diff --git a/404.html b/404.html index 75c16707e..d106f40cb 100644 --- a/404.html +++ b/404.html @@ -5,8 +5,8 @@ Page Not Found | CyclOps - - + +
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diff --git a/api/_modules/cyclops/report/report.html b/api/_modules/cyclops/report/report.html index 58251b9f5..7c19d8532 100644 --- a/api/_modules/cyclops/report/report.html +++ b/api/_modules/cyclops/report/report.html @@ -297,7 +297,7 @@

Source code for cyclops.report.report

-"""Cyclops report module."""
+"""Cyclops model report module."""
 
 import base64
 import glob
@@ -361,8 +361,8 @@ 
Source code for cyclops.report.report
 )
 
 
-_TEMPLATE_DIR = os.path.join(os.path.dirname(__file__), "templates")
-_DEFAULT_TEMPLATE_FILENAME = "cyclops_generic_template.jinja"
+_TEMPLATE_DIR = os.path.join(os.path.dirname(__file__), "templates", "model_report")
+_DEFAULT_TEMPLATE_FILENAME = "model_report.jinja"
 
 
 

diff --git a/api/reference/api/_autosummary/cyclops.report.report.html b/api/reference/api/_autosummary/cyclops.report.report.html
index 48bb63729..f9f0fc798 100644
--- a/api/reference/api/_autosummary/cyclops.report.report.html
+++ b/api/reference/api/_autosummary/cyclops.report.report.html
@@ -300,7 +300,7 @@
         

           

 
cyclops.report.report#

-
Cyclops report module.

+
Cyclops model report module.

 
Classes

 

 
diff --git a/api/reference/api/cyclops.report.html b/api/reference/api/cyclops.report.html
index 4bbf7e102..bd1221aa6 100644
--- a/api/reference/api/cyclops.report.html
+++ b/api/reference/api/cyclops.report.html
@@ -305,7 +305,7 @@
 

-
+

 
 
report
Cyclops report module.
Cyclops model report module.

 

 
 

