8440251713

Committed 26 Mar 2024 05:22PM UTC coverage: 93.575%. First build

Build # 8440251713

Build Type

Pull #587

github

Committed by

web-flow

Commit Message

Merge de0ce3198 into 5df1590c0

Pull Request Pull Request #587: Revert GitHub-based registry for backend

Run Details

1034 of 1105 relevant lines covered (93.57%)

2.55 hits per line

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92.0

/pysr/export_numpy.py

"""Code for exporting discovered expressions to numpy"""
import warnings

import numpy as np
import pandas as pd
from sympy import lambdify


def sympy2numpy(eqn, sympy_symbols, *, selection=None):
    return CallableEquation(eqn, sympy_symbols, selection=selection)


class CallableEquation:
    """Simple wrapper for numpy lambda functions built with sympy"""

    def __init__(self, eqn, sympy_symbols, selection=None):
        self._sympy = eqn
        self._sympy_symbols = sympy_symbols
        self._selection = selection

    def __repr__(self):
        return f"PySRFunction(X=>{self._sympy})"

    def __call__(self, X):
        expected_shape = (X.shape[0],)
        if isinstance(X, pd.DataFrame):
            # Lambda function takes as argument:
            return self._lambda(
                **{k: X[k].values for k in map(str, self._sympy_symbols)}
            ) * np.ones(expected_shape)
        if self._selection is not None:
            if X.shape[1] != len(self._selection):
                warnings.warn(
                    "`X` should be of shape (n_samples, len(self._selection)). "
                    "Automatically filtering `X` to selection. "
                    "Note: Filtered `X` column order may not match column order in fit "
                    "this may lead to incorrect predictions and other errors."
                )
                X = X[:, self._selection]
        return self._lambda(*X.T) * np.ones(expected_shape)

    @property
    def _lambda(self):
        return lambdify(self._sympy_symbols, self._sympy)

1	"""Code for exporting discovered expressions to numpy"""
2	import warnings	3✔
3
4	import numpy as np	3✔
5	import pandas as pd	3✔
6	from sympy import lambdify	3✔
7
8
9	def sympy2numpy(eqn, sympy_symbols, *, selection=None):	3✔
10	return CallableEquation(eqn, sympy_symbols, selection=selection)	3✔
11
12
13	class CallableEquation:	3✔
14	"""Simple wrapper for numpy lambda functions built with sympy"""
15
16	def __init__(self, eqn, sympy_symbols, selection=None):	3✔
17	self._sympy = eqn	3✔
18	self._sympy_symbols = sympy_symbols	3✔
19	self._selection = selection	3✔
20
21	def __repr__(self):	3✔
22	return f"PySRFunction(X=>{self._sympy})"	3✔
23
24	def __call__(self, X):	3✔
25	expected_shape = (X.shape[0],)	3✔
26	if isinstance(X, pd.DataFrame):	3✔
27	# Lambda function takes as argument:
28	return self._lambda(	3✔
29	**{k: X[k].values for k in map(str, self._sympy_symbols)}
30	) * np.ones(expected_shape)
31	if self._selection is not None:	3✔
32	if X.shape[1] != len(self._selection):	3✔
33	warnings.warn(	×
34	"`X` should be of shape (n_samples, len(self._selection)). "
35	"Automatically filtering `X` to selection. "
36	"Note: Filtered `X` column order may not match column order in fit "
37	"this may lead to incorrect predictions and other errors."
38	)
39	X = X[:, self._selection]	×
40	return self._lambda(X.T) np.ones(expected_shape)	3✔
41
42	@property	3✔
43	def _lambda(self):	2✔
44	return lambdify(self._sympy_symbols, self._sympy)	3✔

MilesCranmer / PySR / 8440251713

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