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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 4cfc92667..9af3330a6 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-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
+/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
   from .autonotebook import tqdm as notebook_tqdm
-2023-09-21 11:13:32,085 INFO cyclops.query.orm - Database setup, ready to run queries!
+2023-09-21 13:53:43,487 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 11:13:32,731 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 11:13:32,732 INFO cyclops.utils.profile - Finished executing function run_query in 0.042269 s
+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

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

Example 2. Get all patient encounters with diagnoses ( 
-2023-09-21 11:13:32,811 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 11:13:32,812 INFO cyclops.utils.profile - Finished executing function run_query in 0.068682 s
+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

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

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

-2023-09-21 11:13:32,883 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 11:13:32,884 INFO cyclops.utils.profile - Finished executing function run_query in 0.036568 s
+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
@@ -667,8 +667,8 @@

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

-2023-09-21 11:13:33,061 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 11:13:33,062 INFO cyclops.utils.profile - Finished executing function run_query in 0.158932 s
+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
diff --git a/api/tutorials/eicu/query_api.ipynb b/api/tutorials/eicu/query_api.ipynb index 10855de68..36a12c32f 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": "2023-09-21T15:13:29.994261Z", - "iopub.status.busy": "2023-09-21T15:13:29.993598Z", - "iopub.status.idle": "2023-09-21T15:13:32.675511Z", - "shell.execute_reply": "2023-09-21T15:13:32.674003Z" + "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" }, "tags": [] }, @@ -47,7 +47,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/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", + "/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", " from .autonotebook import tqdm as notebook_tqdm\n" ] }, @@ -55,7 +55,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:13:32,085 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n" + "2023-09-21 13:53:43,487 \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": "2023-09-21T15:13:32.680552Z", - "iopub.status.busy": "2023-09-21T15:13:32.679674Z", - "iopub.status.idle": "2023-09-21T15:13:32.735778Z", - "shell.execute_reply": "2023-09-21T15:13:32.735178Z" + "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" }, "tags": [] }, @@ -144,14 +144,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:13:32,731 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-09-21 13:53:44,237 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:13:32,732 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.042269 s\n" + "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" ] }, { @@ -188,10 +188,10 @@ "id": "a7ab5fa3-e26b-47a7-818f-1bf367a55760", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T15:13:32.739071Z", - "iopub.status.busy": "2023-09-21T15:13:32.738807Z", - "iopub.status.idle": "2023-09-21T15:13:32.815828Z", - "shell.execute_reply": "2023-09-21T15:13:32.814793Z" + "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" }, "tags": [] }, @@ -200,14 +200,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:13:32,811 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-09-21 13:53:44,324 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:13:32,812 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.068682 s\n" + "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" ] }, { @@ -244,10 +244,10 @@ "id": "24043abc-1878-4e00-8229-36d4a0368b98", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T15:13:32.820582Z", - "iopub.status.busy": "2023-09-21T15:13:32.819523Z", - "iopub.status.idle": "2023-09-21T15:13:32.888583Z", - "shell.execute_reply": "2023-09-21T15:13:32.887723Z" + "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" }, "tags": [] }, @@ -256,14 +256,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:13:32,883 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-09-21 13:53:44,396 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:13:32,884 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.036568 s\n" + "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" ] }, { @@ -306,10 +306,10 @@ "id": "f6142f27-e8d1-453c-bfe2-2265d9ff1914", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T15:13:32.892256Z", - "iopub.status.busy": "2023-09-21T15:13:32.891751Z", - "iopub.status.idle": "2023-09-21T15:13:33.070327Z", - "shell.execute_reply": "2023-09-21T15:13:33.068395Z" + "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" }, "tags": [] }, @@ -318,14 +318,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:13:33,061 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-09-21 13:53:44,580 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:13:33,062 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.158932 s\n" + "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" ] }, { diff --git a/api/tutorials/kaggle/heart_failure_prediction.html b/api/tutorials/kaggle/heart_failure_prediction.html index 14fe54e78..754ba64da 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-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
+/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
   from .autonotebook import tqdm as notebook_tqdm

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

Data Loading 
-2023-09-21 11:13:41,041 INFO cyclops.utils.file - Loading DataFrame from ./data/heart.csv
+2023-09-21 13:53:52,715 INFO cyclops.utils.file - Loading DataFrame from ./data/heart.csv
@@ -632,9 +632,9 @@

