Add files using upload-large-folder tool

airfoil_self_noise/019d7366-4673-7d9a-9821-35604af616f6/container_metadata.json

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+{
+  "uuid": "019d7366-4673-7d9a-9821-35604af616f6",
+  "checksum": "6c79451403f65fbbea152a878f96ae30c9a051a5dbce73a5ee7c204a675cab89",
+  "version_comment": null
+}

airfoil_self_noise/019d7366-4673-7d9a-9821-35604af616f6/dataset_metadata.dataset-mold-v1.json

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+{
+  "unique_name": "airfoil_self_noise",
+  "dataset_year": "2014",
+  "domain_str": "physics & astronomy",
+  "dataset_source": "UCI",
+  "original_dataset_source_download_link": "https://doi.org/10.24432/C5VW2C",
+  "download_description": "\nmkdir -p local-data-warehouse/airfoil_self_noise/ && wget -P local-data-warehouse/airfoil_self_noise/ https://archive.ics.uci.edu/static/public/291/airfoil+self+noise.zip && unzip local-data-warehouse/airfoil_self_noise/airfoil+self+noise.zip -d local-data-warehouse/airfoil_self_noise/\n",
+  "academic_reference_bibtex": "@techreport{brooks1989airfoil,\n  title={Airfoil self-noise and prediction},\n  author={Brooks, Thomas F and Pope, D Stuart and Marcolini, Michael A},\n  year={1989}\n}\n",
+  "academic_reference_bibtex_key": "brooks1989airfoil",
+  "license": "CC BY 4.0",
+  "data_tags": [
+    "IID"
+  ],
+  "curation_comments": "N/A",
+  "version_from_unique_name": null,
+  "version_comment": null,
+  "local_data_directory_base": "/home/lennart_priorlabs_ai/code/large_data_ensemble/data-foundry/local-data-warehouse",
+  "type_adapter_id": "dataset-mold-v1"
+}

airfoil_self_noise/019d7366-4673-7d9a-9821-35604af616f6/dtypes.json

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+{
+  "frequency": "int64",
+  "attack-angle": "category",
+  "chord-length": "float64",
+  "free-stream-velocity": "float64",
+  "suction-side-displacement-thickness": "float64",
+  "scaled-sound-pressure": "float64"
+}

airfoil_self_noise/019d7366-4673-7d9a-9821-35604af616f6/task_metadata.predictive-ml-task-mold-v1.json

@@ -0,0 +1,11 @@
+{
+  "target_column_name": "scaled-sound-pressure",
+  "problem_type": "regression",
+  "objective_metric_name": "rmse",
+  "stratify_on": null,
+  "time_on": null,
+  "group_on": null,
+  "group_labels": null,
+  "group_time_on": null,
+  "type_adapter_id": "predictive-ml-task-mold-v1"
+}

asp_potassco_classification/019d73ec-935f-7f88-8a82-af3ad2f44e9f/container_metadata.json

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+{
+  "uuid": "019d73ec-935f-7f88-8a82-af3ad2f44e9f",
+  "checksum": "15cbbd0aad0f3c2b0289ee141aab0c3be0b8a9850bfe34be428e75869ba26aa2",
+  "version_comment": null
+}

asp_potassco_classification/019d73ec-935f-7f88-8a82-af3ad2f44e9f/dataset_metadata.dataset-mold-v1.json

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+{
+  "unique_name": "asp_potassco_classification",
+  "dataset_year": "2014",
+  "domain_str": "technology & internet",
+  "dataset_source": "ASlib",
+  "original_dataset_source_download_link": "https://github.com/coseal/aslib_data/tree/master/ASP-POTASSCO",
+  "download_description": "\nwget https://raw.githubusercontent.com/coseal/aslib_data/refs/heads/master/ASP-POTASSCO/algorithm_runs.arff \\\n&& wget https://raw.githubusercontent.com/coseal/aslib_data/refs/heads/master/ASP-POTASSCO/feature_values.arff \\\n&& mkdir -p local-data-warehouse/asp_potassco_classification \\\n&& mv feature_values.arff algorithm_runs.arff local-data-warehouse/asp_potassco_classification/\n",
+  "academic_reference_bibtex": "@article{hoos2014claspfolio,\n  title={claspfolio 2: Advances in algorithm selection for answer set programming},\n  author={Hoos, Holger and Lindauer, Marius and Schaub, Torsten},\n  journal={Theory and Practice of Logic Programming},\n  volume={14},\n  number={4-5},\n  pages={569--585},\n  year={2014},\n  publisher={Cambridge University Press}\n}\n@article{bischl_aslib_2016,\n\ttitle = {{ASlib}: {A} {Benchmark} {Library} for {Algorithm} {Selection}},\n\tnumber = {237},\n\tjournal = {Artificial Intelligence Journal (AIJ)},\n\tauthor = {Bischl, Bernd and Kerschke, Pascal and Kotthoff, Lars and Lindauer, Marius and Malitsky, Yuri and Fr\u00e9chette, Alexandre and Hoos, Holger H. and Hutter, Frank and Leyton-Brown, Kevin and Tierney, Kevin and Vanschoren, Joaquin},\n\tyear = {2016},\n\tpages = {41--58}\n}\n",
+  "academic_reference_bibtex_key": "hoos2014claspfolio,bischl_aslib_2016",
+  "license": "GPLv3",
+  "data_tags": [
+    "Non-IID",
+    "Grouped"
+  ],
+  "curation_comments": "\nWe get the data from ASlib and merge them into one file.\n\n- We treat it as a multiclass classification task to solve the algorithm selection task as in the OpenML version (https://openml.org/d/41705).\n- We drop all cases where no algorithm was able to finish before the timeout as these are essentially random labels.\n- We resole the instance ID to a task ID by mapping the instance ID back to the source task based on their naming convention. From our understanding, the data contains multiple instance from the same solver task, differing only in seed or task configurations. We want to avoid having samples from the same task in both train and test set, as this would leak what algorithms are best for this task, and since in real-world you might not have samples from your new task. Thus, we treat the data as a grouped task, where we aim to generalize the predicting the best algorithm to new tasks across a set of instances of this task.\n",
+  "version_from_unique_name": null,
+  "version_comment": null,
+  "local_data_directory_base": "/home/lennart_priorlabs_ai/code/large_data_ensemble/data-foundry/local-data-warehouse",
+  "type_adapter_id": "dataset-mold-v1"
+}

asp_potassco_classification/019d73ec-935f-7f88-8a82-af3ad2f44e9f/dtypes.json

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+{
+  "algorithm": "category",
+  "Frac_Neg_Body": "float64",
+  "Frac_Pos_Body": "float64",
+  "Frac_Unary_Rules": "float64",
+  "Frac_Binary_Rules": "float64",
+  "Frac_Ternary_Rules": "float64",
+  "Frac_Integrity_Rules": "float64",
+  "Tight": "float64",
+  "Problem_Variables": "float64",
+  "Free_Problem_Variables": "float64",
+  "Assigned_Problem_Variables": "float64",
+  "Constraints": "float64",
+  "Constraints/Vars": "float64",
+  "Created_Bodies": "float64",
+  "Program_Atoms": "float64",
+  "SCCS": "float64",
+  "Nodes_in_Positive_BADG": "float64",
+  "Rules": "float64",
+  "Normal_Rules": "float64",
+  "Cardinality_Rules": "float64",
+  "Choice_Rules": "float64",
+  "Weight_Rules": "float64",
+  "Frac_Normal_Rules": "float64",
+  "Frac_Cardinality_Rules": "float64",
+  "Frac_Choice_Rules": "float64",
+  "Frac_Weight_Rules": "float64",
+  "Equivalences": "float64",
+  "Atom-Atom_Equivalences": "float64",
+  "Body-Body_Equivalences": "float64",
+  "Other_Equivalences": "float64",
+  "Frac_Atom-Atom_Equivalences": "float64",
+  "Frac_Body-Body_Equivalences": "float64",
+  "Frac_Other_Equivalences": "float64",
+  "Binary_Constraints": "float64",
+  "Ternary_Constraints": "float64",
+  "Other_Constraints": "float64",
+  "Frac_Binary_Constraints": "float64",
+  "Frac_Ternary_Constraints": "float64",
+  "Frac_Other_Constraints": "float64",
+  "Choices-1": "float64",
+  "Conflicts/Choices-1": "float64",
+  "Avg_Conflict_Levels-1": "float64",
+  "Avg_LBD_Levels-1": "float64",
+  "Learnt_from_Conflict-1": "float64",
+  "Learnt_from_Loop-1": "float64",
+  "Frac_Learnt_from_Conflict-1": "float64",
+  "Frac_Learnt_from_Loop-1": "float64",
+  "Literals_in_Conflict_Nogoods-1": "float64",
+  "Literals_in_Loop_Nogoods-1": "float64",
+  "Frac_Literals_in_Conflict_Nogoods-1": "float64",
+  "Frac_Literals_in_Loop_Nogoods-1": "float64",
+  "Removed_Nogoods-1": "float64",
+  "Learnt_Binary-1": "float64",
+  "Learnt_Ternary-1": "float64",
+  "Learnt_Others-1": "float64",
+  "Frac_Learnt_Binary-1": "float64",
+  "Frac_Learnt_Ternary-1": "float64",
+  "Frac_Learnt_Others-1": "float64",
+  "Skipped_Levels_while_Backjumping-1": "float64",
+  "Avg_Skipped_Levels_while_Backjumping-1": "float64",
+  "Longest_Backjumping-1": "float64",
+  "Running_Avg_Conflictlevel-1": "float64",
+  "Running_Avg_LBD-1": "float64",
+  "Choices-2": "float64",
+  "Conflicts/Choices-2": "float64",
+  "Avg_Conflict_Levels-2": "float64",
+  "Avg_LBD_Levels-2": "float64",
+  "Learnt_from_Conflict-2": "float64",
+  "Learnt_from_Loop-2": "float64",
+  "Frac_Learnt_from_Conflict-2": "float64",
+  "Frac_Learnt_from_Loop-2": "float64",
+  "Literals_in_Conflict_Nogoods-2": "float64",
+  "Literals_in_Loop_Nogoods-2": "float64",
+  "Frac_Literals_in_Conflict_Nogoods-2": "float64",
+  "Frac_Literals_in_Loop_Nogoods-2": "float64",
+  "Removed_Nogoods-2": "float64",
+  "Learnt_Binary-2": "float64",
+  "Learnt_Ternary-2": "float64",
+  "Learnt_Others-2": "float64",
+  "Frac_Learnt_Binary-2": "float64",
+  "Frac_Learnt_Ternary-2": "float64",
+  "Frac_Learnt_Others-2": "float64",
+  "Skipped_Levels_while_Backjumping-2": "float64",
+  "Avg_Skipped_Levels_while_Backjumping-2": "float64",
+  "Longest_Backjumping-2": "float64",
+  "Running_Avg_Conflictlevel-2": "float64",
+  "Running_Avg_LBD-2": "float64",
+  "Choices-3": "float64",
+  "Conflicts/Choices-3": "float64",
+  "Avg_Conflict_Levels-3": "float64",
+  "Avg_LBD_Levels-3": "float64",
+  "Learnt_from_Conflict-3": "float64",
+  "Learnt_from_Loop-3": "float64",
+  "Frac_Learnt_from_Conflict-3": "float64",
+  "Frac_Learnt_from_Loop-3": "float64",
+  "Literals_in_Conflict_Nogoods-3": "float64",
+  "Literals_in_Loop_Nogoods-3": "float64",
+  "Frac_Literals_in_Conflict_Nogoods-3": "float64",
+  "Frac_Literals_in_Loop_Nogoods-3": "float64",
+  "Removed_Nogoods-3": "float64",
+  "Learnt_Binary-3": "float64",
+  "Learnt_Ternary-3": "float64",
+  "Learnt_Others-3": "float64",
+  "Frac_Removed_Nogood-3": "float64",
+  "Frac_Learnt_Binary-3": "float64",
+  "Frac_Learnt_Ternary-3": "float64",
+  "Frac_Learnt_Others-3": "float64",
+  "Skipped_Levels_while_Backjumping-3": "float64",
+  "Avg_Skipped_Levels_while_Backjumping-3": "float64",
+  "Longest_Backjumping-3": "float64",
+  "Running_Avg_Conflictlevel-3": "float64",
+  "Running_Avg_LBD-3": "float64",
+  "Choices-4": "float64",
+  "Conflicts/Choices-4": "float64",
+  "Avg_Conflict_Levels-4": "float64",
+  "Avg_LBD_Levels-4": "float64",
+  "Learnt_from_Conflict-4": "float64",
+  "Learnt_from_Loop-4": "float64",
+  "Frac_Learnt_from_Conflict-4": "float64",
+  "Frac_Learnt_from_Loop-4": "float64",
+  "Literals_in_Conflict_Nogoods-4": "float64",
+  "Literals_in_Loop_Nogoods-4": "float64",
+  "Frac_Literals_in_Conflict_Nogoods-4": "float64",
+  "Frac_Literals_in_Loop_Nogoods-4": "float64",
+  "Removed_Nogoods-4": "float64",
+  "Learnt_Binary-4": "float64",
+  "Learnt_Ternary-4": "float64",
+  "Learnt_Others-4": "float64",
+  "Frac_Removed_Nogood-4": "float64",
+  "Frac_Learnt_Binary-4": "float64",
+  "Frac_Learnt_Ternary-4": "float64",
+  "Frac_Learnt_Others-4": "float64",
+  "Skipped_Levels_while_Backjumping-4": "float64",
+  "Avg_Skipped_Levels_while_Backjumping-4": "float64",
+  "Longest_Backjumping-4": "float64",
+  "Running_Avg_Conflictlevel-4": "float64",
+  "Running_Avg_LBD-4": "float64",
+  "task_id": "category"
+}

