localmode_webinar.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9d4c60f4",
   "metadata": {},
   "source": [
    "# Install and Instatiate Featureform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ef24a3da",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/six-1.16.0-py3.11.egg-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/pycparser-2.21-py3.11.egg-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/six-1.16.0-py3.11.egg-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/pycparser-2.21-py3.11.egg-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/cffi-1.15.1-py3.11.egg-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
      "\u001b[0mCollecting featureform==1.10.3\n",
      "  Downloading featureform-1.10.3-py3-none-any.whl (2.0 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m0m\n",
      "\u001b[?25hRequirement already satisfied: click>=7.1.2 in /opt/homebrew/lib/python3.11/site-packages (from featureform==1.10.3) (8.1.3)\n",
      "Requirement already satisfied: protobuf>=3.20.0 in /opt/homebrew/lib/python3.11/site-packages (from featureform==1.10.3) (4.23.3)\n",
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      "Requirement already satisfied: itsdangerous>=2.0 in /opt/homebrew/lib/python3.11/site-packages (from flask==2.2.1->featureform==1.10.3) (2.1.2)\n",
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      "Requirement already satisfied: cryptography>=3.2 in /opt/homebrew/lib/python3.11/site-packages (from authlib>=1.1.0->weaviate-client->featureform==1.10.3) (41.0.1)\n",
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      "Requirement already satisfied: mdurl~=0.1 in /opt/homebrew/lib/python3.11/site-packages (from markdown-it-py>=2.2.0->rich->featureform==1.10.3) (0.1.2)\n",
      "Requirement already satisfied: cffi>=1.12 in /opt/homebrew/lib/python3.11/site-packages (from cryptography>=3.2->authlib>=1.1.0->weaviate-client->featureform==1.10.3) (1.15.1)\n",
      "Requirement already satisfied: pycparser in /opt/homebrew/lib/python3.11/site-packages (from cffi>=1.12->cryptography>=3.2->authlib>=1.1.0->weaviate-client->featureform==1.10.3) (2.21)\n",
      "\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/six-1.16.0-py3.11.egg-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/pycparser-2.21-py3.11.egg-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/cffi-1.15.1-py3.11.egg-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
      "\u001b[0mInstalling collected packages: featureform\n",
      "  Attempting uninstall: featureform\n",
      "\u001b[33m    WARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/six-1.16.0-py3.11.egg-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
      "\u001b[0m    Found existing installation: featureform 1.10.3rc0\n",
      "    Uninstalling featureform-1.10.3rc0:\n",
      "      Successfully uninstalled featureform-1.10.3rc0\n",
      "\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/six-1.16.0-py3.11.egg-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
      "\u001b[0mSuccessfully installed featureform-1.10.3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/six-1.16.0-py3.11.egg-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\r\n",
      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/pycparser-2.21-py3.11.egg-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\r\n",
      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/six-1.16.0-py3.11.egg-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\r\n",
      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/six-1.16.0-py3.11.egg-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\r\n",
      "\u001b[0m\r\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\r\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3.11 -m pip install --upgrade pip\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!pip install featureform==1.10.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "067ce903",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from featureform import local\n",
    "import featureform as ff\n",
    "\n",
    "client = ff.Client(local=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49419bdf",
   "metadata": {},
   "source": [
    "# Set a run name, to group everything we build in this notebook\n",
    "\n",
    "everything in featureform is versioned. We generate a default version when its not specificfied, but you can set the default version by using `set_run()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "27736fde",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ff.set_run(\"webinar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53cc08aa",
   "metadata": {},
   "source": [
