From 51796de6f0dfbe4bde65b5962fb494a64a0c285f Mon Sep 17 00:00:00 2001 From: amrit110 Date: Mon, 27 Nov 2023 21:38:15 -0500 Subject: [PATCH] deploy: 03f6c5cefdb8be5b8685c950eaad48ec7d59d62e --- api/_modules/cyclops/evaluate/evaluator.html | 178 +- .../cyclops/evaluate/fairness/evaluator.html | 35 +- .../cyclops/tasks/classification.html | 12 +- .../kaggle/heart_failure_prediction.ipynb.txt | 2 +- .../mimiciv/mortality_prediction.ipynb.txt | 4 +- .../nihcxr/cxr_classification.ipynb.txt | 16 +- .../synthea/los_prediction.ipynb.txt | 4 +- .../cyclops.evaluate.evaluator.evaluate.html | 39 +- ....fairness.evaluator.evaluate_fairness.html | 4 +- api/searchindex.js | 2 +- .../kaggle/heart_failure_prediction.html | 72 +- .../kaggle/heart_failure_prediction.ipynb | 462 +- .../kaggle/heart_failure_report_periodic.html | 170 +- .../mimiciv/mortality_prediction.html | 144 +- .../mimiciv/mortality_prediction.ipynb | 4384 +++++++++-------- .../mimiciv/mortality_report_periodic.html | 138 +- api/tutorials/nihcxr/cxr_classification.html | 146 +- api/tutorials/nihcxr/cxr_classification.ipynb | 392 +- api/tutorials/nihcxr/monitor_api.html | 20 +- api/tutorials/nihcxr/monitor_api.ipynb | 132 +- .../nihcxr/nihcxr_report_periodic.html | 6 +- .../length_of_stay_report_periodic.html | 136 +- api/tutorials/synthea/los_prediction.html | 136 +- api/tutorials/synthea/los_prediction.ipynb | 3240 ++++++------ 24 files changed, 4843 insertions(+), 5031 deletions(-) diff --git a/api/_modules/cyclops/evaluate/evaluator.html b/api/_modules/cyclops/evaluate/evaluator.html index 28b25bae2..2de466d05 100644 --- a/api/_modules/cyclops/evaluate/evaluator.html +++ b/api/_modules/cyclops/evaluate/evaluator.html @@ -169,11 +169,10 @@

Source code for cyclops.evaluate.evaluator

 import logging
 import warnings
 from dataclasses import asdict
-from typing import Any, Callable, Dict, List, Optional, Sequence, Union, get_args
+from typing import Any, Dict, List, Optional, Sequence, Union
 
 from datasets import Dataset, DatasetDict, config, load_dataset
 from datasets.splits import Split
-from sklearn.compose import ColumnTransformer
 
 from cyclops.data.slicer import SliceSpec
 from cyclops.data.utils import (
@@ -184,8 +183,7 @@ 
Source code for cyclops.evaluate.evaluator
 from cyclops.evaluate.fairness.config import FairnessConfig
 from cyclops.evaluate.fairness.evaluator import evaluate_fairness
 from cyclops.evaluate.metrics.metric import Metric, MetricCollection
-from cyclops.evaluate.utils import choose_split
-from cyclops.models.wrappers import WrappedModel
+from cyclops.evaluate.utils import _format_column_names, choose_split
 from cyclops.utils.log import setup_logging
 
 
@@ -199,13 +197,8 @@ 
Source code for cyclops.evaluate.evaluator
     dataset: Union[str, Dataset, DatasetDict],
     metrics: Union[Metric, Sequence[Metric], Dict[str, Metric], MetricCollection],
     target_columns: Union[str, List[str]],
-    feature_columns: Optional[Union[str, List[str]]] = None,
-    prediction_column_prefix: str = "predictions",
-    remove_columns: Optional[Union[str, List[str]]] = None,
-    models: Optional[
-        Union[WrappedModel, Sequence[WrappedModel], Dict[str, WrappedModel]]
-    ] = None,
-    transforms: Optional[Union[Callable[..., Any], ColumnTransformer]] = None,
+    prediction_columns: Union[str, List[str]],
+    ignore_columns: Optional[Union[str, List[str]]] = None,
     slice_spec: Optional[SliceSpec] = None,
     split: Optional[Union[str, Split]] = None,
     batch_size: Optional[int] = config.DEFAULT_MAX_BATCH_SIZE,
@@ -225,30 +218,19 @@ 
Source code for cyclops.evaluate.evaluator
     metrics : Union[Metric, Sequence[Metric], Dict[str, Metric], MetricCollection]
         The metrics to compute.
     target_columns : Union[str, List[str]]
-        The name of the column(s) containing the target values.
-    feature_columns : Union[str, List[str]], optional
-        The name of the column(s) containing the feature values. This must be provided
-        if `models` is not None.
-    prediction_column_prefix : str, optional
-        The prefix of the column(s) containing the predictions. If `models` is not
-        None, the predictions will be added to the dataset and the column names will
-        be `{prediction_column_prefix}.{model_name}`. If `models` is None, the
-        predictions will be read from the dataset and the column names must start
-        with `prediction_column_prefix`.
-    remove_columns : Union[str, List[str]], optional
-        The name of the column(s) to remove from the dataset before filtering
-        and computing metrics. This is useful if the dataset contains columns
-        that are not needed for computing metrics but may be expensive to
-        keep in memory (e.g. image columns).
-    models : Union[WrappedModel, Sequence[WrappedModel], Dict[str, WrappedModel]]
-        The model(s) to evaluate. If a `Sequence` of `WrappedModel`, each model will
-        be evaluated on the entire dataset and the model class name will be used as
-        the model name. If a `Dict` of `WrappedModel`, each model will be evaluated
-        on the entire dataset and the keys will be used as the model names.
-    transforms : Callable, optional
-        A function that transforms the dataset before doing inference. This is
-        useful if the dataset needs to be transformed before being passed to
-        the model.
+        The name of the column(s) containing the target values. A string value
+        indicates a single column. A list of strings indicates a multi-label
+        task - the target values will be the union of the columns.
+    prediction_columns : Union[str, List[str]]
+        The names of the prediction columns used to compute metrics. If a string, it
+        should be the name of a column in the dataset. If a list, it should be a list
+        of column names in the dataset. Lists allow for evaluating multiple models
+        on the same dataset.
+    ignore_columns : Union[str, List[str]], optional
+        The name of the column(s) to ignore while filtering the dataset and computing
+        metrics. This is useful if the dataset contains columns that are not needed
+        for computing metrics but may be expensive to keep in memory
+        (e.g. image columns).
     slice_spec : SliceSpec, optional
         The slice specification to use for computing metrics. If None, no slices
         will be computed - the metrics will be computed on the entire dataset.
@@ -291,84 +273,33 @@ 
Source code for cyclops.evaluate.evaluator
     ------
     ValueError
         - If `dataset` is a `DatasetDict` and `split` is None.
-        - If `models` is None and `dataset` does not have a column that starts
-          with `prediction_column_prefix`.
-        - If `models` is not None and `feature_columns` is None.
-        - If multiple models are provided and only one set of results is found
-          after computing metrics.
 
     """
     dataset = _load_data(dataset, split, **(load_dataset_kwargs or {}))
+    metrics = _prepare_metrics(metrics)
 
-    column_names: List[str] = dataset.column_names
     check_required_columns(
-        column_names,
+        dataset.column_names,
         target_columns,
-        feature_columns,
-        remove_columns,
+        prediction_columns,
+        ignore_columns,
     )
 
-    metrics = _prepare_metrics(metrics)
-
-    if models is None and not any(
-        col.startswith(prediction_column_prefix) for col in column_names
-    ):
-        raise ValueError(
-            "Got `model=None` but `dataset` does not have a column that "
-            f"starts with `{prediction_column_prefix}`. Please specify a "
-            f"model or add a column that starts with `{prediction_column_prefix}` "
-            "to the dataset.",
-        )
-
-    if models is not None:
-        if feature_columns is None:
-            raise ValueError(
-                "Got `models` but `feature_columns` is None. Please specify "
-                "`feature_columns` argument.",
-            )
-        models = _prepare_models(models)
-        for model_name, model in models.items():
-            dataset = model.predict_proba(
-                dataset,
-                feature_columns=feature_columns,
-                prediction_column_prefix=prediction_column_prefix,
-                model_name=model_name,
-                transforms=transforms,
-                only_predictions=False,
-            )
-
-    # compute metrics for each model
-    results = {}
-
     if slice_spec is None:
         slice_spec = SliceSpec()
 
     metric_results = _compute_metrics(
-        dataset,
-        metrics,
-        slice_spec,
+        dataset=dataset,
+        metrics=metrics,
+        slice_spec=slice_spec,
         target_columns=target_columns,
-        prediction_column_prefix=prediction_column_prefix,
-        remove_columns=remove_columns,
+        prediction_columns=prediction_columns,
+        ignore_columns=ignore_columns,
         batch_size=batch_size,
         raise_on_empty_slice=raise_on_empty_slice,
     )
-    if "default" in metric_results:
-        if models is not None and len(models) > 1:
-            raise ValueError(
-                "Got multiple models but only one set of predictions. "
-                "Please make sure that the predictions for each model "
-                f"starts with `{prediction_column_prefix}` followed by "
-                "the model name. For example, if the model name is "
-                "`my_model`, the predictions should be in a column "
-                f"called `{prediction_column_prefix}.my_model`.",
-            )
-        if models is not None:  # only one model; replace "default" with model name
-            model_name = list(models.keys())[0]
-            metric_results[model_name] = metric_results.pop("default")
-        else:  # no models; don't name the results
-            metric_results = metric_results.pop("default")
 
+    results = {}
     results.update(metric_results)
 
     if fairness_config is not None:
@@ -377,13 +308,9 @@ 
Source code for cyclops.evaluate.evaluator
 
         fairness_config.dataset = dataset
         fairness_config.target_columns = target_columns
-        fairness_config.prediction_columns = [
-            col
-            for col in dataset.column_names
-            if col.startswith(prediction_column_prefix)
-        ]
+        fairness_config.prediction_columns = prediction_columns
         fairness_config.batch_size = batch_size
-        fairness_config.remove_columns = remove_columns
+        fairness_config.remove_columns = ignore_columns
 
         fairness_results = evaluate_fairness(**asdict(fairness_config))
         results["fairness"] = fairness_results
@@ -463,47 +390,19 @@ 
Source code for cyclops.evaluate.evaluator
     )
 
 
-def _prepare_models(
-    model: Union[WrappedModel, Sequence[WrappedModel], Dict[str, WrappedModel]],
-) -> Dict[str, WrappedModel]:
-    """Prepare models for evaluation."""
-    if isinstance(model, get_args(WrappedModel)):
-        model_name: str = model.model_.__class__.__name__
-        return {model_name: model}
-    if isinstance(model, (list, tuple)):
-        assert all(isinstance(m, get_args(WrappedModel)) for m in model)
-        return {m.getattr("model_").__class__.__name__: m for m in model}
-    if isinstance(model, dict):
-        assert all(isinstance(m, get_args(WrappedModel)) for m in model.values())
-        return model
-
-    raise TypeError(
-        f"Invalid type for `model`: {type(model)}. "
-        "Expected one of: WrappedModel, Sequence[WrappedModel], "
-        "Dict[str, WrappedModel].",
-    )
-
-
 def _compute_metrics(
     dataset: Dataset,
     metrics: MetricCollection,
     slice_spec: SliceSpec,
     target_columns: Union[str, List[str]],
-    prediction_column_prefix: str = "predictions",
-    remove_columns: Optional[Union[str, List[str]]] = None,
+    prediction_columns: Union[str, List[str]],
+    ignore_columns: Optional[Union[str, List[str]]] = None,
     batch_size: Optional[int] = config.DEFAULT_MAX_BATCH_SIZE,
     raise_on_empty_slice: bool = False,
 ) -> Dict[str, Dict[str, Any]]:
     """Compute metrics for a dataset."""
-    if isinstance(target_columns, str):
-        target_columns = [target_columns]
-
-    # get the predictions (there could be multiple)
-    # any column starting with `prediction_column_prefix` is considered a
-    # prediction column, for a single model
-    prediction_columns = [
-        col for col in dataset.column_names if col.startswith(prediction_column_prefix)
-    ]
+    target_columns = _format_column_names(target_columns)
+    prediction_columns = _format_column_names(prediction_columns)
 
     # temporarily stop decoding features to save memory
     set_decode(dataset, False, exclude=target_columns + prediction_columns)
@@ -514,9 +413,8 @@ 
Source code for cyclops.evaluate.evaluator
         output_all_columns=True,
     ):
         results: Dict[str, Dict[str, Any]] = {}
-
         for slice_name, slice_fn in slice_spec.slices():
-            sliced_dataset = dataset.remove_columns(remove_columns or []).filter(
+            sliced_dataset = dataset.remove_columns(ignore_columns or []).filter(
                 slice_fn,
                 batched=True,
                 batch_size=batch_size,
@@ -569,13 +467,7 @@ 
Source code for cyclops.evaluate.evaluator
                     metric_output = metrics.compute()
                     metrics.reset_state()
 
-                # get the model name from the prediction column name
-                # model name is everything after the first `prediction_column_prefix.`
-                model_name: str = "default"
-                pred_col_split = prediction_column.split(".", 1)
-                if len(pred_col_split) == 2:
-                    model_name = pred_col_split[1]
-
+                model_name: str = "model_for_%s" % prediction_column
                 results.setdefault(model_name, {})
                 results[model_name][slice_name] = metric_output
 
diff --git a/api/_modules/cyclops/evaluate/fairness/evaluator.html b/api/_modules/cyclops/evaluate/fairness/evaluator.html
index 8efebf209..22e88731c 100644
--- a/api/_modules/cyclops/evaluate/fairness/evaluator.html
+++ b/api/_modules/cyclops/evaluate/fairness/evaluator.html
@@ -196,6 +196,7 @@ 
Source code for cyclops.evaluate.fairness.evaluator
_check_thresholds,
     _get_value_if_singleton_array,
 )
+from cyclops.evaluate.utils import _format_column_names
 from cyclops.utils.log import setup_logging
 
 
@@ -210,7 +211,7 @@ 
Source code for cyclops.evaluate.fairness.evaluator
dataset: Dataset,
     groups: Union[str, List[str]],
     target_columns: Union[str, List[str]],
-    prediction_columns: Union[str, List[str]] = "predictions",
+    prediction_columns: Union[str, List[str]],
     group_values: Optional[Dict[str, Any]] = None,
     group_bins: Optional[Dict[str, Union[int, List[Any]]]] = None,
     group_base_values: Optional[Dict[str, Any]] = None,
@@ -245,7 +246,7 @@ 
Source code for cyclops.evaluate.fairness.evaluator
        The target or targets columns used to compute metrics. If a string, it should
         be the name of a column in the dataset. If a list, it should be a list of
         column names in the dataset. Lists will be treated as multilabel targets.
-    prediction_columns : Union[str, List[str]], default="predictions"
+    prediction_columns : Union[str, List[str]]
         The names of the prediction columns used to compute metrics. If a string, it
         should be the name of a column in the dataset. If a list, it should be a list
         of column names in the dataset. Lists allow for evaluating multiple models
@@ -583,36 +584,6 @@ 
Source code for cyclops.evaluate.fairness.evaluator
)
 
