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Merge pull request #13 from SCAI-BIO/main-script-for-paper-reference
Add AD/PD evaluation script
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| import os | ||
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| from datastew.process.parsing import MappingSource, DataDictionarySource | ||
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| from datastew.embedding import GPT4Adapter, MPNetAdapter | ||
| from datastew.evaluation import MatchingMethod, enrichment_analysis, evaluate | ||
| from datastew.mapping import MappingTable | ||
| from dotenv import load_dotenv | ||
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| from datastew.visualisation import enrichment_plot, scatter_plot_all_cohorts, scatter_plot_two_distributions | ||
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| EVAL_PD = True | ||
| EVAL_AD = True | ||
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| # Constants for file paths | ||
| PD_CDM_SRC = "resources/cdm_pd.xlsx" | ||
| AD_CDM_SRC = "resources/cdm_ad.csv" | ||
| CDM_CURIE_CSV = "resources/cdm_curie.csv" | ||
| LRRK2_DICT_XLSX = "resources/dictionaries/pd/LRRK2.xlsx" | ||
| OPDC_DICT_CSV = "resources/dictionaries/pd/OPDC.csv" | ||
| TPD_DICT_CSV = "resources/dictionaries/pd/TPD.csv" | ||
| A4_DICT_CSV = "resources/dictionaries/ad/a4.csv" | ||
| ABVIB_DICT_CSV = "resources/dictionaries/ad/abvib.csv" | ||
| ADNI_DICT_CSV = "resources/dictionaries/ad/ADNIMERGE_DICT_27Nov2023 2.csv" | ||
| AIBL_DICT_CSV = "resources/dictionaries/ad/aibl.csv" | ||
| ARWIBO_DICT_CSV = "resources/dictionaries/ad/arwibo.csv" | ||
| DOD_ADNI_DICT_CSV = "resources/dictionaries/ad/dod-adni.csv" | ||
| EDSD_DICT_XLSX = "resources/dictionaries/ad/edsd.xlsx" | ||
| EMIF_DICT_XLSX = "resources/dictionaries/ad/emif.xlsx" | ||
| I_ADNI_DICT_CSV = "resources/dictionaries/ad/i-adni.csv" | ||
| JADNI_DICT_TSV = "resources/dictionaries/ad/jadni.tsv" | ||
| PHARMACOG_DICT_CSV = "resources/dictionaries/ad/pharmacog.csv" | ||
| PREVENT_AD_DICT_CSV = "resources/dictionaries/ad/prevent-ad.csv" | ||
| VITA_DICT_CSV = "resources/dictionaries/ad/vita.csv" | ||
| PPMI_DICT_SRC = "resources/dictionaries/pd/ppmi.csv" | ||
| LUXPARK_DICT_SRC = "resources/dictionaries/pd/luxpark.xlsx" | ||
| BIOFIND_DICT_SRC = "resources/dictionaries/pd/biofind.csv" | ||
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| load_dotenv() | ||
| gpt4 = GPT4Adapter(api_key=os.getenv("GPT_KEY")) | ||
| mpnet = MPNetAdapter() | ||
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| # PD Mappings | ||
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| if EVAL_PD: | ||
| cdm_pd_gpt = MappingTable(MappingSource(PD_CDM_SRC, "Feature", "CURIE")) | ||
| cdm_pd_gpt.joined_mapping_table["identifier"].to_csv("resources/cdm_curie.csv", index=False) | ||
| cdm_pd_gpt.add_descriptions(DataDictionarySource(PD_CDM_SRC, "Feature", "Definition")) | ||
| cdm_pd_gpt.compute_embeddings(gpt4) | ||
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| cdm_pd_mpnet = MappingTable(MappingSource(PD_CDM_SRC, "Feature", "CURIE")) | ||
| cdm_pd_mpnet.joined_mapping_table["identifier"].to_csv("resources/cdm_curie.csv", index=False) | ||
| cdm_pd_mpnet.add_descriptions(DataDictionarySource(PD_CDM_SRC, "Feature", "Definition")) | ||
| cdm_pd_mpnet.compute_embeddings(mpnet) | ||
