Example Modeling Problem

We’ll begin by laying out a simple modeling problem, based on the Fenl Quickstart guide. The goal will be to build a model predicting if a given purchase will result in a fraud report within the next 30 days given two event tables; a Purchase table and a FraudReport table.


from kaskada import table
import pandas
import requests

  table_name = "Purchase",
  time_column_name = "purchase_time",
  entity_key_column_name = "id",
  subsort_column_name = "subsort_id",
  table_name = "PurchaseByCustomer",
  time_column_name = "purchase_time",
  entity_key_column_name = "customer_id",
  subsort_column_name = "subsort_id",

purchases_url = "https://drive.google.com/uc?export=download&id=1SLdIw9uc0RGHY-eKzS30UBhN0NJtslkk"
r = requests.get(purchases_url, allow_redirects=True)
open('purchases.parquet', 'wb').write(r.content)
table.load('Purchase', 'purchases.parquet')
table.load('PurchaseByCustomer', 'purchases.parquet')

  table_name = "FraudReport",
  time_column_name = "time",
  entity_key_column_name = "purchase_id",
  subsort_column_name = "subsort_id",

frauds_url = "https://drive.google.com/uc?export=download&id=1WXRW1zt1EEPcbrw4nw9rCdhTqlatxTSR"
f = requests.get(frauds_url, allow_redirects=True)
open('frauds.parquet', 'wb').write(r.content)
table.load('FraudReporting', 'frauds.parquet')

Feature Definitions

To train a model we must produce separate training and validation datasets. These datasets will be created by partitioning the full set of training examples into two time ranges, one spanning the years 2000-2015 and another spanning the years 2015-2020.

We’ll execute the feature query twice, once to produce the training dataset and another time to produce the validation dataset. The training query will provide start and end times corresponding to the time interval 2000-2015, while the validation query will times corresponding to the time interval 2015-2020.


The fenlmagic extension is designed to make it easy to interactively explore your dataset. When you’re ready to train a model we recommend using the Python client. The python client exposes the full functionality of the Kaskada API and is better-suited to tasks such as managing Views, Tables, and making multiple queries with different query variables.

%%fenl --var examples

let average_purchase_by_customer = PurchaseByCustomer.amount
  | mean()

let predictors = {
    entity: Purchase.id,
    purchase_total: Purchase.amount | last(),
    mean_purchase: lookup(Purchase.customer_id, average_purchase_by_customer),
let target = {
  target: count(FraudReport),

let shifted =  predictors
  | shift_to(time_of($input) | add_time(days(30)))

in shifted | extend(lookup($input.entity, target))

We’ve provided the fenl-magic flag --var examples which causes the query string to be assigned to a local variable named examples. We’ll use this variable to create a view we can re-use when we make the training and validation queries.

from kaskada import view

    view_name = "Examples",
    expression = examples.query,

Training a Model Locally

Depending on the size of your training dataset and how you intend to train a model, you may want to copy the training features locally or transfer them to a remote data store. We’ll show the simple case of training locally.

Begin by limiting the examples to the training time range.

%%fenl --var training

  | when(time_of($input) > ("2020-01-01T00:00:00Z" as timestamp_ns))
    and time_of($input) <= ("2020-02-03T00:00:00Z" as timestamp_ns))

Next we limit the examples to different time bounds to produce a validation dataset.

%%fenl --var validation

  | when(time_of($input) > ("2020-02-03T00:00:00Z" as timestamp_ns))
    and time_of($input) <= ("2020-04-12T00:00:00Z" as timestamp_ns))

We’re finally ready to train a model. This shows a simple linear regression model.

from sklearn.linear_model import LogisticRegression
from sklearn import preprocessing

X_train = training.dataframe.drop(['_time', '_subsort', '_key', '_key_hash', 'entity', 'target'], axis = 1)
y_train = training['target']
X_validation = validation.drop(['_time', '_subsort', '_key', '_key_hash', 'entity', 'target'], axis = 1)
y_validation = validation['target']

scaler = preprocessing.StandardScaler().fit(X_train)
X_train_scaled = scaler.transform(X_train)
X_validation_scaled = scaler.transform(X_validation)

model = LogisticRegression(max_iter=1000)
model.fit(X_train_scaled, y_train)

model.score(X_validation_scaled, y_validation)