 
-def _format_column_names(column_names: Union[str, List[str]]) -> List[str]:
-    """Format the column names to list of strings if not already a list.
-
-    Parameters
-    ----------
-    column_names : Union[str, List[str]]
-        The column names to format.
-
-    Returns
-    -------
-    List[str]
-        The formatted column names.
-
-    Raises
-    ------
-    TypeError
-        If any of the column names are not strings or list of strings.
-
-    """
-    if isinstance(column_names, str):
-        return [column_names]
-    if isinstance(column_names, list):
-        return column_names
-
-    raise TypeError(
-        f"Expected column name {column_names} to be a string or "
-        f"list of strings, but got {type(column_names)}.",
-    )
-
-
 def _get_unique_values(
     dataset: Dataset,
     groups: List[str],
diff --git a/api/_modules/cyclops/tasks/classification.html b/api/_modules/cyclops/tasks/classification.html
index 801f0a618..c0cfe2b83 100644
--- a/api/_modules/cyclops/tasks/classification.html
+++ b/api/_modules/cyclops/tasks/classification.html
@@ -516,8 +516,10 @@ 
Source code for cyclops.tasks.classification
metrics=metrics_collection,
             target_columns=self.task_target,
             slice_spec=slice_spec,
-            prediction_column_prefix=prediction_column_prefix,
-            remove_columns=remove_columns,
+            prediction_columns=[
+                f"{prediction_column_prefix}.{model_name}" for model_name in model_names
+            ],
+            ignore_columns=remove_columns,
             split=splits_mapping["test"],
             batch_size=batch_size,
             fairness_config=fairness_config,
@@ -718,8 +720,10 @@ 
Source code for cyclops.tasks.classification
metrics=metrics_collection,
             slice_spec=slice_spec,
             target_columns=self.task_target,
-            prediction_column_prefix=prediction_column_prefix,
-            remove_columns=remove_columns,
+            prediction_columns=[
+                f"{prediction_column_prefix}.{model_name}" for model_name in model_names
+            ],
+            ignore_columns=remove_columns,
             split=splits_mapping["test"],
             batch_size=batch_size,
             fairness_config=fairness_config,
diff --git a/api/_sources/tutorials/kaggle/heart_failure_prediction.ipynb.txt b/api/_sources/tutorials/kaggle/heart_failure_prediction.ipynb.txt
index 2722c0ba2..8601a4fc1 100644
--- a/api/_sources/tutorials/kaggle/heart_failure_prediction.ipynb.txt
+++ b/api/_sources/tutorials/kaggle/heart_failure_prediction.ipynb.txt
@@ -885,12 +885,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "model_name = f\"model_for_preds.{model_name}\"\n",
     "results_flat = flatten_results_dict(\n",
     "    results=results,\n",
     "    remove_metrics=[\"BinaryROCCurve\", \"BinaryPrecisionRecallCurve\"],\n",
     "    model_name=model_name,\n",
     ")\n",
-    "\n",
     "results_female_flat = flatten_results_dict(\n",
     "    results=results_female,\n",
     "    model_name=model_name,\n",
diff --git a/api/_sources/tutorials/mimiciv/mortality_prediction.ipynb.txt b/api/_sources/tutorials/mimiciv/mortality_prediction.ipynb.txt
index 1574cc617..0658f18e5 100644
--- a/api/_sources/tutorials/mimiciv/mortality_prediction.ipynb.txt
+++ b/api/_sources/tutorials/mimiciv/mortality_prediction.ipynb.txt
@@ -916,12 +916,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "model_name = f\"model_for_preds.{model_name}\"\n",
     "results_flat = flatten_results_dict(\n",
     "    results=results,\n",
     "    remove_metrics=[\"BinaryROCCurve\", \"BinaryPrecisionRecallCurve\"],\n",
     "    model_name=model_name,\n",
-    ")\n",
-    "print(results_flat)"
+    ")"
    ]
   },
   {
diff --git a/api/_sources/tutorials/nihcxr/cxr_classification.ipynb.txt b/api/_sources/tutorials/nihcxr/cxr_classification.ipynb.txt
index 06de21e8a..32974c7e9 100644
--- a/api/_sources/tutorials/nihcxr/cxr_classification.ipynb.txt
+++ b/api/_sources/tutorials/nihcxr/cxr_classification.ipynb.txt
@@ -294,10 +294,9 @@
     "nih_eval_results_gender = evaluator.evaluate(\n",
     "    dataset=nih_ds,\n",
     "    metrics=[ppv, npv, sensitivity, specificity],\n",
-    "    feature_columns=\"image\",\n",
     "    target_columns=pathologies,\n",
-    "    prediction_column_prefix=\"predictions\",\n",
-    "    remove_columns=\"image\",\n",
+    "    prediction_columns=\"predictions.densenet\",\n",
+    "    ignore_columns=\"image\",\n",
     "    slice_spec=slice_spec,\n",
     ")"
    ]
@@ -354,10 +353,9 @@
     "nih_eval_results_age = evaluator.evaluate(\n",
     "    dataset=nih_ds,\n",
     "    metrics=[ppv, npv, sensitivity, specificity],\n",
-    "    feature_columns=\"image\",\n",
     "    target_columns=pathologies,\n",
-    "    prediction_column_prefix=\"predictions\",\n",
-    "    remove_columns=\"image\",\n",
+    "    prediction_columns=\"predictions.densenet\",\n",
+    "    ignore_columns=\"image\",\n",
     "    slice_spec=slice_spec,\n",
     ")"
    ]
@@ -459,7 +457,7 @@
    "outputs": [],
    "source": [
     "results_flat = {}\n",
-    "for slice_, metrics in nih_eval_results_age[\"densenet\"].items():\n",
+    "for slice_, metrics in nih_eval_results_age[\"model_for_predictions.densenet\"].items():\n",
     "    for name, metric in metrics.items():\n",
     "        results_flat[f\"{slice_}/{name}\"] = metric.mean()\n",
     "        for itr, m in enumerate(metric):\n",
@@ -467,7 +465,9 @@
     "                results_flat[f\"pathology:{pathologies[itr]}/{name}\"] = m\n",
     "            else:\n",
     "                results_flat[f\"{slice_}&pathology:{pathologies[itr]}/{name}\"] = m\n",
-    "for slice_, metrics in nih_eval_results_gender[\"densenet\"].items():\n",
+    "for slice_, metrics in nih_eval_results_gender[\n",
+    "    \"model_for_predictions.densenet\"\n",
+    "].items():\n",
     "    for name, metric in metrics.items():\n",
     "        results_flat[f\"{slice_}/{name}\"] = metric.mean()\n",
     "        for itr, m in enumerate(metric):\n",
diff --git a/api/_sources/tutorials/synthea/los_prediction.ipynb.txt b/api/_sources/tutorials/synthea/los_prediction.ipynb.txt
index c1f864997..c0130d714 100644
--- a/api/_sources/tutorials/synthea/los_prediction.ipynb.txt
+++ b/api/_sources/tutorials/synthea/los_prediction.ipynb.txt
@@ -1092,12 +1092,12 @@
    },
    "outputs": [],
    "source": [
+    "model_name = f\"model_for_preds.{model_name}\"\n",
     "results_flat = flatten_results_dict(\n",
     "    results=results,\n",
     "    remove_metrics=[\"BinaryROCCurve\", \"BinaryPrecisionRecallCurve\"],\n",
     "    model_name=model_name,\n",
-    ")\n",
-    "print(results_flat)"
+    ")"
    ]
   },
   {
diff --git a/api/reference/api/_autosummary/cyclops.evaluate.evaluator.evaluate.html b/api/reference/api/_autosummary/cyclops.evaluate.evaluator.evaluate.html
index 7419e9b03..eb04977b7 100644
--- a/api/reference/api/_autosummary/cyclops.evaluate.evaluator.evaluate.html
+++ b/api/reference/api/_autosummary/cyclops.evaluate.evaluator.evaluate.html
@@ -191,7 +191,7 @@
 
cyclops.evaluate.evaluator.evaluate

 

 

-evaluate(dataset, metrics, target_columns, feature_columns=None, prediction_column_prefix='predictions', remove_columns=None, models=None, transforms=None, slice_spec=None, split=None, batch_size=1000, raise_on_empty_slice=False, fairness_config=None, override_fairness_metrics=True, load_dataset_kwargs=None)[source]

+evaluate(dataset, metrics, target_columns, prediction_columns, ignore_columns=None, slice_spec=None, split=None, batch_size=1000, raise_on_empty_slice=False, fairness_config=None, override_fairness_metrics=True, load_dataset_kwargs=None)[source]
 
Evaluate one or more models on a dataset using one or more metrics.

 

 
Parameters:

@@ -200,25 +200,17 @@ 
cyclops.evaluate.evaluator.evaluateMetric, Sequence[Metric], Dict[str, Metric], MetricCollection]) – The metrics to compute.

-
  • target_columns (Union[str, List[str]]) – The name of the column(s) containing the target values.
    • 
-
  • feature_columns (Union[str, List[str]], optional) – The name of the column(s) containing the feature values. This must be provided
-if models is not None.
    • 
-
  • prediction_column_prefix (str, optional) – The prefix of the column(s) containing the predictions. If models is not
-None, the predictions will be added to the dataset and the column names will
-be {prediction_column_prefix}.{model_name}. If models is None, the
-predictions will be read from the dataset and the column names must start
-with prediction_column_prefix.
    • 
-
  • remove_columns (Union[str, List[str]], optional) – The name of the column(s) to remove from the dataset before filtering
-and computing metrics. This is useful if the dataset contains columns
-that are not needed for computing metrics but may be expensive to
-keep in memory (e.g. image columns).
    • 
-
  • models (Union[WrappedModel, Sequence[WrappedModel], Dict[str, WrappedModel]]) – The model(s) to evaluate. If a Sequence of WrappedModel, each model will
-be evaluated on the entire dataset and the model class name will be used as
-the model name. If a Dict of WrappedModel, each model will be evaluated
-on the entire dataset and the keys will be used as the model names.
    • 
-
  • transforms (Callable, optional) – A function that transforms the dataset before doing inference. This is
-useful if the dataset needs to be transformed before being passed to
-the model.
    • 
+
  • target_columns (Union[str, List[str]]) – The name of the column(s) containing the target values. A string value
+indicates a single column. A list of strings indicates a multi-label
+task - the target values will be the union of the columns.
    • 
+
  • prediction_columns (Union[str, List[str]]) – The names of the prediction columns used to compute metrics. If a string, it
+should be the name of a column in the dataset. If a list, it should be a list
+of column names in the dataset. Lists allow for evaluating multiple models
+on the same dataset.
    • 
+
  • ignore_columns (Union[str, List[str]], optional) – The name of the column(s) to ignore while filtering the dataset and computing
+metrics. This is useful if the dataset contains columns that are not needed
+for computing metrics but may be expensive to keep in memory
+(e.g. image columns).
    • 
 
  • slice_spec (SliceSpec, optional) – The slice specification to use for computing metrics. If None, no slices
 will be computed - the metrics will be computed on the entire dataset.
 Note that this is not used for computing fairness metrics.
    • 
@@ -254,12 +246,7 @@ 
    cyclops.evaluate.evaluator.evaluateRaises:
 
    ValueError – 
      
-
    • If dataset is a DatasetDict and split is None.
-    - If models is None and dataset does not have a column that starts
-      with prediction_column_prefix.
-    - If models is not None and feature_columns is None.
-    - If multiple models are provided and only one set of results is found
-      after computing metrics.
      • 
+
    • If dataset is a DatasetDict and split is None.
      • 
 
    
 
    
 
    
diff --git a/api/reference/api/_autosummary/cyclops.evaluate.fairness.evaluator.evaluate_fairness.html b/api/reference/api/_autosummary/cyclops.evaluate.fairness.evaluator.evaluate_fairness.html
index 085a42ef9..24c395f4b 100644
--- a/api/reference/api/_autosummary/cyclops.evaluate.fairness.evaluator.evaluate_fairness.html
+++ b/api/reference/api/_autosummary/cyclops.evaluate.fairness.evaluator.evaluate_fairness.html
@@ -192,7 +192,7 @@
 
    cyclops.evaluate.fairness.evaluator.evaluate_fairness
    
 
    
 
    
-evaluate_fairness(metrics, dataset, groups, target_columns, prediction_columns='predictions', group_values=None, group_bins=None, group_base_values=None, thresholds=None, compute_optimal_threshold=False, remove_columns=None, batch_size=1000, raise_on_empty_slice=False, metric_name=None, metric_kwargs=None)[source]
    
+evaluate_fairness(metrics, dataset, groups, target_columns, prediction_columns, group_values=None, group_bins=None, group_base_values=None, thresholds=None, compute_optimal_threshold=False, remove_columns=None, batch_size=1000, raise_on_empty_slice=False, metric_name=None, metric_kwargs=None)[source]
 
    Compute fairness indicators.
    
 
    This function computes fairness indicators for a dataset that includes
 predictions and targets.
    
@@ -211,7 +211,7 @@ 
    cyclops.evaluate.fairness.evaluator.evaluate_fairness
    target_columns (Union[str, List[str]]) – The target or targets columns used to compute metrics. If a string, it should
 be the name of a column in the dataset. If a list, it should be a list of
 column names in the dataset. Lists will be treated as multilabel targets.
    
-
  • prediction_columns (Union[str, List[str]], default="predictions") – The names of the prediction columns used to compute metrics. If a string, it
+
  • prediction_columns (Union[str, List[str]]) – The names of the prediction columns used to compute metrics. If a string, it
 should be the name of a column in the dataset. If a list, it should be a list
 of column names in the dataset. Lists allow for evaluating multiple models
 on the same dataset.
    • 
diff --git a/api/searchindex.js b/api/searchindex.js
index e60162c50..0c04aae86 100644
--- a/api/searchindex.js
+++ b/api/searchindex.js
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"Drop-NaNs-based-on-the-NAN_THRESHOLD"]], "Gender distribution": [[132, "Gender-distribution"], [135, "Gender-distribution"]], "Chest X-Ray Disease Classification": [[133, "Chest-X-Ray-Disease-Classification"]], "Generate Historical Reports": [[133, "Generate-Historical-Reports"]], "Initialize Periodic Report": [[133, "Initialize-Periodic-Report"]], "Load Dataset": [[133, "Load-Dataset"]], "Multilabel AUROC by Pathology and Sex": [[133, "Multilabel-AUROC-by-Pathology-and-Sex"]], "Multilabel AUROC by Pathology and Age": [[133, "Multilabel-AUROC-by-Pathology-and-Age"]], "Log Performance Metrics as Tests w/ Thresholds": [[133, "Log-Performance-Metrics-as-Tests-w/-Thresholds"]], "Populate Model Card Fields": [[133, "Populate-Model-Card-Fields"]], "NIHCXR Clinical Drift Experiments Tutorial": [[134, "NIHCXR-Clinical-Drift-Experiments-Tutorial"]], "Import Libraries and Load NIHCXR Dataset": [[134, "Import-Libraries-and-Load-NIHCXR-Dataset"]], "Example 1. Generate Source/Target Dataset for Experiments (1-2)": [[134, "Example-1.-Generate-Source/Target-Dataset-for-Experiments-(1-2)"]], "Example 2. Sensitivity test experiment with 3 dimensionality reduction techniques": [[134, "Example-2.-Sensitivity-test-experiment-with-3-dimensionality-reduction-techniques"]], "Example 3. Sensitivity test experiment with models trained on different datasets": [[134, "Example-3.-Sensitivity-test-experiment-with-models-trained-on-different-datasets"]], "Example 4. Sensitivity test experiment with different clinical shifts": [[134, "Example-4.-Sensitivity-test-experiment-with-different-clinical-shifts"]], "Example 5. Rolling window experiment with synthetic timestamps using biweekly window": [[134, "Example-5.-Rolling-window-experiment-with-synthetic-timestamps-using-biweekly-window"]], "Prolonged Length of Stay Prediction": [[135, "Prolonged-Length-of-Stay-Prediction"]], "Data Querying": [[135, "Data-Querying"]], "Compute length of stay (labels)": [[135, "Compute-length-of-stay-(labels)"]], "Length of stay distribution": [[135, "Length-of-stay-distribution"]], "monitor API": [[136, "monitor-api"]], "Example use cases": [[137, "example-use-cases"]], "Tabular data": [[137, "tabular-data"]], "Kaggle Heart Failure Prediction": [[137, "kaggle-heart-failure-prediction"]], "MIMICIV Mortality Prediction": [[137, "mimiciv-mortality-prediction"]], "Synthea Prolonged Length of Stay Prediction": [[137, "synthea-prolonged-length-of-stay-prediction"]], "Image data": [[137, "image-data"]], "NIH Chest X-ray classification": [[137, "nih-chest-x-ray-classification"]]}, "indexentries": 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Generate Source/Target Dataset for Experiments (1-2)": [[134, "Example-1.-Generate-Source/Target-Dataset-for-Experiments-(1-2)"]], "Example 2. Sensitivity test experiment with 3 dimensionality reduction techniques": [[134, "Example-2.-Sensitivity-test-experiment-with-3-dimensionality-reduction-techniques"]], "Example 3. Sensitivity test experiment with models trained on different datasets": [[134, "Example-3.-Sensitivity-test-experiment-with-models-trained-on-different-datasets"]], "Example 4. Sensitivity test experiment with different clinical shifts": [[134, "Example-4.-Sensitivity-test-experiment-with-different-clinical-shifts"]], "Example 5. Rolling window experiment with synthetic timestamps using biweekly window": [[134, "Example-5.-Rolling-window-experiment-with-synthetic-timestamps-using-biweekly-window"]], "Prolonged Length of Stay Prediction": [[135, "Prolonged-Length-of-Stay-Prediction"]], "Data Querying": [[135, "Data-Querying"]], "Compute length of stay (labels)": [[135, "Compute-length-of-stay-(labels)"]], "Length of stay distribution": [[135, "Length-of-stay-distribution"]], "monitor API": [[136, "monitor-api"]], "Example use cases": [[137, "example-use-cases"]], "Tabular data": [[137, "tabular-data"]], "Kaggle Heart Failure Prediction": [[137, "kaggle-heart-failure-prediction"]], "MIMICIV Mortality Prediction": [[137, "mimiciv-mortality-prediction"]], "Synthea Prolonged Length of Stay Prediction": [[137, "synthea-prolonged-length-of-stay-prediction"]], "Image data": [[137, "image-data"]], "NIH Chest X-ray classification": [[137, "nih-chest-x-ray-classification"]]}, "indexentries": 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(multilabelimageclassificationtask property)": [[124, "cyclops.tasks.classification.MultilabelImageClassificationTask.data_type"]], "evaluate() (multilabelimageclassificationtask method)": [[124, "cyclops.tasks.classification.MultilabelImageClassificationTask.evaluate"]], "get_model() (multilabelimageclassificationtask method)": [[124, "cyclops.tasks.classification.MultilabelImageClassificationTask.get_model"]], "list_models() (multilabelimageclassificationtask method)": [[124, "cyclops.tasks.classification.MultilabelImageClassificationTask.list_models"]], "list_models_params() (multilabelimageclassificationtask method)": [[124, "cyclops.tasks.classification.MultilabelImageClassificationTask.list_models_params"]], "load_model() (multilabelimageclassificationtask method)": [[124, "cyclops.tasks.classification.MultilabelImageClassificationTask.load_model"]], "models_count (multilabelimageclassificationtask property)": [[124, "cyclops.tasks.classification.MultilabelImageClassificationTask.models_count"]], "predict() (multilabelimageclassificationtask method)": [[124, "cyclops.tasks.classification.MultilabelImageClassificationTask.predict"]], "save_model() (multilabelimageclassificationtask method)": [[124, "cyclops.tasks.classification.MultilabelImageClassificationTask.save_model"]], "task_type (multilabelimageclassificationtask property)": [[124, "cyclops.tasks.classification.MultilabelImageClassificationTask.task_type"]], "cyclops.data": [[125, "module-cyclops.data"]], "cyclops.data.features": [[125, "module-cyclops.data.features"]], "cyclops.evaluate": [[126, "module-cyclops.evaluate"]], "cyclops.evaluate.fairness": [[126, "module-cyclops.evaluate.fairness"]], "cyclops.evaluate.metrics": [[126, "module-cyclops.evaluate.metrics"]], "cyclops.evaluate.metrics.functional": [[126, "module-cyclops.evaluate.metrics.functional"]], "cyclops.monitor": [[127, "module-cyclops.monitor"]], "cyclops.report": [[128, "module-cyclops.report"]], "cyclops.tasks": [[129, "module-cyclops.tasks"]]}})
\ 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 24d2d3dc8..60286c981 100644
--- a/api/tutorials/kaggle/heart_failure_prediction.html
+++ b/api/tutorials/kaggle/heart_failure_prediction.html
@@ -557,7 +557,7 @@ 
    Data Loading
 
