{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "45e05b72",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nasbench201.search_model_darts_proj import TinyNetworkDartsProj\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from nasbench201.cell_operations import SearchSpaceNames\n",
    "import nasbench201.utils as ig_utils\n",
    "import torch.utils\n",
    "import torchvision.datasets as dset\n",
    "import numpy as np\n",
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "eaa02532",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(1)\n",
    "torch.manual_seed(1)\n",
    "torch.cuda.manual_seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "29976057",
   "metadata": {},
   "outputs": [],
   "source": [
    "class AGRS():\n",
    "    def __init__(self):\n",
    "        self.data = '../data'\n",
    "        self.dataset = 'cifar10'\n",
    "        self.train_portion = 0.5\n",
    "        self.batch_size = 64\n",
    "        self.init_channels=16\n",
    "        self.layers = 8\n",
    "        self.learning_rate = 0.025\n",
    "        self.learning_rate_min = 0.001\n",
    "        self.momentum = 0.9\n",
    "        self.nesterov = False\n",
    "        self.weight_decay = 3e-4\n",
    "        self.grad_clip = 5\n",
    "        self.cutout = False\n",
    "args = AGRS()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "3725b779",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Jocab_Score(ori_model, input, target, weights=None):\n",
    "    model = copy.deepcopy(ori_model)\n",
    "    model.eval()\n",
    "    model.proj_weights = weights\n",
    "    num_edge, num_op = model.num_edge, model.num_op\n",
    "    for i in range(num_edge):\n",
    "        model.candidate_flags[i] = False\n",
    "    batch_size = input.shape[0]\n",
    "    model.K = torch.zeros(batch_size, batch_size).cuda()\n",
    "    model.K_list = {}\n",
    "    def counting_forward_hook(module, inp, out):\n",
    "        if isinstance(inp, tuple):\n",
    "            inp = inp[0]\n",
    "        inp = inp.view(inp.size(0), -1)\n",
    "        x = (inp > 0).float()\n",
    "        K = x @ x.t()\n",
    "        if x.cpu().numpy().sum() == 0:\n",
    "            model.K = model.K\n",
    "        else:\n",
    "            K2 = (1.-x) @ (1.-x.t())\n",
    "            model.K = model.K + K + K2\n",
    "        model.K_list[module.name]=K\n",
    "        #print(module)\n",
    "        \n",
    "\n",
    "    for name, module in model.named_modules():\n",
    "        if isinstance(module, nn.ReLU):\n",
    "            module.name = name\n",
    "            module.register_forward_hook(counting_forward_hook)\n",
    "    \n",
    "    input = input.cuda()\n",
    "    model(input)\n",
    "    K = model.K.cpu().numpy()\n",
    "    score = hooklogdet(model.K.cpu().numpy())\n",
    "    #print(model.K_list)\n",
    "    K_list = model.K_list\n",
    "    del model\n",
    "    del input\n",
    "    return score, K,K_list\n",
    "\n",
    "def hooklogdet(K, labels=None):\n",
    "    s, ld = np.linalg.slogdet(K)\n",
    "    return ld"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "ae134d08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "train_transform, valid_transform = ig_utils._data_transforms_cifar10(args)\n",
    "train_data = dset.CIFAR10(root=args.data, train=True, download=True, transform=train_transform)\n",
    "valid_data = dset.CIFAR10(root=args.data, train=False, download=True, transform=valid_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "cd9923d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_train = len(train_data)\n",
    "indices = list(range(num_train))\n",
    "split = 64\n",
    "\n",
    "train_queue = torch.utils.data.DataLoader(\n",
    "    train_data, batch_size=args.batch_size,\n",
    "    sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),\n",
    "    pin_memory=True)\n",
    "input, target = next(iter(train_queue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1529,
   "id": "e08b4613",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(2)\n",
    "torch.manual_seed(2)\n",
