Available Decorators#

While the 1:1 mapping of column -> function implementation is powerful, we’ve implemented a few decorators to promote business-logic reuse. The decorators we’ve defined are as follows (source can be found in function_modifiers):

@tag#

Allows you to attach metadata to a node (any node decorated with the function). A common use of this is to enable marking nodes as part of some data product, or for GDPR/privacy purposes.

For instance:

import pandas as pd
from hamilton.function_modifiers import tag

def intermediate_column() -> pd.Series:
    pass

@tag(data_product='final', pii='true')
def final_column(intermediate_column: pd.Series) -> pd.Series:
    pass

How do I query by tags?#

Right now, we don’t have a specific interface to query by tags, however we do expose them via the driver. Using the list_available_variables() capability exposes tags along with their names & types, enabling querying of the available outputs for specific tag matches. E.g.

from hamilton import driver
dr = driver.Driver(...)  # create driver as required
all_possible_outputs = dr.list_available_variables()
desired_outputs = [o.name for o in all_possible_outputs
                   if 'my_tag_value' == o.tags.get('my_tag_key')]
output = dr.execute(desired_outputs)

@extract_columns#

This works on a function that outputs a dataframe, that we want to extract the columns from and make them individually available for consumption. So it expands a single function into n functions, each of which take in the output dataframe and output a specific column as named in the extract_columns decorator.

import pandas as pd
from hamilton.function_modifiers import extract_columns

@extract_columns('fiscal_date', 'fiscal_week_name', 'fiscal_month', 'fiscal_quarter', 'fiscal_year')
def fiscal_columns(date_index: pd.Series, fiscal_dates: pd.DataFrame) -> pd.DataFrame:
    """Extracts the fiscal column data.
    We want to ensure that it has the same spine as date_index.
    :param fiscal_dates: the input dataframe to extract.
    :return:
    """
    df = pd.DataFrame({'date_index': date_index}, index=date_index.index)
    merged = df.join(fiscal_dates, how='inner')
    return merged

Note: if you have a list of columns to extract, then when you call @extract_columns you should call it with an asterisk like this:

import pandas as pd
from hamilton.function_modifiers import extract_columns

@extract_columns(*my_list_of_column_names)
def my_func(...) -> pd.DataFrame:
   """..."""

@extract_fields#

This works on a function that outputs a dictionary, that we want to extract the fields from and make them individually available for consumption. So it expands a single function into n functions, each of which take in the output dictionary and output a specific field as named in the extract_fields decorator.

import pandas as pd
from hamilton.function_modifiers import extract_columns

@function_modifiers.extract_fields(
    {'X_train': np.ndarray, 'X_test': np.ndarray, 'y_train': np.ndarray, 'y_test': np.ndarray})
def train_test_split_func(feature_matrix: np.ndarray,
                          target: np.ndarray,
                          test_size_fraction: float,
                          shuffle_train_test_split: bool) -> Dict[str, np.ndarray]:
    ...
    return {'X_train': ... }

The input to the decorator is a dictionary of field_name to field_type – this information is used for static compilation to ensure downstream uses are expecting the right type.

@config.when*#

@config.when allows you to specify different implementations depending on configuration parameters.

The following use cases are supported:

  1. A column is present for only one value of a config parameter – in this case, we define a function only once, with a @config.when

import pandas as pd
from hamilton.function_modifiers import config

# signups_parent_before_launch is only present in the kids business line
@config.when(business_line='kids')
def signups_parent_before_launch(signups_from_existing_womens_tf: pd.Series) -> pd.Series:
    """TODO:
    :param signups_from_existing_womens_tf:
    :return:
    """
    return signups_from_existing_womens_tf

  1. A column is implemented differently for different business inputs, e.g. in the case of Stitch Fix gender intent.

import pandas as pd
from hamilton.function_modifiers import config, model
import internal_package_with_logic

# Some 21 day autoship cadence does not exist for kids, so we just return 0s
@config.when(gender_intent='kids')
def percent_clients_something__kids(date_index: pd.Series) -> pd.Series:
    return pd.Series(index=date_index.index, data=0.0)

# In other business lines, we have a model for it
@config.when_not(gender_intent='kids')
@model(internal_package_with_logic.GLM, 'some_model_name', output_column='percent_clients_something')
def percent_clients_something_model() -> pd.Series:
    pass

Note the following:

  • The function cannot have the same name in the same file (or python gets unhappy), so we name it with a __ (dunderscore) as a suffix. The dunderscore is removed before it goes into the DAG.

  • There is currently no @config.otherwise(...) decorator, so make sure to have config.when specify set of configuration possibilities. Any missing cases will not have that output column (and subsequent downstream nodes may error out if they ask for it). To make this easier, we have a few more @config decorators:

    • @config.when_not(param=value) Will be included if the parameter is _not_ equal to the value specified.