    -2023-11-27 15:05:05,242 INFO cyclops.utils.file - Loading DataFrame from ./data/heart.csv
+2023-11-27 21:25:45,844 INFO cyclops.utils.file - Loading DataFrame from ./data/heart.csv
 
    
 
    
 
    
@@ -639,9 +639,9 @@ 
    Sex values
 
    
 
    
-
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1353,7 +1353,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1361,7 +1361,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1424,7 +1424,7 @@ 
    Quantitative Analysis
    
     
     
    
       
-        1.0
+        0.95
       
       
         
@@ -1457,7 +1457,7 @@ 
    Quantitative Analysis
    
         
     
    
       
-        0.89
+        0.56
       
       
         
@@ -1490,7 +1490,7 @@ 
    Quantitative Analysis
    
         
     
    
       
-        0.89
+        0.57
       
       
         
@@ -1523,7 +1523,7 @@ 
    Quantitative Analysis
    
         
     
    
       
-        0.83
+        0.8
       
       
         
@@ -1556,7 +1556,7 @@ 
    Quantitative Analysis
    
         
     
    
       
-        1.0
+        0.54
       
       
         
@@ -1588,7 +1588,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1596,7 +1596,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1604,7 +1604,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1657,7 +1657,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1930,8 +1930,8 @@ 
    Model Parameters
    
   
     
     
    
-      
    Shuffle
    
-      True
+      
    Penalty
    
+      l2
     
    
     
   
@@ -1939,8 +1939,8 @@ 
    Shuffle
    
   
     
     
    
-      
    L1_ratio
    
-      0.15
+      
    Verbose
    
+      0
     
    
     
   
@@ -1948,8 +1948,8 @@ 
    L1_ratio
    
   
     
     
    
-      
    Max_iter
    
-      1000
+      
    Eta0
    
+      0.01
     
    
     
   
@@ -1966,8 +1966,8 @@ 
    N_iter_no_change
    
   
     
     
    
-      
    Tol
    
-      0.001
+      
    Fit_intercept
    
+      True
     
    
     
   
@@ -1975,7 +1975,7 @@ 
    Tol
    
   
     
     
    
-      
    Epsilon
    
+      
    Validation_fraction
    
       0.1
     
    
     
@@ -1984,8 +1984,8 @@ 
    Epsilon
    
   
     
     
    
-      
    Verbose
    
-      0
+      
    Tol
    
+      0.001
     
    
     
   
@@ -1993,8 +1993,8 @@ 
    Verbose
    
   
     
     
    
-      
    Class_weight
    
-      balanced
+      
    Loss
    
+      log_loss
     
    
     
   
@@ -2002,8 +2002,8 @@ 
    Class_weight
    
   
     
     
    
-      
    Alpha
    
-      0.001
+      
    Max_iter
    
+      1000
     
    
     
   
@@ -2011,22 +2011,26 @@ 
    Alpha
    
   
     
     
    
-      
    Fit_intercept
    
-      True
+      
    Alpha
    
+      0.001
     
    
     
   
 
   
     
+    
    
+      
    Power_t
    
+      0.5
+    
    
     
   
 
   
     
     
    
-      
    Loss
    
-      log_loss
+      
    Random_state
    
+      123
     
    
     
   
@@ -2034,8 +2038,8 @@ 
    Loss
    
   
     
     
    
-      
    Random_state
    
-      123
+      
    Class_weight
    
+      balanced
     
    
     
   
@@ -2052,26 +2056,22 @@ 
    Early_stopping
    
   
     
     
    
-      
    Average
    
-      False
+      
    L1_ratio
    
+      0.15
     
    
     
   
 
   
     
-    
    
-      
    Learning_rate
    
-      adaptive
-    
    
     
   
 
   
     
     
    
-      
    Penalty
    
-      l2
+      
    Shuffle
    
+      True
     
    
     
   
@@ -2079,8 +2079,8 @@ 
    Penalty
    
   
     
     
    
-      
    Power_t
    
-      0.5
+      
    Learning_rate
    
+      adaptive
     
    
     
   
@@ -2088,8 +2088,8 @@ 
    Power_t
    
   
     
     
    
-      
    Validation_fraction
    
-      0.1
+      
    Warm_start
    
+      False
     
    
     
   
@@ -2097,8 +2097,8 @@ 
    Validation_fraction
    
   
     
     
    
-      
    Eta0
    
-      0.01
+      
    Average
    
+      False
     
    
     
   
@@ -2106,8 +2106,8 @@ 
    Eta0
    
   
     
     
    
-      
    Warm_start
    
-      False
+      
    Epsilon
    
+      0.1
     
    
     
   
@@ -2409,7 +2409,7 @@ 
    Ethical Considerations
    
   function generate_model_card_plot() {
     var model_card_plots = []
     var overall_indices = [11, 12, 13, 14, 15]
-    var histories = JSON.parse("{\"0\": [\"0.8421052631578947\", \"0.8599295531435974\", \"0.8743556923105937\", \"1.0\", \"1.0\"], \"1\": [\"0.796875\", \"0.7452972655503003\", \"0.8100437979383417\", \"0.9567413040229017\", \"0.9791972659279498\"], \"2\": [\"0.8260869565217391\", \"0.710807703727726\", \"0.6302922057200199\", \"0.5105935853038549\", \"0.5242048980662645\"], \"3\": [\"0.6785714285714286\", \"0.5813403753197157\", \"0.46040900879753677\", \"0.2907742596668287\", \"0.10593973638852061\"], \"4\": [\"0.7450980392156863\", \"0.6454185012514664\", \"0.44704801572543995\", \"0.45348093041138887\", \"0.4278179100561411\"], \"5\": [\"0.8819444444444444\", \"0.8960674994614873\", \"0.9052147172151933\", \"0.9476037683963476\", \"0.9967724009056905\"], \"6\": [\"0.8623853211009175\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"7\": [\"0.8676470588235294\", \"0.8313592315974797\", \"1.0\", \"0.8851948120576105\", \"0.9635843662296566\"], \"8\": [\"0.9076923076923077\", \"0.8273682060598722\", \"0.8859887097616485\", \"0.7762754540105692\", \"0.8356108955342884\"], \"9\": [\"0.8872180451127819\", \"0.8740684867448608\", \"0.7860893785257995\", \"0.7115098592940149\", \"0.6246821821007127\"], \"10\": [\"0.927972027972028\", \"0.8816020282017657\", \"0.7571451406719181\", \"0.9096101695506389\", \"1.0\"], \"11\": [\"0.842391304347826\", \"1.0\", \"1.0\", \"0.9082864560389727\", \"1.0\"], \"12\": [\"0.8686868686868687\", \"0.9697456797963158\", \"0.8328469768771936\", \"0.7556496414399212\", \"0.8921013620193099\"], \"13\": [\"0.8431372549019608\", \"0.9629701192039404\", \"1.0\", \"1.0\", \"0.8878464399707588\"], \"14\": [\"0.8557213930348259\", \"0.6891914985116139\", \"0.7332597316328251\", \"0.8386632504869617\", \"0.8345898783491332\"], \"15\": [\"0.9152319464371114\", \"0.9327809613168279\", \"0.9532958465260944\", \"0.8893136467753834\", \"1.0\"]}");
+    var histories = JSON.parse("{\"0\": [\"0.8421052631578947\", \"0.7060408145501588\", \"0.8638071857712474\", \"0.8414151407475001\", \"0.891252901558953\"], \"1\": [\"0.796875\", \"0.7315650401072841\", \"0.6859140538679289\", \"0.6742335038980826\", \"0.5666047629967271\"], \"2\": [\"0.8260869565217391\", \"0.7923439353251733\", \"0.7041071864428252\", \"0.6338502546967777\", \"0.711462691198528\"], \"3\": [\"0.6785714285714286\", \"0.4401830314310702\", \"0.41989197300517594\", \"0.4377085604515158\", \"0.7323243399353481\"], \"4\": [\"0.7450980392156863\", \"0.5725765636712422\", \"0.5251512278871754\", \"0.36824059607342274\", \"0.5483714783045526\"], \"5\": [\"0.8819444444444444\", \"0.934517961364451\", \"0.9732057198226989\", \"0.9083297359372854\", \"0.9104344943630669\"], \"6\": [\"0.8623853211009175\", \"1.0\", \"1.0\", \"0.9275280854237214\", \"0.9024088441301951\"], \"7\": [\"0.8676470588235294\", \"0.9560064574456584\", \"0.8650526865753256\", \"0.9450905277732021\", \"0.9483228290394246\"], \"8\": [\"0.9076923076923077\", \"0.9382728307654931\", \"0.8453266046589205\", \"0.9213893688603425\", \"0.9371771844350184\"], \"9\": [\"0.8872180451127819\", \"0.9018023660418806\", \"0.9306565239251938\", \"0.8895917787939817\", \"0.81797436851719\"], \"10\": [\"0.927972027972028\", \"0.91690230509804\", \"0.7769647250154791\", \"0.6934412503454002\", \"0.7611875474664198\"], \"11\": [\"0.842391304347826\", \"0.8697475843349362\", \"1.0\", \"0.8913948148968026\", \"0.9482884832406678\"], \"12\": [\"0.8686868686868687\", \"0.7543296661890938\", \"0.5714291749141898\", \"0.5694317913945552\", \"0.560126593179871\"], \"13\": [\"0.8431372549019608\", \"0.8279452634267033\", \"0.7712100078931869\", \"0.7118352817513972\", \"0.567254429734088\"], \"14\": [\"0.8557213930348259\", \"0.8256455428227429\", \"0.8940883939832679\", \"0.8220754761601221\", \"0.7968626590015472\"], \"15\": [\"0.9152319464371114\", \"0.6775364643798298\", \"0.48119608789773305\", \"0.4409306697604034\", \"0.5371162072514168\"]}");
     var thresholds = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\"}");
     var timestamps = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"1\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"2\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"3\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"4\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"5\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"6\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"7\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"8\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"9\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"10\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"11\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"12\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"13\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"14\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"15\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"]}");
 
@@ -2690,9 +2690,9 @@ 
    Ethical Considerations
    
       }
     }
     var slices_all = JSON.parse("{\"0\": [\"metric:Accuracy\", \"Sex:F\", \"Age:overall_Age\"], \"1\": [\"metric:Accuracy\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"2\": [\"metric:Precision\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"3\": [\"metric:Recall\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"4\": [\"metric:F1 Score\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"5\": [\"metric:AUROC\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"6\": [\"metric:Accuracy\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"7\": [\"metric:Precision\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"8\": [\"metric:Recall\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"9\": [\"metric:F1 Score\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"10\": [\"metric:AUROC\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"11\": [\"metric:Accuracy\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"12\": [\"metric:Precision\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"13\": [\"metric:Recall\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"14\": [\"metric:F1 Score\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"15\": [\"metric:AUROC\", \"Sex:overall_Sex\", \"Age:overall_Age\"]}");
-    var histories_all = JSON.parse("{\"0\": [\"0.8421052631578947\", \"0.8599295531435974\", \"0.8743556923105937\", \"1.0\", \"1.0\"], \"1\": [\"0.796875\", \"0.7452972655503003\", \"0.8100437979383417\", \"0.9567413040229017\", \"0.9791972659279498\"], \"2\": [\"0.8260869565217391\", \"0.710807703727726\", \"0.6302922057200199\", \"0.5105935853038549\", \"0.5242048980662645\"], \"3\": [\"0.6785714285714286\", \"0.5813403753197157\", \"0.46040900879753677\", \"0.2907742596668287\", \"0.10593973638852061\"], \"4\": [\"0.7450980392156863\", \"0.6454185012514664\", \"0.44704801572543995\", \"0.45348093041138887\", \"0.4278179100561411\"], \"5\": [\"0.8819444444444444\", \"0.8960674994614873\", \"0.9052147172151933\", \"0.9476037683963476\", \"0.9967724009056905\"], \"6\": [\"0.8623853211009175\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"7\": [\"0.8676470588235294\", \"0.8313592315974797\", \"1.0\", \"0.8851948120576105\", \"0.9635843662296566\"], \"8\": [\"0.9076923076923077\", \"0.8273682060598722\", \"0.8859887097616485\", \"0.7762754540105692\", \"0.8356108955342884\"], \"9\": [\"0.8872180451127819\", \"0.8740684867448608\", \"0.7860893785257995\", \"0.7115098592940149\", \"0.6246821821007127\"], \"10\": [\"0.927972027972028\", \"0.8816020282017657\", \"0.7571451406719181\", \"0.9096101695506389\", \"1.0\"], \"11\": [\"0.842391304347826\", \"1.0\", \"1.0\", \"0.9082864560389727\", \"1.0\"], \"12\": [\"0.8686868686868687\", \"0.9697456797963158\", \"0.8328469768771936\", \"0.7556496414399212\", \"0.8921013620193099\"], \"13\": [\"0.8431372549019608\", \"0.9629701192039404\", \"1.0\", \"1.0\", \"0.8878464399707588\"], \"14\": [\"0.8557213930348259\", \"0.6891914985116139\", \"0.7332597316328251\", \"0.8386632504869617\", \"0.8345898783491332\"], \"15\": [\"0.9152319464371114\", \"0.9327809613168279\", \"0.9532958465260944\", \"0.8893136467753834\", \"1.0\"]}");
+    var histories_all = JSON.parse("{\"0\": [\"0.8421052631578947\", \"0.7060408145501588\", \"0.8638071857712474\", \"0.8414151407475001\", \"0.891252901558953\"], \"1\": [\"0.796875\", \"0.7315650401072841\", \"0.6859140538679289\", \"0.6742335038980826\", \"0.5666047629967271\"], \"2\": [\"0.8260869565217391\", \"0.7923439353251733\", \"0.7041071864428252\", \"0.6338502546967777\", \"0.711462691198528\"], \"3\": [\"0.6785714285714286\", \"0.4401830314310702\", \"0.41989197300517594\", \"0.4377085604515158\", \"0.7323243399353481\"], \"4\": [\"0.7450980392156863\", \"0.5725765636712422\", \"0.5251512278871754\", \"0.36824059607342274\", \"0.5483714783045526\"], \"5\": [\"0.8819444444444444\", \"0.934517961364451\", \"0.9732057198226989\", \"0.9083297359372854\", \"0.9104344943630669\"], \"6\": [\"0.8623853211009175\", \"1.0\", \"1.0\", \"0.9275280854237214\", \"0.9024088441301951\"], \"7\": [\"0.8676470588235294\", \"0.9560064574456584\", \"0.8650526865753256\", \"0.9450905277732021\", \"0.9483228290394246\"], \"8\": [\"0.9076923076923077\", \"0.9382728307654931\", \"0.8453266046589205\", \"0.9213893688603425\", \"0.9371771844350184\"], \"9\": [\"0.8872180451127819\", \"0.9018023660418806\", \"0.9306565239251938\", \"0.8895917787939817\", \"0.81797436851719\"], \"10\": [\"0.927972027972028\", \"0.91690230509804\", \"0.7769647250154791\", \"0.6934412503454002\", \"0.7611875474664198\"], \"11\": [\"0.842391304347826\", \"0.8697475843349362\", \"1.0\", \"0.8913948148968026\", \"0.9482884832406678\"], \"12\": [\"0.8686868686868687\", \"0.7543296661890938\", \"0.5714291749141898\", \"0.5694317913945552\", \"0.560126593179871\"], \"13\": [\"0.8431372549019608\", \"0.8279452634267033\", \"0.7712100078931869\", \"0.7118352817513972\", \"0.567254429734088\"], \"14\": [\"0.8557213930348259\", \"0.8256455428227429\", \"0.8940883939832679\", \"0.8220754761601221\", \"0.7968626590015472\"], \"15\": [\"0.9152319464371114\", \"0.6775364643798298\", \"0.48119608789773305\", \"0.4409306697604034\", \"0.5371162072514168\"]}");
     var thresholds_all = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\"}");
-    var trends_all = JSON.parse("{\"0\": \"positive\", \"1\": \"positive\", \"2\": \"negative\", \"3\": \"negative\", \"4\": \"negative\", \"5\": \"positive\", \"6\": \"positive\", \"7\": \"positive\", \"8\": \"negative\", \"9\": \"negative\", \"10\": \"positive\", \"11\": \"positive\", \"12\": \"negative\", \"13\": \"positive\", \"14\": \"positive\", \"15\": \"positive\"}");
+    var trends_all = JSON.parse("{\"0\": \"positive\", \"1\": \"negative\", \"2\": \"negative\", \"3\": \"positive\", \"4\": \"negative\", \"5\": \"neutral\", \"6\": \"neutral\", \"7\": \"positive\", \"8\": \"neutral\", \"9\": \"negative\", \"10\": \"negative\", \"11\": \"positive\", \"12\": \"negative\", \"13\": \"negative\", \"14\": \"negative\", \"15\": \"negative\"}");
     var passed_all = JSON.parse("{\"0\": true, \"1\": true, \"2\": true, \"3\": false, \"4\": true, \"5\": true, \"6\": true, \"7\": true, \"8\": true, \"9\": true, \"10\": true, \"11\": true, \"12\": true, \"13\": true, \"14\": true, \"15\": true}");
     var names_all = JSON.parse("{\"0\": \"Accuracy\", \"1\": \"Accuracy\", \"2\": \"Precision\", \"3\": \"Recall\", \"4\": \"F1 Score\", \"5\": \"AUROC\", \"6\": \"Accuracy\", \"7\": \"Precision\", \"8\": \"Recall\", \"9\": \"F1 Score\", \"10\": \"AUROC\", \"11\": \"Accuracy\", \"12\": \"Precision\", \"13\": \"Recall\", \"14\": \"F1 Score\", \"15\": \"AUROC\"}");
     var timestamps_all = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"1\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"2\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"3\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"4\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"5\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"6\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"7\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"8\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"9\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"10\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"11\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"12\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"13\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"14\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"15\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"]}");
@@ -2966,9 +2966,9 @@ 
    Ethical Considerations
    
