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train_concat_ternary.py
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import tensorflow as tf
import os
import json
import sys
from datetime import datetime
import numpy as np
import pandas as pd
import random
from pathlib import Path
from argparse import ArgumentParser
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.callbacks import ModelCheckpoint
from gnn_multisol import *
import nfp
import sklearn
from sklearn.model_selection import KFold, train_test_split
from sklearn.model_selection import ShuffleSplit,StratifiedShuffleSplit,GroupKFold,GroupShuffleSplit,StratifiedGroupKFold,LeaveOneGroupOut
import rdkit
rand_seed = 0
random.seed(rand_seed)
np.random.seed(rand_seed)
tf.random.set_seed(rand_seed)
gpus = tf.config.experimental.list_physical_devices('GPU')
if len(gpus) > 0:
tf.config.experimental.set_memory_growth(gpus[0], True)
device = "/gpu:0"
else:
device = "/cpu:0"
print("Using device:",device)
print("\n\n\n\nTraining Ternary Subgraph GNN - start at MACHINE TIME",datetime.now())
def data_process_MixSolDBv4_CT(target,
data_subset_str = 'b',
):
data_frac = 1.0
data_dir = Path.cwd()/"data/csvs"
print(f"\n ! Using DB MixSol_v4 Combined Target (CT) with target {target}")
data_in_path = data_dir/"MixSolDB_v4_CombinedTarget.csv"
print("\nDATA IN",data_in_path,"\n")
data = pd.read_csv(data_in_path, low_memory=False)
data = data.reset_index() #! Necessary for later code, shouldn't impact index assignments at all.
print("\nData after loading:\n\t",data.shape, data.columns.shape,list(data.columns))
# Below added because DGSolv_constant already exists in MixSolv4CT
target_str_constant = f"{target}_constant"
print("Renamed columns:\n\t",list(data.columns))
data[target_str_constant] = data[target_str_constant].astype(float)
single_solv_mask = (data.mol_frac_solvent1 != 0) & (
data.mol_frac_solvent2 == 0) & (
data.mol_frac_solvent3 == 0)
binary_solv_mask = (data.mol_frac_solvent1 != 0) & (
data.mol_frac_solvent2 != 0) & (
data.mol_frac_solvent3 == 0)
ternary_solv_mask = (data.mol_frac_solvent1 != 0) & (
data.mol_frac_solvent2 != 0) & (
data.mol_frac_solvent3 != 0)
data_lookup_dict = {
"s": single_solv_mask,
"b": binary_solv_mask,
"t": ternary_solv_mask,
"s+b": single_solv_mask | binary_solv_mask,
"s+t": single_solv_mask | ternary_solv_mask,
"b+t": binary_solv_mask | ternary_solv_mask,
"s+b+t": single_solv_mask | binary_solv_mask | ternary_solv_mask,
}
print(f"\n\n~~~~~~Chosen data subset is '{data_subset_str}'.")
data = data.loc[data_lookup_dict[data_subset_str], :]
if (data_subset_str.lower().count('t') > 0):
print("\n\n!^|^|^! Keeping ternary datapoints !^|^|^!")
cols_dropna_dict = {
"s": ["can_smiles_solute", "can_smiles_solvent1", "mol_frac_solvent1"],
"b": ["can_smiles_solute", "can_smiles_solvent1", "can_smiles_solvent2",
"mol_frac_solvent1", "mol_frac_solvent2"],
"t": ["can_smiles_solute", "can_smiles_solvent1", "can_smiles_solvent2",
"can_smiles_solvent3",
"mol_frac_solvent1", "mol_frac_solvent2",
"mol_frac_solvent3"],
"s+b": ["can_smiles_solute", "can_smiles_solvent1", "mol_frac_solvent1"],
"s+t": ["can_smiles_solute", "can_smiles_solvent1", "mol_frac_solvent1"],
"b+t": ["can_smiles_solute", "can_smiles_solvent1", "can_smiles_solvent2",
"mol_frac_solvent1", "mol_frac_solvent2"],
"s+b+t": ["can_smiles_solute", "can_smiles_solvent1", "mol_frac_solvent1"],
}
cols_to_dropna = cols_dropna_dict[data_subset_str]
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)
print(f"! SKIPPING dummy SMILES assignment...")
