123 lines
5.3 KiB
Python
123 lines
5.3 KiB
Python
# Adds on the pareto all the snapped versions of a given expression (all paramters are snapped in the end)
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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import torch.utils.data as utils
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from torch.autograd import Variable
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import copy
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import warnings
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warnings.filterwarnings("ignore")
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import sympy
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from S_snap import integerSnap
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from S_snap import zeroSnap
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from S_snap import rationalSnap
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from S_get_symbolic_expr_error import get_symbolic_expr_error
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from get_pareto import Point, ParetoSet
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from S_brute_force_number import brute_force_number
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from sympy import preorder_traversal, count_ops
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from sympy.abc import x,y
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from sympy.parsing.sympy_parser import parse_expr
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from sympy import Symbol, lambdify, N, simplify, powsimp
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from RPN_to_eq import RPN_to_eq
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from S_get_number_DL_snapped import get_number_DL_snapped
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# parameters: path to data, math (not RPN) expression
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def add_bf_on_numbers_on_pareto(pathdir, filename, PA, math_expr):
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def unsnap_recur(expr, param_dict, unsnapped_param_dict):
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"""Recursively transform each numerical value into a learnable parameter."""
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import sympy
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from sympy import Symbol
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if isinstance(expr, sympy.numbers.Float) or isinstance(expr, sympy.numbers.Integer) or isinstance(expr, sympy.numbers.Rational) or isinstance(expr, sympy.numbers.Pi):
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used_param_names = list(param_dict.keys()) + list(unsnapped_param_dict)
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unsnapped_param_name = get_next_available_key(used_param_names, "p", is_underscore=False)
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unsnapped_param_dict[unsnapped_param_name] = float(expr)
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unsnapped_expr = Symbol(unsnapped_param_name)
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return unsnapped_expr
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elif isinstance(expr, sympy.symbol.Symbol):
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return expr
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else:
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unsnapped_sub_expr_list = []
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for sub_expr in expr.args:
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unsnapped_sub_expr = unsnap_recur(sub_expr, param_dict, unsnapped_param_dict)
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unsnapped_sub_expr_list.append(unsnapped_sub_expr)
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return expr.func(*unsnapped_sub_expr_list)
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def get_next_available_key(iterable, key, midfix="", suffix="", is_underscore=True):
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"""Get the next available key that does not collide with the keys in the dictionary."""
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if key + suffix not in iterable:
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return key + suffix
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else:
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i = 0
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underscore = "_" if is_underscore else ""
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while "{}{}{}{}{}".format(key, underscore, midfix, i, suffix) in iterable:
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i += 1
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new_key = "{}{}{}{}{}".format(key, underscore, midfix, i, suffix)
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return new_key
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eq = parse_expr(str(math_expr))
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expr = eq
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# Get the numbers appearing in the expression
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is_atomic_number = lambda expr: expr.is_Atom and expr.is_number
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eq_numbers = [subexpression for subexpression in preorder_traversal(expr) if is_atomic_number(subexpression)]
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# Do bf on one parameter at a time
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bf_on_numbers_expr = []
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for w in range(len(eq_numbers)):
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try:
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param_dict = {}
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unsnapped_param_dict = {'p':1}
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eq_ = unsnap_recur(expr,param_dict,unsnapped_param_dict)
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eq = eq_
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np.savetxt(pathdir+"number_for_bf_%s.txt" %w, [eq_numbers[w]])
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brute_force_number(pathdir,"number_for_bf_%s.txt" %w)
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# Load the predictions made by the bf code
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bf_numbers = np.loadtxt("results.dat",usecols=(1,),dtype="str")
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new_numbers = copy.deepcopy(eq_numbers)
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# replace the number under consideration by all the proposed bf numbers
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for kk in range(len(bf_numbers)):
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eq = eq_
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new_numbers[w] = parse_expr(RPN_to_eq(bf_numbers[kk]))
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jj = 0
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for parm in unsnapped_param_dict:
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if parm!="p":
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eq = eq.subs(parm, new_numbers[jj])
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jj = jj + 1
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bf_on_numbers_expr = bf_on_numbers_expr + [eq]
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except:
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continue
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for i in range(len(bf_on_numbers_expr)):
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try:
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# Calculate the error of the new, snapped expression
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snapped_error = get_symbolic_expr_error(pathdir,filename,str(bf_on_numbers_expr[i]))
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# Calculate the complexity of the new, snapped expression
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expr = simplify(powsimp(bf_on_numbers_expr[i]))
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is_atomic_number = lambda expr: expr.is_Atom and expr.is_number
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numbers_expr = [subexpression for subexpression in preorder_traversal(expr) if is_atomic_number(subexpression)]
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snapped_complexity = 0
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for j in numbers_expr:
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snapped_complexity = snapped_complexity + get_number_DL_snapped(float(j))
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# Add the complexity due to symbols
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n_variables = len(expr.free_symbols)
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n_operations = len(count_ops(expr,visual=True).free_symbols)
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if n_operations!=0 or n_variables!=0:
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snapped_complexity = snapped_complexity + (n_variables+n_operations)*np.log2((n_variables+n_operations))
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PA.add(Point(x=snapped_complexity, y=snapped_error, data=str(expr)))
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except:
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continue
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return(PA)
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