Test MI-and outDP _ LOAN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "92655fa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "\n",
    "from torch import nn,optim\n",
    "import torch\n",
    "\n",
    "import algo\n",
    "import json\n",
    "import scripts\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import attack\n",
    "import random\n",
    "\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c81dbbad",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net_attack(nn.Module):\n",
    "\n",
    "    def __init__(self, h_neurons, do, input_size):\n",
    "        super(Net_attack, self).__init__()\n",
    "        self.input_size = input_size\n",
    "        self.h_neurons = h_neurons\n",
    "        self.do = do\n",
    "        self.fc1 = nn.Linear(input_size, h_neurons)\n",
    "        self.fc2 = nn.Linear(h_neurons, 2)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.drop = nn.Dropout(do)\n",
    "        self.softmax = nn.Softmax(dim=1)   \n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.view(-1, self.input_size)\n",
    "        x = self.fc1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.fc2(x)\n",
    "        x = self.drop(x)\n",
    "        x = self.softmax(x)\n",
    "        return x\n",
    "\n",
    "class Train_args():\n",
    "\n",
    "    def __init__(self, learning_rate, weight_decay, epoch):\n",
    "        self.learning_rate = learning_rate\n",
    "        self.weight_decay = weight_decay\n",
    "        self.epoch = epoch\n",
    "         \n",
    "def train_attack_model(model, train_data, train_target, train_args):\n",
    "\n",
    "    optimizer = optim.Adam(model.parameters(), lr=train_args.learning_rate, weight_decay=train_args.weight_decay)\n",
    "    \n",
    "    model.train()\n",
    "    optimizer.zero_grad()\n",
    "    output = model(train_data)\n",
    "    loss = nn.CrossEntropyLoss()(output, train_target.to(torch.long))\n",
    "    loss.backward(retain_graph=True)\n",
    "    optimizer.step()\n",
    "\n",
    "    return model\n",
    "\n",
    "def attack_evaluation(model, x, y, dev=\"cpu\", extended=False):\n",
    "\n",
    "    model.eval()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        output =  model(x)\n",
    "        out_target = output.argmax(1, keepdim=True)\n",
    "        correct = out_target.to(dev).eq(y.to(dev).view_as(out_target.to(dev))).sum().item()\n",
    "        acc = correct/y.shape[0]\n",
    "\n",
    "        predicted_positive = output.argmax(1, keepdim=True) == 1\n",
    "        labeled_positive = y == 1\n",
    "        tp = predicted_positive.to(dev) * labeled_positive.to(dev).view_as(out_target)\n",
    "        tp_count = tp.to(dev).sum().item()\n",
    "        \n",
    "        if predicted_positive.to(dev).sum().item() != 0:\n",
    "            pre = tp_count / predicted_positive.to(dev).sum().item()\n",
    "        else:\n",
    "            pre = 0\n",
    "        if labeled_positive.to(dev).sum().item() !=0:\n",
    "            rec = tp_count / labeled_positive.to(dev).sum().item()\n",
    "        else:\n",
    "            rec = 0\n",
    "    if extended:\n",
    "        predicted_negative = output.argmax(1, keepdim=True) == 0\n",
    "        labeled_negative = y == 0\n",
    "        tn = predicted_negative.to(dev) * labeled_negative.to(dev).view_as(out_target)\n",
    "        tn_count = tn.to(dev).sum().item()\n",
    "\n",
    "        fp_count = predicted_positive.to(dev).sum().item() - tp.to(dev).sum().item()\n",
    "        fn_count = labeled_positive.to(dev).sum().item() - tp.to(dev).sum().item()\n",
    "        \n",
    "        return acc, pre, rec, tp_count, tn_count, fp_count, fn_count\n",
    "    else:\n",
    "        return acc, pre, rec\n",
    "\n",
    "class model_params(object):\n",
    "\n",
    "    def __init__(self, model_name):\n",
    "        self.model_name      = model_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a5546008",
   "metadata": {},
   "outputs": [],
   "source": [
    "# raw_data = pd.read_csv('../datasets/dataset_purchase', )\n",
    "# y=raw_data['63']\n",
    "# X=raw_data.drop('63', axis=1)\n",
    "# y =  y.replace(100, 0)\n",
    "# print(y.nunique())\n",
    "\n",
    "# X_train, x_shadow, y_train, y_shadow = train_test_split(X, y, train_size=0.1, random_state=42)\n",
    "# print(X_train.shape, x_shadow.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5ae5ebd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('../DP-UTIL.nosync/loan_preprocessed.csv', index_col=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f72e0999",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(661283, 167)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "1c487064",
   "metadata": {},
   "outputs": [],
   "source": [
    "rand_seed=42\n",
    "np.random.seed(rand_seed)\n",
    "torch.manual_seed(rand_seed)\n",
    "\n",
    "#Loan\n",
    "y = data['grade']\n",
    "X = data.drop('grade', axis=1)\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "tr_size = 10000\n",
    "X_train, X_test, y_train, y_test = train_test_split(X[0:100000], y[0:100000], train_size=0.5, random_state=rand_seed)\n",
    "x_target_train = np.array(X_train)[:tr_size]\n",
    "y_target_train = np.array(y_train)[:tr_size]\n",
    "x_target_test = np.array(X_test)[:tr_size]\n",
    "y_target_test = np.array(y_test)[:tr_size]\n",
    "\n",
    "x_shadow = X[100000:]\n",
    "y_shadow = y[100000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "801cefc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.save(f'loan_rs{rand_seed}_size{tr_size}_xtrain', x_target_train)\n",
    "# np.save(f'loan_rs{rand_seed}_size{tr_size}_ytrain', y_target_train)\n",
    "# np.save(f'loan_rs{rand_seed}_size{tr_size}_xtest', x_target_test)\n",
    "# np.save(f'loan_rs{rand_seed}_size{tr_size}_ytest', y_target_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "cc383cd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_target_train = np.load(f'data/loan_rs{rand_seed}_size{tr_size}_xtrain.npy')\n",
    "y_target_train = np.load(f'data/loan_rs{rand_seed}_size{tr_size}_ytrain.npy')\n",
    "x_target_test = np.load(f'data/loan_rs{rand_seed}_size{tr_size}_xtest.npy')\n",
    "y_target_test = np.load(f'data/loan_rs{rand_seed}_size{tr_size}_ytest.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "62909b9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = algo.LogisticRegression_DPSGD()\n",
    "\n",
    "model.n_classes      = len(np.unique(y_target_test))\n",
    "model.alpha          = 0.001\n",
    "model.max_iter       = 100\n",
    "model.lambda_        = 1e-5\n",
    "model.tolerance      = 1e-5\n",
    "model.DP             = False\n",
    "model.L              = 1\n",
    "model.epsilon        = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "b9b4d426",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_target_train.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "8eef77de",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [111]\u001b[0m, in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m random\u001b[38;5;241m.\u001b[39mseed(rand_seed)\n\u001b[1;32m      6\u001b[0m X,y \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39minit_theta(x_target_train, y_target_train)\n\u001b[0;32m----> 7\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      8\u001b[0m model\u001b[38;5;241m.\u001b[39mevaluate(x_target_train, y_target_train, acc\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m      9\u001b[0m model\u001b[38;5;241m.\u001b[39mevaluate(x_target_test, y_target_test, acc\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "File \u001b[0;32m~/Documents/Projects/dp_fl/algo.py:302\u001b[0m, in \u001b[0;36mLogisticRegression_DPSGD.train\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    300\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mDP_SGD(X, y)\n\u001b[1;32m    301\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 302\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSGD\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/Projects/dp_fl/algo.py:231\u001b[0m, in \u001b[0;36mLogisticRegression_DPSGD.SGD\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    229\u001b[0m x_sample \u001b[38;5;241m=\u001b[39m X[i]\n\u001b[1;32m    230\u001b[0m y_sample \u001b[38;5;241m=\u001b[39m y[i]\n\u001b[0;32m--> 231\u001b[0m error \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpred_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_sample\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreshape\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtheta\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtheta\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m-\u001b[39m y_sample\n\u001b[1;32m    232\u001b[0m gradient \u001b[38;5;241m=\u001b[39m x_sample\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,error\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m])\u001b[38;5;241m.\u001b[39mdot(np\u001b[38;5;241m.\u001b[39marray(error))\u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlambda_ \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtheta\n\u001b[1;32m    233\u001b[0m lots_gradients\u001b[38;5;241m.\u001b[39mappend(gradient)\n",
      "File \u001b[0;32m~/Documents/Projects/dp_fl/algo.py:141\u001b[0m, in \u001b[0;36mLogisticRegression_DPSGD.__softmax\u001b[0;34m(self, z)\u001b[0m\n\u001b[1;32m    125\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__softmax\u001b[39m(\u001b[38;5;28mself\u001b[39m, z):\n\u001b[1;32m    127\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    128\u001b[0m \u001b[38;5;124;03m    Compute the softmax function.\u001b[39;00m\n\u001b[1;32m    129\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    138\u001b[0m \u001b[38;5;124;03m    Value of sigmoid function at z\u001b[39;00m\n\u001b[1;32m    139\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 141\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msoftmax\u001b[49m\u001b[43m(\u001b[49m\u001b[43mz\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/anaconda3/envs/myenv/lib/python3.10/site-packages/scipy/special/_logsumexp.py:214\u001b[0m, in \u001b[0;36msoftmax\u001b[0;34m(x, axis)\u001b[0m\n\u001b[1;32m    131\u001b[0m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    132\u001b[0m \u001b[38;5;124;03mSoftmax function\u001b[39;00m\n\u001b[1;32m    133\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    210\u001b[0m \n\u001b[1;32m    211\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    213\u001b[0m \u001b[38;5;66;03m# compute in log space for numerical stability\u001b[39;00m\n\u001b[0;32m--> 214\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39mexp(x \u001b[38;5;241m-\u001b[39m \u001b[43mlogsumexp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m)\n",
      "File \u001b[0;32m/opt/anaconda3/envs/myenv/lib/python3.10/site-packages/scipy/special/_logsumexp.py:113\u001b[0m, in \u001b[0;36mlogsumexp\u001b[0;34m(a, axis, b, keepdims, return_sign)\u001b[0m\n\u001b[1;32m    110\u001b[0m     tmp \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mexp(a \u001b[38;5;241m-\u001b[39m a_max)\n\u001b[1;32m    112\u001b[0m \u001b[38;5;66;03m# suppress warnings about log of zero\u001b[39;00m\n\u001b[0;32m--> 113\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m np\u001b[38;5;241m.\u001b[39merrstate(divide\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m    114\u001b[0m     s \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msum(tmp, axis\u001b[38;5;241m=\u001b[39maxis, keepdims\u001b[38;5;241m=\u001b[39mkeepdims)\n\u001b[1;32m    115\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m return_sign:\n",
      "File \u001b[0;32m/opt/anaconda3/envs/myenv/lib/python3.10/site-packages/numpy/core/_ufunc_config.py:430\u001b[0m, in \u001b[0;36merrstate.__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    429\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__enter__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 430\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moldstate \u001b[38;5;241m=\u001b[39m \u001b[43mseterr\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    431\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcall \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _Unspecified:\n\u001b[1;32m    432\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moldcall \u001b[38;5;241m=\u001b[39m seterrcall(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcall)\n",
      "File \u001b[0;32m/opt/anaconda3/envs/myenv/lib/python3.10/site-packages/numpy/core/_ufunc_config.py:127\u001b[0m, in \u001b[0;36mseterr\u001b[0;34m(all, divide, over, under, invalid)\u001b[0m\n\u001b[1;32m    121\u001b[0m maskvalue \u001b[38;5;241m=\u001b[39m ((_errdict[divide] \u001b[38;5;241m<<\u001b[39m SHIFT_DIVIDEBYZERO) \u001b[38;5;241m+\u001b[39m\n\u001b[1;32m    122\u001b[0m              (_errdict[over] \u001b[38;5;241m<<\u001b[39m SHIFT_OVERFLOW) \u001b[38;5;241m+\u001b[39m\n\u001b[1;32m    123\u001b[0m              (_errdict[under] \u001b[38;5;241m<<\u001b[39m SHIFT_UNDERFLOW) \u001b[38;5;241m+\u001b[39m\n\u001b[1;32m    124\u001b[0m              (_errdict[invalid] \u001b[38;5;241m<<\u001b[39m SHIFT_INVALID))\n\u001b[1;32m    126\u001b[0m pyvals[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m=\u001b[39m maskvalue\n\u001b[0;32m--> 127\u001b[0m \u001b[43mumath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mseterrobj\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpyvals\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    128\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m old\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "rand_seed=42\n",
    "np.random.seed(rand_seed)\n",
    "torch.manual_seed(rand_seed)\n",
    "random.seed(rand_seed)\n",
    "\n",
    "X,y = model.init_theta(x_target_train, y_target_train)\n",
    "model.train(X,y)\n",
    "model.evaluate(x_target_train, y_target_train, acc=True)\n",
    "model.evaluate(x_target_test, y_target_test, acc=True)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9161b439",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the model : 84.39999999999999 %\n",
      "The accuracy of the model : 57.99999999999999 %\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.58"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_target_train, y_target_train, acc=True)\n",
    "model.evaluate(x_target_test, y_target_test, acc=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d38bdc4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/66/0p0lnj8d6zj532q0yvm1zt580000gn/T/ipykernel_15824/3543387768.py:7: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
      "  y_shadow_train = np.array(y_shadow[:shadow_size])\n",
      "/var/folders/66/0p0lnj8d6zj532q0yvm1zt580000gn/T/ipykernel_15824/3543387768.py:9: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
      "  y_shadow_test = np.array(y_shadow[shadow_size:2*shadow_size])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shadow model:  0\n",
      "The accuracy of the model : 83.3 %\n",
      "The accuracy of the model : 59.8 %\n",
      "Shadow model:  1\n",
      "The accuracy of the model : 85.3 %\n",
      "The accuracy of the model : 55.400000000000006 %\n",
      "Shadow model:  2\n",
      "The accuracy of the model : 84.8 %\n",
      "The accuracy of the model : 58.3 %\n",
      "Shadow model:  3\n",
      "The accuracy of the model : 80.4 %\n",
      "The accuracy of the model : 57.9 %\n",
      "Shadow model:  4\n",
      "The accuracy of the model : 86.2 %\n",
      "The accuracy of the model : 57.4 %\n",
      "Shadow model:  5\n",
      "The accuracy of the model : 83.0 %\n",
      "The accuracy of the model : 60.9 %\n",
      "Shadow model:  6\n",
      "The accuracy of the model : 83.89999999999999 %\n",
      "The accuracy of the model : 58.099999999999994 %\n",
      "Shadow model:  7\n",
      "The accuracy of the model : 84.8 %\n",
      "The accuracy of the model : 54.800000000000004 %\n",
      "Shadow model:  8\n",
      "The accuracy of the model : 83.7 %\n",
      "The accuracy of the model : 58.3 %\n",
      "Shadow model:  9\n",
      "The accuracy of the model : 84.8 %\n",
      "The accuracy of the model : 60.6 %\n",
      "Shadow model:  10\n",
      "The accuracy of the model : 83.89999999999999 %\n",
      "The accuracy of the model : 58.4 %\n",
      "Shadow model:  11\n",
      "The accuracy of the model : 84.6 %\n",
      "The accuracy of the model : 56.99999999999999 %\n",
      "Shadow model:  12\n",
      "The accuracy of the model : 85.1 %\n",
      "The accuracy of the model : 59.3 %\n",
      "Shadow model:  13\n",
      "The accuracy of the model : 82.6 %\n",
      "The accuracy of the model : 59.099999999999994 %\n",
      "Shadow model:  14\n",
      "The accuracy of the model : 83.89999999999999 %\n",
      "The accuracy of the model : 58.3 %\n",
      "Shadow model:  15\n",
      "The accuracy of the model : 84.7 %\n",
      "The accuracy of the model : 60.5 %\n",
      "Shadow model:  16\n",
      "The accuracy of the model : 83.8 %\n",
      "The accuracy of the model : 59.099999999999994 %\n",
      "Shadow model:  17\n",
      "The accuracy of the model : 85.6 %\n",
      "The accuracy of the model : 59.099999999999994 %\n",
      "Shadow model:  18\n",
      "The accuracy of the model : 84.0 %\n",
      "The accuracy of the model : 54.7 %\n",
      "Shadow model:  19\n",
      "The accuracy of the model : 81.8 %\n",
      "The accuracy of the model : 58.599999999999994 %\n",
      "Shadow model:  20\n",
      "The accuracy of the model : 85.3 %\n",
      "The accuracy of the model : 56.599999999999994 %\n",
      "Shadow model:  21\n",
      "The accuracy of the model : 84.6 %\n",
      "The accuracy of the model : 62.1 %\n",
      "Shadow model:  22\n",
      "The accuracy of the model : 85.3 %\n",
      "The accuracy of the model : 59.8 %\n",
      "Shadow model:  23\n",
      "The accuracy of the model : 83.7 %\n",
      "The accuracy of the model : 59.099999999999994 %\n",
      "Shadow model:  24\n",
      "The accuracy of the model : 83.6 %\n",
      "The accuracy of the model : 59.5 %\n",
      "Shadow model:  25\n",
      "The accuracy of the model : 84.1 %\n",
      "The accuracy of the model : 60.0 %\n",
      "Shadow model:  26\n",
      "The accuracy of the model : 85.1 %\n",
      "The accuracy of the model : 58.9 %\n",
      "Shadow model:  27\n",
      "The accuracy of the model : 83.7 %\n",
      "The accuracy of the model : 59.8 %\n",
      "Shadow model:  28\n",
      "The accuracy of the model : 83.8 %\n",
      "The accuracy of the model : 58.599999999999994 %\n",
      "Shadow model:  29\n",
      "The accuracy of the model : 83.6 %\n",
      "The accuracy of the model : 61.1 %\n",
      "Shadow model:  30\n",
      "The accuracy of the model : 85.39999999999999 %\n",
      "The accuracy of the model : 57.8 %\n",
      "Shadow model:  31\n",
      "The accuracy of the model : 84.2 %\n",
      "The accuracy of the model : 60.6 %\n",
      "Shadow model:  32\n",
      "The accuracy of the model : 84.7 %\n",
      "The accuracy of the model : 62.0 %\n",
      "Shadow model:  33\n",