print(f"Dropping datapoints w/ NaN {target}...")
print("\tBefore:\t", data.shape)
data = data.dropna(subset=target_str_constant)
print("\tAfter:\t", data.shape)
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]}"
data["solvent_system"] = data.apply(get_solvent_system, axis=1)
return data
def data_process_all(database, target, data_subset_str):
db_options = {
"MixSol_v4_CT": ["logS", "DGsolv"],
}
if database not in list(db_options.keys()):
print(f"\nWARNING!!!: Could not find database '{database}' in database options. Options:\n{db_options}")
sys.exit()
avail_targets = db_options[database]
if target not in avail_targets:
print(f"\nWARNING!!!: Could not find target '{target}' for database '{database}'. Options:\n{db_options}")
sys.exit()
db_functions = {
"MixSol_v4_CT": data_process_MixSolDBv4_CT,
}
data = db_functions[database](target, data_subset_str = data_subset_str,)
return data
def get_train_test_NFPx2_AltSplits_Ternary(data, sample_weight, batch_size, fold_number, split_type='shuffle', output_val_col = "DGsolv_constant"):
print(f"Using output val col of '{output_val_col}'.")
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] # Lets 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)):
#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)):
#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
for i, (train_valid_test_index, logo_index) in enumerate(logo_split.split(X_data, y_data, groups_solute)):
X_data_logo = X_data.iloc[logo_index, :]
y_data_logo = y_data.iloc[logo_index]
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)):
#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
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('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
#### == Construct Preprocessor ==
preprocessor = CustomPreprocessor_NFPx2_ternary(
explicit_hs=False,
atom_features=atom_features,
bond_features=bond_features,
)
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)")
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']) ) )
#* 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:
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),
tf.TensorSpec(shape=(), dtype=tf.float32),
)
#* 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)
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
def create_concat_GNN_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",
):
##################
#* Beginning of GNN Construction and Operations
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
#* 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!
# Solute
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
# Solvent 1
atom_Input_solvent1 = layers.Input(shape=[None], dtype=tf.int32, name='atom_solvent1')
bond_Input_solvent1 = layers.Input(shape=[None], dtype=tf.int32, name='bond_solvent1')
connectivity_Input_solvent1 = layers.Input(shape=[None, 2], dtype=tf.int32, name='connectivity_solvent1')
ratio_Input_solvent1 = layers.Input(shape=[1], dtype=tf.float32, name='ratio_solvent1') # Necessary
global_Input_solvent1 = layers.Input(shape=[5], dtype=tf.float32, name='mol_features_solvent1') #! Change shape as needed to fit global features
# Solvent 2
atom_Input_solvent2 = layers.Input(shape=[None], dtype=tf.int32, name='atom_solvent2')
bond_Input_solvent2 = layers.Input(shape=[None], dtype=tf.int32, name='bond_solvent2')
connectivity_Input_solvent2 = layers.Input(shape=[None, 2], dtype=tf.int32, name='connectivity_solvent2')
ratio_Input_solvent2 = layers.Input(shape=[1], dtype=tf.float32, name='ratio_solvent2') # Necessary
