Pipeline_ISMRM2019_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU available: True\n"
     ]
    }
   ],
   "source": [
    "import torch, torch.utils.data\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim \n",
    "from torch.autograd import Variable\n",
    "from torch.utils.data import DataLoader\n",
    "import torchvision\n",
    "import torch.nn.functional as F\n",
    "from torchvision import datasets, models, transforms\n",
    "\n",
    "import numpy as np\n",
    "import gc \n",
    "import math\n",
    "from scipy import stats\n",
    "import nibabel as ni \n",
    "from sklearn.model_selection import KFold, StratifiedShuffleSplit\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "import os,sys, glob\n",
    "import trainer_dataugmentation_fix_t as ptt\n",
    "from PIL import Image\n",
    "%matplotlib inline\n",
    "cuda_aval = torch.cuda.is_available()\n",
    "print('GPU available:',cuda_aval)\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "torch.set_printoptions(threshold=5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_labels(y_out,ind_end,ind_start,nlabels):\n",
    "    ind_end_pred = np.add.accumulate(np.array(ind_end)-np.array(ind_start))\n",
    "    ind_start_pred = np.zeros_like(ind_end_pred)\n",
    "    ind_start_pred[1:] = ind_end_pred[:-1]\n",
    "\n",
    "    y_prob_pred = [np.sum(y_out[i:j]==0)/(j-i).astype(float) for i,j in zip(ind_start_pred,ind_end_pred)]\n",
    "    for label_true in nlabels[1:]:\n",
    "        y_prob_temp = [np.sum(y_out[i:j]==label_true)/(j-i).astype(float) \n",
    "                       for i,j in zip(ind_start_pred,ind_end_pred)]\n",
    "        y_prob_pred = np.vstack((y_prob_pred,y_prob_temp))\n",
    "    return y_prob_pred, np.argmax(y_prob_pred,axis=0)\n",
    "\n",
    "def divisorGenerator(n):\n",
    "    large_divisors = []\n",
    "    for i in range(1, int(math.sqrt(n) + 1)):\n",
    "        if n % i == 0:\n",
    "            yield i\n",
    "            if i*i != n:\n",
    "                large_divisors.append(n / i)\n",
    "    for divisor in reversed(large_divisors):\n",
    "        yield divisor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14000, 3, 100, 100)\n",
      "(14000, 1)\n",
      "350\n",
      "351\n",
      "(2,)\n",
      "350\n"
     ]
    }
   ],
   "source": [
    "dir_bas =   '/home/mariana/Documents/Data_2D/NCMCI/'+'images_total.npy'\n",
    "dir_bas2 =  '/home/mariana/Documents/Data_2D/NCMCI/'+'labels.npy'\n",
    "dir_bas3 =  '/home/mariana/Documents/Data_2D/NCMCI/'+'labels_suj.npy'\n",
    "dir_bas4 =  '/home/mariana/Documents/Data_2D/NCMCI/'+'ind_suj.npy'\n",
    "dir_bas5 =  '/home/mariana/Documents/Data_2D/NCMCI/'+'n_labels.npy'\n",
    "dir_bas6 =  '/home/mariana/Documents/Data_2D/NCMCI/'+'num_suj.npy'\n",
    "\n",
    "images_total = np.load(dir_bas)\n",
    "labels       = np.load(dir_bas2)\n",
    "labels_suj   = np.load(dir_bas3)\n",
    "ind_suj      = np.load(dir_bas4)\n",
    "n_labels     = np.load(dir_bas5)\n",
    "num_suj      = np.load(dir_bas6)\n",
    "\n",
    "print(images_total.shape)\n",
    "print(labels.shape)\n",
    "print(len(labels_suj))\n",
    "print(len(ind_suj))\n",
    "print(n_labels.shape)\n",
    "print(num_suj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MyResNet(\n",
      "  (mrnc): MyResNetConv(\n",
      "    (my_resnet34_model): ResNet(\n",
      "      (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "      (layer1): Sequential(\n",
      "        (0): BasicBlock(\n",
      "          (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu): ReLU(inplace)\n",
      "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        )\n",
      "        (1): BasicBlock(\n",
      "          (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu): ReLU(inplace)\n",
      "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        )\n",
      "        (2): BasicBlock(\n",
      "          (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu): ReLU(inplace)\n",
      "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        )\n",
      "      )\n",
      "      (layer2): Sequential(\n",
      "        (0): BasicBlock(\n",
      "          (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu): ReLU(inplace)\n",
      "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (downsample): Sequential(\n",
      "            (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          )\n",
      "        )\n",
      "        (1): BasicBlock(\n",
      "          (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu): ReLU(inplace)\n",
      "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        )\n",
      "        (2): BasicBlock(\n",
      "          (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu): ReLU(inplace)\n",
      "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        )\n",
      "        (3): BasicBlock(\n",
      "          (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu): ReLU(inplace)\n",
      "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        )\n",
      "      )\n",
      "      (avgpool): AvgPool2d(kernel_size=7, stride=7, padding=0)\n",
      "    )\n",
      "  )\n",
      "  (mrnd): MyResNetDens(\n",
      "    (dens3): Linear(in_features=130, out_features=64, bias=True)\n",
      "    (drop3): Dropout2d(p=0.25)\n",
      "    (dens4): Linear(in_features=64, out_features=2, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "\n",
    "class MyResNetConv(torch.nn.Module):\n",
    "    def __init__(self, fixed_feature=True):\n",
    "        super().__init__()\n",
    "        self.my_resnet34_model = models.resnet34(pretrained=True)\n",
    "        del self.my_resnet34_model.layer3\n",
    "        del self.my_resnet34_model.layer4\n",
    "        del self.my_resnet34_model.fc\n",
    "        for param in self.parameters():\n",
    "            param.requires_grad = False\n",
    "    def forward(self, x):        \n",
    "        x = self.my_resnet34_model.conv1(x)\n",
    "        x = self.my_resnet34_model.bn1(x)\n",
    "        x = self.my_resnet34_model.relu(x)\n",
    "        x = self.my_resnet34_model.maxpool(x)\n",
    "        x = self.my_resnet34_model.layer1(x)\n",
    "        x = self.my_resnet34_model.layer2(x) \n",
    "        x = self.my_resnet34_model.avgpool(x)\n",
    "        x = x.view(x.size(0), -1)\n",
    "        return x\n",
    "\n",
    "\n",
    "class MyResNetDens(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.dens3 = torch.nn.Linear(in_features=130, out_features=64)\n",
    "        self.drop3 = torch.nn.Dropout2d(p=0.25)\n",
    "        self.dens4 = torch.nn.Linear(in_features=64, out_features=2) \n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.dens3(x)\n",
    "        x = torch.nn.functional.relu(x)\n",
    "        x = self.drop3(x)\n",
    "        x = self.dens4(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "class MyResNet(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.mrnc = MyResNetConv()\n",
    "        self.mrnd = MyResNetDens()\n",
    "        \n",
    "    def forward(self, x):\n",
    "        fea = self.mrnc(x[0])\n",
    "        x_aux = torch.squeeze(x[1].data,1)\n",
    "        print('here',x_aux.cpu())\n",
    "        x = torch.cat((fea, x_aux),1) \n",
    "        x = self.mrnd(x)\n",
    "        return x\n",
    "\n",
    "model = MyResNet()\n",
    "if cuda_aval:\n",
    "    model = model.cuda()\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14000, 3, 100, 100])\n"
     ]
    }
   ],
   "source": [
    "inp_set = torch.Tensor(images_total)\n",
    "if cuda_aval:\n",
    "    inp_set = inp_set.cuda()\n",
    "feat_set = inp_set\n",
    "\n",
    "print(feat_set.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_epochs = 1000 \n",
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "data_transf = transforms.Compose([ptt.RandomCrop(64)]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start training for 1000 epochs\n",
      "  1:   3.1s   T: 0.02167 0.51121   V: 0.02178 0.49214 best\n",
      "  2:   2.8s   T: 0.02164 0.52207   V: 0.02187 0.50000 \n",
      "  3:   2.7s   T: 0.02158 0.53314   V: 0.02173 0.49821 best\n",
      "  4:   2.7s   T: 0.02149 0.54071   V: 0.02163 0.54536 best\n",
      "  5:   2.7s   T: 0.02146 0.54343   V: 0.02160 0.53607 best\n",
      "  6:   2.8s   T: 0.02137 0.55743   V: 0.02157 0.53071 best\n",
      "  7:   2.9s   T: 0.02128 0.56050   V: 0.02155 0.54143 best\n",
      "  8:   3.0s   T: 0.02125 0.56350   V: 0.02145 0.55250 best\n",
      "  9:   2.8s   T: 0.02125 0.56157   V: 0.02152 0.54464 \n",
      " 10:   2.8s   T: 0.02118 0.56600   V: 0.02145 0.55750 best\n",
      " 11:   2.8s   T: 0.02116 0.56771   V: 0.02139 0.55536 best\n",
      " 12:   2.8s   T: 0.02114 0.57321   V: 0.02143 0.55036 \n",
      " 13:   3.0s   T: 0.02110 0.57100   V: 0.02147 0.55143 \n",
      " 14:   2.8s   T: 0.02108 0.57493   V: 0.02132 0.56464 best\n",
      " 15:   2.7s   T: 0.02103 0.57814   V: 0.02148 0.54143 \n",
      " 16:   2.8s   T: 0.02106 0.57179   V: 0.02161 0.54036 \n",
      " 17:   2.8s   T: 0.02104 0.57921   V: 0.02151 0.53643 \n",
      " 18:   2.8s   T: 0.02101 0.57793   V: 0.02145 0.54500 \n",
