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Rearrange operators section
Browse files- docs/options.md +26 -26
docs/options.md
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You likely don't need to tune the hyperparameters yourself,
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but if you would like, you can use `hyperparamopt.py` as an example.
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Common options
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- `niterations`
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- `procs`
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- `populations`
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- `binary_operators`, `unary_operators`
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- `weights`
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- `maxsize`, `maxdepth`
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- `batching`, `batchSize`
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which is `hall_of_fame.csv` by default. It also prints the
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equations to stdout.
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##
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This is the total number of generations that `pysr` will run for.
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I usually set this to a large number, and exit when I am satisfied
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with the equations.
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## Processors
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One can adjust the number of workers used by Julia with the
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`procs` option. You should set this equal to the number of cores
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you want `pysr` to use. This will also run `procs` number of
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populations simultaneously by default.
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## Populations
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By default, `populations=procs`, but you can set a different
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number of populations with this option. More populations may increase
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the diversity of equations discovered, though will take longer to train.
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However, it may be more efficient to have `populations>procs`,
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as there are multiple populations running
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on each core.
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## Custom operators
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A list of operators can be found on the operators page.
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One can define custom operators in Julia by passing a string:
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when constructing a useable function. This step is optional, but
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is necessary for the `lambda_format` to work.
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One can also edit `operators.jl`.
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## Weighted data
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You likely don't need to tune the hyperparameters yourself,
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but if you would like, you can use `hyperparamopt.py` as an example.
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Common options to `PySR` include:
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- `binary_operators`, `unary_operators`
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- `niterations`
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- `procs`
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- `populations`
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- `weights`
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- `maxsize`, `maxdepth`
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- `batching`, `batchSize`
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which is `hall_of_fame.csv` by default. It also prints the
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equations to stdout.
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## Operators
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A list of operators can be found on the operators page.
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One can define custom operators in Julia by passing a string:
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when constructing a useable function. This step is optional, but
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is necessary for the `lambda_format` to work.
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One can also edit `operators.jl`.
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## Iterations
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This is the total number of generations that `pysr` will run for.
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I usually set this to a large number, and exit when I am satisfied
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with the equations.
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## Processors
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One can adjust the number of workers used by Julia with the
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`procs` option. You should set this equal to the number of cores
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you want `pysr` to use. This will also run `procs` number of
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populations simultaneously by default.
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## Populations
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By default, `populations=procs`, but you can set a different
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number of populations with this option. More populations may increase
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+
the diversity of equations discovered, though will take longer to train.
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However, it may be more efficient to have `populations>procs`,
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as there are multiple populations running
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on each core.
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## Weighted data
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