Proper pydoc markdown format

pysr/sr.py

@@ -132,122 +132,122 @@ def pysr(X, y, weights=None,
 
     # Arguments
 
-    X (np.ndarray/pandas.DataFrame): 2D array. Rows are examples,
-        columns are features. If pandas DataFrame, the columns are used
+    X (np.ndarray/pandas.DataFrame): 2D array. Rows are examples, \
+        columns are features. If pandas DataFrame, the columns are used \
         for variable names (so make sure they don't contain spaces).
-    y (np.ndarray): 1D array (rows are examples) or 2D array (rows
-        are examples, columns are outputs). Putting in a 2D array will
+    y (np.ndarray): 1D array (rows are examples) or 2D array (rows \
+        are examples, columns are outputs). Putting in a 2D array will \
         trigger a search for equations for each feature of y.
-    weights (np.ndarray): same shape as y. Each element is how to
-        weight the mean-square-error loss for that particular element
+    weights (np.ndarray): same shape as y. Each element is how to \
+        weight the mean-square-error loss for that particular element \
         of y.
-    binary_operators (list): List of strings giving the binary operators
+    binary_operators (list): List of strings giving the binary operators \
         in Julia's Base. Default is ["+", "-", "*", "/",].
-    unary_operators (list): Same but for operators taking a single scalar.
+    unary_operators (list): Same but for operators taking a single scalar. \
         Default is [].
     procs (int): Number of processes (=number of populations running).
-    loss (str): String of Julia code specifying the loss function.
-        Can either be a loss from LossFunctions.jl, or your own
-        loss written as a function. Examples of custom written losses
-        include: `myloss(x, y) = abs(x-y)` for non-weighted, or 
-        `myloss(x, y, w) = w*abs(x-y)` for weighted.
-        Among the included losses, these are as follows. Regression:
-        `LPDistLoss{P}()`, `L1DistLoss()`, `L2DistLoss()` (mean square),
-        `LogitDistLoss()`, `HuberLoss(d)`, `L1EpsilonInsLoss(ϵ)`,
-        `L2EpsilonInsLoss(ϵ)`, `PeriodicLoss(c)`, `QuantileLoss(τ)`.
-        Classification: `ZeroOneLoss()`, `PerceptronLoss()`, `L1HingeLoss()`,
-        `SmoothedL1HingeLoss(γ)`, `ModifiedHuberLoss()`, `L2MarginLoss()`,
+    loss (str): String of Julia code specifying the loss function. \
+        Can either be a loss from LossFunctions.jl, or your own \
+        loss written as a function. Examples of custom written losses \
+        include: `myloss(x, y) = abs(x-y)` for non-weighted, or  \
+        `myloss(x, y, w) = w*abs(x-y)` for weighted. \
+        Among the included losses, these are as follows. Regression: \
+        `LPDistLoss{P}()`, `L1DistLoss()`, `L2DistLoss()` (mean square), \
+        `LogitDistLoss()`, `HuberLoss(d)`, `L1EpsilonInsLoss(ϵ)`, \
+        `L2EpsilonInsLoss(ϵ)`, `PeriodicLoss(c)`, `QuantileLoss(τ)`. \
+        Classification: `ZeroOneLoss()`, `PerceptronLoss()`, `L1HingeLoss()`, \
+        `SmoothedL1HingeLoss(γ)`, `ModifiedHuberLoss()`, `L2MarginLoss()`, \
         `ExpLoss()`, `SigmoidLoss()`, `DWDMarginLoss(q)`.
     populations (int): Number of populations running.
-    niterations (int): Number of iterations of the algorithm to run. The best
-        equations are printed, and migrate between populations, at the
+    niterations (int): Number of iterations of the algorithm to run. The best \
+        equations are printed, and migrate between populations, at the \
         end of each.
-    ncyclesperiteration (int): Number of total mutations to run, per 10
+    ncyclesperiteration (int): Number of total mutations to run, per 10 \
         samples of the population, per iteration.
     alpha (float): Initial temperature.
     annealing (bool): Whether to use annealing. You should (and it is default).
-    fractionReplaced (float): How much of population to replace with migrating
+    fractionReplaced (float): How much of population to replace with migrating \
         equations from other populations.
-    fractionReplacedHof (float): How much of population to replace with migrating
+    fractionReplacedHof (float): How much of population to replace with migrating \
         equations from hall of fame.
     npop (int): Number of individuals in each population
     parsimony (float): Multiplicative factor for how much to punish complexity.
     migration (bool): Whether to migrate.
     hofMigration (bool): Whether to have the hall of fame migrate.
-    shouldOptimizeConstants (bool): Whether to numerically optimize
+    shouldOptimizeConstants (bool): Whether to numerically optimize \
         constants (Nelder-Mead/Newton) at the end of each iteration.
     topn (int): How many top individuals migrate from each population.
-    perturbationFactor (float): Constants are perturbed by a max
-        factor of (perturbationFactor*T + 1). Either multiplied by this
+    perturbationFactor (float): Constants are perturbed by a max \
+        factor of (perturbationFactor*T + 1). Either multiplied by this \
         or divided by this.
     weightAddNode (float): Relative likelihood for mutation to add a node
     weightInsertNode (float): Relative likelihood for mutation to insert a node
     weightDeleteNode (float): Relative likelihood for mutation to delete a node
     weightDoNothing (float): Relative likelihood for mutation to leave the individual
-    weightMutateConstant (float): Relative likelihood for mutation to change
+    weightMutateConstant (float): Relative likelihood for mutation to change \
         the constant slightly in a random direction.
-    weightMutateOperator (float): Relative likelihood for mutation to swap
+    weightMutateOperator (float): Relative likelihood for mutation to swap \
         an operator.
-    weightRandomize (float): Relative likelihood for mutation to completely
+    weightRandomize (float): Relative likelihood for mutation to completely \
         delete and then randomly generate the equation
-    weightSimplify (float): Relative likelihood for mutation to simplify