       }
     }
     var slices_all = JSON.parse("{\"0\": [\"metric:Accuracy\", \"Sex:F\", \"Age:overall_Age\"], \"1\": [\"metric:Accuracy\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"2\": [\"metric:Precision\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"3\": [\"metric:Recall\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"4\": [\"metric:F1 Score\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"5\": [\"metric:AUROC\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"6\": [\"metric:Accuracy\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"7\": [\"metric:Precision\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"8\": [\"metric:Recall\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"9\": [\"metric:F1 Score\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"10\": [\"metric:AUROC\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"11\": [\"metric:Accuracy\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"12\": [\"metric:Precision\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"13\": [\"metric:Recall\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"14\": [\"metric:F1 Score\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"15\": [\"metric:AUROC\", \"Sex:overall_Sex\", \"Age:overall_Age\"]}");
-    var histories_all = JSON.parse("{\"0\": [\"0.8421052631578947\", \"0.8599295531435974\", \"0.8743556923105937\", \"1.0\", \"1.0\"], \"1\": [\"0.796875\", \"0.7452972655503003\", \"0.8100437979383417\", \"0.9567413040229017\", \"0.9791972659279498\"], \"2\": [\"0.8260869565217391\", \"0.710807703727726\", \"0.6302922057200199\", \"0.5105935853038549\", \"0.5242048980662645\"], \"3\": [\"0.6785714285714286\", \"0.5813403753197157\", \"0.46040900879753677\", \"0.2907742596668287\", \"0.10593973638852061\"], \"4\": [\"0.7450980392156863\", \"0.6454185012514664\", \"0.44704801572543995\", \"0.45348093041138887\", \"0.4278179100561411\"], \"5\": [\"0.8819444444444444\", \"0.8960674994614873\", \"0.9052147172151933\", \"0.9476037683963476\", \"0.9967724009056905\"], \"6\": [\"0.8623853211009175\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"7\": [\"0.8676470588235294\", \"0.8313592315974797\", \"1.0\", \"0.8851948120576105\", \"0.9635843662296566\"], \"8\": [\"0.9076923076923077\", \"0.8273682060598722\", \"0.8859887097616485\", \"0.7762754540105692\", \"0.8356108955342884\"], \"9\": [\"0.8872180451127819\", \"0.8740684867448608\", \"0.7860893785257995\", \"0.7115098592940149\", \"0.6246821821007127\"], \"10\": [\"0.927972027972028\", \"0.8816020282017657\", \"0.7571451406719181\", \"0.9096101695506389\", \"1.0\"], \"11\": [\"0.842391304347826\", \"1.0\", \"1.0\", \"0.9082864560389727\", \"1.0\"], \"12\": [\"0.8686868686868687\", \"0.9697456797963158\", \"0.8328469768771936\", \"0.7556496414399212\", \"0.8921013620193099\"], \"13\": [\"0.8431372549019608\", \"0.9629701192039404\", \"1.0\", \"1.0\", \"0.8878464399707588\"], \"14\": [\"0.8557213930348259\", \"0.6891914985116139\", \"0.7332597316328251\", \"0.8386632504869617\", \"0.8345898783491332\"], \"15\": [\"0.9152319464371114\", \"0.9327809613168279\", \"0.9532958465260944\", \"0.8893136467753834\", \"1.0\"]}");
+    var histories_all = JSON.parse("{\"0\": [\"0.8421052631578947\", \"0.7060408145501588\", \"0.8638071857712474\", \"0.8414151407475001\", \"0.891252901558953\"], \"1\": [\"0.796875\", \"0.7315650401072841\", \"0.6859140538679289\", \"0.6742335038980826\", \"0.5666047629967271\"], \"2\": [\"0.8260869565217391\", \"0.7923439353251733\", \"0.7041071864428252\", \"0.6338502546967777\", \"0.711462691198528\"], \"3\": [\"0.6785714285714286\", \"0.4401830314310702\", \"0.41989197300517594\", \"0.4377085604515158\", \"0.7323243399353481\"], \"4\": [\"0.7450980392156863\", \"0.5725765636712422\", \"0.5251512278871754\", \"0.36824059607342274\", \"0.5483714783045526\"], \"5\": [\"0.8819444444444444\", \"0.934517961364451\", \"0.9732057198226989\", \"0.9083297359372854\", \"0.9104344943630669\"], \"6\": [\"0.8623853211009175\", \"1.0\", \"1.0\", \"0.9275280854237214\", \"0.9024088441301951\"], \"7\": [\"0.8676470588235294\", \"0.9560064574456584\", \"0.8650526865753256\", \"0.9450905277732021\", \"0.9483228290394246\"], \"8\": [\"0.9076923076923077\", \"0.9382728307654931\", \"0.8453266046589205\", \"0.9213893688603425\", \"0.9371771844350184\"], \"9\": [\"0.8872180451127819\", \"0.9018023660418806\", \"0.9306565239251938\", \"0.8895917787939817\", \"0.81797436851719\"], \"10\": [\"0.927972027972028\", \"0.91690230509804\", \"0.7769647250154791\", \"0.6934412503454002\", \"0.7611875474664198\"], \"11\": [\"0.842391304347826\", \"0.8697475843349362\", \"1.0\", \"0.8913948148968026\", \"0.9482884832406678\"], \"12\": [\"0.8686868686868687\", \"0.7543296661890938\", \"0.5714291749141898\", \"0.5694317913945552\", \"0.560126593179871\"], \"13\": [\"0.8431372549019608\", \"0.8279452634267033\", \"0.7712100078931869\", \"0.7118352817513972\", \"0.567254429734088\"], \"14\": [\"0.8557213930348259\", \"0.8256455428227429\", \"0.8940883939832679\", \"0.8220754761601221\", \"0.7968626590015472\"], \"15\": [\"0.9152319464371114\", \"0.6775364643798298\", \"0.48119608789773305\", \"0.4409306697604034\", \"0.5371162072514168\"]}");
     var thresholds_all = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\"}");
-    var trends_all = JSON.parse("{\"0\": \"positive\", \"1\": \"positive\", \"2\": \"negative\", \"3\": \"negative\", \"4\": \"negative\", \"5\": \"positive\", \"6\": \"positive\", \"7\": \"positive\", \"8\": \"negative\", \"9\": \"negative\", \"10\": \"positive\", \"11\": \"positive\", \"12\": \"negative\", \"13\": \"positive\", \"14\": \"positive\", \"15\": \"positive\"}");
+    var trends_all = JSON.parse("{\"0\": \"positive\", \"1\": \"negative\", \"2\": \"negative\", \"3\": \"positive\", \"4\": \"negative\", \"5\": \"neutral\", \"6\": \"neutral\", \"7\": \"positive\", \"8\": \"neutral\", \"9\": \"negative\", \"10\": \"negative\", \"11\": \"positive\", \"12\": \"negative\", \"13\": \"negative\", \"14\": \"negative\", \"15\": \"negative\"}");
     var passed_all = JSON.parse("{\"0\": true, \"1\": true, \"2\": true, \"3\": false, \"4\": true, \"5\": true, \"6\": true, \"7\": true, \"8\": true, \"9\": true, \"10\": true, \"11\": true, \"12\": true, \"13\": true, \"14\": true, \"15\": true}");
     var names_all = JSON.parse("{\"0\": \"Accuracy\", \"1\": \"Accuracy\", \"2\": \"Precision\", \"3\": \"Recall\", \"4\": \"F1 Score\", \"5\": \"AUROC\", \"6\": \"Accuracy\", \"7\": \"Precision\", \"8\": \"Recall\", \"9\": \"F1 Score\", \"10\": \"AUROC\", \"11\": \"Accuracy\", \"12\": \"Precision\", \"13\": \"Recall\", \"14\": \"F1 Score\", \"15\": \"AUROC\"}");
     var timestamps_all = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"1\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"2\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"3\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"4\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"5\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"6\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"7\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"8\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"9\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"10\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"11\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"12\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"13\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"14\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"15\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"]}");
diff --git a/api/tutorials/mimiciv/mortality_prediction.html b/api/tutorials/mimiciv/mortality_prediction.html
index 96f28aaa8..fdf46fca8 100644
--- a/api/tutorials/mimiciv/mortality_prediction.html
+++ b/api/tutorials/mimiciv/mortality_prediction.html
@@ -713,9 +713,9 @@ 
    Compute mortality (labels)
 
    -2023-11-27 15:05:26,852 INFO cycquery.orm    - Database setup, ready to run queries!
-2023-11-27 15:05:34,061 INFO cycquery.orm    - Query returned successfully!
-2023-11-27 15:05:34,062 INFO cycquery.utils.profile - Finished executing function run_query in 5.079080 s
+2023-11-27 21:26:05,334 INFO cycquery.orm    - Database setup, ready to run queries!
+2023-11-27 21:26:12,296 INFO cycquery.orm    - Query returned successfully!
+2023-11-27 21:26:12,297 INFO cycquery.utils.profile - Finished executing function run_query in 4.960876 s
 
    
 
    
 
    
@@ -734,8 +734,8 @@ 
    Compute mortality (labels)
 
    -2023-11-27 15:06:02,832 INFO cycquery.orm    - Query returned successfully!
-2023-11-27 15:06:02,833 INFO cycquery.utils.profile - Finished executing function run_query in 28.008839 s
+2023-11-27 21:26:44,065 INFO cycquery.orm    - Query returned successfully!
+2023-11-27 21:26:44,066 INFO cycquery.utils.profile - Finished executing function run_query in 31.037258 s
 
    
 
    
 
    
@@ -800,9 +800,9 @@ 
    Drop NaNs based on the 
 
    
 
    
-
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1052,7 +1052,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1060,7 +1060,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1068,7 +1068,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1182,7 +1182,7 @@ 
    Quantitative Analysis
    
     
     
    
       
-        0.0
+        0.77
       
       
         
@@ -1215,7 +1215,7 @@ 
    Quantitative Analysis
    
         
     
    
       
-        0.2
+        0.0
       
       
         
@@ -1248,7 +1248,7 @@ 
    Quantitative Analysis
    
         
     
    
       
-        0.0
+        0.25
       
       
         
@@ -1281,7 +1281,7 @@ 
    Quantitative Analysis
    
         
     
    
       
-        1.0
+        0.77
       
       
         
@@ -1313,7 +1313,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1321,7 +1321,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1329,7 +1329,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1382,7 +1382,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1654,6 +1654,10 @@ 
    Model Parameters
    
 
   
     
+    
    
+      
    Max_depth
    
+      5
+    
    
     
   
 
@@ -1664,33 +1668,29 @@ 
    Model Parameters
    
 
   
     
-    
    
-      
    N_estimators
    
-      500
-    
    
     
   
 
   
     
     
    
-      
    Eval_metric
    
-      logloss
+      
    Colsample_bytree
    
+      1
     
    
     
   
 
   
     
-    
    
-      
    Min_child_weight
    
-      3
-    
    
     
   
 
   
     
+    
    
+      
    Learning_rate
    
+      0.01
+    
    
     
   
 
@@ -1706,6 +1706,10 @@ 
    Min_child_weight
    
 
   
     
+    
    
+      
    N_estimators
    
+      500
+    
    
     
   
 
@@ -1716,6 +1720,10 @@ 
    Min_child_weight
    
 
   
     
+    
    
+      
    Min_child_weight
    
+      3
+    
    
     
   
 
@@ -1726,10 +1734,6 @@ 
    Min_child_weight
    
 
   
     
-    
    
-      
    Colsample_bytree
    
-      0.8
-    
    
     
   
 
@@ -1750,14 +1754,18 @@ 
    Colsample_bytree
    
 
   
     
+    
    
+      
    Reg_lambda
    
+      0
+    
    
     
   
 
   
     
     
    
-      
    Random_state
    
-      123
+      
    Enable_categorical
    
+      False
     
    
     
   
@@ -1779,29 +1787,29 @@ 
    Random_state
    
 
   
     
-    
    
-      
    Reg_lambda
    
-      1
-    
    
     
   
 
   
     
-    
    
-      
    Learning_rate
    
-      0.01
-    
    
     
   
 
   
     
+    
    
+      
    Random_state
    
+      123
+    
    
     
   
 
   
     
+    
    
+      
    Missing
    
+      nan
+    
    
     
   
 
@@ -1817,25 +1825,33 @@ 
    Learning_rate
    
 
   
     
+    
    
+      
    Seed
    
+      123
+    
    
     
   
 
   
     
+    
    
+      
    Objective
    
+      binary:logistic
+    
    
     
   
 
   
     
+    
    
+      
    Gamma
    
+      10
+    
    
     
   
 
   
     
-    
    
-      
    Objective
    
-      binary:logistic
-    
    
     
   
 
@@ -1857,8 +1873,8 @@ 
    Objective
    
   
     
     
    
-      
    Missing
    
-      nan
+      
    Eval_metric
    
+      logloss
     
    
     
   
@@ -1875,37 +1891,21 @@ 
    Missing
    
 
   
     
-    
    
-      
    Seed
    
-      123
-    
    
     
   
 
   
     
-    
    
-      
    Enable_categorical
    
-      False
-    
    
     
   
 
   
     
-    
    
-      
    Max_depth
    
-      5
-    
    
     
   
 
   
     
-    
    
-      
    Gamma
    
-      10
-    
    
     
   
