Tutorial.ipynb

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   "source": [
    "# Using Featuretools to Predict Missed Appointments\n",
    "In this notebook, we use [Featuretools](https://github.com/Featuretools/featuretools) to automatically generate features relating to when patients don't show up for doctor appointments. We quickly reconstruct the features that were made by hand in the most popular [kernel](https://www.kaggle.com/somrikbanerjee/predicting-show-up-no-show) and make some other interesting features automatically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Featuretools version 0.16.0\n",
      "110527 Appointments, 14 Columns\n",
      "Appointments: 110527\n",
      "Schedule times: 103549\n",
      "Patients: 62299\n",
      "Neighborhoods: 81\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduled_time</th>\n",
       "      <th>appointment_day</th>\n",
       "      <th>age</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handicap</th>\n",
       "      <th>sms_received</th>\n",
       "      <th>no_show</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AppointmentID</th>\n",
       "      <th></th>\n",
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       "      <td>2016-04-29 23:59:59</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "                    patient_id  appointment_id gender      scheduled_time  \\\n",
       "AppointmentID                                                               \n",
       "5642903         29872499824296         5642903      F 2016-04-29 18:38:08   \n",
       "5642503        558997776694438         5642503      M 2016-04-29 16:08:27   \n",
       "5642549          4262962299951         5642549      F 2016-04-29 16:19:04   \n",
       "\n",
       "                  appointment_day  age     neighborhood  scholarship  \\\n",
       "AppointmentID                                                          \n",
       "5642903       2016-04-29 23:59:59   62  JARDIM DA PENHA        False   \n",
       "5642503       2016-04-29 23:59:59   56  JARDIM DA PENHA        False   \n",
       "5642549       2016-04-29 23:59:59   62    MATA DA PRAIA        False   \n",
       "\n",
       "               hypertension  diabetes  alcoholism  handicap  sms_received  \\\n",
       "AppointmentID                                                               \n",
       "5642903                True     False       False     False         False   \n",
       "5642503               False     False       False     False         False   \n",
       "5642549               False     False       False     False         False   \n",
       "\n",
       "               no_show  \n",
       "AppointmentID           \n",
       "5642903          False  \n",
       "5642503          False  \n",
       "5642549          False  "
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   ],
   "source": [
    "import utils\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import featuretools as ft\n",
    "print('Featuretools version {}'.format(ft.__version__))\n",
    "\n",
    "data = utils.load_data(\"data/KaggleV2-May-2016.csv\")\n",
    "# Convert datetimes to be not timezone aware\n",
    "data['scheduled_time'] = data['scheduled_time'].dt.tz_convert(None)\n",
    "data['appointment_day'] = data['appointment_day'].dt.tz_convert(None)\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset is a single table of appointments with more than sixty thousand unique patients. Each row represents a scheduled appointment and our goal is to predict if the patient actually shows up for that appointment. From that table, we use Featuretools to automatically generate the features below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Structuring the Data\n",
    "We are given a single table of data. Feature engineering requires that we use what we understand about the data to build numeric rows (feature vectors) which we can use as input into machine learning algorithms. The primary benefit of Featuretools is that it does not require you make those features by hand. The requirement instead is that you pass in what you know about the data.\n",
    "\n",
    "That knowledge is stored in a Featuretools [EntitySet](https://docs.featuretools.com/loading_data/using_entitysets.html). `EntitySets` are a collection of tables with  information about relationships between tables and semantic typing for every column. We're going to show how to\n",
    "+ pass in information about semantic types of columns,\n",
    "+ load in a dataframe to an `EntitySet` and\n",
    "+ tell the `EntitySet` about reasonable new `Entities` to make from that dataframe."
