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Tasks package.

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"cyclops-evaluate-metrics-stat-scores-multilabelstatscores"]], "cyclops.evaluate.metrics.stat_scores.StatScores": [[110, "cyclops-evaluate-metrics-stat-scores-statscores"]], "cyclops.monitor.clinical_applicator": [[111, "module-cyclops.monitor.clinical_applicator"]], "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator": [[112, "cyclops-monitor-clinical-applicator-clinicalshiftapplicator"]], "cyclops.monitor.synthetic_applicator": [[113, "module-cyclops.monitor.synthetic_applicator"]], "cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator": [[114, "cyclops-monitor-synthetic-applicator-syntheticshiftapplicator"]], "cyclops.monitor.synthetic_applicator.binary_noise_shift": [[115, "cyclops-monitor-synthetic-applicator-binary-noise-shift"]], "cyclops.monitor.synthetic_applicator.feature_association_shift": [[116, "cyclops-monitor-synthetic-applicator-feature-association-shift"]], "cyclops.monitor.synthetic_applicator.feature_swap_shift": [[117, "cyclops-monitor-synthetic-applicator-feature-swap-shift"]], "cyclops.monitor.synthetic_applicator.gaussian_noise_shift": [[118, "cyclops-monitor-synthetic-applicator-gaussian-noise-shift"]], "cyclops.monitor.synthetic_applicator.knockout_shift": [[119, "cyclops-monitor-synthetic-applicator-knockout-shift"]], "cyclops.query.base": [[120, "module-cyclops.query.base"]], "cyclops.query.base.DatasetQuerier": [[121, "cyclops-query-base-datasetquerier"]], "cyclops.query.eicu": [[122, "module-cyclops.query.eicu"]], "cyclops.query.eicu.EICUQuerier": [[123, "cyclops-query-eicu-eicuquerier"]], "cyclops.query.gemini": [[124, "module-cyclops.query.gemini"]], "cyclops.query.gemini.GEMINIQuerier": [[125, "cyclops-query-gemini-geminiquerier"]], "cyclops.query.interface": [[126, "module-cyclops.query.interface"]], "cyclops.query.interface.QueryInterface": [[127, "cyclops-query-interface-queryinterface"]], "cyclops.query.mimiciii": [[128, "module-cyclops.query.mimiciii"]], "cyclops.query.mimiciii.MIMICIIIQuerier": [[129, "cyclops-query-mimiciii-mimiciiiquerier"]], "cyclops.query.mimiciv": [[130, "module-cyclops.query.mimiciv"]], "cyclops.query.mimiciv.MIMICIVQuerier": [[131, "cyclops-query-mimiciv-mimicivquerier"]], "cyclops.query.omop": [[132, "module-cyclops.query.omop"]], "cyclops.query.omop.OMOPQuerier": [[133, "cyclops-query-omop-omopquerier"]], "cyclops.query.ops": [[134, "module-cyclops.query.ops"]], "cyclops.query.ops.AddColumn": [[135, "cyclops-query-ops-addcolumn"]], "cyclops.query.ops.AddDeltaColumn": [[136, "cyclops-query-ops-adddeltacolumn"]], "cyclops.query.ops.AddDeltaConstant": [[137, "cyclops-query-ops-adddeltaconstant"]], "cyclops.query.ops.AddNumeric": [[138, "cyclops-query-ops-addnumeric"]], "cyclops.query.ops.And": [[139, "cyclops-query-ops-and"]], "cyclops.query.ops.Apply": [[140, "cyclops-query-ops-apply"]], "cyclops.query.ops.Cast": [[141, "cyclops-query-ops-cast"]], "cyclops.query.ops.ConditionAfterDate": [[142, "cyclops-query-ops-conditionafterdate"]], "cyclops.query.ops.ConditionBeforeDate": [[143, "cyclops-query-ops-conditionbeforedate"]], "cyclops.query.ops.ConditionEndsWith": [[144, "cyclops-query-ops-conditionendswith"]], "cyclops.query.ops.ConditionEquals": [[145, "cyclops-query-ops-conditionequals"]], "cyclops.query.ops.ConditionGreaterThan": [[146, "cyclops-query-ops-conditiongreaterthan"]], "cyclops.query.ops.ConditionIn": [[147, "cyclops-query-ops-conditionin"]], "cyclops.query.ops.ConditionInMonths": [[148, "cyclops-query-ops-conditioninmonths"]], "cyclops.query.ops.ConditionInYears": [[149, "cyclops-query-ops-conditioninyears"]], "cyclops.query.ops.ConditionLessThan": [[150, "cyclops-query-ops-conditionlessthan"]], "cyclops.query.ops.ConditionLike": [[151, "cyclops-query-ops-conditionlike"]], "cyclops.query.ops.ConditionRegexMatch": [[152, "cyclops-query-ops-conditionregexmatch"]], "cyclops.query.ops.ConditionStartsWith": [[153, "cyclops-query-ops-conditionstartswith"]], "cyclops.query.ops.ConditionSubstring": [[154, "cyclops-query-ops-conditionsubstring"]], "cyclops.query.ops.Distinct": [[155, "cyclops-query-ops-distinct"]], "cyclops.query.ops.Drop": [[156, "cyclops-query-ops-drop"]], "cyclops.query.ops.DropEmpty": [[157, "cyclops-query-ops-dropempty"]], "cyclops.query.ops.DropNulls": [[158, "cyclops-query-ops-dropnulls"]], "cyclops.query.ops.ExtractTimestampComponent": [[159, "cyclops-query-ops-extracttimestampcomponent"]], "cyclops.query.ops.FillNull": [[160, "cyclops-query-ops-fillnull"]], "cyclops.query.ops.GroupByAggregate": [[161, "cyclops-query-ops-groupbyaggregate"]], "cyclops.query.ops.Join": [[162, "cyclops-query-ops-join"]], "cyclops.query.ops.Keep": [[163, "cyclops-query-ops-keep"]], "cyclops.query.ops.Limit": [[164, "cyclops-query-ops-limit"]], "cyclops.query.ops.Literal": [[165, "cyclops-query-ops-literal"]], "cyclops.query.ops.Or": [[166, "cyclops-query-ops-or"]], "cyclops.query.ops.OrderBy": [[167, "cyclops-query-ops-orderby"]], "cyclops.query.ops.QueryOp": [[168, "cyclops-query-ops-queryop"]], "cyclops.query.ops.RandomizeOrder": [[169, "cyclops-query-ops-randomizeorder"]], "cyclops.query.ops.Rename": [[170, "cyclops-query-ops-rename"]], "cyclops.query.ops.Reorder": [[171, "cyclops-query-ops-reorder"]], "cyclops.query.ops.ReorderAfter": [[172, "cyclops-query-ops-reorderafter"]], "cyclops.query.ops.Sequential": [[173, "cyclops-query-ops-sequential"]], "cyclops.query.ops.Substring": [[174, "cyclops-query-ops-substring"]], "cyclops.query.ops.Trim": [[175, "cyclops-query-ops-trim"]], "cyclops.query.ops.Union": [[176, "cyclops-query-ops-union"]], "cyclops.report.report": [[177, "module-cyclops.report.report"]], "cyclops.report.report.ModelCardReport": [[178, "cyclops-report-report-modelcardreport"]], "cyclops.tasks.cxr_classification": [[179, "module-cyclops.tasks.cxr_classification"]], "cyclops.tasks.cxr_classification.CXRClassificationTask": [[180, "cyclops-tasks-cxr-classification-cxrclassificationtask"]], "cyclops.tasks.mortality_prediction": [[181, "module-cyclops.tasks.mortality_prediction"]], "cyclops.tasks.mortality_prediction.MortalityPredictionTask": [[182, "cyclops-tasks-mortality-prediction-mortalitypredictiontask"]], "cyclops.data": [[183, "module-cyclops.data"]], "cyclops.data.features": [[183, "module-cyclops.data.features"]], "cyclops.evaluate": [[184, "module-cyclops.evaluate"]], "cyclops.evaluate.metrics": [[184, "module-cyclops.evaluate.metrics"]], "cyclops.evaluate.metrics.functional": [[184, "module-cyclops.evaluate.metrics.functional"]], "cyclops.evaluate.fairness": [[184, "module-cyclops.evaluate.fairness"]], "cyclops.monitor": [[185, "module-cyclops.monitor"]], "cyclops.query": [[186, "module-cyclops.query"]], "dataset APIs": [[186, "dataset-apis"]], "cyclops.report": [[187, "module-cyclops.report"]], "cyclops.tasks": [[188, "module-cyclops.tasks"]], "Tutorials": [[189, "tutorials"]], "eICU-CRD query API tutorial": [[190, "eICU-CRD-query-API-tutorial"]], "Imports and instantiate EICUQuerier": [[190, "Imports-and-instantiate-EICUQuerier"]], "Example 1. Get all female patients discharged in 2014 (limit to 100 rows).": [[190, "Example-1.-Get-all-female-patients-discharged-in-2014-(limit-to-100-rows)."]], "Example 2. Get all patient encounters with diagnoses (schizophrenia in diagnosisstring), discharged in the year 2015.": [[190, "Example-2.-Get-all-patient-encounters-with-diagnoses-(schizophrenia-in-diagnosisstring),-discharged-in-the-year-2015."]], "Example 3. Get potassium lab tests for patients discharged in the year 2014, for all teaching hospitals.": [[190, "Example-3.-Get-potassium-lab-tests-for-patients-discharged-in-the-year-2014,-for-all-teaching-hospitals."]], "Example 4. Get glucose medications (substring search) for female patients discharged in 2014.": [[190, "Example-4.-Get-glucose-medications-(substring-search)-for-female-patients-discharged-in-2014."]], "GEMINI query API tutorial": [[191, "GEMINI-query-API-tutorial"]], "Imports and instantiate GEMINIQuerier.": [[191, "Imports-and-instantiate-GEMINIQuerier."]], "Example 1a. Create a table with only one hospitalization per patient, keeping the most recent encounter for each patient. Sort the dataset by patient_id_hashed and discharge_date_time, and then keep the recent record.": [[191, "Example-1a.-Create-a-table-with-only-one-hospitalization-per-patient,-keeping-the-most-recent-encounter-for-each-patient.-Sort-the-dataset-by-patient_id_hashed-and-discharge_date_time,-and-then-keep-the-recent-record."]], "Example 1b. From the above set of encounters, take a subset of patients who were discharged between April 1, 2015 and March 31, 2016.": [[191, "Example-1b.-From-the-above-set-of-encounters,-take-a-subset-of-patients-who-were-discharged-between-April-1,-2015-and-March-31,-2016."]], "Example 1c. From the above set of encounters, get the total number of admissions for each hospital.": [[191, "Example-1c.-From-the-above-set-of-encounters,-get-the-total-number-of-admissions-for-each-hospital."]], "Example 2a. How many sodium tests were placed between Apr 1, 2015 and May 31, 2015 at hospital 101?": [[191, "Example-2a.-How-many-sodium-tests-were-placed-between-Apr-1,-2015-and-May-31,-2015-at-hospital-101?"]], "Heart Failure Prediction": [[192, "Heart-Failure-Prediction"]], "Import Libraries": [[192, "Import-Libraries"], [195, "Import-Libraries"], [198, "Import-Libraries"]], "Constants": [[192, "Constants"], [198, "Constants"]], "Data Loading": [[192, "Data-Loading"]], "Sex values": [[192, "Sex-values"]], "Age distribution": [[192, "Age-distribution"], [198, "Age-distribution"]], "Outcome distribution": [[192, "Outcome-distribution"], [198, "Outcome-distribution"]], "Identifying feature types": [[192, "Identifying-feature-types"], [198, "Identifying-feature-types"]], "Creating data preprocessors": [[192, "Creating-data-preprocessors"], [198, "Creating-data-preprocessors"]], "Creating Hugging Face Dataset": [[192, "Creating-Hugging-Face-Dataset"], [198, "Creating-Hugging-Face-Dataset"]], "Model Creation": [[192, "Model-Creation"], [198, "Model-Creation"]], "Task Creation": [[192, "Task-Creation"], [198, "Task-Creation"]], "Training": [[192, "Training"], [198, "Training"]], "Prediction": [[192, "Prediction"], [198, "Prediction"]], "Evaluation": [[192, "Evaluation"], [198, "Evaluation"]], "Performance over time": [[192, "Performance-over-time"], [195, "Performance-over-time"], [198, "Performance-over-time"]], "Report Generation": [[192, "Report-Generation"], [198, "Report-Generation"]], "MIMIC-III query API tutorial": [[193, "MIMIC-III-query-API-tutorial"]], "Imports and instantiate MIMICIIIQuerier": [[193, "Imports-and-instantiate-MIMICIIIQuerier"]], "Example 1. Get all male patients with a mortality outcome.": [[193, "Example-1.-Get-all-male-patients-with-a-mortality-outcome."]], "Example 2. Get all female patient encounters with diagnoses (gastroenteritis in ICD-9 long title).": [[193, "Example-2.-Get-all-female-patient-encounters-with-diagnoses-(gastroenteritis-in-ICD-9-long-title)."]], "Example 3. Get potassium lab tests for female patients.": [[193, "Example-3.-Get-potassium-lab-tests-for-female-patients."]], "Example 4. Get AaDO2 carevue chart events for male patients that have a valuenum of less than 20.": [[193, "Example-4.-Get-AaDO2-carevue-chart-events-for-male-patients-that-have-a-valuenum-of-less-than-20."]], "MIMIC-IV query API tutorial": [[194, "MIMIC-IV-query-API-tutorial"]], "Imports and instantiate MIMICIVQuerier": [[194, "Imports-and-instantiate-MIMICIVQuerier"]], "Example 1. Get all patient admissions from 2021 or later (approx year of admission)": [[194, "Example-1.-Get-all-patient-admissions-from-2021-or-later-(approx-year-of-admission)"]], "Example 2. Get all patient encounters with diagnoses (schizophrenia in ICD-10 long title), in the year 2015.": [[194, "Example-2.-Get-all-patient-encounters-with-diagnoses-(schizophrenia-in-ICD-10-long-title),-in-the-year-2015."]], "Example 3. Advanced - uses ConditionRegexMatch from cyclops.query.ops. Get all patient encounters with diagnoses (ICD-9 long title contains schizophrenia and chronic ), in the year 2015.": [[194, "Example-3.-Advanced---uses-ConditionRegexMatch-from-cyclops.query.ops.-Get-all-patient-encounters-with-diagnoses-(ICD-9-long-title-contains-schizophrenia-and-chronic-),-in-the-year-2015."]], "Example 4. Get routine vital signs for patients from year 2015.": [[194, "Example-4.-Get-routine-vital-signs-for-patients-from-year-2015."]], "Example 5. Get hemoglobin lab tests for patients from year 2009.": [[194, "Example-5.-Get-hemoglobin-lab-tests-for-patients-from-year-2009."]], "Example 6. Get radiology reports and filter on keywords lymphadenopathy and infectious occurring together from year 2009.": [[194, "Example-6.-Get-radiology-reports-and-filter-on-keywords-lymphadenopathy-and-infectious-occurring-together-from-year-2009."]], "Example 7. Get all female patient encounters from year 2015, and return as dask dataframe (lazy evaluation) with 4 partitions (batches) aggregated based on subject_id.": [[194, "Example-7.