Feature Engineering.ipynb

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        "**Note**: This notebook has been adapted from the one [here](https://github.com/Featuretools/predict-customer-churn/blob/master/churn/3.%20Feature%20Engineering.ipynb) to remove any dependencies on AWS and S3 so as to just focus on the featuretools functionality without requireming you to make an AWS account and set up the `aws` cli. You should check out the full repo for some other cool notebooks and examples using Spark and Dask."
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        "<a href=\"https://colab.research.google.com/github/springboard-curriculum/featuretools/blob/master/Feature%20Engineering.ipynb\" target=\"_blank\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# uncomment and run below to install required libraries if need to\n",
        "# !pip install featuretools"
      ]
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      "source": [
        "# Introduction: Automated Feature Engineering with Featuretools\n",
        "\n",
        "__Problem:__ we have a set of cutoff times and labels - in a label times table - and we need to build relevant features for each label using only data from before the cutoff time. Traditionally, we would do this by hand, a painstaking and error prone process that makes developing useable machine learning solutions extremely difficult.\n",
        "\n",
        "__Solution__: Use automated feature engineering as implemented in Featuretools to build hundreds or thousands of relevant features from a relational dataset with a reusable framework that also automatically filters the data based on the cutoff times. This approachs overcomes the limitations of manual feature engineering, letting us buidl better predictive models in a fraction of the time.\n",
        "\n",
        "The general process of feature engineering is shown below:\n",
        "\n",
        "![](https://github.com/springboard-curriculum/featuretools/blob/master/images/feature_engineering_process.png?raw=1)\n",
        "\n",
        "Currently, the only option for automated feature engineering using multiple related tables is [Featuretools](https://github.com/Featuretools/featuretools), an open-source Python library.\n",
        "\n",
        "![](https://github.com/springboard-curriculum/featuretools/blob/master/images/featuretools-logo.png?raw=1)\n",
        "\n",
        "In this notebook, we'll work with Featuretools to develop an automated feature engineering workflow for the customer churn dataset. The end outcome is a function that takes in a dataset and label times for customers and builds a feature matrix that can be used to train a machine learning model. Because we already partitioned the data into independent subsets (in `Partitioning Data`) we'll be able to apply this function to all of the partitions in parallel using Spark with PySpark.\n",
        "\n",
        "## Featuretools Resources\n",
        "\n",
        "We won't spend too much time on the basics of Featuretools here, so refer to the following sources for more information:\n",
        "\n",
        "* [Featuretools Documentation](https://docs.featuretools.com/)\n",
        "* [Featuretools GitHub](https://github.com/Featuretools/featuretools)\n",
        "* [Introductory tutorial on Featuretools](https://towardsdatascience.com/automated-feature-engineering-in-python-99baf11cc219)\n",
        "* [Why Automated Feature Engineering Will Change Machine Learning](https://towardsdatascience.com/why-automated-feature-engineering-will-change-the-way-you-do-machine-learning-5c15bf188b96)\n",
        "\n",
        "The basics are relatively easy to pick up, and if you're new, you can probably follow along with all the code here! Learning Featuretools requires only a few minutes and it can be applied to any relational dataset.\n",
        "\n",
        "\n",
        "With that in mind, let's get started."
      ]
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        "# Data science helpers\n",
        "from pathlib import Path\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import featuretools as ft\n",
        "\n",
        "# Useful for showing multiple outputs\n",
        "from IPython.core.interactiveshell import InteractiveShell\n",
        "InteractiveShell.ast_node_interactivity = \"all\"\n",
        "\n",
        "# make some folders we will need if they do not exist\n",
        "Path(\"./data/churn\").mkdir(parents=True, exist_ok=True)"
      ]
    },
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        "outputId": "eab99b36-04d3-4f0d-8449-16bcfdaf87b5"
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          "text": [
            "<ipython-input-3-24c4445007ac>:2: FutureWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
            "  members = pd.read_csv('https://raw.githubusercontent.com/springboard-curriculum/featuretools/master/data/members.csv',\n",
            "<ipython-input-3-24c4445007ac>:7: FutureWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
            "  trans = pd.read_csv('https://raw.githubusercontent.com/springboard-curriculum/featuretools/master/data/transactions.csv',\n"
          ]
        }
      ],
      "source": [
        "# Read in all data\n",
        "members = pd.read_csv('https://raw.githubusercontent.com/springboard-curriculum/featuretools/master/data/members.csv',\n",
        "                      parse_dates=['registration_init_time'],\n",
        "                      infer_datetime_format = True,\n",
        "                      dtype = {'gender': 'category'})\n",
        "\n",
        "trans = pd.read_csv('https://raw.githubusercontent.com/springboard-curriculum/featuretools/master/data/transactions.csv',\n",
        "                   parse_dates=['transaction_date', 'membership_expire_date'],\n",
        "                    infer_datetime_format = True)\n",
        "\n",
        "logs = pd.read_csv(f'https://raw.githubusercontent.com/springboard-curriculum/featuretools/master/data/logs.csv', parse_dates = ['date'])\n",
        "\n",
        "cutoff_times = pd.read_csv(f'https://raw.githubusercontent.com/springboard-curriculum/featuretools/master/data/MS-31_labels.csv', parse_dates = ['cutoff_time'])"
      ]
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      "source": [
        "The 3 data tables are represented by the following schema.\n",
        "\n",
        "![](https://github.com/springboard-curriculum/featuretools/blob/master/images/data_schema.png?raw=1)\n",
        "\n",
        "This schema is all the domain knowledge needed to perform automated feature engineering in Featuretools."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3jaeqHxGzulL"
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      "source": [
        "# Define Entities and EntitySet\n",
        "\n",
        "The first step in using Featuretools is to make an `EntitySet` and add all the `entitys` - tables - to it. An EntitySet is a data structure that holds the tables and the relationships between them. This makes it easier to keep track of all the data in a problem with multiple relational tables."
      ]
    },
    {
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      "execution_count": null,
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        "id": "xBmBMT7tzulL"
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      "source": [
        "import featuretools as ft\n",
        "import woodwork as ww\n",
        "\n",
        "# Make entityset\n",
        "es = ft.EntitySet(id = 'customers')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ulf4EJw4zulL"
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      "source": [
        "## Entities\n",
        "\n",
        "When creating entities from a dataframe, we need to make sure to include:\n",
        "\n",
        "* The `index` if there is one or a name for the created index. This is a unique identifier for each observation.\n",
        "* `make_index = True` if there is no index, we need to supply a name under `index` and set this to `True`.\n",
        "* A `time_index` if present. This is the time at which the information in the row becomes known. Featuretools will use the `time_index` and the `cutoff_time` to make valid features for each label.\n",
        "* `variable_types`. In some cases our data will have variables for which we should specify the type. An example would be a boolean that is represented as a float. This prevents Featuretools from making features such as the `min` or `max` of a True/False varaibles.\n",
        "\n",
        "For this problem these are the only arguments we'll need. There are additional arguments that can be used as shown in [the documentation](https://docs.featuretools.com/api_reference.html#entityset-entity-relationship-variable-types).\n",
        "\n",
        "### Members Table\n",
        "\n",
        "The `members` table holds basic information about each customer. The important point for this table is to specify that the `city` and `registered_via` columns are discrete, categorical variables and not numerical and that `registration_init_time` is the `time_index`. The `msno` is the unique index identifying each customer."
