.00000e+00, 1.50000e-05],\n",
       "       [7.60000e+00, 6.40000e-01, 4.70000e+00, 1.10000e+00, 4.90000e+01,\n",
       "        1.76000e+00, 4.00000e+00, 1.59300e+02, 9.70000e-01, 1.10000e+00,\n",
       "        9.70000e-01, 6.00000e-01, 4.00000e-05],\n",
       "       [6.93000e+00, 2.20000e-01, 4.50000e+00, 3.40000e+00, 7.20000e+01,\n",
       "        1.77000e+00, 4.00000e+00, 9.33000e+01, 9.50000e-01, 1.04000e+00,\n",
       "        1.05000e+00, 2.10000e+00, 3.73000e-05],\n",
       "       [6.54000e+00, 1.74000e-01, 3.90000e+00, 2.70000e+00, 5.70000e+01,\n",
       "        2.55000e+00, 8.50000e+01, 9.01000e+01, 9.50000e-01, 1.06000e+00,\n",
       "        1.08000e+00, 2.60000e+00, 1.58000e-07],\n",
       "       [9.00000e+00, 2.56000e-01, 6.30000e+00, 1.60000e+00, 7.10000e+01,\n",
       "        2.53000e+00, 6.60000e+01, 8.11000e+01, 1.01000e+00, 1.03000e+00,\n",
       "        9.20000e-01, 1.60000e+00, 1.82000e-07],\n",
       "       [6.93000e+00, 2.20000e-01, 4.90000e+00, 2.50000e+00, 6.70000e+01,\n",
       "        2.08000e+00, 1.20000e+01, 5.48000e+01, 9.50000e-01, 1.03000e+00,\n",
       "        1.02000e+00, 5.00000e+00, 4.25000e-06],\n",
       "       [6.93000e+00, 2.80000e-01, 3.90000e+00, 3.40000e+00, 6.30000e+01,\n",
       "        1.33000e+00, 1.00000e+00, 1.91300e+02, 9.60000e-01, 1.10000e+00,\n",
       "        1.24000e+00, 1.00000e+00, 8.10588e-04]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Pum2wHkNgGeS"
   },
   "source": [
    "## Get the local plots\n",
    "\n",
    "CIN = Case index number. It stands for the serial number corresponding to your case history.\n",
    "Put that serial number/row number of which you want to find SHAP plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4193,
     "status": "ok",
     "timestamp": 1678857596811,
     "user": {
      "displayName": "Kaushik Jas ce19d202",
      "userId": "01693998378593910946"
     },
     "user_tz": -330
    },
    "id": "y-lkANHabruX",
    "outputId": "ca4f7c2e-931a-4fc4-be83-d46015b8a416"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter CIN of your site to get Local Plot: 1\n"
     ]
    }
   ],
   "source": [
    "i = int(input(\"Enter CIN of your site to get Local Plot: \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 525
    },
    "executionInfo": {
     "elapsed": 1251,
     "status": "ok",
     "timestamp": 1678857600778,
     "user": {
      "displayName": "Kaushik Jas ce19d202",
      "userId": "01693998378593910946"
     },
     "user_tz": -330
    },
    "id": "KyPx1eGobtKr",
    "outputId": "3226c077-4cc2-4752-995f-36fd32396636"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Local plots\n",
    "shap.plots.waterfall(shap_values[i], max_display=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "w697V7aUgL8m"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zD54kE5hgME6"
   },
   "source": [
    "### GET THE GLOBAL PLOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 500
    },
    "executionInfo": {
     "elapsed": 1373,
     "status": "ok",
     "timestamp": 1678857611846,
     "user": {
      "displayName": "Kaushik Jas ce19d202",
      "userId": "01693998378593910946"
     },
     "user_tz": -330
    },
    "id": "UiYZKxine6m0",
    "outputId": "11d7aba8-e573-4023-bfbb-25dabc3931ce"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Global Bar plots\n",
    "shap.plots.bar(shap_values, max_display=17, show=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dvQh7yxwwSJ9"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FDKD11VjoInG"
   },
   "source": [
    "# Prepared by: \n",
    "\n",
    "#### Mr. KAUSHIK JAS, IITM, Chennai, Tamil Nadu, India\n",
    "\n",
    "Contact: ce19d202@smail.iitm.ac.in OR kaushik.jas.2013@gmail.com\n",
    "\n",
    "Website: https://sites.google.com/view/kaushikjasoffice3/home?authuser=0\n",
    "\n",
    "### Cite this model as:\n",
    "Jas, K., and Dodagoudar, G.R. (2023). \"Explainable machine learning model for liquefaction potential assessment of soils using XGBoost-SHAP.\" Soil Dynamics and Earthquake Engineering.165:107662. https://doi.org/10.1016/j.soildyn.2022.107662."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mRqLSpucoPgS"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "17nS3lW9oD77"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyMZXeyxkxJvb7ZbuAOYzpcO",
   "provenance": [
    {
     "file_id": "1UAlXbyRSk4Pz6CQB6F0Wr0dqgCJm7O3E",
     "timestamp": 1678795592563
    }
   ]
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}