{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='https://raw.githubusercontent.com/autonomio/hyperio/master/logo.png'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, '/Users/mikko/Documents/GitHub/hyperio')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hyperio as hy\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this experiment, we're going to use the famous Iris dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for converting the Name to categorical, we'll use Keras\n",
    "\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "def iris():\n",
    "\n",
    "    '''\n",
    "    Returns (x, y)\n",
    "\n",
    "    '''\n",
    "\n",
    "    df = pd.read_csv('iris.csv')\n",
    "    df['Name'] = df['Name'].factorize()[0]\n",
    "    df = df.sample(len(df))\n",
    "    y = to_categorical(df['Name'])\n",
    "    x = df.iloc[:,:-1].values\n",
    "\n",
    "    return x, y\n",
    "\n",
    "x, y = iris()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Model Preparation \n",
    "Hyperio works with any Keras model, without changing the structure of the model in anyway, or without introducing any new syntax. The below example shows clearly how this works. First we have to import two helper functions from Hyperio, one for early stopping callout, and the other for using normalized learning rate values. Because we might want to work on trying out several optimizers in a single scan, without normalization, inputting of the values would become cumbersome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperio import lr_normalizer, early_stopper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the only difference in the model below is how instead of using a label or value to define a given model parameter, we do it using a dictionary label. Also for optimizer we are using a learning rate parameter, which involves the use of two dictionary labels. Finally, we're using a callback with a preset value 'strict'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dropout, Dense\n",
    "\n",
    "def iris_model(x_train, y_train, x_val, y_val, params):\n",
    "    \n",
    "    model = Sequential()                            \n",
    "    model.add(Dense(params['first_neuron'],\n",
    "                    input_dim=x_train.shape[1],\n",
    "                    activation='relu'))\n",
    "    \n",
    "    model.add(Dropout(params['dropout']))\n",
    "    model.add(Dense(y_train.shape[1],\n",
    "                    activation=params['last_activation']))\n",
    "\n",
    "    model.compile(optimizer=params['optimizer'](lr=lr_normalizer(params['lr'], params['optimizer'])),\n",
    "                  loss=params['loss'],\n",
    "                  metrics=['acc'])\n",
    "\n",
    "    out = model.fit(x_train, y_train,\n",
    "                    batch_size=params['batch_size'],\n",
    "                    epochs=params['epochs'],\n",
    "                    verbose=0,\n",
    "                    validation_data=[x_val, y_val],\n",
    "                    callbacks=early_stopper(params['epochs'], mode='strict'))\n",
    "    \n",
    "    return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Setting the Parameter Space Boundaries\n",
    "In the last and final step, we're going to create the dictionary, which will then be passed on to hyperio together with the model above. Here we have three different ways to input values:\n",
    "\n",
    "- as stepped ranges (min, max, steps)\n",
    "- as multiple values [in a list]\n",
    "- as a single value [in a list]\n",
    "\n",
    "For values we don't want to use, it's ok to set it as None.\n",
    "\n",
    "NOTE: at this point you have to import from Keras the optimizer, activations, and losses you want to scan for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.optimizers import Adam, Nadam\n",
    "from keras.activations import softmax\n",
    "from keras.losses import categorical_crossentropy, logcosh\n",
    "\n",
    "p = {'lr': (2, 10, 30),\n",
    "     'first_neuron':[4, 8, 16, 32, 64, 128],\n",
    "     'batch_size': [2, 3, 4],\n",
    "     'epochs': [500],\n",
    "     'dropout': (0, 0.40, 10),\n",
    "     'optimizer': [Adam, Nadam],\n",
    "     'loss': [categorical_crossentropy, logcosh],\n",
    "     'last_activation': [softmax],\n",
    "     'weight_regulizer':[None],\n",
    "     'emb_output_dims': [None]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Running the Model\n",
    "Now we are ready to run the model based on the parameters and the layer configuration above. The exact same process would apply with any other model, just make sure to pass the model function name in the Hyperio() command as in the below example. To get started quickly, we're going to invoke the 'grid_downsample' parameter to 1/100 of the entire permutations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57 scans will take roughly 57 seconds\n",
      "Scan Finished!\n"
     ]
    }
   ],
   "source": [
