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- acquire_valued_shoppers_challenge/019d7376-a3c5-7df7-88c0-ba4e87c3dfa0/container_metadata.json +5 -0
- acquire_valued_shoppers_challenge/019d7376-a3c5-7df7-88c0-ba4e87c3dfa0/dataset_metadata.dataset-mold-v1.json +21 -0
- acquire_valued_shoppers_challenge/019d7376-a3c5-7df7-88c0-ba4e87c3dfa0/dtypes.json +114 -0
- acquire_valued_shoppers_challenge/019d7376-a3c5-7df7-88c0-ba4e87c3dfa0/experiment_metadata.predictive-ml-splits-mold-v1.json +0 -0
- acquire_valued_shoppers_challenge/019d7376-a3c5-7df7-88c0-ba4e87c3dfa0/task_metadata.predictive-ml-task-mold-v1.json +11 -0
- allstate_claims_severity/019d736a-2321-76da-a36a-ddc5e1fbf7b6/container_metadata.json +5 -0
- allstate_claims_severity/019d736a-2321-76da-a36a-ddc5e1fbf7b6/dataset_metadata.dataset-mold-v1.json +20 -0
- allstate_claims_severity/019d736a-2321-76da-a36a-ddc5e1fbf7b6/dtypes.json +133 -0
- allstate_claims_severity/019d736a-2321-76da-a36a-ddc5e1fbf7b6/task_metadata.predictive-ml-task-mold-v1.json +11 -0
- aps_failure/019d7366-7373-7942-9438-aa122a6ca492/container_metadata.json +5 -0
- aps_failure/019d7366-7373-7942-9438-aa122a6ca492/dtypes.json +173 -0
- bioresponse/019d7366-c770-7d3c-8dd9-b9cc456542d6/container_metadata.json +5 -0
- bioresponse/019d7366-c770-7d3c-8dd9-b9cc456542d6/dataset_metadata.dataset-mold-v1.json +19 -0
- bioresponse/019d7366-c770-7d3c-8dd9-b9cc456542d6/dtypes.json +1779 -0
- bioresponse/019d7366-c770-7d3c-8dd9-b9cc456542d6/experiment_metadata.predictive-ml-splits-mold-v1.json +0 -0
- bioresponse/019d7366-c770-7d3c-8dd9-b9cc456542d6/task_metadata.predictive-ml-task-mold-v1.json +11 -0
- body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9/container_metadata.json +5 -0
- body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9/dataset_metadata.dataset-mold-v1.json +19 -0
- body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9/dtypes.json +16 -0
- body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9/experiment_metadata.predictive-ml-splits-mold-v1.json +1 -0
- body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9/task_metadata.predictive-ml-task-mold-v1.json +11 -0
- cardiotocography/019d7392-12c2-7a87-991c-13a7246555d4/container_metadata.json +5 -0
- cardiotocography/019d7392-12c2-7a87-991c-13a7246555d4/dataset_metadata.dataset-mold-v1.json +20 -0
- cardiotocography/019d7392-12c2-7a87-991c-13a7246555d4/dtypes.json +26 -0
- cardiotocography/019d7392-12c2-7a87-991c-13a7246555d4/experiment_metadata.predictive-ml-splits-mold-v1.json +0 -0
- cardiotocography/019d7392-12c2-7a87-991c-13a7246555d4/task_metadata.predictive-ml-task-mold-v1.json +11 -0
- clock_protein_toxicity/019d7375-2841-7bfb-a6da-5adfdcd43d98/container_metadata.json +5 -0
- clock_protein_toxicity/019d7375-2841-7bfb-a6da-5adfdcd43d98/dataset_metadata.dataset-mold-v1.json +19 -0
- clock_protein_toxicity/019d7375-2841-7bfb-a6da-5adfdcd43d98/dtypes.json +1120 -0
- clock_protein_toxicity/019d7375-2841-7bfb-a6da-5adfdcd43d98/experiment_metadata.predictive-ml-splits-mold-v1.json +1 -0
- clock_protein_toxicity/019d7375-2841-7bfb-a6da-5adfdcd43d98/task_metadata.predictive-ml-task-mold-v1.json +11 -0
- coil_2000/019d7366-fdc2-7225-a22c-314c624711f6/container_metadata.json +5 -0
- coil_2000/019d7366-fdc2-7225-a22c-314c624711f6/dataset_metadata.dataset-mold-v1.json +19 -0
- coil_2000/019d7366-fdc2-7225-a22c-314c624711f6/dtypes.json +88 -0
- coil_2000/019d7366-fdc2-7225-a22c-314c624711f6/experiment_metadata.predictive-ml-splits-mold-v1.json +0 -0
- coil_2000/019d7366-fdc2-7225-a22c-314c624711f6/task_metadata.predictive-ml-task-mold-v1.json +11 -0
- early_stage_diabetes_risk_prediction/019d736a-c5ee-7069-9b14-686535f9caa9/container_metadata.json +5 -0
- early_stage_diabetes_risk_prediction/019d736a-c5ee-7069-9b14-686535f9caa9/dataset_metadata.dataset-mold-v1.json +19 -0
- early_stage_diabetes_risk_prediction/019d736a-c5ee-7069-9b14-686535f9caa9/dtypes.json +19 -0
- early_stage_diabetes_risk_prediction/019d736a-c5ee-7069-9b14-686535f9caa9/experiment_metadata.predictive-ml-splits-mold-v1.json +1 -0
- early_stage_diabetes_risk_prediction/019d736a-c5ee-7069-9b14-686535f9caa9/task_metadata.predictive-ml-task-mold-v1.json +11 -0
- ecoli_proteins/019d736a-d6d2-7e28-aeff-7da6b7126b9d/container_metadata.json +5 -0
- ecoli_proteins/019d736a-d6d2-7e28-aeff-7da6b7126b9d/dataset_metadata.dataset-mold-v1.json +19 -0
- ecoli_proteins/019d736a-d6d2-7e28-aeff-7da6b7126b9d/dtypes.json +9 -0
- ecoli_proteins/019d736a-d6d2-7e28-aeff-7da6b7126b9d/experiment_metadata.predictive-ml-splits-mold-v1.json +1 -0
- ecoli_proteins/019d736a-d6d2-7e28-aeff-7da6b7126b9d/task_metadata.predictive-ml-task-mold-v1.json +11 -0
- homeq_default_prediction/019d7374-cab8-700e-97f3-9c55ea980153/container_metadata.json +5 -0
- homeq_default_prediction/019d7374-cab8-700e-97f3-9c55ea980153/dataset_metadata.dataset-mold-v1.json +19 -0
- homeq_default_prediction/019d7374-cab8-700e-97f3-9c55ea980153/dtypes.json +15 -0
- homeq_default_prediction/019d7374-cab8-700e-97f3-9c55ea980153/experiment_metadata.predictive-ml-splits-mold-v1.json +0 -0
acquire_valued_shoppers_challenge/019d7376-a3c5-7df7-88c0-ba4e87c3dfa0/container_metadata.json
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{
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"uuid": "019d7376-a3c5-7df7-88c0-ba4e87c3dfa0",
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"checksum": "1c0cedffb41253a46d6f71108321fac7b1951e8a7ffa79ed6b21583f08f79972",
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"version_comment": null
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}
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acquire_valued_shoppers_challenge/019d7376-a3c5-7df7-88c0-ba4e87c3dfa0/dataset_metadata.dataset-mold-v1.json
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{
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"unique_name": "acquire_valued_shoppers_challenge",
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"dataset_year": "2014",
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"domain_str": "business & marketing",
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"dataset_source": "Kaggle",
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"original_dataset_source_download_link": "https://www.kaggle.com/c/acquire-valued-shoppers-challenge",
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"download_description": "\nWe start with the data from Kaggle.\n\nkaggle competitions download -c acquire-valued-shoppers-challenge\nmkdir -p local-data-warehouse/acquire_valued_shoppers_challenge && mv acquire-valued-shoppers-challenge.zip local-data-warehouse/acquire_valued_shoppers_challenge/ && cd local-data-warehouse/acquire_valued_shoppers_challenge/ && unzip acquire-valued-shoppers-challenge.zip && rm acquire-valued-shoppers-challenge.zip testHistory.csv.gz sampleSubmission.csv.gz\n",
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"academic_reference_bibtex": "@misc{DMDave2014AcquireValuedShoppersChallenge,\n author = {DMDave and Todd B and Will Cukierski},\n title = {Acquire Valued Shoppers Challenge},\n year = {2014},\n howpublished = {\\url{https://kaggle.com/competitions/acquire-valued-shoppers-challenge}},\n note = {Kaggle competition}\n}\n",
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"academic_reference_bibtex_key": "DMDave2014AcquireValuedShoppersChallenge",
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"license": "Kaggle Competition Rules",
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"data_tags": [
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"Non-IID",
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"Temporal",
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"Anonymized"
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],
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"curation_comments": "\nWe follow the preprocessing from TabRed https://github.com/yandex-research/tabred/tree/main/preprocessing#ecom-offers-acquire-valued-shoppers-by-dmdave (which follows a top solution https://github.com/MLWave/kaggle_acquire-valued-shoppers-challenge).\n\n- The preprocessing by TabRed collapses customer groups into single entries per customer. So we go from grouped-temporal data to only temporal data. Future work could look into a version without preprocessing.\n",
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"version_from_unique_name": null,
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"version_comment": null,
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"local_data_directory_base": "/home/lennart_priorlabs_ai/code/large_data_ensemble/data-foundry/local-data-warehouse",
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"type_adapter_id": "dataset-mold-v1"
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}
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acquire_valued_shoppers_challenge/019d7376-a3c5-7df7-88c0-ba4e87c3dfa0/dtypes.json
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{
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"total_spend": "float64",
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"target": "category",
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"offervalue": "float64",
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"offerdate": "datetime64[ns]",
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"day_of_week": "int8",
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"day_of_month": "int8",
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"day_of_year": "int16",
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"has_bought_company": "uint32",
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"has_bought_company_q": "float64",
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"has_bought_company_a": "float64",
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"has_bought_category": "uint32",
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"has_bought_category_q": "float64",
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"has_bought_category_a": "float64",
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"has_bought_brand": "uint32",
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"has_bought_brand_q": "float64",
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"has_bought_brand_a": "float64",
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"has_bought_company_3": "uint32",
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"has_bought_company_q_3": "float64",
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"has_bought_company_a_3": "float64",
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"has_bought_category_3": "uint32",
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"has_bought_category_q_3": "float64",
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"has_bought_category_a_3": "float64",
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"has_bought_brand_3": "uint32",
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"has_bought_brand_q_3": "float64",
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"has_bought_brand_a_3": "float64",
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"has_bought_company_7": "uint32",
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"has_bought_company_q_7": "float64",
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"has_bought_company_a_7": "float64",
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"has_bought_category_7": "uint32",
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"has_bought_category_q_7": "float64",
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"has_bought_category_a_7": "float64",
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"has_bought_brand_7": "uint32",
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"has_bought_brand_q_7": "float64",
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"has_bought_brand_a_7": "float64",
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"has_bought_company_14": "uint32",
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"has_bought_company_q_14": "float64",
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"has_bought_company_a_14": "float64",
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"has_bought_category_14": "uint32",
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+
"has_bought_category_q_14": "float64",
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"has_bought_category_a_14": "float64",
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"has_bought_brand_14": "uint32",
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"has_bought_brand_q_14": "float64",
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"has_bought_brand_a_14": "float64",
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"has_bought_company_21": "uint32",
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"has_bought_company_q_21": "float64",
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"has_bought_company_a_21": "float64",
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"has_bought_category_21": "uint32",
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"has_bought_category_q_21": "float64",
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"has_bought_category_a_21": "float64",
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"has_bought_brand_21": "uint32",
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"has_bought_brand_q_21": "float64",
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"has_bought_brand_a_21": "float64",
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"has_bought_company_28": "uint32",
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"has_bought_company_q_28": "float64",
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"has_bought_company_a_28": "float64",
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"has_bought_category_28": "uint32",
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"has_bought_category_q_28": "float64",
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"has_bought_category_a_28": "float64",
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"has_bought_brand_28": "uint32",
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"has_bought_brand_q_28": "float64",
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"has_bought_brand_a_28": "float64",
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"has_bought_company_60": "uint32",
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"has_bought_company_q_60": "float64",
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"has_bought_company_a_60": "float64",
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"has_bought_category_60": "uint32",
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"has_bought_category_q_60": "float64",
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"has_bought_category_a_60": "float64",
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"has_bought_brand_60": "uint32",
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"has_bought_brand_q_60": "float64",
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"has_bought_brand_a_60": "float64",
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"has_bought_company_90": "uint32",
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"has_bought_company_q_90": "float64",
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"has_bought_company_a_90": "float64",
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"has_bought_category_90": "uint32",
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"has_bought_category_q_90": "float64",
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"has_bought_category_a_90": "float64",
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"has_bought_brand_90": "uint32",
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"has_bought_brand_q_90": "float64",
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"has_bought_brand_a_90": "float64",
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"has_bought_company_120": "uint32",
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"has_bought_company_q_120": "float64",
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"has_bought_company_a_120": "float64",
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"has_bought_category_120": "uint32",
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"has_bought_category_q_120": "float64",
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| 86 |
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"has_bought_category_a_120": "float64",
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"has_bought_brand_120": "uint32",
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| 88 |
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"has_bought_brand_q_120": "float64",
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| 89 |
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"has_bought_brand_a_120": "float64",
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"has_bought_company_150": "uint32",
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| 91 |
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"has_bought_company_q_150": "float64",
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| 92 |
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"has_bought_company_a_150": "float64",
