dataset
dataset
Aligned datasets that bridge feature and label research with model fitting.
Classes
| Name | Description |
|---|---|
| Dataset | One aligned collection of model inputs, targets, weights, and metadata. |
| DatasetView | A partition-backed view over one aligned :class:Dataset. |
| Fold | A dataset facade bound to one split within a split scheme. |
| Partition | A named subset and its role in model fitting or evaluation. |
| PurgedTimeSeriesSplit | Walk-forward folds purged by label horizons and optional embargo. |
| Split | One assignment of dataset rows to named partitions. |
| SplitScheme | A named collection of splits, such as the folds of walk-forward CV. |
| TemporalSplit | Create one ordered train/validation/test assignment. |
| TimeSeriesSplit | Wrap sklearn’s expanding or rolling time-series cross-validator. |
Dataset
dataset.Dataset(
X,
y=None,
sample_weight=None,
metadata=None,
*,
on=None,
splits=None,
)One aligned collection of model inputs, targets, weights, and metadata.
The pandas index is the dataset’s row identity: X, y, sample_weight, and metadata must describe the same indexed rows. When on is provided, each component must expose those keys either as DataFrame columns or as its complete named index. Key columns become the canonical dataset index, and component rows are reordered to X order.
Attributes
| Name | Description |
|---|---|
| index | Return the canonical row index shared by every dataset component. |
| is_split | Return whether at least one split scheme is attached. |
| partitions | Return named views from the first split of the default scheme. |
| test | Return the default split’s conventional test partition. |
| train | Return the default split’s conventional train partition. |
| validation | Return the default split’s conventional validation partition. |
Methods
| Name | Description |
|---|---|
| fold | Return a dataset facade bound to one fold in scheme. |
| load | Load a dataset written by :meth:save. |
| partition | Return a row view for one partition in one split scheme. |
| save | Serialize the aligned dataset and all split metadata with joblib. |
| split | Return a dataset with the split or scheme produced by splitter. |
| to_pandas | Return selected aligned components as one optionally bounded frame. |
| with_split | Return a dataset sharing the same data with an added split scheme. |
fold
dataset.Dataset.fold(scheme, fold=0)Return a dataset facade bound to one fold in scheme.
load
dataset.Dataset.load(path)Load a dataset written by :meth:save.
Only load files from trusted sources because joblib uses pickle.
partition
dataset.Dataset.partition(name, *, scheme='default', fold=0)Return a row view for one partition in one split scheme.
save
dataset.Dataset.save(path)Serialize the aligned dataset and all split metadata with joblib.
split
dataset.Dataset.split(splitter)Return a dataset with the split or scheme produced by splitter.
to_pandas
dataset.Dataset.to_pandas(components=_COMPONENTS, *, start=None, end=None)Return selected aligned components as one optionally bounded frame.
with_split
dataset.Dataset.with_split(split)Return a dataset sharing the same data with an added split scheme.
DatasetView
dataset.DatasetView(dataset, split, partition)A partition-backed view over one aligned :class:Dataset.
Attributes
| Name | Description |
|---|---|
| X | Return feature rows assigned to this partition. |
| index | Return row labels assigned to this partition. |
| metadata | Return row metadata assigned to this partition, when present. |
| sample_weight | Return sample weights assigned to this partition, when present. |
| y | Return target rows assigned to this partition, when present. |
Methods
| Name | Description |
|---|---|
| to_pandas | Return selected partition components as one optionally bounded frame. |
to_pandas
dataset.DatasetView.to_pandas(components=_COMPONENTS, *, start=None, end=None)Return selected partition components as one optionally bounded frame.
Fold
dataset.Fold(dataset, scheme, split)A dataset facade bound to one split within a split scheme.
Attributes
| Name | Description |
|---|---|
| X | Return the complete feature frame governed by this fold. |
| index | Return the parent dataset’s canonical row index. |
| is_split | Return True because this facade is bound to one split. |
| metadata | Return the complete row metadata governed by this fold. |
| name | Return this fold’s split name. |
| partitions | Return named partition views for this fold. |
| sample_weight | Return the complete aligned sample weights governed by this fold. |
| test | Return this fold’s conventional test partition. |
| train | Return this fold’s conventional train partition. |
| validation | Return this fold’s conventional validation partition. |
| y | Return the complete aligned target governed by this fold. |
Methods
| Name | Description |
|---|---|
| partition | Return one named partition view from this fold. |
partition
dataset.Fold.partition(name)Return one named partition view from this fold.
