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Fix orderly condition #156

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merged 29 commits into from
Jan 20, 2024
Merged

Fix orderly condition #156

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7f97413
rm rxn with no solv set to False
dswigh Jan 15, 2024
6c577e2
rm rxn with no agents set to F
dswigh Jan 15, 2024
b11d0dc
added orderly-cond-prelim
dswigh Jan 17, 2024
b22e6d5
added test case for removing C when Pd present
dswigh Jan 17, 2024
d92c33e
added [Pd]/[C] -> [Pd] replacement
dswigh Jan 17, 2024
9f39b6e
delete [C] when transition metal present
dswigh Jan 17, 2024
8861346
change back to df.applymap
dswigh Jan 17, 2024
a880d9c
find [H][H] regex fix
dswigh Jan 17, 2024
7b4b0f5
changed paths
dswigh Jan 17, 2024
1899235
make black
dswigh Jan 17, 2024
957060a
regen test data
dswigh Jan 17, 2024
79028d7
fix strict mypy
dswigh Jan 17, 2024
0af6073
test passes
dswigh Jan 17, 2024
e0d32c1
added prelim orderly-cond with 100 min freq
dswigh Jan 17, 2024
d0172d0
test case [H][H] reactant
dswigh Jan 17, 2024
714eeb7
inspect reactions to find test cases
dswigh Jan 17, 2024
a25b5af
make black
dswigh Jan 17, 2024
b80ccb6
H2 always reactant, never an agent
dswigh Jan 20, 2024
a326340
del C if charcoal in procedure details
dswigh Jan 20, 2024
f73e2e0
scramble reactant, prod, solv, but not agent
dswigh Jan 20, 2024
eda76fd
added comment on sorting
dswigh Jan 20, 2024
0075db0
added no_min_freq as orderly-cond dataset
dswigh Jan 20, 2024
e544336
looking for bugs in prelim orderly-condition
dswigh Jan 20, 2024
ac5fbfa
move benchmark generation
dswigh Jan 20, 2024
9611e08
make black
dswigh Jan 20, 2024
e7b8516
restore extractor from random comment insertions
dswigh Jan 20, 2024
776ed92
make black
dswigh Jan 20, 2024
ac67db1
regen test data
dswigh Jan 20, 2024
1ab7a30
fix strict mypy
dswigh Jan 20, 2024
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32 changes: 24 additions & 8 deletions Makefile
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Expand Up @@ -123,11 +123,16 @@ run_python_310:
# 3. Plot histograms of the number of non-empty columns of each type (reactants, products, solvents, agents)
# 4. Run a cleaning with decided upon number of columns to keep
# 5. Plot histogram showing dataset size as a function of min_frequency_of_occurrence (can probably use the min_frequency code from the cleaner within the plotter)
# 6. Generate the four datasets we need for the paper (split into train and test set)
# 6. Generate the six condition prediction datasets we need for the paper (split into train and test set)
# 7. Plot histograms with the occurrence of the most common reactants, products, solvents, agents
# 8. Generate fingerprints for each dataset
# 9. Train & evaluate a model on each dataset

### Benchmark generation
# I. Extract data
# II. Clean data


# 1. Extract

paper_extract_uspto_no_trust:
Expand Down Expand Up @@ -161,10 +166,10 @@ paper_3: paper_plot_uspto_no_trust_unfiltered_num_rxn_components paper_plot_uspt
# 4. clean (filtered)

