KernelSHAP-Pure-Numpy.ipynb

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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "F26XnfmDnrCd"
      },
      "source": [
        "First, let's have a simple and fast to train model to predict local predictions. Dataset is famous Breast Cancer Wisconsin (Diagnostic) Data Set that we need to classify whether cells are malign or benign.\n",
        "\n",
        "I won't go into details for params and etc.\n",
        "\n",
        "We just have an object : model, that we can use its \"predict\" function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9iw1VswijDTu",
        "outputId": "9f6d2fab-2256-48d0-9ad6-13cf1917aaea"
      },
      "source": [
        "\n",
        "#### THIS IS THE CODE I'VE OBTAINED BY WRITING \"LIGHTGBM EXAMPLE CODE\" -- SORRY I CAN'T FIND RN. BUT THE MODEL CODE DOESN'T BELONG TO ME. \n",
        "#### BUT, IT'S A SIMPLE FAST MODEL. THERE'S NO PARTICULAR REASON TO CHOOSE LIGHTGBM. EVERY MODEL WORKS WITH KERNEL SHAP\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import lightgbm\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "\n",
        "\n",
        "#\n",
        "# Prepare the data\n",
        "#\n",
        "\n",
        "train = pd.read_csv('/content/data.csv')\n",
        "\n",
        "# get the labels\n",
        "y = (train.diagnosis == \"M\")*1\n",
        "train.drop([\"diagnosis\", \"id\", \"Unnamed: 32\"], inplace=True, axis=1)\n",
        "\n",
        "x = train.values\n",
        "\n",
        "#\n",
        "# Create training and validation sets\n",
        "#\n",
        "x, x_test, y, y_test = train_test_split(x, y, test_size=0.2, random_state=42, stratify=y)\n",
        "x_test = pd.DataFrame(x_test,columns = train.columns)\n",
        "x = pd.DataFrame(x,columns = train.columns)\n",
        "\n",
        "\n",
        "#### I ONLY GET FIRST 10 FEATURES.TO MAKE THINGS FASTER.\n",
        "num_feature_to_use = 4\n",
        "x = x.iloc[:,  :num_feature_to_use]\n",
        "x_test = x_test.iloc[:,  :num_feature_to_use]\n",
        "\n",
        "\n",
        "\n",
        "pd.DataFrame(x,columns = train.columns)\n",
        "#\n",
        "# Create the LightGBM data containers\n",
        "#\n",
        "categorical_features = [c for c, col in enumerate(train.columns) if 'cat' in col]\n",
        "train_data = lightgbm.Dataset(x, label=y, categorical_feature=categorical_features)\n",
        "test_data = lightgbm.Dataset(x_test, label=y_test)\n",
        "\n",
        "\n",
        "#\n",
        "# Train the model\n",
        "#\n",
        "\n",
        "parameters = {\n",
        "    'application': 'binary',\n",
        "    'objective': 'binary',\n",
        "    'metric': 'auc',\n",
        "    'is_unbalance': 'true',\n",
        "    'boosting': 'gbdt',\n",
        "    'num_leaves': 31,\n",
        "    'feature_fraction': 0.5,\n",
        "    'bagging_fraction': 0.5,\n",
        "    'bagging_freq': 20,\n",
        "    'learning_rate': 0.05,\n",
        "    'verbose': 0\n",
        "}\n",
        "\n",
        "model = lightgbm.train(parameters,\n",
        "                       train_data,\n",
        "                       valid_sets=test_data,\n",
        "                       num_boost_round=100,\n",
        "                       early_stopping_rounds=30)\n",
        "#\n",
        "# Create a submission\n",
        "#\n",
        "\"\"\"\n",
        "submission = pd.read_csv('../input/test.csv')\n",
        "ids = submission['id'].values\n",
        "submission.drop('id', inplace=True, axis=1)\n",
        "\n",
        "\n",
        "x = submission.values\n",
        "y = model.predict(x)\n",
        "\n",
        "output = pd.DataFrame({'id': ids, 'target': y})\"\"\""
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1]\tvalid_0's auc: 0.913525\n",
            "Training until validation scores don't improve for 30 rounds.\n",
            "[2]\tvalid_0's auc: 0.922619\n",
            "[3]\tvalid_0's auc: 0.919147\n",
            "[4]\tvalid_0's auc: 0.922784\n",
            "[5]\tvalid_0's auc: 0.9208\n",
            "[6]\tvalid_0's auc: 0.935185\n",
            "[7]\tvalid_0's auc: 0.933201\n",
            "[8]\tvalid_0's auc: 0.940476\n",
            "[9]\tvalid_0's auc: 0.941799\n",
            "[10]\tvalid_0's auc: 0.944775\n",
            "[11]\tvalid_0's auc: 0.943122\n",
            "[12]\tvalid_0's auc: 0.945437\n",
            "[13]\tvalid_0's auc: 0.949735\n",
            "[14]\tvalid_0's auc: 0.951389\n",
            "[15]\tvalid_0's auc: 0.951224\n",
            "[16]\tvalid_0's auc: 0.953538\n",