      ]
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              "      border-right-color: var(--fill-color);\n",
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              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-09b08cd5-fea2-4502-ac07-18f69c299aa0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                           msno  city  bd gender  \\\n",
              "0  8hW4+CV3D1oNM0CIsA39YljsF8M3m7g1LAX6AQd3C8I=     4  24   male   \n",
              "1  yhcODfebyTYezE6KAPklcV1us9zdOYJ+7eHS7f/xgoU=     8  37   male   \n",
              "2  sBlgSL0AIq49XsmBQ2KceKZNUyIxT1BwSkN/xYQLGMc=    15  21   male   \n",
              "3  Xy3Au8sZKlEeHBQ+C7ro8Ni3X/dxgrtmx0Tt+jqM1zY=     1   0    NaN   \n",
              "4  NiCu2GVWgT5QZbI85oYRBEDqHUZbzz2azS48jvM+khg=    12  21   male   \n",
              "\n",
              "   registered_via registration_init_time  \n",
              "0               3             2014-11-04  \n",
              "1               9             2007-02-11  \n",
              "2               3             2013-02-08  \n",
              "3               9             2015-02-01  \n",
              "4               3             2015-02-12  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "members.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ORAY7Nn9zulM",
        "outputId": "4536c2dd-b8c8-4e4f-e936-536d93075afa"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "members['msno'].is_unique"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P6sAk1zpzulM",
        "outputId": "33b8b462-5cd0-4213-e16e-d4cc705498dc"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/woodwork/type_sys/utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
            "  pd.to_datetime(\n",
            "/usr/local/lib/python3.10/dist-packages/woodwork/type_sys/utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
            "  pd.to_datetime(\n",
            "/usr/local/lib/python3.10/dist-packages/woodwork/type_sys/utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
            "  pd.to_datetime(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "Entityset: customers\n",
              "  DataFrames:\n",
              "    members [Rows: 6658, Columns: 6]\n",
              "  Relationships:\n",
              "    No relationships"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "\n",
        "# Add the dataframe to the EntitySet with proper logical types\n",
        "es = es.add_dataframe(dataframe_name='members',\n",
        "                      dataframe=members,\n",
        "                      index='msno',\n",
        "                      time_index='registration_init_time',\n",
        "                      logical_types={'city': ww.logical_types.Categorical,\n",
        "                                     'registered_via': ww.logical_types.Categorical})\n",
        "es"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Qh3SE-7zulM"
      },
      "source": [
        "### Transactions Table\n",
        "\n",
        "The transactions table contains payments made by the customers. Each row records one payment."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "HVK2jMkezulM",
        "outputId": "a8c8db8f-59c2-4318-f926-5aa11fc1017d"
      },
      "outputs": [
        {
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              "\n",
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3aa08d55-76b0-40c1-add8-30dd995694f8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                           msno  payment_method_id  \\\n",
              "0  5F7G3pHKf5ijGQpoKuko0G7Jm3Bde6ktfPKBZySWoDI=                 41   \n",
              "1  DQMPoCSc6EB39ytgnKCRsUIZnR6ZWSrHeDmX7nbxAKs=                 41   \n",
              "2  Lrais3nsgqYwpfpSoyK3fHuPutf6cloTI5T5dQfs4lA=                 38   \n",
              "3  ZPOjgxQw1/J7v5xgBJTCLXWuwq5Xmk33nO6AoUO1+mY=                 41   \n",
              "4  MvR23u4bIiWM+U+VE1Mvw3qqdj/0Ixs1sf7avavjhRs=                 38   \n",
              "\n",
              "   payment_plan_days  plan_list_price  actual_amount_paid  is_auto_renew  \\\n",
              "0                 30               99                  99              1   \n",
              "1                 30              149                 149              1   \n",
              "2                 30              149                 149              0   \n",
              "3                 30              149                 119              1   \n",
              "4                 30              149                 149              0   \n",
              "\n",
              "  transaction_date membership_expire_date  is_cancel  \n",
              "0       2017-02-10             2017-03-10          0  \n",
              "1       2016-02-01             2016-03-02          0  \n",
              "2       2016-02-23             2016-04-23          0  \n",
              "3       2015-09-06             2016-08-01          0  \n",
              "4       2016-10-28             2016-11-27          0  "
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trans.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "oonap2Z0zulM",
        "outputId": "42bff63c-08c6-4982-9626-5096fc5ce75e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.style.use('fivethirtyeight')\n",
        "plt.rcParams['figure.figsize'] = (10, 6)\n",
        "\n",
        "trans.loc[trans['actual_amount_paid'] < 250, 'actual_amount_paid'].dropna().plot.hist(bins = 30);\n",
        "plt.title('Distribution of Actual Amount Paid');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SQNW1ceczulM"
      },
      "source": [
        "#### Domain Knowledge Features\n",
        "\n",
        "Before creating the entity from this dataframe, we can create a few new variables based on domain knowledge. Just because we are automatically going to make hundreds of features doesn't mean we can't use our own expertise. Featuretools will build on top of our knowledge by stacking more primitives on top of any variables that we define."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "YoNSotO3zulM",
        "outputId": "c6978a8f-512e-4919-a88f-b6a81f15aaba"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Difference between listing price and price paid\n",
        "trans['price_difference'] = trans['plan_list_price'] - trans['actual_amount_paid']\n",
        "\n",
        "# Planned price per day\n",
        "trans['planned_daily_price'] = trans['plan_list_price'] / trans['payment_plan_days']\n",
        "\n",
        "# Actual price per day\n",
        "trans['daily_price'] = trans['actual_amount_paid'] / trans['payment_plan_days']\n",
        "\n",
        "trans.loc[trans['price_difference'] > 0, 'price_difference'].plot.hist(bins = 30,\n",
        "                                                                       figsize = (8, 6));\n",
        "plt.title('Dfiference between List Price and Price Paid');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6TagvutXzulM"
      },
      "source": [
        "There is no `index` in this dataframe so we have to specify to make an index and pass in a name. There is a `time_index`, the time of the transaction, which will be critical when filtering data based on cutoff times to make features. Again, we also need to specify several variable types.\n",
        "\n",
        "There is one slight anomaly with the transactions where some membership expire dates are after the transactions date, so we will filter those out."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dm2qIrAzzulM"
      },
      "outputs": [],
      "source": [
        "# Filter anomalies\n",
        "trans = trans[trans['membership_expire_date'] > trans['transaction_date']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-J5FRrQezulM",
        "outputId": "73d97c53-fdc0-4be7-d646-5b9a313b9d18"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/woodwork/type_sys/utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
            "  pd.to_datetime(\n",
            "/usr/local/lib/python3.10/dist-packages/woodwork/type_sys/utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
            "  pd.to_datetime(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Entityset: customers\n",
            "  DataFrames:\n",
            "    members [Rows: 6658, Columns: 6]\n",
            "    transactions [Rows: 22329, Columns: 13]\n",
            "  Relationships:\n",
            "    No relationships\n"
          ]
        }
      ],
      "source": [
        "# Add the transactions dataframe to the EntitySet\n",
        "es = es.add_dataframe(dataframe_name='transactions',\n",
        "                      dataframe=trans,\n",
        "                      index='transactions_index',\n",
        "                      make_index=True,\n",
        "                      time_index='transaction_date',\n",
        "                      logical_types={'payment_method_id': ww.logical_types.Categorical,\n",
        "                                     'is_auto_renew': ww.logical_types.Boolean,\n",
        "                                     'is_cancel': ww.logical_types.Boolean})\n",
        "print(es)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aHhvNif-zulM"
      },
      "source": [
        "### Logs\n",
        "\n",
        "The `logs` contain user listening behavior. As before we'll make a few domain knowledge columns before adding to the `EntitySet`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1PdyfDLJzulM",
        "outputId": "3319363a-e400-4718-db45-a3c9e26110e8"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "logs"
            },
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              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5ae023cf-ecc6-4b63-8710-af3539b11e87')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5ae023cf-ecc6-4b63-8710-af3539b11e87 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5ae023cf-ecc6-4b63-8710-af3539b11e87');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-dc57ef29-127d-4b9c-85ff-a14baeb76f80\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dc57ef29-127d-4b9c-85ff-a14baeb76f80')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-dc57ef29-127d-4b9c-85ff-a14baeb76f80 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                           msno       date  num_25  num_50  \\\n",
              "0  6+/V1NwBbqjBOCvRSDueeJZ58F4DY7h7fG6fSZtHaAE= 2017-03-04      29      28   \n",
              "1  E2aBGFTKR6jzp+1knh7JOOF39gLuu+CoZMWaAL/DA0M= 2017-03-27       1       0   \n",
              "2  g7exJzakJlHXwzUydnShY5w24WXSwJyS6QqgoFeyr7g= 2017-03-15       0       0   \n",
              "3  X+i9OmM3P42cETt5gPkOnz8vXGViQL5/M/NMiMQ+Olc= 2017-03-13       3       1   \n",
              "4  tbl8blAVl6j4A8zW1Gnyg78Hc0LAQzzcYesmzgJ7ofs= 2017-03-27       6       5   \n",
              "\n",
              "   num_75  num_985  num_100  num_unq  total_secs  \n",
              "0      18       11      111       79   34727.142  \n",
              "1       2        0      184      173   33408.719  \n",
              "2       0        0       21       21    4951.000  \n",