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"has_bought_category_150": "uint32",
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| 94 |
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"has_bought_category_q_150": "float64",
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| 95 |
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"has_bought_category_a_150": "float64",
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| 96 |
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"has_bought_brand_150": "uint32",
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| 97 |
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"has_bought_brand_q_150": "float64",
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| 98 |
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"has_bought_brand_a_150": "float64",
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| 99 |
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"has_bought_company_180": "uint32",
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| 100 |
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"has_bought_company_q_180": "float64",
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| 101 |
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"has_bought_company_a_180": "float64",
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| 102 |
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"has_bought_category_180": "uint32",
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| 103 |
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"has_bought_category_q_180": "float64",
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| 104 |
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"has_bought_category_a_180": "float64",
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| 105 |
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"has_bought_brand_180": "uint32",
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| 106 |
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"has_bought_brand_q_180": "float64",
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| 107 |
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"has_bought_brand_a_180": "float64",
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| 108 |
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"never_bought_company": "category",
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| 109 |
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"never_bought_category": "category",
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| 110 |
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"never_bought_brand": "category",
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| 111 |
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"has_bought_brand_company_category": "category",
|
| 112 |
+
"has_bought_brand_category": "category",
|
| 113 |
+
"has_bought_brand_company": "category"
|
| 114 |
+
}
|
acquire_valued_shoppers_challenge/019d7376-a3c5-7df7-88c0-ba4e87c3dfa0/experiment_metadata.predictive-ml-splits-mold-v1.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
acquire_valued_shoppers_challenge/019d7376-a3c5-7df7-88c0-ba4e87c3dfa0/task_metadata.predictive-ml-task-mold-v1.json
ADDED
|
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"target_column_name": "target",
|
| 3 |
+
"problem_type": "binary_classification",
|
| 4 |
+
"objective_metric_name": "roc_auc",
|
| 5 |
+
"stratify_on": "target",
|
| 6 |
+
"time_on": "offerdate",
|
| 7 |
+
"group_on": null,
|
| 8 |
+
"group_labels": null,
|
| 9 |
+
"group_time_on": null,
|
| 10 |
+
"type_adapter_id": "predictive-ml-task-mold-v1"
|
| 11 |
+
}
|
allstate_claims_severity/019d736a-2321-76da-a36a-ddc5e1fbf7b6/container_metadata.json
ADDED
|
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"uuid": "019d736a-2321-76da-a36a-ddc5e1fbf7b6",
|
| 3 |
+
"checksum": "60063ec5bdf2dc0c3d32868eb69ef1f5c98048268ee767916690ac018704d83c",
|
| 4 |
+
"version_comment": null
|
| 5 |
+
}
|
allstate_claims_severity/019d736a-2321-76da-a36a-ddc5e1fbf7b6/dataset_metadata.dataset-mold-v1.json
ADDED
|
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"unique_name": "allstate_claims_severity",
|
| 3 |
+
"dataset_year": "2016",
|
| 4 |
+
"domain_str": "insurance",
|
| 5 |
+
"dataset_source": "Kaggle",
|
| 6 |
+
"original_dataset_source_download_link": "https://www.kaggle.com/competitions/allstate-claims-severity",
|
| 7 |
+
"download_description": "\nWe use the train.csv from the Kaggle competition.\n\nkaggle competitions download -c allstate-claims-severity -f train.csv && unzip train.csv.zip && rm train.csv.zip\nmkdir -p local-data-warehouse/allstate_claims_severity && mv train.csv local-data-warehouse/allstate_claims_severity/\n",
|
| 8 |
+
"academic_reference_bibtex": "@misc{Ferguson2016AllstateClaimsSeverity,\n author = {Dana Ferguson and Meg Risdal and NoTrick and Sara R. Sillah and Tim Emmerling and Will Cukierski},\n title = {Allstate Claims Severity},\n year = {2016},\n howpublished = {\\url{https://kaggle.com/competitions/allstate-claims-severity},\n note = {Kaggle competition}\n}\n",
|
| 9 |
+
"academic_reference_bibtex_key": "Ferguson2016AllstateClaimsSeverity",
|
| 10 |
+
"license": "Kaggle Competition Rules",
|
| 11 |
+
"data_tags": [
|
| 12 |
+
"IID",
|
| 13 |
+
"Anonymized"
|
| 14 |
+
],
|
| 15 |
+
"curation_comments": "\nWe start with the train.csv from Kaggle.\n\n- The data has been anonymized.\n- Top Kaggle solutions did not perform any relevant preprocessing.\n- We drop the ID column, as it does not contain any signal.\n- The data contains 1 duplicate row when ignoring the target. We drop this artifact.\n- We log scale the target.\n",
|
| 16 |
+
"version_from_unique_name": null,
|
| 17 |
+
"version_comment": null,
|
| 18 |
+
"local_data_directory_base": "/home/lennart_priorlabs_ai/code/large_data_ensemble/data-foundry/local-data-warehouse",
|
| 19 |
+
"type_adapter_id": "dataset-mold-v1"
|
| 20 |
+
}
|
allstate_claims_severity/019d736a-2321-76da-a36a-ddc5e1fbf7b6/dtypes.json
ADDED
|
@@ -0,0 +1,133 @@
|
|
|
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|
|
|
|
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|
|
|
|
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|
|
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|
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|
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
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|
|
|
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|
|
|
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|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
| 1 |
+
{
|
| 2 |
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"cat1": "category",
|
| 3 |
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"cat2": "category",
|
| 4 |
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|
| 5 |
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"cat4": "category",
|
| 6 |
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"cat5": "category",
|
| 7 |
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|
| 8 |
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|
| 9 |
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"cat8": "category",
|
| 10 |
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"cat9": "category",
|
| 11 |
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"cat10": "category",
|
| 12 |
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|
| 13 |
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|
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| 15 |
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| 23 |
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"cat22": "category",
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| 24 |
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"cat23": "category",
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| 25 |
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"cat24": "category",
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| 26 |
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"cat25": "category",
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| 27 |
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"cat26": "category",
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| 28 |
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"cat27": "category",
|
| 29 |
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"cat28": "category",
|
| 30 |
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"cat29": "category",
|
| 31 |
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"cat30": "category",
|
| 32 |
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"cat31": "category",
|
| 33 |
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"cat32": "category",
|
| 34 |
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"cat33": "category",
|
| 35 |
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"cat34": "category",
|
| 36 |
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"cat35": "category",
|
| 37 |
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"cat36": "category",
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| 38 |
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"cat37": "category",
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| 39 |
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"cat38": "category",
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| 40 |
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"cat39": "category",
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| 41 |
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"cat40": "category",
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| 42 |
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"cat42": "category",
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| 44 |
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"cat44": "category",
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| 46 |
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| 48 |
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| 49 |
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"cat48": "category",
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| 50 |
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"cat49": "category",
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| 51 |
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"cat50": "category",
|
| 52 |
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"cat51": "category",
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| 53 |
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"cat52": "category",
|
| 54 |
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"cat53": "category",
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| 55 |
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"cat54": "category",
|
| 56 |
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"cat55": "category",
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"cat56": "category",
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| 58 |
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"cat57": "category",
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"cat58": "category",
|
| 60 |
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"cat59": "category",
|
| 61 |
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"cat60": "category",
|
| 62 |
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"cat61": "category",
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| 63 |
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"cat62": "category",
|
| 64 |
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"cat63": "category",
|
| 65 |
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"cat64": "category",
|
| 66 |
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"cat65": "category",
|
| 67 |
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"cat66": "category",
|
| 68 |
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"cat67": "category",
|
| 69 |
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"cat68": "category",
|
| 70 |
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"cat69": "category",
|
| 71 |
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"cat70": "category",
|
| 72 |
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"cat71": "category",
|
| 73 |
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"cat72": "category",
|
| 74 |
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"cat73": "category",
|
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"cat74": "category",
|
| 76 |
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"cat75": "category",
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| 77 |
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"cat76": "category",
|
| 78 |
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"cat77": "category",
|
| 79 |
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"cat78": "category",
|
| 80 |
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"cat79": "category",
|
| 81 |
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"cat80": "category",
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"cat81": "category",
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| 83 |
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"cat82": "category",
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| 84 |
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"cat83": "category",
|
| 85 |
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"cat84": "category",
|
| 86 |
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"cat85": "category",
|
| 87 |
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"cat86": "category",
|
| 88 |
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"cat87": "category",
|
| 89 |
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"cat88": "category",
|
| 90 |
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"cat89": "category",
|
| 91 |
+
"cat90": "category",
|
| 92 |
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"cat91": "category",
|
| 93 |
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"cat92": "category",
|
| 94 |
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"cat93": "category",
|
| 95 |
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"cat94": "category",
|
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"cat95": "category",
|
| 97 |
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"cat96": "category",
|
| 98 |
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"cat97": "category",
|
| 99 |
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"cat98": "category",
|
| 100 |
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"cat99": "category",
|
| 101 |
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"cat100": "category",
|
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"cat101": "category",
|
| 103 |
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"cat102": "category",
|
| 104 |
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"cat103": "category",
|
| 105 |
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"cat104": "category",
|
| 106 |
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"cat105": "category",
|
| 107 |
+
"cat106": "category",
|
| 108 |
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"cat107": "category",
|
| 109 |
+
"cat108": "category",
|
| 110 |
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"cat109": "category",
|
| 111 |
+
"cat110": "category",
|
| 112 |
+
"cat111": "category",
|
| 113 |
+
"cat112": "category",
|
| 114 |
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"cat113": "category",
|
| 115 |
+
"cat114": "category",
|
| 116 |
+
"cat115": "category",
|
| 117 |
+
"cat116": "category",
|
| 118 |
+
"cont1": "float64",
|
| 119 |
+
"cont2": "float64",
|
| 120 |
+
"cont3": "float64",
|
| 121 |
+
"cont4": "float64",
|
| 122 |
+
"cont5": "float64",
|
| 123 |
+
"cont6": "float64",
|
| 124 |
+
"cont7": "float64",
|
| 125 |
+
"cont8": "float64",
|
| 126 |
+
"cont9": "float64",
|
| 127 |
+
"cont10": "float64",
|
| 128 |
+
"cont11": "float64",
|
| 129 |
+
"cont12": "float64",
|
| 130 |
+
"cont13": "float64",
|
| 131 |
+
"cont14": "float64",
|
| 132 |
+
"loss": "float64"
|
| 133 |
+
}
|
allstate_claims_severity/019d736a-2321-76da-a36a-ddc5e1fbf7b6/task_metadata.predictive-ml-task-mold-v1.json
ADDED
|
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"target_column_name": "loss",
|
| 3 |
+
"problem_type": "regression",
|
| 4 |
+
"objective_metric_name": "mae",
|
| 5 |
+
"stratify_on": null,
|
| 6 |
+
"time_on": null,
|
| 7 |
+
"group_on": null,
|
| 8 |
+
"group_labels": null,
|
| 9 |
+
"group_time_on": null,
|
| 10 |
+
"type_adapter_id": "predictive-ml-task-mold-v1"
|
| 11 |
+
}
|
aps_failure/019d7366-7373-7942-9438-aa122a6ca492/container_metadata.json
ADDED
|
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"uuid": "019d7366-7373-7942-9438-aa122a6ca492",
|
| 3 |
+
"checksum": "1ac3f2dc79c0fa354d9d5849e5426b80067272da833d936957abb9e989db66ca",
|
| 4 |
+
"version_comment": null
|
| 5 |
+
}
|
aps_failure/019d7366-7373-7942-9438-aa122a6ca492/dtypes.json
ADDED
|
@@ -0,0 +1,173 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
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|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
