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ssd_functions.py
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import os
import json
import sys
from datetime import datetime
import random
from pathlib import Path
from argparse import ArgumentParser
from collections import namedtuple
from tqdm import tqdm
import numpy as np
import pandas as pd
import sklearn
from sklearn.model_selection import KFold, train_test_split
from sklearn.model_selection import ShuffleSplit,StratifiedShuffleSplit,GroupKFold,GroupShuffleSplit,LeaveOneGroupOut
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
import tensorflow as tf
gpus = tf.config.experimental.list_physical_devices('GPU')
if len(gpus) > 0:
tf.config.experimental.set_memory_growth(gpus[0], True)
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
os.environ['TF_MKL_REUSE_PRIMITIVE_MEMORY'] = '0'
rand_seed = 0
random.seed(rand_seed)
np.random.seed(rand_seed)
tf.random.set_seed(rand_seed)
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.callbacks import ModelCheckpoint
import tensorflow_addons as tfa
import nfp
import rdkit
import rdkit.Chem
from rdkit.Chem.Descriptors import MolWt
from rdkit.Chem.rdMolDescriptors import CalcNumHBA, CalcNumHBD, CalcTPSA, CalcLabuteASA
from rdkit.Chem.GraphDescriptors import Chi0v, Chi1v, BalabanJ
### data processing functions
def get_solvent_system(row):
if type(row['can_smiles_solvent2']) == float:
smi_2_placeholder = 'None'
else:
smi_2_placeholder = row['can_smiles_solvent2']
if type(row['can_smiles_solvent3']) == float:
smi_3_placeholder = 'None'
else:
smi_3_placeholder = row['can_smiles_solvent3']
sorted_solvents = sorted([row['can_smiles_solvent1'], smi_2_placeholder, smi_3_placeholder])
return f"{sorted_solvents[0]}/{sorted_solvents[1]}/{sorted_solvents[2]}"
def process_all_data(data):
data.reset_index(inplace=True)
print("\nData after loading:\n\t",data.shape, data.columns.shape,list(data.columns))
cols_chosen = ['can_smiles_solute', 'can_smiles_solvent1', 'can_smiles_solvent2', 'can_smiles_solvent3', 'mol_frac_solvent1', 'mol_frac_solvent2', 'mol_frac_solvent3', 'T_K', 'DGsolv', 'DGsolv_converted']
data = data[cols_chosen]
print(f"Columns chosen:{list(data.columns)}")
data['tag'] = data['DGsolv'].apply(lambda x: 'converted' if pd.isna(x) else 'exp')
cols_to_dropna = ['can_smiles_solute', 'can_smiles_solvent1', 'mol_frac_solvent1']
print(f"Dropping datapoints w/ NaN SMILES or mole fractions...")
print(f"Columns to dropna: {cols_to_dropna}")
print("\tBefore:\t", data.shape)
data = data.dropna(subset = cols_to_dropna)
print("\tAfter:\t",data.shape)
dummy_smi = 'C'
print(f"Using dummy SMILES of '{dummy_smi}'...")
nan_smiles2 = data.loc[data.can_smiles_solvent2.isna() == True, :]
if nan_smiles2.shape[0] > 0:
print(f"\tReplacing {nan_smiles2.shape[0]} missing solvent 2 SMILES with '{dummy_smi}'")
data.loc[(data.can_smiles_solvent2.isna()), 'can_smiles_solvent2'] = dummy_smi
print(f"Done.")
else:
print("Could not find any NaN SMILES to replace for solvent 2. Shape:",nan_smiles2.shape)
nan_smiles3 = data.loc[data.can_smiles_solvent3.isna() == True,:]
if nan_smiles3.shape[0] > 0:
print(f"\tReplacing {nan_smiles3.shape[0]} missing solvent 3 SMILES with '{dummy_smi}'")
data.loc[(data.can_smiles_solvent3.isna()), 'can_smiles_solvent3'] = dummy_smi
print(f"Done.")
else:
print("Could not find any NaN SMILES to replace for solvent 3. Shape:",nan_smiles3.shape)
data['target'] = data.apply(lambda row: row['DGsolv_converted'] if row['tag'] == 'converted' else row['DGsolv'], axis=1)
target = 'target'
print(f"Dropping datapoints w/ NaN {target}...")
print("\tBefore:\t", data.shape)
data = data.dropna(subset=[target])
print("\tAfter:\t", data.shape)
target_const = tf.constant(list(data[target]))
target_str = f"{target}_constant"
data[target_str] = target_const
data['solvent_system'] = data.apply(get_solvent_system, axis=1)
return data
def process_cosmors_data(data):
data.reset_index(inplace=True)
print("\nData after loading:\n\t",data.shape, data.columns.shape,list(data.columns))
cols_chosen = ['can_smiles_solute', 'can_smiles_solvent1', 'can_smiles_solvent2', 'can_smiles_solvent3', 'mol_frac_solvent1', 'mol_frac_solvent2', 'mol_frac_solvent3', 'T_K', 'DGsolv_cosmors']
data = data[cols_chosen]
data.mol_frac_solvent2.fillna(0, inplace=True)
data.mol_frac_solvent3.fillna(0, inplace=True)
print(f"Columns chosen:{list(data.columns)}")
data['tag'] = 'cosmors' # exp / converted / cosmors
# data['DGsolv'].apply(lambda x: 'converted' if pd.isna(x) else 'exp')
cols_to_dropna = ['can_smiles_solute', 'can_smiles_solvent1', 'mol_frac_solvent1']
print(f"Dropping datapoints w/ NaN SMILES or mole fractions...")
print(f"Columns to dropna: {cols_to_dropna}")
print("\tBefore:\t", data.shape)
data = data.dropna(subset = cols_to_dropna)
print("\tAfter:\t",data.shape)
dummy_smi = 'C'
print(f"Using dummy SMILES of '{dummy_smi}'...")