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| ppmi_gpt = MappingTable(MappingSource(PD_CDM_SRC, "PPMI", "CURIE")) | ||
| ppmi_gpt.add_descriptions(DataDictionarySource(PPMI_DICT_SRC, "ITM_NAME", "DSCR")) | ||
| ppmi_gpt.compute_embeddings(gpt4) | ||
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| ppmi_mpnet = MappingTable(MappingSource(PD_CDM_SRC, "PPMI", "CURIE")) | ||
| ppmi_mpnet.add_descriptions(DataDictionarySource(PPMI_DICT_SRC, "ITM_NAME", "DSCR")) | ||
| ppmi_mpnet.compute_embeddings(mpnet) | ||
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| luxpark_gpt = MappingTable(MappingSource(PD_CDM_SRC, "LuxPARK", "CURIE")) | ||
| luxpark_gpt.add_descriptions(DataDictionarySource(LUXPARK_DICT_SRC, "Variable / Field Name", "Field Label")) | ||
| luxpark_gpt.compute_embeddings(gpt4) | ||
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| luxpark_mpnet = MappingTable(MappingSource(PD_CDM_SRC, "LuxPARK", "CURIE")) | ||
| luxpark_mpnet.add_descriptions(DataDictionarySource(LUXPARK_DICT_SRC, "Variable / Field Name", "Field Label")) | ||
| luxpark_mpnet.compute_embeddings(mpnet) | ||
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| biofind_gpt = MappingTable(MappingSource(PD_CDM_SRC, "BIOFIND", "CURIE")) | ||
| biofind_gpt.add_descriptions(DataDictionarySource(BIOFIND_DICT_SRC, "ITM_NAME", "DSCR")) | ||
| biofind_gpt.compute_embeddings(gpt4) | ||
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| biofind_mpnet = MappingTable(MappingSource(PD_CDM_SRC, "BIOFIND", "CURIE")) | ||
| biofind_mpnet.add_descriptions(DataDictionarySource(BIOFIND_DICT_SRC, "ITM_NAME", "DSCR")) | ||
| biofind_mpnet.compute_embeddings(mpnet) | ||
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| lrrk2_gpt = MappingTable(MappingSource(PD_CDM_SRC, "LRRK2", "CURIE")) | ||
| lrrk2_gpt.add_descriptions(DataDictionarySource("resources/dictionaries/pd/LRRK2.xlsx", "Variable", "Label")) | ||
| lrrk2_gpt.compute_embeddings(gpt4) | ||
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| lrrk2_mpnet = MappingTable(MappingSource(PD_CDM_SRC, "LRRK2", "CURIE")) | ||
| lrrk2_mpnet.add_descriptions(DataDictionarySource("resources/dictionaries/pd/LRRK2.xlsx", "Variable", "Label")) | ||
| lrrk2_mpnet.compute_embeddings(mpnet) | ||
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| opdc_gpt = MappingTable(MappingSource(PD_CDM_SRC, "OPDC", "CURIE")) | ||
| opdc_gpt.add_descriptions(DataDictionarySource("resources/dictionaries/pd/OPDC.csv", "Variable Name", "Variable description")) | ||
| opdc_gpt.compute_embeddings(gpt4) | ||
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| opdc_mpnet = MappingTable(MappingSource(PD_CDM_SRC, "OPDC", "CURIE")) | ||
| opdc_mpnet.add_descriptions(DataDictionarySource("resources/dictionaries/pd/OPDC.csv", "Variable Name", "Variable description")) | ||
| opdc_mpnet.compute_embeddings(mpnet) | ||
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| tpd_gpt = MappingTable(MappingSource(PD_CDM_SRC, "TPD", "CURIE")) | ||
| tpd_gpt.add_descriptions(DataDictionarySource("resources/dictionaries/pd/TPD.csv", "Variable Name", "Variable description")) | ||
| tpd_gpt.compute_embeddings(gpt4) | ||
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| tpd_mpnet = MappingTable(MappingSource(PD_CDM_SRC, "TPD", "CURIE")) | ||
| tpd_mpnet.add_descriptions(DataDictionarySource("resources/dictionaries/pd/TPD.csv", "Variable Name", "Variable description")) | ||
| tpd_mpnet.compute_embeddings(mpnet) | ||