Sex values

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

Performance Over Time

-
+
@@ -827,8 +827,8 @@

Average

-

Class_weight

- balanced +

Epsilon

+ 0.1
@@ -836,8 +836,8 @@

Class_weight

-

Learning_rate

- adaptive +

Loss

+ log_loss
@@ -845,8 +845,8 @@

Learning_rate

-

Fit_intercept

- True +

Power_t

+ 0.5
@@ -854,8 +854,8 @@

Fit_intercept

-

Warm_start

- False +

Fit_intercept

+ True
@@ -863,8 +863,8 @@

Warm_start

-

Shuffle

- True +

N_iter_no_change

+ 5
@@ -872,8 +872,8 @@

Shuffle

-

Power_t

- 0.5 +

Eta0

+ 0.01
@@ -881,31 +881,40 @@

Power_t

-

Alpha

- 0.001 +

Penalty

+ l2
+ + + + +
-

Epsilon

- 0.1 +

Learning_rate

+ adaptive
+
+

Tol

+ 0.001 +
-

Eta0

- 0.01 +

Validation_fraction

+ 0.1
@@ -922,8 +931,8 @@

L1_ratio

-

Penalty

- l2 +

Class_weight

+ balanced
@@ -940,8 +949,8 @@

Random_state

-

Validation_fraction

- 0.1 +

Early_stopping

+ True
@@ -958,17 +967,8 @@

Verbose

-

Max_iter

- 1000 -
- - - - - -
-

Loss

- log_loss +

Warm_start

+ False
@@ -976,8 +976,8 @@

Loss

-

N_iter_no_change

- 5 +

Shuffle

+ True
@@ -985,7 +985,7 @@

N_iter_no_change

-

Tol

+

Alpha

0.001
@@ -994,8 +994,8 @@

Tol

-

Early_stopping

- True +

Max_iter

+ 1000
@@ -1300,29 +1300,23 @@

Sensitive Data

-

License

+

Citation

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

Example 2. Get all female patient encounters with diagnoses ( 
-2023-09-21 11:14:11,795 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 11:14:11,796 INFO cyclops.utils.profile - Finished executing function run_query in 0.096709 s
+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

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

Example 3. Get potassium lab tests for female patients. 
-2023-09-21 11:14:11,865 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 11:14:11,866 INFO cyclops.utils.profile - Finished executing function run_query in 0.032713 s
+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

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

Example 4. Get AaDO2 carevue chart events for male patients that have a 
-2023-09-21 11:15:24,022 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 11:15:24,023 INFO cyclops.utils.profile - Finished executing function run_query in 72.113653 s
+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

diff --git a/api/tutorials/mimiciii/query_api.ipynb b/api/tutorials/mimiciii/query_api.ipynb index f2d2ac74b..66c7e0364 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": "2023-09-21T15:13:56.082278Z", - "iopub.status.busy": "2023-09-21T15:13:56.081599Z", - "iopub.status.idle": "2023-09-21T15:14:11.604495Z", - "shell.execute_reply": "2023-09-21T15:14:11.602826Z" + "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" }, "tags": [] }, @@ -47,7 +47,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/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", + "/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", " from .autonotebook import tqdm as notebook_tqdm\n" ] }, @@ -55,7 +55,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:14:04,262 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n" + "2023-09-21 13:54:12,092 \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": "2023-09-21T15:14:11.611824Z", - "iopub.status.busy": "2023-09-21T15:14:11.611095Z", - "iopub.status.idle": "2023-09-21T15:14:11.663843Z", - "shell.execute_reply": "2023-09-21T15:14:11.662029Z" + "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" }, "tags": [] }, @@ -114,14 +114,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:14:11,656 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-09-21 13:54:17,932 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:14:11,657 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.037987 s\n" + "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" ] }, { @@ -158,10 +158,10 @@ "id": "a7ab5fa3-e26b-47a7-818f-1bf367a55760", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T15:14:11.670802Z", - "iopub.status.busy": "2023-09-21T15:14:11.670277Z", - "iopub.status.idle": "2023-09-21T15:14:11.803055Z", - "shell.execute_reply": "2023-09-21T15:14:11.801537Z" + "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" }, "tags": [] }, @@ -170,14 +170,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:14:11,795 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-09-21 13:54:18,079 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:14:11,796 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.096709 s\n" + "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" ] }, { @@ -219,10 +219,10 @@ "id": "24043abc-1878-4e00-8229-36d4a0368b98", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T15:14:11.809563Z", - "iopub.status.busy": "2023-09-21T15:14:11.808863Z", - "iopub.status.idle": "2023-09-21T15:14:11.871782Z", - "shell.execute_reply": "2023-09-21T15:14:11.870700Z" + "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" }, "tags": [] }, @@ -231,14 +231,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:14:11,865 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-09-21 13:54:18,154 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:14:11,866 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.032713 s\n" + "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" ] }, { @@ -272,10 +272,10 @@ "id": "f6142f27-e8d1-453c-bfe2-2265d9ff1914", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T15:14:11.879018Z", - "iopub.status.busy": "2023-09-21T15:14:11.878318Z", - "iopub.status.idle": "2023-09-21T15:15:24.026868Z", - "shell.execute_reply": "2023-09-21T15:15:24.026125Z" + "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" }, "tags": [] }, @@ -284,14 +284,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:15:24,022 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-09-21 13:55:28,127 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:15:24,023 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 72.113653 s\n" + "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" ] }, { diff --git a/api/tutorials/mimiciv/query_api.html b/api/tutorials/mimiciv/query_api.html index 5d505e229..932425822 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-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
+/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
   from .autonotebook import tqdm as notebook_tqdm
-2023-09-21 11:15:29,005 INFO cyclops.query.orm - Database setup, ready to run queries!
+2023-09-21 13:55:33,975 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 11:15:30,728 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 11:15:30,729 INFO cyclops.utils.profile - Finished executing function run_query in 0.269253 s
+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
@@ -580,8 +580,8 @@