      "The accuracy of the model : 84.8 %\n",
      "The accuracy of the model : 59.3 %\n",
      "Shadow model:  34\n",
      "The accuracy of the model : 83.5 %\n",
      "The accuracy of the model : 57.4 %\n",
      "Shadow model:  35\n",
      "The accuracy of the model : 85.1 %\n",
      "The accuracy of the model : 58.599999999999994 %\n",
      "Shadow model:  36\n",
      "The accuracy of the model : 84.1 %\n",
      "The accuracy of the model : 58.4 %\n",
      "Shadow model:  37\n",
      "The accuracy of the model : 83.39999999999999 %\n",
      "The accuracy of the model : 61.5 %\n",
      "Shadow model:  38\n",
      "The accuracy of the model : 84.39999999999999 %\n",
      "The accuracy of the model : 59.099999999999994 %\n",
      "Shadow model:  39\n",
      "The accuracy of the model : 83.8 %\n",
      "The accuracy of the model : 57.99999999999999 %\n"
     ]
    }
   ],
   "source": [
    "s_ms = {}\n",
    "number_of_sms = 40\n",
    "shadow_size = 40000\n",
    "shadow_batch_size = int(shadow_size/number_of_sms)\n",
    "\n",
    "x_shadow_train = np.array(x_shadow[:shadow_size])\n",
    "y_shadow_train = np.array(y_shadow[:shadow_size])\n",
    "x_shadow_test = np.array(x_shadow[shadow_size:2*shadow_size])\n",
    "y_shadow_test = np.array(y_shadow[shadow_size:2*shadow_size])\n",
    "\n",
    "for i in range(number_of_sms):\n",
    "    batch_start = i*shadow_batch_size\n",
    "    batch_end = (i+1)*shadow_batch_size\n",
    "    \n",
    "    shadow_model = algo.LogisticRegression_DPSGD()\n",
    "\n",
    "    shadow_model.n_classes      = len(np.unique(y_target_test))\n",
    "    shadow_model.alpha          = 0.001\n",
    "    shadow_model.max_iter       = 100\n",
    "    shadow_model.lambda_        = 1e-5\n",
    "    shadow_model.tolerance      = 1e-5\n",
    "    shadow_model.DP             = False\n",
    "\n",
    "    X,y = shadow_model.init_theta(x_shadow_train[batch_start:batch_end], y_shadow_train[batch_start:batch_end] )\n",
    "    shadow_model.SGD(X,y)\n",
    "    print('Shadow model: ', i)\n",
    "    shadow_model.evaluate(x_shadow_train[batch_start:batch_end], y_shadow_train[batch_start:batch_end], acc=True)\n",
    "    shadow_model.evaluate(x_shadow_test[batch_start:batch_end], y_shadow_test[batch_start:batch_end], acc=True)\n",
    "    s_ms[i] = shadow_model\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "bd0debd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "shadow_train_pred = []\n",
    "shadow_test_pred = []\n",
    "\n",
    "for i in range(number_of_sms): \n",
    "    batch_start = i*shadow_batch_size\n",
    "    batch_end = (i+1)*shadow_batch_size\n",
    "    \n",
    "    train_prediciton = s_ms[i].predict(x_shadow_train[batch_start:batch_end], y_shadow_train[batch_start:batch_end])\n",
    "    test_prediciton = s_ms[i].predict(x_shadow_test[batch_start:batch_end], y_shadow_test[batch_start:batch_end])\n",
    "    \n",
    "    shadow_train_pred.append(train_prediciton)\n",
    "    shadow_test_pred.append(test_prediciton)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "bfe63562",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_shadow_train_ohe = OneHotEncoder(sparse=False).fit_transform(y_shadow_train.reshape(-1,1)) #encoode the target values\n",
    "y_shadow_test_ohe = OneHotEncoder(sparse=False).fit_transform(y_shadow_test.reshape(-1,1)) #encoode the target values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "83ca69b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 7)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shadow_train_pred[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "4e98c5bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "sh_train_pred = np.concatenate(shadow_train_pred)\n",
    "sh_test_pred = np.concatenate(shadow_test_pred)\n",
    "\n",
    "# members\n",
    "labels = np.ones(sh_train_pred.shape[0])\n",
    "# non-members\n",
    "test_labels = np.zeros(sh_test_pred.shape[0])\n",
    "\n",
    "x_1 = np.concatenate((sh_train_pred, sh_test_pred))\n",
    "x_2 = np.concatenate((y_shadow_train_ohe, y_shadow_test_ohe))#.reshape((-1, 1))\n",
    "y_new = np.concatenate((labels, test_labels))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "31cc63d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "attack_train_data = np.concatenate((x_1,x_2),axis=1)\n",
    "attack_train_target = y_new\n",
    "df = pd.DataFrame(attack_train_data)\n",
    "df['a_target'] = attack_train_target\n",
    "df = df.sample(frac = 1)\n",
    "\n",
    "attack_train_data = torch.tensor(np.array(df.drop(['a_target'], axis=1)), dtype=torch.float, requires_grad=True)\n",
    "attack_train_target = torch.tensor(np.array(df['a_target']), dtype=torch.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "6e6e21ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "k=1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "11572efe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 0: (0.5000875, 0.64, 0.0004)\n",
      "epoch 1: (0.5013, 0.5136125654450262, 0.04905)\n",
      "epoch 2: (0.506475, 0.5283183905532474, 0.1208)\n",
      "epoch 3: (0.5093125, 0.5310080745858653, 0.159475)\n",
      "epoch 4: (0.508825, 0.5267586416009703, 0.173725)\n",
      "epoch 5: (0.5072375, 0.5208408321935066, 0.180875)\n",
      "epoch 6: (0.5041125, 0.5113880235375563, 0.184675)\n",
      "epoch 7: (0.5015875, 0.5042580299067927, 0.188)\n",
      "epoch 8: (0.50055, 0.5013686699017046, 0.201475)\n",
      "epoch 9: (0.5125, 0.5204365241559715, 0.318325)\n",
      "epoch 10: (0.5153125, 0.5198934684465231, 0.400175)\n",
      "epoch 11: (0.51415, 0.5166922260233573, 0.438)\n",
      "epoch 12: (0.5102, 0.5113478333426045, 0.459625)\n",
      "epoch 13: (0.5079375, 0.5085132055235286, 0.474125)\n",
      "epoch 14: (0.507575, 0.5081011710603711, 0.4751)\n",
      "epoch 15: (0.5070125, 0.5074833924712536, 0.47555)\n",
      "epoch 16: (0.5064875, 0.5068915150710397, 0.477175)\n",
      "epoch 17: (0.50705, 0.507530844415959, 0.475125)\n",
      "epoch 18: (0.5078375, 0.508420402352878, 0.473225)\n",
      "epoch 19: (0.5086375, 0.5093224683629691, 0.4719)\n",
      "epoch 20: (0.50835, 0.5089823580034424, 0.47315)\n",
      "epoch 21: (0.5091625, 0.5099067441546155, 0.4716)\n",
      "epoch 22: (0.509525, 0.5103224058520726, 0.4709)\n",
      "epoch 23: (0.5099625, 0.5108326310925055, 0.4698)\n",
      "epoch 24: (0.51045, 0.5113927500681384, 0.469075)\n",
      "epoch 25: (0.51145, 0.5125348951776233, 0.468175)\n",
      "epoch 26: (0.5117375, 0.5128661861828944, 0.467875)\n",
      "epoch 27: (0.5123625, 0.5136169626875946, 0.4663)\n",
      "epoch 28: (0.5124125, 0.5136742956291829, 0.466275)\n",
      "epoch 29: (0.5130125, 0.5144019257906532, 0.464775)\n",
      "epoch 30: (0.5147125, 0.5161175471749788, 0.471125)\n",
      "epoch 31: (0.5172125, 0.5185985574975013, 0.47995)\n",
      "epoch 32: (0.520975, 0.522139539793118, 0.494675)\n",
      "epoch 33: (0.52455, 0.5259294465568229, 0.49795)\n",
      "epoch 34: (0.5267125, 0.5276419609364894, 0.5099)\n",
      "epoch 35: (0.5302375, 0.5319677547244615, 0.503175)\n",
      "epoch 36: (0.5322875, 0.5333833070540491, 0.515875)\n",
      "epoch 37: (0.53445, 0.5352844779023915, 0.522625)\n",
      "epoch 38: (0.5351875, 0.5359376994765734, 0.52475)\n",
      "epoch 39: (0.5367375, 0.5373909060838147, 0.528)\n",
      "epoch 40: (0.5374875, 0.53806900403666, 0.52985)\n",
      "epoch 41: (0.538725, 0.5394911278808893, 0.529025)\n",
      "epoch 42: (0.5399375, 0.5405425982793188, 0.532475)\n",
      "epoch 43: (0.5405125, 0.5410700255974858, 0.533725)\n",
      "epoch 44: (0.5413, 0.5417614641791799, 0.535775)\n",
      "epoch 45: (0.543325, 0.5433271663583179, 0.5433)\n",
      "epoch 46: (0.544225, 0.5443402847403248, 0.542925)\n",
      "epoch 47: (0.545875, 0.5453355074612116, 0.551825)\n",
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      "epoch 164: (0.5805125, 0.5678728740331724, 0.673625)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 165: (0.5792, 0.5683554136279291, 0.658525)\n",
      "epoch 166: (0.5803625, 0.5678808995882166, 0.6723)\n",
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      "epoch 0: (0.499175, 0.0, 0.0)\n",
      "epoch 1: (0.49885, 0.04, 0.0001)\n",
      "epoch 2: (0.49855, 0.07971014492753623, 0.000275)\n",
      "epoch 3: (0.49795, 0.10194174757281553, 0.000525)\n",
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      "epoch 100: (0.5738875, 0.5588147499552247, 0.702025)\n",
      "epoch 101: (0.573775, 0.5595944908922008, 0.69275)\n",
      "epoch 102: (0.5738875, 0.5590167535294235, 0.699875)\n",
      "epoch 103: (0.5744375, 0.5600896046497548, 0.693825)\n",
      "epoch 104: (0.5741625, 0.5622521142425451, 0.669825)\n",
      "epoch 105: (0.57425, 0.5621989528795811, 0.671125)\n",
      "epoch 106: (0.574475, 0.5606671554252199, 0.688275)\n",
      "epoch 107: (0.57465, 0.5604013269682013, 0.6926)\n",
      "epoch 108: (0.574325, 0.5614586348038202, 0.679)\n",
      "epoch 109: (0.5748875, 0.5596487385252593, 0.702625)\n",
      "epoch 110: (0.574875, 0.558313862928349, 0.716875)\n",
      "epoch 111: (0.5747625, 0.5604104801729188, 0.69355)\n",
      "epoch 112: (0.575, 0.5578793023614755, 0.7229)\n",
      "epoch 113: (0.5750375, 0.5598683554403111, 0.701725)\n",
      "epoch 114: (0.5752875, 0.560751245688003, 0.694925)\n",
      "epoch 115: (0.57515, 0.5593344123800876, 0.708425)\n",
      "epoch 116: (0.5750375, 0.5586631486367634, 0.7146)\n",
      "epoch 117: (0.5752875, 0.5615006024465456, 0.687375)\n",
      "epoch 118: (0.5740125, 0.562209754354998, 0.668875)\n",
      "epoch 119: (0.575, 0.5613496932515337, 0.68625)\n",
      "epoch 120: (0.5757375, 0.5597051693896454, 0.71)\n",
      "epoch 121: (0.575425, 0.5577129084092126, 0.728875)\n",
      "epoch 122: (0.5748875, 0.5591984348135413, 0.7074)\n",
      "epoch 123: (0.57535, 0.558683800623053, 0.71735)\n",
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      "epoch 125: (0.575775, 0.5589115646258503, 0.7189)\n",
      "epoch 126: (0.575825, 0.5599644128113879, 0.708075)\n",
      "epoch 127: (0.5745125, 0.5609048368310277, 0.686225)\n",
      "epoch 128: (0.574975, 0.5604198565557257, 0.695425)\n",
      "epoch 129: (0.57445, 0.560259004451639, 0.6922)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 130: (0.5748625, 0.5621252671106408, 0.677375)\n",
      "epoch 131: (0.5748375, 0.559801825918453, 0.70055)\n",
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      "epoch 133: (0.575075, 0.5625208194536976, 0.675475)\n",
      "epoch 134: (0.5748875, 0.5603092472165737, 0.69575)\n",
      "epoch 135: (0.5753, 0.5619217959787838, 0.683325)\n",
      "epoch 136: (0.5754875, 0.5621283512684924, 0.683)\n",
      "epoch 137: (0.5757375, 0.5611316262082047, 0.6952)\n",
      "epoch 138: (0.5751, 0.5625416389073951, 0.6755)\n",
      "epoch 139: (0.57605, 0.5618317817797471, 0.691025)\n",
      "epoch 140: (0.57595, 0.5604264460179808, 0.7044)\n",
      "epoch 141: (0.5764375, 0.5607092508389095, 0.705975)\n",
      "epoch 142: (0.57585, 0.5623023532793955, 0.684575)\n",
      "epoch 143: (0.5756625, 0.5621190041255311, 0.684675)\n",
      "epoch 144: (0.57555, 0.5616131136845539, 0.68865)\n",
      "epoch 145: (0.576525, 0.5614239274390979, 0.69945)\n",
      "epoch 146: (0.5753375, 0.5620738665623004, 0.682175)\n",
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      "epoch 149: (0.5762375, 0.5617495190847424, 0.69355)\n",
      "epoch 150: (0.576, 0.5611325611325612, 0.6976)\n",
      "epoch 151: (0.575475, 0.562170510708402, 0.682475)\n",
      "epoch 152: (0.57605, 0.5620790988122933, 0.688575)\n",
      "epoch 153: (0.5751875, 0.5632372421623667, 0.669675)\n",
      "epoch 154: (0.5764, 0.562247932537581, 0.690075)\n",
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      "epoch 157: (0.5755875, 0.5634482613896292, 0.67125)\n",
      "epoch 158: (0.576075, 0.5626983145835909, 0.68275)\n",
      "epoch 159: (0.575175, 0.5635863818989215, 0.6663)\n",
      "epoch 160: (0.57635, 0.5622046602574547, 0.69005)\n",
      "epoch 161: (0.5763, 0.5629148629148629, 0.682675)\n",
      "epoch 162: (0.575275, 0.5640801906869839, 0.662625)\n",
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      "epoch 167: (0.5760125, 0.563993012438701, 0.669925)\n",
      "epoch 168: (0.57695, 0.5629241965818955, 0.6884)\n",
      "epoch 169: (0.5762125, 0.5636310505333028, 0.675075)\n",
      "epoch 170: (0.577, 0.563006300630063, 0.68805)\n",
      "epoch 171: (0.5767125, 0.5622855982949355, 0.692525)\n",
      "epoch 172: (0.5765625, 0.5622826462752435, 0.6912)\n",
      "epoch 173: (0.5766, 0.5628564395027285, 0.685925)\n",
      "epoch 174: (0.576475, 0.5637212015164771, 0.67655)\n",
      "epoch 175: (0.576625, 0.5627662188728703, 0.687025)\n",
      "epoch 176: (0.5763625, 0.564453166212994, 0.66875)\n",
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      "epoch 185: (0.57705, 0.564088168018299, 0.678175)\n",
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      "epoch 0: (0.5031375, 0.5015760193894337, 0.998525)\n",
      "epoch 1: (0.50535, 0.5027039320731831, 0.99465)\n",
      "epoch 2: (0.5105375, 0.5053837605855079, 0.989175)\n",
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      "epoch 13: (0.523575, 0.524009573276301, 0.514525)\n",
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      "epoch 92: (0.5757875, 0.5635281544039062, 0.672275)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 93: (0.574625, 0.5638885321689996, 0.65865)\n",
      "epoch 94: (0.576275, 0.5638471518854895, 0.6736)\n",
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      "epoch 98: (0.57685, 0.5633579290160353, 0.683325)\n",
      "epoch 99: (0.575275, 0.5642442604762311, 0.661125)\n",
      "epoch 100: (0.5761125, 0.5637391395373181, 0.673175)\n",
      "epoch 101: (0.5753, 0.5642272262026612, 0.6615)\n",
      "epoch 102: (0.5763125, 0.5640272679601468, 0.67225)\n",
      "epoch 103: (0.5753125, 0.5643435357440355, 0.66055)\n",
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      "epoch 111: (0.5756125, 0.564610882057636, 0.66075)\n",
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      "epoch 136: (0.57975, 0.5656270572745227, 0.68735)\n",
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      "epoch 139: (0.579425, 0.5676130075764025, 0.666775)\n",
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      "epoch 153: (0.58005, 0.5678562346359244, 0.6699)\n",
      "epoch 154: (0.581325, 0.5669672266139657, 0.688525)\n",
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      "epoch 199: (0.582, 0.5681884329133924, 0.683275)\n",
      "epoch 0: (0.48525, 0.4817732468334878, 0.389875)\n",
      "epoch 1: (0.486975, 0.4832626574145464, 0.376075)\n",
      "epoch 2: (0.488625, 0.48500131856540085, 0.367825)\n",
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      "epoch 30: (0.5523625, 0.5473429624104338, 0.605375)\n",
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      "epoch 39: (0.565325, 0.5591417319270291, 0.6176)\n",
      "epoch 40: (0.557775, 0.5620735965619125, 0.52315)\n",
      "epoch 41: (0.569225, 0.5583758485474554, 0.66215)\n",
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      "epoch 47: (0.5735, 0.5637688703800104, 0.6498)\n",
      "epoch 48: (0.5741375, 0.5635977610499904, 0.657)\n",
      "epoch 49: (0.5743625, 0.5660075893748752, 0.63765)\n",
      "epoch 50: (0.575575, 0.5637037973616555, 0.66875)\n",
      "epoch 51: (0.5746875, 0.5660323143911765, 0.640225)\n",
      "epoch 52: (0.573075, 0.5675556993621151, 0.613925)\n",
      "epoch 53: (0.5735, 0.5676452993419538, 0.616775)\n",
      "epoch 54: (0.572125, 0.5678504233301975, 0.603625)\n",
      "epoch 55: (0.5757375, 0.5668100121292314, 0.64255)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 56: (0.57435, 0.5674315254852168, 0.62565)\n",
      "epoch 57: (0.5751, 0.5676271949572265, 0.63035)\n",
      "epoch 58: (0.5757875, 0.5674821360995481, 0.637325)\n",
      "epoch 59: (0.57255, 0.5693296383009222, 0.595775)\n",
      "epoch 60: (0.577375, 0.5678430512932924, 0.647625)\n",
      "epoch 61: (0.5737875, 0.5682317312804864, 0.6145)\n",
      "epoch 62: (0.574, 0.5686392727947315, 0.61305)\n",
      "epoch 63: (0.5776875, 0.5668408939364609, 0.658825)\n",
      "epoch 64: (0.5777, 0.5668991347023118, 0.658425)\n",
      "epoch 65: (0.57435, 0.5681922406677061, 0.6195)\n",
      "epoch 66: (0.5754, 0.5675596971461853, 0.633425)\n",
      "epoch 67: (0.5779125, 0.566048532372576, 0.667725)\n",
      "epoch 68: (0.5777875, 0.5662260817742588, 0.665075)\n",
      "epoch 69: (0.5765125, 0.5683071085816315, 0.636575)\n",
      "epoch 70: (0.5770875, 0.5676280293891874, 0.647025)\n",
      "epoch 71: (0.5783125, 0.5668437786740073, 0.6641)\n",
      "epoch 72: (0.5783875, 0.5676381129063572, 0.65785)\n",
      "epoch 73: (0.5777875, 0.5682032397360864, 0.64805)\n",
      "epoch 74: (0.57765, 0.5680961150574411, 0.6478)\n",
      "epoch 75: (0.575325, 0.5688937668633101, 0.622)\n",
      "epoch 76: (0.578575, 0.5680833549952344, 0.655625)\n",
      "epoch 77: (0.5778375, 0.5685596635324687, 0.6455)\n",
      "epoch 78: (0.5786875, 0.56793507586713, 0.657825)\n",
      "epoch 79: (0.5787, 0.5660761512950758, 0.674225)\n",
      "epoch 80: (0.5796, 0.5659459011639949, 0.683125)\n",
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      "epoch 82: (0.5787, 0.5664920581277458, 0.6705)\n",
      "epoch 83: (0.5796875, 0.5669403784362728, 0.6749)\n",
      "epoch 84: (0.5788, 0.5679867132565463, 0.658325)\n",
      "epoch 85: (0.5765, 0.568974844468488, 0.63105)\n",
      "epoch 86: (0.5789125, 0.5674019346159596, 0.6643)\n",
      "epoch 87: (0.57825, 0.5676551962649145, 0.65655)\n",
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      "epoch 95: (0.5791625, 0.5669606039459494, 0.670275)\n",
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      "epoch 98: (0.5782, 0.5679911315915316, 0.653275)\n",
      "epoch 99: (0.57885, 0.5680621493310315, 0.6581)\n",
      "epoch 100: (0.578925, 0.5683718109758739, 0.6561)\n",
      "epoch 101: (0.5798375, 0.5658494339856074, 0.68605)\n",
      "epoch 102: (0.57885, 0.5674941151294671, 0.662975)\n",
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      "epoch 199: (0.5841875, 0.5668433275769665, 0.713925)\n",
      "epoch 0: (0.5, 0, 0.0)\n",
      "epoch 1: (0.5, 0, 0.0)\n",
      "epoch 2: (0.5, 0, 0.0)\n",
      "epoch 3: (0.498325, 0.16161616161616163, 0.0008)\n",
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      "epoch 7: (0.519525, 0.5189012584704743, 0.536025)\n",
      "epoch 8: (0.5257, 0.5230039384174723, 0.5843)\n",
      "epoch 9: (0.52295, 0.5217814264698903, 0.549775)\n",
      "epoch 10: (0.5404875, 0.5301779558371378, 0.7113)\n",
      "epoch 11: (0.529825, 0.5336815358554489, 0.472575)\n",
      "epoch 12: (0.5426375, 0.5311603602945206, 0.7268)\n",
      "epoch 13: (0.534225, 0.5367181632872009, 0.500275)\n",
      "epoch 14: (0.5521875, 0.5387284094914752, 0.72595)\n",
      "epoch 15: (0.5370375, 0.5386320373412605, 0.5164)\n",
      "epoch 16: (0.5538625, 0.540010028041375, 0.726975)\n",