      " 19:   2.8s   T: 0.02102 0.57250   V: 0.02157 0.55000 \n",
      " 20:   2.8s   T: 0.02101 0.58229   V: 0.02140 0.54107 \n",
      " 21:   2.8s   T: 0.02098 0.58314   V: 0.02157 0.55036 \n",
      " 22:   2.9s   T: 0.02102 0.57736   V: 0.02138 0.55964 \n",
      " 23:   3.0s   T: 0.02095 0.58150   V: 0.02136 0.54786 \n",
      " 24:   3.0s   T: 0.02093 0.58557   V: 0.02135 0.56107 \n",
      " 25:   2.9s   T: 0.02094 0.58250   V: 0.02124 0.56107 best\n",
      " 26:   2.9s   T: 0.02092 0.58336   V: 0.02128 0.56464 \n",
      " 27:   2.9s   T: 0.02088 0.58200   V: 0.02145 0.55750 \n",
      " 28:   2.9s   T: 0.02094 0.58086   V: 0.02135 0.54893 \n",
      " 29:   2.9s   T: 0.02092 0.58129   V: 0.02140 0.56500 \n",
      " 30:   2.9s   T: 0.02093 0.58214   V: 0.02139 0.55036 \n",
      " 31:   2.9s   T: 0.02087 0.58393   V: 0.02131 0.57500 \n",
      " 32:   2.9s   T: 0.02092 0.58164   V: 0.02137 0.56107 \n",
      " 33:   2.9s   T: 0.02081 0.58507   V: 0.02149 0.54750 \n",
      " 34:   2.9s   T: 0.02086 0.58086   V: 0.02121 0.56250 best\n",
      " 35:   2.9s   T: 0.02085 0.58721   V: 0.02151 0.55679 \n",
      " 36:   2.9s   T: 0.02082 0.58843   V: 0.02125 0.57214 \n",
      " 37:   2.9s   T: 0.02081 0.59279   V: 0.02122 0.56286 \n",
      " 38:   2.9s   T: 0.02086 0.58257   V: 0.02116 0.57750 best\n",
      " 39:   2.9s   T: 0.02082 0.58793   V: 0.02143 0.56321 \n",
      " 40:   2.8s   T: 0.02074 0.59100   V: 0.02120 0.57643 \n",
      " 41:   2.9s   T: 0.02077 0.58964   V: 0.02124 0.56607 \n",
      " 42:   2.9s   T: 0.02075 0.58929   V: 0.02152 0.56536 \n",
      " 43:   2.9s   T: 0.02085 0.58236   V: 0.02119 0.55429 \n",
      " 44:   2.9s   T: 0.02074 0.59507   V: 0.02112 0.56929 best\n",
      " 45:   2.9s   T: 0.02070 0.59993   V: 0.02120 0.57179 \n",
      " 46:   2.9s   T: 0.02081 0.58943   V: 0.02122 0.56500 \n",
      " 47:   2.9s   T: 0.02073 0.59000   V: 0.02138 0.56679 \n",
      " 48:   2.9s   T: 0.02073 0.59421   V: 0.02100 0.58964 best\n",
      " 49:   2.9s   T: 0.02075 0.59271   V: 0.02121 0.56679 \n",
      " 50:   2.9s   T: 0.02065 0.59664   V: 0.02127 0.56286 \n",
      " 51:   2.9s   T: 0.02069 0.59007   V: 0.02114 0.58464 \n",
      " 52:   2.9s   T: 0.02074 0.59664   V: 0.02119 0.57107 \n",
      " 53:   3.0s   T: 0.02068 0.59114   V: 0.02112 0.57500 \n",
      " 54:   3.0s   T: 0.02066 0.59521   V: 0.02129 0.55821 \n",
      " 55:   3.0s   T: 0.02065 0.59521   V: 0.02117 0.55143 \n",
      " 56:   3.0s   T: 0.02069 0.59464   V: 0.02113 0.57179 \n",
      " 57:   2.9s   T: 0.02066 0.59793   V: 0.02117 0.57393 \n",
      " 58:   2.9s   T: 0.02068 0.59271   V: 0.02101 0.56857 \n",
      " 59:   3.0s   T: 0.02068 0.59636   V: 0.02139 0.56964 \n",
      " 60:   3.0s   T: 0.02069 0.59486   V: 0.02094 0.57429 best\n",
      " 61:   3.0s   T: 0.02066 0.60150   V: 0.02109 0.56964 \n",
      " 62:   3.2s   T: 0.02056 0.60086   V: 0.02113 0.57357 \n",
      " 63:   2.9s   T: 0.02053 0.60207   V: 0.02120 0.56000 \n",
      " 64:   2.9s   T: 0.02063 0.59386   V: 0.02116 0.55821 \n",
      " 65:   2.9s   T: 0.02062 0.59436   V: 0.02103 0.56964 \n",
      " 66:   2.9s   T: 0.02056 0.60229   V: 0.02097 0.56714 \n",
      " 67:   2.9s   T: 0.02055 0.60071   V: 0.02104 0.57679 \n",
      " 68:   2.9s   T: 0.02058 0.60664   V: 0.02104 0.56750 \n",
      " 69:   2.9s   T: 0.02055 0.59843   V: 0.02134 0.56429 \n",
      " 70:   3.0s   T: 0.02065 0.58836   V: 0.02090 0.58429 best\n",
      " 71:   3.0s   T: 0.02059 0.59957   V: 0.02097 0.58607 \n",
      " 72:   2.9s   T: 0.02052 0.60093   V: 0.02099 0.56679 \n",
      " 73:   2.9s   T: 0.02055 0.60207   V: 0.02094 0.57607 \n",
      " 74:   2.9s   T: 0.02051 0.60093   V: 0.02086 0.57357 best\n",
      " 75:   2.9s   T: 0.02063 0.60029   V: 0.02085 0.58143 best\n",
      " 76:   2.9s   T: 0.02047 0.60043   V: 0.02088 0.57821 \n",
      " 77:   2.9s   T: 0.02050 0.59964   V: 0.02082 0.58357 best\n",
      " 78:   2.9s   T: 0.02048 0.60657   V: 0.02107 0.57179 \n",
      " 79:   2.9s   T: 0.02056 0.60200   V: 0.02115 0.55964 \n",
      " 80:   3.8s   T: 0.02046 0.60450   V: 0.02112 0.56179 \n",
      " 81:   5.3s   T: 0.02049 0.60293   V: 0.02095 0.57786 \n",
      " 82:   5.4s   T: 0.02048 0.60014   V: 0.02106 0.57786 \n",
      " 83:   5.4s   T: 0.02047 0.60057   V: 0.02115 0.57000 \n",
      " 84:   5.5s   T: 0.02059 0.60307   V: 0.02101 0.57429 \n",
      " 85:   5.4s   T: 0.02047 0.60236   V: 0.02090 0.58036 \n",
      " 86:   5.4s   T: 0.02038 0.60643   V: 0.02090 0.57250 \n",
      " 87:   5.4s   T: 0.02046 0.60500   V: 0.02101 0.56464 \n",
      " 88:   5.4s   T: 0.02042 0.60671   V: 0.02132 0.55643 \n",
      " 89:   5.4s   T: 0.02051 0.59979   V: 0.02091 0.58143 \n",
      " 90:   5.4s   T: 0.02043 0.60343   V: 0.02081 0.58429 best\n",
      " 91:   5.4s   T: 0.02047 0.60757   V: 0.02076 0.56857 best\n",
      " 92:   5.4s   T: 0.02045 0.60221   V: 0.02099 0.58000 \n",
      " 93:   5.4s   T: 0.02043 0.60414   V: 0.02108 0.56893 \n",
      " 94:   5.4s   T: 0.02049 0.59786   V: 0.02074 0.58643 best\n",
      " 95:   5.4s   T: 0.02028 0.61657   V: 0.02126 0.56857 \n",
      " 96:   5.5s   T: 0.02036 0.61114   V: 0.02070 0.58929 best\n",
      " 97:   5.6s   T: 0.02037 0.61221   V: 0.02059 0.59786 best\n",
      " 98:   5.5s   T: 0.02049 0.60200   V: 0.02059 0.59179 \n",
      " 99:   5.5s   T: 0.02038 0.60771   V: 0.02081 0.58893 \n",
      "100:   5.5s   T: 0.02035 0.60821   V: 0.02073 0.58286 \n",
      "101:   5.5s   T: 0.02045 0.60429   V: 0.02088 0.56964 \n",
      "102:   5.5s   T: 0.02036 0.60286   V: 0.02083 0.57750 \n",
      "103:   5.5s   T: 0.02037 0.60829   V: 0.02082 0.57786 \n",
      "104:   5.5s   T: 0.02036 0.60986   V: 0.02097 0.57571 \n",
      "105:   5.5s   T: 0.02038 0.60364   V: 0.02072 0.58429 \n",
      "106:   5.5s   T: 0.02037 0.60707   V: 0.02074 0.58357 \n",
      "107:   5.4s   T: 0.02036 0.60807   V: 0.02075 0.58357 \n",
      "108:   5.4s   T: 0.02030 0.61379   V: 0.02095 0.58321 \n",
      "109:   5.4s   T: 0.02036 0.60779   V: 0.02066 0.59571 \n",
      "110:   5.4s   T: 0.02034 0.60686   V: 0.02067 0.58357 \n",
      "111:   5.4s   T: 0.02028 0.61186   V: 0.02082 0.57964 \n",
      "112:   5.4s   T: 0.02026 0.60793   V: 0.02076 0.59179 \n",
      "113:   5.4s   T: 0.02024 0.61307   V: 0.02048 0.59464 best\n",
      "114:   5.4s   T: 0.02027 0.61093   V: 0.02071 0.59607 \n",
      "115:   5.4s   T: 0.02034 0.60293   V: 0.02072 0.59214 \n",
      "116:   5.4s   T: 0.02036 0.61221   V: 0.02064 0.58607 \n",
      "117:   5.4s   T: 0.02026 0.61221   V: 0.02056 0.59214 \n",
      "118:   5.4s   T: 0.02030 0.61150   V: 0.02060 0.59143 \n",
      "119:   5.4s   T: 0.02028 0.61136   V: 0.02068 0.58643 \n",
      "120:   5.4s   T: 0.02022 0.61743   V: 0.02066 0.58286 \n",
      "121:   5.4s   T: 0.02018 0.61914   V: 0.02054 0.59643 \n",
      "122:   5.4s   T: 0.02031 0.60807   V: 0.02050 0.60143 \n",
      "123:   5.4s   T: 0.02029 0.61371   V: 0.02067 0.58964 \n",
      "124:   5.4s   T: 0.02034 0.60636   V: 0.02036 0.60643 best\n",
      "125:   5.4s   T: 0.02032 0.61036   V: 0.02073 0.58143 \n",
      "126:   5.4s   T: 0.02016 0.61664   V: 0.02060 0.59036 \n",
      "127:   5.4s   T: 0.02025 0.61407   V: 0.02075 0.58679 \n",
      "128:   5.4s   T: 0.02022 0.61279   V: 0.02050 0.60250 \n",
      "129:   5.4s   T: 0.02024 0.61314   V: 0.02066 0.59107 \n",
      "130:   5.4s   T: 0.02017 0.61321   V: 0.02043 0.60857 \n",
      "131:   5.4s   T: 0.02028 0.61007   V: 0.02053 0.58893 \n",
      "132:   5.4s   T: 0.02016 0.61750   V: 0.02078 0.58893 \n",
      "133:   5.4s   T: 0.02014 0.61893   V: 0.02058 0.58250 \n",
      "134:   5.4s   T: 0.02019 0.61114   V: 0.02049 0.59607 \n",
      "135:   5.4s   T: 0.02019 0.61500   V: 0.02056 0.60393 \n",
      "136:   5.4s   T: 0.02009 0.61600   V: 0.02040 0.60607 \n",
      "137:   5.3s   T: 0.02016 0.61486   V: 0.02060 0.58143 \n",
      "138:   5.3s   T: 0.02019 0.61729   V: 0.02059 0.59321 \n",
      "139:   5.3s   T: 0.02022 0.60893   V: 0.02052 0.58786 \n",
      "140:   5.3s   T: 0.02030 0.61207   V: 0.02044 0.59643 \n",
      "141:   5.3s   T: 0.02006 0.62529   V: 0.02092 0.57750 \n",
      "142:   5.3s   T: 0.02020 0.61457   V: 0.02058 0.58679 \n",
      "143:   5.3s   T: 0.02020 0.61707   V: 0.02039 0.60750 \n",
      "144:   5.3s   T: 0.02020 0.61436   V: 0.02039 0.60607 \n",
      "145:   5.3s   T: 0.02009 0.61971   V: 0.02061 0.59786 \n",
      "146:   5.3s   T: 0.02013 0.61943   V: 0.02050 0.59643 \n",
      "147:   5.3s   T: 0.02020 0.61100   V: 0.02066 0.59643 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "148:   5.3s   T: 0.02012 0.61586   V: 0.02054 0.58821 \n",
      "149:   5.3s   T: 0.02009 0.61557   V: 0.02061 0.60036 \n",
      "150:   5.3s   T: 0.02014 0.61950   V: 0.02040 0.60393 \n",
      "151:   5.3s   T: 0.02016 0.61771   V: 0.02079 0.59321 \n",
      "152:   5.3s   T: 0.02008 0.61486   V: 0.02055 0.59286 \n",
      "153:   5.3s   T: 0.02015 0.61700   V: 0.02048 0.58393 \n",
      "154:   5.3s   T: 0.02009 0.62100   V: 0.02092 0.58786 \n",
      "155:   5.3s   T: 0.02009 0.61729   V: 0.02042 0.60286 \n",
      "156:   5.3s   T: 0.02008 0.62193   V: 0.02059 0.60179 \n",