              "3       0        0       33       27    8755.599  \n",
              "4       0        0        2        6    1035.853  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "logs.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605
        },
        "id": "0P-NdeUUzulM",
        "outputId": "238da076-aa23-477e-f825-d463f672b281"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Make a few features by hand\n",
        "logs['total'] = logs[['num_25', 'num_50', 'num_75', 'num_985', 'num_100']].sum(axis = 1)\n",
        "logs['percent_100'] = logs['num_100'] / logs['total']\n",
        "logs['percent_unique'] = logs['num_unq'] / logs['total']\n",
        "logs['seconds_per_song'] = logs['total_secs'] / logs['total']\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "sns.kdeplot(logs['total']);\n",
        "plt.title('Distribution of Total Number of Songs');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605
        },
        "id": "bGxEWI_2zulM",
        "outputId": "580adad9-e562-4fbc-d6b1-19ddf8059eba"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.kdeplot(logs['percent_100']);\n",
        "plt.title('Distribution of Percentage of Songs Listened to Completion');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HICiHrAQzulN"
      },
      "source": [
        "There is `time_index` in the logs although no `index` present."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "scokPFRJzulN",
        "outputId": "9cf5e8f4-3987-4b0c-8b27-77fda6f4ebb9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/woodwork/type_sys/utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
            "  pd.to_datetime(\n",
            "/usr/local/lib/python3.10/dist-packages/woodwork/type_sys/utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
            "  pd.to_datetime(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Entityset: customers\n",
            "  DataFrames:\n",
            "    members [Rows: 6658, Columns: 6]\n",
            "    transactions [Rows: 22329, Columns: 13]\n",
            "    logs [Rows: 424252, Columns: 14]\n",
            "  Relationships:\n",
            "    No relationships\n"
          ]
        }
      ],
      "source": [
        "es = es.add_dataframe(dataframe_name='logs',\n",
        "                      dataframe=logs,\n",
        "                      index='logs_index',\n",
        "                      make_index=True,\n",
        "                      time_index='date')\n",
        "print(es)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I08kATHzzulN"
      },
      "source": [
        "Making features by hand may seem counterintuitive if we are using automated feature engineering, but the benefits of doing this before using Featuretools is that these features can be stacked on top of to build deep features. Automated feature engineering will therefore take our existing hand-built features and extract more value from them by combining them with other features.\n",
        "\n",
        "Another method to improve the power of deep feature synthesis is through interesting values, which specify conditional statements used to build features."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0CC5eLIXzulN"
      },
      "source": [
        "### Interesting Values\n",
        "\n",
        "In order to create conditional features, we can set interesting values for existing columns in the data. The following code will be used to build features conditional on the value of `is_cancel` and `is_auto_renew` in the transactions data. The primitives used for the conditional features are specified as `where_primitives` in the call to Deep Feature Synthesis. For example, if we used a `mean` primitive along with the following interesting values, we will get a mean of transactions where the transaction was cancelled, as well as the mean of transactions where the transaction was not cancelled."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mITyHUz0zulN"
      },
      "outputs": [],
      "source": [
        "es['transactions']['is_cancel'].interesting_values = [0, 1]\n",
        "es['transactions']['is_auto_renew'].interesting_values = [0, 1]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VmkZNULHzulN"
      },
      "source": [
        "# Relationships\n",
        "\n",
        "Table relationships should be familiar to anyone who has worked with relational databases and the idea is the same in Featuretools. We use relationships to specify how examples in one table relate to examples in other tables. The entityset structure for this problem is fairly simple as there are only three entities with two relationships.  `members` is the parent of `logs` and `transactions`. In both relationships, the parent and child variable is `msno`, the customer id.\n",
        "\n",
        "The two relationships are: one linking `members` to `transactions` and one linking `members` to `logs`. The order for relationships in featuretools is parent variable, child variable."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jmtR4bY_2iWY",
        "outputId": "31b959fc-c33b-46fd-c7f3-738af3d0fdfb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Entityset: customers\n",
            "  DataFrames:\n",
            "    members [Rows: 6658, Columns: 6]\n",
            "    transactions [Rows: 22329, Columns: 13]\n",
            "    logs [Rows: 424252, Columns: 14]\n",
            "  Relationships:\n",
            "    No relationships\n"
          ]
        }
      ],
      "source": [
        "print(es)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LnW5-tuP3h5g",
        "outputId": "80597125-e439-4e2a-8c61-e33cb3710dd0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Entityset: customers\n",
            "  DataFrames:\n",
            "    members [Rows: 6658, Columns: 6]\n",
            "    transactions [Rows: 22329, Columns: 13]\n",
            "    logs [Rows: 424252, Columns: 14]\n",
            "  Relationships:\n",
            "    transactions.msno -> members.msno\n",
            "    logs.msno -> members.msno\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/featuretools/entityset/entityset.py:393: UserWarning: Logical type Categorical for child column msno does not match parent column msno logical type Unknown. Changing child logical type to match parent.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "# Define the relationships explicitly with the EntitySet\n",
        "r_member_transactions = ft.Relationship(entityset=es,\n",
        "                                        parent_dataframe_name='members',\n",
        "                                        parent_column_name='msno',\n",
        "                                        child_dataframe_name='transactions',\n",
        "                                        child_column_name='msno')\n",
        "\n",
        "r_member_logs = ft.Relationship(entityset=es,\n",
        "                                parent_dataframe_name='members',\n",
        "                                parent_column_name='msno',\n",
        "                                child_dataframe_name='logs',\n",
        "                                child_column_name='msno')\n",
        "\n",
        "# Add the relationships to the EntitySet\n",
        "es = es.add_relationships([r_member_transactions, r_member_logs])\n",
        "\n",
        "print(es)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ECXdHC27zulN"
      },
      "source": [
        "### Cutoff Times\n",
        "\n",
        "`cutoff_times` are a critical piece of any time based machine learning problem. The label times dataframe has columns of member id, cutoff time, and label. __For each cutoff time, only data from before the cutoff time can be used to build features for that label.__ This is one of the greatest advantages of Featuretools compared to manual feature engineering: __Featuretools automatically filters our data based on the cutoff times to ensure that all the features are valid for machine learning.__ Normally, we would have to take extreme care to make sure all of our features are valid, but Featreutools is able to implement the filtering logic behind the scenes for us.\n",
        "\n",
        "All we have to do is make sure to pass in the correct label times for the prediction problem we want to solve."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "N1I1uVDRzulN",
        "outputId": "dd6e7453-87b0-471a-bcf2-d8715d491d19"
      },
      "outputs": [
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            "text/plain": [
              "                                           msno cutoff_time  label  \\\n",
              "0  ++bK0FRJecXCogqXdjtO2Kyb3jq7uLM7qThTk+nN8tE=  2015-08-01    0.0   \n",
              "1  ++bK0FRJecXCogqXdjtO2Kyb3jq7uLM7qThTk+nN8tE=  2015-09-01    0.0   \n",
              "2  ++bK0FRJecXCogqXdjtO2Kyb3jq7uLM7qThTk+nN8tE=  2015-10-01    0.0   \n",
              "3  ++bK0FRJecXCogqXdjtO2Kyb3jq7uLM7qThTk+nN8tE=  2015-11-01    0.0   \n",
              "4  ++bK0FRJecXCogqXdjtO2Kyb3jq7uLM7qThTk+nN8tE=  2015-12-01    0.0   \n",
              "\n",
              "   days_to_churn churn_date  \n",
              "0            NaN        NaN  \n",
              "1            NaN        NaN  \n",
              "2            NaN        NaN  \n",
              "3            NaN        NaN  \n",
              "4            NaN        NaN  "
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cutoff_times = cutoff_times.drop_duplicates(subset = ['msno', 'cutoff_time'])\n",
        "cutoff_times.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ik1t_i5bzulN"
      },
      "source": [
        "# Deep Feature Synthesis\n",
        "\n",
        "With the entities and relationships fully defined, we are ready to run [Deep Feature Synthesis (DFS)](https://www.featurelabs.com/blog/deep-feature-synthesis/). This process applies feature engineering building blocks called [feature primitives](https://docs.featuretools.com/automated_feature_engineering/primitives.html) to a dataset to build hundreds of features. Feature primitives are basic operations of two types - transforms and aggregations - that stack to build deep features (for more information see the previous linked resources). These includes many operations that we would traditionally carry out by hand, but automated feature engineering saves us from having to implement these features one at a time.\n",
        "\n",
        "The call to `ft.dfs` needs the entityset which holds all the tables and relationships between them, the `target_entity` to make features for, the specific primitives, the maximum stacking of primitives (`max_depth`), the `cutoff_times`, and a number of optional parameters.\n",
        "\n",
        "To start, we'll use the default aggregation and transformation primitives as well as two `where_primitives` and see how many features this generates. To only generate the definitions of the features, we pass in `features_only = True`.\n",
        "\n",
        "For full details on Deep Feature Synthesis, take a look at [the documentation](https://docs.featuretools.com/api_reference.html#deep-feature-synthesis)."