paper_clean_uspto_no_trust_filtered: #requires: paper_extract_uspto_no_trust
python -m orderly.clean --output_path="data/orderly/uspto_no_trust/filtered/filtered_orderly_ord.parquet" --ord_extraction_path="data/orderly/uspto_no_trust/extracted_ords" --molecules_to_remove_path="data/orderly/uspto_no_trust/all_molecule_names.csv" --min_frequency_of_occurrence=0 --map_rare_molecules_to_other=True --set_unresolved_names_to_none_if_mapped_rxn_str_exists_else_del_rxn=True --remove_rxn_with_unresolved_names=False --set_unresolved_names_to_none=False --num_product=1 --num_reactant=2 --num_solv=2 --num_agent=3 --num_cat=0 --num_reag=0 --consistent_yield=False --remove_reactions_with_no_solvents=True --remove_reactions_with_no_agents=True --train_size=0.0
python -m orderly.clean --output_path="data/orderly/uspto_no_trust/filtered/filtered_orderly_ord.parquet" --ord_extraction_path="data/orderly/uspto_no_trust/extracted_ords" --molecules_to_remove_path="data/orderly/uspto_no_trust/all_molecule_names.csv" --min_frequency_of_occurrence=0 --map_rare_molecules_to_other=True --set_unresolved_names_to_none_if_mapped_rxn_str_exists_else_del_rxn=True --remove_rxn_with_unresolved_names=False --set_unresolved_names_to_none=False --num_product=1 --num_reactant=2 --num_solv=2 --num_agent=3 --num_cat=0 --num_reag=0 --consistent_yield=False --remove_reactions_with_no_solvents=False --remove_reactions_with_no_agents=False --train_size=0.0

paper_clean_uspto_with_trust_filtered: #requires: paper_extract_uspto_with_trust
python -m orderly.clean --output_path="data/orderly/uspto_with_trust/filtered/filtered_orderly_ord.parquet" --ord_extraction_path="data/orderly/uspto_with_trust/extracted_ords" --molecules_to_remove_path="data/orderly/uspto_with_trust/all_molecule_names.csv" --min_frequency_of_occurrence=0 --map_rare_molecules_to_other=True --set_unresolved_names_to_none_if_mapped_rxn_str_exists_else_del_rxn=True --remove_rxn_with_unresolved_names=False --set_unresolved_names_to_none=False --num_product=1 --num_reactant=2 --num_solv=2 --num_agent=0 --num_cat=1 --num_reag=2 --consistent_yield=False --remove_reactions_with_no_solvents=True --remove_reactions_with_no_agents=True --train_size=0.0
python -m orderly.clean --output_path="data/orderly/uspto_with_trust/filtered/filtered_orderly_ord.parquet" --ord_extraction_path="data/orderly/uspto_with_trust/extracted_ords" --molecules_to_remove_path="data/orderly/uspto_with_trust/all_molecule_names.csv" --min_frequency_of_occurrence=0 --map_rare_molecules_to_other=True --set_unresolved_names_to_none_if_mapped_rxn_str_exists_else_del_rxn=True --remove_rxn_with_unresolved_names=False --set_unresolved_names_to_none=False --num_product=1 --num_reactant=2 --num_solv=2 --num_agent=0 --num_cat=1 --num_reag=2 --consistent_yield=False --remove_reactions_with_no_solvents=False --remove_reactions_with_no_agents=False --train_size=0.0

paper_4: paper_clean_uspto_no_trust_filtered paper_clean_uspto_with_trust_filtered

Expand All @@ -185,7 +190,7 @@ paper_plot_uspto_with_trust_filtered_min_frequency_of_occurrence_100_1000:
paper_5 : paper_plot_uspto_no_trust_filtered_min_frequency_of_occurrence_10_100 paper_plot_uspto_no_trust_filtered_min_frequency_of_occurrence_100_1000 paper_plot_uspto_with_trust_filtered_min_frequency_of_occurrence_10_100 paper_plot_uspto_with_trust_filtered_min_frequency_of_occurrence_100_1000


# 6. clean (final)
# 6. ORDerly-condition alternatives cleaning
paper_gen_uspto_no_trust_no_map: #requires: paper_extract_uspto_no_trust
python -m orderly.clean --output_path="data/orderly/datasets_$(dataset_version)/orderly_no_trust_no_map.parquet" --ord_extraction_path="data/orderly/uspto_no_trust/extracted_ords" --molecules_to_remove_path="data/orderly/uspto_no_trust/all_molecule_names.csv" --min_frequency_of_occurrence=100 --map_rare_molecules_to_other=False --set_unresolved_names_to_none_if_mapped_rxn_str_exists_else_del_rxn=True --remove_rxn_with_unresolved_names=False --set_unresolved_names_to_none=False --num_product=1 --num_reactant=2 --num_solv=2 --num_agent=3 --num_cat=0 --num_reag=0 --consistent_yield=False --scramble=True --train_size=0.9