    "# Register and read our primary dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "0f3a5f11",
   "metadata": {},
   "outputs": [],
   "source": [
    "#RUN THIS IN COLAB ENVIRON TO GET FILE EASILY\n",
    "# import requests\n",
    "# url = \"https://featureform-demo-files.s3.amazonaws.com/transactions_new.csv\"\n",
    "# r = requests.get(url)\n",
    "# with open(\"transactions_new.csv\" , 'wb') as f:\n",
    "#      f.write(r.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "8104ac18",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "transactions = local.register_file(\"transactions\", path=\"./transactions_new.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "413d072c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applying Run: webinar\n",
      "Creating provider local-mode \n",
      "Creating source transactions  webinar\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>TransactionID</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>CustomerDOB</th>\n",
       "      <th>CustLocation</th>\n",
       "      <th>CustAccountBalance</th>\n",
       "      <th>TransactionAmount</th>\n",
       "      <th>Timestamp</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>T1</td>\n",
       "      <td>C5841053</td>\n",
       "      <td>10/1/94</td>\n",
       "      <td>JAMSHEDPUR</td>\n",
       "      <td>17819.05</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2022-04-09 11:33:09</td>\n",
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       "      <td>2022-03-27 01:04:21</td>\n",
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       "      <td>459.0</td>\n",
       "      <td>2022-04-07 00:48:14</td>\n",
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       "      <td>MUMBAI</td>\n",
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       "      <td>2060.0</td>\n",
       "      <td>2022-04-14 07:56:59</td>\n",
       "      <td>True</td>\n",
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       "      <td>24/3/88</td>\n",
       "      <td>NAVI MUMBAI</td>\n",
       "      <td>6714.43</td>\n",
       "      <td>1762.5</td>\n",
       "      <td>2022-04-13 07:39:19</td>\n",
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       "      <td>T1048563</td>\n",
       "      <td>C8020229</td>\n",
       "      <td>8/4/90</td>\n",
       "      <td>NEW DELHI</td>\n",
       "      <td>7635.19</td>\n",
       "      <td>799.0</td>\n",
       "      <td>2022-04-02 05:12:36</td>\n",
       "      <td>False</td>\n",
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       "      <td>NASHIK</td>\n",
       "      <td>27311.42</td>\n",
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       "      <td>2022-04-23 03:58:33</td>\n",
       "      <td>False</td>\n",
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       "      <td>HYDERABAD</td>\n",
       "      <td>221757.06</td>\n",
       "      <td>770.0</td>\n",
       "      <td>2022-04-01 15:08:34</td>\n",
       "      <td>False</td>\n",
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       "      <td>VISAKHAPATNAM</td>\n",
       "      <td>10117.87</td>\n",
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       "      <td>2022-04-15 12:50:15</td>\n",
       "      <td>False</td>\n",
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       "      <td>PUNE</td>\n",
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       "      <td>1166.0</td>\n",
       "      <td>2022-03-30 20:56:02</td>\n",
       "      <td>False</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1046198 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0 TransactionID CustomerID CustomerDOB   CustLocation  \\\n",
       "0                 0            T1   C5841053     10/1/94     JAMSHEDPUR   \n",
       "1                 1            T2   C2142763      4/4/57        JHAJJAR   \n",
       "2                 2            T3   C4417068    26/11/96         MUMBAI   \n",
       "3                 3            T4   C5342380     14/9/73         MUMBAI   \n",
       "4                 4            T5   C9031234     24/3/88    NAVI MUMBAI   \n",
       "...             ...           ...        ...         ...            ...   \n",
       "1046193     1048562      T1048563   C8020229      8/4/90      NEW DELHI   \n",
       "1046194     1048563      T1048564   C6459278     20/2/92         NASHIK   \n",
       "1046195     1048564      T1048565   C6412354     18/5/89      HYDERABAD   \n",
       "1046196     1048565      T1048566   C6420483     30/8/78  VISAKHAPATNAM   \n",
       "1046197     1048566      T1048567   C8337524      5/3/84           PUNE   \n",
       "\n",
       "         CustAccountBalance  TransactionAmount            Timestamp  IsFraud  \n",