            "[17]\tvalid_0's auc: 0.953538\n",
            "[18]\tvalid_0's auc: 0.95453\n",
            "[19]\tvalid_0's auc: 0.95453\n",
            "[20]\tvalid_0's auc: 0.955522\n",
            "[21]\tvalid_0's auc: 0.955522\n",
            "[22]\tvalid_0's auc: 0.957837\n",
            "[23]\tvalid_0's auc: 0.956184\n",
            "[24]\tvalid_0's auc: 0.957507\n",
            "[25]\tvalid_0's auc: 0.956845\n",
            "[26]\tvalid_0's auc: 0.956515\n",
            "[27]\tvalid_0's auc: 0.956515\n",
            "[28]\tvalid_0's auc: 0.954861\n",
            "[29]\tvalid_0's auc: 0.957176\n",
            "[30]\tvalid_0's auc: 0.956845\n",
            "[31]\tvalid_0's auc: 0.957837\n",
            "[32]\tvalid_0's auc: 0.957837\n",
            "[33]\tvalid_0's auc: 0.958168\n",
            "[34]\tvalid_0's auc: 0.958168\n",
            "[35]\tvalid_0's auc: 0.958829\n",
            "[36]\tvalid_0's auc: 0.95916\n",
            "[37]\tvalid_0's auc: 0.958829\n",
            "[38]\tvalid_0's auc: 0.959325\n",
            "[39]\tvalid_0's auc: 0.958995\n",
            "[40]\tvalid_0's auc: 0.958664\n",
            "[41]\tvalid_0's auc: 0.960648\n",
            "[42]\tvalid_0's auc: 0.962632\n",
            "[43]\tvalid_0's auc: 0.965608\n",
            "[44]\tvalid_0's auc: 0.966931\n",
            "[45]\tvalid_0's auc: 0.968254\n",
            "[46]\tvalid_0's auc: 0.966601\n",
            "[47]\tvalid_0's auc: 0.968254\n",
            "[48]\tvalid_0's auc: 0.967262\n",
            "[49]\tvalid_0's auc: 0.967923\n",
            "[50]\tvalid_0's auc: 0.967262\n",
            "[51]\tvalid_0's auc: 0.965939\n",
            "[52]\tvalid_0's auc: 0.965278\n",
            "[53]\tvalid_0's auc: 0.965608\n",
            "[54]\tvalid_0's auc: 0.965278\n",
            "[55]\tvalid_0's auc: 0.964947\n",
            "[56]\tvalid_0's auc: 0.964616\n",
            "[57]\tvalid_0's auc: 0.964286\n",
            "[58]\tvalid_0's auc: 0.964286\n",
            "[59]\tvalid_0's auc: 0.963955\n",
            "[60]\tvalid_0's auc: 0.963624\n",
            "[61]\tvalid_0's auc: 0.964286\n",
            "[62]\tvalid_0's auc: 0.962963\n",
            "[63]\tvalid_0's auc: 0.964286\n",
            "[64]\tvalid_0's auc: 0.963294\n",
            "[65]\tvalid_0's auc: 0.964947\n",
            "[66]\tvalid_0's auc: 0.964286\n",
            "[67]\tvalid_0's auc: 0.965608\n",
            "[68]\tvalid_0's auc: 0.964616\n",
            "[69]\tvalid_0's auc: 0.965608\n",
            "[70]\tvalid_0's auc: 0.964947\n",
            "[71]\tvalid_0's auc: 0.965608\n",
            "[72]\tvalid_0's auc: 0.965278\n",
            "[73]\tvalid_0's auc: 0.964947\n",
            "[74]\tvalid_0's auc: 0.963294\n",
            "[75]\tvalid_0's auc: 0.963955\n",
            "Early stopping, best iteration is:\n",
            "[45]\tvalid_0's auc: 0.968254\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Using categorical_feature in Dataset.\n",
            "categorical_feature in Dataset is overridden.\n",
            "New categorical_feature is []\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"\\nsubmission = pd.read_csv('../input/test.csv')\\nids = submission['id'].values\\nsubmission.drop('id', inplace=True, axis=1)\\n\\n\\nx = submission.values\\ny = model.predict(x)\\n\\noutput = pd.DataFrame({'id': ids, 'target': y})\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WRDAwcNyN_qw"
      },
      "source": [
        ""
      ],
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jw98H-hKG14r",
        "outputId": "96b48a2b-e982-4508-db0d-a259b5e7b404"
      },
      "source": [
        "print(\"Test accuracy %{}\".format( 100*(1-sum(abs(y_test.values-model.predict(x_test).round()))/len(y_test))))\n"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test accuracy %89.47368421052632\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BCucpbZwpchq"
      },
      "source": [
        "Now, in order to create all the combinations of features, we need to create binary mask, that we called it z() on the video. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pk3UBlc5pcub"
      },
      "source": [
        "from itertools import  product\n",
        "\n",
        "def create_mask_arrays(k):\n",
        "  mask = np.array(list(product(range(2), repeat=k)))\n",
        "  mask = mask[~np.all(mask == 0, axis=1)]\n",
        "  mask = mask[~np.all(mask == 1, axis=1)]\n",
        "  return mask\n",
        "\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "It is important to note that for z = full zero and z = full one, weight is infinite. In practice, these infinite weights can be avoided during optimization by analytically\n",