      "157:   5.3s   T: 0.02006 0.61850   V: 0.02037 0.59929 \n",
      "158:   5.3s   T: 0.02006 0.61836   V: 0.02051 0.59643 \n",
      "159:   5.4s   T: 0.02003 0.62514   V: 0.02046 0.60607 \n",
      "160:   5.4s   T: 0.02003 0.62307   V: 0.02049 0.58857 \n",
      "161:   5.5s   T: 0.02003 0.62321   V: 0.02049 0.60036 \n",
      "162:   5.4s   T: 0.02003 0.62050   V: 0.02090 0.59821 \n",
      "163:   5.3s   T: 0.02000 0.62579   V: 0.02062 0.60500 \n",
      "164:   5.3s   T: 0.02004 0.61936   V: 0.02052 0.59643 \n",
      "165:   5.3s   T: 0.02000 0.61950   V: 0.02040 0.60964 \n",
      "166:   5.3s   T: 0.01998 0.62343   V: 0.02057 0.59857 \n",
      "167:   5.3s   T: 0.02001 0.62000   V: 0.02034 0.60214 best\n",
      "168:   5.3s   T: 0.02004 0.61986   V: 0.02045 0.60571 \n",
      "169:   5.3s   T: 0.02006 0.61964   V: 0.02029 0.59857 best\n",
      "170:   5.3s   T: 0.02007 0.61679   V: 0.02049 0.59786 \n",
      "171:   5.3s   T: 0.02016 0.61629   V: 0.02030 0.60286 \n",
      "172:   5.3s   T: 0.02008 0.61700   V: 0.02021 0.61321 best\n",
      "173:   5.3s   T: 0.02000 0.62079   V: 0.02046 0.60750 \n",
      "174:   5.3s   T: 0.02015 0.61771   V: 0.02070 0.58286 \n",
      "175:   5.3s   T: 0.01997 0.62164   V: 0.02060 0.59571 \n",
      "176:   5.3s   T: 0.01997 0.62836   V: 0.02077 0.59036 \n",
      "177:   5.3s   T: 0.02004 0.61871   V: 0.01999 0.61786 best\n",
      "178:   5.3s   T: 0.01997 0.62300   V: 0.02032 0.60071 \n",
      "179:   5.3s   T: 0.01988 0.62493   V: 0.02061 0.59179 \n",
      "180:   5.3s   T: 0.01997 0.62621   V: 0.02020 0.60643 \n",
      "181:   5.3s   T: 0.01997 0.62464   V: 0.02013 0.61429 \n",
      "182:   5.3s   T: 0.02010 0.61900   V: 0.02033 0.60679 \n",
      "183:   5.3s   T: 0.02007 0.61964   V: 0.02029 0.61036 \n",
      "184:   5.3s   T: 0.01999 0.62243   V: 0.02039 0.60071 \n",
      "185:   5.3s   T: 0.01988 0.62371   V: 0.02041 0.60643 \n",
      "186:   5.3s   T: 0.01984 0.63086   V: 0.02012 0.61679 \n",
      "187:   5.3s   T: 0.01998 0.61986   V: 0.02017 0.62464 \n",
      "188:   5.3s   T: 0.01996 0.62407   V: 0.02044 0.60643 \n",
      "189:   5.3s   T: 0.01997 0.62521   V: 0.02023 0.61250 \n",
      "190:   5.3s   T: 0.02001 0.62764   V: 0.02031 0.60000 \n",
      "191:   5.3s   T: 0.02001 0.62014   V: 0.02000 0.61214 \n",
      "192:   5.3s   T: 0.01998 0.62350   V: 0.02006 0.61607 \n",
      "193:   5.3s   T: 0.01994 0.62900   V: 0.02013 0.62179 \n",
      "194:   5.3s   T: 0.02001 0.61857   V: 0.02026 0.61607 \n",
      "195:   5.3s   T: 0.01992 0.62457   V: 0.02011 0.61464 \n",
      "196:   5.3s   T: 0.01994 0.62000   V: 0.02060 0.59429 \n",
      "197:   5.3s   T: 0.01994 0.62236   V: 0.02018 0.61179 \n",
      "198:   5.3s   T: 0.02000 0.62114   V: 0.01999 0.62107 \n",
      "199:   5.3s   T: 0.01995 0.62300   V: 0.02029 0.61250 \n",
      "200:   5.3s   T: 0.01998 0.62436   V: 0.02022 0.60643 \n",
      "201:   5.3s   T: 0.02005 0.62057   V: 0.02030 0.60143 \n",
      "202:   5.3s   T: 0.01987 0.62521   V: 0.01989 0.62429 best\n",
      "203:   5.3s   T: 0.01997 0.62957   V: 0.02047 0.60321 \n",
      "204:   5.3s   T: 0.01997 0.62593   V: 0.02055 0.60179 \n",
      "205:   5.3s   T: 0.01984 0.62407   V: 0.02004 0.62107 \n",
      "206:   5.3s   T: 0.01978 0.63771   V: 0.02040 0.61286 \n",
      "207:   5.3s   T: 0.01989 0.62650   V: 0.02032 0.60286 \n",
      "208:   5.3s   T: 0.01991 0.62514   V: 0.02015 0.62107 \n",
      "209:   5.3s   T: 0.01988 0.62657   V: 0.02018 0.62000 \n",
      "210:   5.3s   T: 0.01989 0.62971   V: 0.02009 0.61429 \n",
      "211:   5.3s   T: 0.01999 0.61879   V: 0.01989 0.62107 \n",
      "212:   5.3s   T: 0.02003 0.62107   V: 0.02011 0.62107 \n",
      "213:   5.3s   T: 0.01991 0.62593   V: 0.02021 0.61500 \n",
      "214:   5.3s   T: 0.01975 0.63164   V: 0.01995 0.62607 \n",
      "215:   5.3s   T: 0.01987 0.62793   V: 0.02009 0.62143 \n",
      "216:   5.3s   T: 0.01985 0.63014   V: 0.02009 0.61929 \n",
      "217:   5.3s   T: 0.02002 0.61714   V: 0.02019 0.61536 \n",
      "218:   5.3s   T: 0.01995 0.62793   V: 0.02047 0.60607 \n",
      "219:   5.3s   T: 0.01991 0.62393   V: 0.02010 0.62429 \n",
      "220:   5.3s   T: 0.01984 0.62729   V: 0.02041 0.59929 \n",
      "221:   5.3s   T: 0.01977 0.62914   V: 0.02010 0.60536 \n",
      "222:   5.3s   T: 0.01982 0.63414   V: 0.02007 0.61964 \n",
      "223:   5.3s   T: 0.01970 0.63400   V: 0.02009 0.61643 \n",
      "224:   5.3s   T: 0.01984 0.63293   V: 0.02086 0.59071 \n",
      "225:   5.3s   T: 0.01986 0.62736   V: 0.02004 0.61357 \n",
      "226:   5.3s   T: 0.01975 0.62821   V: 0.01986 0.62286 best\n",
      "227:   5.3s   T: 0.01982 0.63007   V: 0.02044 0.61393 \n",
      "228:   5.3s   T: 0.01981 0.63036   V: 0.02005 0.62071 \n",
      "229:   5.3s   T: 0.01990 0.63100   V: 0.02008 0.61821 \n",
      "230:   5.3s   T: 0.01988 0.62779   V: 0.02048 0.59929 \n",
      "231:   5.3s   T: 0.01989 0.63093   V: 0.01998 0.62607 \n",
      "232:   5.3s   T: 0.01983 0.62643   V: 0.02020 0.61571 \n",
      "233:   5.3s   T: 0.01978 0.63429   V: 0.02007 0.60071 \n",
      "234:   5.3s   T: 0.01987 0.62414   V: 0.02040 0.59714 \n",
      "235:   5.3s   T: 0.01991 0.62214   V: 0.02062 0.60000 \n",
      "236:   5.3s   T: 0.01982 0.62143   V: 0.02025 0.60536 \n",
      "237:   5.3s   T: 0.01974 0.62979   V: 0.02010 0.60929 \n",
      "238:   5.3s   T: 0.01980 0.62886   V: 0.02012 0.62179 \n",
      "239:   5.3s   T: 0.01977 0.63186   V: 0.02023 0.61179 \n",
      "240:   5.3s   T: 0.01977 0.63093   V: 0.02023 0.61929 \n",
      "241:   5.3s   T: 0.01986 0.62693   V: 0.01991 0.63143 \n",
      "242:   5.3s   T: 0.01982 0.62879   V: 0.02009 0.62321 \n",
      "243:   5.3s   T: 0.01981 0.63236   V: 0.01970 0.62893 best\n",
      "244:   5.3s   T: 0.01957 0.64036   V: 0.02007 0.61464 \n",
      "245:   5.3s   T: 0.01977 0.63000   V: 0.02025 0.60893 \n",
      "246:   5.3s   T: 0.01979 0.63264   V: 0.02021 0.61214 \n",
      "247:   5.3s   T: 0.01999 0.62264   V: 0.01968 0.63714 best\n",
      "248:   5.3s   T: 0.01980 0.63057   V: 0.02001 0.62786 \n",
      "249:   5.3s   T: 0.01974 0.62893   V: 0.02002 0.62036 \n",
      "250:   5.3s   T: 0.01982 0.63114   V: 0.02004 0.61786 \n",
      "251:   5.4s   T: 0.01977 0.62571   V: 0.01994 0.61393 \n",
      "252:   5.4s   T: 0.01987 0.62643   V: 0.01982 0.61893 \n",
      "253:   5.4s   T: 0.01989 0.62593   V: 0.02071 0.58750 \n",
      "254:   5.4s   T: 0.01985 0.62907   V: 0.01999 0.61679 \n",
      "255:   5.4s   T: 0.01981 0.63000   V: 0.01995 0.62429 \n",
      "256:   5.4s   T: 0.01976 0.62750   V: 0.02002 0.62643 \n",
      "257:   5.3s   T: 0.01977 0.62900   V: 0.02021 0.60857 \n",
      "258:   5.3s   T: 0.01973 0.63429   V: 0.01998 0.62214 \n",
      "259:   5.3s   T: 0.01985 0.62900   V: 0.02018 0.60750 \n",
      "260:   5.3s   T: 0.01974 0.63414   V: 0.02000 0.62750 \n",
      "261:   5.3s   T: 0.01970 0.63550   V: 0.01987 0.61964 \n",
      "262:   5.3s   T: 0.01972 0.63321   V: 0.02016 0.61393 \n",
      "263:   5.3s   T: 0.01986 0.62793   V: 0.02001 0.62250 \n",
      "264:   5.3s   T: 0.01964 0.63036   V: 0.02014 0.61679 \n",
      "265:   5.3s   T: 0.01973 0.63193   V: 0.02009 0.60964 \n",
      "266:   5.3s   T: 0.01981 0.63036   V: 0.02018 0.60821 \n",
      "267:   5.3s   T: 0.01972 0.63471   V: 0.01965 0.63929 best\n",
      "268:   5.3s   T: 0.01968 0.63129   V: 0.01988 0.62429 \n",
      "269:   5.3s   T: 0.01966 0.63279   V: 0.01996 0.61036 \n",
      "270:   5.3s   T: 0.01973 0.62993   V: 0.01978 0.62393 \n",
      "271:   5.3s   T: 0.01970 0.62957   V: 0.01966 0.63429 \n",
      "272:   5.3s   T: 0.01969 0.63350   V: 0.01985 0.63357 \n",
      "273:   5.3s   T: 0.01963 0.63436   V: 0.02004 0.62143 \n",
      "274:   5.3s   T: 0.01964 0.63700   V: 0.01999 0.62250 \n",
      "275:   5.3s   T: 0.01974 0.63229   V: 0.01987 0.62250 \n",
      "276:   5.3s   T: 0.01971 0.62950   V: 0.02030 0.61714 \n",
      "277:   5.3s   T: 0.01976 0.63071   V: 0.02036 0.61107 \n",
      "278:   5.3s   T: 0.01973 0.63179   V: 0.02030 0.60893 \n",
      "279:   5.3s   T: 0.01960 0.63200   V: 0.02017 0.60500 \n",
      "280:   5.3s   T: 0.01969 0.63250   V: 0.01976 0.62250 \n",
      "281:   5.3s   T: 0.01972 0.63421   V: 0.02031 0.60929 \n",
      "282:   5.3s   T: 0.01972 0.63179   V: 0.01979 0.62107 \n",
      "283:   5.3s   T: 0.01969 0.63164   V: 0.02005 0.62964 \n",
      "284:   5.3s   T: 0.01969 0.63236   V: 0.01964 0.62857 best\n",
      "285:   5.3s   T: 0.01979 0.62850   V: 0.01983 0.62571 \n",
      "286:   5.3s   T: 0.01969 0.63000   V: 0.02012 0.61643 \n",
      "287:   5.3s   T: 0.01973 0.63079   V: 0.01988 0.62179 \n",
      "288:   5.3s   T: 0.01963 0.63379   V: 0.02048 0.61321 \n",
      "289:   5.3s   T: 0.01962 0.63700   V: 0.02018 0.61429 \n",
      "290:   5.3s   T: 0.01967 0.63600   V: 0.02004 0.61679 \n",
      "291:   5.3s   T: 0.01975 0.63021   V: 0.02018 0.61286 \n",
      "292:   5.3s   T: 0.01970 0.63221   V: 0.02019 0.61929 \n",
      "293:   5.3s   T: 0.01974 0.63129   V: 0.01947 0.64393 best\n",
      "294:   5.3s   T: 0.01973 0.63093   V: 0.01966 0.63107 \n",
      "295:   5.3s   T: 0.01973 0.62964   V: 0.01970 0.63000 \n",
