from model_base import ModelBase
[docs]class H2OClusteringModel(ModelBase):
[docs] def size(self, train=False, valid=False, xval=False):
"""
Get the sizes of each cluster.
If all are False (default), then return the training metric value.
If more than one options is set to True, then return a dictionary of metrics where
the keys are "train", "valid", and "xval"
Parameters
----------
train : bool, optional
If True, then return cluster sizes for the training data.
valid : bool, optional
If True, then return the cluster sizes for the validation data.
xval : bool, optional
If True, then return the cluster sizes for each of the cross-validated splits.
Returns
-------
Returns the cluster sizes for the specified key(s).
"""
tm = ModelBase._get_metrics(self, train, valid, xval)
m = {}
for k,v in zip(tm.keys(),tm.values()): m[k] = None if v is None else [ v[2] for v in v._metric_json["centroid_stats"].cell_values]
return m.values()[0] if len(m) == 1 else m
[docs] def num_iterations(self):
"""
Get the number of iterations that it took to converge or reach max iterations.
Returns
-------
The number of iterations (integer).
"""
o = self._model_json["output"]
return o["model_summary"].cell_values[0][o["model_summary"].col_header.index('number_of_iterations')]
[docs] def betweenss(self, train=False, valid=False, xval=False):
"""
Get the between cluster sum of squares.
If all are False (default), then return the training metric value.
If more than one options is set to True, then return a dictionary of metrics where
the keys are "train", "valid", and "xval".
Parameters
----------
train : bool, optional
If True, then return the between cluster sum of squares value for the
training data.
valid : bool, optional
If True, then return the between cluster sum of squares value for the
validation data.
xval : bool, optional
If True, then return the between cluster sum of squares value for each of
the cross-validated splits.
Returns
-------
Returns the between sum of squares values for the specified key(s).
"""
tm = ModelBase._get_metrics(self, train, valid, xval)
m = {}
for k,v in zip(tm.keys(),tm.values()): m[k] = None if v is None else v._metric_json["betweenss"]
return m.values()[0] if len(m) == 1 else m
[docs] def totss(self, train=False, valid=False, xval=False):
"""
Get the total sum of squares.
If all are False (default), then return the training metric value.
If more than one options is set to True, then return a dictionary of metrics where
the keys are "train", "valid", and "xval".
Parameters
----------
train : bool, optional
If True, then return the total sum of squares value for the training
data.
valid : bool, optional
If True, then return the total sum of squares value for the validation
data.
xval : bool, optional
If True, then return the total sum of squares value for each of the
cross-validated splits.
Returns
-------
Returns the total sum of squares values for the specified key(s).
"""
tm = ModelBase._get_metrics(self, train, valid, xval)
m = {}
for k,v in zip(tm.keys(),tm.values()): m[k] = None if v is None else v._metric_json["totss"]
return m.values()[0] if len(m) == 1 else m
[docs] def tot_withinss(self, train=False, valid=False, xval=False):
"""
Get the total within cluster sum of squares.
If all are False (default), then return the training metric value.
If more than one options is set to True, then return a dictionary of metrics where
the keys are "train", "valid", and "xval".
Parameters
----------
train : bool, optional
If True, then return the total within cluster sum of squares value for
the training data.
valid : bool, optional
If True, then return the total within cluster sum of squares value for
the validation data.
xval : bool, optional
If True, then return the total within cluster sum of squares value for
each of the cross-validated splits.
Returns
-------
Returns the total within cluster sum of squares values for the specified key(s).
"""
tm = ModelBase._get_metrics(self, train, valid, xval)
m = {}
for k,v in zip(tm.keys(),tm.values()): m[k] = None if v is None else v._metric_json["tot_withinss"]
return m.values()[0] if len(m) == 1 else m
[docs] def withinss(self, train=False, valid=False, xval=False):
"""
Get the within cluster sum of squares for each cluster.
If all are False (default), then return the training metric value.
If more than one options is set to True, then return a dictionary of metrics where
the keys are "train", "valid", and "xval".
Parameters
----------
train : bool, optional
If True, then return the within cluster sum of squares value for the
training data.
valid : bool, optional
If True, then return the within cluster sum of squares value for the
validation data.
xval : bool, optional
If True, then return the within cluster sum of squares value for each of
the cross-validated splits.
Returns
-------
Returns the total sum of squares values for the specified key(s).
"""
tm = ModelBase._get_metrics(self, train, valid, xval)
m = {}
for k,v in zip(tm.keys(),tm.values()): m[k] = None if v is None else [ z[-1] for z in v._metric_json["centroid_stats"].cell_values]
return m.values()[0] if len(m) == 1 else m
[docs] def centroid_stats(self, train=False, valid=False, xval=False):
"""
Get the centroid statistics for each cluster.
If all are False (default), then return the training metric value.
If more than one options is set to True, then return a dictionary of metrics where
the keys are "train", "valid", and "xval".
Parameters
----------
train : bool, optional
If True, then return the centroid statistics for the training data.
valid : bool, optional
If True, then return the centroid statistics for the validation data.
xval : bool, optional
If True, then return the centroid statistics for each of the cross-validated
splits.
Returns
-------
Returns the centroid statistics for the specified key(s).
"""
tm = ModelBase._get_metrics(self, train, valid, xval)
m = {}
for k,v in zip(tm.keys(),tm.values()): m[k] = None if v is None else v._metric_json["centroid_stats"]
return m.values()[0] if len(m) == 1 else m
[docs] def centers(self):
"""
Returns
-------
The centers for the KMeans model.
"""
o = self._model_json["output"]
cvals = o["centers"].cell_values
centers = [list(cval[1:]) for cval in cvals]
centers = [list(x) for x in zip(*centers)]
return centers
[docs] def centers_std(self):
"""
Returns
-------
The standardized centers for the kmeans model.
"""
o = self._model_json["output"]
cvals = o["centers_std"].cell_values
centers_std = [list(cval[1:]) for cval in cvals]
centers_std = [list(x) for x in zip(*centers_std)]
return centers_std