Feyn

Feyn

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›Primitive Operations

Getting Started

  • Quick start
  • Using Feyn
  • Installation
  • Transition to Feyn 3.0
  • What is a QLattice?
  • Community edition
  • Commercial use

Essentials

  • Auto Run
  • Visualise a model
  • Summary plot
  • Semantic types
  • Categorical features
  • Estimating priors
  • Model parameters
  • Predicting with a model
  • Saving and loading models
  • Filtering models
  • Seeding a QLattice
  • Privacy

Evaluate Regressors

  • Regression plot
  • Residuals plot

Evaluate Classifiers

  • ROC curve
  • Confusion matrix
  • Plot probability scores

Understand Your Models

  • Plot response
  • Plot response 2D
  • Model signal
  • Segmented loss
  • Interactive flow

Primitive Operations

  • Using the primitives
  • Updating priors
  • Sample models
  • Fitting models
  • Pruning models
  • Diverse models
  • Updating a QLattice
  • Validate data

Advanced

  • Converting a model to SymPy
  • Setting themes
  • Saving a graph as an image
  • Using the query language
  • Model complexity

Diverse models

by: Kevin Broløs & Chris Cave
(Feyn version 3.0 or newer)

The number of potential models that can explain a dataset can vary. The function get_diverse_models finds the best performing Models from a list that have evolved independently in the QLattice.

This allows you to filter down the list of possible models to choose from, and only look at the ones that are most likely to be structurally or behaviourally different.

import feyn
from feyn.datasets import make_classification

train, test = make_classification()

ql = feyn.QLattice()

models = ql.sample_models(train.columns, 'y', 'classification', max_complexity=10)

models = feyn.fit_models(models, train, 'binary_cross_entropy', 'bic', 4)

models = feyn.prune_models(models, True, True)

models = feyn.get_diverse_models(
    models=models,
    n=10
)

models

The list of models to find the diverse models from. The function expects the list to be sorted by some metric, such as the loss.

n

The maximum amount of models to return from the list.

← Pruning modelsUpdating a QLattice →
  • models
  • n
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