From 058fe7dcb6de3c49c87b7f0a2bd317b3a9d30837 Mon Sep 17 00:00:00 2001 From: kamikaseda Date: Fri, 17 May 2024 14:15:33 +0900 Subject: [PATCH] Remove Multi Touch Attribution --- .../multi-touch-attribution/LICENSE | 201 ------------------ .../multi-touch-attribution/README.md | 124 ----------- .../multi-touch-attribution/config/model.json | 17 -- .../multi-touch-attribution/config/params.yml | 15 -- .../docs/images/dashboard.png | Bin 302906 -> 0 bytes .../docs/images/shapley_by_day.png | Bin 317711 -> 0 bytes .../multi-touch-attribution/docs/more.md | 115 ---------- .../multi-touch-attribution/mta_shapley.dig | 41 ---- .../py_scripts/data.py | 26 --- .../py_scripts/main.py | 46 ---- .../queries/shuffle.sql | 25 --- .../multi-touch-attribution/requirements.txt | 46 ---- .../multi-touch-attribution/td_mta/README.md | 79 ------- .../td_mta/__init__.py | 0 .../multi-touch-attribution/td_mta/config.py | 40 ---- .../td_mta/data/__init__.py | 3 - .../td_mta/dataset_from_tfrecords.py | 30 --- .../td_mta/example_extractor.py | 55 ----- .../multi-touch-attribution/td_mta/main.py | 48 ----- .../multi-touch-attribution/td_mta/model.py | 49 ----- .../td_mta/mta_train.py | 59 ----- .../td_mta/parse_and_save_tfrecords.py | 79 ------- .../multi-touch-attribution/td_mta/parser.py | 38 ---- .../multi-touch-attribution/td_mta/shapley.py | 140 ------------ .../td_mta/td_connector.py | 64 ------ .../td_mta/train_model_from_tfrecords.py | 85 -------- 26 files changed, 1425 deletions(-) delete mode 100644 machine-learning-box/multi-touch-attribution/LICENSE delete mode 100644 machine-learning-box/multi-touch-attribution/README.md delete mode 100644 machine-learning-box/multi-touch-attribution/config/model.json delete mode 100644 machine-learning-box/multi-touch-attribution/config/params.yml delete mode 100644 machine-learning-box/multi-touch-attribution/docs/images/dashboard.png delete mode 100644 machine-learning-box/multi-touch-attribution/docs/images/shapley_by_day.png delete mode 100644 machine-learning-box/multi-touch-attribution/docs/more.md delete mode 100644 machine-learning-box/multi-touch-attribution/mta_shapley.dig delete mode 100644 machine-learning-box/multi-touch-attribution/py_scripts/data.py delete mode 100644 machine-learning-box/multi-touch-attribution/py_scripts/main.py delete mode 100644 machine-learning-box/multi-touch-attribution/queries/shuffle.sql delete mode 100644 machine-learning-box/multi-touch-attribution/requirements.txt delete mode 100644 machine-learning-box/multi-touch-attribution/td_mta/README.md delete mode 100644 machine-learning-box/multi-touch-attribution/td_mta/__init__.py delete mode 100644 machine-learning-box/multi-touch-attribution/td_mta/config.py delete mode 100644 machine-learning-box/multi-touch-attribution/td_mta/data/__init__.py delete mode 100644 machine-learning-box/multi-touch-attribution/td_mta/dataset_from_tfrecords.py delete mode 100644 machine-learning-box/multi-touch-attribution/td_mta/example_extractor.py delete mode 100644 machine-learning-box/multi-touch-attribution/td_mta/main.py delete mode 100644 machine-learning-box/multi-touch-attribution/td_mta/model.py delete mode 100644 machine-learning-box/multi-touch-attribution/td_mta/mta_train.py delete mode 100644 machine-learning-box/multi-touch-attribution/td_mta/parse_and_save_tfrecords.py delete mode 100644 machine-learning-box/multi-touch-attribution/td_mta/parser.py delete mode 100644 machine-learning-box/multi-touch-attribution/td_mta/shapley.py delete mode 100644 machine-learning-box/multi-touch-attribution/td_mta/td_connector.py delete mode 100644 machine-learning-box/multi-touch-attribution/td_mta/train_model_from_tfrecords.py diff --git a/machine-learning-box/multi-touch-attribution/LICENSE b/machine-learning-box/multi-touch-attribution/LICENSE deleted file mode 100644 index 532931d6..00000000 --- a/machine-learning-box/multi-touch-attribution/LICENSE +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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Eventually, the insights