 
@@ -2176,7 +2176,7 @@ 
    Ethical Considerations
    
   function generate_model_card_plot() {
     var model_card_plots = []
     var overall_indices = [20, 21, 22, 23, 24]
-    var histories = JSON.parse("{\"0\": [\"0.9942279942279942\", \"1.0\", \"0.8021606116347475\", \"0.91483841672335\", \"1.0\"], \"1\": [\"0.0\", \"0.0\", \"0.1054589377479996\", \"0.06446839097661214\", \"0.0834196181191203\"], \"2\": [\"0.0\", \"0.0\", \"0.01284458874174532\", \"0.1330984703400093\", \"0.03722961601573457\"], \"3\": [\"0.0\", \"0.2819621395795328\", \"0.37014888253003336\", \"0.5969546258961874\", \"0.5939194401325512\"], \"4\": [\"0.7688679245283019\", \"0.7719239132483345\", \"0.7125435741443202\", \"0.6949896269200626\", \"0.7019828347883943\"], \"5\": [\"0.9848771266540642\", \"1.0\", \"1.0\", \"1.0\", \"0.9598740097797964\"], \"6\": [\"0.0\", \"0.0\", \"0.0\", \"0.15347997711141814\", \"0.17172264997260875\"], \"7\": [\"0.0\", \"0.0\", \"0.0\", \"0.0\", \"0.10613833912469997\"], \"8\": [\"0.0\", \"0.013027109918884124\", \"0.0\", \"0.14015805410912\", \"0.07351443732433577\"], \"9\": [\"0.9124596908706772\", \"0.841566744649705\", \"0.9190764041739317\", \"0.9415547048073998\", \"0.8777109441484907\"], \"10\": [\"0.9763663220088626\", \"0.989912582760708\", \"1.0\", \"0.9355676469364977\", \"0.9869858868968788\"], \"11\": [\"0.0\", \"0.03430122977182886\", \"0.2216711491565862\", \"0.24360659936216117\", \"0.3452521175564264\"], \"12\": [\"0.0\", \"0.0\", \"0.0\", \"0.0\", \"0.0068967232218273295\"], \"13\": [\"0.0\", \"0.0028339286181728383\", \"0.0\", \"0.0\", \"0.12756489808356938\"], \"14\": [\"0.8732743948562784\", \"0.9901082803891144\", \"0.9749283548341042\", \"1.0\", \"1.0\"], \"15\": [\"0.9866577718478986\", \"0.9987861013155545\", \"1.0\", \"1.0\", \"1.0\"], \"16\": [\"0.0\", \"0.0\", \"0.0\", \"0.03851185514041444\", \"0.027908811968215368\"], \"17\": [\"0.0\", \"0.06846161161707012\", \"0.0\", \"0.16823362423670318\", \"0.12216062575434482\"], \"18\": [\"0.0\", \"0.0\", \"0.0\", \"0.0\", \"0.06729634456708374\"], \"19\": [\"0.856987908961593\", \"0.9226407410160536\", \"1.0\", \"0.9343322658547919\", \"0.8785251669509954\"], \"20\": [\"0.9817735716789344\", \"0.916493952176769\", \"0.9442974994805379\", \"1.0\", \"0.9707540750350074\"], \"21\": [\"0.0\", \"0.0\", \"0.03225594094967265\", \"0.0\", \"0.0\"], \"22\": [\"0.0\", \"0.10264127921613297\", \"0.1234690286363817\", \"0.1632794391103451\", \"0.196281778054396\"], \"23\": [\"0.0\", \"0.08419609021940222\", \"0.0042690359717813126\", \"0.0\", \"0.0\"], \"24\": [\"0.867867489608263\", \"0.9167894375837232\", \"1.0\", \"1.0\", \"1.0\"]}");
+    var histories = JSON.parse("{\"0\": [\"0.9942279942279942\", \"0.8782049823430107\", \"0.8855124189717378\", \"1.0\", \"0.7738737874551314\"], \"1\": [\"0.0\", \"0.036000144088568074\", \"0.09853050735249438\", \"0.055011350068792195\", \"0.1619894499482346\"], \"2\": [\"0.0\", \"0.0\", \"0.15066152276631192\", \"0.18359546675003308\", \"0.0\"], \"3\": [\"0.0\", \"0.0071863364384208706\", \"0.08142250805582295\", \"0.12105588947862309\", \"0.057379525535859\"], \"4\": [\"0.7862844702467343\", \"0.8447744173849981\", \"1.0\", \"0.8550442648950125\", \"0.9875989767985858\"], \"5\": [\"0.9848771266540642\", \"1.0\", \"0.8619729248098779\", \"0.8206862006829606\", \"0.8911746595234663\"], \"6\": [\"0.3333333333333333\", \"0.42828143595965845\", \"0.37299515019258644\", \"0.23930233334429898\", \"0.3146447991258896\"], \"7\": [\"0.043478260869565216\", \"0.06477596070399107\", \"0.1530728723912005\", \"0.20419016514975202\", \"0.26374933651832944\"], \"8\": [\"0.07692307692307693\", \"0.15609026472811377\", \"0.0\", \"0.03374768696285382\", \"0.0783527821064881\"], \"9\": [\"0.9072890025575449\", \"0.9726703268510903\", \"0.7641081146023275\", \"0.8025555118079636\", \"0.7121148405267389\"], \"10\": [\"0.9771048744460856\", \"0.8354893334540037\", \"0.6679118462690455\", \"0.7579534041278262\", \"0.6834291438705408\"], \"11\": [\"1.0\", \"1.0\", \"0.9174517277851223\", \"1.0\", \"0.8976729177215944\"], \"12\": [\"0.03125\", \"0.10039797517512597\", \"0.16893019424555925\", \"0.21017123685947187\", \"0.2842883492370025\"], \"13\": [\"0.06060606060606061\", \"0.1334114439992532\", \"0.13353394646121972\", \"0.18372919137674398\", \"0.22368915661029218\"], \"14\": [\"0.8735816944024206\", \"0.9836828660200002\", \"0.932215258596031\", \"0.8480789793356489\", \"0.6800928276482259\"], \"15\": [\"0.9859906604402935\", \"0.9444299700643859\", \"0.8044314476954962\", \"0.8509907570422153\", \"0.8192431799747109\"], \"16\": [\"0.0\", \"0.0\", \"0.04048633092606849\", \"0.18976336285323178\", \"0.10291466057123591\"], \"17\": [\"0.0\", \"0.06645940675666698\", \"0.10300036870259255\", \"0.2092439270146798\", \"0.26906453307242356\"], \"18\": [\"0.0\", \"0.024479461823030416\", \"0.0\", \"0.01002005637411237\", \"0.0\"], \"19\": [\"0.8555120910384068\", \"1.0\", \"0.9804570633407543\", \"1.0\", \"0.9960020018728102\"], \"20\": [\"0.9817735716789344\", \"1.0\", \"1.0\", \"1.0\", \"0.9748802592613818\"], \"21\": [\"0.3333333333333333\", \"0.4471676796762688\", \"0.5958269860326022\", \"0.6685703484133385\", \"0.7665769773505952\"], \"22\": [\"0.0196078431372549\", \"0.0\", \"0.0\", \"0.0\", \"0.004721174344962717\"], \"23\": [\"0.037037037037037035\", \"0.10009832439653214\", \"0.358461550273736\", \"0.3298621252048033\", \"0.24906173062260095\"], \"24\": [\"0.8671677093392675\", \"0.828637000275013\", \"0.7681927254815382\", \"0.721214483012441\", \"0.7706882816474631\"]}");
     var thresholds = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\", \"16\": \"0.7\", \"17\": \"0.7\", \"18\": \"0.7\", \"19\": \"0.7\", \"20\": \"0.7\", \"21\": \"0.7\", \"22\": \"0.7\", \"23\": \"0.7\", \"24\": \"0.7\"}");
     var timestamps = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"1\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"2\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"3\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"4\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"5\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"6\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"7\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"8\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"9\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"10\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"11\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"12\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"13\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"14\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"15\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"16\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"17\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"18\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"19\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"20\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"21\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"22\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"23\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"24\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"]}");
 
@@ -2457,10 +2457,10 @@ 
    Ethical Considerations
    
       }
     }
     var slices_all = JSON.parse("{\"0\": [\"metric:Accuracy\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"1\": [\"metric:Precision\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"2\": [\"metric:Recall\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"3\": [\"metric:F1 Score\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"4\": [\"metric:AUROC\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"5\": [\"metric:Accuracy\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"6\": [\"metric:Precision\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"7\": [\"metric:Recall\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"8\": [\"metric:F1 Score\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"9\": [\"metric:AUROC\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"10\": [\"metric:Accuracy\", \"gender:M\", \"age:overall_age\"], \"11\": [\"metric:Precision\", \"gender:M\", \"age:overall_age\"], \"12\": [\"metric:Recall\", \"gender:M\", \"age:overall_age\"], \"13\": [\"metric:F1 Score\", \"gender:M\", \"age:overall_age\"], \"14\": [\"metric:AUROC\", \"gender:M\", \"age:overall_age\"], \"15\": [\"metric:Accuracy\", \"gender:F\", \"age:overall_age\"], \"16\": [\"metric:Precision\", \"gender:F\", \"age:overall_age\"], \"17\": [\"metric:Recall\", \"gender:F\", \"age:overall_age\"], \"18\": [\"metric:F1 Score\", \"gender:F\", \"age:overall_age\"], \"19\": [\"metric:AUROC\", \"gender:F\", \"age:overall_age\"], \"20\": [\"metric:Accuracy\", \"age:overall_age\", \"gender:overall_gender\"], \"21\": [\"metric:Precision\", \"age:overall_age\", \"gender:overall_gender\"], \"22\": [\"metric:Recall\", \"age:overall_age\", \"gender:overall_gender\"], \"23\": [\"metric:F1 Score\", \"age:overall_age\", \"gender:overall_gender\"], \"24\": [\"metric:AUROC\", \"age:overall_age\", \"gender:overall_gender\"]}");
-    var histories_all = JSON.parse("{\"0\": [\"0.9942279942279942\", \"1.0\", \"0.8021606116347475\", \"0.91483841672335\", \"1.0\"], \"1\": [\"0.0\", \"0.0\", \"0.1054589377479996\", \"0.06446839097661214\", \"0.0834196181191203\"], \"2\": [\"0.0\", \"0.0\", \"0.01284458874174532\", \"0.1330984703400093\", \"0.03722961601573457\"], \"3\": [\"0.0\", \"0.2819621395795328\", \"0.37014888253003336\", \"0.5969546258961874\", \"0.5939194401325512\"], \"4\": [\"0.7688679245283019\", \"0.7719239132483345\", \"0.7125435741443202\", \"0.6949896269200626\", \"0.7019828347883943\"], \"5\": [\"0.9848771266540642\", \"1.0\", \"1.0\", \"1.0\", \"0.9598740097797964\"], \"6\": [\"0.0\", \"0.0\", \"0.0\", \"0.15347997711141814\", \"0.17172264997260875\"], \"7\": [\"0.0\", \"0.0\", \"0.0\", \"0.0\", \"0.10613833912469997\"], \"8\": [\"0.0\", \"0.013027109918884124\", \"0.0\", \"0.14015805410912\", \"0.07351443732433577\"], \"9\": [\"0.9124596908706772\", \"0.841566744649705\", \"0.9190764041739317\", \"0.9415547048073998\", \"0.8777109441484907\"], \"10\": [\"0.9763663220088626\", \"0.989912582760708\", \"1.0\", \"0.9355676469364977\", \"0.9869858868968788\"], \"11\": [\"0.0\", \"0.03430122977182886\", \"0.2216711491565862\", \"0.24360659936216117\", \"0.3452521175564264\"], \"12\": [\"0.0\", \"0.0\", \"0.0\", \"0.0\", \"0.0068967232218273295\"], \"13\": [\"0.0\", \"0.0028339286181728383\", \"0.0\", \"0.0\", \"0.12756489808356938\"], \"14\": [\"0.8732743948562784\", \"0.9901082803891144\", \"0.9749283548341042\", \"1.0\", \"1.0\"], \"15\": [\"0.9866577718478986\", \"0.9987861013155545\", \"1.0\", \"1.0\", \"1.0\"], \"16\": [\"0.0\", \"0.0\", \"0.0\", \"0.03851185514041444\", \"0.027908811968215368\"], \"17\": [\"0.0\", \"0.06846161161707012\", \"0.0\", \"0.16823362423670318\", \"0.12216062575434482\"], \"18\": [\"0.0\", \"0.0\", \"0.0\", \"0.0\", \"0.06729634456708374\"], \"19\": [\"0.856987908961593\", \"0.9226407410160536\", \"1.0\", \"0.9343322658547919\", \"0.8785251669509954\"], \"20\": [\"0.9817735716789344\", \"0.916493952176769\", \"0.9442974994805379\", \"1.0\", \"0.9707540750350074\"], \"21\": [\"0.0\", \"0.0\", \"0.03225594094967265\", \"0.0\", \"0.0\"], \"22\": [\"0.0\", \"0.10264127921613297\", \"0.1234690286363817\", \"0.1632794391103451\", \"0.196281778054396\"], \"23\": [\"0.0\", \"0.08419609021940222\", \"0.0042690359717813126\", \"0.0\", \"0.0\"], \"24\": [\"0.867867489608263\", \"0.9167894375837232\", \"1.0\", \"1.0\", \"1.0\"]}");
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     var thresholds_all = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\", \"16\": \"0.7\", \"17\": \"0.7\", \"18\": \"0.7\", \"19\": \"0.7\", \"20\": \"0.7\", \"21\": \"0.7\", \"22\": \"0.7\", \"23\": \"0.7\", \"24\": \"0.7\"}");
-    var trends_all = JSON.parse("{\"0\": \"neutral\", \"1\": \"positive\", \"2\": \"positive\", \"3\": \"positive\", \"4\": \"negative\", \"5\": \"neutral\", \"6\": \"positive\", \"7\": \"positive\", \"8\": \"positive\", \"9\": \"neutral\", \"10\": \"neutral\", \"11\": \"positive\", \"12\": \"neutral\", \"13\": \"positive\", \"14\": \"positive\", \"15\": \"neutral\", \"16\": \"neutral\", \"17\": \"positive\", \"18\": \"positive\", \"19\": \"neutral\", \"20\": \"neutral\", \"21\": \"neutral\", \"22\": \"positive\", \"23\": \"neutral\", \"24\": \"positive\"}");
-    var passed_all = JSON.parse("{\"0\": true, \"1\": false, \"2\": false, \"3\": false, \"4\": true, \"5\": true, \"6\": false, \"7\": false, \"8\": false, \"9\": true, \"10\": true, \"11\": false, \"12\": false, \"13\": false, \"14\": true, \"15\": true, \"16\": false, \"17\": false, \"18\": false, \"19\": true, \"20\": true, \"21\": false, \"22\": false, \"23\": false, \"24\": true}");
+    var trends_all = JSON.parse("{\"0\": \"negative\", \"1\": \"positive\", \"2\": \"positive\", \"3\": \"positive\", \"4\": \"positive\", \"5\": \"negative\", \"6\": \"negative\", \"7\": \"positive\", \"8\": \"negative\", \"9\": \"negative\", \"10\": \"negative\", \"11\": \"negative\", \"12\": \"positive\", \"13\": \"positive\", \"14\": \"negative\", \"15\": \"negative\", \"16\": \"positive\", \"17\": \"positive\", \"18\": \"neutral\", \"19\": \"positive\", \"20\": \"neutral\", \"21\": \"positive\", \"22\": \"neutral\", \"23\": \"positive\", \"24\": \"negative\"}");
+    var passed_all = JSON.parse("{\"0\": true, \"1\": false, \"2\": false, \"3\": false, \"4\": true, \"5\": true, \"6\": false, \"7\": false, \"8\": false, \"9\": true, \"10\": true, \"11\": true, \"12\": false, \"13\": false, \"14\": true, \"15\": true, \"16\": false, \"17\": false, \"18\": false, \"19\": true, \"20\": true, \"21\": false, \"22\": false, \"23\": false, \"24\": true}");
     var names_all = JSON.parse("{\"0\": \"Accuracy\", \"1\": \"Precision\", \"2\": \"Recall\", \"3\": \"F1 Score\", \"4\": \"AUROC\", \"5\": \"Accuracy\", \"6\": \"Precision\", \"7\": \"Recall\", \"8\": \"F1 Score\", \"9\": \"AUROC\", \"10\": \"Accuracy\", \"11\": \"Precision\", \"12\": \"Recall\", \"13\": \"F1 Score\", \"14\": \"AUROC\", \"15\": \"Accuracy\", \"16\": \"Precision\", \"17\": \"Recall\", \"18\": \"F1 Score\", \"19\": \"AUROC\", \"20\": \"Accuracy\", \"21\": \"Precision\", \"22\": \"Recall\", \"23\": \"F1 Score\", \"24\": \"AUROC\"}");
     var timestamps_all = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"1\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"2\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"3\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"4\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"5\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"6\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"7\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"8\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"9\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"10\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"11\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"12\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"13\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"14\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"15\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"16\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"17\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"18\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"19\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"20\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"21\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"22\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"23\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"24\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"]}");
 