asp_potassco_classification/019d73ec-935f-7f88-8a82-af3ad2f44e9f/task_metadata.predictive-ml-task-mold-v1.json

@@ -0,0 +1,11 @@
+{
+  "target_column_name": "algorithm",
+  "problem_type": "multiclass_classification",
+  "objective_metric_name": "log_loss",
+  "stratify_on": "algorithm",
+  "time_on": null,
+  "group_on": "task_id",
+  "group_labels": "per_sample",
+  "group_time_on": null,
+  "type_adapter_id": "predictive-ml-task-mold-v1"
+}

audiology_diagnosis/019d736a-3b3d-73da-8d65-b4b7a39e1590/container_metadata.json

@@ -0,0 +1,5 @@
+{
+  "uuid": "019d736a-3b3d-73da-8d65-b4b7a39e1590",
+  "checksum": "5dc7bf9e3b5bc68eef4db323228529b096a0fa33615b4069e451e2788d1c63ab",
+  "version_comment": null
+}

audiology_diagnosis/019d736a-3b3d-73da-8d65-b4b7a39e1590/dataset_metadata.dataset-mold-v1.json

@@ -0,0 +1,19 @@
+{
+  "unique_name": "audiology_diagnosis",
+  "dataset_year": "1987",
+  "domain_str": "medical & healthcare",
+  "dataset_source": "UCI",
+  "original_dataset_source_download_link": "https://doi.org/10.24432/C5TP4R",
+  "download_description": "\nWe get the data from UCI.\nwget https://archive.ics.uci.edu/static/public/8/audiology+standardized.zip && unzip audiology+standardized.zip && rm audiology+standardized.zip audiology.standardized.names && mkdir -p local-data-warehouse/audiology_diagnosis && mv audiology.standardized.data audiology.standardized.test local-data-warehouse/audiology_diagnosis/\n",
+  "academic_reference_bibtex": "@incollection{bareiss1990protos,\n  title={Protos: An exemplar-based learning apprentice},\n  author={Bareiss, E Ray and Porter, Bruce W and Wier, Craig C},\n  booktitle={Machine learning},\n  pages={112--127},\n  year={1990},\n  publisher={Elsevier}\n}\n",
+  "academic_reference_bibtex_key": "bareiss1990protos",
+  "license": "CC BY 4.0",
+  "data_tags": [
+    "IID"
+  ],
+  "curation_comments": "\nWe start with the data from UCI and merge train and test data.\n\n- We drop the ID column as it is uninformative here.\n- The target label contains groups of labels with specifications. We merge labels into groups that represent general a diagnosis. We create 3 labels: \"normal\", \"cochlear\", \"other\". There is likely a much better way to partition these labels, but this is most reasonable partitioning for an acutal task, going from names and my limited domain knowledge about the various diagnoses.\n- We drop duplicated rows as they might introduce too much information leakage for such small data and are likely not natural but rather an artifact of the limited number of features.\n- We remove one constant column (history_fullness).\n",
+  "version_from_unique_name": null,
+  "version_comment": null,
+  "local_data_directory_base": "/home/lennart_priorlabs_ai/code/large_data_ensemble/data-foundry/local-data-warehouse",
+  "type_adapter_id": "dataset-mold-v1"
+}

audiology_diagnosis/019d736a-3b3d-73da-8d65-b4b7a39e1590/dtypes.json

@@ -0,0 +1,71 @@
+{
+  "age_gt_60": "category",
+  "air": "category",
+  "airBoneGap": "category",
+  "ar_c": "category",
+  "ar_u": "category",
+  "bone": "category",
+  "boneAbnormal": "category",
+  "bser": "category",
+  "history_buzzing": "category",
+  "history_dizziness": "category",
+  "history_fluctuating": "category",
+  "history_heredity": "category",
+  "history_nausea": "category",
+  "history_noise": "category",
+  "history_recruitment": "category",
+  "history_ringing": "category",
+  "history_roaring": "category",
+  "history_vomiting": "category",
+  "late_wave_poor": "category",
+  "m_at_2k": "category",
+  "m_cond_lt_1k": "category",
+  "m_gt_1k": "category",
+  "m_m_gt_2k": "category",
+  "m_m_sn": "category",
+  "m_m_sn_gt_1k": "category",
+  "m_m_sn_gt_2k": "category",
+  "m_m_sn_gt_500": "category",
+  "m_p_sn_gt_2k": "category",
+  "m_s_gt_500": "category",
+  "m_s_sn": "category",
+  "m_s_sn_gt_1k": "category",
+  "m_s_sn_gt_2k": "category",
+  "m_s_sn_gt_3k": "category",
+  "m_s_sn_gt_4k": "category",
+  "m_sn_2_3k": "category",
+  "m_sn_gt_1k": "category",
+  "m_sn_gt_2k": "category",
+  "m_sn_gt_3k": "category",
+  "m_sn_gt_4k": "category",
+  "m_sn_gt_500": "category",
+  "m_sn_gt_6k": "category",
+  "m_sn_lt_1k": "category",
+  "m_sn_lt_2k": "category",
+  "m_sn_lt_3k": "category",
+  "middle_wave_poor": "category",
+  "mod_gt_4k": "category",
+  "mod_mixed": "category",
+  "mod_s_mixed": "category",
+  "mod_s_sn_gt_500": "category",
+  "mod_sn": "category",
+  "mod_sn_gt_1k": "category",
+  "mod_sn_gt_2k": "category",
+  "mod_sn_gt_3k": "category",
+  "mod_sn_gt_4k": "category",
+  "mod_sn_gt_500": "category",
+  "notch_4k": "category",
+  "notch_at_4k": "category",
+  "o_ar_c": "category",
+  "o_ar_u": "category",
+  "s_sn_gt_1k": "category",
+  "s_sn_gt_2k": "category",
+  "s_sn_gt_4k": "category",
+  "speech": "category",
+  "static_normal": "category",
+  "tymp": "category",
+  "viith_nerve_signs": "category",
+  "wave_V_delayed": "category",
+  "waveform_ItoV_prolonged": "category",
+  "diagnosis": "category"
+}

audiology_diagnosis/019d736a-3b3d-73da-8d65-b4b7a39e1590/experiment_metadata.predictive-ml-splits-mold-v1.json

@@ -0,0 +1 @@
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198], [1, 4, 7, 10, 18, 20, 24, 26, 32, 33, 35, 39, 42, 47, 50, 51, 52, 54, 57, 60, 62, 63, 64, 65, 66, 67, 71, 72, 73, 76, 82, 88, 92, 95, 97, 98, 104, 105, 110, 115, 117, 124, 128, 129, 131, 134, 137, 138, 149, 151, 152, 155, 157, 158, 159, 165, 169, 171, 178, 180, 182, 184, 185, 186, 188, 197]]}, "19": {"0": [[1, 2, 5, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 25, 26, 27, 30, 33, 34, 36, 37, 38, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 56, 57, 63, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 88, 89, 90, 92, 94, 95, 97, 99, 100, 103, 105, 106, 107, 108, 109, 110, 111, 113, 114, 116, 118, 120, 121, 122, 124, 126, 128, 129, 130, 132, 134, 138, 139, 141, 142, 143, 144, 145, 146, 148, 150, 152, 153, 154, 155, 156, 160, 161, 162, 163, 164, 167, 168, 169, 171, 172, 173, 174, 178, 179, 180, 182, 183, 184, 186, 187, 188, 189, 190, 191, 194, 196, 198], [0, 3, 4, 6, 9, 17, 22, 23, 24, 28, 29, 31, 32, 35, 39, 45, 53, 54, 55, 58, 59, 60, 61, 62, 71, 75, 85, 86, 87, 91, 93, 96, 98, 101, 102, 104, 112, 115, 117, 119, 123, 125, 127, 131, 133, 135, 136, 137, 140, 147, 149, 151, 157, 158, 159, 165, 166, 170, 175, 176, 177, 181, 185, 192, 193, 195, 197]], "1": [[0, 2, 3, 4, 6, 8, 9, 15, 17, 18, 19, 21, 22, 23, 24, 28, 29, 30, 31, 32, 35, 36, 39, 41, 43, 45, 46, 47, 48, 49, 51, 53, 54, 55, 56, 58, 59, 60, 61, 62, 64, 65, 66, 68, 69, 71, 72, 74, 75, 76, 78, 80, 81, 85, 86, 87, 88, 89, 91, 92, 93, 95, 96, 97, 98, 100, 101, 102, 103, 104, 105, 107, 109, 112, 113, 115, 116, 117, 119, 120, 121, 123, 124, 125, 126, 127, 128, 130, 131, 132, 133, 135, 136, 137, 138, 140, 141, 142, 145, 146, 147, 148, 149, 151, 154, 157, 158, 159, 164, 165, 166, 170, 171, 173, 174, 175, 176, 177, 179, 181, 182, 185, 186, 187, 190, 191, 192, 193, 194, 195, 196, 197, 198], [1, 5, 7, 10, 11, 12, 13, 14, 16, 20, 25, 26, 27, 33, 34, 37, 38, 40, 42, 44, 50, 52, 57, 63, 67, 70, 73, 77, 79, 82, 83, 84, 90, 94, 99, 106, 108, 110, 111, 114, 118, 122, 129, 134, 139, 143, 144, 150, 152, 153, 155, 156, 160, 161, 162, 163, 167, 168, 169, 172, 178, 180, 183, 184, 188, 189]], "2": [[0, 1, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 16, 17, 20, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 40, 42, 44, 45, 50, 52, 53, 54, 55, 57, 58, 59, 60, 61, 62, 63, 67, 70, 71, 73, 75, 77, 79, 82, 83, 84, 85, 86, 87, 90, 91, 93, 94, 96, 98, 99, 101, 102, 104, 106, 108, 110, 111, 112, 114, 115, 117, 118, 119, 122, 123, 125, 127, 129, 131, 133, 134, 135, 136, 137, 139, 140, 143, 144, 147, 149, 150, 151, 152, 153, 155, 156, 157, 158, 159, 160, 161, 162, 163, 165, 166, 167, 168, 169, 170, 172, 175, 176, 177, 178, 180, 181, 183, 184, 185, 188, 189, 192, 193, 195, 197], [2, 8, 15, 18, 19, 21, 30, 36, 41, 43, 46, 47, 48, 49, 51, 56, 64, 65, 66, 68, 69, 72, 74, 76, 78, 80, 81, 88, 89, 92, 95, 97, 100, 103, 105, 107, 109, 113, 116, 120, 121, 124, 126, 128, 130, 132, 138, 141, 142, 145, 146, 148, 154, 164, 171, 173, 174, 179, 182, 186, 187, 190, 191, 194, 196, 198]]}}, "time_horizon": null, "time_horizon_unit": null, "type_adapter_id": "predictive-ml-splits-mold-v1"}