Example 2. Get all patient encounters with diagnoses ( 
-2023-09-21 11:15:31,581 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 11:15:31,582 INFO cyclops.utils.profile - Finished executing function run_query in 0.819763 s
+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

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

Example 3. Advanced - uses 
-2023-09-21 11:15:33,139 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 11:15:33,140 INFO cyclops.utils.profile - Finished executing function run_query in 1.516252 s
+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

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

Example 4. Get routine vital signs for patients from year 2015. 
-2023-09-21 11:16:55,506 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 11:16:55,507 INFO cyclops.utils.profile - Finished executing function run_query in 82.326835 s
+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

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

Example 5. Get hemoglobin lab tests for patients from year 2009. 
-2023-09-21 11:18:06,785 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 11:18:06,786 INFO cyclops.utils.profile - Finished executing function run_query in 71.250068 s
+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

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

Example 6. Get radiology reports and filter on keywords 
-2023-09-21 11:18:14,480 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 11:18:14,482 INFO cyclops.utils.profile - Finished executing function run_query in 7.654839 s
+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

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

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

-2023-09-21 11:18:14,852 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 11:18:14,853 INFO cyclops.utils.profile - Finished executing function run_query in 0.330419 s
+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
@@ -834,8 +834,8 @@

Example 8. Running a raw SQL string. 
-2023-09-21 11:18:15,647 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 11:18:15,648 INFO cyclops.utils.profile - Finished executing function run_query in 0.009600 s
+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

