def test_tangible(self):
eq = ['sin(x*2)', 'exp(x)+x', 'x/3', '3*exp(2/x)']
grammar_weights = "../save_model/grammar_ae_model.pt"
print(grammar_weights)
grammar_model = equation_vae.EquationGrammarModel(grammar_weights,
latent_rep_size=25)
# grammar_model = equation_vae.EquationGrammarModel(grammar_weights, latent_rep_size=LATENT_REP_SIZE)
z, _none = grammar_model.encode(eq)
for i, s in enumerate(grammar_model.decode(z)):
print(eq[i] + " --> " + s)
from __future__ import division
import sys
import equation_vae
import numpy as np
from numpy import sin, exp, cos
from matplotlib import pyplot as plt
import pdb
# 1. load grammar VAE
grammar_weights = "pretrained/eq_vae_grammar_h100_c234_L25_E50_batchB.hdf5"
grammar_weights = "eq_vae_grammar_h100_c234_L25_E50_batchB.hdf5"
print(grammar_weights)
grammar_model = equation_vae.EquationGrammarModel(grammar_weights,
latent_rep_size=25)
# 2. let's encode and decode some example equations
eq = ['sin(x*2)', 'exp(x)+x', 'x/3', '3*exp(2/x)']
# z: encoded latent points
# NOTE: this operation returns the mean of the encoding distribution
# if you would like it to sample from that distribution instead
# replace line 62 in equation_vae.py with: return self.vae.encoder.predict(one_hot)
z = grammar_model.encode(eq)
# mol: decoded equations
# NOTE: decoding is stochastic so calling this function many
# times for the same latent point will return different answers
# let's plot how well the true functions match the decoded functions
domain = np.linspace(-10, 10)
import sys
sys.path.insert(0, '../../../')
import equation_vae
from numpy import * # need this for evaluating equations
from sparse_gp import SparseGP
import scipy.stats as sps
import numpy as np
import os.path
import os
import copy
import time
# here we load the grammar VAE in order to see if the equations parse correctly
# diff_model = equation_vae.EquationGrammarModel("../eq_vae_grammar_h100_c234_L25_E50_batchB.hdf5",25)
diff_model = equation_vae.EquationGrammarModel(
"../../../pretrained/eq_vae_grammar_h100_c234_L25_E50_batchB.hdf5")
def decode_from_latent_space(latent_points, grammar_model):
decode_attempts = 25
decoded_molecules = []
for i in range(decode_attempts):
current_decoded_molecules = grammar_model.decode(latent_points)
decoded_molecules.append(current_decoded_molecules)
# We see which ones are decoded by rdkit
x = 0 # make x a dummy variable in order to see if equations parse correctly
rdkit_molecules = []
for i in range(decode_attempts):
rdkit_molecules.append([])