@@ -2733,10 +2733,10 @@ 
    Ethical Considerations
    
       }
     }
     var slices_all = JSON.parse("{\"0\": [\"metric:Accuracy\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"1\": [\"metric:Precision\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"2\": [\"metric:Recall\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"3\": [\"metric:F1 Score\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"4\": [\"metric:AUROC\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"5\": [\"metric:Accuracy\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"6\": [\"metric:Precision\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"7\": [\"metric:Recall\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"8\": [\"metric:F1 Score\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"9\": [\"metric:AUROC\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"10\": [\"metric:Accuracy\", \"gender:M\", \"age:overall_age\"], \"11\": [\"metric:Precision\", \"gender:M\", \"age:overall_age\"], \"12\": [\"metric:Recall\", \"gender:M\", \"age:overall_age\"], \"13\": [\"metric:F1 Score\", \"gender:M\", \"age:overall_age\"], \"14\": [\"metric:AUROC\", \"gender:M\", \"age:overall_age\"], \"15\": [\"metric:Accuracy\", \"gender:F\", \"age:overall_age\"], \"16\": [\"metric:Precision\", \"gender:F\", \"age:overall_age\"], \"17\": [\"metric:Recall\", \"gender:F\", \"age:overall_age\"], \"18\": [\"metric:F1 Score\", \"gender:F\", \"age:overall_age\"], \"19\": [\"metric:AUROC\", \"gender:F\", \"age:overall_age\"], \"20\": [\"metric:Accuracy\", \"age:overall_age\", \"gender:overall_gender\"], \"21\": [\"metric:Precision\", \"age:overall_age\", \"gender:overall_gender\"], \"22\": [\"metric:Recall\", \"age:overall_age\", \"gender:overall_gender\"], \"23\": [\"metric:F1 Score\", \"age:overall_age\", \"gender:overall_gender\"], \"24\": [\"metric:AUROC\", \"age:overall_age\", \"gender:overall_gender\"]}");
-    var histories_all = JSON.parse("{\"0\": [\"0.9942279942279942\", \"1.0\", \"0.8021606116347475\", \"0.91483841672335\", \"1.0\"], \"1\": [\"0.0\", \"0.0\", \"0.1054589377479996\", \"0.06446839097661214\", \"0.0834196181191203\"], \"2\": [\"0.0\", \"0.0\", \"0.01284458874174532\", \"0.1330984703400093\", \"0.03722961601573457\"], \"3\": [\"0.0\", \"0.2819621395795328\", \"0.37014888253003336\", \"0.5969546258961874\", \"0.5939194401325512\"], \"4\": [\"0.7688679245283019\", \"0.7719239132483345\", \"0.7125435741443202\", \"0.6949896269200626\", \"0.7019828347883943\"], \"5\": [\"0.9848771266540642\", \"1.0\", \"1.0\", \"1.0\", \"0.9598740097797964\"], \"6\": [\"0.0\", \"0.0\", \"0.0\", \"0.15347997711141814\", \"0.17172264997260875\"], \"7\": [\"0.0\", \"0.0\", \"0.0\", \"0.0\", \"0.10613833912469997\"], \"8\": [\"0.0\", \"0.013027109918884124\", \"0.0\", \"0.14015805410912\", \"0.07351443732433577\"], \"9\": [\"0.9124596908706772\", \"0.841566744649705\", \"0.9190764041739317\", \"0.9415547048073998\", \"0.8777109441484907\"], \"10\": [\"0.9763663220088626\", \"0.989912582760708\", \"1.0\", \"0.9355676469364977\", \"0.9869858868968788\"], \"11\": [\"0.0\", \"0.03430122977182886\", \"0.2216711491565862\", \"0.24360659936216117\", \"0.3452521175564264\"], \"12\": [\"0.0\", \"0.0\", \"0.0\", \"0.0\", \"0.0068967232218273295\"], \"13\": [\"0.0\", \"0.0028339286181728383\", \"0.0\", \"0.0\", \"0.12756489808356938\"], \"14\": [\"0.8732743948562784\", \"0.9901082803891144\", \"0.9749283548341042\", \"1.0\", \"1.0\"], \"15\": [\"0.9866577718478986\", \"0.9987861013155545\", \"1.0\", \"1.0\", \"1.0\"], \"16\": [\"0.0\", \"0.0\", \"0.0\", \"0.03851185514041444\", \"0.027908811968215368\"], \"17\": [\"0.0\", \"0.06846161161707012\", \"0.0\", \"0.16823362423670318\", \"0.12216062575434482\"], \"18\": [\"0.0\", \"0.0\", \"0.0\", \"0.0\", \"0.06729634456708374\"], \"19\": [\"0.856987908961593\", \"0.9226407410160536\", \"1.0\", \"0.9343322658547919\", \"0.8785251669509954\"], \"20\": [\"0.9817735716789344\", \"0.916493952176769\", \"0.9442974994805379\", \"1.0\", \"0.9707540750350074\"], \"21\": [\"0.0\", \"0.0\", \"0.03225594094967265\", \"0.0\", \"0.0\"], \"22\": [\"0.0\", \"0.10264127921613297\", \"0.1234690286363817\", \"0.1632794391103451\", \"0.196281778054396\"], \"23\": [\"0.0\", \"0.08419609021940222\", \"0.0042690359717813126\", \"0.0\", \"0.0\"], \"24\": [\"0.867867489608263\", \"0.9167894375837232\", \"1.0\", \"1.0\", \"1.0\"]}");
+    var histories_all = JSON.parse("{\"0\": [\"0.9942279942279942\", \"0.8782049823430107\", \"0.8855124189717378\", \"1.0\", \"0.7738737874551314\"], \"1\": [\"0.0\", \"0.036000144088568074\", \"0.09853050735249438\", \"0.055011350068792195\", \"0.1619894499482346\"], \"2\": [\"0.0\", \"0.0\", \"0.15066152276631192\", \"0.18359546675003308\", \"0.0\"], \"3\": [\"0.0\", \"0.0071863364384208706\", \"0.08142250805582295\", \"0.12105588947862309\", \"0.057379525535859\"], \"4\": [\"0.7862844702467343\", \"0.8447744173849981\", \"1.0\", \"0.8550442648950125\", \"0.9875989767985858\"], \"5\": [\"0.9848771266540642\", \"1.0\", \"0.8619729248098779\", \"0.8206862006829606\", \"0.8911746595234663\"], \"6\": [\"0.3333333333333333\", \"0.42828143595965845\", \"0.37299515019258644\", \"0.23930233334429898\", \"0.3146447991258896\"], \"7\": [\"0.043478260869565216\", \"0.06477596070399107\", \"0.1530728723912005\", \"0.20419016514975202\", \"0.26374933651832944\"], \"8\": [\"0.07692307692307693\", \"0.15609026472811377\", \"0.0\", \"0.03374768696285382\", \"0.0783527821064881\"], \"9\": [\"0.9072890025575449\", \"0.9726703268510903\", \"0.7641081146023275\", \"0.8025555118079636\", \"0.7121148405267389\"], \"10\": [\"0.9771048744460856\", \"0.8354893334540037\", \"0.6679118462690455\", \"0.7579534041278262\", \"0.6834291438705408\"], \"11\": [\"1.0\", \"1.0\", \"0.9174517277851223\", \"1.0\", \"0.8976729177215944\"], \"12\": [\"0.03125\", \"0.10039797517512597\", \"0.16893019424555925\", \"0.21017123685947187\", \"0.2842883492370025\"], \"13\": [\"0.06060606060606061\", \"0.1334114439992532\", \"0.13353394646121972\", \"0.18372919137674398\", \"0.22368915661029218\"], \"14\": [\"0.8735816944024206\", \"0.9836828660200002\", \"0.932215258596031\", \"0.8480789793356489\", \"0.6800928276482259\"], \"15\": [\"0.9859906604402935\", \"0.9444299700643859\", \"0.8044314476954962\", \"0.8509907570422153\", \"0.8192431799747109\"], \"16\": [\"0.0\", \"0.0\", \"0.04048633092606849\", \"0.18976336285323178\", \"0.10291466057123591\"], \"17\": [\"0.0\", \"0.06645940675666698\", \"0.10300036870259255\", \"0.2092439270146798\", \"0.26906453307242356\"], \"18\": [\"0.0\", \"0.024479461823030416\", \"0.0\", \"0.01002005637411237\", \"0.0\"], \"19\": [\"0.8555120910384068\", \"1.0\", \"0.9804570633407543\", \"1.0\", \"0.9960020018728102\"], \"20\": [\"0.9817735716789344\", \"1.0\", \"1.0\", \"1.0\", \"0.9748802592613818\"], \"21\": [\"0.3333333333333333\", \"0.4471676796762688\", \"0.5958269860326022\", \"0.6685703484133385\", \"0.7665769773505952\"], \"22\": [\"0.0196078431372549\", \"0.0\", \"0.0\", \"0.0\", \"0.004721174344962717\"], \"23\": [\"0.037037037037037035\", \"0.10009832439653214\", \"0.358461550273736\", \"0.3298621252048033\", \"0.24906173062260095\"], \"24\": [\"0.8671677093392675\", \"0.828637000275013\", \"0.7681927254815382\", \"0.721214483012441\", \"0.7706882816474631\"]}");
     var thresholds_all = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\", \"16\": \"0.7\", \"17\": \"0.7\", \"18\": \"0.7\", \"19\": \"0.7\", \"20\": \"0.7\", \"21\": \"0.7\", \"22\": \"0.7\", \"23\": \"0.7\", \"24\": \"0.7\"}");
-    var trends_all = JSON.parse("{\"0\": \"neutral\", \"1\": \"positive\", \"2\": \"positive\", \"3\": \"positive\", \"4\": \"negative\", \"5\": \"neutral\", \"6\": \"positive\", \"7\": \"positive\", \"8\": \"positive\", \"9\": \"neutral\", \"10\": \"neutral\", \"11\": \"positive\", \"12\": \"neutral\", \"13\": \"positive\", \"14\": \"positive\", \"15\": \"neutral\", \"16\": \"neutral\", \"17\": \"positive\", \"18\": \"positive\", \"19\": \"neutral\", \"20\": \"neutral\", \"21\": \"neutral\", \"22\": \"positive\", \"23\": \"neutral\", \"24\": \"positive\"}");
-    var passed_all = JSON.parse("{\"0\": true, \"1\": false, \"2\": false, \"3\": false, \"4\": true, \"5\": true, \"6\": false, \"7\": false, \"8\": false, \"9\": true, \"10\": true, \"11\": false, \"12\": false, \"13\": false, \"14\": true, \"15\": true, \"16\": false, \"17\": false, \"18\": false, \"19\": true, \"20\": true, \"21\": false, \"22\": false, \"23\": false, \"24\": true}");
+    var trends_all = JSON.parse("{\"0\": \"negative\", \"1\": \"positive\", \"2\": \"positive\", \"3\": \"positive\", \"4\": \"positive\", \"5\": \"negative\", \"6\": \"negative\", \"7\": \"positive\", \"8\": \"negative\", \"9\": \"negative\", \"10\": \"negative\", \"11\": \"negative\", \"12\": \"positive\", \"13\": \"positive\", \"14\": \"negative\", \"15\": \"negative\", \"16\": \"positive\", \"17\": \"positive\", \"18\": \"neutral\", \"19\": \"positive\", \"20\": \"neutral\", \"21\": \"positive\", \"22\": \"neutral\", \"23\": \"positive\", \"24\": \"negative\"}");
+    var passed_all = JSON.parse("{\"0\": true, \"1\": false, \"2\": false, \"3\": false, \"4\": true, \"5\": true, \"6\": false, \"7\": false, \"8\": false, \"9\": true, \"10\": true, \"11\": true, \"12\": false, \"13\": false, \"14\": true, \"15\": true, \"16\": false, \"17\": false, \"18\": false, \"19\": true, \"20\": true, \"21\": false, \"22\": false, \"23\": false, \"24\": true}");
     var names_all = JSON.parse("{\"0\": \"Accuracy\", \"1\": \"Precision\", \"2\": \"Recall\", \"3\": \"F1 Score\", \"4\": \"AUROC\", \"5\": \"Accuracy\", \"6\": \"Precision\", \"7\": \"Recall\", \"8\": \"F1 Score\", \"9\": \"AUROC\", \"10\": \"Accuracy\", \"11\": \"Precision\", \"12\": \"Recall\", \"13\": \"F1 Score\", \"14\": \"AUROC\", \"15\": \"Accuracy\", \"16\": \"Precision\", \"17\": \"Recall\", \"18\": \"F1 Score\", \"19\": \"AUROC\", \"20\": \"Accuracy\", \"21\": \"Precision\", \"22\": \"Recall\", \"23\": \"F1 Score\", \"24\": \"AUROC\"}");
     var timestamps_all = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"1\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"2\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"3\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"4\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"5\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"6\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"7\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"8\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"9\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"10\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"11\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"12\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"13\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"14\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"15\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"16\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"17\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"18\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"19\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"20\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"21\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"22\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"23\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"24\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"]}");
 
diff --git a/api/tutorials/nihcxr/cxr_classification.html b/api/tutorials/nihcxr/cxr_classification.html
index a083a9b1f..aed4ee7f5 100644
--- a/api/tutorials/nihcxr/cxr_classification.html
+++ b/api/tutorials/nihcxr/cxr_classification.html
@@ -504,74 +504,74 @@ 
    Generate Historical Reports
 
    -Flattening the indices: 100%|████████| 1000/1000 [00:53<00:00, 18.66 examples/s]
-Flattening the indices: 100%|████| 1000/1000 [00:00<00:00, 490906.37 examples/s]
-Filter: 100%|████████████████████| 1000/1000 [00:00<00:00, 214773.11 examples/s]
-Map: 100%|███████████████████████████| 400/400 [00:00<00:00, 1643.65 examples/s]
-Filter -> Patient Gender:M: 100%|███| 400/400 [00:00<00:00, 42922.75 examples/s]
-Filter -> Patient Gender:F: 100%|███| 400/400 [00:00<00:00, 35339.05 examples/s]
-Filter -> overall: 100%|████████████| 400/400 [00:00<00:00, 43264.78 examples/s]
-Filter -> Patient Age:[19 - 35]: 100%|█| 400/400 [00:00<00:00, 40339.54 examples
-Filter -> Patient Age:[35 - 65]: 100%|█| 400/400 [00:00<00:00, 40435.80 examples
-Filter -> Patient Age:[65 - 100]: 100%|█| 400/400 [00:00<00:00, 39774.34 example
+Flattening the indices: 100%|████████| 1000/1000 [00:50<00:00, 19.97 examples/s]
+Flattening the indices: 100%|████| 1000/1000 [00:00<00:00, 221780.03 examples/s]
+Filter: 100%|████████████████████| 1000/1000 [00:00<00:00, 136903.22 examples/s]
+Map: 100%|███████████████████████████| 400/400 [00:00<00:00, 1147.93 examples/s]
+Filter -> Patient Gender:M: 100%|███| 400/400 [00:00<00:00, 43015.19 examples/s]
+Filter -> Patient Gender:F: 100%|███| 400/400 [00:00<00:00, 43700.91 examples/s]
+Filter -> overall: 100%|████████████| 400/400 [00:00<00:00, 45745.65 examples/s]
+Filter -> Patient Age:[19 - 35]: 100%|█| 400/400 [00:00<00:00, 41513.38 examples
+Filter -> Patient Age:[35 - 65]: 100%|█| 400/400 [00:00<00:00, 42320.75 examples
+Filter -> Patient Age:[65 - 100]: 100%|█| 400/400 [00:00<00:00, 42055.54 example
 Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|█| 400/400 [00:00<00:00,
 Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|█| 400/400 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|█| 400/400 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:F: 100%|█| 400/400 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|█| 400/400 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|█| 400/400 [00:00<00:00,
-Filter -> overall: 100%|████████████| 400/400 [00:00<00:00, 44234.38 examples/s]
-Flattening the indices: 100%|████████| 1000/1000 [00:53<00:00, 18.67 examples/s]
-Flattening the indices: 100%|████| 1000/1000 [00:00<00:00, 547702.27 examples/s]
-Filter: 100%|████████████████████| 1000/1000 [00:00<00:00, 230620.99 examples/s]
-Map: 100%|███████████████████████████| 396/396 [00:00<00:00, 1845.53 examples/s]
-Filter -> Patient Gender:M: 100%|███| 396/396 [00:00<00:00, 42850.92 examples/s]
-Filter -> Patient Gender:F: 100%|███| 396/396 [00:00<00:00, 43490.47 examples/s]
-Filter -> overall: 100%|████████████| 396/396 [00:00<00:00, 45366.12 examples/s]
-Filter -> Patient Age:[19 - 35]: 100%|█| 396/396 [00:00<00:00, 40392.62 examples
-Filter -> Patient Age:[35 - 65]: 100%|█| 396/396 [00:00<00:00, 42426.23 examples
-Filter -> Patient Age:[65 - 100]: 100%|█| 396/396 [00:00<00:00, 40831.52 example
+Filter -> overall: 100%|████████████| 400/400 [00:00<00:00, 46741.00 examples/s]
+Flattening the indices: 100%|████████| 1000/1000 [00:51<00:00, 19.56 examples/s]
+Flattening the indices: 100%|████| 1000/1000 [00:00<00:00, 567872.19 examples/s]
+Filter: 100%|████████████████████| 1000/1000 [00:00<00:00, 242683.79 examples/s]
+Map: 100%|███████████████████████████| 396/396 [00:00<00:00, 1150.22 examples/s]
+Filter -> Patient Gender:M: 100%|███| 396/396 [00:00<00:00, 43286.45 examples/s]
+Filter -> Patient Gender:F: 100%|███| 396/396 [00:00<00:00, 43913.61 examples/s]
+Filter -> overall: 100%|████████████| 396/396 [00:00<00:00, 45805.26 examples/s]
+Filter -> Patient Age:[19 - 35]: 100%|█| 396/396 [00:00<00:00, 41569.34 examples
+Filter -> Patient Age:[35 - 65]: 100%|█| 396/396 [00:00<00:00, 43008.48 examples
+Filter -> Patient Age:[65 - 100]: 100%|█| 396/396 [00:00<00:00, 42367.79 example
 Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|█| 396/396 [00:00<00:00,
 Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|█| 396/396 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|█| 396/396 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:F: 100%|█| 396/396 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|█| 396/396 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|█| 396/396 [00:00<00:00,
-Filter -> overall: 100%|████████████| 396/396 [00:00<00:00, 46050.36 examples/s]
-Flattening the indices: 100%|████████| 1000/1000 [00:51<00:00, 19.49 examples/s]
-Flattening the indices: 100%|████| 1000/1000 [00:00<00:00, 540991.10 examples/s]
-Filter: 100%|████████████████████| 1000/1000 [00:00<00:00, 211353.19 examples/s]
-Map: 100%|███████████████████████████| 383/383 [00:00<00:00, 1757.34 examples/s]
-Filter -> Patient Gender:M: 100%|███| 383/383 [00:00<00:00, 41916.77 examples/s]
-Filter -> Patient Gender:F: 100%|███| 383/383 [00:00<00:00, 38010.04 examples/s]
-Filter -> overall: 100%|████████████| 383/383 [00:00<00:00, 42929.41 examples/s]
-Filter -> Patient Age:[19 - 35]: 100%|█| 383/383 [00:00<00:00, 38396.16 examples
-Filter -> Patient Age:[35 - 65]: 100%|█| 383/383 [00:00<00:00, 40972.75 examples
-Filter -> Patient Age:[65 - 100]: 100%|█| 383/383 [00:00<00:00, 22248.64 example
+Filter -> overall: 100%|████████████| 396/396 [00:00<00:00, 48601.15 examples/s]
+Flattening the indices: 100%|████████| 1000/1000 [00:50<00:00, 19.73 examples/s]
+Flattening the indices: 100%|████| 1000/1000 [00:00<00:00, 300258.00 examples/s]
+Filter: 100%|████████████████████| 1000/1000 [00:00<00:00, 126281.21 examples/s]
+Map: 100%|████████████████████████████| 383/383 [00:00<00:00, 768.53 examples/s]
+Filter -> Patient Gender:M: 100%|███| 383/383 [00:00<00:00, 43070.98 examples/s]
+Filter -> Patient Gender:F: 100%|███| 383/383 [00:00<00:00, 42860.68 examples/s]
+Filter -> overall: 100%|████████████| 383/383 [00:00<00:00, 45216.83 examples/s]
+Filter -> Patient Age:[19 - 35]: 100%|█| 383/383 [00:00<00:00, 40842.53 examples
+Filter -> Patient Age:[35 - 65]: 100%|█| 383/383 [00:00<00:00, 41961.67 examples
+Filter -> Patient Age:[65 - 100]: 100%|█| 383/383 [00:00<00:00, 41460.24 example
 Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|█| 383/383 [00:00<00:00,
 Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|█| 383/383 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|█| 383/383 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:F: 100%|█| 383/383 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|█| 383/383 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|█| 383/383 [00:00<00:00,
-Filter -> overall: 100%|████████████| 383/383 [00:00<00:00, 46180.03 examples/s]
-Flattening the indices: 100%|████████| 1000/1000 [00:50<00:00, 19.68 examples/s]
-Flattening the indices: 100%|████| 1000/1000 [00:00<00:00, 300516.16 examples/s]
-Filter: 100%|████████████████████| 1000/1000 [00:00<00:00, 125266.67 examples/s]
-Map: 100%|███████████████████████████| 411/411 [00:00<00:00, 1583.10 examples/s]
-Filter -> Patient Gender:M: 100%|███| 411/411 [00:00<00:00, 43730.57 examples/s]
-Filter -> Patient Gender:F: 100%|███| 411/411 [00:00<00:00, 43618.81 examples/s]
-Filter -> overall: 100%|████████████| 411/411 [00:00<00:00, 47375.68 examples/s]
-Filter -> Patient Age:[19 - 35]: 100%|█| 411/411 [00:00<00:00, 40968.18 examples
-Filter -> Patient Age:[35 - 65]: 100%|█| 411/411 [00:00<00:00, 43210.98 examples
-Filter -> Patient Age:[65 - 100]: 100%|█| 411/411 [00:00<00:00, 41431.95 example
+Filter -> overall: 100%|████████████| 383/383 [00:00<00:00, 46573.65 examples/s]
+Flattening the indices: 100%|████████| 1000/1000 [00:52<00:00, 18.97 examples/s]
+Flattening the indices: 100%|████| 1000/1000 [00:00<00:00, 556716.75 examples/s]
+Filter: 100%|████████████████████| 1000/1000 [00:00<00:00, 240526.67 examples/s]
+Map: 100%|███████████████████████████| 411/411 [00:00<00:00, 1164.24 examples/s]
+Filter -> Patient Gender:M: 100%|███| 411/411 [00:00<00:00, 44328.81 examples/s]
+Filter -> Patient Gender:F: 100%|███| 411/411 [00:00<00:00, 44277.58 examples/s]
+Filter -> overall: 100%|████████████| 411/411 [00:00<00:00, 46382.69 examples/s]
+Filter -> Patient Age:[19 - 35]: 100%|█| 411/411 [00:00<00:00, 41921.62 examples
+Filter -> Patient Age:[35 - 65]: 100%|█| 411/411 [00:00<00:00, 43253.27 examples
+Filter -> Patient Age:[65 - 100]: 100%|█| 411/411 [00:00<00:00, 42642.33 example
 Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|█| 411/411 [00:00<00:00,
 Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|█| 411/411 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|█| 411/411 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:F: 100%|█| 411/411 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|█| 411/411 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|█| 411/411 [00:00<00:00,
-Filter -> overall: 100%|████████████| 411/411 [00:00<00:00, 46774.09 examples/s]
+Filter -> overall: 100%|████████████| 411/411 [00:00<00:00, 47510.17 examples/s]
 