diff --git a/api/tutorials/mimiciv/query_api.ipynb b/api/tutorials/mimiciv/query_api.ipynb index 3690e92e8..e49efd6c8 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": "2023-09-21T15:15:26.614909Z", - "iopub.status.busy": "2023-09-21T15:15:26.614291Z", - "iopub.status.idle": "2023-09-21T15:15:30.431295Z", - "shell.execute_reply": "2023-09-21T15:15:30.429985Z" + "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" }, "tags": [] }, @@ -47,7 +47,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/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", + "/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", " from .autonotebook import tqdm as notebook_tqdm\n" ] }, @@ -55,7 +55,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:15:29,005 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n" + "2023-09-21 13:55:33,975 \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": "2023-09-21T15:15:30.437531Z", - "iopub.status.busy": "2023-09-21T15:15:30.436712Z", - "iopub.status.idle": "2023-09-21T15:15:30.731938Z", - "shell.execute_reply": "2023-09-21T15:15:30.731372Z" + "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" } }, "outputs": [ @@ -124,14 +124,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:15:30,728 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-09-21 13:55:36,941 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:15:30,729 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.269253 s\n" + "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" ] }, { @@ -168,10 +168,10 @@ "id": "a89a9cf0", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T15:15:30.737267Z", - "iopub.status.busy": "2023-09-21T15:15:30.736904Z", - "iopub.status.idle": "2023-09-21T15:15:31.588167Z", - "shell.execute_reply": "2023-09-21T15:15:31.586916Z" + "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" } }, "outputs": [ @@ -179,14 +179,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:15:31,581 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-09-21 13:55:37,813 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:15:31,582 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.819763 s\n" + "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" ] }, { @@ -233,10 +233,10 @@ "id": "03936cee", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T15:15:31.592844Z", - "iopub.status.busy": "2023-09-21T15:15:31.592045Z", - "iopub.status.idle": "2023-09-21T15:15:33.143620Z", - "shell.execute_reply": "2023-09-21T15:15:33.142769Z" + "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" } }, "outputs": [ @@ -244,14 +244,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:15:33,139 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-09-21 13:55:39,349 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:15:33,140 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 1.516252 s\n" + "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" ] }, { @@ -298,10 +298,10 @@ "id": "56a72377", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T15:15:33.151407Z", - "iopub.status.busy": "2023-09-21T15:15:33.150642Z", - "iopub.status.idle": "2023-09-21T15:16:55.511076Z", - "shell.execute_reply": "2023-09-21T15:16:55.510453Z" + "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" } }, "outputs": [ @@ -309,14 +309,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:16:55,506 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-09-21 13:57:01,574 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:16:55,507 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 82.326835 s\n" + "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" ] }, { @@ -359,10 +359,10 @@ "id": "bce11f81", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T15:16:55.518731Z", - "iopub.status.busy": "2023-09-21T15:16:55.518490Z", - "iopub.status.idle": "2023-09-21T15:18:06.790720Z", - "shell.execute_reply": "2023-09-21T15:18:06.789736Z" + "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" } }, "outputs": [ @@ -370,14 +370,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:18:06,785 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-09-21 13:58:04,841 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:18:06,786 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 71.250068 s\n" + "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" ] }, { @@ -420,10 +420,10 @@ "id": "f00d270c-d78f-4dc0-8dae-ff4d52958c8b", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T15:18:06.795571Z", - "iopub.status.busy": "2023-09-21T15:18:06.795175Z", - "iopub.status.idle": "2023-09-21T15:18:14.487593Z", - "shell.execute_reply": "2023-09-21T15:18:14.486250Z" + "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" }, "tags": [] }, @@ -432,14 +432,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:18:14,480 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-09-21 13:58:12,804 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:18:14,482 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 7.654839 s\n" + "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" ] }, { @@ -488,10 +488,10 @@ "id": "28683d70-376e-4d9b-883d-1a7de634e455", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T15:18:14.493378Z", - "iopub.status.busy": "2023-09-21T15:18:14.492864Z", - "iopub.status.idle": "2023-09-21T15:18:15.630195Z", - "shell.execute_reply": "2023-09-21T15:18:15.628606Z" + "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" } }, "outputs": [ @@ -499,14 +499,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:18:14,852 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-09-21 13:58:13,268 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:18:14,853 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.330419 s\n" + "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" ] }, { @@ -554,10 +554,10 @@ "id": "a853deec", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T15:18:15.636606Z", - "iopub.status.busy": "2023-09-21T15:18:15.636072Z", - "iopub.status.idle": "2023-09-21T15:18:15.653563Z", - "shell.execute_reply": "2023-09-21T15:18:15.652154Z" + "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" }, "tags": [] }, @@ -566,14 +566,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:18:15,647 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-09-21 13:58:14,056 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:18:15,648 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.009600 s\n" + "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" ] }, { diff --git a/api/tutorials/nihcxr/cxr_classification.html b/api/tutorials/nihcxr/cxr_classification.html index f095df7b1..3164e826c 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-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
+/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
   from .autonotebook import tqdm as notebook_tqdm

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

Load Model and get Predictions 
-Filter: 100%|██████████| 4000/4000 [00:00<00:00, 212857.51 examples/s]
-Map: 100%|██████████| 2511/2511 [00:00<00:00, 3764.42 examples/s]
+Filter: 100%|██████████| 4000/4000 [00:00<00:00, 231652.71 examples/s]
+Map: 100%|██████████| 2511/2511 [00:00<00:00, 3710.70 examples/s]
@@ -664,9 +664,9 @@

Multilabel AUROC by Pathology and Sex 
-Filter -> Patient Gender:M: 100%|██████████| 2511/2511 [00:00<00:00, 59679.94 examples/s]
-Filter -> Patient Gender:F: 100%|██████████| 2511/2511 [00:00<00:00, 60636.52 examples/s]
-Filter -> overall: 100%|██████████| 2511/2511 [00:00<00:00, 60682.64 examples/s]
+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]