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|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"AirPressureSystemFailure": "category",
|
| 3 |
+
"aa_000": "int64",
|
| 4 |
+
"ab_000": "float64",
|
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| 7 |
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|
| 163 |
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|
| 164 |
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|
| 165 |
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|
| 166 |
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| 167 |
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| 168 |
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|
| 171 |
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|
| 172 |
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"eg_000": "float64"
|
| 173 |
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}
|
bioresponse/019d7366-c770-7d3c-8dd9-b9cc456542d6/container_metadata.json
ADDED
|
@@ -0,0 +1,5 @@
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| 1 |
+
{
|
| 2 |
+
"uuid": "019d7366-c770-7d3c-8dd9-b9cc456542d6",
|
| 3 |
+
"checksum": "54cbe67a82a6a8b8c5524964343d1a3479e0ace3618e163263e3b54140f6bcf8",
|
| 4 |
+
"version_comment": null
|
| 5 |
+
}
|
bioresponse/019d7366-c770-7d3c-8dd9-b9cc456542d6/dataset_metadata.dataset-mold-v1.json
ADDED
|
@@ -0,0 +1,19 @@
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|
| 1 |
+
{
|
| 2 |
+
"unique_name": "bioresponse",
|
| 3 |
+
"dataset_year": "2012",
|
| 4 |
+
"domain_str": "biology & life sciences",
|
| 5 |
+
"dataset_source": "Kaggle",
|
| 6 |
+
"original_dataset_source_download_link": "https://www.kaggle.com/c/bioresponse",
|
| 7 |
+
"download_description": "\nWe download the data from Kaggle and uzip it to a predefined folder.\n\nmkdir -p local-data-warehouse/bioresponse && cd local-data-warehouse/bioresponse && kaggle competitions download -c bioresponse && unzip bioresponse.zip && rm bioresponse.zip\n",
|
| 8 |
+
"academic_reference_bibtex": "@misc{bioresponse2012hamner,\n author = {Ben Hamner and dcthompson and Jorg},\n title = {Predicting a Biological Response},\n year = {2012},\n howpublished = {https://kaggle.com/competitions/bioresponse},\n note = {Kaggle}\n}\n",
|
| 9 |
+
"academic_reference_bibtex_key": "bioresponse2012hamner",
|
| 10 |
+
"license": "Public Domain",
|
| 11 |
+
"data_tags": [
|
| 12 |
+
"IID"
|
| 13 |
+
],
|
| 14 |
+
"curation_comments": "\n- We changed the name of the target and mapped it to yes/no\n- Anomaly: Only train data is used, since test data is empty for target\n",
|
| 15 |
+
"version_from_unique_name": null,
|
| 16 |
+
"version_comment": null,
|
| 17 |
+
"local_data_directory_base": "/home/lennart_priorlabs_ai/code/large_data_ensemble/data-foundry/local-data-warehouse",
|
| 18 |
+
"type_adapter_id": "dataset-mold-v1"
|
| 19 |
+
}
|
bioresponse/019d7366-c770-7d3c-8dd9-b9cc456542d6/dtypes.json
ADDED
|
@@ -0,0 +1,1779 @@
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|
| 1 |
+
{
|
| 2 |
+
"MoleculeElicitsResponse": "category",
|
| 3 |
+
"D1": "float64",
|
| 4 |
+
"D2": "float64",
|
| 5 |
+
"D3": "float64",
|
| 6 |
+
"D4": "float64",
|
| 7 |
+
"D5": "float64",
|
| 8 |
+
"D6": "float64",
|
| 9 |
+
"D7": "float64",
|
| 10 |
+
"D8": "float64",
|
| 11 |
+
"D9": "float64",
|
| 12 |
+
"D10": "float64",
|
| 13 |
+
"D11": "float64",
|
| 14 |
+
"D12": "float64",
|
| 15 |
+
"D13": "float64",
|
| 16 |
+
"D14": "float64",
|
| 17 |
+
"D15": "float64",
|
| 18 |
+
"D16": "float64",
|
| 19 |
+
"D17": "float64",
|
| 20 |
+
"D18": "float64",
|
| 21 |
+
"D19": "float64",
|
| 22 |
+
"D20": "float64",
|
| 23 |
+
"D21": "float64",
|
| 24 |
+
"D22": "float64",
|
| 25 |
+
"D23": "int64",
|
| 26 |
+
"D24": "float64",
|
| 27 |
+
"D25": "float64",
|
| 28 |
+
"D26": "float64",
|
| 29 |
+
"D27": "int64",
|
| 30 |
+
"D28": "int64",
|
| 31 |
+
"D29": "float64",
|
| 32 |
+
"D30": "float64",
|
| 33 |
+
"D31": "float64",
|
| 34 |
+
"D32": "float64",
|
| 35 |
+
"D33": "float64",
|
| 36 |
+
"D34": "float64",
|
| 37 |
+
"D35": "float64",
|
| 38 |
+
"D36": "float64",
|
| 39 |
+
"D37": "float64",
|
| 40 |
+
"D38": "float64",
|
| 41 |
+
"D39": "float64",
|
| 42 |
+
"D40": "float64",
|
| 43 |
+
"D41": "float64",
|
| 44 |
+
"D42": "float64",
|
| 45 |
+
"D43": "float64",
|
| 46 |
+
"D44": "float64",
|
| 47 |
+
"D45": "float64",
|
| 48 |
+
"D46": "float64",
|
| 49 |
+
"D47": "float64",
|
| 50 |
+
"D48": "float64",
|
| 51 |
+
"D49": "float64",
|
| 52 |
+
"D50": "float64",
|
| 53 |
+
"D51": "int64",
|
| 54 |
+
"D52": "float64",
|
| 55 |
+
"D53": "float64",
|
| 56 |
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"D54": "float64",
|
| 57 |
+
"D55": "float64",
|
| 58 |
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"D56": "float64",
|
| 59 |
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"D57": "float64",
|
| 60 |
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"D58": "float64",
|
| 61 |
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"D59": "float64",
|
| 62 |
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"D60": "float64",
|
| 63 |
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"D61": "float64",
|
| 64 |
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"D62": "float64",
|
| 65 |
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"D63": "float64",
|
| 66 |
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"D64": "float64",
|
| 67 |
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"D65": "float64",
|
| 68 |
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"D66": "float64",
|
| 69 |
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"D67": "float64",
|
| 70 |
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"D68": "float64",
|
| 71 |
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"D69": "float64",
|
| 72 |
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"D70": "float64",
|
| 73 |
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"D71": "float64",
|
| 74 |
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"D72": "int64",
|
| 75 |
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"D73": "float64",
|
| 76 |
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"D74": "float64",
|
| 77 |
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"D75": "float64",
|
| 78 |
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"D76": "float64",
|
| 79 |
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"D77": "float64",
|
| 80 |
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"D78": "float64",
|
| 81 |
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"D79": "float64",
|
| 82 |
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"D80": "float64",
|
| 83 |
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"D81": "float64",
|
| 84 |
+
"D82": "float64",
|
| 85 |
+
"D83": "float64",
|
| 86 |
+
"D84": "float64",
|
| 87 |
+
"D85": "float64",
|
| 88 |
+
"D86": "float64",
|
| 89 |
+
"D87": "float64",
|
| 90 |
+
"D88": "float64",
|
| 91 |
+
"D89": "float64",
|
| 92 |
+
"D90": "float64",
|
| 93 |
+
"D91": "float64",
|
| 94 |
+
"D92": "float64",
|
| 95 |
+
"D93": "float64",
|
| 96 |
+
"D94": "int64",
|
| 97 |
+
"D95": "float64",
|
| 98 |
+
"D96": "float64",
|
| 99 |
+
"D97": "float64",
|
| 100 |
+
"D98": "float64",
|
| 101 |
+
"D99": "float64",
|
| 102 |
+
"D100": "float64",
|
| 103 |
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"D101": "float64",
|
| 104 |
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"D102": "float64",
|
| 105 |
+
"D103": "float64",
|
| 106 |
+
"D104": "float64",
|
| 107 |
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"D105": "float64",
|
| 108 |
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"D106": "float64",
|
| 109 |
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"D107": "float64",
|
| 110 |
+
"D108": "float64",
|
| 111 |
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"D109": "float64",
|
| 112 |
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"D110": "float64",
|
| 113 |
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"D111": "float64",
|
| 114 |
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"D112": "float64",
|
| 115 |
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"D113": "float64",
|
| 116 |
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"D114": "float64",
|
| 117 |
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"D115": "float64",
|
| 118 |
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"D116": "float64",
|
| 119 |
+
"D117": "float64",
|
| 120 |
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"D118": "float64",
|
| 121 |
+
"D119": "float64",
|
| 122 |
+
"D120": "float64",
|
| 123 |
+
"D121": "float64",
|
| 124 |
+
"D122": "float64",
|
| 125 |
+
"D123": "float64",
|
| 126 |
+
"D124": "float64",
|
| 127 |
+
"D125": "float64",
|
| 128 |
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"D126": "float64",
|
| 129 |
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"D127": "float64",
|
| 130 |
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"D128": "float64",
|
| 131 |
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"D129": "float64",
|
| 132 |
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"D130": "float64",
|
| 133 |
+
"D131": "float64",
|
| 134 |
+
"D132": "float64",
|
| 135 |
+
"D133": "float64",
|
| 136 |
+
"D134": "float64",
|
| 137 |
+
"D135": "float64",
|
| 138 |
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"D136": "float64",
|
| 139 |
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"D137": "float64",
|
| 140 |
+
"D138": "float64",
|
| 141 |
+
"D139": "float64",
|
| 142 |
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"D140": "float64",
|
| 143 |
+
"D141": "float64",
|
| 144 |
+
"D142": "float64",
|
| 145 |
+
"D143": "float64",
|
| 146 |
+
"D144": "float64",
|
| 147 |
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"D145": "float64",
|
| 148 |
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"D146": "float64",
|
| 149 |
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"D147": "float64",
|
| 150 |
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"D148": "float64",
|
| 151 |
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"D149": "float64",
|
| 152 |
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"D150": "float64",
|
| 153 |
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"D151": "float64",
|
| 154 |
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"D152": "float64",
|
| 155 |
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"D153": "float64",
|
| 156 |
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"D154": "float64",
|
| 157 |
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"D155": "float64",
|
| 158 |
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"D156": "float64",
|
| 159 |
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"D157": "float64",
|
| 160 |
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"D158": "float64",
|
| 161 |
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"D159": "float64",
|
| 162 |
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"D160": "float64",
|
| 163 |
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"D161": "float64",
|
| 164 |
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"D162": "float64",
|
| 165 |
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"D163": "float64",
|
| 166 |
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"D164": "float64",
|
| 167 |
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"D165": "float64",
|
| 168 |
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"D166": "float64",
|
| 169 |
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"D167": "float64",
|
| 170 |
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"D168": "float64",
|
| 171 |
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"D169": "float64",
|
| 172 |
+
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bioresponse/019d7366-c770-7d3c-8dd9-b9cc456542d6/experiment_metadata.predictive-ml-splits-mold-v1.json
ADDED
|
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|
|
bioresponse/019d7366-c770-7d3c-8dd9-b9cc456542d6/task_metadata.predictive-ml-task-mold-v1.json
ADDED
|
@@ -0,0 +1,11 @@
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|
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|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"target_column_name": "MoleculeElicitsResponse",
|
| 3 |
+
"problem_type": "binary_classification",
|
| 4 |
+
"objective_metric_name": "roc_auc",
|
| 5 |
+
"stratify_on": "MoleculeElicitsResponse",
|
| 6 |
+
"time_on": null,
|
| 7 |
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"group_on": null,
|
| 8 |
+
"group_labels": null,
|
| 9 |
+
"group_time_on": null,
|
| 10 |
+
"type_adapter_id": "predictive-ml-task-mold-v1"
|
| 11 |
+
}
|
body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9/container_metadata.json
ADDED
|
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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| 1 |
+
{
|
| 2 |
+
"uuid": "019d736a-6e2b-77a5-ae25-1222ef4fe4e9",
|
| 3 |
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"checksum": "41cd5300c6ee91c69f8ad587290847cba1f4ab3b42261a7dd258c9a0d690e3f6",
|
| 4 |
+
"version_comment": null
|
| 5 |
+
}
|
body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9/dataset_metadata.dataset-mold-v1.json
ADDED
|
@@ -0,0 +1,19 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"unique_name": "body_density_prediction",
|
| 3 |
+
"dataset_year": "1985",
|
| 4 |
+
"domain_str": "medical & healthcare",
|
| 5 |
+
"dataset_source": "Kaggle",
|
| 6 |
+
"original_dataset_source_download_link": "https://www.kaggle.com/datasets/fedesoriano/body-fat-prediction-dataset",
|
| 7 |
+
"download_description": "\nWe download the data from Kaggle.\n\nkaggle datasets download fedesoriano/body-fat-prediction-dataset && unzip body-fat-prediction-dataset.zip && rm body-fat-prediction-dataset.zip\nmkdir -p local-data-warehouse/body_density_prediction && mv bodyfat.csv local-data-warehouse/body_density_prediction/\n",
|
| 8 |
+
"academic_reference_bibtex": "@article{penrose1985generalized,\n title={Generalized body composition prediction equation for men using simple measurement techniques},\n author={Penrose, Keith W and Nelson, Arnold G and Fisher, Arnold Garth},\n journal={Medicine \\& Science in Sports \\& Exercise},\n volume={17},\n number={2},\n pages={189},\n year={1985},\n publisher={Ovid Technologies (Wolters Kluwer Health)}\n}\n",
|
| 9 |
+
"academic_reference_bibtex_key": "penrose1985generalized",
|
| 10 |
+
"license": "None",
|
| 11 |
+
"data_tags": [
|
| 12 |
+
"IID"
|
| 13 |
+
],
|
| 14 |
+
"curation_comments": "\nWe start with the data from Kaggle.\n\n- Note, the task is not about bodyfat prediction as this is determined by a deterministic formula. Instead, the task is to estimate the density (which can be used to get the body fat via the deterministic formula). However, density requires a test. So we aim to predict from data the density such that we can skip this test. Which can make a lot of sense, if the test is too expensive as it requires underwater weighing.\n",
|
| 15 |
+
"version_from_unique_name": null,
|
| 16 |
+
"version_comment": null,
|
| 17 |
+
"local_data_directory_base": "/home/lennart_priorlabs_ai/code/large_data_ensemble/data-foundry/local-data-warehouse",
|
| 18 |
+
"type_adapter_id": "dataset-mold-v1"
|
| 19 |
+
}
|
body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9/dtypes.json
ADDED
|
@@ -0,0 +1,16 @@
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"Density": "float64",
|
| 3 |
+
"Age": "int64",
|
| 4 |
+
"Weight": "float64",
|
| 5 |
+
"Height": "float64",
|
| 6 |
+
"Neck": "float64",
|
| 7 |
+
"Chest": "float64",
|
| 8 |
+
"Abdomen": "float64",
|
| 9 |
+
"Hip": "float64",
|
| 10 |
+
"Thigh": "float64",
|
| 11 |
+
"Knee": "float64",
|
| 12 |
+
"Ankle": "float64",
|
| 13 |
+
"Biceps": "float64",
|
| 14 |
+
"Forearm": "float64",
|
| 15 |
+
"Wrist": "float64"
|
| 16 |
+
}
|