    
 
    
 
    CyclOps offers a package for documentation of the model through a model report. The ModelCardReport class is used to populate and generate the model report as an HTML file. The model report has the following sections:
    
@@ -669,8 +669,8 @@ 
    Model Creation
 
    -Filter: 100%|██████████| 1000/1000 [00:00<00:00, 101969.32 examples/s]
-Map: 100%|██████████| 661/661 [00:00<00:00, 1783.15 examples/s]
+Filter: 100%|██████████| 1000/1000 [00:00<00:00, 119014.36 examples/s]
+Map: 100%|██████████| 661/661 [00:00<00:00, 3501.48 examples/s]
 
    
 
    
 
    
@@ -783,10 +783,9 @@ 
    Multilabel AUROC by Pathology and Sexnih_eval_results_gender = evaluator.evaluate(
     dataset=nih_ds,
     metrics=[ppv, npv, sensitivity, specificity],
-    feature_columns="image",
     target_columns=pathologies,
-    prediction_column_prefix="predictions",
-    remove_columns="image",
+    prediction_columns="predictions.densenet",
+    ignore_columns="image",
     slice_spec=slice_spec,
 )
 
    
@@ -797,9 +796,9 @@ 
    Multilabel AUROC by Pathology and Sex
 
    -Filter -> Patient Gender:M: 100%|██████████| 661/661 [00:00<00:00, 46857.00 examples/s]
-Filter -> Patient Gender:F: 100%|██████████| 661/661 [00:00<00:00, 45079.51 examples/s]
-Filter -> overall: 100%|██████████| 661/661 [00:00<00:00, 45122.80 examples/s]
+Filter -> Patient Gender:M: 100%|██████████| 661/661 [00:00<00:00, 46195.70 examples/s]
+Filter -> Patient Gender:F: 100%|██████████| 661/661 [00:00<00:00, 46198.78 examples/s]
+Filter -> overall: 100%|██████████| 661/661 [00:00<00:00, 47727.37 examples/s]
 
    
 
    
 
@@ -846,10 +845,9 @@ 
    Multilabel AUROC by Pathology and Agenih_eval_results_age = evaluator.evaluate(
     dataset=nih_ds,
     metrics=[ppv, npv, sensitivity, specificity],
-    feature_columns="image",
     target_columns=pathologies,
-    prediction_column_prefix="predictions",
-    remove_columns="image",
+    prediction_columns="predictions.densenet",
+    ignore_columns="image",
     slice_spec=slice_spec,
 )
 
    
@@ -860,16 +858,16 @@ 
    Multilabel AUROC by Pathology and Age
 
    -Filter -> Patient Age:[19 - 35]: 100%|██████████| 661/661 [00:00<00:00, 44839.64 examples/s]
-Filter -> Patient Age:[35 - 65]: 100%|██████████| 661/661 [00:00<00:00, 43468.04 examples/s]
-Filter -> Patient Age:[65 - 100]: 100%|██████████| 661/661 [00:00<00:00, 44189.27 examples/s]
-Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|██████████| 661/661 [00:00<00:00, 43112.49 examples/s]
-Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|██████████| 661/661 [00:00<00:00, 48012.52 examples/s]
-Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|██████████| 661/661 [00:00<00:00, 45367.94 examples/s]
-Filter -> Patient Age:[35 - 65]&Patient Gender:F: 100%|██████████| 661/661 [00:00<00:00, 43131.94 examples/s]
-Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|██████████| 661/661 [00:00<00:00, 43892.64 examples/s]
-Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|██████████| 661/661 [00:00<00:00, 41827.24 examples/s]
-Filter -> overall: 100%|██████████| 661/661 [00:00<00:00, 47371.00 examples/s]
+Filter -> Patient Age:[19 - 35]: 100%|██████████| 661/661 [00:00<00:00, 44943.59 examples/s]
+Filter -> Patient Age:[35 - 65]: 100%|██████████| 661/661 [00:00<00:00, 46006.35 examples/s]
+Filter -> Patient Age:[65 - 100]: 100%|██████████| 661/661 [00:00<00:00, 44317.12 examples/s]
+Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|██████████| 661/661 [00:00<00:00, 45493.76 examples/s]
+Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|██████████| 661/661 [00:00<00:00, 44271.13 examples/s]
+Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|██████████| 661/661 [00:00<00:00, 41095.03 examples/s]
+Filter -> Patient Age:[35 - 65]&Patient Gender:F: 100%|██████████| 661/661 [00:00<00:00, 45549.81 examples/s]
+Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|██████████| 661/661 [00:00<00:00, 44374.58 examples/s]
+Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|██████████| 661/661 [00:00<00:00, 44286.68 examples/s]
+Filter -> overall: 100%|██████████| 661/661 [00:00<00:00, 48460.67 examples/s]
 
    
 
    
 
    
@@ -926,9 +924,9 @@ 
    Multilabel AUROC by Pathology and Age
 
    
 
    
-
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -6615,7 +6615,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -6623,7 +6623,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
diff --git a/api/tutorials/synthea/length_of_stay_report_periodic.html b/api/tutorials/synthea/length_of_stay_report_periodic.html
index e2432edb9..2006f5667 100644
--- a/api/tutorials/synthea/length_of_stay_report_periodic.html
+++ b/api/tutorials/synthea/length_of_stay_report_periodic.html
@@ -647,7 +647,7 @@ 
    A quick glance of your most import
     
     
    
       
-        0.72
+        0.85
       
       
         
@@ -680,7 +680,7 @@ 
    A quick glance of your most import
     
     
    
       
-        0.61
+        0.83
       
       
         
@@ -713,7 +713,7 @@ 
    A quick glance of your most import
     
     
    
       
-        1.0
+        0.8
       
       
         
@@ -746,7 +746,7 @@ 
    A quick glance of your most import
     
     
    
       
-        1.0
+        0.9
       
       
         
@@ -779,7 +779,7 @@ 
    A quick glance of your most import
     
     
    
       
-        0.91
+        0.8
       
       
         
@@ -1044,7 +1044,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1052,7 +1052,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1060,7 +1060,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1068,7 +1068,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1076,7 +1076,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1157,7 +1157,7 @@ 
    Quantitative Analysis
    
         
     
    
       
-        0.72
+        0.85
       
       
         
@@ -1190,7 +1190,7 @@ 
    Quantitative Analysis
    
         
     
    
       
-        0.61
+        0.83
       
       
         
@@ -1223,7 +1223,7 @@ 
    Quantitative Analysis
    
         
     
    
       
-        1.0
+        0.8
       
       
         
@@ -1256,7 +1256,7 @@ 
    Quantitative Analysis
    
         
     
    
       
-        1.0
+        0.9
       
       
         
@@ -1289,7 +1289,7 @@ 
    Quantitative Analysis
    
         
     
    
       
-        0.91
+        0.8
       
       
         
@@ -1321,7 +1321,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1329,7 +1329,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1337,7 +1337,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1345,7 +1345,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1398,7 +1398,7 @@ 
    Graphics
    
     
 
    
   
-    
                                
                        
    
+    
                                
                        
    
   
 
    
 
@@ -1670,6 +1670,15 @@ 
    Model Parameters
    
 
   
     
+    
+  
+
+  
+    
+    
    
+      
    Eval_metric
    
+      logloss
+    
    
     
   
 
@@ -1686,22 +1695,26 @@ 
    Model Parameters
    
   
     
     
    
-      
    Random_state
    
-      123
+      
    Reg_lambda
    
+      1
     
    
     
   
 
   
     
+    
    
+      
    Gamma
    
+      1
+    
    
     
   
 
   
     
     
    
-      
    Seed
    
-      123
+      
    Colsample_bytree
    
+      0.8
     
    
     
   
@@ -1724,8 +1737,8 @@ 
    Seed
    
   
     
     
    
-      
    Max_depth
    
-      2
+      
    Random_state
    
+      123
     
    
     
   
@@ -1737,14 +1750,18 @@ 
    Max_depth
    
 
   
     
+    
    
+      
    Enable_categorical
    
+      False
+    
    
     
   
 
   
     
     
    
-      
    Eval_metric
    
-      logloss
+      
    N_estimators
    
+      250
     
    
     
   
@@ -1771,6 +1788,10 @@ 
    Eval_metric
    
 
   
     
+    
    
+      
    Min_child_weight
    
+      3
+    
    
     
   
 
@@ -1786,19 +1807,11 @@ 
    Eval_metric
    
 
   
     
-    
    
-      
    Enable_categorical
    
-      False
-    
    
     
   
 
   
     
-    
    
-      
    Reg_lambda
    
-      1
-    
    
     
   
 
@@ -1809,6 +1822,10 @@ 
    Reg_lambda
    
 
   
     
+    
    
+      
    Seed
    
+      123
+    
    
     
   
 
@@ -1819,32 +1836,28 @@ 
    Reg_lambda
    
 
   
     
-    
    
-      
    N_estimators
    
-      500
-    
    
     
   
 
   
     
+    
    
+      
    Max_depth
    
+      5
+    
    
     
   
 
   
     
-    
    
-      
    Learning_rate
    
-      0.1
-    
    
     
   
 
   
     
     
    
-      
    Gamma
    
-      0
+      
    Missing
    
+      nan
     
    
     
   
@@ -1856,10 +1869,6 @@ 
    Gamma
    
 
   
     
-    
    
-      
    Min_child_weight
    
-      3
-    
    
     
   
 
@@ -1890,10 +1899,6 @@ 
    Min_child_weight
    
 
   
     
-    
    
-      
    Objective
    
-      binary:logistic
-    
    
     
   
 
@@ -1905,22 +1910,17 @@ 
    Objective
    
   
     
     
    
-      
    Colsample_bytree
    
-      0.8
+      
    Objective
    
+      binary:logistic
     
    
     
   
 
   
     
-    
-  
-
-  
-    
     
    
-      
    Missing
    
-      nan
+      
    Learning_rate
    
+      0.1
     
    
     
   
@@ -2192,7 +2192,7 @@ 
    Ethical Considerations
    
   function generate_model_card_plot() {
     var model_card_plots = []
     var overall_indices = [20, 21, 22, 23, 24]
-    var histories = JSON.parse("{\"0\": [\"0.8016528925619835\", \"0.6936076628105141\", \"0.6871314730971221\", \"0.7926497184811672\", \"0.7201177840517814\"], \"1\": [\"0.8823529411764706\", \"0.8339808949419706\", \"0.8042143949288626\", \"0.8854569942989211\", \"0.8910349652620954\"], \"2\": [\"0.7894736842105263\", \"0.8497857047884345\", \"0.9180487667877562\", \"0.9073890687519838\", \"0.8552361719628085\"], \"3\": [\"0.8333333333333334\", \"0.8203350141150871\", \"0.5858710517835515\", \"0.5092051191371252\", \"0.5362486278970567\"], \"4\": [\"0.926608187134503\", \"1.0\", \"0.9143093673249056\", \"0.8605851358656552\", \"0.7901468807602272\"], \"5\": [\"0.9056603773584906\", \"0.8238259039252782\", \"0.8074809018915355\", \"0.8526276043002695\", \"0.8352624674534691\"], \"6\": [\"0.9523809523809523\", \"1.0\", \"1.0\", \"1.0\", \"0.9227045140882221\"], \"7\": [\"0.8333333333333334\", \"0.6386602332326536\", \"0.5955417188874538\", \"0.5413595931197926\", \"0.4814721433971116\"], \"8\": [\"0.8888888888888888\", \"0.9215992453848099\", \"0.899752486661807\", \"0.8645449986258291\", \"0.9046888602094814\"], \"9\": [\"0.9698275862068966\", \"0.9666504976180167\", \"1.0\", \"0.8353544975986249\", \"0.7899943713247332\"], \"10\": [\"0.8650793650793651\", \"0.7261154219780679\", \"0.6361974719898206\", \"0.6275130235158937\", \"0.4306534214195932\"], \"11\": [\"0.8961038961038961\", \"0.7714865957933226\", \"0.876354380113162\", \"0.8442572223999412\", \"0.8486374375081782\"], \"12\": [\"0.8846153846153846\", \"0.858103552926385\", \"0.8012276812960967\", \"0.7834322795983695\", \"0.9266855599956503\"], \"13\": [\"0.8903225806451613\", \"0.9643863406696443\", \"0.8157641548296728\", \"0.8176830538956785\", \"0.7415177617307707\"], \"14\": [\"0.9552617521367521\", \"1.0\", \"0.9797737161515213\", \"1.0\", \"0.8175695048575996\"], \"15\": [\"0.86\", \"0.7448714123011388\", \"0.5447307233058087\", \"0.42013358558061104\", \"0.39409052124812166\"], \"16\": [\"0.9818181818181818\", \"0.9960446256571348\", \"1.0\", \"1.0\", \"1.0\"], \"17\": [\"0.8059701492537313\", \"0.7521150994055207\", \"0.8644304380235543\", \"0.7418327487651996\", \"0.7565124393157227\"], \"18\": [\"0.8852459016393442\", \"0.84143896462059\", \"0.7275641526621126\", \"0.7248208829392547\", \"0.6900141192119377\"], \"19\": [\"0.9739936680235187\", \"0.9819497224849731\", \"1.0\", \"0.6945202702464877\", \"0.6355461379802541\"], \"20\": [\"0.8628318584070797\", \"0.9207345753209007\", \"0.9340228593688318\", \"0.7832610679826659\", \"0.7205573701359043\"], \"21\": [\"0.9318181818181818\", \"0.7191095633817272\", \"0.6346136972402535\", \"0.5877278662426885\", \"0.6113507607836166\"], \"22\": [\"0.8482758620689655\", \"0.897422857946215\", \"1.0\", \"1.0\", \"1.0\"], \"23\": [\"0.8880866425992779\", \"0.8057073452801526\", \"0.8750326692067953\", \"0.9609805211952467\", \"1.0\"], \"24\": [\"0.9593018305661984\", \"1.0\", \"1.0\", \"1.0\", \"0.911481983922187\"]}");
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     var thresholds = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\", \"16\": \"0.7\", \"17\": \"0.7\", \"18\": \"0.7\", \"19\": \"0.7\", \"20\": \"0.7\", \"21\": \"0.7\", \"22\": \"0.7\", \"23\": \"0.7\", \"24\": \"0.7\"}");
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@@ -2473,9 +2473,9 @@ 
    Ethical Considerations
    