nan_smiles2 = data.loc[data.can_smiles_solvent2.isna() == True, :]
if nan_smiles2.shape[0] > 0:
print(f"\tReplacing {nan_smiles2.shape[0]} missing solvent 2 SMILES with '{dummy_smi}'")
data.loc[(data.can_smiles_solvent2.isna()), 'can_smiles_solvent2'] = dummy_smi
print(f"Done.")
else:
print("Could not find any NaN SMILES to replace for solvent 2. Shape:",nan_smiles2.shape)
nan_smiles3 = data.loc[data.can_smiles_solvent3.isna() == True,:]
if nan_smiles3.shape[0] > 0:
print(f"\tReplacing {nan_smiles3.shape[0]} missing solvent 3 SMILES with '{dummy_smi}'")
data.loc[(data.can_smiles_solvent3.isna()), 'can_smiles_solvent3'] = dummy_smi
print(f"Done.")
else:
print("Could not find any NaN SMILES to replace for solvent 3. Shape:",nan_smiles3.shape)
data['target'] = data['DGsolv_cosmors']
# data.apply(lambda row: row['DGsolv_converted'] if row['tag'] == 'converted' else row['DGsolv'], axis=1)
target = 'target'
print(f"Target is DGsolv_cosmors")
print(f"Dropping datapoints w/ NaN {target}...")
print("\tBefore:\t", data.shape)
data = data.dropna(subset=[target])
print("\tAfter:\t", data.shape)
target_const = tf.constant(list(data[target]))
target_str = f"{target}_constant"
data[target_str] = target_const
data['solvent_system'] = data.apply(get_solvent_system, axis=1)
return data
### Train Test Split Functions
def atom_features(atom):
atom_type = namedtuple('Atom', ['totalHs', 'symbol', 'aromatic', 'fc', 'ring_size'])
return str((atom.GetTotalNumHs(),
atom.GetSymbol(),
atom.GetIsAromatic(),
atom.GetFormalCharge(), # 220829
nfp.preprocessing.features.get_ring_size(atom, max_size=6)
))
def bond_features(bond, flipped=False):
bond_type = namedtuple('Bond', ['bond_type', 'ring_size', 'symbol_1', 'symbol_2'])
if not flipped:
atom1 = bond.GetBeginAtom()
atom2 = bond.GetEndAtom()
else:
atom1 = bond.GetEndAtom()
atom2 = bond.GetBeginAtom()
return str((bond.GetBondType(),
nfp.preprocessing.features.get_ring_size(bond, max_size=6),
atom1.GetSymbol(),
atom2.GetSymbol()
))
def global_features(smiles, row, solute_or_solvent):
mol = rdkit.Chem.MolFromSmiles(smiles)
if solute_or_solvent == 'solute':
return tf.constant([CalcNumHBA(mol),
CalcNumHBD(mol),
CalcLabuteASA(mol),
CalcTPSA(mol),
row['T_K'], # REMOVED TEMPORARILY 2024.03.20
#row['atomcharges_min_solute'],
#row['atomcharges_max_solute'],
#row['dispersionenergies_avg_solute'],
#row['homo_lumo_gap_solute'] REMOVED 4.25.23
])
else:
return tf.constant([CalcNumHBA(mol),
CalcNumHBD(mol),
CalcLabuteASA(mol),
CalcTPSA(mol),
row['T_K'], # REMOVED TEMPORARILY 2024.03.20
#row['atomcharges_min_solvent'],
#row['atomcharges_max_solvent'],
#row['dispersionenergies_avg_solvent'],
#row['homo_lumo_gap_solvent'] REMOVED 4.25.23. Update hardcoded lengths as needed
])
class CustomPreprocessor_NFPx2_ternary(nfp.SmilesPreprocessor):
def construct_feature_matrices(self, smiles, train=None):
features = super(CustomPreprocessor_NFPx2_ternary, self).construct_feature_matrices(smiles, train)
#features['mol_features'] = global_features(smiles)
return features
#output_signature = {**nfp.SmilesPreprocessor.output_signature,
# **{'mol_features': tf.TensorSpec(shape=(1,), dtype=tf.float32) }}
output_signature = {'atom_solute': tf.TensorSpec(shape=(None,), dtype=tf.int32),
'bond_solute': tf.TensorSpec(shape=(None,), dtype=tf.int32),
'connectivity_solute': tf.TensorSpec(shape=(None, 2), dtype=tf.int32),
#'ratio_solute': tf.TensorSpec(shape=(None,), dtype=tf.float32),
'mol_features_solute': tf.TensorSpec(shape=(5,), dtype=tf.float32), #! Change shape as needed
'atom_solvent1': tf.TensorSpec(shape=(None,), dtype=tf.int32),
'bond_solvent1': tf.TensorSpec(shape=(None,), dtype=tf.int32),
'connectivity_solvent1': tf.TensorSpec(shape=(None, 2), dtype=tf.int32),
'ratio_solvent1': tf.TensorSpec(shape=(None,), dtype=tf.float32),
'mol_features_solvent1': tf.TensorSpec(shape=(5,), dtype=tf.float32), #! Change shape as needed
'atom_solvent2': tf.TensorSpec(shape=(None,), dtype=tf.int32),
'bond_solvent2': tf.TensorSpec(shape=(None,), dtype=tf.int32),
'connectivity_solvent2': tf.TensorSpec(shape=(None, 2), dtype=tf.int32),
'ratio_solvent2': tf.TensorSpec(shape=(None,), dtype=tf.float32),
'mol_features_solvent2': tf.TensorSpec(shape=(5,), dtype=tf.float32),
'atom_solvent3': tf.TensorSpec(shape=(None,), dtype=tf.int32),
'bond_solvent3': tf.TensorSpec(shape=(None,), dtype=tf.int32),
'connectivity_solvent3': tf.TensorSpec(shape=(None, 2), dtype=tf.int32),
'ratio_solvent3': tf.TensorSpec(shape=(None,), dtype=tf.float32),
'mol_features_solvent3': tf.TensorSpec(shape=(5,), dtype=tf.float32),
'connectivity_edges': tf.TensorSpec(shape=(None, 2), dtype=tf.int32), # Shape, name, dtype need to match layers.Input in main.py
#'weight_edges': tf.TensorSpec(shape=(None, 128), dtype=tf.float32), # Shape, name, dtype need to match layers.Input in main.py
'weight_edges': tf.TensorSpec(shape=(None, 4), dtype=tf.float32), # Shape, name, dtype need to match layers.Input in main.py
'temp_val': tf.TensorSpec(shape=(None,), dtype=tf.float32),
'num_solvents': tf.TensorSpec(shape=(None,), dtype=tf.float32),
} #! Change shape as needed
def create_tf_dataset_NFPx2_ternary_ShareWeights(df, preprocessor, sample_weight = 1.0, train=True, output_val_col = "DGsolv_constant"):
for _, row in df.iterrows():
inputs_solute = preprocessor.construct_feature_matrices(row['can_smiles_solute'], train=train)
inputs_solvent1 = preprocessor.construct_feature_matrices(row['can_smiles_solvent1'], train=train)
inputs_solvent2 = preprocessor.construct_feature_matrices(row['can_smiles_solvent2'], train=train)
if not train:
one_data_sample_w = 1.0
else:
try:
#one_data_sample_w = np.exp( -np.abs(row['DGsolv'] - row['DGsolv_cosmo']) / ( 1.9872E-3 * 298.15 ))
one_data_sample_w = 1.0
except: #Exp dataset
one_data_sample_w = 1.0
mol_solute = rdkit.Chem.MolFromSmiles(row['can_smiles_solute'])
mol_solvent1 = rdkit.Chem.MolFromSmiles(row['can_smiles_solvent1'])
mol_solvent2 = rdkit.Chem.MolFromSmiles(row['can_smiles_solvent2'])
output_val = tf.constant(row[output_val_col])
"""
How do we choose which edge weights to represent?