      "296:   5.3s   T: 0.01969 0.63407   V: 0.01977 0.63250 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "297:   5.3s   T: 0.01951 0.63914   V: 0.01999 0.62321 \n",
      "298:   5.3s   T: 0.01962 0.63736   V: 0.02027 0.61643 \n",
      "299:   5.3s   T: 0.01957 0.63793   V: 0.01988 0.62571 \n",
      "300:   5.3s   T: 0.01963 0.62886   V: 0.01986 0.63821 \n",
      "301:   5.3s   T: 0.01964 0.63200   V: 0.01979 0.60857 \n",
      "302:   5.3s   T: 0.01967 0.63507   V: 0.01973 0.63286 \n",
      "303:   5.3s   T: 0.01975 0.63893   V: 0.02007 0.62357 \n",
      "304:   5.3s   T: 0.01962 0.63536   V: 0.02025 0.61500 \n",
      "305:   5.3s   T: 0.01967 0.63821   V: 0.01990 0.61929 \n",
      "306:   5.3s   T: 0.01953 0.63657   V: 0.01999 0.62464 \n",
      "307:   5.3s   T: 0.01970 0.63021   V: 0.01969 0.62643 \n",
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    {
     "name": "stdout",
     "output_type": "stream",
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      "530:   5.4s   T: 0.01939 0.64279   V: 0.01981 0.62321 \n",
      "531:   5.4s   T: 0.01945 0.63979   V: 0.01943 0.64214 \n",
      "532:   5.4s   T: 0.01938 0.63929   V: 0.01952 0.65321 \n",
      "533:   5.4s   T: 0.01932 0.64729   V: 0.01940 0.64536 \n",
      "534:   5.4s   T: 0.01929 0.64093   V: 0.01932 0.65750 \n",
      "535:   5.4s   T: 0.01938 0.64414   V: 0.01938 0.65250 \n",
      "536:   5.4s   T: 0.01937 0.64143   V: 0.01954 0.63536 \n",
      "537:   5.4s   T: 0.01946 0.63736   V: 0.01949 0.64536 \n",
      "538:   5.4s   T: 0.01945 0.64386   V: 0.01947 0.63643 \n",
      "539:   5.4s   T: 0.01944 0.63614   V: 0.01977 0.63607 \n",
      "540:   5.3s   T: 0.01932 0.64350   V: 0.01927 0.65643 \n",
      "541:   5.3s   T: 0.01937 0.64321   V: 0.01957 0.64500 \n",
      "542:   5.3s   T: 0.01938 0.63929   V: 0.01953 0.64643 \n",
      "543:   5.4s   T: 0.01921 0.64950   V: 0.01978 0.62571 \n",
      "544:   5.3s   T: 0.01932 0.64564   V: 0.01942 0.63964 \n",
      "545:   5.3s   T: 0.01923 0.64400   V: 0.01961 0.64321 \n",
      "546:   5.3s   T: 0.01941 0.63871   V: 0.01923 0.64893 \n",
      "547:   5.3s   T: 0.01936 0.64500   V: 0.01956 0.63714 \n",
      "548:   5.3s   T: 0.01933 0.64664   V: 0.01945 0.64464 \n",
      "549:   5.3s   T: 0.01932 0.64714   V: 0.01919 0.65286 \n",
      "550:   5.3s   T: 0.01930 0.64729   V: 0.01955 0.62821 \n",
      "551:   5.3s   T: 0.01934 0.64543   V: 0.01962 0.62357 \n",
      "552:   5.3s   T: 0.01935 0.64493   V: 0.01967 0.63464 \n",
      "553:   5.3s   T: 0.01932 0.64464   V: 0.01957 0.64714 \n",
      "554:   5.3s   T: 0.01929 0.64607   V: 0.01948 0.64536 \n",
      "555:   5.3s   T: 0.01927 0.64143   V: 0.01955 0.64286 \n",
      "556:   5.3s   T: 0.01934 0.64729   V: 0.01901 0.65607 best\n",
      "557:   5.3s   T: 0.01935 0.64121   V: 0.01935 0.65571 \n",
      "558:   5.3s   T: 0.01928 0.64757   V: 0.01935 0.64321 \n",
      "559:   5.3s   T: 0.01929 0.64079   V: 0.01941 0.65000 \n",
      "560:   5.3s   T: 0.01935 0.64150   V: 0.01956 0.63107 \n",
      "561:   5.3s   T: 0.01944 0.64057   V: 0.01930 0.64750 \n",
      "562:   5.3s   T: 0.01935 0.64371   V: 0.01938 0.64214 \n",
      "563:   5.3s   T: 0.01922 0.64936   V: 0.01933 0.64107 \n",
      "564:   5.3s   T: 0.01933 0.64214   V: 0.01934 0.65607 \n",
      "565:   5.3s   T: 0.01936 0.63579   V: 0.01925 0.64571 \n",
      "566:   5.3s   T: 0.01923 0.64650   V: 0.01965 0.63536 \n",
      "567:   5.3s   T: 0.01928 0.64871   V: 0.01980 0.63714 \n",
      "568:   5.3s   T: 0.01938 0.64229   V: 0.01955 0.63964 \n",
      "569:   5.3s   T: 0.01940 0.64393   V: 0.01912 0.65679 \n",
      "570:   5.3s   T: 0.01927 0.64793   V: 0.01925 0.65250 \n",
      "571:   5.3s   T: 0.01945 0.63943   V: 0.01968 0.62929 \n",
      "572:   5.3s   T: 0.01938 0.63943   V: 0.01946 0.64964 \n",
      "573:   5.3s   T: 0.01934 0.64021   V: 0.01924 0.65571 \n",
      "574:   5.3s   T: 0.01931 0.64650   V: 0.01958 0.64286 \n",
      "575:   5.3s   T: 0.01938 0.64250   V: 0.01977 0.62821 \n",
      "576:   5.3s   T: 0.01943 0.63521   V: 0.01953 0.65000 \n",
      "577:   5.3s   T: 0.01930 0.64700   V: 0.01925 0.65571 \n",
      "578:   5.3s   T: 0.01935 0.64100   V: 0.01947 0.64893 \n",
      "579:   5.3s   T: 0.01953 0.63757   V: 0.01950 0.63571 \n",
      "580:   5.3s   T: 0.01932 0.64614   V: 0.01933 0.63643 \n",
      "581:   5.3s   T: 0.01935 0.64250   V: 0.01938 0.65071 \n",
      "582:   5.3s   T: 0.01922 0.64679   V: 0.01963 0.63643 \n",
      "583:   5.3s   T: 0.01925 0.64843   V: 0.01921 0.64857 \n",
      "584:   5.3s   T: 0.01931 0.64829   V: 0.01952 0.64214 \n",
      "585:   5.3s   T: 0.01944 0.63864   V: 0.01949 0.64429 \n",
      "586:   5.3s   T: 0.01931 0.64243   V: 0.01944 0.64679 \n",
      "587:   5.3s   T: 0.01935 0.64871   V: 0.01959 0.64000 \n",
      "588:   5.3s   T: 0.01928 0.64336   V: 0.01955 0.63071 \n",
      "589:   5.3s   T: 0.01943 0.63971   V: 0.01915 0.64857 \n",
      "590:   5.3s   T: 0.01940 0.64400   V: 0.01936 0.64214 \n",
      "591:   5.3s   T: 0.01935 0.64450   V: 0.01906 0.65821 \n",
      "592:   5.3s   T: 0.01936 0.64400   V: 0.01932 0.65036 \n",
      "593:   5.3s   T: 0.01930 0.64621   V: 0.01917 0.64893 \n",
      "594:   5.3s   T: 0.01947 0.64029   V: 0.01951 0.63750 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "595:   5.3s   T: 0.01918 0.65350   V: 0.01960 0.63929 \n",
      "596:   5.3s   T: 0.01931 0.64714   V: 0.01934 0.65107 \n",
      "597:   5.3s   T: 0.01936 0.64307   V: 0.01918 0.65000 \n",
      "598:   5.3s   T: 0.01933 0.64243   V: 0.01930 0.64393 \n",
      "599:   5.3s   T: 0.01936 0.63979   V: 0.01952 0.63571 \n",
      "600:   5.3s   T: 0.01934 0.64057   V: 0.01954 0.64071 \n",
      "601:   5.3s   T: 0.01933 0.64486   V: 0.01952 0.64071 \n",
      "602:   5.3s   T: 0.01922 0.64671   V: 0.01943 0.64750 \n",
      "603:   5.3s   T: 0.01938 0.64386   V: 0.01956 0.62750 \n",
      "604:   5.3s   T: 0.01932 0.64329   V: 0.01932 0.64857 \n",
      "605:   5.3s   T: 0.01937 0.64536   V: 0.01948 0.63929 \n",
      "606:   5.3s   T: 0.01933 0.64657   V: 0.01953 0.63500 \n",
      "607:   5.3s   T: 0.01926 0.64307   V: 0.01878 0.66214 best\n",
      "608:   5.3s   T: 0.01926 0.64400   V: 0.01912 0.64571 \n",
      "609:   5.3s   T: 0.01930 0.64650   V: 0.01913 0.64857 \n",
      "610:   5.3s   T: 0.01918 0.64864   V: 0.01987 0.64179 \n",
      "611:   5.3s   T: 0.01930 0.64493   V: 0.01932 0.64250 \n",
      "612:   5.3s   T: 0.01931 0.64550   V: 0.01966 0.63643 \n",
      "613:   5.3s   T: 0.01928 0.64479   V: 0.01905 0.64857 \n",
      "614:   5.3s   T: 0.01932 0.64493   V: 0.01952 0.64429 \n",
      "615:   5.3s   T: 0.01945 0.64000   V: 0.01963 0.63464 \n",
      "616:   5.3s   T: 0.01919 0.64750   V: 0.01917 0.63536 \n",
      "617:   5.3s   T: 0.01929 0.64407   V: 0.01941 0.64679 \n",
      "618:   5.3s   T: 0.01926 0.64543   V: 0.01919 0.65179 \n",
      "619:   5.3s   T: 0.01927 0.64214   V: 0.01934 0.64750 \n",
      "620:   5.3s   T: 0.01929 0.64800   V: 0.01933 0.63964 \n",
      "621:   5.3s   T: 0.01937 0.64093   V: 0.01928 0.63964 \n",
      "622:   5.3s   T: 0.01937 0.64271   V: 0.01897 0.66286 \n",
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      "627:   5.3s   T: 0.01922 0.64871   V: 0.01909 0.64964 \n",
      "628:   5.3s   T: 0.01933 0.64071   V: 0.01930 0.64214 \n",
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      "630:   5.3s   T: 0.01912 0.65343   V: 0.01954 0.64036 \n",
      "631:   5.3s   T: 0.01924 0.64421   V: 0.01951 0.64393 \n",
      "632:   5.3s   T: 0.01931 0.64543   V: 0.01908 0.63714 \n",
      "633:   5.3s   T: 0.01929 0.64736   V: 0.01930 0.65679 \n",
      "634:   5.3s   T: 0.01919 0.65000   V: 0.01968 0.62643 \n",
      "635:   5.3s   T: 0.01924 0.64679   V: 0.01922 0.65286 \n",
      "636:   5.3s   T: 0.01926 0.64564   V: 0.01941 0.64643 \n",
      "637:   5.3s   T: 0.01930 0.64607   V: 0.01968 0.63357 \n",
      "638:   5.3s   T: 0.01910 0.65421   V: 0.01919 0.66429 \n",
      "639:   5.3s   T: 0.01934 0.64507   V: 0.01944 0.64107 \n",
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      "641:   5.3s   T: 0.01908 0.64550   V: 0.01900 0.65786 \n",
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      "643:   5.3s   T: 0.01921 0.64893   V: 0.01923 0.64464 \n",
      "644:   5.3s   T: 0.01929 0.64593   V: 0.01926 0.65571 \n",
      "645:   5.3s   T: 0.01919 0.64321   V: 0.01917 0.64357 \n",
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      "648:   5.3s   T: 0.01929 0.64114   V: 0.01906 0.65679 \n",
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      "655:   5.3s   T: 0.01920 0.64600   V: 0.01925 0.64214 \n",
      "656:   5.4s   T: 0.01940 0.64150   V: 0.01905 0.66036 \n",
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      "659:   5.4s   T: 0.01914 0.65143   V: 0.01941 0.64679 \n",
      "660:   5.4s   T: 0.01923 0.64871   V: 0.01935 0.64893 \n",
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      "674:   5.4s   T: 0.01910 0.65400   V: 0.01911 0.66143 \n",
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      "680:   5.3s   T: 0.01918 0.64593   V: 0.01915 0.65321 \n",