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bJPVvvhNzulN",
        "outputId": "498754c6-61da-4210-971b-f9daf0ff9856"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/featuretools/synthesis/dfs.py:321: UnusedPrimitiveWarning: Some specified primitives were not used during DFS:\n",
            "  where_primitives: ['mean', 'sum']\n",
            "This may be caused by a using a value of max_depth that is too small, not setting interesting values, or it may indicate no compatible columns for the primitive were found in the data. If the DFS call contained multiple instances of a primitive in the list above, none of them were used.\n",
            "  warnings.warn(warning_msg, UnusedPrimitiveWarning)\n"
          ]
        }
      ],
      "source": [
        "# Run Deep Feature Synthesis (DFS) with the updated argument names\n",
        "feature_defs = ft.dfs(entityset=es,\n",
        "                      target_dataframe_name='members',  # Updated keyword\n",
        "                      cutoff_time=cutoff_times,\n",
        "                      where_primitives=['sum', 'mean'],\n",
        "                      max_depth=2,\n",
        "                      features_only=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4an3-y8QzulN",
        "outputId": "a6527549-3f97-45dd-f7db-06ef5a47fc49"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "This will generate 140 features.\n"
          ]
        }
      ],
      "source": [
        "print(f'This will generate {len(feature_defs)} features.')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ia3kVDebzulN",
        "outputId": "f987ebce-ed5f-43a1-a4ba-836d0e00492c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<Feature: SKEW(transactions.daily_price)>,\n",
              " <Feature: MAX(transactions.daily_price)>,\n",
              " <Feature: MIN(logs.num_50)>,\n",
              " <Feature: MEAN(logs.num_unq)>,\n",
              " <Feature: MEAN(logs.num_100)>,\n",
              " <Feature: STD(transactions.payment_plan_days)>,\n",
              " <Feature: PERCENT_TRUE(transactions.is_cancel)>,\n",
              " <Feature: NUM_UNIQUE(logs.YEAR(date))>,\n",
              " <Feature: MIN(transactions.price_difference)>,\n",
              " <Feature: SUM(logs.percent_unique)>]"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import random; random.seed(42)\n",
        "\n",
        "random.sample(feature_defs, 10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b2NOkRIHzulN"
      },
      "source": [
        "We can see that Featuretools has built almost 200 features automatically for us using the table relationships and feature primitives. If built by hand, each of these features would require minutes of work, totaling many hours to build 188 features. Moreover, although the features are not necessarily intuitive, they are easy to explain in natural language because they are simple operations stacked on top of each other."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZfaCJV50zulQ"
      },
      "source": [
        "## Specify Primitives\n",
        "\n",
        "Now we'll do a call to `ft.dfs` specifying the primitives to use. Often, these will depend on the problem and can involve domain knowledge. The best way to choose primitives is by trying out a variety and seeing which perform the best. Like many operations in machine learning, choosing primitives is still largely an empirical, rather than theoretical, practice."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1s7lOd9bzulQ"
      },
      "source": [
        "### Aggregation Primitives"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "KsjvBacEzulQ",
        "outputId": "ffc417d7-c6a8-4e9d-db9d-da5a4f051f50"
      },
      "outputs": [
        {
          "data": {
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              "summary": "{\n  \"name\": \"agg_p\",\n  \"rows\": 65,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 65,\n        \"samples\": [\n          \"time_since_last_false\",\n          \"num_zero_crossings\",\n          \"time_since_last_min\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"aggregation\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 65,\n        \"samples\": [\n          \"Calculates the time since the last `False` value.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"valid_inputs\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          \"<ColumnSchema (Logical Type = Boolean)>, <ColumnSchema (Logical Type = BooleanNullable)>\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"return_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"<ColumnSchema (Semantic Tags = ['numeric'])>\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "agg_p"
            },
            "text/html": [
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              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>type</th>\n",
              "      <th>description</th>\n",
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              "      <th>return_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>time_since_last_min</td>\n",
              "      <td>aggregation</td>\n",
              "      <td>Calculates the time since the minimum value occurred.</td>\n",
              "      <td>&lt;ColumnSchema (Logical Type = Datetime) (Semantic Tags = ['time_index'])&gt;, &lt;ColumnSchema (Semant...</td>\n",
              "      <td>&lt;ColumnSchema (Logical Type = Double) (Semantic Tags = ['numeric'])&gt;</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>first</td>\n",
              "      <td>aggregation</td>\n",
              "      <td>Determines the first value in a list.</td>\n",
              "      <td>&lt;ColumnSchema&gt;</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>n_most_common</td>\n",
              "      <td>aggregation</td>\n",
              "      <td>Determines the `n` most common elements.</td>\n",
              "      <td>&lt;ColumnSchema (Semantic Tags = ['category'])&gt;</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>first_last_time_delta</td>\n",
              "      <td>aggregation</td>\n",
              "      <td>Determines the time between the first and last time value in seconds.</td>\n",
              "      <td>&lt;ColumnSchema (Logical Type = Datetime) (Semantic Tags = ['time_index'])&gt;</td>\n",
              "      <td>&lt;ColumnSchema (Logical Type = Double) (Semantic Tags = ['numeric'])&gt;</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>median</td>\n",
              "      <td>aggregation</td>\n",
              "      <td>Determines the middlemost number in a list of values.</td>\n",
              "      <td>&lt;ColumnSchema (Semantic Tags = ['numeric'])&gt;</td>\n",
              "      <td>&lt;ColumnSchema (Semantic Tags = ['numeric'])&gt;</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-de20332a-bdf7-40e2-ace7-5f557fccd2f0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-de20332a-bdf7-40e2-ace7-5f557fccd2f0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-47570057-cf35-45de-b9db-6d6e34cfcf2c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-47570057-cf35-45de-b9db-6d6e34cfcf2c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "    }\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-47570057-cf35-45de-b9db-6d6e34cfcf2c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                    name         type  \\\n",
              "0    time_since_last_min  aggregation   \n",
              "1                  first  aggregation   \n",
              "2          n_most_common  aggregation   \n",
              "3  first_last_time_delta  aggregation   \n",
              "4                 median  aggregation   \n",
              "\n",
              "                                                             description  \\\n",
              "0                  Calculates the time since the minimum value occurred.   \n",
              "1                                  Determines the first value in a list.   \n",
              "2                               Determines the `n` most common elements.   \n",
              "3  Determines the time between the first and last time value in seconds.   \n",
              "4                  Determines the middlemost number in a list of values.   \n",
              "\n",
              "                                                                                          valid_inputs  \\\n",
              "0  <ColumnSchema (Logical Type = Datetime) (Semantic Tags = ['time_index'])>, <ColumnSchema (Semant...   \n",
              "1                                                                                       <ColumnSchema>   \n",
              "2                                                        <ColumnSchema (Semantic Tags = ['category'])>   \n",
              "3                            <ColumnSchema (Logical Type = Datetime) (Semantic Tags = ['time_index'])>   \n",
              "4                                                         <ColumnSchema (Semantic Tags = ['numeric'])>   \n",
              "\n",
              "                                                            return_type  \n",
              "0  <ColumnSchema (Logical Type = Double) (Semantic Tags = ['numeric'])>  \n",
              "1                                                                  None  \n",
              "2                                                                  None  \n",
              "3  <ColumnSchema (Logical Type = Double) (Semantic Tags = ['numeric'])>  \n",
              "4                          <ColumnSchema (Semantic Tags = ['numeric'])>  "
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "all_p = ft.list_primitives()\n",
        "trans_p = all_p.loc[all_p['type'] == 'transform'].copy()\n",
        "agg_p = all_p.loc[all_p['type'] == 'aggregation'].copy()\n",
        "\n",
        "pd.options.display.max_colwidth = 100\n",
        "agg_p.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UtDtifSnzulQ"
      },
      "outputs": [],
      "source": [
        "# Specify aggregation primitives\n",
        "agg_primitives = ['sum', 'time_since_last', 'avg_time_between', 'all', 'mode', 'num_unique', 'min', 'last',\n",
        "                  'mean', 'percent_true', 'max', 'std', 'count']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2eZcH8TozulQ"
      },
      "source": [
        "## Transform Primitives"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "9NTUfEEXzulQ",
        "outputId": "4d541fa7-aee7-4b8d-c226-792c1407bd8c"
      },
      "outputs": [
        {
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            "text/plain": [
              "                   name       type  \\\n",
              "198        greater_than  transform   \n",
              "199                hour  transform   \n",
              "200  less_than_equal_to  transform   \n",
              "201      divide_numeric  transform   \n",
              "202       is_year_start  transform   \n",
              "\n",
              "                                                                  description  \\\n",
              "198           Determines if values in one list are greater than another list.   \n",
              "199                                  Determines the hour value of a datetime.   \n",
              "200  Determines if values in one list are less than or equal to another list.   \n",
              "201                              Performs element-wise division of two lists.   \n",
              "202                        Determines if a date falls on the start of a year.   \n",
              "\n",
              "                                                                                            valid_inputs  \\\n",
              "198  <ColumnSchema (Logical Type = Ordinal: None)>, <ColumnSchema (Logical Type = Datetime)>, <Column...   \n",
              "199                                                             <ColumnSchema (Logical Type = Datetime)>   \n",
              "200  <ColumnSchema (Logical Type = Ordinal: None)>, <ColumnSchema (Logical Type = Datetime)>, <Column...   \n",
              "201                                                         <ColumnSchema (Semantic Tags = ['numeric'])>   \n",
              "202                                                             <ColumnSchema (Logical Type = Datetime)>   \n",
              "\n",
              "                                                                                             return_type  \n",
              "198                                                      <ColumnSchema (Logical Type = BooleanNullable)>  \n",
              "199  <ColumnSchema (Logical Type = Ordinal: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16...  \n",
              "200                                                      <ColumnSchema (Logical Type = BooleanNullable)>  \n",
              "201                                                         <ColumnSchema (Semantic Tags = ['numeric'])>  \n",
              "202                                                      <ColumnSchema (Logical Type = BooleanNullable)>  "
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
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      "source": [
        "trans_p.tail()"
      ]
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    {
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      "execution_count": null,
      "metadata": {
        "id": "A9QNhgPbzulQ"
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      "source": [
        "# Specify transformation primitives\n",
        "trans_primitives = ['weekday', 'cum_sum', 'day', 'month', 'diff', 'time_since_previous']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yYm_8LuQzulQ"
      },
      "source": [
        "### Where Primitives\n",
        "\n",
        "These primitives are applied to the `interesting_values` to build conditional features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o6xZvupczulQ"
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      "source": [
        "# Specify where primitives\n",
        "where_primitives = ['sum', 'mean', 'percent_true', 'all', 'any']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kN4tvYq4zulQ"
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      "source": [
        "## Custom Primitives\n",
        "\n",
        "[Custom primitives](https://docs.featuretools.com/automated_feature_engineering/primitives.html#defining-custom-primitives) are one of the most powerful options in Featuretools. We use custom primitives to write our own functions based on domain knowledge and then pass them to `dfs` like any other primitives. Featuretools will then stack our custom primitives with the other primitives, again, in effect, amplifying our domain knowledge.\n",
        "\n",
        "For this problem, I wrote a custom primitive that calculates the sum of a value in the month prior to the cutoff time. This is actually a primitive I [wrote for another problem](https://github.com/Featuretools/Automated-Manual-Comparison/tree/master/Retail%20Spending) but I can apply it to this problem because primitives are data agnostic. That's one of the benefits of feature primitives: they can work for any problem and writing a custom primitive will pay off many times over."
