From d82f87793f09e1067af67ab9bf048bcc8419bfde Mon Sep 17 00:00:00 2001 From: Stefan Krawczyk Date: Tue, 14 Feb 2023 10:53:08 -0800 Subject: [PATCH 1/7] Adds basic scenario 1 for feature engineering This example shows how you can use the same feature definitions in Hamilton in an offline setting and use them in an online setting. Assumptions: - the API request can provide the same raw data that training provides. - if you have aggregation features, you need to store the training result for them, and provide them to the online side. --- examples/feature_engineering/README.md | 30 ++++ .../feature_engineering/scenario_1/README.md | 46 +++++++ .../scenario_1/constants.py | 11 ++ .../feature_engineering/scenario_1/etl.py | 44 ++++++ .../scenario_1/fastapi_server.py | 111 +++++++++++++++ .../scenario_1/features.py | 129 ++++++++++++++++++ .../scenario_1/offline_loader.py | 65 +++++++++ 7 files changed, 436 insertions(+) create mode 100644 examples/feature_engineering/README.md create mode 100644 examples/feature_engineering/scenario_1/README.md create mode 100644 examples/feature_engineering/scenario_1/constants.py create mode 100644 examples/feature_engineering/scenario_1/etl.py create mode 100644 examples/feature_engineering/scenario_1/fastapi_server.py create mode 100644 examples/feature_engineering/scenario_1/features.py create mode 100644 examples/feature_engineering/scenario_1/offline_loader.py diff --git a/examples/feature_engineering/README.md b/examples/feature_engineering/README.md new file mode 100644 index 00000000..54daf5f8 --- /dev/null +++ b/examples/feature_engineering/README.md @@ -0,0 +1,30 @@ +# write features once, use anywhere +A not too uncommon task is that you need to do feature engineering in an offline (e.g. batch via airflow) +setting, as well as an online (e.g. synchronous request via FastAPI). What commonly +happens is that the code for features is not shared, and results in two implementations +that result in subtle bugs and hard to maintain code. + +With this example we show how you can use Hamilton to: + +1. write a feature once. +2. leverage the feature code anywhere that python runs. e.g. in batch and online. +3. show how to modularize components so that if you have values cached in a feature store, +you can inject those values into your feature computation needs. + +# Scenarios +We provide two examples for two common scenarios. The example code here tries to be illustrative about +how to think and frame using Hamilton to solve these two scenarios; it contains minimal features so as to not +overwhelm you. + +## Scenario 1: the simple case - ETL + Online API +Assume we can get the same raw inputs at prediction time, as it was provided in at training time. +However, we don't want to recompute `age_mean` and `age_std_dev` because recomputing them doesn't make sense. +Instead, we store the result of that at training time, and then use it at prediction time to get the right +features for the model. + +## Scenario 2: the more complex case - request doesn't have all the raw data - ETL + Online API +At prediction time we might only have some of the raw data required to compute a prediction. To get the rest +we need to make an API call, e.g. a feature store or a database, that will provide us with that information. + +# What next? +Jump into each directory and read the README, it'll explain how the example is set up and how things should work. diff --git a/examples/feature_engineering/scenario_1/README.md b/examples/feature_engineering/scenario_1/README.md new file mode 100644 index 00000000..45f9a9d7 --- /dev/null +++ b/examples/feature_engineering/scenario_1/README.md @@ -0,0 +1,46 @@ +# Scenario 1: the simple case - ETL + Online API +Assumptions: +1. you have an ETL process to create features that ingests raw data, and then creates a training set for you to train on. +2. you have an online API from where you want to serve the predictions, and you can provide the same raw data to it that, +you would have access to in your ETL process. + +TODO: picture + +## ETL Process +ETL stands for Extract, Transform, Load. This is the process of taking raw data, transforming it into features, +and then loading the data somewhere/doing something with it. E.g. you pull raw data, transform it into features, +and then create a training set with which to train a model. This is exactly what we have in this example and is +what etl.py orchestrates; it is however not complete (i.e. doesn't save, or fit a model) and is just illustrative +for this example. + +### File Descriptions +Here is a description of all the files and what they do. +Note: aggregation features, like `mean()` or `std_dev()` make sense only in an +offline setting where you have all the data. In an online setting, computing them +does not make sense. In `etl.py` there is a note that you need to store `age_mean` and +`age_std_dev` and then somehow get those values to plug into the code in `fastapi_server.py`. +If you're getting started, these could be hardcoded values, or stored to a file that +is loaded much like the model, or queried from a database, etc. Though you'll want +to ensure these values match whatever values the model was trained with. + +#### offline_loader.py +Contains logic to load raw data. Here it's a flat file, but it could be going +to a database, etc. + +#### features.py +The feature transform logic that takes raw data and transforms it into features. + +#### etl.py +This script that mimics what one might do to fit a model: extract data, transform into features, +and then load features somewhere or fit a model. It's pretty basic and is meant +to be illustrative. + +#### constants.py +Rather than hardcoding what features the model should have in two places, we define +it in a single place and import it where needed. + +#### fastapi_server.py +The FastAPI server that serves the predictions. It's pretty basic and is meant to +illustrate the steps of what's required to serve a prediction from a model, where +you want to use the same feature computation logic as in your ETL process. +Note: aggregation features diff --git a/examples/feature_engineering/scenario_1/constants.py b/examples/feature_engineering/scenario_1/constants.py new file mode 100644 index 00000000..920fbf1a --- /dev/null +++ b/examples/feature_engineering/scenario_1/constants.py @@ -0,0 +1,11 @@ +model_x_features = [ + "is_summer", + "has_pet", + "has_children", + "day_of_the_week_2", + "day_of_the_week_3", + "day_of_the_week_4", + "day_of_the_week_5", + "day_of_the_week_6", + "age_zero_mean_unit_variance", +] diff --git a/examples/feature_engineering/scenario_1/etl.py b/examples/feature_engineering/scenario_1/etl.py new file mode 100644 index 00000000..c84951bf --- /dev/null +++ b/examples/feature_engineering/scenario_1/etl.py @@ -0,0 +1,44 @@ +""" +This is part of an ETL that you'd likely have. +You pull data from a source, and transform it into features, and then save/fit a model with them. +""" +import constants +import features +import offline_loader +import pandas as pd + +from hamilton import driver + + +def create_features(source_location: str) -> pd.DataFrame: + model_features = constants.model_x_features + config = {} + dr = driver.Driver(config, offline_loader, features) + # Visualize the DAG if you need to: + # dr.display_all_functions('./offline_my_full_dag.dot', {"format": "png"}) + # dr.visualize_execution(model_features, + # './offline_execution.dot', + # {"format": "png"}, + # inputs={"location": "../data_quality/pandera/Absenteeism_at_work.csv"}) + df = dr.execute( + # add age_mean and age_std_dev to the features + model_features + ["age_mean", "age_std_dev"], + inputs={"location": source_location}, + ) + return df + + +if __name__ == "__main__": + # stick in command line args here + _source_location = "../../data_quality/pandera/Absenteeism_at_work.csv" + _features_df = create_features(_source_location) + # we need to store `age_mean` and `age_std_dev` somewhere for the online side. + # exercise for the reader: where would you store them for your context? + # ideas: with the model? in a database? in a file? in a feature store? + _age_mean = _features_df["age_mean"].values[0] + _age_std_dev = _features_df["age_std_dev"].values[0] + print(_features_df) + # Then do something with the features_df, e.g.: + # save_features(features_df[constants.model_x_features], "my_model_features.csv") + # train_model(features_df[constants.model_x_features]) + # etc. diff --git a/examples/feature_engineering/scenario_1/fastapi_server.py b/examples/feature_engineering/scenario_1/fastapi_server.py new file mode 100644 index 00000000..5593f9c0 --- /dev/null +++ b/examples/feature_engineering/scenario_1/fastapi_server.py @@ -0,0 +1,111 @@ +import constants +import fastapi +import features +import pandas as pd +import pydantic + +from hamilton import base +from hamilton.experimental import h_async + +app = fastapi.FastAPI() + +# know the model schema somehow. +model_input_features = constants.model_x_features + + +def load_invariant_feature_values() -> dict: + """This function would load the invariant feature values from a database or file. + :return: a dictionary of invariant feature values. + """ + return { + "age_mean": 33.0, + "age_std_dev": 12.0, + } + + +def fake_model(df: pd.DataFrame) -> pd.Series: + """Function to simulate a model""" + # do some transformation. + return df.sum() # this is nonsensical but provides a single number. + + +# you would load the model from disk or a registry +model = fake_model +# need to load the invariant features somehow. +invariant_feature_values = load_invariant_feature_values() + + +# can instantiate an async driver once for the life of the app: +dr = h_async.AsyncDriver({}, features, result_builder=base.SimplePythonDataFrameGraphAdapter()) + + +class PredictRequest(pydantic.BaseModel): + """Assumption here is that all this data is available for the API.""" + + id: int + reason_for_absence: int + month_of_absence: int + day_of_the_week: int + seasons: int + transportation_expense: int + distance_from_residence_to_work: int + service_time: int + age: int + work_load_average_per_day: float + hit_target: int + disciplinary_failure: int + education: int + son: int + social_drinker: int + social_smoker: int + pet: int + weight: int + height: int + body_mass_index: int + + +@app.post("/predict") +async def predict_model_version1(request: PredictRequest) -> dict: + """Illustrates how a prediction could be made that needs to compute some features first. + In this version we go to the feature store, and then pass in what we get from the feature + store as overrides to the model. + """ + # one liner to quickly create some series from the request. + input_series = pd.DataFrame([request.dict()]).to_dict(orient="series") + # create the features -- point here is we're reusing the same code as in the training! + # with the ability to provide static values for things like `age_mean` and `age_std_dev`. + features = await dr.execute( + model_input_features, inputs=input_series, overrides=invariant_feature_values + ) + prediction = model(features) + return {"prediction": prediction.values[0]} + + +if __name__ == "__main__": + # If you run this as a script, then the app will be started on localhost:8000 + import uvicorn + + uvicorn.run(app, host="0.0.0.0", port=8000) + + example_request_input = { + "id": 11, + "reason_for_absence": 26, + "month_of_absence": 7, + "day_of_the_week": 3, + "seasons": 2, + "transportation_expense": 1, + "distance_from_residence_to_work": 1, + "service_time": 289, + "age": 36, + "work_load_average_per_day": 13, + "hit_target": 33, + "disciplinary_failure": 239.554, + "education": 97, + "son": 0, + "social_drinker": 1, + "social_smoker": 2, + "pet": 1, + "weight": 90, + "height": 172, + "body_mass_index": 30, + } diff --git a/examples/feature_engineering/scenario_1/features.py b/examples/feature_engineering/scenario_1/features.py new file mode 100644 index 00000000..f6842731 --- /dev/null +++ b/examples/feature_engineering/scenario_1/features.py @@ -0,0 +1,129 @@ +""" +Here are some features that are based on the absenteeism data set. +They are just supposed to be illustrative of the kind of features one might have. +""" +import pandas as pd +import pandera as pa + +from hamilton.function_modifiers import check_output, extract_columns + + +@check_output(range=(20.0, 60.0), data_type=float) +def age_mean(age: pd.Series) -> float: + """Average of age""" + return age.mean() + + +age_zero_mean_schema = pa.SeriesSchema( + float, + checks=[ + pa.Check.in_range(-120.0, 120.0), + ], + nullable=False, +) + + +@check_output(schema=age_zero_mean_schema) +def age_zero_mean(age: pd.Series, age_mean: float) -> pd.Series: + """Zero mean of age""" + return age - age_mean + + +age_std_dev_schema = pa.SeriesSchema( + float, + checks=[ + pa.Check.in_range(0.0, 40.0), + ], +) + + +@check_output(range=(0.0, 40.0), data_type=float) +def age_std_dev(age: pd.Series) -> float: + """Standard deviation of age.""" + return age.std() + + +age_zero_mean_unit_variance_schema = pa.SeriesSchema( + float, + checks=[ + pa.Check.in_range(-4.0, 4.0), + ], + nullable=False, +) + + +@check_output(schema=age_zero_mean_unit_variance_schema) +def age_zero_mean_unit_variance(age_zero_mean: pd.Series, age_std_dev: float) -> pd.Series: + """Zero mean unit variance value of age""" + return age_zero_mean / age_std_dev + + +@extract_columns("seasons_1", "seasons_2", "seasons_3", "seasons_4", fill_with=0) +def seasons_encoded(seasons: pd.Series) -> pd.DataFrame: + """One hot encodes seasons into 4 dimensions: + 1 - first season + 2 - second season + 3 - third season + 4 - fourth season + """ + return pd.get_dummies(seasons, prefix="seasons") + + +@extract_columns( + "day_of_the_week_2", + "day_of_the_week_3", + "day_of_the_week_4", + "day_of_the_week_5", + "day_of_the_week_6", + fill_with=0, +) +def day_of_week_encoded(day_of_the_week: pd.Series) -> pd.DataFrame: + """One hot encodes day of week into five dimensions -- Saturday & Sunday weren't present. + 1 - Sunday, 2 - Monday, 3 - Tuesday, 4 - Wednesday, 5 - Thursday, 6 - Friday, 7 - Saturday. + """ + return pd.get_dummies(day_of_the_week, prefix="day_of_the_week") + + +has_children_schema = pa.SeriesSchema( + int, + checks=[ + pa.Check.isin([0, 1]), + ], + nullable=False, +) + + +@check_output(schema=has_children_schema) +def has_children(son: pd.Series) -> pd.Series: + """Single variable that says whether someone has any children or not.""" + return (son > 0).astype(int) + + +has_pet_schema = pa.SeriesSchema( + int, + checks=[ + pa.Check.isin([0, 1]), + ], + nullable=False, +) + + +@check_output(schema=has_pet_schema) +def has_pet(pet: pd.Series) -> pd.Series: + """Single variable that says whether someone