Restructure likelihood.py for #310 and estimagic's comparison plot. #313
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tobiasraabe
commented
Jan 29, 2020
tobiasraabe
commented
Jan 29, 2020
mo2561057
approved these changes
Feb 13, 2020
| from respy.shared import downcast_to_smallest_dtype | ||
| from respy.shared import generate_column_dtype_dict_for_estimation | ||
| from respy.shared import rename_labels_to_internal | ||
| from respy.solve import solve_with_backward_induction | ||
| from respy.state_space import StateSpace | ||
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| def get_crit_func(params, options, df, version="log_like"): | ||
| def get_crit_func( |
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I like the new interface. I think just passing the params is the best option!
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@@ Coverage Diff @@
## master #313 +/- ##
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Coverage ? 81.3%
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Files ? 38
Lines ? 2493
Branches ? 0
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Hits ? 2027
Misses ? 466
Partials ? 0
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Solution / Implementation
We need to change some parts of the likelihood calculation from NumPy arrays to DataFrames which makes the distribution of the estimation over the dense state space easier. It is similar to what happened in Simplify simulation and prepare for #310. #312.
The likelihood function has two indicators.
return_scalardefines whether the mean log likehood or contributions will be returned. This is essential to switch between estimation and standard error calculation.return_comparison_plot_dataallows to return a DataFrame in the tidy data format which can be used to visualize the evolution of likelihoods during the estimation! One can plot the distributions of choice and wage likelihoods for periods, individuals, choices, and types.Type probabilities are added to the comparison plot in Separate core and dense state space. #310.