   ]
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List the semantic type for each column\n",
    "\n",
    "import featuretools.variable_types as vtypes\n",
    "variable_types = {'gender': vtypes.Categorical,\n",
    "                  'patient_id': vtypes.Categorical,\n",
    "                  'age': vtypes.Ordinal,\n",
    "                  'scholarship': vtypes.Boolean,\n",
    "                  'hypertension': vtypes.Boolean,\n",
    "                  'diabetes': vtypes.Boolean,\n",
    "                  'alcoholism': vtypes.Boolean,\n",
    "                  'handicap': vtypes.Boolean,\n",
    "                  'no_show': vtypes.Boolean,\n",
    "                  'sms_received': vtypes.Boolean}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `variable_types` dictionary is a place to store information about the semantic type of each column. While many types can be detected automatically, some are necessarily tricky. As an example, computers tend to read `age` as numeric. Even though ages are numbers, it's can be useful to think of them as `Categorical` or `Ordinal`. Changing the variable type will change which functions are automatically applied to generate features.\n",
    "\n",
    "Next, we make an entity `appointments`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Entity: appointments\n",
       "  Variables:\n",
       "    appointment_id (dtype: index)\n",
       "    scheduled_time (dtype: datetime_time_index)\n",
       "    appointment_day (dtype: datetime)\n",
       "    neighborhood (dtype: categorical)\n",
       "    gender (dtype: categorical)\n",
       "    patient_id (dtype: categorical)\n",
       "    age (dtype: ordinal)\n",
       "    scholarship (dtype: boolean)\n",
       "    hypertension (dtype: boolean)\n",
       "    diabetes (dtype: boolean)\n",
       "    alcoholism (dtype: boolean)\n",
       "    handicap (dtype: boolean)\n",
       "    no_show (dtype: boolean)\n",
       "    sms_received (dtype: boolean)\n",
       "  Shape:\n",
       "    (Rows: 110527, Columns: 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make an entity named 'appointments' which stores dataset metadata with the dataframe\n",
    "es = ft.EntitySet('Appointments')\n",
    "es = es.entity_from_dataframe(entity_id=\"appointments\",\n",
    "                              dataframe=data,\n",
    "                              index='appointment_id',\n",
    "                              time_index='scheduled_time',\n",
    "                              secondary_time_index={'appointment_day': ['no_show', 'sms_received']},\n",
    "                              variable_types=variable_types)\n",
    "es['appointments']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have turned the dataframe into an entity by calling the function `entity_from_dataframe`. Notice that we specified an index, a time index, a secondary time index and the `variable_types` from the last cell as keyword arguments. \n",
    "\n",
    "The time index and secondary time index notate what time the data is recorded. By doing that, we can avoid using data from the future while creating features. Since the label is in the dataframe, we either need to specify a time index or drop the column entirely.\n",
    "\n",
    "Finally, we build new entities from our existing one using `normalize_entity`. We take unique values from `patient`, `age`, `neighborhood` and `gender` and make a new `Entity` for each whose rows are the unique values. To do that we only need to specify where we start (`appointments`), the name of the new entity (e.g. `patients`) and what the index should be (e.g. `patient_id`). Having those additional `Entities` and `Relationships` tells the algorithm about reasonable groupings which allows for some neat aggregations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Entityset: Appointments\n",
       "  Entities:\n",
       "    appointments [Rows: 110527, Columns: 9]\n",
       "    patients [Rows: 62299, Columns: 7]\n",
       "    locations [Rows: 81, Columns: 1]\n",
       "    ages [Rows: 104, Columns: 1]\n",
       "    genders [Rows: 2, Columns: 1]\n",
       "  Relationships:\n",
       "    appointments.patient_id -> patients.patient_id\n",
       "    appointments.neighborhood -> locations.neighborhood\n",
       "    appointments.age -> ages.age\n",