global_Input_solvent2 = layers.Input(shape=[5], dtype=tf.float32, name='mol_features_solvent2') #! Change shape as needed to fit global features
# Solvent 3
atom_Input_solvent3 = layers.Input(shape=[None], dtype=tf.int32, name='atom_solvent3')
bond_Input_solvent3 = layers.Input(shape=[None], dtype=tf.int32, name='bond_solvent3')
connectivity_Input_solvent3 = layers.Input(shape=[None, 2], dtype=tf.int32, name='connectivity_solvent3')
ratio_Input_solvent3 = layers.Input(shape=[1], dtype=tf.float32, name='ratio_solvent3') # Necessary
global_Input_solvent3 = layers.Input(shape=[5], dtype=tf.float32, name='mol_features_solvent3') #! Change shape as needed to fit global features
connectivity_Input_edges = layers.Input(shape=[None, 2], dtype=tf.int32, name='connectivity_edges') # None here allows for variable # of edge connections
weight_Input_edges = layers.Input(shape=[None, 4], dtype=tf.float32, name='weight_edges') # None here allows for variable # of edge weights
print("WEIGHT INPUT EDGES",weight_Input_edges)
######
temp_Input = layers.Input(shape=[1], dtype=tf.float32, name='temp_val')
num_solvents_Input = layers.Input(shape=[1], dtype=tf.float32, name='num_solvents')
#! Define embedding and dense layers for solute/solvent
# Solute
atom_state_solute = layers.Embedding(preprocessor.atom_classes, features_dim,
name='atom_embedding_solute', mask_zero=True,
embeddings_regularizer='l2')(atom_Input_solute)
bond_state_solute = layers.Embedding(preprocessor.bond_classes, features_dim,
name='bond_embedding_solute', mask_zero=True,
embeddings_regularizer='l2')(bond_Input_solute)
global_state_solute = layers.Dense(features_dim, activation='relu')(global_Input_solute)
# Solvent 1
atom_state_solvent1 = layers.Embedding(preprocessor.atom_classes, features_dim,
name='atom_embedding_solvent1', mask_zero=True,
embeddings_regularizer='l2')(atom_Input_solvent1)
bond_state_solvent1 = layers.Embedding(preprocessor.bond_classes, features_dim,
name='bond_embedding_solvent1', mask_zero=True,
embeddings_regularizer='l2')(bond_Input_solvent1)
global_state_solvent1 = layers.Dense(features_dim, activation='relu')(global_Input_solvent1)
# Solvent 2
atom_state_solvent2 = layers.Embedding(preprocessor.atom_classes, features_dim,
name='atom_embedding_solvent2', mask_zero=True,
embeddings_regularizer='l2')(atom_Input_solvent2)
bond_state_solvent2 = layers.Embedding(preprocessor.bond_classes, features_dim,
name='bond_embedding_solvent2', mask_zero=True,
embeddings_regularizer='l2')(bond_Input_solvent2)
global_state_solvent2 = layers.Dense(features_dim, activation='relu')(global_Input_solvent2)
# Solvent 3
atom_state_solvent3 = layers.Embedding(preprocessor.atom_classes, features_dim,
name='atom_embedding_solvent3', mask_zero=True,
embeddings_regularizer='l2')(atom_Input_solvent3)
bond_state_solvent3 = layers.Embedding(preprocessor.bond_classes, features_dim,
name='bond_embedding_solvent3', mask_zero=True,
embeddings_regularizer='l2')(bond_Input_solvent3)
global_state_solvent3 = layers.Dense(features_dim, activation='relu')(global_Input_solvent3)
# Edge Weight Feature Embedding
weight_embedding_edges = layers.Dense(features_dim, name='weight_Embedding_edges')(weight_Input_edges)
print("WEIGHT EMBEDDING EDGES",weight_embedding_edges)
global_state_GNN2_initial = layers.Dense(features_dim, activation='relu')(temp_Input)
#* Create message passing blocks
#* If curious about layers, go to GNN.py and look at message_block function
if share_weights.count("all") > 0:
print("\n !! GNN1: Sharing Solute and Solvents Weights.\n")
Layers_In = [
[layers.GlobalAveragePooling1D(), # atom_av,