@@ -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 13790a09b..54d1747eb 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-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
+/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
   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, 59651.02 examples/s]
+Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 67311.63 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, 49186.86 examples/s]
-Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 47503.36 examples/s]
-Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 47554.34 examples/s]
-Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 47736.93 examples/s]
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+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, 47213.04 examples/s]
+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, 46966.92 examples/s]
diff --git a/api/tutorials/nihcxr/monitor_api.ipynb b/api/tutorials/nihcxr/monitor_api.ipynb index 3d915f820..3d6839766 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": "2023-09-21T15:19:40.741494Z", - "iopub.status.busy": "2023-09-21T15:19:40.740965Z", - "iopub.status.idle": "2023-09-21T15:19:47.826976Z", - "shell.execute_reply": "2023-09-21T15:19:47.826290Z" + "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" } }, "outputs": [ @@ -33,7 +33,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/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", + "/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", " from .autonotebook import tqdm as notebook_tqdm\n" ] } @@ -69,10 +69,10 @@ "id": "e11920db", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T15:19:47.831059Z", - "iopub.status.busy": "2023-09-21T15:19:47.830850Z", - "iopub.status.idle": "2023-09-21T15:19:48.421859Z", - "shell.execute_reply": "2023-09-21T15:19:48.420755Z" + "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" } }, "outputs": [ @@ -89,7 +89,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 33676.51 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 33866.90 examples/s]" ] }, { @@ -97,7 +97,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 67%|██████▋ | 17064/25596 [00:00<00:00, 74589.12 examples/s]" + "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 88514.67 examples/s]" ] }, { @@ -105,7 +105,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 59651.02 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 67311.63 examples/s]" ] }, { @@ -159,16 +159,16 @@ "id": "54a3523a", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T15:19:48.427311Z", - "iopub.status.busy": "2023-09-21T15:19:48.427053Z", - "iopub.status.idle": "2023-09-21T15:20:00.190060Z", - "shell.execute_reply": "2023-09-21T15:20:00.189377Z" + "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" } }, "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": "2023-09-21T15:20:00.195951Z", - "iopub.status.busy": "2023-09-21T15:20:00.195751Z", - "iopub.status.idle": "2023-09-21T15:20:08.181487Z", - "shell.execute_reply": "2023-09-21T15:20:08.180840Z" + "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" } }, "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": "2023-09-21T15:20:08.187450Z", - "iopub.status.busy": "2023-09-21T15:20:08.187246Z", - "iopub.status.idle": "2023-09-21T15:20:24.439511Z", - "shell.execute_reply": "2023-09-21T15:20:24.437810Z" + "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" } }, "outputs": [ @@ -305,7 +305,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 32117.36 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 32885.86 examples/s]" ] }, { @@ -313,7 +313,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 59308.24 examples/s]" + "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 60226.40 examples/s]" ] }, { @@ -321,7 +321,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 68146.59 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 76067.48 examples/s]" ] }, { @@ -329,7 +329,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 50082.92 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 50791.85 examples/s]" ] }, { @@ -352,7 +352,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 30327.52 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 31789.60 examples/s]" ] }, { @@ -360,7 +360,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 67%|██████▋ | 17064/25596 [00:00<00:00, 65741.29 examples/s]" + "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 57330.27 examples/s]" ] }, { @@ -368,7 +368,15 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 49186.86 examples/s]" + "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 65867.94 examples/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 49247.74 examples/s]" ] }, { @@ -391,7 +399,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 27875.77 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 30620.49 examples/s]" ] }, { @@ -399,7 +407,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 54151.32 examples/s]" + "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 51340.50 examples/s]" ] }, { @@ -407,7 +415,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 64771.65 examples/s]" + "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 60968.84 examples/s]" ] }, { @@ -415,7 +423,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 47503.36 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 44759.52 examples/s]" ] }, { @@ -438,7 +446,15 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 26967.65 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 30416.40 examples/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 56194.37 examples/s]" ] }, { @@ -446,7 +462,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 67%|██████▋ | 17064/25596 [00:00<00:00, 59482.00 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 73573.79 examples/s]" ] }, { @@ -454,7 +470,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 47554.34 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 50134.96 examples/s]" ] }, { @@ -477,7 +493,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 31119.55 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 30705.09 examples/s]" ] }, { @@ -485,7 +501,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 58048.25 examples/s]" + "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 51688.20 examples/s]" ] }, { @@ -493,7 +509,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 65662.26 examples/s]" + "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 61192.63 examples/s]" ] }, { @@ -501,7 +517,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 47736.93 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 46152.58 examples/s]" ] }, { @@ -524,7 +540,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 29740.41 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 28207.56 examples/s]" ] }, { @@ -532,7 +548,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 53287.44 examples/s]" + "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 54832.62 examples/s]" ] }, { @@ -540,7 +556,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 62028.81 examples/s]" + "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 62902.11 examples/s]" ] }, { @@ -548,7 +564,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 45598.12 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 47213.04 examples/s]" ] }, { @@ -571,7 +587,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 30003.42 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 29575.29 examples/s]" ] }, { @@ -579,7 +595,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 54249.63 examples/s]" + "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 54485.36 examples/s]" ] }, { @@ -587,7 +603,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 64059.31 examples/s]" + "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 61190.33 examples/s]" ] }, { @@ -595,7 +611,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 45676.58 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 46946.69 examples/s]" ] }, { @@ -618,7 +634,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 31113.54 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 31376.30 examples/s]" ] }, { @@ -626,7 +642,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 56549.55 examples/s]" + "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 57991.21 examples/s]" ] }, { @@ -634,7 +650,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 65331.50 examples/s]" + "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 64886.52 examples/s]" ] }, { @@ -642,7 +658,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 47465.24 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 46966.92 examples/s]" ] }, { @@ -654,7 +670,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -718,17 +734,17 @@ "id": "77e4b383", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T15:20:24.445864Z", - "iopub.status.busy": "2023-09-21T15:20:24.445335Z", - "iopub.status.idle": "2023-09-21T15:20:26.691388Z", - "shell.execute_reply": "2023-09-21T15:20:26.690729Z" + "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" }, "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 46e741409..244c5a616 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-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
+/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
   from .autonotebook import tqdm as notebook_tqdm
-2023-09-21 11:20:32,084 INFO cyclops.query.orm - Database setup, ready to run queries!
+2023-09-21 14:00:29,101 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 11:20:32,762 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 11:20:32,764 INFO cyclops.utils.profile - Finished executing function run_query in 0.073394 s
+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

