demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Kittylyst demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_moons, make_blobs\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from micrograd.engine import Value\n",
    "from micrograd.nn import Neuron, Layer, MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kittylyst import (\n",
    "    MicroLoader, MicroCriterion, MicroOptimizer, MicroScheduler,\n",
    "    Experiment, IRunner,SupervisedRunner, Callback,\n",
    "    CriterionCallback, AccuracyCallback, \n",
    "    OptimizerCallback, SchedulerCallback,\n",
    "    LoggerCallback\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make up a dataset\n",
    "def make_dataset(seed=42, n_samples=100):\n",
    "    np.random.seed(seed)\n",
    "    random.seed(seed)\n",
    "    X, y = make_moons(n_samples=100, noise=0.1)\n",
    "\n",
    "    y = y*2 - 1 # make y be -1 or 1\n",
    "    return X, y\n",
    "\n",
    "def visualize_dataset(X, y):\n",
    "    plt.figure(figsize=(5,5))\n",
    "    plt.scatter(X[:,0], X[:,1], c=y, s=20, cmap='jet')\n",
    "\n",
    "# let's create train data\n",
    "X_train, y_train = make_dataset()\n",
    "visualize_dataset(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# valid data\n",
    "X_valid, y_valid = make_dataset(seed=137)\n",
    "visualize_dataset(X_valid, y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# and another train one (why not?)\n",
    "X_train2, y_train2 = make_dataset(seed=1337)\n",
    "visualize_dataset(X_train2, y_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP of [Layer of [ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2)], Layer of [ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16)], Layer of [LinearNeuron(16)]]\n",
      "number of parameters 337\n"
     ]
    }
   ],
   "source": [
    "# initialize a model \n",
    "model = MLP(2, [16, 16, 1]) # 2-layer neural network\n",
    "print(model)\n",
    "print(\"number of parameters\", len(model.parameters()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_decision_boundary(X, y, model):\n",
    "    h = 0.25\n",
    "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                         np.arange(y_min, y_max, h))\n",
    "    Xmesh = np.c_[xx.ravel(), yy.ravel()]\n",
    "    inputs = [list(map(Value, xrow)) for xrow in Xmesh]\n",
    "    scores = list(map(model, inputs))\n",
    "    Z = np.array([s.data > 0 for s in scores])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "\n",
    "    fig = plt.figure()\n",
    "    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral, alpha=0.8)\n",
    "    plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)\n",
    "    plt.xlim(xx.min(), xx.max())\n",
    "    plt.ylim(yy.min(), yy.max())\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_decision_boundary(X_valid, y_valid, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "loaders = {\n",
    "    \"train_1\": MicroLoader(X_train, y_train), \n",
    "    \"train_2\": MicroLoader(X_train2, y_train2), \n",
    "    \"valid\": MicroLoader(X_valid, y_valid), \n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Act 1 - ``CustomRunner`` solution\n",
    "\n",
    "Suppose you have your favorite `for-loop` pipeline and want to get rid of `for-for-for` stuff to make code more clean. In this case you can define your own ``CustomRunner`` with ``_handle_batch`` method and just run it.\n",
    "\n",