      ]
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      "metadata": {
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      "source": [
        "import featuretools as ft\n",
        "from featuretools.primitives import AggregationPrimitive\n",
        "from woodwork.column_schema import ColumnSchema\n",
        "from woodwork.logical_types import Double, Datetime\n",
        "import pandas as pd\n",
        "\n",
        "# Define the custom aggregation primitive\n",
        "class TotalPreviousMonth(AggregationPrimitive):\n",
        "    \"\"\"Return total of `numeric` column in the month prior to `time`.\"\"\"\n",
        "    name = \"total_previous_month\"\n",
        "    input_types = [ColumnSchema(logical_type=Double), ColumnSchema(logical_type=Datetime)]\n",
        "    return_type = ColumnSchema(logical_type=Double)\n",
        "\n",
        "    def get_function(self):\n",
        "        def total_previous_month(numeric, datetime):\n",
        "            df = pd.DataFrame({'value': numeric, 'date': datetime})\n",
        "            df['date'] = pd.to_datetime(df['date'])\n",
        "\n",
        "            # Get previous month and year\n",
        "            current_time = df['date'].max()  # assuming the last date in datetime is the \"current\" time\n",
        "            previous_month = current_time.month - 1\n",
        "            year = current_time.year\n",
        "\n",
        "            # Handle January\n",
        "            if previous_month == 0:\n",
        "                previous_month = 12\n",
        "                year = current_time.year - 1\n",
        "\n",
        "            # Filter data and sum up total\n",
        "            df = df[(df['date'].dt.month == previous_month) & (df['date'].dt.year == year)]\n",
        "            total = df['value'].sum()\n",
        "\n",
        "            return total\n",
        "        return total_previous_month\n"
      ]
    },
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        "outputId": "bd0cacd0-3b1a-4d1f-ef11-4b8ad23e5996"
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19</td>\n",
              "      <td>2018-01-27 05:20:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>22</td>\n",
              "      <td>2018-02-02 18:40:00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-27eb5c49-56a6-430f-a93c-a49ec0dcef71')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-27eb5c49-56a6-430f-a93c-a49ec0dcef71 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "\n",
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              "\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-71d2ff57-5a48-4443-b7fd-dcd279213c98 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   value                date\n",
              "0     10 2018-01-01 00:00:00\n",
              "1     12 2018-01-07 13:20:00\n",
              "2     14 2018-01-14 02:40:00\n",
              "3     15 2018-01-20 16:00:00\n",
              "4     19 2018-01-27 05:20:00\n",
              "5     22 2018-02-02 18:40:00"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "47\n"
          ]
        }
      ],
      "source": [
        "numeric = [10, 12, 14, 15, 19, 22, 9, 8, 8, 11]\n",
        "dates = pd.date_range('2018-01-01', '2018-03-01', periods = len(numeric))\n",
        "pd.DataFrame({'value': numeric, 'date': dates}).head(6)\n",
        "\n",
        "# Instantiate the custom aggregation primitive\n",
        "primitive = TotalPreviousMonth()\n",
        "# Get the function from the custom primitive\n",
        "total_func = primitive.get_function()\n",
        "# Test it with sample data\n",
        "print(total_func(numeric, dates))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "ZWHt8-JQzulR",
        "outputId": "02cc7bae-0bc3-4be7-f429-30edf809ed94"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"print(total_func(numeric, dates))\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 5,\n        \"max\": 14,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          10,\n          12,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-01-01 00:00:00\",\n        \"max\": \"2018-03-01 00:00:00\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"2018-01-01 00:00:00\",\n          \"2018-01-12 19:12:00\",\n          \"2018-03-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
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              "<style scoped>\n",
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              "      <td>10</td>\n",
              "      <td>2018-01-01 00:00:00</td>\n",
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              "      <th>1</th>\n",
              "      <td>12</td>\n",
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              "      <th>2</th>\n",
              "      <td>14</td>\n",
              "      <td>2018-01-24 14:24:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>2018-02-05 09:36:00</td>\n",
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              "      <td>7</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8ff99211-e98f-4c24-81f1-13756762a509 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   value                date\n",
              "0     10 2018-01-01 00:00:00\n",
              "1     12 2018-01-12 19:12:00\n",
              "2     14 2018-01-24 14:24:00\n",
              "3      5 2018-02-05 09:36:00\n",
              "4      7 2018-02-17 04:48:00\n",
              "5      8 2018-03-01 00:00:00"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "12\n"
          ]
        }
      ],
      "source": [
        "numeric = [10, 12, 14, 5, 7, 8]\n",
        "dates = pd.date_range('2018-01-01', '2018-03-01', periods = len(numeric))\n",
        "pd.DataFrame({'value': numeric, 'date': dates}).head(6)\n",
        "\n",
        "# Instantiate the custom aggregation primitive\n",
        "primitive = TotalPreviousMonth()\n",
        "# Get the function from the custom primitive\n",
        "total_func = primitive.get_function()\n",
        "# Test it with sample data\n",
        "print(total_func(numeric, dates))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ZXmWLlKzulR"
      },
      "source": [
        "### Custom Primitive Implementation\n",
        "\n",
        "Making a custom primitive is simple: first we define a function (`total_previous_month`) and then we `make_agg_primitive` with `input_type[s]`, a `return_type`, and whether or not the primitive requires the `cutoff_time` through `uses_calc_time`.\n",
        "\n",
        "This primitive is an aggregation primitive because it takes in multiple numbers - transactions for the previous month - and returns a single number - the total of the transactions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "utIbOj1gzulR"
      },
      "outputs": [],
      "source": [
        "import featuretools as ft\n",
        "from featuretools.primitives import AggregationPrimitive\n",
        "from woodwork.column_schema import ColumnSchema\n",
        "from woodwork.logical_types import Double, Datetime\n",
        "import pandas as pd\n",
        "\n",
        "# Define the custom aggregation primitive\n",
        "class TotalPreviousMonth(AggregationPrimitive):\n",
        "    \"\"\"Return total of `numeric` column in the month prior to `time`.\"\"\"\n",
        "    name = \"total_previous_month\"\n",
        "    input_types = [ColumnSchema(logical_type=Double), ColumnSchema(logical_type=Datetime)]\n",
        "    return_type = ColumnSchema(logical_type=Double)\n",
        "    uses_calc_time = True\n",
        "\n",
        "    def get_function(self):\n",
        "        def total_previous_month(numeric, datetime):\n",
        "            df = pd.DataFrame({'value': numeric, 'date': datetime})\n",
        "            df['date'] = pd.to_datetime(df['date'])\n",
        "\n",
        "            # Get previous month and year\n",
        "            current_time = df['date'].max()  # assuming the last date in datetime is the \"current\" time\n",
        "            previous_month = current_time.month - 1\n",
        "            year = current_time.year\n",
        "\n",
        "            # Handle January\n",
        "            if previous_month == 0:\n",
        "                previous_month = 12\n",
        "                year = current_time.year - 1\n",
        "\n",
        "            # Filter data and sum up total\n",
        "            df = df[(df['date'].dt.month == previous_month) & (df['date'].dt.year == year)]\n",
        "            total = df['value'].sum()\n",
        "\n",
        "            return total\n",
        "        return total_previous_month\n",
        "\n",
        "# Example of instantiating and using the custom primitive\n",
        "total_previous = TotalPreviousMonth()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2-XdKQ7zulR"
      },
      "source": [
        "Now just have to pass this in as another aggregation primitive for Featuretools to use it in calculations.\n",
        "\n",
        "\n",
        "The second custom primitive finds the time since a previous true value. This is originally intended for the `is_cancel` variable in the `transactions` dataframe, but it can work for any Boolean variable. It simply finds the time between True examples."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VK_zycCBzulR"
      },
      "outputs": [],
      "source": [
        "def time_since_true(boolean, datetime):\n",
        "    \"\"\"Calculate time since previous true value\"\"\"\n",
        "\n",
        "    if np.any(np.array(list(boolean)) == 1):\n",
        "        # Create dataframe sorted from oldest to newest\n",
        "        df = pd.DataFrame({'value': boolean, 'date': datetime}).\\\n",
        "                sort_values('date', ascending = False).reset_index()\n",
        "\n",
        "        older_date = None\n",
        "\n",
        "        # Iterate through each date in reverse order\n",
        "        for date in df.loc[df['value'] == 1, 'date']:\n",
        "\n",
        "            # If there was no older true value\n",
        "            if older_date == None:\n",
        "                # Subset to times on or after true\n",
        "                times_after_idx = df.loc[df['date'] >= date].index\n",
        "\n",
        "            else:\n",
        "                # Subset to times on or after true but before previous true\n",
        "                times_after_idx = df.loc[(df['date'] >= date) & (df['date'] < older_date)].index\n",
        "            older_date = date\n",
        "            # Calculate time since previous true\n",
        "            df.loc[times_after_idx, 'time_since_previous'] = (df.loc[times_after_idx, 'date'] - date).dt.total_seconds()\n",
        "\n",
        "        return list(df['time_since_previous'])[::-1]\n",
        "\n",
        "    # Handle case with no true values\n",
        "    else:\n",
        "        return [np.nan for _ in range(len(boolean))]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CclSc-TizulR",
        "outputId": "2ab007a6-97c6-4552-ebda-5badc02fa42b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "booleans = []\n",