    "h = hy.Hyperio(x, y, params=p, dataset_name='iris', experiment_no='1', model=iris_model, grid_downsample=.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Accessing the hyperio object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5760"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of combinations with the selection\n",
    "h.combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>train_peak</th>\n",
       "      <th>val_peak</th>\n",
       "      <th>train_acc</th>\n",
       "      <th>val_acc</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>train_score</th>\n",
       "      <th>val_score</th>\n",
       "      <th>batch_size</th>\n",
       "      <th>epochs</th>\n",
       "      <th>round_epochs</th>\n",
       "      <th>dropout</th>\n",
       "      <th>normalized_lr</th>\n",
       "      <th>first_neuron</th>\n",
       "      <th>loss</th>\n",
       "      <th>optimizer</th>\n",
       "      <th>last_activation</th>\n",
       "      <th>embed_dims</th>\n",
       "      <th>weight_regul</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0.961905</td>\n",
       "      <td>0.955556</td>\n",
       "      <td>0.013299</td>\n",
       "      <td>0.016764</td>\n",
       "      <td>0.948606</td>\n",
       "      <td>0.938791</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "      <td>9</td>\n",
       "      <td>0.12</td>\n",
       "      <td>7</td>\n",
       "      <td>128</td>\n",
       "      <td>&lt;function logcosh at 0x11e2be730&gt;</td>\n",
       "      <td>&lt;class 'keras.optimizers.Adam'&gt;</td>\n",
       "      <td>&lt;function softmax at 0x11e2debf8&gt;</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>0.885714</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.274503</td>\n",
       "      <td>0.254995</td>\n",
       "      <td>0.611211</td>\n",
       "      <td>0.678338</td>\n",
       "      <td>4</td>\n",
       "      <td>500</td>\n",
       "      <td>17</td>\n",
       "      <td>0.16</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>&lt;function categorical_crossentropy at 0x11e2be...</td>\n",
       "      <td>&lt;class 'keras.optimizers.Adam'&gt;</td>\n",
       "      <td>&lt;function softmax at 0x11e2debf8&gt;</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>0.961905</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.169008</td>\n",
       "      <td>0.137809</td>\n",
       "      <td>0.792897</td>\n",
       "      <td>0.839969</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>0.12</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>&lt;function categorical_crossentropy at 0x11e2be...</td>\n",
       "      <td>&lt;class 'keras.optimizers.Nadam'&gt;</td>\n",
       "      <td>&lt;function softmax at 0x11e2debf8&gt;</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   train_peak  val_peak  train_acc   val_acc  train_loss  val_loss  \\\n",
       "0           4         6   0.961905  0.955556    0.013299  0.016764   \n",
       "1          16        14   0.885714  0.933333    0.274503  0.254995   \n",
       "2          19        17   0.961905  0.977778    0.169008  0.137809   \n",
       "\n",
       "   train_score  val_score  batch_size  epochs  round_epochs  dropout  \\\n",
       "0     0.948606   0.938791           2     500             9     0.12   \n",
       "1     0.611211   0.678338           4     500            17     0.16   \n",
       "2     0.792897   0.839969           2     500            20     0.12   \n",
       "\n",
       "   normalized_lr  first_neuron  \\\n",
       "0              7           128   \n",
       "1              9             8   \n",
       "2              6             8   \n",
       "\n",
       "                                                loss  \\\n",
       "0                  <function logcosh at 0x11e2be730>   \n",
       "1  <function categorical_crossentropy at 0x11e2be...   \n",
       "2  <function categorical_crossentropy at 0x11e2be...   \n",
       "\n",
       "                          optimizer                    last_activation  \\\n",
       "0   <class 'keras.optimizers.Adam'>  <function softmax at 0x11e2debf8>   \n",
       "1   <class 'keras.optimizers.Adam'>  <function softmax at 0x11e2debf8>   \n",
       "2  <class 'keras.optimizers.Nadam'>  <function softmax at 0x11e2debf8>   \n",
       "\n",
       "  embed_dims weight_regul  \n",
       "0       None         None  \n",
       "1       None         None  \n",
       "2       None         None  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the results dataframe\n",
    "h.result.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basically everything related with the experiment are stored in the object, so have a look around. But for now, let's move on. The other main functionality has to do with interpreting the results. For that we have separate command."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Analyzing the results\n",