Possible solution: change order of edge connections (instead of 0->1, 0->2, 1->2)
0->1, 1->2, 2->0.
This way we can use each source node's properties as a sequential list, which makes sense in a way
"""
if row['mol_frac_solvent3'] > 0:
inputs_solvent3 = preprocessor.construct_feature_matrices(row['can_smiles_solvent3'], train=train)
mol_solvent3 = rdkit.Chem.MolFromSmiles(row['can_smiles_solvent3'])
edge_weight = tf.constant([
# Solute is source node
[CalcNumHBA(mol_solute), CalcNumHBD(mol_solute), BalabanJ(mol_solute),
MolWt(mol_solute),
],
# Solvent 1 is source node
[CalcNumHBA(mol_solvent1), CalcNumHBD(mol_solvent1), BalabanJ(mol_solvent1),
MolWt(mol_solvent1),
],
# Solvent 2 is source node
[CalcNumHBA(mol_solvent2), CalcNumHBD(mol_solvent2), BalabanJ(mol_solvent2),
MolWt(mol_solvent2),
],
# Solvent 3 is source node
[CalcNumHBA(mol_solvent3), CalcNumHBD(mol_solvent2), BalabanJ(mol_solvent2),
MolWt(mol_solvent3),
],
])
#edge_connectivity_bidirect = tf.constant([(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]) # bidirectional
edge_connectivity_bidirect = tf.constant([(0, 1), (0, 2), (0, 3), # bidirectional, 4 component
(1, 0), (1, 2), (1, 3),
(2, 0), (2, 1), (2, 3),
(3, 0), (3, 1), (3, 2)])
edge_weight_bidirect = tf.constant([
# Solute is source node (0,1)
[CalcNumHBA(mol_solute), CalcNumHBD(mol_solute), BalabanJ(mol_solute),
MolWt(mol_solute),
],
# Solute is source node (0,2)
[CalcNumHBA(mol_solute), CalcNumHBD(mol_solute), BalabanJ(mol_solute),
MolWt(mol_solute),
],
# Solute is source node (0,3)
[CalcNumHBA(mol_solute), CalcNumHBD(mol_solute), BalabanJ(mol_solute),
MolWt(mol_solute),
],
# Solvent 1 is source node (1,0)
[CalcNumHBA(mol_solvent1), CalcNumHBD(mol_solvent1), BalabanJ(mol_solvent1),
MolWt(mol_solvent1),
],
# Solvent 1 is source node (1,2)
[CalcNumHBA(mol_solvent1), CalcNumHBD(mol_solvent1), BalabanJ(mol_solvent1),
MolWt(mol_solvent1),
],
# Solvent 1 is source node (1,3)
[CalcNumHBA(mol_solvent1), CalcNumHBD(mol_solvent1), BalabanJ(mol_solvent1),
MolWt(mol_solvent1),
],
# Solvent 2 is source node (2,0)
[CalcNumHBA(mol_solvent2), CalcNumHBD(mol_solvent2), BalabanJ(mol_solvent2),
MolWt(mol_solvent2),
],
# Solvent 2 is source node (2,1)
[CalcNumHBA(mol_solvent2), CalcNumHBD(mol_solvent2), BalabanJ(mol_solvent2),
MolWt(mol_solvent2),
],
# Solvent 2 is source node (2,3)
[CalcNumHBA(mol_solvent2), CalcNumHBD(mol_solvent2), BalabanJ(mol_solvent2),
MolWt(mol_solvent2),
],
# Solvent 3 is source node (3,0)
[CalcNumHBA(mol_solvent3), CalcNumHBD(mol_solvent3), BalabanJ(mol_solvent3),
MolWt(mol_solvent3),
],
# Solvent 3 is source node (3,1)
[CalcNumHBA(mol_solvent3), CalcNumHBD(mol_solvent3), BalabanJ(mol_solvent3),
MolWt(mol_solvent3),
],
# Solvent 3 is source node (3,2)
[CalcNumHBA(mol_solvent3), CalcNumHBD(mol_solvent3), BalabanJ(mol_solvent3),
MolWt(mol_solvent3),
],
])
stoich_vec_6edge = tf.constant([[1.0],[1.0],[1.0],
[row['mol_frac_solvent1']], [row['mol_frac_solvent1']], [row['mol_frac_solvent1']],
[row['mol_frac_solvent2']], [row['mol_frac_solvent2']], [row['mol_frac_solvent2']],
[row['mol_frac_solvent3']], [row['mol_frac_solvent3']], [row['mol_frac_solvent3']],
])
elif row['mol_frac_solvent2'] > 0:
solv3_fake_smi = 'C'
#print("USING FAKE SMILES",solv3_fake_smi)
inputs_solvent3 = preprocessor.construct_feature_matrices(solv3_fake_smi, train=train)
mol_solvent3 = rdkit.Chem.MolFromSmiles(solv3_fake_smi)
edge_weight = tf.constant([
# Solute is source node
[CalcNumHBA(mol_solute), CalcNumHBD(mol_solute), BalabanJ(mol_solute),
MolWt(mol_solute),
],
# Solvent 1 is source node
[CalcNumHBA(mol_solvent1), CalcNumHBD(mol_solvent1), BalabanJ(mol_solvent1),
MolWt(mol_solvent1),
],
# Solvent 2 is source node
[CalcNumHBA(mol_solvent2), CalcNumHBD(mol_solvent2), BalabanJ(mol_solvent2),