       "    appointments.gender -> genders.gender"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a patients entity with patient-specific variables\n",
    "es.normalize_entity('appointments', 'patients', 'patient_id',\n",
    "                    additional_variables=['scholarship',\n",
    "                                          'hypertension',\n",
    "                                          'diabetes',\n",
    "                                          'alcoholism',\n",
    "                                          'handicap'])\n",
    "\n",
    "# Make locations, ages and genders\n",
    "es.normalize_entity('appointments', 'locations', 'neighborhood',\n",
    "                    make_time_index=False)\n",
    "es.normalize_entity('appointments', 'ages', 'age',\n",
    "                    make_time_index=False)\n",
    "es.normalize_entity('appointments', 'genders', 'gender',\n",
    "                    make_time_index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handicap</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2015-11-10 07:13:56</td>\n",
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       "                      patient_id  scholarship  hypertension  diabetes  \\\n",
       "832256398961987  832256398961987        False         False     False   \n",
       "91637474953513    91637474953513        False          True     False   \n",
       "\n",
       "                 alcoholism  handicap first_appointments_time  \n",
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       "91637474953513        False     False     2015-12-03 08:17:28  "
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   "source": [
    "# Show the patients entity\n",
    "es['patients'].df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize EntitySet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "<text text-anchor=\"start\" x=\"8\" y=\"-21.3\" font-family=\"Times,serif\" font-size=\"14.00\">handicap : boolean</text>\n",
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       "<polyline fill=\"none\" stroke=\"black\" points=\"452.88,-64.5 558.3,-64.5 \"/>\n",
       "<text text-anchor=\"start\" x=\"460.88\" y=\"-49.3\" font-family=\"Times,serif\" font-size=\"14.00\">age : index</text>\n",
       "</g>\n",
       "<!-- appointments&#45;&gt;ages -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>appointments&#45;&gt;ages</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M502.47,-165.25C502.47,-165.25 502.47,-96.71 502.47,-96.71\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"505.97,-96.71 502.47,-86.71 498.97,-96.71 505.97,-96.71\"/>\n",
       "<text text-anchor=\"middle\" x=\"492.76\" y=\"-133.78\" font-family=\"Times,serif\" font-size=\"14.00\">age</text>\n",
       "</g>\n",
       "<!-- genders -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>genders</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"576.55,-42.5 576.55,-86.5 686.63,-86.5 686.63,-42.5 576.55,-42.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"631.59\" y=\"-71.3\" font-family=\"Times,serif\" font-size=\"14.00\">genders (2 rows)</text>\n",
       "<polyline fill=\"none\" stroke=\"black\" points=\"576.55,-64.5 686.63,-64.5 \"/>\n",
       "<text text-anchor=\"start\" x=\"584.55\" y=\"-49.3\" font-family=\"Times,serif\" font-size=\"14.00\">gender : index</text>\n",
       "</g>\n",
       "<!-- appointments&#45;&gt;genders -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>appointments&#45;&gt;genders</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M552.15,-243C594.24,-243 631.59,-243 631.59,-243 631.59,-243 631.59,-96.75 631.59,-96.75\"/>\n",
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       "<text text-anchor=\"middle\" x=\"612.54\" y=\"-212.39\" font-family=\"Times,serif\" font-size=\"14.00\">gender</text>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.dot.Digraph at 0x137dc4ed0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "es.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating Features with Deep Feature Synthesis\n",
    "With our data  structued in an `EntitySet`, we can immediately build features across our entity and relationships with Deep Feature Synthesis (DFS). As an example, the feature `locations.PERCENT_TRUE(no_show)` will calculate percentage of patients of at this location that haven't shown up in the past.\n",
    "\n",
