# -*- encoding: utf-8 -*-
import warnings
from h2o.utils.compatibility import * # NOQA
import itertools
import h2o
from h2o.base import Keyed
from h2o.display import H2ODisplay, display
from h2o.job import H2OJob
from h2o.frame import H2OFrame
from h2o.exceptions import H2OValueError, H2OJobCancelled
from h2o.estimators.estimator_base import H2OEstimator
from h2o.two_dim_table import H2OTwoDimTable
from h2o.grid.metrics import * # NOQA
from h2o.utils.metaclass import backwards_compatibility, deprecated_fn, h2o_meta
from h2o.utils.mixin import assign, mixin
from h2o.utils.shared_utils import quoted, stringify_dict_as_map
from h2o.utils.typechecks import assert_is_type, is_type
[docs]@backwards_compatibility(
instance_attrs=dict(
giniCoef=lambda self, *args, **kwargs: self.gini(*args, **kwargs)
)
)
class H2OGridSearch(h2o_meta(Keyed, H2ODisplay)):
"""
Grid Search of a Hyper-Parameter Space for a Model
Examples
--------
>>> from h2o.grid.grid_search import H2OGridSearch
>>> from h2o.estimators.glm import H2OGeneralizedLinearEstimator
>>> hyper_parameters = {'alpha': [0.01,0.5], 'lambda': [1e-5,1e-6]}
>>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
... hyper_parameters)
>>> training_data = h2o.import_file("smalldata/logreg/benign.csv")
>>> gs.train(x=[3, 4-11], y=3, training_frame=training_data)
>>> gs.show()
"""
def __init__(self, model, hyper_params, grid_id=None, search_criteria=None, export_checkpoints_dir=None,
recovery_dir=None, parallelism=1):
"""
:param model: The type of model to be explored initialized with optional parameters that will be
unchanged across explored models.
:param hyper_params: A dictionary of string parameters (keys) and a list of values to be explored by grid
search (values).
:param str grid_id: The unique id assigned to the resulting grid object. If none is given, an id will
automatically be generated.
:param search_criteria: The optional dictionary of directives which control the search of the hyperparameter space.
The dictionary can include values for: ``strategy``, ``max_models``, ``max_runtime_secs``, ``stopping_metric``,
``stopping_tolerance``, ``stopping_rounds`` and ``seed``.
The default strategy, "Cartesian", covers the entire space of hyperparameter combinations.
If you want to use cartesian grid search, you can leave the search_criteria argument unspecified.
Specify the "RandomDiscrete" strategy to get random search of all the combinations of
your hyperparameters with three ways of specifying when to stop the search:
max number of models, max time, and metric-based early stopping
(e.g., stop if MSE hasn’t improved by 0.0001 over the 5 best models).
:param export_checkpoints_dir: Directory to automatically export the grid and its models to.
:param recovery_dir: When specified, the grid and all necessary data (frames, models) will be saved to this
directory (use HDFS or other distributed file-system).
Should the cluster crash during training, the grid can be reloaded from this directory
via ``h2o.load_grid``, and training can be resumed.
:param parallelism: Level of parallelism during grid model building.
1 = sequential building (default).
Use the value of 0 for adaptive parallelism - decided by H2O.
Any number > 1 sets the exact number of models built in parallel.
:returns: a new H2OGridSearch instance
:examples:
>>> criteria = {"strategy": "RandomDiscrete", "max_runtime_secs": 600,
... "max_models": 100, "stopping_metric": "AUTO",
... "stopping_tolerance": 0.00001, "stopping_rounds": 5,
... "seed": 123456}
>>> criteria = {"strategy": "RandomDiscrete", "max_models": 42,
... "max_runtime_secs": 28800, "seed": 1234}
>>> criteria = {"strategy": "RandomDiscrete", "stopping_metric": "AUTO",
... "stopping_tolerance": 0.001, "stopping_rounds": 10}
>>> criteria = {"strategy": "RandomDiscrete", "stopping_rounds": 5,
... "stopping_metric": "misclassification",
... "stopping_tolerance": 0.00001}
"""
assert_is_type(model, None, H2OEstimator, lambda mdl: issubclass(mdl, H2OEstimator))
assert_is_type(hyper_params, dict)
assert_is_type(grid_id, None, str)
assert_is_type(search_criteria, None, dict)
assert_is_type(export_checkpoints_dir, None, str)
assert_is_type(recovery_dir, None, str)
if not (model is None or is_type(model, H2OEstimator)): model = model()
self._id = grid_id
self.model = model
self.hyper_params = dict(hyper_params)
self.search_criteria = None if search_criteria is None else dict(search_criteria)
self.export_checkpoints_dir = export_checkpoints_dir
self.recovery_dir = recovery_dir
self._parallelism = parallelism # Degree of parallelism during model building
self._grid_json = None
self.models = [] # list of H2O Estimator instances
self._parms = {} # internal, for object recycle #
self.parms = {} # external#
self._future = False # used by __repr__/show to query job state#
self._job = None # used when _future is True#
@property
def key(self):
return self._id
@property
def grid_id(self):
"""A key that identifies this grid search object in H2O.
:examples:
>>> from h2o.grid.grid_search import H2OGridSearch
>>> from h2o.estimators.glm import H2OGeneralizedLinearEstimator
>>> training_data = h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/logreg/benign.csv")
>>> hyper_parameters = {'alpha': [0.01,0.5],
... 'lambda': [1e-5,1e-6]}
>>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
... hyper_parameters)
>>> gs.train(x=range(3)+range(4,11), y=3, training_frame=training_data)
>>> gs.grid_id
"""
return self._id
@grid_id.setter
def grid_id(self, value):
oldname = self.grid_id
self._id = value
h2o.rapids('(rename "{}" "{}")'.format(oldname, value))
@property
def model_ids(self):
"""
Returns model ids.
:examples:
>>> from h2o.grid.grid_search import H2OGridSearch
>>> from h2o.estimators.glm import H2OGeneralizedLinearEstimator
>>> training_data = h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/logreg/benign.csv")
>>> hyper_parameters = {'alpha': [0.01,0.5],
... 'lambda': [1e-5,1e-6]}
>>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
... hyper_parameters)
>>> gs.train(x=range(3)+range(4,11), y=3, training_frame=training_data)
>>> gs.model_ids
"""
return [i['name'] for i in self._grid_json["model_ids"]]
@property
def hyper_names(self):
"""
Return the hyperparameter names.
:examples:
>>> from h2o.grid.grid_search import H2OGridSearch
>>> from h2o.estimators.glm import H2OGeneralizedLinearEstimator
>>> training_data = h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/logreg/benign.csv")
>>> hyper_parameters = {'alpha': [0.01,0.5],
... 'lambda': [1e-5,1e-6]}
>>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
... hyper_parameters)
>>> gs.train(x=range(3)+range(4,11), y=3, training_frame=training_data)
>>> gs.hyper_names
"""
return self._grid_json["hyper_names"]
@property
def failed_params(self):
"""
Return a list of failed parameters.