Expand All @@ -198,17 +203,25 @@ paper_gen_uspto_with_trust_with_map: #requires: paper_extract_uspto_with_trust
paper_gen_uspto_with_trust_no_map: #requires: paper_extract_uspto_with_trust
python -m orderly.clean --output_path="data/orderly/datasets_$(dataset_version)/orderly_with_trust_no_map.parquet" --ord_extraction_path="data/orderly/uspto_with_trust/extracted_ords" --molecules_to_remove_path="data/orderly/uspto_with_trust/all_molecule_names.csv" --min_frequency_of_occurrence=100 --map_rare_molecules_to_other=False --set_unresolved_names_to_none_if_mapped_rxn_str_exists_else_del_rxn=True --remove_rxn_with_unresolved_names=False --set_unresolved_names_to_none=False --num_product=1 --num_reactant=2 --num_solv=2 --num_agent=0 --num_cat=1 --num_reag=2 --consistent_yield=False --scramble=True --train_size=0.9

paper_6: paper_gen_uspto_no_trust_no_map paper_gen_uspto_no_trust_with_map paper_gen_uspto_with_trust_with_map paper_gen_uspto_with_trust_no_map
paper_gen_uspto_no_trust_no_min_freq: #requires: paper_extract_uspto_no_trust
python -m orderly.clean --output_path="data/orderly/datasets_$(dataset_version)/orderly_no_trust_no_min_freq.parquet" --ord_extraction_path="data/orderly/uspto_no_trust/extracted_ords" --molecules_to_remove_path="data/orderly/uspto_no_trust/all_molecule_names.csv" --min_frequency_of_occurrence=0 --map_rare_molecules_to_other=False --set_unresolved_names_to_none_if_mapped_rxn_str_exists_else_del_rxn=True --remove_rxn_with_unresolved_names=False --set_unresolved_names_to_none=False --num_product=1 --num_reactant=2 --num_solv=2 --num_agent=3 --num_cat=0 --num_reag=0 --consistent_yield=False --scramble=True --train_size=0.9

paper_gen_uspto_with_trust_no_min_freq: #requires: paper_extract_uspto_with_trust
python -m orderly.clean --output_path="data/orderly/datasets_$(dataset_version)/orderly_with_trust_no_min_freq.parquet" --ord_extraction_path="data/orderly/uspto_with_trust/extracted_ords" --molecules_to_remove_path="data/orderly/uspto_with_trust/all_molecule_names.csv" --min_frequency_of_occurrence=0 --map_rare_molecules_to_other=False --set_unresolved_names_to_none_if_mapped_rxn_str_exists_else_del_rxn=True --remove_rxn_with_unresolved_names=False --set_unresolved_names_to_none=False --num_product=1 --num_reactant=2 --num_solv=2 --num_agent=0 --num_cat=1 --num_reag=2 --consistent_yield=False --scramble=True --train_size=0.9


paper_6: paper_gen_uspto_no_trust_no_map paper_gen_uspto_no_trust_with_map paper_gen_uspto_with_trust_with_map paper_gen_uspto_with_trust_no_map paper_gen_uspto_no_trust_no_min_freq paper_gen_uspto_with_trust_no_min_freq

# 7. Plot plot_molecule_popularity_histograms
paper_plot_uspto_no_trust_no_map:
python -m orderly.plot --clean_data_path="data/orderly/datasets/orderly_no_trust_no_map_train.parquet" --plot_output_path="data/orderly/plot_no_trust/" --plot_num_rxn_components_bool=False --plot_frequency_of_occurrence_bool=False --plot_molecule_popularity_histograms=True
python -m orderly.plot --clean_data_path="data/orderly/datasets_$(dataset_version)/orderly_no_trust_no_map_train.parquet" --plot_output_path="data/orderly/plot_no_trust/" --plot_num_rxn_components_bool=False --plot_frequency_of_occurrence_bool=False --plot_molecule_popularity_histograms=True