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      "epoch 18: (0.5542875, 0.5408814503831165, 0.71825)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 19: (0.5435, 0.5420716669084579, 0.560475)\n",
      "epoch 20: (0.5562375, 0.5421041795346921, 0.724075)\n",
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      "epoch 26: (0.562, 0.5462686567164179, 0.732)\n",
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      "epoch 29: (0.5587625, 0.5519642738708465, 0.624175)\n",
      "epoch 30: (0.564175, 0.5477865892252132, 0.73565)\n",
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      "epoch 46: (0.575175, 0.5584519088717829, 0.718225)\n",
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      "epoch 50: (0.5753125, 0.5588643335873537, 0.715025)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 181: (0.5821125, 0.5684484734812962, 0.681925)\n",
      "epoch 182: (0.5841, 0.5667116170229644, 0.714425)\n",
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      "epoch 0: (0.486175, 0.4907722600453878, 0.735275)\n",
      "epoch 1: (0.4912875, 0.4923873391729832, 0.563525)\n",
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      "epoch 144: (0.58355, 0.5668293073108303, 0.70865)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 145: (0.5812625, 0.5692937389413545, 0.667625)\n",
      "epoch 146: (0.5837125, 0.5668056580811205, 0.71025)\n",
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      "epoch 0: (0.5136375, 0.5073609780452589, 0.939975)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 109: (0.5645, 0.5924797476521615, 0.413225)\n",
      "epoch 110: (0.5859375, 0.5642319263037913, 0.7549)\n",
      "epoch 111: (0.5645375, 0.5925235654636034, 0.4133)\n",
      "epoch 112: (0.58595, 0.5642544761335178, 0.754775)\n",
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      "epoch 0: (0.5004, 0.5002001451052013, 0.999675)\n",
      "epoch 1: (0.4962625, 0.49604967631126967, 0.469325)\n",
      "epoch 2: (0.507525, 0.5061398498694517, 0.620325)\n",
      "epoch 3: (0.49865, 0.4153605015673981, 0.006625)\n",
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      "epoch 63: (0.559, 0.6030477687538206, 0.345275)\n",
      "epoch 64: (0.5850625, 0.5599866010824915, 0.794075)\n",
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      "epoch 69: (0.5593375, 0.6029405386650475, 0.34755)\n",
      "epoch 70: (0.585125, 0.5605354857061584, 0.788225)\n",
      "epoch 71: (0.5655, 0.5825300825300825, 0.462325)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 72: (0.5850125, 0.5594482613940316, 0.800025)\n",
      "epoch 73: (0.5658125, 0.5834469204678734, 0.46015)\n",
      "epoch 74: (0.585225, 0.5597692685321551, 0.798175)\n",
      "epoch 75: (0.5652125, 0.5856397124002758, 0.44595)\n",
      "epoch 76: (0.5851625, 0.5597076402643156, 0.798325)\n",
      "epoch 77: (0.5655875, 0.5843270868824532, 0.454475)\n",
      "epoch 78: (0.5851625, 0.5597160136734157, 0.798225)\n",
      "epoch 79: (0.565575, 0.5849472116069694, 0.45155)\n",
      "epoch 80: (0.5848625, 0.5593309212941114, 0.800025)\n",
      "epoch 81: (0.5658, 0.5846084608460846, 0.45465)\n",
      "epoch 82: (0.5851, 0.559744453805111, 0.7973)\n",
      "epoch 83: (0.565975, 0.588646288209607, 0.4381)\n",
      "epoch 84: (0.5850625, 0.559656351363198, 0.798)\n",
      "epoch 85: (0.5665625, 0.5883348263163133, 0.443325)\n",
      "epoch 86: (0.5851625, 0.559875555868033, 0.796325)\n",
      "epoch 87: (0.5662875, 0.5897505331212132, 0.435575)\n",
      "epoch 88: (0.5851625, 0.5595385825395438, 0.80035)\n",
      "epoch 89: (0.5668, 0.5888888888888889, 0.44255)\n",
      "epoch 90: (0.584775, 0.5591178521617852, 0.801775)\n",
      "epoch 91: (0.56655, 0.5889765358646969, 0.440525)\n",
      "epoch 92: (0.585, 0.559438481171987, 0.800025)\n",
      "epoch 93: (0.5659, 0.5895562954406469, 0.433825)\n",
      "epoch 94: (0.584975, 0.5594189217537235, 0.800025)\n",
      "epoch 95: (0.5662625, 0.5889041693221078, 0.438925)\n",
      "epoch 96: (0.584975, 0.5595960304379843, 0.7979)\n",
      "epoch 97: (0.56495, 0.5894073921123271, 0.428175)\n",
      "epoch 98: (0.5849125, 0.5595407134718204, 0.797975)\n",
      "epoch 99: (0.5651375, 0.5893978383942358, 0.42945)\n",
      "epoch 100: (0.5850625, 0.5597212714794727, 0.797225)\n",
      "epoch 101: (0.56515, 0.589252688540311, 0.430125)\n",
      "epoch 102: (0.585025, 0.5596938954610875, 0.7972)\n",
      "epoch 103: (0.5651875, 0.5891300632370535, 0.430875)\n",
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      "epoch 105: (0.565225, 0.5892026805251641, 0.430825)\n",
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      "epoch 109: (0.5651375, 0.5886254634511378, 0.432625)\n",
      "epoch 110: (0.58535, 0.5597061909758657, 0.8001)\n",
      "epoch 111: (0.5653, 0.5888677191072401, 0.4327)\n",
      "epoch 112: (0.585325, 0.5596699185286199, 0.8003)\n",
      "epoch 113: (0.5654875, 0.5888960532120677, 0.433825)\n",
      "epoch 114: (0.5852, 0.5596179413616962, 0.79975)\n",
      "epoch 115: (0.565625, 0.5891220207781626, 0.4338)\n",
      "epoch 116: (0.585325, 0.5597555851250088, 0.799275)\n",
      "epoch 117: (0.56575, 0.588713485799096, 0.436325)\n",
      "epoch 118: (0.5851625, 0.5595594020456334, 0.8001)\n",
      "epoch 119: (0.5658625, 0.5887305917618133, 0.437)\n",
      "epoch 120: (0.585175, 0.5595671025945871, 0.800125)\n",
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      "epoch 122: (0.585225, 0.5595937347038669, 0.800275)\n",
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      "epoch 163: (0.5670625, 0.582181918446126, 0.475075)\n",
      "epoch 164: (0.5852625, 0.5598700956727816, 0.797325)\n",
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      "epoch 170: (0.5854, 0.5597495277408522, 0.80005)\n",
      "epoch 171: (0.5672125, 0.5815265184825787, 0.479425)\n",
      "epoch 172: (0.5854, 0.559745347698335, 0.8001)\n",
      "epoch 173: (0.5672875, 0.5816026437861929, 0.479575)\n",
      "epoch 174: (0.5854, 0.5597788044239115, 0.7997)\n",
      "epoch 175: (0.5674, 0.5814550728140673, 0.481125)\n",
      "epoch 176: (0.58545, 0.5598640885526132, 0.79915)\n",
      "epoch 177: (0.5671375, 0.5814355459865967, 0.47935)\n",
      "epoch 178: (0.5853625, 0.5598730470462396, 0.798225)\n",
      "epoch 179: (0.5672875, 0.5816075922500834, 0.47955)\n",
      "epoch 180: (0.585425, 0.5598969289019773, 0.798525)\n",
      "epoch 181: (0.5672875, 0.5815581346019818, 0.4798)\n",
      "epoch 182: (0.5853625, 0.5598289849485728, 0.79875)\n",
      "epoch 183: (0.5674, 0.5816870682341534, 0.47995)\n",
      "epoch 184: (0.5854125, 0.5598451540576994, 0.799025)\n",
      "epoch 185: (0.5674, 0.5816722205392305, 0.480025)\n",
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      "epoch 187: (0.5675375, 0.5817719526591397, 0.4805)\n",
      "epoch 188: (0.5854375, 0.5598584765207644, 0.7991)\n",
      "epoch 189: (0.567575, 0.5818049754857454, 0.4806)\n",
      "epoch 190: (0.5855, 0.559926406167864, 0.798875)\n",
      "epoch 191: (0.5674, 0.5817068735604316, 0.47985)\n",
      "epoch 192: (0.58535, 0.5598254652507623, 0.798675)\n",
      "epoch 193: (0.5676, 0.5816622372553757, 0.4815)\n",
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      "epoch 195: (0.567675, 0.5817133542622555, 0.481775)\n",
      "epoch 196: (0.5854, 0.5597683451726914, 0.799825)\n",
      "epoch 197: (0.5677375, 0.5817616705392438, 0.481975)\n",
      "epoch 198: (0.585475, 0.5598606344982142, 0.799425)\n",
      "epoch 199: (0.5677125, 0.5818006100691613, 0.4816)\n",
      "epoch 0: (0.5019625, 0.5012981429114782, 0.75785)\n",
      "epoch 1: (0.499825, 0.3055555555555556, 0.000275)\n",
      "epoch 2: (0.509475, 0.5048505170471997, 0.986175)\n",
      "epoch 3: (0.510525, 0.6263505402160864, 0.052175)\n",
      "epoch 4: (0.521475, 0.511179364377001, 0.98195)\n",
      "epoch 5: (0.5181125, 0.6277102062400846, 0.089025)\n",
      "epoch 6: (0.533, 0.5175667403050225, 0.972275)\n",
      "epoch 7: (0.5237375, 0.631055900621118, 0.1143)\n",
      "epoch 8: (0.545275, 0.5249400941967114, 0.95295)\n",
      "epoch 9: (0.5287125, 0.6256702046175731, 0.14295)\n",
      "epoch 10: (0.5520125, 0.5293180953454617, 0.93905)\n",
      "epoch 11: (0.5377375, 0.6212547192545586, 0.19335)\n",
      "epoch 12: (0.5585375, 0.5338548067608403, 0.923075)\n",
      "epoch 13: (0.5434375, 0.616275179013585, 0.230225)\n",
      "epoch 14: (0.563625, 0.5378506201850144, 0.9041)\n",
      "epoch 15: (0.54535, 0.6134175315743404, 0.245275)\n",
      "epoch 16: (0.5698125, 0.5426824608330149, 0.887625)\n",
      "epoch 17: (0.5484875, 0.6099427470098067, 0.269)\n",
      "epoch 18: (0.5760375, 0.5478667317165294, 0.8703)\n",
      "epoch 19: (0.5505125, 0.6080770259427655, 0.2842)\n",
      "epoch 20: (0.5796125, 0.5516436112417495, 0.8504)\n",
      "epoch 21: (0.549125, 0.6098010728654448, 0.272825)\n",
      "epoch 22: (0.5811125, 0.553528121030142, 0.838775)\n",
      "epoch 23: (0.551125, 0.6095692241748821, 0.284425)\n",
      "epoch 24: (0.58245, 0.5549007857237981, 0.83335)\n",
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      "epoch 27: (0.5549875, 0.6065287935293263, 0.313075)\n",
      "epoch 28: (0.5839, 0.5566949352974964, 0.823825)\n",
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      "epoch 30: (0.5838, 0.5569525621856735, 0.8195)\n",
      "epoch 31: (0.5566125, 0.6058424865622809, 0.32405)\n",
      "epoch 32: (0.583975, 0.5571842015662241, 0.818225)\n",
      "epoch 33: (0.5569125, 0.6055547827699727, 0.3265)\n",
      "epoch 34: (0.5844, 0.5577094017094018, 0.81565)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 35: (0.5575125, 0.6053294263083192, 0.330525)\n",
      "epoch 36: (0.5845, 0.5577343536485379, 0.8163)\n",
      "epoch 37: (0.557675, 0.6053809610816737, 0.331325)\n",
      "epoch 38: (0.5840125, 0.5574945679139078, 0.814625)\n",
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      "epoch 40: (0.583825, 0.5572028115190392, 0.816525)\n",
      "epoch 41: (0.5577875, 0.6051111818471193, 0.332675)\n",
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      "epoch 43: (0.557425, 0.6047710271848203, 0.331475)\n",
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      "epoch 45: (0.5579125, 0.6046249040242084, 0.334675)\n",
      "epoch 46: (0.5840375, 0.5572803953310045, 0.8176)\n",
      "epoch 47: (0.558025, 0.6049656295224313, 0.334425)\n",
      "epoch 48: (0.584125, 0.5573586063477994, 0.81745)\n",
      "epoch 49: (0.5581625, 0.6053716200914897, 0.33415)\n",
      "epoch 50: (0.5840625, 0.5573443388986477, 0.817025)\n",
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      "epoch 64: (0.5843375, 0.5575751369617531, 0.81675)\n",
      "epoch 65: (0.558, 0.6055408970976254, 0.332775)\n",
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      "epoch 73: (0.5581625, 0.6061601642710472, 0.3321)\n",
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      "epoch 77: (0.558025, 0.6060398391812866, 0.331625)\n",
      "epoch 78: (0.5844125, 0.5576420779486829, 0.816625)\n",
      "epoch 79: (0.55815, 0.6062391522791633, 0.331825)\n",
      "epoch 80: (0.5843875, 0.5576250064018301, 0.8166)\n",
      "epoch 81: (0.5582375, 0.6061420695311432, 0.332575)\n",
      "epoch 82: (0.58435, 0.5575689325689326, 0.81695)\n",
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      "epoch 170: (0.58415, 0.5573952187702486, 0.817225)\n",
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      "epoch 196: (0.5841625, 0.5573890659893285, 0.817425)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 197: (0.5586, 0.6062942136767641, 0.33425)\n",
      "epoch 198: (0.584175, 0.557398568019093, 0.817425)\n",
      "epoch 199: (0.5585875, 0.6062763593487823, 0.334225)\n",
      "epoch 0: (0.5083, 0.5067603339442068, 0.622175)\n",
      "epoch 1: (0.5492375, 0.5287833394227256, 0.90455)\n",
      "epoch 2: (0.512625, 0.5979061651803025, 0.0771)\n",
      "epoch 3: (0.52365, 0.5123318385650224, 0.98255)\n",
      "epoch 4: (0.5180875, 0.5796017163604357, 0.1317)\n",
      "epoch 5: (0.5473, 0.5261001517450683, 0.953425)\n",
      "epoch 6: (0.5335375, 0.6098779588827914, 0.18615)\n",
      "epoch 7: (0.5586, 0.5342109872146652, 0.91505)\n",
      "epoch 8: (0.5324625, 0.6133368246486864, 0.175675)\n",
      "epoch 9: (0.564225, 0.5384534786253143, 0.899325)\n",
      "epoch 10: (0.5342625, 0.6162524387140554, 0.181625)\n",
      "epoch 11: (0.5715875, 0.5450682279616601, 0.8658)\n",
      "epoch 12: (0.53485, 0.616691779675205, 0.184175)\n",
      "epoch 13: (0.5752, 0.5480020426401123, 0.8585)\n",
      "epoch 14: (0.536125, 0.6200764500581685, 0.18655)\n",
      "epoch 15: (0.5787, 0.5514278246095536, 0.84385)\n",
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      "epoch 154: (0.55535, 0.6056297709923664, 0.31735)\n",
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      "epoch 158: (0.555525, 0.605071435329738, 0.31975)\n",
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      "epoch 160: (0.5555375, 0.6050404274433779, 0.3199)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 161: (0.58495, 0.5582427753590895, 0.814225)\n",
      "epoch 162: (0.555575, 0.6050666414594952, 0.32005)\n",
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      "epoch 166: (0.555425, 0.6046149490373726, 0.320325)\n",
      "epoch 167: (0.5849, 0.558286420431141, 0.8132)\n",
      "epoch 168: (0.5555125, 0.6051721688059489, 0.319425)\n",
      "epoch 169: (0.584925, 0.5582855770220652, 0.81345)\n",
      "epoch 170: (0.555525, 0.605210800568451, 0.3194)\n",
      "epoch 171: (0.5849, 0.5582704186684969, 0.8134)\n",
      "epoch 172: (0.5554875, 0.6051148472649776, 0.319425)\n",
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      "epoch 174: (0.5554625, 0.6050873951968169, 0.31935)\n",
      "epoch 175: (0.5848875, 0.5582688380553601, 0.8133)\n",
      "epoch 176: (0.5554, 0.6049441182042053, 0.31935)\n",
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      "epoch 182: (0.55535, 0.6044438154542882, 0.320325)\n",
      "epoch 183: (0.5849, 0.5582704186684969, 0.8134)\n",
      "epoch 184: (0.5555, 0.6046380090497737, 0.3207)\n",
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      "epoch 199: (0.5849, 0.5582984275218018, 0.81305)\n",
      "epoch 0: (0.5, 0, 0.0)\n",
      "epoch 1: (0.5039625, 0.5019942876555481, 0.997425)\n",
      "epoch 2: (0.5, 0, 0.0)\n",
      "epoch 3: (0.5060125, 0.5030415702343464, 0.9944)\n",
      "epoch 4: (0.501425, 0.8238636363636364, 0.003625)\n",
      "epoch 5: (0.5101875, 0.5052000255215977, 0.98975)\n",
      "epoch 6: (0.5048625, 0.7202718006795017, 0.0159)\n",
      "epoch 7: (0.532, 0.5169191318370476, 0.977675)\n",
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      "epoch 9: (0.5475375, 0.5260532986230048, 0.95985)\n",
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      "epoch 68: (0.5412125, 0.6240126382306477, 0.207375)\n",
      "epoch 69: (0.5814625, 0.5530190858946615, 0.8497)\n",
      "epoch 70: (0.541225, 0.6241529890076796, 0.20725)\n",
      "epoch 71: (0.581475, 0.553024633106635, 0.84975)\n",
      "epoch 72: (0.54125, 0.6241534988713319, 0.207375)\n",
      "epoch 73: (0.5814875, 0.5530284542925472, 0.849825)\n",
      "epoch 74: (0.5413125, 0.6242574629671404, 0.20755)\n",
      "epoch 75: (0.5815125, 0.5530412714938752, 0.8499)\n",
      "epoch 76: (0.5413625, 0.6243704427572728, 0.20765)\n",
      "epoch 77: (0.581475, 0.5530073842750723, 0.85)\n",
      "epoch 78: (0.5413875, 0.6244082062072593, 0.207725)\n",
      "epoch 79: (0.5815, 0.5530322748568454, 0.8499)\n",
      "epoch 80: (0.541425, 0.6244554604176056, 0.20785)\n",
      "epoch 81: (0.5815, 0.5530305495006019, 0.849925)\n",
      "epoch 82: (0.54145, 0.6245118654250525, 0.2079)\n",
      "epoch 83: (0.5815125, 0.553037820252135, 0.84995)\n",
      "epoch 84: (0.5414625, 0.6245026649650927, 0.207975)\n",
      "epoch 85: (0.5815375, 0.5530523610455943, 0.85)\n",
      "epoch 86: (0.54155, 0.6246624662466247, 0.2082)\n",
      "epoch 87: (0.5815375, 0.5530523610455943, 0.85)\n",
      "epoch 88: (0.5415875, 0.6247469066366704, 0.208275)\n",
      "epoch 89: (0.5815875, 0.553081439794408, 0.8501)\n",
      "epoch 90: (0.5415875, 0.6246908027883967, 0.20835)\n",
      "epoch 91: (0.5816625, 0.5531527133675047, 0.84985)\n",
      "epoch 92: (0.5415875, 0.6247281997450701, 0.2083)\n",
      "epoch 93: (0.5817125, 0.5531800654073314, 0.849975)\n",
      "epoch 94: (0.5416125, 0.6247844666016943, 0.20835)\n",
      "epoch 95: (0.5817, 0.5531710650483226, 0.849975)\n",
      "epoch 96: (0.5415625, 0.6245598261781674, 0.2084)\n",
      "epoch 97: (0.581725, 0.5531856045815436, 0.850025)\n",
      "epoch 98: (0.5415875, 0.6246160761105701, 0.20845)\n",
      "epoch 99: (0.581725, 0.5531856045815436, 0.850025)\n",
      "epoch 100: (0.5415625, 0.6245598261781674, 0.2084)\n",
      "epoch 101: (0.581725, 0.5531856045815436, 0.850025)\n",
      "epoch 102: (0.5415375, 0.6244476069208299, 0.208425)\n",
      "epoch 103: (0.5817, 0.5531676048547164, 0.850025)\n",
      "epoch 104: (0.541575, 0.6245133273435161, 0.208525)\n",
      "epoch 105: (0.5817125, 0.5531766045717075, 0.850025)\n",
      "epoch 106: (0.5415375, 0.6243917047241146, 0.2085)\n",
      "epoch 107: (0.5817, 0.5531676048547164, 0.850025)\n",
      "epoch 108: (0.541575, 0.624457416554408, 0.2086)\n",
      "epoch 109: (0.5817125, 0.5531748743228073, 0.85005)\n",
      "epoch 110: (0.54165, 0.6245886927909063, 0.2088)\n",
      "epoch 111: (0.5817125, 0.5531748743228073, 0.85005)\n",
      "epoch 112: (0.541575, 0.6244946848330588, 0.20855)\n",
      "epoch 113: (0.5817125, 0.5531731441865005, 0.850075)\n",
      "epoch 114: (0.54165, 0.6246259724715739, 0.20875)\n",
      "epoch 115: (0.581725, 0.5531769528581189, 0.85015)\n",
      "epoch 116: (0.5417125, 0.6247663201974127, 0.208875)\n",
      "epoch 117: (0.581725, 0.5531769528581189, 0.85015)\n",
      "epoch 118: (0.5417625, 0.6248785228377065, 0.208975)\n",
      "epoch 119: (0.5817125, 0.5531679544530297, 0.85015)\n",
      "epoch 120: (0.5417625, 0.6248038849458349, 0.209075)\n",
      "epoch 121: (0.5817125, 0.5531679544530297, 0.85015)\n",
      "epoch 122: (0.5417625, 0.6247665994473075, 0.209125)\n",
      "epoch 123: (0.5817, 0.5531606858183947, 0.850125)\n",
      "epoch 124: (0.5417625, 0.6247665994473075, 0.209125)\n",
      "epoch 125: (0.581725, 0.5531752228511939, 0.850175)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 126: (0.5417375, 0.6246732880292734, 0.209125)\n",
      "epoch 127: (0.581725, 0.5531786829776159, 0.850125)\n",
      "epoch 128: (0.5416875, 0.6245983710677725, 0.208975)\n",
      "epoch 129: (0.5817375, 0.5531859515559676, 0.85015)\n",
      "epoch 130: (0.541775, 0.6247759856630825, 0.209175)\n",
      "epoch 131: (0.5817375, 0.5531859515559676, 0.85015)\n",
      "epoch 132: (0.541725, 0.6246080334478125, 0.20915)\n",
      "epoch 133: (0.5817, 0.5531606858183947, 0.850125)\n",
      "epoch 134: (0.5417375, 0.6245616652988137, 0.209275)\n",
      "epoch 135: (0.581675, 0.5531426898301777, 0.850125)\n",
      "epoch 136: (0.541775, 0.6246270883054893, 0.209375)\n",