:examples:
>>> from h2o.grid.grid_search import H2OGridSearch
>>> from h2o.estimators.glm import H2OGeneralizedLinearEstimator
>>> training_data = h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/logreg/benign.csv")
>>> hyper_parameters = {'alpha': [0.01,0.5],
... 'lambda': [1e-5,1e-6],
... 'beta_epsilon': [0.05]}
>>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
... hyper_parameters)
>>> gs.train(x=range(3)+range(4,11), y=3, training_frame=training_data)
>>> gs.failed_params
"""
return self._grid_json.get("failed_params", None)
@property
def failure_details(self):
return self._grid_json.get("failure_details", None)
@property
def failure_stack_traces(self):
return self._grid_json.get("failure_stack_traces", None)
@property
def failed_raw_params(self):
return self._grid_json.get("failed_raw_params", None)
[docs] def detach(self):
self._id = None
[docs] def start(self, x, y=None, training_frame=None, offset_column=None, fold_column=None, weights_column=None,
validation_frame=None, **params):
"""
Asynchronous model build by specifying the predictor columns, response column, and any
additional frame-specific values.
To block for results, call :meth:`join`.
:param x: A list of column names or indices indicating the predictor columns.
:param y: An index or a column name indicating the response column.
:param training_frame: The H2OFrame having the columns indicated by x and y (as well as any
additional columns specified by fold, offset, and weights).
:param offset_column: The name or index of the column in training_frame that holds the offsets.
:param fold_column: The name or index of the column in training_frame that holds the per-row fold
assignments.
:param weights_column: The name or index of the column in training_frame that holds the per-row weights.
:param validation_frame: H2OFrame with validation data to be scored on while training.
:examples:
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv")
>>> insurance["offset"] = insurance["Holders"].log()
>>> insurance["Group"] = insurance["Group"].asfactor()
>>> insurance["Age"] = insurance["Age"].asfactor()
>>> insurance["District"] = insurance["District"].asfactor()
>>> hyper_params = {'huber_alpha': [0.2,0.5],
... 'quantile_alpha': [0.2,0.6]}
>>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5), hyper_params)
>>> gs.start(x=list(range(3)),y="Claims", training_frame=insurance)
>>> gs.join()
"""
self._future = True
self.train(x=x,
y=y,
training_frame=training_frame,
offset_column=offset_column,
fold_column=fold_column,
weights_column=weights_column,
validation_frame=validation_frame,
**params)
[docs] def join(self):
"""Wait until grid finishes computing.
:examples:
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv")
>>> insurance["offset"] = insurance["Holders"].log()
>>> insurance["Group"] = insurance["Group"].asfactor()
>>> insurance["Age"] = insurance["Age"].asfactor()
>>> insurance["District"] = insurance["District"].asfactor()
>>> hyper_params = {'huber_alpha': [0.2,0.5],
... 'quantile_alpha': [0.2,0.6]}
>>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5), hyper_params)
>>> gs.start(x=list(range(3)),y="Claims", training_frame=insurance)
>>> gs.join()
"""
self._future = False
self._job.poll()
self._job = None
[docs] def cancel(self):
"""Cancel grid execution."""
if self._job is None:
raise H2OValueError("Grid is not running.")
self._job.cancel()
[docs] def train(self, x=None, y=None, training_frame=None, offset_column=None, fold_column=None, weights_column=None,
validation_frame=None, **params):
"""
Train the model synchronously (i.e. do not return until the model finishes training).
To train asynchronously call :meth:`start`.
:param x: A list of column names or indices indicating the predictor columns.
:param y: An index or a column name indicating the response column.
:param training_frame: The H2OFrame having the columns indicated by x and y (as well as any
additional columns specified by fold, offset, and weights).
:param offset_column: The name or index of the column in training_frame that holds the offsets.
:param fold_column: The name or index of the column in training_frame that holds the per-row fold
assignments.
:param weights_column: The name or index of the column in training_frame that holds the per-row weights.
:param validation_frame: H2OFrame with validation data to be scored on while training.
:examples:
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv")
>>> insurance["offset"] = insurance["Holders"].log()
>>> insurance["Group"] = insurance["Group"].asfactor()
>>> insurance["Age"] = insurance["Age"].asfactor()
>>> insurance["District"] = insurance["District"].asfactor()
>>> hyper_params = {'huber_alpha': [0.2,0.5],
... 'quantile_alpha': [0.2,0.6]}
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5),
... hyper_params)
>>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance)
"""
algo_params = locals()
parms = self._parms.copy()
parms.update({k: v for k, v in algo_params.items() if k not in ["self", "params", "algo_params", "parms"]})
# dictionaries have special handling in grid search, avoid the implicit conversion
parms["search_criteria"] = None if self.search_criteria is None else stringify_dict_as_map(self.search_criteria)
parms["export_checkpoints_dir"] = self.export_checkpoints_dir
parms["recovery_dir"] = self.recovery_dir
parms["parallelism"] = self._parallelism
parms["hyper_parameters"] = None if self.hyper_params is None else stringify_dict_as_map(self.hyper_params) # unique to grid search
parms.update({k: v for k, v in list(self.model._parms.items()) if v is not None}) # unique to grid search
parms.update(params)
if '__class__' in parms: # FIXME: hackt for PY3
del parms['__class__']
y = algo_params["y"]
tframe = algo_params["training_frame"]
if tframe is None:
raise ValueError("Missing training_frame")
if y is not None:
if is_type(y, list, tuple):
if len(y) == 1:
parms["y"] = y[0]
else:
raise ValueError('y must be a single column reference')
if x is None:
if isinstance(y, int):
xset = set(range(training_frame.ncols)) - {y}
else:
xset = set(training_frame.names) - {y}
else:
xset = set()
if is_type(x, int, str): x = [x]
for xi in x:
if is_type(xi, int):
if not (-training_frame.ncols <= xi < training_frame.ncols):
raise H2OValueError("Column %d does not exist in the training frame" % xi)
xset.add(training_frame.names[xi])
else:
if xi not in training_frame.names:
raise H2OValueError("Column %s not in the training frame" % xi)
xset.add(xi)
x = list(xset)
parms["x"] = x
self.build_model(parms)
return self
[docs] def resume(self, recovery_dir=None, **kwargs):
"""
Resume previously stopped grid training.
:param recovery_dir: When specified, the grid and all necessary data (frames, models) will be saved to this
directory (use HDFS or other distributed file-system). Should the cluster crash during training, the grid
can be reloaded from this directory via ``h2o.load_grid``, and training can be resumed.
