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  {
   "cell_type": "markdown",
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   "source": [
    "## Active Learning\n",
    "\n",
    "The class provides tools for active learning of regression models using expected model change (EMC) and query by committee (QBC) methods and approaches for distribution shift alleviations.\n",
    "\n",
    "The central idea behind any active learning approach is to achieve lower prediction errors (higher accuracy) in machine learning models by choosing less but more informative data points.\n",
    "\n",
    "The implementation of this algorithm follows an interactive approach. In other words, we often ask you to provide labels for the selected data points.\n",
    "\n",
    "Here is a list of main methods to carry out an active learning approach:\n",
    "\n",
    "- initialize\n",
    "- deposit\n",
    "- ignore\n",
    "- search\n",
    "- visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from chemml.optimization import ActiveLearning\n",
    "from chemml.datasets import load_organic_density\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this tutorial, we load a sample data from ChemML datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "labels  : density,     <class 'pandas.core.frame.DataFrame'> , shape: (500, 1)\n",
      "features: molecular descriptors, <class 'pandas.core.frame.DataFrame'> , shape: (500, 200)\n"
     ]
    }
   ],
   "source": [
    "smi, density, features = load_organic_density()\n",
    "print('labels  : density,    ', type(density), ', shape:', density.shape)\n",
    "print('features: molecular descriptors,', type(features), ', shape:', features.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Keras model example\n",
    "\n",
    "The current version of the EMC approach only work with Keras deep learning models. You should create a function that returns a Keras model. You can also set the optimizer parameters and compile the model inside the function. Here is a toy example for the Keras model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Input, Dense, Concatenate\n",
    "from keras.models import Sequential, Model\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.initializers import glorot_uniform\n",
    "from keras import regularizers\n",
    "from keras import losses\n",
    "from keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_creator(nneurons=features.shape[1], activation=['relu','tanh'], lr = 0.001):\n",
    "    # branch 1\n",
    "    b1_in = Input(shape=(nneurons, ), name='inp1')\n",
    "    l1 = Dense(12, name='l1', activation=activation[0])(b1_in)\n",
    "    b1_l1 = Dense(6, name='b1_l1', activation=activation[0])(l1)\n",
    "    b1_l2 = Dense(3, name='b1_l2', activation=activation[0])(b1_l1)\n",
    "    # branch 2\n",
    "    b2_l1 = Dense(16, name='b2_l1', activation=activation[1])(l1)\n",
    "    b2_l2 = Dense(8, name='b2_l2', activation=activation[1])(b2_l1)\n",
    "    # merge branches\n",
    "    merged = Concatenate(name='merged')([b1_l2, b2_l2])\n",
    "    # linear output\n",
    "    out = Dense(1, name='outp', activation='linear')(merged)\n",
    "    ###\n",
    "    model = Model(inputs = b1_in, outputs = out)\n",
    "    adam = Adam(learning_rate=lr, beta_1=0.9, beta_2=0.999, epsilon=1e-8, decay=0.0)\n",
    "    model.compile(optimizer = adam,\n",
    "                  loss = 'mean_squared_error',\n",
    "                  metrics=['mean_absolute_error'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional\"</span>\n",
       "</pre>\n"
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       "\u001b[1mModel: \"functional\"\u001b[0m\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)        </span>┃<span style=\"font-weight: bold\"> Output Shape      </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ inp1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">200</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ l1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>)        │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,412</span> │ inp1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ b1_l1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">78</span> │ l1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ b2_l1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">208</span> │ l1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ b1_l2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span> │ b1_l1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ b2_l2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)         │        <span style=\"color: #00af00; text-decoration-color: #00af00\">136</span> │ b2_l1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ merged              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ b1_l2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],      │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)       │                   │            │ b2_l2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ outp (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span> │ merged[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
       "</pre>\n"