    "torch.cuda.manual_seed(2)\n",
    "from scipy.stats import rankdata\n",
    "input, target = next(iter(train_queue))\n",
    "LAYER=8\n",
    "OPN=4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1530,
   "id": "58c0ad9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nasbench201.cell_operations import OPS\n",
    "class TinyNetwork(nn.Module):\n",
    "    def __init__(self, C, N, num_classes, criterion, affine=False, track_running_stats=True, stem_channels=3):\n",
    "        super(TinyNetwork, self).__init__()\n",
    "        self.stem = nn.Sequential(\n",
    "                nn.Conv2d(stem_channels, C, kernel_size=3, padding=1, bias=False),\n",
    "                nn.BatchNorm2d(C))\n",
    "        op_names=['skip_connect','nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3']\n",
    "        self.N=N\n",
    "        self.edges = nn.ModuleDict()\n",
    "        for i in range(N):\n",
    "            self.edges[str(i)]=nn.ModuleList([OPS[op_name](C, C, 1, affine, track_running_stats) for op_name in op_names])\n",
    "        \n",
    "        self.lastact    = nn.Sequential(nn.BatchNorm2d(C), nn.ReLU(inplace=True))\n",
    "        self.global_pooling = nn.AdaptiveAvgPool2d(1)\n",
    "        self.classifier = nn.Linear(C, num_classes)\n",
    "        self.cos = nn.CosineSimilarity(dim=1, eps=1e-6)\n",
    "        self.weights=[[1/4, 1/4, 1/4, 1/4],[1/4, 1/4, 1/4, 1/4],[1/4, 1/4, 1/4, 1/4],[1/4, 1/4, 1/4, 1/4],[1/4, 1/4, 1/4, 1/4],[1/4, 1/4, 1/4, 1/4],[1/4, 1/4, 1/4, 1/4],[1/4, 1/4, 1/4, 1/4]]\n",
    "        self.weights = np.array(self.weights)\n",
    "        self.weights=torch.from_numpy(self.weights)\n",
    "    \n",
    "    def forward(self, inputs):\n",
    "        weights = self.weights\n",
    "        sum_value=[]\n",
    "        feature = self.stem(inputs)\n",
    "        for i in range(self.N):\n",
    "            feature=sum(op(feature, block_input=True)*w if w==0 else op(feature) * w for op, w in zip(self.edges[str(i)], weights[i]))\n",
    "#             with torch.no_grad():\n",
    "#                 print(self.calc_k(feature))\n",
    "#                 print(torch.mean(torch.abs(feature)))\n",
    "#                 print(torch.count_nonzero((feature>0).float()))\n",
    "                \n",
    "        out = self.lastact(feature)\n",
    "        out = self.global_pooling( out )\n",
    "        out = out.view(out.size(0), -1)\n",
    "        logits = self.classifier(out)\n",
    "        #print(sum_value)\n",
    "        #print('model end')\n",
    "        return logits\n",
    "    \n",
    "    def calc_k(self, inp):\n",
    "        inp = inp.view(inp.size(0), -1)\n",
    "        x = (inp > 0).float()\n",
    "        K = x @ x.t()\n",
    "        if x.cpu().numpy().sum() == 0:\n",
    "            return 0\n",
    "        else:\n",
    "            K2 = (1.-x) @ (1.-x.t())\n",
    "            K =  K + K2\n",
    "            return hooklogdet(K.cpu().numpy())\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1533,
   "id": "b84ded93",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = TinyNetwork(C=16, N=LAYER, num_classes=10, criterion=nn.CrossEntropyLoss())\n",
    "#model.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1534,
   "id": "baddff20",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hongkaiw/anaconda2/envs/mct/lib/python3.7/site-packages/torch/tensor.py:593: RuntimeWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).\n",
      "  'incorrect results).', category=RuntimeWarning)\n",
      "/home/hongkaiw/.local/lib/python3.7/site-packages/ipykernel_launcher.py:27: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n"
     ]
    }
   ],
   "source": [
    "import torch.onnx \n",
    "model.eval()\n",
    "  \n",
    "torch.onnx.export(model,         # model being run \n",
    "     input,       # model input (or a tuple for multiple inputs) \n",
    "     \"toy.onnx\",       # where to save the model  \n",
    "     export_params=False,  # store the trained parameter weights inside the model file \n",
    "     opset_version=10,    # the ONNX version to export the model to \n",