audiology_diagnosis/019d736a-3b3d-73da-8d65-b4b7a39e1590/task_metadata.predictive-ml-task-mold-v1.json

@@ -0,0 +1,11 @@
+{
+  "target_column_name": "diagnosis",
+  "problem_type": "multiclass_classification",
+  "objective_metric_name": "log_loss",
+  "stratify_on": "diagnosis",
+  "time_on": null,
+  "group_on": null,
+  "group_labels": null,
+  "group_time_on": null,
+  "type_adapter_id": "predictive-ml-task-mold-v1"
+}

bank_customer_churn/019d7366-99cc-77cc-8d41-5c3ccc8eff96/container_metadata.json

@@ -0,0 +1,5 @@
+{
+  "uuid": "019d7366-99cc-77cc-8d41-5c3ccc8eff96",
+  "checksum": "631a7b4311ef96cce0271f48f074a4fe344c661f839a30eb632e990647912c5a",
+  "version_comment": null
+}

bank_customer_churn/019d7366-99cc-77cc-8d41-5c3ccc8eff96/dataset_metadata.dataset-mold-v1.json

@@ -0,0 +1,19 @@
+{
+  "unique_name": "bank_customer_churn",
+  "dataset_year": "2020",
+  "domain_str": "business & marketing",
+  "dataset_source": "Kaggle",
+  "original_dataset_source_download_link": "https://www.kaggle.com/datasets/gauravtopre/bank-customer-churn-dataset",
+  "download_description": "\nWe download the data from Kaggle to a .csv file in a predefined folder.\n    \nmkdir -p local-data-warehouse/bank_customer_churn && cd local-data-warehouse/bank_customer_churn && kaggle datasets download gauravtopre/bank-customer-churn-dataset && unzip bank-customer-churn-dataset.zip && rm bank-customer-churn-dataset.zip && cd ../../\n",
+  "academic_reference_bibtex": "@misc{Topre2022BankCustomerChurn,\n  title={Bank Customer Churn Dataset},\n  author={Gaurav Topre},\n  year={2022},\n  publisher={Kaggle},\n  url={https://www.kaggle.com/datasets/gauravtopre/bank-customer-churn-dataset}\n}\n",
+  "academic_reference_bibtex_key": "Topre2022BankCustomerChurn",
+  "license": "Public Domain",
+  "data_tags": [
+    "IID"
+  ],
+  "curation_comments": "\n- We remove the customer_id column.\n",
+  "version_from_unique_name": null,
+  "version_comment": null,
+  "local_data_directory_base": "/home/lennart_priorlabs_ai/code/large_data_ensemble/data-foundry/local-data-warehouse",
+  "type_adapter_id": "dataset-mold-v1"
+}

bank_customer_churn/019d7366-99cc-77cc-8d41-5c3ccc8eff96/dtypes.json

@@ -0,0 +1,13 @@
+{
+  "credit_score": "int64",
+  "country": "category",
+  "gender": "category",
+  "age": "int64",
+  "tenure": "int64",
+  "balance": "float64",
+  "products_number": "int64",
+  "credit_card": "category",
+  "active_member": "category",
+  "estimated_salary": "float64",
+  "churn": "category"
+}

bank_customer_churn/019d7366-99cc-77cc-8d41-5c3ccc8eff96/task_metadata.predictive-ml-task-mold-v1.json

@@ -0,0 +1,11 @@
+{
+  "target_column_name": "churn",
+  "problem_type": "binary_classification",
+  "objective_metric_name": "roc_auc",
+  "stratify_on": "churn",
+  "time_on": null,
+  "group_on": null,
+  "group_labels": null,
+  "group_time_on": null,
+  "type_adapter_id": "predictive-ml-task-mold-v1"
+}

eryhemato_squamous_disease/019d736b-020a-7fbc-9785-6438802781f2/container_metadata.json

@@ -0,0 +1,5 @@
+{
+  "uuid": "019d736b-020a-7fbc-9785-6438802781f2",
+  "checksum": "1a5dabd5da4cd914969b97bfb894a13df7bb21b1b7a000d63085a6e63feaba9a",
+  "version_comment": null
+}

eryhemato_squamous_disease/019d736b-020a-7fbc-9785-6438802781f2/dataset_metadata.dataset-mold-v1.json

@@ -0,0 +1,19 @@
+{
+  "unique_name": "eryhemato_squamous_disease",
+  "dataset_year": "1997",
+  "domain_str": "medical & healthcare",
+  "dataset_source": "UCI",
+  "original_dataset_source_download_link": "https://doi.org/10.24432/C5FK5P",
+  "download_description": "\nWe take the original data from UCI.\n\nwget https://archive.ics.uci.edu/static/public/33/dermatology.zip && unzip dermatology.zip && rm dermatology.zip dermatology.names\nmkdir -p local-data-warehouse/eryhemato_squamous_disease && mv dermatology.data local-data-warehouse/eryhemato_squamous_disease/\n",
+  "academic_reference_bibtex": "@article{guvenir1998learning,\n  title={Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals},\n  author={G{\\\"u}venir, H Altay and Demir{\\\"o}z, G{\\\"u}l{\\c{s}}en and Ilter, Nilsel},\n  journal={Artificial intelligence in medicine},\n  volume={13},\n  number={3},\n  pages={147--165},\n  year={1998},\n  publisher={Elsevier}\n}\n",
+  "academic_reference_bibtex_key": "guvenir1998learning",
+  "license": "CC BY 4.0",
+  "data_tags": [
+    "IID"
+  ],
+  "curation_comments": "\nWe start with the data from UCI.\n\n- We encode all features but age as categorical, since they are ordinal features in nature.\n- We ensure missing values in age are encoded as NaN.\n",
+  "version_from_unique_name": null,
+  "version_comment": null,
+  "local_data_directory_base": "/home/lennart_priorlabs_ai/code/large_data_ensemble/data-foundry/local-data-warehouse",
+  "type_adapter_id": "dataset-mold-v1"
+}

eryhemato_squamous_disease/019d736b-020a-7fbc-9785-6438802781f2/dtypes.json

@@ -0,0 +1,37 @@
+{
+  "erythema": "category",
+  "scaling": "category",
+  "definite borders": "category",
+  "itching": "category",
+  "koebner phenomenon": "category",
+  "polygonal papules": "category",
+  "follicular papules": "category",
+  "oral mucosal involvement": "category",
+  "knee and elbow involvement": "category",
+  "scalp involvement": "category",
+  "family history": "category",
+  "melanin incontinence": "category",
+  "eosinophils in the infiltrate": "category",
+  "PNL infiltrate": "category",
+  "fibrosis of the papillary dermis": "category",
+  "exocytosis": "category",
+  "acanthosis": "category",
+  "hyperkeratosis": "category",
+  "parakeratosis": "category",
+  "clubbing of the rete ridges": "category",
+  "elongation of the rete ridges": "category",
+  "thinning of the suprapapillary epidermis": "category",
+  "spongiform pustule": "category",
+  "munro microabcess": "category",
+  "focal hypergranulosis": "category",
+  "disappearance of the granular layer": "category",
+  "vacuolisation and damage of basal layer": "category",
+  "spongiosis": "category",
+  "saw-tooth appearance of retes": "category",
+  "follicular horn plug": "category",
+  "perifollicular parakeratosis": "category",
+  "inflammatory monoluclear inflitrate": "category",
+  "band-like infiltrate": "category",
+  "age": "float64",
+  "class": "category"
+}

eryhemato_squamous_disease/019d736b-020a-7fbc-9785-6438802781f2/task_metadata.predictive-ml-task-mold-v1.json

@@ -0,0 +1,11 @@
+{
+  "target_column_name": "class",
+  "problem_type": "multiclass_classification",
+  "objective_metric_name": "log_loss",
+  "stratify_on": "class",
+  "time_on": null,
+  "group_on": null,
+  "group_labels": null,
+  "group_time_on": null,
+  "type_adapter_id": "predictive-ml-task-mold-v1"
+}

ghanas_indigenous_intel/019dd375-4259-77c1-a177-5acc1f4d0134/container_metadata.json

@@ -0,0 +1,5 @@
+{
+  "uuid": "019dd375-4259-77c1-a177-5acc1f4d0134",
+  "checksum": "d47199f80bd667dacc88b8dd87bf8b5cd6d7892fd9bbcfba5002a87d8e7b2d36",
+  "version_comment": null
+}

ghanas_indigenous_intel/019dd375-4259-77c1-a177-5acc1f4d0134/dataset_metadata.dataset-mold-v1.json