has any pets or not.""" + return (pet > 0).astype(int) + + +is_summer_schema = pa.SeriesSchema( + int, + checks=[ + pa.Check.isin([0, 1]), + ], + nullable=False, +) + + +@check_output(schema=is_summer_schema) +def is_summer(month_of_absence: pd.Series) -> pd.Series: + """Is it summer in Brazil? i.e. months of December, January, February.""" + return month_of_absence.isin([1, 2, 12]).astype(int) diff --git a/examples/feature_engineering/scenario_1/offline_loader.py b/examples/feature_engineering/scenario_1/offline_loader.py new file mode 100644 index 00000000..3a2e26b6 --- /dev/null +++ b/examples/feature_engineering/scenario_1/offline_loader.py @@ -0,0 +1,65 @@ +""" +Module that contains logic to load data, and is the "base" that we can build off of +to build our featurization process. +""" + +from typing import List + +import pandas as pd + +from hamilton.function_modifiers import extract_columns + +# full set of available columns from the data source +data_columns = [ + "id", + "reason_for_absence", + "month_of_absence", + "day_of_the_week", + "seasons", + "transportation_expense", + "distance_from_residence_to_work", + "service_time", + "age", + "work_load_average_per_day", + "hit_target", + "disciplinary_failure", + "education", + "son", + "social_drinker", + "social_smoker", + "pet", + "weight", + "height", + "body_mass_index", + "absenteeism_time_in_hours", +] + + +def _sanitize_columns(df_columns: List[str]) -> List[str]: + """Renames columns to be valid hamilton names -- and lower cases them. + + :param df_columns: the current column names. + :return: sanitize column names that work with Hamilton + """ + return [c.strip().replace("/", "_per_").replace(" ", "_").lower() for c in df_columns] + + +@extract_columns(*data_columns) +def raw_data(location: str) -> pd.DataFrame: + """Extracts the raw data, renames the columns to be valid python variable names, and assigns an index. + :param location: the location to load from + :return: + """ + df = pd.read_csv(location, sep=";") + # rename columns to be valid hamilton names -- and lower case it + df.columns = _sanitize_columns(df.columns) + # create proper index -- ID-Month-Day; + index = ( + df["id"].astype(str) + + "-" + + df["month_of_absence"].astype(str) + + "-" + + df["day_of_the_week"].astype(str) + ) + df.index = index + return df From d47023e38a3ac84a891a0ebe0cc8f1028ef90b60 Mon Sep 17 00:00:00 2001 From: Stefan Krawczyk Date: Sun, 19 Feb 2023 14:33:46 -0800 Subject: [PATCH 2/7] Completes feature engineering scenario 1 example This example shows how one might use Hamilton to compute features in an offline and online fashion. The assumption here is that the request passed into the API has all the raw data required to compute features. This example also shows how one might "override" some values that are required for computing features, in this example they are `age_mean` and `age_std_dev`. This can be required when you computing aggregation features does not make sense at inference time. --- examples/feature_engineering/README.md | 10 +++-- .../feature_engineering/scenario_1/README.md | 35 ++++++++++++++---- .../feature_engineering/scenario_1/etl.py | 24 +++++++++--- .../scenario_1/fastapi_server.py | 23 +++++++++++- .../scenario_1/offline_execution.dot.png | Bin 0 -> 272404 bytes .../scenario_1/online_execution.dot.png | Bin 0 -> 248363 bytes .../scenario_1/requirements.txt | 5 +++ .../feature_engineering/scenario_2/README.md | 1 + 8 files changed, 80 insertions(+), 18 deletions(-) create mode 100644 examples/feature_engineering/scenario_1/offline_execution.dot.png create mode 100644 examples/feature_engineering/scenario_1/online_execution.dot.png create mode 100644 examples/feature_engineering/scenario_1/requirements.txt create mode 100644 examples/feature_engineering/scenario_2/README.md diff --git a/examples/feature_engineering/README.md b/examples/feature_engineering/README.md index 54daf5f8..571e1189 100644 --- a/examples/feature_engineering/README.md +++ b/examples/feature_engineering/README.md @@ -1,6 +1,6 @@ # write features once, use anywhere A not too uncommon task is that you need to do feature engineering in an offline (e.g. batch via airflow) -setting, as well as an online (e.g. synchronous request via FastAPI). What commonly +setting, as well as an online setting (e.g. synchronous request via FastAPI). What commonly happens is that the code for features is not shared, and results in two implementations that result in subtle bugs and hard to maintain code. @@ -18,13 +18,15 @@ overwhelm you. ## Scenario 1: the simple case - ETL + Online API Assume we can get the same raw inputs at prediction time, as it was provided in at training time. -However, we don't want to recompute `age_mean` and `age_std_dev` because recomputing them doesn't make sense. -Instead, we store the result of that at training time, and then use it at prediction time to get the right -features for the model. +However, we don't want to recompute `age_mean` and `age_std_dev` because recomputing aggregation features +doesn't make sense in an online setting (usually). Instead, we "store" the values for them when we compute features, +and then use those "stored" at prediction time to get the right computation to happen. ## Scenario 2: the more complex case - request doesn't have all the raw data - ETL + Online API At prediction time we might only have some of the raw data required to compute a prediction. To get the rest we need to make an API call, e.g. a feature store or a database, that will provide us with that information. +This example shows one way to modularize your Hamilton code so that you can swap out the "source" of the data. +A good exercise would be to make note of the differences with this scenario (2) and scenario 1. # What next? Jump into each directory and read the README, it'll explain how the example is set up and how things should work. diff --git a/examples/feature_engineering/scenario_1/README.md b/examples/feature_engineering/scenario_1/README.md index 45f9a9d7..016414c9 100644 --- a/examples/feature_engineering/scenario_1/README.md +++ b/examples/feature_engineering/scenario_1/README.md @@ -4,8 +4,6 @@ Assumptions: 2. you have an online API from where you want to serve the predictions, and you can provide the same raw data to it that, you would have access to in your ETL process. -TODO: picture - ## ETL Process ETL stands for Extract, Transform, Load. This is the process of taking raw data, transforming it into features, and then loading the data somewhere/doing something with it. E.g. you pull raw data, transform it into features, @@ -17,7 +15,7 @@ for this example. Here is a description of all the files and what they do. Note: aggregation features, like `mean()` or `std_dev()` make sense only in an offline setting where you have all the data. In an online setting, computing them -does not make sense. In `etl.py` there is a note that you need to store `age_mean` and +does not make sense (probably). In `etl.py` there is a note that you need to store `age_mean` and `age_std_dev` and then somehow get those values to plug into the code in `fastapi_server.py`. If you're getting started, these could be hardcoded values, or stored to a file that is loaded much like the model, or queried from a database, etc. Though you'll want @@ -28,19 +26,40 @@ Contains logic to load raw data. Here it's a flat