diff --git a/api/searchindex.js b/api/searchindex.js
index 5e5360f93..b330882db 100644
--- a/api/searchindex.js
+++ b/api/searchindex.js
@@ -1 +1 @@
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"multiclass_sensitivity": [[54, "multiclass-sensitivity"]], "multilabel_sensitivity": [[54, "multilabel-sensitivity"]], "Specificity": [[55, "specificity"], [55, "id1"]], "BinarySpecificity": [[55, "binaryspecificity"]], "MulticlassSpecificity": [[55, "multiclassspecificity"]], "MultilabelSpecificity": [[55, "multilabelspecificity"]], "specificity": [[55, "id2"]], "binary_specificity": [[55, "binary-specificity"]], "multiclass_specificity": [[55, "multiclass-specificity"]], "multilabel_specificity": [[55, "multilabel-specificity"]], "StatScores": [[56, "statscores"], [56, "id1"]], "BinaryStatScores": [[56, "binarystatscores"]], "MulticlassStatScores": [[56, "multiclassstatscores"]], "MultilabelStatScores": [[56, "multilabelstatscores"]], "stat_scores": [[56, "stat-scores"]], "binary_stat_scores": [[56, "binary-stat-scores"]], "multiclass_stat_scores": [[56, "multiclass-stat-scores"]], "multilabel_stat_scores": [[56, "multilabel-stat-scores"]], "Tutorials": [[57, "tutorials"]], "Heart Failure Prediction": [[58, "Heart-Failure-Prediction"]], "Import Libraries": [[58, "Import-Libraries"], [59, "Import-Libraries"], [60, "Import-Libraries"], [62, "Import-Libraries"]], "Constants": [[58, "Constants"], [59, "Constants"], [62, "Constants"]], "Data Loading": [[58, "Data-Loading"]], "Sex values": [[58, "Sex-values"]], "Age distribution": [[58, "Age-distribution"], [59, "Age-distribution"], [62, "Age-distribution"]], "Outcome distribution": [[58, "Outcome-distribution"], [59, "Outcome-distribution"], [62, "Outcome-distribution"]], "Identifying feature types": [[58, "Identifying-feature-types"], [59, "Identifying-feature-types"], [62, "Identifying-feature-types"]], "Creating data preprocessors": [[58, "Creating-data-preprocessors"], [59, "Creating-data-preprocessors"], [62, "Creating-data-preprocessors"]], "Creating Hugging Face Dataset": [[58, "Creating-Hugging-Face-Dataset"], [59, "Creating-Hugging-Face-Dataset"], [62, "Creating-Hugging-Face-Dataset"]], "Model Creation": [[58, "Model-Creation"], [59, "Model-Creation"], [60, "Model-Creation"], [62, "Model-Creation"]], "Task Creation": [[58, "Task-Creation"], [59, "Task-Creation"], [62, "Task-Creation"]], "Training": [[58, "Training"], [59, "Training"], [62, "Training"]], "Prediction": [[58, "Prediction"], [59, "Prediction"], [62, "Prediction"]], "Report Generation": [[58, "Report-Generation"], [59, "Report-Generation"], [62, "Report-Generation"]], "Mortality Prediction": [[59, "Mortality-Prediction"]], "Data Querying & Processing": [[59, "Data-Querying-&-Processing"]], "Compute mortality (labels)": [[59, "Compute-mortality-(labels)"]], "Data Inspection and Preprocessing": [[59, "Data-Inspection-and-Preprocessing"], [62, "Data-Inspection-and-Preprocessing"]], "Drop NaNs based on the NAN_THRESHOLD": [[59, "Drop-NaNs-based-on-the-NAN_THRESHOLD"], [62, "Drop-NaNs-based-on-the-NAN_THRESHOLD"]], "Gender distribution": [[59, "Gender-distribution"], [62, "Gender-distribution"]], "Chest X-Ray Disease Classification": [[60, "Chest-X-Ray-Disease-Classification"]], "Generate Historical Reports": [[60, "Generate-Historical-Reports"]], "Initialize Periodic Report": [[60, "Initialize-Periodic-Report"]], "Load Dataset": [[60, "Load-Dataset"]], "Multilabel AUROC by Pathology and Sex": [[60, "Multilabel-AUROC-by-Pathology-and-Sex"]], "Multilabel AUROC by Pathology and Age": [[60, "Multilabel-AUROC-by-Pathology-and-Age"]], "Log Performance Metrics as Tests w/ Thresholds": [[60, "Log-Performance-Metrics-as-Tests-w/-Thresholds"]], "Populate Model Card Fields": [[60, "Populate-Model-Card-Fields"]], "NIHCXR Clinical Drift Experiments Tutorial": [[61, "NIHCXR-Clinical-Drift-Experiments-Tutorial"]], "Import Libraries and Load NIHCXR Dataset": [[61, "Import-Libraries-and-Load-NIHCXR-Dataset"]], "Example 1. Generate Source/Target Dataset for Experiments (1-2)": [[61, "Example-1.-Generate-Source/Target-Dataset-for-Experiments-(1-2)"]], "Example 2. Sensitivity test experiment with 3 dimensionality reduction techniques": [[61, "Example-2.-Sensitivity-test-experiment-with-3-dimensionality-reduction-techniques"]], "Example 3. Sensitivity test experiment with models trained on different datasets": [[61, "Example-3.-Sensitivity-test-experiment-with-models-trained-on-different-datasets"]], "Example 4. Sensitivity test experiment with different clinical shifts": [[61, "Example-4.-Sensitivity-test-experiment-with-different-clinical-shifts"]], "Example 5. Rolling window experiment with synthetic timestamps using biweekly window": [[61, "Example-5.-Rolling-window-experiment-with-synthetic-timestamps-using-biweekly-window"]], "Prolonged Length of Stay Prediction": [[62, "Prolonged-Length-of-Stay-Prediction"]], "Data Querying": [[62, "Data-Querying"]], "Compute length of stay (labels)": [[62, "Compute-length-of-stay-(labels)"]], "Length of stay distribution": [[62, "Length-of-stay-distribution"]], "monitor API": [[63, "monitor-api"]], "Example use cases": [[64, "example-use-cases"]], "Tabular data": [[64, "tabular-data"]], "Kaggle Heart Failure Prediction": [[64, "kaggle-heart-failure-prediction"]], "MIMICIV Mortality Prediction": [[64, "mimiciv-mortality-prediction"]], "Synthea Prolonged Length of Stay Prediction": [[64, "synthea-prolonged-length-of-stay-prediction"]], "Image data": [[64, "image-data"]], "NIH Chest X-ray classification": [[64, "nih-chest-x-ray-classification"]], "User Guide": [[65, "user-guide"]]}, "indexentries": 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"fit() (aggregator method)": [[9, "cyclops.data.aggregate.Aggregator.fit"]], "fit_transform() (aggregator method)": [[9, "cyclops.data.aggregate.Aggregator.fit_transform"]], "imputer (aggregator attribute)": [[9, "cyclops.data.aggregate.Aggregator.imputer"]], "num_timesteps (aggregator attribute)": [[9, "cyclops.data.aggregate.Aggregator.num_timesteps"]], "set_output() (aggregator method)": [[9, "cyclops.data.aggregate.Aggregator.set_output"]], "time_by (aggregator attribute)": [[9, "cyclops.data.aggregate.Aggregator.time_by"]], "timestamp_col (aggregator attribute)": [[9, "cyclops.data.aggregate.Aggregator.timestamp_col"]], "timestep_size (aggregator attribute)": [[9, "cyclops.data.aggregate.Aggregator.timestep_size"]], "transform() (aggregator method)": [[9, "cyclops.data.aggregate.Aggregator.transform"]], "window_duration (aggregator attribute)": [[9, "cyclops.data.aggregate.Aggregator.window_duration"]], "window_start_time (aggregator attribute)": [[9, "cyclops.data.aggregate.Aggregator.window_start_time"]], "window_stop_time (aggregator attribute)": [[9, "cyclops.data.aggregate.Aggregator.window_stop_time"]], "window_times (aggregator attribute)": [[9, "cyclops.data.aggregate.Aggregator.window_times"]], "tabular_as_aggregated() (in module cyclops.data.aggregate)": [[10, "cyclops.data.aggregate.tabular_as_aggregated"]], "timestamp_ffill_agg() (in module cyclops.data.aggregate)": [[11, "cyclops.data.aggregate.timestamp_ffill_agg"]], "cyclops.data.features.medical_image": [[12, "module-cyclops.data.features.medical_image"]], "medicalimage (class in cyclops.data.features.medical_image)": [[13, "cyclops.data.features.medical_image.MedicalImage"]], "__call__() (medicalimage method)": [[13, "cyclops.data.features.medical_image.MedicalImage.__call__"]], "cast_storage() (medicalimage method)": [[13, "cyclops.data.features.medical_image.MedicalImage.cast_storage"]], "decode_example() (medicalimage method)": [[13, 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"overall() (in module cyclops.data.slicer)": [[23, "cyclops.data.slicer.overall"]], "cyclops.monitor.clinical_applicator": [[24, "module-cyclops.monitor.clinical_applicator"]], "clinicalshiftapplicator (class in cyclops.monitor.clinical_applicator)": [[25, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator"]], "age() (clinicalshiftapplicator method)": [[25, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.age"]], "apply_shift() (clinicalshiftapplicator method)": [[25, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.apply_shift"]], "custom() (clinicalshiftapplicator method)": [[25, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.custom"]], "hospital_type() (clinicalshiftapplicator method)": [[25, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.hospital_type"]], "month() (clinicalshiftapplicator method)": [[25, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.month"]], "sex() (clinicalshiftapplicator method)": [[25, 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\ No newline at end of file
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Generate Source/Target Dataset for Experiments (1-2)": [[61, "Example-1.-Generate-Source/Target-Dataset-for-Experiments-(1-2)"]], "Example 2. Sensitivity test experiment with 3 dimensionality reduction techniques": [[61, "Example-2.-Sensitivity-test-experiment-with-3-dimensionality-reduction-techniques"]], "Example 3. Sensitivity test experiment with models trained on different datasets": [[61, "Example-3.-Sensitivity-test-experiment-with-models-trained-on-different-datasets"]], "Example 4. Sensitivity test experiment with different clinical shifts": [[61, "Example-4.-Sensitivity-test-experiment-with-different-clinical-shifts"]], "Example 5. Rolling window experiment with synthetic timestamps using biweekly window": [[61, "Example-5.-Rolling-window-experiment-with-synthetic-timestamps-using-biweekly-window"]], "Prolonged Length of Stay Prediction": [[62, "Prolonged-Length-of-Stay-Prediction"]], "Data Querying": [[62, "Data-Querying"]], "Compute length of stay (labels)": [[62, "Compute-length-of-stay-(labels)"]], "Length of stay distribution": [[62, "Length-of-stay-distribution"]], "monitor API": [[63, "monitor-api"]], "Example use cases": [[64, "example-use-cases"]], "Tabular data": [[64, "tabular-data"]], "Kaggle Heart Failure Prediction": [[64, "kaggle-heart-failure-prediction"]], "MIMICIV Mortality Prediction": [[64, "mimiciv-mortality-prediction"]], "Synthea Prolonged Length of Stay Prediction": [[64, "synthea-prolonged-length-of-stay-prediction"]], "Image data": [[64, "image-data"]], "NIH Chest X-ray classification": [[64, "nih-chest-x-ray-classification"]], "User Guide": [[65, "user-guide"]]}, "indexentries": 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[[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.__init__"]], "add_model() (binarytabularclassificationtask method)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.add_model"]], "data_type (binarytabularclassificationtask property)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.data_type"]], "evaluate() (binarytabularclassificationtask method)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.evaluate"]], "get_model() (binarytabularclassificationtask method)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.get_model"]], "list_models() (binarytabularclassificationtask method)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.list_models"]], "list_models_params() (binarytabularclassificationtask method)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.list_models_params"]], "load_model() (binarytabularclassificationtask method)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.load_model"]], "models_count (binarytabularclassificationtask property)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.models_count"]], "predict() (binarytabularclassificationtask method)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.predict"]], "save_model() (binarytabularclassificationtask method)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.save_model"]], "task_type (binarytabularclassificationtask property)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.task_type"]], "train() (binarytabularclassificationtask method)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.train"]], "multilabelimageclassificationtask (class in cyclops.tasks.classification)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask"]], "__init__() (multilabelimageclassificationtask method)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask.__init__"]], "add_model() (multilabelimageclassificationtask method)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask.add_model"]], "data_type (multilabelimageclassificationtask property)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask.data_type"]], "evaluate() (multilabelimageclassificationtask method)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask.evaluate"]], "get_model() (multilabelimageclassificationtask method)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask.get_model"]], "list_models() (multilabelimageclassificationtask method)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask.list_models"]], "list_models_params() (multilabelimageclassificationtask method)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask.list_models_params"]], "load_model() (multilabelimageclassificationtask method)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask.load_model"]], "models_count (multilabelimageclassificationtask property)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask.models_count"]], "predict() (multilabelimageclassificationtask method)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask.predict"]], "save_model() (multilabelimageclassificationtask method)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask.save_model"]], "task_type (multilabelimageclassificationtask property)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask.task_type"]], "cyclops.data": [[38, "module-cyclops.data"]], "cyclops.data.features": [[38, "module-cyclops.data.features"]], "cyclops.monitor": [[39, "module-cyclops.monitor"]], "cyclops.report": [[40, "module-cyclops.report"]], "cyclops.tasks": [[41, "module-cyclops.tasks"]], "evaluate() (in module cyclops.evaluate.evaluator)": [[42, "cyclops.evaluate.evaluator.evaluate"]], "evaluate_fairness() (in module cyclops.evaluate.fairness.evaluator)": [[43, "cyclops.evaluate.fairness.evaluator.evaluate_fairness"]], "accuracy (class in cyclops.evaluate.metrics)": [[45, "cyclops.evaluate.metrics.Accuracy"]], "binaryaccuracy (class in cyclops.evaluate.metrics)": [[45, "cyclops.evaluate.metrics.BinaryAccuracy"]], "multiclassaccuracy (class in cyclops.evaluate.metrics)": [[45, "cyclops.evaluate.metrics.MulticlassAccuracy"]], "multilabelaccuracy (class in cyclops.evaluate.metrics)": [[45, "cyclops.evaluate.metrics.MultilabelAccuracy"]], "binary_accuracy() (in module cyclops.evaluate.metrics.functional.accuracy)": [[45, "cyclops.evaluate.metrics.functional.accuracy.binary_accuracy"]], "multiclass_accuracy() (in module cyclops.evaluate.metrics.functional.accuracy)": [[45, "cyclops.evaluate.metrics.functional.accuracy.multiclass_accuracy"]], "multilabel_accuracy() (in module cyclops.evaluate.metrics.functional.accuracy)": [[45, "cyclops.evaluate.metrics.functional.accuracy.multilabel_accuracy"]], "auroc (class in cyclops.evaluate.metrics)": [[46, "cyclops.evaluate.metrics.AUROC"]], "binaryauroc (class in cyclops.evaluate.metrics)": [[46, "cyclops.evaluate.metrics.BinaryAUROC"]], "multiclassauroc (class in cyclops.evaluate.metrics)": [[46, "cyclops.evaluate.metrics.MulticlassAUROC"]], "multilabelauroc (class in cyclops.evaluate.metrics)": [[46, "cyclops.evaluate.metrics.MultilabelAUROC"]], "auroc() (in module cyclops.evaluate.metrics.functional.auroc)": [[46, "cyclops.evaluate.metrics.functional.auroc.auroc"]], "binary_auroc() (in module cyclops.evaluate.metrics.functional.auroc)": [[46, "cyclops.evaluate.metrics.functional.auroc.binary_auroc"]], "multiclass_auroc() (in module cyclops.evaluate.metrics.functional.auroc)": [[46, "cyclops.evaluate.metrics.functional.auroc.multiclass_auroc"]], "multilabel_auroc() (in module cyclops.evaluate.metrics.functional.auroc)": [[46, "cyclops.evaluate.metrics.functional.auroc.multilabel_auroc"]], "binaryaverageprecision (class in cyclops.evaluate.metrics)": [[47, "cyclops.evaluate.metrics.BinaryAveragePrecision"]], "average_precision() (in module cyclops.evaluate.metrics.functional.average_precision)": [[47, "cyclops.evaluate.metrics.functional.average_precision.average_precision"]], "binary_average_precision() (in module cyclops.evaluate.metrics.functional.average_precision)": [[47, "cyclops.evaluate.metrics.functional.average_precision.binary_average_precision"]], "binaryf1score (class in cyclops.evaluate.metrics)": [[48, "cyclops.evaluate.metrics.BinaryF1Score"]], "f1score (class in cyclops.evaluate.metrics)": [[48, "cyclops.evaluate.metrics.F1Score"]], "multiclassf1score (class in cyclops.evaluate.metrics)": [[48, "cyclops.evaluate.metrics.MulticlassF1Score"]], "multilabelf1score (class in cyclops.evaluate.metrics)": [[48, "cyclops.evaluate.metrics.MultilabelF1Score"]], "binary_f1_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[48, "cyclops.evaluate.metrics.functional.f_beta.binary_f1_score"]], "f1_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[48, "cyclops.evaluate.metrics.functional.f_beta.f1_score"]], "multiclass_f1_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[48, "cyclops.evaluate.metrics.functional.f_beta.multiclass_f1_score"]], "multilabel_f1_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[48, "cyclops.evaluate.metrics.functional.f_beta.multilabel_f1_score"]], "binaryfbetascore (class in cyclops.evaluate.metrics)": [[49, "cyclops.evaluate.metrics.BinaryFbetaScore"]], "fbetascore (class in cyclops.evaluate.metrics)": [[49, "cyclops.evaluate.metrics.FbetaScore"]], "multiclassfbetascore (class in cyclops.evaluate.metrics)": [[49, "cyclops.evaluate.metrics.MulticlassFbetaScore"]], "multilabelfbetascore (class in cyclops.evaluate.metrics)": [[49, "cyclops.evaluate.metrics.MultilabelFbetaScore"]], "binary_fbeta_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[49, "cyclops.evaluate.metrics.functional.f_beta.binary_fbeta_score"]], "fbeta_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[49, "cyclops.evaluate.metrics.functional.f_beta.fbeta_score"]], "multiclass_fbeta_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[49, "cyclops.evaluate.metrics.functional.f_beta.multiclass_fbeta_score"]], "multilabel_fbeta_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[49, "cyclops.evaluate.metrics.functional.f_beta.multilabel_fbeta_score"]], "binaryprecision (class in cyclops.evaluate.metrics)": [[50, "cyclops.evaluate.metrics.BinaryPrecision"]], "multiclassprecision (class in cyclops.evaluate.metrics)": [[50, "cyclops.evaluate.metrics.MulticlassPrecision"]], "multilabelprecision (class in cyclops.evaluate.metrics)": [[50, "cyclops.evaluate.metrics.MultilabelPrecision"]], "precision (class in cyclops.evaluate.metrics)": [[50, "cyclops.evaluate.metrics.Precision"]], "binary_precision() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[50, "cyclops.evaluate.metrics.functional.precision_recall.binary_precision"]], "multiclass_precision() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[50, "cyclops.evaluate.metrics.functional.precision_recall.multiclass_precision"]], "multilabel_precision() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[50, "cyclops.evaluate.metrics.functional.precision_recall.multilabel_precision"]], "precision() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[50, "cyclops.evaluate.metrics.functional.precision_recall.precision"]], "binaryprecisionrecallcurve (class in cyclops.evaluate.metrics)": [[51, "cyclops.evaluate.metrics.BinaryPrecisionRecallCurve"]], "multiclassprecisionrecallcurve (class in cyclops.evaluate.metrics)": [[51, "cyclops.evaluate.metrics.MulticlassPrecisionRecallCurve"]], "multilabelprecisionrecallcurve (class in cyclops.evaluate.metrics)": [[51, "cyclops.evaluate.metrics.MultilabelPrecisionRecallCurve"]], "precisionrecallcurve (class in cyclops.evaluate.metrics)": [[51, "cyclops.evaluate.metrics.PrecisionRecallCurve"]], "multiclass_precision_recall_curve() (in module cyclops.evaluate.metrics.functional.precision_recall_curve)": [[51, "cyclops.evaluate.metrics.functional.precision_recall_curve.multiclass_precision_recall_curve"]], "multilabel_precision_recall_curve() (in module cyclops.evaluate.metrics.functional.precision_recall_curve)": [[51, "cyclops.evaluate.metrics.functional.precision_recall_curve.multilabel_precision_recall_curve"]], "precision_recall_curve() (in module cyclops.evaluate.metrics.functional.precision_recall_curve)": [[51, "cyclops.evaluate.metrics.functional.precision_recall_curve.precision_recall_curve"]], "binaryrecall (class in cyclops.evaluate.metrics)": [[52, "cyclops.evaluate.metrics.BinaryRecall"]], "multiclassrecall (class in cyclops.evaluate.metrics)": [[52, "cyclops.evaluate.metrics.MulticlassRecall"]], "multilabelrecall (class in cyclops.evaluate.metrics)": [[52, "cyclops.evaluate.metrics.MultilabelRecall"]], "recall (class in cyclops.evaluate.metrics)": [[52, "cyclops.evaluate.metrics.Recall"]], "multiclass_recall() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[52, "cyclops.evaluate.metrics.functional.precision_recall.multiclass_recall"]], "multilabel_recall() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[52, "cyclops.evaluate.metrics.functional.precision_recall.multilabel_recall"]], "recall() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[52, "cyclops.evaluate.metrics.functional.precision_recall.recall"]], "binaryroccurve (class in cyclops.evaluate.metrics)": [[53, "cyclops.evaluate.metrics.BinaryROCCurve"]], "multiclassroccurve (class in cyclops.evaluate.metrics)": [[53, "cyclops.evaluate.metrics.MulticlassROCCurve"]], "multilabelroccurve (class in cyclops.evaluate.metrics)": [[53, "cyclops.evaluate.metrics.MultilabelROCCurve"]], "roccurve (class in cyclops.evaluate.metrics)": [[53, "cyclops.evaluate.metrics.ROCCurve"]], "binary_roc_curve() (in module cyclops.evaluate.metrics.functional.roc)": [[53, "cyclops.evaluate.metrics.functional.roc.binary_roc_curve"]], "multiclass_roc_curve() (in module cyclops.evaluate.metrics.functional.roc)": [[53, "cyclops.evaluate.metrics.functional.roc.multiclass_roc_curve"]], "multilabel_roc_curve() (in module cyclops.evaluate.metrics.functional.roc)": [[53, "cyclops.evaluate.metrics.functional.roc.multilabel_roc_curve"]], "roc_curve() (in module cyclops.evaluate.metrics.functional.roc)": [[53, "cyclops.evaluate.metrics.functional.roc.roc_curve"]], "binarysensitivity (class in cyclops.evaluate.metrics)": [[54, "cyclops.evaluate.metrics.BinarySensitivity"]], "multiclasssensitivity (class in cyclops.evaluate.metrics)": [[54, "cyclops.evaluate.metrics.MulticlassSensitivity"]], "multilabelsensitivity (class in cyclops.evaluate.metrics)": [[54, "cyclops.evaluate.metrics.MultilabelSensitivity"]], "sensitivity (class in cyclops.evaluate.metrics)": [[54, "cyclops.evaluate.metrics.Sensitivity"]], "binary_sensitivity() (in module cyclops.evaluate.metrics.functional.sensitivity)": [[54, "cyclops.evaluate.metrics.functional.sensitivity.binary_sensitivity"]], "multiclass_sensitivity() (in module cyclops.evaluate.metrics.functional.sensitivity)": [[54, "cyclops.evaluate.metrics.functional.sensitivity.multiclass_sensitivity"]], "multilabel_sensitivity() (in module cyclops.evaluate.metrics.functional.sensitivity)": [[54, "cyclops.evaluate.metrics.functional.sensitivity.multilabel_sensitivity"]], "sensitivity() (in module cyclops.evaluate.metrics.functional.sensitivity)": [[54, "cyclops.evaluate.metrics.functional.sensitivity.sensitivity"]], "binaryspecificity (class in cyclops.evaluate.metrics)": [[55, "cyclops.evaluate.metrics.BinarySpecificity"]], "multiclassspecificity (class in cyclops.evaluate.metrics)": [[55, "cyclops.evaluate.metrics.MulticlassSpecificity"]], "multilabelspecificity (class in cyclops.evaluate.metrics)": [[55, "cyclops.evaluate.metrics.MultilabelSpecificity"]], "specificity (class in cyclops.evaluate.metrics)": [[55, "cyclops.evaluate.metrics.Specificity"]], "binary_specificity() (in module cyclops.evaluate.metrics.functional.specificity)": [[55, "cyclops.evaluate.metrics.functional.specificity.binary_specificity"]], "multiclass_specificity() (in module cyclops.evaluate.metrics.functional.specificity)": [[55, "cyclops.evaluate.metrics.functional.specificity.multiclass_specificity"]], "multilabel_specificity() (in module cyclops.evaluate.metrics.functional.specificity)": [[55, "cyclops.evaluate.metrics.functional.specificity.multilabel_specificity"]], "specificity() (in module cyclops.evaluate.metrics.functional.specificity)": [[55, "cyclops.evaluate.metrics.functional.specificity.specificity"]], "binarystatscores (class in cyclops.evaluate.metrics)": [[56, "cyclops.evaluate.metrics.BinaryStatScores"]], "multiclassstatscores (class in cyclops.evaluate.metrics)": [[56, "cyclops.evaluate.metrics.MulticlassStatScores"]], "multilabelstatscores (class in cyclops.evaluate.metrics)": [[56, "cyclops.evaluate.metrics.MultilabelStatScores"]], "statscores (class in cyclops.evaluate.metrics)": [[56, "cyclops.evaluate.metrics.StatScores"]], "binary_stat_scores() (in module cyclops.evaluate.metrics.functional.stat_scores)": [[56, "cyclops.evaluate.metrics.functional.stat_scores.binary_stat_scores"]], "multiclass_stat_scores() (in module cyclops.evaluate.metrics.functional.stat_scores)": [[56, "cyclops.evaluate.metrics.functional.stat_scores.multiclass_stat_scores"]], "multilabel_stat_scores() (in module cyclops.evaluate.metrics.functional.stat_scores)": [[56, "cyclops.evaluate.metrics.functional.stat_scores.multilabel_stat_scores"]], "stat_scores() (in module cyclops.evaluate.metrics.functional.stat_scores)": [[56, "cyclops.evaluate.metrics.functional.stat_scores.stat_scores"]]}})
\ No newline at end of file
diff --git a/api/tutorials/kaggle/heart_failure_prediction.html b/api/tutorials/kaggle/heart_failure_prediction.html
index dd58d4133..cfd4c5716 100644
--- a/api/tutorials/kaggle/heart_failure_prediction.html
+++ b/api/tutorials/kaggle/heart_failure_prediction.html
@@ -407,7 +407,7 @@ 
Data Loading
 
