01_online_prediction_plotting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## Setup\n",
    "### Import libraries"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autotime extension is already loaded. To reload it, use:\n",
      "  %reload_ext autotime\n",
      "time: 1.38 ms (started: 2022-11-26 21:57:41 -06:00)\n"
     ]
    }
   ],
   "source": [
    "#!pip install ipython-autotime\n",
    "%load_ext autotime\n",
    "import os\n",
    "from tabulate import tabulate\n",
    "import numpy as np\n",
    "from google.cloud import aiplatform as aip\n",
    "from google.cloud import bigquery\n",
    "import pandas as pd"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Set configurations and constants"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 925 µs (started: 2022-11-26 21:57:42 -06:00)\n"
     ]
    }
   ],
   "source": [
    "NOTEBOOK ='Vertex_AI_Streamlit'\n",
    "REGION = \"us-central1\"\n",
    "PROJECT = 'babyweight-prediction'\n",
    "BUCKET = 'b_w_bucket'\n",
    "BQ_DATASET = \"bw_dataset\"\n",
    "APPNAME = \"bw-prediction\"\n",
    "GOOGLE_APPLICATION_CREDENTIALS = 'key/babyweight-prediction-ff79f406c099.json'\n",
    "\n",
    "os.environ[\"REGION\"] = REGION\n",
    "os.environ[\"PROJECT\"] = PROJECT\n",
    "os.environ[\"BUCKET\"] = BUCKET\n",
    "os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = GOOGLE_APPLICATION_CREDENTIALS\n",
    "GCS_BUCKET=f\"gs://{BUCKET}\""
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "---\n",
    "## 1. Retrieve the Endpoint"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 439 µs (started: 2022-11-26 21:57:42 -06:00)\n"
     ]
    }
   ],
   "source": [
    "aip.init(\n",
    "    project=PROJECT,\n",
    "    location=REGION,\n",
    "    staging_bucket=GCS_BUCKET)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 353 µs (started: 2022-11-26 21:57:42 -06:00)\n"
     ]
    }
   ],
   "source": [
    "ENDPOINT_NAME = 'projects/69318036822/locations/us-central1/endpoints/4074389870305345536'"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 14.7 ms (started: 2022-11-26 21:57:42 -06:00)\n"
     ]
    }
   ],
   "source": [
    "endpoint = aip.Endpoint(\n",
    "    project=PROJECT,\n",
    "    location=REGION,\n",
    "    endpoint_name=ENDPOINT_NAME\n",
    ")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 2. Make Online Predictions\n",
    "### 2.1. One Sample"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 477 µs (started: 2022-11-26 21:57:42 -06:00)\n"
     ]
    }
   ],
   "source": [
    "instance= [\n",
    "    {'is_male': 'true',\n",
    "     'mother_age': '45.0',\n",
    "     'plurality': 'Single(1)',\n",
    "     'gestation_weeks': '35.0',\n",
    "     'cigarette_use': 'true',\n",
    "     'alcohol_use': 'false',\n",
    "     },\n",
    "]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Get Prediction"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "Prediction(predictions=[{'lower_bound': 2.85607647895813, 'upper_bound': 4.929884910583496, 'value': 6.105488300323486}], deployed_model_id='3368705715412664320', model_version_id='1', model_resource_name='projects/69318036822/locations/us-central1/models/697490594242494464', explanations=None)"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.65 s (started: 2022-11-26 21:57:42 -06:00)\n"
     ]
    }
   ],
   "source": [
    "endpoint.predict(instance)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "6.105488300323486"
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 825 ms (started: 2022-11-26 21:57:43 -06:00)\n"
     ]
    }
   ],
   "source": [
    "predicted_value = endpoint.predict(instance).predictions[0]['value']\n",
    "predicted_value"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 3. Explaination\n",
    "### 3.1.1"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 2.09 s (started: 2022-11-26 21:57:44 -06:00)\n"
     ]
    }
   ],
   "source": [
    "explain=endpoint.explain(instance)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 410 µs (started: 2022-11-26 21:57:46 -06:00)\n"
     ]
    }
   ],
   "source": [
    "FEATURE_COLUMNS = [\n",
    "    'is_male',\n",
    "    'mother_age',\n",
    "    'plurality',\n",
    "    'gestation_weeks',\n",
    "    'cigarette_use',\n",
    "    'alcohol_use'\n",
    "]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.13 ms (started: 2022-11-26 21:57:46 -06:00)\n"
     ]
    }
   ],
   "source": [
    "def get_feature_attributions(\n",
    "        prediction_expl, instance_index, feature_columns=FEATURE_COLUMNS):\n",
    "    \"\"\"Returns the feature attributions with the baseline for a prediction example\"\"\"\n",
    "\n",
    "    rows = []\n",
    "    attribution = prediction_expl.explanations[instance_index].attributions[0]\n",
    "    baseline_score = attribution.baseline_output_value\n",
    "    total_att_val = baseline_score\n",
    "    for key in feature_columns:\n",
    "        feature_val = instance[instance_index][key]\n",
    "        att_val = attribution.feature_attributions[key]\n",
    "        total_att_val += att_val\n",
    "        rows.append([key,feature_val,att_val])\n",
    "\n",
    "    feature_attributions_rows = sorted(rows, key=lambda row: row[2], reverse=True)\n",
    "    feature_attributions_rows.insert(0,[\"Baseline_Score\", \"--\", baseline_score])\n",