"""
parms = kwargs
if "detach" in kwargs.keys():
self._future = kwargs.pop("detach")
parms["grid_id"] = self.grid_id
parms["recovery_dir"] = recovery_dir
self._run_grid_job(parms, end_point="/resume")
[docs] def build_model(self, algo_params):
"""(internal)"""
if algo_params["training_frame"] is None:
raise ValueError("Missing training_frame")
x = algo_params.pop("x")
y = algo_params.pop("y", None)
training_frame = algo_params.pop("training_frame")
validation_frame = algo_params.pop("validation_frame", None)
is_auto_encoder = (algo_params is not None) and ("autoencoder" in algo_params and algo_params["autoencoder"])
is_uplift = (algo_params is not None) and ("treatment_column" in algo_params and algo_params["treatment_column"])
if is_auto_encoder and y is not None:
raise ValueError("y should not be specified for autoencoder.")
if self.model.supervised_learning:
if y is None:
raise ValueError("Missing response")
elif is_uplift:
y = y if y in training_frame.names else training_frame.names[y]
self.model._estimator_type = "binomial_uplift"
else:
y = y if y in training_frame.names else training_frame.names[y]
self.model._estimator_type = "classifier" if training_frame.types[y] == "enum" else "regressor"
else:
self.model._estimator_type = "unsupervised"
self._model_build(x, y, training_frame, validation_frame, algo_params)
def _model_build(self, x, y, tframe, vframe, kwargs):
kwargs['training_frame'] = tframe
if vframe is not None: kwargs["validation_frame"] = vframe
if is_type(y, int): y = tframe.names[y]
if y is not None: kwargs['response_column'] = y
if not is_type(x, list, tuple): x = [x]
if is_type(x[0], int):
x = [tframe.names[i] for i in x]
offset = kwargs["offset_column"]
folds = kwargs["fold_column"]
weights = kwargs["weights_column"]
treatment = kwargs["treatment_column"] if "treatment_column" in kwargs else None
ignored_columns = list(set(tframe.names) - set(x + [y, offset, folds, weights, treatment]))
kwargs["ignored_columns"] = None if not ignored_columns else [quoted(col) for col in ignored_columns]
kwargs = {k: H2OEstimator._keyify(kwargs[k]) for k in kwargs}
if self.grid_id is not None: kwargs["grid_id"] = self.grid_id
rest_ver = kwargs.pop("_rest_version") if "_rest_version" in kwargs else None
self._run_grid_job(kwargs, rest_ver=rest_ver)
def _run_grid_job(self, params, end_point="", rest_ver=None):
algo = self.model.algo
grid = H2OJob(h2o.api("POST /99/Grid/%s%s" % (algo, end_point), data=params), job_type=(algo + " Grid Build"))
if self._future:
self._job = grid
else:
try:
grid.poll()
self._handle_build_finish(grid, rest_ver)
except H2OJobCancelled:
self._handle_build_finish(grid, rest_ver)
raise
def _handle_build_finish(self, grid, rest_ver=None):
grid_json = h2o.api("GET /99/Grids/%s" % grid.dest_key)
failure_messages_stacks = ""
error_index = 0
if len(grid_json["warning_details"]) > 0:
for w_message in grid_json["warning_details"]:
warnings.warn(w_message)
if len(grid_json["failure_details"]) > 0:
print("Errors/Warnings building gridsearch model\n")
# will raise error if no grid model is returned, store error messages here
for error_message in grid_json["failure_details"]:
if isinstance(grid_json["failed_params"][error_index], dict):
for h_name in grid_json['hyper_names']:
print("Hyper-parameter: {0}, {1}".format(h_name,
grid_json['failed_params'][error_index][h_name]))
if len(grid_json["failure_stack_traces"]) > error_index:
print("failure_details: {0}\nfailure_stack_traces: "
"{1}\n".format(error_message, grid_json['failure_stack_traces'][error_index]))
failure_messages_stacks += error_message+'\n'
error_index += 1
self.models = [h2o.get_model(key['name']) for key in grid_json['model_ids']]
for model in self.models:
model._estimator_type = self.model._estimator_type
# get first model returned in list of models from grid search to get model class (binomial, multinomial, etc)
# sometimes no model is returned due to bad parameter values provided by the user.
if len(grid_json['model_ids']) > 0:
first_model_json = h2o.api("GET /%d/Models/%s" %
(rest_ver or 3, grid_json['model_ids'][0]['name']))['models'][0]
self._resolve_grid(grid.dest_key, grid_json, first_model_json)
else:
if len(failure_messages_stacks)>0:
raise ValueError(failure_messages_stacks)
else:
raise ValueError("Gridsearch returns no model due to bad parameter values or other reasons....")
def _resolve_grid(self, grid_id, grid_json, first_model_json):
model_class = H2OGridSearch._metrics_class(first_model_json)
m = model_class()
m._id = grid_id
m._grid_json = grid_json
# m._metrics_class = metrics_class
m._parms = self._parms
self.export_checkpoints_dir = m._grid_json["export_checkpoints_dir"]
mixin(self, model_class)
assign(self, m)
def __getitem__(self, item):
return self.models[item]
def __iter__(self):
nmodels = len(self.models)
return (self[i] for i in range(nmodels))
def __len__(self):
return len(self.models)
[docs] def predict(self, test_data):
"""
Predict on a dataset.
:param H2OFrame test_data: Data to be predicted on.
:returns: H2OFrame filled with predictions.
:examples:
>>> from h2o.estimators import H2OGeneralizedLinearEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
>>> y = 3
>>> x = [4,5,6,7,8,9,10,11]
>>> hyper_params = {'alpha': [0.01,0.3,0.5],
... 'lambda': [1e-5, 1e-6, 1e-7]}
>>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
... hyper_params)
>>> gs.train(x=x,y=y, training_frame=benign)
>>> gs.predict(benign)
"""
return {model.model_id: model.predict(test_data) for model in self.models}
[docs] def is_cross_validated(self):
"""Return True if the model was cross-validated.
:examples:
>>> from h2o.estimators import H2OGeneralizedLinearEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
>>> y = 3
>>> x = [4,5,6,7,8,9,10,11]
>>> hyper_params = {'alpha': [0.01,0.3,0.5],
... 'lambda': [1e-5, 1e-6, 1e-7]}
>>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
... hyper_params)
>>> gs.train(x=x,y=y, training_frame=benign)
>>> gs.is_cross_validated()
"""
return {model.model_id: model.is_cross_validated() for model in self.models}
[docs] def xval_keys(self):
"""Model keys for the cross-validated model.
:examples:
>>> from h2o.estimators import H2OGeneralizedLinearEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
>>> y = 3
>>> x = [4,5,6,7,8,9,10,11]
>>> hyper_params = {'alpha': [0.01,0.3,0.5],
... 'lambda': [1e-5, 1e-6, 1e-7]}
>>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
... hyper_params)
>>> gs.train(x=x,y=y, training_frame=benign)
>>> gs.xval_keys()
"""
return {model.model_id: model.xval_keys() for model in self.models}
[docs] def get_xval_models(self, key=None):
"""
Return a Model object.
:param str key: If None, return all cross-validated models; otherwise return the model
specified by the key.
:returns: A model or a list of models.