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| pd_datasets_gpt = [opdc_gpt, tpd_gpt, biofind_gpt, lrrk2_gpt, luxpark_gpt, ppmi_gpt, cdm_pd_gpt] | ||
| pd_datasets_mpnet = [opdc_mpnet, tpd_mpnet, biofind_mpnet, lrrk2_mpnet, luxpark_mpnet, ppmi_mpnet, cdm_pd_mpnet] | ||
| pd_datasets_labels = ["OPDC", "PRoBaND", "BIOFIND", "LCC", "LuxPARK", "PPMI", "PASSIONATE"] | ||
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| # enrichment analysis | ||
| luxpark_passionate_enrichment_gpt = enrichment_analysis(luxpark_gpt, cdm_pd_gpt, 20, MatchingMethod.EUCLIDEAN_EMBEDDING_DISTANCE) | ||
| luxpark_passionate_enrichment_mpnet = enrichment_analysis(luxpark_mpnet, cdm_pd_mpnet, 20, MatchingMethod.COSINE_EMBEDDING_DISTANCE) | ||
| luxpark_passionate_enrichment_fuzzy = enrichment_analysis(luxpark_gpt, cdm_pd_gpt, 20, MatchingMethod.FUZZY_STRING_MATCHING) | ||
| label1 = "Enrichment Plot LuxPARK to CDM" | ||
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| ppmi_passionate_enrichment_gpt = enrichment_analysis(ppmi_gpt, cdm_pd_gpt, 20, MatchingMethod.EUCLIDEAN_EMBEDDING_DISTANCE) | ||
| ppmi_passionate_enrichment_mpnet = enrichment_analysis(ppmi_mpnet, cdm_pd_mpnet, 20, MatchingMethod.COSINE_EMBEDDING_DISTANCE) | ||
| ppmi_passionate_enrichment_fuzzy = enrichment_analysis(ppmi_gpt, cdm_pd_gpt, 20, MatchingMethod.FUZZY_STRING_MATCHING) | ||
| label2 = "Enrichment Plot PPMI to CDM" | ||
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| ppmi_luxpark_enrichment_gpt = enrichment_analysis(ppmi_gpt, luxpark_gpt, 20, MatchingMethod.EUCLIDEAN_EMBEDDING_DISTANCE) | ||
| ppmi_luxpark_enrichment_mpnet = enrichment_analysis(ppmi_mpnet, luxpark_mpnet, 20, MatchingMethod.COSINE_EMBEDDING_DISTANCE) | ||
| ppmi_luxpark_enrichment_fuzzy = enrichment_analysis(ppmi_gpt, luxpark_gpt, 20, MatchingMethod.FUZZY_STRING_MATCHING) | ||
| label3 = "Enrichment Plot PPMI to LuxPARK" | ||
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| enrichment_plot(luxpark_passionate_enrichment_gpt, luxpark_passionate_enrichment_mpnet, luxpark_passionate_enrichment_fuzzy, label1, save_plot=True) | ||
| enrichment_plot(ppmi_passionate_enrichment_gpt, ppmi_passionate_enrichment_mpnet, ppmi_passionate_enrichment_fuzzy, label2, save_plot=True) | ||
| enrichment_plot( ppmi_luxpark_enrichment_gpt, ppmi_luxpark_enrichment_mpnet, ppmi_luxpark_enrichment_fuzzy, label3, save_plot=True) | ||
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| print(luxpark_passionate_enrichment_gpt) | ||
| print(luxpark_passionate_enrichment_mpnet) | ||
| print(luxpark_passionate_enrichment_fuzzy) | ||
| print(ppmi_passionate_enrichment_gpt) | ||
| print(ppmi_passionate_enrichment_mpnet) | ||
| print(ppmi_passionate_enrichment_fuzzy) | ||
| print(ppmi_luxpark_enrichment_gpt) | ||
| print(ppmi_luxpark_enrichment_mpnet) | ||
| print(ppmi_luxpark_enrichment_fuzzy) | ||
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| gpt_table1 = evaluate(pd_datasets_gpt, pd_datasets_labels, store_results=True) | ||
| fuzzy_table1 = evaluate(pd_datasets_gpt, pd_datasets_labels, store_results=True, model="fuzzy") | ||
| mpnet_table1 = evaluate(pd_datasets_mpnet, pd_datasets_labels, store_results=True, model="mpnet") | ||
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| print("PD RESULTS:") | ||
| print("GPT") | ||
| print("-----------") | ||
| print(gpt_table1) | ||
| print("-----------") | ||
| print("MPNet") | ||