    "     do_constant_folding=True,  # whether to execute constant folding for optimization \n",
    "     input_names = ['modelInput'],   # the model's input names \n",
    "     output_names = ['modelOutput'], # the model's output names \n",
    "     dynamic_axes={'modelInput' : {1 : 'batch_size'},    # variable length axes \n",
    "                            'modelOutput' : {0 : 'batch_size'}}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1522,
   "id": "78f85e24",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "def Jocab_Score(ori_model, input, target, weights=None):\n",
    "    model = copy.deepcopy(ori_model)\n",
    "    model.eval()\n",
    "    model.proj_weights = weights\n",
    "    batch_size = input.shape[0]\n",
    "    model.K = torch.zeros(batch_size, batch_size).cuda()\n",
    "    model.K_list = {}\n",
    "    model.count = 0\n",
    "    def counting_forward_hook(module, inp, out):\n",
    "        if isinstance(inp, tuple):\n",
    "            inp = inp[0]\n",
    "        inp = inp.view(inp.size(0), -1)\n",
    "        #with torch.no_grad():\n",
    "            #print(torch.sum((inp > 0).float()), torch.count_nonzero(inp))\n",
    "        x = (inp > 0).float()\n",
    "        K = x @ x.t()\n",
    "        if x.cpu().numpy().sum() == 0:\n",
    "            model.K = model.K\n",
    "        else:\n",
    "            K2 = (1.-x) @ (1.-x.t())\n",
    "            model.K = model.K + K + K2\n",
    "        model.K_list[module.name]=K\n",
    "        #print(module)\n",
    "        \n",
    "\n",
    "    for name, module in model.named_modules():\n",
    "        if isinstance(module, nn.ReLU):\n",
    "        #if 'ReLU' in str(type(module)):\n",
    "            module.name = name\n",
    "            #print(module)\n",
    "            model.count+=1\n",
    "            module.register_forward_hook(counting_forward_hook)\n",
    "    \n",
    "    input = input.cuda()\n",
    "    model(input, weights)\n",
    "    K = model.K.cpu().numpy()\n",
    "    score = hooklogdet(model.K.cpu().numpy())\n",
    "    #print(model.K_list)\n",
    "    K_list = model.K_list\n",
    "    #print(model.count)\n",
    "    del model\n",
    "    del input\n",
    "    return score, K,K_list\n",
    "\n",
    "def hooklogdet(K, labels=None):\n",
    "    s, ld = np.linalg.slogdet(K)\n",
    "    return ld"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1523,
   "id": "42ae5a33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "2\n",
      "[724.5167, 724.62964, 721.42676, 726.57513, 721.924, 724.0039, 724.2308, 724.328, 724.6446, 723.4378, 723.59174, 726.2936, 726.9928, 722.4523, 723.66644, 727.96545, 727.3341, 722.5211, 723.89703, 727.9818, 726.8876, 723.4647, 724.414, 727.43134, 727.0108, 724.00287, 724.3993, 727.34503, 727.5785, 724.18317, 724.01965, 727.66486]\n",
      "[2.625 1.5   1.875 4.   ]\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "weights=[[1/4, 1/4, 1/4, 1/4],[1/4, 1/4, 1/4, 1/4],[1/4, 1/4, 1/4, 1/4],[1/4, 1/4, 1/4, 1/4],[1/4, 1/4, 1/4, 1/4],[1/4, 1/4, 1/4, 1/4],[1/4, 1/4, 1/4, 1/4],[1/4, 1/4, 1/4, 1/4]]\n",
    "\n",
    "\n",
    "pt_score = []\n",
    "avg_skip_rank=np.array([0.0,0.0,0.0,0.0])\n",
    "count_skip = 0\n",
    "crit,K,K_list = Jocab_Score(model, input,  target, weights)\n",
    "for l in range(LAYER):\n",
    "    op_s = []\n",
    "    for o in range(OPN):\n",
    "        w = copy.deepcopy(weights)\n",
    "        w[l][o]=0\n",
    "        crit,K,K_list = Jocab_Score(model, input,  target, w)\n",
    "        pt_score.append(crit)\n",
    "        op_s.append(crit)\n",
    "    avg_skip_rank +=(rankdata(op_s))\n",
    "    select=np.argmin(op_s)\n",
    "    print(select)\n",
    "    if select ==0:\n",
    "        count_skip+=1\n",
    "print(pt_score)\n",
    "print(avg_skip_rank/LAYER)\n",
    "print(count_skip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1467,
   "id": "3133fd42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "[557.9228, 597.0493, 597.9796, 550.9689]\n",
      "[3. 2. 1. 4.]\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "disc_score = []\n",