body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9/experiment_metadata.predictive-ml-splits-mold-v1.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"splits_comment": "Default splits for IID data.", "splits": {"0": {"0": [[0, 2, 3, 5, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 34, 35, 37, 38, 39, 40, 41, 44, 45, 46, 48, 49, 51, 52, 53, 54, 57, 58, 60, 61, 62, 65, 67, 68, 70, 71, 72, 73, 76, 77, 79, 81, 82, 84, 85, 87, 88, 92, 93, 94, 95, 97, 98, 99, 100, 101, 102, 103, 105, 106, 109, 110, 111, 112, 116, 117, 118, 119, 120, 123, 124, 126, 127, 128, 129, 131, 132, 133, 134, 135, 137, 138, 139, 140, 143, 145, 148, 149, 150, 151, 152, 154, 155, 156, 159, 160, 161, 163, 164, 165, 166, 167, 168, 170, 172, 175, 176, 178, 180, 182, 183, 184, 186, 187, 190, 191, 192, 193, 194, 195, 202, 205, 206, 207, 208, 209, 210, 211, 212, 213, 215, 216, 217, 218, 219, 220, 222, 226, 228, 229, 230, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 250, 251], [1, 4, 6, 11, 15, 19, 20, 25, 33, 36, 42, 43, 47, 50, 55, 56, 59, 63, 64, 66, 69, 74, 75, 78, 80, 83, 86, 89, 90, 91, 96, 104, 107, 108, 113, 114, 115, 121, 122, 125, 130, 136, 141, 142, 144, 146, 147, 153, 157, 158, 162, 169, 171, 173, 174, 177, 179, 181, 185, 188, 189, 196, 197, 198, 199, 200, 201, 203, 204, 214, 221, 223, 224, 225, 227, 231, 232, 233, 234, 236, 238, 247, 248, 249]], "1": [[0, 1, 3, 4, 5, 6, 7, 8, 11, 12, 13, 15, 19, 20, 22, 23, 24, 25, 27, 28, 30, 32, 33, 36, 39, 40, 41, 42, 43, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 59, 60, 63, 64, 65, 66, 68, 69, 74, 75, 76, 78, 80, 83, 86, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 104, 105, 107, 108, 111, 112, 113, 114, 115, 116, 118, 121, 122, 123, 125, 130, 131, 133, 134, 136, 137, 138, 141, 142, 143, 144, 145, 146, 147, 149, 150, 152, 153, 154, 157, 158, 161, 162, 166, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 183, 184, 185, 188, 189, 191, 193, 194, 196, 197, 198, 199, 200, 201, 203, 204, 205, 206, 207, 209, 213, 214, 215, 218, 220, 221, 222, 223, 224, 225, 226, 227, 229, 230, 231, 232, 233, 234, 235, 236, 238, 240, 243, 244, 246, 247, 248, 249], [2, 9, 10, 14, 16, 17, 18, 21, 26, 29, 31, 34, 35, 37, 38, 44, 45, 57, 58, 61, 62, 67, 70, 71, 72, 73, 77, 79, 81, 82, 84, 85, 93, 97, 98, 99, 100, 106, 109, 110, 117, 119, 120, 124, 126, 127, 128, 129, 132, 135, 139, 140, 148, 151, 155, 156, 159, 160, 163, 164, 165, 167, 182, 186, 187, 190, 192, 195, 202, 208, 210, 211, 212, 216, 217, 219, 228, 237, 239, 241, 242, 245, 250, 251]], "2": [[1, 2, 4, 6, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 25, 26, 29, 31, 33, 34, 35, 36, 37, 38, 42, 43, 44, 45, 47, 50, 55, 56, 57, 58, 59, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 73, 74, 75, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 89, 90, 91, 93, 96, 97, 98, 99, 100, 104, 106, 107, 108, 109, 110, 113, 114, 115, 117, 119, 120, 121, 122, 124, 125, 126, 127, 128, 129, 130, 132, 135, 136, 139, 140, 141, 142, 144, 146, 147, 148, 151, 153, 155, 156, 157, 158, 159, 160, 162, 163, 164, 165, 167, 169, 171, 173, 174, 177, 179, 181, 182, 185, 186, 187, 188, 189, 190, 192, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 208, 210, 211, 212, 214, 216, 217, 219, 221, 223, 224, 225, 227, 228, 231, 232, 233, 234, 236, 237, 238, 239, 241, 242, 245, 247, 248, 249, 250, 251], [0, 3, 5, 7, 8, 12, 13, 22, 23, 24, 27, 28, 30, 32, 39, 40, 41, 46, 48, 49, 51, 52, 53, 54, 60, 65, 68, 76, 87, 88, 92, 94, 95, 101, 102, 103, 105, 111, 112, 116, 118, 123, 131, 133, 134, 137, 138, 143, 145, 149, 150, 152, 154, 161, 166, 168, 170, 172, 175, 176, 178, 180, 183, 184, 191, 193, 194, 205, 206, 207, 209, 213, 215, 218, 220, 222, 226, 229, 230, 235, 240, 243, 244, 246]]}, "1": {"0": [[1, 2, 3, 6, 7, 8, 11, 12, 13, 14, 15, 16, 17, 19, 22, 23, 24, 25, 26, 27, 28, 33, 35, 37, 38, 41, 42, 43, 44, 45, 46, 47, 48, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 64, 66, 68, 69, 71, 72, 75, 76, 77, 82, 84, 85, 86, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 104, 105, 106, 107, 110, 111, 112, 113, 116, 118, 120, 123, 124, 125, 126, 127, 128, 130, 131, 132, 133, 135, 136, 137, 139, 140, 142, 144, 145, 146, 147, 150, 151, 152, 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"predictive-ml-splits-mold-v1"}
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body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9/task_metadata.predictive-ml-task-mold-v1.json
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{
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"target_column_name": "Density",
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"problem_type": "regression",
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| 4 |
+
"objective_metric_name": "rmse",
|
| 5 |
+
"stratify_on": null,
|
| 6 |
+
"time_on": null,
|
| 7 |
+
"group_on": null,
|
| 8 |
+
"group_labels": null,
|
| 9 |
+
"group_time_on": null,
|
| 10 |
+
"type_adapter_id": "predictive-ml-task-mold-v1"
|
| 11 |
+
}
|
cardiotocography/019d7392-12c2-7a87-991c-13a7246555d4/container_metadata.json
ADDED
|
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"uuid": "019d7392-12c2-7a87-991c-13a7246555d4",
|
| 3 |
+
"checksum": "dfc2f3a1c5e8685180111b7d1720d0ab8f8d95d26a0b495b77ca910b4d203c04",
|
| 4 |
+
"version_comment": null
|
| 5 |
+
}
|
cardiotocography/019d7392-12c2-7a87-991c-13a7246555d4/dataset_metadata.dataset-mold-v1.json
ADDED
|
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"unique_name": "cardiotocography",
|
| 3 |
+
"dataset_year": "2010",
|
| 4 |
+
"domain_str": "medical & healthcare",
|
| 5 |
+
"dataset_source": "UCI",
|
| 6 |
+
"original_dataset_source_download_link": "10.24432/C51S4N",
|
| 7 |
+
"download_description": "\nwget https://archive.ics.uci.edu/static/public/193/cardiotocography.zip && unzip cardiotocography.zip && rm cardiotocography.zip && mkdir -p local-data-warehouse/cardiotocography && mv CTG.xls local-data-warehouse/cardiotocography\n",
|
| 8 |
+
"academic_reference_bibtex": "@misc{campos2010cardiotocography,\n author = {Campos, D. and Bernardes, J.},\n title = {{Cardiotocography}},\n year = {2000},\n howpublished = {UCI Machine Learning Repository},\n note = {{DOI}: https://doi.org/10.24432/C51S4N}\n}\n",
|
| 9 |
+
"academic_reference_bibtex_key": "campos2010cardiotocography",
|
| 10 |
+
"license": "CC BY 4.0",
|
| 11 |
+
"data_tags": [
|
| 12 |
+
"Non-IID",
|
| 13 |
+
"Grouped"
|
| 14 |
+
],
|
| 15 |
+
"curation_comments": "\nWe use the data from UCI and the 3 class problem of predicting NSP as it is more medical relevant.\n\n- We transform the file names into patient IDs to indicate the sub-group of samples recorded from the same patient.\n- The raw data has dates and the start and end index of the recording. We drop these as they are not relevant for the predictive task and would not be available for test samples.\n- We drop all other columns that represent the label.\n",
|
| 16 |
+
"version_from_unique_name": null,
|
| 17 |
+
"version_comment": null,
|
| 18 |
+
"local_data_directory_base": "/home/lennart_priorlabs_ai/code/large_data_ensemble/data-foundry/local-data-warehouse",
|
| 19 |
+
"type_adapter_id": "dataset-mold-v1"
|
| 20 |
+
}
|
cardiotocography/019d7392-12c2-7a87-991c-13a7246555d4/dtypes.json
ADDED
|
@@ -0,0 +1,26 @@
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|
| 1 |
+
{
|
| 2 |
+
"LB": "float64",
|
| 3 |
+
"AC": "float64",
|
| 4 |
+
"FM": "float64",
|
| 5 |
+
"UC": "float64",
|
| 6 |
+
"ASTV": "float64",
|
| 7 |
+
"MSTV": "float64",
|
| 8 |
+
"ALTV": "float64",
|
| 9 |
+
"MLTV": "float64",
|
| 10 |
+
"DL": "float64",
|
| 11 |
+
"DS": "float64",
|
| 12 |
+
"DP": "float64",
|
| 13 |
+
"DR": "float64",
|
| 14 |
+
"Width": "float64",
|
| 15 |
+
"Min": "float64",
|
| 16 |
+
"Max": "float64",
|
| 17 |
+
"Nmax": "float64",
|
| 18 |
+
"Nzeros": "float64",
|
| 19 |
+
"Mode": "float64",
|
| 20 |
+
"Mean": "float64",
|
| 21 |
+
"Median": "float64",
|
| 22 |
+
"Variance": "float64",
|
| 23 |
+
"Tendency": "float64",
|
| 24 |
+
"NSP": "category",
|
| 25 |
+
"patient_id": "category"
|
| 26 |
+
}
|
cardiotocography/019d7392-12c2-7a87-991c-13a7246555d4/experiment_metadata.predictive-ml-splits-mold-v1.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
cardiotocography/019d7392-12c2-7a87-991c-13a7246555d4/task_metadata.predictive-ml-task-mold-v1.json
ADDED
|
@@ -0,0 +1,11 @@
|
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|
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|
|
|
| 1 |
+
{
|
| 2 |
+
"target_column_name": "NSP",
|
| 3 |
+
"problem_type": "multiclass_classification",
|
| 4 |
+
"objective_metric_name": "log_loss",
|
| 5 |
+
"stratify_on": "NSP",
|
| 6 |
+
"time_on": null,
|
| 7 |
+
"group_on": "patient_id",
|
| 8 |
+
"group_labels": "per_sample",
|
| 9 |
+
"group_time_on": null,
|
| 10 |
+
"type_adapter_id": "predictive-ml-task-mold-v1"
|
| 11 |
+
}
|
clock_protein_toxicity/019d7375-2841-7bfb-a6da-5adfdcd43d98/container_metadata.json
ADDED
|
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"uuid": "019d7375-2841-7bfb-a6da-5adfdcd43d98",
|
| 3 |
+
"checksum": "b298aa874aafe48c76b087a8ad8e891324b33c77d858c53f6097b4462c7ec542",
|
| 4 |
+
"version_comment": null
|
| 5 |
+
}
|
clock_protein_toxicity/019d7375-2841-7bfb-a6da-5adfdcd43d98/dataset_metadata.dataset-mold-v1.json
ADDED
|
@@ -0,0 +1,19 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"unique_name": "clock_protein_toxicity",
|
| 3 |
+
"dataset_year": "2021",
|
| 4 |
+
"domain_str": "biology & life sciences",
|
| 5 |
+
"dataset_source": "UCI",
|
| 6 |
+
"original_dataset_source_download_link": "https://doi.org/10.24432/C59313",
|
| 7 |
+
"download_description": "\nWe download the data from the UCI repository and uzip it to a predefined folder. \n\nmkdir -p local-data-warehouse/clock_protein_toxicity && wget -P local-data-warehouse/clock_protein_toxicity/ https://archive.ics.uci.edu/static/public/728/toxicity-2.zip && unzip local-data-warehouse/clock_protein_toxicity/toxicity-2.zip -d local-data-warehouse/clock_protein_toxicity/ && rm local-data-warehouse/clock_protein_toxicity/toxicity-2.zip\n",
|
| 8 |
+
"academic_reference_bibtex": "@article{gul2021structure,\n title={Structure-based design and classifications of small molecules regulating the circadian rhythm period},\n author={Gul, Seref and Rahim, Fatih and Isin, Safak and Yilmaz, Fatma and Ozturk, Nuri and Turkay, Metin and Kavakli, Ibrahim Halil},\n journal={Scientific reports},\n volume={11},\n number={1},\n pages={18510},\n year={2021},\n publisher={Nature Publishing Group UK London}\n}\n",
|
| 9 |
+
"academic_reference_bibtex_key": "gul2021structure",
|
| 10 |
+
"license": "CC BY 4.0",
|
| 11 |
+
"data_tags": [
|
| 12 |
+
"IID"
|
| 13 |
+
],
|
| 14 |
+
"curation_comments": "\n- We remove duplicated columns (same values for all rows).\n",
|
| 15 |
+
"version_from_unique_name": null,
|
| 16 |
+
"version_comment": null,
|
| 17 |
+
"local_data_directory_base": "/home/lennart_priorlabs_ai/code/large_data_ensemble/data-foundry/local-data-warehouse",
|
| 18 |
+
"type_adapter_id": "dataset-mold-v1"
|
| 19 |
+
}
|
clock_protein_toxicity/019d7375-2841-7bfb-a6da-5adfdcd43d98/dtypes.json
ADDED
|
@@ -0,0 +1,1120 @@
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|
|
| 1 |
+
{
|
| 2 |
+
"MATS3v": "float64",
|
| 3 |
+
"nHBint10": "int64",
|
| 4 |
+
"MATS3s": "float64",
|
| 5 |
+
"MATS3p": "float64",
|
| 6 |
+
"nHBDon_Lipinski": "int64",
|
| 7 |
+
"minHBint8": "float64",
|
| 8 |
+
"MATS3e": "float64",
|
| 9 |
+
"MATS3c": "float64",
|
| 10 |
+
"minHBint2": "float64",
|
| 11 |
+
"MATS3m": "float64",
|
| 12 |
+
"minHBint6": "float64",
|
| 13 |
+
"minHBint7": "float64",
|
| 14 |
+
"minHBint4": "float64",
|
| 15 |
+
"MATS3i": "float64",
|
| 16 |
+
"VR3_Dt": "float64",
|
| 17 |
+
"SpMax8_Bhi": "float64",
|
| 18 |
+
"SdsN": "float64",
|
| 19 |
+
"SpMax8_Bhm": "float64",
|
| 20 |
+
"SpMax8_Bhe": "float64",
|
| 21 |
+
"ECCEN": "int64",
|
| 22 |
+
"MDEC-14": "float64",
|
| 23 |
+
"SpMax8_Bhs": "float64",
|
| 24 |
+
"SpMax8_Bhp": "float64",
|
| 25 |
+
"SpMax8_Bhv": "float64",
|
| 26 |
+
"MDEC-11": "float64",
|
| 27 |
+
"MDEC-12": "float64",
|
| 28 |
+
"MDEC-13": "float64",
|
| 29 |
+
"VR2_Dt": "float64",
|
| 30 |
+
"BIC5": "float64",
|
| 31 |
+
"ATS7s": "float64",
|
| 32 |
+
"ATS7p": "float64",
|
| 33 |
+
"ATS7v": "float64",
|
| 34 |
+
"ATS7i": "float64",
|
| 35 |
+
"ATS7m": "float64",
|
| 36 |
+
"ATS7e": "float64",
|
| 37 |
+
"mintN": "float64",
|
| 38 |
+
"nHsNH2": "int64",
|
| 39 |
+
"khs.sssCH": "int64",
|
| 40 |
+
"minHBint3": "float64",
|
| 41 |
+
"maxdssC": "float64",
|
| 42 |
+
"nT6Ring": "int64",
|
| 43 |
+
"minHBint5": "float64",
|
| 44 |
+
"nF8Ring": "int64",
|
| 45 |
+
"minssCH2": "float64",
|
| 46 |
+
"SpMax_DzZ": "float64",
|
| 47 |
+
"ETA_EtaP": "float64",
|
| 48 |
+
"nHsOH": "int64",
|
| 49 |
+
"SpMin1_Bhe": "float64",
|
| 50 |
+
"maxHother": "float64",
|
| 51 |
+
"nHBAcc_Lipinski": "int64",
|
| 52 |
+
"StN": "float64",
|
| 53 |
+
"khs.aaS": "int64",
|
| 54 |
+
"khs.aaO": "int64",
|
| 55 |
+
"khs.aaN": "int64",
|
| 56 |
+
"Sare": "float64",
|
| 57 |
+
"SHAvin": "float64",
|
| 58 |
+
"SpMax3_Bhv": "float64",
|
| 59 |
+
"SpMax3_Bhp": "float64",
|
| 60 |
+
"SpMax3_Bhs": "float64",
|
| 61 |
+
"SpMax3_Bhe": "float64",
|
| 62 |
+
"SpMin6_Bhi": "float64",
|
| 63 |
+
"SpMax3_Bhm": "float64",
|
| 64 |
+
"SpMax3_Bhi": "float64",
|
| 65 |
+
"ETA_EtaP_F_L": "float64",
|
| 66 |
+
"mindCH2": "float64",
|
| 67 |
+
"AATSC2e": "float64",
|
| 68 |
+
"AATSC2c": "float64",
|
| 69 |
+
"AATSC2m": "float64",
|
| 70 |
+
"AATSC2i": "float64",
|
| 71 |
+
"nsBr": "int64",
|
| 72 |
+
"AATS5p": "float64",
|
| 73 |
+
"AATSC2v": "float64",
|
| 74 |
+
"AATSC2p": "float64",
|
| 75 |
+
"AATSC2s": "float64",
|
| 76 |
+
"VABC": "float64",
|
| 77 |
+
"maxdNH": "float64",
|
| 78 |
+
"khs.ddsN": "int64",
|
| 79 |
+
"RotBtFrac": "float64",
|
| 80 |
+
"ATS4e": "float64",
|
| 81 |
+
"ATS4m": "float64",
|
| 82 |
+
"nFRing": "int64",
|
| 83 |
+
"ATS4i": "float64",
|
| 84 |
+
"ATS4s": "float64",
|
| 85 |
+
"ATS4p": "float64",
|
| 86 |
+
"ETA_Alpha": "float64",
|
| 87 |
+
"khs.sssN": "int64",
|
| 88 |
+
"EE_Dzi": "float64",
|
| 89 |
+
"MAXDN": "float64",
|
| 90 |
+
"EE_Dzm": "float64",
|
| 91 |
+
"EE_Dze": "float64",
|
| 92 |
+
"EE_Dzs": "float64",
|
| 93 |
+
"EE_Dzp": "float64",
|
| 94 |
+
"EE_Dzv": "float64",
|
| 95 |
+
"ATS8e": "float64",
|
| 96 |
+
"maxsOH": "float64",
|
| 97 |
+
"minssssNp": "float64",
|
| 98 |
+
"maxsOm": "float64",
|
| 99 |
+
"MDEC-23": "float64",
|
| 100 |
+
"MDEC-22": "float64",
|
| 101 |
+
"MDEC-24": "float64",
|
| 102 |
+
"nFG12HeteroRing": "int64",
|
| 103 |
+
"ATS8s": "float64",
|
| 104 |
+
"ATS8v": "float64",
|
| 105 |
+
"SP-6": "float64",
|
| 106 |
+
"SP-7": "float64",
|
| 107 |
+
"SHsNH2": "float64",
|
| 108 |
+
"SP-5": "float64",
|
| 109 |
+
"SP-2": "float64",
|
| 110 |
+
"SP-3": "float64",
|
| 111 |
+
"SP-0": "float64",
|
| 112 |
+
"SP-1": "float64",
|
| 113 |
+
"minHsOH": "float64",
|
| 114 |
+
"ATSC8v": "float64",
|
| 115 |
+
"MATS2v": "float64",
|
| 116 |
+
"ATSC8s": "float64",
|
| 117 |
+
"MATS2p": "float64",
|
| 118 |
+
"MATS2s": "float64",
|
| 119 |
+
"ATSC8p": "float64",
|
| 120 |
+
"MATS2e": "float64",
|
| 121 |
+
"ATSC8e": "float64",
|
| 122 |
+
"ATSC8c": "float64",
|
| 123 |
+
"MATS2c": "float64",
|
| 124 |
+
"MATS2m": "float64",
|
| 125 |
+
"topoDiameter": "int64",
|
| 126 |
+
"ATSC8m": "float64",
|
| 127 |
+
"MATS2i": "float64",
|
| 128 |
+
"ATSC8i": "float64",
|
| 129 |
+
"ntN": "int64",
|
| 130 |
+
"khs.ssCH2": "int64",
|
| 131 |
+
"SpAD_Dt": "float64",
|
| 132 |
+
"ETA_Eta_R_L": "float64",
|
| 133 |
+
"SHdsCH": "float64",
|
| 134 |
+
"SaasN": "float64",
|
| 135 |
+
"SC-4": "float64",
|
| 136 |
+
"SaasC": "float64",
|
| 137 |
+
"minaaCH": "float64",
|
| 138 |
+
"AATSC3c": "float64",
|
| 139 |
+
"AATSC3e": "float64",
|
| 140 |
+
"AATSC3i": "float64",
|
| 141 |
+
"AATSC3m": "float64",
|
| 142 |
+
"AATSC3s": "float64",
|
| 143 |
+
"AATSC3p": "float64",
|
| 144 |
+
"AATSC3v": "float64",
|
| 145 |
+
"SpMax2_Bhp": "float64",
|
| 146 |
+
"AATS8e": "float64",
|
| 147 |
+
"AATS8i": "float64",
|
| 148 |
+
"AATS8m": "float64",
|
| 149 |
+
"AATS8s": "float64",
|
| 150 |
+
"AATS8p": "float64",
|
| 151 |
+
"AATS8v": "float64",
|
| 152 |
+
"VE3_Dt": "float64",
|
| 153 |
+
"XLogP": "float64",
|
| 154 |
+
"SpMax2_Bhi": "float64",
|
| 155 |
+
"maxssCH2": "float64",
|
| 156 |
+
"minaaS": "float64",
|
| 157 |
+
"SpMax4_Bhv": "float64",
|
| 158 |
+
"SpMax4_Bhs": "float64",
|
| 159 |
+
"SpMax4_Bhp": "float64",
|
| 160 |
+
"SpMax4_Bhm": "float64",
|
| 161 |
+
"minHaaNH": "float64",
|
| 162 |
+
"SpMax4_Bhi": "float64",
|
| 163 |
+
"minaaN": "float64",
|
| 164 |
+
"SpMax4_Bhe": "float64",
|
| 165 |
+
"StsC": "float64",
|
| 166 |
+
"SssCH2": "float64",
|
| 167 |
+
"maxHdNH": "float64",
|
| 168 |
+
"MATS1p": "float64",
|
| 169 |
+
"R_TpiPCTPC": "float64",
|
| 170 |
+
"MATS1s": "float64",
|
| 171 |
+
"MATS1v": "float64",
|
| 172 |
+
"JGI10": "float64",
|
| 173 |
+
"MATS1c": "float64",
|
| 174 |
+
"MATS1e": "float64",
|
| 175 |
+
"VR2_DzZ": "float64",
|
| 176 |
+
"MATS1i": "float64",
|
| 177 |
+
"MATS1m": "float64",
|
| 178 |
+
"MDEC-34": "float64",
|
| 179 |
+
"MDEC-33": "float64",
|
| 180 |
+
"VR2_Dze": "float64",
|
| 181 |
+
"VR2_Dzm": "float64",
|
| 182 |
+
"VR2_Dzs": "float64",
|
| 183 |
+
"VR2_Dzp": "float64",
|
| 184 |
+
"khs.ssssC": "int64",
|
| 185 |
+
"nTG12Ring": "int64",
|
| 186 |
+
"khs.ssssN": "int64",
|
| 187 |
+
"ATS5e": "float64",
|
| 188 |
+
"gmin": "float64",
|
| 189 |
+
"VR2_D": "float64",
|
| 190 |
+
"ATS5m": "float64",
|
| 191 |
+
"ATS5p": "float64",
|
| 192 |
+
"ATS5s": "float64",
|
| 193 |
+
"ATS5v": "float64",
|
| 194 |
+
"AATSC5p": "float64",
|
| 195 |
+
"TpiPC": "float64",
|
| 196 |
+
"maxsCH3": "float64",
|
| 197 |
+
"SdS": "float64",
|
| 198 |
+
"khs.ssO": "int64",
|
| 199 |
+
"ETA_Eta_F_L": "float64",
|
| 200 |
+
"khs.ssS": "int64",
|
| 201 |
+
"SdO": "float64",
|
| 202 |
+
"VE2_Dt": "float64",
|
| 203 |
+
"maxHtCH": "float64",
|
| 204 |
+
"ETA_dEpsilon_B": "float64",
|
| 205 |
+
"ETA_dEpsilon_A": "float64",
|
| 206 |
+
"ETA_dEpsilon_D": "float64",
|
| 207 |
+
"SsNH2": "float64",
|
| 208 |
+
"StCH": "float64",
|
| 209 |
+
"SsCH3": "float64",
|
| 210 |
+
"CIC5": "float64",
|
| 211 |
+
"CIC4": "float64",
|
| 212 |
+
"CIC1": "float64",
|
| 213 |
+
"CIC0": "float64",
|
| 214 |
+
"CIC3": "float64",
|
| 215 |
+
"CIC2": "float64",
|
| 216 |
+
"nF10HeteroRing": "int64",
|
| 217 |
+
"maxssO": "float64",
|
| 218 |
+
"WPOL": "int64",
|
| 219 |
+
"n5HeteroRing": "int64",
|
| 220 |
+
"maxHAvin": "float64",
|
| 221 |
+
"fragC": "float64",
|
| 222 |
+
"ETA_Eta_B_RC": "float64",
|
| 223 |
+
"AATS7m": "float64",
|
| 224 |
+
"SpDiam_Dt": "float64",
|
| 225 |
+