        "dates = []\n",
        "df = pd.DataFrame({'value': booleans, 'date': dates})\n",
        "time_since_true(df['value'], df['date'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q7qWZgoGzulR",
        "outputId": "650a78f5-a3a0-4da8-f69c-956c6d3e14bc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[0.0,\n",
              " 509760.0,\n",
              " 1019520.0,\n",
              " 1529280.0,\n",
              " 2039040.0,\n",
              " 0.0,\n",
              " 0.0,\n",
              " 0.0,\n",
              " 509760.0,\n",
              " 1019520.0,\n",
              " 1529280.0]"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "booleans = [1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0]\n",
        "dates = pd.date_range('2018-01-01', '2018-03-01', periods = len(booleans))\n",
        "df = pd.DataFrame({'value': booleans, 'date': dates})\n",
        "time_since_true(df['value'], df['date'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QpOdA6tozulR",
        "outputId": "62bae132-4e6f-4ef9-920b-63b2f4db9a1d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[0.0, 2548800.0, 5097600.0]"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "booleans = [1, 0, 0]\n",
        "dates = pd.date_range('2018-01-01', '2018-03-01', periods = len(booleans))\n",
        "time_since_true(booleans, dates)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CodFIbC8zulR",
        "outputId": "d3fd2af1-2a93-4424-df01-ba96b8c1104d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[nan, nan]"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "booleans = [0, 0]\n",
        "dates = pd.date_range('2018-01-01', '2018-03-01', periods = len(booleans))\n",
        "time_since_true(booleans, dates)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vGblR8n6zulR"
      },
      "source": [
        "This is a transformation primitive since it acts on multiple columns in the same table. The returned list is the same length as the original column."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J2sLma-5zulR"
      },
      "outputs": [],
      "source": [
        "import featuretools as ft\n",
        "from featuretools.primitives import TransformPrimitive\n",
        "from woodwork.column_schema import ColumnSchema\n",
        "from woodwork.logical_types import Boolean, Datetime, Double\n",
        "import pandas as pd\n",
        "\n",
        "# Define the custom transformation primitive\n",
        "class TimeSinceTrue(TransformPrimitive):\n",
        "    \"\"\"Returns the time difference since the last True in a boolean column.\"\"\"\n",
        "    name = \"time_since_true\"\n",
        "    input_types = [ColumnSchema(logical_type=Boolean), ColumnSchema(logical_type=Datetime)]\n",
        "    return_type = ColumnSchema(logical_type=Double)\n",
        "\n",
        "    def get_function(self):\n",
        "        def time_since_true(boolean, datetime):\n",
        "            df = pd.DataFrame({'bool': boolean, 'date': datetime})\n",
        "            df['date'] = pd.to_datetime(df['date'])\n",
        "\n",
        "            # Find the most recent 'True' event\n",
        "            last_true_date = df.loc[df['bool'] == True, 'date'].max()\n",
        "\n",
        "            # Calculate time since the last 'True'\n",
        "            time_since = (df['date'].max() - last_true_date).total_seconds() / (60 * 60 * 24)  # returns in days\n",
        "            return time_since\n",
        "        return time_since_true\n",
        "\n",
        "# Instantiate the custom transformation primitive\n",
        "time_since = TimeSinceTrue()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "15KuXbySzulR"
      },
      "source": [
        "Let's add the two custom primitives to the respective lists. In the final version of feature engineering, I did not use the `time_since` primitive. I ran into problems with the implementation but would encourage anyone to try and fix it or build their own custom primitive[s]."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "siPNn62KzulR"
      },
      "outputs": [],
      "source": [
        "agg_primitives.append(total_previous)\n",
        "# trans_primitives.append(time_since)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "td2YgKeHzulR"
      },
      "source": [
        "## Deep Feature Synthesis with Specified Primitives\n",
        "\n",
        "We'll again run Deep Feature Synthesis to make the feature definitions this time using the selected primitives and the custom primitives."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cvlc7UgHzulR",
        "outputId": "c24c97e6-a27b-4da9-abb9-34ad67e60c16"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/featuretools/synthesis/dfs.py:321: UnusedPrimitiveWarning: Some specified primitives were not used during DFS:\n",
            "  agg_primitives: ['total_previous_month']\n",
            "  where_primitives: ['all', 'any', 'mean', 'percent_true', 'sum']\n",
            "This may be caused by a using a value of max_depth that is too small, not setting interesting values, or it may indicate no compatible columns for the primitive were found in the data. If the DFS call contained multiple instances of a primitive in the list above, none of them were used.\n",
            "  warnings.warn(warning_msg, UnusedPrimitiveWarning)\n"
          ]
        }
      ],
      "source": [
        "feature_defs = ft.dfs(entityset=es,\n",
        "                      target_dataframe_name='members',  # Use 'target_dataframe_name' instead of 'target_entity'\n",
        "                      cutoff_time=cutoff_times,\n",
        "                      agg_primitives=agg_primitives,\n",
        "                      trans_primitives=trans_primitives,\n",
        "                      where_primitives=where_primitives,\n",
        "                      chunk_size=len(cutoff_times),\n",
        "                      cutoff_time_in_index=True,\n",
        "                      max_depth=2,\n",
        "                      features_only=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c_85JpQjzulR",
        "outputId": "dfe51e07-6b6a-44a0-f4bc-25cd6f4e58ee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "This will generate 607 features.\n"
          ]
        }
      ],
      "source": [
        "print(f'This will generate {len(feature_defs)} features.')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d6qafC3qzulR",
        "outputId": "a2b90569-4a7c-4799-a356-5bc84b7f5a5a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<Feature: MIN(transactions.payment_plan_days)>,\n",
              " <Feature: MIN(transactions.actual_amount_paid)>,\n",
              " <Feature: MIN(logs.percent_100)>,\n",
              " <Feature: SUM(transactions.DIFF(price_difference))>,\n",
              " <Feature: LAST(logs.DIFF(num_25))>,\n",
              " <Feature: DIFF(MAX(logs.percent_100))>,\n",
              " <Feature: MEAN(transactions.plan_list_price)>,\n",
              " <Feature: DIFF(STD(logs.total))>,\n",
              " <Feature: STD(transactions.CUM_SUM(planned_daily_price))>,\n",
              " <Feature: DIFF(MIN(transactions.payment_plan_days))>,\n",
              " <Feature: CUM_SUM(MIN(logs.num_100))>,\n",
              " <Feature: LAST(logs.CUM_SUM(num_100))>,\n",
              " <Feature: CUM_SUM(STD(logs.total_secs))>,\n",
              " <Feature: MONTH(LAST(transactions.transaction_date))>,\n",
              " <Feature: MEAN(logs.CUM_SUM(total_secs))>]"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "random.sample(feature_defs, 15)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "epOkltbXzulR"
      },
      "source": [
        "We can see that our custom primitive `TOTAL_PREVIOUS_MONTH` has been applied to create more features. The benefit of custom primitives are that they can be used to encode specific domain knowledge into the feature engineering process. Moreover, we don't get just the custom primitive itself, we also get features that are stacked on top of the primitive."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_5K4jI5fzulR"
      },
      "source": [
        "# Run Deep Feature Synthesis\n",
        "\n",
        "Once we're happy with the features that will be generated, we can run deep feature synthesis to make the actual features. We need to change `feature_only` to `False` and then we're good to go."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "1twsv65_zulR",
        "outputId": "32c3b243-abe5-4a61-e7d3-098f3145580f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Built 129 features\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/featuretools/synthesis/dfs.py:321: UnusedPrimitiveWarning: Some specified primitives were not used during DFS:\n",
            "  agg_primitives: ['total_previous_month']\n",
            "  where_primitives: ['all', 'any', 'mean', 'percent_true', 'sum']\n",
            "This may be caused by a using a value of max_depth that is too small, not setting interesting values, or it may indicate no compatible columns for the primitive were found in the data. If the DFS call contained multiple instances of a primitive in the list above, none of them were used.\n",
            "  warnings.warn(warning_msg, UnusedPrimitiveWarning)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Elapsed: 02:54 | Progress: 100%|██████████\n",
            "175 seconds elapsed.\n"
          ]
        }
      ],
      "source": [
        "from timeit import default_timer as timer\n",
        "import warnings\n",
        "\n",
        "# Suppress FutureWarnings\n",
        "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
        "\n",
        "cutoff_times.rename(columns={'cutoff_time': 'registration_init_time'}, inplace=True)\n",
        "\n",
        "start = timer()\n",
        "feature_matrix, feature_defs = ft.dfs(entityset=es,\n",
        "                                      target_dataframe_name='members',  # Updated argument name\n",
        "                                      cutoff_time=cutoff_times,\n",
        "                                      agg_primitives=agg_primitives,\n",
        "                                      trans_primitives=trans_primitives,\n",
        "                                      where_primitives=where_primitives,\n",
        "                                      max_depth=1,\n",
        "                                      features_only=False,\n",
        "                                      verbose=1,\n",
        "                                      chunk_size=100,\n",
        "                                      n_jobs=1,\n",
        "                                      cutoff_time_in_index=True)\n",
        "end = timer()\n",
        "print(f'{round(end - start)} seconds elapsed.')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NsJuHRb7zulR"
      },
      "source": [
        "The `chunk_size` is a parameter that may need to be adjusted to optimize the calculation. I suggest playing around with this parameter to find the optimal value. Generally I've found that a large value makes the calculation proceed quicker although it depends on the machine in use and the number of unique cutoff times."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "sTmqmHf3zulS",