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       "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ inp1 (\u001b[38;5;33mInputLayer\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m200\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ -                 │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ l1 (\u001b[38;5;33mDense\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m)        │      \u001b[38;5;34m2,412\u001b[0m │ inp1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ b1_l1 (\u001b[38;5;33mDense\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m)         │         \u001b[38;5;34m78\u001b[0m │ l1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ b2_l1 (\u001b[38;5;33mDense\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)        │        \u001b[38;5;34m208\u001b[0m │ l1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ b1_l2 (\u001b[38;5;33mDense\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)         │         \u001b[38;5;34m21\u001b[0m │ b1_l1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ b2_l2 (\u001b[38;5;33mDense\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)         │        \u001b[38;5;34m136\u001b[0m │ b2_l1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ merged              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m)        │          \u001b[38;5;34m0\u001b[0m │ b1_l2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],      │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │                   │            │ b2_l2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ outp (\u001b[38;5;33mDense\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)         │         \u001b[38;5;34m12\u001b[0m │ merged[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,867</span> (11.20 KB)\n",
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       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,867\u001b[0m (11.20 KB)\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,867</span> (11.20 KB)\n",
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       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,867\u001b[0m (11.20 KB)\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
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       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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   ],
   "source": [
    "m = model_creator()\n",
    "m.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To view workflow, you need to install `pydot`. For example with `pip install pydot`. Then run the following code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<text text-anchor=\"start\" x=\"538.25\" y=\"-376.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Output shape: </text>\n",
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       "<text text-anchor=\"start\" x=\"180\" y=\"-256.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Output shape: </text>\n",
       "<text text-anchor=\"start\" x=\"269.25\" y=\"-256.2\" font-family=\"Helvetica,sans-Serif\" font-weight=\"bold\" font-size=\"14.00\">(None, 3)</text>\n",
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       "<text text-anchor=\"start\" x=\"372.88\" y=\"-256.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Input shape: </text>\n",
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       "<text text-anchor=\"start\" x=\"540\" y=\"-256.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Output shape: </text>\n",
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       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import SVG\n",
    "from tensorflow.keras.utils import model_to_dot\n",
    "\n",
    "SVG(model_to_dot(m, show_shapes= True, show_layer_names=True, dpi=70).create(prog='dot', format='svg'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize active learning: EMC\n",
    "\n",
    "You first need to instantiate the active learning class using the model_creator function and the pool of candidates (i.e., U) represented by your choice of features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "al = ActiveLearning(\n",
    "           model_creator = model_creator,\n",
    "           U = features,\n",
    "           target_layer = ['b1_l2', 'b2_l2'], # we could also enter 'merged' layer because it only does the concatenation\n",
    "           train_size = 50,  # 50 initial training data will be selected randomly\n",
    "           test_size = 50,  # 50 independent test data will be selected randomly for the entire search\n",
    "           batch_size = [20,0,0] # at each round of AL, labels for 20 candidates will be queried using EMC method\n",
    "           )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you first need to initialize the method to get the indices for the training and test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "tr_ind, te_ind = al.initialize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The array of queried points are always available via the queries attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['initial training set',\n",
       "  array([445, 171, 373, 342, 372,  99, 331, 195, 199, 130, 140, 201, 451,\n",
       "         300, 294, 108, 369,  79, 328, 398, 485, 426,  43, 391,  71, 124,\n",
       "         495, 276, 316, 383, 498, 225, 440, 148, 122,  35, 178, 456, 253,\n",
       "         216, 131, 374, 419, 357, 211, 389, 458, 324, 415, 355])],\n",
       " ['test set',\n",
       "  array([ 51,  48, 223, 254, 118, 258, 399, 231, 462, 292, 438, 266,  97,\n",
       "          28, 119, 103, 464, 242, 101, 250, 321, 468, 144, 123, 206, 227,\n",
       "         410, 403, 326, 229, 363,  39, 407, 416,  38, 430, 441, 465,  78,\n",
       "         346,  13, 281, 256,  85, 273, 151, 404, 161, 370, 431])]]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "al.queries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you acquire labels for the queried data (e.g., by experiment or simulation) give them to the al object using deposit method. This can be done partially or all at once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we stored 50 of passed indices. A list of them is in the 'last_deposited_indices_' attribute.\n",
      "we stored 50 of passed indices. A list of them is in the 'last_deposited_indices_' attribute.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "al.deposit(tr_ind, density.values.reshape(-1,1)[tr_ind])\n",