      "epoch 137: (0.5816875, 0.553149958521073, 0.85015)\n",
      "epoch 138: (0.5417625, 0.6245805056305467, 0.209375)\n",
      "epoch 139: (0.5816875, 0.553149958521073, 0.85015)\n",
      "epoch 140: (0.541725, 0.6244407992842231, 0.209375)\n",
      "epoch 141: (0.5816875, 0.5531482294767319, 0.850175)\n",
      "epoch 142: (0.5417375, 0.6244131455399061, 0.209475)\n",
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      "epoch 144: (0.5417625, 0.624469115565159, 0.209525)\n",
      "epoch 145: (0.5816625, 0.55312677888916, 0.850225)\n",
      "epoch 146: (0.5417625, 0.6244134951962463, 0.2096)\n",
      "epoch 147: (0.5817, 0.5531537685826746, 0.850225)\n",
      "epoch 148: (0.5417875, 0.6244694318266438, 0.20965)\n",
      "epoch 149: (0.5817, 0.5531537685826746, 0.850225)\n",
      "epoch 150: (0.5418, 0.6244788564621798, 0.2097)\n",
      "epoch 151: (0.5816875, 0.5531482294767319, 0.850175)\n",
      "epoch 152: (0.5417875, 0.6244138444361742, 0.209725)\n",
      "epoch 153: (0.5817, 0.5531572269755034, 0.850175)\n",
      "epoch 154: (0.5417875, 0.6243768137510232, 0.209775)\n",
      "epoch 155: (0.5817, 0.5531572269755034, 0.850175)\n",
      "epoch 156: (0.54185, 0.6245165129425766, 0.2099)\n",
      "epoch 157: (0.5817125, 0.5531662247669861, 0.850175)\n",
      "epoch 158: (0.5418125, 0.6244141932604329, 0.20985)\n",
      "epoch 159: (0.5817125, 0.5531662247669861, 0.850175)\n",
      "epoch 160: (0.54185, 0.6244979919678715, 0.209925)\n",
      "epoch 161: (0.5817125, 0.5531662247669861, 0.850175)\n",
      "epoch 162: (0.5418, 0.6243492488472409, 0.209875)\n",
      "epoch 163: (0.5817, 0.5531589563406858, 0.85015)\n",
      "epoch 164: (0.5417875, 0.6242658538398632, 0.209925)\n",
      "epoch 165: (0.5817, 0.5531589563406858, 0.85015)\n",
      "epoch 166: (0.541775, 0.6241825208085613, 0.209975)\n",
      "epoch 167: (0.5817, 0.5531589563406858, 0.85015)\n",
      "epoch 168: (0.5418125, 0.6242293693827528, 0.2101)\n",
      "epoch 169: (0.5817, 0.5531589563406858, 0.85015)\n",
      "epoch 170: (0.5418375, 0.6243036470326079, 0.210125)\n",
      "epoch 171: (0.5817, 0.5531589563406858, 0.85015)\n",
      "epoch 172: (0.5418375, 0.6242667260711369, 0.210175)\n",
      "epoch 173: (0.5817, 0.5531589563406858, 0.85015)\n",
      "epoch 174: (0.54185, 0.6242946242946243, 0.2102)\n",
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      "epoch 176: (0.54185, 0.6242577197149644, 0.21025)\n",
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      "epoch 178: (0.541775, 0.6240350356294537, 0.210175)\n",
      "epoch 179: (0.5816875, 0.5531534169472777, 0.8501)\n",
      "epoch 180: (0.5418125, 0.6241003190621058, 0.210275)\n",
      "epoch 181: (0.5816875, 0.5531534169472777, 0.8501)\n",
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      "epoch 0: (0.521, 0.5112476902064754, 0.954525)\n",
      "epoch 1: (0.5, 0, 0.0)\n",
      "epoch 2: (0.5095625, 0.5048590556282473, 0.99355)\n",
      "epoch 3: (0.5, 0, 0.0)\n",
      "epoch 4: (0.5153375, 0.5078660905465875, 0.99025)\n",
      "epoch 5: (0.5002125, 0.84, 0.000525)\n",
      "epoch 6: (0.52705, 0.5141645284599675, 0.9819)\n",
      "epoch 7: (0.5033125, 0.7485928705440901, 0.009975)\n",
      "epoch 8: (0.5368875, 0.5197536649039427, 0.970575)\n",
      "epoch 9: (0.5062625, 0.7287671232876712, 0.01995)\n",
      "epoch 10: (0.5469625, 0.5257770154373927, 0.9579)\n",
      "epoch 11: (0.50885, 0.7240506329113924, 0.0286)\n",
      "epoch 12: (0.5549375, 0.5308615967979776, 0.945)\n",
      "epoch 13: (0.5111625, 0.7279224093925473, 0.03565)\n",
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      "epoch 16: (0.565625, 0.5383626107035338, 0.92095)\n",
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      "epoch 25: (0.5168, 0.7022877784467189, 0.058325)\n",
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      "epoch 40: (0.5804625, 0.5502615757007886, 0.8809)\n",
      "epoch 41: (0.5214, 0.6843238587424634, 0.07945)\n",
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      "epoch 86: (0.5816, 0.5514956455887922, 0.8739)\n",
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      "epoch 89: (0.5264625, 0.6716950527169505, 0.103525)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 90: (0.5815875, 0.5515178303629217, 0.873425)\n",
      "epoch 91: (0.5264625, 0.6716950527169505, 0.103525)\n",
      "epoch 92: (0.581625, 0.5515553450181588, 0.87325)\n",
      "epoch 93: (0.5265125, 0.6719079267304263, 0.103625)\n",
      "epoch 94: (0.5816125, 0.5515450081316218, 0.873275)\n",
      "epoch 95: (0.5283125, 0.6638691940384893, 0.1147)\n",
      "epoch 96: (0.5816125, 0.5515238561214666, 0.8736)\n",
      "epoch 97: (0.528175, 0.6623451454912129, 0.11495)\n",
      "epoch 98: (0.5817375, 0.5516223257282703, 0.873425)\n",
      "epoch 99: (0.52905, 0.6616583194212576, 0.1189)\n",
      "epoch 100: (0.5816125, 0.5515189773534286, 0.873675)\n",
      "epoch 101: (0.5290625, 0.6618855312630553, 0.118825)\n",
      "epoch 102: (0.5816125, 0.5515417528458879, 0.873325)\n",
      "epoch 103: (0.5290875, 0.6620699261735618, 0.118825)\n",
      "epoch 104: (0.5816375, 0.5515673114883537, 0.8732)\n",
      "epoch 105: (0.5291625, 0.6624878116729349, 0.1189)\n",
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      "epoch 107: (0.529225, 0.6623611111111111, 0.119225)\n",
      "epoch 108: (0.5821125, 0.5519149003429908, 0.87295)\n",
      "epoch 109: (0.5292625, 0.6627276518837759, 0.119175)\n",
      "epoch 110: (0.5820875, 0.5519187894312414, 0.872625)\n",
      "epoch 111: (0.529275, 0.6625034693311129, 0.11935)\n",
      "epoch 112: (0.5821125, 0.5519576049988135, 0.8723)\n",
      "epoch 113: (0.5292875, 0.6640067198656027, 0.118575)\n",
      "epoch 114: (0.5821875, 0.552021520689928, 0.872125)\n",
      "epoch 115: (0.5299, 0.6584525702172761, 0.12425)\n",
      "epoch 116: (0.5822, 0.5519907656304355, 0.872725)\n",
      "epoch 117: (0.529875, 0.6584040296924708, 0.124175)\n",
      "epoch 118: (0.582225, 0.5520131574785716, 0.87265)\n",
      "epoch 119: (0.5298625, 0.6583587432056212, 0.12415)\n",
      "epoch 120: (0.5822375, 0.5520367001502808, 0.872425)\n",
      "epoch 121: (0.5297875, 0.6581287325812873, 0.123975)\n",
      "epoch 122: (0.5822375, 0.5520416396399247, 0.87235)\n",
      "epoch 123: (0.5297875, 0.6580448335322987, 0.124025)\n",
      "epoch 124: (0.5822875, 0.552084816836775, 0.872225)\n",
      "epoch 125: (0.5297125, 0.6587418191531988, 0.1233)\n",
      "epoch 126: (0.5822875, 0.5520914112080016, 0.872125)\n",
      "epoch 127: (0.5296625, 0.6584746894617337, 0.12325)\n",
      "epoch 128: (0.582275, 0.5520909177245243, 0.872)\n",
      "epoch 129: (0.529625, 0.6584648301684942, 0.1231)\n",
      "epoch 130: (0.5823, 0.5521430607913327, 0.871475)\n",
      "epoch 131: (0.5296375, 0.6585528955463421, 0.1231)\n",
      "epoch 132: (0.5823125, 0.5521551109632657, 0.871425)\n",
      "epoch 133: (0.5297375, 0.6581992286208272, 0.123725)\n",
      "epoch 134: (0.5824, 0.5522130342489624, 0.871475)\n",
      "epoch 135: (0.529725, 0.6581958488557743, 0.123675)\n",
      "epoch 136: (0.5824125, 0.5522317113748356, 0.871325)\n",
      "epoch 137: (0.529875, 0.6584040296924708, 0.124175)\n",
      "epoch 138: (0.5823625, 0.5522000221824347, 0.871275)\n",
      "epoch 139: (0.5309625, 0.6563960095971714, 0.12995)\n",
      "epoch 140: (0.5822875, 0.552084816836775, 0.872225)\n",
      "epoch 141: (0.530975, 0.6565184436584134, 0.129925)\n",
      "epoch 142: (0.58225, 0.5520701443403393, 0.87205)\n",
      "epoch 143: (0.53095, 0.6564711830131446, 0.12985)\n",
      "epoch 144: (0.5823625, 0.5521752213230287, 0.87165)\n",
      "epoch 145: (0.530925, 0.6563843236409608, 0.1298)\n",
      "epoch 146: (0.582375, 0.5522104262398986, 0.87125)\n",
      "epoch 147: (0.530925, 0.6563843236409608, 0.1298)\n",
      "epoch 148: (0.5823375, 0.5522238959803377, 0.87065)\n",
      "epoch 149: (0.5310875, 0.6559247648902822, 0.130775)\n",
      "epoch 150: (0.5824375, 0.5522492117063587, 0.871325)\n",
      "epoch 151: (0.5311, 0.6560461615654791, 0.13075)\n",
      "epoch 152: (0.5824375, 0.5522574919573382, 0.8712)\n",
      "epoch 153: (0.531075, 0.6559989959839357, 0.130675)\n",
      "epoch 154: (0.5823375, 0.5522056842138634, 0.870925)\n",
      "epoch 155: (0.531025, 0.6558653604621955, 0.13055)\n",
      "epoch 156: (0.5823375, 0.5522056842138634, 0.870925)\n",
      "epoch 157: (0.5310375, 0.6559085771694085, 0.130575)\n",
      "epoch 158: (0.5823875, 0.5522473246135553, 0.870825)\n",
      "epoch 159: (0.5310625, 0.6561910747957259, 0.1305)\n",
      "epoch 160: (0.5823625, 0.5522347830223082, 0.87075)\n",
      "epoch 161: (0.5309875, 0.656403785488959, 0.13005)\n",
      "epoch 162: (0.582475, 0.5523285324535245, 0.870525)\n",
      "epoch 163: (0.5310375, 0.6571717938979618, 0.129775)\n",
      "epoch 164: (0.582475, 0.5523501221873116, 0.8702)\n",
      "epoch 165: (0.5307875, 0.6567392134402443, 0.129)\n",
      "epoch 166: (0.5823875, 0.552305372589477, 0.86995)\n",
      "epoch 167: (0.531025, 0.6562972292191436, 0.130275)\n",
      "epoch 168: (0.5823625, 0.5522662732219631, 0.870275)\n",
      "epoch 169: (0.531, 0.656328794755421, 0.13015)\n",
      "epoch 170: (0.5823625, 0.5522729075763586, 0.870175)\n",
      "epoch 171: (0.531225, 0.6562421816362272, 0.13115)\n",
      "epoch 172: (0.5824875, 0.5523289930693226, 0.87065)\n",
      "epoch 173: (0.5313625, 0.6544, 0.132925)\n",
      "epoch 174: (0.58245, 0.5522894469812278, 0.87085)\n",
      "epoch 175: (0.5313875, 0.6545230769230769, 0.13295)\n",
      "epoch 176: (0.5824625, 0.5523247513444058, 0.87045)\n",
      "epoch 177: (0.53135, 0.6544715447154471, 0.132825)\n",
      "epoch 178: (0.582475, 0.5523301925700327, 0.8705)\n",
      "epoch 179: (0.5314, 0.6546417138635804, 0.132925)\n",
      "epoch 180: (0.582525, 0.5523801967629324, 0.870275)\n",
      "epoch 181: (0.5314875, 0.6550535516434814, 0.133025)\n",
      "epoch 182: (0.582225, 0.5521054465954818, 0.87125)\n",
      "epoch 183: (0.5314625, 0.6549304444170873, 0.133)\n",
      "epoch 184: (0.582275, 0.5521420875847646, 0.871225)\n",
      "epoch 185: (0.5314375, 0.6548836063554625, 0.132925)\n",
      "epoch 186: (0.5822625, 0.5521449059474193, 0.87105)\n",
      "epoch 187: (0.5314, 0.6546798029556651, 0.1329)\n",
      "epoch 188: (0.582275, 0.5521503502044179, 0.8711)\n",
      "epoch 189: (0.531425, 0.6547267355982275, 0.132975)\n",
      "epoch 190: (0.58225, 0.5521394611727417, 0.871)\n",
      "epoch 191: (0.53145, 0.654659454143103, 0.133125)\n",
      "epoch 192: (0.5823, 0.552166196558172, 0.871125)\n",
      "epoch 193: (0.5314875, 0.6549391069012178, 0.1331)\n",
      "epoch 194: (0.582325, 0.5521903131735768, 0.871025)\n",
      "epoch 195: (0.5315125, 0.6550621232623939, 0.133125)\n",
      "epoch 196: (0.5822875, 0.5521706741056569, 0.870925)\n",
      "epoch 197: (0.531475, 0.6547443461160275, 0.133175)\n",
      "epoch 198: (0.582225, 0.5521302225321753, 0.870875)\n",
      "epoch 199: (0.5314875, 0.6547487406315272, 0.133225)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "attack_params = model_params('attack_model')\n",
    "attack_params.learning_rate = 0.001\n",
    "attack_params.weight_decay = 1e-5\n",
    "attack_params.epoch = 200\n",
    "attack_params.h_neurons=64\n",
    "attack_params.do=0\n",
    "\n",
    "for attack_params.learning_rate in [0.001, 0.01]:\n",
    "    for attack_params.h_neurons in [32,64,128]:\n",
    "        for attack_params.do in [0, 0.5]:\n",
    "            # attack_train_data = torch.tensor(np.array(df.drop(['a_target'], axis=1)), dtype=torch.float, requires_grad=True)   \n",
    "            # attack_train_target = torch.tensor(np.array(df['a_target']), dtype=torch.float)\n",
    "            attack_train_args = Train_args(attack_params.learning_rate, attack_params.weight_decay, attack_params.epoch)\n",
    "            attack_model = Net_attack(attack_params.h_neurons, attack_params.do, input_size=attack_train_data.shape[1])\n",
    "            for epoch in range(attack_train_args.epoch):\n",
    "                attack_model = attack.train_attack_model(attack_model, attack_train_data, attack_train_target, attack_train_args)\n",
    "                print(f'epoch {epoch}: {attack_evaluation(attack_model, attack_train_data, attack_train_target)}' )        \n",
    "                \n",
    "                train_res = attack_evaluation(attack_model, attack_train_data, attack_train_target)\n",
    "                test_res = attack.mi_attack_test(model, attack_model, x_target_train, y_target_train, x_target_test, y_target_test)\n",
    "\n",
    "                attack_params.number_of_sms = number_of_sms\n",
    "                attack_params.shadow_size = shadow_size\n",
    "\n",
    "                attack_params.train_acc, attack_params.train_pre, attack_params.train_rec = train_res\n",
    "                attack_params.test_acc, attack_params.test_pre, attack_params.test_rec = test_res\n",
    "\n",
    "                a_param = dict(shadow_model.__dict__)\n",
    "                a_param.pop('theta')\n",
    "                a_param.pop('pred_func')\n",
    "                a_param.pop('accuracy')\n",
    "                a_param.update(dict(attack_params.__dict__))\n",
    "                a_param\n",
    "\n",
    "                k+=1\n",
    "                at_path = 'mia/loan/attack_model'+str(k)\n",
    "                torch.save(attack_model, at_path)\n",
    "                with open(at_path+'_params.json', 'w') as file:\n",
    "                    json.dump(a_param, file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "55dbb858",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 0: (0.500025, 0.5000125003125078, 1.0)\n",
      "epoch 1: (0.50045, 0.5002251125562781, 0.99995)\n",
      "epoch 2: (0.496775, 0.49832471883847174, 0.9593)\n",
      "epoch 3: (0.483525, 0.49034545401271645, 0.83675)\n",
      "epoch 4: (0.49075, 0.4939755112674222, 0.75845)\n",
      "epoch 5: (0.4891, 0.49239994421977407, 0.7062)\n",
      "epoch 6: (0.4881, 0.4905292479108635, 0.61635)\n",
      "epoch 7: (0.496875, 0.49728319930449905, 0.572)\n",
      "epoch 8: (0.510075, 0.5093058698563709, 0.5514)\n",
      "epoch 9: (0.51315, 0.5124033201282777, 0.54325)\n",
      "epoch 10: (0.512525, 0.5121666909514789, 0.52725)\n",
      "epoch 11: (0.51265, 0.512735326688815, 0.5093)\n",
      "epoch 12: (0.513725, 0.514343941056592, 0.49215)\n",
      "epoch 13: (0.5146, 0.5158920213344944, 0.47395)\n",
      "epoch 14: (0.514125, 0.5156085971600641, 0.4666)\n",
      "epoch 15: (0.513975, 0.5161476688427985, 0.4467)\n",
      "epoch 16: (0.515125, 0.5184058411925768, 0.426)\n",
      "epoch 17: (0.51525, 0.5185005459177484, 0.4274)\n",
      "epoch 18: (0.5146, 0.5186891961085509, 0.4052)\n",
      "epoch 19: (0.51555, 0.520824963171287, 0.3889)\n",
      "epoch 20: (0.51825, 0.5231863803836869, 0.4118)\n",
      "epoch 21: (0.521375, 0.5302697727111804, 0.37445)\n",
      "epoch 22: (0.522825, 0.5328630048232669, 0.3701)\n",
      "epoch 23: (0.5376, 0.5426255526584287, 0.47865)\n",
      "epoch 24: (0.5389, 0.5451642865435969, 0.46955)\n",
      "epoch 25: (0.54045, 0.5461231470923603, 0.47895)\n",
      "epoch 26: (0.5413, 0.5474439977024699, 0.47655)\n",
      "epoch 27: (0.546925, 0.5494077388786522, 0.5218)\n",
      "epoch 28: (0.5464, 0.5502381983542659, 0.5082)\n",
      "epoch 29: (0.547875, 0.5515699897667905, 0.51205)\n",
      "epoch 30: (0.5522, 0.5534015345268543, 0.54095)\n",
      "epoch 31: (0.5533, 0.5553478712357217, 0.5348)\n",
      "epoch 32: (0.55385, 0.555942239767297, 0.53515)\n",
      "epoch 33: (0.554125, 0.5560039319157742, 0.53735)\n",
      "epoch 34: (0.554825, 0.556757596148869, 0.5378)\n",
      "epoch 35: (0.5566, 0.554198984966006, 0.57875)\n",
      "epoch 36: (0.555575, 0.5556167125344008, 0.5552)\n",
      "epoch 37: (0.5579, 0.5536111111111112, 0.5979)\n",
      "epoch 38: (0.55645, 0.5544148833622518, 0.57515)\n",
      "epoch 39: (0.55675, 0.5541507633587787, 0.58075)\n",
      "epoch 40: (0.55795, 0.5548613083404336, 0.5861)\n",
      "epoch 41: (0.55905, 0.5555712403538491, 0.59035)\n",
      "epoch 42: (0.560175, 0.5564096554956643, 0.59355)\n",
      "epoch 43: (0.56065, 0.5543702375616315, 0.6184)\n",
      "epoch 44: (0.56155, 0.5564938044974759, 0.6063)\n",
      "epoch 45: (0.560975, 0.5555910106213247, 0.6094)\n",
      "epoch 46: (0.5608, 0.5548637430066775, 0.6149)\n",
      "epoch 47: (0.561425, 0.5556310283928814, 0.6135)\n",
      "epoch 48: (0.5621, 0.5557400592406426, 0.61915)\n",
      "epoch 49: (0.56275, 0.556222560702446, 0.6208)\n",
      "epoch 50: (0.5634, 0.5565012031013279, 0.62445)\n",
      "epoch 51: (0.563225, 0.5564282208041412, 0.62345)\n",
      "epoch 52: (0.563375, 0.5561014473509495, 0.6282)\n",
      "epoch 53: (0.563725, 0.5564712658956976, 0.62795)\n",
      "epoch 54: (0.564375, 0.5566307455465141, 0.63275)\n",
      "epoch 55: (0.5644, 0.5567500881212548, 0.6318)\n",
      "epoch 56: (0.565, 0.5573394495412844, 0.6318)\n",
      "epoch 57: (0.565475, 0.5571310152262118, 0.6385)\n",
      "epoch 58: (0.5657, 0.558141592920354, 0.6307)\n",
      "epoch 59: (0.566325, 0.5574814750617498, 0.64325)\n",
      "epoch 60: (0.5659, 0.5584323461606668, 0.6298)\n",
      "epoch 61: (0.567375, 0.5570032573289903, 0.65835)\n",
      "epoch 62: (0.566375, 0.5581446279181814, 0.63715)\n",
      "epoch 63: (0.56675, 0.5578974759302628, 0.6432)\n",
      "epoch 64: (0.566925, 0.5581122736942647, 0.64275)\n",
      "epoch 65: (0.567275, 0.5581435547297006, 0.6458)\n",
      "epoch 66: (0.567475, 0.5582157801647901, 0.647)\n",
      "epoch 67: (0.567725, 0.558305712194912, 0.6485)\n",
      "epoch 68: (0.56825, 0.5584933150497086, 0.65165)\n",
      "epoch 69: (0.568275, 0.5586529788239337, 0.6503)\n",
      "epoch 70: (0.568625, 0.5582678836765018, 0.6575)\n",
      "epoch 71: (0.568325, 0.5593227696982852, 0.6442)\n",
      "epoch 72: (0.5694, 0.5575551501078122, 0.6723)\n",
      "epoch 73: (0.569175, 0.5591669161356541, 0.65375)\n",
      "epoch 74: (0.569725, 0.5587578477225804, 0.66305)\n",
      "epoch 75: (0.569125, 0.5593067650465445, 0.6519)\n",
      "epoch 76: (0.5701, 0.5578430563577853, 0.67605)\n",
      "epoch 77: (0.570175, 0.5598073890995866, 0.65685)\n",
      "epoch 78: (0.5706, 0.5581884117695541, 0.67725)\n",
      "epoch 79: (0.5702, 0.5597141884994896, 0.658)\n",
      "epoch 80: (0.571325, 0.5586578395493236, 0.6793)\n",
      "epoch 81: (0.571275, 0.5603897479347596, 0.6614)\n",
      "epoch 82: (0.571425, 0.5594341585188267, 0.6723)\n",
      "epoch 83: (0.5713, 0.560546875, 0.6601)\n",
      "epoch 84: (0.5721, 0.5591128966139215, 0.68195)\n",
      "epoch 85: (0.572025, 0.5606909627132926, 0.6654)\n",
      "epoch 86: (0.572225, 0.5601398892543403, 0.6727)\n",
      "epoch 87: (0.5726, 0.5614161238473903, 0.66365)\n",
      "epoch 88: (0.57265, 0.5595101572739187, 0.68305)\n",
      "epoch 89: (0.57295, 0.5614626337517904, 0.6664)\n",
      "epoch 90: (0.573325, 0.5600753758551472, 0.6836)\n",
      "epoch 91: (0.57305, 0.5613556190156224, 0.66835)\n",