    "Whole ``Runner`` source code is located [here](github.com/Scitator/kittylyst/blob/master/kittylyst/runner.py) (~100 lines of code)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_1: accuracy 0.15625, loss 1.3142614940551327\n",
      "train_1: accuracy 0.78125, loss 0.6598317508322048\n",
      "train_1: accuracy 0.75, loss 0.7927354575997964\n",
      "train_1: accuracy 0.625, loss 1.8240299288726267\n",
      "train_1: accuracy 0.5625, loss 0.6996691628006418\n",
      "train_1: accuracy 0.875, loss 0.3762774678655899\n",
      "train_1: accuracy 0.875, loss 0.46448401507996706\n",
      "train_1: accuracy 0.84375, loss 0.313181864574222\n",
      "train_1: accuracy 0.875, loss 0.41924003695879297\n",
      "train_1: accuracy 0.875, loss 0.29237872504797224\n",
      "train_2: accuracy 0.84375, loss 0.36252849347984367\n",
      "train_2: accuracy 0.90625, loss 0.23049635069090824\n",
      "train_2: accuracy 0.90625, loss 0.2861263243941025\n",
      "train_2: accuracy 0.90625, loss 0.2427632314364521\n",
      "train_2: accuracy 0.90625, loss 0.2260113554087449\n",
      "train_2: accuracy 0.9375, loss 0.17427040073732888\n",
      "train_2: accuracy 0.90625, loss 0.2317996487584881\n",
      "train_2: accuracy 0.9375, loss 0.15078985747720192\n",
      "train_2: accuracy 0.90625, loss 0.291187602523029\n",
      "train_2: accuracy 0.84375, loss 0.3432754137156298\n",
      "valid: accuracy 0.9375, loss 0.1474493115337908\n",
      "valid: accuracy 0.96875, loss 0.09990144181004182\n",
      "valid: accuracy 0.9375, loss 0.1680025256893815\n",
      "valid: accuracy 0.96875, loss 0.148258954342728\n",
      "valid: accuracy 0.9375, loss 0.1873935410326742\n",
      "valid: accuracy 0.96875, loss 0.16336856456103752\n",
      "valid: accuracy 0.875, loss 0.17102582501262614\n",
      "valid: accuracy 0.9375, loss 0.14233517805117027\n",
      "valid: accuracy 0.90625, loss 0.20186039709225018\n",
      "valid: accuracy 0.96875, loss 0.14318479381156357\n",
      "train_1: accuracy 0.875, loss 0.22827268599953085\n",
      "train_1: accuracy 0.90625, loss 0.23111718146551252\n",
      "train_1: accuracy 0.90625, loss 0.26719846946912557\n",
      "train_1: accuracy 0.9375, loss 0.11483146831037279\n",
      "train_1: accuracy 0.84375, loss 0.2719358187559947\n",
      "train_1: accuracy 1.0, loss 0.19933782936501845\n",
      "train_1: accuracy 0.90625, loss 0.3778133633734559\n",
      "train_1: accuracy 0.9375, loss 0.2337964863015131\n",
      "train_1: accuracy 0.9375, loss 0.17062439862210832\n",
      "train_1: accuracy 0.9375, loss 0.1912249192592238\n",
      "train_2: accuracy 0.875, loss 0.21954725970131905\n",
      "train_2: accuracy 0.96875, loss 0.0886570490153784\n",
      "train_2: accuracy 0.9375, loss 0.136627444439142\n",
      "train_2: accuracy 0.96875, loss 0.09349875422947171\n",
      "train_2: accuracy 0.96875, loss 0.09833153329882595\n",
      "train_2: accuracy 0.9375, loss 0.11095208900863657\n",
      "train_2: accuracy 0.9375, loss 0.14545674194763888\n",
      "train_2: accuracy 0.96875, loss 0.06608834510895631\n",
      "train_2: accuracy 0.90625, loss 0.14124617927826397\n",
      "train_2: accuracy 0.90625, loss 0.15779848379052286\n",
      "valid: accuracy 0.96875, loss 0.06836955780794637\n",
      "valid: accuracy 0.875, loss 0.17787099568178805\n",
      "valid: accuracy 0.90625, loss 0.13833576669594758\n",
      "valid: accuracy 0.9375, loss 0.10236049911723889\n",
      "valid: accuracy 0.96875, loss 0.04589813841052155\n",
      "valid: accuracy 0.96875, loss 0.06818523690980538\n",