    "    feature_attributions_rows.append([\"Final_Prediction\", \"--\", total_att_val])\n",
    "\n",
    "    return feature_attributions_rows"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 674 µs (started: 2022-11-26 21:57:46 -06:00)\n"
     ]
    }
   ],
   "source": [
    "feature_attributions_rows = get_feature_attributions(explain, 0)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.03 ms (started: 2022-11-26 21:57:46 -06:00)\n"
     ]
    }
   ],
   "source": [
    "def generate_dataframe():\n",
    "    feature_list=[]\n",
    "    feature_values=[]\n",
    "    feature_contributions=[]\n",
    "    feature_attributions_rows = get_feature_attributions(explain, 0)\n",
    "\n",
    "    for i in range(len(feature_attributions_rows)):\n",
    "        feature=feature_attributions_rows[i][0]\n",
    "        feature_list.append(feature)\n",
    "\n",
    "    for i in range(len(feature_attributions_rows)):\n",
    "        feature=feature_attributions_rows[i][1]\n",
    "        feature_values.append(feature)\n",
    "\n",
    "    for i in range(len(feature_attributions_rows)):\n",
    "        feature=feature_attributions_rows[i][2]\n",
    "        feature_contributions.append(feature)\n",
    "\n",
    "    zipped = list(zip(feature_list, feature_values, feature_contributions))\n",
    "    df = pd.DataFrame(zipped, columns=['Feature', 'Value', 'Contribution'])\n",
    "\n",
    "    return df, feature_list, feature_values,feature_contributions\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.31 ms (started: 2022-11-26 21:57:46 -06:00)\n"
     ]
    }
   ],
   "source": [
    "df, feature_list, feature_values,feature_contributions=generate_dataframe()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "data": {
      "text/plain": "            Feature      Value  Contribution\n0    Baseline_Score         --      7.805862\n1           is_male       true      0.000000\n2         plurality  Single(1)      0.000000\n3       alcohol_use      false      0.000000\n4        mother_age       45.0     -0.029131\n5     cigarette_use       true     -0.236360\n6   gestation_weeks       35.0     -1.434883\n7  Final_Prediction         --      6.105488",
      "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>Feature</th>\n      <th>Value</th>\n      <th>Contribution</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Baseline_Score</td>\n      <td>--</td>\n      <td>7.805862</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>is_male</td>\n      <td>true</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>plurality</td>\n      <td>Single(1)</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>alcohol_use</td>\n      <td>false</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>mother_age</td>\n      <td>45.0</td>\n      <td>-0.029131</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>cigarette_use</td>\n      <td>true</td>\n      <td>-0.236360</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>gestation_weeks</td>\n      <td>35.0</td>\n      <td>-1.434883</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Final_Prediction</td>\n      <td>--</td>\n      <td>6.105488</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 6.04 ms (started: 2022-11-26 21:57:46 -06:00)\n"
     ]
    }
   ],
   "source": [
    "df"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 11.7 ms (started: 2022-11-26 21:57:46 -06:00)\n"
     ]
    }
   ],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 582 ms (started: 2022-11-26 21:57:46 -06:00)\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot: xlabel='Contribution', ylabel='Feature'>"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x400 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 254 ms (started: 2022-11-26 21:57:47 -06:00)\n"
     ]
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 4))\n",
    "sns.barplot(x = \"Contribution\", y = \"Feature\", data = df)\n",
    "#st.pyplot(fig)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "            Feature      Value  Contribution\n0    Baseline_Score         --      7.805862\n1           is_male       true      0.000000\n2         plurality  Single(1)      0.000000\n3       alcohol_use      false      0.000000\n4        mother_age       45.0     -0.029131\n5     cigarette_use       true     -0.236360\n6   gestation_weeks       35.0     -1.434883\n7  Final_Prediction         --      6.105488",
      "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>Feature</th>\n      <th>Value</th>\n      <th>Contribution</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Baseline_Score</td>\n      <td>--</td>\n      <td>7.805862</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>is_male</td>\n      <td>true</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>plurality</td>\n      <td>Single(1)</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>alcohol_use</td>\n      <td>false</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>mother_age</td>\n      <td>45.0</td>\n      <td>-0.029131</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>cigarette_use</td>\n      <td>true</td>\n      <td>-0.236360</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>gestation_weeks</td>\n      <td>35.0</td>\n      <td>-1.434883</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Final_Prediction</td>\n      <td>--</td>\n      <td>6.105488</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 5.9 ms (started: 2022-11-26 21:57:47 -06:00)\n"
