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Merge pull request #214 from MilesCranmer/organize-params-list
Browse files- docs/_api.md +64 -0
- docs/api.md +0 -16
- docs/gen_docs.sh +9 -3
- docs/gen_param_docs.py +67 -0
- docs/param_groupings.yml +89 -0
docs/_api.md
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# PySRRegressor Reference
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`PySRRegressor` has many options for controlling a symbolic regression search.
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Let's look at them below.
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PARAMSKEY
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## PySRRegressor Functions
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::: pysr.PySRRegressor.fit
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options:
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show_root_heading: true
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heading_level: 3
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show_root_full_path: false
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::: pysr.PySRRegressor.predict
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options:
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show_root_heading: true
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heading_level: 3
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show_root_full_path: false
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::: pysr.PySRRegressor.from_file
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options:
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show_root_heading: true
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heading_level: 3
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show_root_full_path: false
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::: pysr.PySRRegressor.sympy
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options:
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show_root_heading: true
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heading_level: 3
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show_root_full_path: false
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::: pysr.PySRRegressor.latex
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options:
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show_root_heading: true
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heading_level: 3
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show_root_full_path: false
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::: pysr.PySRRegressor.pytorch
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options:
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show_root_heading: true
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heading_level: 3
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show_root_full_path: false
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::: pysr.PySRRegressor.jax
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options:
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show_root_heading: true
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heading_level: 3
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show_root_full_path: false
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::: pysr.PySRRegressor.latex_table
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options:
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show_root_heading: true
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heading_level: 3
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show_root_full_path: false
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::: pysr.PySRRegressor.refresh
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options:
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show_root_heading: true
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heading_level: 3
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show_root_full_path: false
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docs/api.md
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# PySRRegressor Reference
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::: pysr.PySRRegressor
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options:
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members:
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- fit
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- predict
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- from_file
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- sympy
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- latex
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- pytorch
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- jax
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- latex_table
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- refresh
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show_root_members_full_path: true
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heading_level: 2
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docs/gen_docs.sh
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#!/bin/bash -e
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# Generate home page using README.md:
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echo '<div style="width:100%;height:0px;position:relative;padding-bottom:56.250%;"><iframe src="https://streamable.com/e/ncvqhy" frameborder="0" width="100%" height="100%" allowfullscreen style="width:100%;height:100%;position:absolute;left:0px;top:0px;overflow:hidden;"></iframe></div>' >
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cat ../README.md | grep -v 'user-images' | grep -E -v '\<.*div.*\>' >>
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# Transform "### Test status" to "**Test status**":
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sed -i.bak 's/\#\#\# Test status/**Test status**/g' index.md
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sed -i.bak '10,$s/^\# /## /g' index.md
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# Create papers.md
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python generate_papers.py
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#!/bin/bash -e
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# Generate home page using README.md:
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echo '<div style="width:100%;height:0px;position:relative;padding-bottom:56.250%;"><iframe src="https://streamable.com/e/ncvqhy" frameborder="0" width="100%" height="100%" allowfullscreen style="width:100%;height:100%;position:absolute;left:0px;top:0px;overflow:hidden;"></iframe></div>' >index.md
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cat ../README.md | grep -v 'user-images' | grep -E -v '\<.*div.*\>' >>index.md
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# Transform "### Test status" to "**Test status**":
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sed -i.bak 's/\#\#\# Test status/**Test status**/g' index.md
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sed -i.bak '10,$s/^\# /## /g' index.md
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# Create papers.md
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python generate_papers.py
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# Copy _api.md up to but not including PARAMSKEY and all tex after:
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cat _api.md | sed -n '1,/PARAMSKEY/p' | sed '$d' >api.md
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python gen_param_docs.py >> api.md
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# Copy _api.md after PARAMSKEY:
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cat _api.md | sed -n '/PARAMSKEY/,$p' | sed '1d' >>api.md
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docs/gen_param_docs.py
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# Load YAML file param_groupings.yml:
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from pathlib import Path
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from yaml import safe_load
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import sys
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sys.path.append("..")