-Get-all-female-patient-encounters-from-year-2015,-and-return-as-dask-dataframe-(lazy-evaluation)-with-4-partitions-(batches)-aggregated-based-on-subject_id."]], "Example 8. Running a raw SQL string.": [[194, "Example-8.-Running-a-raw-SQL-string."]], "Chest X-Ray Disease Classification": [[195, "Chest-X-Ray-Disease-Classification"]], "Load Dataset": [[195, "Load-Dataset"]], "Load Model and get Predictions": [[195, "Load-Model-and-get-Predictions"]], "Multilabel AUROC by Pathology and Sex": [[195, "Multilabel-AUROC-by-Pathology-and-Sex"]], "Multilabel AUROC by Pathology and Age": [[195, "Multilabel-AUROC-by-Pathology-and-Age"]], "Balanced Error Rate by Pathology and Age": [[195, "Balanced-Error-Rate-by-Pathology-and-Age"]], "Balanced Error Rate Parity by Pathology and Age": [[195, "Balanced-Error-Rate-Parity-by-Pathology-and-Age"]], "Log Performance Metrics as Tests w/ Thresholds": [[195, "Log-Performance-Metrics-as-Tests-w/-Thresholds"]], "Populate Model Card Fields": [[195, "Populate-Model-Card-Fields"]], "NIHCXR Clinical Drift Experiments Tutorial": [[196, "NIHCXR-Clinical-Drift-Experiments-Tutorial"]], "Import Libraries and Load NIHCXR Dataset": [[196, "Import-Libraries-and-Load-NIHCXR-Dataset"]], "Example 1. Generate Source/Target Dataset for Experiments (1-2)": [[196, "Example-1.-Generate-Source/Target-Dataset-for-Experiments-(1-2)"]], "Example 2. Sensitivity test experiment with 3 dimensionality reduction techniques": [[196, "Example-2.-Sensitivity-test-experiment-with-3-dimensionality-reduction-techniques"]], "Example 3. Sensitivity test experiment with models trained on different datasets": [[196, "Example-3.-Sensitivity-test-experiment-with-models-trained-on-different-datasets"]], "Example 4. Sensitivity test experiment with different clinical shifts": [[196, "Example-4.-Sensitivity-test-experiment-with-different-clinical-shifts"]], "Example 5. Rolling window experiment with synthetic timestamps using biweekly window": [[196, "Example-5.-Rolling-window-experiment-with-synthetic-timestamps-using-biweekly-window"]], "OMOP query API tutorial": [[197, "OMOP-query-API-tutorial"]], "Imports and instantiate OMOPQuerier.": [[197, "Imports-and-instantiate-OMOPQuerier."], [197, "id1"]], "Example 1. Get all patient visits in or after 2010.": [[197, "Example-1.-Get-all-patient-visits-in-or-after-2010."]], "Example 2. Get measurements for all visits in or after 2020.": [[197, "Example-2.-Get-measurements-for-all-visits-in-or-after-2020."]], "Example 1. Get all patient visits that ended in a mortality outcome in or after 2010.": [[197, "Example-1.-Get-all-patient-visits-that-ended-in-a-mortality-outcome-in-or-after-2010."]], "Example 2. Get all measurements for female patient visits with sepsis diagnoses, that ended in a mortality outcome.": [[197, "Example-2.-Get-all-measurements-for-female-patient-visits-with-sepsis-diagnoses,-that-ended-in-a-mortality-outcome."]], "Prolonged Length of Stay Prediction": [[198, "Prolonged-Length-of-Stay-Prediction"]], "Data Querying": [[198, "Data-Querying"]], "Compute length of stay (labels)": [[198, "Compute-length-of-stay-(labels)"]], "Data Inspection and Preprocessing": [[198, "Data-Inspection-and-Preprocessing"]], "Drop NaNs based on the NAN_THRESHOLD": [[198, "Drop-NaNs-based-on-the-NAN_THRESHOLD"]], "Length of stay distribution": [[198, "Length-of-stay-distribution"]], "Gender distribution": [[198, "Gender-distribution"]], "monitor API": [[199, "monitor-api"]], "query API": [[200, "query-api"]], "Example use cases": [[201, "example-use-cases"]], "Tabular data": [[201, "tabular-data"]], "Kaggle Heart Failure Prediction": [[201, "kaggle-heart-failure-prediction"]], "Synthea Prolonged Length of Stay Prediction": [[201, "synthea-prolonged-length-of-stay-prediction"]], "Image data": [[201, "image-data"]], "NIH Chest X-ray classification": [[201, "nih-chest-x-ray-classification"]]}, "indexentries": {"cyclops.data.features.medical_image": [[4, "module-cyclops.data.features.medical_image"]], "module": [[4, "module-cyclops.data.features.medical_image"], [6, "module-cyclops.data.slicer"], [16, "module-cyclops.evaluate.evaluator"], [18, "module-cyclops.evaluate.fairness.config"], [20, "module-cyclops.evaluate.fairness.evaluator"], [23, "module-cyclops.evaluate.metrics.accuracy"], [28, "module-cyclops.evaluate.metrics.auroc"], [33, "module-cyclops.evaluate.metrics.f_beta"], [42, "module-cyclops.evaluate.metrics.factory"], [44, "module-cyclops.evaluate.metrics.functional.accuracy"], [45, "module-cyclops.evaluate.metrics.functional.auroc"], [46, "module-cyclops.evaluate.metrics.functional.f_beta"], [55, "module-cyclops.evaluate.metrics.functional.precision_recall"], [64, "module-cyclops.evaluate.metrics.functional.precision_recall_curve"], [65, "module-cyclops.evaluate.metrics.functional.roc"], [70, "module-cyclops.evaluate.metrics.functional.sensitivity"], [71, "module-cyclops.evaluate.metrics.functional.specificity"], [72, "module-cyclops.evaluate.metrics.functional.stat_scores"], [73, "module-cyclops.evaluate.metrics.metric"], [77, "module-cyclops.evaluate.metrics.precision_recall"], [86, "module-cyclops.evaluate.metrics.precision_recall_curve"], [91, "module-cyclops.evaluate.metrics.roc"], [96, "module-cyclops.evaluate.metrics.sensitivity"], [101, "module-cyclops.evaluate.metrics.specificity"], [106, "module-cyclops.evaluate.metrics.stat_scores"], [111, "module-cyclops.monitor.clinical_applicator"], [113, "module-cyclops.monitor.synthetic_applicator"], [120, "module-cyclops.query.base"], [122, "module-cyclops.query.eicu"], [124, "module-cyclops.query.gemini"], [126, "module-cyclops.query.interface"], [128, "module-cyclops.query.mimiciii"], 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"cyclops-query-ops-conditioninmonths"]], "cyclops.query.ops.ConditionInYears": [[149, "cyclops-query-ops-conditioninyears"]], "cyclops.query.ops.ConditionLessThan": [[150, "cyclops-query-ops-conditionlessthan"]], "cyclops.query.ops.ConditionLike": [[151, "cyclops-query-ops-conditionlike"]], "cyclops.query.ops.ConditionRegexMatch": [[152, "cyclops-query-ops-conditionregexmatch"]], "cyclops.query.ops.ConditionStartsWith": [[153, "cyclops-query-ops-conditionstartswith"]], "cyclops.query.ops.ConditionSubstring": [[154, "cyclops-query-ops-conditionsubstring"]], "cyclops.query.ops.Distinct": [[155, "cyclops-query-ops-distinct"]], "cyclops.query.ops.Drop": [[156, "cyclops-query-ops-drop"]], "cyclops.query.ops.DropEmpty": [[157, "cyclops-query-ops-dropempty"]], "cyclops.query.ops.DropNulls": [[158, "cyclops-query-ops-dropnulls"]], "cyclops.query.ops.ExtractTimestampComponent": [[159, "cyclops-query-ops-extracttimestampcomponent"]], "cyclops.query.ops.FillNull": [[160, "cyclops-query-ops-fillnull"]], "cyclops.query.ops.GroupByAggregate": [[161, "cyclops-query-ops-groupbyaggregate"]], "cyclops.query.ops.Join": [[162, "cyclops-query-ops-join"]], "cyclops.query.ops.Keep": [[163, "cyclops-query-ops-keep"]], "cyclops.query.ops.Limit": [[164, "cyclops-query-ops-limit"]], "cyclops.query.ops.Literal": [[165, "cyclops-query-ops-literal"]], "cyclops.query.ops.Or": [[166, "cyclops-query-ops-or"]], "cyclops.query.ops.OrderBy": [[167, "cyclops-query-ops-orderby"]], "cyclops.query.ops.QueryOp": [[168, "cyclops-query-ops-queryop"]], "cyclops.query.ops.RandomizeOrder": [[169, "cyclops-query-ops-randomizeorder"]], "cyclops.query.ops.Rename": [[170, "cyclops-query-ops-rename"]], "cyclops.query.ops.Reorder": [[171, "cyclops-query-ops-reorder"]], "cyclops.query.ops.ReorderAfter": [[172, "cyclops-query-ops-reorderafter"]], "cyclops.query.ops.Sequential": [[173, "cyclops-query-ops-sequential"]], "cyclops.query.ops.Substring": [[174, "cyclops-query-ops-substring"]], "cyclops.query.ops.Trim": [[175, "cyclops-query-ops-trim"]], "cyclops.query.ops.Union": [[176, "cyclops-query-ops-union"]], "cyclops.report.report": [[177, "module-cyclops.report.report"]], "cyclops.report.report.ModelCardReport": [[178, "cyclops-report-report-modelcardreport"]], "cyclops.tasks.cxr_classification": [[179, "module-cyclops.tasks.cxr_classification"]], "cyclops.tasks.cxr_classification.CXRClassificationTask": [[180, "cyclops-tasks-cxr-classification-cxrclassificationtask"]], "cyclops.tasks.mortality_prediction": [[181, "module-cyclops.tasks.mortality_prediction"]], "cyclops.tasks.mortality_prediction.MortalityPredictionTask": [[182, "cyclops-tasks-mortality-prediction-mortalitypredictiontask"]], "cyclops.data": [[183, "module-cyclops.data"]], "cyclops.data.features": [[183, "module-cyclops.data.features"]], "cyclops.evaluate": [[184, "module-cyclops.evaluate"]], "cyclops.evaluate.metrics": [[184, "module-cyclops.evaluate.metrics"]], "cyclops.evaluate.metrics.functional": [[184, "module-cyclops.evaluate.metrics.functional"]], "cyclops.evaluate.fairness": [[184, "module-cyclops.evaluate.fairness"]], "cyclops.monitor": [[185, "module-cyclops.monitor"]], "cyclops.query": [[186, "module-cyclops.query"]], "dataset APIs": [[186, "dataset-apis"]], "cyclops.report": [[187, "module-cyclops.report"]], "cyclops.tasks": [[188, "module-cyclops.tasks"]], "Tutorials": [[189, "tutorials"]], "eICU-CRD query API tutorial": [[190, "eICU-CRD-query-API-tutorial"]], "Imports and instantiate EICUQuerier": [[190, "Imports-and-instantiate-EICUQuerier"]], "Example 1. Get all female patients discharged in 2014 (limit to 100 rows).": [[190, "Example-1.-Get-all-female-patients-discharged-in-2014-(limit-to-100-rows)."]], "Example 2. Get all patient encounters with diagnoses (schizophrenia in diagnosisstring), discharged in the year 2015.": [[190, "Example-2.-Get-all-patient-encounters-with-diagnoses-(schizophrenia-in-diagnosisstring),-discharged-in-the-year-2015."]], "Example 3. Get potassium lab tests for patients discharged in the year 2014, for all teaching hospitals.": [[190, "Example-3.-Get-potassium-lab-tests-for-patients-discharged-in-the-year-2014,-for-all-teaching-hospitals."]], "Example 4. Get glucose medications (substring search) for female patients discharged in 2014.": [[190, "Example-4.-Get-glucose-medications-(substring-search)-for-female-patients-discharged-in-2014."]], "GEMINI query API tutorial": [[191, "GEMINI-query-API-tutorial"]], "Imports and instantiate GEMINIQuerier.": [[191, "Imports-and-instantiate-GEMINIQuerier."]], "Example 1a. Create a table with only one hospitalization per patient, keeping the most recent encounter for each patient. Sort the dataset by patient_id_hashed and discharge_date_time, and then keep the recent record.": [[191, "Example-1a.-Create-a-table-with-only-one-hospitalization-per-patient,-keeping-the-most-recent-encounter-for-each-patient.-Sort-the-dataset-by-patient_id_hashed-and-discharge_date_time,-and-then-keep-the-recent-record."]], "Example 1b. From the above set of encounters, take a subset of patients who were discharged between April 1, 2015 and March 31, 2016.": [[191, "Example-1b.-From-the-above-set-of-encounters,-take-a-subset-of-patients-who-were-discharged-between-April-1,-2015-and-March-31,-2016."]], "Example 1c. From the above set of encounters, get the total number of admissions for each hospital.": [[191, "Example-1c.-From-the-above-set-of-encounters,-get-the-total-number-of-admissions-for-each-hospital."]], "Example 2a. How many sodium tests were placed between Apr 1, 2015 and May 31, 2015 at hospital 101?": [[191, "Example-2a.-How-many-sodium-tests-were-placed-between-Apr-1,-2015-and-May-31,-2015-at-hospital-101?"]], "Heart Failure Prediction": [[192, "Heart-Failure-Prediction"]], "Import Libraries": [[192, "Import-Libraries"], [195, "Import-Libraries"], [198, "Import-Libraries"]], "Constants": [[192, "Constants"], [198, "Constants"]], "Data Loading": [[192, "Data-Loading"]], "Sex values": [[192, "Sex-values"]], "Age distribution": [[192, "Age-distribution"], [198, "Age-distribution"]], "Outcome distribution": [[192, "Outcome-distribution"], [198, "Outcome-distribution"]], "Identifying feature types": [[192, "Identifying-feature-types"], [198, "Identifying-feature-types"]], "Creating data preprocessors": [[192, "Creating-data-preprocessors"], [198, "Creating-data-preprocessors"]], "Creating Hugging Face Dataset": [[192, "Creating-Hugging-Face-Dataset"], [198, "Creating-Hugging-Face-Dataset"]], "Model Creation": [[192, "Model-Creation"], [198, "Model-Creation"]], "Task Creation": [[192, "Task-Creation"], [198, "Task-Creation"]], "Training": [[192, "Training"], [198, "Training"]], "Prediction": [[192, "Prediction"], [198, "Prediction"]], "Evaluation": [[192, "Evaluation"], [198, "Evaluation"]], "Performance over time": [[192, "Performance-over-time"], [195, "Performance-over-time"], [198, "Performance-over-time"]], "Report Generation": [[192, "Report-Generation"], [198, "Report-Generation"]], "MIMIC-III query API tutorial": [[193, "MIMIC-III-query-API-tutorial"]], "Imports and instantiate MIMICIIIQuerier": [[193, "Imports-and-instantiate-MIMICIIIQuerier"]], "Example 1. Get all male patients with a mortality outcome.": [[193, "Example-1.-Get-all-male-patients-with-a-mortality-outcome."]], "Example 2. Get all female patient encounters with diagnoses (gastroenteritis in ICD-9 long title).": [[193, "Example-2.