MolWt(mol_solvent2),
],
# Solvent 3 is source node
[CalcNumHBA(mol_solvent3), CalcNumHBD(mol_solvent2), BalabanJ(mol_solvent2),
MolWt(mol_solvent3),
],
])
#edge_connectivity_bidirect = tf.constant([(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]) # bidirectional
edge_connectivity_bidirect = tf.constant([(0, 1), (0, 2), (0, 3), # bidirectional, 4 component
(1, 0), (1, 2), (1, 3),
(2, 0), (2, 1), (2, 3),
(3, 0), (3, 1), (3, 2)])
edge_weight_bidirect = tf.constant([
# Solute is source node (0,1)
[CalcNumHBA(mol_solute), CalcNumHBD(mol_solute), BalabanJ(mol_solute),
MolWt(mol_solute),
],
# Solute is source node (0,2)
[CalcNumHBA(mol_solute), CalcNumHBD(mol_solute), BalabanJ(mol_solute),
MolWt(mol_solute),
],
# Solute is source node (0,3)
[CalcNumHBA(mol_solute), CalcNumHBD(mol_solute), BalabanJ(mol_solute),
MolWt(mol_solute),
],
# Solvent 1 is source node (1,0)
[CalcNumHBA(mol_solvent1), CalcNumHBD(mol_solvent1), BalabanJ(mol_solvent1),
MolWt(mol_solvent1),
],
# Solvent 1 is source node (1,2)
[CalcNumHBA(mol_solvent1), CalcNumHBD(mol_solvent1), BalabanJ(mol_solvent1),
MolWt(mol_solvent1),
],
# Solvent 1 is source node (1,3)
[CalcNumHBA(mol_solvent1), CalcNumHBD(mol_solvent1), BalabanJ(mol_solvent1),
MolWt(mol_solvent1),
],
# Solvent 2 is source node (2,0)
[CalcNumHBA(mol_solvent2), CalcNumHBD(mol_solvent2), BalabanJ(mol_solvent2),
MolWt(mol_solvent2),
],
# Solvent 2 is source node (2,1)
[CalcNumHBA(mol_solvent2), CalcNumHBD(mol_solvent2), BalabanJ(mol_solvent2),
MolWt(mol_solvent2),
],
# Solvent 2 is source node (2,3)
[CalcNumHBA(mol_solvent2), CalcNumHBD(mol_solvent2), BalabanJ(mol_solvent2),
MolWt(mol_solvent2),
],
# Solvent 3 is source node (3,0)
[CalcNumHBA(mol_solvent3), CalcNumHBD(mol_solvent3), BalabanJ(mol_solvent3),
MolWt(mol_solvent3),
],
# Solvent 3 is source node (3,1)
[CalcNumHBA(mol_solvent3), CalcNumHBD(mol_solvent3), BalabanJ(mol_solvent3),
MolWt(mol_solvent3),
],
# Solvent 3 is source node (3,2)
[CalcNumHBA(mol_solvent3), CalcNumHBD(mol_solvent3), BalabanJ(mol_solvent3),
MolWt(mol_solvent3),
],
])
stoich_vec_6edge = tf.constant([[1.0],[1.0],[1.0],
[row['mol_frac_solvent1']], [row['mol_frac_solvent1']], [row['mol_frac_solvent1']],
[row['mol_frac_solvent2']], [row['mol_frac_solvent2']], [row['mol_frac_solvent2']],
[row['mol_frac_solvent3']], [row['mol_frac_solvent3']], [row['mol_frac_solvent3']],
])
else: # If not multi-solvent
solv2_fake_smi = 'C'
#print("USING FAKE SMILES 2",solv2_fake_smi)
inputs_solvent2 = preprocessor.construct_feature_matrices(solv2_fake_smi, train=train)
mol_solvent2 = rdkit.Chem.MolFromSmiles(solv2_fake_smi)
solv3_fake_smi = 'C'
#print("USING FAKE SMILES 3",solv3_fake_smi)
inputs_solvent3 = preprocessor.construct_feature_matrices(solv3_fake_smi, train=train)
mol_solvent3 = rdkit.Chem.MolFromSmiles(solv3_fake_smi)
edge_weight = tf.constant([
# Solute is source node
[CalcNumHBA(mol_solute), CalcNumHBD(mol_solute), BalabanJ(mol_solute),
MolWt(mol_solute),
],
# Solvent 1 is source node
[CalcNumHBA(mol_solvent1), CalcNumHBD(mol_solvent1), BalabanJ(mol_solvent1),
MolWt(mol_solvent1),
],
# Solvent 2 is source node
[CalcNumHBA(mol_solvent2), CalcNumHBD(mol_solvent2), BalabanJ(mol_solvent2),
MolWt(mol_solvent2),
],
# Solvent 3 is source node
[CalcNumHBA(mol_solvent3), CalcNumHBD(mol_solvent2), BalabanJ(mol_solvent2),
MolWt(mol_solvent3),
],
])
#edge_connectivity_bidirect = tf.constant([(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]) # bidirectional
edge_connectivity_bidirect = tf.constant([(0, 1), (0, 2), (0, 3), # bidirectional, 4 component
(1, 0), (1, 2), (1, 3),
(2, 0), (2, 1), (2, 3),
(3, 0), (3, 1), (3, 2)])
edge_weight_bidirect = tf.constant([