-2024-02-27 19:03:08,034 INFO cyclops.utils.file - Loading DataFrame from ./data/heart.csv
+2024-02-28 16:28:08,739 INFO cyclops.utils.file - Loading DataFrame from ./data/heart.csv
 

 

 

@@ -489,9 +489,9 @@ 
Sex values
 

 

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@@ -989,7 +989,7 @@ 
Training
 
-2024-02-27 19:03:14,998 INFO cyclops.models.wrappers.sk_model - Best alpha: 0.001
+2024-02-28 16:28:16,008 INFO cyclops.models.wrappers.sk_model - Best alpha: 0.001
@@ -997,7 +997,7 @@

Training 

-2024-02-27 19:03:14,999 INFO cyclops.models.wrappers.sk_model - Best eta0: 0.01
+2024-02-28 16:28:16,009 INFO cyclops.models.wrappers.sk_model - Best eta0: 0.01
@@ -1005,7 +1005,7 @@

Training 

-2024-02-27 19:03:14,999 INFO cyclops.models.wrappers.sk_model - Best learning_rate: adaptive
+2024-02-28 16:28:16,010 INFO cyclops.models.wrappers.sk_model - Best learning_rate: adaptive
@@ -1069,7 +1069,7 @@

Prediction

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Evaluation

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[36]:
@@ -1278,31 +1278,31 @@ 
Evaluation
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Log the performance metrics to the report.

We can add a performance metric to the model card using the log_performance_metric method, which expects a dictionary where the keys are in the following format: slice_name/metric_name. For instance, overall/accuracy.

@@ -1419,9 +1419,9 @@

Evaluation
-
diff --git a/api/tutorials/kaggle/heart_failure_prediction.ipynb b/api/tutorials/kaggle/heart_failure_prediction.ipynb index 37fb23b75..31ded3c75 100644 --- a/api/tutorials/kaggle/heart_failure_prediction.ipynb +++ b/api/tutorials/kaggle/heart_failure_prediction.ipynb @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:03:03.003066Z", - "iopub.status.busy": "2024-02-28T00:03:03.002427Z", - "iopub.status.idle": "2024-02-28T00:03:07.525473Z", - "shell.execute_reply": "2024-02-28T00:03:07.524579Z" + "iopub.execute_input": "2024-02-28T21:28:03.688756Z", + "iopub.status.busy": "2024-02-28T21:28:03.688073Z", + "iopub.status.idle": "2024-02-28T21:28:08.313462Z", + "shell.execute_reply": "2024-02-28T21:28:08.312675Z" }, "tags": [] }, @@ -84,10 +84,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:03:07.531768Z", - "iopub.status.busy": "2024-02-28T00:03:07.531023Z", - "iopub.status.idle": "2024-02-28T00:03:07.535695Z", - "shell.execute_reply": "2024-02-28T00:03:07.534784Z" + "iopub.execute_input": "2024-02-28T21:28:08.319117Z", + "iopub.status.busy": "2024-02-28T21:28:08.318441Z", + "iopub.status.idle": "2024-02-28T21:28:08.322336Z", + "shell.execute_reply": "2024-02-28T21:28:08.321758Z" } }, "outputs": [], @@ -107,10 +107,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:03:07.540887Z", - "iopub.status.busy": "2024-02-28T00:03:07.540497Z", - "iopub.status.idle": "2024-02-28T00:03:07.545482Z", - "shell.execute_reply": "2024-02-28T00:03:07.544273Z" + "iopub.execute_input": "2024-02-28T21:28:08.327290Z", + "iopub.status.busy": "2024-02-28T21:28:08.327101Z", + "iopub.status.idle": "2024-02-28T21:28:08.330273Z", + "shell.execute_reply": "2024-02-28T21:28:08.329703Z" }, "tags": [] }, @@ -136,10 +136,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:03:07.551183Z", - "iopub.status.busy": "2024-02-28T00:03:07.550564Z", - "iopub.status.idle": "2024-02-28T00:03:08.024752Z", - "shell.execute_reply": "2024-02-28T00:03:08.022909Z" + "iopub.execute_input": "2024-02-28T21:28:08.335296Z", + "iopub.status.busy": "2024-02-28T21:28:08.334932Z", + "iopub.status.idle": "2024-02-28T21:28:08.730000Z", + "shell.execute_reply": "2024-02-28T21:28:08.728648Z" }, "tags": [] }, @@ -159,10 +159,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:03:08.031778Z", - "iopub.status.busy": "2024-02-28T00:03:08.031061Z", - "iopub.status.idle": "2024-02-28T00:03:08.060369Z", - "shell.execute_reply": "2024-02-28T00:03:08.059289Z" + "iopub.execute_input": "2024-02-28T21:28:08.736415Z", + "iopub.status.busy": "2024-02-28T21:28:08.735718Z", + "iopub.status.idle": "2024-02-28T21:28:08.765854Z", + "shell.execute_reply": "2024-02-28T21:28:08.764818Z" }, "tags": [] }, @@ -171,7 +171,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-02-27 19:03:08,034 \u001b[1;37mINFO\u001b[0m cyclops.utils.file - Loading DataFrame from ./data/heart.csv\n" + "2024-02-28 16:28:08,739 \u001b[1;37mINFO\u001b[0m cyclops.utils.file - Loading DataFrame from ./data/heart.csv\n" ] }, { @@ -227,10 +227,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:03:08.091596Z", - "iopub.status.busy": "2024-02-28T00:03:08.090811Z", - "iopub.status.idle": "2024-02-28T00:03:08.349146Z", - "shell.execute_reply": "2024-02-28T00:03:08.348398Z" + "iopub.execute_input": "2024-02-28T21:28:08.792991Z", + "iopub.status.busy": "2024-02-28T21:28:08.792217Z", + "iopub.status.idle": "2024-02-28T21:28:09.410194Z", + "shell.execute_reply": "2024-02-28T21:28:09.408899Z" }, "tags": [] }, @@ -2038,9 +2038,9 @@ } }, "text/html": [ - "
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- 0.63 + 0.7 @@ -1468,11 +1468,11 @@

