Metadata-Version: 2.4
Name: pnne_search
Version: 0.1.2
Summary: A package for search model estimation
Home-page: https://pnnehome.github.io/
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: tensorflow>=2.0.0
Requires-Dist: numpy>=1.15
Requires-Dist: scipy>=1.0.0
Requires-Dist: pandas>=1.0.0
Requires-Dist: joblib>=0.14.0
Requires-Dist: absl-py>=0.13.0
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

This package `pnne_search` implements the pre-trained neural network estimator for sequential search model, as described by "Pre-Training Estimators for Structural Models: Application to Consumer Search"

See below for a simple demonstration of how to use `pnne_search`:

import time 
import pnne_search

data = pnne_search.load_example_data()

pnne_search.pnne_estimate(data['Y'], data['Xp'], data['Xa'], data['Xc'], 
                          data['consumer_idx'], checks = True)

start_time = time.time()
pnne_search.pnne_estimate(data['Y'], data['Xp'], data['Xa'], data['Xc'], 
                          data['consumer_idx'], checks = True, se=True)
time.time() - start_time

start_time = time.time()
pnne_search.pnne_estimate(data['Y'], data['Xp'], data['Xa'], data['Xc'], 
                          data['consumer_idx'], checks = True, se=True, use_parallel=False)
time.time() - start_time