| print("-----------") | ||
| print(mpnet_table1) | ||
| print("-----------") | ||
| print("Fuzzy") | ||
| print("-----------") | ||
| print(fuzzy_table1) | ||
| print("-----------") | ||
|
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| # AD Mappings | ||
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| if EVAL_AD: | ||
| cdm_ad_gpt = MappingTable(MappingSource(AD_CDM_SRC, "Feature", "CDM")) | ||
| cdm_ad_gpt.add_descriptions(DataDictionarySource(AD_CDM_SRC, "Feature", "Definition")) | ||
| cdm_ad_gpt.compute_embeddings(gpt4) | ||
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| cdm_ad_mpnet = MappingTable(MappingSource(AD_CDM_SRC, "Feature", "CDM")) | ||
| cdm_ad_mpnet.add_descriptions(DataDictionarySource(AD_CDM_SRC, "Feature", "Definition")) | ||
| cdm_ad_mpnet.compute_embeddings(mpnet) | ||
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| abvib_gpt = MappingTable(MappingSource(AD_CDM_SRC, "abvib", "CDM")) | ||
| abvib_gpt.add_descriptions(DataDictionarySource(ABVIB_DICT_CSV, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| abvib_gpt.compute_embeddings(gpt4) | ||
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| abvib_mpnet = MappingTable(MappingSource(AD_CDM_SRC, "abvib", "CDM")) | ||
| abvib_mpnet.add_descriptions(DataDictionarySource(ABVIB_DICT_CSV, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| abvib_mpnet.compute_embeddings(mpnet) | ||
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| adni_gpt = MappingTable(MappingSource(AD_CDM_SRC, "adni", "CDM")) | ||
| adni_gpt.add_descriptions(DataDictionarySource(ADNI_DICT_CSV, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| adni_gpt.compute_embeddings(gpt4) | ||
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| adni_mpnet = MappingTable(MappingSource(AD_CDM_SRC, "adni", "CDM")) | ||
| adni_mpnet.add_descriptions(DataDictionarySource(ADNI_DICT_CSV, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| adni_mpnet.compute_embeddings(mpnet) | ||
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| a4_gpt = MappingTable(MappingSource(AD_CDM_SRC, "a4", "CDM")) | ||
| a4_gpt.add_descriptions(DataDictionarySource(A4_DICT_CSV, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| a4_gpt.compute_embeddings(gpt4) | ||
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| a4_mpnet = MappingTable(MappingSource(AD_CDM_SRC, "a4", "CDM")) | ||
| a4_mpnet.add_descriptions(DataDictionarySource(A4_DICT_CSV, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| a4_mpnet.compute_embeddings(mpnet) | ||
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| aibl_gpt = MappingTable(MappingSource(AD_CDM_SRC, "aibl", "CDM")) | ||
| aibl_gpt.add_descriptions(DataDictionarySource(AIBL_DICT_CSV, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| aibl_gpt.compute_embeddings(gpt4) | ||
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| aibl_mpnet = MappingTable(MappingSource(AD_CDM_SRC, "aibl", "CDM")) | ||
| aibl_mpnet.add_descriptions(DataDictionarySource(AIBL_DICT_CSV, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| aibl_mpnet.compute_embeddings(mpnet) | ||
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| arwibo_gpt = MappingTable(MappingSource(AD_CDM_SRC, "arwibo", "CDM")) | ||
| arwibo_gpt.add_descriptions(DataDictionarySource(ARWIBO_DICT_CSV, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| arwibo_gpt.compute_embeddings(gpt4) | ||