"SdssC": "float64",
|
| 226 |
+
"ETA_Epsilon_3": "float64",
|
| 227 |
+
"AATS7i": "float64",
|
| 228 |
+
"AATS7e": "float64",
|
| 229 |
+
"nT9Ring": "int64",
|
| 230 |
+
"minsCl": "float64",
|
| 231 |
+
"AATS7v": "float64",
|
| 232 |
+
"AATS7s": "float64",
|
| 233 |
+
"AATS7p": "float64",
|
| 234 |
+
"nHdCH2": "int64",
|
| 235 |
+
"ETA_Epsilon_5": "float64",
|
| 236 |
+
"ETA_Epsilon_4": "float64",
|
| 237 |
+
"SsssCH": "float64",
|
| 238 |
+
"maxHsOH": "float64",
|
| 239 |
+
"GATS1v": "float64",
|
| 240 |
+
"maxaaaC": "float64",
|
| 241 |
+
"GATS1s": "float64",
|
| 242 |
+
"minsNH2": "float64",
|
| 243 |
+
"BIC4": "float64",
|
| 244 |
+
"SpMin7_Bhs": "float64",
|
| 245 |
+
"SpMin7_Bhp": "float64",
|
| 246 |
+
"SpMin7_Bhv": "float64",
|
| 247 |
+
"nHtCH": "int64",
|
| 248 |
+
"GATS1e": "float64",
|
| 249 |
+
"mintsC": "float64",
|
| 250 |
+
"GATS1c": "float64",
|
| 251 |
+
"SpMin7_Bhm": "float64",
|
| 252 |
+
"GATS1m": "float64",
|
| 253 |
+
"SpMin7_Bhe": "float64",
|
| 254 |
+
"GATS1i": "float64",
|
| 255 |
+
"maxtsC": "float64",
|
| 256 |
+
"minHAvin": "float64",
|
| 257 |
+
"MDEC-44": "float64",
|
| 258 |
+
"AATS2v": "float64",
|
| 259 |
+
"SPC-6": "float64",
|
| 260 |
+
"SPC-4": "float64",
|
| 261 |
+
"SPC-5": "float64",
|
| 262 |
+
"SpAD_D": "float64",
|
| 263 |
+
"MATS6c": "float64",
|
| 264 |
+
"ETA_BetaP_s": "float64",
|
| 265 |
+
"minaasC": "float64",
|
| 266 |
+
"minaasN": "float64",
|
| 267 |
+
"minssNH": "float64",
|
| 268 |
+
"nT7HeteroRing": "int64",
|
| 269 |
+
"RotBFrac": "float64",
|
| 270 |
+
"nF10Ring": "int64",
|
| 271 |
+
"ETA_BetaP_ns": "float64",
|
| 272 |
+
"nH": "int64",
|
| 273 |
+
"nL": "int64",
|
| 274 |
+
"nN": "int64",
|
| 275 |
+
"nO": "int64",
|
| 276 |
+
"nA": "int64",
|
| 277 |
+
"nC": "int64",
|
| 278 |
+
"nF": "int64",
|
| 279 |
+
"nX": "int64",
|
| 280 |
+
"nQ": "int64",
|
| 281 |
+
"nS": "int64",
|
| 282 |
+
"nV": "int64",
|
| 283 |
+
"ATS1m": "float64",
|
| 284 |
+
"mindsN": "float64",
|
| 285 |
+
"SHCsats": "float64",
|
| 286 |
+
"SHCsatu": "float64",
|
| 287 |
+
"CrippenMR": "float64",
|
| 288 |
+
"GATS1p": "float64",
|
| 289 |
+
"SRW10": "float64",
|
| 290 |
+
"ETA_dPsi_A": "float64",
|
| 291 |
+
"AATS6m": "float64",
|
| 292 |
+
"AATS6i": "float64",
|
| 293 |
+
"AATS6e": "float64",
|
| 294 |
+
"minsBr": "float64",
|
| 295 |
+
"nF9HeteroRing": "int64",
|
| 296 |
+
"SpMin7_Bhi": "float64",
|
| 297 |
+
"AATS6v": "float64",
|
| 298 |
+
"AATS6p": "float64",
|
| 299 |
+
"AATS6s": "float64",
|
| 300 |
+
"naAromAtom": "int64",
|
| 301 |
+
"nBase": "int64",
|
| 302 |
+
"minHBint10": "float64",
|
| 303 |
+
"SpDiam_DzZ": "float64",
|
| 304 |
+
"SaaNH": "float64",
|
| 305 |
+
"khs.dNH": "int64",
|
| 306 |
+
"maxaaN": "float64",
|
| 307 |
+
"maxaaO": "float64",
|
| 308 |
+
"SpDiam_Dzv": "float64",
|
| 309 |
+
"SpDiam_Dzs": "float64",
|
| 310 |
+
"SpDiam_Dzp": "float64",
|
| 311 |
+
"SpDiam_Dze": "float64",
|
| 312 |
+
"SpDiam_Dzm": "float64",
|
| 313 |
+
"GATS6i": "float64",
|
| 314 |
+
"SpDiam_Dzi": "float64",
|
| 315 |
+
"Mi": "float64",
|
| 316 |
+
"Mv": "float64",
|
| 317 |
+
"Mp": "float64",
|
| 318 |
+
"GGI10": "float64",
|
| 319 |
+
"bpol": "float64",
|
| 320 |
+
"MW": "float64",
|
| 321 |
+
"GATS6v": "float64",
|
| 322 |
+
"MATS7s": "float64",
|
| 323 |
+
"MATS7p": "float64",
|
| 324 |
+
"C1SP1": "int64",
|
| 325 |
+
"MATS7v": "float64",
|
| 326 |
+
"MATS7i": "float64",
|
| 327 |
+
"MATS7m": "float64",
|
| 328 |
+
"MATS7c": "float64",
|
| 329 |
+
"GATS6s": "float64",
|
| 330 |
+
"MATS7e": "float64",
|
| 331 |
+
"maxtN": "float64",
|
| 332 |
+
"SpMin8_Bhe": "float64",
|
| 333 |
+
"SpMin8_Bhi": "float64",
|
| 334 |
+
"SpMin8_Bhm": "float64",
|
| 335 |
+
"SpMin8_Bhp": "float64",
|
| 336 |
+
"SpMin8_Bhs": "float64",
|
| 337 |
+
"SpMin8_Bhv": "float64",
|
| 338 |
+
"maxHCsatu": "float64",
|
| 339 |
+
"maxHCsats": "float64",
|
| 340 |
+
"ATSC3v": "float64",
|
| 341 |
+
"ATSC3s": "float64",
|
| 342 |
+
"ATSC3p": "float64",
|
| 343 |
+
"minHdCH2": "float64",
|
| 344 |
+
"ATSC3e": "float64",
|
| 345 |
+
"ATSC3c": "float64",
|
| 346 |
+
"maxssNH": "float64",
|
| 347 |
+
"ATSC3m": "float64",
|
| 348 |
+
"ATSC3i": "float64",
|
| 349 |
+
"minHCsatu": "float64",
|
| 350 |
+
"minHCsats": "float64",
|
| 351 |
+
"SpMax7_Bhe": "float64",
|
| 352 |
+
"SpMax7_Bhi": "float64",
|
| 353 |
+
"SpMax7_Bhm": "float64",
|
| 354 |
+
"ETA_BetaP_ns_d": "float64",
|
| 355 |
+
"SpMax7_Bhp": "float64",
|
| 356 |
+
"SpMax7_Bhs": "float64",
|
| 357 |
+
"SpMax7_Bhv": "float64",
|
| 358 |
+
"SdsCH": "float64",
|
| 359 |
+
"minssO": "float64",
|
| 360 |
+
"minssS": "float64",
|
| 361 |
+
"SpMin3_Bhe": "float64",
|
| 362 |
+
"SpMin3_Bhm": "float64",
|
| 363 |
+
"SpMin3_Bhi": "float64",
|
| 364 |
+
"SpMin3_Bhv": "float64",
|
| 365 |
+
"SpMin3_Bhs": "float64",
|
| 366 |
+
"SpMin3_Bhp": "float64",
|
| 367 |
+
"TPC": "int64",
|
| 368 |
+
"khs.tCH": "int64",
|
| 369 |
+
"VP-5": "float64",
|
| 370 |
+
"VP-4": "float64",
|
| 371 |
+
"VP-7": "float64",
|
| 372 |
+
"VP-6": "float64",
|
| 373 |
+
"VP-1": "float64",
|
| 374 |
+
"VP-0": "float64",
|
| 375 |
+
"VP-3": "float64",
|
| 376 |
+
"VP-2": "float64",
|
| 377 |
+
"MIC5": "float64",
|
| 378 |
+
"MIC4": "float64",
|
| 379 |
+
"MIC3": "float64",
|
| 380 |
+
"MIC2": "float64",
|
| 381 |
+
"MIC1": "float64",
|
| 382 |
+
"MIC0": "float64",
|
| 383 |
+
"ATSC5p": "float64",
|
| 384 |
+
"piPC10": "float64",
|
| 385 |
+
"ATSC5s": "float64",
|
| 386 |
+
"minsOm": "float64",
|
| 387 |
+
"nHBa": "int64",
|
| 388 |
+
"nHBd": "int64",
|
| 389 |
+
"SddssS": "float64",
|
| 390 |
+
"nCl": "int64",
|
| 391 |
+
"minsOH": "float64",
|
| 392 |
+
"SHaaCH": "float64",
|
| 393 |
+
"nHBDon": "int64",
|
| 394 |
+
"nF11HeteroRing": "int64",
|
| 395 |
+
"AATS5i": "float64",
|
| 396 |
+
"AATS5m": "float64",
|
| 397 |
+
"SpMin6_Bhs": "float64",
|
| 398 |
+
"SpMin6_Bhp": "float64",
|
| 399 |
+
"SpMin6_Bhv": "float64",
|
| 400 |
+
"AATS5e": "float64",
|
| 401 |
+
"ETA_dBeta": "float64",
|
| 402 |
+
"khs.sCH3": "int64",
|
| 403 |
+
"ALogP": "float64",
|
| 404 |
+
"SpMin6_Bhm": "float64",
|
| 405 |
+
"BCUTp-1l": "float64",
|
| 406 |
+
"AATS5s": "float64",
|
| 407 |
+
"BCUTp-1h": "float64",
|
| 408 |
+
"AATS5v": "float64",
|
| 409 |
+
"SpMin6_Bhe": "float64",
|
| 410 |
+
"ATS8i": "float64",
|
| 411 |
+
"ATS8m": "float64",
|
| 412 |
+
"BCUTw-1h": "float64",
|
| 413 |
+
"BCUTw-1l": "float64",
|
| 414 |
+
"nBondsS3": "int64",
|
| 415 |
+
"nBondsS2": "int64",
|
| 416 |
+
"ATS8p": "float64",
|
| 417 |
+
"GATS3p": "float64",
|
| 418 |
+
"GATS3s": "float64",
|
| 419 |
+
"GATS3v": "float64",
|
| 420 |
+
"GATS3c": "float64",
|
| 421 |
+
"GATS3e": "float64",
|
| 422 |
+
"SC-5": "float64",
|
| 423 |
+
"GATS3i": "float64",
|
| 424 |
+
"SC-6": "float64",
|
| 425 |
+
"GATS3m": "float64",
|
| 426 |
+
"SC-3": "float64",
|
| 427 |
+
"minsCH3": "float64",
|
| 428 |
+
"SssssC": "float64",
|
| 429 |
+
"nAtomLC": "int64",
|
| 430 |
+
"nT12HeteroRing": "int64",
|
| 431 |
+
"minHaaCH": "float64",
|
| 432 |
+
"MLFER_BH": "float64",
|
| 433 |
+
"MLFER_BO": "float64",
|
| 434 |
+
"SaaaC": "float64",
|
| 435 |
+
"mindsCH": "float64",
|
| 436 |
+
"nddssS": "int64",
|
| 437 |
+
"maxaasC": "float64",
|
| 438 |
+
"maxsssN": "float64",
|
| 439 |
+
"MATS6p": "float64",
|
| 440 |
+
"MATS6s": "float64",
|
| 441 |
+
"MATS6v": "float64",
|
| 442 |
+
"MATS6i": "float64",
|
| 443 |
+
"MATS6m": "float64",
|
| 444 |
+
"MATS6e": "float64",
|
| 445 |
+
"ETA_Beta_ns_d": "float64",
|
| 446 |
+
"hmax": "float64",
|
| 447 |
+
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|
| 448 |
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"nHaaCH": "int64",
|
| 449 |
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"khs.aaaC": "int64",
|
| 450 |
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|
| 451 |
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"nAromBond": "int64",
|
| 452 |
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"ATSC2v": "float64",
|
| 453 |
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"ATSC2p": "float64",
|
| 454 |
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"ATSC2s": "float64",
|
| 455 |
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"ATS4v": "float64",
|
| 456 |
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|
| 457 |
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"ATSC2c": "float64",
|
| 458 |
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"ATSC2m": "float64",
|
| 459 |
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|
| 460 |
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|
| 461 |
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|
| 462 |
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|
| 463 |
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"AATS4e": "float64",
|
| 464 |
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"ETA_BetaP": "float64",
|
| 465 |
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"AATS4m": "float64",
|
| 466 |
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"AATS4i": "float64",
|
| 467 |
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"SHsOH": "float64",
|
| 468 |
+
"SpMax_D": "float64",
|
| 469 |
+
"MDEN-13": "float64",
|
| 470 |
+
"MDEN-12": "float64",
|
| 471 |
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"MDEN-11": "float64",
|
| 472 |
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"ATS3m": "float64",
|
| 473 |
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"PetitjeanNumber": "float64",
|
| 474 |
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"khs.aasN": "int64",
|
| 475 |
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"khs.aasC": "int64",
|
| 476 |
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"MWC10": "float64",
|
| 477 |
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"MPC7": "int64",
|
| 478 |
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"TWC": "float64",
|
| 479 |
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"topoRadius": "int64",
|
| 480 |
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"WPATH": "int64",
|
| 481 |
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"nG": "int64",
|
| 482 |
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"ndsN": "int64",
|
| 483 |
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"MAXDP": "float64",
|
| 484 |
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"naaaC": "int64",
|
| 485 |
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"SM1_DzZ": "float64",
|
| 486 |
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"SpAbs_DzZ": "float64",
|
| 487 |
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"SpAbs_Dze": "float64",
|
| 488 |
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"khs.aaNH": "int64",
|
| 489 |
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"SpAbs_Dzm": "float64",
|
| 490 |
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"SM1_Dzv": "float64",
|
| 491 |
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"SpAbs_Dzi": "float64",
|
| 492 |
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"SM1_Dzp": "float64",
|
| 493 |
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"SM1_Dzs": "float64",
|
| 494 |
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|
| 495 |
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"SpAbs_Dzv": "float64",
|
| 496 |
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|
| 497 |
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"SpAbs_Dzp": "float64",
|
| 498 |
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"SpAbs_Dzs": "float64",
|
| 499 |
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"SM1_Dze": "float64",
|
| 500 |
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"maxaaCH": "float64",
|
| 501 |
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"maxdO": "float64",
|
| 502 |
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"nF11Ring": "int64",
|
| 503 |
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|
| 504 |
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|
| 505 |
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"ETA_Beta": "float64",
|
| 506 |
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|
| 507 |
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|
| 508 |
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|
| 509 |
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|
| 510 |
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"nHaaNH": "int64",
|
| 511 |
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|
| 512 |
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"ATSC1s": "float64",
|
| 513 |
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"ATSC1v": "float64",
|
| 514 |
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"ATSC1c": "float64",
|
| 515 |
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|
| 516 |
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|
| 517 |
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"ATSC1m": "float64",
|
| 518 |
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"MWC9": "float64",
|
| 519 |
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"MWC8": "float64",
|
| 520 |
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"MWC5": "float64",
|
| 521 |
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"MWC4": "float64",
|
| 522 |
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"MWC7": "float64",
|
| 523 |
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"MWC6": "float64",
|
| 524 |
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"ndssC": "int64",
|
| 525 |
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"MWC2": "float64",
|
| 526 |
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"MAXDN2": "float64",
|
| 527 |
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"MATS5e": "float64",
|
| 528 |
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"SpMin2_Bhe": "float64",
|
| 529 |
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"MATS5c": "float64",
|
| 530 |
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"MATS5m": "float64",
|
| 531 |
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"SpMin2_Bhm": "float64",
|
| 532 |
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"MATS5i": "float64",
|
| 533 |
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"SpMin2_Bhi": "float64",
|
| 534 |
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"SpMin2_Bhv": "float64",
|
| 535 |
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"MATS5v": "float64",
|
| 536 |
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|
| 537 |
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"SpMin2_Bhs": "float64",
|
| 538 |
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"SpMin2_Bhp": "float64",
|
| 539 |
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"VE1_Dzi": "float64",
|
| 540 |
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"Mare": "float64",
|
| 541 |
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"BCUTc-1l": "float64",
|
| 542 |
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"BCUTc-1h": "float64",
|
| 543 |
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"SssNH": "float64",
|
| 544 |
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"MPC9": "int64",
|
| 545 |
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"VE1_Dzp": "float64",
|
| 546 |
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"naaN": "int64",
|
| 547 |
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"SHBd": "float64",
|
| 548 |
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"TSRW": "float64",
|
| 549 |
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"SHBa": "float64",
|
| 550 |
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"khs.ssNH": "int64",
|
| 551 |
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"SpMax6_Bhp": "float64",
|
| 552 |
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"SpMax6_Bhs": "float64",
|
| 553 |
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|
| 554 |
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|
| 555 |
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|
| 556 |
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|
| 557 |
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|
| 558 |
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|
| 559 |
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|
| 560 |
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|
| 561 |
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|
| 562 |
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"AATSC8s": "float64",
|
| 563 |
+
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|
| 564 |
+
"AATSC8v": "float64",
|
| 565 |
+
"minHssNH": "float64",
|
| 566 |
+
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|
| 567 |
+
"AATSC8e": "float64",
|
| 568 |
+
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|
| 569 |
+
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|
| 570 |
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"AATS3s": "float64",
|
| 571 |
+
"AATS3p": "float64",
|
| 572 |
+
"AATS3v": "float64",
|
| 573 |
+
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|
| 574 |
+
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|
| 575 |
+
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|
| 576 |
+
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|
| 577 |
+
"SpMin5_Bhi": "float64",
|
| 578 |
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|
| 579 |
+
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|