enable you to effectively and efficiently optimize the marketing campaigns with optimal budget allocation. - -The implementation is based on a [state-of-the-art academic paper](https://arxiv.org/abs/1902.00215) employing a Deep Learning technique, and one of the key concepts used in the technique is called the [Shapley value](https://en.wikipedia.org/wiki/Shapley_value) calculation. The overall performance of this template has been proven on the Treasure Data platform with some of our real datasets. - -The template ultimately generates deeper insights about conversion histories and allows you to build the following dashboard, for example: - -![dashboard](./docs/images/dashboard.png) - -## Input - -Assume we have a following [`touchpoints`](https://gist.github.com/takuti/c890cdcbae7946f21a0afc3a4d88ec9f) table that collects user behaviors and conversion events with their sources (i.e., marketing channels): - -| `time` | `user_id` | `source` | `conversion` | -|:---:|:---:|:---:|:---:| -| 1596012307 | yl38g61s2x | sfmc | 0 | -| 1596012340 | d4dbvpwcyj | instagram | 0 | -| 1596012427 | egeaf1po46 | facebook | 0 | -| 1596012553 | gls9vyk2de | google | 1 | -| 1596012645 | ps6cc25f24 | instagram | 0 | -| ... | ... | ...| ... | - - -If you have a `pageviews` table collected by [td-js-sdk](https://github.com/treasure-data/td-js-sdk), preprocess the records in advance and parse `td_url` & `td_referrer` for extracting source channel of every single touchpoint. A query snippet below is an example of how to extract a variety of sources from the `pageviews` data: - -```sql -select - ${time_col}, - ${unique_id}, - ( - CASE - WHEN url_extract_parameter(td_url, 'utm_source') IS NULL THEN ( - CASE - WHEN regexp_like(td_referrer, 'google.co') THEN 'google' - WHEN regexp_like(td_referrer, 'instagram') THEN 'instagram' - WHEN regexp_like(td_referrer, 'facebook') THEN 'facebook' - WHEN regexp_like(td_referrer, 'youtube') THEN 'youtube' - WHEN regexp_like(td_referrer, 'twitter') THEN 'twitter' - WHEN regexp_like(td_referrer, 'linkedin') THEN 'linkedin' - -- ... (as many possible sources as you want) - ELSE 'direct & others' - END - ) - ELSE url_extract_parameter(td_url, 'utm_source') - END - ) as ${source_col} , - -- ... (assign 0 or 1 depending on your definition of "conversion") - as ${conversion_col} -from - pageviews -where - TD_INTERVAL(${time_col}, '-30d') -- last 30 days, for example -``` - -## Workflow - -```sh -# Push workflow to TD -$ td wf push mta - -# Set secrets from STDIN like: td.apikey=1/xxxxx, td.apiserver=https://api.treasuredata.com -$ td workflow secrets \ - --project mta \ - --set td.apikey \ - --set td.apiserver - -$ td wf start mta mta_shapley --session now -``` - -By default, the workflow automatically imports a dummy dataset to `mta_sample.touchpoints`. Edit [`config/params.yml`](./config/params.yml) if you use your own dataset. - -Meanwhile, [`config/model.json`](./config/model.json) enables you to further customize the model. See [`docs/more.md`](./docs/more.md) for more information about the advanced config parameters. - -## Output - -Eventually, three tables are derived as a result of successful workflow execution. - -### Table: `metrics` - -| `loss` | `rmse` | `val_loss` | `val_rmse` | -|:---|:---|:---|:---| -|0.49751710891723633|0.4046436846256256|0.6924436688423157|0.499650239944458| -|0.42745542526245117|0.37608763575553894|0.6913946270942688|0.4991241991519928| -|0.3927740454673767|0.3654404878616333|0.6890262365341187|0.4979383051395416| - -The table contains evaluation metrics obtained from the training and validation process. A single row corresponds to a single epoch, and `loss` / `rmse` and `val_loss` / `val_rmse` are respectively represent the values for training and validation. - -Check if `val_loss` or `val_rmse` stop decreasing and start getting bigger; next time, you might want to stop the model training at a specific epoch. You also want to make sure there is not a huge difference between training and validation metrics, because such a gap might point to