{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Built-in Macrostate Visualizations\n",
    "\n",
    "This notebook demonstrates how to use the `visualize_macrostates` function\n",
    "to visualize the macrostates that CFL constructs over cause and effect spaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from cfl.experiment import Experiment\n",
    "from cfl.visualization.basic_visualizations import visualize_macrostates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CFL helper function\n",
    "\n",
    "First, we will create a helper function to quickly define and train\n",
    "CFL experiments. If you are unfamiliar with how to define a CFL experiment,\n",
    "please refer to the cfl_code_intro notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_and_train_CFL_experiment(X, Y):\n",
    "    ''' Helper function that will create a CFL experiment and train it for\n",
    "        this demo.\n",
    "    '''\n",
    "\n",
    "    # set up pipeline parameters\n",
    "    data_info = {'X_dims' : X.shape, 'Y_dims' : Y.shape, 'Y_type' : 'continuous'}\n",
    "    CDE_params = {'model' : 'CondExpMod', 'model_params' : \n",
    "        {'n_epochs' : 5, 'verbose' : 0, 'show_plot' : False}}\n",
    "    cause_cluster_params =  {'model' : 'KMeans', 'model_params' : {'n_clusters' : 4}, \n",
    "        'verbose' : 0, 'tune' : False}\n",
    "    effect_cluster_params = {'model' : 'KMeans', 'model_params' : {'n_clusters' : 2}, \n",
    "        'verbose' : 0, 'tune' : False}\n",
    "\n",
    "    block_names = ['CondDensityEstimator', 'CauseClusterer', 'EffectClusterer']\n",
    "    block_params = [CDE_params, cause_cluster_params, effect_cluster_params]\n",
    "    save_path = 'basic_vis_results'\n",
    "\n",
    "    # Create a new CFL experiment with specified parameters\n",
    "    my_exp = Experiment(X_train=X, Y_train=Y, data_info=data_info, \n",
    "                        block_names=block_names, block_params=block_params, \n",
    "                        results_path=save_path, verbose=0)\n",
    "    my_exp.train()\n",
    "    return my_exp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing macrostates over 1D data\n",
    "Here, we construct a dataset where the variables in both X and Y are \n",
    "1-dimensional. Here, when we say 1-dimensional, we mean that the data is not\n",
    "spatially organized in 2D (like an image), or any other higher dimensional\n",
    "organization. Note that the data can still contain many variables, and in that\n",
    "sense it is high-dimensional, but in the context of visualization it is 1D."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape:  (1000, 10)\n",
      "Y shape:  (1000, 2)\n",
      "Block: verbose not specified in input, defaulting to 1\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x1a4929b0e0> 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 0x1a4929b0e0> 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 0x1a494e2c20> 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 0x1a494e2c20> 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_predict_function.<locals>.predict_function at 0x1a4a3a1ef0> 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 0x1a4a3a1ef0> 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": [
      "100%|██████████| 1000/1000 [00:00<00:00, 6930.31it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment saved to: \n",
      "basic_vis_results/experiment0000\n"
     ]
    }
   ],
   "source": [
    "# define the parameters of our toy dataset\n",
    "n_samples = 1000\n",
    "n_cause_features = 10\n",
    "n_effect_features = 2\n",
    "\n",
    "# randomly sampled X and Y values\n",
    "X = np.random.normal(loc=1, size=(n_samples,n_cause_features))\n",
    "Y = np.random.normal(loc=2, size=(n_samples,n_effect_features))\n",
    "print('X shape: ', X.shape)\n",
    "print('Y shape: ', Y.shape)\n",
    "\n",
    "# name each feature in X and Y (you may have more specific variable names that\n",
    "# you'd want to supply here)\n",
    "X_feature_names = [f'cause_{i}' for i in range(n_cause_features)]\n",
    "Y_feature_names = [f'effect_{i}' for i in range(n_effect_features)]\n",
    "\n",
    "# create and train experiment\n",
    "my_exp = create_and_train_CFL_experiment(X, Y)\n",
    "\n",
    "# print out where the experiment was saved\n",
    "print('Experiment saved to: ')\n",
    "print(my_exp.get_save_path())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simplest way to visualize macrostates is by taking all of the samples\n",
    "in our dataset that fall into a certain macrostate and looking at the mean\n",
    "across those samples. We can then compare these means across macrostates to \n",
    "see how variables differ. \n",
    "\n",
    "Here's how you would visualize the means of the macrostates CFL found on the cause side:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize cause side macrostates\n",
    "visualize_macrostates(  data=X, # data to visualize\n",
    "                        feature_names=X_feature_names, \n",
    "                        cause_or_effect='cause', \n",
    "                        exp_path='basic_vis_results/experiment0000', # where was the data saved?\n",
    "                        data_series='dataset_train', # if you've passed multiple datasets through CFL's predict method, you can select which one you want\n",
    "                        subtract_global_mean=True) # do you want to plot the raw data or relative to the global mean?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The process is quite similar for visualizing results on the effect side, \n",
    "except that now you change the data, feature_names, and cause_or_effect values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize effect side macrostates\n",
    "visualize_macrostates(  data=Y,\n",
    "                        feature_names=Y_feature_names,\n",
    "                        cause_or_effect='effect', # change the space you want to plot in along with the data\n",
    "                        exp_path='basic_vis_results/experiment0000',\n",
    "                        data_series='dataset_train',\n",
    "                        subtract_global_mean=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Up to now, we told `visualize_macrostates` to subtract the global mean\n",