@@ -0,0 +1,21 @@
+{
+  "unique_name": "ghanas_indigenous_intel",
+  "dataset_year": "2025",
+  "domain_str": "environmental science & climate",
+  "dataset_source": "Zindi",
+  "original_dataset_source_download_link": "https://www.kaggle.com/datasets/phantom50/ghanas-indigenous-intel-dataset",
+  "download_description": "\nWe download the data from Kaggle, although it was originally a Zindi competition.\n\nkaggle datasets download phantom50/ghanas-indigenous-intel-dataset \\\n&& unzip ghanas-indigenous-intel-dataset.zip \\\n&& rm ghanas-indigenous-intel-dataset.zip \\\n&& mkdir -p local-data-warehouse/ghanas_indigenous_intel \\\n&& mv *.csv local-data-warehouse/ghanas_indigenous_intel/\n",
+  "academic_reference_bibtex": "@misc{zindi_ghana_indigenous_intel_2025,\n    author       = {{Zindi}},\n    title        = {Ghana's Indigenous Intel Challenge [BEGINNERS ONLY]: Data},\n    year         = {2025},\n    howpublished = {\\url{https://zindi.africa/competitions/ghana-indigenous-intel-challenge/data}},\n    note         = {Zindi dataset page. Accessed 2026-04-11}\n}\n",
+  "academic_reference_bibtex_key": "zindi_ghana_indigenous_intel_2025",
+  "license": "CC-BY-SA 4.0",
+  "data_tags": [
+    "Non-IID",
+    "Temporal",
+    "Grouped"
+  ],
+  "curation_comments": "\n- Information about the data source: The data was collected by local farmers in the Pra River Basin of Ghana using the Smart Indigenous Weather App, a custom-built mobile app for data collection. Farmers made weather forecasts based on traditional signs such as cloud formations, sun position, wind, moon, heat, and specific tree or animal behaviors. The task is to predict the farmers' forecasts based on these traditional signs, which are represented as features in the dataset. The target variable is the actual amount of rain in four categories, therefore the actual task is to make a prediction correcting the farmers' forecasts.\n- The data was initially used in a Zindi competition with a temporal split, we do the same.\n- We create 6 splits using a sliding window approach with a window size of 3 days and a step size of 2 days. This means that one day overlaps among consecutive test splits.\n- We ensure that each split uses at least half of the available samples for training.\n- We drop the ID column.\n- We convert prediction_time to datetime and sort by it.\n- We rename the target column to \"rainfall\" for better clarity.\n- We transform object columns and user_id to categorical.\n\n- Note: The split that would be ideal for the task would be grouped+temporal. However, the available data is not enough for this scenario. In general the available data size reduces the task quality a lot.\n- Anomaly: For this dataset it is possible that test folds don't contain some classes.\n",
+  "version_from_unique_name": null,
+  "version_comment": null,
+  "local_data_directory_base": "/home/lennart_priorlabs_ai/code/large_data_ensemble/data-foundry/local-data-warehouse",
+  "type_adapter_id": "dataset-mold-v1"
+}

ghanas_indigenous_intel/019dd375-4259-77c1-a177-5acc1f4d0134/dtypes.json

@@ -0,0 +1,13 @@
+{
+  "user_id": "category",
+  "confidence": "float64",
+  "predicted_intensity": "float64",
+  "community": "category",
+  "district": "category",
+  "prediction_time": "datetime64[ns]",
+  "indicator": "category",
+  "indicator_description": "category",
+  "time_observed": "category",
+  "rainfall": "category",
+  "forecast_length": "int64"
+}

ghanas_indigenous_intel/019dd375-4259-77c1-a177-5acc1f4d0134/task_metadata.predictive-ml-task-mold-v1.json

@@ -0,0 +1,11 @@
+{
+  "target_column_name": "rainfall",
+  "problem_type": "multiclass_classification",
+  "objective_metric_name": "f1_macro",
+  "stratify_on": "rainfall",
+  "time_on": "prediction_time",
+  "group_on": null,
+  "group_labels": null,
+  "group_time_on": null,
+  "type_adapter_id": "predictive-ml-task-mold-v1"
+}

hazelnut_spread_contaminant_detection/019d7367-e731-79fc-9677-4c5d1bab73ff/container_metadata.json

@@ -0,0 +1,5 @@
+{
+  "uuid": "019d7367-e731-79fc-9677-4c5d1bab73ff",
+  "checksum": "e689d9a3232f54e29da4bb6c4c515cea599fd03c812efd560ec84b52d7961d60",
+  "version_comment": null
+}

hazelnut_spread_contaminant_detection/019d7367-e731-79fc-9677-4c5d1bab73ff/dataset_metadata.dataset-mold-v1.json

@@ -0,0 +1,19 @@
+{
+  "unique_name": "hazelnut_spread_contaminant_detection",
+  "dataset_year": "2020",
+  "domain_str": "biology & life sciences",
+  "dataset_source": "OpenML",
+  "original_dataset_source_download_link": "https://www.openml.org/d/45538",
+  "download_description": "\nmkdir -p local-data-warehouse/hazelnut_spread_contaminant_detection/ && wget https://api.openml.org/data/download/22116506/dataset -O ./local-data-warehouse/hazelnut_spread_contaminant_detection/hazelnut.arff\n",
+  "academic_reference_bibtex": "@article{ricci2021machine,\n  title={Machine-learning-based microwave sensing: A case study for the food industry},\n  author={Ricci, Marco and {\\v{S}}titi{\\'c}, Bernardita and Urbinati, Luca and Di Guglielmo, Giuseppe and Vasquez, Jorge A Tob{\\'o}n and Carloni, Luca P and Vipiana, Francesca and Casu, Mario R},\n  journal={IEEE Journal on Emerging and Selected Topics in Circuits and Systems},\n  volume={11},\n  number={3},\n  pages={503--514},\n  year={2021},\n  publisher={IEEE}\n}\n",
+  "academic_reference_bibtex_key": "ricci2021machine",
+  "license": "CC BY-SA",
+  "data_tags": [
+    "IID"
+  ],
+  "curation_comments": "\n- We select a features from a single frequency (10 GHz) as the authors also only considered this frequency for the final experiments.\n- Anomaly: we use the publicly available dataset state, which is without preprocessing. Moreover, we were not able to inverse the original ordinal encoding of the label.\n",
+  "version_from_unique_name": null,
+  "version_comment": null,
+  "local_data_directory_base": "/home/lennart_priorlabs_ai/code/large_data_ensemble/data-foundry/local-data-warehouse",
+  "type_adapter_id": "dataset-mold-v1"
+}

hazelnut_spread_contaminant_detection/019d7367-e731-79fc-9677-4c5d1bab73ff/dtypes.json

@@ -0,0 +1,33 @@
+{
+  "s12": "float64",
+  "s13": "float64",
+  "s14": "float64",
+  "s15": "float64",
+  "s16": "float64",
+  "s21": "float64",
+  "s23": "float64",
+  "s24": "float64",
+  "s25": "float64",
+  "s26": "float64",
+  "s31": "float64",
+  "s32": "float64",
+  "s34": "float64",
+  "s35": "float64",
+  "s36": "float64",
+  "s41": "float64",
+  "s42": "float64",
+  "s43": "float64",
+  "s45": "float64",
+  "s46": "float64",
+  "s51": "float64",
+  "s52": "float64",
+  "s53": "float64",
+  "s54": "float64",
+  "s56": "float64",
+  "s61": "float64",
+  "s62": "float64",
+  "s63": "float64",
+  "s64": "float64",
+  "s65": "float64",
+  "Contaminated": "category"
+}

hazelnut_spread_contaminant_detection/019d7367-e731-79fc-9677-4c5d1bab73ff/task_metadata.predictive-ml-task-mold-v1.json

@@ -0,0 +1,11 @@
+{
+  "target_column_name": "Contaminated",
+  "problem_type": "binary_classification",
+  "objective_metric_name": "roc_auc",
+  "stratify_on": "Contaminated",
+  "time_on": null,
+  "group_on": null,
+  "group_labels": null,
+  "group_time_on": null,
+  "type_adapter_id": "predictive-ml-task-mold-v1"
+}

heart_failure_followup_survival/019d736b-8d9f-7f20-8f5c-45207fb0251d/container_metadata.json

@@ -0,0 +1,5 @@
+{
+  "uuid": "019d736b-8d9f-7f20-8f5c-45207fb0251d",
+  "checksum": "7cae11dfdde54eca6e9b4c486dd42d87b208d0c480ef02f2561922e15fadb32e",
+  "version_comment": null
+}

heart_failure_followup_survival/019d736b-8d9f-7f20-8f5c-45207fb0251d/dataset_metadata.dataset-mold-v1.json

@@ -0,0 +1,19 @@
+{
+  "unique_name": "heart_failure_followup_survival",
+  "dataset_year": "2020",
+  "domain_str": "medical & healthcare",
+  "dataset_source": "UCI",
+  "original_dataset_source_download_link": "https://doi.org/10.24432/C5Z89R",
+  "download_description": "\nWe get the data from UCI.\n\nwget https://archive.ics.uci.edu/static/public/519/heart+failure+clinical+records.zip && unzip heart+failure+clinical+records.zip && rm heart+failure+clinical+records.zip && mkdir -p local-data-warehouse/heart_failure_followup_survival && mv heart_failure_clinical_records_dataset.csv local-data-warehouse/heart_failure_followup_survival/\n",
+  "academic_reference_bibtex": "@article{chicco2020machine,\n  title={Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone},\n  author={Chicco, Davide and Jurman, Giuseppe},\n  journal={BMC medical informatics and decision making},\n  volume={20},\n  number={1},\n  pages={16},\n  year={2020},\n  publisher={Springer}\n}\n",
+  "academic_reference_bibtex_key": "chicco2020machine",
+  "license": "CC BY 4.0",
+  "data_tags": [
+    "IID"
+  ],
+  "curation_comments": "\nWe use the data as is from UCI.\n\n- We keep all features of the dataset. The study that introduced the dataset also curated a subset of features. We leave it to the pipeline to select the relevant features for the task.\n- There is a \"time\" feature in the data which is the follow-up time in days. The authors used this in some part of the experiments and showed it improved the predictive performance. We also keep this feature and follow their preprocessing to transform it into months (from days). Note, this does not make the data temporal, as the split is across unseen patients (not across time) and we only have one entry per patient.\n",
+  "version_from_unique_name": null,
+  "version_comment": null,
+  "local_data_directory_base": "/home/lennart_priorlabs_ai/code/large_data_ensemble/data-foundry/local-data-warehouse",
+  "type_adapter_id": "dataset-mold-v1"
+}

heart_failure_followup_survival/019d736b-8d9f-7f20-8f5c-45207fb0251d/dtypes.json

@@ -0,0 +1,15 @@
+{
+  "age": "float64",
+  "anaemia": "category",
+  "creatinine_phosphokinase": "int64",
+  "diabetes": "category",
+  "ejection_fraction": "int64",
+  "high_blood_pressure": "category",
+  "platelets": "float64",
+  "serum_creatinine": "float64",
+  "serum_sodium": "int64",
+  "sex": "category",
+  "smoking": "category",
+  "DEATH_EVENT": "category",
+  "month_to_follow_up": "float64"
+}

heart_failure_followup_survival/019d736b-8d9f-7f20-8f5c-45207fb0251d/experiment_metadata.predictive-ml-splits-mold-v1.json

@@ -0,0 +1 @@
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heart_failure_followup_survival/019d736b-8d9f-7f20-8f5c-45207fb0251d/task_metadata.predictive-ml-task-mold-v1.json

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+{
+  "target_column_name": "DEATH_EVENT",
+  "problem_type": "binary_classification",
+  "objective_metric_name": "roc_auc",
+  "stratify_on": "DEATH_EVENT",
+  "time_on": null,
+  "group_on": null,
+  "group_labels": null,
+  "group_time_on": null,
+  "type_adapter_id": "predictive-ml-task-mold-v1"
+}

hiva_agnostic/019d7368-2dd6-7aa8-b7f3-42a416dc1315/container_metadata.json

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+{
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+  "checksum": "60279c5b9326e96f4816b6c37d3761e111e4d54ae897898037145cab7217a91c",
+  "version_comment": null
+}

hiva_agnostic/019d7368-2dd6-7aa8-b7f3-42a416dc1315/dataset_metadata.dataset-mold-v1.json