:examples:
>>> from h2o.estimators import H2OGradientBoostingEstimator
>>> fr = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate_train.csv")
>>> m = H2OGradientBoostingEstimator(nfolds=10,
... ntrees=10,
... keep_cross_validation_models=True)
>>> m.train(x=list(range(2,fr.ncol)), y=1, training_frame=fr)
>>> m.get_xval_models()
"""
return {model.model_id: model.get_xval_models(key) for model in self.models}
[docs] def xvals(self):
"""Return the list of cross-validated models."""
return {model.model_id: model.xvals for model in self.models}
[docs] def deepfeatures(self, test_data, layer):
"""
Obtain a hidden layer's details on a dataset.
:param test_data: Data to create a feature space on.
:param int layer: Index of the hidden layer.
:returns: A dictionary of hidden layer details for each model.
:examples:
>>> from h2o.estimators import H2OAutoEncoderEstimator
>>> resp = 784
>>> nfeatures = 20
>>> train = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/train.csv.gz")
>>> train[resp] = train[resp].asfactor()
>>> test = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/test.csv.gz")
>>> test[resp] = test[resp].asfactor()
>>> sid = train[0].runif(0)
>>> train_unsup = train[sid >= 0.5]
>>> train_unsup.pop(resp)
>>> train_sup = train[sid < 0.5]
>>> ae_model = H2OAutoEncoderEstimator(activation="Tanh",
... hidden=[nfeatures],
... model_id="ae_model",
... epochs=1,
... ignore_const_cols=False,
... reproducible=True,
... seed=1234)
>>> ae_model.train(list(range(resp)), training_frame=train_unsup)
>>> ae_model.deepfeatures(train_sup[0:resp], 0)
"""
return {model.model_id: model.deepfeatures(test_data, layer) for model in self.models}
[docs] def weights(self, matrix_id=0):
"""
Return the frame for the respective weight matrix.
:param: matrix_id: an integer, ranging from 0 to number of layers, that specifies the weight matrix to return.
:returns: an H2OFrame which represents the weight matrix identified by matrix_id
:examples:
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris.csv")
>>> hh = H2ODeepLearningEstimator(hidden=[],
... loss="CrossEntropy",
... export_weights_and_biases=True)
>>> hh.train(x=list(range(4)), y=4, training_frame=iris)
>>> hh.weights(0)
"""
return {model.model_id: model.weights(matrix_id) for model in self.models}
[docs] def biases(self, vector_id=0):
"""
Return the frame for the respective bias vector.
:param vector_id: an integer, ranging from 0 to number of layers, that specifies the bias vector to return.
:returns: an H2OFrame which represents the bias vector identified by vector_id
:examples:
>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris.csv")
>>> hh = H2ODeepLearningEstimator(hidden=[],
... loss="CrossEntropy",
... export_weights_and_biases=True)
>>> hh.train(x=list(range(4)), y=4, training_frame=iris)
>>> hh.biases(0)
"""
return {model.model_id: model.biases(vector_id) for model in self.models}
[docs] def normmul(self):
"""Normalization/Standardization multipliers for numeric predictors.
:examples:
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv")
>>> insurance["offset"] = insurance["Holders"].log()
>>> insurance["Group"] = insurance["Group"].asfactor()
>>> insurance["Age"] = insurance["Age"].asfactor()
>>> insurance["District"] = insurance["District"].asfactor()
>>> hyper_params = {'huber_alpha': [0.2,0.5],
... 'quantile_alpha': [0.2,0.6]}
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5),
... hyper_params)
>>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance)
>>> gs.normmul()
"""
return {model.model_id: model.normmul() for model in self.models}
[docs] def normsub(self):
"""Normalization/Standardization offsets for numeric predictors.
:examples:
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv")
>>> insurance["offset"] = insurance["Holders"].log()
>>> insurance["Group"] = insurance["Group"].asfactor()
>>> insurance["Age"] = insurance["Age"].asfactor()
>>> insurance["District"] = insurance["District"].asfactor()
>>> hyper_params = {'huber_alpha': [0.2,0.5],
... 'quantile_alpha': [0.2,0.6]}
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5),
... hyper_params)
>>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance)
>>> gs.normsub()
"""
return {model.model_id: model.normsub() for model in self.models}
[docs] def respmul(self):
"""Normalization/Standardization multipliers for numeric response.
:examples:
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv")
>>> insurance["offset"] = insurance["Holders"].log()
>>> insurance["Group"] = insurance["Group"].asfactor()
>>> insurance["Age"] = insurance["Age"].asfactor()
>>> insurance["District"] = insurance["District"].asfactor()
>>> hyper_params = {'huber_alpha': [0.2,0.5],
... 'quantile_alpha': [0.2,0.6]}
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5),
... hyper_params)
>>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance)
>>> gs.respmul()
"""
return {model.model_id: model.respmul() for model in self.models}
[docs] def respsub(self):
"""Normalization/Standardization offsets for numeric response.
:examples:
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv")
>>> insurance["offset"] = insurance["Holders"].log()
>>> insurance["Group"] = insurance["Group"].asfactor()
>>> insurance["Age"] = insurance["Age"].asfactor()
>>> insurance["District"] = insurance["District"].asfactor()
>>> hyper_params = {'huber_alpha': [0.2,0.5],
... 'quantile_alpha': [0.2,0.6]}
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5),
... hyper_params)
>>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance)
>>> gs.respsub()
"""
return {model.model_id: model.respsub() for model in self.models}
[docs] def catoffsets(self):
"""
Categorical offsets for one-hot encoding
:examples:
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris.csv")
>>> hh = H2ODeepLearningEstimator(hidden=[],
... loss="CrossEntropy",
... export_weights_and_biases=True)
>>> hh.train(x=list(range(4)), y=4, training_frame=iris)
>>> hh.catoffsets()
"""
return {model.model_id: model.catoffsets() for model in self.models}
[docs] def scoring_history(self):
"""
Retrieve model scoring history.
:returns: Score history (H2OTwoDimTable)
:examples:
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv")
>>> insurance["offset"] = insurance["Holders"].log()
>>> insurance["Group"] = insurance["Group"].asfactor()
>>> insurance["Age"] = insurance["Age"].asfactor()
>>> insurance["District"] = insurance["District"].asfactor()
>>> hyper_params = {'huber_alpha': [0.2,0.5],
... 'quantile_alpha': [0.2,0.6]}
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5),
... hyper_params)
>>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance)
>>> gs.scoring_history()
"""
return {model.model_id: model.scoring_history() for model in self.models}
def _as_table(self):
hyper_combos = itertools.product(*list(self.hyper_params.values()))
if not self.models:
# what the hell is this?
# if we don't have models yet, then we display all possible combinations?
# there can be literally trillions of them when using a random search!!
c_values = [[idx + 1, list(val)] for idx, val in enumerate(hyper_combos)]
return H2OTwoDimTable(
table_header="Grid Search of Model {}".format(self.model.__class__.__name__),
col_header=["Model", "Hyperparameters: [{}]".format(', '.join(list(self.hyper_params.keys())))],
cell_values=c_values)
else:
return self.sorted_metric_table(use_pandas=False)
def _str_(self, verbosity=None):
return self._as_table().to_str(verbosity=verbosity)
[docs] def show(self, verbosity=None, fmt=None):
"""
Renders all models in the grid, sorted by performance metric.
