Welcome to fermi’s documentation!

fermi is a modular Python framework for analyzing the main Economic Complexity metrics and features. It provides tools to explore the hidden structure of economies through:

  • 📊 Matrix preprocessing: raw cleaning, sparse conversion, Comparative advantage RCA/ICA, transformation and thresholding.

  • 🧠 Fitness & complexity: compute Fitness, Complexity ECI, PCI and other metrics via multiple methods.

  • 🌐 Relatedness metrics: product space, taxonomy, assist matrix.

  • 📈 Prediction models: GDP forecasting, density models, XGBoost.

  • ✅ Validation metrics: AUC, confusion matrix, prediction@k.

Basic functionalities: Fitness and Complexity module

The main module to generate an Economic Complexity object and initialize it (with a biadjacency matrix):

import fermi myefc = fermi.efc() myefc.load(my_biadjacency_matrix, possible kwargs)

To compute the Revealed Comparative Advantage (Balassa index) and binarize its value

myefc.compute_rca().binarize()

To compute the Fitness and the Complexity (using the original [Tacchella2012] algorithm)

fitness, complexity = myefc.get_fitness_complexity()

To compute the diversification and the ubiquity

div, ubi = myefc.get_diversification_ubiquity()

To compute the ECI index (using the eigenvalue method)

eci, pci = myefc.get_eci_pci()

Basic functionalities: Relatedness module

The module to generate cooccurrences and similar relatedness measures is

myproj = fermi.RelatednessMetrics() myproj.load(my_biadjacency_matrix, possible kwargs)

The cooccurrence can be evaluated using

relatedness = myproj.get_projection(projection_method=”cooccurrence”) validated_relatedness, validated_values = myproj.get_bicm_projection(projection_method=”cooccurrence”, validation_method=”fdr”)

Contents:

Indices and tables