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+{
+  "unique_name": "hiva_agnostic",
+  "dataset_year": "2007",
+  "domain_str": "chemistry & material science",
+  "dataset_source": "Other",
+  "original_dataset_source_download_link": "https://www.agnostic.inf.ethz.ch",
+  "download_description": "\nWe download the dataset from the challenge platform into predefined folder:\n    \nmkdir -p local-data-warehouse/hiva_agnostic/ && wget -P local-data-warehouse/hiva_agnostic/ https://www.agnostic.inf.ethz.ch/datasets/DataAgnos/HIVA.zip && wget -P local-data-warehouse/hiva_agnostic/ https://www.agnostic.inf.ethz.ch/datasets/ValidAgnos.zip && unzip local-data-warehouse/hiva_agnostic/HIVA.zip -d local-data-warehouse/hiva_agnostic/ && unzip local-data-warehouse/hiva_agnostic/ValidAgnos.zip -d local-data-warehouse/hiva_agnostic/ && rm local-data-warehouse/hiva_agnostic/HIVA.zip && rm local-data-warehouse/hiva_agnostic/ValidAgnos.zip\n",
+  "academic_reference_bibtex": "@inproceedings{guyon2007agnostic,\n  author={Isabelle Guyon and Amir Saffari and Gideon Dror and Gavin Cawley},\n  booktitle={2007 International Joint Conference on Neural Networks}, \n  title={Agnostic Learning vs. Prior Knowledge Challenge}, \n  year={2007},\n  pages={829-834},\n  doi={10.1109/IJCNN.2007.4371065}\n}\n",
+  "academic_reference_bibtex_key": "guyon2007agnostic",
+  "license": "Public Domain",
+  "data_tags": [
+    "IID"
+  ],
+  "curation_comments": "\n- We take the original data with the original labels with three classes.\n- We only take the training data from the challenge, as the original labels are not available for the validation data.\n",
+  "version_from_unique_name": null,
+  "version_comment": null,
+  "local_data_directory_base": "/home/lennart_priorlabs_ai/code/large_data_ensemble/data-foundry/local-data-warehouse",
+  "type_adapter_id": "dataset-mold-v1"
+}

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+}

hiva_agnostic/019d7368-2dd6-7aa8-b7f3-42a416dc1315/task_metadata.predictive-ml-task-mold-v1.json

@@ -0,0 +1,11 @@
+{
+  "target_column_name": "CompoundActive",
+  "problem_type": "binary_classification",
+  "objective_metric_name": "roc_auc",
+  "stratify_on": "CompoundActive",
+  "time_on": null,
+  "group_on": null,
+  "group_labels": null,
+  "group_time_on": null,
+  "type_adapter_id": "predictive-ml-task-mold-v1"
+}

home_credit_default_risk/019d736d-6a16-7087-999b-e953d48f7516/container_metadata.json

@@ -0,0 +1,5 @@
+{
+  "uuid": "019d736d-6a16-7087-999b-e953d48f7516",
+  "checksum": "617c029f4d1c721d1f71666f8cf191ea1cbf13e22f9cf65ae036d5a7d12906f8",
+  "version_comment": null
+}

home_credit_default_risk/019d736d-6a16-7087-999b-e953d48f7516/dataset_metadata.dataset-mold-v1.json

@@ -0,0 +1,19 @@
+{
+  "unique_name": "home_credit_default_risk",
+  "dataset_year": "2018",
+  "domain_str": "finance",
+  "dataset_source": "Kaggle",
+  "original_dataset_source_download_link": "https://www.kaggle.com/c/home-credit-default-risk",
+  "download_description": "\nWe download the files from the Kaggle data. This dataset starts as a SQL database with multiple tables, so it has multiple files.\n\nkaggle competitions download -c home-credit-default-risk && unzip home-credit-default-risk.zip -d data_files && rm home-credit-default-risk.zip\nmkdir -p local-data-warehouse/home_credit_default_risk && mv data_files local-data-warehouse/home_credit_default_risk/\n",
+  "academic_reference_bibtex": "@misc{Montoya2018HomeCreditDefaultRisk,\n  author = {Anna Montoya and inversion and Kirill Odintsov and Martin Kotek},\n  title  = {Home Credit Default Risk},\n  year   = {2018},\n  howpublished = {\\url{https://kaggle.com/competitions/home-credit-default-risk}},\n  note   = {Kaggle competition}\n}\n",
+  "academic_reference_bibtex_key": "Montoya2018HomeCreditDefaultRisk",
+  "license": "Kaggle Competition Rules",
+  "data_tags": [
+    "IID"
+  ],
+  "curation_comments": "\nWe start with the SQL tables from Kaggle. We then merge these tables into a single file following the top Kaggle solutions.\nWe start from this file.\n\n- The top solutions start with various features from public notebooks/kernels. When selecting the kernel to use as our starting point, we looked among the most popular and best performance on the leaderboard. We found https://www.kaggle.com/code/jsaguiar/lightgbm-with-simple-features?scriptVersionId=6025993 and its follow-up https://www.kaggle.com/code/ogrellier/lighgbm-with-selected-features. We use the later one, because it performance better and comes with a reduced set of features from the initial raw merges. Note, this may introduce a slight bias, as the feature selection was done for LGBM.\n- We do not follow the script exactly. We do not ordinal encode binary features. Moreover, we do not apply one-hot encoding to all categorical columns. Instead, we do it only when need for aggregations and otherwise keep the categorical column with the original semantics. Moreover, we fix some bugs in the original script that cause it to crash when run out of the box.\n- We drop a duplicated column after preprocessing.\n- We replace values that became inf after aggregation with NaN. This occurred across several aggregated columns.\n",
+  "version_from_unique_name": null,
+  "version_comment": null,
+  "local_data_directory_base": "/home/lennart_priorlabs_ai/code/large_data_ensemble/data-foundry/local-data-warehouse",
+  "type_adapter_id": "dataset-mold-v1"
+}