        "eliminating two variables using these constraints.\n",
        "https://arxiv.org/pdf/1705.07874.pdf\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yWjGm4pZ3jcM"
      },
      "source": [
        "Weight is defined in the paper https://arxiv.org/pdf/1705.07874.pdf . "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "drmWddXU3jlX"
      },
      "source": [
        "from scipy.special import comb\n",
        "\n",
        "\n",
        "def calculate_weight(mask_row):\n",
        "  M = no_of_features = len(mask_row)\n",
        "  z = no_of_masked_feature = np.sum(mask_row)\n",
        "  \n",
        "  weight = (M-1) /  (  comb(M,z) * z * (M-z)   )\n",
        "  return weight\n",
        "\n",
        "\n"
      ],
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fc9BTV-DAecy"
      },
      "source": [
        "We'll simulate missingness by replacing 0's with mean."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MuBQ537VAeFi"
      },
      "source": [
        "mean_ref_array = x.mean()\n",
        "## random training subset, an instance compare\n"
      ],
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zh_Eydsr-4cj"
      },
      "source": [
        "Our reference array will be the mean of features. We'll explain our predictions with comparing the mean array."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mhPDYw9A-3kU",
        "outputId": "d1154231-7c37-4a41-c1c2-d2c108b90f91"
      },
      "source": [
        "mean_ref_array"
      ],
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "radius_mean        14.166077\n",
              "texture_mean       19.417692\n",
              "perimeter_mean     92.215868\n",
              "area_mean         659.578242\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pf7t-JnmqMiI"
      },
      "source": [
        ""
      ],
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2eocpa_IBYw6"
      },
      "source": [
        "We'll try to explain one of the predictions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "GpMZ7sO3m41s",
        "outputId": "65ade74c-defa-452a-f361-e10f6f7bf18f"
      },
      "source": [
        "instance_to_explain = x_test.iloc[0:1]\n",
        "instance_to_explain"
      ],
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11.41</td>\n",
              "      <td>10.82</td>\n",
              "      <td>73.34</td>\n",
              "      <td>403.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   radius_mean  texture_mean  perimeter_mean  area_mean\n",
              "0        11.41         10.82           73.34      403.3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-EWS7z13Bhv-"
      },
      "source": [
        "Why our model predicts this instance as "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WCXn7tKJBk_e",
        "outputId": "2cd2b08d-5ac4-4404-91fe-976bb49dbbe7"
      },
      "source": [
        "print(\"this?:\",model.predict(instance_to_explain))"
      ],
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "this?: [0.10101002]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZOjMTpxt_dNM"
      },
      "source": [
        "Our mask array is as follows:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A6-dYKsfLn6g",
        "outputId": "e237ba53-3375-4755-c4da-05ba0ddb854c"
      },
      "source": [
        "mask = create_mask_arrays(len(x.columns))\n",
        "mask"
      ],
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0, 1],\n",
              "       [0, 0, 1, 0],\n",
              "       [0, 0, 1, 1],\n",
              "       [0, 1, 0, 0],\n",
              "       [0, 1, 0, 1],\n",
              "       [0, 1, 1, 0],\n",
              "       [0, 1, 1, 1],\n",
              "       [1, 0, 0, 0],\n",
              "       [1, 0, 0, 1],\n",
              "       [1, 0, 1, 0],\n",
              "       [1, 0, 1, 1],\n",
              "       [1, 1, 0, 0],\n",
              "       [1, 1, 0, 1],\n",
              "       [1, 1, 1, 0]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "187t9HYFPPlq",
        "outputId": "2979acb6-483c-465b-d35e-87bc8f7c53ca"
      },
      "source": [
        "S_with_zero = mask*instance_to_explain.values\n",
        "print(S_with_zero)\n",
        "print(\"-000-\")\n",
        "print(instance_to_explain)"
      ],
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[  0.     0.     0.   403.3 ]\n",
            " [  0.     0.    73.34   0.  ]\n",
            " [  0.     0.    73.34 403.3 ]\n",
