Skip to content

NumerFrame

NumerFrame is an extension of pd.DataFrame tailored specifically for the data format and workflow commonly used by Numerai participants. It builds upon the base functionalities of a Pandas DataFrame by offering utilities that simplify working with Numerai datasets.

Why?

  • Intuitive Data Handling: With built-in features like get_feature_data, get_target_data, and more, it simplifies extracting data subsets specific to Numerai competitions.

  • Automated Column Grouping: Automatically parses columns into recognizable groups such as features, targets, predictions, making data retrieval more intuitive and less error-prone.

  • Support for Multiple Formats: Through create_numerframe, it supports initializing from various data formats such as CSV, Parquet, Excel, and Pickle, providing a flexible interface for users.

  • Optimized for Numerai: Whether you're trying to fetch specific eras, feature groups or patterns like all 20-day targets, NumerFrame is designed to simplify those tasks for Numerai participants.

  • Chainable Operations: Since most operations return another NumerFrame, they can be conveniently chained for more complex workflows.

  • Tailored for Machine Learning: With methods like get_feature_target_pair, it aids in easily splitting the data for machine learning tasks specific to the Numerai competition.

By using NumerFrame, participants can focus more on model development and less on data wrangling, leading to a smoother and more efficient workflow in the Numerai competition.

Initialization

A NumerFrame can be initialized either from an existing pd.DataFrame or with create_numerframe. The create_numerframe function takes a path to a file and returns a NumerFrame object. This function automatically parses the file and supports CSV, Parquet, Excel and Pickle formats.

NumerFrame automatically parses columns into groups so you can easily retrieve what you need. It automatically is aware of the era column for its operations.

NumerFrame follows a convention for feature groups.

  • Features are all columns that start with feature.

  • Targets are all columns that start with target.

  • Predictions are all columns that start with prediction.

  • Aux columns are all that fall in none of these buckets, like era, data_type and id.

  • Era column is either era or date.

import pandas as pd
from numerblox.numerframe import NumerFrame, create_numerframe
# From DataFrame
data = pd.read_parquet('train.parquet')
df = NumerFrame(data)

# With create_numerframe
df = create_numerframe('train.parquet')

Examples

Basic functionality: 

# Get data for features, targets, predictions, and aux
features = df.get_feature_data
targets = df.get_target_data
predictions = df.get_prediction_data
aux_data = df.get_aux_data

Additionally it is possible to get groups specific to Numerai Classic like FNCv3 and internal feature groups. The examples below show some advanced functionality in NumerFrame.

# Get data for features, targets and predictions
features = df.get_feature_data
targets = df.get_target_data
predictions = df.get_prediction_data

# Get specific data groups
fncv3_features = df.get_fncv3_feature_data
group_features = df.get_group_features(group='rain')
small_features = df.get_small_feature_data
medium_features = df.get_medium_feature_data

# Fetch columns by pattern. For example all 20 day targets.
pattern_data = df.get_pattern_data(pattern='_20')
# Or for example Jerome targets.
jerome_targets = df.get_pattern_data(pattern='_jerome_')

# Split into feature and target pairs. Will get single target by default.
X, y = df.get_feature_target_pair()
# Optionally get all targets
X, y = df.get_feature_target_pair(multi_target=True)

# Fetch data for specified eras
X, y = df.get_era_batch(eras=['0001', '0002'])

# Since every operation returns a NumerFrame they can be chained.
# An example chained operation is getting features and targets for the last 2 eras.
X, y = df.get_last_eras(2).get_feature_target_pair()