      "valid: accuracy 0.9375, loss 0.08834167812787137\n",
      "valid: accuracy 0.875, loss 0.17635925490669888\n",
      "valid: accuracy 0.9375, loss 0.100988275812585\n",
      "valid: accuracy 0.9375, loss 0.0868299373527822\n",
      "train_1: accuracy 0.9375, loss 0.11368181500229675\n",
      "train_1: accuracy 0.96875, loss 0.05958319093917941\n",
      "train_1: accuracy 0.96875, loss 0.06271656286677857\n",
      "train_1: accuracy 0.9375, loss 0.0960974993374915\n",
      "train_1: accuracy 0.96875, loss 0.08171256035062921\n",
      "train_1: accuracy 0.96875, loss 0.06446721277863994\n",
      "train_1: accuracy 0.9375, loss 0.1183011413154769\n",
      "train_1: accuracy 0.96875, loss 0.09177626495788474\n",
      "train_1: accuracy 0.96875, loss 0.0845696512276944\n",
      "train_1: accuracy 1.0, loss 0.028705688601890787\n",
      "train_2: accuracy 0.90625, loss 0.17563650457945842\n",
      "train_2: accuracy 1.0, loss 0.07476901977481708\n",
      "train_2: accuracy 0.96875, loss 0.11360803023004437\n",
      "train_2: accuracy 1.0, loss 0.012994541284905112\n",
      "train_2: accuracy 1.0, loss 0.03246210627239752\n",
      "train_2: accuracy 1.0, loss 0.039389930717253094\n",
      "train_2: accuracy 1.0, loss 0.019757776014066062\n",
      "train_2: accuracy 1.0, loss 0.05897310690434179\n",
      "train_2: accuracy 0.96875, loss 0.07010052501675902\n",
      "train_2: accuracy 1.0, loss 0.011731661883674601\n",
      "valid: accuracy 1.0, loss 0.015135237402272116\n",
      "valid: accuracy 1.0, loss 0.019516570798421964\n",
      "valid: accuracy 0.96875, loss 0.07986825814256159\n",
      "valid: accuracy 1.0, loss 0.03207407579926418\n",
      "valid: accuracy 0.96875, loss 0.059521996557408946\n",
      "valid: accuracy 1.0, loss 0.01172781421411155\n",
      "valid: accuracy 0.9375, loss 0.12741422424864493\n",
      "valid: accuracy 0.96875, loss 0.059273780320194924\n",
      "valid: accuracy 1.0, loss 0.015135237402272116\n",
      "valid: accuracy 1.0, loss 0.0161091476102614\n",
      "train_1: accuracy 1.0, loss 0.017436982093771272\n",
      "train_1: accuracy 1.0, loss 0.035965423943386365\n",
      "train_1: accuracy 1.0, loss 0.021785258757928035\n",
      "train_1: accuracy 0.96875, loss 0.06928632366129193\n",
      "train_1: accuracy 1.0, loss 0.015958819856300887\n",
      "train_1: accuracy 0.9375, loss 0.12976594837090422\n",
      "train_1: accuracy 1.0, loss 0.0481978511599334\n",
      "train_1: accuracy 1.0, loss 0.011788204303024444\n",
      "train_1: accuracy 1.0, loss 0.011784762398645324\n",
      "train_1: accuracy 0.96875, loss 0.059486835501186\n",
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     ]
    }
   ],
   "source": [
    "model = MLP(2, [16, 16, 1])\n",
    "experiment = Experiment(model=model, loaders=loaders, num_epochs=10)\n",
    "\n",
    "class CustomRunner(IRunner):\n",
    "    def _handle_batch(self, batch):\n",
    "        features, targets = batch\n",
    "        scores = list(map(self.model, features))\n",
    "        \n",
    "        losses = [(1 + -yi*scorei).relu() for yi, scorei in zip(targets, scores)]\n",
    "        data_loss = sum(losses) * (1.0 / len(losses))\n",
    "        # L2 regularization\n",
    "        alpha = 1e-4\n",
    "        reg_loss = alpha * sum((p*p for p in self.model.parameters()))\n",
    "        total_loss = data_loss + reg_loss\n",
    "\n",
    "        # also get accuracy\n",
    "        accuracy = [(yi > 0) == (scorei.data > 0) for yi, scorei in zip(targets, scores)]\n",