            " [  0.    10.82   0.     0.  ]\n",
            " [  0.    10.82   0.   403.3 ]\n",
            " [  0.    10.82  73.34   0.  ]\n",
            " [  0.    10.82  73.34 403.3 ]\n",
            " [ 11.41   0.     0.     0.  ]\n",
            " [ 11.41   0.     0.   403.3 ]\n",
            " [ 11.41   0.    73.34   0.  ]\n",
            " [ 11.41   0.    73.34 403.3 ]\n",
            " [ 11.41  10.82   0.     0.  ]\n",
            " [ 11.41  10.82   0.   403.3 ]\n",
            " [ 11.41  10.82  73.34   0.  ]]\n",
            "-000-\n",
            "   radius_mean  texture_mean  perimeter_mean  area_mean\n",
            "0        11.41         10.82           73.34      403.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jtpz0W0x_jAC"
      },
      "source": [
        "Now we need to create S_full which is the imputed input array.  We will predict all instances of imputed array(rows) and use it on matrix algebra solution."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H9iJ32MEROLT",
        "outputId": "3ae3368c-d5d6-4053-e3c0-7ee0943f7d5f"
      },
      "source": [
        "S_full = (S_with_zero == 0)*mean_ref_array.values + S_with_zero\n",
        "print(S_full)"
      ],
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 14.16607692  19.41769231  92.21586813 403.3       ]\n",
            " [ 14.16607692  19.41769231  73.34       659.57824176]\n",
            " [ 14.16607692  19.41769231  73.34       403.3       ]\n",
            " [ 14.16607692  10.82        92.21586813 659.57824176]\n",
            " [ 14.16607692  10.82        92.21586813 403.3       ]\n",
            " [ 14.16607692  10.82        73.34       659.57824176]\n",
            " [ 14.16607692  10.82        73.34       403.3       ]\n",
            " [ 11.41        19.41769231  92.21586813 659.57824176]\n",
            " [ 11.41        19.41769231  92.21586813 403.3       ]\n",
            " [ 11.41        19.41769231  73.34       659.57824176]\n",
            " [ 11.41        19.41769231  73.34       403.3       ]\n",
            " [ 11.41        10.82        92.21586813 659.57824176]\n",
            " [ 11.41        10.82        92.21586813 403.3       ]\n",
            " [ 11.41        10.82        73.34       659.57824176]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oyx6PQ72C63J",
        "outputId": "0051d2cf-3fe8-4a89-b4a5-171b15f055d3"
      },
      "source": [
        "print(\"Our mean model predicts\",model.predict(mean_ref_array), \"and prediction for the instance we want to explain is:\",model.predict(instance_to_explain))"
      ],
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Our mean model predicts [0.39885547] and prediction for the instance we want to explain is: [0.10101002]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Converting data to scipy sparse matrix.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Re1I6lPOCv3L"
      },
      "source": [
        "We also need to have weights. We'll apply calculate_weights function  in a row-wise manner."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2tY8qfz8R48L",
        "outputId": "c408f941-2db2-4e33-e358-5fbdb2bf0f6c"
      },
      "source": [
        "calculate_weight([0,1,0,0])"
      ],
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.25"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ulO8m-bCvWS",
        "outputId": "7df57ba2-9451-46f3-8510-a548422f7e7a"
      },
      "source": [
        "weights  = np.apply_along_axis(calculate_weight, 1, mask)\n",
        "weights"
      ],
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.25 , 0.25 , 0.125, 0.25 , 0.125, 0.125, 0.25 , 0.25 , 0.125,\n",
              "       0.125, 0.25 , 0.125, 0.25 , 0.25 ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 183
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ziXlpF2eLKqv"
      },
      "source": [
        "Here we need to implement matrix solution."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hUql0vXC0TF0",
        "outputId": "c9deb26b-9f6f-4721-bc32-7b19954552a1"
      },
      "source": [
        "X = mask \n",
        "y = model.predict(S_full)\n",
        "\n",
        "B0 = model.predict(mean_ref_array)[0]\n",
        "W = np.diag(weights)\n",
        "\n",
        "y = y - B0\n",
        "\n",
        "B = np.dot(np.linalg.inv(np.dot(np.dot(X.transpose(),W),X)), np.dot(np.dot(X.transpose(),W),y))\n",
        "\n",
        "print(\"Our Shapley values are:\", B)"
      ],
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Our Shapley values are: [-0.0679206  -0.14358621 -0.18380165  0.0256743 ]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Converting data to scipy sparse matrix.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rvNZ6O54DYQC"