# Solute is source node (0,1)
[CalcNumHBA(mol_solute), CalcNumHBD(mol_solute), BalabanJ(mol_solute),
MolWt(mol_solute),
],
# Solute is source node (0,2)
[CalcNumHBA(mol_solute), CalcNumHBD(mol_solute), BalabanJ(mol_solute),
MolWt(mol_solute),
],
# Solute is source node (0,3)
[CalcNumHBA(mol_solute), CalcNumHBD(mol_solute), BalabanJ(mol_solute),
MolWt(mol_solute),
],
# Solvent 1 is source node (1,0)
[CalcNumHBA(mol_solvent1), CalcNumHBD(mol_solvent1), BalabanJ(mol_solvent1),
MolWt(mol_solvent1),
],
# Solvent 1 is source node (1,2)
[CalcNumHBA(mol_solvent1), CalcNumHBD(mol_solvent1), BalabanJ(mol_solvent1),
MolWt(mol_solvent1),
],
# Solvent 1 is source node (1,3)
[CalcNumHBA(mol_solvent1), CalcNumHBD(mol_solvent1), BalabanJ(mol_solvent1),
MolWt(mol_solvent1),
],
# Solvent 2 is source node (2,0)
[CalcNumHBA(mol_solvent2), CalcNumHBD(mol_solvent2), BalabanJ(mol_solvent2),
MolWt(mol_solvent2),
],
# Solvent 2 is source node (2,1)
[CalcNumHBA(mol_solvent2), CalcNumHBD(mol_solvent2), BalabanJ(mol_solvent2),
MolWt(mol_solvent2),
],
# Solvent 2 is source node (2,3)
[CalcNumHBA(mol_solvent2), CalcNumHBD(mol_solvent2), BalabanJ(mol_solvent2),
MolWt(mol_solvent2),
],
# Solvent 3 is source node (3,0)
[CalcNumHBA(mol_solvent3), CalcNumHBD(mol_solvent3), BalabanJ(mol_solvent3),
MolWt(mol_solvent3),
],
# Solvent 3 is source node (3,1)
[CalcNumHBA(mol_solvent3), CalcNumHBD(mol_solvent3), BalabanJ(mol_solvent3),
MolWt(mol_solvent3),
],
# Solvent 3 is source node (3,2)
[CalcNumHBA(mol_solvent3), CalcNumHBD(mol_solvent3), BalabanJ(mol_solvent3),
MolWt(mol_solvent3),
],
])
stoich_vec_6edge = tf.constant([[1.0],[1.0],[1.0],
[row['mol_frac_solvent1']], [row['mol_frac_solvent1']], [row['mol_frac_solvent1']],
[row['mol_frac_solvent2']], [row['mol_frac_solvent2']], [row['mol_frac_solvent2']],
[row['mol_frac_solvent3']], [row['mol_frac_solvent3']], [row['mol_frac_solvent3']],
])
edge_weight_bidirect_stoichbias = stoich_vec_6edge*edge_weight_bidirect #UNUSED!!
#;;if _ == 1:
#;; print("DIVIDING BY EDGE WEIGHTS")
#;;edge_weight = edge_weight*row['T_K']
yield ({
'atom_solute': inputs_solute['atom'],
'bond_solute': inputs_solute['bond'],
'connectivity_solute': inputs_solute['connectivity'],
#'ratio_solute': tf.constant([row['ratio_solute']]),
'mol_features_solute': global_features(row['can_smiles_solute'], row,'solute'),
'atom_solvent1': inputs_solvent1['atom'],
'bond_solvent1': inputs_solvent1['bond'],
'connectivity_solvent1': inputs_solvent1['connectivity'],
'ratio_solvent1': tf.constant([row['mol_frac_solvent1']]),
'mol_features_solvent1': global_features(row['can_smiles_solvent1'], row,'solvent'),
'atom_solvent2': inputs_solvent2['atom'],
'bond_solvent2': inputs_solvent2['bond'],
'connectivity_solvent2': inputs_solvent2['connectivity'],
'ratio_solvent2': tf.constant([row['mol_frac_solvent2']]),
'mol_features_solvent2': global_features(row['can_smiles_solvent2'], row, 'solvent'),
'atom_solvent3': inputs_solvent3['atom'],
'bond_solvent3': inputs_solvent3['bond'],
'connectivity_solvent3': inputs_solvent3['connectivity'],
'ratio_solvent3': tf.constant([row['mol_frac_solvent3']]),
'mol_features_solvent3': global_features(row['can_smiles_solvent3'], row, 'solvent'),
'connectivity_edges': edge_connectivity_bidirect, #was edge_connectivity
'weight_edges': edge_weight_bidirect, #was edge_weight or edge_weight_bidirect
'temp_val': tf.constant([row['T_K']]),
'num_solvents': tf.constant([2]),
},
#row['logS_constant'],
output_val,
one_data_sample_w) #! CHANGE. WHAT DOES one_data??
def get_train_test_NFPx2_AltSplits_Ternary(data, sample_weight, batch_size, fold_number, rand_seed, split_type='shuffle', output_val_col = "DGsolv_constant"):
print(f"Using output val col of '{output_val_col}'.")