+    weightSimplify (float): Relative likelihood for mutation to simplify \
         constant parts by evaluation
     timeout (float): Time in seconds to timeout search
     equation_file (str): Where to save the files (.csv separated by |)
     verbosity (int): What verbosity level to use. 0 means minimal print statements.
     progress (bool): Whether to use a progress bar instead of printing to stdout.
     maxsize (int): Max size of an equation.
-    maxdepth (int): Max depth of an equation. You can use both maxsize and maxdepth.
+    maxdepth (int): Max depth of an equation. You can use both maxsize and maxdepth. \
         maxdepth is by default set to = maxsize, which means that it is redundant.
-    fast_cycle (bool): (experimental) - batch over population subsamples. This
-        is a slightly different algorithm than regularized evolution, but does cycles
+    fast_cycle (bool): (experimental) - batch over population subsamples. This \
+        is a slightly different algorithm than regularized evolution, but does cycles \
         15% faster. May be algorithmically less efficient.
-    variable_names (list): a list of names for the variables, other
+    variable_names (list): a list of names for the variables, other \
         than "x0", "x1", etc.
-    batching (bool): whether to compare population members on small batches
-        during evolution. Still uses full dataset for comparing against
+    batching (bool): whether to compare population members on small batches \
+        during evolution. Still uses full dataset for comparing against \
         hall of fame.
     batchSize (int): the amount of data to use if doing batching.
-    select_k_features (None/int), whether to run feature selection in
-        Python using random forests, before passing to the symbolic regression
-        code. None means no feature selection; an int means select that many
+    select_k_features (None/int), whether to run feature selection in \
+        Python using random forests, before passing to the symbolic regression \
+        code. None means no feature selection; an int means select that many \
         features.
-    warmupMaxsizeBy (float): whether to slowly increase max size from
-        a small number up to the maxsize (if greater than 0).
-        If greater than 0, says the fraction of training time at which
+    warmupMaxsizeBy (float): whether to slowly increase max size from \
+        a small number up to the maxsize (if greater than 0). \
+        If greater than 0, says the fraction of training time at which \
         the current maxsize will reach the user-passed maxsize.
-    constraints (dict): Dictionary of `int` (unary operators)
-        or tuples of two `int`s (binary),
-        this enforces maxsize constraints on the individual
-        arguments of operators. e.g., `'pow': (-1, 1)`
-        says that power laws can have any complexity left argument, but only
+    constraints (dict): Dictionary of `int` (unary operators) \
+        or tuples of two `int`s (binary), \
+        this enforces maxsize constraints on the individual \
+        arguments of operators. e.g., `'pow': (-1, 1)` \
+        says that power laws can have any complexity left argument, but only \
         1 complexity exponent. Use this to force more interpretable solutions.
-    useFrequency (bool): whether to measure the frequency of complexities,
-        and use that instead of parsimony to explore equation space. Will
+    useFrequency (bool): whether to measure the frequency of complexities, \
+        and use that instead of parsimony to explore equation space. Will \
         naturally find equations of all complexities.
     julia_optimization (int): Optimization level (0, 1, 2, 3)
     tempdir (str/None): directory for the temporary files
     delete_tempfiles (bool): whether to delete the temporary files after finishing
-    julia_project (str/None): a Julia environment location containing
-        a Project.toml (and potentially the source code for SymbolicRegression.jl).
-        Default gives the Python package directory, where a Project.toml file
+    julia_project (str/None): a Julia environment location containing \
+        a Project.toml (and potentially the source code for SymbolicRegression.jl). \
+        Default gives the Python package directory, where a Project.toml file \
         should be present from the install.
-    user_input (bool): Whether to ask for user input or not for installing (to
+    user_input (bool): Whether to ask for user input or not for installing (to \
         be used for automated scripts). Will choose to install when asked.
     update (bool): Whether to automatically update Julia packages.
-    temp_equation_file (bool): Whether to put the hall of fame file in
-        the temp directory. Deletion is then controlled with the
+    temp_equation_file (bool): Whether to put the hall of fame file in \
+        the temp directory. Deletion is then controlled with the \
         delete_tempfiles argument.
-    output_jax_format (bool): Whether to create a 'jax_format' column in the output,
+    output_jax_format (bool): Whether to create a 'jax_format' column in the output, \
         containing jax-callable functions and the default parameters in a jax array.
-    output_torch_format (bool): Whether to create a 'torch_format' column in the output,
+    output_torch_format (bool): Whether to create a 'torch_format' column in the output, \
         containing a torch module with trainable parameters.
     
     # Returns
 
-    equations (pd.DataFrame/list): Results dataframe,
-        giving complexity, MSE, and equations (as strings), as well as functional
-        forms. If list, each element corresponds to a dataframe of equations
+    equations (pd.DataFrame/list): Results dataframe, \
+        giving complexity, MSE, and equations (as strings), as well as functional \
+        forms. If list, each element corresponds to a dataframe of equations \
         for each output.
     """
     if binary_operators is None: