Add files via upload

CMATER-JUCS · Jul 25, 2021 · 0dde686 · 0dde686
1 parent 1b6d149
commit 0dde686
Show file tree

Hide file tree

Showing 2 changed files with 599 additions and 0 deletions.
diff --git a/driver.ipynb b/driver.ipynb
@@ -0,0 +1,216 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "driver.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iKYmsVyQifJ0",
+        "outputId": "dd5d7e6f-5534-4e45-a8d1-442623b5c2ad"
+      },
+      "source": [
+        "!pip install pyitlib"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Requirement already satisfied: pyitlib in /usr/local/lib/python3.7/dist-packages (0.2.2)\n",
+            "Requirement already satisfied: pandas>=0.20.2numpy>=1.9.2 in /usr/local/lib/python3.7/dist-packages (from pyitlib) (1.1.5)\n",
+            "Requirement already satisfied: scikit-learn>=0.16.0 in /usr/local/lib/python3.7/dist-packages (from pyitlib) (0.22.2.post1)\n",
+            "Requirement already satisfied: future>=0.16.0 in /usr/local/lib/python3.7/dist-packages (from pyitlib) (0.16.0)\n",
+            "Requirement already satisfied: scipy>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from pyitlib) (1.4.1)\n",
+            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.20.2numpy>=1.9.2->pyitlib) (1.19.5)\n",
+            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.20.2numpy>=1.9.2->pyitlib) (2.8.1)\n",
+            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.20.2numpy>=1.9.2->pyitlib) (2018.9)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.20.2numpy>=1.9.2->pyitlib) (1.15.0)\n",
+            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.16.0->pyitlib) (1.0.1)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "LunZbhmzksAX"
+      },
+      "source": [
+        "from lambda_fuzzy_script3 import *"
+      ],
+      "execution_count": 39,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "HzqXM3c8iiCP"
+      },
+      "source": [
+        "df = pd.read_csv(r'/content/JBHI_val_predmatrix.csv')"
+      ],
+      "execution_count": 40,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "yuapuz_uixLe"
+      },
+      "source": [
+        "predict = df[['vgg16', 'xcep', 'incep']]\n",
+        "y_data = df['True Label']"
+      ],
+      "execution_count": 41,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "oFBSW5NLix-N"
+      },
+      "source": [
+        "pred_mat = predict.values.tolist()\n",
+        "Y = y_data.values.tolist()"
+      ],
+      "execution_count": 42,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "AjF4MtI4jFNk"
+      },
+      "source": [
+        "df_1 = pd.read_csv(r'/content/JBHI_test_confidencematrix.csv')"
+      ],
+      "execution_count": 43,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "w2X_7qlMjG4j"
+      },
+      "source": [
+        "class_0 = df_1[['vgg16_class0','xcep_class0', 'incep_class0']]\n",
+        "class_1 = df_1[['vgg16_class1', 'xcep_class1', 'incep_class1']]\n",
+        "class_2 = df_1[['vgg16_class2', 'xcep_class2', 'incep_class2']]"
+      ],
+      "execution_count": 44,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "oNESsPPUjLlN"
+      },
+      "source": [
+        "class_0 = class_0.values.tolist()\n",
+        "class_1 = class_1.values.tolist()\n",
+        "class_2 = class_2.values.tolist()"
+      ],
+      "execution_count": 45,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "suH4Sc3ji5sS"
+      },
+      "source": [
+        "val = [.9498, .9674, .9649]\n",
+        "w = wt1(val)\n",
+        "shapley_value_list = shapley_calculation(pred_mat=pred_mat, w=w, Y=Y)\n",
+        "Subset_list=subset_list()\n",
+        "list_mu = Cal_lambda_fuzzy(shapley_value_list=shapley_value_list, Subset_list=Subset_list)\n",
+        "fa_cl0 = class_0_pred(class_0=class_0,list_mu=list_mu, Subset_list=Subset_list)\n",
+        "fa_cl1 = class_1_pred(class_1=class_1,list_mu=list_mu, Subset_list=Subset_list)\n",
+        "fa_cl2 = class_2_pred(class_2=class_2,list_mu=list_mu, Subset_list=Subset_list)\n",
+        "result1 = cal_result(fa_cl0, fa_cl1, fa_cl2)\n",
+        "print(result1)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zRI8lK-4i6RM"
+      },
+      "source": [
+        "val = [.9498, .9674, .9649]\n",
+        "w = wt2(val)\n",
+        "shapley_value_list = shapley_calculation(pred_mat=pred_mat, w=w, Y=Y)\n",
+        "Subset_list=subset_list()\n",
+        "list_mu = Cal_lambda_fuzzy(shapley_value_list=shapley_value_list, Subset_list=Subset_list)\n",
+        "fa_cl0 = class_0_pred(class_0=class_0,list_mu=list_mu, Subset_list=Subset_list)\n",
+        "fa_cl1 = class_1_pred(class_1=class_1,list_mu=list_mu, Subset_list=Subset_list)\n",
+        "fa_cl2 = class_2_pred(class_2=class_2,list_mu=list_mu, Subset_list=Subset_list)\n",
+        "result2 = cal_result(fa_cl0, fa_cl1, fa_cl2)\n",
+        "print(result2)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "SfZzGA5pjAMJ"
+      },
+      "source": [
+        "val = [.9498, .9674, .9649]\n",
+        "w = wt3(val)\n",
+        "shapley_value_list = shapley_calculation(pred_mat=pred_mat, w=w, Y=Y)\n",
+        "Subset_list=subset_list()\n",
+        "list_mu = Cal_lambda_fuzzy(shapley_value_list=shapley_value_list, Subset_list=Subset_list)\n",
+        "fa_cl0 = class_0_pred(class_0=class_0,list_mu=list_mu, Subset_list=Subset_list)\n",
+        "fa_cl1 = class_1_pred(class_1=class_1,list_mu=list_mu, Subset_list=Subset_list)\n",
+        "fa_cl2 = class_2_pred(class_2=class_2,list_mu=list_mu, Subset_list=Subset_list)\n",
+        "result3 = cal_result(fa_cl0, fa_cl1, fa_cl2)\n",
+        "print(result3)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "xz7NHykZySDj"
+      },
+      "source": [
+        "result = majority_aggregation(result1, result2, result3)"
+      ],
+      "execution_count": 49,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "0M4hMH8lyf51"
+      },
+      "source": [
+        "print(result)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}