      "epoch 92: (0.5741, 0.5612143742255267, 0.67935)\n",
      "epoch 93: (0.573725, 0.5620920537331031, 0.6674)\n",
      "epoch 94: (0.5738, 0.5599756196668021, 0.68905)\n",
      "epoch 95: (0.574275, 0.5623086279937922, 0.6703)\n",
      "epoch 96: (0.57535, 0.5619603651015541, 0.6834)\n",
      "epoch 97: (0.574675, 0.5628974520951779, 0.6683)\n",
      "epoch 98: (0.575175, 0.5611253404886775, 0.6901)\n",
      "epoch 99: (0.57515, 0.5631724949562879, 0.66995)\n",
      "epoch 100: (0.575425, 0.5612389883489628, 0.69125)\n",
      "epoch 101: (0.57555, 0.5633012149141181, 0.6723)\n",
      "epoch 102: (0.5758, 0.5616109891896286, 0.69095)\n",
      "epoch 103: (0.575875, 0.5635921719817291, 0.67245)\n",
      "epoch 104: (0.576175, 0.5618077812487322, 0.6924)\n",
      "epoch 105: (0.57645, 0.5640392025464902, 0.67335)\n",
      "epoch 106: (0.576475, 0.5619205700174082, 0.694)\n",
      "epoch 107: (0.577525, 0.5649043492820963, 0.67475)\n",
      "epoch 108: (0.57725, 0.5626978329681033, 0.6933)\n",
      "epoch 109: (0.5775, 0.5649078726968174, 0.6745)\n",
      "epoch 110: (0.57815, 0.5633563032022699, 0.6949)\n",
      "epoch 111: (0.5783, 0.5655175299138148, 0.67585)\n",
      "epoch 112: (0.578625, 0.5639305606374762, 0.69355)\n",
      "epoch 113: (0.5783, 0.5656053623795559, 0.67505)\n",
      "epoch 114: (0.57935, 0.5643500121644636, 0.6959)\n",
      "epoch 115: (0.5787, 0.5658467202141901, 0.6763)\n",
      "epoch 116: (0.57945, 0.5648201027984009, 0.6923)\n",
      "epoch 117: (0.57885, 0.5660495895459876, 0.67575)\n",
      "epoch 118: (0.580125, 0.5651767194045634, 0.6948)\n",
      "epoch 119: (0.57915, 0.5661899983274795, 0.67705)\n",
      "epoch 120: (0.580575, 0.5656977455257043, 0.6938)\n",
      "epoch 121: (0.5792, 0.5664373794144787, 0.67525)\n",
      "epoch 122: (0.580675, 0.5657149839123529, 0.6945)\n",
      "epoch 123: (0.5794, 0.5666834635088603, 0.67475)\n",
      "epoch 124: (0.58055, 0.5657765801077903, 0.69285)\n",
      "epoch 125: (0.579975, 0.5670790522122038, 0.6761)\n",
      "epoch 126: (0.581075, 0.5660784873059211, 0.69455)\n",
      "epoch 127: (0.57995, 0.5671453766691862, 0.6753)\n",
      "epoch 128: (0.58135, 0.5664244304727688, 0.6937)\n",
      "epoch 129: (0.580225, 0.5674414694632424, 0.675)\n",
      "epoch 130: (0.58125, 0.5662724306688418, 0.69425)\n",
      "epoch 131: (0.580775, 0.567886708408623, 0.6757)\n",
      "epoch 132: (0.5816, 0.5664603355595373, 0.6955)\n",
      "epoch 133: (0.58095, 0.5680080651936487, 0.6761)\n",
      "epoch 134: (0.58205, 0.5669741245612603, 0.6946)\n",
      "epoch 135: (0.581125, 0.5681751334089667, 0.6761)\n",
      "epoch 136: (0.5823, 0.5670850994457124, 0.6957)\n",
      "epoch 137: (0.581425, 0.568300968837814, 0.6775)\n",
      "epoch 138: (0.5823, 0.5670960378281429, 0.6956)\n",
      "epoch 139: (0.581275, 0.568203751101414, 0.6771)\n",
      "epoch 140: (0.5821, 0.5668077142159655, 0.69655)\n",
      "epoch 141: (0.581475, 0.5682341610485323, 0.6785)\n",
      "epoch 142: (0.582325, 0.5672123117116381, 0.69475)\n",
      "epoch 143: (0.58155, 0.5684431389005455, 0.6773)\n",
      "epoch 144: (0.582425, 0.5670476267946476, 0.6971)\n",
      "epoch 145: (0.5816, 0.5683188211654387, 0.6788)\n",
      "epoch 146: (0.582675, 0.5672838250254324, 0.69705)\n",
      "epoch 147: (0.58175, 0.5684501381562421, 0.6789)\n",
      "epoch 148: (0.582475, 0.5669902124030378, 0.69805)\n",
      "epoch 149: (0.58175, 0.5683357017470534, 0.6799)\n",
      "epoch 150: (0.58225, 0.5669570172582221, 0.69645)\n",
      "epoch 151: (0.5816, 0.5682845188284519, 0.6791)\n",
      "epoch 152: (0.582375, 0.5669307332927077, 0.69775)\n",
      "epoch 153: (0.5819, 0.5683867735470942, 0.6807)\n",
      "epoch 154: (0.582475, 0.5670773860355415, 0.69725)\n",
      "epoch 155: (0.581725, 0.5682548962291728, 0.6804)\n",
      "epoch 156: (0.5828, 0.5672077922077922, 0.6988)\n",
      "epoch 157: (0.5816, 0.56802834514381, 0.68135)\n",
      "epoch 158: (0.58255, 0.5671356538711776, 0.69735)\n",
      "epoch 159: (0.5815, 0.5680186947087298, 0.6806)\n",
      "epoch 160: (0.5824, 0.5668017835427644, 0.69915)\n",
      "epoch 161: (0.58175, 0.5680626092748314, 0.6823)\n",
      "epoch 162: (0.582375, 0.5667788091281262, 0.69915)\n",
      "epoch 163: (0.58195, 0.5682291233036383, 0.6825)\n",
      "epoch 164: (0.5825, 0.5668449197860963, 0.6996)\n",
      "epoch 165: (0.5819, 0.5682386268955174, 0.682)\n",
      "epoch 166: (0.58245, 0.5667881733495342, 0.6997)\n",
      "epoch 167: (0.58165, 0.5679906736614206, 0.6821)\n",
      "epoch 168: (0.582525, 0.5667975231696952, 0.70025)\n",
      "epoch 169: (0.58155, 0.5677944966331366, 0.683)\n",
      "epoch 170: (0.582275, 0.566535926569892, 0.70055)\n",
      "epoch 171: (0.58185, 0.5680438939230193, 0.6833)\n",
      "epoch 172: (0.58215, 0.5664160401002506, 0.7006)\n",
      "epoch 173: (0.5819, 0.5680402093544903, 0.68375)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 174: (0.582225, 0.5664149266992448, 0.70125)\n",
      "epoch 175: (0.58225, 0.5682912653603454, 0.68445)\n",
      "epoch 176: (0.581975, 0.566175580221998, 0.70135)\n",
      "epoch 177: (0.5823, 0.5683100929614874, 0.6847)\n",
      "epoch 178: (0.58205, 0.5662227602905568, 0.70155)\n",
      "epoch 179: (0.582375, 0.568261860368759, 0.68575)\n",
      "epoch 180: (0.581725, 0.5658568032555703, 0.7022)\n",
      "epoch 181: (0.582425, 0.5683316062176166, 0.68555)\n",
      "epoch 182: (0.581675, 0.5657582222937885, 0.7027)\n",
      "epoch 183: (0.582425, 0.5681620839363242, 0.68705)\n",
      "epoch 184: (0.5821, 0.5661137059107747, 0.703)\n",
      "epoch 185: (0.582475, 0.5682598799917236, 0.6866)\n",
      "epoch 186: (0.58215, 0.5660369774919615, 0.70415)\n",
      "epoch 187: (0.5823, 0.5680559001075002, 0.68695)\n",
      "epoch 188: (0.58235, 0.5661871081819643, 0.70445)\n",
      "epoch 189: (0.582175, 0.5679441068254165, 0.6869)\n",
      "epoch 190: (0.58235, 0.566155205655527, 0.70475)\n",
      "epoch 191: (0.582075, 0.5677941601618965, 0.6874)\n",
      "epoch 192: (0.5826, 0.5662549129702414, 0.70595)\n",
      "epoch 193: (0.5821, 0.5677616375041268, 0.6879)\n",
      "epoch 194: (0.5829, 0.5664475793523566, 0.7067)\n",
      "epoch 195: (0.581875, 0.5675062868450345, 0.6883)\n",
      "epoch 196: (0.58335, 0.5667013444302177, 0.70815)\n",
      "epoch 197: (0.581975, 0.5675553174832091, 0.6887)\n",
      "epoch 198: (0.5833, 0.5666453316265301, 0.70825)\n",
      "epoch 199: (0.58185, 0.5673219279486758, 0.68975)\n"
     ]
    }
   ],
   "source": [
    "# attack_train_data = torch.tensor(np.array(df.drop(['a_target'], axis=1)), dtype=torch.float, requires_grad=True)   \n",
    "# attack_train_target = torch.tensor(np.array(df['a_target']), dtype=torch.float)\n",
    "attack_train_args = Train_args(attack_params.learning_rate, attack_params.weight_decay, attack_params.epoch)\n",
    "attack_model = Net_attack(attack_params.h_neurons, attack_params.do, input_size=attack_train_data.shape[1])\n",
    "for epoch in range(attack_train_args.epoch):\n",
    "    attack_model = attack.train_attack_model(attack_model, attack_train_data, attack_train_target, attack_train_args)\n",
    "    print(f'epoch {epoch}: {attack_evaluation(attack_model, attack_train_data, attack_train_target)}' )        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "97b6468a",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_res = attack_evaluation(attack_model, attack_train_data, attack_train_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "78651070",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_res = attack.mi_attack_test(model, attack_model, x_target_train, y_target_train, x_target_test, y_target_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "f45b84db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_classes': 7,\n",
       " 'alpha': 0.001,\n",
       " 'max_iter': 100,\n",
       " 'lambda_': 1e-05,\n",
       " 'tolerance': 1e-05,\n",
       " 'sgdDP': False,\n",
       " 'L': 1,\n",
       " 'C': 1,\n",
       " 'epsilon': 1,\n",
       " 'delta': 1e-05,\n",
       " 'sigma': 0,\n",
       " 'DP': False,\n",
       " 'model_name': 'attack_model',\n",
       " 'learning_rate': 0.001,\n",
       " 'weight_decay': 1e-05,\n",
       " 'epoch': 200,\n",
       " 'h_neurons': 64,\n",
       " 'do': 0,\n",
       " 'number_of_sms': 10,\n",
       " 'shadow_size': 20000,\n",
       " 'train_acc': 0.58185,\n",
       " 'train_pre': 0.5673219279486758,\n",
       " 'train_rec': 0.68975,\n",
       " 'test_acc': 0.6395,\n",
       " 'test_pre': 0.6368989205103042,\n",
       " 'test_rec': 0.649}"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attack_params = model_params('attack_model')\n",
    "attack_params.learning_rate = 0.001\n",
    "attack_params.weight_decay = 1e-5\n",
    "attack_params.epoch = 200\n",
    "attack_params.h_neurons=64\n",
    "attack_params.do=0\n",
    "attack_params.number_of_sms = number_of_sms\n",
    "attack_params.shadow_size = shadow_size\n",
    "\n",
    "attack_params.train_acc, attack_params.train_pre, attack_params.train_rec = train_res\n",
    "attack_params.test_acc, attack_params.test_pre, attack_params.test_rec = test_res\n",
    "\n",
    "a_param = dict(shadow_model.__dict__)\n",
    "a_param.pop('theta')\n",
    "a_param.pop('pred_func')\n",
    "a_param.pop('accuracy')\n",
    "a_param.update(dict(attack_params.__dict__))\n",
    "a_param\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe6604cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "k=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "036497f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "k+=1\n",
    "\n",
    "at_path = 'mia/loan/'\n",
    "torch.save(attack_model, at_path)\n",
    "with open(at_path+'_params.json', 'w') as file:\n",
    "    json.dump(a_param, file)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4a633399",
   "metadata": {},
   "outputs": [],
   "source": [
    "sh_path = f'mia/shms{number_of_sms}_shtrsize{shadow_size}_shlr{shadow_model.alpha}_shiter{int(shadow_model.max_iter/shadow_batch_size)}_shreg{shadow_model.lambda_}/'      \n",
    "os.mkdir(sh_path)\n",
    "\n",
    "torch.save(attack_train_data, sh_path+'attack_train_data.pt')\n",
    "torch.save(attack_train_target, sh_path+'attack_train_target.pt')\n",
    "\n",
    "at_path = sh_path+f'attack_model_aneur{attack_model.h_neurons}_ado{attack_model.do}_alr{attack_train_args.learning_rate}_alreg{attack_train_args.weight_decay}_aepoch{attack_train_args.epoch}'\n",
    "torch.save(attack_model, at_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "8ebbf2c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.59217, 0.5930014328093153, 0.5877)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_acc, train_pre, train_rec = attack_evaluation(attack_model, attack_train_data, attack_train_target)\n",
    "train_acc, train_pre, train_rec "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c6b37350",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6260389215487533, 0.5935737236701178, 0.8313)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_acc, test_pre, test_rec = attack_evaluation(attack_model, attack_test_data, attack_test_target)\n",
    "test_acc, test_pre, test_rec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41cc9ef5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# MI optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "7fe0eb0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "path = 'mia/loan/'\n",
    "params = {}\n",
    "ams = {}\n",
    "for r,d,f in os.walk(path):\n",
    "    for file in f:\n",
    "        if \".json\" in file:\n",
    "            with open(r+'/'+file) as json_file:\n",
    "                params[r+file.replace('_params.json', '')] = json.load(json_file)\n",
    "for r,d,f in os.walk(path):\n",
    "    for file in f:\n",
    "        if \".json\" not in file and '.DS_Store' not in file:\n",
    "            ams[r+file] = Net_attack(h_neurons=params[r+file]['h_neurons'], do=params[r+file]['do'], input_size=attack_train_data.shape[1])\n",
    "            ams[r+file] = torch.load(r+'/'+file)      \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "a1fa079b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mia/loan/attack_model6063\n",
      "0.6515 0.7050359712230216 0.6111518708730741 0.833\n",
      "mia/loan/attack_model6065\n",
      "0.6515 0.7050359712230216 0.6111518708730741 0.833\n",
      "mia/loan/attack_model6069\n",
      "0.6515 0.7047861075815331 0.6113152094048494 0.832\n",
      "mia/loan/attack_model6071\n",
      "0.6515 0.7045358202628232 0.6114790286975718 0.831\n"
     ]
    }
   ],
   "source": [
    "for i in params:\n",
    "    if (params[i]['test_pre']+params[i]['test_rec']) == 0 :\n",
    "        continue\n",
    "    f1 = 2*params[i]['test_pre']*params[i]['test_rec']/(params[i]['test_pre']+params[i]['test_rec'])\n",
    "    if f1>0.7 and params[i]['test_pre']>0.61 and params[i]['test_acc']>0.651 :\n",
    "        print(i)\n",
    "        print(params[i]['test_acc'], f1, params[i]['test_pre'], params[i]['test_rec'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "feb454df",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_ams = ['mia/loan/attack_model3475', 'mia/loan/attack_model6063',\n",
    "'mia/loan/attack_model6073',\n",
    "'mia/loan/attack_model6065',\n",
    "'mia/loan/attack_model6069',\n",
    "'mia/loan/attack_model6071']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "7c148f5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import shutil\n",
    "\n",
    "for i,file in enumerate(best_ams):\n",
    "    src_path = file\n",
    "    dst_path = f\"mia/loan/best_ams/attack_model{i}\"\n",
    "    shutil.copy(src_path, dst_path)\n",
    "    shutil.copy(src_path+'_params.json', dst_path+'_params.json')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "c592bfb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'mia/loan/best_ams/'\n",
    "params = {}\n",
    "best_ams = {}\n",
    "for r,d,f in os.walk(path):\n",
    "    for file in f:\n",
    "        if \".json\" in file:\n",
    "            with open(r+'/'+file) as json_file:\n",
    "                params[r+file.replace('_params.json', '')] = json.load(json_file)\n",
    "for r,d,f in os.walk(path):\n",
    "    for file in f:\n",
    "        if \".json\" not in file and '.DS_Store' not in file:\n",
    "            best_ams[r+file] = Net_attack(h_neurons=params[r+file]['h_neurons'], do=params[r+file]['do'], input_size=attack_train_data.shape[1])\n",
    "            best_ams[r+file] = torch.load(r+'/'+file)      \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "3652bf00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attack_train_data.shape[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c09221aa",
   "metadata": {},
   "source": [
    "# OutDP "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "e0cf0e68",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'loan/centr/'\n",
    "tms_params = {}\n",
    "tms = {}\n",
    "outdp = {}\n",
    "for r,d,f in os.walk(path):\n",
    "    for file in f:\n",
    "        if \".json\" in file:\n",
    "            with open(r+'/'+file) as json_file:\n",
    "                tms_params[file.replace('_params.json', '')] = json.load(json_file)\n",
    "        if \"target_model.npy\" in file:\n",
    "            tms[file.replace('.npy', '')] = np.load(path+'/'+file)\n",
    "        if '_outdp_results.csv' in file:\n",
    "            outdp[file.replace('_outdp_results.csv','')] = pd.read_csv(path+'/'+file,index_col=0)\n",
    "                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "3f334d42",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'mia/loan/best_ams/'\n",
    "aparams = {}\n",
    "ams = {}\n",
    "for r,d,f in os.walk(path):\n",
    "    for file in f:\n",
    "        if \".json\" in file:\n",
    "            with open(r+'/'+file) as json_file:\n",
    "                aparams[r+file.replace('_params.json', '')] = json.load(json_file)\n",
    "            \n",
    "for r,d,f in os.walk(path):\n",
    "    for file in f:\n",
    "        if \".json\" not in file and '.DS_Store' not in file:\n",
    "            ams[r+file] = Net_attack(h_neurons=aparams[r+file]['h_neurons'], do=aparams[r+file]['do'], input_size=14)\n",
    "            ams[r+file] = torch.load(r+'/'+file)      \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "98b5545b",
   "metadata": {},
   "outputs": [],
   "source": [
    "files_to_check = ['rs42_lr0.001_iter300_reg1e-07_target_model',\n",
    " 'rs42_lr0.001_iter300_reg1e-06_target_model',\n",
    " 'rs42_lr0.001_iter300_reg0.0001_target_model',\n",
    " 'rs42_lr0.001_iter300_reg1e-05_target_model',\n",
    " 'rs42_lr0.001_iter200_reg1e-05_target_model',\n",
    " 'rs42_lr0.001_iter200_reg1e-06_target_model',\n",
    " 'rs42_lr0.001_iter200_reg0.0001_target_model',\n",
    " 'rs42_lr0.01_iter300_reg1e-06_target_model',\n",
    " 'rs42_lr0.01_iter100_reg1e-06_target_model',\n",
    " 'rs42_lr0.01_iter200_reg1e-05_target_model',\n",
    " 'rs42_lr0.01_iter200_reg1e-06_target_model',\n",
    " 'rs42_lr0.01_iter300_reg1e-05_target_model',\n",
    " 'rs42_lr0.001_iter100_reg1e-06_target_model',\n",
    " 'rs42_lr0.001_iter100_reg0.0001_target_model',\n",
    " 'rs42_lr0.001_iter200_reg1e-07_target_model']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "eaae46d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "epsilon_list = [0.1, 0.5, 1, 5, 10, 50, 100, 500, 1000, 5000, 10000]\n",
    "#Load model and parameters\n",
    "                  \n",
    "for file in files_to_check:\n",
    "    tm_params = tms_params[file]\n",
    "\n",
    "    rand_seed = int(file[file.find('rs')+2:file.find('_lr')])\n",
    "    x_target_train, y_target_train, x_target_test, y_target_test = scripts.load_loan(rand_seed, tr_size=10000)\n",
    "\n",
    "    target_model = algo.LogisticRegression_DPSGD()\n",
    "    scripts.set_model_params(target_model, tm_params)\n",
    "    target_model.theta = tms[file]\n",
    "    target_model.train_acc = target_model.evaluate(x_target_train, y_target_train, acc=False)\n",
    "    target_model.test_acc = target_model.evaluate(x_target_test, y_target_test, acc=False)\n",
    "\n",
    "    outdp_results = {}\n",
    "    for i,target_model.outDP_local_epsilon in enumerate(epsilon_list):\n",
    "        # Output DP\n",
    "        noise_model = scripts.output_DP(target_model,  x_target_train.shape[0], target_model.outDP_local_epsilon)\n",
    "        noise_model.train_acc = noise_model.evaluate(x_target_train, y_target_train, acc=False)\n",
    "        noise_model.test_acc = noise_model.evaluate(x_target_test, y_target_test, acc=False)\n",
    "\n",
    "        attack_dict = attack.test_mi_attack(ams, noise_model, x_target_train, y_target_train, x_target_test, y_target_test)\n",
    "\n",
    "        outdp_results[i] = {}\n",
    "        outdp_results[i]['epsilon_out'] = noise_model.__dict__['epsilon_out']\n",
    "        outdp_results[i]['train_acc'] = noise_model.__dict__['train_acc']\n",