| 580 |
+
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|
| 581 |
+
"SpMAD_Dzs": "float64",
|
| 582 |
+
"AATS3m": "float64",
|
| 583 |
+
"MLFER_L": "float64",
|
| 584 |
+
"GATS5s": "float64",
|
| 585 |
+
"GATS5p": "float64",
|
| 586 |
+
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|
| 587 |
+
"MLFER_A": "float64",
|
| 588 |
+
"GATS5i": "float64",
|
| 589 |
+
"GATS5m": "float64",
|
| 590 |
+
"IC3": "float64",
|
| 591 |
+
"IC2": "float64",
|
| 592 |
+
"IC1": "float64",
|
| 593 |
+
"IC0": "float64",
|
| 594 |
+
"IC5": "float64",
|
| 595 |
+
"IC4": "float64",
|
| 596 |
+
"ETA_Eta_R": "float64",
|
| 597 |
+
"ETA_Eta_L": "float64",
|
| 598 |
+
"ETA_Eta_B": "float64",
|
| 599 |
+
"ETA_Eta_F": "float64",
|
| 600 |
+
"SaaCH": "float64",
|
| 601 |
+
"nAcid": "int64",
|
| 602 |
+
"maxsssCH": "float64",
|
| 603 |
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"SsCl": "float64",
|
| 604 |
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"nF6Ring": "int64",
|
| 605 |
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"maxsF": "float64",
|
| 606 |
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|
| 607 |
+
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|
| 608 |
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|
| 609 |
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|
| 610 |
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|
| 611 |
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|
| 612 |
+
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|
| 613 |
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|
| 614 |
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"VC-6": "float64",
|
| 615 |
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"SsssN": "float64",
|
| 616 |
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"minaaaC": "float64",
|
| 617 |
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"minwHBa": "float64",
|
| 618 |
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"nT6HeteroRing": "int64",
|
| 619 |
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"khs.dssC": "int64",
|
| 620 |
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|
| 621 |
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"EE_Dt": "float64",
|
| 622 |
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"minsF": "float64",
|
| 623 |
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|
| 624 |
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|
| 625 |
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|
| 626 |
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|
| 627 |
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|
| 628 |
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|
| 629 |
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|
| 630 |
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"VE2_D": "float64",
|
| 631 |
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"SpMAD_D": "float64",
|
| 632 |
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"nHBint2": "int64",
|
| 633 |
+
"nHBint3": "int64",
|
| 634 |
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|
| 635 |
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"nHBint6": "int64",
|
| 636 |
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"nHBint7": "int64",
|
| 637 |
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"nHBint4": "int64",
|
| 638 |
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"nHBint5": "int64",
|
| 639 |
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"nHBint8": "int64",
|
| 640 |
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"nHBint9": "int64",
|
| 641 |
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"nBondsS": "int64",
|
| 642 |
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|
| 643 |
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|
| 644 |
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|
| 645 |
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|
| 646 |
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|
| 647 |
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|
| 648 |
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"nBondsM": "int64",
|
| 649 |
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"nBonds2": "int64",
|
| 650 |
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|
| 651 |
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"Sp": "float64",
|
| 652 |
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"Sv": "float64",
|
| 653 |
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"Si": "float64",
|
| 654 |
+
"FMF": "float64",
|
| 655 |
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"nRotB": "int64",
|
| 656 |
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"minssssC": "float64",
|
| 657 |
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"SwHBa": "float64",
|
| 658 |
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"Zagreb": "int64",
|
| 659 |
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"SRW6": "float64",
|
| 660 |
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"SRW7": "float64",
|
| 661 |
+
"SRW4": "float64",
|
| 662 |
+
"SRW5": "float64",
|
| 663 |
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"SRW2": "float64",
|
| 664 |
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"SRW8": "float64",
|
| 665 |
+
"SRW9": "float64",
|
| 666 |
+
"maxHsNH2": "float64",
|
| 667 |
+
"SCH-5": "float64",
|
| 668 |
+
"SCH-6": "float64",
|
| 669 |
+
"SCH-7": "float64",
|
| 670 |
+
"ATS6p": "float64",
|
| 671 |
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"maxaasN": "float64",
|
| 672 |
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"MAXDP2": "float64",
|
| 673 |
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|
| 674 |
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|
| 675 |
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|
| 676 |
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|
| 677 |
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|
| 678 |
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|
| 679 |
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|
| 680 |
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|
| 681 |
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|
| 682 |
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"ndsCH": "int64",
|
| 683 |
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|
| 684 |
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|
| 685 |
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|
| 686 |
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|
| 687 |
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"McGowan_Volume": "float64",
|
| 688 |
+
"n5Ring": "int64",
|
| 689 |
+
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|
| 690 |
+
"ATSC7s": "float64",
|
| 691 |
+
"ATSC7p": "float64",
|
| 692 |
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|
| 693 |
+
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|
| 694 |
+
"ATSC7i": "float64",
|
| 695 |
+
"ATSC7m": "float64",
|
| 696 |
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"ATSC7c": "float64",
|
| 697 |
+
"VC-4": "float64",
|
| 698 |
+
"VC-5": "float64",
|
| 699 |
+
"VC-3": "float64",
|
| 700 |
+
"ATSC7e": "float64",
|
| 701 |
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"JGI2": "float64",
|
| 702 |
+
"JGI3": "float64",
|
| 703 |
+
"JGI1": "float64",
|
| 704 |
+
"JGI6": "float64",
|
| 705 |
+
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|
| 706 |
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|
| 707 |
+
"JGI5": "float64",
|
| 708 |
+
"JGI8": "float64",
|
| 709 |
+
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|
| 710 |
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|
| 711 |
+
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|
| 712 |
+
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|
| 713 |
+
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|
| 714 |
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|
| 715 |
+
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|
| 716 |
+
"SpAD_Dzs": "float64",
|
| 717 |
+
"nBonds": "int64",
|
| 718 |
+
"LipinskiFailures": "int64",
|
| 719 |
+
"SpDiam_D": "float64",
|
| 720 |
+
"minHdsCH": "float64",
|
| 721 |
+
"SssO": "float64",
|
| 722 |
+
"C2SP3": "int64",
|
| 723 |
+
"C2SP2": "int64",
|
| 724 |
+
"C2SP1": "int64",
|
| 725 |
+
"GATS2p": "float64",
|
| 726 |
+
"ETA_Shape_X": "float64",
|
| 727 |
+
"ETA_Shape_Y": "float64",
|
| 728 |
+
"ETA_Shape_P": "float64",
|
| 729 |
+
"khs.dCH2": "int64",
|
| 730 |
+
"AVP-5": "float64",
|
| 731 |
+
"AVP-4": "float64",
|
| 732 |
+
"AVP-7": "float64",
|
| 733 |
+
"AVP-1": "float64",
|
| 734 |
+
"AVP-0": "float64",
|
| 735 |
+
"AVP-2": "float64",
|
| 736 |
+
"AATS1v": "float64",
|
| 737 |
+
"AATS1p": "float64",
|
| 738 |
+
"AATS1s": "float64",
|
| 739 |
+
"AATS1m": "float64",
|
| 740 |
+
"MDEN-22": "float64",
|
| 741 |
+
"AATS1i": "float64",
|
| 742 |
+
"AATS1e": "float64",
|
| 743 |
+
"maxaaNH": "float64",
|
| 744 |
+
"GATS7c": "float64",
|
| 745 |
+
"GATS7m": "float64",
|
| 746 |
+
"GATS7v": "float64",
|
| 747 |
+
"GATS7p": "float64",
|
| 748 |
+
"GATS7s": "float64",
|
| 749 |
+
"SsOm": "float64",
|
| 750 |
+
"SsOH": "float64",
|
| 751 |
+
"AATSC4v": "float64",
|
| 752 |
+
"maxHBd": "float64",
|
| 753 |
+
"AATSC4s": "float64",
|
| 754 |
+
"maxHBa": "float64",
|
| 755 |
+
"SHssNH": "float64",
|
| 756 |
+
"AATSC4e": "float64",
|
| 757 |
+
"AATSC4c": "float64",
|
| 758 |
+
"AATSC4m": "float64",
|
| 759 |
+
"AATSC4i": "float64",
|
| 760 |
+
"SHBint10": "float64",
|
| 761 |
+
"ATS2v": "float64",
|
| 762 |
+
"ATS2p": "float64",
|
| 763 |
+
"ATS2s": "float64",
|
| 764 |
+
"CrippenLogP": "float64",
|
| 765 |
+
"ATS2i": "float64",
|
| 766 |
+
"JGT": "float64",
|
| 767 |
+
"MPC10": "int64",
|
| 768 |
+
"VAdjMat": "float64",
|
| 769 |
+
"ATSC6p": "float64",
|
| 770 |
+
"ATSC6s": "float64",
|
| 771 |
+
"ATSC6v": "float64",
|
| 772 |
+
"ATSC6i": "float64",
|
| 773 |
+
"ATSC6m": "float64",
|
| 774 |
+
"ATSC6c": "float64",
|
| 775 |
+
"ATSC6e": "float64",
|
| 776 |
+
"SpMin1_Bhi": "float64",
|
| 777 |
+
"SpMin1_Bhm": "float64",
|
| 778 |
+
"SpMin1_Bhp": "float64",
|
| 779 |
+
"SpMin1_Bhs": "float64",
|
| 780 |
+
"SpMin1_Bhv": "float64",
|
| 781 |
+
"SHother": "float64",
|
| 782 |
+
"AATS0s": "float64",
|
| 783 |
+
"SpMin4_Bhv": "float64",
|
| 784 |
+
"AATS0p": "float64",
|
| 785 |
+
"SpMin4_Bhp": "float64",
|
| 786 |
+
"AATS0v": "float64",
|
| 787 |
+
"SpMin4_Bhs": "float64",
|
| 788 |
+
"SpMin4_Bhm": "float64",
|
| 789 |
+
"AATS0i": "float64",
|
| 790 |
+
"SpMin4_Bhi": "float64",
|
| 791 |
+
"AATS0m": "float64",
|
| 792 |
+
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|
| 793 |
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|
| 794 |
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|
| 795 |
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|
| 796 |
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|
| 797 |
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|
| 798 |
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|
| 799 |
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|
| 800 |
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|
| 801 |
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|
| 802 |
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|
| 803 |
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|
| 804 |
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|
| 805 |
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|
| 806 |
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|
| 807 |
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|
| 808 |
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|
| 809 |
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|
| 810 |
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|
| 811 |
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|
| 812 |
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|
| 813 |
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|
| 814 |
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|
| 815 |
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|
| 816 |
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|
| 817 |
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|
| 818 |
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|
| 819 |
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|
| 820 |
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|
| 821 |
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|
| 822 |
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|
| 823 |
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|
| 824 |
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|
| 825 |
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|
| 826 |
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|
| 827 |
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|
| 828 |
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|
| 829 |
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|
| 830 |
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|
| 831 |
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|
| 832 |
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|
| 833 |
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|
| 834 |
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|
| 835 |
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|
| 836 |
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|
| 837 |
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|
| 838 |
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|
| 839 |
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|
| 840 |
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|
| 841 |
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|
| 842 |
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|
| 843 |
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|
| 844 |
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|
| 845 |
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|
| 846 |
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|
| 847 |
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|
| 848 |
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|
| 849 |
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|
| 850 |
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|
| 851 |
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|
| 852 |
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|
| 853 |
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|
| 854 |
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|
| 855 |
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|
| 856 |
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|
| 857 |
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|
| 858 |
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|
| 859 |
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|
| 860 |
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|
| 861 |
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|
| 862 |
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|
| 863 |
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|
| 864 |
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|
| 865 |
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|
| 866 |
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|
| 867 |
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|
| 868 |
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|
| 869 |
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|
| 870 |
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|
| 871 |
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|
| 872 |
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|
| 873 |
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|
| 874 |
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|
| 875 |
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|
| 876 |
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|
| 877 |
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"nwHBa": "int64",
|
| 878 |
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|
| 879 |
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|
| 880 |
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|
| 881 |
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|
| 882 |
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|
| 883 |
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|
| 884 |
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|
| 885 |
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|
| 886 |
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|
| 887 |
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|
| 888 |
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|
| 889 |
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|
| 890 |
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|
| 891 |
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|
| 892 |
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|
| 893 |
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|
| 894 |
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"ASP-2": "float64",
|
| 895 |
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|
| 896 |
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|
| 897 |
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|
| 898 |