home_credit_default_risk/019d736d-6a16-7087-999b-e953d48f7516/dtypes.json

@@ -0,0 +1,507 @@
+{
+  "TARGET": "category",
+  "NAME_CONTRACT_TYPE": "category",
+  "CODE_GENDER": "category",
+  "FLAG_OWN_REALTY": "category",
+  "AMT_INCOME_TOTAL": "float64",
+  "AMT_CREDIT": "float64",
+  "AMT_ANNUITY": "float64",
+  "AMT_GOODS_PRICE": "float64",
+  "NAME_TYPE_SUITE": "category",
+  "NAME_INCOME_TYPE": "category",
+  "NAME_EDUCATION_TYPE": "category",
+  "NAME_FAMILY_STATUS": "category",
+  "NAME_HOUSING_TYPE": "category",
+  "REGION_POPULATION_RELATIVE": "float64",
+  "DAYS_BIRTH": "int64",
+  "DAYS_EMPLOYED": "float64",
+  "DAYS_REGISTRATION": "float64",
+  "DAYS_ID_PUBLISH": "int64",
+  "OWN_CAR_AGE": "float64",
+  "FLAG_WORK_PHONE": "category",
+  "FLAG_PHONE": "category",
+  "OCCUPATION_TYPE": "category",
+  "CNT_FAM_MEMBERS": "float64",
+  "REGION_RATING_CLIENT": "int64",
+  "REGION_RATING_CLIENT_W_CITY": "int64",
+  "WEEKDAY_APPR_PROCESS_START": "category",
+  "HOUR_APPR_PROCESS_START": "int64",
+  "REG_CITY_NOT_LIVE_CITY": "category",
+  "LIVE_CITY_NOT_WORK_CITY": "category",
+  "ORGANIZATION_TYPE": "category",
+  "EXT_SOURCE_1": "float64",
+  "EXT_SOURCE_2": "float64",
+  "EXT_SOURCE_3": "float64",
+  "APARTMENTS_AVG": "float64",
+  "BASEMENTAREA_AVG": "float64",
+  "YEARS_BEGINEXPLUATATION_AVG": "float64",
+  "YEARS_BUILD_AVG": "float64",
+  "COMMONAREA_AVG": "float64",
+  "ELEVATORS_AVG": "float64",
+  "ENTRANCES_AVG": "float64",
+  "FLOORSMAX_AVG": "float64",
+  "FLOORSMIN_AVG": "float64",
+  "LANDAREA_AVG": "float64",
+  "LIVINGAPARTMENTS_AVG": "float64",
+  "LIVINGAREA_AVG": "float64",
+  "NONLIVINGAPARTMENTS_AVG": "float64",
+  "NONLIVINGAREA_AVG": "float64",
+  "APARTMENTS_MODE": "float64",
+  "BASEMENTAREA_MODE": "float64",
+  "YEARS_BEGINEXPLUATATION_MODE": "float64",
+  "YEARS_BUILD_MODE": "float64",
+  "COMMONAREA_MODE": "float64",
+  "FLOORSMIN_MODE": "float64",
+  "LANDAREA_MODE": "float64",
+  "LIVINGAPARTMENTS_MODE": "float64",
+  "LIVINGAREA_MODE": "float64",
+  "NONLIVINGAPARTMENTS_MODE": "float64",
+  "NONLIVINGAREA_MODE": "float64",
+  "APARTMENTS_MEDI": "float64",
+  "BASEMENTAREA_MEDI": "float64",
+  "YEARS_BEGINEXPLUATATION_MEDI": "float64",
+  "YEARS_BUILD_MEDI": "float64",
+  "COMMONAREA_MEDI": "float64",
+  "ENTRANCES_MEDI": "float64",
+  "FLOORSMAX_MEDI": "float64",
+  "FLOORSMIN_MEDI": "float64",
+  "LANDAREA_MEDI": "float64",
+  "LIVINGAPARTMENTS_MEDI": "float64",
+  "LIVINGAREA_MEDI": "float64",
+  "NONLIVINGAPARTMENTS_MEDI": "float64",
+  "NONLIVINGAREA_MEDI": "float64",
+  "FONDKAPREMONT_MODE": "category",
+  "HOUSETYPE_MODE": "category",
+  "TOTALAREA_MODE": "float64",
+  "WALLSMATERIAL_MODE": "category",
+  "EMERGENCYSTATE_MODE": "category",
+  "OBS_30_CNT_SOCIAL_CIRCLE": "float64",
+  "DEF_30_CNT_SOCIAL_CIRCLE": "float64",
+  "OBS_60_CNT_SOCIAL_CIRCLE": "float64",
+  "DEF_60_CNT_SOCIAL_CIRCLE": "float64",
+  "DAYS_LAST_PHONE_CHANGE": "float64",
+  "FLAG_DOCUMENT_3": "category",
+  "FLAG_DOCUMENT_8": "category",
+  "FLAG_DOCUMENT_18": "category",
+  "AMT_REQ_CREDIT_BUREAU_MON": "float64",
+  "AMT_REQ_CREDIT_BUREAU_QRT": "float64",
+  "AMT_REQ_CREDIT_BUREAU_YEAR": "float64",
+  "NEW_DOC_IND_AVG": "float64",
+  "NEW_DOC_IND_STD": "float64",
+  "NEW_DOC_IND_KURT": "float64",
+  "NEW_LIVE_IND_SUM": "int64",
+  "NEW_LIVE_IND_STD": "float64",
+  "NEW_LIVE_IND_KURT": "float64",
+  "NEW_INC_BY_ORG": "float64",
+  "NEW_CREDIT_TO_ANNUITY_RATIO": "float64",
+  "NEW_CREDIT_TO_GOODS_RATIO": "float64",
+  "NEW_INC_PER_CHLD": "float64",
+  "NEW_EMPLOY_TO_BIRTH_RATIO": "float64",
+  "NEW_ANNUITY_TO_INCOME_RATIO": "float64",
+  "NEW_SOURCES_PROD": "float64",
+  "NEW_EXT_SOURCES_MEAN": "float64",
+  "NEW_SCORES_STD": "float64",
+  "NEW_CAR_TO_BIRTH_RATIO": "float64",
+  "NEW_CAR_TO_EMPLOY_RATIO": "float64",
+  "NEW_PHONE_TO_BIRTH_RATIO": "float64",
+  "NEW_PHONE_TO_EMPLOY_RATIO": "float64",
+  "NEW_CREDIT_TO_INCOME_RATIO": "float64",
+  "BURO_DAYS_CREDIT_MIN": "float64",
+  "BURO_DAYS_CREDIT_MAX": "float64",
+  "BURO_DAYS_CREDIT_MEAN": "float64",
+  "BURO_DAYS_CREDIT_VAR": "float64",
+  "BURO_DAYS_CREDIT_ENDDATE_MIN": "float64",
+  "BURO_DAYS_CREDIT_ENDDATE_MAX": "float64",
+  "BURO_DAYS_CREDIT_ENDDATE_MEAN": "float64",
+  "BURO_DAYS_CREDIT_UPDATE_MEAN": "float64",
+  "BURO_AMT_CREDIT_MAX_OVERDUE_MEAN": "float64",
+  "BURO_AMT_CREDIT_SUM_MAX": "float64",
+  "BURO_AMT_CREDIT_SUM_MEAN": "float64",
+  "BURO_AMT_CREDIT_SUM_SUM": "float64",
+  "BURO_AMT_CREDIT_SUM_DEBT_MAX": "float64",
+  "BURO_AMT_CREDIT_SUM_DEBT_MEAN": "float64",
+  "BURO_AMT_CREDIT_SUM_DEBT_SUM": "float64",
+  "BURO_AMT_CREDIT_SUM_OVERDUE_MEAN": "float64",
+  "BURO_AMT_CREDIT_SUM_LIMIT_MEAN": "float64",
+  "BURO_AMT_CREDIT_SUM_LIMIT_SUM": "float64",
+  "BURO_AMT_ANNUITY_MAX": "float64",
+  "BURO_AMT_ANNUITY_MEAN": "float64",
+  "BURO_MONTHS_BALANCE_MIN_MIN": "float64",
+  "BURO_MONTHS_BALANCE_SIZE_MEAN": "float64",
+  "BURO_MONTHS_BALANCE_SIZE_SUM": "float64",
+  "BURO_STATUS_0_MEAN_MEAN": "float64",
+  "BURO_STATUS_1_MEAN_MEAN": "float64",
+  "BURO_STATUS_C_MEAN_MEAN": "float64",
+  "BURO_STATUS_X_MEAN_MEAN": "float64",
+  "BURO_CREDIT_ACTIVE_Active_MEAN": "float64",
+  "BURO_CREDIT_ACTIVE_Closed_MEAN": "float64",
+  "BURO_CREDIT_ACTIVE_Sold_MEAN": "float64",
+  "BURO_CREDIT_TYPE_Another type of loan_MEAN": "float64",
+  "BURO_CREDIT_TYPE_Car loan_MEAN": "float64",
+  "BURO_CREDIT_TYPE_Consumer credit_MEAN": "float64",
+  "BURO_CREDIT_TYPE_Credit card_MEAN": "float64",
+  "BURO_CREDIT_TYPE_Microloan_MEAN": "float64",
+  "BURO_CREDIT_TYPE_Mortgage_MEAN": "float64",
+  "ACTIVE_DAYS_CREDIT_MIN": "float64",
+  "ACTIVE_DAYS_CREDIT_MAX": "float64",
+  "ACTIVE_DAYS_CREDIT_MEAN": "float64",
+  "ACTIVE_DAYS_CREDIT_VAR": "float64",
+  "ACTIVE_DAYS_CREDIT_ENDDATE_MIN": "float64",
+  "ACTIVE_DAYS_CREDIT_ENDDATE_MAX": "float64",
+  "ACTIVE_DAYS_CREDIT_ENDDATE_MEAN": "float64",
+  "ACTIVE_DAYS_CREDIT_UPDATE_MEAN": "float64",
+  "ACTIVE_CREDIT_DAY_OVERDUE_MAX": "float64",
+  "ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN": "float64",
+  "ACTIVE_AMT_CREDIT_SUM_MAX": "float64",
+  "ACTIVE_AMT_CREDIT_SUM_MEAN": "float64",
+  "ACTIVE_AMT_CREDIT_SUM_SUM": "float64",
+  "ACTIVE_AMT_CREDIT_SUM_DEBT_MAX": "float64",
+  "ACTIVE_AMT_CREDIT_SUM_DEBT_MEAN": "float64",
+  "ACTIVE_AMT_CREDIT_SUM_DEBT_SUM": "float64",
+  "ACTIVE_AMT_CREDIT_SUM_OVERDUE_MEAN": "float64",
+  "ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN": "float64",
+  "ACTIVE_AMT_CREDIT_SUM_LIMIT_SUM": "float64",
+  "ACTIVE_AMT_ANNUITY_MAX": "float64",
+  "ACTIVE_AMT_ANNUITY_MEAN": "float64",
+  "ACTIVE_MONTHS_BALANCE_MIN_MIN": "float64",
+  "ACTIVE_MONTHS_BALANCE_MAX_MAX": "float64",
+  "ACTIVE_MONTHS_BALANCE_SIZE_MEAN": "float64",
+  "ACTIVE_MONTHS_BALANCE_SIZE_SUM": "float64",
+  "CLOSED_DAYS_CREDIT_MIN": "float64",
+  "CLOSED_DAYS_CREDIT_MAX": "float64",
+  "CLOSED_DAYS_CREDIT_MEAN": "float64",
+  "CLOSED_DAYS_CREDIT_VAR": "float64",
+  "CLOSED_DAYS_CREDIT_ENDDATE_MIN": "float64",
+  "CLOSED_DAYS_CREDIT_ENDDATE_MAX": "float64",
+  "CLOSED_DAYS_CREDIT_ENDDATE_MEAN": "float64",
+  "CLOSED_DAYS_CREDIT_UPDATE_MEAN": "float64",
+  "CLOSED_AMT_CREDIT_MAX_OVERDUE_MEAN": "float64",
+  "CLOSED_AMT_CREDIT_SUM_MAX": "float64",
+  "CLOSED_AMT_CREDIT_SUM_MEAN": "float64",
+  "CLOSED_AMT_CREDIT_SUM_SUM": "float64",
+  "CLOSED_AMT_CREDIT_SUM_DEBT_MAX": "float64",
+  "CLOSED_AMT_CREDIT_SUM_DEBT_MEAN": "float64",
+  "CLOSED_AMT_CREDIT_SUM_DEBT_SUM": "float64",
+  "CLOSED_AMT_ANNUITY_MAX": "float64",
+  "CLOSED_AMT_ANNUITY_MEAN": "float64",
+  "CLOSED_MONTHS_BALANCE_MIN_MIN": "float64",
+  "CLOSED_MONTHS_BALANCE_SIZE_MEAN": "float64",
+  "CLOSED_MONTHS_BALANCE_SIZE_SUM": "float64",
+  "NEW_RATIO_BURO_DAYS_CREDIT_MIN": "float64",
+  "NEW_RATIO_BURO_DAYS_CREDIT_MAX": "float64",
+  "NEW_RATIO_BURO_DAYS_CREDIT_MEAN": "float64",
+  "NEW_RATIO_BURO_DAYS_CREDIT_VAR": "float64",
+  "NEW_RATIO_BURO_DAYS_CREDIT_ENDDATE_MIN": "float64",
+  "NEW_RATIO_BURO_DAYS_CREDIT_ENDDATE_MAX": "float64",
+  "NEW_RATIO_BURO_DAYS_CREDIT_ENDDATE_MEAN": "float64",
