- 2.17.0 (latest)
- 2.16.0
- 2.15.0
- 2.14.0
- 2.13.0
- 2.12.0
- 2.11.0
- 2.10.0
- 2.9.0
- 2.8.0
- 2.7.0
- 2.6.0
- 2.5.0
- 2.4.0
- 2.3.0
- 2.2.0
- 2.0.0-dev0
- 1.36.0
- 1.35.0
- 1.34.0
- 1.33.0
- 1.32.0
- 1.31.0
- 1.30.0
- 1.29.0
- 1.28.0
- 1.27.0
- 1.26.0
- 1.25.0
- 1.24.0
- 1.22.0
- 1.21.0
- 1.20.0
- 1.19.0
- 1.18.0
- 1.17.0
- 1.16.0
- 1.15.0
- 1.14.0
- 1.13.0
- 1.12.0
- 1.11.1
- 1.10.0
- 1.9.0
- 1.8.0
- 1.7.0
- 1.6.0
- 1.5.0
- 1.4.0
- 1.3.0
- 1.2.0
- 1.1.0
- 1.0.0
- 0.26.0
- 0.25.0
- 0.24.0
- 0.23.0
- 0.22.0
- 0.21.0
- 0.20.1
- 0.19.2
- 0.18.0
- 0.17.0
- 0.16.0
- 0.15.0
- 0.14.1
- 0.13.0
- 0.12.0
- 0.11.0
- 0.10.0
- 0.9.0
- 0.8.0
- 0.7.0
- 0.6.0
- 0.5.0
- 0.4.0
- 0.3.0
- 0.2.0
Functions for test/train split and model tuning. This module is styled after scikit-learn's model_selection module: https://scikit-learn.org/stable/modules/classes.html#module-sklearn.model_selection .
Classes
KFold
KFold
(
n_splits
:
int
=
5
,
*
,
random_state
:
typing
.
Optional
[
int
]
=
None
)
K-Fold cross-validator.
Split data in train/test sets. Split dataset into k consecutive folds.
Each fold is then used once as a validation while the k - 1 remaining folds form the training set.
n_splits
int
Number of folds. Must be at least 2. Default to 5.
random_state
Optional[int]
A seed to use for randomly choosing the rows of the split. If not set, a random split will be generated each time. Default to None.
Modules Functions
cross_validate
cross_validate
(
estimator
,
X
:
typing
.
Union
[
bigframes
.
dataframe
.
DataFrame
,
bigframes
.
series
.
Series
,
pandas
.
core
.
frame
.
DataFrame
,
pandas
.
core
.
series
.
Series
,
],
y
:
typing
.
Optional
[
typing
.
Union
[
bigframes
.
dataframe
.
DataFrame
,
bigframes
.
series
.
Series
,
pandas
.
core
.
frame
.
DataFrame
,
pandas
.
core
.
series
.
Series
,
]
]
=
None
,
*
,
cv
:
typing
.
Optional
[
typing
.
Union
[
int
,
bigframes
.
ml
.
model_selection
.
KFold
]]
=
None
)
-
> dict
[
str
,
list
]
Evaluate metric(s) by cross-validation and also record fit/score times.
X
y
bigframes.dataframe.DataFrame
, bigframes.series.Series
or None
The target variable to try to predict in the case of supe()rvised learning. Default to None.
cv
int, bigframes.ml.model_selection.KFold
or None
Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 5-fold cross validation, - int, to specify the number of folds in a KFold
, - bigframes.ml.model_selection.KFold
instance.
Dict[str, List]
dict
are: test_score
The score array for test scores on each cv split. fit_time
The time for fitting the estimator on the train set for each cv split. score_time
The time for scoring the estimator on the test set for each cv split.train_test_split
train_test_split
(
*
arrays
:
typing
.
Union
[
bigframes
.
dataframe
.
DataFrame
,
bigframes
.
series
.
Series
,
pandas
.
core
.
frame
.
DataFrame
,
pandas
.
core
.
series
.
Series
,
],
test_size
:
typing
.
Optional
[
float
]
=
None
,
train_size
:
typing
.
Optional
[
float
]
=
None
,
random_state
:
typing
.
Optional
[
int
]
=
None
,
stratify
:
typing
.
Optional
[
bigframes
.
series
.
Series
]
=
None
)
-
> typing
.
List
[
typing
.
Union
[
bigframes
.
dataframe
.
DataFrame
,
bigframes
.
series
.
Series
]]
Splits dataframes or series into random train and test subsets.
\*arrays
bigframes.dataframe.DataFrame
or bigframes.series.Series
A sequence of BigQuery DataFrames or Series that can be joined on their indexes.
test_size
default None
The proportion of the dataset to include in the test split. If None, this will default to the complement of train_size. If both are none, it will be set to 0.25.
train_size
default None
The proportion of the dataset to include in the train split. If None, this will default to the complement of test_size.
random_state
default None
A seed to use for randomly choosing the rows of the split. If not set, a random split will be generated each time.
List[Union[ bigframes.dataframe.DataFrame
, bigframes.series.Series
]]
