"""
This module implements the base model class. All model things inherit from this class.
"""
import h2o
from . import H2OFrame
from . import H2OConnection
[docs]class ModelBase(object):
def __init__(self, dest_key, model_json, metrics_class):
self._id = dest_key
self._model_json = model_json
self._metrics_class = metrics_class
self._is_xvalidated=False
self._xval_keys=None
if dest_key is not None and model_json is not None and metrics_class is not None:
# build Metric objects out of each metrics
for metric in ["training_metrics", "validation_metrics", "cross_validation_metrics"]:
if metric in model_json["output"]:
if model_json["output"][metric] is not None:
if metric=="cross_validation_metrics":
self._is_xvalidated=True
model_json["output"][metric] = metrics_class(model_json["output"][metric],metric,model_json["algo"])
if self._is_xvalidated: self._xval_keys= [i["name"] for i in model_json["output"]["cross_validation_models"]]
# build a useful dict of the params
self._params={}
for p in self._model_json["parameters"]: self._params[p["label"]]=p
@property
[docs] def id(self):
"""
:return: Retrieve this model's identifier.
"""
return self._id
@property
[docs] def params(self):
"""
Get the parameters and the actual/default values only.
:return: A dictionary of parameters used to build this model.
"""
params = {}
for p in self._params:
params[p] = {"default":self._params[p]["default_value"], "actual":self._params[p]["actual_value"]}
return params
@property
[docs] def full_parameters(self):
"""
Get the full specification of all parameters.
:return: a dictionary of parameters used to build this model.
"""
return self._params
def __repr__(self):
self.show()
return ""
[docs] def predict(self, test_data):
"""
Predict on a dataset.
:param test_data: Data to be predicted on.
:return: A new H2OFrame filled with predictions.
"""
if not test_data: raise ValueError("Must specify test data")
test_data._eager()
j = H2OConnection.post_json("Predictions/models/" + self._id + "/frames/" + test_data._id)
prediction_frame_id = j["model_metrics"][0]["predictions"]["frame_id"]["name"]
return h2o.get_frame(prediction_frame_id)
[docs] def is_cross_validated(self):
"""
:return: True if the model was cross-validated.
"""
return self._is_xvalidated
[docs] def xval_keys(self):
"""
:return: The model keys for the cross-validated model.
"""
return self._xval_keys
[docs] def get_xval_models(self,key=None):
"""
Return a Model object.
:param key: If None, return all cross-validated models; otherwise return the model that key points to.
:return: A model or list of models.
"""
return h2o.get_model(key) if key is not None else [h2o.get_model(k) for k in self._xval_keys]
@property
[docs] def xvals(self):
"""
Return a list of the cross-validated models.
:return: A list of models
"""
return self.get_xval_models()
[docs] def deepfeatures(self, test_data, layer):
"""
Return hidden layer details
:param test_data: Data to create a feature space on
:param layer: 0 index hidden layer
"""
if test_data is None: raise ValueError("Must specify test data")
test_data._eager()
j = H2OConnection.post_json("Predictions/models/" + self._id + "/frames/" + test_data._id, deep_features_hidden_layer=layer)
return h2o.get_frame(j["predictions_frame"]["name"])
[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.
:return: an H2OFrame which represents the weight matrix identified by matrix_id
"""
num_weight_matrices = len(self._model_json['output']['weights'])
if matrix_id not in range(num_weight_matrices):
raise ValueError("Weight matrix does not exist. Model has {0} weight matrices (0-based indexing), but matrix {1} "
"was requested.".format(num_weight_matrices, matrix_id))
return h2o.get_frame(self._model_json['output']['weights'][matrix_id]['URL'].split('/')[3])
[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.