        "outputId": "bd95b112-ab27-42f3-91d9-70f45b2524e3"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "feature_matrix"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-e80081d0-1131-4d30-b9b1-7abb9089da82\" class=\"colab-df-container\">\n",
              "    <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></th>\n",
              "      <th>city</th>\n",
              "      <th>bd</th>\n",
              "      <th>gender</th>\n",
              "      <th>registered_via</th>\n",
              "      <th>ALL(transactions.is_auto_renew)</th>\n",
              "      <th>ALL(transactions.is_cancel)</th>\n",
              "      <th>AVG_TIME_BETWEEN(transactions.transaction_date)</th>\n",
              "      <th>COUNT(transactions)</th>\n",
              "      <th>LAST(transactions.actual_amount_paid)</th>\n",
              "      <th>LAST(transactions.daily_price)</th>\n",
              "      <th>...</th>\n",
              "      <th>TIME_SINCE_LAST(logs.date)</th>\n",
              "      <th>CUM_SUM(bd)</th>\n",
              "      <th>DAY(registration_init_time)</th>\n",
              "      <th>DIFF(bd)</th>\n",
              "      <th>MONTH(registration_init_time)</th>\n",
              "      <th>TIME_SINCE_PREVIOUS(registration_init_time)</th>\n",
              "      <th>WEEKDAY(registration_init_time)</th>\n",
              "      <th>label</th>\n",
              "      <th>days_to_churn</th>\n",
              "      <th>churn_date</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>msno</th>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">++bK0FRJecXCogqXdjtO2Kyb3jq7uLM7qThTk+nN8tE=</th>\n",
              "      <th>2015-08-01</th>\n",
              "      <td>NaN</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-09-01</th>\n",
              "      <td>NaN</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-10-01</th>\n",
              "      <td>NaN</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-11-01</th>\n",
              "      <td>NaN</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-12-01</th>\n",
              "      <td>NaN</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 132 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e80081d0-1131-4d30-b9b1-7abb9089da82')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e80081d0-1131-4d30-b9b1-7abb9089da82 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e80081d0-1131-4d30-b9b1-7abb9089da82');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c938c0b7-b571-4ea5-a00f-a939cff1e95f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c938c0b7-b571-4ea5-a00f-a939cff1e95f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c938c0b7-b571-4ea5-a00f-a939cff1e95f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                                        city    bd gender  \\\n",
              "msno                                         time                           \n",
              "++bK0FRJecXCogqXdjtO2Kyb3jq7uLM7qThTk+nN8tE= 2015-08-01  NaN  <NA>    NaN   \n",
              "                                             2015-09-01  NaN  <NA>    NaN   \n",
              "                                             2015-10-01  NaN  <NA>    NaN   \n",
              "                                             2015-11-01  NaN  <NA>    NaN   \n",
              "                                             2015-12-01  NaN  <NA>    NaN   \n",
              "\n",
              "                                                        registered_via  \\\n",
              "msno                                         time                        \n",
              "++bK0FRJecXCogqXdjtO2Kyb3jq7uLM7qThTk+nN8tE= 2015-08-01            NaN   \n",
              "                                             2015-09-01            NaN   \n",
              "                                             2015-10-01            NaN   \n",
              "                                             2015-11-01            NaN   \n",
              "                                             2015-12-01            NaN   \n",
              "\n",
              "                                                         ALL(transactions.is_auto_renew)  \\\n",
              "msno                                         time                                          \n",
              "++bK0FRJecXCogqXdjtO2Kyb3jq7uLM7qThTk+nN8tE= 2015-08-01                             <NA>   \n",
              "                                             2015-09-01                             <NA>   \n",
              "                                             2015-10-01                             <NA>   \n",
              "                                             2015-11-01                             <NA>   \n",
              "                                             2015-12-01                             <NA>   \n",
              "\n",
              "                                                         ALL(transactions.is_cancel)  \\\n",
              "msno                                         time                                      \n",
              "++bK0FRJecXCogqXdjtO2Kyb3jq7uLM7qThTk+nN8tE= 2015-08-01                         <NA>   \n",
              "                                             2015-09-01                         <NA>   \n",
              "                                             2015-10-01                         <NA>   \n",
              "                                             2015-11-01                         <NA>   \n",
              "                                             2015-12-01                         <NA>   \n",
              "\n",
              "                                                         AVG_TIME_BETWEEN(transactions.transaction_date)  \\\n",
              "msno                                         time                                                          \n",
              "++bK0FRJecXCogqXdjtO2Kyb3jq7uLM7qThTk+nN8tE= 2015-08-01                                              NaN   \n",
              "                                             2015-09-01                                              NaN   \n",
              "                                             2015-10-01                                              NaN   \n",
              "                                             2015-11-01                                              NaN   \n",
              "                                             2015-12-01                                              NaN   \n",
              "\n",
              "                                                         COUNT(transactions)  \\\n",
              "msno                                         time                              \n",
              "++bK0FRJecXCogqXdjtO2Kyb3jq7uLM7qThTk+nN8tE= 2015-08-01                    0   \n",
              "                                             2015-09-01                    0   \n",
              "                                             2015-10-01                    0   \n",
              "                                             2015-11-01                    0   \n",
              "                                             2015-12-01                    0   \n",
              "\n",
              "                                                         LAST(transactions.actual_amount_paid)  \\\n",
              "msno                                         time                                                \n",
              "++bK0FRJecXCogqXdjtO2Kyb3jq7uLM7qThTk+nN8tE= 2015-08-01                                   <NA>   \n",
              "                                             2015-09-01                                   <NA>   \n",
              "                                             2015-10-01                                   <NA>   \n",
              "                                             2015-11-01                                   <NA>   \n",
              "                                             2015-12-01                                   <NA>   \n",
              "\n",
              "                                                         LAST(transactions.daily_price)  \\\n",
              "msno                                         time                                         \n",
              "++bK0FRJecXCogqXdjtO2Kyb3jq7uLM7qThTk+nN8tE= 2015-08-01                             NaN   \n",
              "                                             2015-09-01                             NaN   \n",
              "                                             2015-10-01                             NaN   \n",
              "                                             2015-11-01                             NaN   \n",
              "                                             2015-12-01                             NaN   \n",
              "\n",
              "                                                         ...  \\\n",
              "msno                                         time        ...   \n",
              "++bK0FRJecXCogqXdjtO2Kyb3jq7uLM7qThTk+nN8tE= 2015-08-01  ...   \n",
              "                                             2015-09-01  ...   \n",
              "                                             2015-10-01  ...   \n",
              "                                             2015-11-01  ...   \n",
              "                                             2015-12-01  ...   \n",
              "\n",
              "                                                         TIME_SINCE_LAST(logs.date)  \\\n",
              "msno                                         time                                     \n",
              "++bK0FRJecXCogqXdjtO2Kyb3jq7uLM7qThTk+nN8tE= 2015-08-01                         NaN   \n",
              "                                             2015-09-01                         NaN   \n",
              "                                             2015-10-01                         NaN   \n",
              "                                             2015-11-01                         NaN   \n",
              "                                             2015-12-01                         NaN   \n",
              "\n",
              "                                                         CUM_SUM(bd)  \\\n",
              "msno                                         time                      \n",
              "++bK0FRJecXCogqXdjtO2Kyb3jq7uLM7qThTk+nN8tE= 2015-08-01          NaN   \n",
              "                                             2015-09-01          NaN   \n",
              "                                             2015-10-01          NaN   \n",
              "                                             2015-11-01          NaN   \n",
              "                                             2015-12-01          NaN   \n",
              "\n",
              "                                                        DAY(registration_init_time)  \\\n",
              "msno                                         time                                     \n",