    "This is where the time indices get used. We set the `cutoff_time` for each row to be when the patient schedules the appointment. That means that DFS, while building features, will only use the data that is known as the appointment is made. In particular, it won't use the label to create features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "      <th></th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>time</th>\n",
       "      <th>label</th>\n",
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      ],
      "text/plain": [
       "         appointment_id                time  label\n",
       "5030230         5030230 2015-11-10 07:13:56  False\n",
       "5122866         5122866 2015-12-03 08:17:28   True\n",
       "5134197         5134197 2015-12-07 10:40:59   True\n",
       "5134220         5134220 2015-12-07 10:42:42  False\n",
       "5134223         5134223 2015-12-07 10:43:01  False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take the index and the appointment time to use as a cutoff time\n",
    "cutoff_times = es['appointments'].df[['appointment_id', 'scheduled_time', 'no_show']].sort_values(by='scheduled_time')\n",
    "\n",
    "# Rename columns to avoid confusion\n",
    "cutoff_times.rename(columns = {'scheduled_time': 'time', \n",
    "                               'no_show': 'label'},\n",
    "                    inplace = True)\n",
    "cutoff_times.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Built 46 features\n",
      "Elapsed: 01:03 | Progress: 100%|██████████\n"
     ]
    }
   ],
   "source": [
    "# Generate features using the constructed entityset\n",
    "fm, features = ft.dfs(entityset=es,\n",
    "                      target_entity='appointments',\n",
    "                      agg_primitives=['count', 'percent_true'],\n",
    "                      trans_primitives=['is_weekend', 'weekday', 'day', 'month', 'year'],\n",
    "                      max_depth=3,\n",
    "                      approximate='6h',\n",
    "                      cutoff_time=cutoff_times[20000:],\n",
    "                      verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have applied and stacked **primitives** like `MONTH`, `WEEKDAY` and `PERCENT_TRUE` to build features accross all the `Entities` in our `EntitySet`.\n",
    "\n",
    "Feel free to fork this kernel and modify the parameters. By doing so, you can get very different feature matrices. Here's a short overview of the keywords used:\n",
    "+ `target_entity` is the entity for which we're building features. It would be equally easy to make a feature matrix for the `locations` entity\n",
    "+ `agg_primitives` and `trans_primitives` are lists of which primitives will be used while constructing features. The full list can be found by running `ft.list_primitives()`\n",
    "+ `max_depth=3` says to stack up to 3 primitives deep.\n",
    "+ `approximate='6h'` rounds cutoff times into blocks that are 3 hours long for faster computation\n",
    "+ `cutoff_time` is a dataframe that says when to calculate each row\n",
    "+ `verbose=True` makes the progress bar\n",
    "\n",
    "For more information, see the [documentation](https://docs.featuretools.com/automated_feature_engineering/afe.html) of dfs.\n",
    "## Machine Learning\n",
    "We can put the created feature matrix directly into sklearn. Similar to the other kernels, we do not do a good job predicting no-shows. With one unshuffled train test split, our `roc_auc_score` is roughly .5 with similar scores for F1 and K-first. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = [c for c in fm.columns if c != 'no_show']\n",
    "fm = fm[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score of 0.515\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
    "\n",
    "X = fm.copy()\n",
    "label = X.pop('label')\n",
    "X = X.drop(['patient_id', 'neighborhood', 'gender'], axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, label, test_size=0.30, shuffle=False)\n",
    "clf = RandomForestClassifier(n_estimators=150)\n",
    "clf.fit(X_train, y_train)\n",
    "probs = clf.predict_proba(X_test)\n",
    "print('AUC score of {:.3f}'.format(roc_auc_score(y_test, probs[:,1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sms_received', 'IS_WEEKEND(appointment_day)',\n",
       "       'IS_WEEKEND(scheduled_time)', 'WEEKDAY(appointment_day)',\n",
       "       'WEEKDAY(scheduled_time)', 'DAY(appointment_day)',\n",
       "       'DAY(scheduled_time)', 'MONTH(appointment_day)',\n",