layers.Dense(features_dim, activation='relu'), # global_embed_dense1,
layers.Dense(features_dim), layers.Add(), # global_embed_dense2, global_residcon,
nfp.EdgeUpdate(dropout = dropout), layers.Add(), # nfp_edgeupdate, bond_residcon,
nfp.NodeUpdate(dropout = dropout), layers.Add() # nfp_nodeupdate, atom_residcon
],
[layers.GlobalAveragePooling1D(), # atom_av,
layers.Dense(features_dim, activation='relu'), # global_embed_dense1,
layers.Dense(features_dim), layers.Add(), # global_embed_dense2, global_residcon,
nfp.EdgeUpdate(dropout = dropout), layers.Add(), # nfp_edgeupdate, bond_residcon,
nfp.NodeUpdate(dropout = dropout), layers.Add() # nfp_nodeupdate, atom_residcon
],
[layers.GlobalAveragePooling1D(), # atom_av,
layers.Dense(features_dim, activation='relu'), # global_embed_dense1,
layers.Dense(features_dim), layers.Add(), # global_embed_dense2, global_residcon,
nfp.EdgeUpdate(dropout = dropout), layers.Add(), # nfp_edgeupdate, bond_residcon,
nfp.NodeUpdate(dropout = dropout), layers.Add() # nfp_nodeupdate, atom_residcon
],
[layers.GlobalAveragePooling1D(), # atom_av,
layers.Dense(features_dim, activation='relu'), # global_embed_dense1,
layers.Dense(features_dim), layers.Add(), # global_embed_dense2, global_residcon,
nfp.EdgeUpdate(dropout = dropout), layers.Add(), # nfp_edgeupdate, bond_residcon,
nfp.NodeUpdate(dropout = dropout), layers.Add() # nfp_nodeupdate, atom_residcon
],
[layers.GlobalAveragePooling1D(), # atom_av,
layers.Dense(features_dim, activation='relu'), # global_embed_dense1,
layers.Dense(features_dim), layers.Add(), # global_embed_dense2, global_residcon,
nfp.EdgeUpdate(dropout = dropout), layers.Add(), # nfp_edgeupdate, bond_residcon,
nfp.NodeUpdate(dropout = dropout), layers.Add() # nfp_nodeupdate, atom_residcon
],
]
for i in range(num_messages):
surv_prob_i = 1.0
# If on first loop, print atom/bond/global/connectivity states
if i == 0:
print('atom:\n\t',atom_state_solute,'\nbond:\n\t',bond_state_solute,'\nglobal:\n\t',
global_state_solute,'\nconnectivity\n\t',connectivity_Input_solute)
atom_states_all_in = [atom_state_solute, atom_state_solvent1, atom_state_solvent2, atom_state_solvent3]
bond_states_all_in = [bond_state_solute, bond_state_solvent1, bond_state_solvent2, bond_state_solvent3]
global_states_all_in = [global_state_solute, global_state_solvent1, global_state_solvent2, global_state_solvent3]
connectivity_Input_all_in = [connectivity_Input_solute, connectivity_Input_solvent1, connectivity_Input_solvent2, connectivity_Input_solvent3]
atom_states_out, bond_states_out, global_states_out = message_block_solu_solv_shared_ternary(atom_states_all_in,
bond_states_all_in,
global_states_all_in,
connectivity_Input_all_in,
features_dim, i,
Layers_In)
atom_state_solute, atom_state_solvent1, atom_state_solvent2, atom_state_solvent3 = atom_states_out
bond_state_solute, bond_state_solvent1, bond_state_solvent2, bond_state_solvent3 = bond_states_out
global_state_solute, global_state_solvent1, global_state_solvent2, global_state_solvent3 = global_states_out
elif share_weights.count("all_solt_last") > 0:
print("\n !! GNN1: Sharing Solute and Solvents Weights. Solute goes last! \n")
Layers_In = [
[layers.GlobalAveragePooling1D(), # atom_av,
layers.Dense(features_dim, activation='relu'), # global_embed_dense1,
layers.Dense(features_dim), layers.Add(), # global_embed_dense2, global_residcon,
nfp.EdgeUpdate(dropout = dropout), layers.Add(), # nfp_edgeupdate, bond_residcon,
nfp.NodeUpdate(dropout = dropout), layers.Add() # nfp_nodeupdate, atom_residcon
],
[layers.GlobalAveragePooling1D(), # atom_av,
