{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CFL Package In-Depth Tutorial\n",
    "\n",
    "In this example, we will:\n",
    "\n",
    "    1) set up a CFL pipeline that takes visual bars data as input,\n",
    "    2) tune the hyperparameters for all models in the pipeline\n",
    "    3) visualize the results\n",
    "\n",
    "While CFL has built-in support for tuning `CauseClusterer` and `EffectClusterer`\n",
    "hyperparameters, we must use Optuna (or a similar neural network tuning package) \n",
    "to tune the `CondDensityEstimator` neural network hyperparameters. For this \n",
    "reason, we will setup one CFL pipeline for tuning hyperparameters for the \n",
    "`CondDensityEstimator` with Optuna first, and then setup a second pipeline that \n",
    "trains the `CondDensityEstimator` with the optimal hyperparameters found by the \n",
    "first pipeline and then tunes and trains the `CauseClusterer` and \n",
    "`EffectClusterers`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# cfl imports\n",
    "from cfl import Experiment\n",
    "from visual_bars.generate_visual_bars_data import VisualBarsData\n",
    "from cfl.visualization.basic_visualizations import visualize_macrostates\n",
    "\n",
    "# hyperparameter optimization\n",
    "import optuna\n",
    "from optuna.integration import TFKerasPruningCallback\n",
    "from optuna.trial import TrialState\n",
    "from keras import Sequential\n",
    "from keras.layers import Dense, Dropout"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Load Data\n",
    "We will use the VisualBarsData class to generate a toy visual bars dataset.\n",
    "The process used to generate this data is described in the visual bars tutorial. \n",
    "To keep things simple, we will flatten the cause images from 10x10 images to \n",
    "100-dimensional vectors so that we can use a standard feedforward neural\n",
    "network for conditional density estimation instead of a convolutional neural\n",
    "network (though this would likely improve performance). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X raw shape:  (5000, 10, 10)\n",
      "Y raw shape:  (5000,)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X final shape:  (5000, 100)\n",
      "Y final shape:  (5000, 1)\n"
     ]
    }
   ],
   "source": [
    "# instantiate `VisualBarsData` object\n",
    "vb_data = VisualBarsData(n_samples=5000, im_shape=(10, 10), \n",
    "                         noise_lvl=0.03, set_random_seed=42)\n",
    "\n",
    "# retrieve the images and the target \n",
    "X = vb_data.getImages()\n",
    "Y = vb_data.getTarget()\n",
    "print('X raw shape: ', X.shape)\n",
    "print('Y raw shape: ', Y.shape)\n",
    "\n",
    "# visualize an example sample\n",
    "plt.imshow(X[22])\n",
    "plt.axis('off')\n",
    "plt.show()\n",
    "\n",
    "# vectorize X images and make Y images 2D to be cfl-compatible\n",
    "X = np.reshape(X, (X.shape[0], np.product(X.shape[1:])))\n",
    "Y = np.expand_dims(Y, -1)\n",
    "print('X final shape: ', X.shape)\n",
    "print('Y final shape: ', Y.shape)\n",
    "\n",
    "# define DATA_INFO, which must be passed into our CFL Experiment\n",
    "DATA_INFO = {'X_dims': X.shape, 'Y_dims': Y.shape, 'Y_type': 'categorical'}\n",
    "\n",
    "# define indices for training and valadation subsets of the data so that\n",
    "# we can evaluate various hyperparameters on the same withheld subset\n",
    "IN_SAMPLE_IDX,OUT_SAMPLE_IDX = train_test_split(np.arange(X.shape[0]),\n",
    "                                                 train_size=0.75,\n",
    "                                                 random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Tune hyperparameters for CondDensityEstimator\n",
    "The first step in defining our CFL pipeline is to learn optimal hyperparameters\n",
    "for the conditional density estimator. To simplify the problem of learning\n",
    "a conditional distribution, we will estimate the expectation of this distribution\n",
    "instead. A conditional expectation estimator can be specified through `cfl` with\n",
    "a `CondExpDIY` model. \n",
    "\n",
    "To tune the hyperparameters for this model, we will use the Optuna library. \n",
    "Detailed instructions on using Optuna can be found in the Optuna tutorial or\n",
    "in their documentation. The basic idea is to 1. specify your neural network\n",
    "for a given set of hyperparameter values, 2. define an objective function \n",