# Change below if ternary or NOT deltaG!
X_data = data[['can_smiles_solute', 'can_smiles_solvent1',
'can_smiles_solvent2', 'can_smiles_solvent3',
'mol_frac_solvent1', 'mol_frac_solvent2', 'mol_frac_solvent3',
'T_K', ]]
y_data = data[output_val_col]
num_splits = 5
if split_type == 'shuffle':
# Separates training+validation from test
index_train_valid, index_test, dummy_train_valid, dummy_test = train_test_split(data.index,
data.index, test_size = 0.1, random_state = rand_seed)
test_exp = data[data.index.isin(index_test)]
train_valid = data[data.index.isin(index_train_valid)] # BOTH training and validation set
kfold = KFold(n_splits = num_splits, shuffle = True, random_state = rand_seed) # Split training and validation into 10
train_valid_split = list(kfold.split(train_valid))[fold_number] # Let's you choose chunk which is validation set (0-9)
train_index, valid_index = train_valid_split
train_exp = train_valid.iloc[train_index]
valid_exp = train_valid.iloc[valid_index]
train = train_exp
valid = valid_exp
test = test_exp
elif split_type == 'group_kfold_solute':
print("Performing Group KFold on Solute Smiles!")
group_kfold = GroupShuffleSplit(n_splits=num_splits, train_size=0.9, random_state=0)
groups_solute = list(data.can_smiles_solute)
folds_dict = {}
for i, (train_valid_index, test_index) in enumerate(group_kfold.split(X_data, y_data, groups_solute)):
#print(f"\nFold {i}:")
#NOTE tv = train_valid
X_data_test = X_data.iloc[test_index, :]
X_data_tv = X_data.iloc[train_valid_index, :]
y_data_tv = y_data.iloc[train_valid_index]
train_valid_size = X_data_tv.shape[0]
X_data_train, X_data_valid, y_data_train, y_data_valid = train_test_split(X_data_tv, y_data_tv,
test_size=0.1, random_state=0)
train_size = X_data_train.shape[0]
valid_size = X_data_valid.shape[0]
test_size = X_data_test.shape[0]
train_ratio = train_size/(X_data.shape[0])
valid_ratio = valid_size/(X_data.shape[0])
test_ratio = test_size/(X_data.shape[0])
idx_X_train = list(X_data_train.index)
idx_X_valid = list(X_data_valid.index)
idx_X_test = list(X_data_test.index)
folds_dict[f"fold_{i}"] = {
"train_ratio":train_ratio,
"valid_ratio":valid_ratio,
"test_ratio":test_ratio,
"train_idx":idx_X_train, # USE LOC TO RETRIEVE FROM X_data!
"valid_idx":idx_X_valid, # USE LOC TO RETRIEVE FROM X_data!
"test_idx":idx_X_test, # USE LOC TO RETRIEVE FROM X_data!
}
fold_params = folds_dict[f"fold_{fold_number}"]
print(f" Using Fold {fold_number}")
print(f" Ratios: {fold_params['train_ratio']:.2f}/{fold_params['valid_ratio']:.2f}/{fold_params['test_ratio']:.2f}")
train = data.loc[fold_params['train_idx'],:]
valid = data.loc[fold_params['valid_idx'],:]
test = data.loc[fold_params['test_idx'],:]
train_exp = train
valid_exp = valid
test_exp = test
elif split_type == 'group_kfold_solvent_system':
print("Performing Group KFold on Solvent Systems!")
group_kfold = GroupShuffleSplit(n_splits=num_splits, train_size=0.9, random_state=0)
groups_solv_system = list(data.solvent_system)
folds_dict = {}
for i, (train_valid_index, test_index) in enumerate(group_kfold.split(X_data, y_data, groups_solv_system)):
#print(f"\nFold {i}:")
#NOTE tv = train_valid
X_data_test = X_data.iloc[test_index, :]
X_data_tv = X_data.iloc[train_valid_index, :]
y_data_tv = y_data.iloc[train_valid_index]
train_valid_size = X_data_tv.shape[0]
X_data_train, X_data_valid, y_data_train, y_data_valid = train_test_split(X_data_tv, y_data_tv,
test_size=0.1, random_state=0)
train_size = X_data_train.shape[0]
valid_size = X_data_valid.shape[0]
test_size = X_data_test.shape[0]
train_ratio = train_size/(X_data.shape[0])
valid_ratio = valid_size/(X_data.shape[0])
test_ratio = test_size/(X_data.shape[0])
idx_X_train = list(X_data_train.index)
idx_X_valid = list(X_data_valid.index)
idx_X_test = list(X_data_test.index)
folds_dict[f"fold_{i}"] = {
"train_ratio":train_ratio,
"valid_ratio":valid_ratio,
"test_ratio":test_ratio,
"train_idx":idx_X_train, # USE LOC TO RETRIEVE FROM X_data!
"valid_idx":idx_X_valid, # USE LOC TO RETRIEVE FROM X_data!
"test_idx":idx_X_test, # USE LOC TO RETRIEVE FROM X_data!
}
fold_params = folds_dict[f"fold_{fold_number}"]
print(f" Using Fold {fold_number}")
print(f" Ratios: {fold_params['train_ratio']:.2f}/{fold_params['valid_ratio']:.2f}/{fold_params['test_ratio']:.2f}")
train = data.loc[fold_params['train_idx'],:]
valid = data.loc[fold_params['valid_idx'],:]
test = data.loc[fold_params['test_idx'],:]
train_exp = train
valid_exp = valid
test_exp = test
elif split_type == 'group_kfold_leave_one_solute':
print("Performing 'Leave One Solute Out' splitting!")