    "avg_skip_rank=np.array([0.0,0.0,0.0,0.0])\n",
    "count_skip=0\n",
    "for l in range(LAYER):\n",
    "    op_s = []\n",
    "    for o in range(OPN):\n",
    "        w = copy.deepcopy(weights)\n",
    "        w[l]=np.zeros_like(w[l])\n",
    "        w[l][o]=1\n",
    "        #w[l][0]=1\n",
    "        crit,K,K_list = Jocab_Score(model, input,  target, w)\n",
    "       #print(w)\n",
    "        op_s.append(crit)\n",
    "        disc_score.append(crit)\n",
    "    #print([5-x for x in rankdata(op_s)])\n",
    "    avg_skip_rank +=(5-rankdata(op_s))\n",
    "    select=np.argmax(op_s)\n",
    "    print(select)\n",
    "    if select ==0:\n",
    "        count_skip+=1\n",
    "print(disc_score)\n",
    "print(avg_skip_rank/LAYER)\n",
    "print(count_skip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1468,
   "id": "e091015c",
   "metadata": {},
   "outputs": [],
   "source": [
    "w = copy.deepcopy(weights)\n",
    "arch=[1,1,1,1,1,1,1,1]\n",
    "for i in range(len(arch)):    \n",
    "    w[i]=np.zeros_like(w[i])\n",
    "    w[i][arch[i]]=1\n",
    "crit,K,K_list = Jocab_Score(model, input,  target, w)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1469,
   "id": "05ffd6a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7116abcb5edb4d99822e2a5f26b3cfb1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[557.9228, 597.0493, 597.9796, 550.9689]\n"
     ]
    }
   ],
   "source": [
    "from itertools import combinations_with_replacement,permutations,product\n",
    "from tqdm.notebook import tqdm\n",
    "final_score = []\n",
    "archs=[]\n",
    "archs =list(product([0,1,2,3], repeat=LAYER)) \n",
    "                 \n",
    "archs = [list(x) for x in archs]\n",
    "#print(archs)\n",
    "for i in tqdm(range(len(archs))):\n",
    "    arch = archs[i]\n",
    "    w = copy.deepcopy(weights)\n",
    "    for i in range(len(arch)):\n",
    "        w[i]=np.zeros_like(w[i])\n",
    "        w[i][arch[i]]=1\n",
    "    crit,K,K_list = Jocab_Score(model, input,  target, w)\n",
    "    final_score.append(crit)\n",
    "print(final_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1470,
   "id": "24aab655",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>arch</th>\n",
       "      <th>naswot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[0]</td>\n",
       "      <td>557.922791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[1]</td>\n",
       "      <td>597.049316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[2]</td>\n",
       "      <td>597.979614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[3]</td>\n",
       "      <td>550.968872</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  arch      naswot\n",
       "0  [0]  557.922791\n",
       "1  [1]  597.049316\n",
       "2  [2]  597.979614\n",
       "3  [3]  550.968872"
      ]
     },
     "execution_count": 1470,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(list(zip(archs, final_score)),columns =['arch', 'naswot'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1471,
   "id": "2da3ceab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[557.9227905273438, 597.04931640625, 597.9796142578125, 550.9688720703125]\n"
     ]
    }
   ],
   "source": [
    "index=0\n",
    "best_nwot=[]\n",
    "for l in range(LAYER):\n",
    "    for o in range(OPN): \n",
    "        max_nwot=max(df[df.apply(lambda x: x['arch'][l]==o, axis=1)]['naswot'])\n",
    "        best_nwot.append(max_nwot)\n",
    "print(best_nwot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1472,
   "id": "a03d51ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "[3. 2. 1. 4.]\n"
     ]
    }
   ],
   "source": [
    "avg_rank=np.array([0.0,0.0,0.0,0.0])\n",
    "\n",
    "for i in range(LAYER):\n",
    "    #print(np.argmax(best_nwot[i*4:(i+1)*4]))\n",
    "    avg_rank +=(5-rankdata(best_nwot[i*4:(i+1)*4]))\n",
    "    #print((5-rankdata(best_nwot[i*4:(i+1)*4])))\n",
    "print(avg_rank/LAYER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1473,
   "id": "5357b16a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SpearmanrResult(correlation=1.0, pvalue=0.0)"