    • @config.when_in(param=[value1, value2, ...]) Will be included if the parameter is equal to one of the specified values.

    • @config.when_not_in(param=[value1, value2, ...]) Will be included if the parameter is not equal to any of the specified values.

    • @config If you’re feeling adventurous, you can pass in a lambda function that takes in the entire configuration and resolves to True or False. You probably don’t want to do this.

@check_output#

The @check_output decorator enables you to add simple data quality checks to your code.

For example:

import pandas as pd
import numpy as np
from hamilton.function_modifiers import check_output

@check_output(
    data_type=np.int64,
    data_in_range=(0,100),
)
def some_int_data_between_0_and_100() -> pd.Series:
    pass

The check_output validator takes in arguments that each correspond to one of the default validators. These arguments tell it to add the default validator to the list. The above thus creates two validators, one that checks the datatype of the series, and one that checks whether the data is in a certain range.

Note that you can also specify custom decorators using the @check_output_custom decorator.

See data_quality for more information on available validators and how to build custom ones.

@parameterize#

Expands a single function into n, each of which correspond to a function in which the parameter value is replaced either by:

  1. A specified value

  2. The value from a specified upstream node.

Note that this can take the place of any of the @parameterize decorators below. In fact, they delegate to this!

import pandas as pd
from hamilton.function_modifiers import parameterize
from hamilton.function_modifiers import value, source


@parameterize(
    D_ELECTION_2016_shifted=dict(n_off_date=source('D_ELECTION_2016'), shift_by=value(3)),
    SOME_OUTPUT_NAME=dict(n_off_date=source('SOME_INPUT_NAME'), shift_by=value(1)),
)
def date_shifter(n_off_date: pd.Series, shift_by: int = 1) -> pd.Series:
    """{one_off_date} shifted by shift_by to create {output_name}"""
    return n_off_date.shift(shift_by)

By choosing literal or upstream, you can determine the source of your dependency. Note that you can also pass documentation. If you don’t, it will use the parameterized docstring.

@parameterize(
    D_ELECTION_2016_shifted=(dict(n_off_date=source('D_ELECTION_2016'), shift_by=value(3)), "D_ELECTION_2016 shifted by 3"),
    SOME_OUTPUT_NAME=(dict(n_off_date=source('SOME_INPUT_NAME'), shift_by=value(1)),"SOME_INPUT_NAME shifted by 1")
)
def date_shifter(n_off_date: pd.Series, shift_by: int=1) -> pd.Series:
    """{one_off_date} shifted by shift_by to create {output_name}"""
    return n_off_date.shift(shift_by)

@parameterize_values (replacing @parametrized)#

Expands a single function into n, each of which corresponds to a function in which the parameter value is replaced by that specific value.

import pandas as pd
from hamilton.function_modifiers import parameterize_values
import internal_package_with_logic

ONE_OFF_DATES = {
     #output name        # doc string               # input value to function
    ('D_ELECTION_2016', 'US Election 2016 Dummy'): '2016-11-12',
    ('SOME_OUTPUT_NAME', 'Doc string for this thing'): 'value to pass to function',
}
            # parameter matches the name of the argument in the function below
@parameterize_values(parameter='one_off_date', assigned_output=ONE_OFF_DATES)
def create_one_off_dates(date_index: pd.Series, one_off_date: str) -> pd.Series:
    """Given a date index, produces a series where a 1 is placed at the date index that would contain that event."""
    one_off_dates = internal_package_with_logic.get_business_week(one_off_date)
    return internal_package_with_logic.bool_to_int(date_index.isin([one_off_dates]))

We see here that parameterized allows you keep your code DRY by reusing the same function to create multiple distinct outputs. The parameter key word argument has to match one of the arguments in the function. The rest of the arguments are pulled from outside the DAG. The _assigned_output_ key word argument takes in a dictionary of tuple(Output Name, Documentation string) -> value.

Note that @parametrized is deprecated, and we intend for you to use @parameterize_vales. We’re consolidating to make the parameterization decorators more consistent! You have plenty of time to migrate, we wont make this a hard change until we have a Hamilton 2.0.0 to release.

@parameterize_sources (replacing @parameterized_inputs)#

Expands a single function into n, each of which corresponds to a function in which the parameters specified are mapped to the specified inputs. Note this decorator and @parameterize_values are quite similar, except that the input here is another DAG node(s), i.e. column/input, rather than a specific scalar/static value.

import pandas as pd
from hamilton.function_modifiers import parameterize_sources

@parameterize_sources(
    D_ELECTION_2016_shifted=dict(one_off_date='D_ELECTION_2016'),
    SOME_OUTPUT_NAME=dict(one_off_date='SOME_INPUT_NAME')
)
def date_shifter(one_off_date: pd.Series) -> pd.Series:
    """{one_off_date} shifted by 1 to create {output_name}"""
    return one_off_date.shift(1)

We see here that parameterize_sources allows you to keep your code DRY by reusing the same function to create multiple distinct outputs. The key word arguments passed have to have the following structure:

OUTPUT_NAME = Mapping of function argument to input that should go into it.