logo_split = LeaveOneGroupOut()
groups_solute = list(data_in.can_smiles_solute)
folds_dict = {}
# Iterate over all possible solutes left out (69 of them)
# For each, create test by
for i, (train_valid_test_index, logo_index) in enumerate(logo_split.split(X_data, y_data, groups_solute)):
#print("LOGO INDEX IS",logo_index)
X_data_logo = X_data.iloc[logo_index, :]
y_data_logo = y_data.iloc[logo_index]
#group_kfold_1fold = GroupShuffleSplit(n_splits=1, train_size=0.9, random_state=0)
X_data_tvt = X_data.iloc[train_valid_test_index, :]
y_data_tvt = y_data.iloc[train_valid_test_index]
train_valid_test_size = X_data_tvt.shape[0]
# Split off test from train and validation (shuffle split)
X_data_tv, X_data_test_nologo, y_data_tv, y_data_test_nologo = train_test_split(X_data_tvt, y_data_tvt, test_size=0.1, random_state=0)
train_valid_size = X_data_tv.shape[0]
# Split off validation from train (shuffle split)
X_data_train, X_data_valid, y_data_train, y_data_valid = train_test_split(X_data_tv, y_data_tv, test_size=0.1, random_state=0)
X_data_test = pd.concat([X_data_test_nologo, X_data_logo])
y_data_test = pd.concat([y_data_test_nologo, y_data_logo])
logo_size = X_data_logo.shape[0]
train_size = X_data_train.shape[0]
valid_size = X_data_valid.shape[0]
test_size = X_data_test.shape[0]
logo_ratio = logo_size/(X_data.shape[0])
train_ratio = train_size/(X_data.shape[0])
valid_ratio = valid_size/(X_data.shape[0])
test_ratio = test_size/(X_data.shape[0])
idx_X_logo = list(X_data_logo.index)
idx_X_train = list(X_data_train.index)
idx_X_valid = list(X_data_valid.index)
idx_X_test = list(X_data_test.index)
folds_dict[f"fold_{i}"] = {
"logo_ratio":logo_ratio,
"train_ratio":train_ratio,
"valid_ratio":valid_ratio,
"test_ratio":test_ratio,
"logo_idx":idx_X_logo,
"train_idx":idx_X_train, # USE LOC TO RETRIEVE FROM X_data!
"valid_idx":idx_X_valid, # USE LOC TO RETRIEVE FROM X_data!
"test_idx":idx_X_test, # USE LOC TO RETRIEVE FROM X_data!
}
fold_params = folds_dict[f"fold_{fold_number}"]
print(f" Using Fold {fold_number} - LOGO!")
print(f" Ratios: {fold_params['train_ratio']:.2f}/{fold_params['valid_ratio']:.2f}/{fold_params['test_ratio']:.2f} logo: {fold_params['logo_ratio']:.2f}")
print(f" LOGO Indices:\t {fold_params['logo_idx']}")
train = data.loc[fold_params['train_idx'],:]
valid = data.loc[fold_params['valid_idx'],:]
test = data.loc[fold_params['test_idx'],:]
train_exp = train
valid_exp = valid
test_exp = test
elif split_type == 'group_kfold_NumSolvShuffle':
print("Performing Group Kfold Shuffle Split based on Single/Binary/Ternary label!")
group_kfold = GroupShuffleSplit(n_splits=num_splits, train_size=0.9, random_state=0)
groups_sin_bin_tern = list(data.sin_bin_term)
folds_dict = {}
for i, (train_valid_index, test_index) in enumerate(group_kfold.split(X_data, y_data, groups_sin_bin_tern)):
#print(f"\nFold {i}:")
#NOTE tv = train_valid
X_data_test = X_data.iloc[test_index, :]
X_data_tv = X_data.iloc[train_valid_index, :]
y_data_tv = y_data.iloc[train_valid_index]
train_valid_size = X_data_tv.shape[0]
X_data_train, X_data_valid, y_data_train, y_data_valid = train_test_split(X_data_tv, y_data_tv,
test_size=0.1, random_state=0)
train_size = X_data_train.shape[0]
valid_size = X_data_valid.shape[0]
test_size = X_data_test.shape[0]
train_ratio = train_size/(X_data.shape[0])
valid_ratio = valid_size/(X_data.shape[0])
test_ratio = test_size/(X_data.shape[0])
idx_X_train = list(X_data_train.index)
idx_X_valid = list(X_data_valid.index)
idx_X_test = list(X_data_test.index)
folds_dict[f"fold_{i}"] = {
"train_ratio":train_ratio,
"valid_ratio":valid_ratio,
"test_ratio":test_ratio,
"train_idx":idx_X_train, # USE LOC TO RETRIEVE FROM X_data!
"valid_idx":idx_X_valid, # USE LOC TO RETRIEVE FROM X_data!
"test_idx":idx_X_test, # USE LOC TO RETRIEVE FROM X_data!
}
fold_params = folds_dict[f"fold_{fold_number}"]
print(f" Using Fold {fold_number}")
print(f" Ratios: {fold_params['train_ratio']:.2f}/{fold_params['valid_ratio']:.2f}/{fold_params['test_ratio']:.2f}")
train = data.loc[fold_params['train_idx'],:]
valid = data.loc[fold_params['valid_idx'],:]
test = data.loc[fold_params['test_idx'],:]
train_exp = train
valid_exp = valid
test_exp = test
#! Ignore
print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
print(len(train_exp), len(train) - len(train_exp), len(train))
print(len(valid_exp), len(valid) - len(valid_exp), len(valid))
print(len(test_exp), len(test) - len(test_exp), len(test))
print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
# Set labels - after training, concatenate train/valid/test
last_col_idx = len(list(train.columns)) - 1
train.insert(loc=last_col_idx, column='Train/Valid/Test', value='Train')
valid.insert(loc=last_col_idx, column='Train/Valid/Test', value='Valid')
test.insert(loc=last_col_idx, column='Train/Valid/Test', value='Test')
print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
print(data.shape[0],"total datapoints")
print("Train/valid/test datapoints:",train.shape[0], valid.shape[0], test.shape[0])
print("Total datapoints vs train/valid/test sum:",data.shape[0] - (train.shape[0]+valid.shape[0]+test.shape[0]))
print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
# Set labels - after training, concatenate train/valid/test
train['Train/Valid/Test'] = 'Train'
valid['Train/Valid/Test'] = 'Valid'
test['Train/Valid/Test'] = 'Test'
#### == Construct Preprocessor ==
#* Beginning of actual GNN Part
#* Preprocessor in GNN.py - converts SMILES into Atom and Bond Feature vectors
#* output_signature: describes shape of input vectors
#* tf.TensorSpec: dummy placeholder to specify shape (if shape is None, can be variable). If array given doesnt make shape (if shape not None), error
#* Construct_feature_matrices: from NFP package (by peter st. john and YJ) https://github.com/NREL/nfp
#* Converts SMILES to mol, gets atoms and bonds, and gets specific features requested for each.