-Get-all-female-patient-encounters-with-diagnoses-(gastroenteritis-in-ICD-9-long-title)."]], "Example 3. Get potassium lab tests for female patients.": [[193, "Example-3.-Get-potassium-lab-tests-for-female-patients."]], "Example 4. Get AaDO2 carevue chart events for male patients that have a valuenum of less than 20.": [[193, "Example-4.-Get-AaDO2-carevue-chart-events-for-male-patients-that-have-a-valuenum-of-less-than-20."]], "MIMIC-IV query API tutorial": [[194, "MIMIC-IV-query-API-tutorial"]], "Imports and instantiate MIMICIVQuerier": [[194, "Imports-and-instantiate-MIMICIVQuerier"]], "Example 1. Get all patient admissions from 2021 or later (approx year of admission)": [[194, "Example-1.-Get-all-patient-admissions-from-2021-or-later-(approx-year-of-admission)"]], "Example 2. Get all patient encounters with diagnoses (schizophrenia in ICD-10 long title), in the year 2015.": [[194, "Example-2.-Get-all-patient-encounters-with-diagnoses-(schizophrenia-in-ICD-10-long-title),-in-the-year-2015."]], "Example 3. Advanced - uses ConditionRegexMatch from cyclops.query.ops. Get all patient encounters with diagnoses (ICD-9 long title contains schizophrenia and chronic ), in the year 2015.": [[194, "Example-3.-Advanced---uses-ConditionRegexMatch-from-cyclops.query.ops.-Get-all-patient-encounters-with-diagnoses-(ICD-9-long-title-contains-schizophrenia-and-chronic-),-in-the-year-2015."]], "Example 4. Get routine vital signs for patients from year 2015.": [[194, "Example-4.-Get-routine-vital-signs-for-patients-from-year-2015."]], "Example 5. Get hemoglobin lab tests for patients from year 2009.": [[194, "Example-5.-Get-hemoglobin-lab-tests-for-patients-from-year-2009."]], "Example 6. Get radiology reports and filter on keywords lymphadenopathy and infectious occurring together from year 2009.": [[194, "Example-6.-Get-radiology-reports-and-filter-on-keywords-lymphadenopathy-and-infectious-occurring-together-from-year-2009."]], "Example 7. Get all female patient encounters from year 2015, and return as dask dataframe (lazy evaluation) with 4 partitions (batches) aggregated based on subject_id.": [[194, "Example-7.-Get-all-female-patient-encounters-from-year-2015,-and-return-as-dask-dataframe-(lazy-evaluation)-with-4-partitions-(batches)-aggregated-based-on-subject_id."]], "Example 8. Running a raw SQL string.": [[194, "Example-8.-Running-a-raw-SQL-string."]], "Chest X-Ray Disease Classification": [[195, "Chest-X-Ray-Disease-Classification"]], "Load Dataset": [[195, "Load-Dataset"]], "Load Model and get Predictions": [[195, "Load-Model-and-get-Predictions"]], "Multilabel AUROC by Pathology and Sex": [[195, "Multilabel-AUROC-by-Pathology-and-Sex"]], "Multilabel AUROC by Pathology and Age": [[195, "Multilabel-AUROC-by-Pathology-and-Age"]], "Balanced Error Rate by Pathology and Age": [[195, "Balanced-Error-Rate-by-Pathology-and-Age"]], "Balanced Error Rate Parity by Pathology and Age": [[195, "Balanced-Error-Rate-Parity-by-Pathology-and-Age"]], "Log Performance Metrics as Tests w/ Thresholds": [[195, "Log-Performance-Metrics-as-Tests-w/-Thresholds"]], "Populate Model Card Fields": [[195, "Populate-Model-Card-Fields"]], "NIHCXR Clinical Drift Experiments Tutorial": [[196, "NIHCXR-Clinical-Drift-Experiments-Tutorial"]], "Import Libraries and Load NIHCXR Dataset": [[196, "Import-Libraries-and-Load-NIHCXR-Dataset"]], "Example 1. Generate Source/Target Dataset for Experiments (1-2)": [[196, "Example-1.-Generate-Source/Target-Dataset-for-Experiments-(1-2)"]], "Example 2. Sensitivity test experiment with 3 dimensionality reduction techniques": [[196, "Example-2.-Sensitivity-test-experiment-with-3-dimensionality-reduction-techniques"]], "Example 3. Sensitivity test experiment with models trained on different datasets": [[196, "Example-3.-Sensitivity-test-experiment-with-models-trained-on-different-datasets"]], "Example 4. Sensitivity test experiment with different clinical shifts": [[196, "Example-4.-Sensitivity-test-experiment-with-different-clinical-shifts"]], "Example 5. Rolling window experiment with synthetic timestamps using biweekly window": [[196, "Example-5.-Rolling-window-experiment-with-synthetic-timestamps-using-biweekly-window"]], "OMOP query API tutorial": [[197, "OMOP-query-API-tutorial"]], "Imports and instantiate OMOPQuerier.": [[197, "Imports-and-instantiate-OMOPQuerier."], [197, "id1"]], "Example 1. Get all patient visits in or after 2010.": [[197, "Example-1.-Get-all-patient-visits-in-or-after-2010."]], "Example 2. Get measurements for all visits in or after 2020.": [[197, "Example-2.-Get-measurements-for-all-visits-in-or-after-2020."]], "Example 1. Get all patient visits that ended in a mortality outcome in or after 2010.": [[197, "Example-1.-Get-all-patient-visits-that-ended-in-a-mortality-outcome-in-or-after-2010."]], "Example 2. Get all measurements for female patient visits with sepsis diagnoses, that ended in a mortality outcome.": [[197, "Example-2.-Get-all-measurements-for-female-patient-visits-with-sepsis-diagnoses,-that-ended-in-a-mortality-outcome."]], "Prolonged Length of Stay Prediction": [[198, "Prolonged-Length-of-Stay-Prediction"]], "Data Querying": [[198, "Data-Querying"]], "Compute length of stay (labels)": [[198, "Compute-length-of-stay-(labels)"]], "Data Inspection and Preprocessing": [[198, "Data-Inspection-and-Preprocessing"]], "Drop NaNs based on the NAN_THRESHOLD": [[198, "Drop-NaNs-based-on-the-NAN_THRESHOLD"]], "Length of stay distribution": [[198, "Length-of-stay-distribution"]], "Gender distribution": [[198, "Gender-distribution"]], "monitor API": [[199, "monitor-api"]], "query API": [[200, "query-api"]], "Example use cases": [[201, "example-use-cases"]], "Tabular data": [[201, "tabular-data"]], "Kaggle Heart Failure Prediction": [[201, "kaggle-heart-failure-prediction"]], "Synthea Prolonged Length of Stay Prediction": [[201, "synthea-prolonged-length-of-stay-prediction"]], "Image data": [[201, "image-data"]], "NIH Chest X-ray classification": [[201, "nih-chest-x-ray-classification"]]}, "indexentries": {"cyclops.data.features.medical_image": [[4, "module-cyclops.data.features.medical_image"]], "module": [[4, "module-cyclops.data.features.medical_image"], [6, "module-cyclops.data.slicer"], [16, "module-cyclops.evaluate.evaluator"], [18, "module-cyclops.evaluate.fairness.config"], [20, "module-cyclops.evaluate.fairness.evaluator"], [23, "module-cyclops.evaluate.metrics.accuracy"], [28, "module-cyclops.evaluate.metrics.auroc"], [33, "module-cyclops.evaluate.metrics.f_beta"], [42, "module-cyclops.evaluate.metrics.factory"], [44, "module-cyclops.evaluate.metrics.functional.accuracy"], [45, "module-cyclops.evaluate.metrics.functional.auroc"], [46, "module-cyclops.evaluate.metrics.functional.f_beta"], [55, "module-cyclops.evaluate.metrics.functional.precision_recall"], [64, "module-cyclops.evaluate.metrics.functional.precision_recall_curve"], [65, "module-cyclops.evaluate.metrics.functional.roc"], [70, "module-cyclops.evaluate.metrics.functional.sensitivity"], [71, "module-cyclops.evaluate.metrics.functional.specificity"], [72, "module-cyclops.evaluate.metrics.functional.stat_scores"], [73, "module-cyclops.evaluate.metrics.metric"], [77, "module-cyclops.evaluate.metrics.precision_recall"], [86, "module-cyclops.evaluate.metrics.precision_recall_curve"], [91, "module-cyclops.evaluate.metrics.roc"], [96, "module-cyclops.evaluate.metrics.sensitivity"], [101, "module-cyclops.evaluate.metrics.specificity"], [106, "module-cyclops.evaluate.metrics.stat_scores"], [111, "module-cyclops.monitor.clinical_applicator"], [113, "module-cyclops.monitor.synthetic_applicator"], [120, "module-cyclops.query.base"], [122, "module-cyclops.query.eicu"], [124, "module-cyclops.query.gemini"], [126, "module-cyclops.query.interface"], [128, "module-cyclops.query.mimiciii"], [130, "module-cyclops.query.mimiciv"], [132, "module-cyclops.query.omop"], [134, "module-cyclops.query.ops"], [177, "module-cyclops.report.report"], [179, "module-cyclops.tasks.cxr_classification"], [181, "module-cyclops.tasks.mortality_prediction"], [183, "module-cyclops.data"], [183, "module-cyclops.data.features"], [184, "module-cyclops.evaluate"], [184, "module-cyclops.evaluate.fairness"], [184, "module-cyclops.evaluate.metrics"], [184, "module-cyclops.evaluate.metrics.functional"], [185, "module-cyclops.monitor"], [186, "module-cyclops.query"], [187, "module-cyclops.report"], [188, "module-cyclops.tasks"]], "medicalimage (class in cyclops.data.features.medical_image)": [[5, "cyclops.data.features.medical_image.MedicalImage"]], "__call__() (medicalimage method)": [[5, "cyclops.data.features.medical_image.MedicalImage.__call__"]], "cast_storage() (medicalimage method)": [[5, "cyclops.data.features.medical_image.MedicalImage.cast_storage"]], "decode_example() (medicalimage method)": [[5, "cyclops.data.features.medical_image.MedicalImage.decode_example"]], "embed_storage() (medicalimage method)": [[5, "cyclops.data.features.medical_image.MedicalImage.embed_storage"]], "encode_example() (medicalimage method)": [[5, "cyclops.data.features.medical_image.MedicalImage.encode_example"]], "flatten() (medicalimage method)": [[5, "cyclops.data.features.medical_image.MedicalImage.flatten"]], "cyclops.data.slicer": [[6, "module-cyclops.data.slicer"]], "slicespec (class in cyclops.data.slicer)": [[7, "cyclops.data.slicer.SliceSpec"]], "_registry (slicespec attribute)": [[7, "cyclops.data.slicer.SliceSpec._registry"]], "add_slice_spec() (slicespec method)": [[7, "cyclops.data.slicer.SliceSpec.add_slice_spec"]], "column_names (slicespec attribute)": [[7, "cyclops.data.slicer.SliceSpec.column_names"]], "get_slices() (slicespec method)": [[7, "cyclops.data.slicer.SliceSpec.get_slices"]], "include_overall (slicespec attribute)": [[7, "cyclops.data.slicer.SliceSpec.include_overall"]], "slices() (slicespec method)": [[7, "cyclops.data.slicer.SliceSpec.slices"]], "spec_list (slicespec attribute)": [[7, "cyclops.data.slicer.SliceSpec.spec_list"]], "validate (slicespec attribute)": [[7, "cyclops.data.slicer.SliceSpec.validate"]], "compound_filter() (in module cyclops.data.slicer)": [[8, "cyclops.data.slicer.compound_filter"]], "filter_datetime() (in module cyclops.data.slicer)": [[9, "cyclops.data.slicer.filter_datetime"]], "filter_non_null() (in module cyclops.data.slicer)": [[10, "cyclops.data.slicer.filter_non_null"]], "filter_range() (in module cyclops.data.slicer)": [[11, "cyclops.data.slicer.filter_range"]], "filter_string_contains() (in module cyclops.data.slicer)": [[12, "cyclops.data.slicer.filter_string_contains"]], "filter_value() (in module cyclops.data.slicer)": [[13, "cyclops.data.slicer.filter_value"]], "is_datetime() (in module cyclops.data.slicer)": [[14, "cyclops.data.slicer.is_datetime"]], "overall() (in module cyclops.data.slicer)": [[15, "cyclops.data.slicer.overall"]], "cyclops.evaluate.evaluator": [[16, "module-cyclops.evaluate.evaluator"]], "evaluate() (in module cyclops.evaluate.evaluator)": [[17, "cyclops.evaluate.evaluator.evaluate"]], "cyclops.evaluate.fairness.config": [[18, "module-cyclops.evaluate.fairness.config"]], "fairnessconfig (class in cyclops.evaluate.fairness.config)": [[19, "cyclops.evaluate.fairness.config.FairnessConfig"]], "cyclops.evaluate.fairness.evaluator": [[20, "module-cyclops.evaluate.fairness.evaluator"]], "evaluate_fairness() (in module cyclops.evaluate.fairness.evaluator)": [[21, "cyclops.evaluate.fairness.evaluator.evaluate_fairness"]], "warn_too_many_unique_values() (in module cyclops.evaluate.fairness.evaluator)": [[22, "cyclops.evaluate.fairness.evaluator.warn_too_many_unique_values"]], "cyclops.evaluate.metrics.accuracy": [[23, "module-cyclops.evaluate.metrics.accuracy"]], "accuracy (class in cyclops.evaluate.metrics.accuracy)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy"]], "__add__() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.__add__"]], "__call__() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.__call__"]], "__init__() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.__init__"]], "__mul__() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.__mul__"]], "add_state() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.add_state"]], "clone() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.clone"]], "compute() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.compute"]], "reset_state() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.reset_state"]], "update_state() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.update_state"]], "binaryaccuracy (class in cyclops.evaluate.metrics.accuracy)": [[25, 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"module-cyclops.evaluate.metrics"]], "cyclops.evaluate.metrics.functional": [[184, "module-cyclops.evaluate.metrics.functional"]], "cyclops.monitor": [[185, "module-cyclops.monitor"]], "cyclops.query": [[186, "module-cyclops.query"]], "cyclops.report": [[187, "module-cyclops.report"]], "cyclops.tasks": [[188, "module-cyclops.tasks"]]}}) \ No newline at end of file diff --git a/api/tutorials/eicu/query_api.html b/api/tutorials/eicu/query_api.html index 9af3330a6..45e6d3737 100644 --- a/api/tutorials/eicu/query_api.html +++ b/api/tutorials/eicu/query_api.html @@ -483,9 +483,9 @@