:return: an H2OFrame which represents the bias vector identified by vector_id
"""
num_bias_vectors = len(self._model_json['output']['biases'])
if vector_id not in range(num_bias_vectors):
raise ValueError("Bias vector does not exist. Model has {0} bias vectors (0-based indexing), but vector {1} "
"was requested.".format(num_bias_vectors, vector_id))
return h2o.get_frame(self._model_json['output']['biases'][vector_id]['URL'].split('/')[3])
[docs] def score_history(self):
"""
Retrieve Model Score History
:return: the score history (H2OTwoDimTable)
"""
model = self._model_json["output"]
if 'scoring_history' in model.keys() and model["scoring_history"] != None:
s = model["scoring_history"]
if h2o.can_use_pandas():
import pandas
pandas.options.display.max_rows = 20
return pandas.DataFrame(s.cell_values,columns=s.col_header)
return model["scoring_history"]
else: print "No score history for this model"
[docs] def summary(self):
"""
Print a detailed summary of the model.
:return:
"""
model = self._model_json["output"]
if model["model_summary"]:
model["model_summary"].show() # H2OTwoDimTable object
[docs] def show(self):
"""
Print innards of model, without regards to type
:return: None
"""
model = self._model_json["output"]
print "Model Details"
print "============="
print self.__class__.__name__, ": ", self._model_json["algo_full_name"]
print "Model Key: ", self._id
self.summary()
print
# training metrics
tm = model["training_metrics"]
if tm: tm.show()
vm = model["validation_metrics"]
if vm: vm.show()
xm = model["cross_validation_metrics"]
if xm: xm.show()
if "scoring_history" in model.keys() and model["scoring_history"]: model["scoring_history"].show()
if "variable_importances" in model.keys() and model["variable_importances"]: model["variable_importances"].show()
[docs] def varimp(self, return_list=False):
"""
Pretty print the variable importances, or return them in a list
:param return_list: if True, then return the variable importances in an list (ordered from most important to least
important). Each entry in the list is a 4-tuple of (variable, relative_importance, scaled_importance, percentage).
:return: None or ordered list
"""
model = self._model_json["output"]
if "variable_importances" in model.keys() and model["variable_importances"]:
if not return_list: return model["variable_importances"].show()
else: return model["variable_importances"].cell_values
else:
print "Warning: This model doesn't have variable importances"
[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 train: Get the residual deviance for the training set. If both train and valid are False, then train is selected by default.
:param valid: Get the residual deviance for the validation set. If both train and valid are True, then train is selected by default.
:return: Return the residual deviance, or None if it is not present.
"""
if xval: raise ValueError("Cross-validation metrics are not available.")
if not train and not valid: train = True
if train and valid: train = True
return self._model_json["output"]["training_metrics"].residual_deviance() if train else self._model_json["output"]["validation_metrics"].residual_deviance()
[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 train: Get the residual dof for the training set. If both train and valid are False, then train is selected by default.
:param valid: Get the residual dof for the validation set. If both train and valid are True, then train is selected by default.
:return: Return the residual dof, or None if it is not present.
"""
if xval: raise ValueError("Cross-validation metrics are not available.")
if not train and not valid: train = True
if train and valid: train = True
return self._model_json["output"]["training_metrics"].residual_degrees_of_freedom() if train else self._model_json["output"]["validation_metrics"].residual_degrees_of_freedom()
[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: train Get the null deviance for the training set. If both train and valid are False, then train is selected by default.
:param: valid Get the null deviance for the validation set. If both train and valid are True, then train is selected by default.
:return: Return the null deviance, or None if it is not present.
"""
if xval: raise ValueError("Cross-validation metrics are not available.")
if not train and not valid: train = True
if train and valid: train = True
return self._model_json["output"]["training_metrics"].null_deviance() if train else self._model_json["output"]["validation_metrics"].null_deviance()
[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 train: Get the null dof for the training set. If both train and valid are False, then train is selected by default.
:param valid: Get the null dof for the validation set. If both train and valid are True, then train is selected by default.
:return: Return the null dof, or None if it is not present.