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Graphics

-
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Graphics

-
+
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Version

- Date: 2024-02-27 + Date: 2024-02-28
@@ -1987,8 +1987,8 @@

Model Parameters

-

Epsilon

- 0.1 +

Tol

+ 0.001
@@ -1996,8 +1996,8 @@

Epsilon

-

Max_iter

- 1000 +

Epsilon

+ 0.1
@@ -2005,8 +2005,8 @@

Max_iter

-

Warm_start

- False +

Penalty

+ l2
@@ -2014,8 +2014,8 @@

Warm_start

-

Class_weight

- balanced +

Warm_start

+ False
@@ -2023,8 +2023,8 @@

Class_weight

-

Verbose

- 0 +

Eta0

+ 0.01
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Verbose

-

Random_state

- 123 +

Average

+ False
@@ -2041,8 +2041,8 @@

Random_state

-

Learning_rate

- adaptive +

L1_ratio

+ 0.15
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Learning_rate

-

Loss

- log_loss +

Validation_fraction

+ 0.1
@@ -2059,8 +2059,8 @@

Loss

-

Early_stopping

- True +

Max_iter

+ 1000
@@ -2068,8 +2068,8 @@

Early_stopping

-

L1_ratio

- 0.15 +

Shuffle

+ True
@@ -2077,8 +2077,8 @@

L1_ratio

-

N_iter_no_change

- 5 +

Verbose

+ 0
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N_iter_no_change

-

Tol

- 0.001 +

Power_t

+ 0.5
@@ -2095,8 +2095,8 @@

Tol

-

Validation_fraction

- 0.1 +

Class_weight

+ balanced
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Validation_fraction

-

Alpha

- 0.001 +

Random_state

+ 123
@@ -2118,8 +2118,8 @@

Alpha

-

Average

- False +

Loss

+ log_loss
@@ -2127,8 +2127,8 @@

Average

-

Penalty

- l2 +

Learning_rate

+ adaptive
@@ -2136,8 +2136,8 @@

Penalty

-

Shuffle

- True +

N_iter_no_change

+ 5
@@ -2145,8 +2145,8 @@

Shuffle

-

Eta0

- 0.01 +

Early_stopping

+ True
@@ -2163,8 +2163,8 @@

Fit_intercept

-

Power_t

- 0.5 +

Alpha

+ 0.001
@@ -2424,643 +2424,525 @@

Ethical Considerations

- + + + + \ No newline at end of file diff --git a/api/tutorials/mimiciv/mortality_prediction.html b/api/tutorials/mimiciv/mortality_prediction.html index 6b12a62ec..fd967c6a3 100644 --- a/api/tutorials/mimiciv/mortality_prediction.html +++ b/api/tutorials/mimiciv/mortality_prediction.html @@ -567,7 +567,7 @@

Compute mortality (labels) 
-2024-02-27 19:03:29,396 INFO cycquery.orm    - Database setup, ready to run queries!
+2024-02-28 16:28:29,766 INFO cycquery.orm    - Database setup, ready to run queries!

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

Compute mortality (labels) 
-2024-02-27 19:03:37,099 INFO cycquery.orm    - Query returned successfully!
+2024-02-28 16:28:37,234 INFO cycquery.orm    - Query returned successfully!

@@ -583,7 +583,7 @@

Compute mortality (labels) 
-2024-02-27 19:03:37,101 INFO cycquery.utils.profile - Finished executing function run_query in 5.432613 s
+2024-02-28 16:28:37,235 INFO cycquery.utils.profile - Finished executing function run_query in 5.235178 s

@@ -602,7 +602,7 @@

Compute mortality (labels) 
-2024-02-27 19:04:03,105 INFO cycquery.orm    - Query returned successfully!
+2024-02-28 16:29:12,415 INFO cycquery.orm    - Query returned successfully!

@@ -610,7 +610,7 @@

Compute mortality (labels) 
-2024-02-27 19:04:03,106 INFO cycquery.utils.profile - Finished executing function run_query in 25.225979 s
+2024-02-28 16:29:12,416 INFO cycquery.utils.profile - Finished executing function run_query in 34.431019 s

@@ -682,9 +682,9 @@

Drop NaNs based on the

-
+
@@ -1162,7 +1162,7 @@

Training 
-2024-02-27 19:08:14,183 INFO cyclops.models.wrappers.sk_model - Best reg_lambda: 0
+2024-02-28 16:31:45,374 INFO cyclops.models.wrappers.sk_model - Best reg_lambda: 1
@@ -1170,7 +1170,7 @@

Training 

-2024-02-27 19:08:14,184 INFO cyclops.models.wrappers.sk_model - Best n_estimators: 500
+2024-02-28 16:31:45,375 INFO cyclops.models.wrappers.sk_model - Best n_estimators: 500
@@ -1178,7 +1178,7 @@

Training 

-2024-02-27 19:08:14,184 INFO cyclops.models.wrappers.sk_model - Best max_depth: 5
+2024-02-28 16:31:45,375 INFO cyclops.models.wrappers.sk_model - Best max_depth: 5
@@ -1186,7 +1186,7 @@

Training 

-2024-02-27 19:08:14,185 INFO cyclops.models.wrappers.sk_model - Best learning_rate: 0.1
+2024-02-28 16:31:45,376 INFO cyclops.models.wrappers.sk_model - Best learning_rate: 0.1
@@ -1194,7 +1194,7 @@

Training 

-2024-02-27 19:08:14,185 INFO cyclops.models.wrappers.sk_model - Best gamma: 10
+2024-02-28 16:31:45,376 INFO cyclops.models.wrappers.sk_model - Best gamma: 10
@@ -1202,7 +1202,7 @@

Training 

-2024-02-27 19:08:14,186 INFO cyclops.models.wrappers.sk_model - Best colsample_bytree: 1
+2024-02-28 16:31:45,377 INFO cyclops.models.wrappers.sk_model - Best colsample_bytree: 0.7
@@ -1211,26 +1211,26 @@

Training 

XGBClassifier(base_score=None, booster=None, callbacks=None,
-              colsample_bylevel=None, colsample_bynode=None, colsample_bytree=1,
-              early_stopping_rounds=None, enable_categorical=False,
-              eval_metric='logloss', feature_types=None, gamma=10, gpu_id=None,
-              grow_policy=None, importance_type=None,
-              interaction_constraints=None, learning_rate=0.1, max_bin=None,
-              max_cat_threshold=None, max_cat_to_onehot=None,
-              max_delta_step=None, max_depth=5, max_leaves=None,
-              min_child_weight=3, missing=nan, monotone_constraints=None,
-              n_estimators=500, n_jobs=None, num_parallel_tree=None,
-              predictor=None, random_state=123, ...)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
+ colsample_bylevel=None, colsample_bynode=None, + colsample_bytree=0.7, early_stopping_rounds=None, + enable_categorical=False, eval_metric='logloss', + feature_types=None, gamma=10, gpu_id=None, grow_policy=None, + importance_type=None, interaction_constraints=None, + learning_rate=0.1, max_bin=None, max_cat_threshold=None, + max_cat_to_onehot=None, max_delta_step=None, max_depth=5, + max_leaves=None, min_child_weight=3, missing=nan, + monotone_constraints=None, n_estimators=500, n_jobs=None, + num_parallel_tree=None, predictor=None, random_state=123, ...)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. 
[25]:
@@ -1246,7 +1246,7 @@ 
Training
 
-{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 1, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': 'logloss', 'feature_types': None, 'gamma': 10, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': 3, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 500, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': 0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None, 'seed': 123}
+{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.7, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': 'logloss', 'feature_types': None, 'gamma': 10, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': 3, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 500, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': 1, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None, 'seed': 123}

Log the model parameters to the report.

@@ -1282,7 +1282,7 @@

Prediction

-
+
@@ -1407,91 +1407,91 @@

Evaluation

-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
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-
+

Log the performance metrics to the report.

We can add a performance metric to the model card using the log_performance_metric method, which expects a dictionary where the keys are in the following format: slice_name/metric_name. For instance, overall/accuracy.

@@ -1617,9 +1617,9 @@

Evaluation
-

diff --git a/api/tutorials/mimiciv/mortality_prediction.ipynb b/api/tutorials/mimiciv/mortality_prediction.ipynb index ae55e7037..076feec69 100644 --- a/api/tutorials/mimiciv/mortality_prediction.ipynb +++ b/api/tutorials/mimiciv/mortality_prediction.ipynb @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:03:21.607288Z", - "iopub.status.busy": "2024-02-28T00:03:21.606617Z", - "iopub.status.idle": "2024-02-28T00:03:27.106309Z", - "shell.execute_reply": "2024-02-28T00:03:27.105288Z" + "iopub.execute_input": "2024-02-28T21:28:22.604959Z", + "iopub.status.busy": "2024-02-28T21:28:22.604312Z", + "iopub.status.idle": "2024-02-28T21:28:27.369421Z", + "shell.execute_reply": "2024-02-28T21:28:27.368491Z" } }, "outputs": [], @@ -89,10 +89,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:03:27.112426Z", - "iopub.status.busy": "2024-02-28T00:03:27.112004Z", - "iopub.status.idle": "2024-02-28T00:03:27.117534Z", - "shell.execute_reply": "2024-02-28T00:03:27.116795Z" + "iopub.execute_input": "2024-02-28T21:28:27.375755Z", + "iopub.status.busy": "2024-02-28T21:28:27.375057Z", + "iopub.status.idle": "2024-02-28T21:28:27.381366Z", + "shell.execute_reply": "2024-02-28T21:28:27.380431Z" } }, "outputs": [], @@ -112,10 +112,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:03:27.122719Z", - "iopub.status.busy": "2024-02-28T00:03:27.122345Z", - "iopub.status.idle": "2024-02-28T00:03:27.126401Z", - "shell.execute_reply": "2024-02-28T00:03:27.125638Z" + "iopub.execute_input": "2024-02-28T21:28:27.387144Z", + "iopub.status.busy": "2024-02-28T21:28:27.386736Z", + "iopub.status.idle": "2024-02-28T21:28:27.391310Z", + "shell.execute_reply": "2024-02-28T21:28:27.390291Z" } }, "outputs": [], @@ -147,10 +147,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:03:27.132038Z", - "iopub.status.busy": "2024-02-28T00:03:27.131531Z", - "iopub.status.idle": "2024-02-28T00:04:52.490174Z", - "shell.execute_reply": "2024-02-28T00:04:52.489242Z" + "iopub.execute_input": "2024-02-28T21:28:27.396839Z", + "iopub.status.busy": "2024-02-28T21:28:27.396384Z", + "iopub.status.idle": "2024-02-28T21:29:57.842618Z", + "shell.execute_reply": "2024-02-28T21:29:57.841811Z" } }, "outputs": [ @@ -158,21 +158,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-02-27 19:03:29,396 \u001b[1;37mINFO\u001b[0m cycquery.orm - Database setup, ready to run queries!\n" + "2024-02-28 16:28:29,766 \u001b[1;37mINFO\u001b[0m cycquery.orm - Database setup, ready to run queries!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2024-02-27 19:03:37,099 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" + "2024-02-28 16:28:37,234 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2024-02-27 19:03:37,101 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 5.432613 s\n" + "2024-02-28 16:28:37,235 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 5.235178 s\n" ] }, { @@ -189,14 +189,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-02-27 19:04:03,105 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" + "2024-02-28 16:29:12,415 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2024-02-27 19:04:03,106 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 25.225979 s\n" + "2024-02-28 16:29:12,416 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 34.431019 s\n" ] }, { @@ -394,10 +394,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:04:52.496159Z", - "iopub.status.busy": "2024-02-28T00:04:52.495726Z", - "iopub.status.idle": "2024-02-28T00:04:52.648814Z", - "shell.execute_reply": "2024-02-28T00:04:52.647928Z" + "iopub.execute_input": "2024-02-28T21:29:57.848008Z", + "iopub.status.busy": "2024-02-28T21:29:57.847660Z", + "iopub.status.idle": "2024-02-28T21:29:57.990955Z", + "shell.execute_reply": "2024-02-28T21:29:57.990251Z" } }, "outputs": [ @@ -2395,9 +2395,9 @@ } }, "text/html": [ - "
+
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Graphics