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

Example 2. Get measurements for all visits in or after 2020. 
-2023-09-21 11:20:32,876 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 11:20:32,877 INFO cyclops.utils.profile - Finished executing function run_query in 0.060985 s
+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

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

Imports and instantiate 
-2023-09-21 11:20:39,668 INFO cyclops.query.orm - Database setup, ready to run queries!
+2023-09-21 14:00:37,437 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 11:20:47,657 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 11:20:47,658 INFO cyclops.utils.profile - Finished executing function run_query in 0.982651 s
+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
@@ -770,8 +770,8 @@

Example 2. Get all measurements for female patient visits with 
-2023-09-21 11:21:03,713 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 11:21:03,714 INFO cyclops.utils.profile - Finished executing function run_query in 15.987421 s
+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

diff --git a/api/tutorials/omop/query_api.ipynb b/api/tutorials/omop/query_api.ipynb index 284848849..e8c5daa1d 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": "2023-09-21T15:20:29.968455Z", - "iopub.status.busy": "2023-09-21T15:20:29.967832Z", - "iopub.status.idle": "2023-09-21T15:20:32.670741Z", - "shell.execute_reply": "2023-09-21T15:20:32.669483Z" + "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" } }, "outputs": [ @@ -56,7 +56,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/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", + "/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", " from .autonotebook import tqdm as notebook_tqdm\n" ] }, @@ -64,7 +64,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:20:32,084 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n" + "2023-09-21 14:00:29,101 \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": "2023-09-21T15:20:32.675784Z", - "iopub.status.busy": "2023-09-21T15:20:32.675350Z", - "iopub.status.idle": "2023-09-21T15:20:32.780285Z", - "shell.execute_reply": "2023-09-21T15:20:32.778884Z" + "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" } }, "outputs": [ @@ -169,14 +169,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:20:32,762 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-09-21 14:00:30,605 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:20:32,764 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.073394 s\n" + "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" ] }, { @@ -233,10 +233,10 @@ "id": "030e2491-a7cc-42f3-a1ca-618212b3524c", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T15:20:32.787408Z", - "iopub.status.busy": "2023-09-21T15:20:32.787048Z", - "iopub.status.idle": "2023-09-21T15:20:32.883542Z", - "shell.execute_reply": "2023-09-21T15:20:32.882113Z" + "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" } }, "outputs": [ @@ -244,14 +244,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:20:32,876 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-09-21 14:00:30,733 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:20:32,877 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.060985 s\n" + "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" ] }, { @@ -309,10 +309,10 @@ "id": "0622b3df-2864-4f32-bd98-806019f59c50", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T15:20:32.887960Z", - "iopub.status.busy": "2023-09-21T15:20:32.887658Z", - "iopub.status.idle": "2023-09-21T15:20:46.636115Z", - "shell.execute_reply": "2023-09-21T15:20:46.634435Z" + "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" }, "tags": [] }, @@ -321,7 +321,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:20:39,668 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n" + "2023-09-21 14:00:37,437 \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": "2023-09-21T15:20:46.643291Z", - "iopub.status.busy": "2023-09-21T15:20:46.642703Z", - "iopub.status.idle": "2023-09-21T15:20:47.661643Z", - "shell.execute_reply": "2023-09-21T15:20:47.661030Z" + "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" }, "tags": [] }, @@ -375,14 +375,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:20:47,657 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-09-21 14:00:45,407 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:20:47,658 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.982651 s\n" + "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" ] }, { @@ -425,10 +425,10 @@ "id": "46fd771c-5da7-4bce-aec7-08a5210a069b", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T15:20:47.668887Z", - "iopub.status.busy": "2023-09-21T15:20:47.668641Z", - "iopub.status.idle": "2023-09-21T15:21:03.719854Z", - "shell.execute_reply": "2023-09-21T15:21:03.718648Z" + "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" }, "tags": [] }, @@ -437,14 +437,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:21:03,713 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" + "2023-09-21 14:01:01,902 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-09-21 11:21:03,714 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 15.987421 s\n" + "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" ] }, { @@ -494,10 +494,10 @@ "id": "d20a2581-f613-4ab8-9feb-3e84b8835db1", "metadata": { "execution": { - "iopub.execute_input": "2023-09-21T15:21:03.726216Z", - "iopub.status.busy": "2023-09-21T15:21:03.725634Z", - "iopub.status.idle": "2023-09-21T15:21:03.736676Z", - "shell.execute_reply": "2023-09-21T15:21:03.735134Z" + "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" }, "tags": [] }, diff --git a/api/tutorials/synthea/los_prediction.html b/api/tutorials/synthea/los_prediction.html index 13de9722c..a2782712f 100644 --- a/api/tutorials/synthea/los_prediction.html +++ b/api/tutorials/synthea/los_prediction.html @@ -492,7 +492,7 @@

Import Libraries 
-/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
+/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
   from .autonotebook import tqdm as notebook_tqdm

@@ -676,17 +676,17 @@

Compute length of stay (labels) 
-2023-09-21 11:21:11,813 INFO cyclops.query.orm - Database setup, ready to run queries!
-2023-09-21 11:21:16,638 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 11:21:16,638 INFO cyclops.utils.profile - Finished executing function run_query in 3.832714 s
-2023-09-21 11:21:18,455 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 11:21:18,456 INFO cyclops.utils.profile - Finished executing function run_query in 1.816797 s
-2023-09-21 11:21:20,032 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 11:21:20,034 INFO cyclops.utils.profile - Finished executing function run_query in 0.385914 s
-2023-09-21 11:21:20,526 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 11:21:20,528 INFO cyclops.utils.profile - Finished executing function run_query in 0.488658 s
-2023-09-21 11:21:20,627 INFO cyclops.query.orm - Query returned successfully!
-2023-09-21 11:21:20,628 INFO cyclops.utils.profile - Finished executing function run_query in 0.098377 s
+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
@@ -773,9 +773,9 @@