    "Because the analysis represents a different stage in the iterative process of hyperparameter optimization, it's separated to a different command. Actually there are just two commands in the Hyperio namespace 'Hyperio' which is for the setting up of the experiment and running it, and 'Reporting' which is for analyzing.\n",
    "\n",
    "Let's start by loading a previous result with more observations, it's also for the Iris dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model with the best val_score\n",
      "------------------------------\n",
      "The  highest val_score is 99.80%, which was achieved after 11 epochs using logcosh (loss), Adam (optimizer), and softmax (activation). The batch size was 2 with a dropout of 0 and a normalized learning rate of 8.00 and 64 first layer neurons.  \n",
      "        \n",
      "\n",
      "Model with the best train_score\n",
      "------------------------------\n",
      "The  highest val_score is 97.60%, which was achieved after 29 epochs using logcosh (loss), Adam (optimizer), and softmax (activation). The batch size was 2 with a dropout of 0 and a normalized learning rate of 3.00 and 16 first layer neurons.  \n",
      "        \n"
     ]
    },
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       "      <th></th>\n",
       "      <th>first_neuron</th>\n",
       "      <th>batch_size</th>\n",
       "      <th>round_epochs</th>\n",
       "      <th>normalized_lr</th>\n",
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       "      <th>0.998</th>\n",
       "      <td>64</td>\n",
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       "    <tr>\n",
       "      <th>0.998</th>\n",
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       "      <td>13</td>\n",
       "      <td>6</td>\n",
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       "      <td>softmax</td>\n",
       "      <td>logcosh</td>\n",
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       "      <th>0.998</th>\n",
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       "      <td>8</td>\n",
       "      <td>Adam</td>\n",
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       "      <th>0.998</th>\n",
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       "      <td>logcosh</td>\n",
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       "      <td>softmax</td>\n",
       "      <td>logcosh</td>\n",
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       "      <th>0.997</th>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>Nadam</td>\n",
       "      <td>softmax</td>\n",
       "      <td>logcosh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.997</th>\n",
       "      <td>128</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>Nadam</td>\n",
       "      <td>softmax</td>\n",
       "      <td>logcosh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.997</th>\n",
       "      <td>64</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>Nadam</td>\n",
       "      <td>softmax</td>\n",
       "      <td>logcosh</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "           first_neuron  batch_size  round_epochs  normalized_lr optimizer  \\\n",
       "val_score                                                                    \n",
       "0.998                64           3            10              8     Nadam   \n",
       "0.998               128           3            19              5      Adam   \n",
       "0.998                64           2            13              6     Nadam   \n",
       "0.998                64           2            14              8      Adam   \n",
       "0.998                16           2            17              8     Nadam   \n",
       "0.997                16           4            17              7     Nadam   \n",
       "0.997                64           2            22              5      Adam   \n",
       "0.997                32           3            20              7     Nadam   \n",
       "0.997               128           2            11              4     Nadam   \n",
       "0.997                64           3            11              9     Nadam   \n",
       "\n",
       "          last_activation     loss  \n",
       "val_score                           \n",
       "0.998             softmax  logcosh  \n",
       "0.998             softmax  logcosh  \n",
       "0.998             softmax  logcosh  \n",
       "0.998             softmax  logcosh  \n",
       "0.998             softmax  logcosh  \n",
       "0.997             softmax  logcosh  \n",
       "0.997             softmax  logcosh  \n",
       "0.997             softmax  logcosh  \n",
       "0.997             softmax  logcosh  \n",
       "0.997             softmax  logcosh  "
      ]
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       "        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>first_neuron</th>\n",
       "      <th>batch_size</th>\n",
       "      <th>round_epochs</th>\n",
       "      <th>normalized_lr</th>\n",