paper_plot_uspto_with_trust_no_map:
python -m orderly.plot --clean_data_path="data/orderly/datasets/orderly_with_trust_no_map_train.parquet" --plot_output_path="data/orderly/plot_with_trust/" --plot_num_rxn_components_bool=False --plot_frequency_of_occurrence_bool=False --plot_molecule_popularity_histograms=True
python -m orderly.plot --clean_data_path="data/orderly/datasets_$(dataset_version)/orderly_with_trust_no_map_train.parquet" --plot_output_path="data/orderly/plot_with_trust/" --plot_num_rxn_components_bool=False --plot_frequency_of_occurrence_bool=False --plot_molecule_popularity_histograms=True

paper_7 : paper_plot_uspto_no_trust_no_map paper_plot_uspto_with_trust_no_map


# 8. gen fp

fp_no_trust_no_map_test:
Expand All @@ -234,7 +247,7 @@ fp_with_trust_no_map_train:
paper_8: fp_no_trust_no_map_test fp_no_trust_no_map_train fp_no_trust_with_map_test fp_no_trust_with_map_train fp_with_trust_with_map_test fp_with_trust_with_map_train fp_with_trust_no_map_test fp_with_trust_no_map_train

#Generate datasets for paper
paper_get_datasets: paper_1 paper_6 paper_8
paper_get_datasets: paper_1 paper_6

paper_gen_all: paper_1 paper_2 paper_3 paper_4 paper_5 paper_6 paper_8

Expand Down Expand Up @@ -266,6 +279,9 @@ with_trust_no_map_train_20:
with_trust_with_map_train_20:
python -m condition_prediction --train_data_path="data/orderly/datasets_$(dataset_version)/orderly_with_trust_with_map_train.parquet" --test_data_path="data/orderly/datasets_$(dataset_version)/orderly_with_trust_with_map_test.parquet" --output_folder_path="models/with_trust_with_map_20" --train_fraction=0.2 --train_val_split=0.8 --overwrite=False --epochs=20 --evaluate_on_test_data=True --early_stopping_patience=5 --wandb_entity=$(WANDB_ENTITY)

################################################
# Generate ORDerly benchmarks
################################################

# Sweeps
RANDOM_SEEDS = 12345 54321 98765
Expand Down
2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -167,7 +167,7 @@ If you would like to extract all data in ORD (instead of just USPTO data) simply

### ORDerly-condition

```python -m orderly.clean --output_path="../orderly_generated_datasets/orderly_condition.parquet" --ord_extraction_path="data/orderly/extracted_ords" --molecules_to_remove_path="data/orderly/all_molecule_names.csv" --min_frequency_of_occurrence=100 --map_rare_molecules_to_other=False --set_unresolved_names_to_none_if_mapped_rxn_str_exists_else_del_rxn=True --remove_rxn_with_unresolved_names=False --set_unresolved_names_to_none=False --num_product=1 --num_reactant=2 --num_solv=2 --num_agent=3 --num_cat=0 --num_reag=0 --consistent_yield=False --scramble=False --train_test_split_fraction=0.9 --remove_reactions_with_no_reactants=True --remove_reactions_with_no_products=True --remove_reactions_with_no_solvents=True --remove_reactions_with_no_agents=True```
```python -m orderly.clean --output_path="../orderly_generated_datasets/orderly_condition.parquet" --ord_extraction_path="data/orderly/extracted_ords" --molecules_to_remove_path="data/orderly/all_molecule_names.csv" --min_frequency_of_occurrence=100 --map_rare_molecules_to_other=False --set_unresolved_names_to_none_if_mapped_rxn_str_exists_else_del_rxn=True --remove_rxn_with_unresolved_names=False --set_unresolved_names_to_none=False --num_product=1 --num_reactant=2 --num_solv=2 --num_agent=3 --num_cat=0 --num_reag=0 --consistent_yield=False --scramble=False --train_test_split_fraction=0.9 --remove_reactions_with_no_reactants=True --remove_reactions_with_no_products=True --remove_reactions_with_no_solvents=False --remove_reactions_with_no_agents=False```

### ORDerly-forward

Expand Down
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