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      "686:   5.3s   T: 0.01927 0.64400   V: 0.01931 0.63786 \n",
      "687:   5.3s   T: 0.01922 0.64814   V: 0.01929 0.64143 \n",
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      "689:   5.3s   T: 0.01915 0.65193   V: 0.01903 0.65036 \n",
      "690:   5.3s   T: 0.01917 0.64543   V: 0.01905 0.65679 \n",
      "691:   5.3s   T: 0.01916 0.65350   V: 0.01935 0.63571 \n",
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      "693:   5.3s   T: 0.01929 0.64507   V: 0.01928 0.64643 \n",
      "694:   5.3s   T: 0.01921 0.64657   V: 0.01946 0.63643 \n",
      "695:   5.3s   T: 0.01930 0.64571   V: 0.01933 0.64821 \n",
      "696:   5.3s   T: 0.01928 0.64221   V: 0.01946 0.63786 \n",
      "697:   5.3s   T: 0.01908 0.65493   V: 0.01944 0.64571 \n",
      "698:   5.3s   T: 0.01920 0.65043   V: 0.01954 0.63607 \n",
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      "700:   5.3s   T: 0.01924 0.64400   V: 0.01922 0.66036 \n",
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      "703:   5.3s   T: 0.01912 0.65357   V: 0.01903 0.66107 \n",
      "704:   5.3s   T: 0.01913 0.65379   V: 0.01918 0.65286 \n",
      "705:   5.3s   T: 0.01927 0.64700   V: 0.01956 0.64607 \n",
      "706:   5.3s   T: 0.01918 0.64293   V: 0.01939 0.64607 \n",
      "707:   5.3s   T: 0.01920 0.65100   V: 0.01902 0.65500 \n",
      "708:   5.3s   T: 0.01922 0.64343   V: 0.01942 0.64607 \n",
      "709:   5.3s   T: 0.01918 0.64886   V: 0.01897 0.66107 \n",
      "710:   5.3s   T: 0.01916 0.64707   V: 0.01958 0.63464 \n",
      "711:   5.3s   T: 0.01914 0.65271   V: 0.01895 0.65607 \n",
      "712:   5.3s   T: 0.01919 0.64900   V: 0.01953 0.64786 \n",
      "713:   5.3s   T: 0.01916 0.64529   V: 0.01904 0.65071 \n",
      "714:   5.3s   T: 0.01922 0.64121   V: 0.01884 0.65893 \n",
      "715:   5.3s   T: 0.01919 0.64814   V: 0.01958 0.63214 \n",
      "716:   5.3s   T: 0.01934 0.64586   V: 0.01920 0.64964 \n",
      "717:   5.3s   T: 0.01912 0.65400   V: 0.01902 0.65571 \n",
      "718:   5.3s   T: 0.01909 0.65143   V: 0.01899 0.65536 \n",
      "719:   5.3s   T: 0.01928 0.64407   V: 0.01946 0.64929 \n",
      "720:   5.3s   T: 0.01919 0.65079   V: 0.01933 0.65321 \n",
      "721:   5.3s   T: 0.01919 0.65293   V: 0.01872 0.66179 best\n",
      "722:   5.3s   T: 0.01919 0.64400   V: 0.01921 0.64821 \n",
      "723:   5.3s   T: 0.01901 0.65300   V: 0.01917 0.64786 \n",
      "724:   5.3s   T: 0.01913 0.65371   V: 0.01927 0.64429 \n",
      "725:   5.3s   T: 0.01927 0.64429   V: 0.01913 0.65607 \n",
      "726:   5.3s   T: 0.01919 0.64657   V: 0.01871 0.65286 best\n",
      "727:   5.3s   T: 0.01919 0.64771   V: 0.01903 0.65179 \n",
      "728:   5.3s   T: 0.01918 0.64993   V: 0.01905 0.65179 \n",
      "729:   5.3s   T: 0.01924 0.64686   V: 0.01908 0.65893 \n",
      "730:   5.3s   T: 0.01918 0.64686   V: 0.01952 0.63821 \n",
      "731:   5.3s   T: 0.01910 0.65471   V: 0.01933 0.65000 \n",
      "732:   5.3s   T: 0.01918 0.65071   V: 0.01893 0.67036 \n",
      "733:   5.3s   T: 0.01933 0.64107   V: 0.01943 0.63964 \n",
      "734:   5.3s   T: 0.01925 0.64157   V: 0.01933 0.63893 \n",
      "735:   5.3s   T: 0.01914 0.65400   V: 0.01875 0.66679 \n",
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      "737:   5.3s   T: 0.01929 0.64750   V: 0.01928 0.65607 \n",
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      "740:   5.3s   T: 0.01922 0.64743   V: 0.01945 0.65571 \n",
      "741:   5.3s   T: 0.01929 0.64829   V: 0.01925 0.64357 \n",
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      "743:   5.3s   T: 0.01929 0.64607   V: 0.01925 0.64500 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "744:   5.4s   T: 0.01927 0.64479   V: 0.01924 0.65107 \n",
      "745:   5.4s   T: 0.01918 0.64764   V: 0.01961 0.63321 \n",
      "746:   5.3s   T: 0.01930 0.64421   V: 0.01911 0.65536 \n",
      "747:   5.3s   T: 0.01918 0.64964   V: 0.01924 0.64607 \n",
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      "750:   5.3s   T: 0.01926 0.64807   V: 0.01911 0.64750 \n",
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      "752:   5.3s   T: 0.01913 0.64664   V: 0.01912 0.64786 \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "973:   5.3s   T: 0.01912 0.64807   V: 0.01912 0.66000 \n",
      "974:   5.3s   T: 0.01914 0.64800   V: 0.01904 0.66536 \n",
      "975:   5.3s   T: 0.01921 0.64500   V: 0.01901 0.65893 \n",
      "976:   5.3s   T: 0.01905 0.64936   V: 0.01908 0.66536 \n",
      "977:   5.3s   T: 0.01916 0.64957   V: 0.01889 0.65821 \n",
      "978:   5.3s   T: 0.01916 0.65014   V: 0.01931 0.64464 \n",
      "979:   5.3s   T: 0.01925 0.64829   V: 0.01896 0.66643 \n",
      "980:   5.3s   T: 0.01910 0.64864   V: 0.01928 0.64429 \n",
      "981:   5.3s   T: 0.01913 0.65071   V: 0.01907 0.65679 \n",
      "982:   5.3s   T: 0.01915 0.64836   V: 0.01942 0.63536 \n",
      "983:   5.3s   T: 0.01910 0.64429   V: 0.01944 0.64107 \n",
      "984:   5.3s   T: 0.01920 0.64921   V: 0.01892 0.66893 \n",
      "985:   5.3s   T: 0.01915 0.65014   V: 0.01891 0.66107 \n",
      "986:   5.3s   T: 0.01905 0.64986   V: 0.01936 0.63643 \n",
      "987:   5.3s   T: 0.01904 0.65400   V: 0.01874 0.66286 \n",
      "988:   5.3s   T: 0.01895 0.65536   V: 0.01874 0.65643 \n",
      "989:   5.3s   T: 0.01897 0.65679   V: 0.01912 0.65357 \n",
      "990:   5.3s   T: 0.01894 0.65429   V: 0.01905 0.65000 \n",
      "991:   5.3s   T: 0.01893 0.65686   V: 0.01871 0.66536 \n",
      "992:   5.3s   T: 0.01904 0.64757   V: 0.01938 0.64679 \n",
      "993:   5.3s   T: 0.01912 0.65279   V: 0.01908 0.65893 \n",
      "994:   5.3s   T: 0.01895 0.65371   V: 0.01881 0.67000 \n",
      "995:   5.3s   T: 0.01902 0.65179   V: 0.01916 0.64857 \n",
      "996:   5.3s   T: 0.01928 0.64093   V: 0.01892 0.65714 \n",
      "997:   5.3s   T: 0.01917 0.64921   V: 0.01904 0.65036 \n",
      "998:   5.3s   T: 0.01909 0.65021   V: 0.01951 0.62929 \n",
      "999:   5.3s   T: 0.01895 0.65793   V: 0.01901 0.65786 \n",
      "1000:   5.3s   T: 0.01902 0.65214   V: 0.01875 0.67107 \n",
      "Best model was saved at epoch 897 with loss 0.01844: ./Resn_NCMCI_Cor0\n",
      "Stop training at epoch: 1000/1000\n"
     ]
    }
   ],
   "source": [
    "skf = StratifiedShuffleSplit(n_splits=1, test_size=70, train_size=280, random_state=996622)\n",
    "batch_size = 32\n",
    "X = np.arange(num_suj)\n",
    "acc_kfold = []\n",
    "fold = 0\n",
    "trainer = {}\n",
    "final_loss_train = np.array([]).reshape(0,n_epochs)\n",
    "final_loss_valid = np.array([]).reshape(0,n_epochs)\n",
    "final_acc_train = np.array([]).reshape(0,n_epochs)\n",
    "final_acc_valid = np.array([]).reshape(0,n_epochs)\n",
    "\n",
    "for train_s, valid_s in skf.split(X, labels_suj):\n",
    "    model = MyResNet()\n",
    "    if cuda_aval: \n",
    "        model = model.cuda()\n",
    "        \n",
    "    optimizer = optim.Adam(model.mrnd.parameters(), lr=0.0005)#, momentum=0.8)\n",
    "    fp_model = './Resn_NCMCI_Cor{}'.format(fold)\n",
    "    cb_chkpt = ptt.ModelCheckpoint(fp_model, reset=True, verbose=1)\n",
    "\n",
    "    trainer[\"string{0}\".format(fold)] = ptt.DeepNetTrainer(model = model, criterion = criterion, optimizer = optimizer,\n",
    "                                        data_transf = data_transf,\n",
    "                                        callbacks = [ptt.AccuracyMetric(), cb_chkpt, ptt.PrintCallback()])\n",
    "\n",
    "    ind_start_train = np.take(ind_suj[:-1],train_s)\n",
    "    ind_end_train = np.take(ind_suj[1:],train_s)\n",
    "\n",
    "    x_train = torch.cat([feat_set[i:j] for i,j in zip(ind_start_train,ind_end_train)])\n",
    "    labels_train = np.concatenate([labels[i:j] for i,j in zip(ind_start_train,ind_end_train)])\n",
    "    y_train = torch.LongTensor(labels_train)\n",
    "\n",
    "    ind_start_valid = np.take(ind_suj[:-1],valid_s)\n",
    "    ind_end_valid = np.take(ind_suj[1:],valid_s)\n",
    "\n",
    "    x_valid = torch.cat([feat_set[i:j] for i,j in zip(ind_start_valid,ind_end_valid)])\n",
    "    labels_valid = np.concatenate([labels[i:j] for i,j in zip(ind_start_valid,ind_end_valid)])\n",
    "    y_valid_true = labels_suj[valid_s].astype(\"uint8\")\n",
    "    y_valid = torch.LongTensor(labels_valid)\n",
    "\n",
    "    y_train = torch.LongTensor(labels)\n",
    "\n",
    "    train_loader = DataLoader(ptt.create_dataloaders(feat_set, y_train, transform=data_transf, n_epochs=n_epochs), \n",
    "                          batch_size=batch_size, shuffle=True)\n",
    "\n",
    "    valid_loader = DataLoader(ptt.create_dataloaders(x_valid, y_valid, transform=data_transf, n_epochs=n_epochs),\n",
    "                           batch_size=batch_size, shuffle=True) \n",
    "\n",
    "    trainer[\"string{0}\".format(fold)].fit_loader(n_epochs, train_loader, valid_data=valid_loader)\n",
    "\n",
    "\n",
    "    final_loss_train = np.vstack((final_loss_train,trainer[\"string{0}\".format(fold)].metrics['train']['losses']))\n",
    "    final_loss_valid = np.vstack((final_loss_valid,trainer[\"string{0}\".format(fold)].metrics['valid']['losses']))\n",
    "    final_acc_train = np.vstack((final_acc_train,trainer[\"string{0}\".format(fold)].metrics['train']['acc']))\n",