       "      <th>optimizer</th>\n",
       "      <th>last_activation</th>\n",
       "      <th>loss</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>val_score</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1.115</th>\n",
       "      <td>128</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>Nadam</td>\n",
       "      <td>softmax</td>\n",
       "      <td>categorical_crossentropy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1.114</th>\n",
       "      <td>128</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>Nadam</td>\n",
       "      <td>softmax</td>\n",
       "      <td>categorical_crossentropy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.926</th>\n",
       "      <td>128</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>Nadam</td>\n",
       "      <td>softmax</td>\n",
       "      <td>categorical_crossentropy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.858</th>\n",
       "      <td>128</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>Nadam</td>\n",
       "      <td>softmax</td>\n",
       "      <td>categorical_crossentropy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.838</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Nadam</td>\n",
       "      <td>softmax</td>\n",
       "      <td>categorical_crossentropy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.836</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>Nadam</td>\n",
       "      <td>softmax</td>\n",
       "      <td>categorical_crossentropy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.821</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>Adam</td>\n",
       "      <td>softmax</td>\n",
       "      <td>categorical_crossentropy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.768</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>Nadam</td>\n",
       "      <td>softmax</td>\n",
       "      <td>categorical_crossentropy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.768</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>Adam</td>\n",
       "      <td>softmax</td>\n",
       "      <td>categorical_crossentropy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.767</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>Nadam</td>\n",
       "      <td>softmax</td>\n",
       "      <td>categorical_crossentropy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           first_neuron  batch_size  round_epochs  normalized_lr optimizer  \\\n",
       "val_score                                                                    \n",
       "-1.115              128           4            12              6     Nadam   \n",
       "-1.114              128           4            12              4     Nadam   \n",
       "-0.926              128           2             5              7     Nadam   \n",
       "-0.858              128           4             8              9     Nadam   \n",
       "-0.838                8           4             5              5     Nadam   \n",
       "-0.836                4           3             8              3     Nadam   \n",
       "-0.821                4           2            19              3      Adam   \n",
       "-0.768                4           2             7              8     Nadam   \n",
       "-0.768                4           2            16              5      Adam   \n",
       "-0.767                4           3             6              7     Nadam   \n",
       "\n",
       "          last_activation                      loss  \n",
       "val_score                                            \n",
       "-1.115            softmax  categorical_crossentropy  \n",
       "-1.114            softmax  categorical_crossentropy  \n",
       "-0.926            softmax  categorical_crossentropy  \n",
       "-0.858            softmax  categorical_crossentropy  \n",
       "-0.838            softmax  categorical_crossentropy  \n",
       "-0.836            softmax  categorical_crossentropy  \n",
       "-0.821            softmax  categorical_crossentropy  \n",
       "-0.768            softmax  categorical_crossentropy  \n",
       "-0.768            softmax  categorical_crossentropy  \n",
       "-0.767            softmax  categorical_crossentropy  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " NOTE: you have more options in the Reporting object.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "r = hy.Reporting('iris_e.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to the default report, we have several options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'batch_size': (2, 4),\n",
       " 'epochs': (2, 500),\n",
       " 'dropout': (0.0, 0.36),\n",
       " 'normalized_lr': (2, 9),\n",
       " 'embed_dims': ('None', 'None'),\n",
       " 'weight_regul': ('None', 'None'),\n",
       " 'loss': ['logcosh', 'categorical_crossentropy'],\n",
       " 'optimizer': ['Nadam', 'Adam'],\n",
       " 'last_activation': ['softmax']}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gives the dictionary for top result params\n",
