203 lines
9.5 KiB
Python
203 lines
9.5 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 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, Rational, symbols, S,Float
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import re
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from S_get_number_DL_snapped import get_number_DL_snapped
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def intify(expr):
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floats = S(expr).atoms(Float)
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ints = [i for i in floats if int(i) == i]
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return expr.xreplace(dict(zip(ints, [int(i) for i in ints])))
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# parameters: path to data, math (not RPN) expression
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def add_snap_expr_on_pareto(pathdir, filename, math_expr, PA, DR_file=""):
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input_data = np.loadtxt(pathdir+filename)
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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, "pp", 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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#
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# # Do zero snap one parameter at a time
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# zero_snapped_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 = {'pp':1}
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# eq = unsnap_recur(expr,param_dict,unsnapped_param_dict)
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# new_numbers = zeroSnap(eq_numbers,w+1)
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# for kk in range(len(new_numbers)):
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# eq_numbers[new_numbers[kk][0]] = new_numbers[kk][1]
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# jj = 0
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# for parm in unsnapped_param_dict:
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# if parm!="pp":
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# eq = eq.subs(parm, eq_numbers[jj])
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# jj = jj + 1
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# zero_snapped_expr = zero_snapped_expr + [eq]
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# except:
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# continue
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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 integer snap one parameter at a time
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integer_snapped_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 = {'pp':1}
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eq = unsnap_recur(expr,param_dict,unsnapped_param_dict)
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del unsnapped_param_dict["pp"]
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temp_unsnapped_param_dict = copy.deepcopy(unsnapped_param_dict)
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new_numbers = integerSnap(eq_numbers,w+1)
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new_numbers = {"pp"+str(k): v for k, v in new_numbers.items()}
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temp_unsnapped_param_dict.update(new_numbers)
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#for kk in range(len(new_numbers)):
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# eq_numbers[new_numbers[kk][0]] = new_numbers[kk][1]
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new_eq = re.sub(r"(pp\d*)",r"{\1}",str(eq))
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new_eq = new_eq.format_map(temp_unsnapped_param_dict)
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integer_snapped_expr = integer_snapped_expr + [parse_expr(new_eq)]
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except:
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continue
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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 rational snap one parameter at a time
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rational_snapped_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 = {'pp':1}
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eq = unsnap_recur(expr,param_dict,unsnapped_param_dict)
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del unsnapped_param_dict["pp"]
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temp_unsnapped_param_dict = copy.deepcopy(unsnapped_param_dict)
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new_numbers = rationalSnap(eq_numbers,w+1)
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new_numbers = {"pp"+str(k): v for k, v in new_numbers.items()}
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temp_unsnapped_param_dict.update(new_numbers)
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#for kk in range(len(new_numbers)):
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# eq_numbers_snap[new_numbers[kk][0]] = new_numbers[kk][1][1:3]
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new_eq = re.sub(r"(pp\d*)",r"{\1}",str(eq))
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new_eq = new_eq.format_map(temp_unsnapped_param_dict)
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rational_snapped_expr = rational_snapped_expr + [parse_expr(new_eq)]
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except:
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continue
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snapped_expr = np.append(integer_snapped_expr,rational_snapped_expr)
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# snapped_expr = np.append(snapped_expr,rational_snapped_expr)
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for i in range(len(snapped_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(input_data,str(snapped_expr[i]))
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# Calculate the complexity of the new, snapped expression
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#expr = simplify(powsimp(snapped_expr[i]))
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expr = snapped_expr[i]
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for s in (expr.free_symbols):
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s = symbols(str(s), real = True)
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expr = parse_expr(str(snapped_expr[i]),locals())
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expr = intify(expr)
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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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if DR_file=="":
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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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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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# If a da file is provided, replace the variables with the actual ones before calculating the complexity
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else:
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dr_data = np.loadtxt(DR_file,dtype="str",delimiter=",")
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expr = str(expr)
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old_vars = ["x%s" %k for k in range(len(dr_data)-3)]
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for i_dr in range(len(old_vars)):
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expr = expr.replace(old_vars[i_dr],"("+dr_data[i_dr+2]+")")
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expr = "("+dr_data[1]+")*(" + expr +")"
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expr = parse_expr(expr)
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for s in (expr.free_symbols):
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s = symbols(str(s), real = True)
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#expr = simplify(parse_expr(str(expr),locals()))
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expr = parse_expr(str(expr),locals())
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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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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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