       "       'MONTH(scheduled_time)', 'YEAR(appointment_day)',\n",
       "       'YEAR(scheduled_time)', 'patients.scholarship', 'patients.hypertension',\n",
       "       'patients.diabetes', 'patients.alcoholism', 'patients.handicap',\n",
       "       'patients.COUNT(appointments)',\n",
       "       'patients.PERCENT_TRUE(appointments.no_show)',\n",
       "       'patients.PERCENT_TRUE(appointments.sms_received)',\n",
       "       'patients.IS_WEEKEND(first_appointments_time)',\n",
       "       'patients.WEEKDAY(first_appointments_time)',\n",
       "       'patients.DAY(first_appointments_time)',\n",
       "       'patients.MONTH(first_appointments_time)',\n",
       "       'patients.YEAR(first_appointments_time)',\n",
       "       'locations.COUNT(appointments)',\n",
       "       'locations.PERCENT_TRUE(appointments.no_show)',\n",
       "       'locations.PERCENT_TRUE(appointments.sms_received)',\n",
       "       'ages.COUNT(appointments)', 'ages.PERCENT_TRUE(appointments.no_show)',\n",
       "       'ages.PERCENT_TRUE(appointments.sms_received)',\n",
       "       'genders.COUNT(appointments)',\n",
       "       'genders.PERCENT_TRUE(appointments.no_show)',\n",
       "       'genders.PERCENT_TRUE(appointments.sms_received)',\n",
       "       'patients.PERCENT_TRUE(appointments.IS_WEEKEND(appointment_day))',\n",
       "       'patients.PERCENT_TRUE(appointments.IS_WEEKEND(scheduled_time))',\n",
       "       'locations.PERCENT_TRUE(appointments.IS_WEEKEND(appointment_day))',\n",
       "       'locations.PERCENT_TRUE(appointments.IS_WEEKEND(scheduled_time))',\n",
       "       'ages.PERCENT_TRUE(appointments.IS_WEEKEND(appointment_day))',\n",
       "       'ages.PERCENT_TRUE(appointments.IS_WEEKEND(scheduled_time))',\n",
       "       'genders.PERCENT_TRUE(appointments.IS_WEEKEND(appointment_day))',\n",
       "       'genders.PERCENT_TRUE(appointments.IS_WEEKEND(scheduled_time))'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Feature Importances:\n",
      "1: locations.COUNT(appointments) [1.000]\n",
      "2: ages.COUNT(appointments) [0.930]\n",
      "3: age [0.914]\n",
      "4: DAY(appointment_day) [0.825]\n",
      "5: locations.PERCENT_TRUE(appointments.no_show) [0.654]\n",
      "6: ages.PERCENT_TRUE(appointments.no_show) [0.644]\n",
      "7: locations.PERCENT_TRUE(appointments.sms_received) [0.641]\n",
      "8: ages.PERCENT_TRUE(appointments.sms_received) [0.638]\n"
     ]
    }
   ],
   "source": [
    "feature_imps = [(imp, X.columns[i]) for i, imp in enumerate(clf.feature_importances_)]\n",
    "feature_imps.sort()\n",
    "feature_imps.reverse()\n",
    "print('Random Forest Feature Importances:')\n",
    "for i, f in enumerate(feature_imps[0:8]):\n",
    "    print('{}: {} [{:.3f}]'.format(i + 1, f[1], f[0]/feature_imps[0][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };var element = document.getElementById(\"1001\");\n",
       "  if (element == null) {\n",
       "    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.2.0.min.js\"];\n",
       "  var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.2.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.2.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.2.0.min.css\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {} // ensure no trailing comma for IE\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };var element = document.getElementById(\"1001\");\n  if (element == null) {\n    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n    return false;\n  }\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.2.0.min.js\"];\n  var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.2.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.2.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.2.0.min.css\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nate.parsons/dev/featuretools-demos/predict-appointment-noshow/env/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1045\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1045\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };var element = document.getElementById(\"1045\");\n",
       "  if (element == null) {\n",
       "    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1045' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.2.0.min.js\"];\n",
