{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import sys\n",
    "sys.path.insert(1, '../')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from models.resnet_simclr import ResNetSimCLR\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets\n",
    "import numpy as np\n",
    "from sklearn.neighbors import KNeighborsClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 512\n",
    "out_dim = 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _load_stl10(prefix=\"train\"):\n",
    "    X_train = np.fromfile('../data/stl10_binary/' + prefix + '_X.bin', dtype=np.uint8)\n",
    "    y_train = np.fromfile('../data/stl10_binary/' + prefix + '_y.bin', dtype=np.uint8)\n",
    "\n",
    "    X_train = np.reshape(X_train, (-1, 3, 96, 96))\n",
    "    X_train = np.transpose(X_train, (0, 3, 2, 1))\n",
    "    print(\"{} images\".format(prefix))\n",
    "    print(X_train.shape)\n",
    "    print(y_train.shape)\n",
    "    return X_train, y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train images\n",
      "(5000, 96, 96, 3)\n",
      "(5000,)\n"
     ]
    }
   ],
   "source": [
    "# load STL-10 train data\n",
    "X_train, y_train = _load_stl10(\"train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test images\n",
      "(8000, 96, 96, 3)\n",
      "(8000,)\n"
     ]
    }
   ],
   "source": [
    "# load STL-10 test data\n",
    "X_test, y_test = _load_stl10(\"test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test protocol #1 PCA features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PCA features\n",
      "(5000, 128)\n",
      "(8000, 128)\n"
     ]
    }
   ],
   "source": [
    "scaler = preprocessing.StandardScaler()\n",
    "scaler.fit(X_train.reshape((X_train.shape[0],-1)))\n",
    "\n",
    "pca = PCA(n_components=out_dim)\n",
    "\n",
    "X_train_pca = pca.fit_transform(scaler.transform(X_train.reshape(X_train.shape[0], -1)))\n",
    "X_test_pca = pca.transform(scaler.transform(X_test.reshape(X_test.shape[0], -1)))\n",
    "\n",
    "print(\"PCA features\")\n",
    "print(X_train_pca.shape)\n",
    "print(X_test_pca.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def linear_model_eval(X_train, y_train, X_test, y_test):\n",
    "    \n",
    "    clf = LogisticRegression(random_state=0, max_iter=1000, solver='lbfgs', C=1.0)\n",
    "    clf.fit(X_train, y_train)\n",
    "    print(\"PCA feature evaluation\")\n",
    "    print(\"Train score:\", clf.score(X_train, y_train))\n",
    "    print(\"Test score:\", clf.score(X_test, y_test))\n",
    "    print(\"-------------------------------\")\n",
    "    neigh = KNeighborsClassifier(n_neighbors=10)\n",
    "    neigh.fit(X_train, y_train)\n",
    "    print(\"KNN with PCA features\")\n",
    "    print(\"Train score:\", neigh.score(X_train, y_train))\n",
    "    print(\"Test score:\", neigh.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PCA feature evaluation\n",
      "Train score: 0.436\n",
      "Test score: 0.360875\n",
      "-------------------------------\n",
      "KNN with PCA features\n",
      "Train score: 0.4222\n",
      "Test score: 0.318\n"
     ]
    }
   ],
   "source": [
    "linear_model_eval(X_train_pca, y_train, X_test_pca, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Protocol #2 Logisitc Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ResNetSimCLR(out_dim=out_dim)\n",
    "model.eval()\n",
    "\n",
    "state_dict = torch.load('../checkpoints/checkpoint.pth')\n",
    "model.load_state_dict(state_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def next_batch(X, y, batch_size):\n",
    "    for i in range(0, X.shape[0], batch_size):\n",
    "        X_batch = torch.tensor(X[i: i+batch_size]) / 255.\n",
    "        y_batch = torch.tensor(y[i: i+batch_size])\n",
    "        yield X_batch.permute((0,3,1,2)), y_batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train features\n",
      "(5000, 512)\n"
     ]
    }
   ],
   "source": [
    "X_train_feature = []\n",
    "\n",
    "for batch_x, batch_y in next_batch(X_train, y_train, batch_size):\n",
    "    features, _ = model(batch_x)\n",
    "    X_train_feature.extend(features.detach().numpy())\n",
    "    \n",
    "X_train_feature = np.array(X_train_feature)\n",
    "\n",
    "print(\"Train features\")\n",
    "print(X_train_feature.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test features\n",
      "(8000, 512)\n"
     ]
    }
   ],
   "source": [
    "X_test_feature = []\n",
    "\n",
    "for batch_x, batch_y in next_batch(X_test, y_test, batch_size):\n",
    "    features, _ = model(batch_x)\n",
    "    X_test_feature.extend(features.detach().numpy())\n",
    "    \n",
    "X_test_feature = np.array(X_test_feature)\n",
    "\n",
    "print(\"Test features\")\n",
    "print(X_test_feature.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/thalles/anaconda3/envs/pytorch/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PCA feature evaluation\n",
      "Train score: 0.937\n",
      "Test score: 0.71875\n",
      "-------------------------------\n",
      "KNN with PCA features\n",
      "Train score: 0.7378\n",
      "Test score: 0.6675\n"
     ]
    }
   ],
   "source": [
    "scaler = preprocessing.StandardScaler()\n",
    "scaler.fit(X_train_feature)\n",
    "\n",
    "linear_model_eval(scaler.transform(X_train_feature), y_train, scaler.transform(X_test_feature), y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SimCLR feature evaluation\n",
    "# Train score: 0.8966\n",
    "# Test score: 0.634125"
   ]
  }
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