file, but it could be going to a database, etc. #### features.py -The feature transform logic that takes raw data and transforms it into features. +The feature transform logic that takes raw data and transforms it into features. It contains some runtime +dataquality checks using Pandera. + +Important not, there are two aggregations features defined: `age_mean` and `age_std_dev`, that are computed on the +`age` column. These make sense to compute in an offline setting as you have all the data, but in an online setting where +you'd be performing inference, that doesn't makse sense. So for the online case, these computations be "overridden" in +`fastapi_server.py` with the values that were computed in the offline setting that you have stored (as mentioned above +and below it's up to you how to store them/sync them). #### etl.py -This script that mimics what one might do to fit a model: extract data, transform into features, +This script mimics what one might do to fit a model: extract data, transform into features, and then load features somewhere or fit a model. It's pretty basic and is meant -to be illustrative. +to be illustrative. It is not complete, i.e. doesn't save, or fit a model, it just extracts and transforms data +into features to create a dataframe. + +As seen in this image of what is executed - we see the that data is pulled from a data source, and transformed into features. +![offline execution](offline_execution.dot.png) #### constants.py Rather than hardcoding what features the model should have in two places, we define -it in a single place and import it where needed. +it in a single place and import it where needed; this is simple if you can share the code eaisly. +However, this is something you'll have to determine how to best do in your set up. There are many ways to do this, +come ask in the slack channel if you need help. #### fastapi_server.py The FastAPI server that serves the predictions. It's pretty basic and is meant to illustrate the steps of what's required to serve a prediction from a model, where you want to use the same feature computation logic as in your ETL process. -Note: aggregation features + +Note: the aggregation feature values are provided at run time and are the same +for all predictions -- how you "link" or "sync" these values to the webservice & model +is up to you; in this example we just hardcode them. + +Here is the DAG that is executed when a request is made. As you can see, no data is loaded, as we're assuming +that data comes from the API request. Note: `age_mean` and `age_std_dev` are overridden with values and would +not be executed (our visualization doesn't take into account overrides just yet). +![online execution](online_execution.dot.png) diff --git a/examples/feature_engineering/scenario_1/etl.py b/examples/feature_engineering/scenario_1/etl.py index c84951bf..03733c31 100644 --- a/examples/feature_engineering/scenario_1/etl.py +++ b/examples/feature_engineering/scenario_1/etl.py @@ -11,15 +11,28 @@ def create_features(source_location: str) -> pd.DataFrame: + """Extracts and transforms data to create feature set. + + Hamilton functions encode: + - pulling the data + - transforming the data into features + + Hamilton then handles building a dataframe. + + :param source_location: the location to load data from. + :return: a pandas dataframe. + """ model_features = constants.model_x_features config = {} dr = driver.Driver(config, offline_loader, features) # Visualize the DAG if you need to: # dr.display_all_functions('./offline_my_full_dag.dot', {"format": "png"}) - # dr.visualize_execution(model_features, - # './offline_execution.dot', - # {"format": "png"}, - # inputs={"location": "../data_quality/pandera/Absenteeism_at_work.csv"}) + dr.visualize_execution( + model_features, + "./offline_execution.dot", + {"format": "png"}, + inputs={"location": source_location}, + ) df = dr.execute( # add age_mean and age_std_dev to the features model_features + ["age_mean", "age_std_dev"], @@ -34,7 +47,8 @@ def create_features(source_location: str) -> pd.DataFrame: _features_df = create_features(_source_location) # we need to store `age_mean` and `age_std_dev` somewhere for the online side. # exercise for the reader: where would you store them for your context? - # ideas: with the model? in a database? in a file? in a feature store? + # ideas: with the model? in a database? in a file? in a feature store? (all reasonable answers it just + # depends on your context). _age_mean = _features_df["age_mean"].values[0] _age_std_dev = _features_df["age_std_dev"].values[0] print(_features_df) diff --git a/examples/feature_engineering/scenario_1/fastapi_server.py b/examples/feature_engineering/scenario_1/fastapi_server.py index 5593f9c0..4e6b2d20 100644 --- a/examples/feature_engineering/scenario_1/fastapi_server.py +++ b/examples/feature_engineering/scenario_1/fastapi_server.py @@ -1,3 +1,14 @@ +"""" +This is a simple example of a FastAPI server that uses Hamilton on the request +path to transform the data into features, and then uses a fake model to make +a prediction. + +The assumption here is that you get all the raw data passed in via the request. + +Otherwise for aggregration type features, you need to pass in a stored value +that we have mocked out with `load_invariant_feature_values`. +""" + import constants import fastapi import features @@ -69,6 +80,15 @@ async def predict_model_version1(request: PredictRequest) -> dict: """Illustrates how a prediction could be made that needs to compute some features first. In this version we go to the feature store, and then pass in what we get from the feature store as overrides to the model. + + If you wanted to visualize execution, you could do something like: + dr.visualize_execution(model_input_features, + './online_execution.dot', + {"format": "png"}, + inputs=input_series) + + :param request: the request body. + :return: a dictionary with the prediction value. """ # one liner to quickly create some series from the request. input_series = pd.DataFrame([request.dict()]).to_dict(orient="series") @@ -87,6 +107,7 @@ async def predict_model_version1(request: PredictRequest) -> dict: uvicorn.run(app, host="0.0.0.0", port=8000) + # here's a request you can cut and past into http://localhost:8000/docs example_request_input = { "id": 11, "reason_for_absence": 26, @@ -107,5 +128,5 @@ async def predict_model_version1(request: PredictRequest) -> dict: "pet": 1, "weight": 90, "height": 172, - "body_mass_index": 30, + "body_mass_index": 30, # remove this comma to make it valid json. } diff --git a/examples/feature_engineering/scenario_1/offline_execution.dot.png b/examples/feature_engineering/scenario_1/offline_execution.dot.png new file mode 100644 index 0000000000000000000000000000000000000000..1bea672330f415bfeb331d32c088ea34335ee7a2 GIT binary patch literal 272404 zcmeFacRben|37}UbWV}eX-VN!Dk=@xLX)HrMYdBKcA1gY-eg2J31xkbrFR^VUj+4P=P}E 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zG7(Q00yZ)M)_2t}2K_vbN?jIX5O51whK}khbyrq7_~7=D2YuDE%LyK) z#++IjsB)zgWHLH1#~ymJRC>F((Hci;X6K4;NO9hZ41!R^U&;YcSg1Va4d}%MfvH?+ zG057=J0gW3u&l&niT(P9=?lpIkmPVs{eNu&bl`KatCoQ*0F~#dxwB_q+VF;^G5zfr zOG$;Af$6l{iMJp=Z(aM3&m4emi6wbsWD#EWlsz*Z-!hK=xeS4`SMKaNbAZeD8y&x2 zz8q`wl18)p{<9MJ|Emp61CPPJC%ImD(;hiC+iXe~LWg9}7jQ-lO8S!?HSZBA`&Q}- zd1{b~a*+MX z!t7brz(^J!