       "  var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.2.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.2.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.2.0.min.css\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {} // ensure no trailing comma for IE\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1045\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1045\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };var element = document.getElementById(\"1045\");\n  if (element == null) {\n    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1045' but no matching script tag was found. \")\n    return false;\n  }\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.2.0.min.js\"];\n  var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.2.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.2.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.2.0.min.css\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1045\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1125\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1125\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };var element = document.getElementById(\"1125\");\n",
       "  if (element == null) {\n",
       "    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1125' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.2.0.min.js\"];\n",
       "  var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.2.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.2.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.2.0.min.css\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {} // ensure no trailing comma for IE\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1125\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1125\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };var element = document.getElementById(\"1125\");\n  if (element == null) {\n    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1125' but no matching script tag was found. \")\n    return false;\n  }\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.2.0.min.js\"];\n  var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.2.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.2.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.2.0.min.css\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1125\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p1 = utils.plot_roc_auc(y_test, probs)\n",
    "p2 = utils.plot_f1(y_test, probs, 1000)\n",
    "p3 = utils.plot_kfirst(y_test, probs, 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"191e82bd-89ce-4a22-8d23-ae505d25e3f7\" data-root-id=\"1204\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
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       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
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\",\"dtype\":\"float64\",\"shape\":[1174]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1174]}},\"selected\":{\"id\":\"1172\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1171\",\"type\":\"UnionRenderers\"}},\"id\":\"1034\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"data_source\":{\"id\":\"1034\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1035\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1036\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"1038\",\"type\":\"CDSView\"}},\"id\":\"1037\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1021\",\"type\":\"PanTool\"},{\"id\":\"1022\",\"type\":\"WheelZoomTool\"},{\"id\":\"1023\",\"type\":\"BoxZoomTool\"},{\"id\":\"1024\",\"type\":\"SaveTool\"},{\"id\":\"1025\",\"type\":\"ResetTool\"},{\"id\":\"1026\",\"type\":\"HelpTool\"}]},\"id\":\"1027\",\"type\":\"Toolbar\"},{\"attributes\":{\"line_color\":\"red\",\"line_dash\":[6],\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1041\",\"type\":\"Line\"},{\"attributes\":{\"axis_label\":\"False Positive