    "that computes a measure of how good this network is for a given set of hyperparameter\n",
    "values, 3. run an optuna study that will smartly iterate over many hyperarameter\n",
    "value combinations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. build a function that will define our neural network\n",
    "\n",
    "def build_network(trial):\n",
    "    # vary number of layers\n",
    "    n_layers = trial.suggest_int(\"n_layers\", 1, 3)\n",
    "    model = Sequential()\n",
    "    \n",
    "    for i in range(n_layers):\n",
    "        # vary size of each layer\n",
    "        num_hidden = trial.suggest_int(\"n_units_l{}\".format(i), 4, 256, log=True)\n",
    "        model.add(Dense(num_hidden, activation=\"relu\"))\n",
    "\n",
    "        # vary amount of dropout for each layer\n",
    "        dropout = trial.suggest_float(\"dropout_l{}\".format(i), 0.0, 0.5)\n",
    "        model.add(Dropout(rate=dropout))\n",
    "\n",
    "    # add output layer with size of target    \n",
    "    model.add(Dense(Y.shape[1], activation=\"linear\"))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. define an objective function that evaluates a given network's performance\n",
    "def objective(trial):\n",
    "    CDE_params = {\n",
    "        'model'           : 'CondExpDIY',\n",
    "        'verbose' : 1,\n",
    "        'model_params'    : {\n",
    "            # specify our function from step 1 for building the network\n",
    "            'build_network'     : build_network,\n",
    "            # add a callback that automatically stops training early for \n",
    "            # hyperparameter value combinations that look not promising\n",
    "            'optuna_callback' : TFKerasPruningCallback(trial, \"val_loss\"),\n",
    "            # provide the `trial` object, which specifies hyperparameter\n",
    "            # combinations for the current study iteration\n",
    "            'optuna_trial'    : trial,\n",
    "            # network training hyperparameters\n",
    "            'batch_size'      : trial.suggest_int(\"batch_size\", 16, 128),\n",
    "            'n_epochs'        : 100,\n",
    "            'optimizer'       : 'adam',\n",
    "            'opt_config'      : {'lr' : trial.suggest_float(\"lr\", 1e-5, 1e-3, log=True)},\n",
    "            'loss'            : 'mean_squared_error',\n",
    "            'best'            : True, \n",
    "            'early_stopping'  : True,\n",
    "            'verbose'         : 0,\n",
    "        },\n",
    "    }\n",
    "\n",
    "    # for now, we will specify an Experiment that only contains a\n",
    "    # CondDensityEstimator, since we don't need to know clustering results\n",
    "    # in order to evaluate how good a given CondDensityEstimator network is\n",
    "    my_exp = Experiment(X_train=X, \n",
    "                        Y_train=Y, \n",
    "                        in_sample_idx=IN_SAMPLE_IDX,\n",
    "                        out_sample_idx=OUT_SAMPLE_IDX,\n",
    "                        data_info=DATA_INFO, \n",
    "                        block_names=['CondDensityEstimator'], \n",
    "                        block_params=[CDE_params], \n",
    "                        verbose=0,\n",
    "                        results_path=None) # we don't want to save 100's of results\n",
    "\n",
    "    # train the CFL pipeline and take the minimum validation loss from the\n",
    "    # CondDensityEstimator training as our evaluation metric\n",
    "    train_results = my_exp.train()     \n",
    "    score = np.min(train_results['CondDensityEstimator']['val_loss'])\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-03-23 17:20:59,261]\u001b[0m A new study created in memory with name: no-name-f6fb7154-40d7-4fe6-91b4-54a3d2df53a5\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x10c0e57a0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x10c0e57a0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x1a523b13b0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x1a523b13b0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x1a531b9830> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x1a531b9830> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-03-23 17:21:10,778]\u001b[0m Trial 0 finished with value: 0.1501224786043167 and parameters: {'batch_size': 70, 'lr': 0.0004469301425517348, 'n_layers': 1, 'n_units_l0': 48, 'dropout_l0': 0.24737793557393845}. Best is trial 0 with value: 0.1501224786043167.