Drop NaNs based on the
-
+
@@ -695,7 +695,7 @@

Performance Over Time

-
+
@@ -952,10 +952,6 @@

Model Parameters

-
-

Objective

- binary:logistic -
@@ -991,20 +987,24 @@

Objective

+
+

Max_depth

+ 2 +
+
+

Missing

+ nan +
-
-

Enable_categorical

- False -
@@ -1020,10 +1020,6 @@

Enable_categorical

-
-

N_estimators

- 500 -
@@ -1035,8 +1031,8 @@

N_estimators

-

Learning_rate

- 0.01 +

Reg_lambda

+ 1
@@ -1048,6 +1044,10 @@

Learning_rate

+
+

Seed

+ 123 +
@@ -1059,59 +1059,63 @@

Learning_rate

-

Max_depth

- 5 +

N_estimators

+ 250
+
+

Gamma

+ 1 +
-
-

Min_child_weight

- 3 -
-

Colsample_bytree

- 0.8 +

Eval_metric

+ logloss
+
+

Enable_categorical

+ False +
+
+

Colsample_bytree

+ 0.7 +
-
-

Seed

- 123 -
-

Reg_lambda

- 1 +

Learning_rate

+ 0.1
@@ -1139,8 +1143,8 @@

Reg_lambda

-

Random_state

- 123 +

Min_child_weight

+ 3
@@ -1152,10 +1156,6 @@

Random_state

-
-

Gamma

- 2 -
@@ -1167,8 +1167,8 @@

Gamma

-

Eval_metric

- logloss +

Random_state

+ 123
@@ -1186,8 +1186,8 @@

Eval_metric

-

Missing

- nan +

Objective

+ binary:logistic
@@ -1220,7 +1220,7 @@

Graphics

-
+
@@ -1228,7 +1228,7 @@

Graphics

-
+
@@ -1236,7 +1236,7 @@

Graphics

-
+
@@ -1244,7 +1244,7 @@

Graphics

-
+
@@ -1252,7 +1252,7 @@

Graphics

-
+
@@ -1496,7 +1496,7 @@

Quantitative Analysis

BinaryAccuracy age:[20 - 50) - 0.89 + 0.86 0.6 Passed @@ -1506,7 +1506,7 @@

Quantitative Analysis

BinaryPrecision age:[20 - 50) - 0.89 + 0.94 0.6 Passed @@ -1516,7 +1516,7 @@

Quantitative Analysis

BinaryRecall age:[20 - 50) - 0.93 + 0.84 0.6 Passed @@ -1526,7 +1526,7 @@

Quantitative Analysis

BinaryF1Score age:[20 - 50) - 0.91 + 0.89 0.6 Passed @@ -1546,7 +1546,7 @@

Quantitative Analysis

BinaryAccuracy age:[50 - 80) - 0.89 + 0.72 0.6 Passed @@ -1556,7 +1556,7 @@

Quantitative Analysis

BinaryPrecision age:[50 - 80) - 0.89 + 0.7 0.6 Passed @@ -1566,7 +1566,7 @@

Quantitative Analysis

BinaryRecall age:[50 - 80) - 0.89 + 0.73 0.6 Passed @@ -1576,7 +1576,7 @@

Quantitative Analysis

BinaryF1Score age:[50 - 80) - 0.89 + 0.72 0.6 Passed @@ -1586,7 +1586,7 @@

Quantitative Analysis

BinaryAUROC age:[50 - 80) - 0.97 + 0.88 0.8 Passed @@ -1596,7 +1596,7 @@

Quantitative Analysis

BinaryAccuracy gender:M - 0.91 + 0.85 0.6 Passed @@ -1616,7 +1616,7 @@

Quantitative Analysis

BinaryRecall gender:M - 0.94 + 0.84 0.6 Passed @@ -1626,7 +1626,7 @@

Quantitative Analysis

BinaryF1Score gender:M - 0.93 + 0.88 0.6 Passed @@ -1636,7 +1636,7 @@

Quantitative Analysis

BinaryAUROC gender:M - 0.98 + 0.95 0.8 Passed @@ -1646,7 +1646,7 @@

Quantitative Analysis

BinaryAccuracy gender:F - 0.9 + 0.85 0.6 Passed @@ -1656,7 +1656,7 @@

Quantitative Analysis

BinaryPrecision gender:F - 0.92 + 0.88 0.6 Passed @@ -1666,7 +1666,7 @@

Quantitative Analysis

BinaryRecall gender:F - 0.92 + 0.88 0.6 Passed @@ -1676,7 +1676,7 @@

Quantitative Analysis

BinaryF1Score gender:F - 0.92 + 0.88 0.6 Passed @@ -1686,7 +1686,7 @@

Quantitative Analysis

BinaryAUROC gender:F - 0.97 + 0.95 0.8 Passed @@ -1696,7 +1696,7 @@

Quantitative Analysis

BinaryAccuracy overall - 0.91 + 0.85 0.6 Passed @@ -1706,7 +1706,7 @@

Quantitative Analysis

BinaryPrecision overall - 0.92 + 0.91 0.6 Passed @@ -1716,7 +1716,7 @@

Quantitative Analysis

BinaryRecall overall - 0.93 + 0.86 0.6 Passed @@ -1726,7 +1726,7 @@

Quantitative Analysis

BinaryF1Score overall - 0.93 + 0.88 0.6 Passed @@ -1736,7 +1736,7 @@

Quantitative Analysis

BinaryAUROC overall - 0.97 + 0.95 0.8 Passed @@ -1766,7 +1766,7 @@

Graphics

-
+
@@ -1774,7 +1774,7 @@

Graphics

-
+
@@ -1782,7 +1782,7 @@

Graphics

-
+
@@ -1790,7 +1790,7 @@

Graphics

-
+
@@ -1841,7 +1841,7 @@

Graphics

-
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