       "0                  17819.05               25.0  2022-04-09 11:33:09    False  \n",
       "1                   2270.69            27999.0  2022-03-27 01:04:21     True  \n",
       "2                  17874.44              459.0  2022-04-07 00:48:14    False  \n",
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       "4                   6714.43             1762.5  2022-04-13 07:39:19    False  \n",
       "...                     ...                ...                  ...      ...  \n",
       "1046193             7635.19              799.0  2022-04-02 05:12:36    False  \n",
       "1046194            27311.42              460.0  2022-04-23 03:58:33    False  \n",
       "1046195           221757.06              770.0  2022-04-01 15:08:34    False  \n",
       "1046196            10117.87             1000.0  2022-04-15 12:50:15    False  \n",
       "1046197            75734.42             1166.0  2022-03-30 20:56:02    False  \n",
       "\n",
       "[1046198 rows x 9 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.dataframe(\"transactions\", ff.get_run())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "558fefbf",
   "metadata": {},
   "source": [
    "# Create a percentage balance transformation for training and an equivalent ondemand feature for serving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "aab16360",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applying Run: webinar\n",
      "Creating provider local-mode \n",
      "Creating source perc_balance  webinar\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "         Unnamed: 0 TransactionID CustomerID CustomerDOB   CustLocation  \\\n",
       "0                 0            T1   C5841053     10/1/94     JAMSHEDPUR   \n",
       "1                 1            T2   C2142763      4/4/57        JHAJJAR   \n",
       "2                 2            T3   C4417068    26/11/96         MUMBAI   \n",
       "3                 3            T4   C5342380     14/9/73         MUMBAI   \n",
       "4                 4            T5   C9031234     24/3/88    NAVI MUMBAI   \n",
       "...             ...           ...        ...         ...            ...   \n",
       "1046193     1048562      T1048563   C8020229      8/4/90      NEW DELHI   \n",
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       "1046195     1048564      T1048565   C6412354     18/5/89      HYDERABAD   \n",
       "1046196     1048565      T1048566   C6420483     30/8/78  VISAKHAPATNAM   \n",
       "1046197     1048566      T1048567   C8337524      5/3/84           PUNE   \n",
       "\n",
       "         CustAccountBalance  TransactionAmount            Timestamp  IsFraud  \\\n",
       "0                  17819.05               25.0  2022-04-09 11:33:09    False   \n",
       "1                   2270.69            27999.0  2022-03-27 01:04:21     True   \n",
       "2                  17874.44              459.0  2022-04-07 00:48:14    False   \n",
       "3                 866503.21             2060.0  2022-04-14 07:56:59     True   \n",
       "4                   6714.43             1762.5  2022-04-13 07:39:19    False   \n",
       "...                     ...                ...                  ...      ...   \n",
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       "1046194            27311.42              460.0  2022-04-23 03:58:33    False   \n",
       "1046195           221757.06              770.0  2022-04-01 15:08:34    False   \n",
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       "1046197            75734.42             1166.0  2022-03-30 20:56:02    False   \n",
       "\n",
       "         BalancePercent  \n",
       "0              0.001403  \n",
       "1             12.330613  \n",
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       "\n",
       "[1046198 rows x 10 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@local.df_transformation(inputs=[(\"transactions\", ff.get_run())])\n",
    "def perc_balance(df):\n",
    "    df[\"BalancePercent\"] = df[\"TransactionAmount\"]/df[\"CustAccountBalance\"].replace(0.0, 1.0)\n",
    "    return df\n",
    "\n",
    "client.dataframe(perc_balance)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77d66632",
   "metadata": {},
   "source": [
    "# Create a rolling window average of transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "741507c9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applying Run: webinar\n",
      "Creating provider local-mode \n",
      "Creating source window_aggs  webinar\n"
     ]
    },
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "        CustomerID           Timestamp    mean  count\n",
       "0         C1010011 2022-03-31 12:25:23  4750.0    1.0\n",