#* CustomPreprocessor inherits SMILESPreprocessor, and MolPreprocessor is superclass of SMILESPreprocessor
#* Only purpose of CustomPreprocessor is to allow for global features (mol_features). However, this is *different* now! it is in create_tf_dataset.
#* Could just use SMILESPreprocessor
#* To use customized atom and bond features, can define customized feature function. Specify them when constructing CustomPreprocessor
#* Other than atom +bond features, we have mol features. global_features function takes SMILES as input and calculates various rdkit features.
#* QM features are brought from dataframe rows. Can use different global features for solute and solvent
preprocessor = CustomPreprocessor_NFPx2_ternary(
explicit_hs=False,
atom_features=atom_features,
bond_features=bond_features,
# MODIFIED
# TO ADD GLOBAL FEATURES, LOOK AT gnn.py AND MODIFY MOL_FEATURES SHAPE AND VALS
# DOESNT WORK NO MOLglobal_features=tf.constant([CalcNumHBA(g),
# DOESNT WORK NO MOL CalcNumHBD(mol),
# DOESNT WORK NO MOL CalcLabuteASA(mol),
# DOESNT WORK NO MOL CalcTPSA(mol)])
)
print(f"Atom classes before: {preprocessor.atom_classes} (includes 'none' and 'missing' classes)")
print(f"Bond classes before: {preprocessor.bond_classes} (includes 'none' and 'missing' classes)")
# Raul: to speed up, create preprocessor once and then load it
train_all_smiles = list( set(list(train['can_smiles_solvent1']) +
list(train['can_smiles_solvent2']) +
list(train['can_smiles_solvent3']) +
list(train['can_smiles_solute']) ) ) #! CHANGED:
print("TRAIN ALL SMILES", train_all_smiles)
#* Initially preprocessor has no info about atom and bond types, so we iterate over all SMILES to get atom and bond classes
#* Also have bond_tokenizer - shortening/classifying atom and bond feature info. Class #1/2/x will be converted to 64dim vector
for smiles in train_all_smiles:
print("SMI",smiles,"| Mol Obj:", rdkit.Chem.MolFromSmiles(smiles))
preprocessor.construct_feature_matrices(smiles, train=True)
print(f'Atom classes after: {preprocessor.atom_classes}')
print(f'Bond classes after: {preprocessor.bond_classes}')
# Below must match output signature specified in gnn.py
output_signature = (preprocessor.output_signature,
tf.TensorSpec(shape=(), dtype=tf.float32), #! CHANGE
tf.TensorSpec(shape=(), dtype=tf.float32) #! Code breaks if removed (??)
) # deprecated. May need to modify other parts if removed
#* Generates input data (incl. all atom,bond,global features defined in preprocessor)
train_data = tf.data.Dataset.from_generator(
lambda: create_tf_dataset_NFPx2_ternary_ShareWeights(train, preprocessor, sample_weight, True, output_val_col = output_val_col), output_signature=output_signature)\
.cache().shuffle(buffer_size=1000)\
.padded_batch(batch_size=batch_size)\
.prefetch(tf.data.experimental.AUTOTUNE)
print("\nTRAIN DATA\n",train_data) # Seems fine based on main_copoly results
valid_data = tf.data.Dataset.from_generator(
lambda: create_tf_dataset_NFPx2_ternary_ShareWeights(valid, preprocessor, sample_weight, False, output_val_col = output_val_col), output_signature=output_signature)\
.cache()\
.padded_batch(batch_size=batch_size)\
.prefetch(tf.data.experimental.AUTOTUNE)
test_data = tf.data.Dataset.from_generator(
lambda: create_tf_dataset_NFPx2_ternary_ShareWeights(test, preprocessor, sample_weight, False, output_val_col = output_val_col), output_signature=output_signature)\
.cache()\
.padded_batch(batch_size=batch_size)\
.prefetch(tf.data.experimental.AUTOTUNE)
train_data_final = tf.data.Dataset.from_generator(
lambda: create_tf_dataset_NFPx2_ternary_ShareWeights(train, preprocessor, sample_weight, False, output_val_col = output_val_col), output_signature=output_signature)\
.cache()\
.padded_batch(batch_size=batch_size)\
.prefetch(tf.data.experimental.AUTOTUNE)
dataframes = [train,valid,test]
datasets = [train_data_final, train_data,valid_data,test_data]
return preprocessor, output_signature, datasets, dataframes
### GNN Functions
def create_GNN_NFPx2_ShareWeights_ternary(model_name_in, train_data, valid_data, test_data,
train_df, valid_df, test_df,
td_final,
preprocessor, output_signature, batch_size, sample_weight,
num_hidden, num_messages, learn_rate, num_epochs,
node_aggreg_op,
do_stoich_multiply,
dropout = 1.0e-10,
fold_number = 0,
output_val_col = "DGsolv_constant",
share_weights = "noshare",
split_type = "shuffle"
):
##################
#* Beginning of ACTUAL GNN OPERATORS
features_dim = num_hidden
print(f"\nFold number is: {fold_number} - previously defaulted to 0, check to match data split\n")
#! Define input for solute/solvent
# solute
#* layers.Input is a placeholder to receive dict w/ atom_feature_matrix, bond_feature_matrix, connectivity, and global features
# layers.Input is NOT a model layer, but a function to construct Tensors!
#* In graph: connectivity gives source atom and target atom
atom_Input_solute = layers.Input(shape=[None], dtype=tf.int32, name='atom_solute')
bond_Input_solute = layers.Input(shape=[None], dtype=tf.int32, name='bond_solute')
connectivity_Input_solute = layers.Input(shape=[None, 2], dtype=tf.int32, name='connectivity_solute')
global_Input_solute = layers.Input(shape=[5], dtype=tf.float32, name='mol_features_solute') #! Change shape as needed to fit global features
#fake_ratio_Input_solute = layers.Input(shape=[1], dtype=tf.float32, name='fake_ratio_solute1')
# Solvent 1
atom_Input_solvent1 = layers.Input(shape=[None], dtype=tf.int32, name='atom_solvent1')