Lab_1__Data_Hackathon_Setup_&_Preparation.ipynb

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   "source": [
    "# Copyright 2023 Google LLC\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_TFkjzedQKR9",
    "tags": []
   },
   "source": [
    "# **Lab 1:** Datathon Preparation\n",
    "This notebooks sets up the enrvironment and prepares the datasets from the original events data \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OJ1XZxTPkvsL"
   },
   "source": [
    "![overview](assets/overview.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "my-fGMqmNxZM"
   },
   "source": [
    "### **Step 1:** Parameters and Authentication\n",
    "Authenticate yourself against Google Cloud Platform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pGwpOfseKFRP"
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   "outputs": [],
   "source": [
    "project_id = \"\" \n",
    "team_name  = \"\" \n",
    "\n",
    "location   = \"us\" \n",
    "dataset_name = \"datathon_ds_{}\".format(team_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "IxIxhR9qNb6C"
   },
   "outputs": [],
   "source": [
    "from google.cloud import bigquery\n",
    "\n",
    "client = bigquery.Client(project=project_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ySL_6DB8N-9W"
   },
   "source": [
    "### **Step 2:** Create the Team Dataset\n",
    "Create a dataset for your team. We do using the command line bq command that is installed in a CoLab. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Nb-cuRBCNORF",
    "outputId": "bccd05e1-eb64-459e-aab8-351aa3e65389"
   },
   "outputs": [],
   "source": [
    "!bq mk --location=$location --dataset $project_id:$dataset_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tE1-FrCdBKIr"
   },
   "source": [
    "Let's check if the dataset is existing. Just list all the datasets in the project. We can do this also via the BQ python library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dDQoJAJKNOXH",
    "outputId": "68b26aa9-9d7f-4932-a0d6-13cedc633c99"
   },
   "outputs": [],
   "source": [
    "for dataset in client.list_datasets():\n",
    "  print(dataset.dataset_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uKGfZpydBWKD"
   },
   "source": [
    "### **Step 3:** Test Access to Raw Dataset\n",
    "Check if the access to the dataset is working properly. This time we use BigQuery magic (pre-installed in CoLab and Vertex Workbenchs) to execute a BigQuery query directly from here:  "
   ]
  },
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   "source": [
    "%%bigquery --project $project_id\n",
    "\n",
    "SELECT *\n",
    "FROM `firebase-public-project.analytics_153293282.events_*`\n",
    "LIMIT 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W-PhQrKZPWEv"
   },
   "source": [
    "### **Step 4:** Data Preparation\n",
    "Now we prepare some Views on top of the dataset. These views already execute some data preparations and feature engineering tasks. We will come back to the details of this later. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3D7H1SNinT63"
   },
   "source": [
    "![overview_2](assets/overview-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eBlPq0gePa5B"
   },
   "source": [
    "**View 1** analyzes the user and its first and latest appearance.  \n",
    "\n",
    "This view aggregates the first and the last engagement of by user and defines a churned user\n",
    "\n",
    "Our label field/column is **Churned** i.e If last activity was within 24h of sign up\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f2rVqiRjPMhS",
    "outputId": "92cc2bdf-79f5-435f-dd4a-dda9ed9b4326"