    "al.deposit(te_ind, density.values.reshape(-1,1)[te_ind])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 200) (50, 1)\n",
      "(50, 200) (50, 1)\n"
     ]
    }
   ],
   "source": [
    "print (al.X_train.shape, al.Y_train.shape)\n",
    "print (al.X_test.shape, al.Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "al.queries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can not initialize the method or deposit any more data once you empty the list of queries by providing requested data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#al.initialize()\n",
    "# the above code raises following error message:\n",
    "# ValueError: The class has been already initialized and it can not be initialized again!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Start the active search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n"
     ]
    }
   ],
   "source": [
    "q = al.search(n_evaluation=1, epochs=10, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 40, 138, 153, 155, 160, 173, 214, 248, 283, 310, 343, 356, 385,\n",
       "       393, 402, 408, 427, 433, 477, 482])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['batch #1',\n",
       "  array([ 40, 138, 153, 155, 160, 173, 214, 248, 283, 310, 343, 356, 385,\n",
       "         393, 402, 408, 427, 433, 477, 482])]]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "al.queries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_query</th>\n",
       "      <th>num_training</th>\n",
       "      <th>num_test</th>\n",
       "      <th>mae</th>\n",
       "      <th>mae_std</th>\n",
       "      <th>rmse</th>\n",
       "      <th>rmse_std</th>\n",
       "      <th>r2</th>\n",
       "      <th>r2_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>52.235659</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.67875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.340604</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_query  num_training  num_test        mae  mae_std      rmse  rmse_std  \\\n",
       "0          0            50        50  52.235659      0.0  63.67875       0.0   \n",
       "\n",
       "         r2  r2_std  \n",
       "0  0.340604     0.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "al.results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now you need to deposit the labels for the first batch of queried data using EMC approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we stored 20 of passed indices. A list of them is in the 'last_deposited_indices_' attribute.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "al.deposit(q, density.values.reshape(-1,1)[q])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n"
     ]
    }
   ],
   "source": [
    "q = al.search(epochs=10, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  3,   8,  15,  16,  47,  50,  75,  91, 112, 165, 175, 197, 217,\n",
       "       224, 264, 278, 338, 353, 423, 483])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a data frame of the results in terms of mean absolute error, root mean square error, and r-squared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_query</th>\n",
       "      <th>num_training</th>\n",
       "      <th>num_test</th>\n",
       "      <th>mae</th>\n",
       "      <th>mae_std</th>\n",
       "      <th>rmse</th>\n",
       "      <th>rmse_std</th>\n",
       "      <th>r2</th>\n",
       "      <th>r2_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>52.235659</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>63.678750</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.340604</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>50</td>\n",
       "      <td>47.562849</td>\n",
       "      <td>9.865081</td>\n",
       "      <td>58.327748</td>\n",
       "      <td>11.807654</td>\n",
       "      <td>0.424096</td>\n",
       "      <td>0.212494</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_query  num_training  num_test        mae   mae_std       rmse  \\\n",
       "0          0            50        50  52.235659  0.000000  63.678750   \n",
       "1          1            70        50  47.562849  9.865081  58.327748   \n",
       "\n",
       "    rmse_std        r2    r2_std  \n",
       "0   0.000000  0.340604  0.000000  \n",
       "1  11.807654  0.424096  0.212494  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "al.results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get a baseline: random sampling\n",
    "\n",
    "You can get a baseline learning curve by running a `random_search`. This method starts from the initial sets of training and test data, and adds the same number of data as batch_size every round."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n",
      "WARNING:tensorflow:5 out of the last 19 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x000001F5E2936CA0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 71ms/stepWARNING:tensorflow:6 out of the last 20 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x000001F5E2936CA0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "al.random_search(density.values.reshape(-1,1), n_evaluation=2, epochs=10, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results of random sampling is also accessible using `random_results` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_query</th>\n",
       "      <th>num_training</th>\n",
       "      <th>num_test</th>\n",
       "      <th>mae</th>\n",
       "      <th>mae_std</th>\n",
       "      <th>rmse</th>\n",
       "      <th>rmse_std</th>\n",
       "      <th>r2</th>\n",
       "      <th>r2_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>45.216390</td>\n",
       "      <td>7.688320</td>\n",
       "      <td>57.787159</td>\n",
       "      <td>9.361563</td>\n",
       "      <td>0.442724</td>\n",
       "      <td>0.175941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>50</td>\n",