layers.Dense(features_dim, activation='relu'), # global_embed_dense1,
layers.Dense(features_dim), layers.Add(), # global_embed_dense2, global_residcon,
nfp.EdgeUpdate(dropout = dropout), layers.Add(), # nfp_edgeupdate, bond_residcon,
nfp.NodeUpdate(dropout = dropout), layers.Add() # nfp_nodeupdate, atom_residcon
],
[layers.GlobalAveragePooling1D(), # atom_av,
layers.Dense(features_dim, activation='relu'), # global_embed_dense1,
layers.Dense(features_dim), layers.Add(), # global_embed_dense2, global_residcon,
nfp.EdgeUpdate(dropout = dropout), layers.Add(), # nfp_edgeupdate, bond_residcon,
nfp.NodeUpdate(dropout = dropout), layers.Add() # nfp_nodeupdate, atom_residcon
],
[layers.GlobalAveragePooling1D(), # atom_av,
layers.Dense(features_dim, activation='relu'), # global_embed_dense1,
layers.Dense(features_dim), layers.Add(), # global_embed_dense2, global_residcon,
nfp.EdgeUpdate(dropout = dropout), layers.Add(), # nfp_edgeupdate, bond_residcon,
nfp.NodeUpdate(dropout = dropout), layers.Add() # nfp_nodeupdate, atom_residcon
],
[layers.GlobalAveragePooling1D(), # atom_av,
layers.Dense(features_dim, activation='relu'), # global_embed_dense1,
layers.Dense(features_dim), layers.Add(), # global_embed_dense2, global_residcon,
nfp.EdgeUpdate(dropout = dropout), layers.Add(), # nfp_edgeupdate, bond_residcon,
nfp.NodeUpdate(dropout = dropout), layers.Add() # nfp_nodeupdate, atom_residcon
],
]
for i in range(num_messages):
surv_prob_i = 1.0
# If on first loop, print atom/bond/global/connectivity states
if i == 0:
print('atom:\n\t',atom_state_solute,'\nbond:\n\t',bond_state_solute,'\nglobal:\n\t',
global_state_solute,'\nconnectivity\n\t',connectivity_Input_solute)
atom_states_all_in = [atom_state_solute, atom_state_solvent1, atom_state_solvent2, atom_state_solvent3]
bond_states_all_in = [bond_state_solute, bond_state_solvent1, bond_state_solvent2, bond_state_solvent3]
global_states_all_in = [global_state_solute, global_state_solvent1, global_state_solvent2, global_state_solvent3]
connectivity_Input_all_in = [connectivity_Input_solute, connectivity_Input_solvent1, connectivity_Input_solvent2, connectivity_Input_solvent3]
atom_states_out, bond_states_out, global_states_out = message_block_solu_solv_shared_ternary_SoluteLast(atom_states_all_in,
bond_states_all_in,
global_states_all_in,
connectivity_Input_all_in,
features_dim, i,
#dropout,
Layers_In)
atom_state_solute, atom_state_solvent1, atom_state_solvent2, atom_state_solvent3 = atom_states_out
bond_state_solute, bond_state_solvent1, bond_state_solvent2, bond_state_solvent3 = bond_states_out
global_state_solute, global_state_solvent1, global_state_solvent2, global_state_solvent3 = global_states_out
elif share_weights.count("solvs") > 0:
print("\n !!GNN 1: Solvents share weights, Solute has independent weights.\n")
Layers_In = [
[layers.GlobalAveragePooling1D(), # atom_av,
layers.Dense(features_dim, activation='relu'), # global_embed_dense1,
layers.Dense(features_dim), layers.Add(), # global_embed_dense2, global_residcon,
nfp.EdgeUpdate(dropout = dropout), layers.Add(), # nfp_edgeupdate, bond_residcon,
nfp.NodeUpdate(dropout = dropout), layers.Add() # nfp_nodeupdate, atom_residcon
],
[layers.GlobalAveragePooling1D(), # atom_av,
layers.Dense(features_dim, activation='relu'), # global_embed_dense1,
layers.Dense(features_dim), layers.Add(), # global_embed_dense2, global_residcon,
nfp.EdgeUpdate(dropout = dropout), layers.Add(), # nfp_edgeupdate, bond_residcon,
nfp.NodeUpdate(dropout = dropout), layers.Add() # nfp_nodeupdate, atom_residcon
],
[layers.GlobalAveragePooling1D(), # atom_av,