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   "source": [
    "view_id  = \"{}.{}.user_returninginfo\".format(project_id, dataset_name)\n",
    "view     = bigquery.Table(view_id)\n",
    "\n",
    "view.view_query = f'''\n",
    "WITH firstlasttouch AS (\n",
    "    SELECT\n",
    "      user_pseudo_id,\n",
    "      MIN(event_timestamp) AS user_first_engagement,\n",
    "      MAX(event_timestamp) AS user_last_engagement\n",
    "    FROM\n",
    "      `firebase-public-project.analytics_153293282.events_*`\n",
    "    WHERE event_name=\"user_engagement\"\n",
    "    GROUP BY\n",
    "      user_pseudo_id\n",
    "\n",
    "  )\n",
    "  SELECT\n",
    "    user_pseudo_id,\n",
    "    user_first_engagement,\n",
    "    user_last_engagement,\n",
    "    EXTRACT(MONTH from TIMESTAMP_MICROS(user_first_engagement)) as month,\n",
    "    EXTRACT(DAYOFYEAR from TIMESTAMP_MICROS(user_first_engagement)) as julianday,\n",
    "    EXTRACT(DAYOFWEEK from TIMESTAMP_MICROS(user_first_engagement)) as dayofweek,\n",
    "\n",
    "    #add 24 hr to user's first touch\n",
    "    (user_first_engagement + 86400000000) AS ts_24hr_after_first_engagement,\n",
    "\n",
    "#churned = 1 if last_touch within 24 hr of app installation, else 0\n",
    "IF (user_last_engagement < (user_first_engagement + 86400000000),\n",
    "    1,\n",
    "    0 ) AS churned,\n",
    "\n",
    "#bounced = 1 if last_touch within 10 min, else 0\n",
    "IF (user_last_engagement <= (user_first_engagement + 600000000),\n",
    "    1,\n",
    "    0 ) AS bounced,\n",
    "  FROM\n",
    "    firstlasttouch\n",
    "  GROUP BY\n",
    "    1,2,3\n",
    "\n",
    "'''\n",
    "\n",
    "# Create the view\n",
    "view = client.create_table(view, exists_ok=True)\n",
    "print(f\"Created {view.table_type}: {str(view.reference)}\")\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "id": "Zz8v_2heeJz5"
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   "source": [
    "**View 2** aggregates the demographics of the users.\n",
    "\n",
    "This view extracts some demographic data from the user that has been collected by Google Analytics\n",
    "\n",
    "E.g. country, device/OS, language\n"
   ]
  },
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   "source": [
    "view_id  = \"{}.{}.user_demographics\".format(project_id, dataset_name)\n",
    "view     = bigquery.Table(view_id)\n",
    "\n",
    "view.view_query = f'''\n",
    "  WITH first_values AS (\n",
    "      SELECT\n",
    "          user_pseudo_id,\n",
    "          geo.country as country,\n",
    "          device.operating_system as operating_system,\n",
    "          device.language as language,\n",
    "          ROW_NUMBER() OVER (PARTITION BY user_pseudo_id ORDER BY event_timestamp DESC) AS row_num\n",
    "      FROM `firebase-public-project.analytics_153293282.events_*`\n",
    "      WHERE event_name=\"user_engagement\"\n",
    "      )\n",
    "  SELECT * EXCEPT (row_num)\n",
    "  FROM first_values\n",
    "  WHERE row_num = 1\n",
    "'''\n",
    "\n",
    "# Create the view\n",
    "view = client.create_table(view, exists_ok=True)\n",
    "print(f\"Created {view.table_type}: {str(view.reference)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qlyoq6SoeYCi"
   },
   "source": [
    "**View 3** creates a aggregated user behaviour view\n",
    "\n",
    "This view aggregates KPIs that describe the user behaviour. \n",
    "\n",
    "E.g.  count of completed levels, sum of scores, # of challenges to a friend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yqOd-a9-ef5T",