      },
      "source": [
        "In a better form:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "o0xVfkAQDYYb",
        "outputId": "c656337b-ddaf-4605-c124-ceea1b8af012"
      },
      "source": [
        "pd.DataFrame([B],columns = x.columns)"
      ],
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.067921</td>\n",
              "      <td>-0.143586</td>\n",
              "      <td>-0.183802</td>\n",
              "      <td>0.025674</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   radius_mean  texture_mean  perimeter_mean  area_mean\n",
              "0    -0.067921     -0.143586       -0.183802   0.025674"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fMvYWeJCkRXY",
        "outputId": "11991e42-6c7c-447f-f2e0-793a04d3ca98"
      },
      "source": [
        ""
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-0.36963416664564963\n",
            "[0.10101002]\n",
            "[0.39885547]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Converting data to scipy sparse matrix.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mSNSy2eMDfBw"
      },
      "source": [
        "Our results are matching with the original SHAP."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103,
          "referenced_widgets": [
            "4764ff258837450e9d27c8da41dc71a1",
            "7790bda4d7ed4e7991aad2aedbd4a3b1",
            "9f3d9e80187d414282b9ed3789630dba",
            "2d15f0b2401047239d31eba48a5548eb",
            "3487f1172dbb4ac1b1dfc75208735057",
            "40b2e3c704484a00b7645fc0cee135fb",
            "ab253f27e0584cbd94a1eb79f12c2547",
            "63eaf027e61b4b7a9ffa3f511f5a7975"
          ]
        },
        "id": "glgMrPuNjTpx",
        "outputId": "c9469c87-9694-44eb-f7eb-afbb67623927"
      },
      "source": [
        "import shap\n",
        "\n",
        "explainer = shap.KernelExplainer(model.predict, x, link=\"logit\",algorithm = \"linear\")\n",
        "shap_values = explainer.shap_values(x_test, nsamples=100)\n"
      ],
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using 455 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4764ff258837450e9d27c8da41dc71a1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=114.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "KO8xKK8_Dqlm",
        "outputId": "78bc851c-8d2c-49d2-b2b4-e35dce9bbfd8"
      },
      "source": [
        "shap.initjs()\n",
        "shap.force_plot(explainer.expected_value, shap_values[0], instance_to_explain, link=\"logit\")\n",
        "\n",
        "## https://github.com/slundberg/shap/blob/master/notebooks/tabular_examples/model_agnostic/Iris%20classification%20with%20scikit-learn.ipynb"
      ],
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "display_data",
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n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?v.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;A.postMountWrapper(t)}function s(){var t=this;P.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\n",
              "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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              "  );\n",
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            ],
            "text/plain": [
              "<shap.plots._force.AdditiveForceVisualizer at 0x7fee1f271d10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 189
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QJUrMxgdDz5D"
      },
      "source": [
        "Signs are definitely matching. Most important feature is matching. But the order of importance for radius_mean - texture_mean is different.   Since I don't know the exact details of implementation of original SHAP library, small changes are OK for now. I really did my best."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "85ol4TndSypl",
        "outputId": "9d545b83-5606-498a-c6b3-5e6dff56a459"
      },
      "source": [
        "pd.DataFrame([B],columns = x.columns)"
      ],
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.067921</td>\n",
              "      <td>-0.143586</td>\n",
              "      <td>-0.183802</td>\n",
              "      <td>0.025674</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   radius_mean  texture_mean  perimeter_mean  area_mean\n",
              "0    -0.067921     -0.143586       -0.183802   0.025674"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hUlVwK6yLZb9"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}