@DE4#d|Fm#uFXR4S94g|KyR+aF6!ev)y(}ZXbKL!;{T6((7Hi)*N;o(knorxl9QAloU zzv$YCOXDc4wqH8xJ8tUKyOVM|)(;$1zZWRp5u@L?Cnx_W(7wUb3iJcazN*>Utm24~ zJ81sos;7}#dLAu%IGf^xm$_kcBC{B{@W+oHkx~uE1?m<9au*-_VAbi=TlI`FeJTfe z=JwlXT_<)ubGpb%BWxGt%(oYn3=>rQxa*jS^Sh_lr@2R4JM5wm#t#kc zf8m=x)k$b$o^v zw7Yk6+MYzHDN-g_CLBiSbiEdV2e$RXS54 zKzc3j*whc?=3;MlWcFf}#W(g%DDJEjd>w60Ac}ue++qFenS&ronEC06`>eY#?J$VD z<<-qPVcN1Yl$kYRO*Wvxu>Ze Date: Mon, 20 Feb 2023 16:48:47 -0800 Subject: [PATCH 3/7] Renames constants to `named_model_feature_sets` I think that this makes things clearer what this file is, and is a lightweight way to register feature sets that are used by a model. --- examples/feature_engineering/scenario_1/etl.py | 8 ++++---- examples/feature_engineering/scenario_1/fastapi_server.py | 4 ++-- .../{constants.py => named_model_feature_sets.py} | 6 ++++++ 3 files changed, 12 insertions(+), 6 deletions(-) rename examples/feature_engineering/scenario_1/{constants.py => named_model_feature_sets.py} (52%) diff --git a/examples/feature_engineering/scenario_1/etl.py b/examples/feature_engineering/scenario_1/etl.py index 03733c31..3b50ebbd 100644 --- a/examples/feature_engineering/scenario_1/etl.py +++ b/examples/feature_engineering/scenario_1/etl.py @@ -2,8 +2,8 @@ This is part of an ETL that you'd likely have. You pull data from a source, and transform it into features, and then save/fit a model with them. """ -import constants import features +import named_model_feature_sets import offline_loader import pandas as pd @@ -22,7 +22,7 @@ def create_features(source_location: str) -> pd.DataFrame: :param source_location: the location to load data from. :return: a pandas dataframe. """ - model_features = constants.model_x_features + model_features = named_model_feature_sets.model_x_features config = {} dr = driver.Driver(config, offline_loader, features) # Visualize the DAG if you need to: @@ -53,6 +53,6 @@ def create_features(source_location: str) -> pd.DataFrame: _age_std_dev = _features_df["age_std_dev"].values[0] print(_features_df) # Then do something with the features_df, e.g.: - # save_features(features_df[constants.model_x_features], "my_model_features.csv") - # train_model(features_df[constants.model_x_features]) + # save_features(features_df[named_model_feature_sets.model_x_features], "my_model_features.csv") + # train_model(features_df[named_model_feature_sets.model_x_features]) # etc. diff --git a/examples/feature_engineering/scenario_1/fastapi_server.py b/examples/feature_engineering/scenario_1/fastapi_server.py index 4e6b2d20..0e48a3cf 100644 --- a/examples/feature_engineering/scenario_1/fastapi_server.py +++ b/examples/feature_engineering/scenario_1/fastapi_server.py @@ -9,9 +9,9 @@ that we have mocked out with `load_invariant_feature_values`. """ -import constants import fastapi import features +import named_model_feature_sets import pandas as pd import pydantic @@ -21,7 +21,7 @@ app = fastapi.FastAPI() # know the model schema somehow. -model_input_features = constants.model_x_features +model_input_features = named_model_feature_sets.model_x_features def load_invariant_feature_values() -> dict: diff --git a/examples/feature_engineering/scenario_1/constants.py b/examples/feature_engineering/scenario_1/named_model_feature_sets.py similarity index 52% rename from examples/feature_engineering/scenario_1/constants.py rename to examples/feature_engineering/scenario_1/named_model_feature_sets.py index 920fbf1a..bb1be2b4 100644 --- a/examples/feature_engineering/scenario_1/constants.py +++ b/examples/feature_engineering/scenario_1/named_model_feature_sets.py @@ -1,3 +1,9 @@ +""" +This module is a light weight way to "record" feature sets that are used in a model; just define it in code! + +No need for a database! Just use code and `git` to version what is or isn't used/available. +""" + model_x_features = [ "is_summer", "has_pet", From f1759381ef5b3eda7f62f1d4a13b60794140e346 Mon Sep 17 00:00:00 2001 From: Stefan Krawczyk Date: Mon, 20 Feb 2023 16:55:47 -0800 Subject: [PATCH 4/7] Adds more context to the feature engineering example README To help set the tone and explain what feature engineering is, as well as more context about the scenarios and the task. --- examples/feature_engineering/README.md | 128 +++++++++++++++++++++---- 1 file changed, 110 insertions(+), 18 deletions(-) diff --git a/examples/feature_engineering/README.md b/examples/feature_engineering/README.md index 571e1189..930b091d 100644 --- a/examples/feature_engineering/README.md +++ b/examples/feature_engineering/README.md @@ -1,32 +1,124 @@ -# write features once, use anywhere +# Feature Engineering + +What is feature engineering? It's the process of transforming data for input to a "model". + +To make models better, it's common to perform and try a lot of "transforms". This is where Hamilton comes in. +Hamilton allows you to: +* write different transformations in a straightforward and formulaic manner +* keep them managed and versioned with computational lineage (if using something like git) +* has a great testing and documentation story + +which allows you to sanely iterate, maintain, and determine what works best for your modeling domain. + +In this examples series, we'll skip talking about the benefits of Hamilton here, and instead focus on how to use it +for feature engineering. But first, some context on what challenges you're likely to face with feature engineering +in general. + +# What is hard about feature engineering? +There are certain dimensions that make feature engineering hard: + +1. Code: Organizing and maintaining code for reuse/collaboration/discoverability. +2. Lineage: Keeping track of what data is being used for what purpose. +3. Deployment: Offline vs online vs streaming needs. + +## Code: Organizing and maintaining code for reuse/collaboration/discoverability. +> Individuals build features, but teams own them. + +Have you ever dreaded taking over someone else's code? This is a common problem with feature engineering! + +Why? The code for feature engineering is often spread out across many different files, and created by many individuals. +E.g. scripts, notebooks, libraries, etc., and written in many different ways. This makes it hard to reuse code, +collaborate, discover what code is available, and therefore maintain what is actually being used in "production" and +what is not. + +## Lineage: Keeping track of what data is being used for what purpose +With the growth of data teams, along with data governance & privacy regulations, the need for knowing and understanding what +data is being used and for what purpose is important for the business to easily answer. A "modeler" a lot of the times +is not a stakeholder in needing this visibility, they just want to build models, but these concerns are often put on +their plate to address, which slows down their ability to build and ship features and thus models. + +Not having lineage or visbility into what data is being used for what purpose can lead to a lot of problems: + - teams break data assumptions without knowing it, e.g. upstream team stops updating data used downstream. + - teams are not aware of what data is available to them, e.g. duplication of data & effort. + - teams have to spend time figuring out what data is being used for what purpose, e.g. to audit models. + - teams struggle to debug inherited feature workflows, e.g. to fix bugs or add new features. + +## Deployment: Offline vs online vs