    "        accuracy = sum(accuracy) / len(accuracy)\n",
    "        \n",
    "        print(f\"{self.loader_name}: accuracy {accuracy}, loss {total_loss.data}\")\n",
    "        \n",
    "        if self.is_train_loader:\n",
    "            # backward\n",
    "            self.model.zero_grad()\n",
    "            total_loss.backward()\n",
    "\n",
    "            # update (sgd)\n",
    "            learning_rate = 1.0 - 0.9 * self.epoch / self.num_epochs\n",
    "            for p in self.model.parameters():\n",
    "                p.data -= learning_rate * p.grad\n",
    "\n",
    "CustomRunner().run_experiment(experiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_decision_boundary(X_valid, y_valid, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Act 2 - ``SupervisedRunner`` solution\n",
    "\n",
    "Let's make a bit more abstract. Let's introduce ``SupervisedRunner``, that knows how to execute Supervised models and ``CriterionCallback/OptimizerCallback/SchedulerCallback`` for typical criterions/optimizers/schedulers steps. \n",
    "\n",
    "Additionaly let's wrap accuracy with ``AccuracyCallback``, as far as metrics are general too. \n",
    "\n",
    "Finally, let's make our logs looks consistent with ``LoggerCallback``.\n",
    "\n",
    "Whole ``Callback`` source code is located [here](github.com/Scitator/kittylyst/blob/master/kittylyst/callback.py) (~100 lines of code)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/10 Epoch (train_1) accuracy: 0.71875 loss: 0.785281554267671\n",
      "1/10 Epoch (train_2) accuracy: 0.73125 loss: 0.7427673170707891\n",
      "1/10 Epoch (valid) accuracy: 0.753125 loss: 0.6237755706891819\n",
      "2/10 Epoch (train_1) accuracy: 0.725 loss: 0.873129456211746\n",
      "2/10 Epoch (train_2) accuracy: 0.903125 loss: 0.27634106504414263\n",
      "2/10 Epoch (valid) accuracy: 0.85625 loss: 0.3620674022545227\n",
      "3/10 Epoch (train_1) accuracy: 0.95 loss: 0.1447585259765843\n",
      "3/10 Epoch (train_2) accuracy: 0.98125 loss: 0.06889329382392614\n",
      "3/10 Epoch (valid) accuracy: 0.9625 loss: 0.07871991095724752\n",
      "4/10 Epoch (train_1) accuracy: 0.96875 loss: 0.09869389158524669\n",
      "4/10 Epoch (train_2) accuracy: 0.990625 loss: 0.04151205921220884\n",
      "4/10 Epoch (valid) accuracy: 0.996875 loss: 0.02621252590693186\n",
      "5/10 Epoch (train_1) accuracy: 0.99375 loss: 0.03757581261556087\n",
      "5/10 Epoch (train_2) accuracy: 0.9875 loss: 0.04990766902591705\n",
      "5/10 Epoch (valid) accuracy: 0.99375 loss: 0.02435732621803047\n",
      "6/10 Epoch (train_1) accuracy: 0.99375 loss: 0.029049422796950025\n",
      "6/10 Epoch (train_2) accuracy: 1.0 loss: 0.01639232843102455\n",
      "6/10 Epoch (valid) accuracy: 0.990625 loss: 0.027775804239404046\n",
      "7/10 Epoch (train_1) accuracy: 1.0 loss: 0.019021064621160842\n",
      "7/10 Epoch (train_2) accuracy: 1.0 loss: 0.014796119994869109\n",
      "7/10 Epoch (valid) accuracy: 0.99375 loss: 0.0317453722485309\n",
      "8/10 Epoch (train_1) accuracy: 1.0 loss: 0.012836074392126856\n",
      "8/10 Epoch (train_2) accuracy: 1.0 loss: 0.01281247664631655\n",
      "8/10 Epoch (valid) accuracy: 0.984375 loss: 0.04146243637179545\n",
      "9/10 Epoch (train_1) accuracy: 1.0 loss: 0.012790993796967232\n",
      "9/10 Epoch (train_2) accuracy: 1.0 loss: 0.0127720764285023\n",
      "9/10 Epoch (valid) accuracy: 0.9875 loss: 0.031930683016207856\n",
      "10/10 Epoch (train_1) accuracy: 1.0 loss: 0.012755252863518533\n",