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x1a51f83a70> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x1a51f83a70> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x1a533c7320> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x1a533c7320> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x1a531b97a0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x1a531b97a0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-03-23 17:21:32,525]\u001b[0m Trial 1 finished with value: 0.14899791777133942 and parameters: {'batch_size': 72, 'lr': 3.691282812195019e-05, 'n_layers': 1, 'n_units_l0': 159, 'dropout_l0': 0.09148617746139015}. Best is trial 1 with value: 0.14899791777133942.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x1a535e59e0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x1a535e59e0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x1a52bb54d0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x1a52bb54d0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x1a52bb5680> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x1a52bb5680> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-03-23 17:22:15,020]\u001b[0m Trial 2 finished with value: 0.16023316979408264 and parameters: {'batch_size': 61, 'lr': 0.00020870803053454806, 'n_layers': 2, 'n_units_l0': 50, 'dropout_l0': 0.30642245072016777, 'n_units_l1': 22, 'dropout_l1': 0.2054317561537234}. Best is trial 1 with value: 0.14899791777133942.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x1a52d17a70> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x1a52d17a70> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x1a52ed8710> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x1a52ed8710> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x1a52ed8b90> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x1a52ed8b90> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-03-23 17:22:49,291]\u001b[0m Trial 3 finished with value: 0.1694500744342804 and parameters: {'batch_size': 48, 'lr': 4.8197525706368465e-05, 'n_layers': 3, 'n_units_l0': 64, 'dropout_l0': 0.4597960452158009, 'n_units_l1': 6, 'dropout_l1': 0.0992273678564991, 'n_units_l2': 199, 'dropout_l2': 0.290903942636899}. Best is trial 1 with value: 0.14899791777133942.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Study statistics: \n",
      "  Number of finished trials:  4\n",
      "  Number of pruned trials:  0\n",
      "  Number of complete trials:  4\n",
      "Best trial:\n",
      "  Value:  0.14899791777133942\n",
      "  Params: \n",
      "    batch_size: 72\n",
      "    lr: 3.691282812195019e-05\n",
      "    n_layers: 1\n",
      "    n_units_l0: 159\n",
      "    dropout_l0: 0.09148617746139015\n"
     ]
    }
   ],
   "source": [
    "# 3. Run an Optuna study, trying 4 different hyperparameter combinations\n",
    "#    (We only try a few trials here to keep the notebook short. Depending\n",
    "#    on how many hyperparameters you're tuning, you should significantly\n",
    "#    increase the number of trials.)\n",
    "study = optuna.create_study(direction=\"minimize\", pruner=optuna.pruners.MedianPruner())\n",
    "study.optimize(objective, n_trials=4)\n",
    "\n",
    "# print out tuning results\n",
    "pruned_trials = study.get_trials(deepcopy=False, states=[TrialState.PRUNED])\n",
    "complete_trials = study.get_trials(deepcopy=False, states=[TrialState.COMPLETE])\n",
    "print(\"Study statistics: \")\n",
    "print(\"  Number of finished trials: \", len(study.trials))\n",
    "print(\"  Number of pruned trials: \", len(pruned_trials))\n",
    "print(\"  Number of complete trials: \", len(complete_trials))\n",
    "print(\"Best trial:\")\n",
    "trial = study.best_trial\n",
    "print(\"  Value: \", trial.value)\n",
    "print(\"  Params: \")\n",
    "for key, value in trial.params.items():\n",
    "    print(\"    {}: {}\".format(key, value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Specify CondDensityEstimator with optimal hyperparameters\n",
    "Now that we have the optimal hyperparameters (store in the\n",
    "`study.best_trial.params` dictionary), we can rebuild our network with these\n",
    "specific hyperparameters and specify the function that does this into the \n",
    "parameters for the `CondDensityEstimator`. These are the hyperparameters we\n",
    "will use in our full run of CFL (at which point we will tune the `CauseClusterer`\n",