streaming needs +This is a big topic. We wont do it justice here, but let's try to give a brief overview of two main problems: + +(1) There are a lot of different deployment needs when you get something to production. For example, you might want to: + - run a batch job to generate features for a model + - hit a webservice to make predictions in realtime that needs features computed on the fly, or retrieved from a cache (e.g. feature store). + - run a streaming job to generate features for a model in realtime + - require all three or a subset of the above ways of deploying features. + +So the challenge is, how do you design your processes to take in account your deployment needs? + +(2) Implement features once or twice or thrice? To enable (1), you need to ask yourself, can we share features? or +do we need to reimplement them for every system that we want to use them in? + +With (1) and (2) in mind, you can see that there are a lot of different dimensions to consider when designing your +feature engineering processes. They have to connect with each other, and be flexible enough to support your specific +deployment needs. + +# Using Hamilton for Feature Engineering for Batch/Offline +If you fall into **only** needing to deploy features for batch jobs, then stop right there. You don't need these examples, +as they are focused on how to bridge the gap between "offline" and "online" feature engineering. You should instead +browse the other examples like `data_quality`. + +# Using Hamilton for Feature Engineering for Batch/Offline and Online/Streaming +These example scenarios here are for the people who have to deal with both batch and online feature engineering. + +We provide two examples for two common scenarios that occur if you have this need. Note, the example code in these +scenarios tries to be illustrative about how to think and frame using Hamilton. It contains minimal features so as to +not overwhelm you, and leaves out some implementation details that you would need to fill in for your specific use case, +e.g. like fitting a model using the features, or where to store aggregate feature values, etc. + +## Scenario Context A not too uncommon task is that you need to do feature engineering in an offline (e.g. batch via airflow) setting, as well as an online setting (e.g. synchronous request via FastAPI). What commonly happens is that the code for features is not shared, and results in two implementations that result in subtle bugs and hard to maintain code. -With this example we show how you can use Hamilton to: +With this example series, we show how you can use Hamilton to: -1. write a feature once. -2. leverage the feature code anywhere that python runs. e.g. in batch and online. +1. write a feature once. (scenarios 1 and 2) +2. leverage that feature code anywhere that python runs. e.g. in batch and online. (scenarios 1 and 2) 3. show how to modularize components so that if you have values cached in a feature store, -you can inject those values into your feature computation needs. +you can inject those values into your feature computation needs. (scenario 2) -# Scenarios -We provide two examples for two common scenarios. The example code here tries to be illustrative about -how to think and frame using Hamilton to solve these two scenarios; it contains minimal features so as to not -overwhelm you. +The task that we're modeling here isn't that important, but if you must know, we're trying to predict the number of +hours of absence that an employee will have given some information about them; this is based off the `data_quality` +example, which is based off of the [Metaflow+Hamilton example](https://outerbounds.com/blog/developing-scalable-feature-engineering-dags/), +where Hamilton was used for the feature engineering process -- in that example only offline feature engineering was modeled. + +Assumptions we're using: +1. You have a fixed set of features that you want to compute for a model that you have determined as being useful a priori. +2. We are agnostic of the actual model -- and skip any details of that in the examples. + +Let's explain the context of the two scenarios a bit more. ## Scenario 1: the simple case - ETL + Online API -Assume we can get the same raw inputs at prediction time, as it was provided in at training time. -However, we don't want to recompute `age_mean` and `age_std_dev` because recomputing aggregation features -doesn't make sense in an online setting (usually). Instead, we "store" the values for them when we compute features, -and then use those "stored" at prediction time to get the right computation to happen. +In this scenario we assume we can get the same raw inputs at prediction time, as would be provided at training time. + +This is a straightforward process if all your feature transforms are [map operations](https://en.wikipedia.org/wiki/Map_(higher-order_function)). +If however you have some transforms that are aggregations, then you need to be careful about how you connect your offline +ETL with online. + +In this example, there are two features, `age_mean` and `age_std_dev`, that we avoid recomputing in an online setting. +Instead, we "store" the values for them when we compute features in the offline ETL, and then use those "stored" values +at prediction time to get the right feature computation to happen. ## Scenario 2: the more complex case - request doesn't have all the raw data - ETL + Online API -At prediction time we might only have some of the raw data required to compute a prediction. To get the rest -we need to make an API call, e.g. a feature store or a database, that will provide us with that information. -This example shows one way to modularize your Hamilton code so that you can swap out the "source" of the data. -A good exercise would be to make note of the differences with this scenario (2) and scenario 1. +In this scenario we assume we are not passed in data, but need to fetch it ourselves as part of the online API request. + +We will pretend to hit a feature store, that will provide us with the required data to compute the features for +input to the model. This example shows one way to modularize your Hamilton code so that you can swap out the "source" +of the data. To simplify the example, we assume that we can get all the input data we need from a feature store, rather +than it also coming in via the request. + +A good exercise would be to make note of the differences with this scenario (2) and scenario 1 in how they structure +the code with Hamilton. -# What next? +# What's next? Jump into each directory and read the README, it'll explain how the example is set up and how things should work. From f6ee7e102436a1e8de7f9ba4a43d0b0f2dafbc1e Mon Sep 17 00:00:00 2001 From: Stefan Krawczyk Date: Mon, 20 Feb 2023 17:21:01 -0800 Subject: [PATCH 5/7] Updates feature engineering scenario 1 with more docs I expand on the docs, and hopefully explain it a bit more that is understandable to a novice. --- .../feature_engineering/scenario_1/README.md | 50 ++++++++++--------- .../feature_engineering/scenario_1/etl.py | 7 ++- .../scenario_1/fastapi_server.py | 7 ++- .../scenario_1/features.py | 4 ++ .../scenario_1/offline_loader.py | 7 +-- 5 files changed, 43 insertions(+), 32 deletions(-) diff --git a/examples/feature_engineering/scenario_1/README.md b/examples/feature_engineering/scenario_1/README.md index 