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|
| 899 |
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|
| 900 |
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|
| 901 |
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|
| 902 |
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"ATS0s": "float64",
|
| 903 |
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|
| 904 |
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|
| 905 |
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|
| 906 |
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|
| 907 |
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|
| 908 |
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|
| 909 |
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|
| 910 |
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"ATS0e": "float64",
|
| 911 |
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"ATS0i": "float64",
|
| 912 |
+
"ATS0m": "float64",
|
| 913 |
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"GGI9": "float64",
|
| 914 |
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"GGI8": "float64",
|
| 915 |
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|
| 916 |
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|
| 917 |
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|
| 918 |
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|
| 919 |
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|
| 920 |
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"GGI7": "float64",
|
| 921 |
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|
| 922 |
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|
| 923 |
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|
| 924 |
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"TIC1": "float64",
|
| 925 |
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|
| 926 |
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|
| 927 |
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"TIC4": "float64",
|
| 928 |
+
"TIC5": "float64",
|
| 929 |
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"ntCH": "int64",
|
| 930 |
+
"ZMIC4": "float64",
|
| 931 |
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"ZMIC5": "float64",
|
| 932 |
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"ZMIC0": "float64",
|
| 933 |
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"ZMIC1": "float64",
|
| 934 |
+
"ZMIC2": "float64",
|
| 935 |
+
"ZMIC3": "float64",
|
| 936 |
+
"minHother": "float64",
|
| 937 |
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"ATSC4c": "float64",
|
| 938 |
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"ATSC4e": "float64",
|
| 939 |
+
"ATSC4i": "float64",
|
| 940 |
+
"ATSC4m": "float64",
|
| 941 |
+
"ATSC4s": "float64",
|
| 942 |
+
"ATSC4p": "float64",
|
| 943 |
+
"ATSC4v": "float64",
|
| 944 |
+
"SpMax1_Bhs": "float64",
|
| 945 |
+
"SpMax1_Bhp": "float64",
|
| 946 |
+
"SpMax1_Bhv": "float64",
|
| 947 |
+
"hmin": "float64",
|
| 948 |
+
"SpMax1_Bhe": "float64",
|
| 949 |
+
"SpMax1_Bhi": "float64",
|
| 950 |
+
"SpMax1_Bhm": "float64",
|
| 951 |
+
"MPC6": "int64",
|
| 952 |
+
"nAtomP": "int64",
|
| 953 |
+
"MPC4": "int64",
|
| 954 |
+
"MPC5": "int64",
|
| 955 |
+
"MPC2": "int64",
|
| 956 |
+
"VR3_Dzv": "float64",
|
| 957 |
+
"VR3_Dzs": "float64",
|
| 958 |
+
"MPC8": "int64",
|
| 959 |
+
"VR3_Dzp": "float64",
|
| 960 |
+
"VR3_Dzm": "float64",
|
| 961 |
+
"SpMAD_Dzp": "float64",
|
| 962 |
+
"VR3_Dzi": "float64",
|
| 963 |
+
"VR3_Dze": "float64",
|
| 964 |
+
"SsF": "float64",
|
| 965 |
+
"minaaO": "float64",
|
| 966 |
+
"VR3_DzZ": "float64",
|
| 967 |
+
"mintCH": "float64",
|
| 968 |
+
"Sse": "float64",
|
| 969 |
+
"C3SP2": "int64",
|
| 970 |
+
"VR1_DzZ": "float64",
|
| 971 |
+
"VR1_Dzp": "float64",
|
| 972 |
+
"VR1_Dzs": "float64",
|
| 973 |
+
"SHtCH": "float64",
|
| 974 |
+
"VR1_Dzi": "float64",
|
| 975 |
+
"VR1_Dzm": "float64",
|
| 976 |
+
"VR1_Dze": "float64",
|
| 977 |
+
"maxdsN": "float64",
|
| 978 |
+
"AATSC7v": "float64",
|
| 979 |
+
"AATSC7s": "float64",
|
| 980 |
+
"AATSC7p": "float64",
|
| 981 |
+
"AATSC7m": "float64",
|
| 982 |
+
"AATSC7i": "float64",
|
| 983 |
+
"AATSC7e": "float64",
|
| 984 |
+
"BIC2": "float64",
|
| 985 |
+
"BIC3": "float64",
|
| 986 |
+
"BIC0": "float64",
|
| 987 |
+
"BIC1": "float64",
|
| 988 |
+
"SIC5": "float64",
|
| 989 |
+
"SIC4": "float64",
|
| 990 |
+
"SIC1": "float64",
|
| 991 |
+
"SIC0": "float64",
|
| 992 |
+
"SIC3": "float64",
|
| 993 |
+
"SIC2": "float64",
|
| 994 |
+
"minHBd": "float64",
|
| 995 |
+
"minHBa": "float64",
|
| 996 |
+
"AATSC1p": "float64",
|
| 997 |
+
"AATSC0i": "float64",
|
| 998 |
+
"ALogp2": "float64",
|
| 999 |
+
"AATSC1i": "float64",
|
| 1000 |
+
"SaaO": "float64",
|
| 1001 |
+
"SaaN": "float64",
|
| 1002 |
+
"GATS8m": "float64",
|
| 1003 |
+
"SpMax2_Bhv": "float64",
|
| 1004 |
+
"GATS8i": "float64",
|
| 1005 |
+
"SpMax2_Bhs": "float64",
|
| 1006 |
+
"GATS8e": "float64",
|
| 1007 |
+
"GATS8c": "float64",
|
| 1008 |
+
"SpMax2_Bhe": "float64",
|
| 1009 |
+
"SpMax2_Bhm": "float64",
|
| 1010 |
+
"GATS8v": "float64",
|
| 1011 |
+
"GATS8p": "float64",
|
| 1012 |
+
"GATS8s": "float64",
|
| 1013 |
+
"piPC3": "float64",
|
| 1014 |
+
"piPC2": "float64",
|
| 1015 |
+
"piPC1": "float64",
|
| 1016 |
+
"piPC7": "float64",
|
| 1017 |
+
"piPC6": "float64",
|
| 1018 |
+
"piPC5": "float64",
|
| 1019 |
+
"piPC4": "float64",
|
| 1020 |
+
"piPC9": "float64",
|
| 1021 |
+
"piPC8": "float64",
|
| 1022 |
+
"maxHdsCH": "float64",
|
| 1023 |
+
"ATS1v": "float64",
|
| 1024 |
+
"ATS1p": "float64",
|
| 1025 |
+
"ATS1s": "float64",
|
| 1026 |
+
"ATS1i": "float64",
|
| 1027 |
+
"SpMAD_Dt": "float64",
|
| 1028 |
+
"ATS1e": "float64",
|
| 1029 |
+
"VR2_Dzi": "float64",
|
| 1030 |
+
"MPC3": "int64",
|
| 1031 |
+
"ATS2e": "float64",
|
| 1032 |
+
"VR2_Dzv": "float64",
|
| 1033 |
+
"minHBint9": "float64",
|
| 1034 |
+
"VE2_Dzs": "float64",
|
| 1035 |
+
"VE2_Dzp": "float64",
|
| 1036 |
+
"VE2_Dzv": "float64",
|
| 1037 |
+
"ATS2m": "float64",
|
| 1038 |
+
"VE2_Dzi": "float64",
|
| 1039 |
+
"VE2_Dzm": "float64",
|
| 1040 |
+
"VE2_Dze": "float64",
|
| 1041 |
+
"VE2_DzZ": "float64",
|
| 1042 |
+
"nHAvin": "int64",
|
| 1043 |
+
"meanI": "float64",
|
| 1044 |
+
"nHCsatu": "int64",
|
| 1045 |
+
"nHCsats": "int64",
|
| 1046 |
+
"apol": "float64",
|
| 1047 |
+
"ATS5i": "float64",
|
| 1048 |
+
"nF12Ring": "int64",
|
| 1049 |
+
"mindO": "float64",
|
| 1050 |
+
"MolIP": "float64",
|
| 1051 |
+
"maxdsCH": "float64",
|
| 1052 |
+
"ndCH2": "int64",
|
| 1053 |
+
"TopoPSA": "float64",
|
| 1054 |
+
"ETA_EtaP_B_RC": "float64",
|
| 1055 |
+
"MDEO-11": "float64",
|
| 1056 |
+
"MDEO-12": "float64",
|
| 1057 |
+
"AATSC0s": "float64",
|
| 1058 |
+
"SpMax5_Bhv": "float64",
|
| 1059 |
+
"AATSC0p": "float64",
|
| 1060 |
+
"SpMax5_Bhs": "float64",
|
| 1061 |
+
"AATSC0v": "float64",
|
| 1062 |
+
"SpMax5_Bhp": "float64",
|
| 1063 |
+
"SpMax5_Bhm": "float64",
|
| 1064 |
+
"SpMax5_Bhi": "float64",
|
| 1065 |
+
"AATSC0c": "float64",
|
| 1066 |
+
"SpMax5_Bhe": "float64",
|
| 1067 |
+
"AATSC0e": "float64",
|
| 1068 |
+
"VE3_DzZ": "float64",
|
| 1069 |
+
"C3SP3": "int64",
|
| 1070 |
+
"VE3_Dzv": "float64",
|
| 1071 |
+
"minddssS": "float64",
|
| 1072 |
+
"ATS6s": "float64",
|
| 1073 |
+
"VE3_Dzs": "float64",
|
| 1074 |
+
"VE3_Dzp": "float64",
|
| 1075 |
+
"ATS6v": "float64",
|
| 1076 |
+
"ATS6i": "float64",
|
| 1077 |
+
"VE3_Dzm": "float64",
|
| 1078 |
+
"ATS6m": "float64",
|
| 1079 |
+
"VE3_Dzi": "float64",
|
| 1080 |
+
"VE3_Dze": "float64",
|
| 1081 |
+
"ATS6e": "float64",
|
| 1082 |
+
"VPC-6": "float64",
|
| 1083 |
+
"VPC-5": "float64",
|
| 1084 |
+
"VPC-4": "float64",
|
| 1085 |
+
"MLFER_E": "float64",
|
| 1086 |
+
"nBondsD2": "int64",
|
| 1087 |
+
"Mpe": "float64",
|
| 1088 |
+
"nHeavyAtom": "int64",
|
| 1089 |
+
"nRing": "int64",
|
| 1090 |
+
"GATS5c": "float64",
|
| 1091 |
+
"GATS5e": "float64",
|
| 1092 |
+
"VR1_Dzv": "float64",
|
| 1093 |
+
"minsssN": "float64",
|
| 1094 |
+
"topoShape": "float64",
|
| 1095 |
+
"ndS": "int64",
|
| 1096 |
+
"VE1_Dt": "float64",
|
| 1097 |
+
"maxHaaCH": "float64",
|
| 1098 |
+
"nHBAcc": "int64",
|
| 1099 |
+
"VE3_D": "float64",
|
| 1100 |
+
"VE1_D": "float64",
|
| 1101 |
+
"AATSC1s": "float64",
|
| 1102 |
+
"AATSC1v": "float64",
|
| 1103 |
+
"AATSC1m": "float64",
|
| 1104 |
+
"AATSC1c": "float64",
|
| 1105 |
+
"AATSC1e": "float64",
|
| 1106 |
+
"LipoaffinityIndex": "float64",
|
| 1107 |
+
"n6Ring": "int64",
|
| 1108 |
+
"ETA_Eta": "float64",
|
| 1109 |
+
"WTPT-1": "float64",
|
| 1110 |
+
"WTPT-2": "float64",
|
| 1111 |
+
"WTPT-3": "float64",
|
| 1112 |
+
"WTPT-4": "float64",
|
| 1113 |
+
"WTPT-5": "float64",
|
| 1114 |
+
"ETA_EtaP_L": "float64",
|
| 1115 |
+
"ETA_EtaP_F": "float64",
|
| 1116 |
+
"ETA_EtaP_B": "float64",
|
| 1117 |
+
"ETA_dEpsilon_C": "float64",
|
| 1118 |
+
"MDEO-22": "float64",
|
| 1119 |
+
"Toxic": "category"
|
| 1120 |
+
}
|
clock_protein_toxicity/019d7375-2841-7bfb-a6da-5adfdcd43d98/experiment_metadata.predictive-ml-splits-mold-v1.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"splits_comment": "Default splits.", "splits": {"0": {"0": [[0, 1, 2, 3, 5, 6, 7, 9, 10, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 29, 31, 32, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 52, 53, 56, 57, 59, 60, 62, 65, 68, 69, 71, 72, 73, 74, 76, 78, 79, 80, 84, 87, 88, 90, 91, 92, 93, 94, 95, 97, 98, 99, 101, 103, 104, 105, 106, 107, 108, 109, 112, 113, 117, 118, 119, 121, 123, 124, 125, 126, 127, 129, 130, 133, 134, 135, 137, 138, 139, 140, 142, 143, 145, 146, 147, 148, 149, 154, 155, 157, 158, 159, 162, 166, 167, 169, 170], [4, 8, 11, 13, 23, 28, 30, 33, 34, 38, 42, 47, 51, 54, 55, 58, 61, 63, 64, 66, 67, 70, 75, 77, 81, 82, 83, 85, 86, 89, 96, 100, 102, 110, 111, 114, 115, 116, 120, 122, 128, 131, 132, 136, 141, 144, 150, 151, 152, 153, 156, 160, 161, 163, 164, 165, 168]], "1": [[0, 1, 4, 6, 7, 8, 10, 11, 12, 13, 15, 17, 19, 21, 23, 25, 26, 28, 30, 32, 33, 34, 35, 38, 39, 41, 42, 44, 45, 46, 47, 51, 54, 55, 56, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 70, 75, 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clock_protein_toxicity/019d7375-2841-7bfb-a6da-5adfdcd43d98/task_metadata.predictive-ml-task-mold-v1.json
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{
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"target_column_name": "Toxic",
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"problem_type": "binary_classification",
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"objective_metric_name": "roc_auc",
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"stratify_on": "Toxic",
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"time_on": null,
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"type_adapter_id": "predictive-ml-task-mold-v1"
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}
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coil_2000/019d7366-fdc2-7225-a22c-314c624711f6/container_metadata.json
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"version_comment": null
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coil_2000/019d7366-fdc2-7225-a22c-314c624711f6/dataset_metadata.dataset-mold-v1.json
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{
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"unique_name": "coil_2000",
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| 3 |
+
"dataset_year": "2000",
|
| 4 |
+
"domain_str": "business & marketing",
|
| 5 |
+
"dataset_source": "UCI",
|
| 6 |
+
"original_dataset_source_download_link": "https://doi.org/10.24432/C5630S",
|
| 7 |
+
"download_description": "\nWe download the data from the UCI repository and unzip it to a predefined folder.\n\nmkdir -p local-data-warehouse/coil_2000/ && wget -P local-data-warehouse/coil_2000/ https://archive.ics.uci.edu/static/public/125/insurance+company+benchmark+coil+2000.zip && unzip local-data-warehouse/coil_2000/insurance+company+benchmark+coil+2000.zip -d local-data-warehouse/coil_2000/\n",
|
| 8 |
+
"academic_reference_bibtex": "@techreport{van2000coil,\n title={CoIL challenge 2000: The insurance company case},\n author={Van Der Putten, Peter and van Someren, Maarten and others},\n year={2000},\n institution={Technical Report 2000--09, Leiden Institute of Advanced Computer Science}\n}\n",
|
| 9 |
+
"academic_reference_bibtex_key": "van2000coil",
|
| 10 |
+
"license": "CC BY 4.0",
|
| 11 |
+
"data_tags": [
|
| 12 |
+
"IID"
|
| 13 |
+
],
|
| 14 |
+
"curation_comments": "\n- We created semantic meaningful names for the features.\n- We combined the original training and validation data into one new dataset.\n- We reversed the ordinal encoding of the original data where possible.\n- Anomaly: the data has 15% duplicates.\n",
|
| 15 |
+
"version_from_unique_name": null,
|
| 16 |
+
"version_comment": null,
|
| 17 |
+
"local_data_directory_base": "/home/lennart_priorlabs_ai/code/large_data_ensemble/data-foundry/local-data-warehouse",
|
| 18 |
+
"type_adapter_id": "dataset-mold-v1"
|
| 19 |
+
}
|
coil_2000/019d7366-fdc2-7225-a22c-314c624711f6/dtypes.json
ADDED
|
@@ -0,0 +1,88 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"customerSubtype": "category",
|
| 3 |
+
"numberOfHouses": "int64",
|
| 4 |
+
"avgSizeHousehold": "int64",
|
| 5 |
+
"avgAge": "category",
|
| 6 |
+
"customerMainType": "category",
|
| 7 |
+
"romanCatholic": "category",
|
| 8 |
+
"protestant": "int64",
|
| 9 |
+
"otherReligion": "int64",
|
| 10 |
+
"noReligion": "int64",
|
| 11 |
+
"married": "int64",
|
| 12 |
+
"livingTogether": "int64",
|
| 13 |
+
"otherRelation": "int64",
|
| 14 |
+
"singles": "int64",
|
| 15 |
+
"householdWithoutChildren": "int64",
|
| 16 |
+
"householdWithChildren": "int64",
|
| 17 |
+
"highLevelEducation": "int64",
|
| 18 |
+
"mediumLevelEducation": "int64",
|
| 19 |
+
"lowerLevelEducation": "int64",
|
| 20 |
+
"highStatus": "int64",
|
| 21 |
+
"entrepreneur": "int64",
|
| 22 |
+
"farmer": "int64",
|
| 23 |
+
"middleManagement": "int64",
|
| 24 |
+
"skilledLabourers": "int64",
|
| 25 |
+
"unskilledLabourers": "int64",
|
| 26 |
+
"socialClassA": "int64",
|
| 27 |
+
"socialClassB1": "int64",
|
| 28 |
+
"socialClassB2": "int64",
|
| 29 |
+
"socialClassC": "int64",
|
| 30 |
+
"socialClassD": "int64",
|
| 31 |
+
"rentedHouse": "int64",
|
| 32 |
+
"homeOwners": "int64",
|
| 33 |
+
"oneCar": "int64",
|
| 34 |
+
"twoCars": "int64",
|
| 35 |
+
"noCar": "int64",
|
| 36 |
+
"nationalHealthService": "int64",
|
| 37 |
+
"privateHealthInsurance": "int64",
|
| 38 |
+
"incomeLessThan30k": "int64",
|
| 39 |
+
"income30To45k": "int64",
|
| 40 |
+
"income45To75k": "int64",
|
| 41 |
+
"income75To122k": "int64",
|
| 42 |
+
"incomeAbove123k": "int64",
|
| 43 |
+
"averageIncome": "int64",
|
| 44 |
+
"purchasingPowerClass": "int64",
|
| 45 |
+
"contributionPrivateThirdPartyInsurance": "category",
|
| 46 |
+
"contributionThirdPartyInsuranceFirms": "int64",
|
| 47 |
+
"contributionThirdPartyInsuranceAgriculture": "int64",
|
| 48 |
+
"contributionCarPolicies": "int64",
|
| 49 |
+
"contributionDeliveryVanPolicies": "int64",
|
| 50 |
+
"contributionMotorcycleScooterPolicies": "int64",
|
| 51 |
+
"contributionLorryPolicies": "int64",
|
| 52 |
+
"contributionTrailerPolicies": "int64",
|
| 53 |
+
"contributionTractorPolicies": "int64",
|
| 54 |
+
"contributionAgriculturalMachinesPolicies": "int64",
|
| 55 |
+
"contributionMopedPolicies": "int64",
|
| 56 |
+
"contributionLifeInsurances": "int64",
|
| 57 |
+
"contributionPrivateAccidentInsurancePolicies": "int64",
|
| 58 |
+
"contributionFamilyAccidentsInsurancePolicies": "int64",
|
| 59 |
+
"contributionDisabilityInsurancePolicies": "int64",
|
| 60 |
+
"contributionFirePolicies": "int64",
|
| 61 |
+
"contributionSurfboardPolicies": "int64",
|
| 62 |
+
"contributionBoatPolicies": "int64",
|
| 63 |
+
"contributionBicyclePolicies": "int64",
|
| 64 |
+
"contributionPropertyInsurancePolicies": "int64",
|
| 65 |
+
"contributionSocialSecurityInsurancePolicies": "int64",
|
| 66 |
+
"numberOfPrivateThirdPartyInsurance": "int64",
|
| 67 |
+
"numberOfThirdPartyInsuranceFirms": "int64",
|
| 68 |
+
"numberOfThirdPartyInsuranceAgriculture": "int64",
|
| 69 |
+
"numberOfCarPolicies": "int64",
|
| 70 |
+
"numberOfDeliveryVanPolicies": "int64",
|
| 71 |
+
"numberOfMotorcycleScooterPolicies": "int64",
|
| 72 |
+
"numberOfLorryPolicies": "int64",
|
| 73 |
+
"numberOfTrailerPolicies": "int64",
|
| 74 |
+
"numberOfTractorPolicies": "int64",
|
| 75 |
+
"numberOfAgriculturalMachinesPolicies": "int64",
|
| 76 |
+
"numberOfMopedPolicies": "int64",
|
| 77 |
+
"numberOfLifeInsurances": "int64",
|
| 78 |
+
"numberOfPrivateAccidentInsurancePolicies": "int64",
|
| 79 |
+
"numberOfFamilyAccidentsInsurancePolicies": "int64",
|
| 80 |
+
"numberOfDisabilityInsurancePolicies": "int64",
|
| 81 |
+
"numberOfFirePolicies": "int64",
|
| 82 |
+
"numberOfSurfboardPolicies": "int64",
|
| 83 |
+
"numberOfBoatPolicies": "int64",
|
| 84 |
+
"numberOfBicyclePolicies": "int64",
|
| 85 |
+
"numberOfPropertyInsurancePolicies": "int64",
|
| 86 |
+
"numberOfSocialSecurityInsurancePolicies": "int64",
|
| 87 |
+
"MobileHomePolicy": "category"
|
| 88 |
+
}
|
coil_2000/019d7366-fdc2-7225-a22c-314c624711f6/experiment_metadata.predictive-ml-splits-mold-v1.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
coil_2000/019d7366-fdc2-7225-a22c-314c624711f6/task_metadata.predictive-ml-task-mold-v1.json
ADDED
|
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
