mlshell.Dataset

class mlshell.Dataset(*args, **kwargs)

Bases: dict

Unified data interface.

Implements interface to access arbitrary data.

Interface: x, y, data, meta, subset, dump_pred and whole dict api.

Parameters

• *args (list) – Passed to parent class constructor.

• **kwrags (dict) – Passed to parent class constructor.

data

Underlying data.

Type

pandas.DataFrame

subsets

{‘subset_id’ : array-like subset indices, ..}.

Type

dict

meta

Extracted auxiliary information from data: {

‘index’: list

List of index column label(s).

‘features’: list

List of feature column label(s).

‘categoric_features’: list

List of categorical feature column label(s).

‘targets’: list

List of target column label(s),

‘indices’: list

List of rows indices.

‘classes’: list of numpy.ndarray

List of sorted unique labels for each target(s) (n_outputs, n_classes).

‘pos_labels’: list

List of “positive” label(s) for target(s) (n_outputs,).

‘pos_labels_ind’: list

List of “positive” label(s) index in numpy.unique() for target(s) (n_outputs).

categoric_ind_namedict

Dictionary with categorical feature indices as key, and tuple (‘feature_name’, categories) as value: {‘column_index’: (‘feature_name’, [‘cat1’, ‘cat2’])}.

numeric_ind_namedict

Dictionary with numeric features indices as key, and tuple (‘feature_name’, ) as value: {‘columns_index’:(‘feature_name’,)}.

}

Type

dict

Notes

Inherited from dict class, so attributes section describes keys.

Attributes

data

pandas.DataFrame : Access data.

meta

dict: Access meta.

oid

str: Dataset identifier.

x

pandas.DataFrame : Extracted features columns.

y

pandas.DataFrame : Extracted targets columns.

Methods

clear()
copy()
dump_pred(filepath, y_pred, **kwargs)	Dump columns to disk.
fromkeys(iterable[, value])	Create a new dictionary with keys from iterable and values set to value.
get(key[, default])	Return the value for key if key is in the dictionary, else default.
items()
keys()
pop(k[,d])	If key is not found, d is returned if given, otherwise KeyError is raised
popitem()	2-tuple; but raise KeyError if D is empty.
setdefault(key[, default])	Insert key with a value of default if key is not in the dictionary.
subset(subset_id)	mlshell.Dataset : Access subset.
update([E, ]**F)	If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values()

__init__(*args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(*args, **kwargs)	Initialize self.
clear()
copy()
dump_pred(filepath, y_pred, **kwargs)	Dump columns to disk.
fromkeys(iterable[, value])	Create a new dictionary with keys from iterable and values set to value.
get(key[, default])	Return the value for key if key is in the dictionary, else default.
items()
keys()
pop(k[,d])	If key is not found, d is returned if given, otherwise KeyError is raised
popitem()	2-tuple; but raise KeyError if D is empty.
setdefault(key[, default])	Insert key with a value of default if key is not in the dictionary.
subset(subset_id)	mlshell.Dataset : Access subset.
update([E, ]**F)	If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values()

Attributes

data	pandas.DataFrame : Access data.
meta	dict: Access meta.
oid	str: Dataset identifier.
x	pandas.DataFrame : Extracted features columns.
y	pandas.DataFrame : Extracted targets columns.

property oid

str: Dataset identifier.

property x

pandas.DataFrame : Extracted features columns.

property y

pandas.DataFrame : Extracted targets columns.

property meta

dict: Access meta.

property data

pandas.DataFrame : Access data.

subset(subset_id)

mlshell.Dataset : Access subset.

dump_pred(filepath, y_pred, **kwargs)

Dump columns to disk.

Parameters

• filepath (str) – File path without extension.

• y_pred (array-like) – pipeline.predict() result.

• **kwargs (dict) –

• Additional kwargs to pass in .to_csv(**kwargs) (`) –

Returns

fullpath – Full filepath.

Return type

str

clear() → None. Remove all items from D.

copy() → a shallow copy of D

fromkeys(iterable, value=None, /)

Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /)

Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D’s items

keys() → a set-like object providing a view on D’s keys

pop(k[, d]) → v, remove specified key and return the corresponding value.

If key is not found, d is returned if given, otherwise KeyError is raised

popitem() → (k, v), remove and return some (key, value) pair as a

2-tuple; but raise KeyError if D is empty.

setdefault(key, default=None, /)

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None. Update D from dict/iterable E and F.

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D’s values