016414c9..b60eb0fd 100644 --- a/examples/feature_engineering/scenario_1/README.md +++ b/examples/feature_engineering/scenario_1/README.md @@ -1,31 +1,32 @@ # Scenario 1: the simple case - ETL + Online API -Assumptions: -1. you have an ETL process to create features that ingests raw data, and then creates a training set for you to train on. -2. you have an online API from where you want to serve the predictions, and you can provide the same raw data to it that, -you would have access to in your ETL process. -## ETL Process -ETL stands for Extract, Transform, Load. This is the process of taking raw data, transforming it into features, -and then loading the data somewhere/doing something with it. E.g. you pull raw data, transform it into features, -and then create a training set with which to train a model. This is exactly what we have in this example and is -what etl.py orchestrates; it is however not complete (i.e. doesn't save, or fit a model) and is just illustrative -for this example. +Context required to understand this scenario: +1. you have read the main README in the directory above. +2. you understand what it means to have an extract, transform, load (ETL) process to create features. It ingests raw data, +runs transformations, and then creates a training set for you to train a model on. +3. you understand the need to have an online API from where you want to serve the predictions. The assumption here is +that you can provide the same raw data to it that you would have access to in your ETL process. +4. you understand why aggregation features, like `mean()` or `std_dev()`, make sense only in an +offline setting where you have all the data. In an online setting, computing them likely does not make sense. -### File Descriptions -Here is a description of all the files and what they do. -Note: aggregation features, like `mean()` or `std_dev()` make sense only in an -offline setting where you have all the data. In an online setting, computing them -does not make sense (probably). In `etl.py` there is a note that you need to store `age_mean` and -`age_std_dev` and then somehow get those values to plug into the code in `fastapi_server.py`. -If you're getting started, these could be hardcoded values, or stored to a file that +# How it works + +1. You run `etl.py`, and in addition to storing the features/training a model, you store the aggregate global values +for `age_mean` and `age_std_dev`. `etl.py` uses `offline_loader.py`, `features.py`, and `named_model_feature_sets.py`. +2. You then run `fastapi_server.py`, which is the online webservice with the trained model (not shown here). `fastapi_server.py` +uses `features.py`, and `named_model_feature_sets.py`. +Note in real-life you would need to figure out a process to inject the aggregate global values for `age_mean` and `age_std_dev` +into the feature computation process. E.g. If you're getting started, these could be hardcoded values, or stored to a file that is loaded much like the model, or queried from a database, etc. Though you'll want -to ensure these values match whatever values the model was trained with. +to ensure these values match whatever values the model was trained with. If you need help here, join our slack and we're happy to help you figure this out! + +Otherwise, here is a description of all the files and what they do: -#### offline_loader.py +## offline_loader.py Contains logic to load raw data. Here it's a flat file, but it could be going to a database, etc. -#### features.py +## features.py The feature transform logic that takes raw data and transforms it into features. It contains some runtime dataquality checks using Pandera. @@ -35,7 +36,7 @@ you'd be performing inference, that doesn't makse sense. So for the online case, `fastapi_server.py` with the values that were computed in the offline setting that you have stored (as mentioned above and below it's up to you how to store them/sync them). -#### etl.py +## etl.py This script mimics what one might do to fit a model: extract data, transform into features, and then load features somewhere or fit a model. It's pretty basic and is meant to be illustrative. It is not complete, i.e. doesn't save, or fit a model, it just extracts and transforms data @@ -44,13 +45,16 @@ into features to create a dataframe. As seen in this image of what is executed - we see the that data is pulled from a data source, and transformed into features. ![offline execution](offline_execution.dot.png) -#### constants.py +Note, you need to store `age_mean` and +`age_std_dev` and then somehow get those values to plug into the code in `fastapi_server.py` - we "hand-waive" this here. + +## named_model_feature_sets.py Rather than hardcoding what features the model should have in two places, we define it in a single place and import it where needed; this is simple if you can share the code eaisly. However, this is something you'll have to determine how to best do in your set up. There are many ways to do this, come ask in the slack channel if you need help. -#### fastapi_server.py +## fastapi_server.py The FastAPI server that serves the predictions. It's pretty basic and is meant to illustrate the steps of what's required to serve a prediction from a model, where you want to use the same feature computation logic as in your ETL process. diff --git a/examples/feature_engineering/scenario_1/etl.py b/examples/feature_engineering/scenario_1/etl.py index 3b50ebbd..e9a0e077 100644 --- a/examples/feature_engineering/scenario_1/etl.py +++ b/examples/feature_engineering/scenario_1/etl.py @@ -1,6 +1,9 @@ """ -This is part of an ETL that you'd likely have. +This is part of an offline ETL that you'd likely have. You pull data from a source, and transform it into features, and then save/fit a model with them. + +Here we ONLY use Hamilton to create the features, with comment stubs for the rest of the ETL that would normally +be here. """ import features import named_model_feature_sets @@ -52,7 +55,7 @@ def create_features(source_location: str) -> pd.DataFrame: _age_mean = _features_df["age_mean"].values[0] _age_std_dev = _features_df["age_std_dev"].values[0] print(_features_df) - # Then do something with the features_df, e.g.: + # Then do something with the features_df, e.g. define functions to do the following: # save_features(features_df[named_model_feature_sets.model_x_features], "my_model_features.csv") # train_model(features_df[named_model_feature_sets.model_x_features]) # etc. diff --git a/examples/feature_engineering/scenario_1/fastapi_server.py b/examples/feature_engineering/scenario_1/fastapi_server.py index 0e48a3cf..d081a4e3 100644 --- a/examples/feature_engineering/scenario_1/fastapi_server.py +++ b/examples/feature_engineering/scenario_1/fastapi_server.py @@ -40,18 +40,17 @@ def fake_model(df: pd.DataFrame) -> pd.Series: return df.sum() # this is nonsensical but provides a single number. -# you would load the model from disk or a registry +# you would load the model from disk or a registry -- here it's a function. model = fake_model # need to load the invariant features somehow. invariant_feature_values = load_invariant_feature_values() - -# can instantiate an async driver once for the life of the app: +# we instantiate an async driver once for the life of the app: dr = h_async.AsyncDriver({}, features, result_builder=base.SimplePythonDataFrameGraphAdapter()) class