      "10/10 Epoch (train_2) accuracy: 1.0 loss: 0.012740974577848235\n",
      "10/10 Epoch (valid) accuracy: 0.990625 loss: 0.03286910977967583\n"
     ]
    }
   ],
   "source": [
    "model = MLP(2, [16, 16, 1])\n",
    "criterion = MicroCriterion()\n",
    "optimizer = MicroOptimizer(model)\n",
    "scheduler = MicroScheduler(optimizer, num_epochs=10)\n",
    "experiment = Experiment(\n",
    "    model=model, \n",
    "    criterion=criterion, \n",
    "    optimizer=optimizer, \n",
    "    scheduler=scheduler,\n",
    "    loaders=loaders, \n",
    "    num_epochs=10,\n",
    "    callbacks=[\n",
    "        CriterionCallback(), \n",
    "        AccuracyCallback(), \n",
    "        OptimizerCallback(), \n",
    "        SchedulerCallback(),\n",
    "        LoggerCallback(),\n",
    "    ],\n",
    "    verbose=False,\n",
    ")\n",
    "\n",
    "SupervisedRunner().run_experiment(experiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_decision_boundary(X_valid, y_valid, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Act 3 - ``CustomExperiment`` solution - multi-stage experiment with ``VisualizationCallback``\n",
    "\n",
    "Let's go even harder. Suppose we want to firstly train our model on `train_1` data, and only after that - on `train_2`. This case could be easily handled with ``CustomExperiment``, where you could redefine your experiment components for each stage of your experiment.\n",
    "\n",
    "Whole ``Experiemnt`` source code is located [here](github.com/Scitator/kittylyst/blob/master/kittylyst/experiment.py) (~100 lines of code)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/10 Epoch (train_1) accuracy: 0.71875 loss: 0.785281554267671\n",
      "1/10 Epoch (valid) accuracy: 0.721875 loss: 0.6940299711337519\n",
      "2/10 Epoch (train_1) accuracy: 0.825 loss: 0.5505549582580421\n",
      "2/10 Epoch (valid) accuracy: 0.878125 loss: 0.5176625016049039\n",
      "3/10 Epoch (train_1) accuracy: 0.840625 loss: 0.4816616578259919\n",
      "3/10 Epoch (valid) accuracy: 0.86875 loss: 0.25247263037071416\n",
      "4/10 Epoch (train_1) accuracy: 0.940625 loss: 0.14985338301717985\n",
      "4/10 Epoch (valid) accuracy: 0.978125 loss: 0.11139035194477467\n",
      "5/10 Epoch (train_1) accuracy: 0.94375 loss: 0.1643548936585497\n",
      "5/10 Epoch (valid) accuracy: 0.984375 loss: 0.11174302043807763\n",
      "6/10 Epoch (train_1) accuracy: 0.98125 loss: 0.08354336042231081\n",
      "6/10 Epoch (valid) accuracy: 0.975 loss: 0.05898081107929618\n",
      "7/10 Epoch (train_1) accuracy: 0.990625 loss: 0.051306623433875244\n",
      "7/10 Epoch (valid) accuracy: 1.0 loss: 0.032472787227006895\n",
      "8/10 Epoch (train_1) accuracy: 1.0 loss: 0.023781156996373325\n",
      "8/10 Epoch (valid) accuracy: 1.0 loss: 0.04340243647048116\n",
      "9/10 Epoch (train_1) accuracy: 1.0 loss: 0.015649671475612734\n",
      "9/10 Epoch (valid) accuracy: 1.0 loss: 0.03103500877611328\n",
      "10/10 Epoch (train_1) accuracy: 1.0 loss: 0.014355740334118364\n",
      "10/10 Epoch (valid) accuracy: 1.0 loss: 0.03150174856877102\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/10 Epoch (train_2) accuracy: 1.0 loss: 0.015204068895861076\n",
      "1/10 Epoch (valid) accuracy: 0.984375 loss: 0.04979577101262032\n",
      "2/10 Epoch (train_2) accuracy: 1.0 loss: 0.01809742569123933\n",
      "2/10 Epoch (valid) accuracy: 0.98125 loss: 0.08383076867659356\n",
      "3/10 Epoch (train_2) accuracy: 0.984375 loss: 0.08231697447288436\n",
