Predict the Next Release - ElasticNet Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "amazing-revelation",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "import warnings\n",
    "import os\n",
    "\n",
    "import pandas\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import mean_absolute_percentage_error as mape\n",
    "from sklearn.metrics import RocCurveDisplay, roc_curve\n",
    "import numpy as np\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn import set_config\n",
    "\n",
    "from get_model_training_data_05 import get_features_and_data\n",
    "from get_model_training_data_05 import weighted_absolute_percentage_error as wape\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = \"retina\"\n",
    "\n",
    "sns.set(rc={\"figure.figsize\" : (25, 15)})\n",
    "sns.set(font_scale=2)\n",
    "sns.set_style(\"ticks\")\n",
    "\n",
    "set_config(transform_output=\"pandas\")\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "os.environ[\"PYTHONWARNINGS\"] = \"ignore\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb75d1c2",
   "metadata": {},
   "source": [
    "## Load Training and Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "noble-newcastle",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training examples = 2323, testing examples = 259\n"
     ]
    }
   ],
   "source": [
    "(df, train_df, test_df, features, next_month) = get_features_and_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "263a6450",
   "metadata": {},
   "source": [
    "## Model-Building and Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d6c18742",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(train_df):\n",
    "    ct = ColumnTransformer(remainder=\"passthrough\",\n",
    "                           transformers=[\n",
    "                               (\"ss\", StandardScaler(), [\"days_since_previous_release\", \"month_holidays\"])])\n",
    "\n",
    "    pipeline = Pipeline(\n",
    "        [(\"ct\", ct), (\"en\", ElasticNet(random_state=1024))])\n",
    "\n",
    "    model = GridSearchCV(\n",
    "        pipeline,\n",
    "        param_grid={\"en__alpha\" : [0.00001, 0.0001, 0.001, 0.1, 0.5, 0.7, 0.9, 0.95, 0.99, 1, 1.5, 2],\n",
    "                    \"en__l1_ratio\" : [0, 0.00001, 0.0001, 0.001, 0.1, 0.5, 0.7, 0.9, 0.95, 0.99, 1]},\n",
    "        scoring=\"neg_mean_absolute_percentage_error\", cv=3, refit=True)\n",
    "    \n",
    "    model.fit(train_df[features], train_df[\"prob_of_release\"])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "35409473",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = train_model(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2ebea1f2-7412-4ff7-b7fb-9326164a1d57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'en__alpha': 1e-05, 'en__l1_ratio': 0}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ba6dcd43-93fc-4432-a760-da20e4ece3b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.37761296621648627"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_score_ * -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6a41dfb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('days_since_previous_release', 0.16852015073994084),\n",
       " ('M_Sep', 0.04525186546834289),\n",
       " ('WD_Wednesday', 0.031762673225715396),\n",
       " ('WD_Thursday', 0.02946624737433628),\n",
       " ('month_holidays', 0.026367849628158635),\n",
       " ('M_Apr', 0.016936251024990957),\n",
       " ('M_Jul', 0.016714709264599146),\n",
       " ('M_Jun', 0.01562567951347666),\n",
       " ('M_Oct', 0.011701377472064749),\n",
       " ('M_May', 0.010297604760891444),\n",
       " ('M_Aug', 0.002815257048914652),\n",
       " ('WD_Tuesday', 0.001059104374898595),\n",
       " ('WD_Friday', -0.0026789820549391313),\n",
       " ('M_Jan', -0.010291330024345305),\n",
       " ('M_Nov', -0.011908609083788303),\n",
       " ('M_Dec', -0.01978252351721564),\n",
       " ('WD_Saturday', -0.03879984716870491),\n",
       " ('WD_Monday', -0.039562080225230424),\n",
       " ('M_Mar', -0.04436758806042964),\n",
       " ('WD_Sunday', -0.05289424728100277),\n",
       " ('M_Feb', -0.08618018062994037),\n",
       " ('previous_release_post', -0.2128181605444271)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(zip(model.feature_names_in_, model.best_estimator_[\"en\"].coef_), key=lambda x : x[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "02e79171",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df[\"predicted_prob\"] = model.predict(test_df[features])\n",
    "test_df[\"predicted_prob\"] = test_df[\"predicted_prob\"].apply(lambda x : 0 if x < 0 else (1 if x > 1 else x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9fe3ad7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3182619487550408"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mape(test_df[\"prob_of_release\"], test_df[\"predicted_prob\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5d6d65c4-5b44-41ad-a1ac-c4a18f96ee13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25523329275328044"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wape(test_df[\"prob_of_release\"], test_df[\"predicted_prob\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c549dce2",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df[\"residuals\"] = test_df[\"predicted_prob\"] - test_df[\"prob_of_release\"]\n",
    "# sns.scatterplot(data=test_df, x=\"predicted_prob\", y=\"residuals\", s=75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "84f01d30",
   "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>index</th>\n",
       "      <th>prob_of_release</th>\n",
       "      <th>predicted_prob</th>\n",
       "      <th>residuals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2507</th>\n",
       "      <td>2023-12-30</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.393400</td>\n",
       "      <td>-0.606600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2373</th>\n",
       "      <td>2023-08-18</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.429084</td>\n",
       "      <td>-0.570916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2562</th>\n",
       "      <td>2024-02-23</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.432226</td>\n",
       "      <td>-0.567774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2534</th>\n",
       "      <td>2024-01-26</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.462046</td>\n",
       "      <td>-0.537954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>2023-07-28</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.466018</td>\n",
       "      <td>-0.533982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2343</th>\n",
       "      <td>2023-07-19</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.758454</td>\n",
       "      <td>0.258454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2449</th>\n",
       "      <td>2023-11-02</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.515609</td>\n",
       "      <td>0.265609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2339</th>\n",
       "      <td>2023-07-15</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.481496</td>\n",
       "      <td>0.267210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2407</th>\n",
       "      <td>2023-09-21</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.526700</td>\n",
       "      <td>0.276700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2338</th>\n",
       "      <td>2023-07-14</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.466018</td>\n",
       "      <td>0.323161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>259 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          index  prob_of_release  predicted_prob  residuals\n",
       "2507 2023-12-30         1.000000        0.393400  -0.606600\n",
       "2373 2023-08-18         1.000000        0.429084  -0.570916\n",
       "2562 2024-02-23         1.000000        0.432226  -0.567774\n",
       "2534 2024-01-26         1.000000        0.462046  -0.537954\n",
       "2352 2023-07-28         1.000000        0.466018  -0.533982\n",
       "...         ...              ...             ...        ...\n",
       "2343 2023-07-19         0.500000        0.758454   0.258454\n",
       "2449 2023-11-02         0.250000        0.515609   0.265609\n",
       "2339 2023-07-15         0.214286        0.481496   0.267210\n",
       "2407 2023-09-21         0.250000        0.526700   0.276700\n",
       "2338 2023-07-14         0.142857        0.466018   0.323161\n",
       "\n",
       "[259 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df[[\"index\", \"prob_of_release\", \"predicted_prob\", \"residuals\"]].sort_values(by=[\"residuals\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b677c28f",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2500x1500 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1252,
       "width": 2040
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.histplot(x=test_df[\"prob_of_release\"], alpha=0.8, label=\"Actual Test Set Distribution\")\n",
    "sns.histplot(x=test_df[\"predicted_prob\"], ax=ax, label=\"Predicted Test Set Distribution\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d37df1",
   "metadata": {},
   "source": [
    "## Predictions for the Next Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "97ea4e14",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = train_model(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fd0fcdf9-6c93-4960-9f37-57795fa22a16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1858122943633413"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# based on https://machinelearningmastery.com/prediction-intervals-for-machine-learning/\n",
    "\n",
    "preds = model.predict(df[features])\n",
    "preds[preds < 0] = 0\n",
    "preds[preds > 1] = 1\n",
    "\n",
    "sum_errs = np.sum((df[\"prob_of_release\"] - preds) ** 2)\n",
    "stdev = np.sqrt(1 / (len(df[\"prob_of_release\"]) - 2) * sum_errs)\n",
    "stdev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "90e12ae4",
   "metadata": {},
   "outputs": [],
   "source": [
    "next_month[\"predicted_prob\"] = model.predict(next_month[features])\n",
    "next_month[\"predicted_prob\"] = next_month[\"predicted_prob\"].apply(lambda x : 0 if x < 0 else (1 if x > 1 else x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "13e0f709-2218-4d9c-87f6-86a131ca7dd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "next_month[\"predicted_min\"] = next_month[\"predicted_prob\"] - (1.96 * stdev)\n",
    "next_month[\"predicted_min\"] = next_month[\"predicted_min\"].apply(lambda x : 0 if x < 0 else x)\n",
    "next_month[\"predicted_max\"] = next_month[\"predicted_prob\"] + (1.96 * stdev)\n",
    "next_month[\"predicted_max\"] = next_month[\"predicted_max\"].apply(lambda x : 1 if x > 1 else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "162572ad",
   "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>index</th>\n",
       "      <th>predicted_min</th>\n",
       "      <th>predicted_prob</th>\n",
       "      <th>predicted_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2024-03-19</td>\n",
       "      <td>0.226285</td>\n",
       "      <td>0.590477</td>\n",
       "      <td>0.954669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2024-03-20</td>\n",
       "      <td>0.308653</td>\n",
       "      <td>0.672845</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2024-03-21</td>\n",
       "      <td>0.357656</td>\n",
       "      <td>0.721848</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2024-03-22</td>\n",
       "      <td>0.378570</td>\n",
       "      <td>0.742762</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2024-03-23</td>\n",
       "      <td>0.385330</td>\n",
       "      <td>0.749522</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2024-03-24</td>\n",
       "      <td>0.422018</td>\n",
       "      <td>0.786210</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2024-03-25</td>\n",
       "      <td>0.493403</td>\n",
       "      <td>0.857595</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2024-03-26</td>\n",
       "      <td>0.587066</td>\n",
       "      <td>0.951258</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2024-03-27</td>\n",
       "      <td>0.635808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2024-03-28</td>\n",
       "      <td>0.635808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2024-03-29</td>\n",
       "      <td>0.635808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2024-03-30</td>\n",
       "      <td>0.635808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2024-03-31</td>\n",
       "      <td>0.635808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2024-04-01</td>\n",
       "      <td>0.635808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2024-04-02</td>\n",
       "      <td>0.635808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2024-04-03</td>\n",
       "      <td>0.635808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2024-04-04</td>\n",
       "      <td>0.635808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2024-04-05</td>\n",
       "      <td>0.635808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2024-04-06</td>\n",
       "      <td>0.635808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2024-04-07</td>\n",
       "      <td>0.635808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2024-04-08</td>\n",
       "      <td>0.635808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2024-04-09</td>\n",
       "      <td>0.635808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2024-04-10</td>\n",
       "      <td>0.635808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2024-04-11</td>\n",
       "      <td>0.635808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2024-04-12</td>\n",
       "      <td>0.635808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2024-04-13</td>\n",
       "      <td>0.635808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        index  predicted_min  predicted_prob  predicted_max\n",
       "5  2024-03-19       0.226285        0.590477       0.954669\n",
       "6  2024-03-20       0.308653        0.672845       1.000000\n",
       "7  2024-03-21       0.357656        0.721848       1.000000\n",
       "8  2024-03-22       0.378570        0.742762       1.000000\n",
       "9  2024-03-23       0.385330        0.749522       1.000000\n",
       "10 2024-03-24       0.422018        0.786210       1.000000\n",
       "11 2024-03-25       0.493403        0.857595       1.000000\n",
       "12 2024-03-26       0.587066        0.951258       1.000000\n",
       "13 2024-03-27       0.635808        1.000000       1.000000\n",
       "14 2024-03-28       0.635808        1.000000       1.000000\n",
       "15 2024-03-29       0.635808        1.000000       1.000000\n",
       "16 2024-03-30       0.635808        1.000000       1.000000\n",
       "17 2024-03-31       0.635808        1.000000       1.000000\n",
       "18 2024-04-01       0.635808        1.000000       1.000000\n",
       "19 2024-04-02       0.635808        1.000000       1.000000\n",
       "20 2024-04-03       0.635808        1.000000       1.000000\n",
       "21 2024-04-04       0.635808        1.000000       1.000000\n",
       "22 2024-04-05       0.635808        1.000000       1.000000\n",
       "23 2024-04-06       0.635808        1.000000       1.000000\n",
       "24 2024-04-07       0.635808        1.000000       1.000000\n",
       "25 2024-04-08       0.635808        1.000000       1.000000\n",
       "26 2024-04-09       0.635808        1.000000       1.000000\n",
       "27 2024-04-10       0.635808        1.000000       1.000000\n",
       "28 2024-04-11       0.635808        1.000000       1.000000\n",
       "29 2024-04-12       0.635808        1.000000       1.000000\n",
       "30 2024-04-13       0.635808        1.000000       1.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next_month[[\"index\", \"predicted_min\", \"predicted_prob\", \"predicted_max\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1fd68ab5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2500x1500 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1249,
       "width": 2049
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.lineplot(data=next_month[:14], x=\"index\", y=\"predicted_prob\", linewidth=3, color=\"darkgrey\", alpha=0.8)\n",
    "sns.scatterplot(data=next_month[:14][next_month[\"predicted_prob\"] >= 0.50], x=\"index\", y=\"predicted_prob\",\n",
    "                s=300, ax=ax, color=\"tab:green\", label=\"At Least 50% Probability of a Release\")\n",
    "\n",
    "ax.fill_between(x=next_month[:14][\"index\"],\n",
    "                y1=next_month[:14][\"predicted_min\"],\n",
    "                y2=next_month[:14][\"predicted_max\"],\n",
    "                label=\"95% Prediction Interval\",\n",
    "                alpha=0.2,\n",
    "                color=\"darkgrey\")\n",
    "\n",
    "ax.set_xlabel(None)\n",
    "ax.set_ylabel(\"Predicted Probability of a Release\")\n",
    "ax.set_title(\"Predicted Probability of a Troon Release Over the Next Two Weeks (Beginning {})\".format(\n",
    "    next_month[\"index\"].iloc[0].date()))\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fbf3f38",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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