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181b6d4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | #!/bin/bash
chemprop_dir=../../../chemprop # location of chemprop directory, CHANGE ME
results_dir=results_sampl
results_dir2=results_sampl_production
train_path=../data/logP/train.csv
val_path=../data/logP/val.csv
test_path=../data/logP/test.csv
path=../data/logP/logP_without_overlap.csv
#Hyperparameter optimization
python $chemprop_dir/hyperparameter_optimization.py \
--dataset_type regression \
--data_path $train_path \
--separate_val_path $val_path \
--separate_test_path $val_path \
--num_iters 30 \
--epochs 50 \
--aggregation norm \
--search_parameter_keywords depth ffn_num_layers hidden_size ffn_hidden_size dropout \
--config_save_path $results_dir/config.json \
--hyperopt_checkpoint_dir $results_dir \
--log_dir $results_dir
#Training with optimized hyperparameters
python $chemprop_dir/train.py \
--dataset_type regression \
--data_path $train_path \
--separate_val_path $val_path \
--separate_test_path $test_path \
--epochs 50 \
--aggregation norm \
--config_path $results_dir/config.json \
--save_dir $results_dir \
--ensemble_size 5 \
--save_preds \
--extra_metrics mae
#Train production model
python $chemprop_dir/train.py \
--dataset_type regression \
--data_path $path \
--separate_val_path $path \
--separate_test_path $path \
--epochs 40 \
--aggregation norm \
--config_path $results_dir/config.json \
--save_dir $results_dir2 \
--ensemble_size 5
#Predict on Sample 6
python $chemprop_dir/predict.py \
--test_path "../data/logP/sampl6_experimental.csv" \
--preds_path $results_dir2/pred_SAMPL6.csv \
--checkpoint_dir $results_dir2 \
--smiles_column "Isomeric SMILES"
echo SAMPL6 >> $results_dir2/sampl.csv
python -c 'import pandas as pd; from sklearn import metrics; print("rmse", metrics.mean_squared_error(pd.read_csv("results_sampl_production/pred_SAMPL6.csv")["logP"],pd.read_csv("../data/logP/sampl6_experimental.csv")["logP mean"],squared=False))' >> $results_dir2/sampl.csv
#Predict on Sample 7
python $chemprop_dir/predict.py \
--test_path "../data/logP/sampl7_experimental.csv" \
--preds_path $results_dir2/pred_SAMPL7.csv \
--checkpoint_dir $results_dir2 \
--smiles_column "Isomeric SMILES"
echo SAMPL7 >> $results_dir2/sampl.csv
python -c 'import pandas as pd; from sklearn import metrics; print("rmse", metrics.mean_squared_error(pd.read_csv("results_sampl_production/pred_SAMPL7.csv")["logP"],pd.read_csv("../data/logP/sampl7_experimental.csv")["logP mean"],squared=False))' >> $results_dir2/sampl.csv
#Predict on Sample 9
python $chemprop_dir/predict.py \
--test_path "../data/logP/sampl9_experimental.csv" \
--preds_path $results_dir2/pred_SAMPL9.csv \
--checkpoint_dir $results_dir2 \
--smiles_column smiles
echo SAMPL9 >> $results_dir2/sampl.csv
python -c 'import pandas as pd; from sklearn import metrics; print("rmse", metrics.mean_squared_error(pd.read_csv("results_sampl_production/pred_SAMPL9.csv")["logP"],pd.read_csv("../data/logP/sampl9_experimental.csv")["new_logPexp_reviewed"],squared=False))' >> $results_dir2/sampl.csv
echo "Saved results to" $results_dir2"/sampl.csv"
cat >> $results_dir2/sampl.csv
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