    "        outdp_results[i]['test_acc'] = noise_model.__dict__['test_acc']\n",
    "        outdp_results[i].update(attack_dict)\n",
    "\n",
    "    df = pd.DataFrame.from_dict(outdp_results, orient='index')\n",
    "    df.to_csv('loan/centr/'+file+'_outdp_results.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "70a323ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for mod in outdp:\n",
    "    plt.figure()\n",
    "    plt.title(mod)\n",
    "    plt.plot(outdp[mod]['epsilon_out'], outdp[mod]['test_acc'], label='test_acc')\n",
    "    plt.plot(outdp[mod]['epsilon_out'], outdp[mod]['attack_acc_mean'], label='attack_acc')\n",
    "    plt.xscale('log')\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45ef219d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "d10652c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "import algo\n",
    "import scripts\n",
    "# import attack\n",
    "\n",
    "import torch\n",
    "import os\n",
    "import json\n",
    "\n",
    "rand_seed=42\n",
    "np.random.seed(rand_seed)\n",
    "torch.manual_seed(rand_seed)\n",
    "\n",
    "dataset = 'loan'\n",
    "# x_target_train, y_target_train, x_target_test, y_target_test = scripts.load_purchase(rand_seed)\n",
    "x_target_train, y_target_train, x_target_test, y_target_test = scripts.load_loan(rand_seed, tr_size=10000)\n",
    "#x_target_train, y_target_train, x_target_test, y_target_test = scripts.load_texas()\n",
    "tr_size = 1000\n",
    "x_target_train = x_target_train[:tr_size]\n",
    "y_target_train = y_target_train[:tr_size]\n",
    "x_target_test = x_target_test[:tr_size]\n",
    "y_target_test = y_target_test[:tr_size]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "368da0c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start training\n",
      "c: 1; lam: 1e-06\n",
      "DP-SGD with sampling rate = 2% and noise_multiplier = 0.2176172972207593 iterated over 15000 steps satisfies differential privacy with eps = 1e+04 and delta = 1e-05.\n",
      "The accuracy of the model : 65.60000000000001 %\n",
      "The accuracy of the model : 45.800000000000004 %\n",
      "Start training\n",
      "c: 1.5; lam: 1e-06\n",
      "DP-SGD with sampling rate = 2% and noise_multiplier = 0.2176172972207593 iterated over 15000 steps satisfies differential privacy with eps = 1e+04 and delta = 1e-05.\n",
      "The accuracy of the model : 69.19999999999999 %\n",
      "The accuracy of the model : 47.699999999999996 %\n",
      "Start training\n",
      "c: 2; lam: 1e-06\n",
      "DP-SGD with sampling rate = 2% and noise_multiplier = 0.2176172972207593 iterated over 15000 steps satisfies differential privacy with eps = 1e+04 and delta = 1e-05.\n",
      "The accuracy of the model : 71.2 %\n",
      "The accuracy of the model : 49.3 %\n",
      "Start training\n",
      "c: 3; lam: 1e-06\n",
      "DP-SGD with sampling rate = 2% and noise_multiplier = 0.2176172972207593 iterated over 15000 steps satisfies differential privacy with eps = 1e+04 and delta = 1e-05.\n",
      "The accuracy of the model : 76.8 %\n",
      "The accuracy of the model : 52.1 %\n",
      "Start training\n",
      "c: 4; lam: 1e-06\n",
      "DP-SGD with sampling rate = 2% and noise_multiplier = 0.2176172972207593 iterated over 15000 steps satisfies differential privacy with eps = 1e+04 and delta = 1e-05.\n",
      "The accuracy of the model : 80.30000000000001 %\n",
      "The accuracy of the model : 55.2 %\n",
      "Start training\n",
      "c: 5; lam: 1e-06\n",
      "DP-SGD with sampling rate = 2% and noise_multiplier = 0.2176172972207593 iterated over 15000 steps satisfies differential privacy with eps = 1e+04 and delta = 1e-05.\n",
      "The accuracy of the model : 82.5 %\n",
      "The accuracy of the model : 55.800000000000004 %\n",
      "Start training\n",
      "c: 6; lam: 1e-06\n",
      "DP-SGD with sampling rate = 2% and noise_multiplier = 0.2176172972207593 iterated over 15000 steps satisfies differential privacy with eps = 1e+04 and delta = 1e-05.\n",
      "The accuracy of the model : 85.1 %\n",
      "The accuracy of the model : 59.099999999999994 %\n",
      "Start training\n",
      "c: 7; lam: 1e-06\n",
      "DP-SGD with sampling rate = 2% and noise_multiplier = 0.2176172972207593 iterated over 15000 steps satisfies differential privacy with eps = 1e+04 and delta = 1e-05.\n",
      "The accuracy of the model : 86.5 %\n",
      "The accuracy of the model : 58.599999999999994 %\n",
      "Start training\n",
      "c: 8; lam: 1e-06\n",
      "DP-SGD with sampling rate = 2% and noise_multiplier = 0.2176172972207593 iterated over 15000 steps satisfies differential privacy with eps = 1e+04 and delta = 1e-05.\n",
      "The accuracy of the model : 86.8 %\n",
      "The accuracy of the model : 59.0 %\n",
      "Start training\n",
      "c: 9; lam: 1e-06\n",
      "DP-SGD with sampling rate = 2% and noise_multiplier = 0.2176172972207593 iterated over 15000 steps satisfies differential privacy with eps = 1e+04 and delta = 1e-05.\n",
      "The accuracy of the model : 87.8 %\n",
      "The accuracy of the model : 59.5 %\n",
      "Start training\n",
      "c: 10; lam: 1e-06\n",
      "DP-SGD with sampling rate = 2% and noise_multiplier = 0.2176172972207593 iterated over 15000 steps satisfies differential privacy with eps = 1e+04 and delta = 1e-05.\n",
      "The accuracy of the model : 86.6 %\n",
      "The accuracy of the model : 59.3 %\n",
      "Start training\n",
      "c: 1; lam: 0.0001\n",
      "DP-SGD with sampling rate = 2% and noise_multiplier = 0.2176172972207593 iterated over 15000 steps satisfies differential privacy with eps = 1e+04 and delta = 1e-05.\n",
      "The accuracy of the model : 65.2 %\n",
      "The accuracy of the model : 45.2 %\n",
      "Start training\n",
      "c: 1.5; lam: 0.0001\n",
      "DP-SGD with sampling rate = 2% and noise_multiplier = 0.2176172972207593 iterated over 15000 steps satisfies differential privacy with eps = 1e+04 and delta = 1e-05.\n",
      "The accuracy of the model : 68.5 %\n",
      "The accuracy of the model : 47.3 %\n",
      "Start training\n",
      "c: 2; lam: 0.0001\n",
      "DP-SGD with sampling rate = 2% and noise_multiplier = 0.2176172972207593 iterated over 15000 steps satisfies differential privacy with eps = 1e+04 and delta = 1e-05.\n",
      "The accuracy of the model : 71.89999999999999 %\n",
      "The accuracy of the model : 50.3 %\n",
      "Start training\n",
      "c: 3; lam: 0.0001\n",
      "DP-SGD with sampling rate = 2% and noise_multiplier = 0.2176172972207593 iterated over 15000 steps satisfies differential privacy with eps = 1e+04 and delta = 1e-05.\n",
      "The accuracy of the model : 76.6 %\n",
      "The accuracy of the model : 52.7 %\n",
      "Start training\n",
      "c: 4; lam: 0.0001\n",
      "DP-SGD with sampling rate = 2% and noise_multiplier = 0.2176172972207593 iterated over 15000 steps satisfies differential privacy with eps = 1e+04 and delta = 1e-05.\n",
      "The accuracy of the model : 80.2 %\n",
      "The accuracy of the model : 55.400000000000006 %\n",
      "Start training\n",
      "c: 5; lam: 0.0001\n",
      "DP-SGD with sampling rate = 2% and noise_multiplier = 0.2176172972207593 iterated over 15000 steps satisfies differential privacy with eps = 1e+04 and delta = 1e-05.\n",
      "The accuracy of the model : 82.5 %\n",
      "The accuracy of the model : 56.599999999999994 %\n",
      "Start training\n",
      "c: 6; lam: 0.0001\n",
      "DP-SGD with sampling rate = 2% and noise_multiplier = 0.2176172972207593 iterated over 15000 steps satisfies differential privacy with eps = 1e+04 and delta = 1e-05.\n",
      "The accuracy of the model : 84.5 %\n",
      "The accuracy of the model : 59.099999999999994 %\n",
      "Start training\n",
      "c: 7; lam: 0.0001\n",
      "DP-SGD with sampling rate = 2% and noise_multiplier = 0.2176172972207593 iterated over 15000 steps satisfies differential privacy with eps = 1e+04 and delta = 1e-05.\n",
      "The accuracy of the model : 87.0 %\n",
      "The accuracy of the model : 59.199999999999996 %\n",
      "Start training\n",
      "c: 8; lam: 0.0001\n",
      "DP-SGD with sampling rate = 2% and noise_multiplier = 0.2176172972207593 iterated over 15000 steps satisfies differential privacy with eps = 1e+04 and delta = 1e-05.\n",
      "The accuracy of the model : 87.1 %\n",
      "The accuracy of the model : 60.199999999999996 %\n",
      "Start training\n",
      "c: 9; lam: 0.0001\n",
      "DP-SGD with sampling rate = 2% and noise_multiplier = 0.2176172972207593 iterated over 15000 steps satisfies differential privacy with eps = 1e+04 and delta = 1e-05.\n",
      "The accuracy of the model : 87.8 %\n",
      "The accuracy of the model : 59.099999999999994 %\n",
      "Start training\n",
      "c: 10; lam: 0.0001\n",
      "DP-SGD with sampling rate = 2% and noise_multiplier = 0.2176172972207593 iterated over 15000 steps satisfies differential privacy with eps = 1e+04 and delta = 1e-05.\n",
      "The accuracy of the model : 87.0 %\n",
      "The accuracy of the model : 59.9 %\n"
     ]
    }
   ],
   "source": [
    "res = {}\n",
    "for lam in [1e-6, 1e-4]:\n",
    "\n",
    "    np.random.seed(rand_seed)\n",
    "    torch.manual_seed(rand_seed)\n",
    "    \n",
    "    #for epsilon in [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,5,10,30,50,70,100]:\n",
    "    \n",
    "    #for C in [1,1.5,2,3,4,5]:\n",
    "    for C in [1, 1.5, 2, 3, 4, 5, 6, 7, 8, 9, 10]:\n",
    "        model = algo.LogisticRegression_DPSGD()\n",
    "\n",
    "        model.n_classes      = len(np.unique(y_target_train))\n",
    "        model.alpha          = 0.01\n",
    "        model.max_iter       = 300\n",
    "        model.lambda_        = lam\n",
    "        model.tolerance      = 1e-5\n",
    "        model.sgdDP          = True\n",
    "        model.L              = 20 #should be 1 if DP == False\n",
    "        model.epsilon        = 10000\n",
    "        model.C              = C\n",
    "        model.outDP_local          = False\n",
    "        model.outDP_local_epsilon  = 1\n",
    "\n",
    "        params = dict(model.__dict__) #save model's parameters to json file later\n",
    "\n",
    "        print(\"Start training\")\n",
    "        print(f'c: {C}; lam: {lam}')\n",
    "        tm_path = f'small_loan/c{C}_lam{lam}'\n",
    "        X,y = model.init_theta(x_target_train, y_target_train)\n",
    "        model.train(X,y)\n",
    "        params['train_acc'] = model.evaluate(x_target_train, y_target_train, acc=True)\n",
    "        params['test_acc'] = model.evaluate(x_target_test, y_target_test, acc=True)\n",
    "\n",
    "        if model.outDP_local:\n",
    "            noise_model = scripts.output_DP(model,  x_target_train.shape[0], model.outDP_local_epsilon)\n",
    "            params['train_acc_output_dp'] = noise_model.evaluate(x_target_train, y_target_train, acc=True)\n",
    "            params['test_acc_output_dp'] = noise_model.evaluate(x_target_test, y_target_test, acc=True)\n",
    "        \n",
    "        attack_dict = attack.test_mi_attack(ams, model, x_target_train, y_target_train, x_target_test, y_target_test)\n",
    "        params.update(attack_dict)\n",
    "    \n",
    "        np.save(tm_path+'_target_model', model.theta)\n",
    "        with open(tm_path+'_target_model_params.json', 'w') as file:\n",
    "            json.dump(params, file)\n",
    "\n",
    "           \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "aa554d1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'small_loan/'\n",
    "params = {}\n",
    "results = {}\n",
    "models = {}\n",
    "for r,d,f in os.walk(path):\n",
    "    for file in f:\n",
    "        if \"params.json\" in file:\n",
    "            with open(r+'/'+file) as json_file:\n",
    "                params[file] = json.load(json_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "0702cddc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'c3_lam0.0001_target_model_params.json': {'n_classes': 7,\n",
       "  'alpha': 0.01,\n",
       "  'max_iter': 300,\n",
       "  'lambda_': 0.0001,\n",
       "  'tolerance': 1e-05,\n",
       "  'sgdDP': True,\n",
       "  'L': 20,\n",
       "  'C': 3,\n",
       "  'epsilon': 10000,\n",
       "  'delta': 1e-05,\n",
       "  'sigma': 0,\n",
       "  'outDP_local': False,\n",
       "  'outDP_local_epsilon': 1,\n",
       "  'train_acc': 0.766,\n",
       "  'test_acc': 0.527,\n",
       "  'attack_acc_mean': 0.6269166666666667,\n",
       "  'attack_acc_std': 0.0022806553639006756,\n",
       "  'attack_pre_mean': 0.6035853962110685,\n",
       "  'attack_pre_std': 0.01342545166715442,\n",
       "  'attack_rec_mean': 0.7468333333333333,\n",
       "  'attack_rec_std': 0.054038309456244916},\n",
       " 'c8_lam0.0001_target_model_params.json': {'n_classes': 7,\n",
       "  'alpha': 0.01,\n",
       "  'max_iter': 300,\n",
       "  'lambda_': 0.0001,\n",
       "  'tolerance': 1e-05,\n",
       "  'sgdDP': True,\n",
       "  'L': 20,\n",
       "  'C': 8,\n",
       "  'epsilon': 10000,\n",
       "  'delta': 1e-05,\n",
       "  'sigma': 0,\n",
       "  'outDP_local': False,\n",
       "  'outDP_local_epsilon': 1,\n",
       "  'train_acc': 0.871,\n",
       "  'test_acc': 0.602,\n",
       "  'attack_acc_mean': 0.6410833333333333,\n",
       "  'attack_acc_std': 0.007446568934005015,\n",
       "  'attack_pre_mean': 0.6027008076562118,\n",
       "  'attack_pre_std': 0.007473937557817051,\n",
       "  'attack_rec_mean': 0.8334999999999999,\n",
       "  'attack_rec_std': 0.08385254915624209},\n",
       " 'c2_lam1e-06_target_model_params.json': {'n_classes': 7,\n",
       "  'alpha': 0.01,\n",
       "  'max_iter': 300,\n",
       "  'lambda_': 1e-06,\n",
       "  'tolerance': 1e-05,\n",
       "  'sgdDP': True,\n",
       "  'L': 20,\n",
       "  'C': 2,\n",
       "  'epsilon': 10000,\n",
       "  'delta': 1e-05,\n",
       "  'sigma': 0,\n",
       "  'outDP_local': False,\n",
       "  'outDP_local_epsilon': 1,\n",
       "  'train_acc': 0.712,\n",
       "  'test_acc': 0.493,\n",
       "  'attack_acc_mean': 0.619,\n",
       "  'attack_acc_std': 0.0018484227510682277,\n",
       "  'attack_pre_mean': 0.6051696619914181,\n",
       "  'attack_pre_std': 0.01187037801694791,\n",
       "  'attack_rec_mean': 0.69,\n",
       "  'attack_rec_std': 0.044721359549995794},\n",
       " 'c5_lam0.0001_target_model_params.json': {'n_classes': 7,\n",
       "  'alpha': 0.01,\n",
       "  'max_iter': 300,\n",
       "  'lambda_': 0.0001,\n",
       "  'tolerance': 1e-05,\n",
       "  'sgdDP': True,\n",
       "  'L': 20,\n",
       "  'C': 5,\n",
       "  'epsilon': 10000,\n",
       "  'delta': 1e-05,\n",
       "  'sigma': 0,\n",
       "  'outDP_local': False,\n",
       "  'outDP_local_epsilon': 1,\n",
       "  'train_acc': 0.825,\n",
       "  'test_acc': 0.566,\n",
       "  'attack_acc_mean': 0.6366666666666666,\n",
       "  'attack_acc_std': 0.0029814239699997224,\n",
       "  'attack_pre_mean': 0.6040076335877863,\n",
       "  'attack_pre_std': 0.008961341131201441,\n",
       "  'attack_rec_mean': 0.7989999999999999,\n",
       "  'attack_rec_std': 0.06484597134749388},\n",
       " 'c10_lam0.0001_target_model_params.json': {'n_classes': 7,\n",
       "  'alpha': 0.01,\n",
       "  'max_iter': 300,\n",
       "  'lambda_': 0.0001,\n",
       "  'tolerance': 1e-05,\n",
       "  'sgdDP': True,\n",
       "  'L': 20,\n",
       "  'C': 10,\n",
       "  'epsilon': 10000,\n",
       "  'delta': 1e-05,\n",
       "  'sigma': 0,\n",
       "  'outDP_local': False,\n",
       "  'outDP_local_epsilon': 1,\n",
       "  'train_acc': 0.87,\n",
       "  'test_acc': 0.599,\n",
       "  'attack_acc_mean': 0.6373333333333333,\n",
       "  'attack_acc_std': 0.006039223643997812,\n",
       "  'attack_pre_mean': 0.6002919949722899,\n",
       "  'attack_pre_std': 0.007505424171751087,\n",
       "  'attack_rec_mean': 0.8273333333333334,\n",
       "  'attack_rec_std': 0.0775170232199927},\n",
       " 'c1.5_lam1e-06_target_model_params.json': {'n_classes': 7,\n",
       "  'alpha': 0.01,\n",
       "  'max_iter': 300,\n",
       "  'lambda_': 1e-06,\n",
       "  'tolerance': 1e-05,\n",
       "  'sgdDP': True,\n",
       "  'L': 20,\n",
       "  'C': 1.5,\n",
       "  'epsilon': 10000,\n",
       "  'delta': 1e-05,\n",
       "  'sigma': 0,\n",
       "  'outDP_local': False,\n",
       "  'outDP_local_epsilon': 1,\n",
       "  'train_acc': 0.692,\n",
       "  'test_acc': 0.477,\n",
       "  'attack_acc_mean': 0.6133333333333334,\n",
       "  'attack_acc_std': 0.002867441755680878,\n",
       "  'attack_pre_mean': 0.6053115834030883,\n",
       "  'attack_pre_std': 0.007915330747614911,\n",
       "  'attack_rec_mean': 0.655,\n",
       "  'attack_rec_std': 0.05053711507397311},\n",
       " 'c2_lam0.0001_target_model_params.json': {'n_classes': 7,\n",
       "  'alpha': 0.01,\n",
       "  'max_iter': 300,\n",
       "  'lambda_': 0.0001,\n",
       "  'tolerance': 1e-05,\n",
       "  'sgdDP': True,\n",
       "  'L': 20,\n",
       "  'C': 2,\n",
       "  'epsilon': 10000,\n",
       "  'delta': 1e-05,\n",
       "  'sigma': 0,\n",
       "  'outDP_local': False,\n",
       "  'outDP_local_epsilon': 1,\n",
       "  'train_acc': 0.719,\n",
       "  'test_acc': 0.503,\n",
       "  'attack_acc_mean': 0.62075,\n",
       "  'attack_acc_std': 0.0006291528696059147,\n",
       "  'attack_pre_mean': 0.605450183817969,\n",
       "  'attack_pre_std': 0.010933864119749214,\n",
       "  'attack_rec_mean': 0.6986666666666667,\n",
       "  'attack_rec_std': 0.0521749194749951},\n",
       " 'c3_lam1e-06_target_model_params.json': {'n_classes': 7,\n",
       "  'alpha': 0.01,\n",
       "  'max_iter': 300,\n",
       "  'lambda_': 1e-06,\n",
       "  'tolerance': 1e-05,\n",
       "  'sgdDP': True,\n",
       "  'L': 20,\n",
       "  'C': 3,\n",
       "  'epsilon': 10000,\n",
       "  'delta': 1e-05,\n",
       "  'sigma': 0,\n",
       "  'outDP_local': False,\n",
       "  'outDP_local_epsilon': 1,\n",
       "  'train_acc': 0.768,\n",
       "  'test_acc': 0.521,\n",
       "  'attack_acc_mean': 0.6315833333333334,\n",
       "  'attack_acc_std': 0.002008661798865665,\n",
       "  'attack_pre_mean': 0.6080808264514029,\n",
       "  'attack_pre_std': 0.013606831672853887,\n",
       "  'attack_rec_mean': 0.7473333333333333,\n",
       "  'attack_rec_std': 0.05381656085464979},\n",
       " 'c9_lam0.0001_target_model_params.json': {'n_classes': 7,\n",
       "  'alpha': 0.01,\n",
       "  'max_iter': 300,\n",
       "  'lambda_': 0.0001,\n",
       "  'tolerance': 1e-05,\n",
       "  'sgdDP': True,\n",
       "  'L': 20,\n",
       "  'C': 9,\n",
       "  'epsilon': 10000,\n",
       "  'delta': 1e-05,\n",
       "  'sigma': 0,\n",
       "  'outDP_local': False,\n",
       "  'outDP_local_epsilon': 1,\n",
       "  'train_acc': 0.878,\n",
       "  'test_acc': 0.591,\n",
       "  'attack_acc_mean': 0.6458333333333334,\n",
       "  'attack_acc_std': 0.0077602978178819,\n",
       "  'attack_pre_mean': 0.6067732679100352,\n",
       "  'attack_pre_std': 0.0061110888497393585,\n",