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Quantitative Analysis

- 0.07 + 0.16 @@ -1206,7 +1206,7 @@

Quantitative Analysis
- 0.23 + 0.0 @@ -1239,7 +1239,7 @@

Quantitative Analysis
- 0.24 + 0.32 @@ -1272,11 +1272,11 @@

Quantitative Analysis
- 0.8 + 0.19 - ▲ + ▼ @@ -1305,7 +1305,7 @@

Quantitative Analysis
- 0.3 + 0.19 @@ -1337,7 +1337,7 @@

Graphics

-
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- Date: 2024-02-27 + Date: 2024-02-28
@@ -1689,10 +1689,6 @@

Model Parameters

-
-

Learning_rate

- 0.1 -
@@ -1714,8 +1710,8 @@

Learning_rate

-

Gamma

- 10 +

Seed

+ 123
@@ -1732,11 +1728,6 @@

Gamma

- - - - - @@ -1752,8 +1743,8 @@

Min_child_weight

-

Seed

- 123 +

N_estimators

+ 500
@@ -1761,19 +1752,27 @@

Seed

-

Enable_categorical

- False +

Objective

+ binary:logistic
+
+

Missing

+ nan +
+
+

Learning_rate

+ 0.1 +
@@ -1794,19 +1793,11 @@

Enable_categorical

-
-

Objective

- binary:logistic -
-
-

Max_depth

- 5 -
@@ -1822,6 +1813,10 @@

Max_depth

+
+

Max_depth

+ 5 +
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Random_state

-
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Colsample_bytree

- 1 -
-
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N_estimators

- 500 -
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N_estimators

-
-

Missing

- nan -
@@ -1893,11 +1876,19 @@

Missing

+
+

Enable_categorical

+ False +
+
+

Reg_lambda

+ 1 +
@@ -1908,10 +1899,6 @@

Missing

-
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Eval_metric

- logloss -
@@ -1923,14 +1910,27 @@

Eval_metric

-

Reg_lambda

- 0 +

Colsample_bytree

+ 0.7 +
+ + + + + +
+

Eval_metric

+ logloss
+
+

Gamma

+ 10 +
@@ -2169,643 +2169,525 @@

Ethical Considerations

- + + + + \ No newline at end of file diff --git a/api/tutorials/nihcxr/cxr_classification.html b/api/tutorials/nihcxr/cxr_classification.html index eb155fd97..05e46dcea 100644 --- a/api/tutorials/nihcxr/cxr_classification.html +++ b/api/tutorials/nihcxr/cxr_classification.html @@ -428,13 +428,13 @@

Model Creation

-
+
-
+
@@ -508,19 +508,19 @@

Multilabel AUROC by Pathology and Sex

-
+
-
+
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Multilabel AUROC by Pathology and Age
-
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[8]:
@@ -686,9 +686,9 @@ 
Multilabel AUROC by Pathology and Age
-