Imports and instantiate 
-/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
+/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
   from .autonotebook import tqdm as notebook_tqdm
-2023-09-21 13:53:43,487 INFO cyclops.query.orm - Database setup, ready to run queries!
+2023-10-06 10:16:23,020 INFO cyclops.query.orm - Database setup, ready to run queries!
@@ -549,8 +549,8 @@

Example 1. Get all female patients discharged in 2014 (limit to 100 rows). 
-2023-09-21 13:53:44,237 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 13:53:44,238 INFO cyclops.utils.profile - Finished executing function run_query in 0.050105 s
+2023-10-06 10:16:23,822 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:16:23,823 INFO cyclops.utils.profile - Finished executing function run_query in 0.050423 s

@@ -585,8 +585,8 @@

Example 2. Get all patient encounters with diagnoses ( 
-2023-09-21 13:53:44,324 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 13:53:44,325 INFO cyclops.utils.profile - Finished executing function run_query in 0.069920 s
+2023-10-06 10:16:23,905 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:16:23,907 INFO cyclops.utils.profile - Finished executing function run_query in 0.066299 s

@@ -627,8 +627,8 @@

Example 3. Get potassium lab tests for patients discharged in the year 2014, 

-2023-09-21 13:53:44,396 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 13:53:44,397 INFO cyclops.utils.profile - Finished executing function run_query in 0.039890 s
+2023-10-06 10:16:23,971 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:16:23,973 INFO cyclops.utils.profile - Finished executing function run_query in 0.034082 s
@@ -667,8 +667,8 @@