layers.Dense(features_dim, activation='relu'), # global_embed_dense1,
layers.Dense(features_dim), layers.Add(), # global_embed_dense2, global_residcon,
nfp.EdgeUpdate(dropout = dropout), layers.Add(), # nfp_edgeupdate, bond_residcon,
nfp.NodeUpdate(dropout = dropout), layers.Add() # nfp_nodeupdate, atom_residcon
],
[layers.GlobalAveragePooling1D(), # atom_av,
layers.Dense(features_dim, activation='relu'), # global_embed_dense1,
layers.Dense(features_dim), layers.Add(), # global_embed_dense2, global_residcon,
nfp.EdgeUpdate(dropout = dropout), layers.Add(), # nfp_edgeupdate, bond_residcon,
nfp.NodeUpdate(dropout = dropout), layers.Add() # nfp_nodeupdate, atom_residcon
],
[layers.GlobalAveragePooling1D(), # atom_av,
layers.Dense(features_dim, activation='relu'), # global_embed_dense1,
layers.Dense(features_dim), layers.Add(), # global_embed_dense2, global_residcon,
nfp.EdgeUpdate(dropout = dropout), layers.Add(), # nfp_edgeupdate, bond_residcon,
nfp.NodeUpdate(dropout = dropout), layers.Add() # nfp_nodeupdate, atom_residcon
],
]
for i in range(num_messages):
surv_prob_i = 1.0
# If on first loop, print atom/bond/global/connectivity states
if i == 0:
print('atom:\n\t',atom_state_solute,'\nbond:\n\t',bond_state_solute,'\nglobal:\n\t',
global_state_solute,'\nconnectivity\n\t',connectivity_Input_solute)
# SOLUTE
atom_state_solute, bond_state_solute, global_state_solute = message_block(atom_state_solute,
bond_state_solute,
global_state_solute,
connectivity_Input_solute,
features_dim, i, dropout, surv_prob_i)
# SOLVENTS
atom_states_solvents = [atom_state_solvent1, atom_state_solvent2, atom_state_solvent3]
bond_states_solvents = [bond_state_solvent1, bond_state_solvent2, bond_state_solvent3]
global_states_solvents = [global_state_solvent1, global_state_solvent2, global_state_solvent3]
connectivity_Input_solvents = [connectivity_Input_solvent1, connectivity_Input_solvent2, connectivity_Input_solvent3]
atom_states_out, bond_states_out, global_states_out = message_block_solv_shared_only_ternary(atom_states_solvents,
bond_states_solvents,
global_states_solvents,
connectivity_Input_solvents,
features_dim, i,
Layers_In)
atom_state_solvent1, atom_state_solvent2, atom_state_solvent3 = atom_states_out
bond_state_solvent1, bond_state_solvent2, bond_state_solvent3 = bond_states_out
global_state_solvent1, global_state_solvent2, global_state_solvent3 = global_states_out
elif share_weights.count("noshare") > 0:#
print("\n !!GNN 1: NOT Sharing any weights!\n")
for i in range(num_messages):
surv_prob_i = 1.0
if i == 0:
print('atom:\n\t',atom_state_solute,'\nbond:\n\t',bond_state_solute,'\nglobal:\n\t',
global_state_solute,'\nconnectivity\n\t',connectivity_Input_solute)
atom_state_solute, bond_state_solute, global_state_solute = message_block(atom_state_solute,
bond_state_solute,
global_state_solute,
connectivity_Input_solute,
features_dim, i, dropout, surv_prob_i)
atom_state_solvent1, bond_state_solvent1, global_state_solvent1 = message_block(atom_state_solvent1,
bond_state_solvent1,
global_state_solvent1,
connectivity_Input_solvent1,
features_dim, i, dropout, surv_prob_i)
atom_state_solvent2, bond_state_solvent2, global_state_solvent2 = message_block(atom_state_solvent2,
bond_state_solvent2,
global_state_solvent2,
connectivity_Input_solvent2,
features_dim, i, dropout, surv_prob_i)
atom_state_solvent3, bond_state_solvent3, global_state_solvent3 = message_block(atom_state_solvent3,
bond_state_solvent3,
global_state_solvent3,
connectivity_Input_solvent3,
features_dim, i, dropout, surv_prob_i)
else:
print(f"Could not find a valid share weights option (Given: {share_weights})")
print("Valid options are ['all', 'all_solt_last', 'solvs', 'noshare']")
X1 = tf.tile(ratio_Input_solvent1, [1,features_dim])
X2 = tf.tile(ratio_Input_solvent2, [1,features_dim])
X3 = tf.tile(ratio_Input_solvent3, [1,features_dim])
solvent1_vector = tf.math.multiply(X1, global_state_solvent1)
solvent2_vector = tf.math.multiply(X2, global_state_solvent2)
solvent3_vector = tf.math.multiply(X3, global_state_solvent3)
solvent12_vector = tf.concat([solvent1_vector, solvent2_vector], -1)
solvent12_vector = layers.Dense(features_dim, activation='relu')(solvent12_vector)
solvent12_vector = layers.Dense(features_dim, activation='relu')(solvent12_vector)
solvent123_vector = tf.concat([solvent12_vector, solvent3_vector], -1)
solvent123_vector = layers.Dense(features_dim, activation='relu')(solvent123_vector)
solvent123_vector = layers.Dense(features_dim, activation='relu')(solvent123_vector)
# Combine solvent vector with solute vector
# prediction is final output
readout_vector = tf.concat([solvent123_vector, global_state_solute], -1)
readout_vector = layers.Dense(features_dim, activation='relu')(readout_vector)
readout_vector = layers.Dense(features_dim)(readout_vector)
prediction = layers.Dense(1)(readout_vector)
# NOTE THAT ORDER MATTERS HERE. MUST MATCH GNN (create_tensor_dataset func yield)
input_tensors = [
# SOLUTE
atom_Input_solute,
bond_Input_solute,
connectivity_Input_solute,
global_Input_solute,
# SOLVENT 1
atom_Input_solvent1,
bond_Input_solvent1,
connectivity_Input_solvent1,
ratio_Input_solvent1,
global_Input_solvent1,
# SOLVENT 2
atom_Input_solvent2,
bond_Input_solvent2,
connectivity_Input_solvent2,
ratio_Input_solvent2,
global_Input_solvent2,
# SOLVENT 3
atom_Input_solvent3,
bond_Input_solvent3,
connectivity_Input_solvent3,
ratio_Input_solvent3,
global_Input_solvent3,
# EDGE MATRICES
connectivity_Input_edges,
weight_Input_edges,
# GNN 2 GLOBAL
temp_Input,
num_solvents_Input,
]
print("INPUT TENSORS\n",*input_tensors,sep='\n\t-\t')
model = tf.keras.Model(input_tensors, [prediction])
model_metrics = [
tf.keras.metrics.mean_absolute_error,
tf.keras.metrics.mean_squared_error,
]
model.compile(
loss=tf.keras.losses.MeanAbsoluteError(),
optimizer=tf.keras.optimizers.Adam(learn_rate),
metrics=model_metrics,
)
model_path = "model_files/"+model_name_in+"/best_model.h5"
checkpoint = ModelCheckpoint(model_path, monitor="val_loss",\
verbose=2, save_best_only = True, mode='auto', period=1,
#custom
)
reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5,
patience=5, min_lr=1e-6)
tensorboard = keras.callbacks.TensorBoard(log_dir=("model_files/"+model_name_in))
csv_log_callback = keras.callbacks.CSVLogger("model_files/" + model_name_in + "/all_training_metrics.log")
callbacks_lst = [checkpoint,
reduce_lr,
tensorboard,
csv_log_callback
]
hist = model.fit(train_data,
validation_data=valid_data,
epochs=num_epochs,
verbose=1,
callbacks = callbacks_lst)
model.load_weights(model_path)
print("Using argument of td_final...",)
train_results = model.predict(td_final).squeeze()
print("Shape train results",model.predict(td_final).shape,)
valid_results = model.predict(valid_data).squeeze()
test_results = model.predict(test_data).squeeze()
train_labels = tf.convert_to_tensor(list(train_df[output_val_col]), dtype=tf.float32)
print("Train labels",train_labels)
valid_labels = tf.convert_to_tensor(list(valid_df[output_val_col]), dtype=tf.float32)
test_labels = tf.convert_to_tensor(list(test_df[output_val_col]), dtype=tf.float32)
train_df['predicted'] = train_results