diff --git a/api/tutorials/nihcxr/cxr_classification.ipynb b/api/tutorials/nihcxr/cxr_classification.ipynb index bd981062f..916e4d9f9 100644 --- a/api/tutorials/nihcxr/cxr_classification.ipynb +++ b/api/tutorials/nihcxr/cxr_classification.ipynb @@ -24,10 +24,10 @@ "id": "fc1eb72a", "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:08:28.333642Z", - "iopub.status.busy": "2024-02-28T00:08:28.332976Z", - "iopub.status.idle": "2024-02-28T00:08:33.952838Z", - "shell.execute_reply": "2024-02-28T00:08:33.951811Z" + "iopub.execute_input": "2024-02-28T21:31:59.803422Z", + "iopub.status.busy": "2024-02-28T21:31:59.802808Z", + "iopub.status.idle": "2024-02-28T21:32:04.015270Z", + "shell.execute_reply": "2024-02-28T21:32:04.014166Z" } }, "outputs": [], @@ -69,10 +69,10 @@ "id": "25c2a16f", "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:08:33.958977Z", - "iopub.status.busy": "2024-02-28T00:08:33.958423Z", - "iopub.status.idle": "2024-02-28T00:13:54.310815Z", - "shell.execute_reply": "2024-02-28T00:13:54.308890Z" + "iopub.execute_input": "2024-02-28T21:32:04.020774Z", + "iopub.status.busy": "2024-02-28T21:32:04.020390Z", + "iopub.status.idle": "2024-02-28T21:37:22.616124Z", + "shell.execute_reply": "2024-02-28T21:37:22.614225Z" }, "nbsphinx": "hidden" }, @@ -90,16 +90,10 @@ "output_type": "stream", "text": [ "\r", - "Flattening the indices: 100%|████████| 1000/1000 [00:52<00:00, 18.92 examples/s]\r", - "Flattening the indices: 100%|████████| 1000/1000 [00:52<00:00, 18.92 examples/s]\r\n", + "Flattening the indices: 100%|████████| 1000/1000 [00:52<00:00, 19.03 examples/s]\r", + "Flattening the indices: 100%|████████| 1000/1000 [00:52<00:00, 19.03 examples/s]\r\n", "\r", - "Flattening the indices: 0%| | 0/1000 [00:00 Patient Gender:M: 0%| | 0/400 [00:00 Patient Gender:M: 100%|███| 400/400 [00:00<00:00, 42654.30 examples/s]\r\n" + "Map: 100%|███████████████████████████| 400/400 [00:00<00:00, 1868.51 examples/s]\r", + "Map: 100%|███████████████████████████| 400/400 [00:00<00:00, 1834.55 examples/s]\r\n", + "\r", + "Filter -> Patient Gender:M: 0%| | 0/400 [00:00 Patient Gender:F: 0%| | 0/400 [00:00 Patient Gender:F: 100%|███| 400/400 [00:00<00:00, 43024.02 examples/s]\r\n", + "Filter -> Patient Gender:M: 100%|███| 400/400 [00:00<00:00, 43805.89 examples/s]\r\n", "\r", - "Filter -> overall: 0%| | 0/400 [00:00 overall: 100%|████████████| 400/400 [00:00<00:00, 45738.16 examples/s]\r\n" + "Filter -> Patient Gender:F: 0%| | 0/400 [00:00 Patient Gender:F: 100%|███| 400/400 [00:00<00:00, 45095.19 examples/s]\r\n" ] }, { @@ -137,10 +136,10 @@ "output_type": "stream", "text": [ "\r", - "Filter -> Patient Age:[19 - 35]: 0%| | 0/400 [00:00 Patient Age:[19 - 35]: 100%|█| 400/400 [00:00<00:00, 41190.29 examples\r\n", + "Filter -> overall: 0%| | 0/400 [00:00 overall: 100%|████████████| 400/400 [00:00<00:00, 46842.80 examples/s]\r\n", "\r", - "Filter -> Patient Age:[35 - 65]: 0%| | 0/400 [00:00 Patient Age:[19 - 35]: 0%| | 0/400 [00:00 Patient Age:[35 - 65]: 100%|█| 400/400 [00:00<00:00, 42177.12 examples\r\n", - "\r", - "Filter -> Patient Age:[65 - 100]: 0%| | 0/400 [00:00 Patient Age:[65 - 100]: 100%|█| 400/400 [00:00<00:00, 42179.24 example\r\n", + "Filter -> Patient Age:[19 - 35]: 100%|█| 400/400 [00:00<00:00, 42537.50 examples\r\n", "\r", - "Filter -> Patient Age:[19 - 35]&Patient Gender:M: 0%| | 0/400 [00:00 Patient Age:[35 - 65]: 0%| | 0/400 [00:00 Patient Age:[35 - 65]: 100%|█| 400/400 [00:00<00:00, 44311.49 examples\r\n" ] }, { @@ -161,13 +158,14 @@ "output_type": "stream", "text": [ "\r", + "Filter -> Patient Age:[65 - 100]: 0%| | 0/400 [00:00 Patient Age:[65 - 100]: 100%|█| 400/400 [00:00<00:00, 43617.97 example\r\n", + "\r", + "Filter -> Patient Age:[19 - 35]&Patient Gender:M: 0%| | 0/400 [00:00 Patient Age:[19 - 35]&Patient Gender:M: 100%|█| 400/400 [00:00<00:00, \r\n", "\r", "Filter -> Patient Age:[19 - 35]&Patient Gender:F: 0%| | 0/400 [00:00 Patient Age:[19 - 35]&Patient Gender:F: 100%|█| 400/400 [00:00<00:00, \r\n", - "\r", - "Filter -> Patient Age:[35 - 65]&Patient Gender:M: 0%| | 0/400 [00:00 Patient Age:[35 - 65]&Patient Gender:M: 100%|█| 400/400 [00:00<00:00, \r\n" + "Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|█| 400/400 [00:00<00:00, \r\n" ] }, { @@ -175,23 +173,26 @@ "output_type": "stream", "text": [ "\r", - "Filter -> Patient Age:[35 - 65]&Patient Gender:F: 0%| | 0/400 [00:00 Patient Age:[35 - 65]&Patient Gender:F: 100%|█| 400/400 [00:00<00:00, \r\n", + "Filter -> Patient Age:[35 - 65]&Patient Gender:M: 0%| | 0/400 [00:00 Patient Age:[35 - 65]&Patient Gender:M: 100%|█| 400/400 [00:00<00:00, \r\n", "\r", - "Filter -> Patient Age:[65 - 100]&Patient Gender:M: 0%| | 0/400 [00:00 Patient Age:[65 - 100]&Patient Gender:M: 100%|█| 400/400 [00:00<00:00,\r\n" + "Filter -> Patient Age:[35 - 65]&Patient Gender:F: 0%| | 0/400 [00:00 Patient Age:[35 - 65]&Patient Gender:F: 100%|█| 400/400 [00:00<00:00, \r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "\r", + "Filter -> Patient Age:[65 - 100]&Patient Gender:M: 0%| | 0/400 [00:00 Patient Age:[65 - 100]&Patient Gender:M: 100%|█| 400/400 [00:00<00:00,\r\n", "\r", "Filter -> Patient Age:[65 - 100]&Patient Gender:F: 0%| | 0/400 [00:00 Patient Age:[65 - 100]&Patient Gender:F: 100%|█| 400/400 [00:00<00:00,\r\n", "\r", "Filter -> overall: 0%| | 0/400 [00:00 overall: 100%|████████████| 400/400 [00:00<00:00, 46765.76 examples/s]\r\n" + "Filter -> overall: 100%|████████████| 400/400 [00:00<00:00, 48728.48 examples/s]\r\n" ] }, { @@ -207,14 +208,14 @@ "output_type": "stream", "text": [ "\r", - "Flattening the indices: 100%|████████| 1000/1000 [00:53<00:00, 18.81 examples/s]\r", - "Flattening the indices: 100%|████████| 1000/1000 [00:53<00:00, 18.81 examples/s]\r\n", + "Flattening the indices: 100%|████████| 1000/1000 [00:53<00:00, 18.78 examples/s]\r", + "Flattening the indices: 100%|████████| 1000/1000 [00:53<00:00, 18.77 examples/s]\r\n", "\r", "Flattening the indices: 0%| | 0/1000 [00:00 Patient Gender:M: 0%| | 0/396 [00:00 Patient Gender:M: 100%|███| 396/396 [00:00<00:00, 43202.01 examples/s]\r\n", + "Filter -> Patient Gender:M: 100%|███| 396/396 [00:00<00:00, 43129.09 examples/s]\r\n", "\r", "Filter -> Patient Gender:F: 0%| | 0/396 [00:00 Patient Gender:F: 100%|███| 396/396 [00:00<00:00, 43233.49 examples/s]\r\n", + "Filter -> Patient Gender:F: 100%|███| 396/396 [00:00<00:00, 42483.74 examples/s]\r\n", "\r", "Filter -> overall: 0%| | 0/396 [00:00 overall: 100%|████████████| 396/396 [00:00<00:00, 45937.01 examples/s]\r\n" + "Filter -> overall: 100%|████████████| 396/396 [00:00<00:00, 45715.74 examples/s]\r\n" ] }, { @@ -256,10 +257,10 @@ "text": [ "\r", "Filter -> Patient Age:[19 - 35]: 0%| | 0/396 [00:00 Patient Age:[19 - 35]: 100%|█| 396/396 [00:00<00:00, 41657.96 examples\r\n", + "Filter -> Patient Age:[19 - 35]: 100%|█| 396/396 [00:00<00:00, 41539.19 examples\r\n", "\r", "Filter -> Patient Age:[35 - 65]: 0%| | 0/396 [00:00 Patient Age:[35 - 65]: 100%|█| 396/396 [00:00<00:00, 43111.18 examples\r\n" + "Filter -> Patient Age:[35 - 65]: 100%|█| 396/396 [00:00<00:00, 42914.02 examples\r\n" ] }, { @@ -268,23 +269,24 @@ "text": [ "\r", "Filter -> Patient Age:[65 - 100]: 0%| | 0/396 [00:00 Patient Age:[65 - 100]: 100%|█| 396/396 [00:00<00:00, 42233.13 example\r\n", + "Filter -> Patient Age:[65 - 100]: 100%|█| 396/396 [00:00<00:00, 41686.19 example\r\n", "\r", - "Filter -> Patient Age:[19 - 35]&Patient Gender:M: 0%| | 0/396 [00:00 Patient Age:[19 - 35]&Patient Gender:M: 0%| | 0/396 [00:00 Patient Age:[19 - 35]&Patient Gender:M: 100%|█| 396/396 [00:00<00:00, \r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\r", - "Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|█| 396/396 [00:00<00:00, \r\n", "\r", "Filter -> Patient Age:[19 - 35]&Patient Gender:F: 0%| | 0/396 [00:00 Patient Age:[19 - 35]&Patient Gender:F: 100%|█| 396/396 [00:00<00:00, \r\n", "\r", "Filter -> Patient Age:[35 - 65]&Patient Gender:M: 0%| | 0/396 [00:00 Patient Age:[35 - 65]&Patient Gender:M: 100%|█| 396/396 [00:00<00:00, \r\n" + "Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|█| 396/396 [00:00<00:00, \r\n", + "\r", + "Filter -> Patient Age:[35 - 65]&Patient Gender:F: 0%| | 0/396 [00:00 Patient Age:[35 - 65]&Patient Gender:F: 0%| | 0/396 [00:00 Patient Age:[35 - 65]&Patient Gender:F: 100%|█| 396/396 [00:00<00:00, \r\n", "\r", "Filter -> Patient Age:[65 - 100]&Patient Gender:M: 0%| | 0/396 [00:00 Patient Age:[65 - 100]&Patient Gender:F: 100%|█| 396/396 [00:00<00:00,\r\n", "\r", "Filter -> overall: 0%| | 0/396 [00:00 overall: 100%|████████████| 396/396 [00:00<00:00, 47306.88 examples/s]\r\n" + "Filter -> overall: 100%|████████████| 396/396 [00:00<00:00, 47118.99 examples/s]\r\n" ] }, { @@ -325,11 +326,11 @@ "output_type": "stream", "text": [ "\r", - "Flattening the indices: 100%|████████| 1000/1000 [00:53<00:00, 18.66 examples/s]\r", - "Flattening the indices: 100%|████████| 1000/1000 [00:53<00:00, 18.65 examples/s]\r\n", + "Flattening the indices: 100%|████████| 1000/1000 [00:52<00:00, 18.99 examples/s]\r", + "Flattening the indices: 100%|████████| 1000/1000 [00:52<00:00, 18.98 examples/s]\r\n", "\r", "Flattening the indices: 0%| | 0/1000 [00:00 Patient Gender:M: 0%| | 0/383 [00:00 Patient Gender:M: 100%|███| 383/383 [00:00<00:00, 42746.63 examples/s]\r\n" + "Filter -> Patient Gender:M: 100%|███| 383/383 [00:00<00:00, 42714.81 examples/s]\r\n" ] }, { @@ -362,10 +363,10 @@ "text": [ "\r", "Filter -> Patient Gender:F: 0%| | 0/383 [00:00 Patient Gender:F: 100%|███| 383/383 [00:00<00:00, 42702.32 examples/s]\r\n", + "Filter -> Patient Gender:F: 100%|███| 383/383 [00:00<00:00, 42180.93 examples/s]\r\n", "\r", "Filter -> overall: 0%| | 0/383 [00:00 overall: 100%|████████████| 383/383 [00:00<00:00, 45304.82 examples/s]\r\n" + "Filter -> overall: 100%|████████████| 383/383 [00:00<00:00, 45748.66 examples/s]\r\n" ] }, { @@ -374,19 +375,21 @@ "text": [ "\r", "Filter -> Patient Age:[19 - 35]: 0%| | 0/383 [00:00 Patient Age:[19 - 35]: 100%|█| 383/383 [00:00<00:00, 41143.80 examples\r\n" + "Filter -> Patient Age:[19 - 35]: 100%|█| 383/383 [00:00<00:00, 41404.67 examples\r\n", + "\r", + "Filter -> Patient Age:[35 - 65]: 0%| | 0/383 [00:00 Patient Age:[35 - 65]: 100%|█| 383/383 [00:00<00:00, 41535.28 examples\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\r", - "Filter -> Patient Age:[35 - 65]: 0%| | 0/383 [00:00 Patient Age:[35 - 65]: 100%|█| 383/383 [00:00<00:00, 41730.57 examples\r\n", "\r", "Filter -> Patient Age:[65 - 100]: 0%| | 0/383 [00:00 Patient Age:[65 - 100]: 100%|█| 383/383 [00:00<00:00, 41212.41 example\r\n" + "Filter -> Patient Age:[65 - 100]: 100%|█| 383/383 [00:00<00:00, 40434.40 example\r\n", + "\r", + "Filter -> Patient Age:[19 - 35]&Patient Gender:M: 0%| | 0/383 [00:00 Patient Age:[19 - 35]&Patient Gender:M: 0%| | 0/383 [00:00 Patient Age:[19 - 35]&Patient Gender:M: 100%|█| 383/383 [00:00<00:00, \r\n", "\r", "Filter -> Patient Age:[19 - 35]&Patient Gender:F: 0%| | 0/383 [00:00 Patient Age:[19 - 35]&Patient Gender:F: 100%|█| 383/383 [00:00<00:00, \r\n", "\r", - "Filter -> Patient Age:[35 - 65]&Patient Gender:M: 0%| | 0/383 [00:00 Patient Age:[35 - 65]&Patient Gender:M: 0%| | 0/383 [00:00 Patient Age:[35 - 65]&Patient Gender:M: 100%|█| 383/383 [00:00<00:00, \r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\r", - "Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|█| 383/383 [00:00<00:00, \r\n", "\r", "Filter -> Patient Age:[35 - 65]&Patient Gender:F: 0%| | 0/383 [00:00 Patient Age:[35 - 65]&Patient Gender:F: 100%|█| 383/383 [00:00<00:00, \r\n", "\r", "Filter -> Patient Age:[65 - 100]&Patient Gender:M: 0%| | 0/383 [00:00 Patient Age:[65 - 100]&Patient Gender:M: 100%|█| 383/383 [00:00<00:00,\r\n" + "Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|█| 383/383 [00:00<00:00,\r\n", + "\r", + "Filter -> Patient Age:[65 - 100]&Patient Gender:F: 0%| | 0/383 [00:00 Patient Age:[65 - 100]&Patient Gender:F: 0%| | 0/383 [00:00 Patient Age:[65 - 100]&Patient Gender:F: 100%|█| 383/383 [00:00<00:00,\r\n", "\r", "Filter -> overall: 0%| | 0/383 [00:00 overall: 100%|████████████| 383/383 [00:00<00:00, 46013.36 examples/s]\r\n" + "Filter -> overall: 100%|████████████| 383/383 [00:00<00:00, 46898.62 examples/s]\r\n" ] }, { @@ -442,14 +444,14 @@ "output_type": "stream", "text": [ "\r", - "Flattening the indices: 100%|████████| 1000/1000 [00:53<00:00, 18.58 examples/s]\r", - "Flattening the indices: 100%|████████| 1000/1000 [00:53<00:00, 18.57 examples/s]\r\n", + "Flattening the indices: 100%|████████| 1000/1000 [00:52<00:00, 19.10 examples/s]\r", + "Flattening the indices: 100%|████████| 1000/1000 [00:52<00:00, 19.10 examples/s]\r\n", "\r", "Flattening the indices: 0%| | 0/1000 [00:00 Patient Gender:M: 0%| | 0/411 [00:00 Patient Gender:M: 100%|███| 411/411 [00:00<00:00, 43819.50 examples/s]\r\n" + "Filter -> Patient Gender:M: 100%|███| 411/411 [00:00<00:00, 44609.86 examples/s]\r\n", + "\r", + "Filter -> Patient Gender:F: 0%| | 0/411 [00:00 Patient Gender:F: 0%| | 0/411 [00:00 Patient Gender:F: 100%|███| 411/411 [00:00<00:00, 43590.13 examples/s]\r\n", + "Filter -> Patient Gender:F: 100%|███| 411/411 [00:00<00:00, 43444.03 examples/s]\r\n", "\r", "Filter -> overall: 0%| | 0/411 [00:00 overall: 100%|████████████| 411/411 [00:00<00:00, 46568.13 examples/s]\r\n" + "Filter -> overall: 100%|████████████| 411/411 [00:00<00:00, 46593.30 examples/s]\r\n" ] }, { @@ -484,7 +493,9 @@ "text": [ "\r", "Filter -> Patient Age:[19 - 35]: 0%| | 0/411 [00:00 Patient Age:[19 - 35]: 100%|█| 411/411 [00:00<00:00, 41915.50 examples\r\n" + "Filter -> Patient Age:[19 - 35]: 100%|█| 411/411 [00:00<00:00, 42187.34 examples\r\n", + "\r", + "Filter -> Patient Age:[35 - 65]: 0%| | 0/411 [00:00 Patient Age:[35 - 65]: 0%| | 0/411 [00:00 Patient Age:[35 - 65]: 100%|█| 411/411 [00:00<00:00, 42847.96 examples\r\n", + "Filter -> Patient Age:[35 - 65]: 100%|█| 411/411 [00:00<00:00, 42666.61 examples\r\n", "\r", - "Filter -> Patient Age:[65 - 100]: 0%| | 0/411 [00:00 Patient Age:[65 - 100]: 0%| | 0/411 [00:00 Patient Age:[65 - 100]: 100%|█| 411/411 [00:00<00:00, 42613.87 example\r\n", + "\r", + "Filter -> Patient Age:[19 - 35]&Patient Gender:M: 0%| | 0/411 [00:00 Patient Age:[65 - 100]: 100%|█| 411/411 [00:00<00:00, 43293.46 example\r\n", - "\r", - "Filter -> Patient Age:[19 - 35]&Patient Gender:M: 0%| | 0/411 [00:00 Patient Age:[19 - 35]&Patient Gender:M: 100%|█| 411/411 [00:00<00:00, \r\n", "\r", "Filter -> Patient Age:[19 - 35]&Patient Gender:F: 0%| | 0/411 [00:00 Patient Age:[19 - 35]&Patient Gender:F: 100%|█| 411/411 [00:00<00:00, \r\n", "\r", - "Filter -> Patient Age:[35 - 65]&Patient Gender:M: 0%| | 0/411 [00:00 Patient Age:[35 - 65]&Patient Gender:M: 0%| | 0/411 [00:00 Patient Age:[35 - 65]&Patient Gender:M: 100%|█| 411/411 [00:00<00:00, \r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\r", - "Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|█| 411/411 [00:00<00:00, \r\n", "\r", "Filter -> Patient Age:[35 - 65]&Patient Gender:F: 0%| | 0/411 [00:00 Patient Age:[35 - 65]&Patient Gender:F: 100%|█| 411/411 [00:00<00:00, \r\n", "\r", - "Filter -> Patient Age:[65 - 100]&Patient Gender:M: 0%| | 0/411 [00:00 Patient Age:[65 - 100]&Patient Gender:M: 0%| | 0/411 [00:00 Patient Age:[65 - 100]&Patient Gender:M: 100%|█| 411/411 [00:00<00:00,\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\r", - "Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|█| 411/411 [00:00<00:00,\r\n", "\r", "Filter -> Patient Age:[65 - 100]&Patient Gender:F: 0%| | 0/411 [00:00 Patient Age:[65 - 100]&Patient Gender:F: 100%|█| 411/411 [00:00<00:00,\r\n", "\r", "Filter -> overall: 0%| | 0/411 [00:00 overall: 100%|████████████| 411/411 [00:00<00:00, 47531.13 examples/s]\r\n" + "Filter -> overall: 100%|████████████| 411/411 [00:00<00:00, 47419.99 examples/s]\r\n" ] } ], @@ -586,10 +594,10 @@ "id": "03edf1c0", "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:13:54.321405Z", - "iopub.status.busy": "2024-02-28T00:13:54.320823Z", - "iopub.status.idle": "2024-02-28T00:13:54.329756Z", - "shell.execute_reply": "2024-02-28T00:13:54.328008Z" + "iopub.execute_input": "2024-02-28T21:37:22.623513Z", + "iopub.status.busy": "2024-02-28T21:37:22.622921Z", + "iopub.status.idle": "2024-02-28T21:37:22.630956Z", + "shell.execute_reply": "2024-02-28T21:37:22.629693Z" } }, "outputs": [], @@ -611,10 +619,10 @@ "id": "6514120e", "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:13:54.335935Z", - "iopub.status.busy": "2024-02-28T00:13:54.335411Z", - "iopub.status.idle": "2024-02-28T00:13:57.686305Z", - "shell.execute_reply": "2024-02-28T00:13:57.685292Z" + "iopub.execute_input": "2024-02-28T21:37:22.636993Z", + "iopub.status.busy": "2024-02-28T21:37:22.636353Z", + "iopub.status.idle": "2024-02-28T21:37:25.936372Z", + "shell.execute_reply": "2024-02-28T21:37:25.935490Z" } }, "outputs": [], @@ -658,17 +666,17 @@ "id": "5f624ed4", "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:13:57.693104Z", - "iopub.status.busy": "2024-02-28T00:13:57.692897Z", - "iopub.status.idle": "2024-02-28T00:14:11.359098Z", - "shell.execute_reply": "2024-02-28T00:14:11.358115Z" + "iopub.execute_input": "2024-02-28T21:37:25.944410Z", + "iopub.status.busy": "2024-02-28T21:37:25.944199Z", + "iopub.status.idle": "2024-02-28T21:37:39.785448Z", + "shell.execute_reply": "2024-02-28T21:37:39.784769Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1cb695175ad24419b8e1fab6c83346cd", + "model_id": "2c16739537e14c638fef93d1c9c2638d", "version_major": 2, "version_minor": 0 }, @@ -682,7 +690,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "561cb40aa9a34248be08891d82335ce5", + "model_id": "c8e02ac63dbd4853be7c0e4bcc561af1", "version_major": 2, "version_minor": 0 }, @@ -858,17 +866,17 @@ "id": "8c38ef9e", "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:14:11.605371Z", - "iopub.status.busy": "2024-02-28T00:14:11.605040Z", - "iopub.status.idle": "2024-02-28T00:14:12.111464Z", - "shell.execute_reply": "2024-02-28T00:14:12.110195Z" + "iopub.execute_input": "2024-02-28T21:37:40.017479Z", + "iopub.status.busy": "2024-02-28T21:37:40.016955Z", + "iopub.status.idle": "2024-02-28T21:37:40.516901Z", + "shell.execute_reply": "2024-02-28T21:37:40.515833Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "56c6af40b9874efc84b7d261fa3010d6", + "model_id": "897ed197d5584c07a0cea280cc7cda4f", "version_major": 2, "version_minor": 0 }, @@ -882,7 +890,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "24787ac519e945b99e36606a2eec4764", + "model_id": "2f38a868b96a4dca80a24f7bb085655a", "version_major": 2, "version_minor": 0 }, @@ -896,7 +904,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c803ed395e1044ffa91bdbbafd745834", + "model_id": "f0adc266b60545289d856fe6007434ae", "version_major": 2, "version_minor": 0 }, @@ -910,7 +918,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d1d91e4b26ca4442b45b9755f08eba57", + "model_id": "fcbbebad6b14491d895018ede9bced74", "version_major": 2, "version_minor": 0 }, @@ -924,7 +932,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aaf3a6ecd928458caf90c711690985ed", + "model_id": "9e61c85de31d4e84b6289a7cda65dbfc", "version_major": 2, "version_minor": 0 }, @@ -938,7 +946,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a38192b0ae4a4f118e720c7ee5cefced", + "model_id": "c6187525100d4ece83e2111974dd6610", "version_major": 2, "version_minor": 0 }, @@ -952,7 +960,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3779ff16c1364716adf86cf67f94656f", + "model_id": "909d95be86274cb99935306352a670db", "version_major": 2, "version_minor": 0 }, @@ -966,7 +974,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "635c4668b5e243898cf7f3b7d52ebde8", + "model_id": "548631a67f8b4034bf7d9ea0e41e968d", "version_major": 2, "version_minor": 0 }, @@ -980,7 +988,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b4d3ab7be37142ef8d6183d013d7af81", + "model_id": "9672d0ab3e354e5bac0e6daf20c2e94e", "version_major": 2, "version_minor": 0 }, @@ -994,7 +1002,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d64bb7d922764d95835c66b57d4b64f4", + "model_id": "a7c769396a334f74bd8072e8f9437bfa", "version_major": 2, "version_minor": 0 }, @@ -1057,10 +1065,10 @@ "id": "3e674b7a", "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:14:12.117155Z", - "iopub.status.busy": "2024-02-28T00:14:12.116754Z", - "iopub.status.idle": "2024-02-28T00:14:12.360922Z", - "shell.execute_reply": "2024-02-28T00:14:12.360136Z" + "iopub.execute_input": "2024-02-28T21:37:40.521834Z", + "iopub.status.busy": "2024-02-28T21:37:40.521632Z", + "iopub.status.idle": "2024-02-28T21:37:40.750494Z", + "shell.execute_reply": "2024-02-28T21:37:40.749561Z" } }, "outputs": [ @@ -1955,9 +1963,9 @@ } }, "text/html": [ - 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@@ -526,49 +526,49 @@

