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Commit
·
c6f5c09
1
Parent(s):
d423f0c
Refactor LaTeX table to export_latex.py
Browse files- pysr/export_latex.py +62 -0
- pysr/sr.py +4 -54
pysr/export_latex.py
CHANGED
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@@ -1,6 +1,8 @@
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"""Functions to help export PySR equations to LaTeX."""
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import sympy
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from sympy.printing.latex import LatexPrinter
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class PreciseLatexPrinter(LatexPrinter):
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@@ -51,3 +53,63 @@ def generate_table_environment(columns=["equation", "complexity", "loss"]):
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bottom_latex_table = "\n".join(bottom_pieces)
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return top_latex_table, bottom_latex_table
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"""Functions to help export PySR equations to LaTeX."""
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import sympy
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from sympy.printing.latex import LatexPrinter
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import pandas as pd
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from typing import List
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class PreciseLatexPrinter(LatexPrinter):
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bottom_latex_table = "\n".join(bottom_pieces)
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return top_latex_table, bottom_latex_table
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def generate_table(
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equations: List[pd.DataFrame],
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indices: List[List[int]],
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precision=3,
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columns=["equation", "complexity", "loss", "score"],
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):
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latex_top, latex_bottom = generate_table_environment(columns)
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latex_equations = [
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[to_latex(eq, prec=precision) for eq in equation_set["sympy_format"]]
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for equation_set in equations
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]
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all_latex_table_str = []
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for output_feature, index_set in enumerate(indices):
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latex_table_content = []
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for i in index_set:
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latex_equation = latex_equations[output_feature][i]
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complexity = str(equations[output_feature].iloc[i]["complexity"])
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loss = to_latex(
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sympy.Float(equations[output_feature].iloc[i]["loss"]),
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prec=precision,
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)
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score = to_latex(
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sympy.Float(equations[output_feature].iloc[i]["score"]),
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prec=precision,
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)
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row_pieces = []
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for col in columns:
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if col == "equation":
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row_pieces.append(latex_equation)
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elif col == "complexity":
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row_pieces.append(complexity)
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elif col == "loss":
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row_pieces.append(loss)
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elif col == "score":
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row_pieces.append(score)
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else:
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raise ValueError(f"Unknown column: {col}")
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row_pieces = ["$" + piece + "$" for piece in row_pieces]
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latex_table_content.append(
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" & ".join(row_pieces) + r" \\",
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)
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this_latex_table = "\n".join(
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[
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latex_top,
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*latex_table_content,
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latex_bottom,
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]
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)
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all_latex_table_str.append(this_latex_table)
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return "\n\n".join(all_latex_table_str)
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pysr/sr.py
CHANGED
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@@ -27,7 +27,7 @@ from .julia_helpers import (
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import_error_string,
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)
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from .export_numpy import CallableEquation
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from .export_latex import to_latex,
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from .deprecated import make_deprecated_kwargs_for_pysr_regressor
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@@ -2033,8 +2033,6 @@ class PySRRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):
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else:
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indices = list(range(len(self.equations_)))
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latex_top, latex_bottom = generate_table_environment(columns)
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-
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equations = self.equations_
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if isinstance(indices[0], int):
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@@ -2044,57 +2042,9 @@ class PySRRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):
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assert len(indices) == self.nout_
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]
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all_latex_table_str = []
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for output_feature, index_set in enumerate(indices):
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latex_table_content = []
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for i in index_set:
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latex_equation = latex_equations[output_feature][i]
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complexity = str(equations[output_feature].iloc[i]["complexity"])
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loss = to_latex(
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sympy.Float(equations[output_feature].iloc[i]["loss"]),
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prec=precision,
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)
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score = to_latex(
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sympy.Float(equations[output_feature].iloc[i]["score"]),
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prec=precision,
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)
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row_pieces = []
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for col in columns:
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if col == "equation":
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row_pieces.append(latex_equation)
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elif col == "complexity":
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row_pieces.append(complexity)
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elif col == "loss":
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row_pieces.append(loss)
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elif col == "score":
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row_pieces.append(score)
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else:
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raise ValueError(f"Unknown column: {col}")
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row_pieces = ["$" + piece + "$" for piece in row_pieces]
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latex_table_content.append(
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" & ".join(row_pieces) + r" \\",
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)
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all_latex_table_str.append(
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"\n".join(
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[
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latex_top,
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*latex_table_content,
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latex_bottom,
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]
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)
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)
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return "\n\n".join(all_latex_table_str)
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def _denoise(X, y, Xresampled=None, random_state=None):
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import_error_string,
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)
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from .export_numpy import CallableEquation
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from .export_latex import to_latex, generate_table
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from .deprecated import make_deprecated_kwargs_for_pysr_regressor
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else:
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indices = list(range(len(self.equations_)))
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equations = self.equations_
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if isinstance(indices[0], int):
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assert len(indices) == self.nout_
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return generate_table(
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equations, indices=indices, precision=precision, columns=columns
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)
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def _denoise(X, y, Xresampled=None, random_state=None):
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