     ]
    }
   ],
   "source": [
    "df"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "data": {
      "text/plain": "            Feature Value  Contribution\n0    Baseline_Score    --      7.805862\n7  Final_Prediction    --      6.105488",
      "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>Feature</th>\n      <th>Value</th>\n      <th>Contribution</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Baseline_Score</td>\n      <td>--</td>\n      <td>7.805862</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Final_Prediction</td>\n      <td>--</td>\n      <td>6.105488</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 9.05 ms (started: 2022-11-26 21:57:47 -06:00)\n"
     ]
    }
   ],
   "source": [
    "df2=df.query(\"Feature in ('Baseline_Score','Final_Prediction')\")\n",
    "df2"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "data": {
      "text/plain": "            Feature      Value  Contribution\n0    Baseline_Score         --      7.805862\n1           is_male       true      0.000000\n2         plurality  Single(1)      0.000000\n3       alcohol_use      false      0.000000\n4        mother_age       45.0     -0.029131\n5     cigarette_use       true     -0.236360\n6   gestation_weeks       35.0     -1.434883\n7  Final_Prediction         --      6.105488",
      "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>Feature</th>\n      <th>Value</th>\n      <th>Contribution</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Baseline_Score</td>\n      <td>--</td>\n      <td>7.805862</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>is_male</td>\n      <td>true</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>plurality</td>\n      <td>Single(1)</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>alcohol_use</td>\n      <td>false</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>mother_age</td>\n      <td>45.0</td>\n      <td>-0.029131</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>cigarette_use</td>\n      <td>true</td>\n      <td>-0.236360</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>gestation_weeks</td>\n      <td>35.0</td>\n      <td>-1.434883</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Final_Prediction</td>\n      <td>--</td>\n      <td>6.105488</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 6.22 ms (started: 2022-11-26 21:58:14 -06:00)\n"
     ]
    }
   ],
   "source": [
    "df"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "data": {
      "text/plain": "0.0"
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 2.86 ms (started: 2022-11-26 21:59:23 -06:00)\n"
     ]
    }
   ],
   "source": [
    "df['Contribution'][1]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'is_male'",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "Cell \u001B[0;32mIn [50], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[43mdf\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mloc\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mis_male\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mContribution\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m\n",
      "File \u001B[0;32m~/opt/anaconda3/envs/vertex_streamlit/lib/python3.9/site-packages/pandas/core/indexing.py:1066\u001B[0m, in \u001B[0;36m_LocationIndexer.__getitem__\u001B[0;34m(self, key)\u001B[0m\n\u001B[1;32m   1064\u001B[0m     key \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mtuple\u001B[39m(com\u001B[38;5;241m.\u001B[39mapply_if_callable(x, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mobj) \u001B[38;5;28;01mfor\u001B[39;00m x \u001B[38;5;129;01min\u001B[39;00m key)\n\u001B[1;32m   1065\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_is_scalar_access(key):\n\u001B[0;32m-> 1066\u001B[0m         \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mobj\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_get_value\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkey\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtakeable\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_takeable\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   1067\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_getitem_tuple(key)\n\u001B[1;32m   1068\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m   1069\u001B[0m     \u001B[38;5;66;03m# we by definition only have the 0th axis\u001B[39;00m\n",
      "File \u001B[0;32m~/opt/anaconda3/envs/vertex_streamlit/lib/python3.9/site-packages/pandas/core/frame.py:3922\u001B[0m, in \u001B[0;36mDataFrame._get_value\u001B[0;34m(self, index, col, takeable)\u001B[0m\n\u001B[1;32m   3916\u001B[0m engine \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mindex\u001B[38;5;241m.\u001B[39m_engine\n\u001B[1;32m   3918\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mindex, MultiIndex):\n\u001B[1;32m   3919\u001B[0m     \u001B[38;5;66;03m# CategoricalIndex: Trying to use the engine fastpath may give incorrect\u001B[39;00m\n\u001B[1;32m   3920\u001B[0m     \u001B[38;5;66;03m#  results if our categories are integers that dont match our codes\u001B[39;00m\n\u001B[1;32m   3921\u001B[0m     \u001B[38;5;66;03m# IntervalIndex: IntervalTree has no get_loc\u001B[39;00m\n\u001B[0;32m-> 3922\u001B[0m     row \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mindex\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mindex\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   3923\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m series\u001B[38;5;241m.\u001B[39m_values[row]\n\u001B[1;32m   3925\u001B[0m \u001B[38;5;66;03m# For MultiIndex going through engine effectively restricts us to\u001B[39;00m\n\u001B[1;32m   3926\u001B[0m \u001B[38;5;66;03m#  same-length tuples; see test_get_set_value_no_partial_indexing\u001B[39;00m\n",