Partition
dataset.Partition(name, role, metadata=dict())A named subset and its role in model fitting or evaluation.
PurgedTimeSeriesSplit
dataset.PurgedTimeSeriesSplit(
n_splits=5,
max_train_size=None,
test_size=None,
gap=0,
name='purged_walk_forward',
label_end='label_end_time',
embargo=0,
)Walk-forward folds purged by label horizons and optional embargo.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| label_end | str | Metadata column containing each row’s inclusive label end. | 'label_end_time' |
| embargo | int | str | pd.Timedelta | Additional separation before each test partition, expressed as a row count or pandas-compatible timedelta string. | 0 |
Methods
| Name | Description |
|---|---|
| split | Return sklearn folds after removing overlapping training labels. |
split
dataset.PurgedTimeSeriesSplit.split(dataset)Return sklearn folds after removing overlapping training labels.
Split
dataset.Split(membership, partitions, name='default', metadata=dict())One assignment of dataset rows to named partitions.
Attributes
| Name | Description |
|---|---|
| fit_partitions | Return names of partitions from which learned state may be fitted. |
Methods
| Name | Description |
|---|---|
| partition | Return one partition definition by name. |
partition
dataset.Split.partition(name)Return one partition definition by name.
SplitScheme
dataset.SplitScheme(splits, name='default', metadata=dict())A named collection of splits, such as the folds of walk-forward CV.
Methods
| Name | Description |
|---|---|
| split | Return a split by zero-based fold position or name. |
split
dataset.SplitScheme.split(fold=0)Return a split by zero-based fold position or name.
TemporalSplit
dataset.TemporalSplit(
train_end=None,
validation_end=None,
test_end=None,
name='default',
train_size=None,
validation_size=None,
test_size=None,
)Create one ordered train/validation/test assignment.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| train_end | Any | None | Inclusive end of the training partition. Alternatively, use the size arguments to assign partitions by row count or proportion. | None |
| validation_end | Any | None | Inclusive end of validation. Omit validation by leaving this as None. |
None |
| test_end | Any | None | Inclusive end of test. Rows after it are excluded. By default, test extends to the end of the dataset. | None |
| name | str | Split name. | 'default' |
| train_size | int | float | None | Training row count or proportion. | None |
| validation_size | int | float | None | Validation row count or proportion. Leaving it unset omits validation in size mode. | None |
| test_size | int | float | None | Test row count or proportion. When either train_size or test_size is unset, it receives the remaining rows. |
None |
Methods
| Name | Description |
|---|---|
| apply | Return dataset with this split attached as the default scheme. |
| split | Return the ordered assignment for dataset. |
apply
dataset.TemporalSplit.apply(dataset)Return dataset with this split attached as the default scheme.
split
dataset.TemporalSplit.split(dataset)Return the ordered assignment for dataset.
TimeSeriesSplit
dataset.TimeSeriesSplit(
n_splits=5,
max_train_size=None,
test_size=None,
gap=0,
name='walk_forward',
)Wrap sklearn’s expanding or rolling time-series cross-validator.
max_train_size selects a rolling window; leaving it unset produces expanding training windows. gap and test_size retain sklearn’s row semantics. Rows not selected in a fold receive the excluded role.
Methods
| Name | Description |
|---|---|
| apply | Return dataset with the generated scheme attached. |
| split | Return sklearn-generated folds as a QRT split scheme. |
apply
dataset.TimeSeriesSplit.apply(dataset)Return dataset with the generated scheme attached.
split
dataset.TimeSeriesSplit.split(dataset)Return sklearn-generated folds as a QRT split scheme.
Functions
| Name | Description |
|---|---|
| audit_splits | Audit all folds for ordering and label-horizon leakage. |
| split_diagnostics | Summarize partition roles, sizes, proportions, and index boundaries. |
audit_splits
dataset.audit_splits(dataset, scheme, *, label_end='label_end_time')Audit all folds for ordering and label-horizon leakage.
A fold passes when every fit row precedes every evaluate/holdout row and, when label-end metadata exists, each fit label ends before evaluation starts.
split_diagnostics
dataset.split_diagnostics(dataset, scheme, fold=0)Summarize partition roles, sizes, proportions, and index boundaries.