valid_df['predicted'] = valid_results
test_df['predicted'] = test_results
diff_pred_train = train_labels - train_results
print("DIFF TRAIN VS PRED TRAIN",diff_pred_train)
mae_train = np.abs(train_labels - train_results).mean()
mae_valid = np.abs(valid_labels - valid_results).mean()
mae_test = np.abs(test_labels - test_results).mean()
rmse_train = sklearn.metrics.mean_squared_error(y_true=train_labels, y_pred=train_results, squared=False) # Will fail in scitkit-learn >1.3
rmse_valid = sklearn.metrics.mean_squared_error(y_true=valid_labels, y_pred=valid_results, squared=False) # Will fail in scikit-learn >1.3
rmse_test = sklearn.metrics.mean_squared_error(y_true=test_labels, y_pred=test_results, squared=False) # Will fail in scikit-learn > 1.3
r2_train = sklearn.metrics.r2_score(y_true=train_labels,
y_pred=train_results)
r2_valid = sklearn.metrics.r2_score(y_true=valid_labels,
y_pred=valid_results)
r2_test = sklearn.metrics.r2_score(y_true=test_labels,
y_pred=test_results)
print(f"Fold number is: {fold_number} with {split_type} - previously defaulted to 0, check to match data split")
print(len(train_df),len(valid_df),len(test_df))
mae_string = f"{mae_train:.2f},{mae_valid:.2f},{mae_test:.2f}"
rmse_string = f"{rmse_train:.2f},{rmse_valid:.2f},{rmse_test:.2f}"
r2_string = f"{r2_train:.2f},{r2_valid:.2f},{r2_test:.2f}"
print("MAEs:\t",mae_string)
print("RMSEs:\t",rmse_string)
print("R2s:\t",r2_string)
with open("model_files/" + model_name_in + "/results.txt",'w') as f:
f.write("MAEs:\t" + mae_string)
f.write('\n')
f.write("RMSEs:\t" + rmse_string)
f.write('\n')
f.write("R2s:\t" + r2_string)
pd.concat([train_df, valid_df, test_df], ignore_index=True).to_csv('model_files/' + model_name_in +'/kfold_'+str(fold_number)+'.csv',index=False)
preprocessor.to_json("model_files/"+ model_name_in +"/preprocessor.json")
return model, pd.concat([train_df,valid_df,test_df])
#~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&~^&
if __name__ == '__main__':
with tf.device(device):
parser = ArgumentParser()
parser.add_argument('-n', '--modelname', type=str, required=True, default='unnamed_model', help='Model name (REQ) - impacts save directory! (default=unnamed_model)')
parser.add_argument('--db', type=str, default='MixSol_v4_CT', help='Database of binary solubilities to be used (default=MixSol_v1). Options: ["MixSol_v1" (logS, DGsolv), "MixSol_v3" (logS, DGsolv), "ComboDB_v1" (logS), "MixSol_v4" (logS, DGsolv)]')
parser.add_argument('-t', '--target', type=str, default='DGsolv', help='Prediction target to be used ("DGsolv" or "logS", default=DGsolv). DGsolv not available for all databases and may throw an error!')
parser.add_argument('-lr', '--lr', type=float, default=1.0e-4, help='Learning rate for training - note this is only an initial LR and further LR is specified in code. (default=1.0e-4)')
parser.add_argument('-b', '--batch_size', type=int, default=1000, help='Batch size for training. (default=1000)')
parser.add_argument('-e', '--epochs', type=int, default=1000, help='# Epochs for training. (default=1000)')
parser.add_argument('-m', '--num_messages', type=int, default=5, help='number of message-passing blocks (default=5)')
parser.add_argument('-hid', '--num_hidden', type=int, default=128, help='number of nodes in hidden layers (default=128)')
parser.add_argument('-f','--fold_number', type=int, default=0, help='Fold number for Kfold (default=0). Relevant for all splitting types).')
parser.add_argument('-s', '--split_option', type=str, default='shuffle', help='Split Options (default=shuffle) \
0: Shuffle split ("shuffle"). ~80:10:10 split,\