      "File \u001B[0;32m~/opt/anaconda3/envs/vertex_streamlit/lib/python3.9/site-packages/pandas/core/indexes/range.py:395\u001B[0m, in \u001B[0;36mRangeIndex.get_loc\u001B[0;34m(self, key, method, tolerance)\u001B[0m\n\u001B[1;32m    393\u001B[0m             \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[1;32m    394\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_indexing_error(key)\n\u001B[0;32m--> 395\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key)\n\u001B[1;32m    396\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28msuper\u001B[39m()\u001B[38;5;241m.\u001B[39mget_loc(key, method\u001B[38;5;241m=\u001B[39mmethod, tolerance\u001B[38;5;241m=\u001B[39mtolerance)\n",
      "\u001B[0;31mKeyError\u001B[0m: 'is_male'"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 2.13 s (started: 2022-11-26 22:28:54 -06:00)\n"
     ]
    }
   ],
   "source": [
    "df.loc['is_male', 'Contribution']"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 528 µs (started: 2022-11-26 22:31:13 -06:00)\n"
     ]
    }
   ],
   "source": [
    "df3=df.copy()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [
    {
     "data": {
      "text/plain": "            Feature      Value  Contribution\n0    Baseline_Score         --      7.805862\n1           is_male       true      0.000000\n2         plurality  Single(1)      0.000000\n3       alcohol_use      false      0.000000\n4        mother_age       45.0     -0.029131\n5     cigarette_use       true     -0.236360\n6   gestation_weeks       35.0     -1.434883\n7  Final_Prediction         --      6.105488",
      "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>Feature</th>\n      <th>Value</th>\n      <th>Contribution</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Baseline_Score</td>\n      <td>--</td>\n      <td>7.805862</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>is_male</td>\n      <td>true</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>plurality</td>\n      <td>Single(1)</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>alcohol_use</td>\n      <td>false</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>mother_age</td>\n      <td>45.0</td>\n      <td>-0.029131</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>cigarette_use</td>\n      <td>true</td>\n      <td>-0.236360</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>gestation_weeks</td>\n      <td>35.0</td>\n      <td>-1.434883</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Final_Prediction</td>\n      <td>--</td>\n      <td>6.105488</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 5.4 ms (started: 2022-11-26 22:31:15 -06:00)\n"
     ]
    }
   ],
   "source": [
    "df3"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [
    {
     "data": {
      "text/plain": "                      Value  Contribution\nFeature                                  \nBaseline_Score           --      7.805862\nis_male                true      0.000000\nplurality         Single(1)      0.000000\nalcohol_use           false      0.000000\nmother_age             45.0     -0.029131\ncigarette_use          true     -0.236360\ngestation_weeks        35.0     -1.434883\nFinal_Prediction         --      6.105488",
      "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>Value</th>\n      <th>Contribution</th>\n    </tr>\n    <tr>\n      <th>Feature</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Baseline_Score</th>\n      <td>--</td>\n      <td>7.805862</td>\n    </tr>\n    <tr>\n      <th>is_male</th>\n      <td>true</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>plurality</th>\n      <td>Single(1)</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>alcohol_use</th>\n      <td>false</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>mother_age</th>\n      <td>45.0</td>\n      <td>-0.029131</td>\n    </tr>\n    <tr>\n      <th>cigarette_use</th>\n      <td>true</td>\n      <td>-0.236360</td>\n    </tr>\n    <tr>\n      <th>gestation_weeks</th>\n      <td>35.0</td>\n      <td>-1.434883</td>\n    </tr>\n    <tr>\n      <th>Final_Prediction</th>\n      <td>--</td>\n      <td>6.105488</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 9.66 ms (started: 2022-11-26 22:35:17 -06:00)\n"
     ]
    }
   ],
   "source": [
    "df3=df3.set_index('Feature')\n",
    "df3"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [
    {
     "data": {
      "text/plain": "-0.0291309654712677"
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 3.05 ms (started: 2022-11-26 22:35:44 -06:00)\n"
     ]
    }
   ],
   "source": [
    "df3.loc['mother_age', 'Contribution']"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
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