    "outputId": "ffd7a4b1-b9c0-48f6-a9fa-af59fa55eb84"
   },
   "outputs": [],
   "source": [
    "view_id  = \"{}.{}.user_aggregate_behavior\".format(project_id, dataset_name)\n",
    "view     = bigquery.Table(view_id)\n",
    "\n",
    "view.view_query = f'''\n",
    "WITH events_first24hr AS (\n",
    "    #select user data only from first 24 hr of using the app\n",
    "    SELECT\n",
    "      e.*\n",
    "    FROM\n",
    "      `firebase-public-project.analytics_153293282.events_*` e\n",
    "    JOIN\n",
    "      {dataset_name}.user_returninginfo r\n",
    "    ON\n",
    "      e.user_pseudo_id = r.user_pseudo_id\n",
    "    WHERE\n",
    "      e.event_timestamp <= r.ts_24hr_after_first_engagement\n",
    "    )\n",
    "SELECT\n",
    "  user_pseudo_id,\n",
    "  SUM(IF(event_name = 'user_engagement', 1, 0)) AS cnt_user_engagement,\n",
    "  SUM(IF(event_name = 'level_start_quickplay', 1, 0)) AS cnt_level_start_quickplay,\n",
    "  SUM(IF(event_name = 'level_end_quickplay', 1, 0)) AS cnt_level_end_quickplay,\n",
    "  SUM(IF(event_name = 'level_complete_quickplay', 1, 0)) AS cnt_level_complete_quickplay,\n",
    "  SUM(IF(event_name = 'level_reset_quickplay', 1, 0)) AS cnt_level_reset_quickplay,\n",
    "  SUM(IF(event_name = 'post_score', 1, 0)) AS cnt_post_score,\n",
    "  SUM(IF(event_name = 'spend_virtual_currency', 1, 0)) AS cnt_spend_virtual_currency,\n",
    "  SUM(IF(event_name = 'ad_reward', 1, 0)) AS cnt_ad_reward,\n",
    "  SUM(IF(event_name = 'challenge_a_friend', 1, 0)) AS cnt_challenge_a_friend,\n",
    "  SUM(IF(event_name = 'completed_5_levels', 1, 0)) AS cnt_completed_5_levels,\n",
    "  SUM(IF(event_name = 'use_extra_steps', 1, 0)) AS cnt_use_extra_steps,\n",
    "FROM\n",
    "  events_first24hr\n",
    "GROUP BY\n",
    "  1\n",
    "'''\n",
    "\n",
    "# Create the view\n",
    "view = client.create_table(view, exists_ok=True)\n",
    "print(f\"Created {view.table_type}: {str(view.reference)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7fUIR5f-fK1Y"
   },
   "source": [
    "**View 4** combines all the other views into a single flat view for training purposes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pkZjE-Oj_IdJ"
   },
   "source": [
    "![overview_3](assets/overview-3.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eJDqeTw-eyll",
    "outputId": "d9ce1f12-cb19-42d4-fcad-7b0009260a3e"
   },
   "outputs": [],
   "source": [
    "# Create view for the full dataset\n",
    "view_id  = \"{}.{}.cc_full_dataset\".format(project_id, dataset_name)\n",
    "view     = bigquery.Table(view_id)\n",
    "\n",
    "view.view_query = f'''\n",
    "  \n",
    "  SELECT\n",
    "    dem.*,\n",
    "    IFNULL(beh.cnt_user_engagement, 0) AS cnt_user_engagement,\n",
    "    IFNULL(beh.cnt_level_start_quickplay, 0) AS cnt_level_start_quickplay,\n",
    "    IFNULL(beh.cnt_level_end_quickplay, 0) AS cnt_level_end_quickplay,\n",
    "    IFNULL(beh.cnt_level_complete_quickplay, 0) AS cnt_level_complete_quickplay,\n",
    "    IFNULL(beh.cnt_level_reset_quickplay, 0) AS cnt_level_reset_quickplay,\n",
    "    IFNULL(beh.cnt_post_score, 0) AS cnt_post_score,\n",
    "    IFNULL(beh.cnt_spend_virtual_currency, 0) AS cnt_spend_virtual_currency,\n",
    "    IFNULL(beh.cnt_ad_reward, 0) AS cnt_ad_reward,\n",
    "    IFNULL(beh.cnt_challenge_a_friend, 0) AS cnt_challenge_a_friend,\n",
    "    IFNULL(beh.cnt_completed_5_levels, 0) AS cnt_completed_5_levels,\n",
    "    IFNULL(beh.cnt_use_extra_steps, 0) AS cnt_use_extra_steps,\n",
    "    ret.user_first_engagement,\n",
    "    ret.churned\n",
    "  FROM\n",
    "    {dataset_name}.user_returninginfo ret\n",