model overfitting. - -### Table: `shapley` - -| `instagram` | `google` | `sfmc` | `facebook` | `direct` | `days_before_conversion` | -|:---|:---|:---|:---|:---|:---| -|-0.0010403504129499197|-0.004529354628175497|-0.0004913342418149114|-0.004142973572015762|-0.0002199627342633903|2| -|-0.0007449646363966167|-0.006828240118920803|-0.000607220979873091|-0.00694135669618845|-0.0003867909254040569|1| -|0.18350449204444885|0.34311071038246155|0.08801353722810745|0.33066612482070923|0.08063764125108719|0| - -This is the main table we've all been waiting for. The number of rows is equal to the number of lookback days that we chose in [`config/params.yml`](./config/params.yml). - -In a column called `days_before_conversion`, `0` indicates, for example, the effect of each channel on the conversion events when users are exposed to a channel in less than 24 hours before their conversion. Going up the day's index, you will see how Shapley values and attribution percentages change at farther touchpoints from the final conversion event. - -By visualizing the values as follows, this table gives insights into how different channels perform throughout the customer journey to conversion and which ones are more effective as a first-touch vs. last-touch on that journey. - -![shapley](./docs/images/shapley_by_day.png) - -### Table: `shapley_channel` - -| `instagram` | `google` | `sfmc` | `facebook` | `direct` | -|:---|:---|:---|:---|:---| -|0.18171918392181396|0.33175310492515564|0.08691497892141342|0.3195818066596985|0.0800308883190155| - -This table has the aggregate Shapley values summed across all days of the lookback window. These values can be considered as the total attribution percentage that each channel should be assigned towards conversions. - -## How this workflow works - -For further reading for algorithm and workflow details, refer [`docs/more.md`](./docs/more.md). - -## Want to learn more & try on your data? - -The model is fully customizable for your data depending on your own definition of conversion. Contact your Customer Success Representative if you are interested in building and testing the advanced MTA solution. \ No newline at end of file diff --git a/machine-learning-box/multi-touch-attribution/config/model.json b/machine-learning-box/multi-touch-attribution/config/model.json deleted file mode 100644 index 3ac2eb96..00000000 --- a/machine-learning-box/multi-touch-attribution/config/model.json +++ /dev/null @@ -1,17 +0,0 @@ -{ - "metrics_table": "metrics", - "shapley_table": "shapley", - "if_exists": "overwrite", - "time_column": "time", - "action_column": "source", - "conversion_column": "conversion", - "users_per_tfrecord": 10000, - "lookback_window_days": 3, - "dropout_rate": 0.5, - "model_width": 128, - "train_epochs": 3, - "train_batch_size": 100, - "validation_batch_size": 1000, - "validation_fraction": 0.1, - "shuffle_buffer_size": 10000 -} \ No newline at end of file diff --git a/machine-learning-box/multi-touch-attribution/config/params.yml b/machine-learning-box/multi-touch-attribution/config/params.yml deleted file mode 100644 index a3bcbd8c..00000000 --- a/machine-learning-box/multi-touch-attribution/config/params.yml +++ /dev/null @@ -1,15 +0,0 @@ -# Name of database where model will read and write data to. -in_db: mta_sample - -# Enter name of a touchpoints table. `queries/shuffle.sql` enriches the table -# with a rand attribute specified by `rnd_column` below. -in_table: touchpoints - -# This points to the column that contains unique user IDs, most commonly -# 'canonical_id' since this is typically the output of our ID unification -# algorithm. -unique_id: user_id - -# Name of rand digit attribute for random -# shuffling. -rnd_column: ab_rnd diff --git a/machine-learning-box/multi-touch-attribution/docs/images/dashboard.png b/machine-learning-box/multi-touch-attribution/docs/images/dashboard.png deleted file mode 100644 index 3fb5e5c223328a7f70584349e6943286f99f84c3..0000000000000000000000000000000000000000 GIT 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