    "and `EffectClusterer` as well)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_optimized_network():\n",
    "    # vary number of layers\n",
    "    n_layers = study.best_trial.params['n_layers']\n",
    "    model = Sequential()\n",
    "    for i in range(n_layers):\n",
    "        # vary size of each layer\n",
    "        num_hidden = study.best_trial.params[f'n_units_l{i}']\n",
    "        model.add(Dense(num_hidden, activation=\"relu\"))\n",
    "\n",
    "        # vary amount of dropout for each layer\n",
    "        dropout = study.best_trial.params[f'dropout_l{i}']\n",
    "        model.add(Dropout(rate=dropout))\n",
    "\n",
    "    # add output layer with size of target    \n",
    "    model.add(Dense(Y.shape[1], activation=\"linear\"))\n",
    "    return model\n",
    "\n",
    "opt_CDE_params = {\n",
    "    'model'        : 'CondExpDIY',\n",
    "    'model_params' : {\n",
    "        'build_network'   : build_optimized_network,\n",
    "        'batch_size'      : study.best_trial.params['batch_size'],\n",
    "        'n_epochs'        : 100,\n",
    "        'optimizer'       : 'adam',\n",
    "        'opt_config'      : {'lr' : study.best_trial.params['lr']},\n",
    "        'loss'            : 'mean_squared_error',\n",
    "        'best'            : True, \n",
    "        'verbose'         : 0,\n",
    "        'early_stopping'  : True\n",
    "    }\n",
    "}  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Tune hyperparameters for CauseClusterer and EffectClusterer and train\n",
    "To tune the `CauseClusterer` and `EffectClusterer`, we need to \n",
    "\n",
    "1. set 'tune' to True in the parameter dictionary, and\n",
    "2. specify ranges of hyperparameters to iterate over instead of single values\n",
    "\n",
    "Once we do this, CFL will retrain the clustering model for each combination\n",
    "of hyperparameters specified, and display a plot evaluating how good the\n",
    "model is with each set of hyperparameters. Specifically, it evaluates how \n",
    "well the conditional distribution that was learned by the `CondDensityEstimator`\n",
    "can be predicted from the cluster assignments found. From this plot, you can\n",
    "select the \"elbow\" where the error stops significantly dropping. This is the point\n",
    "at which finer grained clusters are more likely overfitting than truly describing\n",
    "distinct conditional probability regions for this dataset. \n",
    "\n",
    "CFL will then prompt you to enter your chosen hyperparameters from the error plot,\n",
    "and will retrain with these final hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "save_path 'demo_results' doesn't exist, creating now.\n",
      "All results from this run will be saved to demo_results/experiment0000\n",
      "Block: verbose not specified in input, defaulting to 1\n",
      "#################### Beginning CFL Experiment training. ####################\n",
      "Beginning CondDensityEstimator training...\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x1a5309e290> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x1a5309e290> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x1a4fea2170> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x1a4fea2170> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x1a534aa440> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x1a534aa440> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CondDensityEstimator training complete.\n",
      "Beginning CauseClusterer training...\n",
      "Beginning clusterer tuning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "9it [00:07,  1.27it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please choose your final clustering parameters.\n",
      "Final parameters:  {'n_clusters': 4}\n",
      "CauseClusterer training complete.\n",
      "Beginning EffectClusterer training...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning clusterer tuning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1it [00:00,  3.46it/s]/Users/imanwahle/Desktop/cfl/cfl/clustering/cluster_tuning_util.py:173: ConvergenceWarning: Number of distinct clusters (2) found smaller than n_clusters (3). Possibly due to duplicate points in X.\n",
      "  tmp_model.fit(data_to_cluster)\n",
      "2it [00:00,  3.45it/s]/Users/imanwahle/Desktop/cfl/cfl/clustering/cluster_tuning_util.py:173: ConvergenceWarning: Number of distinct clusters (2) found smaller than n_clusters (4). Possibly due to duplicate points in X.\n",