:examples:
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv")
>>> insurance["offset"] = insurance["Holders"].log()
>>> insurance["Group"] = insurance["Group"].asfactor()
>>> insurance["Age"] = insurance["Age"].asfactor()
>>> insurance["District"] = insurance["District"].asfactor()
>>> hyper_params = {'huber_alpha': [0.2,0.5],
... 'quantile_alpha': [0.2,0.6]}
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5),
... hyper_params)
>>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance)
>>> gs.show()
"""
self._as_table().show(rows=-1, verbosity=verbosity, fmt=fmt)
def get_summary(self):
table = []
for model in self.models:
model_summary = model._model_json["output"]["model_summary"]
r_values = list(model_summary.cell_values[0])
r_values[0] = model.model_id
table.append(r_values)
return H2OTwoDimTable(table_header="Grid Summary",
col_header=['Model Id'] + model_summary.col_header[1:],
cell_values=table)
[docs] def show_summary(self):
"""
Renders a detailed summary of the explored models.
:examples:
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv")
>>> insurance["offset"] = insurance["Holders"].log()
>>> insurance["Group"] = insurance["Group"].asfactor()
>>> insurance["Age"] = insurance["Age"].asfactor()
>>> insurance["District"] = insurance["District"].asfactor()
>>> hyper_params = {'huber_alpha': [0.2,0.5],
... 'quantile_alpha': [0.2,0.6]}
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5),
... hyper_params)
>>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance)
>>> gs.show_summary()
"""
self.get_summary().show(rows=-1) # always display all models in the grid
[docs] def summary(self):
"""Deprecated. Please use `show_summary()` instead"""
self.show_summary()
[docs] def varimp(self, use_pandas=False):
"""
Return the variable importances as a list/pandas DataFrame.
:param bool use_pandas: If True, then the variable importances will be returned as a pandas data frame.
:returns: A dictionary of lists or Pandas DataFrame instances.
:examples:
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv")
>>> insurance["offset"] = insurance["Holders"].log()
>>> insurance["Group"] = insurance["Group"].asfactor()
>>> insurance["Age"] = insurance["Age"].asfactor()
>>> insurance["District"] = insurance["District"].asfactor()
>>> hyper_params = {'huber_alpha': [0.2,0.5],
... 'quantile_alpha': [0.2,0.6]}
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5),
... hyper_params)
>>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance)
>>> gs.varimp(use_pandas=True)
"""
return {model.model_id: model.varimp(use_pandas) for model in self.models}
[docs] def residual_deviance(self, train=False, valid=False, xval=False):
"""
Retreive the residual deviance if this model has the attribute, or None otherwise.
:param bool train: Get the residual deviance for the training set. If both train and valid are False,
then train is selected by default.
:param bool valid: Get the residual deviance for the validation set. If both train and valid are True,
then train is selected by default.
:param bool xval: Get the residual deviance for the cross-validated models.
:returns: the residual deviance, or None if it is not present.
:examples:
>>> from h2o.estimators import H2OGeneralizedLinearEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
>>> y = 3
>>> x = [4,5,6,7,8,9,10,11]
>>> hyper_params = {'alpha': [0.01,0.3,0.5],
... 'lambda': [1e-5, 1e-6, 1e-7]}
>>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
... hyper_params)
>>> gs.train(x=x,y=y, training_frame=benign)
>>> gs.residual_deviance()
"""
return {model.model_id: model.residual_deviance(train, valid, xval) for model in self.models}
[docs] def residual_degrees_of_freedom(self, train=False, valid=False, xval=False):
"""
Retreive the residual degress of freedom if this model has the attribute, or None otherwise.
:param bool train: Get the residual dof for the training set. If both train and valid are False, then
train is selected by default.
:param bool valid: Get the residual dof for the validation set. If both train and valid are True, then
train is selected by default.
:param bool xval: Get the residual dof for the cross-validated models.
:returns: the residual degrees of freedom, or None if they are not present.
:examples:
>>> from h2o.estimators import H2OGeneralizedLinearEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
>>> y = 3
>>> x = [4,5,6,7,8,9,10,11]
>>> hyper_params = {'alpha': [0.01,0.3,0.5],
... 'lambda': [1e-5, 1e-6, 1e-7]}
>>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
... hyper_params)
>>> gs.train(x=x,y=y, training_frame=benign)
>>> gs.residual_degrees_of_freedom()
"""
return {model.model_id: model.residual_degrees_of_freedom(train, valid, xval) for model in self.models}
[docs] def null_deviance(self, train=False, valid=False, xval=False):
"""
Retreive the null deviance if this model has the attribute, or None otherwise.
:param bool train: Get the null deviance for the training set. If both train and valid are False, then
train is selected by default.
:param bool valid: Get the null deviance for the validation set. If both train and valid are True, then
train is selected by default.
:param bool xval: Get the null deviance for the cross-validated models.
:returns: the null deviance, or None if it is not present.
:examples:
>>> from h2o.estimators import H2OGeneralizedLinearEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
>>> y = 3
>>> x = [4,5,6,7,8,9,10,11]
>>> hyper_params = {'alpha': [0.01,0.3,0.5],
... 'lambda': [1e-5, 1e-6, 1e-7]}
>>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
... hyper_params)
>>> gs.train(x=x,y=y, training_frame=benign)
>>> gs.null_deviance()
"""
return {model.model_id: model.null_deviance(train, valid, xval) for model in self.models}
[docs] def null_degrees_of_freedom(self, train=False, valid=False, xval=False):
"""
Retreive the null degress of freedom if this model has the attribute, or None otherwise.
:param bool train: Get the null dof for the training set. If both train and valid are False, then train is
selected by default.
:param bool valid: Get the null dof for the validation set. If both train and valid are True, then train is
selected by default.
:param bool xval: Get the null dof for the cross-validated models.
:returns: the null dof, or None if it is not present.
:examples:
>>> from h2o.estimators import H2OGeneralizedLinearEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
>>> y = 3
>>> x = [4,5,6,7,8,9,10,11]
>>> hyper_params = {'alpha': [0.01,0.3,0.5],
... 'lambda': [1e-5, 1e-6, 1e-7]}
>>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
... hyper_params)
>>> gs.train(x=x,y=y, training_frame=benign)
>>> gs.null_degrees_of_freedom()
"""
return {model.model_id: model.null_degrees_of_freedom(train, valid, xval) for model in self.models}
[docs] def pprint_coef(self):
"""Pretty print the coefficents table (includes normalized coefficients).