    "from each of macrostate means. You can also plot the raw means by setting\n",
    "`subtract_global_mean` to False:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize effect side macrostates\n",
    "visualize_macrostates(  data=Y,\n",
    "                        feature_names=Y_feature_names,\n",
    "                        cause_or_effect='effect', # change the space you want to plot in along with the data\n",
    "                        exp_path='basic_vis_results/experiment0000',\n",
    "                        data_series='dataset_train',\n",
    "                        subtract_global_mean=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2D visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape:  (1000, 10, 5)\n",
      "X flattened (for CFL) shape:  (1000, 50)\n",
      "Y shape:  (1000, 2)\n",
      "Block: verbose not specified in input, defaulting to 1\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x1a49c38dd0> 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 0x1a49c38dd0> 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 0x1a49ccfc20> 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 0x1a49ccfc20> 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_predict_function.<locals>.predict_function at 0x1a49e729e0> 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 0x1a49e729e0> 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": [
      "100%|██████████| 1000/1000 [00:00<00:00, 6702.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment saved to: \n",
      "basic_vis_results/experiment0001\n"
     ]
    }
   ],
   "source": [
    "# define the parameters of our toy dataset\n",
    "n_samples = 1000\n",
    "n_cause_features = [10,5] # our cause samples are now 10x5 images\n",
    "n_effect_features = 2\n",
    "\n",
    "# randomly sampled X and Y values\n",
    "# note that we construct X_flattened here to pass in vectorized samples to CFL\n",
    "X = np.random.normal(loc=1, size=np.concatenate([[n_samples], n_cause_features]))\n",
    "X_flattened = np.reshape(X, (n_samples, np.product(n_cause_features)))\n",
    "Y = np.random.normal(loc=2, size=(n_samples,n_effect_features))\n",
    "print('X shape: ', X.shape)\n",
    "print('X flattened (for CFL) shape: ', X_flattened.shape)\n",
    "print('Y shape: ', Y.shape)\n",
    "\n",
    "# name each feature in X and Y (you may have more specific variable names that\n",
    "# you'd want to supply here)\n",
    "X_feature_names = [[f'cause{j}_{i}' for i in range(n_cause_features[j])] for j in range(len(n_cause_features))]\n",
    "Y_feature_names = [f'effect_{i}' for i in range(n_effect_features)]\n",
    "\n",
    "# create and train experiment\n",
    "my_exp = create_and_train_CFL_experiment(X_flattened, Y)\n",
    "\n",
    "# print out where the experiment was saved\n",
    "print('Experiment saved to: ')\n",
    "print(my_exp.get_save_path())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize cause side macrostates\n",
    "visualize_macrostates(  data=X,\n",
    "                        feature_names=X_feature_names,\n",
    "                        cause_or_effect='cause',\n",
    "                        exp_path='basic_vis_results/experiment0001',\n",
    "                        data_series='dataset_train',\n",
    "                        subtract_global_mean=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3D visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape:  (1000, 10, 5, 3)\n",
      "X flattened (for CFL) shape:  (1000, 150)\n",
      "Y shape:  (1000, 2)\n",
      "Block: verbose not specified in input, defaulting to 1\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x1a4adbf560> 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 0x1a4adbf560> 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 0xf160dd560> 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 0xf160dd560> 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_predict_function.<locals>.predict_function at 0x1a49fd34d0> 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 0x1a49fd34d0> 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": [
      "100%|██████████| 1000/1000 [00:00<00:00, 1872.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment saved to: \n",
      "basic_vis_results/experiment0002\n"
     ]
    }
   ],
   "source": [
    "# define the parameters of our toy dataset\n",
    "n_samples = 1000\n",
    "n_cause_features = [10,5,3] # our cause samples are now 10x5x3 volumes\n",
    "n_effect_features = 2\n",
    "\n",
    "# randomly sampled X and Y values\n",
    "# note that we construct X_flattened here to pass in vectorized samples to CFL\n",
    "X = np.random.normal(loc=1, size=np.concatenate([[n_samples], n_cause_features]))\n",
    "X_flattened = np.reshape(X, (n_samples, np.product(n_cause_features)))\n",
    "Y = np.random.normal(loc=2, size=(n_samples,n_effect_features))\n",
    "print('X shape: ', X.shape)\n",
    "print('X flattened (for CFL) shape: ', X_flattened.shape)\n",
    "print('Y shape: ', Y.shape)\n",
    "\n",
    "# name each feature in X and Y (you may have more specific variable names that\n",
    "# you'd want to supply here)\n",
    "X_feature_names = [[f'cause{j}_{i}' for i in range(n_cause_features[j])] for j in range(len(n_cause_features))]\n",
    "Y_feature_names = [f'effect_{i}' for i in range(n_effect_features)]\n",
    "\n",
    "# create and train experiment\n",
    "my_exp = create_and_train_CFL_experiment(X_flattened, Y)\n",
    "\n",
    "# print out where the experiment was saved\n",
    "print('Experiment saved to: ')\n",
    "print(my_exp.get_save_path())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x1296 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize cause side macrostates\n",
    "visualize_macrostates(  data=X,\n",
    "                        feature_names=X_feature_names,\n",
    "                        cause_or_effect='cause',\n",
    "                        exp_path='basic_vis_results/experiment0002',\n",
    "                        data_series='dataset_train',\n",
    "                        subtract_global_mean=True)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "b9f90a7866deec4ec91106178f5b99f4479e17c39f9cdadf9218e367e68d50a4"
  },
  "kernelspec": {
   "display_name": "Python 3.7.9 64-bit ('cfl_env': conda)",
   "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
}