    "    final_acc_valid = np.vstack((final_acc_valid,trainer[\"string{0}\".format(fold)].metrics['valid']['acc']))\n",
    "\n",
    "    del x_train, y_train, x_valid, y_valid\n",
    "    gc.collect()\n",
    "    fold += 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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O0qINDSSbm7GxWMbNQFbbZENDTw5NREREjkEK0goUKCsDwHvwIPV33EEiIy9a+PXXsxsb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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,8))\n",
    "fold = 1 \n",
    "colors2 = np.array(['maroon', 'k','olive','orange', 'blue','green','blue'])\n",
    "colors1 = np.array(['lightcoral','slategrey','darkseagreen', 'darkgoldenrod', 'slateblue' ])\n",
    "labels_kfold = np.array(['Fold 1','Fold 2', 'Fold 3','Fold 4','Fold 5','Fold 6'])\n",
    "labels_kfold = np.array(['Train 1', 'Train 2',  'Train 3', 'Train 4' ,'Train 5'])\n",
    "labels_kfold2 = np.array(['Validation','Validation','Validation', 'Validation','Validation'])\n",
    "\n",
    "for i in range(fold):\n",
    "    plt.plot(final_loss_train[i,:], color=colors2[i], label=labels_kfold[i])\n",
    "    plt.grid()\n",
    "    plt.plot(final_loss_valid[i,:], color=colors1[i], linestyle= 'dashed', label=labels_kfold2[i])\n",
    "    \n",
    "    plt.grid()\n",
    "    plt.title('Loss') \n",
    "    plt.legend(loc= 'upper left')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8s/VoFNH334d74EBIRUWGJVuWoWza5JhcclmGHo3Cn+1dYSwZA2BapsU1p/lz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\n0YULEX72WSAWsxNEWQZRVSjduqXJONxnn83Jn+8b3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Q4AACAASURB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\nJVkBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "fold = 1\n",
    "colors2 = np.array(['maroon', 'k','olive','orange', 'blue','green','blue'])\n",
    "colors1 = np.array(['lightcoral','slategrey','darkseagreen', 'darkgoldenrod', 'slateblue' ])\n",
    "labels_kfold = np.array(['Fold 1','Fold 2', 'Fold 3','Fold 4','Fold 5','Fold 6'])\n",
    "labels_kfold = np.array(['Train 1', 'Train 2',  'Train 3', 'Train 4' ,'Train 5'])\n",
    "labels_kfold2 = np.array(['Validation','Validation','Validation', 'Validation','Validation'])\n",
    "\n",
    "for i in range(fold):\n",
    "    plt.plot(final_acc_train[i,:], color=colors2[i], label=labels_kfold[i])\n",
    "    plt.plot(final_acc_valid[i,:], color=colors1[i], linestyle= 'dashed', label=labels_kfold2[i]) \n",
    "    plt.grid()\n",
    "    plt.title('Accuracy') \n",
    "    plt.legend(loc= 'upper left')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(720, 3, 100, 100)\n",
      "(720, 1)\n",
      "18\n",
      "19\n",
      "(2,)\n",
      "18\n"
     ]
    }
   ],
   "source": [
    "dir_bas =   '/home/mariana/Documents/Data_2D/NCAD/'+'images_total_t.npy'\n",
    "dir_bas2 =  '/home/mariana/Documents/Data_2D/NCAD/'+'labels_t.npy'\n",
    "dir_bas3 =  '/home/mariana/Documents/Data_2D/NCAD/'+'labels_suj_t.npy'\n",
    "dir_bas4 =  '/home/mariana/Documents/Data_2D/NCAD/'+'ind_suj_t.npy'\n",
    "dir_bas5 =  '/home/mariana/Documents/Data_2D/NCAD/'+'n_labels_t.npy'\n",
    "dir_bas6 =  '/home/mariana/Documents/Data_2D/NCAD/'+'num_suj_t.npy' \n",
    "\n",
    "images_total = np.load(dir_bas)\n",
    "labels       = np.load(dir_bas2)\n",
    "labels_suj   = np.load(dir_bas3)\n",
    "ind_suj      = np.load(dir_bas4)\n",
    "n_labels     = np.load(dir_bas5)\n",
    "num_suj      = np.load(dir_bas6)\n",
    "\n",
    "print(images_total.shape)\n",
    "print(labels.shape)\n",
    "print(len(labels_suj))\n",
    "print(len(ind_suj))\n",
    "print(n_labels.shape)\n",
    "print(num_suj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([720, 3, 100, 100])\n"
     ]
    }
   ],
   "source": [
    "inp_set = torch.Tensor(images_total)\n",
    "if cuda_aval:\n",
    "    inp_set = inp_set.cuda()\n",
    "feat_set = inp_set\n",
    "\n",
    "print(feat_set.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(720,)\n",
      "[[0.675 0.2   0.65  0.175 0.675 1.    0.95  0.7   0.575 0.75  0.825 0.15\n",
      "  0.9   0.975 0.6   0.525 0.3   0.775]\n",
      " [0.325 0.8   0.35  0.825 0.325 0.    0.05  0.3   0.425 0.25  0.175 0.85\n",
      "  0.1   0.025 0.4   0.475 0.7   0.225]]\n",
      "0.5555555555555556\n",
      "[1 1 0 1 1 0 0 0 1 0 1 1 0 0 1 1 0 1]\n",
      "[0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0]\n",
      "Confusion matrix, without normalization\n",
      "[[7 1]\n",
      " [7 3]]\n",
      "Normalized confusion matrix\n",
      "[[0.88 0.12]\n",
      " [0.7  0.3 ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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JGlH7GdgLuK/RNm4am1nhag9vapF1gCtTwhwIXBIR1zXawInQzEqhVVeNI+JfwBbNbONE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\nRGhmledEaGaV50RoZpXnRGhmlff/ATtdPddBmQOnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sPxv4GlANLAfOiogXGywzomkuiElaHhGdJE0BxgIDgb+RvJ93hKSngS9HxLP1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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inp_test = torch.Tensor(images_total)\n",
    "if cuda_aval:\n",
    "    inp_test = inp_test.cuda()\n",
    "f = 1\n",
    "\n",
    "for f in range(f):\n",
    "    fp_model = './Resn_NCMCI_Cor{}'.format(f)\n",
    "\n",
    "    cb_chkpt = ptt.ModelCheckpoint(fp_model, reset=True, verbose=1) \n",
    "    criterion = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "    model_test = ptt.DeepNetTrainer(model = model, criterion = criterion, optimizer = optimizer,\n",
    "                                            data_transf = data_transf,\n",
    "                                            #lr_scheduler  = schd,\n",
    "                                            callbacks = [ptt.AccuracyMetric(), cb_chkpt, ptt.PrintCallback()])\n",
    "\n",
    "    model_test.load_state(fp_model)\n",
    "    y_better_out = model_test.predict(inp_test, labels)\n",
    " \n",
    "    __, y_better_out = torch.max(y_better_out, 1)\n",
    "    y_better_out = y_better_out.cpu().numpy()\n",
    "    print(y_better_out.shape)\n",
    "\n",
    "    y_prob_pred, y_better_pred = get_labels(y_better_out,ind_suj[1:],ind_suj[:-1],n_labels)\n",
    "    print(y_prob_pred)\n",
    "    acc_k = accuracy_score(labels_suj, y_better_pred)\n",
    "    print(acc_k)\n",
    "    print(labels_suj.astype(int))\n",
    "    print(y_better_pred)\n",
    "    \n",
    "    # Compute confusion matrix\n",
    "    cnf_matrix = confusion_matrix(labels_suj, y_better_pred)\n",
    "    np.set_printoptions(precision=2)\n",
    "\n",
    "    # Plot non-normalized confusion matrix\n",
    "    plt.figure()\n",
    "    plot_confusion_matrix(cnf_matrix, classes=class_names,\n",
    "                          title='Confusion matrix, without normalization')\n",
    "\n",
    "    # Plot normalized confusion matrix\n",
    "    plt.figure()\n",
    "    plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,\n",
    "                          title='Normalized confusion matrix')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.68 0.2  0.65 0.17 0.68 1.   0.95 0.7  0.57 0.75 0.82 0.15 0.9  0.97\n",
      "  0.6  0.53 0.3  0.78]\n",
      " [0.33 0.8  0.35 0.82 0.33 0.   0.05 0.3  0.42 0.25 0.17 0.85 0.1  0.03\n",
      "  0.4  0.47 0.7  0.23]]\n"
     ]
    }
   ],
   "source": [
    "print(y_prob_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.68 0.2  0.65 0.17 0.68 1.   0.95 0.7  0.57 0.75 0.82 0.15 0.9  0.97\n",
      "  0.6  0.53 0.3  0.78]\n",
      " [0.33 0.8  0.35 0.82 0.33 0.   0.05 0.3  0.42 0.25 0.17 0.85 0.1  0.03\n",
      "  0.4  0.47 0.7  0.23]] (2, 18)\n"
     ]
    }
   ],
   "source": [
    "print(y_prob_pred, y_prob_pred.shape)\n",
    "dir_bas =  '/home/mariana/Documents/Data_2D/Ens_Coronal/'+'NCMCI_only.npy'\n",
    "np.save(dir_bas, y_prob_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1040, 3, 100, 100)\n",
      "(1040, 1)\n",
      "26\n",
      "27\n",
      "(3,)\n",
      "26\n"
     ]
    }
   ],
   "source": [
    "dir_bas =   '/home/mariana/Documents/Data_2D/Test_1_Cor/'+'images_total.npy'\n",
    "dir_bas2 =  '/home/mariana/Documents/Data_2D/Test_1_Cor/'+'labels.npy'\n",
    "dir_bas3 =  '/home/mariana/Documents/Data_2D/Test_1_Cor/'+'labels_suj.npy'\n",
    "dir_bas4 =  '/home/mariana/Documents/Data_2D/Test_1_Cor/'+'ind_suj.npy'\n",
    "dir_bas5 =  '/home/mariana/Documents/Data_2D/Test_1_Cor/'+'n_labels.npy'\n",
    "dir_bas6 =  '/home/mariana/Documents/Data_2D/Test_1_Cor/'+'num_suj.npy' \n",
    "\n",
    "images_total = np.load(dir_bas)\n",
    "labels       = np.load(dir_bas2)\n",
    "labels_suj   = np.load(dir_bas3)\n",
    "ind_suj      = np.load(dir_bas4)\n",
    "n_labels     = np.load(dir_bas5)\n",
    "num_suj      = np.load(dir_bas6)\n",
    "\n",
    "print(images_total.shape)\n",
    "print(labels.shape)\n",
    "print(len(labels_suj))\n",
    "print(len(ind_suj))\n",
    "print(n_labels.shape)\n",
    "print(num_suj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1040, 3, 100, 100)\n",
      "(1040, 1)\n",
      "26\n",
      "27\n",
      "(3,)\n",
      "26\n"
     ]
    }
   ],
   "source": [