:examples:
>>> from h2o.estimators import H2OGeneralizedLinearEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
>>> y = 3
>>> x = [4,5,6,7,8,9,10,11]
>>> hyper_params = {'alpha': [0.01,0.3,0.5],
... 'lambda': [1e-5, 1e-6, 1e-7]}
>>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
... hyper_params)
>>> gs.train(x=x,y=y, training_frame=benign)
>>> gs.pprint_coef()
"""
for i, model in enumerate(self.models):
print('Model', i)
model.pprint_coef()
print()
[docs] def coef(self):
"""Return the coefficients that can be applied to the non-standardized data.
Note: standardize = True by default. If set to False, then coef() returns the coefficients that are fit directly.
:examples:
>>> from h2o.grid.grid_search import H2OGridSearch
>>> from h2o.estimators import H2OGeneralizedLinearEstimator
>>> training_data = h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/logreg/benign.csv")
>>> hyper_parameters = {'alpha': [0.01,0.5],
... 'lambda': [1e-5,1e-6]}
>>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
... hyper_parameters)
>>> gs.train(x=range(3)+range(4,11), y=3, training_frame=training_data)
>>> gs.coef()
"""
return {model.model_id: model.coef() for model in self.models}
[docs] def coef_norm(self):
"""Return coefficients fitted on the standardized data (requires standardize = True, which is on by default). These coefficients can be used to evaluate variable importance.
:examples:
>>> from h2o.grid.grid_search import H2OGridSearch
>>> from h2o.estimators import H2OGeneralizedLinearEstimator
>>> training_data = h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/logreg/benign.csv")
>>> hyper_parameters = {'alpha': [0.01,0.5],
... 'lambda': [1e-5,1e-6]}
>>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
... hyper_parameters)
>>> gs.train(x=range(3)+range(4,11), y=3, training_frame=training_data)
>>> gs.coef_norm()
"""
return {model.model_id: model.coef_norm() for model in self.models}
[docs] def r2(self, train=False, valid=False, xval=False):
"""
Return the R^2 for this regression model.
The R^2 value is defined to be ``1 - MSE/var``, where ``var`` is computed as ``sigma^2``.
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".
:param bool train: If train is True, then return the R^2 value for the training data.
:param bool valid: If valid is True, then return the R^2 value for the validation data.
:param bool xval: If xval is True, then return the R^2 value for the cross validation data.
:returns: The R^2 for this regression model.
:examples:
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv")
>>> insurance["offset"] = insurance["Holders"].log()
>>> insurance["Group"] = insurance["Group"].asfactor()
>>> insurance["Age"] = insurance["Age"].asfactor()
>>> insurance["District"] = insurance["District"].asfactor()
>>> hyper_params = {'huber_alpha': [0.2,0.5],
... 'quantile_alpha': [0.2,0.6]}
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5),
... hyper_params)
>>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance)
>>> gs.r2()
"""
return {model.model_id: model.r2(train, valid, xval) for model in self.models}
[docs] def mse(self, train=False, valid=False, xval=False):
"""
Get the MSE(s).
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".
:param bool train: If train is True, then return the MSE value for the training data.
:param bool valid: If valid is True, then return the MSE value for the validation data.
:param bool xval: If xval is True, then return the MSE value for the cross validation data.
:returns: The MSE for this regression model.
:examples:
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv")
>>> insurance["offset"] = insurance["Holders"].log()
>>> insurance["Group"] = insurance["Group"].asfactor()
>>> insurance["Age"] = insurance["Age"].asfactor()
>>> insurance["District"] = insurance["District"].asfactor()
>>> hyper_params = {'huber_alpha': [0.2,0.5],
... 'quantile_alpha': [0.2,0.6]}
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5),
... hyper_params)
>>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance)
>>> gs.mse()
"""
return {model.model_id: model.mse(train, valid, xval) for model in self.models}
def rmse(self, train=False, valid=False, xval=False):
return {model.model_id: model.rmse(train, valid, xval) for model in self.models}
def mae(self, train=False, valid=False, xval=False):
return {model.model_id: model.mae(train, valid, xval) for model in self.models}
def rmsle(self, train=False, valid=False, xval=False):
return {model.model_id: model.rmsle(train, valid, xval) for model in self.models}
[docs] def logloss(self, train=False, valid=False, xval=False):
"""
Get the Log Loss(s).
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".
:param bool train: If train is True, then return the Log Loss value for the training data.
:param bool valid: If valid is True, then return the Log Loss value for the validation data.
:param bool xval: If xval is True, then return the Log Loss value for the cross validation data.
:returns: The Log Loss for this binomial model.
:examples:
>>> from h2o.estimators import H2OGeneralizedLinearEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
>>> y = 3
>>> x = [4,5,6,7,8,9,10,11]
>>> hyper_params = {'alpha': [0.01,0.3,0.5],
... 'lambda': [1e-5, 1e-6, 1e-7]}
>>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
... hyper_params)
>>> gs.train(x=x,y=y, training_frame=benign)
>>> gs.logloss()
"""
return {model.model_id: model.logloss(train, valid, xval) for model in self.models}
[docs] def mean_residual_deviance(self, train=False, valid=False, xval=False):
"""
Get the Mean Residual Deviances(s).
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".
:param bool train: If train is True, then return the Mean Residual Deviance value for the training data.
:param bool valid: If valid is True, then return the Mean Residual Deviance value for the validation data.
:param bool xval: If xval is True, then return the Mean Residual Deviance value for the cross validation data.
:returns: The Mean Residual Deviance for this regression model.
:examples:
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv")
>>> insurance["offset"] = insurance["Holders"].log()
>>> insurance["Group"] = insurance["Group"].asfactor()
>>> insurance["Age"] = insurance["Age"].asfactor()
>>> insurance["District"] = insurance["District"].asfactor()
>>> hyper_params = {'huber_alpha': [0.2,0.5],
... 'quantile_alpha': [0.2,0.6]}
>>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5),
... hyper_params)
>>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance)
>>> gs.mean_residual_deviance()
"""
return {model.model_id: model.mean_residual_deviance(train, valid, xval) for model in self.models}
[docs] def auc(self, train=False, valid=False, xval=False):
"""
Get the AUC(s).
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".
:param bool train: If train is True, then return the AUC value for the training data.
:param bool valid: If valid is True, then return the AUC value for the validation data.
:param bool xval: If xval is True, then return the AUC value for the validation data.
:returns: The AUC.