      "3/10 Epoch (valid) accuracy: 0.828125 loss: 0.4213829661783479\n",
      "4/10 Epoch (train_2) accuracy: 0.890625 loss: 0.3243339002094888\n",
      "4/10 Epoch (valid) accuracy: 0.996875 loss: 0.09912316988030219\n",
      "5/10 Epoch (train_2) accuracy: 0.996875 loss: 0.04078078209342447\n",
      "5/10 Epoch (valid) accuracy: 0.975 loss: 0.05557860372341974\n",
      "6/10 Epoch (train_2) accuracy: 1.0 loss: 0.022288604992755146\n",
      "6/10 Epoch (valid) accuracy: 1.0 loss: 0.029794771150518633\n",
      "7/10 Epoch (train_2) accuracy: 1.0 loss: 0.019741230552452572\n",
      "7/10 Epoch (valid) accuracy: 0.971875 loss: 0.05227168965444916\n",
      "8/10 Epoch (train_2) accuracy: 1.0 loss: 0.0148558049132897\n",
      "8/10 Epoch (valid) accuracy: 0.98125 loss: 0.03843664487496198\n",
      "9/10 Epoch (train_2) accuracy: 1.0 loss: 0.01444589875891345\n",
      "9/10 Epoch (valid) accuracy: 0.9875 loss: 0.028072820587907027\n",
      "10/10 Epoch (train_2) accuracy: 1.0 loss: 0.013592906541379879\n",
      "10/10 Epoch (valid) accuracy: 0.96875 loss: 0.05166795322455673\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class CustomExperiment(Experiment):\n",
    "    @property\n",
    "    def stages(self):\n",
    "        return self._stage\n",
    "\n",
    "    def get_loaders(self, stage: str):\n",
    "        return self._loaders[stage]\n",
    "\n",
    "    \n",
    "class VisualizationCallback(Callback):\n",
    "    def on_stage_end(self, runner):\n",
    "        visualize_decision_boundary(X_valid, y_valid, runner.model)\n",
    "\n",
    "\n",
    "loaders = {\n",
    "    \"loader_1\": {\n",
    "        \"train_1\": MicroLoader(X_train, y_train), \n",
    "        \"valid\": MicroLoader(X_valid, y_valid), \n",
    "    },\n",
    "    \"loader_2\": {\n",
    "        \"train_2\": MicroLoader(X_train2, y_train2), \n",
    "        \"valid\": MicroLoader(X_valid, y_valid), \n",
    "    },\n",
    "}\n",
    "model = MLP(2, [16, 16, 1])\n",
    "criterion = MicroCriterion()\n",
    "optimizer = MicroOptimizer(model)\n",
    "scheduler = MicroScheduler(optimizer, num_epochs=10)\n",
    "experiment = CustomExperiment(\n",
    "    model=model, \n",
    "    criterion=criterion, \n",
    "    optimizer=optimizer, \n",
    "    scheduler=scheduler,\n",
    "    loaders=loaders, \n",
    "    num_epochs=10,\n",
    "    callbacks=[\n",
    "        CriterionCallback(), \n",
    "        AccuracyCallback(), \n",
    "        OptimizerCallback(), \n",
    "        SchedulerCallback(),\n",
    "        LoggerCallback(),\n",
    "        VisualizationCallback(),  # <-- here is our new Callback\n",
    "    ],\n",
    "    verbose=False,\n",
    "    stage=loaders.keys()  # <-- here is the trick for multi-stage support\n",
    ")\n",
    "\n",
    "SupervisedRunner().run_experiment(experiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_decision_boundary(X_valid, y_valid, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "🎉 You have passed ``Kittylyst`` tutorial! This is just a minimal educational demo, but I hope you found it interesting for your deep learning research code organisation.\n",
    "\n",
    "For more advanced and production-ready solution please follow our [Catalyst](https://github.com/catalyst-team/catalyst) repository.\n",
    "\n",
    "PS. If you are interested in deep learning you could also try out our [dl-course](https://github.com/catalyst-team/dl-course)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}