       }
     }
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     var thresholds_all = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\", \"16\": \"0.7\", \"17\": \"0.7\", \"18\": \"0.7\", \"19\": \"0.7\", \"20\": \"0.7\", \"21\": \"0.7\", \"22\": \"0.7\", \"23\": \"0.7\", \"24\": \"0.7\"}");
-    var trends_all = JSON.parse("{\"0\": \"neutral\", \"1\": \"neutral\", \"2\": \"positive\", \"3\": \"negative\", \"4\": \"negative\", \"5\": \"negative\", \"6\": \"neutral\", \"7\": \"negative\", \"8\": \"neutral\", \"9\": \"negative\", \"10\": \"negative\", \"11\": \"neutral\", \"12\": \"neutral\", \"13\": \"negative\", \"14\": \"negative\", \"15\": \"negative\", \"16\": \"neutral\", \"17\": \"negative\", \"18\": \"negative\", \"19\": \"negative\", \"20\": \"negative\", \"21\": \"negative\", \"22\": \"positive\", \"23\": \"positive\", \"24\": \"neutral\"}");
+    var trends_all = JSON.parse("{\"0\": \"positive\", \"1\": \"neutral\", \"2\": \"positive\", \"3\": \"positive\", \"4\": \"positive\", \"5\": \"negative\", \"6\": \"negative\", \"7\": \"positive\", \"8\": \"positive\", \"9\": \"positive\", \"10\": \"negative\", \"11\": \"negative\", \"12\": \"negative\", \"13\": \"neutral\", \"14\": \"negative\", \"15\": \"negative\", \"16\": \"neutral\", \"17\": \"negative\", \"18\": \"negative\", \"19\": \"positive\", \"20\": \"neutral\", \"21\": \"negative\", \"22\": \"negative\", \"23\": \"neutral\", \"24\": \"negative\"}");
     var passed_all = JSON.parse("{\"0\": true, \"1\": true, \"2\": true, \"3\": true, \"4\": true, \"5\": true, \"6\": true, \"7\": true, \"8\": true, \"9\": true, \"10\": true, \"11\": true, \"12\": true, \"13\": true, \"14\": true, \"15\": true, \"16\": true, \"17\": true, \"18\": true, \"19\": true, \"20\": true, \"21\": true, \"22\": true, \"23\": true, \"24\": true}");
     var names_all = JSON.parse("{\"0\": \"Accuracy\", \"1\": \"Precision\", \"2\": \"Recall\", \"3\": \"F1 Score\", \"4\": \"AUROC\", \"5\": \"Accuracy\", \"6\": \"Precision\", \"7\": \"Recall\", \"8\": \"F1 Score\", \"9\": \"AUROC\", \"10\": \"Accuracy\", \"11\": \"Precision\", \"12\": \"Recall\", \"13\": \"F1 Score\", \"14\": \"AUROC\", \"15\": \"Accuracy\", \"16\": \"Precision\", \"17\": \"Recall\", \"18\": \"F1 Score\", \"19\": \"AUROC\", \"20\": \"Accuracy\", \"21\": \"Precision\", \"22\": \"Recall\", \"23\": \"F1 Score\", \"24\": \"AUROC\"}");
     var timestamps_all = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"1\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"2\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"3\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"4\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"5\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"6\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"7\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"8\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"9\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"10\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"11\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"12\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"13\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"14\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"15\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"16\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"17\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"18\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"19\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"20\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"21\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"22\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"23\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"24\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"]}");
@@ -2749,9 +2749,9 @@ 
    Ethical Considerations
    
       }
     }
     var slices_all = JSON.parse("{\"0\": [\"metric:Accuracy\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"1\": [\"metric:Precision\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"2\": [\"metric:Recall\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"3\": [\"metric:F1 Score\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"4\": [\"metric:AUROC\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"5\": [\"metric:Accuracy\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"6\": [\"metric:Precision\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"7\": [\"metric:Recall\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"8\": [\"metric:F1 Score\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"9\": [\"metric:AUROC\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"10\": [\"metric:Accuracy\", \"gender:M\", \"age:overall_age\"], \"11\": [\"metric:Precision\", \"gender:M\", \"age:overall_age\"], \"12\": [\"metric:Recall\", \"gender:M\", \"age:overall_age\"], \"13\": [\"metric:F1 Score\", \"gender:M\", \"age:overall_age\"], \"14\": [\"metric:AUROC\", \"gender:M\", \"age:overall_age\"], \"15\": [\"metric:Accuracy\", \"gender:F\", \"age:overall_age\"], \"16\": [\"metric:Precision\", \"gender:F\", \"age:overall_age\"], \"17\": [\"metric:Recall\", \"gender:F\", \"age:overall_age\"], \"18\": [\"metric:F1 Score\", \"gender:F\", \"age:overall_age\"], \"19\": [\"metric:AUROC\", \"gender:F\", \"age:overall_age\"], \"20\": [\"metric:Accuracy\", \"age:overall_age\", \"gender:overall_gender\"], \"21\": [\"metric:Precision\", \"age:overall_age\", \"gender:overall_gender\"], \"22\": [\"metric:Recall\", \"age:overall_age\", \"gender:overall_gender\"], \"23\": [\"metric:F1 Score\", \"age:overall_age\", \"gender:overall_gender\"], \"24\": [\"metric:AUROC\", \"age:overall_age\", \"gender:overall_gender\"]}");
-    var histories_all = JSON.parse("{\"0\": [\"0.8016528925619835\", \"0.6936076628105141\", \"0.6871314730971221\", \"0.7926497184811672\", \"0.7201177840517814\"], \"1\": [\"0.8823529411764706\", \"0.8339808949419706\", \"0.8042143949288626\", \"0.8854569942989211\", \"0.8910349652620954\"], \"2\": [\"0.7894736842105263\", \"0.8497857047884345\", \"0.9180487667877562\", \"0.9073890687519838\", \"0.8552361719628085\"], \"3\": [\"0.8333333333333334\", \"0.8203350141150871\", \"0.5858710517835515\", \"0.5092051191371252\", \"0.5362486278970567\"], \"4\": [\"0.926608187134503\", \"1.0\", \"0.9143093673249056\", \"0.8605851358656552\", \"0.7901468807602272\"], \"5\": [\"0.9056603773584906\", \"0.8238259039252782\", \"0.8074809018915355\", \"0.8526276043002695\", \"0.8352624674534691\"], \"6\": [\"0.9523809523809523\", \"1.0\", \"1.0\", \"1.0\", \"0.9227045140882221\"], \"7\": [\"0.8333333333333334\", \"0.6386602332326536\", \"0.5955417188874538\", \"0.5413595931197926\", \"0.4814721433971116\"], \"8\": [\"0.8888888888888888\", \"0.9215992453848099\", \"0.899752486661807\", \"0.8645449986258291\", \"0.9046888602094814\"], \"9\": [\"0.9698275862068966\", \"0.9666504976180167\", \"1.0\", \"0.8353544975986249\", \"0.7899943713247332\"], \"10\": [\"0.8650793650793651\", \"0.7261154219780679\", \"0.6361974719898206\", \"0.6275130235158937\", \"0.4306534214195932\"], \"11\": [\"0.8961038961038961\", \"0.7714865957933226\", \"0.876354380113162\", \"0.8442572223999412\", \"0.8486374375081782\"], \"12\": [\"0.8846153846153846\", \"0.858103552926385\", \"0.8012276812960967\", \"0.7834322795983695\", \"0.9266855599956503\"], \"13\": [\"0.8903225806451613\", \"0.9643863406696443\", \"0.8157641548296728\", \"0.8176830538956785\", \"0.7415177617307707\"], \"14\": [\"0.9552617521367521\", \"1.0\", \"0.9797737161515213\", \"1.0\", \"0.8175695048575996\"], \"15\": [\"0.86\", \"0.7448714123011388\", \"0.5447307233058087\", \"0.42013358558061104\", \"0.39409052124812166\"], \"16\": [\"0.9818181818181818\", \"0.9960446256571348\", \"1.0\", \"1.0\", \"1.0\"], \"17\": [\"0.8059701492537313\", \"0.7521150994055207\", \"0.8644304380235543\", \"0.7418327487651996\", \"0.7565124393157227\"], \"18\": [\"0.8852459016393442\", \"0.84143896462059\", \"0.7275641526621126\", \"0.7248208829392547\", \"0.6900141192119377\"], \"19\": [\"0.9739936680235187\", \"0.9819497224849731\", \"1.0\", \"0.6945202702464877\", \"0.6355461379802541\"], \"20\": [\"0.8628318584070797\", \"0.9207345753209007\", \"0.9340228593688318\", \"0.7832610679826659\", \"0.7205573701359043\"], \"21\": [\"0.9318181818181818\", \"0.7191095633817272\", \"0.6346136972402535\", \"0.5877278662426885\", \"0.6113507607836166\"], \"22\": [\"0.8482758620689655\", \"0.897422857946215\", \"1.0\", \"1.0\", \"1.0\"], \"23\": [\"0.8880866425992779\", \"0.8057073452801526\", \"0.8750326692067953\", \"0.9609805211952467\", \"1.0\"], \"24\": [\"0.9593018305661984\", \"1.0\", \"1.0\", \"1.0\", \"0.911481983922187\"]}");
+    var histories_all = JSON.parse("{\"0\": [\"0.8717948717948718\", \"1.0\", \"0.9260294388954282\", \"1.0\", \"1.0\"], \"1\": [\"0.8805970149253731\", \"0.8864518186300872\", \"0.797081946779185\", \"0.8368927766555394\", \"0.8688002183880602\"], \"2\": [\"0.8939393939393939\", \"0.9513427326904658\", \"0.9437718965065215\", \"1.0\", \"0.978789025614006\"], \"3\": [\"0.8872180451127819\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"4\": [\"0.9631610219845513\", \"0.9518349043515505\", \"1.0\", \"1.0\", \"1.0\"], \"5\": [\"0.88\", \"1.0\", \"0.8183257226653501\", \"0.8764460848764506\", \"0.8673841678057298\"], \"6\": [\"0.9565217391304348\", \"0.9825044222506764\", \"0.9170264812503323\", \"0.9416114372114078\", \"0.8268460623101885\"], \"7\": [\"0.8148148148148148\", \"0.8167367973115197\", \"0.9333272608973396\", \"0.9643578216343616\", \"0.7990934832388008\"], \"8\": [\"0.88\", \"0.999651165393864\", \"1.0\", \"1.0\", \"0.9808767862920497\"], \"9\": [\"0.9500805152979065\", \"0.8951220073421391\", \"0.936433467560014\", \"1.0\", \"1.0\"], \"10\": [\"0.9193548387096774\", \"0.9648001377992477\", \"0.8330877691073038\", \"0.8684515555514056\", \"0.7208002153978317\"], \"11\": [\"0.9375\", \"1.0\", \"0.8122905559916681\", \"0.7167668811457667\", \"0.6956096239226329\"], \"12\": [\"0.9375\", \"1.0\", \"0.9615581089379361\", \"1.0\", \"0.868681031365583\"], \"13\": [\"0.9375\", \"1.0\", \"1.0\", \"0.9621506691382669\", \"1.0\"], \"14\": [\"0.9755681818181818\", \"0.9083777863454842\", \"0.8331557576642691\", \"0.8190703314509756\", \"0.7941282841512916\"], \"15\": [\"0.8725490196078431\", \"0.8740834672358618\", \"1.0\", \"1.0\", \"0.7391431668169599\"], \"16\": [\"0.9333333333333333\", \"0.9869589152121052\", \"1.0\", \"0.9824768087099318\", \"0.9433515215061776\"], \"17\": [\"0.8615384615384616\", \"0.9597553387744371\", \"0.7328185993460558\", \"0.8444742817504761\", \"0.8644642749352557\"], \"18\": [\"0.896\", \"0.8927289503847454\", \"0.9105348869670588\", \"0.8372212095735406\", \"0.8229679841498414\"], \"19\": [\"0.9496881496881497\", \"0.8968054758243895\", \"1.0\", \"1.0\", \"1.0\"], \"20\": [\"0.8982300884955752\", \"0.9897957786449971\", \"1.0\", \"1.0\", \"0.8542438083210027\"], \"21\": [\"0.9357142857142857\", \"1.0\", \"0.891890217163751\", \"0.9486304681961161\", \"0.8256976895577073\"], \"22\": [\"0.903448275862069\", \"0.9448221268303703\", \"0.8120326963250271\", \"0.7730876829564877\", \"0.7989880475568345\"], \"23\": [\"0.9192982456140351\", \"1.0\", \"0.9963830406784279\", \"0.9881621055393863\", \"0.9025301076085205\"], \"24\": [\"0.9655172413793103\", \"0.7815534224253337\", \"0.7871225167765127\", \"0.7384355239835124\", \"0.8040328837936527\"]}");
     var thresholds_all = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\", \"16\": \"0.7\", \"17\": \"0.7\", \"18\": \"0.7\", \"19\": \"0.7\", \"20\": \"0.7\", \"21\": \"0.7\", \"22\": \"0.7\", \"23\": \"0.7\", \"24\": \"0.7\"}");
-    var trends_all = JSON.parse("{\"0\": \"neutral\", \"1\": \"neutral\", \"2\": \"positive\", \"3\": \"negative\", \"4\": \"negative\", \"5\": \"negative\", \"6\": \"neutral\", \"7\": \"negative\", \"8\": \"neutral\", \"9\": \"negative\", \"10\": \"negative\", \"11\": \"neutral\", \"12\": \"neutral\", \"13\": \"negative\", \"14\": \"negative\", \"15\": \"negative\", \"16\": \"neutral\", \"17\": \"negative\", \"18\": \"negative\", \"19\": \"negative\", \"20\": \"negative\", \"21\": \"negative\", \"22\": \"positive\", \"23\": \"positive\", \"24\": \"neutral\"}");
+    var trends_all = JSON.parse("{\"0\": \"positive\", \"1\": \"neutral\", \"2\": \"positive\", \"3\": \"positive\", \"4\": \"positive\", \"5\": \"negative\", \"6\": \"negative\", \"7\": \"positive\", \"8\": \"positive\", \"9\": \"positive\", \"10\": \"negative\", \"11\": \"negative\", \"12\": \"negative\", \"13\": \"neutral\", \"14\": \"negative\", \"15\": \"negative\", \"16\": \"neutral\", \"17\": \"negative\", \"18\": \"negative\", \"19\": \"positive\", \"20\": \"neutral\", \"21\": \"negative\", \"22\": \"negative\", \"23\": \"neutral\", \"24\": \"negative\"}");
     var passed_all = JSON.parse("{\"0\": true, \"1\": true, \"2\": true, \"3\": true, \"4\": true, \"5\": true, \"6\": true, \"7\": true, \"8\": true, \"9\": true, \"10\": true, \"11\": true, \"12\": true, \"13\": true, \"14\": true, \"15\": true, \"16\": true, \"17\": true, \"18\": true, \"19\": true, \"20\": true, \"21\": true, \"22\": true, \"23\": true, \"24\": true}");
     var names_all = JSON.parse("{\"0\": \"Accuracy\", \"1\": \"Precision\", \"2\": \"Recall\", \"3\": \"F1 Score\", \"4\": \"AUROC\", \"5\": \"Accuracy\", \"6\": \"Precision\", \"7\": \"Recall\", \"8\": \"F1 Score\", \"9\": \"AUROC\", \"10\": \"Accuracy\", \"11\": \"Precision\", \"12\": \"Recall\", \"13\": \"F1 Score\", \"14\": \"AUROC\", \"15\": \"Accuracy\", \"16\": \"Precision\", \"17\": \"Recall\", \"18\": \"F1 Score\", \"19\": \"AUROC\", \"20\": \"Accuracy\", \"21\": \"Precision\", \"22\": \"Recall\", \"23\": \"F1 Score\", \"24\": \"AUROC\"}");
     var timestamps_all = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"1\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"2\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"3\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"4\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"5\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"6\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"7\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"8\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"9\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"10\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"11\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"12\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"13\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"14\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"15\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"16\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"17\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"18\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"19\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"20\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"21\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"22\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"23\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"24\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"]}");
diff --git a/api/tutorials/synthea/los_prediction.html b/api/tutorials/synthea/los_prediction.html
index 1db4e555f..3763d9f2a 100644
--- a/api/tutorials/synthea/los_prediction.html
+++ b/api/tutorials/synthea/los_prediction.html
@@ -685,17 +685,17 @@ 
    Compute length of stay (labels)
 
    -2023-11-27 15:14:27,819 INFO cycquery.orm    - Database setup, ready to run queries!
-2023-11-27 15:14:33,079 INFO cycquery.orm    - Query returned successfully!
-2023-11-27 15:14:33,080 INFO cycquery.utils.profile - Finished executing function run_query in 3.934621 s
-2023-11-27 15:14:34,873 INFO cycquery.orm    - Query returned successfully!
-2023-11-27 15:14:34,875 INFO cycquery.utils.profile - Finished executing function run_query in 1.793854 s
-2023-11-27 15:14:36,431 INFO cycquery.orm    - Query returned successfully!
-2023-11-27 15:14:36,432 INFO cycquery.utils.profile - Finished executing function run_query in 0.391219 s
-2023-11-27 15:14:36,933 INFO cycquery.orm    - Query returned successfully!
-2023-11-27 15:14:36,935 INFO cycquery.utils.profile - Finished executing function run_query in 0.498290 s
-2023-11-27 15:14:37,040 INFO cycquery.orm    - Query returned successfully!
-2023-11-27 15:14:37,041 INFO cycquery.utils.profile - Finished executing function run_query in 0.105283 s
+2023-11-27 21:35:13,870 INFO cycquery.orm    - Database setup, ready to run queries!
+2023-11-27 21:35:18,615 INFO cycquery.orm    - Query returned successfully!
+2023-11-27 21:35:18,617 INFO cycquery.utils.profile - Finished executing function run_query in 3.939373 s
+2023-11-27 21:35:20,425 INFO cycquery.orm    - Query returned successfully!
+2023-11-27 21:35:20,425 INFO cycquery.utils.profile - Finished executing function run_query in 1.807781 s
+2023-11-27 21:35:22,059 INFO cycquery.orm    - Query returned successfully!
+2023-11-27 21:35:22,060 INFO cycquery.utils.profile - Finished executing function run_query in 0.359256 s
+2023-11-27 21:35:22,510 INFO cycquery.orm    - Query returned successfully!
+2023-11-27 21:35:22,511 INFO cycquery.utils.profile - Finished executing function run_query in 0.446829 s
+2023-11-27 21:35:22,594 INFO cycquery.orm    - Query returned successfully!
+2023-11-27 21:35:22,595 INFO cycquery.utils.profile - Finished executing function run_query in 0.082727 s
 
    
 
    
 
@@ -748,9 +748,9 @@ 
    Drop NaNs based on the 
 
    
 
    
-