+  "NEW_RATIO_BURO_DAYS_CREDIT_UPDATE_MEAN": "float64",
+  "NEW_RATIO_BURO_AMT_CREDIT_MAX_OVERDUE_MEAN": "float64",
+  "NEW_RATIO_BURO_AMT_CREDIT_SUM_MAX": "float64",
+  "NEW_RATIO_BURO_AMT_CREDIT_SUM_MEAN": "float64",
+  "NEW_RATIO_BURO_AMT_CREDIT_SUM_SUM": "float64",
+  "NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_MAX": "float64",
+  "NEW_RATIO_BURO_AMT_CREDIT_SUM_DEBT_SUM": "float64",
+  "NEW_RATIO_BURO_AMT_CREDIT_SUM_LIMIT_MEAN": "float64",
+  "NEW_RATIO_BURO_AMT_ANNUITY_MAX": "float64",
+  "NEW_RATIO_BURO_AMT_ANNUITY_MEAN": "float64",
+  "NEW_RATIO_BURO_MONTHS_BALANCE_MIN_MIN": "float64",
+  "NEW_RATIO_BURO_MONTHS_BALANCE_SIZE_MEAN": "float64",
+  "NEW_RATIO_BURO_MONTHS_BALANCE_SIZE_SUM": "float64",
+  "PREV_AMT_ANNUITY_MIN": "float64",
+  "PREV_AMT_ANNUITY_MAX": "float64",
+  "PREV_AMT_ANNUITY_MEAN": "float64",
+  "PREV_AMT_APPLICATION_MIN": "float64",
+  "PREV_AMT_APPLICATION_MAX": "float64",
+  "PREV_AMT_APPLICATION_MEAN": "float64",
+  "PREV_AMT_CREDIT_MIN": "float64",
+  "PREV_AMT_CREDIT_MAX": "float64",
+  "PREV_AMT_CREDIT_MEAN": "float64",
+  "PREV_APP_CREDIT_PERC_MIN": "float64",
+  "PREV_APP_CREDIT_PERC_MAX": "float64",
+  "PREV_APP_CREDIT_PERC_MEAN": "float64",
+  "PREV_APP_CREDIT_PERC_VAR": "float64",
+  "PREV_AMT_DOWN_PAYMENT_MIN": "float64",
+  "PREV_AMT_DOWN_PAYMENT_MAX": "float64",
+  "PREV_AMT_DOWN_PAYMENT_MEAN": "float64",
+  "PREV_AMT_GOODS_PRICE_MIN": "float64",
+  "PREV_AMT_GOODS_PRICE_MAX": "float64",
+  "PREV_AMT_GOODS_PRICE_MEAN": "float64",
+  "PREV_HOUR_APPR_PROCESS_START_MIN": "float64",
+  "PREV_HOUR_APPR_PROCESS_START_MAX": "float64",
+  "PREV_HOUR_APPR_PROCESS_START_MEAN": "float64",
+  "PREV_RATE_DOWN_PAYMENT_MIN": "float64",
+  "PREV_RATE_DOWN_PAYMENT_MAX": "float64",
+  "PREV_RATE_DOWN_PAYMENT_MEAN": "float64",
+  "PREV_DAYS_DECISION_MIN": "float64",
+  "PREV_DAYS_DECISION_MAX": "float64",
+  "PREV_DAYS_DECISION_MEAN": "float64",
+  "PREV_CNT_PAYMENT_MEAN": "float64",
+  "PREV_CNT_PAYMENT_SUM": "float64",
+  "PREV_NAME_CONTRACT_TYPE_Cash loans_MEAN": "float64",
+  "PREV_NAME_CONTRACT_TYPE_Consumer loans_MEAN": "float64",
+  "PREV_NAME_CONTRACT_TYPE_Revolving loans_MEAN": "float64",
+  "PREV_WEEKDAY_APPR_PROCESS_START_FRIDAY_MEAN": "float64",
+  "PREV_WEEKDAY_APPR_PROCESS_START_MONDAY_MEAN": "float64",
+  "PREV_WEEKDAY_APPR_PROCESS_START_SATURDAY_MEAN": "float64",
+  "PREV_WEEKDAY_APPR_PROCESS_START_SUNDAY_MEAN": "float64",
+  "PREV_WEEKDAY_APPR_PROCESS_START_THURSDAY_MEAN": "float64",
+  "PREV_WEEKDAY_APPR_PROCESS_START_TUESDAY_MEAN": "float64",
+  "PREV_WEEKDAY_APPR_PROCESS_START_WEDNESDAY_MEAN": "float64",
+  "PREV_NAME_CASH_LOAN_PURPOSE_Car repairs_MEAN": "float64",
+  "PREV_NAME_CASH_LOAN_PURPOSE_Medicine_MEAN": "float64",
+  "PREV_NAME_CASH_LOAN_PURPOSE_Repairs_MEAN": "float64",
+  "PREV_NAME_CASH_LOAN_PURPOSE_Urgent needs_MEAN": "float64",
+  "PREV_NAME_CASH_LOAN_PURPOSE_XNA_MEAN": "float64",
+  "PREV_NAME_CONTRACT_STATUS_Approved_MEAN": "float64",
+  "PREV_NAME_CONTRACT_STATUS_Canceled_MEAN": "float64",
+  "PREV_NAME_CONTRACT_STATUS_Refused_MEAN": "float64",
+  "PREV_NAME_PAYMENT_TYPE_Cash through the bank_MEAN": "float64",
+  "PREV_NAME_PAYMENT_TYPE_XNA_MEAN": "float64",
+  "PREV_CODE_REJECT_REASON_HC_MEAN": "float64",
+  "PREV_CODE_REJECT_REASON_LIMIT_MEAN": "float64",
+  "PREV_CODE_REJECT_REASON_SCO_MEAN": "float64",
+  "PREV_CODE_REJECT_REASON_SCOFR_MEAN": "float64",
+  "PREV_CODE_REJECT_REASON_XAP_MEAN": "float64",
+  "PREV_NAME_TYPE_SUITE_Children_MEAN": "float64",
+  "PREV_NAME_TYPE_SUITE_Family_MEAN": "float64",
+  "PREV_NAME_TYPE_SUITE_Other_A_MEAN": "float64",
+  "PREV_NAME_TYPE_SUITE_Other_B_MEAN": "float64",
+  "PREV_NAME_TYPE_SUITE_Spouse, partner_MEAN": "float64",
+  "PREV_NAME_TYPE_SUITE_Unaccompanied_MEAN": "float64",
+  "PREV_NAME_TYPE_SUITE_nan_MEAN": "float64",
+  "PREV_NAME_CLIENT_TYPE_New_MEAN": "float64",
+  "PREV_NAME_CLIENT_TYPE_Refreshed_MEAN": "float64",
+  "PREV_NAME_CLIENT_TYPE_Repeater_MEAN": "float64",
+  "PREV_NAME_GOODS_CATEGORY_Audio/Video_MEAN": "float64",
+  "PREV_NAME_GOODS_CATEGORY_Computers_MEAN": "float64",
+  "PREV_NAME_GOODS_CATEGORY_Consumer Electronics_MEAN": "float64",
+  "PREV_NAME_GOODS_CATEGORY_Furniture_MEAN": "float64",
+  "PREV_NAME_GOODS_CATEGORY_Mobile_MEAN": "float64",
+  "PREV_NAME_GOODS_CATEGORY_Photo / Cinema Equipment_MEAN": "float64",
+  "PREV_NAME_GOODS_CATEGORY_Sport and Leisure_MEAN": "float64",
+  "PREV_NAME_PORTFOLIO_Cards_MEAN": "float64",
+  "PREV_NAME_PORTFOLIO_Cash_MEAN": "float64",
+  "PREV_NAME_PORTFOLIO_POS_MEAN": "float64",
+  "PREV_NAME_PORTFOLIO_XNA_MEAN": "float64",
+  "PREV_NAME_PRODUCT_TYPE_XNA_MEAN": "float64",
+  "PREV_NAME_PRODUCT_TYPE_walk-in_MEAN": "float64",
+  "PREV_NAME_PRODUCT_TYPE_x-sell_MEAN": "float64",
+  "PREV_CHANNEL_TYPE_AP+ (Cash loan)_MEAN": "float64",
+  "PREV_CHANNEL_TYPE_Channel of corporate sales_MEAN": "float64",
+  "PREV_CHANNEL_TYPE_Contact center_MEAN": "float64",
+  "PREV_CHANNEL_TYPE_Country-wide_MEAN": "float64",
+  "PREV_CHANNEL_TYPE_Credit and cash offices_MEAN": "float64",
+  "PREV_CHANNEL_TYPE_Regional / Local_MEAN": "float64",
+  "PREV_CHANNEL_TYPE_Stone_MEAN": "float64",
+  "PREV_NAME_SELLER_INDUSTRY_Auto technology_MEAN": "float64",
+  "PREV_NAME_SELLER_INDUSTRY_Clothing_MEAN": "float64",
+  "PREV_NAME_SELLER_INDUSTRY_Connectivity_MEAN": "float64",
+  "PREV_NAME_SELLER_INDUSTRY_Consumer electronics_MEAN": "float64",
+  "PREV_NAME_SELLER_INDUSTRY_XNA_MEAN": "float64",
+  "PREV_NAME_YIELD_GROUP_XNA_MEAN": "float64",
+  "PREV_NAME_YIELD_GROUP_high_MEAN": "float64",
+  "PREV_NAME_YIELD_GROUP_low_action_MEAN": "float64",
+  "PREV_NAME_YIELD_GROUP_low_normal_MEAN": "float64",
+  "PREV_NAME_YIELD_GROUP_middle_MEAN": "float64",
+  "PREV_PRODUCT_COMBINATION_Card Street_MEAN": "float64",
+  "PREV_PRODUCT_COMBINATION_Card X-Sell_MEAN": "float64",
+  "PREV_PRODUCT_COMBINATION_Cash_MEAN": "float64",
+  "PREV_PRODUCT_COMBINATION_Cash Street: high_MEAN": "float64",
+  "PREV_PRODUCT_COMBINATION_Cash Street: low_MEAN": "float64",
+  "PREV_PRODUCT_COMBINATION_Cash Street: middle_MEAN": "float64",
+  "PREV_PRODUCT_COMBINATION_Cash X-Sell: high_MEAN": "float64",
+  "PREV_PRODUCT_COMBINATION_Cash X-Sell: low_MEAN": "float64",
+  "PREV_PRODUCT_COMBINATION_Cash X-Sell: middle_MEAN": "float64",
+  "PREV_PRODUCT_COMBINATION_POS household with interest_MEAN": "float64",
+  "PREV_PRODUCT_COMBINATION_POS household without interest_MEAN": "float64",
+  "PREV_PRODUCT_COMBINATION_POS industry with interest_MEAN": "float64",
+  "PREV_PRODUCT_COMBINATION_POS mobile with interest_MEAN": "float64",
+  "PREV_PRODUCT_COMBINATION_POS other with interest_MEAN": "float64",
+  "APPROVED_AMT_ANNUITY_MIN": "float64",
+  "APPROVED_AMT_ANNUITY_MAX": "float64",
+  "APPROVED_AMT_ANNUITY_MEAN": "float64",
+  "APPROVED_AMT_APPLICATION_MIN": "float64",
+  "APPROVED_AMT_APPLICATION_MAX": "float64",
+  "APPROVED_AMT_APPLICATION_MEAN": "float64",
+  "APPROVED_AMT_CREDIT_MIN": "float64",
+  "APPROVED_AMT_CREDIT_MAX": "float64",
+  "APPROVED_AMT_CREDIT_MEAN": "float64",
+  "APPROVED_APP_CREDIT_PERC_MIN": "float64",
+  "APPROVED_APP_CREDIT_PERC_MAX": "float64",
+  "APPROVED_APP_CREDIT_PERC_MEAN": "float64",
+  "APPROVED_APP_CREDIT_PERC_VAR": "float64",
+  "APPROVED_AMT_DOWN_PAYMENT_MIN": "float64",
+  "APPROVED_AMT_DOWN_PAYMENT_MAX": "float64",
+  "APPROVED_AMT_DOWN_PAYMENT_MEAN": "float64",
+  "APPROVED_AMT_GOODS_PRICE_MIN": "float64",
+  "APPROVED_AMT_GOODS_PRICE_MAX": "float64",
+  "APPROVED_AMT_GOODS_PRICE_MEAN": "float64",
+  "APPROVED_HOUR_APPR_PROCESS_START_MIN": "float64",
+  "APPROVED_HOUR_APPR_PROCESS_START_MAX": "float64",
+  "APPROVED_HOUR_APPR_PROCESS_START_MEAN": "float64",
+  "APPROVED_RATE_DOWN_PAYMENT_MIN": "float64",
+  "APPROVED_RATE_DOWN_PAYMENT_MAX": "float64",
+  "APPROVED_RATE_DOWN_PAYMENT_MEAN": "float64",
+  "APPROVED_DAYS_DECISION_MIN": "float64",
+  "APPROVED_DAYS_DECISION_MAX": "float64",
+  "APPROVED_DAYS_DECISION_MEAN": "float64",
+  "APPROVED_CNT_PAYMENT_MEAN": "float64",