       "      <td>39.659886</td>\n",
       "      <td>2.165134</td>\n",
       "      <td>52.483417</td>\n",
       "      <td>4.332977</td>\n",
       "      <td>0.549026</td>\n",
       "      <td>0.073960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_query  num_training  num_test        mae   mae_std       rmse  \\\n",
       "0          0            50        50  45.216390  7.688320  57.787159   \n",
       "1          1            70        50  39.659886  2.165134  52.483417   \n",
       "\n",
       "   rmse_std        r2    r2_std  \n",
       "0  9.361563  0.442724  0.175941  \n",
       "1  4.332977  0.549026  0.073960  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "al.random_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize distributions\n",
    "\n",
    "We keep track of many metrics to analyze your active learning search. You can store the info during your search or visualize them at each round. The `visualize` method is able to return a dictionary of matplotlib figures for the distribution of features and labels, and the learning curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "plots = al.visualize(density.values.reshape(-1,1)) # Let's provide all the labels, now that we have all of them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dist_pc': (<Figure size 640x480 with 1 Axes>,\n",
       "  <Figure size 640x480 with 1 Axes>,\n",
       "  <Figure size 640x480 with 1 Axes>),\n",
       " 'dist_y': (<Figure size 640x480 with 1 Axes>,\n",
       "  <Figure size 640x480 with 1 Axes>,\n",
       "  <Figure size 640x480 with 1 Axes>),\n",
       " 'learning_curve': <Figure size 640x480 with 1 Axes>}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of the first principal component:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plots['dist_pc'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of the labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plots['dist_y'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The learning curves:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plots['learning_curve']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Search in a loop\n",
    "\n",
    "This is how we use this module in a loop:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we stored 100 of passed indices. A list of them is in the 'last_deposited_indices_' attribute.\n",
      "we stored 50 of passed indices. A list of them is in the 'last_deposited_indices_' attribute.\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 933us/step\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "we stored 50 of passed indices. A list of them is in the 'last_deposited_indices_' attribute.\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "we stored 50 of passed indices. A list of them is in the 'last_deposited_indices_' attribute.\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "we stored 50 of passed indices. A list of them is in the 'last_deposited_indices_' attribute.\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 995us/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "we stored 50 of passed indices. A list of them is in the 'last_deposited_indices_' attribute.\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "we stored 50 of passed indices. A list of them is in the 'last_deposited_indices_' attribute.\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "out_dir = 'al_toy_example' # the arbitrary path to the output directory\n",
    "if not os.path.exists(out_dir):\n",
    "    os.makedirs(out_dir)\n",
    "\n",
    "# all data sets must be 2-dimensional\n",
    "y = density.values.reshape(-1,1)\n",
    "\n",
    "al = ActiveLearning(\n",
    "           model_creator = model_creator,\n",
    "           U = features,\n",
    "           target_layer = ['b1_l2', 'b2_l2'], # we could also enter 'merged' layer because it only does the concatenation\n",
    "           train_size = 100,  # 100 initial training data will be selected randomly\n",
    "           test_size = 50,  # 50 independent test data will be selected randomly for the entire search\n",
    "           batch_size = [50,0,0] # at each round of AL, labels for 20 candidates will be queried using EMC method\n",
    "           )\n",
    "\n",
    "tr_ind, te_ind = al.initialize()\n",
    "\n",
    "\n",
    "al.deposit(tr_ind, y[tr_ind])\n",
    "al.deposit(te_ind, y[te_ind])\n",
    "\n",
    "\n",
    "while al.query_number < 5:\n",
    "    early_stopping = EarlyStopping(monitor='val_loss', min_delta=1e-6, patience=20, verbose=0, mode='auto')\n",
    "    tr_ind = al.search(n_evaluation=3, ensemble='kfold', n_ensemble=3, normalize_input=True, normalize_internal=False,\n",
    "    batch_size=32, epochs=5, verbose=0)#, callbacks=[early_stopping],validation_split=0.1)\n",
    "\n",
    "    al.results.to_csv(out_dir+'/emc.csv',index=False)\n",
    "\n",
    "    pd.DataFrame(al.train_indices).to_csv(out_dir+'/train_indices.csv',index=False)\n",
    "    pd.DataFrame(al.test_indices).to_csv(out_dir+'/test_indices.csv',index=False)\n",
    "\n",
    "    al.deposit(tr_ind, y[tr_ind])\n",
    "\n",
    "    # you can run random search later but, I need it to be in my learning curve\n",
    "    al.random_search(y, n_evaluation=3, random_state=13, batch_size=32, epochs=5, verbose=0)#, callbacks=[early_stopping],validation_split=0.05)\n",
    "\n",
    "    al.random_results.to_csv(out_dir+'/random.csv',index=False)\n",
    "\n",
    "    plots = al.visualize(y)\n",
    "    if not os.path.exists(out_dir+\"/plots\"):\n",
    "        os.makedirs(out_dir+\"/plots\")\n",
    "\n",
    "    plots['dist_pc'][0].savefig(out_dir+\"/plots/dist_pc_0_%i.png\"%al.query_number)# , close = True, verbose = True)\n",
    "    plots['dist_y'][0].savefig(out_dir+\"/plots/dist_y_0_%i.png\"%al.query_number)# , close = True, verbose = True)\n",