    "r.top_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'batch_size': (2, 4),\n",
       " 'epochs': (2, 500),\n",
       " 'dropout': (0.0, 0.36),\n",
       " 'normalized_lr': (2, 9),\n",
       " 'embed_dims': ('None', 'None'),\n",
       " 'weight_regul': ('None', 'None'),\n",
       " 'loss': ['logcosh', 'categorical_crossentropy'],\n",
       " 'optimizer': ['Nadam', 'Adam'],\n",
       " 'last_activation': ['softmax']}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gives the dictionary for bottom result params\n",
    "r.bottom_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>train_peak</th>\n",
       "      <th>val_peak</th>\n",
       "      <th>train_acc</th>\n",
       "      <th>val_acc</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>train_score</th>\n",
       "      <th>val_score</th>\n",
       "      <th>batch_size</th>\n",
       "      <th>epochs</th>\n",
       "      <th>round_epochs</th>\n",
       "      <th>dropout</th>\n",
       "      <th>normalized_lr</th>\n",
       "      <th>first_neuron</th>\n",
       "      <th>loss</th>\n",
       "      <th>optimizer</th>\n",
       "      <th>last_activation</th>\n",
       "      <th>embed_dims</th>\n",
       "      <th>weight_regul</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>0.933</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.715</td>\n",
       "      <td>0.894</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>0.32</td>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "      <td>categorical_crossentropy</td>\n",
       "      <td>Adam</td>\n",
       "      <td>softmax</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>0.952</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.243</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.709</td>\n",
       "      <td>0.861</td>\n",
       "      <td>3</td>\n",
       "      <td>500</td>\n",
       "      <td>38</td>\n",
       "      <td>0.16</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>categorical_crossentropy</td>\n",
       "      <td>Adam</td>\n",
       "      <td>softmax</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.924</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.897</td>\n",
       "      <td>0.961</td>\n",
       "      <td>3</td>\n",
       "      <td>500</td>\n",
       "      <td>7</td>\n",
       "      <td>0.04</td>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "      <td>logcosh</td>\n",
       "      <td>Nadam</td>\n",
       "      <td>softmax</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   train_peak  val_peak  train_acc  val_acc  train_loss  val_loss  \\\n",
       "0          17        17      0.933    1.000       0.218     0.106   \n",
       "1          35        35      0.952    1.000       0.243     0.139   \n",
       "2           4         4      0.924    0.978       0.026     0.017   \n",
       "\n",
       "   train_score  val_score  batch_size  epochs  round_epochs  dropout  \\\n",
       "0        0.715      0.894           2     500            20     0.32   \n",
       "1        0.709      0.861           3     500            38     0.16   \n",
       "2        0.897      0.961           3     500             7     0.04   \n",
       "\n",
       "   normalized_lr  first_neuron                      loss optimizer  \\\n",
       "0              6            32  categorical_crossentropy      Adam   \n",
       "1              5             8  categorical_crossentropy      Adam   \n",
       "2              7            32                   logcosh     Nadam   \n",
       "\n",
       "  last_activation embed_dims weight_regul  \n",
       "0         softmax       None         None  \n",
       "1         softmax       None         None  \n",
       "2         softmax       None         None  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access the dataframe\n",
    "r.data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition, we have several useful plotting options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r.plots.hist(r.data, 'val_score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are in total more than 20 plots with completely unified, production quality look. Each plot has a more or less unified parameter space, which focus on the aesthetic aspect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r.plots.regs(r.data, 'val_score', 'train_score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's very easy to add titles and other visual attributes to your plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r.plots.kde(r.data, 'train_score', 'val_score',\n",
    "            title='Kernel Density Estimation',\n",
    "            sub_title='comparing train and validation scores',\n",
    "            x_label='density',\n",
    "            y_label='density',\n",
