Foundry

Foundry is a package for forging interpretable predictive modeling pipelines with a sklearn style-API. It includes:

• A Glm class with a Pytorch backend. This class is highly extensible, supporting (almost) any distribution in pytorch's distributions module.
• A preprocessing module that includes helpful classes like DataFrameTransformer and InteractionFeatures.
• An evaluation module with tools for interpreting any sklearn-API model via MarginalEffects.

You should use Foundry to augment your workflows if any of the following are true:

• You are attempting to model a target that is 'weird': for example, highly skewed data, binomial count-data, censored or truncated data, etc.
• You need some help battling some annoying aspects of feature-engineering: for example, you want an expressive way of specifying interaction-terms in your model; or perhaps you just want consistent support for getting feature-names despite being stuck on python 3.7.
• You want to interpret your model: for example, perform statistical inference on its parameters, or understand the direction and functional-form of its predictors.

Getting Started

foundry can be installed with pip:

pip install git+https://github.com/strongio/foundry.git#egg=foundry

Let's walk through a quick example:

# data:
from foundry.data import get_click_data
# preprocessing:
from foundry.preprocessing import DataFrameTransformer, SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler, PowerTransformer
from sklearn.pipeline import make_pipeline
# glm:
from foundry.glm import Glm
# evaluation:
from foundry.evaluation import MarginalEffects

Here's a dataset of click user pageviews and clicks for domain with lots of pages:

df_train, df_val = get_click_data()
df_train

We'd like to build a model that let's us predict future click-rates for different pages (page_id), page-attributes (e.g. market), and user-attributes (e.g. platform), and also learn about each of these features -- e.g. perform statistical inference on model-coefficients ("are users with missing user-agent data significantly worse than average?")

Unfortunately, these data don't fit nicely into the typical regression/classification divide: each observations captures counts of clicks and counts of pageviews. Our target is the click-rate (clicks/views) and our sample-weight is the pageviews.

One workaround would be to expand our dataset so that each row indicates is_click (True/False) -- then we could use a standard classification algorithm:

df_train_expanded, df_val_expanded = get_click_data(expanded=True)
df_train_expanded

But this is hugely inefficient: our dataset of ~400K explodes to almost 8MM.

Within foundry, we have the Glm, which supports binomial data directly:

Glm('binomial', penalty=10_000)

Glm(family='binomial', penalty=10000)

Let's set up a sklearn model pipeline using this Glm. We'll use foundry's DataFrameTransformer to support passing feature-names to the Glm (newer versions of sklearn support this via the set_output() API).

preproc = DataFrameTransformer([
    (
        'one_hot', 
        make_pipeline(SimpleImputer(strategy='most_frequent'), OneHotEncoder()), 
        ['attributed_source', 'user_agent_platform', 'page_id', 'page_market']
    )
    ,
    (
        'power', 
        PowerTransformer(),
        ['page_feat1', 'page_feat2', 'page_feat3']
    )
])

glm = make_pipeline(
    preproc, 
    Glm('binomial', penalty=1_000)
).fit(
    X=df_train,
    y={
        'value' : df_train['num_clicks'],
        'total_count' : df_train['num_views']
    },
)

Epoch 8; Loss 0.3183; Convergence 0.0003131/0.001:  42%|█████▊        | 5/12 [00:00<00:00, 10.99it/s]

Estimating laplace coefs... (you can safely keyboard-interrupt to cancel)

Epoch 8; Loss 0.3183; Convergence 0.0003131/0.001:  42%|█████▊        | 5/12 [00:07<00:10,  1.55s/it]

By default, the Glm will estimate not just the parameters of our model, but also the uncertainty associated with them. We can access a dataframe of these with the coef_dataframe_ attribute:

df_coefs = glm[-1].coef_dataframe_
df_coefs

Using this, it's easy to plot our model-coefficients:

df_coefs[['param', 'trans', 'term']] = df_coefs['name'].str.split('__', n=3, expand=True)

df_coefs[df_coefs['name'].str.contains('page_feat')].plot('term', 'estimate', kind='bar', yerr='se')
df_coefs[df_coefs['name'].str.contains('user_agent_platform')].plot('term', 'estimate', kind='bar', yerr='se')

<AxesSubplot:xlabel='term'>

Model-coefficients are limited because they only give us a single number, and for non-linear models (like our binomial GLM) this doesn't tell the whole story. For example, how could we translate the importance of page_feat3 into understanable terms? This only gets more difficult if our model includes interaction-terms.

To aid in this, there is MarginalEffects, a tool for plotting our model-predictions as a function of each predictor:

glm_me = MarginalEffects(glm)
glm_me.fit(
    X=df_val_expanded, 
    y=df_val_expanded['is_click'],
    vary_features=['page_feat3']
).plot()

<ggplot: (8777751556441)>

Here we see that how this predictor's impact on click-rates varies due to floor effects.

As a bonus, we plotted the actual values alongside the predictions, and we can see potential room for improvement in our model: it looks like very high values of this predictor have especially high click-rates, so an extra step in feature-engineering that captures this discontinuity may be warranted.