      ]
     },
     "execution_count": 1473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "stats.spearmanr([x*-1 for x in pt_score], best_nwot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1474,
   "id": "4ebc7c45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[557.9228, 597.0493, 597.9796, 550.9689]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SpearmanrResult(correlation=1.0, pvalue=0.0)"
      ]
     },
     "execution_count": 1474,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(disc_score)\n",
    "stats.spearmanr(disc_score, best_nwot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1485,
   "id": "02a823f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "cor_dic={}\n",
    "cor_dic['zc_pt(nwot)']=[1.0,0.80,0.82,0.83,0.79,0.69,0.66,0.65]\n",
    "cor_dic['disc_zc(nwot)']=[1.0,0.85,0.89,0.71,0.63,0.35,0.28,0.07]\n",
    "#cor_dic['zc_pt(nwot)_w/o_skip']  =[1.0,0.5217,0.5533,0.6655,0.7019,0.5058,0.5801,0.637]\n",
    "#cor_dic['disc_zc(nwot)_w/o_skip']=[1.0,0.4638,0.5788,0.7332,0.7419,0.5711,0.6141,0.6845]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1486,
   "id": "66c58b61",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import statistics as stat\n",
    "import itertools\n",
    "marker = itertools.cycle(('^', 'x', 'o', 's', '*', '+', '1')) \n",
    "color = itertools.cycle(('b', 'c', 'r', 'g', 'y', 'm', 'k')) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1488,
   "id": "89d82376",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pathlib\n",
    "import seaborn as sns\n",
    "x = np.array([1, 2, 3, 4, 5, 6,7,8])\n",
    "#plt.figure(figsize=(8.5,5.5))\n",
    "idx=0\n",
    "cor_df = pd.DataFrame.from_dict(cor_dic, orient='index')\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(13, 13))\n",
    "sns.set(font_scale=1.8)\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(200, 55, as_cmap=True)\n",
    "g = sns.heatmap(cor_df, cmap=cmap, center=0,\n",
    "         square=True, linewidths=.5, cbar_kws={\"shrink\": .267}, annot=True,xticklabels=x)\n",
    "plt.xlabel('#N Layers')\n",
    "plt.ylabel('Spearman-$\\\\rho$')\n",
    "#plt.xticks(x)\n",
    "#plt.legend(bbox_to_anchor=(0.55, 0.65), prop={'size': 13})\n",
    "#plt.grid()\n",
    "plt.savefig(pathlib.Path('op_correl_layer_increase_toy').with_suffix('.pdf'), bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 999,
   "id": "7876c4d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "rank_dic={}\n",
    "rank_dic['zc_pt(nwot)']  =[4,4,4,4,2.6,2.67,2.71,2.75]\n",
    "rank_dic['disc_zc(nwot)']=[4,4,4,2.5,2.2,2.33,2.28,2.25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1087,
   "id": "afca5839",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pathlib\n",
    "x = np.array([1, 2, 3, 4, 5, 6,7,8])\n",
    "idx=0\n",
    "cor_df = pd.DataFrame.from_dict(rank_dic, orient='index')\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(13, 13))\n",
    "#sns.set(font_scale=1.8)\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(200, 55, as_cmap=True)\n",
    "g = sns.heatmap(cor_df, cmap=cmap, center=0,\n",
    "         square=True, linewidths=.5, cbar_kws={\"shrink\": .267}, annot=True,xticklabels=x)\n",
    "plt.xlabel('#N Layers in Toy model')\n",
    "plt.ylabel('Everage Rank for Skip')\n",
    "#plt.legend(bbox_to_anchor=(0.55, 0.65), prop={'size': 13})\n",
    "#plt.grid()\n",
    "plt.savefig(pathlib.Path('skip_layer_increase_toy').with_suffix('.pdf'), bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1489,
   "id": "bcc59139",
   "metadata": {},
   "outputs": [],
   "source": [
    "pt_dic={}\n",
    " #[3.         1.66666667 1.33333333 4.        ]             \n",
    "pt_dic['skip']  =     [3,3.00,3.00,3.00,3.00,2.83,2.71,2.75]# 00000011\n",
    "pt_dic['skip_select']=[0,0,0,0,0,0,1,1] \n",
    "pt_dic['conv_1x1']=   [2,1.50,1.67,1.75,1.60,1.83,1.71,1.75]#01112222\n",
    "pt_dic['conv_1x1_select']=[0,1,1,1,2,2,2,2]\n",
    "pt_dic['conv_3x3']=   [1,1.50,1.33,1.25,1.40,1.33,1.57,1.5]#21233445\n",
    "pt_dic['conv_3x3_select']=[2,1,2,3,3,4,4,5]\n",
    "pt_dic['avg_pooling']=[4,4.00,4.00,4.00,4.00,4.00,4.00,4.00]#00000000\n",