    "    plots['learning_curve'].savefig(out_dir+\"/plots/lcurve_%i.png\"%al.query_number)#, close = True, verbose = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_query</th>\n",
       "      <th>num_training</th>\n",
       "      <th>num_test</th>\n",
       "      <th>mae</th>\n",
       "      <th>mae_std</th>\n",
       "      <th>rmse</th>\n",
       "      <th>rmse_std</th>\n",
       "      <th>r2</th>\n",
       "      <th>r2_std</th>\n",
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       "      <td>0.120509</td>\n",
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       "      <td>2.419361</td>\n",
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       "      <td>0.710894</td>\n",
       "      <td>0.044060</td>\n",
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       "      <td>33.343583</td>\n",
       "      <td>6.578607</td>\n",
       "      <td>40.832199</td>\n",
       "      <td>8.060312</td>\n",
       "      <td>0.718315</td>\n",
       "      <td>0.099726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>300</td>\n",
       "      <td>50</td>\n",
       "      <td>29.987748</td>\n",
       "      <td>1.177923</td>\n",
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       "      <td>2.378018</td>\n",
       "      <td>0.770154</td>\n",
       "      <td>0.029591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_query  num_training  num_test        mae   mae_std       rmse  \\\n",
       "0          0           100        50  42.286460  7.604904  55.063699   \n",
       "1          1           150        50  42.174142  4.981477  54.359089   \n",
       "2          2           200        50  34.254397  2.419361  42.046462   \n",
       "3          3           250        50  33.343583  6.578607  40.832199   \n",
       "4          4           300        50  29.987748  1.177923  37.520587   \n",
       "\n",
       "    rmse_std        r2    r2_std  \n",
       "0  10.609402  0.488650  0.202795  \n",
       "1   6.561031  0.512491  0.120509  \n",
       "2   3.157107  0.710894  0.044060  \n",
       "3   8.060312  0.718315  0.099726  \n",
       "4   2.378018  0.770154  0.029591  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "al.results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>num_query</th>\n",
       "      <th>num_training</th>\n",
       "      <th>num_test</th>\n",
       "      <th>mae</th>\n",
       "      <th>mae_std</th>\n",
       "      <th>rmse</th>\n",
       "      <th>rmse_std</th>\n",
       "      <th>r2</th>\n",
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       "      <td>200</td>\n",
       "      <td>50</td>\n",
       "      <td>36.378079</td>\n",
       "      <td>4.676701</td>\n",
       "      <td>45.963672</td>\n",
       "      <td>5.200160</td>\n",
       "      <td>0.652055</td>\n",
       "      <td>0.080589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>250</td>\n",
       "      <td>50</td>\n",
       "      <td>35.486844</td>\n",
       "      <td>9.098630</td>\n",
       "      <td>44.663603</td>\n",
       "      <td>12.942464</td>\n",
       "      <td>0.648373</td>\n",
       "      <td>0.207024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>300</td>\n",
       "      <td>50</td>\n",
       "      <td>23.700192</td>\n",
       "      <td>1.753282</td>\n",
       "      <td>30.448780</td>\n",
       "      <td>1.795343</td>\n",
       "      <td>0.848712</td>\n",
       "      <td>0.017457</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_query  num_training  num_test        mae   mae_std       rmse  \\\n",
       "0          0           100        50  44.773474  6.886911  57.323165   \n",
       "1          1           150        50  37.735247  5.263009  46.901441   \n",
       "2          2           200        50  36.378079  4.676701  45.963672   \n",
       "3          3           250        50  35.486844  9.098630  44.663603   \n",
       "4          4           300        50  23.700192  1.753282  30.448780   \n",
       "\n",
       "    rmse_std        r2    r2_std  \n",
       "0   7.641993  0.456164  0.145890  \n",
       "1   6.405237  0.635620  0.101938  \n",
       "2   5.200160  0.652055  0.080589  \n",
       "3  12.942464  0.648373  0.207024  \n",
       "4   1.795343  0.848712  0.017457  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "al.random_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plots = al.visualize(y)\n",
    "plots['learning_curve']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a video\n",
    "\n",
    "If you store all the plots at each round you will be able to create a video similar to these ones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video controls alt=\"test\" 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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "from base64 import b64encode\n",
    "with open(\"images/dist_y.mov\", \"rb\") as f:\n",
    "    video = f.read()\n",
    "video_encoded = b64encode(video).decode('ascii')\n",
    "video_tag = '<video controls alt=\"test\" src=\"data:video/x-m4v;base64,{0}\">'.format(video_encoded)\n",
    "HTML(data=video_tag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video controls alt=\"test\" 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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "from base64 import b64encode\n",
    "with open(\"images/lcurve.mov\", \"rb\") as f:\n",
    "    video = f.read()\n",
    "video_encoded = b64encode(video).decode('ascii')\n",
    "video_tag = '<video controls alt=\"test\" src=\"data:video/x-m4v;base64,{0}\">'.format(video_encoded)\n",
    "HTML(data=video_tag)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "chemml_dev_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": "3.12.5"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}