Example 4. Get glucose medications (substring search) for female patients di 

-2023-09-21 13:53:44,580 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 13:53:44,581 INFO cyclops.utils.profile - Finished executing function run_query in 0.161098 s
+2023-10-06 10:16:24,161 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:16:24,163 INFO cyclops.utils.profile - Finished executing function run_query in 0.164061 s
diff --git a/api/tutorials/eicu/query_api.ipynb b/api/tutorials/eicu/query_api.ipynb index 36a12c32f..ecdb35f58 100644 --- a/api/tutorials/eicu/query_api.ipynb +++ b/api/tutorials/eicu/query_api.ipynb @@ -35,10 +35,10 @@ "id": "75a140e0-fb27-4319-862f-be54397abe5c", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T17:53:41.653977Z", - "iopub.status.busy": "2023-09-21T17:53:41.653024Z", - "iopub.status.idle": "2023-09-21T17:53:44.171001Z", - "shell.execute_reply": "2023-09-21T17:53:44.169632Z" + "iopub.execute_input": "2023-10-06T14:16:20.666173Z", + "iopub.status.busy": "2023-10-06T14:16:20.665581Z", + "iopub.status.idle": "2023-10-06T14:16:23.756281Z", + "shell.execute_reply": "2023-10-06T14:16:23.755177Z" }, "tags": [] }, @@ -47,7 +47,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + "/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] }, @@ -55,7 +55,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:53:43,487 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n" + "2023-10-06 10:16:23,020 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n" ] }, { @@ -132,10 +132,10 @@ "id": "c1efa964-8978-4a0e-9892-5ea4ce9953a3", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T17:53:44.177867Z", - "iopub.status.busy": "2023-09-21T17:53:44.177091Z", - "iopub.status.idle": "2023-09-21T17:53:44.243526Z", - "shell.execute_reply": "2023-09-21T17:53:44.242691Z" + "iopub.execute_input": "2023-10-06T14:16:23.762086Z", + "iopub.status.busy": "2023-10-06T14:16:23.761430Z", + "iopub.status.idle": "2023-10-06T14:16:23.828348Z", + "shell.execute_reply": "2023-10-06T14:16:23.827671Z" }, "tags": [] }, @@ -144,14 +144,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:53:44,237 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-10-06 10:16:23,822 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:53:44,238 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.050105 s\n" + "2023-10-06 10:16:23,823 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.050423 s\n" ] }, { @@ -188,10 +188,10 @@ "id": "a7ab5fa3-e26b-47a7-818f-1bf367a55760", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T17:53:44.250067Z", - "iopub.status.busy": "2023-09-21T17:53:44.249664Z", - "iopub.status.idle": "2023-09-21T17:53:44.329873Z", - "shell.execute_reply": "2023-09-21T17:53:44.328812Z" + "iopub.execute_input": "2023-10-06T14:16:23.834160Z", + "iopub.status.busy": "2023-10-06T14:16:23.833975Z", + "iopub.status.idle": "2023-10-06T14:16:23.912027Z", + "shell.execute_reply": "2023-10-06T14:16:23.910885Z" }, "tags": [] }, @@ -200,14 +200,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:53:44,324 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-10-06 10:16:23,905 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:53:44,325 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.069920 s\n" + "2023-10-06 10:16:23,907 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.066299 s\n" ] }, { @@ -244,10 +244,10 @@ "id": "24043abc-1878-4e00-8229-36d4a0368b98", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T17:53:44.337315Z", - "iopub.status.busy": "2023-09-21T17:53:44.336859Z", - "iopub.status.idle": "2023-09-21T17:53:44.400785Z", - "shell.execute_reply": "2023-09-21T17:53:44.399999Z" + "iopub.execute_input": "2023-10-06T14:16:23.918853Z", + "iopub.status.busy": "2023-10-06T14:16:23.918163Z", + "iopub.status.idle": "2023-10-06T14:16:23.976962Z", + "shell.execute_reply": "2023-10-06T14:16:23.976263Z" }, "tags": [] }, @@ -256,14 +256,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:53:44,396 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-10-06 10:16:23,971 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:53:44,397 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.039890 s\n" + "2023-10-06 10:16:23,973 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.034082 s\n" ] }, { @@ -306,10 +306,10 @@ "id": "f6142f27-e8d1-453c-bfe2-2265d9ff1914", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T17:53:44.408346Z", - "iopub.status.busy": "2023-09-21T17:53:44.408045Z", - "iopub.status.idle": "2023-09-21T17:53:44.587272Z", - "shell.execute_reply": "2023-09-21T17:53:44.585830Z" + "iopub.execute_input": "2023-10-06T14:16:23.984118Z", + "iopub.status.busy": "2023-10-06T14:16:23.983547Z", + "iopub.status.idle": "2023-10-06T14:16:24.169122Z", + "shell.execute_reply": "2023-10-06T14:16:24.167481Z" }, "tags": [] }, @@ -318,14 +318,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:53:44,580 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-10-06 10:16:24,161 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:53:44,581 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.161098 s\n" + "2023-10-06 10:16:24,163 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.164061 s\n" ] }, { diff --git a/api/tutorials/kaggle/heart_failure_prediction.html b/api/tutorials/kaggle/heart_failure_prediction.html index 754ba64da..96a9bc4e4 100644 --- a/api/tutorials/kaggle/heart_failure_prediction.html +++ b/api/tutorials/kaggle/heart_failure_prediction.html @@ -486,7 +486,7 @@

Import Libraries 
-/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
+/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
   from .autonotebook import tqdm as notebook_tqdm

@@ -550,7 +550,7 @@

Data Loading 
-2023-09-21 13:53:52,715 INFO cyclops.utils.file - Loading DataFrame from ./data/heart.csv
+2023-10-06 10:16:31,911 INFO cyclops.utils.file - Loading DataFrame from ./data/heart.csv
@@ -632,9 +632,9 @@

Sex values

-
+
@@ -575,7 +575,7 @@

Performance Over Time

-
+
@@ -620,7 +620,7 @@

Version

- Date: 2023-09-21 + Date: 2023-10-06
@@ -818,17 +818,8 @@

Model Parameters

-

Average

- False -
- - - - - -
-

Epsilon

- 0.1 +

Max_iter

+ 1000
@@ -845,8 +836,8 @@

Loss

-

Power_t

- 0.5 +

Early_stopping

+ True
@@ -854,8 +845,8 @@

Power_t

-

Fit_intercept

- True +

Epsilon

+ 0.1
@@ -872,8 +863,8 @@

N_iter_no_change

-

Eta0

- 0.01 +

Power_t

+ 0.5
@@ -881,22 +872,17 @@

Eta0

-

Penalty

- l2 +

Learning_rate

+ adaptive
- - - - -
-

Learning_rate

- adaptive +

Eta0

+ 0.01
@@ -922,8 +908,8 @@

Validation_fraction

-

L1_ratio

- 0.15 +

Fit_intercept

+ True
@@ -931,8 +917,8 @@

L1_ratio

-

Class_weight

- balanced +

Penalty

+ l2
@@ -940,8 +926,8 @@

Class_weight

-

Random_state

- 123 +

Average

+ False
@@ -949,8 +935,8 @@

Random_state

-

Early_stopping

- True +

Verbose

+ 0
@@ -958,8 +944,8 @@

Early_stopping

-

Verbose

- 0 +

Alpha

+ 0.001
@@ -976,8 +962,22 @@

Warm_start

-

Shuffle

- True +

Class_weight

+ balanced +
+ + + + + + + + + + +
+

L1_ratio

+ 0.15
@@ -985,8 +985,8 @@

Shuffle

-

Alpha

- 0.001 +

Shuffle

+ True
@@ -994,8 +994,8 @@

Alpha

-

Max_iter

- 1000 +

Random_state

+ 123
@@ -1404,7 +1404,7 @@

Graphics

-
+
@@ -1412,7 +1412,7 @@

Graphics

-
+
@@ -1420,7 +1420,7 @@

Graphics

-
+
@@ -1864,7 +1864,7 @@

Graphics

-
+
@@ -1872,7 +1872,7 @@

Graphics

-
+
@@ -1880,7 +1880,7 @@

Graphics

-
+
@@ -1888,7 +1888,7 @@

Graphics

-
+
@@ -1939,7 +1939,7 @@

Graphics

-
+
diff --git a/api/tutorials/mimiciii/query_api.html b/api/tutorials/mimiciii/query_api.html index 2ebb5656c..3ceffaed2 100644 --- a/api/tutorials/mimiciii/query_api.html +++ b/api/tutorials/mimiciii/query_api.html @@ -483,9 +483,9 @@

Imports and instantiate 
-/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
+/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
   from .autonotebook import tqdm as notebook_tqdm
-2023-09-21 13:54:12,092 INFO cyclops.query.orm - Database setup, ready to run queries!
+2023-10-06 10:16:52,892 INFO cyclops.query.orm - Database setup, ready to run queries!
@@ -519,8 +519,8 @@

Example 1. Get all male patients with a mortality outcome. 
-2023-09-21 13:54:17,932 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 13:54:17,934 INFO cyclops.utils.profile - Finished executing function run_query in 0.032659 s
+2023-10-06 10:16:58,694 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:16:58,695 INFO cyclops.utils.profile - Finished executing function run_query in 0.029403 s

@@ -560,8 +560,8 @@

Example 2. Get all female patient encounters with diagnoses ( 
-2023-09-21 13:54:18,079 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 13:54:18,080 INFO cyclops.utils.profile - Finished executing function run_query in 0.106374 s
+2023-10-06 10:16:58,822 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:16:58,823 INFO cyclops.utils.profile - Finished executing function run_query in 0.095880 s

@@ -593,8 +593,8 @@

Example 3. Get potassium lab tests for female patients. 
-2023-09-21 13:54:18,154 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 13:54:18,155 INFO cyclops.utils.profile - Finished executing function run_query in 0.035972 s
+2023-10-06 10:16:58,889 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:16:58,891 INFO cyclops.utils.profile - Finished executing function run_query in 0.035646 s

@@ -631,8 +631,8 @@

Example 4. Get AaDO2 carevue chart events for male patients that have a 
-2023-09-21 13:55:28,127 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 13:55:28,128 INFO cyclops.utils.profile - Finished executing function run_query in 69.928861 s
+2023-10-06 10:18:12,332 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:18:12,333 INFO cyclops.utils.profile - Finished executing function run_query in 73.401368 s

diff --git a/api/tutorials/mimiciii/query_api.ipynb b/api/tutorials/mimiciii/query_api.ipynb index 66c7e0364..8d2a42846 100644 --- a/api/tutorials/mimiciii/query_api.ipynb +++ b/api/tutorials/mimiciii/query_api.ipynb @@ -35,10 +35,10 @@ "id": "75a140e0-fb27-4319-862f-be54397abe5c", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T17:54:07.506462Z", - "iopub.status.busy": "2023-09-21T17:54:07.505758Z", - "iopub.status.idle": "2023-09-21T17:54:17.886435Z", - "shell.execute_reply": "2023-09-21T17:54:17.884508Z" + "iopub.execute_input": "2023-10-06T14:16:47.087651Z", + "iopub.status.busy": "2023-10-06T14:16:47.086739Z", + "iopub.status.idle": "2023-10-06T14:16:58.659092Z", + "shell.execute_reply": "2023-10-06T14:16:58.658255Z" }, "tags": [] }, @@ -47,7 +47,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + "/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] }, @@ -55,7 +55,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:54:12,092 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n" + "2023-10-06 10:16:52,892 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n" ] }, { @@ -102,10 +102,10 @@ "id": "c1efa964-8978-4a0e-9892-5ea4ce9953a3", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T17:54:17.893917Z", - "iopub.status.busy": "2023-09-21T17:54:17.893025Z", - "iopub.status.idle": "2023-09-21T17:54:17.942042Z", - "shell.execute_reply": "2023-09-21T17:54:17.940193Z" + "iopub.execute_input": "2023-10-06T14:16:58.663505Z", + "iopub.status.busy": "2023-10-06T14:16:58.663064Z", + "iopub.status.idle": "2023-10-06T14:16:58.699024Z", + "shell.execute_reply": "2023-10-06T14:16:58.698126Z" }, "tags": [] }, @@ -114,14 +114,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:54:17,932 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-10-06 10:16:58,694 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:54:17,934 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.032659 s\n" + "2023-10-06 10:16:58,695 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.029403 s\n" ] }, { @@ -158,10 +158,10 @@ "id": "a7ab5fa3-e26b-47a7-818f-1bf367a55760", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T17:54:17.949573Z", - "iopub.status.busy": "2023-09-21T17:54:17.949105Z", - "iopub.status.idle": "2023-09-21T17:54:18.088890Z", - "shell.execute_reply": "2023-09-21T17:54:18.087015Z" + "iopub.execute_input": "2023-10-06T14:16:58.707309Z", + "iopub.status.busy": "2023-10-06T14:16:58.706678Z", + "iopub.status.idle": "2023-10-06T14:16:58.828826Z", + "shell.execute_reply": "2023-10-06T14:16:58.827655Z" }, "tags": [] }, @@ -170,14 +170,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:54:18,079 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-10-06 10:16:58,822 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:54:18,080 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.106374 s\n" + "2023-10-06 10:16:58,823 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.095880 s\n" ] }, { @@ -219,10 +219,10 @@ "id": "24043abc-1878-4e00-8229-36d4a0368b98", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T17:54:18.095706Z", - "iopub.status.busy": "2023-09-21T17:54:18.095141Z", - "iopub.status.idle": "2023-09-21T17:54:18.162760Z", - "shell.execute_reply": "2023-09-21T17:54:18.161116Z" + "iopub.execute_input": "2023-10-06T14:16:58.835421Z", + "iopub.status.busy": "2023-10-06T14:16:58.834722Z", + "iopub.status.idle": "2023-10-06T14:16:58.896753Z", + "shell.execute_reply": "2023-10-06T14:16:58.895611Z" }, "tags": [] }, @@ -231,14 +231,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:54:18,154 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-10-06 10:16:58,889 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:54:18,155 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.035972 s\n" + "2023-10-06 10:16:58,891 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.035646 s\n" ] }, { @@ -272,10 +272,10 @@ "id": "f6142f27-e8d1-453c-bfe2-2265d9ff1914", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T17:54:18.170610Z", - "iopub.status.busy": "2023-09-21T17:54:18.170000Z", - "iopub.status.idle": "2023-09-21T17:55:28.132906Z", - "shell.execute_reply": "2023-09-21T17:55:28.131801Z" + "iopub.execute_input": "2023-10-06T14:16:58.902884Z", + "iopub.status.busy": "2023-10-06T14:16:58.902579Z", + "iopub.status.idle": "2023-10-06T14:18:12.338486Z", + "shell.execute_reply": "2023-10-06T14:18:12.336736Z" }, "tags": [] }, @@ -284,14 +284,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:55:28,127 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-10-06 10:18:12,332 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:55:28,128 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 69.928861 s\n" + "2023-10-06 10:18:12,333 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 73.401368 s\n" ] }, { diff --git a/api/tutorials/mimiciv/query_api.html b/api/tutorials/mimiciv/query_api.html index 932425822..8f7f2087b 100644 --- a/api/tutorials/mimiciv/query_api.html +++ b/api/tutorials/mimiciv/query_api.html @@ -487,9 +487,9 @@