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|
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|
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|
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|
|
| 1 |
+
{
|
| 2 |
+
"target_column_name": "MobileHomePolicy",
|
| 3 |
+
"problem_type": "binary_classification",
|
| 4 |
+
"objective_metric_name": "roc_auc",
|
| 5 |
+
"stratify_on": "MobileHomePolicy",
|
| 6 |
+
"time_on": null,
|
| 7 |
+
"group_on": null,
|
| 8 |
+
"group_labels": null,
|
| 9 |
+
"group_time_on": null,
|
| 10 |
+
"type_adapter_id": "predictive-ml-task-mold-v1"
|
| 11 |
+
}
|
early_stage_diabetes_risk_prediction/019d736a-c5ee-7069-9b14-686535f9caa9/container_metadata.json
ADDED
|
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
| 1 |
+
{
|
| 2 |
+
"uuid": "019d736a-c5ee-7069-9b14-686535f9caa9",
|
| 3 |
+
"checksum": "9d93119a10378fd42fe3d3329abc70223ac482e62ae90b8824da6e80c61d9953",
|
| 4 |
+
"version_comment": null
|
| 5 |
+
}
|
early_stage_diabetes_risk_prediction/019d736a-c5ee-7069-9b14-686535f9caa9/dataset_metadata.dataset-mold-v1.json
ADDED
|
@@ -0,0 +1,19 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
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|
|
|
|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"unique_name": "early_stage_diabetes_risk_prediction",
|
| 3 |
+
"dataset_year": "2019",
|
| 4 |
+
"domain_str": "medical & healthcare",
|
| 5 |
+
"dataset_source": "UCI",
|
| 6 |
+
"original_dataset_source_download_link": "https://doi.org/10.24432/C5VG8H",
|
| 7 |
+
"download_description": "\nWe get the data from UCI.\n\nwget https://archive.ics.uci.edu/static/public/529/early+stage+diabetes+risk+prediction+dataset.zip && unzip early+stage+diabetes+risk+prediction+dataset.zip && rm early+stage+diabetes+risk+prediction+dataset.zip && mkdir -p local-data-warehouse/early_stage_diabetes_risk_prediction && mv diabetes_data_upload.csv local-data-warehouse/early_stage_diabetes_risk_prediction/\n",
|
| 8 |
+
"academic_reference_bibtex": "@inproceedings{islam2019likelihood,\n title={Likelihood prediction of diabetes at early stage using data mining techniques},\n author={Islam, MM Faniqul and Ferdousi, Rahatara and Rahman, Sadikur and Bushra, Humayra Yasmin},\n booktitle={Computer Vision and Machine Intelligence in Medical Image Analysis: International Symposium, ISCMM 2019},\n pages={113--125},\n year={2019},\n organization={Springer}\n}\n",
|
| 9 |
+
"academic_reference_bibtex_key": "islam2019likelihood",
|
| 10 |
+
"license": "CC BY 4.0",
|
| 11 |
+
"data_tags": [
|
| 12 |
+
"IID"
|
| 13 |
+
],
|
| 14 |
+
"curation_comments": "\nWe use the data as is from UCI.\n\n- Note, the dataset contains a lot of naturally occurring duplicates (50%). We drop them to avoid too extreme duplicated-based data leakage biasing the evaluation.\n",
|
| 15 |
+
"version_from_unique_name": null,
|
| 16 |
+
"version_comment": null,
|
| 17 |
+
"local_data_directory_base": "/home/lennart_priorlabs_ai/code/large_data_ensemble/data-foundry/local-data-warehouse",
|
| 18 |
+
"type_adapter_id": "dataset-mold-v1"
|
| 19 |
+
}
|
early_stage_diabetes_risk_prediction/019d736a-c5ee-7069-9b14-686535f9caa9/dtypes.json
ADDED
|
@@ -0,0 +1,19 @@
|
|
|
|
|
|
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|
|
|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"Age": "int64",
|
| 3 |
+
"Gender": "category",
|
| 4 |
+
"Polyuria": "category",
|
| 5 |
+
"Polydipsia": "category",
|
| 6 |
+
"sudden weight loss": "category",
|
| 7 |
+
"weakness": "category",
|
| 8 |
+
"Polyphagia": "category",
|
| 9 |
+
"Genital thrush": "category",
|
| 10 |
+
"visual blurring": "category",
|
| 11 |
+
"Itching": "category",
|
| 12 |
+
"Irritability": "category",
|
| 13 |
+
"delayed healing": "category",
|
| 14 |
+
"partial paresis": "category",
|
| 15 |
+
"muscle stiffness": "category",
|
| 16 |
+
"Alopecia": "category",
|
| 17 |
+
"Obesity": "category",
|
| 18 |
+
"class": "category"
|
| 19 |
+
}
|
early_stage_diabetes_risk_prediction/019d736a-c5ee-7069-9b14-686535f9caa9/experiment_metadata.predictive-ml-splits-mold-v1.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"splits_comment": "Default splits for IID data.", "splits": {"0": {"0": [[0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 17, 18, 19, 23, 24, 26, 27, 28, 29, 30, 32, 33, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 48, 49, 51, 52, 53, 54, 56, 57, 59, 60, 63, 65, 66, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 80, 81, 83, 86, 88, 89, 91, 92, 93, 95, 96, 97, 99, 101, 102, 103, 104, 105, 106, 108, 109, 110, 113, 114, 115, 116, 119, 120, 123, 126, 127, 128, 129, 130, 132, 133, 134, 135, 136, 138, 142, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 154, 157, 158, 159, 160, 161, 162, 167, 170, 171, 173, 174, 175, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 194, 196, 197, 198, 199, 202, 204, 205, 209, 212, 213, 215, 216, 218, 219, 220, 221, 226, 227, 229, 231, 233, 234, 235, 236, 239, 240, 241, 244, 247, 248, 249], [6, 11, 15, 16, 20, 21, 22, 25, 31, 34, 39, 46, 47, 50, 55, 58, 61, 62, 64, 67, 78, 79, 82, 84, 85, 87, 90, 94, 98, 100, 107, 111, 112, 117, 118, 121, 122, 124, 125, 131, 137, 139, 140, 141, 151, 155, 156, 163, 164, 165, 166, 168, 169, 172, 176, 190, 191, 192, 193, 195, 200, 201, 203, 206, 207, 208, 210, 211, 214, 217, 222, 223, 224, 225, 228, 230, 232, 237, 238, 242, 243, 245, 246, 250]], "1": [[2, 4, 6, 7, 8, 9, 11, 13, 15, 16, 20, 21, 22, 23, 25, 30, 31, 32, 33, 34, 36, 37, 38, 39, 42, 43, 45, 46, 47, 48, 50, 55, 57, 58, 59, 61, 62, 64, 67, 68, 70, 71, 72, 73, 74, 75, 78, 79, 80, 82, 83, 84, 85, 87, 88, 90, 92, 94, 96, 98, 99, 100, 101, 103, 104, 105, 106, 107, 109, 110, 111, 112, 117, 118, 119, 121, 122, 124, 125, 127, 128, 129, 131, 132, 135, 137, 138, 139, 140, 141, 142, 145, 146, 147, 151, 153, 155, 156, 159, 160, 162, 163, 164, 165, 166, 167, 168, 169, 170, 172, 173, 176, 177, 178, 179, 184, 186, 188, 190, 191, 192, 193, 194, 195, 196, 197, 199, 200, 201, 203, 204, 206, 207, 208, 210, 211, 213, 214, 215, 216, 217, 218, 220, 221, 222, 223, 224, 225, 227, 228, 230, 231, 232, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 248, 249, 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early_stage_diabetes_risk_prediction/019d736a-c5ee-7069-9b14-686535f9caa9/task_metadata.predictive-ml-task-mold-v1.json
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{
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"target_column_name": "class",
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"problem_type": "binary_classification",
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"objective_metric_name": "roc_auc",
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"stratify_on": "class",
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"time_on": null,
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"group_on": null,
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"group_labels": null,
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"group_time_on": null,
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"type_adapter_id": "predictive-ml-task-mold-v1"
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}
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ecoli_proteins/019d736a-d6d2-7e28-aeff-7da6b7126b9d/container_metadata.json
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{
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"uuid": "019d736a-d6d2-7e28-aeff-7da6b7126b9d",
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"checksum": "886458d37ea9b8b8987d67d68eed72ea032f098b0d8eee99f44e7e262a9f09d0",
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"version_comment": null
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}
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ecoli_proteins/019d736a-d6d2-7e28-aeff-7da6b7126b9d/dataset_metadata.dataset-mold-v1.json
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{
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"unique_name": "ecoli_proteins",
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"dataset_year": "1996",
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"domain_str": "biology & life sciences",
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"dataset_source": "UCI",
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+
"original_dataset_source_download_link": "https://doi.org/10.24432/C5388M",
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"download_description": "\n\nwget https://archive.ics.uci.edu/static/public/39/ecoli.zip && unzip ecoli.zip && rm ecoli.zip ecoli.names\nmkdir -p local-data-warehouse/ecoli_proteins && mv ecoli.data local-data-warehouse/ecoli_proteins/\n",
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| 8 |
+
"academic_reference_bibtex": "@inproceedings{horton1996probabilistic,\n title={A probabilistic classification system for predicting the cellular localization sites of proteins.},\n author={Horton, Paul and Nakai, Kenta},\n booktitle={Ismb},\n volume={4},\n pages={109--115},\n year={1996},\n organization={St. Louis, Missouri, USA}\n}\n",
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+
"academic_reference_bibtex_key": "horton1996probabilistic",
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"license": "CC BY 4.0",
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"data_tags": [
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"IID"
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],
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"curation_comments": "\nWe start with the dataset from UCI.\n\n- We drop the sequence name column, which is a unique identifier.\n- We drop three classes with less than 10 samples each: omL (5), imL (2), and imS (3).\n- We drop \"chg\", which becomes constant after our preprocessing steps.\n",
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"version_from_unique_name": null,
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"version_comment": null,
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"local_data_directory_base": "/home/lennart_priorlabs_ai/code/large_data_ensemble/data-foundry/local-data-warehouse",
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| 18 |
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"type_adapter_id": "dataset-mold-v1"
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}
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ecoli_proteins/019d736a-d6d2-7e28-aeff-7da6b7126b9d/dtypes.json
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{
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"mcg": "float64",
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"gvh": "float64",
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"lip": "category",
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"aac": "float64",
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"alm1": "float64",
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"alm2": "float64",
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| 8 |
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"class": "category"
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| 9 |
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}
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ecoli_proteins/019d736a-d6d2-7e28-aeff-7da6b7126b9d/experiment_metadata.predictive-ml-splits-mold-v1.json
ADDED
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{"splits_comment": "Default splits for IID data.", "splits": {"0": {"0": [[0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 16, 18, 19, 20, 22, 23, 24, 26, 27, 28, 29, 31, 33, 34, 35, 36, 37, 44, 46, 47, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 68, 69, 71, 76, 77, 78, 79, 81, 82, 86, 87, 88, 89, 92, 94, 95, 97, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 116, 118, 121, 125, 126, 128, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 147, 148, 151, 152, 153, 155, 156, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 172, 174, 177, 178, 179, 181, 182, 183, 184, 185, 186, 187, 188, 191, 192, 193, 194, 199, 200, 203, 205, 207, 208, 210, 211, 214, 215, 216, 217, 220, 222, 224, 225, 227, 228, 230, 231, 232, 233, 234, 236, 237, 238, 240, 243, 244, 245, 246, 247, 248, 250, 252, 254, 255, 256, 257, 259, 260, 261, 263, 264, 265, 266, 268, 269, 276, 278, 279, 280, 283, 285, 286, 287, 288, 289, 291, 292, 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ecoli_proteins/019d736a-d6d2-7e28-aeff-7da6b7126b9d/task_metadata.predictive-ml-task-mold-v1.json
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{
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"target_column_name": "class",
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"problem_type": "multiclass_classification",
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"objective_metric_name": "log_loss",
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"stratify_on": "class",
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"time_on": null,
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"group_on": null,
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"group_labels": null,
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| 9 |
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"group_time_on": null,
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| 10 |
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"type_adapter_id": "predictive-ml-task-mold-v1"
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| 11 |
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}
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homeq_default_prediction/019d7374-cab8-700e-97f3-9c55ea980153/container_metadata.json
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{
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"uuid": "019d7374-cab8-700e-97f3-9c55ea980153",
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"checksum": "fe9846eb10af3376d7efaf53f07a6a55217cb74e4986972204d80450c33a826e",
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"version_comment": null
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}
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homeq_default_prediction/019d7374-cab8-700e-97f3-9c55ea980153/dataset_metadata.dataset-mold-v1.json
ADDED
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@@ -0,0 +1,19 @@
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{
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"unique_name": "homeq_default_prediction",
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| 3 |
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"dataset_year": "2016",
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| 4 |
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"domain_str": "business & marketing",
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| 5 |
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"dataset_source": "Other",
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| 6 |
+
"original_dataset_source_download_link": "http://www.creditriskanalytics.net/datasets-private2.html",
|
| 7 |
+
"download_description": "\nWe get the data from the original source, not from the Kaggle reupload (https://www.kaggle.com/datasets/ajay1735/hmeq-data).\n\nwget http://www.creditriskanalytics.net/uploads/1/9/5/1/19511601/hmeq.csv \\\n&& mkdir -p local-data-warehouse/homeq_default_prediction \\\n&& mv hmeq.csv local-data-warehouse/homeq_default_prediction\n",
|
| 8 |
+
"academic_reference_bibtex": "@book{baesens2016credit,\n title={Credit risk analytics: Measurement techniques, applications, and examples in SAS},\n author={Baesens, Bart and Roesch, Daniel and Scheule, Harald},\n year={2016},\n publisher={John Wiley \\& Sons}\n}\n",
|
| 9 |
+
"academic_reference_bibtex_key": "baesens2016credit",
|
| 10 |
+
"license": "None",
|
| 11 |
+
"data_tags": [
|
| 12 |
+
"IID"
|
| 13 |
+
],
|
| 14 |
+
"curation_comments": "\n- We rename the target column and make its values more semantically meaningful (0 -> \"no_default\", 1 -> \"default\").\n- We noticed several columns missing a large number of features. In all these cases the loan also did not have a value for \"REASON\". We drop 252 rows that dont have a reason for their loan in the data as these are most likely data errors that we want to ignore.\n",
|
| 15 |
+
"version_from_unique_name": null,
|
| 16 |
+
"version_comment": null,
|
| 17 |
+
"local_data_directory_base": "/home/lennart_priorlabs_ai/code/large_data_ensemble/data-foundry/local-data-warehouse",
|
| 18 |
+
"type_adapter_id": "dataset-mold-v1"
|
| 19 |
+
}
|
homeq_default_prediction/019d7374-cab8-700e-97f3-9c55ea980153/dtypes.json
ADDED
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@@ -0,0 +1,15 @@
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|
| 1 |
+
{
|
| 2 |
+
"LOAN": "int64",
|
| 3 |
+
"MORTDUE": "float64",
|
| 4 |
+
"VALUE": "float64",
|
| 5 |
+
"REASON": "category",
|
| 6 |
+
"JOB": "category",
|
| 7 |
+
"YOJ": "float64",
|
| 8 |
+
"DEROG": "float64",
|
| 9 |
+
"DELINQ": "float64",
|
| 10 |
+
"CLAGE": "float64",
|
| 11 |
+
"NINQ": "float64",
|
| 12 |
+
"CLNO": "float64",
|
| 13 |
+
"DEBTINC": "float64",
|
| 14 |
+
"DefaultStatus": "category"
|
| 15 |
+
}
|
homeq_default_prediction/019d7374-cab8-700e-97f3-9c55ea980153/experiment_metadata.predictive-ml-splits-mold-v1.json
ADDED
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