      "  tmp_model.fit(data_to_cluster)\n",
      "3it [00:01,  2.78it/s]/Users/imanwahle/Desktop/cfl/cfl/clustering/cluster_tuning_util.py:173: ConvergenceWarning: Number of distinct clusters (2) found smaller than n_clusters (5). Possibly due to duplicate points in X.\n",
      "  tmp_model.fit(data_to_cluster)\n",
      "4it [00:01,  2.77it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please choose your final clustering parameters.\n",
      "Final parameters:  {'n_clusters': 2}\n",
      "EffectClusterer training complete.\n",
      "Experiment training complete.\n"
     ]
    }
   ],
   "source": [
    "cause_clusterer_params = { 'model' : 'KMeans', \n",
    "                           'model_params' : {'n_clusters' : np.arange(2, 11, dtype=int)},\n",
    "                           'tune' : True,\n",
    "                           'verbose' : 0 }\n",
    "effect_clusterer_params = {'model' : 'KMeans', \n",
    "                           'model_params' : {'n_clusters' : np.arange(2, 6, dtype=int)},\n",
    "                           'tune' : True,\n",
    "                           'precompute_distance' : True,\n",
    "                           'verbose' : 0 }\n",
    "block_names = ['CondDensityEstimator', 'CauseClusterer', 'EffectClusterer']\n",
    "block_params = [opt_CDE_params, cause_clusterer_params, effect_clusterer_params]\n",
    "\n",
    "my_exp = Experiment(X_train=X, \n",
    "                    Y_train=Y, \n",
    "                    data_info=DATA_INFO, \n",
    "                    in_sample_idx=IN_SAMPLE_IDX, \n",
    "                    out_sample_idx=OUT_SAMPLE_IDX,\n",
    "                    block_names=block_names, \n",
    "                    block_params=block_params, \n",
    "                    results_path='demo_results')\n",
    "\n",
    "train_results = my_exp.train()   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, we see that the error plot for the CauseClusterer clearly decreases\n",
    "in improvement as n_clusters = 4. \n",
    "For the EffectClusterer, we see that KMeans throws warnings that the number\n",
    "of distinct clusters found is 2, as opposed to the larger numbers of clusters\n",
    "we ask for. For this reason, we select n_clusters = 2 even though it is not\n",
    "evident from the error plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Visualize results\n",
    "We will plot a few examples from each cause macrostate to see what high-level\n",
    "features each macrostate specifies for. \n",
    "For more general visualization methods, refer to the \n",
    "[Macrostate Visualization](https://cfl.readthedocs.io/en/latest/examples/basic_visualizations.html)\n",
    "notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x1440 with 40 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from visual_bars.visual_bars_vis import viewImagesAndLabels\n",
    "viewImagesAndLabels(X, im_shape=(10,10), n_examples=10, \n",
    "                    x_lbls=train_results['CauseClusterer']['x_lbls'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since Y is just a binary variable, we can print out the percent of samples\n",
    "where Y=1 in each effect macrostate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% Y=1 for effect macrostate 0: 0.0\n",
      "% Y=1 for effect macrostate 1: 1.0\n"
     ]
    }
   ],
   "source": [
    "ylbls = train_results['EffectClusterer']['y_lbls']\n",
    "print('% Y=1 for effect macrostate 0:', np.sum(Y[ylbls==0]==1)/np.sum(ylbls==1))\n",
    "print('% Y=1 for effect macrostate 1:', np.sum(Y[ylbls==1]==1)/np.sum(ylbls==1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While this seems like a somewhat trivial situation to have partitioned\n",
    "the effect space (which is just described by a binary variable), the fact that\n",
    "CFL partitions based on the relation between X and Y (instead of just\n",
    "clustering based on the effect space here) makes it so that we could've gotten \n",
    "a very different partition of the effect space if, for example, P(Y=1 | X) = P(Y=0 | X)."
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "a3e2eae926291f18d1afe7b431ab5303b6e9ec8df44d60cf0ff75f0c781aceac"
  },
  "kernelspec": {
   "display_name": "Python 3.7.9 ('cfl_env')",
   "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": "3.7.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}