Example 4. Sensitivity test experiment with different clinical shifts
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@@ -613,7 +613,7 @@

Example 5. Rolling window experiment with synthetic timestamps using biweekl

diff --git a/api/tutorials/nihcxr/monitor_api.ipynb b/api/tutorials/nihcxr/monitor_api.ipynb index 756ee274a..029f1f6e9 100644 --- a/api/tutorials/nihcxr/monitor_api.ipynb +++ b/api/tutorials/nihcxr/monitor_api.ipynb @@ -22,17 +22,17 @@ "id": "8aa3302d", "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:14:18.894647Z", - "iopub.status.busy": "2024-02-28T00:14:18.894102Z", - "iopub.status.idle": "2024-02-28T00:14:26.375318Z", - "shell.execute_reply": "2024-02-28T00:14:26.374684Z" + "iopub.execute_input": "2024-02-28T21:37:46.069486Z", + "iopub.status.busy": "2024-02-28T21:37:46.068989Z", + "iopub.status.idle": "2024-02-28T21:37:53.785413Z", + "shell.execute_reply": "2024-02-28T21:37:53.784602Z" } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 1, @@ -79,17 +79,17 @@ "id": "e11920db", "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:14:26.380688Z", - "iopub.status.busy": "2024-02-28T00:14:26.380360Z", - "iopub.status.idle": "2024-02-28T00:14:26.979363Z", - 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Graphics

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Graphics

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Tradeoffs

- + + + + \ No newline at end of file diff --git a/api/tutorials/synthea/length_of_stay_report_periodic.html b/api/tutorials/synthea/length_of_stay_report_periodic.html index a7bc5583b..63f74b83c 100644 --- a/api/tutorials/synthea/length_of_stay_report_periodic.html +++ b/api/tutorials/synthea/length_of_stay_report_periodic.html @@ -658,7 +658,7 @@

A quick glance of your most import
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A quick glance of your most import
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A quick glance of your most import
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A quick glance of your most import
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A quick glance of your most import
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Graphics

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Quantitative Analysis
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Quantitative Analysis
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Quantitative Analysis
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Quantitative Analysis
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Quantitative Analysis
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Graphics

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Graphics

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Version

- Date: 2024-02-27 + Date: 2024-02-28
@@ -1591,6 +1591,10 @@

Model Parameters

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Objective

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Model Parameters

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Model Parameters

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Enable_categorical

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Objective

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Eval_metric

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Ethical Considerations

- + + + + \ No newline at end of file diff --git a/api/tutorials/synthea/los_prediction.html b/api/tutorials/synthea/los_prediction.html index 815d308a2..579f497bd 100644 --- a/api/tutorials/synthea/los_prediction.html +++ b/api/tutorials/synthea/los_prediction.html @@ -535,7 +535,7 @@

Compute length of stay (labels) 
-2024-02-27 19:15:12,044 INFO cycquery.orm    - Database setup, ready to run queries!
+2024-02-28 16:38:37,054 INFO cycquery.orm    - Database setup, ready to run queries!

@@ -543,7 +543,7 @@

Compute length of stay (labels) 
-2024-02-27 19:15:17,283 INFO cycquery.orm    - Query returned successfully!
+2024-02-28 16:38:42,415 INFO cycquery.orm    - Query returned successfully!

@@ -551,7 +551,7 @@

Compute length of stay (labels) 
-2024-02-27 19:15:17,284 INFO cycquery.utils.profile - Finished executing function run_query in 3.887026 s
+2024-02-28 16:38:42,416 INFO cycquery.utils.profile - Finished executing function run_query in 4.020620 s

@@ -559,7 +559,7 @@

Compute length of stay (labels) 
-2024-02-27 19:15:19,120 INFO cycquery.orm    - Query returned successfully!
+2024-02-28 16:38:44,193 INFO cycquery.orm    - Query returned successfully!

@@ -567,7 +567,7 @@

Compute length of stay (labels) 
-2024-02-27 19:15:19,121 INFO cycquery.utils.profile - Finished executing function run_query in 1.835471 s
+2024-02-28 16:38:44,194 INFO cycquery.utils.profile - Finished executing function run_query in 1.777614 s

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

Compute length of stay (labels) 
-2024-02-27 19:15:20,657 INFO cycquery.orm    - Query returned successfully!
+2024-02-28 16:38:45,752 INFO cycquery.orm    - Query returned successfully!