+  "APPROVED_CNT_PAYMENT_SUM": "float64",
+  "REFUSED_AMT_ANNUITY_MIN": "float64",
+  "REFUSED_AMT_ANNUITY_MAX": "float64",
+  "REFUSED_AMT_ANNUITY_MEAN": "float64",
+  "REFUSED_AMT_APPLICATION_MIN": "float64",
+  "REFUSED_AMT_APPLICATION_MAX": "float64",
+  "REFUSED_AMT_APPLICATION_MEAN": "float64",
+  "REFUSED_AMT_CREDIT_MIN": "float64",
+  "REFUSED_AMT_CREDIT_MAX": "float64",
+  "REFUSED_AMT_CREDIT_MEAN": "float64",
+  "REFUSED_APP_CREDIT_PERC_MIN": "float64",
+  "REFUSED_APP_CREDIT_PERC_MAX": "float64",
+  "REFUSED_APP_CREDIT_PERC_MEAN": "float64",
+  "REFUSED_APP_CREDIT_PERC_VAR": "float64",
+  "REFUSED_AMT_DOWN_PAYMENT_MIN": "float64",
+  "REFUSED_AMT_GOODS_PRICE_MIN": "float64",
+  "REFUSED_AMT_GOODS_PRICE_MAX": "float64",
+  "REFUSED_AMT_GOODS_PRICE_MEAN": "float64",
+  "REFUSED_HOUR_APPR_PROCESS_START_MIN": "float64",
+  "REFUSED_HOUR_APPR_PROCESS_START_MAX": "float64",
+  "REFUSED_HOUR_APPR_PROCESS_START_MEAN": "float64",
+  "REFUSED_RATE_DOWN_PAYMENT_MAX": "float64",
+  "REFUSED_RATE_DOWN_PAYMENT_MEAN": "float64",
+  "REFUSED_DAYS_DECISION_MIN": "float64",
+  "REFUSED_DAYS_DECISION_MAX": "float64",
+  "REFUSED_DAYS_DECISION_MEAN": "float64",
+  "REFUSED_CNT_PAYMENT_MEAN": "float64",
+  "REFUSED_CNT_PAYMENT_SUM": "float64",
+  "NEW_RATIO_PREV_AMT_ANNUITY_MIN": "float64",
+  "NEW_RATIO_PREV_AMT_ANNUITY_MAX": "float64",
+  "NEW_RATIO_PREV_AMT_ANNUITY_MEAN": "float64",
+  "NEW_RATIO_PREV_AMT_APPLICATION_MIN": "float64",
+  "NEW_RATIO_PREV_AMT_APPLICATION_MAX": "float64",
+  "NEW_RATIO_PREV_AMT_APPLICATION_MEAN": "float64",
+  "NEW_RATIO_PREV_AMT_CREDIT_MIN": "float64",
+  "NEW_RATIO_PREV_AMT_CREDIT_MAX": "float64",
+  "NEW_RATIO_PREV_AMT_CREDIT_MEAN": "float64",
+  "NEW_RATIO_PREV_APP_CREDIT_PERC_MIN": "float64",
+  "NEW_RATIO_PREV_APP_CREDIT_PERC_MAX": "float64",
+  "NEW_RATIO_PREV_APP_CREDIT_PERC_MEAN": "float64",
+  "NEW_RATIO_PREV_APP_CREDIT_PERC_VAR": "float64",
+  "NEW_RATIO_PREV_AMT_DOWN_PAYMENT_MAX": "float64",
+  "NEW_RATIO_PREV_AMT_DOWN_PAYMENT_MEAN": "float64",
+  "NEW_RATIO_PREV_AMT_GOODS_PRICE_MIN": "float64",
+  "NEW_RATIO_PREV_AMT_GOODS_PRICE_MAX": "float64",
+  "NEW_RATIO_PREV_AMT_GOODS_PRICE_MEAN": "float64",
+  "NEW_RATIO_PREV_HOUR_APPR_PROCESS_START_MIN": "float64",
+  "NEW_RATIO_PREV_HOUR_APPR_PROCESS_START_MAX": "float64",
+  "NEW_RATIO_PREV_HOUR_APPR_PROCESS_START_MEAN": "float64",
+  "NEW_RATIO_PREV_RATE_DOWN_PAYMENT_MIN": "float64",
+  "NEW_RATIO_PREV_RATE_DOWN_PAYMENT_MEAN": "float64",
+  "NEW_RATIO_PREV_DAYS_DECISION_MIN": "float64",
+  "NEW_RATIO_PREV_DAYS_DECISION_MAX": "float64",
+  "NEW_RATIO_PREV_DAYS_DECISION_MEAN": "float64",
+  "NEW_RATIO_PREV_CNT_PAYMENT_MEAN": "float64",
+  "NEW_RATIO_PREV_CNT_PAYMENT_SUM": "float64",
+  "POS_MONTHS_BALANCE_MAX": "float64",
+  "POS_MONTHS_BALANCE_MEAN": "float64",
+  "POS_MONTHS_BALANCE_SIZE": "float64",
+  "POS_SK_DPD_MAX": "float64",
+  "POS_SK_DPD_MEAN": "float64",
+  "POS_SK_DPD_DEF_MAX": "float64",
+  "POS_SK_DPD_DEF_MEAN": "float64",
+  "POS_NAME_CONTRACT_STATUS_Active_MEAN": "float64",
+  "POS_NAME_CONTRACT_STATUS_Completed_MEAN": "float64",
+  "POS_NAME_CONTRACT_STATUS_Returned to the store_MEAN": "float64",
+  "POS_NAME_CONTRACT_STATUS_Signed_MEAN": "float64",
+  "INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE": "float64",
+  "INSTAL_DPD_MAX": "float64",
+  "INSTAL_DPD_MEAN": "float64",
+  "INSTAL_DPD_SUM": "float64",
+  "INSTAL_DBD_MAX": "float64",
+  "INSTAL_DBD_MEAN": "float64",
+  "INSTAL_DBD_SUM": "float64",
+  "INSTAL_PAYMENT_PERC_MAX": "float64",
+  "INSTAL_PAYMENT_PERC_MEAN": "float64",
+  "INSTAL_PAYMENT_PERC_SUM": "float64",
+  "INSTAL_PAYMENT_PERC_VAR": "float64",
+  "INSTAL_PAYMENT_DIFF_MAX": "float64",
+  "INSTAL_PAYMENT_DIFF_MEAN": "float64",
+  "INSTAL_PAYMENT_DIFF_SUM": "float64",
+  "INSTAL_PAYMENT_DIFF_VAR": "float64",
+  "INSTAL_AMT_INSTALMENT_MAX": "float64",
+  "INSTAL_AMT_INSTALMENT_MEAN": "float64",
+  "INSTAL_AMT_INSTALMENT_SUM": "float64",
+  "INSTAL_AMT_PAYMENT_MIN": "float64",
+  "INSTAL_AMT_PAYMENT_MAX": "float64",
+  "INSTAL_AMT_PAYMENT_MEAN": "float64",
+  "INSTAL_AMT_PAYMENT_SUM": "float64",
+  "INSTAL_DAYS_ENTRY_PAYMENT_MAX": "float64",
+  "INSTAL_DAYS_ENTRY_PAYMENT_MEAN": "float64",
+  "INSTAL_DAYS_ENTRY_PAYMENT_SUM": "float64",
+  "INSTAL_COUNT": "float64",
+  "CC_MONTHS_BALANCE_VAR": "float64",
+  "CC_AMT_BALANCE_MIN": "float64",
+  "CC_AMT_BALANCE_MAX": "float64",
+  "CC_AMT_BALANCE_MEAN": "float64",
+  "CC_AMT_BALANCE_SUM": "float64",
+  "CC_AMT_BALANCE_VAR": "float64",
+  "CC_AMT_CREDIT_LIMIT_ACTUAL_MIN": "float64",
+  "CC_AMT_CREDIT_LIMIT_ACTUAL_MAX": "float64",
+  "CC_AMT_CREDIT_LIMIT_ACTUAL_MEAN": "float64",
+  "CC_AMT_CREDIT_LIMIT_ACTUAL_SUM": "float64",
+  "CC_AMT_CREDIT_LIMIT_ACTUAL_VAR": "float64",
+  "CC_AMT_DRAWINGS_ATM_CURRENT_MAX": "float64",
+  "CC_AMT_DRAWINGS_ATM_CURRENT_MEAN": "float64",
+  "CC_AMT_DRAWINGS_ATM_CURRENT_SUM": "float64",
+  "CC_AMT_DRAWINGS_ATM_CURRENT_VAR": "float64",
+  "CC_AMT_DRAWINGS_CURRENT_MAX": "float64",
+  "CC_AMT_DRAWINGS_CURRENT_MEAN": "float64",
+  "CC_AMT_DRAWINGS_CURRENT_SUM": "float64",
+  "CC_AMT_DRAWINGS_CURRENT_VAR": "float64",
+  "CC_AMT_DRAWINGS_POS_CURRENT_MIN": "float64",
+  "CC_AMT_DRAWINGS_POS_CURRENT_MAX": "float64",
+  "CC_AMT_DRAWINGS_POS_CURRENT_MEAN": "float64",
+  "CC_AMT_DRAWINGS_POS_CURRENT_SUM": "float64",
+  "CC_AMT_DRAWINGS_POS_CURRENT_VAR": "float64",
+  "CC_AMT_INST_MIN_REGULARITY_MAX": "float64",
+  "CC_AMT_INST_MIN_REGULARITY_MEAN": "float64",
+  "CC_AMT_INST_MIN_REGULARITY_SUM": "float64",
+  "CC_AMT_INST_MIN_REGULARITY_VAR": "float64",
+  "CC_AMT_PAYMENT_CURRENT_MIN": "float64",
+  "CC_AMT_PAYMENT_CURRENT_MAX": "float64",
+  "CC_AMT_PAYMENT_CURRENT_MEAN": "float64",
+  "CC_AMT_PAYMENT_CURRENT_SUM": "float64",
+  "CC_AMT_PAYMENT_CURRENT_VAR": "float64",
+  "CC_AMT_PAYMENT_TOTAL_CURRENT_MAX": "float64",
+  "CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN": "float64",
+  "CC_AMT_PAYMENT_TOTAL_CURRENT_SUM": "float64",
+  "CC_AMT_RECEIVABLE_PRINCIPAL_MIN": "float64",
+  "CC_AMT_RECEIVABLE_PRINCIPAL_MAX": "float64",
+  "CC_AMT_RECEIVABLE_PRINCIPAL_MEAN": "float64",
+  "CC_AMT_RECEIVABLE_PRINCIPAL_SUM": "float64",
+  "CC_AMT_RECEIVABLE_PRINCIPAL_VAR": "float64",
+  "CC_AMT_RECIVABLE_MIN": "float64",
+  "CC_AMT_RECIVABLE_MAX": "float64",
+  "CC_AMT_RECIVABLE_MEAN": "float64",
+  "CC_AMT_RECIVABLE_VAR": "float64",
+  "CC_AMT_TOTAL_RECEIVABLE_MEAN": "float64",
+  "CC_CNT_DRAWINGS_ATM_CURRENT_MAX": "float64",
+  "CC_CNT_DRAWINGS_ATM_CURRENT_MEAN": "float64",
+  "CC_CNT_DRAWINGS_ATM_CURRENT_SUM": "float64",
+  "CC_CNT_DRAWINGS_ATM_CURRENT_VAR": "float64",
+  "CC_CNT_DRAWINGS_CURRENT_MAX": "float64",
+  "CC_CNT_DRAWINGS_CURRENT_MEAN": "float64",
+  "CC_CNT_DRAWINGS_CURRENT_SUM": "float64",
+  "CC_CNT_DRAWINGS_CURRENT_VAR": "float64",
+  "CC_CNT_DRAWINGS_POS_CURRENT_MIN": "float64",
+  "CC_CNT_DRAWINGS_POS_CURRENT_MAX": "float64",
+  "CC_CNT_DRAWINGS_POS_CURRENT_MEAN": "float64",
+  "CC_CNT_DRAWINGS_POS_CURRENT_VAR": "float64",
+  "CC_CNT_INSTALMENT_MATURE_CUM_MEAN": "float64",
+  "CC_CNT_INSTALMENT_MATURE_CUM_SUM": "float64",
+  "CC_CNT_INSTALMENT_MATURE_CUM_VAR": "float64",
+  "CC_SK_DPD_DEF_MEAN": "float64",
+  "CC_NAME_CONTRACT_STATUS_Active_MEAN": "float64",
+  "CC_NAME_CONTRACT_STATUS_Active_SUM": "float64",
+  "CC_NAME_CONTRACT_STATUS_Active_VAR": "float64"
+}

home_credit_default_risk/019d736d-6a16-7087-999b-e953d48f7516/task_metadata.predictive-ml-task-mold-v1.json

@@ -0,0 +1,11 @@
+{
+  "target_column_name": "TARGET",
+  "problem_type": "binary_classification",
+  "objective_metric_name": "roc_auc",
+  "stratify_on": "TARGET",
+  "time_on": null,
+  "group_on": null,
+  "group_labels": null,
+  "group_time_on": null,
+  "type_adapter_id": "predictive-ml-task-mold-v1"
+}

home_credit_default_stability/versions/019d7383-f45a-72f7-ac99-09447cf6d41f/container_metadata.json

@@ -0,0 +1,5 @@
+{
+  "uuid": "019d7383-f45a-72f7-ac99-09447cf6d41f",
+  "checksum": "0397277a74691f3f375e5072aa8eb781b0675dba7e074f00e70e17db24647e39",
+  "version_comment": null
+}