    "dir_bas =   '/home/mariana/Documents/Data_2D/Test_2_Cor/'+'images_total.npy'\n",
    "dir_bas2 =  '/home/mariana/Documents/Data_2D/Test_2_Cor/'+'labels.npy'\n",
    "dir_bas3 =  '/home/mariana/Documents/Data_2D/Test_2_Cor/'+'labels_suj.npy'\n",
    "dir_bas4 =  '/home/mariana/Documents/Data_2D/Test_2_Cor/'+'ind_suj.npy'\n",
    "dir_bas5 =  '/home/mariana/Documents/Data_2D/Test_2_Cor/'+'n_labels.npy'\n",
    "dir_bas6 =  '/home/mariana/Documents/Data_2D/Test_2_Cor/'+'num_suj.npy'\n",
    "\n",
    "images_total = np.load(dir_bas)\n",
    "labels       = np.load(dir_bas2)\n",
    "labels_suj   = np.load(dir_bas3)\n",
    "ind_suj      = np.load(dir_bas4)\n",
    "n_labels     = np.load(dir_bas5)\n",
    "num_suj      = np.load(dir_bas6)\n",
    "\n",
    "print(images_total.shape)\n",
    "print(labels.shape)\n",
    "print(len(labels_suj))\n",
    "print(len(ind_suj))\n",
    "print(n_labels.shape)\n",
    "print(num_suj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2080, 3, 100, 100)\n",
      "(2080, 1)\n",
      "52\n",
      "53\n",
      "(3,)\n",
      "52\n"
     ]
    }
   ],
   "source": [
    "dir_bas  =  '/home/mariana/Documents/Data_2D/Ensamble_CERTO/'+'images_total.npy'\n",
    "dir_bas2 =  '/home/mariana/Documents/Data_2D/Ensamble_CERTO/'+'labels.npy'\n",
    "dir_bas3 =  '/home/mariana/Documents/Data_2D/Ensamble_CERTO/'+'labels_suj.npy'\n",
    "dir_bas4 =  '/home/mariana/Documents/Data_2D/Ensamble_CERTO/'+'ind_suj.npy'\n",
    "dir_bas5 =  '/home/mariana/Documents/Data_2D/Ensamble_CERTO/'+'n_labels.npy'\n",
    "dir_bas6 =  '/home/mariana/Documents/Data_2D/Ensamble_CERTO/'+'num_suj.npy'\n",
    "\n",
    "images_total = np.load(dir_bas)\n",
    "labels       = np.load(dir_bas2)\n",
    "labels_suj   = np.load(dir_bas3)\n",
    "ind_suj      = np.load(dir_bas4)\n",
    "n_labels     = np.load(dir_bas5)\n",
    "num_suj      = np.load(dir_bas6)\n",
    "\n",
    "\n",
    "print(images_total.shape)\n",
    "print(labels.shape)\n",
    "print(len(labels_suj))\n",
    "print(len(ind_suj))\n",
    "print(n_labels.shape)\n",
    "print(num_suj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2080, 3, 100, 100])\n"
     ]
    }
   ],
   "source": [
    "inp_set = torch.Tensor(images_total)\n",
    "if cuda_aval:\n",
    "    inp_set = inp_set.cuda()\n",
    "feat_set = inp_set\n",
    "\n",
    "print(feat_set.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here tensor([[0.0000, 0.1143],\n",
      "        [0.3143, 0.1429],\n",
      "        [0.2286, 0.8857],\n",
      "        [0.8000, 0.0857],\n",
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      "        [0.0286, 0.7143],\n",
      "        [0.1429, 0.2571],\n",
      "        [0.8286, 0.6857],\n",
      "        [0.4286, 0.8000],\n",
      "        [0.0286, 0.6286],\n",
      "        [0.5714, 0.7429],\n",
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      "        [1.0000, 0.5143],\n",
      "        [0.3714, 0.9714],\n",
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      "        [0.4857, 0.3143],\n",
      "        [0.4857, 0.3714],\n",
      "        [0.1143, 0.6000],\n",
      "        [0.3429, 0.8286],\n",
      "        [0.0286, 0.4000],\n",
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      "        [0.8000, 0.6286],\n",
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      "        [0.0857, 0.4286],\n",
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      "        [1.0000, 0.6000],\n",
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      "        [0.8571, 0.5429],\n",
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      "        [0.4571, 0.8571],\n",
      "        [0.1429, 0.0857],\n",
      "        [0.3429, 0.6857],\n",
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      "        [0.1429, 0.5429],\n",
      "        [0.4857, 0.0857],\n",
      "        [0.0857, 0.7714],\n",
      "        [0.9429, 0.8857],\n",
      "        [0.5143, 0.9143],\n",
      "        [0.2000, 0.2000],\n",
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      "        [0.6571, 0.7714],\n",
      "        [0.9714, 0.4571],\n",
      "        [0.2571, 0.4857],\n",
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      "        [0.2286, 0.8286],\n",
      "        [0.8286, 0.4286],\n",
      "        [0.1143, 0.6286],\n",
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      "        [0.9143, 0.3143],\n",
      "        [0.8571, 0.2000],\n",
      "        [0.2571, 0.2000],\n",
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      "        [-1.4988,  1.6267],\n",
      "        [ 0.1205,  0.0338],\n",
      "        [ 0.0142,  0.1248],\n",
      "        [ 0.3744, -0.1769],\n",
      "        [-0.1139,  0.2477],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.0623,  0.1054],\n",
      "        [-0.0148,  0.1782],\n",
      "        [ 0.0747,  0.1497],\n",
      "        [-0.9377,  1.0290],\n",
      "        [ 0.9502, -0.7258],\n",
      "        [-0.1704,  0.3127],\n",
      "        [ 0.4371, -0.2535],\n",
      "        [-0.2384,  0.3923],\n",
      "        [-0.0981,  0.2245],\n",
      "        [-1.2275,  1.3977],\n",
      "        [-1.2712,  1.4147],\n",
      "        [ 0.6238, -0.4255],\n",
      "        [-0.4807,  0.6538],\n",
      "        [ 0.0932,  0.0669],\n",
      "        [ 0.6864, -0.5789],\n",
      "        [ 0.3484, -0.1676],\n",
      "        [ 0.1185,  0.0472],\n",
      "        [-0.3861,  0.5635],\n",
      "        [ 0.1126,  0.0381],\n",
      "        [-0.0028,  0.1831],\n",
      "        [ 0.1554,  0.0110],\n",
      "        [ 0.1647,  0.0011],\n",
      "        [ 0.2044, -0.0404],\n",
      "        [ 0.4086, -0.2626],\n",
      "        [ 0.1119,  0.0594],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.0368,  0.1377],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [-0.0482,  0.1954],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.1615,  0.0028],\n",
      "        [-0.0899,  0.2295],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [-0.0662,  0.2347],\n",
      "        [ 0.1457,  0.0119],\n",
      "        [ 0.0188,  0.1209],\n",
      "        [-0.1635,  0.3425],\n",
      "        [-0.1441,  0.3263],\n",
      "        [-0.2530,  0.3708],\n",
      "        [-1.3572,  1.5064],\n",
      "        [-0.4417,  0.6302],\n",
      "        [-0.1511,  0.3451],\n",
      "        [-0.0154,  0.1856],\n",
      "        [-0.1645,  0.3332],\n",
      "        [-0.2326,  0.4251],\n",
      "        [-0.5455,  0.7484],\n",
      "        [ 0.1612,  0.0047],\n",
      "        [-1.0777,  1.3031],\n",
      "        [-0.4673,  0.6770],\n",
      "        [ 0.1160,  0.0527],\n",
      "        [-0.7349,  0.9495],\n",
      "        [ 0.0078,  0.1664],\n",
      "        [-0.1970,  0.3784],\n",
      "        [-0.6712,  0.8850],\n",
      "        [ 0.4489, -0.3102],\n",
      "        [ 0.0066,  0.1730],\n",
      "        [ 0.4809, -0.3412],\n",
      "        [ 0.1735, -0.0082],\n",
      "        [ 0.2014, -0.0875],\n",
      "        [-0.1299,  0.2632],\n",
      "        [ 0.5172, -0.3991],\n",
      "        [ 0.2549, -0.0954],\n",
      "        [ 0.1538,  0.0075],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 1.1111, -0.9380],\n",
      "        [ 0.3387, -0.2421],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.0982,  0.0660],\n",
      "        [ 0.2628, -0.1013],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.0514,  0.0940],\n",
      "        [ 0.2974, -0.1263],\n",
      "        [ 0.8678, -0.6536],\n",
      "        [ 0.2905, -0.1192],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.0128,  0.1662],\n",
      "        [ 0.1679, -0.0022],\n",
      "        [ 0.1613,  0.0015],\n",
      "        [ 0.1482,  0.0205],\n",
      "        [ 0.5304, -0.3063],\n",
      "        [-0.4959,  0.6672],\n",
      "        [ 0.4569, -0.3151],\n",
      "        [ 0.0882,  0.0856],\n",
      "        [ 0.7632, -0.6084],\n",
      "        [ 0.8819, -0.7466],\n",
      "        [ 0.0091,  0.1460],\n",
      "        [ 0.0948,  0.0694],\n",
      "        [ 0.4306, -0.2974],\n",
      "        [ 0.5828, -0.4530],\n",
      "        [ 0.0419,  0.1038],\n",
      "        [ 0.1033,  0.0596],\n",
      "        [ 0.5831, -0.4224],\n",
      "        [ 0.1838, -0.0202],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.7254, -0.6159],\n",
      "        [ 0.5863, -0.4113],\n",
      "        [ 0.3123, -0.1579],\n",
      "        [ 0.0699,  0.0149],\n",
      "        [-0.1544,  0.2867],\n",
      "        [ 0.3235, -0.1507],\n",
      "        [-0.2705,  0.3500],\n",
      "        [-0.1520,  0.2416],\n",
      "        [ 0.5993, -0.4700],\n",
      "        [-0.1988,  0.2786],\n",
      "        [ 0.5534, -0.3535],\n",
      "        [ 0.0197,  0.1215],\n",
      "        [ 0.1029,  0.0963],\n",
      "        [ 0.3791, -0.1910],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.7208, -0.5192],\n",
      "        [ 0.5198, -0.3392],\n",
      "        [ 0.2230, -0.0950],\n",
      "        [ 0.1362,  0.0880],\n",
      "        [-0.8485,  0.9425],\n",
      "        [ 0.3492, -0.1835],\n",
      "        [-1.0621,  1.1944],\n",