:examples:
>>> from h2o.estimators import H2OGradientBoostingEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> data = h2o.import_file("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv")
>>> test = h2o.import_file("https://s3.amazonaws.com/erin-data/higgs/higgs_test_5k.csv")
>>> x = data.columns
>>> y = "response"
>>> x.remove(y)
>>> data[y] = data[y].asfactor()
>>> test[y] = test[y].asfactor()
>>> ss = data.split_frame(seed = 1)
>>> train = ss[0]
>>> valid = ss[1]
>>> gbm_params1 = {'learn_rate': [0.01, 0.1],
... 'max_depth': [3, 5, 9],
... 'sample_rate': [0.8, 1.0],
... 'col_sample_rate': [0.2, 0.5, 1.0]}
>>> gbm_grid1 = H2OGridSearch(model=H2OGradientBoostingEstimator,
... grid_id='gbm_grid1',
... hyper_params=gbm_params1)
>>> gbm_grid1.train(x=x, y=y,
... training_frame=train,
... validation_frame=valid,
... ntrees=100,
... seed=1)
>>> gbm_pridperf1 = gbm_grid1.get_grid(sort_by='auc', decreasing=True)
>>> best_gbm1 = gbm_gridperf1.models[0]
>>> best_gbm_perf1 = best_gbm1.model_performance(test)
>>> best_gbm_perf1.auc()
"""
return {model.model_id: model.auc(train, valid, xval) for model in self.models}
[docs] def aic(self, train=False, valid=False, xval=False):
"""
Get the AIC(s).
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".
:param bool train: If train is True, then return the AIC value for the training data.
:param bool valid: If valid is True, then return the AIC value for the validation data.
:param bool xval: If xval is True, then return the AIC value for the validation data.
:returns: The AIC.
:examples:
>>> from h2o.grid.grid_search import H2OGridSearch
>>> from h2o.estimators import H2OGeneralizedLinearEstimator
>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip")
>>> prostate[2] = prostate[2].asfactor()
>>> prostate[4] = prostate[4].asfactor()
>>> prostate[5] = prostate[5].asfactor()
>>> prostate[8] = prostate[8].asfactor()
>>> predictors = ["AGE","RACE","DPROS","DCAPS","PSA","VOL","GLEASON"]
>>> response = "CAPSULE"
>>> hyper_params = {'alpha': [0.01,0.5],
... 'lambda': [1e-5,1e-6]}
>>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
... hyper_params)
>>> gs.train(x=predictors, y=response, training_frame=prostate)
>>> gs.aic()
"""
return {model.model_id: model.aic(train, valid, xval) for model in self.models}
[docs] def gini(self, train=False, valid=False, xval=False):
"""
Get the Gini Coefficient(s).
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".
:param bool train: If train is True, then return the Gini Coefficient value for the training data.
:param bool valid: If valid is True, then return the Gini Coefficient value for the validation data.
:param bool xval: If xval is True, then return the Gini Coefficient value for the cross validation data.
:returns: The Gini Coefficient for the models in this grid.
:examples:
>>> from h2o.estimators import H2OGeneralizedLinearEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
>>> y = 3
>>> x = [4,5,6,7,8,9,10,11]
>>> hyper_params = {'alpha': [0.01,0.3,0.5],
... 'lambda': [1e-5, 1e-6, 1e-7]}
>>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
... hyper_params)
>>> gs.train(x=x,y=y, training_frame=benign)
>>> gs.gini()
"""
return {model.model_id: model.gini(train, valid, xval) for model in self.models}
# @alias('pr_auc')
[docs] def aucpr(self, train=False, valid=False, xval=False):
"""
Get the aucPR (Area Under PRECISION RECALL Curve).
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".
:param bool train: If train is True, then return the aucpr value for the training data.
:param bool valid: If valid is True, then return the aucpr value for the validation data.
:param bool xval: If xval is True, then return the aucpr value for the validation data.
:returns: The AUCPR for the models in this grid.
"""
return {model.model_id: model.aucpr(train, valid, xval) for model in self.models}
[docs] @deprecated_fn(replaced_by=aucpr)
def pr_auc(self):
pass
[docs] def get_hyperparams(self, id, display=True):
"""
Get the hyperparameters of a model explored by grid search.
:param str id: The model id of the model with hyperparameters of interest.
:param bool display: Flag to indicate whether to display the hyperparameter names.
:returns: A list of the hyperparameters for the specified model.
:examples:
>>> from h2o.estimators import H2OGeneralizedLinearEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
>>> y = 3
>>> x = [4,5,6,7,8,9,10,11]
>>> hyper_params = {'alpha': [0.01,0.3,0.5],
... 'lambda': [1e-5, 1e-6, 1e-7]}
>>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
... hyper_params)
>>> gs.train(x=x,y=y, training_frame=benign)
>>> best_model_id = gs.get_grid(sort_by='F1',
... decreasing=True).model_ids[0]
>>> gs.get_hyperparams(best_model_id)
"""
idx = id if is_type(id, int) else self.model_ids.index(id)
model = self[idx]
# if cross-validation is turned on, parameters in one of the fold model actuall contains the max_runtime_secs
# parameter and not the main model that is returned.
if model._is_xvalidated:
model = h2o.get_model(model._xval_keys[0])
res = [model.params[h]['actual'][0] if isinstance(model.params[h]['actual'], list)
else model.params[h]['actual']
for h in self.hyper_params]
if display:
print('Hyperparameters: [' + ', '.join(list(self.hyper_params.keys())) + ']')
return res
[docs] def get_hyperparams_dict(self, id, display=True):
"""
Derived and returned the model parameters used to train the particular grid search model.
:param str id: The model id of the model with hyperparameters of interest.
:param bool display: Flag to indicate whether to display the hyperparameter names.
:returns: A dict of model pararmeters derived from the hyper-parameters used to train this particular model.
:examples:
>>> from h2o.estimators import H2OGeneralizedLinearEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
>>> y = 3
>>> x = [4,5,6,7,8,9,10,11]
>>> hyper_params = {'alpha': [0.01,0.3,0.5],
... 'lambda': [1e-5, 1e-6, 1e-7]}
>>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
... hyper_params)
>>> gs.train(x=x,y=y, training_frame=benign)
>>> best_model_id = gs.get_grid(sort_by='F1',
... decreasing=True).model_ids[0]
>>> gs.get_hyperparams_dict(best_model_id)
"""
idx = id if is_type(id, int) else self.model_ids.index(id)
model = self[idx]
model_params = dict()
for param_name in self.hyper_names:
# if cross-validation is turned on, parameters in one of the fold model actual contains the max_runtime_secs
# parameter and not the main model that is returned.
if 'max_runtime_secs' == param_name and model._is_xvalidated:
xvalidated_model = h2o.get_model(model._xval_keys[0])
model_params[param_name] = xvalidated_model.params[param_name]['actual']
else:
model_params[param_name] = model.params[param_name]['actual']
if display:
print('Hyperparameters: [' + ', '.join(list(self.hyper_params.keys())) + ']')
return model_params
[docs] def sorted_metric_table(self, use_pandas=True):
"""
Retrieve summary table of an H2O Grid Search.
:param use_pandas: if True and if pandas is available, return the table as a Pandas DataFrame
:returns: The summary table as an H2OTwoDimTable (or a Pandas DataFrame if use_pandas is True).