    "            palette='default')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several options that are particularly useful for analyzing hyperparamater scanning results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1246.22x576 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r.plots.bargrid(r.data, x='loss',y='val_score', hue='optimizer', col='first_neuron', col_wrap=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Accessing your results later \n",
    "Based on the inputs you had provided in the Hyperio() run 'dataset_name' and 'experiment_no' a file will be created in the local folder. In this case you would access it by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hy.Reporting('iris_1.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition, a master log file is created, which by default is called hyperio.log, where the results of every round, for every dataset and experiment is saved. This way you have a full record of every scan you run complete with Hyperio. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>64</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.04</td>\n",
       "      <td>'Nadam'</td>\n",
       "      <td>'categorical_crossentropy'</td>\n",
       "      <td>'softmax'</td>\n",
       "      <td>'None'</td>\n",
       "      <td>'None'</td>\n",
       "      <td>0.436</td>\n",
       "      <td>2</td>\n",
       "      <td>category</td>\n",
       "      <td>3</td>\n",
       "      <td>onehot</td>\n",
       "      <td>0.3</td>\n",
       "      <td>iris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.24</td>\n",
       "      <td>'Adam'</td>\n",
       "      <td>'categorical_crossentropy'</td>\n",
       "      <td>'softmax'</td>\n",
       "      <td>'None'</td>\n",
       "      <td>'None'</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>2</td>\n",
       "      <td>category</td>\n",
       "      <td>3</td>\n",
       "      <td>onehot</td>\n",
       "      <td>0.3</td>\n",
       "      <td>iris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.32</td>\n",
       "      <td>'Nadam'</td>\n",
       "      <td>'logcosh'</td>\n",
       "      <td>'softmax'</td>\n",
       "      <td>'None'</td>\n",
       "      <td>'None'</td>\n",
       "      <td>0.403</td>\n",
       "      <td>2</td>\n",
       "      <td>category</td>\n",
       "      <td>3</td>\n",
       "      <td>onehot</td>\n",
       "      <td>0.3</td>\n",
       "      <td>iris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>128</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.24</td>\n",
       "      <td>'Adam'</td>\n",
       "      <td>'logcosh'</td>\n",
       "      <td>'softmax'</td>\n",
       "      <td>'None'</td>\n",
       "      <td>'None'</td>\n",
       "      <td>0.425</td>\n",
       "      <td>2</td>\n",
       "      <td>category</td>\n",
       "      <td>3</td>\n",
       "      <td>onehot</td>\n",
       "      <td>0.3</td>\n",
       "      <td>iris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.20</td>\n",
       "      <td>'Nadam'</td>\n",
       "      <td>'logcosh'</td>\n",
       "      <td>'softmax'</td>\n",
       "      <td>'None'</td>\n",
       "      <td>'None'</td>\n",
       "      <td>0.245</td>\n",
       "      <td>2</td>\n",
       "      <td>category</td>\n",
       "      <td>3</td>\n",
       "      <td>onehot</td>\n",
       "      <td>0.3</td>\n",
       "      <td>iris</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0    1   2   3     4        5                           6          7   \\\n",
       "0   5   64   2   2  0.04  'Nadam'  'categorical_crossentropy'  'softmax'   \n",
       "1   2   32   2   2  0.24   'Adam'  'categorical_crossentropy'  'softmax'   \n",
       "2   8    4   4   2  0.32  'Nadam'                   'logcosh'  'softmax'   \n",
       "3   6  128   2   2  0.24   'Adam'                   'logcosh'  'softmax'   \n",
       "4   5    4   2   2  0.20  'Nadam'                   'logcosh'  'softmax'   \n",
       "\n",
       "       8       9      10  11        12  13      14   15    16  \n",
       "0  'None'  'None'  0.436   2  category   3  onehot  0.3  iris  \n",
       "1  'None'  'None' -0.100   2  category   3  onehot  0.3  iris  \n",
       "2  'None'  'None'  0.403   2  category   3  onehot  0.3  iris  \n",
       "3  'None'  'None'  0.425   2  category   3  onehot  0.3  iris  \n",
       "4  'None'  'None'  0.245   2  category   3  onehot  0.3  iris  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('hyperio.log', header=None).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It includes information about each parameter used in the round, and the type of dataset in question."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<img align=left src='https://img00.deviantart.net/920c/i/2014/137/a/2/pinkie_pie_thats_all_folks_by_dan232323-d7ipnd4.jpg' width=400>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Back to the repository page >> http://github.com/autonomio/hyperio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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