Imports and instantiate 
-/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
+/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
   from .autonotebook import tqdm as notebook_tqdm
-2023-09-21 13:55:33,975 INFO cyclops.query.orm - Database setup, ready to run queries!
+2023-10-06 10:18:18,022 INFO cyclops.query.orm - Database setup, ready to run queries!

@@ -534,8 +534,8 @@

Example 1. Get all patient admissions from 2021 or later (approx year of adm 

-2023-09-21 13:55:36,941 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 13:55:36,942 INFO cyclops.utils.profile - Finished executing function run_query in 0.191435 s
+2023-10-06 10:18:20,549 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:18:20,550 INFO cyclops.utils.profile - Finished executing function run_query in 0.239686 s
@@ -580,8 +580,8 @@

Example 2. Get all patient encounters with diagnoses ( 
-2023-09-21 13:55:37,813 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 13:55:37,815 INFO cyclops.utils.profile - Finished executing function run_query in 0.825588 s
+2023-10-06 10:18:21,346 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:18:21,347 INFO cyclops.utils.profile - Finished executing function run_query in 0.757002 s

@@ -626,8 +626,8 @@

Example 3. Advanced - uses 
-2023-09-21 13:55:39,349 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 13:55:39,350 INFO cyclops.utils.profile - Finished executing function run_query in 1.488212 s
+2023-10-06 10:18:22,925 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:18:22,926 INFO cyclops.utils.profile - Finished executing function run_query in 1.543901 s

@@ -668,8 +668,8 @@

Example 4. Get routine vital signs for patients from year 2015. 
-2023-09-21 13:57:01,574 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 13:57:01,576 INFO cyclops.utils.profile - Finished executing function run_query in 82.185425 s
+2023-10-06 10:19:42,689 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:19:42,690 INFO cyclops.utils.profile - Finished executing function run_query in 79.723617 s

@@ -710,8 +710,8 @@

Example 5. Get hemoglobin lab tests for patients from year 2009. 
-2023-09-21 13:58:04,841 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 13:58:04,842 INFO cyclops.utils.profile - Finished executing function run_query in 63.230410 s
+2023-10-06 10:20:46,496 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:20:46,498 INFO cyclops.utils.profile - Finished executing function run_query in 63.771282 s

@@ -758,8 +758,8 @@

Example 6. Get radiology reports and filter on keywords 
-2023-09-21 13:58:12,804 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 13:58:12,805 INFO cyclops.utils.profile - Finished executing function run_query in 7.924855 s
+2023-10-06 10:20:53,867 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:20:53,868 INFO cyclops.utils.profile - Finished executing function run_query in 7.347900 s

@@ -803,8 +803,8 @@

Example 7. Get all female patient encounters from year 2015, and return as d 

-2023-09-21 13:58:13,268 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 13:58:13,268 INFO cyclops.utils.profile - Finished executing function run_query in 0.434427 s
+2023-10-06 10:20:54,309 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:20:54,310 INFO cyclops.utils.profile - Finished executing function run_query in 0.401536 s
@@ -834,8 +834,8 @@

Example 8. Running a raw SQL string. 
-2023-09-21 13:58:14,056 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 13:58:14,057 INFO cyclops.utils.profile - Finished executing function run_query in 0.009834 s
+2023-10-06 10:20:55,459 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:20:55,460 INFO cyclops.utils.profile - Finished executing function run_query in 0.010658 s

diff --git a/api/tutorials/mimiciv/query_api.ipynb b/api/tutorials/mimiciv/query_api.ipynb index e49efd6c8..3dbcb06c5 100644 --- a/api/tutorials/mimiciv/query_api.ipynb +++ b/api/tutorials/mimiciv/query_api.ipynb @@ -35,10 +35,10 @@ "id": "53009e6b", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T17:55:30.700107Z", - "iopub.status.busy": "2023-09-21T17:55:30.699586Z", - "iopub.status.idle": "2023-09-21T17:55:36.717628Z", - "shell.execute_reply": "2023-09-21T17:55:36.715848Z" + "iopub.execute_input": "2023-10-06T14:18:15.152876Z", + "iopub.status.busy": "2023-10-06T14:18:15.152350Z", + "iopub.status.idle": "2023-10-06T14:18:20.277536Z", + "shell.execute_reply": "2023-10-06T14:18:20.276264Z" }, "tags": [] }, @@ -47,7 +47,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + "/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] }, @@ -55,7 +55,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:55:33,975 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n" + "2023-10-06 10:18:18,022 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n" ] }, { @@ -113,10 +113,10 @@ "id": "cdfadaa4-6fd6-4fd7-85cf-e012aa0799e1", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T17:55:36.725087Z", - "iopub.status.busy": "2023-09-21T17:55:36.724202Z", - "iopub.status.idle": "2023-09-21T17:55:36.948064Z", - "shell.execute_reply": "2023-09-21T17:55:36.946618Z" + "iopub.execute_input": "2023-10-06T14:18:20.286090Z", + "iopub.status.busy": "2023-10-06T14:18:20.285275Z", + "iopub.status.idle": "2023-10-06T14:18:20.553219Z", + "shell.execute_reply": "2023-10-06T14:18:20.552583Z" } }, "outputs": [ @@ -124,14 +124,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:55:36,941 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-10-06 10:18:20,549 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:55:36,942 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.191435 s\n" + "2023-10-06 10:18:20,550 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.239686 s\n" ] }, { @@ -168,10 +168,10 @@ "id": "a89a9cf0", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T17:55:36.954017Z", - "iopub.status.busy": "2023-09-21T17:55:36.953494Z", - "iopub.status.idle": "2023-09-21T17:55:37.820478Z", - "shell.execute_reply": "2023-09-21T17:55:37.819196Z" + "iopub.execute_input": "2023-10-06T14:18:20.561940Z", + "iopub.status.busy": "2023-10-06T14:18:20.561121Z", + "iopub.status.idle": "2023-10-06T14:18:21.351054Z", + "shell.execute_reply": "2023-10-06T14:18:21.350447Z" } }, "outputs": [ @@ -179,14 +179,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:55:37,813 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-10-06 10:18:21,346 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:55:37,815 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.825588 s\n" + "2023-10-06 10:18:21,347 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.757002 s\n" ] }, { @@ -233,10 +233,10 @@ "id": "03936cee", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T17:55:37.827247Z", - "iopub.status.busy": "2023-09-21T17:55:37.826747Z", - "iopub.status.idle": "2023-09-21T17:55:39.355980Z", - "shell.execute_reply": "2023-09-21T17:55:39.354706Z" + "iopub.execute_input": "2023-10-06T14:18:21.360942Z", + "iopub.status.busy": "2023-10-06T14:18:21.360571Z", + "iopub.status.idle": "2023-10-06T14:18:22.932086Z", + "shell.execute_reply": "2023-10-06T14:18:22.930516Z" } }, "outputs": [ @@ -244,14 +244,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:55:39,349 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-10-06 10:18:22,925 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:55:39,350 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 1.488212 s\n" + "2023-10-06 10:18:22,926 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 1.543901 s\n" ] }, { @@ -298,10 +298,10 @@ "id": "56a72377", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T17:55:39.360124Z", - "iopub.status.busy": "2023-09-21T17:55:39.359622Z", - "iopub.status.idle": "2023-09-21T17:57:01.580197Z", - "shell.execute_reply": "2023-09-21T17:57:01.579390Z" + "iopub.execute_input": "2023-10-06T14:18:22.936380Z", + "iopub.status.busy": "2023-10-06T14:18:22.935778Z", + "iopub.status.idle": "2023-10-06T14:19:42.695080Z", + "shell.execute_reply": "2023-10-06T14:19:42.693742Z" } }, "outputs": [ @@ -309,14 +309,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:57:01,574 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-10-06 10:19:42,689 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:57:01,576 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 82.185425 s\n" + "2023-10-06 10:19:42,690 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 79.723617 s\n" ] }, { @@ -359,10 +359,10 @@ "id": "bce11f81", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T17:57:01.587773Z", - "iopub.status.busy": "2023-09-21T17:57:01.587431Z", - "iopub.status.idle": "2023-09-21T17:58:04.848576Z", - "shell.execute_reply": "2023-09-21T17:58:04.847334Z" + "iopub.execute_input": "2023-10-06T14:19:42.699281Z", + "iopub.status.busy": "2023-10-06T14:19:42.698661Z", + "iopub.status.idle": "2023-10-06T14:20:46.501647Z", + "shell.execute_reply": "2023-10-06T14:20:46.500962Z" } }, "outputs": [ @@ -370,14 +370,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:58:04,841 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-10-06 10:20:46,496 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:58:04,842 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 63.230410 s\n" + "2023-10-06 10:20:46,498 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 63.771282 s\n" ] }, { @@ -420,10 +420,10 @@ "id": "f00d270c-d78f-4dc0-8dae-ff4d52958c8b", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T17:58:04.855102Z", - "iopub.status.busy": "2023-09-21T17:58:04.854778Z", - "iopub.status.idle": "2023-09-21T17:58:12.808817Z", - "shell.execute_reply": "2023-09-21T17:58:12.808181Z" + "iopub.execute_input": "2023-10-06T14:20:46.504974Z", + "iopub.status.busy": "2023-10-06T14:20:46.504700Z", + "iopub.status.idle": "2023-10-06T14:20:53.871972Z", + "shell.execute_reply": "2023-10-06T14:20:53.871010Z" }, "tags": [] }, @@ -432,14 +432,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:58:12,804 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-10-06 10:20:53,867 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:58:12,805 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 7.924855 s\n" + "2023-10-06 10:20:53,868 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 7.347900 s\n" ] }, { @@ -488,10 +488,10 @@ "id": "28683d70-376e-4d9b-883d-1a7de634e455", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T17:58:12.816134Z", - "iopub.status.busy": "2023-09-21T17:58:12.815762Z", - "iopub.status.idle": "2023-09-21T17:58:14.039698Z", - "shell.execute_reply": "2023-09-21T17:58:14.038224Z" + "iopub.execute_input": "2023-10-06T14:20:53.879993Z", + "iopub.status.busy": "2023-10-06T14:20:53.879286Z", + "iopub.status.idle": "2023-10-06T14:20:55.442335Z", + "shell.execute_reply": "2023-10-06T14:20:55.440770Z" } }, "outputs": [ @@ -499,14 +499,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:58:13,268 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-10-06 10:20:54,309 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:58:13,268 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.434427 s\n" + "2023-10-06 10:20:54,310 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.401536 s\n" ] }, { @@ -554,10 +554,10 @@ "id": "a853deec", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T17:58:14.045813Z", - "iopub.status.busy": "2023-09-21T17:58:14.045298Z", - "iopub.status.idle": "2023-09-21T17:58:14.062291Z", - "shell.execute_reply": "2023-09-21T17:58:14.061326Z" + "iopub.execute_input": "2023-10-06T14:20:55.448001Z", + "iopub.status.busy": "2023-10-06T14:20:55.447439Z", + "iopub.status.idle": "2023-10-06T14:20:55.466839Z", + "shell.execute_reply": "2023-10-06T14:20:55.465139Z" }, "tags": [] }, @@ -566,14 +566,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:58:14,056 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-10-06 10:20:55,459 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 13:58:14,057 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.009834 s\n" + "2023-10-06 10:20:55,460 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.010658 s\n" ] }, { diff --git a/api/tutorials/nihcxr/cxr_classification.html b/api/tutorials/nihcxr/cxr_classification.html index 3164e826c..c04024b92 100644 --- a/api/tutorials/nihcxr/cxr_classification.html +++ b/api/tutorials/nihcxr/cxr_classification.html @@ -488,7 +488,7 @@

Import Libraries 
-/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
+/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
   from .autonotebook import tqdm as notebook_tqdm

@@ -574,8 +574,8 @@

Load Model and get Predictions 
-Filter: 100%|██████████| 4000/4000 [00:00<00:00, 231652.71 examples/s]
-Map: 100%|██████████| 2511/2511 [00:00<00:00, 3710.70 examples/s]
+Filter: 100%|██████████| 4000/4000 [00:00<00:00, 233084.87 examples/s]
+Map: 100%|██████████| 2511/2511 [00:00<00:00, 2738.27 examples/s]
@@ -664,9 +664,9 @@

Multilabel AUROC by Pathology and Sex 
-Filter -> Patient Gender:M: 100%|██████████| 2511/2511 [00:00<00:00, 58764.86 examples/s]
-Filter -> Patient Gender:F: 100%|██████████| 2511/2511 [00:00<00:00, 62441.96 examples/s]
-Filter -> overall: 100%|██████████| 2511/2511 [00:00<00:00, 63952.55 examples/s]
+Filter -> Patient Gender:M: 100%|██████████| 2511/2511 [00:00<00:00, 59212.54 examples/s]
+Filter -> Patient Gender:F: 100%|██████████| 2511/2511 [00:00<00:00, 60730.23 examples/s]
+Filter -> overall: 100%|██████████| 2511/2511 [00:00<00:00, 62028.96 examples/s]

@@ -698,9 +698,9 @@

Multilabel AUROC by Pathology and Sex

-
+
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Performance Over Time

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diff --git a/api/tutorials/nihcxr/monitor_api.html b/api/tutorials/nihcxr/monitor_api.html index 54d1747eb..d537b8759 100644 --- a/api/tutorials/nihcxr/monitor_api.html +++ b/api/tutorials/nihcxr/monitor_api.html @@ -472,7 +472,7 @@

Import Libraries and Load NIHCXR Dataset 
-/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
+/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
   from .autonotebook import tqdm as notebook_tqdm
@@ -517,7 +517,7 @@

Example 1. Generate Source/Target Dataset for Experiments (1-2) 
-Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 67311.63 examples/s]
+Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 64729.36 examples/s]
@@ -663,14 +663,14 @@

Example 4. Sensitivity test experiment with different clinical shifts 
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+Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 45792.76 examples/s]
+Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 45625.10 examples/s]
diff --git a/api/tutorials/nihcxr/monitor_api.ipynb b/api/tutorials/nihcxr/monitor_api.ipynb index 3d6839766..7b982ba75 100644 --- a/api/tutorials/nihcxr/monitor_api.ipynb +++ b/api/tutorials/nihcxr/monitor_api.ipynb @@ -22,10 +22,10 @@ "id": "8aa3302d", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T17:59:36.599139Z", - "iopub.status.busy": "2023-09-21T17:59:36.598645Z", - "iopub.status.idle": "2023-09-21T17:59:43.819186Z", - "shell.execute_reply": "2023-09-21T17:59:43.817995Z" + "iopub.execute_input": "2023-10-06T14:22:20.701278Z", + "iopub.status.busy": "2023-10-06T14:22:20.700689Z", + "iopub.status.idle": "2023-10-06T14:22:28.889772Z", + "shell.execute_reply": "2023-10-06T14:22:28.889060Z" } }, "outputs": [ @@ -33,7 +33,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + "/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] } @@ -69,10 +69,10 @@ "id": "e11920db", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T17:59:43.824258Z", - "iopub.status.busy": "2023-09-21T17:59:43.824053Z", - "iopub.status.idle": "2023-09-21T17:59:44.364867Z", - "shell.execute_reply": "2023-09-21T17:59:44.364173Z" + "iopub.execute_input": "2023-10-06T14:22:28.895160Z", + "iopub.status.busy": "2023-10-06T14:22:28.894789Z", + "iopub.status.idle": "2023-10-06T14:22:29.443357Z", + "shell.execute_reply": "2023-10-06T14:22:29.441931Z" } }, "outputs": [ @@ -89,7 +89,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 33866.90 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 29803.27 examples/s]" ] }, { @@ -97,7 +97,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 88514.67 examples/s]" + "Filter (num_proc=6): 67%|██████▋ | 17064/25596 [00:00<00:00, 70487.03 examples/s]" ] }, { @@ -105,7 +105,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 67311.63 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 64729.36 examples/s]" ] }, { @@ -159,16 +159,16 @@ "id": "54a3523a", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T17:59:44.368683Z", - "iopub.status.busy": "2023-09-21T17:59:44.368369Z", - "iopub.status.idle": "2023-09-21T17:59:56.311955Z", - "shell.execute_reply": "2023-09-21T17:59:56.311339Z" + "iopub.execute_input": "2023-10-06T14:22:29.449137Z", + "iopub.status.busy": "2023-10-06T14:22:29.448237Z", + "iopub.status.idle": "2023-10-06T14:22:41.876797Z", + "shell.execute_reply": "2023-10-06T14:22:41.876131Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -227,16 +227,16 @@ "id": "40b5a90f", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T17:59:56.318088Z", - "iopub.status.busy": "2023-09-21T17:59:56.317885Z", - "iopub.status.idle": "2023-09-21T18:00:04.388661Z", - "shell.execute_reply": "2023-09-21T18:00:04.388006Z" + "iopub.execute_input": "2023-10-06T14:22:41.883407Z", + "iopub.status.busy": "2023-10-06T14:22:41.883175Z", + "iopub.status.idle": "2023-10-06T14:22:50.479461Z", + "shell.execute_reply": "2023-10-06T14:22:50.477771Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -285,10 +285,10 @@ "id": "9ba03fac", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T18:00:04.394939Z", - "iopub.status.busy": "2023-09-21T18:00:04.394576Z", - "iopub.status.idle": "2023-09-21T18:00:20.649021Z", - "shell.execute_reply": "2023-09-21T18:00:20.647490Z" + "iopub.execute_input": "2023-10-06T14:22:50.484786Z", + "iopub.status.busy": "2023-10-06T14:22:50.484248Z", + "iopub.status.idle": "2023-10-06T14:23:08.110607Z", + "shell.execute_reply": "2023-10-06T14:23:08.109793Z" } }, "outputs": [ @@ -305,7 +305,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 32885.86 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 27703.86 examples/s]" ] }, { @@ -313,7 +313,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 60226.40 examples/s]" + "Filter (num_proc=6): 67%|██████▋ | 17064/25596 [00:00<00:00, 59928.49 examples/s]" ] }, { @@ -321,15 +321,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 76067.48 examples/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 50791.85 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 48586.81 examples/s]" ] }, { @@ -352,7 +344,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 31789.60 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 30371.07 examples/s]" ] }, { @@ -360,7 +352,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 57330.27 examples/s]" + "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 53132.49 examples/s]" ] }, { @@ -368,7 +360,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 65867.94 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 71355.84 examples/s]" ] }, { @@ -376,7 +368,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 49247.74 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 46788.10 examples/s]" ] }, { @@ -399,7 +391,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 30620.49 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 28172.69 examples/s]" ] }, { @@ -407,7 +399,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 51340.50 examples/s]" + "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 54305.60 examples/s]" ] }, { @@ -415,7 +407,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 60968.84 examples/s]" + "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 62290.69 examples/s]" ] }, { @@ -423,7 +415,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 44759.52 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 48797.96 examples/s]" ] }, { @@ -446,7 +438,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 30416.40 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 30792.60 examples/s]" ] }, { @@ -454,7 +446,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 56194.37 examples/s]" + "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 53635.16 examples/s]" ] }, { @@ -462,7 +454,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 73573.79 examples/s]" + "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 64239.37 examples/s]" ] }, { @@ -470,7 +462,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 50134.96 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 47166.93 examples/s]" ] }, { @@ -493,7 +485,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 30705.09 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 21686.89 examples/s]" ] }, { @@ -501,7 +493,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 51688.20 examples/s]" + "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 39203.53 examples/s]" ] }, { @@ -509,7 +501,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 61192.63 examples/s]" + "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 43684.07 examples/s]" ] }, { @@ -517,7 +509,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 46152.58 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 35272.02 examples/s]" ] }, { @@ -540,7 +532,15 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 28207.56 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 21959.68 examples/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 38832.80 examples/s]" ] }, { @@ -548,7 +548,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 54832.62 examples/s]" + "Filter (num_proc=6): 67%|██████▋ | 17064/25596 [00:00<00:00, 39874.10 examples/s]" ] }, { @@ -556,7 +556,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 62902.11 examples/s]" + "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 36513.79 examples/s]" ] }, { @@ -564,7 +564,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 47213.04 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 32329.00 examples/s]" ] }, { @@ -587,7 +587,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 29575.29 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 27381.83 examples/s]" ] }, { @@ -595,7 +595,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 54485.36 examples/s]" + "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 52349.81 examples/s]" ] }, { @@ -603,7 +603,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 61190.33 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 67214.90 examples/s]" ] }, { @@ -611,7 +611,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 46946.69 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 45792.76 examples/s]" ] }, { @@ -634,7 +634,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 31376.30 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 28349.50 examples/s]" ] }, { @@ -642,7 +642,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 57991.21 examples/s]" + "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 50527.17 examples/s]" ] }, { @@ -650,7 +650,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 64886.52 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 68934.39 examples/s]" ] }, { @@ -658,7 +658,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 46966.92 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 45625.10 examples/s]" ] }, { @@ -670,7 +670,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -734,17 +734,17 @@ "id": "77e4b383", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T18:00:20.653349Z", - "iopub.status.busy": "2023-09-21T18:00:20.652650Z", - "iopub.status.idle": "2023-09-21T18:00:22.903072Z", - "shell.execute_reply": "2023-09-21T18:00:22.902427Z" + "iopub.execute_input": "2023-10-06T14:23:08.117438Z", + "iopub.status.busy": "2023-10-06T14:23:08.117120Z", + "iopub.status.idle": "2023-10-06T14:23:10.642415Z", + "shell.execute_reply": "2023-10-06T14:23:10.640742Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] diff --git a/api/tutorials/omop/query_api.html b/api/tutorials/omop/query_api.html index 244c5a616..3fde090ec 100644 --- a/api/tutorials/omop/query_api.html +++ b/api/tutorials/omop/query_api.html @@ -492,9 +492,9 @@

Imports and instantiate 
-/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
+/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
   from .autonotebook import tqdm as notebook_tqdm
-2023-09-21 14:00:29,101 INFO cyclops.query.orm - Database setup, ready to run queries!
+2023-10-06 10:23:17,092 INFO cyclops.query.orm - Database setup, ready to run queries!