Rate\",\"formatter\":{\"id\":\"1167\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1012\",\"type\":\"BasicTicker\"}},\"id\":\"1011\",\"type\":\"LinearAxis\"},{\"attributes\":{\"axis_label\":\"Threshold\",\"formatter\":{\"id\":\"1085\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1056\",\"type\":\"BasicTicker\"}},\"id\":\"1055\",\"type\":\"LinearAxis\"},{\"attributes\":{\"source\":{\"id\":\"1034\",\"type\":\"ColumnDataSource\"}},\"id\":\"1038\",\"type\":\"CDSView\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1036\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"1017\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"1061\",\"type\":\"BasicTicker\"},{\"attributes\":{\"children\":[[{\"id\":\"1002\",\"subtype\":\"Figure\",\"type\":\"Plot\"},0,0],[{\"id\":\"1046\",\"subtype\":\"Figure\",\"type\":\"Plot\"},1,0],[{\"id\":\"1126\",\"subtype\":\"Figure\",\"type\":\"Plot\"},2,0]]},\"id\":\"1201\",\"type\":\"GridBox\"},{\"attributes\":{},\"id\":\"1056\",\"type\":\"BasicTicker\"},{\"attributes\":{\"toolbar\":{\"id\":\"1202\",\"type\":\"ProxyToolbar\"},\"toolbar_location\":\"above\"},\"id\":\"1203\",\"type\":\"ToolbarBox\"},{\"attributes\":{\"tools\":[{\"id\":\"1021\",\"type\":\"PanTool\"},{\"id\":\"1022\",\"type\":\"WheelZoomTool\"},{\"id\":\"1023\",\"type\":\"BoxZoomTool\"},{\"id\":\"1024\",\"type\":\"SaveTool\"},{\"id\":\"1025\",\"type\":\"ResetTool\"},{\"id\":\"1026\",\"type\":\"HelpTool\"},{\"id\":\"1065\",\"type\":\"PanTool\"},{\"id\":\"1066\",\"type\":\"WheelZoomTool\"},{\"id\":\"1067\",\"type\":\"BoxZoomTool\"},{\"id\":\"1068\",\"type\":\"SaveTool\"},{\"id\":\"1069\",\"type\":\"ResetTool\"},{\"id\":\"1070\",\"type\":\"HelpTool\"},{\"id\":\"1145\",\"type\":\"PanTool\"},{\"id\":\"1146\",\"type\":\"WheelZoomTool\"},{\"id\":\"1147\",\"type\":\"BoxZoomTool\"},{\"id\":\"1148\",\"type\":\"SaveTool\"},{\"id\":\"1149\",\"type\":\"ResetTool\"},{\"id\":\"1150\",\"type\":\"HelpTool\"}]},\"id\":\"1202\",\"type\":\"ProxyToolbar\"},{\"attributes\":{\"ticker\":{\"id\":\"1056\",\"type\":\"BasicTicker\"}},\"id\":\"1059\",\"type\":\"Grid\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"1017\",\"type\":\"BasicTicker\"}},\"id\":\"1020\",\"type\":\"Grid\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"1061\",\"type\":\"BasicTicker\"}},\"id\":\"1064\",\"type\":\"Grid\"},{\"attributes\":{\"formatter\":{\"id\":\"1087\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1061\",\"type\":\"BasicTicker\"}},\"id\":\"1060\",\"type\":\"LinearAxis\"},{\"attributes\":{\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1159\",\"type\":\"Step\"},{\"attributes\":{},\"id\":\"1012\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"1085\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"ticker\":{\"id\":\"1012\",\"type\":\"BasicTicker\"}},\"id\":\"1015\",\"type\":\"Grid\"},{\"attributes\":{\"axis_label\":\"True Positive Rate\",\"formatter\":{\"id\":\"1169\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1017\",\"type\":\"BasicTicker\"}},\"id\":\"1016\",\"type\":\"LinearAxis\"}],\"root_ids\":[\"1204\"]},\"title\":\"Bokeh Application\",\"version\":\"1.2.0\"}};\n",
       "  var render_items = [{\"docid\":\"55e46399-e216-46c3-b7b3-cb54bb42b08b\",\"roots\":{\"1204\":\"191e82bd-89ce-4a22-8d23-ae505d25e3f7\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1204"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.io import show\n",
    "from bokeh.layouts import gridplot\n",
    "show(gridplot([p1, p2, p3], ncols=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Some Plots\n",
    "An interesting workflow with this dataset is to plot generated features to learn about the data. Here, we'll show the number of visits by neighborhood, and the likelihood to show up by neighborhood and age as created by DFS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "p4 = utils.plot_locations(fm)\n",
    "p5 = utils.plot_noshow_by_loc(fm)\n",
    "p6 = utils.plot_ages(fm)\n",
    "p7 = utils.plot_noshow_by_age(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"b6d4c745-c4bb-4e0a-b2c3-c25f3b9801fd\" data-root-id=\"1619\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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       "  var render_items = [{\"docid\":\"5f82078e-d8de-4496-9897-f4f862ab4068\",\"roots\":{\"1619\":\"b6d4c745-c4bb-4e0a-b2c3-c25f3b9801fd\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
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    "show(gridplot([p4, p6, p5, p7], ncols=2)) "
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.featurelabs.com/\">\n",
    "    <img src=\"http://www.featurelabs.com/wp-content/uploads/2017/12/logo.png\" alt=\"Featuretools\" />\n",
    "</a>\n",
    "\n",
    "Featuretools was created by the developers at [Feature Labs](https://www.featurelabs.com/). If building impactful data science pipelines is important to you or your business, please [get in touch](https://www.featurelabs.com/contact)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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