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from pysr import PySRRegressor
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import pysr
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import re
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from docstring_parser import parse
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found_params = []
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def str_param_groups(param_groupings, params, cur_heading=2):
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global found_params
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# Recursively print the parameter descriptions, defaults,
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# with headings from the param groupings dict.
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if isinstance(param_groupings, list):
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return "\n\n".join(
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str_param_groups(param, params, cur_heading) for param in param_groupings
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)
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elif isinstance(param_groupings, dict):
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for heading, param_grouping in param_groupings.items():
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return (
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f"{'#' * cur_heading} {heading}"
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+ "\n\n"
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+ str_param_groups(param_grouping, params, cur_heading + 1)
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)
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elif isinstance(param_groupings, str):
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found_params.append(param_groupings)
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clean_desc = re.sub(r"Default is .*", "", params[param_groupings].description)
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default_value = re.search(
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r"Default is `(.*)`", params[param_groupings].description
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)
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return (
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f"**`{param_groupings}`**"
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+ "\n\n"
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+ clean_desc
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+ ("\n" + f"*Default:* `{default_value.group(1)}`" if default_value else "")
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)
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else:
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raise TypeError(f"Unexpected type {type(param_groupings)}")
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if __name__ == "__main__":
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# This is the path to the param_groupings.yml file
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# relative to the current file.
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path = "param_groupings.yml"
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with open(path, "r") as f:
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param_groupings = safe_load(f)
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# This is basically a dict of lists and dicts.
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# Let's load in the parameter descriptions from the docstring of PySRRegressor:
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raw_params = parse(PySRRegressor.__doc__).params
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params = {
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param.arg_name: param
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for param in raw_params
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if param.arg_name[-1] != "_" and param.arg_name != "**kwargs"
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}
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output = str_param_groups(param_groupings, params, cur_heading=3)
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assert len(set(found_params) ^ set(params.keys())) == 0
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print("## PySRRegressor Parameters")
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print(output)
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docs/param_groupings.yml
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- The Algorithm:
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- Creating the Search Space:
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- binary_operators
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- unary_operators
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- maxsize
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- maxdepth
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- Setting the Search Size:
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- niterations
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- populations
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- population_size
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- ncyclesperiteration
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- The Objective:
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- loss
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- model_selection
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- Working with Complexities:
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- parsimony
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- constraints
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- nested_constraints
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- complexity_of_operators
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- complexity_of_constants
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- complexity_of_variables
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- warmup_maxsize_by
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- use_frequency
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- use_frequency_in_tournament
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- Mutations:
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- weight_add_node
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- weight_insert_node
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- weight_delete_node
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- weight_do_nothing
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- weight_mutate_constant
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- weight_mutate_operator
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- weight_randomize
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- weight_simplify
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- crossover_probability
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- annealing
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- alpha
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- perturbation_factor
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- skip_mutation_failures
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- Tournament Selection:
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- tournament_selection_n
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- tournament_selection_p
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- Constant Optimization:
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- optimizer_algorithm
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- optimizer_nrestarts
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- optimize_probability
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- optimizer_iterations
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- should_optimize_constants
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- Migration between Populations:
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- fraction_replaced
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- fraction_replaced_hof
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- migration
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- hof_migration
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- topn
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- Data Preprocessing:
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- denoise
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- select_k_features
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- Stopping Criteria:
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- max_evals
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- timeout_in_seconds
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- early_stop_condition
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- Performance and Parallelization:
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- procs
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- multithreading
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- cluster_manager
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- batching
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- batch_size
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- precision
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- fast_cycle
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- turbo
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- random_state
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- deterministic
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- warm_start
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- Monitoring:
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- verbosity
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- update_verbosity
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- progress
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- Environment:
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- temp_equation_file
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- tempdir
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- delete_tempfiles
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- julia_project
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- update
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- Exporting the Results:
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- equation_file
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- output_jax_format
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- output_torch_format
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- extra_sympy_mappings
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- extra_torch_mappings
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- extra_jax_mappings
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