rafaelvalero Goto Github PK

aggreating_predictions_sklearn

# Objectives * Create a training function that train the model many time and store the results. * Create a function that collect previous trained model and provide predictions. * I want to do it in parallel using joblib [this library is used to parallelize in Sklearn]

bertopic

Leveraging BERT and c-TF-IDF to create easily interpretable topics.

covid_forecast

Forecast and Predictions Techniques applied to COVID / Coronavirus

different_notebooks

I put here some notebook I have used to research different topics.

factor_analyzer

A Python module to perform exploratory factor analysis.

forecastcovid

Forecast new cases and deaths by day and country

grpc_data_functions_experiements

how_to_test_in_python

I want to have a little cheatsheet and overview for testing procedures. I focus in two procedures Doctest and Unittest.

module_packages

Tutorial about classes, modules and packages

paralleltextprocessing

I would like to process text in the most parallel possible way

pingouin

Statistical package in Python based on Pandas

playing_flex_dashboard_shiny

R Shiny Flex Dashboard Interactive Data Visualization

python_from_commandline

How to use funtions from the command line. Pick up variables from the commandline.

qs-python-samples

Examples of integrating Qlik Sense and Python

reliabilipy

Implementation in Python of the reliability measures such as Omega.

scsl

Synthetic Control with Statistical Learning

smolyak

Smolyak Method for Solving Dynamic Economic Models: Lagrange Interpolation, Anisotropic Grid and Adaptive Domain (with Kenneth L. Judd, Lilia Maliar and Serguei Maliar). Journal of Economic Dynamics & Control 44 (2014) 92–123. First, we propose a more effcient implementation of the Smolyak method for interpolation, namely, we show how to avoid costly evaluations of repeated basis functions in the conventional Smolyak formula. Second, we extend the Smolyak method to include anisotropic constructions; this allows us to target higher quality of approximation in some dimensions than in others. Third, we show how to effectively adapt the Smolyak hypercube to a solution domain of a given economic model. Finally, we advocate the use of low-cost fixed-point iteration, instead of conventional time iteration. In the context of one- and multi-agent growth models, we find that the proposed techniques lead to substantial increases in accuracy and speed of a Smolyak-based projection method for solving dynamic economic models. JEL classif ication : C63, C68 Key Words : Smolyak method; sparse grid; adaptive domain; projection; anisotropic grid; collocation; high-dimensional problem

smolyak-1

Efficient implementations of Smolyak's algorithm for function approxmation in Python and Julia.

sparsegrid

Smolyak sparse grid interpolation sandbox