      "        [-0.1933,  0.3435],\n",
      "        [-0.4250,  0.6271],\n",
      "        [-0.4388,  0.6093],\n",
      "        [-0.3179,  0.4939],\n",
      "        [-0.6854,  0.8153],\n",
      "        [-0.5569,  0.6781],\n",
      "        [-2.1671,  2.2973],\n",
      "        [ 0.0506,  0.1135],\n",
      "        [-1.2674,  1.4153],\n",
      "        [-0.5186,  0.6777],\n",
      "        [-0.5199,  0.7098],\n",
      "        [-0.8442,  0.9248],\n",
      "        [-1.3394,  1.4400],\n",
      "        [-1.6087,  1.7698],\n",
      "        [-0.0089,  0.0965],\n",
      "        [-0.3533,  0.5032],\n",
      "        [ 0.5010, -0.3631],\n",
      "        [-0.5543,  0.6842],\n",
      "        [ 0.5307, -0.4528],\n",
      "        [-0.0750,  0.2241],\n",
      "        [-0.2019,  0.3470],\n",
      "        [ 0.1295,  0.0403],\n",
      "        [-0.3533,  0.4962],\n",
      "        [-0.4599,  0.6033],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.1957, -0.0312],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [-0.0922,  0.2459],\n",
      "        [ 0.2600, -0.1069],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [-0.1708,  0.2860],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.2389, -0.0718],\n",
      "        [ 0.1261,  0.0445],\n",
      "        [ 0.2160, -0.0291],\n",
      "        [ 0.0498,  0.0940],\n",
      "        [-0.1909,  0.3368],\n",
      "        [-0.1138,  0.2600],\n",
      "        [-0.6221,  0.7531],\n",
      "        [-0.4189,  0.6229],\n",
      "        [-0.9756,  1.2037],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [-0.8754,  1.0374],\n",
      "        [-1.7251,  1.9137],\n",
      "        [ 0.1405,  0.0315],\n",
      "        [-2.4838,  2.6514],\n",
      "        [-0.3484,  0.5316],\n",
      "        [-0.7199,  0.9420],\n",
      "        [-1.2462,  1.4440],\n",
      "        [-0.1455,  0.3358],\n",
      "        [-0.3197,  0.5155],\n",
      "        [-0.5141,  0.6338],\n",
      "        [-0.2277,  0.4287],\n",
      "        [-1.2192,  1.3688],\n",
      "        [-0.0365,  0.2104],\n",
      "        [ 0.0017,  0.1728],\n",
      "        [-0.6901,  0.8045],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.3509, -0.1998],\n",
      "        [-0.3137,  0.4642],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [-1.0607,  1.1459],\n",
      "        [-0.9621,  1.0454],\n",
      "        [ 0.1193,  0.0449],\n",
      "        [-0.3050,  0.4920],\n",
      "        [-0.5709,  0.6718],\n",
      "        [ 0.0942,  0.0665],\n",
      "        [-0.3159,  0.3853],\n",
      "        [ 0.2918, -0.1580],\n",
      "        [ 0.0721,  0.1195],\n",
      "        [-0.6521,  0.7284],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.3463, -0.1701],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.7399, -0.5496],\n",
      "        [ 0.6001, -0.4037],\n",
      "        [ 0.0012,  0.1471],\n",
      "        [ 0.1772,  0.0212],\n",
      "        [ 0.0649,  0.0970],\n",
      "        [ 0.0241,  0.1325],\n",
      "        [-0.4269,  0.6117],\n",
      "        [-0.6625,  0.7476],\n",
      "        [-0.1692,  0.3192],\n",
      "        [-0.7925,  0.9164],\n",
      "        [ 0.0044,  0.1487],\n",
      "        [ 0.4607, -0.3377],\n",
      "        [ 0.2572, -0.0979],\n",
      "        [ 0.1392,  0.0016],\n",
      "        [ 0.5089, -0.3763],\n",
      "        [ 0.1349,  0.0276],\n",
      "        [ 0.5140, -0.3864],\n",
      "        [ 0.0858,  0.0875],\n",
      "        [ 0.5784, -0.4549],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.4754, -0.3355],\n",
      "        [ 0.0548,  0.0961],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [-0.1711,  0.2548],\n",
      "        [ 0.5503, -0.4370],\n",
      "        [ 0.1037,  0.0477],\n",
      "        [ 0.1372, -0.0200],\n",
      "        [ 0.2089, -0.0453],\n",
      "        [-0.4400,  0.4939],\n",
      "        [-0.4987,  0.5731],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.3566, -0.2253],\n",
      "        [ 0.1987, -0.0342],\n",
      "        [ 0.2161, -0.0533],\n",
      "        [-0.8461,  0.9709],\n",
      "        [ 0.4385, -0.2951]], device='cuda:0')\n",
      "(2080, 2)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Found input variables with inconsistent numbers of samples: [2080, 52]",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-3b2749c6e534>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0my_prob_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_better_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_better_out\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mind_suj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mind_suj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mn_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0;31m#print(y_prob_pred)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m     \u001b[0macc_k\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_better_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0macc_k\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m     \u001b[0;31m#print(labels.astype(int))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/sklearn/metrics/classification.py\u001b[0m in \u001b[0;36maccuracy_score\u001b[0;34m(y_true, y_pred, normalize, sample_weight)\u001b[0m\n\u001b[1;32m    174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m     \u001b[0;31m# Compute accuracy for each possible representation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 176\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    177\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'multilabel'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m         \u001b[0mdiffering_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcount_nonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/sklearn/metrics/classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0marray\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindicator\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m     \"\"\"\n\u001b[0;32m---> 71\u001b[0;31m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     72\u001b[0m     \u001b[0mtype_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype_of_target\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m     \u001b[0mtype_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype_of_target\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    202\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m         raise ValueError(\"Found input variables with inconsistent numbers of\"\n\u001b[0;32m--> 204\u001b[0;31m                          \" samples: %r\" % [int(l) for l in lengths])\n\u001b[0m\u001b[1;32m    205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [2080, 52]"
     ]
    }
   ],
   "source": [
    "inp_test = torch.Tensor(images_total)\n",
    "if cuda_aval:\n",
    "    inp_test = inp_test.cuda()\n",
    "f = 1\n",
    "\n",
    "for f in range(f):\n",
    "    fp_model = './Resn_NCMCI_Cor{}'.format(f)\n",
    "\n",
    "    optimizer = optim.Adam(model.mrnd.parameters(), lr=0.00005)#, momentum=0.8)\n",
    "    cb_chkpt = ptt.ModelCheckpoint(fp_model, reset=True, verbose=1) \n",
    "    criterion = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "    model_test = ptt.DeepNetTrainer(model = model, criterion = criterion, optimizer = optimizer,\n",
    "                                            data_transf = data_transf,\n",
    "                                            #lr_scheduler  = schd,\n",
    "                                            callbacks = [ptt.AccuracyMetric(), cb_chkpt, ptt.PrintCallback()])\n",
    "\n",
    "    model_test.load_state(fp_model)\n",
    "    y_better_out = model_test.predict(inp_test, labels)\n",
    "    print(y_better_out)\n",
    "    y_better_out = y_better_out.cpu().numpy()\n",
    "    print(y_better_out.shape)\n",
    "\n",
    "    y_prob_pred, y_better_pred = get_labels(y_better_out,ind_suj[1:],ind_suj[:-1],n_labels)\n",
    "    acc_k = accuracy_score(labels, y_better_pred)\n",
    "    print(acc_k)\n",
    "    print(y_better_pred.shape)\n",
    "    \n",
    "    # Compute confusion matrix\n",
    "    cnf_matrix = confusion_matrix(labels, y_better_pred)\n",
    "    np.set_printoptions(precision=2)\n",
    "\n",
    "    # Plot non-normalized confusion matrix\n",
    "    plt.figure()\n",
    "    plot_confusion_matrix(cnf_matrix, classes=class_names,\n",
    "                          title='Confusion matrix, without normalization')\n",
    "\n",
    "    # Plot normalized confusion matrix\n",
    "    plt.figure()\n",
    "    plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,\n",
    "                          title='Normalized confusion matrix')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2080, 2)\n"
     ]
    }
   ],
   "source": [
    "print(NCMCI_cor.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2080, 2)\n"
     ]
    }
   ],
   "source": [
    "print(y_better_out.shape)\n",
    "dir_bas =  '/home/mariana/Documents/Data_2D/Test_2_Cor/'+'NCMCI_cor.npy'\n",
    "np.save(dir_bas, NCMCI_cor)"
   ]
  }
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