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KMeans
(
n_clusters
:
int
=
8
,
*
,
init
:
typing
.
Literal
[
"kmeans++"
,
"random"
,
"custom"
]
=
"kmeans++"
,
init_col
:
typing
.
Optional
[
str
]
=
None
,
distance_type
:
typing
.
Literal
[
"euclidean"
,
"cosine"
]
=
"euclidean"
,
max_iter
:
int
=
20
,
tol
:
float
=
0.01
,
warm_start
:
bool
=
False
)
K-Means clustering.
Examples:
>>> import bigframes.pandas as bpd
>>> from bigframes.ml.cluster import KMeans
>>> X = bpd.DataFrame({"feat0": [1, 1, 1, 10, 10, 10], "feat1": [2, 4, 0, 2, 4, 0]})
>>> kmeans = KMeans(n_clusters=2).fit(X)
>>> kmeans.predict(bpd.DataFrame({"feat0": [0, 12], "feat1": [0, 3]}))["CENTROID_ID"] # doctest:+SKIP
0 1
1 2
Name: CENTROID_ID, dtype: Int64
>>> kmeans.cluster_centers_ # doctest:+SKIP
centroid_id feature numerical_value categorical_value
0 1 feat0 5.5 []
1 1 feat1 1.0 []
2 2 feat0 5.5 []
3 2 feat1 4.0 []
[4 rows x 4 columns]
Properties
cluster_centers_
Information of cluster centers.
Returns
Type
Description
DataFrame of cluster centers, containing following columns: centroid_id: An integer that identifies the centroid. feature: The column name that contains the feature. numerical_value: If feature is numeric, the value of feature for the centroid that centroid_id identifies. If feature is not numeric, the value is NULL. categorical_value: An list of mappings containing information about categorical features. Each mapping contains the following fields: categorical_value.category: The name of each category. categorical_value.value: The value of categorical_value.category for the centroid that centroid_id identifies. The output contains one row per feature per centroid.
Methods
__repr__
__repr__
()
Print the estimator's constructor with all non-default parameter values.
detect_anomalies
detect_anomalies
(
X
:
typing
.
Union
[
bigframes
.
dataframe
.
DataFrame
,
bigframes
.
series
.
Series
,
pandas
.
core
.
frame
.
DataFrame
,
pandas
.
core
.
series
.
Series
,
],
*
,
contamination
:
float
=
0.1
)
-
> bigframes
.
dataframe
.
DataFrame
Detect the anomaly data points of the input.
fit
fit
(
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
,
)
-
> bigframes
.
ml
.
base
.
_T
Compute k-means clustering.
Parameters
Name
Description
X
bigframes.dataframe.DataFrame
or bigframes.series.Series
or pandas.core.frame.DataFrame or pandas.core.series.Series
DataFrame of shape (n_samples, n_features). Training data.
y
default None
Not used, present here for API consistency by convention.
Returns
Type
Description
KMeans
Fitted estimator.
get_params
get_params
(
deep
:
bool
=
True
)
-
> typing
.
Dict
[
str
,
typing
.
Any
]
Get parameters for this estimator.
Parameter
Name
Description
deep
bool, default True
Default True
. If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns
Type
Description
Dictionary
A dictionary of parameter names mapped to their values.
predict
predict
(
X
:
typing
.
Union
[
bigframes
.
dataframe
.
DataFrame
,
bigframes
.
series
.
Series
,
pandas
.
core
.
frame
.
DataFrame
,
pandas
.
core
.
series
.
Series
,
],
)
-
> bigframes
.
dataframe
.
DataFrame
Predict the closest cluster each sample in X belongs to.
Returns
Type
Description
DataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted labels.
register
register
(
vertex_ai_model_id
:
typing
.
Optional
[
str
]
=
None
)
-
> bigframes
.
ml
.
base
.
_T
Register the model to Vertex AI.
After register, go to the Google Cloud console ( https://console.cloud.google.com/vertex-ai/models ) to manage the model registries. Refer to https://cloud.google.com/vertex-ai/docs/model-registry/introduction for more options.
Parameter
Name
Description
vertex_ai_model_id
Optional[str], default None
Optional string id as model id in Vertex. If not set, will default to 'bigframes_{bq_model_id}'. Vertex Ai model id will be truncated to 63 characters due to its limitation.
score
score
(
X
:
typing
.
Union
[
bigframes
.
dataframe
.
DataFrame
,
bigframes
.
series
.
Series
,
pandas
.
core
.
frame
.
DataFrame
,
pandas
.
core
.
series
.
Series
,
],
y
=
None
,
)
-
> bigframes
.
dataframe
.
DataFrame
Calculate evaluation metrics of the model.
to_gbq
to_gbq
(
model_name
:
str
,
replace
:
bool
=
False
)
-
> bigframes
.
ml
.
cluster
.
KMeans
Save the model to BigQuery.
Returns
Type
Description
KMeans
Saved model.