    "  LEFT OUTER JOIN\n",
    "    {dataset_name}.user_demographics dem\n",
    "  ON \n",
    "    ret.user_pseudo_id = dem.user_pseudo_id\n",
    "  LEFT OUTER JOIN \n",
    "    {dataset_name}.user_aggregate_behavior beh\n",
    "  ON\n",
    "    ret.user_pseudo_id = beh.user_pseudo_id\n",
    "  WHERE ret.bounced = 0\n",
    "'''\n",
    "\n",
    "# Create the view\n",
    "view = client.create_table(view, exists_ok=True)\n",
    "print(f\"Created {view.table_type}: {str(view.reference)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OIwxF2VkpnS5"
   },
   "source": [
    "## Step 5: Train & Test Split\n",
    "Split the dataset into a train & test dataset using views and a stable algorithm. We will use [[farm-fingerprint]](https://cloud.google.com/bigquery/docs/reference/standard-sql/hash_functions#farm_fingerprint) to split the data. The output of this function for a particular input will never change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zM6nK8OPvlDt",
    "outputId": "59ea38f4-831d-4edd-9008-c6412b5c15d9"
   },
   "outputs": [],
   "source": [
    "# Create view train_dataset\n",
    "view_id  = \"{}.{}.cc_train_dataset\".format(project_id, dataset_name)\n",
    "view     = bigquery.Table(view_id)\n",
    "\n",
    "view.view_query = f'''\n",
    "  \n",
    "  SELECT * FROM {dataset_name}.cc_full_dataset  \n",
    "  WHERE ABS(MOD(FARM_FINGERPRINT(user_pseudo_id), 10)) < 8\n",
    "  \n",
    "'''\n",
    "\n",
    "# Create the view\n",
    "view = client.create_table(view, exists_ok=True)\n",
    "print(f\"Created {view.table_type}: {str(view.reference)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FEOBLLOcwD9r",
    "outputId": "5332d24b-3685-4f69-8650-b38813c1c298"
   },
   "outputs": [],
   "source": [
    "# Create view test dataset\n",
    "view_id  = \"{}.{}.cc_eval_dataset\".format(project_id, dataset_name)\n",
    "view     = bigquery.Table(view_id)\n",
    "\n",
    "view.view_query = f'''\n",
    "  \n",
    "  SELECT * FROM {dataset_name}.cc_full_dataset   \n",
    "  WHERE ABS(MOD(FARM_FINGERPRINT(user_pseudo_id), 10)) = 8\n",
    "  \n",
    "'''\n",
    "\n",
    "# Create the view\n",
    "view = client.create_table(view, exists_ok=True)\n",
    "print(f\"Created {view.table_type}: {str(view.reference)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_ekx264ukc87"
   },
   "source": [
    "## **Finally**\n",
    "This is the dataset we have prepared now:\n",
    "To remember: This is how the final training dataset looks like\n",
    "\n",
    "| User Demographic Data | User Behavioral Data | Label \n",
    "| --- | --- | --- |\n",
    "| country | cnt_user_engagement | churned\n",
    "| operating_system | cnt_level_start_quickplay |  |\n",
    "| language | cnt_level_end_quickplay |  |\n",
    "|  |  cnt_level_complete_quickplay |  |\n",
    "|  |  cnt_level_reset_quickplay |  |\n",
    "|  |  cnt_post_score |  |\n",
    "|  |  cnt_spend_virtual_currency |  |\n",
    "|  |  cnt_ad_reward |  |\n",
    "|  |  cnt_challenge_a_friend |  |\n",
    "|  |  cnt_completed_5_levels |  |\n",
    "|  |  cnt_use_extra_steps |  |\n",
    "|  |  user_first_engagement |  |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4_vywvaOdhxY"
   },
   "source": [
    "### Cleanup\n",
    "Call this an the entire dataset will be cleaned up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "CfO3W6CLdeLy"
   },
   "outputs": [],
   "source": [
    "# Uncomment this to delete the dataset\n",
    "#!bq rm -f --location=$location --dataset $project_id:$dataset_name"
   ]
  }
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