:examples:
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv")
>>> insurance["offset"] = insurance["Holders"].log()
>>> insurance["Group"] = insurance["Group"].asfactor()
>>> insurance["Age"] = insurance["Age"].asfactor()
>>> insurance["District"] = insurance["District"].asfactor()
>>> hyper_params = {'huber_alpha': [0.2,0.5],
... 'quantile_alpha': [0.2,0.6]}
>>> from h2o.estimators import H2ODeepLearningEstimator
>>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5),
... hyper_params)
>>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance)
>>> gs.sorted_metric_table()
"""
summary = self._grid_json["summary_table"]
if summary is None:
print("No sorted metric table for this grid search")
return summary.as_data_frame() if use_pandas else summary
@staticmethod
def _metrics_class(model_json):
model_type = model_json["output"]["model_category"]
if model_type == "Binomial":
model_class = H2OBinomialGridSearch
elif model_type == "Clustering":
model_class = H2OClusteringGridSearch
elif model_type == "Regression":
model_class = H2ORegressionGridSearch
elif model_type == "Multinomial":
model_class = H2OMultinomialGridSearch
elif model_type == "Ordinal":
model_class = H2OOrdinalGridSearch
elif model_type == "AutoEncoder":
model_class = H2OAutoEncoderGridSearch
elif model_type == "DimReduction":
model_class = H2ODimReductionGridSearch
elif model_type == "AnomalyDetection":
model_class = H2OBinomialGridSearch
elif model_type == "BinomialUplift":
model_class = H2OBinomialGridSearch
else:
raise NotImplementedError(model_type)
return model_class
[docs] def get_grid(self, sort_by=None, decreasing=None):
"""
Retrieve an H2OGridSearch instance.
Optionally specify a metric by which to sort models and a sort order.
Note that if neither cross-validation nor a validation frame is used in the grid search, then the
training metrics will display in the "get grid" output. If a validation frame is passed to the grid, and
``nfolds = 0``, then the validation metrics will display. However, if ``nfolds`` > 1, then cross-validation
metrics will display even if a validation frame is provided.
:param str sort_by: A metric by which to sort the models in the grid space. Choices are: ``"logloss"``,
``"residual_deviance"``, ``"mse"``, ``"auc"``, ``"r2"``, ``"accuracy"``, ``"precision"``, ``"recall"``,
``"f1"``, etc.
:param bool decreasing: Sort the models in decreasing order of metric if true, otherwise sort in increasing
order (default).
:returns: A new H2OGridSearch instance optionally sorted on the specified metric.
:examples:
>>> from h2o.estimators import H2OGeneralizedLinearEstimator
>>> from h2o.grid.grid_search import H2OGridSearch
>>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
>>> y = 3
>>> x = [4,5,6,7,8,9,10,11]
>>> hyper_params = {'alpha': [0.01,0.3,0.5],
... 'lambda': [1e-5, 1e-6, 1e-7]}
>>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
... hyper_params)
>>> gs.train(x=x,y=y, training_frame=benign)
>>> gs.get_grid(sort_by='F1', decreasing=True)
"""
if sort_by is None and decreasing is None: return self
grid_json = h2o.api("GET /99/Grids/%s" % self._id, data={"sort_by": sort_by, "decreasing": decreasing})
grid = H2OGridSearch(self.model, self.hyper_params, self._id)
grid.models = [h2o.get_model(key['name']) for key in grid_json['model_ids']] # reordered
first_model_json = h2o.api("GET /99/Models/%s" % grid_json['model_ids'][0]['name'])['models'][0]
model_class = H2OGridSearch._metrics_class(first_model_json)
m = model_class()
m._id = self._id
m._grid_json = grid_json
# m._metrics_class = metrics_class
m._parms = grid._parms
mixin(grid, model_class)
assign(grid, m)
return grid
[docs] @deprecated_fn("grid.sort_by() is deprecated; use grid.get_grid() instead")
def sort_by(self, metric, increasing=True):
"""Deprecated since 2016-12-12, use grid.get_grid() instead."""
if metric[-1] != ')': metric += '()'
c_values = [list(x) for x in zip(*sorted(eval('self.' + metric + '.items()'), key=lambda k_v: k_v[1]))]
c_values.insert(1, [self.get_hyperparams(model_id, display=False) for model_id in c_values[0]])
if not increasing:
for col in c_values: col.reverse()
if metric[-2] == '(': metric = metric[:-2]
return H2OTwoDimTable(
col_header=['Model Id', 'Hyperparameters: [' + ', '.join(list(self.hyper_params.keys())) + ']', metric],
table_header='Grid Search Results for ' + self.model.__class__.__name__,
cell_values=[list(x) for x in zip(*c_values)])
[docs] def pareto_front(self,
test_frame, # type: H2OFrame
x_metric=None, # type: Optional[str]
y_metric=None, # type: Optional[str]
**kwargs
):
"""
Create Pareto front and plot it. Pareto front contains models that are optimal in a sense that for each model in the
Pareto front there isn't a model that would be better in both criteria. For example, this can be useful in picking
models that are fast to predict and at the same time have high accuracy. For generic data.frames/H2OFrames input
the task is assumed to be minimization for both metrics.
:param test_frame: a frame used to generate the metrics
:param x_metric: metric present in the leaderboard
:param y_metric: metric present in the leaderboard
:param kwargs: key, value mappings
Other keyword arguments are passed through to
:meth:`h2o.explanation.pareto_front`.
:return: object that contains the resulting figure (can be accessed using ``result.figure()``)
:examples:
>>> import h2o
>>> from h2o.automl import H2OAutoML
>>> from h2o.estimators import H2OGradientBoostingEstimator
>>> from h2o.grid import H2OGridSearch
>>>
>>> h2o.connect()
>>>
>>> # Import the wine dataset into H2O:
>>> df = h2o.import_file("h2o://prostate.csv")
>>>
>>> # Set the response
>>> response = "CAPSULE"
>>> df[response] = df[response].asfactor()
>>>
>>>
>>> # Split the dataset into a train and test set:
>>> train, test = df.split_frame([0.8])
>>>
>>> gbm_params1 = {'learn_rate': [0.01, 0.1],
>>> 'max_depth': [3, 5, 9]}
>>> grid = H2OGridSearch(model=H2OGradientBoostingEstimator,
>>> hyper_params=gbm_params1)
>>> grid.train(y=response, training_frame=train)
>>>
>>> # Create the Pareto front
>>> pf = grid.pareto_front(test)
>>> pf.figure() # get the Pareto front plot
>>> pf # H2OFrame containing the Pareto front subset of the leaderboard
"""
leaderboard = h2o.make_leaderboard(self, test_frame, extra_columns="ALL")
if x_metric is None:
x_metric = "predict_time_per_row_ms"
if y_metric is None:
y_metric = leaderboard.columns[1]
higher_is_better = ("auc", "aucpr")
optimum = "{} {}".format(
"top" if y_metric.lower() in higher_is_better else "bottom",
"right" if x_metric.lower() in higher_is_better else "left"
)
if kwargs.get("title") is None:
kwargs["title"] = "Pareto Front for {}".format(self.grid_id)
return h2o.explanation.pareto_front(frame=leaderboard,
x_metric=x_metric,
y_metric=y_metric,
optimum=optimum,
**kwargs)