    "pt_dic['avg_pooling_select']=[0,0,0,0,0,0,0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1490,
   "id": "30f0c50c",
   "metadata": {},
   "outputs": [],
   "source": [
    "disc_dic={}\n",
    "#[3.         1.33333333 1.66666667 4.        ]\n",
    "disc_dic['skip']  =     [3,3.00,3.00,2.75,2.40,2.17,2.14,1.75]#00001225\n",
    "disc_dic['skip_select']=[0,0,0,0,1,2,2,5] \n",
    "disc_dic['conv_1x1']=   [2,2.00,1.33,1.50,2.00,2.00,2.57,2.75]#00221321\n",
    "disc_dic['conv_1x1_select']=[0,0,2,2,1,3,2,1]\n",
    "disc_dic['conv_3x3']=   [1,1.00,1.67,1.75,1.80,2.17,2.00,2.25]#22123132\n",
    "disc_dic['conv_3x3_select']=[2,2,1,2,3,1,3,2]\n",
    "disc_dic['avg_pooling']=[4,4.00,4.00,4.00,3.80,3.67,3.29,3.25]#00000000\n",
    "disc_dic['avg_pooling_select']=[0,0,0,0,0,0,0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1491,
   "id": "acbdfcc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_dic={}\n",
    "\n",
    "best_dic['skip']  =     [3,3.00,3.00,3.00,3.00,3.00,3.00,3.00]#00000000\n",
    "best_dic['skip_select']=[0,0,0,0,0,0,0,0] \n",
    "best_dic['conv_1x1']=   [2,1.50,1.67,2.00,1.80,1.50,1.42,1.5]#01101344\n",
    "best_dic['conv_1x1_select']=[0,1,1,0,1,3,4,4]\n",
    "best_dic['conv_3x3']=   [1,1.50,1.33,1.00,1.20,1.50,1.57,1.5]#21244334\n",
    "best_dic['conv_3x3_select']=[2,1,2,4,4,3,3,4]\n",
    "best_dic['avg_pooling']=[4,4.00,4.00,4.00,4.00,4.00,4.00,4.00]#00000000\n",
    "best_dic['avg_pooling_select']=[0,0,0,0,0,0,0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1496,
   "id": "5c0200f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pathlib\n",
    "x = np.array([1, 2, 3, 4, 5, 6,7,8])\n",
    "idx=0\n",
    "pt_df = pd.DataFrame.from_dict(pt_dic, orient='index')\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(13, 13))\n",
    "#sns.set(font_scale=1.8)\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(200, 55, as_cmap=True)\n",
    "g = sns.heatmap(pt_df, cmap=cmap, center=0,\n",
    "         square=True, linewidths=.5, cbar_kws={\"shrink\": .267}, annot=True,xticklabels=x)\n",
    "plt.xlabel('#N Layers')\n",
    "plt.ylabel('')\n",
    "#plt.legend(bbox_to_anchor=(0.55, 0.65), prop={'size': 13})\n",
    "#plt.grid()\n",
    "plt.savefig(pathlib.Path('op_select_pt').with_suffix('.pdf'), bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1497,
   "id": "c96d7468",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pathlib\n",
    "x = np.array([1, 2, 3, 4, 5, 6,7,8])\n",
    "idx=0\n",
    "disc_df = pd.DataFrame.from_dict(disc_dic, orient='index')\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(13, 13))\n",
    "#sns.set(font_scale=1.8)\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(200, 55, as_cmap=True)\n",
    "g = sns.heatmap(disc_df, cmap=cmap, center=0,\n",
    "         square=True, linewidths=.5, cbar_kws={\"shrink\": .267}, annot=True,xticklabels=x)\n",
    "plt.xlabel('#N Layers')\n",
    "plt.ylabel('')\n",
    "#plt.legend(bbox_to_anchor=(0.55, 0.65), prop={'size': 13})\n",
    "#plt.grid()\n",
    "plt.savefig(pathlib.Path('op_select_disc').with_suffix('.pdf'), bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1498,
   "id": "b9a92916",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pathlib\n",
    "x = np.array([1, 2, 3, 4, 5, 6,7,8])\n",
    "idx=0\n",
    "best_df = pd.DataFrame.from_dict(best_dic, orient='index')\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(13, 13))\n",
    "#sns.set(font_scale=1.8)\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(200, 55, as_cmap=True)\n",
    "g = sns.heatmap(best_df, cmap=cmap, center=0,\n",
    "         square=True, linewidths=.5, cbar_kws={\"shrink\": .267}, annot=True,xticklabels=x)\n",
    "plt.xlabel('#N Layers')\n",
    "plt.ylabel('')\n",
    "#plt.legend(bbox_to_anchor=(0.55, 0.65), prop={'size': 13})\n",
    "#plt.grid()\n",
    "plt.savefig(pathlib.Path('op_select_best').with_suffix('.pdf'), bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ef09034",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mct",
   "language": "python",
   "name": "mct"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}