PredictRequest(pydantic.BaseModel): - """Assumption here is that all this data is available for the API.""" + """Assumption here is that all this data is available via the request that comes in.""" id: int reason_for_absence: int diff --git a/examples/feature_engineering/scenario_1/features.py b/examples/feature_engineering/scenario_1/features.py index f6842731..d51a66f3 100644 --- a/examples/feature_engineering/scenario_1/features.py +++ b/examples/feature_engineering/scenario_1/features.py @@ -1,6 +1,10 @@ """ Here are some features that are based on the absenteeism data set. They are just supposed to be illustrative of the kind of features one might have. + +Note: we use check_output to warn us if the output is not what we expect; this is +used in both the offline ETL and the online webserivce. Use `check_output` to help +encode your expectations about the output of your functions and catch bugs early! """ import pandas as pd import pandera as pa diff --git a/examples/feature_engineering/scenario_1/offline_loader.py b/examples/feature_engineering/scenario_1/offline_loader.py index 3a2e26b6..c3e11c4b 100644 --- a/examples/feature_engineering/scenario_1/offline_loader.py +++ b/examples/feature_engineering/scenario_1/offline_loader.py @@ -1,6 +1,7 @@ """ -Module that contains logic to load data, and is the "base" that we can build off of -to build our featurization process. +Module that contains logic to load data for the offline ETL process. + +We use this to build our offline ETL featurization process. """ from typing import List @@ -53,7 +54,7 @@ def raw_data(location: str) -> pd.DataFrame: df = pd.read_csv(location, sep=";") # rename columns to be valid hamilton names -- and lower case it df.columns = _sanitize_columns(df.columns) - # create proper index -- ID-Month-Day; + # create proper index -- ID-Month-Day - to be able to join features appropriately. index = ( df["id"].astype(str) + "-" From 784ea0f27c93a5058146b767b595f600d782ee17 Mon Sep 17 00:00:00 2001 From: Stefan Krawczyk Date: Mon, 20 Feb 2023 17:27:07 -0800 Subject: [PATCH 6/7] Adds tags to age_mean and age_std_dev As a way to show functionality that can be used to highlight that they should be overridden in an online setting. --- examples/feature_engineering/scenario_1/README.md | 4 +++- examples/feature_engineering/scenario_1/features.py | 9 +++++++-- 2 files changed, 10 insertions(+), 3 deletions(-) diff --git a/examples/feature_engineering/scenario_1/README.md b/examples/feature_engineering/scenario_1/README.md index b60eb0fd..6507ebc1 100644 --- a/examples/feature_engineering/scenario_1/README.md +++ b/examples/feature_engineering/scenario_1/README.md @@ -34,7 +34,9 @@ Important not, there are two aggregations features defined: `age_mean` and `age_ `age` column. These make sense to compute in an offline setting as you have all the data, but in an online setting where you'd be performing inference, that doesn't makse sense. So for the online case, these computations be "overridden" in `fastapi_server.py` with the values that were computed in the offline setting that you have stored (as mentioned above -and below it's up to you how to store them/sync them). +and below it's up to you how to store them/sync them). The nice thing in Hamilton is that we can also "tag" these two +feature transforms with information to indicate to someone reading the code, that they should be overriden in the +online feature computation context. ## etl.py This script mimics what one might do to fit a model: extract data, transform into features, diff --git a/examples/feature_engineering/scenario_1/features.py b/examples/feature_engineering/scenario_1/features.py index d51a66f3..9fc6f9a8 100644 --- a/examples/feature_engineering/scenario_1/features.py +++ b/examples/feature_engineering/scenario_1/features.py @@ -2,16 +2,20 @@ Here are some features that are based on the absenteeism data set. They are just supposed to be illustrative of the kind of features one might have. -Note: we use check_output to warn us if the output is not what we expect; this is +Note (1): we use check_output to warn us if the output is not what we expect; this is used in both the offline ETL and the online webserivce. Use `check_output` to help encode your expectations about the output of your functions and catch bugs early! + +Note (2): we can tag the `aggregation` features with whatever key value pair makes sense +for us to discern/identify that we should not compute these features in an online setting. """ import pandas as pd import pandera as pa -from hamilton.function_modifiers import check_output, extract_columns +from hamilton.function_modifiers import check_output, extract_columns, tag +@tag(inject_at_inference_time="True") @check_output(range=(20.0, 60.0), data_type=float) def age_mean(age: pd.Series) -> float: """Average of age""" @@ -41,6 +45,7 @@ def age_zero_mean(age: pd.Series, age_mean: float) -> pd.Series: ) +@tag(inject_at_inference_time="True") @check_output(range=(0.0, 40.0), data_type=float) def age_std_dev(age: pd.Series) -> float: """Standard deviation of age.""" From c97cec44539ec208497218338b6cc5eee2c5e879 Mon Sep 17 00:00:00 2001 From: Stefan Krawczyk Date: Mon, 20 Feb 2023 21:51:00 -0800 Subject: [PATCH 7/7] Adds SVG image to help explain scenario 1 --- .../scenario_1/FeaturesExampleScenario1.svg | 1 + examples/feature_engineering/scenario_1/README.md | 6 +++++- 2 files changed, 6 insertions(+), 1 deletion(-) create mode 100644 examples/feature_engineering/scenario_1/FeaturesExampleScenario1.svg diff --git a/examples/feature_engineering/scenario_1/FeaturesExampleScenario1.svg b/examples/feature_engineering/scenario_1/FeaturesExampleScenario1.svg new file mode 100644 index 00000000..8453294e --- /dev/null +++ b/examples/feature_engineering/scenario_1/FeaturesExampleScenario1.svg @@ -0,0 +1 @@ + diff --git a/examples/feature_engineering/scenario_1/README.md b/examples/feature_engineering/scenario_1/README.md index 6507ebc1..d340cfb0 100644 --- a/examples/feature_engineering/scenario_1/README.md +++ b/examples/feature_engineering/scenario_1/README.md @@ -15,11 +15,15 @@ offline setting where you have all the data. In an online setting, computing the for `age_mean` and `age_std_dev`. `etl.py` uses `offline_loader.py`, `features.py`, and `named_model_feature_sets.py`. 2. You then run `fastapi_server.py`, which is the online webservice with the trained model (not shown here). `fastapi_server.py` uses `features.py`, and `named_model_feature_sets.py`. -Note in real-life you would need to figure out a process to inject the aggregate global values for `age_mean` and `age_std_dev` +Note, in real-life you would need to figure out a process to inject the aggregate global values for `age_mean` and `age_std_dev` into the feature computation process. E.g. If you're getting started, these could be hardcoded values, or stored to a file that is loaded much like the model, or queried from a database, etc. Though you'll want to ensure these values match whatever values the model was trained with. If you need help here, join our slack and we're happy to help you figure this out! +Here's a mental image of how things work and how they relate to the files/modules & Hamilton: +![offline-online-image](FeaturesExampleScenario1.svg?sanitize=True) + + Otherwise, here is a description of all the files and what they do: ## offline_loader.py