              "++bK0FRJecXCogqXdjtO2Kyb3jq7uLM7qThTk+nN8tE= 2015-08-01                         NaN   \n",
              "                                             2015-09-01                         NaN   \n",
              "                                             2015-10-01                         NaN   \n",
              "                                             2015-11-01                         NaN   \n",
              "                                             2015-12-01                         NaN   \n",
              "\n",
              "                                                         DIFF(bd)  \\\n",
              "msno                                         time                   \n",
              "++bK0FRJecXCogqXdjtO2Kyb3jq7uLM7qThTk+nN8tE= 2015-08-01       NaN   \n",
              "                                             2015-09-01       NaN   \n",
              "                                             2015-10-01       NaN   \n",
              "                                             2015-11-01       NaN   \n",
              "                                             2015-12-01       NaN   \n",
              "\n",
              "                                                         MONTH(registration_init_time)  \\\n",
              "msno                                         time                                        \n",
              "++bK0FRJecXCogqXdjtO2Kyb3jq7uLM7qThTk+nN8tE= 2015-08-01                            NaN   \n",
              "                                             2015-09-01                            NaN   \n",
              "                                             2015-10-01                            NaN   \n",
              "                                             2015-11-01                            NaN   \n",
              "                                             2015-12-01                            NaN   \n",
              "\n",
              "                                                         TIME_SINCE_PREVIOUS(registration_init_time)  \\\n",
              "msno                                         time                                                      \n",
              "++bK0FRJecXCogqXdjtO2Kyb3jq7uLM7qThTk+nN8tE= 2015-08-01                                          NaN   \n",
              "                                             2015-09-01                                          NaN   \n",
              "                                             2015-10-01                                          NaN   \n",
              "                                             2015-11-01                                          NaN   \n",
              "                                             2015-12-01                                          NaN   \n",
              "\n",
              "                                                         WEEKDAY(registration_init_time)  \\\n",
              "msno                                         time                                          \n",
              "++bK0FRJecXCogqXdjtO2Kyb3jq7uLM7qThTk+nN8tE= 2015-08-01                              NaN   \n",
              "                                             2015-09-01                              NaN   \n",
              "                                             2015-10-01                              NaN   \n",
              "                                             2015-11-01                              NaN   \n",
              "                                             2015-12-01                              NaN   \n",
              "\n",
              "                                                         label  days_to_churn  \\\n",
              "msno                                         time                               \n",
              "++bK0FRJecXCogqXdjtO2Kyb3jq7uLM7qThTk+nN8tE= 2015-08-01      0            NaN   \n",
              "                                             2015-09-01      0            NaN   \n",
              "                                             2015-10-01      0            NaN   \n",
              "                                             2015-11-01      0            NaN   \n",
              "                                             2015-12-01      0            NaN   \n",
              "\n",
              "                                                         churn_date  \n",
              "msno                                         time                    \n",
              "++bK0FRJecXCogqXdjtO2Kyb3jq7uLM7qThTk+nN8tE= 2015-08-01         NaT  \n",
              "                                             2015-09-01         NaT  \n",
              "                                             2015-10-01         NaT  \n",
              "                                             2015-11-01         NaT  \n",
              "                                             2015-12-01         NaT  \n",
              "\n",
              "[5 rows x 132 columns]"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "feature_matrix.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yauzXO1wzulS"
      },
      "source": [
        "We can save these feature definitions as a binary file which will allow us to make the same exact features for another entityset of the same format. This is useful when we have multiple partitions and we want to make the same features for each. Instead of remaking the feature definitions, we pass in the same feature definitions to a call to `calculate_feature_matrix`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "lid98OZdzulS"
      },
      "outputs": [],
      "source": [
        "ft.save_features(feature_defs, './data/churn/features.txt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "K7_7FeUpzulS",
        "outputId": "14018be4-b697-425e-a3de-52512a0ffd12"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "feature_matrix.loc[feature_matrix['SUM(logs.num_100)'] < 10000, 'SUM(logs.num_100)'].plot.hist();\n",
        "plt.title('Sum of Number of Songs Listened to 100 %');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "ICp7rYYwzulS",
        "outputId": "1fc367ea-0a79-4f8f-f8f0-e064d79d167a"
      },
      "outputs": [
        {
          "ename": "KeyError",
          "evalue": "'TOTAL_PREVIOUS_MONTH(logs.num_unq, date)'",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3805\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3806\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mindex.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mindex.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'TOTAL_PREVIOUS_MONTH(logs.num_unq, date)'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-47-dda085bbb4e9>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfeature_matrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'TOTAL_PREVIOUS_MONTH(logs.num_unq, date)'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Number of Unique Songs Listened to in Past Month'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4101\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4102\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4103\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4104\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3810\u001b[0m             ):\n\u001b[1;32m   3811\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3812\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3813\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3814\u001b[0m             \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'TOTAL_PREVIOUS_MONTH(logs.num_unq, date)'"
          ]
        }
      ],
      "source": [
        "feature_matrix['TOTAL_PREVIOUS_MONTH(logs.num_unq, date)'].plot.hist()\n",
        "plt.title('Number of Unique Songs Listened to in Past Month');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "AzREWo9SzulS"
      },
      "outputs": [],
      "source": [
        "feature_matrix['TOTAL_PREVIOUS_MONTH(logs.num_unq, date)'].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "fCSRXkfTzulS"
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      "outputs": [],
      "source": [
        "plt.figure(figsize = (6, 8))\n",
        "sns.boxplot(y = feature_matrix['TOTAL_PREVIOUS_MONTH(logs.num_unq, date)']);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
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        "id": "EPuu3iC6zulS"
      },
      "outputs": [],
      "source": [
        "feature_defs = ft.load_features('./data/churn/features.txt')\n",
        "print(f'There are {len(feature_defs)} features.')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XcHq59KKzulS"
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      "source": [
        "# Conclusions\n",
        "\n",
        "Automated feature engineering is a significant improvement over manual feature engineering in terms of both time and modeling performance. In this notebook, we implemented an automated feature engineering workflow with Featuretools for the customer churn problem. Given customer data and label times, we can now calculate a feature matrix with several hundred relevant features for predicting customer churn while ensuring that our features are made with valid data for each cutoff time.\n",
        "\n",
        "Along the way, we implemented a number of Featuretools concepts:\n",
        "\n",
        "1. An entityset and entities\n",
        "2. Relationships between entities\n",
        "3. Cutoff times\n",
        "4. Feature primitives\n",
        "5. Custom primitives\n",
        "6. Deep feature synthesis\n",
        "\n",
        "These concepts will serve us well in future machine learning projects that we can tackle with automated feature engineering.\n",
        "\n",
        "## Next Steps\n",
        "\n",
        "Although we often hear that \"data is the fuel of machine learning\", data is not exactly a fuel but more like crude oil. _Features_ are the refined product that we feed into a machine learning model to make accurate predictions. After performing prediction engineering and automated feature engineering, the next step is to use these features in a predictive model to estimate the _label_ using the _features_.\n",
        "\n",
        "Generating hundreds of features automatically is impressive, but if those features cannot allow a model to learn our prediction problem then they are not mcuch help! The next step is to use our features and labeled historical examples to train a machine learning model to make predictions of customer churn. We'll make sure to test our model using a hold-out testing set to estimate performance on new data. Then, after validating our model, we can use it on new examples by passing the data through the feature engineering process.\n",
        "\n",
        "\n",
        "If you want to see how to parallelize feature engineering in Spark, see the `Feature Engineering on Spark` notebook. Otherwise, the next notebook is `Modeling`, where we develop a machine learning model to predict churn using the historical labeled examples and the automatically engineered features."
      ]
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