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.
Parameters
----------
sort_by : str, optional
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.
decreasing : bool, optional
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.
"""
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
H2OEstimator.mixin(grid, model_class)
grid.__dict__.update(m.__dict__.copy())
return grid
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.
Parameters
----------
sort_by : str, optional
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.
decreasing : bool, optional
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.
"""
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
H2OEstimator.mixin(grid, model_class)
grid.__dict__.update(m.__dict__.copy())
return grid
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.
"""
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
H2OEstimator.mixin(grid, model_class)
grid.__dict__.update(m.__dict__.copy())
return grid
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.
"""
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
H2OEstimator.mixin(grid, model_class)
grid.__dict__.update(m.__dict__.copy())
return grid
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
H2OEstimator.mixin(self, model_class)
self.__dict__.update(m.__dict__.copy())
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
H2OEstimator.mixin(self, model_class)
self.__dict__.update(m.__dict__.copy())
def show_model_performance(model: H2OEstimator):
cross_val_model_performance = model.model_performance(xval=True)
logger.info('Cross validation model performance')
logger.info(cross_val_model_performance)
[[threshold, f1]] = cross_val_model_performance.F1()
logger.info('Threshold = {} for maximum F1 = {}'.format(threshold, f1))