       "1         C1010011 2022-04-20 22:31:25  2553.0    2.0\n",
       "2         C1010012 2022-04-19 23:53:55  1499.0    1.0\n",
       "3         C1010014 2022-04-06 19:57:12   250.0    1.0\n",
       "4         C1010014 2022-04-17 18:49:18   727.5    2.0\n",
       "...            ...                 ...     ...    ...\n",
       "1046193   C9099836 2022-04-22 09:31:33   691.0    1.0\n",
       "1046194   C9099877 2022-04-24 14:31:31   222.0    1.0\n",
       "1046195   C9099919 2022-04-13 07:42:36   126.0    1.0\n",
       "1046196   C9099941 2022-04-17 17:56:18    50.0    1.0\n",
       "1046197   C9099956 2022-04-02 19:54:20   855.0    1.0\n",
       "\n",
       "[1046198 rows x 4 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@local.df_transformation(inputs=[(\"transactions\", ff.get_run())])\n",
    "def window_aggs(df):\n",
    "    df['Timestamp'] = pd.to_datetime(df['Timestamp'])\n",
    "    df = df.sort_values(by='Timestamp')\n",
    "    df.set_index('Timestamp', inplace=True)\n",
    "    return df.groupby('CustomerID')['TransactionAmount'].rolling(window=30, min_periods=1).agg([\"mean\", \"count\"]).reset_index()\n",
    "\n",
    "\n",
    "client.dataframe(window_aggs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d6cd1d5",
   "metadata": {},
   "source": [
    "# Defining our features, label, and training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "31ca5c52",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applying Run: webinar\n",
      "Creating provider local-mode \n",
      "Creating entity user \n",
      "Creating feature avg_trans  webinar\n",
      "Creating feature balance  webinar\n",
      "Creating feature perc  webinar\n",
      "Creating label is_fraud  webinar\n",
      "Creating training-set fraud  webinar\n",
      "Updating Feature Table: |##################################################| 100% Complete\n",
      "\n",
      "Updating Feature Table: |##################################################| 100% Complete\n",
      "\n",
      "Updating Feature Table: |##################################################| 100% Complete\n",
      "\n"
     ]
    }
   ],
   "source": [
    "@ff.entity\n",
    "class User:\n",
    "    is_fraud = ff.Label(transactions[[\"CustomerID\", \"IsFraud\", \"Timestamp\"]], type=ff.Bool)\n",
    "    avg_trans = ff.Feature(window_aggs[[\"CustomerID\", \"mean\", \"Timestamp\"]], type=ff.Float32)\n",
    "    balance = ff.Feature(transactions[[\"CustomerID\", \"CustAccountBalance\", \"Timestamp\"]], type=ff.Float32)\n",
    "    perc = ff.Feature(perc_balance[[\"CustomerID\", \"BalancePercent\", \"Timestamp\"]], type=ff.Float32)\n",
    "    \n",
    "ts = ff.register_training_set(\"fraud\", label=User.is_fraud, features=[(\"avg_trans\", ff.get_run()), (\"perc\", ff.get_run())])\n",
    "\n",
    "client.apply()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b32a28ac",
   "metadata": {},
   "source": [
    "# Training a Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e7a3e26a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = client.training_set(\"fraud\", \"webinar\").dataframe()\n",
    "print(ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "fb8e6593",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9805247562607532\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = ts.drop('label', axis=1)\n",
    "y = ts['label']\n",
    "# Split the data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train, y_train)\n",
    "y_pred = rf.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21ebc9a3",
   "metadata": {},
   "source": [
    "# Serving for Inference / Prod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "522d4448",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applying Run: webinar\n",
      "Creating provider local-mode \n",
      "Creating ondemand_feature ondemand_percent  webinar\n"
     ]
    }
   ],
   "source": [
    "@ff.ondemand_feature()\n",
    "def ondemand_percent(client, params, entities):\n",
    "    return params[\"TransactionAmount\"] / client.features([(\"balance\", \"webinar\")], entities=entities)[0]\n",
    "\n",
    "client.apply()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "4acfe078",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updating Feature Table: |##################################################| 100% Complete\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = client.features([(\"avg_trans\", \"webinar\"), (\"ondemand_percent\", ff.get_run())],\n",
    "                params={\"TransactionAmount\": 1000.0},\n",
    "                entities={\"user\": \"C1010011\"})\n",
    "rf.predict([f])"
   ]
  }
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