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| arwibo_mpnet = MappingTable(MappingSource(AD_CDM_SRC, "arwibo", "CDM")) | ||
| arwibo_mpnet.add_descriptions(DataDictionarySource(ARWIBO_DICT_CSV, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| arwibo_mpnet.compute_embeddings(mpnet) | ||
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| dod_adni_gpt = MappingTable(MappingSource(AD_CDM_SRC, "dod-adni", "CDM")) | ||
| dod_adni_gpt.add_descriptions(DataDictionarySource(DOD_ADNI_DICT_CSV, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| dod_adni_gpt.compute_embeddings(gpt4) | ||
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| dod_adni_mpnet = MappingTable(MappingSource(AD_CDM_SRC, "dod-adni", "CDM")) | ||
| dod_adni_mpnet.add_descriptions(DataDictionarySource(DOD_ADNI_DICT_CSV, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| dod_adni_mpnet.compute_embeddings(mpnet) | ||
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| edsd_gpt = MappingTable(MappingSource(AD_CDM_SRC, "edsd", "CDM")) | ||
| edsd_gpt.add_descriptions(DataDictionarySource(EDSD_DICT_XLSX, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| edsd_gpt.compute_embeddings(gpt4) | ||
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| edsd_mpnet = MappingTable(MappingSource(AD_CDM_SRC, "edsd", "CDM")) | ||
| edsd_mpnet.add_descriptions(DataDictionarySource(EDSD_DICT_XLSX, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| edsd_mpnet.compute_embeddings(mpnet) | ||
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| emif_gpt = MappingTable(MappingSource(AD_CDM_SRC, "emif", "CDM")) | ||
| emif_gpt.add_descriptions(DataDictionarySource(EMIF_DICT_XLSX, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| emif_gpt.compute_embeddings(gpt4) | ||
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| emif_mpnet = MappingTable(MappingSource(AD_CDM_SRC, "emif", "CDM")) | ||
| emif_mpnet.add_descriptions(DataDictionarySource(EMIF_DICT_XLSX, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| emif_mpnet.compute_embeddings(mpnet) | ||
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| iadni_gpt = MappingTable(MappingSource(AD_CDM_SRC, "i-adni", "CDM")) | ||
| iadni_gpt.add_descriptions(DataDictionarySource(I_ADNI_DICT_CSV, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| iadni_gpt.compute_embeddings(gpt4) | ||
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| iadni_mpnet = MappingTable(MappingSource(AD_CDM_SRC, "i-adni", "CDM")) | ||
| iadni_mpnet.add_descriptions(DataDictionarySource(I_ADNI_DICT_CSV, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| iadni_mpnet.compute_embeddings(mpnet) | ||
|
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| jadni_gpt = MappingTable(MappingSource(AD_CDM_SRC, "jadni", "CDM")) | ||
| jadni_gpt.add_descriptions(DataDictionarySource(JADNI_DICT_TSV, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| jadni_gpt.compute_embeddings(gpt4) | ||
|
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| jadni_mpnet = MappingTable(MappingSource(AD_CDM_SRC, "jadni", "CDM")) | ||
| jadni_mpnet.add_descriptions(DataDictionarySource(JADNI_DICT_TSV, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| jadni_mpnet.compute_embeddings(mpnet) | ||
|
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| pharmacog_gpt = MappingTable(MappingSource(AD_CDM_SRC, "pharmacog", "CDM")) | ||