@@ -568,8 +568,8 @@

Example 1. Get all patient visits in or after 2010. 
-2023-09-21 14:00:30,605 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 14:00:30,607 INFO cyclops.utils.profile - Finished executing function run_query in 0.077730 s
+2023-10-06 10:23:18,152 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:23:18,153 INFO cyclops.utils.profile - Finished executing function run_query in 0.051203 s

@@ -626,8 +626,8 @@

Example 2. Get measurements for all visits in or after 2020. 
-2023-09-21 14:00:30,733 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 14:00:30,734 INFO cyclops.utils.profile - Finished executing function run_query in 0.066410 s
+2023-10-06 10:23:18,265 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:23:18,267 INFO cyclops.utils.profile - Finished executing function run_query in 0.065999 s

@@ -673,7 +673,7 @@

Imports and instantiate 
-2023-09-21 14:00:37,437 INFO cyclops.query.orm - Database setup, ready to run queries!
+2023-10-06 10:23:24,830 INFO cyclops.query.orm - Database setup, ready to run queries!

@@ -713,8 +713,8 @@

Example 1. Get all patient visits that ended in a mortality outcome in or af 

-2023-09-21 14:00:45,407 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 14:00:45,408 INFO cyclops.utils.profile - Finished executing function run_query in 1.023836 s
+2023-10-06 10:23:31,842 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:23:31,844 INFO cyclops.utils.profile - Finished executing function run_query in 0.965239 s
@@ -770,8 +770,8 @@

Example 2. Get all measurements for female patient visits with 
-2023-09-21 14:01:01,902 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 14:01:01,904 INFO cyclops.utils.profile - Finished executing function run_query in 16.425851 s
+2023-10-06 10:23:48,457 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:23:48,458 INFO cyclops.utils.profile - Finished executing function run_query in 16.527008 s

diff --git a/api/tutorials/omop/query_api.ipynb b/api/tutorials/omop/query_api.ipynb index e8c5daa1d..4f7c31222 100644 --- a/api/tutorials/omop/query_api.ipynb +++ b/api/tutorials/omop/query_api.ipynb @@ -45,10 +45,10 @@ "id": "53009e6b", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T18:00:26.191031Z", - "iopub.status.busy": "2023-09-21T18:00:26.190524Z", - "iopub.status.idle": "2023-09-21T18:00:30.497727Z", - "shell.execute_reply": "2023-09-21T18:00:30.496385Z" + "iopub.execute_input": "2023-10-06T14:23:14.228965Z", + "iopub.status.busy": "2023-10-06T14:23:14.227858Z", + "iopub.status.idle": "2023-10-06T14:23:18.083298Z", + "shell.execute_reply": "2023-10-06T14:23:18.082438Z" } }, "outputs": [ @@ -56,7 +56,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + "/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] }, @@ -64,7 +64,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 14:00:29,101 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n" + "2023-10-06 10:23:17,092 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n" ] }, { @@ -158,10 +158,10 @@ "id": "3a3d9cb9-fe40-45b8-ba2f-8de52a3b7f4f", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T18:00:30.504207Z", - "iopub.status.busy": "2023-09-21T18:00:30.503480Z", - "iopub.status.idle": "2023-09-21T18:00:30.619969Z", - "shell.execute_reply": "2023-09-21T18:00:30.618613Z" + "iopub.execute_input": "2023-10-06T14:23:18.088588Z", + "iopub.status.busy": "2023-10-06T14:23:18.088222Z", + "iopub.status.idle": "2023-10-06T14:23:18.161618Z", + "shell.execute_reply": "2023-10-06T14:23:18.160929Z" } }, "outputs": [ @@ -169,14 +169,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 14:00:30,605 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-10-06 10:23:18,152 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 14:00:30,607 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.077730 s\n" + "2023-10-06 10:23:18,153 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.051203 s\n" ] }, { @@ -233,10 +233,10 @@ "id": "030e2491-a7cc-42f3-a1ca-618212b3524c", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T18:00:30.626169Z", - "iopub.status.busy": "2023-09-21T18:00:30.625674Z", - "iopub.status.idle": "2023-09-21T18:00:30.740094Z", - "shell.execute_reply": "2023-09-21T18:00:30.738934Z" + "iopub.execute_input": "2023-10-06T14:23:18.167084Z", + "iopub.status.busy": "2023-10-06T14:23:18.166840Z", + "iopub.status.idle": "2023-10-06T14:23:18.270285Z", + "shell.execute_reply": "2023-10-06T14:23:18.269579Z" } }, "outputs": [ @@ -244,14 +244,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 14:00:30,733 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-10-06 10:23:18,265 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 14:00:30,734 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.066410 s\n" + "2023-10-06 10:23:18,267 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.065999 s\n" ] }, { @@ -309,10 +309,10 @@ "id": "0622b3df-2864-4f32-bd98-806019f59c50", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T18:00:30.749217Z", - "iopub.status.busy": "2023-09-21T18:00:30.748713Z", - "iopub.status.idle": "2023-09-21T18:00:44.345207Z", - "shell.execute_reply": "2023-09-21T18:00:44.343636Z" + "iopub.execute_input": "2023-10-06T14:23:18.277310Z", + "iopub.status.busy": "2023-10-06T14:23:18.276890Z", + "iopub.status.idle": "2023-10-06T14:23:30.837556Z", + "shell.execute_reply": "2023-10-06T14:23:30.836115Z" }, "tags": [] }, @@ -321,7 +321,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 14:00:37,437 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n" + "2023-10-06 10:23:24,830 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n" ] }, { @@ -363,10 +363,10 @@ "id": "40ff2e83-75e4-4119-aa33-26f95e63ddaa", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T18:00:44.352021Z", - "iopub.status.busy": "2023-09-21T18:00:44.351427Z", - "iopub.status.idle": "2023-09-21T18:00:45.411512Z", - "shell.execute_reply": "2023-09-21T18:00:45.410904Z" + "iopub.execute_input": "2023-10-06T14:23:30.844987Z", + "iopub.status.busy": "2023-10-06T14:23:30.844339Z", + "iopub.status.idle": "2023-10-06T14:23:31.849585Z", + "shell.execute_reply": "2023-10-06T14:23:31.848266Z" }, "tags": [] }, @@ -375,14 +375,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 14:00:45,407 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-10-06 10:23:31,842 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 14:00:45,408 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 1.023836 s\n" + "2023-10-06 10:23:31,844 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.965239 s\n" ] }, { @@ -425,10 +425,10 @@ "id": "46fd771c-5da7-4bce-aec7-08a5210a069b", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T18:00:45.418735Z", - "iopub.status.busy": "2023-09-21T18:00:45.418495Z", - "iopub.status.idle": "2023-09-21T18:01:01.909212Z", - "shell.execute_reply": "2023-09-21T18:01:01.908113Z" + "iopub.execute_input": "2023-10-06T14:23:31.853541Z", + "iopub.status.busy": "2023-10-06T14:23:31.852868Z", + "iopub.status.idle": "2023-10-06T14:23:48.462064Z", + "shell.execute_reply": "2023-10-06T14:23:48.461316Z" }, "tags": [] }, @@ -437,14 +437,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 14:01:01,902 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-10-06 10:23:48,457 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 14:01:01,904 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 16.425851 s\n" + "2023-10-06 10:23:48,458 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 16.527008 s\n" ] }, { @@ -494,10 +494,10 @@ "id": "d20a2581-f613-4ab8-9feb-3e84b8835db1", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T18:01:01.914610Z", - "iopub.status.busy": "2023-09-21T18:01:01.914179Z", - "iopub.status.idle": "2023-09-21T18:01:01.923687Z", - "shell.execute_reply": "2023-09-21T18:01:01.922384Z" + "iopub.execute_input": "2023-10-06T14:23:48.468815Z", + "iopub.status.busy": "2023-10-06T14:23:48.468433Z", + "iopub.status.idle": "2023-10-06T14:23:48.477650Z", + "shell.execute_reply": "2023-10-06T14:23:48.476437Z" }, "tags": [] }, diff --git a/api/tutorials/synthea/los_prediction.html b/api/tutorials/synthea/los_prediction.html index a2782712f..d0a5dd645 100644 --- a/api/tutorials/synthea/los_prediction.html +++ b/api/tutorials/synthea/los_prediction.html @@ -676,17 +676,17 @@

Compute length of stay (labels) 
-2023-09-21 14:01:10,509 INFO cyclops.query.orm - Database setup, ready to run queries!
-2023-09-21 14:01:15,563 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 14:01:15,564 INFO cyclops.utils.profile - Finished executing function run_query in 3.709101 s
-2023-09-21 14:01:17,366 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 14:01:17,367 INFO cyclops.utils.profile - Finished executing function run_query in 1.802094 s
-2023-09-21 14:01:18,935 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 14:01:18,936 INFO cyclops.utils.profile - Finished executing function run_query in 0.389443 s
-2023-09-21 14:01:19,432 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 14:01:19,434 INFO cyclops.utils.profile - Finished executing function run_query in 0.492748 s
-2023-09-21 14:01:19,537 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 14:01:19,538 INFO cyclops.utils.profile - Finished executing function run_query in 0.102891 s
+2023-10-06 10:23:57,554 INFO cyclops.query.orm - Database setup, ready to run queries!
+2023-10-06 10:24:01,798 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:24:01,800 INFO cyclops.utils.profile - Finished executing function run_query in 3.710401 s
+2023-10-06 10:24:03,884 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:24:03,886 INFO cyclops.utils.profile - Finished executing function run_query in 2.084462 s
+2023-10-06 10:24:05,455 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:24:05,456 INFO cyclops.utils.profile - Finished executing function run_query in 0.374978 s
+2023-10-06 10:24:05,895 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:24:05,896 INFO cyclops.utils.profile - Finished executing function run_query in 0.435460 s
+2023-10-06 10:24:05,981 INFO cyclops.query.orm - Query returned successfully!
+2023-10-06 10:24:05,982 INFO cyclops.utils.profile - Finished executing function run_query in 0.084567 s

@@ -773,9 +773,9 @@

Drop NaNs based on the
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+
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Performance Over Time

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Version

- Date: 2023-09-21 + Date: 2023-10-06
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Model Parameters

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+

Eval_metric

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Model Parameters

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+

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+

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Reg_lambda

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Random_state

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+

Min_child_weight

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Objective

- binary:logistic -
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Graphics

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Graphics

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Quantitative Analysis

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Quantitative Analysis

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Quantitative Analysis

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Quantitative Analysis

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Quantitative Analysis

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Quantitative Analysis

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Quantitative Analysis

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Quantitative Analysis

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Quantitative Analysis

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Quantitative Analysis

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Quantitative Analysis

 - + @@ -1606,7 +1606,7 @@

Quantitative Analysis

 - + @@ -1616,7 +1616,7 @@

Quantitative Analysis

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Quantitative Analysis

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Quantitative Analysis

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Quantitative Analysis

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Quantitative Analysis

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Quantitative Analysis

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Quantitative Analysis

 - + @@ -1686,7 +1686,7 @@

Quantitative Analysis

 - + @@ -1696,7 +1696,7 @@

Quantitative Analysis

 - + @@ -1706,7 +1706,7 @@

Quantitative Analysis

 - + @@ -1716,7 +1716,7 @@

Quantitative Analysis

 - + @@ -1726,7 +1726,7 @@

Quantitative Analysis

 - + @@ -1736,7 +1736,7 @@

Quantitative Analysis

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Graphics

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Graphics

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Graphics

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BinaryAccuracy gender:M0.850.9 0.6 Passed
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BinaryAUROC overall0.950.97 0.8 Passed