@@ -583,7 +583,7 @@

Compute length of stay (labels) 
-2024-02-27 19:15:20,658 INFO cycquery.utils.profile - Finished executing function run_query in 0.382661 s
+2024-02-28 16:38:45,753 INFO cycquery.utils.profile - Finished executing function run_query in 0.380770 s

@@ -591,7 +591,7 @@

Compute length of stay (labels) 
-2024-02-27 19:15:21,150 INFO cycquery.orm    - Query returned successfully!
+2024-02-28 16:38:46,242 INFO cycquery.orm    - Query returned successfully!

@@ -599,7 +599,7 @@

Compute length of stay (labels) 
-2024-02-27 19:15:21,151 INFO cycquery.utils.profile - Finished executing function run_query in 0.488408 s
+2024-02-28 16:38:46,243 INFO cycquery.utils.profile - Finished executing function run_query in 0.485648 s

@@ -607,7 +607,7 @@

Compute length of stay (labels) 
-2024-02-27 19:15:21,232 INFO cycquery.orm    - Query returned successfully!
+2024-02-28 16:38:46,356 INFO cycquery.orm    - Query returned successfully!

@@ -615,7 +615,7 @@

Compute length of stay (labels) 
-2024-02-27 19:15:21,234 INFO cycquery.utils.profile - Finished executing function run_query in 0.081796 s
+2024-02-28 16:38:46,357 INFO cycquery.utils.profile - Finished executing function run_query in 0.112409 s

@@ -668,9 +668,9 @@

Drop NaNs based on the
-
+
@@ -1232,7 +1232,7 @@

Training 
-2024-02-27 19:15:27,257 INFO cyclops.models.wrappers.sk_model - Best reg_lambda: 0
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@@ -1240,7 +1240,7 @@

Training 

-2024-02-27 19:15:27,258 INFO cyclops.models.wrappers.sk_model - Best n_estimators: 500
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@@ -1248,7 +1248,7 @@

Training 

-2024-02-27 19:15:27,259 INFO cyclops.models.wrappers.sk_model - Best max_depth: 2
+2024-02-28 16:38:55,464 INFO cyclops.models.wrappers.sk_model - Best max_depth: 5
@@ -1256,7 +1256,7 @@

Training 

-2024-02-27 19:15:27,259 INFO cyclops.models.wrappers.sk_model - Best learning_rate: 0.1
+2024-02-28 16:38:55,465 INFO cyclops.models.wrappers.sk_model - Best learning_rate: 0.1
@@ -1264,7 +1264,7 @@

Training 

-2024-02-27 19:15:27,259 INFO cyclops.models.wrappers.sk_model - Best gamma: 2
+2024-02-28 16:38:55,465 INFO cyclops.models.wrappers.sk_model - Best gamma: 2
@@ -1272,7 +1272,7 @@

Training 

-2024-02-27 19:15:27,260 INFO cyclops.models.wrappers.sk_model - Best colsample_bytree: 1
+2024-02-28 16:38:55,466 INFO cyclops.models.wrappers.sk_model - Best colsample_bytree: 1
@@ -1287,9 +1287,9 @@

Training grow_policy=None, importance_type=None, interaction_constraints=None, learning_rate=0.1, max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None, - max_delta_step=None, max_depth=2, max_leaves=None, + max_delta_step=None, max_depth=5, max_leaves=None, min_child_weight=3, missing=nan, monotone_constraints=None, - n_estimators=500, n_jobs=None, num_parallel_tree=None, + n_estimators=250, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=123, ...)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

@@ -1316,7 +1316,7 @@

Training 

-{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 1, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': 'logloss', 'feature_types': None, 'gamma': 2, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 2, 'max_leaves': None, 'min_child_weight': 3, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 500, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': 0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None, 'seed': 123}
+{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 1, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': 'logloss', 'feature_types': None, 'gamma': 2, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': 3, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 250, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': 1, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None, 'seed': 123}

Log the model parameters to the report.

@@ -1352,7 +1352,7 @@

Prediction
-
+
@@ -1479,91 +1479,91 @@

Evaluation

-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+

Log the performance metrics to the report.

We can add a performance metric to the model card using the log_performance_metric method, which expects a dictionary where the keys are in the following format: slice_name/metric_name. For instance, overall/accuracy.

@@ -1666,9 +1666,9 @@

Evaluation
-

diff --git a/api/tutorials/synthea/los_prediction.ipynb b/api/tutorials/synthea/los_prediction.ipynb index 48a74a99e..311e1cebc 100644 --- a/api/tutorials/synthea/los_prediction.ipynb +++ b/api/tutorials/synthea/los_prediction.ipynb @@ -33,10 +33,10 @@ "id": "53009e6b", "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:15:06.467554Z", - "iopub.status.busy": "2024-02-28T00:15:06.466909Z", - "iopub.status.idle": "2024-02-28T00:15:11.178021Z", - "shell.execute_reply": "2024-02-28T00:15:11.176698Z" + "iopub.execute_input": "2024-02-28T21:38:31.650717Z", + "iopub.status.busy": "2024-02-28T21:38:31.650084Z", + "iopub.status.idle": "2024-02-28T21:38:36.012351Z", + "shell.execute_reply": "2024-02-28T21:38:36.011597Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "afae58a8-5708-4e05-8695-25ba3ce1a71f", "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:15:11.185669Z", - "iopub.status.busy": "2024-02-28T00:15:11.185090Z", - "iopub.status.idle": "2024-02-28T00:15:11.190668Z", - "shell.execute_reply": "2024-02-28T00:15:11.189753Z" + "iopub.execute_input": "2024-02-28T21:38:36.017704Z", + "iopub.status.busy": "2024-02-28T21:38:36.017345Z", + "iopub.status.idle": "2024-02-28T21:38:36.021809Z", + "shell.execute_reply": "2024-02-28T21:38:36.021013Z" }, "tags": [] }, @@ -123,10 +123,10 @@ "id": "739b109a-011b-4e6e-a3de-964edeffddbd", "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:15:11.196015Z", - "iopub.status.busy": "2024-02-28T00:15:11.195597Z", - "iopub.status.idle": "2024-02-28T00:15:11.200284Z", - "shell.execute_reply": "2024-02-28T00:15:11.199328Z" + "iopub.execute_input": "2024-02-28T21:38:36.026408Z", + "iopub.status.busy": "2024-02-28T21:38:36.026061Z", + "iopub.status.idle": "2024-02-28T21:38:36.030020Z", + "shell.execute_reply": "2024-02-28T21:38:36.029161Z" }, "tags": [] }, @@ -157,10 +157,10 @@ "id": "e497df9f-0f3d-4e9c-845c-539627a37f67", "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:15:11.207390Z", - "iopub.status.busy": "2024-02-28T00:15:11.206847Z", - "iopub.status.idle": "2024-02-28T00:15:21.276171Z", - "shell.execute_reply": "2024-02-28T00:15:21.275221Z" + "iopub.execute_input": "2024-02-28T21:38:36.035226Z", + "iopub.status.busy": "2024-02-28T21:38:36.034827Z", + "iopub.status.idle": "2024-02-28T21:38:46.395301Z", + "shell.execute_reply": "2024-02-28T21:38:46.394564Z" }, "tags": [] }, @@ -169,77 +169,77 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-02-27 19:15:12,044 \u001b[1;37mINFO\u001b[0m cycquery.orm - Database setup, ready to run queries!\n" + "2024-02-28 16:38:37,054 \u001b[1;37mINFO\u001b[0m cycquery.orm - Database setup, ready to run queries!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2024-02-27 19:15:17,283 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" + "2024-02-28 16:38:42,415 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2024-02-27 19:15:17,284 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 3.887026 s\n" + "2024-02-28 16:38:42,416 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 4.020620 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2024-02-27 19:15:19,120 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" + "2024-02-28 16:38:44,193 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2024-02-27 19:15:19,121 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 1.835471 s\n" + "2024-02-28 16:38:44,194 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 1.777614 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2024-02-27 19:15:20,657 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" + "2024-02-28 16:38:45,752 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2024-02-27 19:15:20,658 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 0.382661 s\n" + "2024-02-28 16:38:45,753 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 0.380770 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2024-02-27 19:15:21,150 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" + "2024-02-28 16:38:46,242 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2024-02-27 19:15:21,151 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 0.488408 s\n" + "2024-02-28 16:38:46,243 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 0.485648 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2024-02-27 19:15:21,232 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" + "2024-02-28 16:38:46,356 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2024-02-27 19:15:21,234 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 0.081796 s\n" + "2024-02-28 16:38:46,357 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 0.112409 s\n" ] } ], @@ -398,10 +398,10 @@ "id": "c576ee51-e825-4970-86e8-3e5f221f145c", "metadata": { "execution": { - "iopub.execute_input": "2024-02-28T00:15:21.282439Z", - "iopub.status.busy": "2024-02-28T00:15:21.281973Z", - "iopub.status.idle": "2024-02-28T00:15:21.373941Z", - "shell.execute_reply": "2024-02-28T00:15:21.373002Z" + "iopub.execute_input": "2024-02-28T21:38:46.400695Z", + "iopub.status.busy": "2024-02-28T21:38:46.400298Z", + "iopub.status.idle": "2024-02-28T21:38:46.488217Z", + "shell.execute_reply": "2024-02-28T21:38:46.487513Z" }, "tags": [] }, @@ -1497,9 +1497,9 @@ } }, "text/html": [ - "
- + +

CyclOps 0.2.0 release

· 4 min read
Carolyn Chong
Amrit Krishnan
diff --git a/blog/archive/index.html b/blog/archive/index.html index bcdc0b553..8d926c610 100644 --- a/blog/archive/index.html +++ b/blog/archive/index.html @@ -5,8 +5,8 @@ Archive | CyclOps - - + +

Archive

Archive

2023​

diff --git a/blog/index.html b/blog/index.html index d989da7e4..34b9f2924 100644 --- a/blog/index.html +++ b/blog/index.html @@ -5,8 +5,8 @@ Blog | CyclOps - - + +

· 4 min read
Carolyn Chong
Amrit Krishnan
diff --git a/blog/tags/0-2-0/index.html b/blog/tags/0-2-0/index.html index b5681cec6..bfc539fb4 100644 --- a/blog/tags/0-2-0/index.html +++ b/blog/tags/0-2-0/index.html @@ -5,8 +5,8 @@ One post tagged with "0.2.0" | CyclOps - - + +

One post tagged with "0.2.0"

View All Tags

· 4 min read
Carolyn Chong
Amrit Krishnan
diff --git a/blog/tags/index.html b/blog/tags/index.html index 801a69771..7a073adc1 100644 --- a/blog/tags/index.html +++ b/blog/tags/index.html @@ -5,8 +5,8 @@ Tags | CyclOps - - + +

Tags

diff --git a/docs/intro/index.html b/docs/intro/index.html index bd7fcf04d..8b6faea73 100644 --- a/docs/intro/index.html +++ b/docs/intro/index.html @@ -5,8 +5,8 @@ intro | CyclOps - - + +

intro

Getting Started​

diff --git a/index.html b/index.html index dbbfa52dd..a688c78e7 100644 --- a/index.html +++ b/index.html @@ -5,8 +5,8 @@ CyclOps | CyclOps - - + +

CyclOps

Cyclical development towards Operationalizing ML models for healthcare

Rigorous Evaluation

CyclOps APIs support rigorous evaluation across patient sub-populations

Monitoring

CyclOps supports monitoring of clinical ML models for performance degradation

diff --git a/markdown-page/index.html b/markdown-page/index.html index 30ea67c77..3ccbd5a1f 100644 --- a/markdown-page/index.html +++ b/markdown-page/index.html @@ -5,8 +5,8 @@ Markdown page example | CyclOps - - + +

Markdown page example