| pharmacog_gpt.add_descriptions(DataDictionarySource(PHARMACOG_DICT_CSV, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| pharmacog_gpt.compute_embeddings(gpt4) | ||
|
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| pharmacog_mpnet = MappingTable(MappingSource(AD_CDM_SRC, "pharmacog", "CDM")) | ||
| pharmacog_mpnet.add_descriptions(DataDictionarySource(PHARMACOG_DICT_CSV, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| pharmacog_mpnet.compute_embeddings(mpnet) | ||
|
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| prevent_ad_gpt = MappingTable(MappingSource(AD_CDM_SRC, "prevent-ad", "CDM")) | ||
| prevent_ad_gpt.add_descriptions(DataDictionarySource(PREVENT_AD_DICT_CSV, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| prevent_ad_gpt.compute_embeddings(gpt4) | ||
|
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| prevent_ad_mpnet = MappingTable(MappingSource(AD_CDM_SRC, "prevent-ad", "CDM")) | ||
| prevent_ad_mpnet.add_descriptions(DataDictionarySource(PREVENT_AD_DICT_CSV, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| prevent_ad_mpnet.compute_embeddings(mpnet) | ||
|
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| vita_gpt = MappingTable(MappingSource(AD_CDM_SRC, "vita", "CDM")) | ||
| vita_gpt.add_descriptions(DataDictionarySource(VITA_DICT_CSV, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| vita_gpt.compute_embeddings(gpt4) | ||
|
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| vita_mpnet = MappingTable(MappingSource(AD_CDM_SRC, "vita", "CDM")) | ||
| vita_mpnet.add_descriptions(DataDictionarySource(VITA_DICT_CSV, "FIELD_LABEL", "FIELD_DESCRIPTION")) | ||
| vita_mpnet.compute_embeddings(mpnet) | ||
|
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| ad_datasets_gpt = [a4_gpt, abvib_gpt, adni_gpt, aibl_gpt, arwibo_gpt, dod_adni_gpt, edsd_gpt, emif_gpt, iadni_gpt, jadni_gpt, | ||
| pharmacog_gpt, prevent_ad_gpt, vita_gpt, cdm_ad_gpt] | ||
| ad_datasets_mpnet = [a4_mpnet, abvib_mpnet, adni_mpnet, aibl_mpnet, arwibo_mpnet, dod_adni_mpnet, edsd_mpnet, emif_mpnet, | ||
| iadni_mpnet, jadni_mpnet, pharmacog_mpnet, prevent_ad_mpnet, vita_mpnet, cdm_ad_mpnet] | ||
| ad_datasets_labels = ["A4", "ABVIB", "ADNI", "AIBL", "ARWIBO", "DOD-ADNI", "EDSD", "EMIF", "I-ADNI", "JADNI", "PharmaCog", | ||
| "PREVENT-AD", "VITA", "AD-Mapper"] | ||
| gpt_table2 = evaluate(ad_datasets_gpt, ad_datasets_labels, store_results=True, results_root_dir="resources/results/ad") | ||
| fuzzy_table2 = evaluate(ad_datasets_gpt, ad_datasets_labels, store_results=True, model="fuzzy", results_root_dir="resources/results/ad") | ||
| mpnet_table2 = evaluate(ad_datasets_mpnet, ad_datasets_labels, store_results=True, model="mpnet", results_root_dir="resources/results/ad") | ||
|
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| print("AD RESULTS:") | ||
| print("GPT") | ||
| print("-----------") | ||
| print(gpt_table2.to_string()) | ||
| print("-----------") | ||
| print("MPNet") | ||
| print("-----------") | ||
| print(mpnet_table2.to_string()) | ||
| print("-----------") | ||
| print("Fuzzy") | ||
| print("-----------") | ||
| print(fuzzy_table2.to_string()) | ||
| print("-----------") | ||
|
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| # embedding distribution | ||
| scatter_plot_two_distributions(pd_datasets_gpt, ad_datasets_gpt, "PD", "AD") | ||
| scatter_plot_all_cohorts(pd_datasets_gpt, ad_datasets_gpt, pd_datasets_labels, ad_datasets_labels) |