       "  'attack_rec_mean': 0.8326666666666666,\n",
       "  'attack_rec_std': 0.07632969423639938},\n",
       " 'c1_lam1e-06_target_model_params.json': {'n_classes': 7,\n",
       "  'alpha': 0.01,\n",
       "  'max_iter': 300,\n",
       "  'lambda_': 1e-06,\n",
       "  'tolerance': 1e-05,\n",
       "  'sgdDP': True,\n",
       "  'L': 20,\n",
       "  'C': 1,\n",
       "  'epsilon': 10000,\n",
       "  'delta': 1e-05,\n",
       "  'sigma': 0,\n",
       "  'outDP_local': False,\n",
       "  'outDP_local_epsilon': 1,\n",
       "  'train_acc': 0.656,\n",
       "  'test_acc': 0.458,\n",
       "  'attack_acc_mean': 0.6040833333333334,\n",
       "  'attack_acc_std': 0.005876625070754669,\n",
       "  'attack_pre_mean': 0.6041008499031116,\n",
       "  'attack_pre_std': 0.006049612012966222,\n",
       "  'attack_rec_mean': 0.607,\n",
       "  'attack_rec_std': 0.057691709398607134},\n",
       " 'c4_lam0.0001_target_model_params.json': {'n_classes': 7,\n",
       "  'alpha': 0.01,\n",
       "  'max_iter': 300,\n",
       "  'lambda_': 0.0001,\n",
       "  'tolerance': 1e-05,\n",
       "  'sgdDP': True,\n",
       "  'L': 20,\n",
       "  'C': 4,\n",
       "  'epsilon': 10000,\n",
       "  'delta': 1e-05,\n",
       "  'sigma': 0,\n",
       "  'outDP_local': False,\n",
       "  'outDP_local_epsilon': 1,\n",
       "  'train_acc': 0.802,\n",
       "  'test_acc': 0.554,\n",
       "  'attack_acc_mean': 0.63025,\n",
       "  'attack_acc_std': 0.00024999999999997247,\n",
       "  'attack_pre_mean': 0.6031624764999765,\n",
       "  'attack_pre_std': 0.011253662481973561,\n",
       "  'attack_rec_mean': 0.7678333333333334,\n",
       "  'attack_rec_std': 0.05762064637687511},\n",
       " 'c7_lam1e-06_target_model_params.json': {'n_classes': 7,\n",
       "  'alpha': 0.01,\n",
       "  'max_iter': 300,\n",
       "  'lambda_': 1e-06,\n",
       "  'tolerance': 1e-05,\n",
       "  'sgdDP': True,\n",
       "  'L': 20,\n",
       "  'C': 7,\n",
       "  'epsilon': 10000,\n",
       "  'delta': 1e-05,\n",
       "  'sigma': 0,\n",
       "  'outDP_local': False,\n",
       "  'outDP_local_epsilon': 1,\n",
       "  'train_acc': 0.865,\n",
       "  'test_acc': 0.586,\n",
       "  'attack_acc_mean': 0.6401666666666667,\n",
       "  'attack_acc_std': 0.005906681715556446,\n",
       "  'attack_pre_mean': 0.6026779586196008,\n",
       "  'attack_pre_std': 0.008595123774852808,\n",
       "  'attack_rec_mean': 0.8290000000000001,\n",
       "  'attack_rec_std': 0.08094648437908425},\n",
       " 'c1.5_lam0.0001_target_model_params.json': {'n_classes': 7,\n",
       "  'alpha': 0.01,\n",
       "  'max_iter': 300,\n",
       "  'lambda_': 0.0001,\n",
       "  'tolerance': 1e-05,\n",
       "  'sgdDP': True,\n",
       "  'L': 20,\n",
       "  'C': 1.5,\n",
       "  'epsilon': 10000,\n",
       "  'delta': 1e-05,\n",
       "  'sigma': 0,\n",
       "  'outDP_local': False,\n",
       "  'outDP_local_epsilon': 1,\n",
       "  'train_acc': 0.685,\n",
       "  'test_acc': 0.473,\n",
       "  'attack_acc_mean': 0.6166666666666667,\n",
       "  'attack_acc_std': 0.003659083066683354,\n",
       "  'attack_pre_mean': 0.6078858593845718,\n",
       "  'attack_pre_std': 0.007851176352748068,\n",
       "  'attack_rec_mean': 0.661,\n",
       "  'attack_rec_std': 0.05366563145999496},\n",
       " 'c1_lam0.0001_target_model_params.json': {'n_classes': 7,\n",
       "  'alpha': 0.01,\n",
       "  'max_iter': 300,\n",
       "  'lambda_': 0.0001,\n",
       "  'tolerance': 1e-05,\n",
       "  'sgdDP': True,\n",
       "  'L': 20,\n",
       "  'C': 1,\n",
       "  'epsilon': 10000,\n",
       "  'delta': 1e-05,\n",
       "  'sigma': 0,\n",
       "  'outDP_local': False,\n",
       "  'outDP_local_epsilon': 1,\n",
       "  'train_acc': 0.652,\n",
       "  'test_acc': 0.452,\n",
       "  'attack_acc_mean': 0.6042500000000001,\n",
       "  'attack_acc_std': 0.005039427877580302,\n",
       "  'attack_pre_mean': 0.6046337141787989,\n",
       "  'attack_pre_std': 0.006262389964717866,\n",
       "  'attack_rec_mean': 0.6053333333333334,\n",
       "  'attack_rec_std': 0.05381656085464978},\n",
       " 'c7_lam0.0001_target_model_params.json': {'n_classes': 7,\n",
       "  'alpha': 0.01,\n",
       "  'max_iter': 300,\n",
       "  'lambda_': 0.0001,\n",
       "  'tolerance': 1e-05,\n",
       "  'sgdDP': True,\n",
       "  'L': 20,\n",
       "  'C': 7,\n",
       "  'epsilon': 10000,\n",
       "  'delta': 1e-05,\n",
       "  'sigma': 0,\n",
       "  'outDP_local': False,\n",
       "  'outDP_local_epsilon': 1,\n",
       "  'train_acc': 0.87,\n",
       "  'test_acc': 0.592,\n",
       "  'attack_acc_mean': 0.6371666666666668,\n",
       "  'attack_acc_std': 0.0012133516482134333,\n",
       "  'attack_pre_mean': 0.6009594681330195,\n",
       "  'attack_pre_std': 0.01154225382245346,\n",
       "  'attack_rec_mean': 0.8245,\n",
       "  'attack_rec_std': 0.07133197039196379},\n",
       " 'c10_lam1e-06_target_model_params.json': {'n_classes': 7,\n",
       "  'alpha': 0.01,\n",
       "  'max_iter': 300,\n",
       "  'lambda_': 1e-06,\n",
       "  'tolerance': 1e-05,\n",
       "  'sgdDP': True,\n",
       "  'L': 20,\n",
       "  'C': 10,\n",
       "  'epsilon': 10000,\n",
       "  'delta': 1e-05,\n",
       "  'sigma': 0,\n",
       "  'outDP_local': False,\n",
       "  'outDP_local_epsilon': 1,\n",
       "  'train_acc': 0.866,\n",
       "  'test_acc': 0.593,\n",
       "  'attack_acc_mean': 0.6336666666666666,\n",
       "  'attack_acc_std': 0.003448026810929519,\n",
       "  'attack_pre_mean': 0.5981370351572036,\n",
       "  'attack_pre_std': 0.00897900254304717,\n",
       "  'attack_rec_mean': 0.8209999999999998,\n",
       "  'attack_rec_std': 0.07244998274671981},\n",
       " 'c5_lam1e-06_target_model_params.json': {'n_classes': 7,\n",
       "  'alpha': 0.01,\n",
       "  'max_iter': 300,\n",
       "  'lambda_': 1e-06,\n",
       "  'tolerance': 1e-05,\n",
       "  'sgdDP': True,\n",
       "  'L': 20,\n",
       "  'C': 5,\n",
       "  'epsilon': 10000,\n",
       "  'delta': 1e-05,\n",
       "  'sigma': 0,\n",
       "  'outDP_local': False,\n",
       "  'outDP_local_epsilon': 1,\n",
       "  'train_acc': 0.825,\n",
       "  'test_acc': 0.558,\n",
       "  'attack_acc_mean': 0.6361666666666665,\n",
       "  'attack_acc_std': 0.0009860132971832578,\n",
       "  'attack_pre_mean': 0.6039649312394945,\n",
       "  'attack_pre_std': 0.010362392369673432,\n",
       "  'attack_rec_mean': 0.797,\n",
       "  'attack_rec_std': 0.060822145089871084},\n",
       " 'c8_lam1e-06_target_model_params.json': {'n_classes': 7,\n",
       "  'alpha': 0.01,\n",
       "  'max_iter': 300,\n",
       "  'lambda_': 1e-06,\n",
       "  'tolerance': 1e-05,\n",
       "  'sgdDP': True,\n",
       "  'L': 20,\n",
       "  'C': 8,\n",
       "  'epsilon': 10000,\n",
       "  'delta': 1e-05,\n",
       "  'sigma': 0,\n",
       "  'outDP_local': False,\n",
       "  'outDP_local_epsilon': 1,\n",
       "  'train_acc': 0.868,\n",
       "  'test_acc': 0.59,\n",
       "  'attack_acc_mean': 0.6400833333333333,\n",
       "  'attack_acc_std': 0.004961658548868056,\n",
       "  'attack_pre_mean': 0.6031287664104454,\n",
       "  'attack_pre_std': 0.008929602692097123,\n",
       "  'attack_rec_mean': 0.8255,\n",
       "  'attack_rec_std': 0.07714434522374272},\n",
       " 'c6_lam1e-06_target_model_params.json': {'n_classes': 7,\n",
       "  'alpha': 0.01,\n",
       "  'max_iter': 300,\n",
       "  'lambda_': 1e-06,\n",
       "  'tolerance': 1e-05,\n",
       "  'sgdDP': True,\n",
       "  'L': 20,\n",
       "  'C': 6,\n",
       "  'epsilon': 10000,\n",
       "  'delta': 1e-05,\n",
       "  'sigma': 0,\n",
       "  'outDP_local': False,\n",
       "  'outDP_local_epsilon': 1,\n",
       "  'train_acc': 0.851,\n",
       "  'test_acc': 0.591,\n",
       "  'attack_acc_mean': 0.6318333333333332,\n",
       "  'attack_acc_std': 0.0014907119849998612,\n",
       "  'attack_pre_mean': 0.5980607772056689,\n",
       "  'attack_pre_std': 0.0100044939226399,\n",
       "  'attack_rec_mean': 0.8106666666666666,\n",
       "  'attack_rec_std': 0.0664872585955802},\n",
       " 'c6_lam0.0001_target_model_params.json': {'n_classes': 7,\n",
       "  'alpha': 0.01,\n",
       "  'max_iter': 300,\n",
       "  'lambda_': 0.0001,\n",
       "  'tolerance': 1e-05,\n",
       "  'sgdDP': True,\n",
       "  'L': 20,\n",
       "  'C': 6,\n",
       "  'epsilon': 10000,\n",
       "  'delta': 1e-05,\n",
       "  'sigma': 0,\n",
       "  'outDP_local': False,\n",
       "  'outDP_local_epsilon': 1,\n",
       "  'train_acc': 0.845,\n",
       "  'test_acc': 0.591,\n",
       "  'attack_acc_mean': 0.6349166666666667,\n",
       "  'attack_acc_std': 0.0006066758241066964,\n",
       "  'attack_pre_mean': 0.6002742586943915,\n",
       "  'attack_pre_std': 0.011645123530616445,\n",
       "  'attack_rec_mean': 0.8153333333333332,\n",
       "  'attack_rec_std': 0.06633668333249375},\n",
       " 'c4_lam1e-06_target_model_params.json': {'n_classes': 7,\n",
       "  'alpha': 0.01,\n",
       "  'max_iter': 300,\n",
       "  'lambda_': 1e-06,\n",
       "  'tolerance': 1e-05,\n",
       "  'sgdDP': True,\n",
       "  'L': 20,\n",
       "  'C': 4,\n",
       "  'epsilon': 10000,\n",
       "  'delta': 1e-05,\n",
       "  'sigma': 0,\n",
       "  'outDP_local': False,\n",
       "  'outDP_local_epsilon': 1,\n",
       "  'train_acc': 0.803,\n",
       "  'test_acc': 0.552,\n",
       "  'attack_acc_mean': 0.6330833333333333,\n",
       "  'attack_acc_std': 0.002206367049145626,\n",
       "  'attack_pre_mean': 0.6047041846635534,\n",
       "  'attack_pre_std': 0.013327947178044822,\n",
       "  'attack_rec_mean': 0.7759999999999999,\n",
       "  'attack_rec_std': 0.05590766196745011},\n",
       " 'c9_lam1e-06_target_model_params.json': {'n_classes': 7,\n",
       "  'alpha': 0.01,\n",
       "  'max_iter': 300,\n",
       "  'lambda_': 1e-06,\n",
       "  'tolerance': 1e-05,\n",
       "  'sgdDP': True,\n",
       "  'L': 20,\n",
       "  'C': 9,\n",
       "  'epsilon': 10000,\n",
       "  'delta': 1e-05,\n",
       "  'sigma': 0,\n",
       "  'outDP_local': False,\n",
       "  'outDP_local_epsilon': 1,\n",
       "  'train_acc': 0.878,\n",
       "  'test_acc': 0.595,\n",
       "  'attack_acc_mean': 0.6400833333333333,\n",
       "  'attack_acc_std': 0.0061469278686366955,\n",
       "  'attack_pre_mean': 0.6016779581723138,\n",
       "  'attack_pre_std': 0.007005589850999155,\n",
       "  'attack_rec_mean': 0.8336666666666667,\n",
       "  'attack_rec_std': 0.07498370193285707}}"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "4e5a3481",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22, 21)"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame.from_dict(params, orient='index')\n",
    "# df = df.loc[df['max_iter']==100].loc[df['n_classes']==100].loc[df['alpha']==0.001].loc[df['lambda_']==0.00001].loc[df['tolerance']==0.00001].loc[df['C']==1].loc[df['delta']==0.00001]        \n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "f61996c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_classes</th>\n",
       "      <th>alpha</th>\n",
       "      <th>max_iter</th>\n",
       "      <th>lambda_</th>\n",
       "      <th>tolerance</th>\n",
       "      <th>sgdDP</th>\n",
       "      <th>L</th>\n",
       "      <th>C</th>\n",
       "      <th>epsilon</th>\n",
       "      <th>delta</th>\n",
       "      <th>...</th>\n",
       "      <th>outDP_local</th>\n",
       "      <th>outDP_local_epsilon</th>\n",
       "      <th>train_acc</th>\n",
       "      <th>test_acc</th>\n",
       "      <th>attack_acc_mean</th>\n",
       "      <th>attack_acc_std</th>\n",
       "      <th>attack_pre_mean</th>\n",
       "      <th>attack_pre_std</th>\n",
       "      <th>attack_rec_mean</th>\n",
       "      <th>attack_rec_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c3_lam0.0001_target_model_params.json</th>\n",
       "      <td>7</td>\n",
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       "      <td>...</td>\n",
       "      <td>False</td>\n",
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       "      <td>0.527</td>\n",
       "      <td>0.626917</td>\n",
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       "      <td>0.054038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c8_lam0.0001_target_model_params.json</th>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>300</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>True</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.00001</td>\n",
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       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.871</td>\n",
       "      <td>0.602</td>\n",
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       "      <td>0.833500</td>\n",
       "      <td>0.083853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c2_lam1e-06_target_model_params.json</th>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>300</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.712</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.619000</td>\n",
       "      <td>0.001848</td>\n",
       "      <td>0.605170</td>\n",
       "      <td>0.011870</td>\n",
       "      <td>0.690000</td>\n",
       "      <td>0.044721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c5_lam0.0001_target_model_params.json</th>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>300</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.636667</td>\n",
       "      <td>0.002981</td>\n",
       "      <td>0.604008</td>\n",
       "      <td>0.008961</td>\n",
       "      <td>0.799000</td>\n",
       "      <td>0.064846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c10_lam0.0001_target_model_params.json</th>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>300</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.599</td>\n",
       "      <td>0.637333</td>\n",
       "      <td>0.006039</td>\n",
       "      <td>0.600292</td>\n",
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       "      <td>0.827333</td>\n",
       "      <td>0.077517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c1.5_lam1e-06_target_model_params.json</th>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>300</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>1.5</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.692</td>\n",
       "      <td>0.477</td>\n",
       "      <td>0.613333</td>\n",
       "      <td>0.002867</td>\n",
       "      <td>0.605312</td>\n",
       "      <td>0.007915</td>\n",
       "      <td>0.655000</td>\n",
       "      <td>0.050537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c2_lam0.0001_target_model_params.json</th>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>300</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.503</td>\n",
       "      <td>0.620750</td>\n",
       "      <td>0.000629</td>\n",
       "      <td>0.605450</td>\n",
       "      <td>0.010934</td>\n",
       "      <td>0.698667</td>\n",
       "      <td>0.052175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c3_lam1e-06_target_model_params.json</th>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>300</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.768</td>\n",
       "      <td>0.521</td>\n",
       "      <td>0.631583</td>\n",
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       "      <td>0.053817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c9_lam0.0001_target_model_params.json</th>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>300</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.645833</td>\n",
       "      <td>0.007760</td>\n",
       "      <td>0.606773</td>\n",
       "      <td>0.006111</td>\n",
       "      <td>0.832667</td>\n",
       "      <td>0.076330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c1_lam1e-06_target_model_params.json</th>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>300</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.656</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.604083</td>\n",
       "      <td>0.005877</td>\n",
       "      <td>0.604101</td>\n",
       "      <td>0.006050</td>\n",
       "      <td>0.607000</td>\n",
       "      <td>0.057692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c4_lam0.0001_target_model_params.json</th>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>300</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.802</td>\n",
       "      <td>0.554</td>\n",
       "      <td>0.630250</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>0.603162</td>\n",
       "      <td>0.011254</td>\n",
       "      <td>0.767833</td>\n",
       "      <td>0.057621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c7_lam1e-06_target_model_params.json</th>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>300</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.586</td>\n",
       "      <td>0.640167</td>\n",
       "      <td>0.005907</td>\n",
       "      <td>0.602678</td>\n",
       "      <td>0.008595</td>\n",
       "      <td>0.829000</td>\n",
       "      <td>0.080946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c1.5_lam0.0001_target_model_params.json</th>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>300</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>1.5</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.473</td>\n",
       "      <td>0.616667</td>\n",
       "      <td>0.003659</td>\n",
       "      <td>0.607886</td>\n",
       "      <td>0.007851</td>\n",
       "      <td>0.661000</td>\n",
       "      <td>0.053666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c1_lam0.0001_target_model_params.json</th>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>300</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.652</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.604250</td>\n",
       "      <td>0.005039</td>\n",
       "      <td>0.604634</td>\n",
       "      <td>0.006262</td>\n",
       "      <td>0.605333</td>\n",
       "      <td>0.053817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c7_lam0.0001_target_model_params.json</th>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>300</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.637167</td>\n",
       "      <td>0.001213</td>\n",
       "      <td>0.600959</td>\n",
       "      <td>0.011542</td>\n",
       "      <td>0.824500</td>\n",
       "      <td>0.071332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c10_lam1e-06_target_model_params.json</th>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>300</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.866</td>\n",
       "      <td>0.593</td>\n",
       "      <td>0.633667</td>\n",
       "      <td>0.003448</td>\n",
       "      <td>0.598137</td>\n",
       "      <td>0.008979</td>\n",
       "      <td>0.821000</td>\n",