So in the example, D_ELECTION_2016_shifted is an _output_ that will correspond to replacing one_off_date with D_ELECTION_2016. Then similarly SOME_OUTPUT_NAME is an _output_ that will correspond to replacing one_off_date with SOME_INPUT_NAME. The documentation for both uses the same function doc and will replace values that are templatized with the input parameter names, and the reserved value output_name.

To help visualize what the above is doing, it is equivalent to writing the following two function definitions:

def D_ELECTION_2016_shifted(D_ELECTION_2016: pd.Series) -> pd.Series:
    """D_ELECTION_2016 shifted by 1 to create D_ELECTION_2016_shifted"""
    return D_ELECTION_2016.shift(1)

def SOME_OUTPUT_NAME(SOME_INPUT_NAME: pd.Series) -> pd.Series:
    """SOME_INPUT_NAME shifted by 1 to create SOME_OUTPUT_NAME"""
    return SOME_INPUT_NAME.shift(1)

Note that @parameterized_inputs is deprecated, and we intend for you to use @parameterize_sources. We’re consolidating to make the parameterization decorators more consistent! But we will not break your workflow for a long time.

Note: that the different input variables must all have compatible types with the original decorated input variable.

Migrating @parameterized*#

As we’ve said above, we’re planning on deprecating the following:

  • @parameterized_inputs (replaced by @parameterize_sources)

  • @parametrized (replaced by @parameterize_values, as that’s what its really doing)

  • @parametrized_input (deprecated long ago, migrate to @parameterize_sources as that is more versatile.)

In other words, we’re aligning around the following @parameterize implementations:

  • @parameterize – this does everything you want

  • @parameterize_values – this just changes the values, does not change the input source

  • @parameterize_sources– this just changes the source of the inputs. We also changed the name from inputs -> sources as it was clearer (values are inputs as well).

The only non-drop-in change you’ll have to do is for @parameterized. We won’t update this until hamilton==2.0.0, though, so you’ll have time to migrate for a while.

@does#

@does is a decorator that essentially allows you to run a function over all the input parameters. So you can’t pass any old function to @does, instead the function passed has to take any amount of inputs and process them all in the same way.

import pandas as pd
from hamilton.function_modifiers import does
import internal_package_with_logic

def sum_series(**series: pd.Series) -> pd.Series:
    """This function takes any number of inputs and sums them all together."""
    ...

@does(sum_series)
def D_XMAS_GC_WEIGHTED_BY_DAY(D_XMAS_GC_WEIGHTED_BY_DAY_1: pd.Series,
                              D_XMAS_GC_WEIGHTED_BY_DAY_2: pd.Series) -> pd.Series:
    """Adds D_XMAS_GC_WEIGHTED_BY_DAY_1 and D_XMAS_GC_WEIGHTED_BY_DAY_2"""
    pass

@does(internal_package_with_logic.identity_function)
def copy_of_x(x: pd.Series) -> pd.Series:
    """Just returns x"""
    pass

The example here is a function, that all that it does, is sum all the parameters together. So we can annotate it with the @does decorator and pass it the sum_series function. The @does decorator is currently limited to just allow functions that consist only of one argument, a generic **kwargs.

@model#

@model allows you to abstract a function that is a model. You will need to implement models that make sense for your business case. Reach out if you need examples.

Under the hood, they’re just DAG nodes whose inputs are determined by a configuration parameter. A model takes in two required parameters:

  1. The class it uses to run the model. If external to Stitch Fix you will need to write your own, else internally see the internal docs for this. Basically the class defined determines what the function actually does.

  2. The configuration key that determines how the model functions. This is just the name of a configuration parameter that stores the way the model is run.

The following is an example usage of @model:

import pandas as pd
from hamilton.function_modifiers import model
import internal_package_with_logic

@model(internal_package_with_logic.GLM, 'model_p_cancel_manual_res')
# This runs a GLM (Generalized Linear Model)
# The associated configuration parameter is 'model_p_cancel_manual_res',
# which points to the results of loading the model_p_cancel_manual_res table
def prob_cancel_manual_res() -> pd.Series:
    pass

GLM here is not part of the hamilton framework, and instead a user defined model.

Models (optionally) accept a output_column parameter – this is specifically if the name of the function differs from the output column that it should represent. E.G. if you use the model result as an intermediate object, and manipulate it all later. At Stitch Fix this is necessary because various dependent columns that a model queries (e.g. MULTIPLIER_... and OFFSET_...) are derived from the model’s name.