       "      <td>0.072450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c5_lam1e-06_target_model_params.json</th>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>300</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.558</td>\n",
       "      <td>0.636167</td>\n",
       "      <td>0.000986</td>\n",
       "      <td>0.603965</td>\n",
       "      <td>0.010362</td>\n",
       "      <td>0.797000</td>\n",
       "      <td>0.060822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c8_lam1e-06_target_model_params.json</th>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>300</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.640083</td>\n",
       "      <td>0.004962</td>\n",
       "      <td>0.603129</td>\n",
       "      <td>0.008930</td>\n",
       "      <td>0.825500</td>\n",
       "      <td>0.077144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c6_lam1e-06_target_model_params.json</th>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>300</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.851</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.631833</td>\n",
       "      <td>0.001491</td>\n",
       "      <td>0.598061</td>\n",
       "      <td>0.010004</td>\n",
       "      <td>0.810667</td>\n",
       "      <td>0.066487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c6_lam0.0001_target_model_params.json</th>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>300</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.845</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.634917</td>\n",
       "      <td>0.000607</td>\n",
       "      <td>0.600274</td>\n",
       "      <td>0.011645</td>\n",
       "      <td>0.815333</td>\n",
       "      <td>0.066337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c4_lam1e-06_target_model_params.json</th>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>300</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.803</td>\n",
       "      <td>0.552</td>\n",
       "      <td>0.633083</td>\n",
       "      <td>0.002206</td>\n",
       "      <td>0.604704</td>\n",
       "      <td>0.013328</td>\n",
       "      <td>0.776000</td>\n",
       "      <td>0.055908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c9_lam1e-06_target_model_params.json</th>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>300</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.595</td>\n",
       "      <td>0.640083</td>\n",
       "      <td>0.006147</td>\n",
       "      <td>0.601678</td>\n",
       "      <td>0.007006</td>\n",
       "      <td>0.833667</td>\n",
       "      <td>0.074984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         n_classes  alpha  max_iter   lambda_  \\\n",
       "c3_lam0.0001_target_model_params.json            7   0.01       300  0.000100   \n",
       "c8_lam0.0001_target_model_params.json            7   0.01       300  0.000100   \n",
       "c2_lam1e-06_target_model_params.json             7   0.01       300  0.000001   \n",
       "c5_lam0.0001_target_model_params.json            7   0.01       300  0.000100   \n",
       "c10_lam0.0001_target_model_params.json           7   0.01       300  0.000100   \n",
       "c1.5_lam1e-06_target_model_params.json           7   0.01       300  0.000001   \n",
       "c2_lam0.0001_target_model_params.json            7   0.01       300  0.000100   \n",
       "c3_lam1e-06_target_model_params.json             7   0.01       300  0.000001   \n",
       "c9_lam0.0001_target_model_params.json            7   0.01       300  0.000100   \n",
       "c1_lam1e-06_target_model_params.json             7   0.01       300  0.000001   \n",
       "c4_lam0.0001_target_model_params.json            7   0.01       300  0.000100   \n",
       "c7_lam1e-06_target_model_params.json             7   0.01       300  0.000001   \n",
       "c1.5_lam0.0001_target_model_params.json          7   0.01       300  0.000100   \n",
       "c1_lam0.0001_target_model_params.json            7   0.01       300  0.000100   \n",
       "c7_lam0.0001_target_model_params.json            7   0.01       300  0.000100   \n",
       "c10_lam1e-06_target_model_params.json            7   0.01       300  0.000001   \n",
       "c5_lam1e-06_target_model_params.json             7   0.01       300  0.000001   \n",
       "c8_lam1e-06_target_model_params.json             7   0.01       300  0.000001   \n",
       "c6_lam1e-06_target_model_params.json             7   0.01       300  0.000001   \n",
       "c6_lam0.0001_target_model_params.json            7   0.01       300  0.000100   \n",
       "c4_lam1e-06_target_model_params.json             7   0.01       300  0.000001   \n",
       "c9_lam1e-06_target_model_params.json             7   0.01       300  0.000001   \n",
       "\n",
       "                                         tolerance  sgdDP   L     C  epsilon  \\\n",
       "c3_lam0.0001_target_model_params.json      0.00001   True  20   3.0    10000   \n",
       "c8_lam0.0001_target_model_params.json      0.00001   True  20   8.0    10000   \n",
       "c2_lam1e-06_target_model_params.json       0.00001   True  20   2.0    10000   \n",
       "c5_lam0.0001_target_model_params.json      0.00001   True  20   5.0    10000   \n",
       "c10_lam0.0001_target_model_params.json     0.00001   True  20  10.0    10000   \n",
       "c1.5_lam1e-06_target_model_params.json     0.00001   True  20   1.5    10000   \n",
       "c2_lam0.0001_target_model_params.json      0.00001   True  20   2.0    10000   \n",
       "c3_lam1e-06_target_model_params.json       0.00001   True  20   3.0    10000   \n",
       "c9_lam0.0001_target_model_params.json      0.00001   True  20   9.0    10000   \n",
       "c1_lam1e-06_target_model_params.json       0.00001   True  20   1.0    10000   \n",
       "c4_lam0.0001_target_model_params.json      0.00001   True  20   4.0    10000   \n",
       "c7_lam1e-06_target_model_params.json       0.00001   True  20   7.0    10000   \n",
       "c1.5_lam0.0001_target_model_params.json    0.00001   True  20   1.5    10000   \n",
       "c1_lam0.0001_target_model_params.json      0.00001   True  20   1.0    10000   \n",
       "c7_lam0.0001_target_model_params.json      0.00001   True  20   7.0    10000   \n",
       "c10_lam1e-06_target_model_params.json      0.00001   True  20  10.0    10000   \n",
       "c5_lam1e-06_target_model_params.json       0.00001   True  20   5.0    10000   \n",
       "c8_lam1e-06_target_model_params.json       0.00001   True  20   8.0    10000   \n",
       "c6_lam1e-06_target_model_params.json       0.00001   True  20   6.0    10000   \n",
       "c6_lam0.0001_target_model_params.json      0.00001   True  20   6.0    10000   \n",
       "c4_lam1e-06_target_model_params.json       0.00001   True  20   4.0    10000   \n",
       "c9_lam1e-06_target_model_params.json       0.00001   True  20   9.0    10000   \n",
       "\n",
       "                                           delta  ...  outDP_local  \\\n",
       "c3_lam0.0001_target_model_params.json    0.00001  ...        False   \n",
       "c8_lam0.0001_target_model_params.json    0.00001  ...        False   \n",
       "c2_lam1e-06_target_model_params.json     0.00001  ...        False   \n",
       "c5_lam0.0001_target_model_params.json    0.00001  ...        False   \n",
       "c10_lam0.0001_target_model_params.json   0.00001  ...        False   \n",
       "c1.5_lam1e-06_target_model_params.json   0.00001  ...        False   \n",
       "c2_lam0.0001_target_model_params.json    0.00001  ...        False   \n",
       "c3_lam1e-06_target_model_params.json     0.00001  ...        False   \n",
       "c9_lam0.0001_target_model_params.json    0.00001  ...        False   \n",
       "c1_lam1e-06_target_model_params.json     0.00001  ...        False   \n",
       "c4_lam0.0001_target_model_params.json    0.00001  ...        False   \n",
       "c7_lam1e-06_target_model_params.json     0.00001  ...        False   \n",
       "c1.5_lam0.0001_target_model_params.json  0.00001  ...        False   \n",
       "c1_lam0.0001_target_model_params.json    0.00001  ...        False   \n",
       "c7_lam0.0001_target_model_params.json    0.00001  ...        False   \n",
       "c10_lam1e-06_target_model_params.json    0.00001  ...        False   \n",
       "c5_lam1e-06_target_model_params.json     0.00001  ...        False   \n",
       "c8_lam1e-06_target_model_params.json     0.00001  ...        False   \n",
       "c6_lam1e-06_target_model_params.json     0.00001  ...        False   \n",
       "c6_lam0.0001_target_model_params.json    0.00001  ...        False   \n",
       "c4_lam1e-06_target_model_params.json     0.00001  ...        False   \n",
       "c9_lam1e-06_target_model_params.json     0.00001  ...        False   \n",
       "\n",
       "                                         outDP_local_epsilon  train_acc  \\\n",
       "c3_lam0.0001_target_model_params.json                      1      0.766   \n",
       "c8_lam0.0001_target_model_params.json                      1      0.871   \n",
       "c2_lam1e-06_target_model_params.json                       1      0.712   \n",
       "c5_lam0.0001_target_model_params.json                      1      0.825   \n",
       "c10_lam0.0001_target_model_params.json                     1      0.870   \n",
       "c1.5_lam1e-06_target_model_params.json                     1      0.692   \n",
       "c2_lam0.0001_target_model_params.json                      1      0.719   \n",
       "c3_lam1e-06_target_model_params.json                       1      0.768   \n",
       "c9_lam0.0001_target_model_params.json                      1      0.878   \n",
       "c1_lam1e-06_target_model_params.json                       1      0.656   \n",
       "c4_lam0.0001_target_model_params.json                      1      0.802   \n",
       "c7_lam1e-06_target_model_params.json                       1      0.865   \n",
       "c1.5_lam0.0001_target_model_params.json                    1      0.685   \n",
       "c1_lam0.0001_target_model_params.json                      1      0.652   \n",
       "c7_lam0.0001_target_model_params.json                      1      0.870   \n",
       "c10_lam1e-06_target_model_params.json                      1      0.866   \n",
       "c5_lam1e-06_target_model_params.json                       1      0.825   \n",
       "c8_lam1e-06_target_model_params.json                       1      0.868   \n",
       "c6_lam1e-06_target_model_params.json                       1      0.851   \n",
       "c6_lam0.0001_target_model_params.json                      1      0.845   \n",
       "c4_lam1e-06_target_model_params.json                       1      0.803   \n",
       "c9_lam1e-06_target_model_params.json                       1      0.878   \n",
       "\n",
       "                                         test_acc  attack_acc_mean  \\\n",
       "c3_lam0.0001_target_model_params.json       0.527         0.626917   \n",
       "c8_lam0.0001_target_model_params.json       0.602         0.641083   \n",
       "c2_lam1e-06_target_model_params.json        0.493         0.619000   \n",
       "c5_lam0.0001_target_model_params.json       0.566         0.636667   \n",
       "c10_lam0.0001_target_model_params.json      0.599         0.637333   \n",
       "c1.5_lam1e-06_target_model_params.json      0.477         0.613333   \n",
       "c2_lam0.0001_target_model_params.json       0.503         0.620750   \n",
       "c3_lam1e-06_target_model_params.json        0.521         0.631583   \n",
       "c9_lam0.0001_target_model_params.json       0.591         0.645833   \n",
       "c1_lam1e-06_target_model_params.json        0.458         0.604083   \n",
       "c4_lam0.0001_target_model_params.json       0.554         0.630250   \n",
       "c7_lam1e-06_target_model_params.json        0.586         0.640167   \n",
       "c1.5_lam0.0001_target_model_params.json     0.473         0.616667   \n",
       "c1_lam0.0001_target_model_params.json       0.452         0.604250   \n",
       "c7_lam0.0001_target_model_params.json       0.592         0.637167   \n",
       "c10_lam1e-06_target_model_params.json       0.593         0.633667   \n",
       "c5_lam1e-06_target_model_params.json        0.558         0.636167   \n",
       "c8_lam1e-06_target_model_params.json        0.590         0.640083   \n",
       "c6_lam1e-06_target_model_params.json        0.591         0.631833   \n",
       "c6_lam0.0001_target_model_params.json       0.591         0.634917   \n",
       "c4_lam1e-06_target_model_params.json        0.552         0.633083   \n",
       "c9_lam1e-06_target_model_params.json        0.595         0.640083   \n",
       "\n",
       "                                         attack_acc_std  attack_pre_mean  \\\n",
       "c3_lam0.0001_target_model_params.json          0.002281         0.603585   \n",
       "c8_lam0.0001_target_model_params.json          0.007447         0.602701   \n",
       "c2_lam1e-06_target_model_params.json           0.001848         0.605170   \n",
       "c5_lam0.0001_target_model_params.json          0.002981         0.604008   \n",
       "c10_lam0.0001_target_model_params.json         0.006039         0.600292   \n",
       "c1.5_lam1e-06_target_model_params.json         0.002867         0.605312   \n",
       "c2_lam0.0001_target_model_params.json          0.000629         0.605450   \n",
       "c3_lam1e-06_target_model_params.json           0.002009         0.608081   \n",
       "c9_lam0.0001_target_model_params.json          0.007760         0.606773   \n",
       "c1_lam1e-06_target_model_params.json           0.005877         0.604101   \n",
       "c4_lam0.0001_target_model_params.json          0.000250         0.603162   \n",
       "c7_lam1e-06_target_model_params.json           0.005907         0.602678   \n",
       "c1.5_lam0.0001_target_model_params.json        0.003659         0.607886   \n",
       "c1_lam0.0001_target_model_params.json          0.005039         0.604634   \n",
       "c7_lam0.0001_target_model_params.json          0.001213         0.600959   \n",
       "c10_lam1e-06_target_model_params.json          0.003448         0.598137   \n",
       "c5_lam1e-06_target_model_params.json           0.000986         0.603965   \n",
       "c8_lam1e-06_target_model_params.json           0.004962         0.603129   \n",
       "c6_lam1e-06_target_model_params.json           0.001491         0.598061   \n",
       "c6_lam0.0001_target_model_params.json          0.000607         0.600274   \n",
       "c4_lam1e-06_target_model_params.json           0.002206         0.604704   \n",
       "c9_lam1e-06_target_model_params.json           0.006147         0.601678   \n",
       "\n",
       "                                         attack_pre_std  attack_rec_mean  \\\n",
       "c3_lam0.0001_target_model_params.json          0.013425         0.746833   \n",
       "c8_lam0.0001_target_model_params.json          0.007474         0.833500   \n",
       "c2_lam1e-06_target_model_params.json           0.011870         0.690000   \n",
       "c5_lam0.0001_target_model_params.json          0.008961         0.799000   \n",
       "c10_lam0.0001_target_model_params.json         0.007505         0.827333   \n",
       "c1.5_lam1e-06_target_model_params.json         0.007915         0.655000   \n",
       "c2_lam0.0001_target_model_params.json          0.010934         0.698667   \n",
       "c3_lam1e-06_target_model_params.json           0.013607         0.747333   \n",
       "c9_lam0.0001_target_model_params.json          0.006111         0.832667   \n",
       "c1_lam1e-06_target_model_params.json           0.006050         0.607000   \n",
       "c4_lam0.0001_target_model_params.json          0.011254         0.767833   \n",
       "c7_lam1e-06_target_model_params.json           0.008595         0.829000   \n",
       "c1.5_lam0.0001_target_model_params.json        0.007851         0.661000   \n",
       "c1_lam0.0001_target_model_params.json          0.006262         0.605333   \n",
       "c7_lam0.0001_target_model_params.json          0.011542         0.824500   \n",
       "c10_lam1e-06_target_model_params.json          0.008979         0.821000   \n",
       "c5_lam1e-06_target_model_params.json           0.010362         0.797000   \n",
       "c8_lam1e-06_target_model_params.json           0.008930         0.825500   \n",
       "c6_lam1e-06_target_model_params.json           0.010004         0.810667   \n",
       "c6_lam0.0001_target_model_params.json          0.011645         0.815333   \n",
       "c4_lam1e-06_target_model_params.json           0.013328         0.776000   \n",
       "c9_lam1e-06_target_model_params.json           0.007006         0.833667   \n",
       "\n",
       "                                         attack_rec_std  \n",
       "c3_lam0.0001_target_model_params.json          0.054038  \n",
       "c8_lam0.0001_target_model_params.json          0.083853  \n",
       "c2_lam1e-06_target_model_params.json           0.044721  \n",
       "c5_lam0.0001_target_model_params.json          0.064846  \n",
       "c10_lam0.0001_target_model_params.json         0.077517  \n",
       "c1.5_lam1e-06_target_model_params.json         0.050537  \n",
       "c2_lam0.0001_target_model_params.json          0.052175  \n",
       "c3_lam1e-06_target_model_params.json           0.053817  \n",
       "c9_lam0.0001_target_model_params.json          0.076330  \n",
       "c1_lam1e-06_target_model_params.json           0.057692  \n",
       "c4_lam0.0001_target_model_params.json          0.057621  \n",
       "c7_lam1e-06_target_model_params.json           0.080946  \n",
       "c1.5_lam0.0001_target_model_params.json        0.053666  \n",
       "c1_lam0.0001_target_model_params.json          0.053817  \n",
       "c7_lam0.0001_target_model_params.json          0.071332  \n",
       "c10_lam1e-06_target_model_params.json          0.072450  \n",
       "c5_lam1e-06_target_model_params.json           0.060822  \n",
       "c8_lam1e-06_target_model_params.json           0.077144  \n",
       "c6_lam1e-06_target_model_params.json           0.066487  \n",
       "c6_lam0.0001_target_model_params.json          0.066337  \n",
       "c4_lam1e-06_target_model_params.json           0.055908  \n",
       "c9_lam1e-06_target_model_params.json           0.074984  \n",
       "\n",
       "[22 rows x 21 columns]"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "d5150792",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a6863c40>"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df.loc[df['lambda_']==0.0001].sort_values(by=['C'], ascending=False)\n",
    "plt.plot(data['C'], data['test_acc'], label='test')\n",
    "plt.plot(data['C'], data['attack_acc_mean'], label='attack')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "787b63cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a730fb20>"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "data = df.loc[df['lambda_']==0.000001].sort_values(by=['C'], ascending=False)\n",
    "plt.plot(data['C'], data['test_acc'], label='test')\n",
    "plt.plot(data['C'], data['attack_acc_mean'], label='attack')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "197c80bd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}