"""
if xval: raise ValueError("Cross-validation metrics are not available.")
if not train and not valid: train = True
if train and valid: train = True
return self._model_json["output"]["training_metrics"].null_degrees_of_freedom() if train else self._model_json["output"]["validation_metrics"].null_degrees_of_freedom()
[docs] def pprint_coef(self):
"""
Pretty print the coefficents table (includes normalized coefficients)
:return: None
"""
print self._model_json["output"]["coefficients_table"] # will return None if no coefs!
[docs] def coef(self):
"""
:return: Return the coefficients for this model.
"""
tbl = self._model_json["output"]["coefficients_table"]
if tbl is None: return None
tbl = tbl.cell_values
return {a[0]:a[1] for a in tbl}
[docs] def coef_norm(self):
"""
:return: Return the normalized coefficients
"""
tbl = self._model_json["output"]["coefficients_table"]
if tbl is None: return None
tbl = tbl.cell_values
return {a[0]:a[2] for a in tbl}
[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*sigma.
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 train: If train is True, then return the R^2 value for the training data.
:param valid: If valid is True, then return the R^2 value for the validation data.
:param xval: If xval is True, then return the R^2 value for the cross validation data.
:return: The R^2 for this regression model.
"""
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.r2()
return m.values()[0] if len(m) == 1 else m
[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 train: If train is True, then return the MSE value for the training data.
:param valid: If valid is True, then return the MSE value for the validation data.
:param xval: If xval is True, then return the MSE value for the cross validation data.
:return: The MSE for this regression model.
"""
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.mse()
return m.values()[0] if len(m) == 1 else m
[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 train: If train is True, then return the Log Loss value for the training data.
:param valid: If valid is True, then return the Log Loss value for the validation data.
:param xval: If xval is True, then return the Log Loss value for the cross validation data.
:return: The Log Loss for this binomial model.
"""
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.logloss()
return m.values()[0] if len(m) == 1 else m
[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 train: If train is True, then return the Mean Residual Deviance value for the training data.
:param valid: If valid is True, then return the Mean Residual Deviance value for the validation data.
:param xval: If xval is True, then return the Mean Residual Deviance value for the cross validation data.
:return: The Mean Residual Deviance for this regression model.
"""
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.mean_residual_deviance()
return m.values()[0] if len(m) == 1 else m
[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 train: If train is True, then return the AUC value for the training data.
:param valid: If valid is True, then return the AUC value for the validation data.
:param xval: If xval is True, then return the AUC value for the validation data.
:return: The AUC.
"""
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.auc()
return m.values()[0] if len(m) == 1 else m
[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 train: If train is True, then return the AIC value for the training data.
:param valid: If valid is True, then return the AIC value for the validation data.
:param xval: If xval is True, then return the AIC value for the validation data.
:return: The AIC.
"""
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.aic()
return m.values()[0] if len(m) == 1 else m
[docs] def giniCoef(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 train: If train is True, then return the Gini Coefficient value for the training data.
:param valid: If valid is True, then return the Gini Coefficient value for the validation data.
:param xval: If xval is True, then return the Gini Coefficient value for the cross validation data.
:return: The Gini Coefficient for this binomial model.
"""
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.giniCoef()
return m.values()[0] if len(m) == 1 else m
[docs] def download_pojo(self,path=""):
"""
Download the POJO for this model to the directory specified by path (no trailing slash!).
If path is "", then dump to screen.
:param model: Retrieve this model's scoring POJO.
:param path: An absolute path to the directory where POJO should be saved.
:return: None
"""
h2o.download_pojo(self,path) # call the "package" function
@staticmethod
def _get_metrics(o, train, valid, xval):
metrics = {}
if train: metrics["train"] = o._model_json["output"]["training_metrics"]
if valid: metrics["valid"] = o._model_json["output"]["validation_metrics"]
if xval : metrics["xval"] = o._model_json["output"]["cross_validation_metrics"]
if len(metrics) == 0: metrics["train"] = o._model_json["output"]["training_metrics"]
return metrics
# Delete from cluster as model goes out of scope
# def __del__(self):
# h2o.remove(self._id)
@staticmethod
def _has(dictionary, key):
return key in dictionary and dictionary[key] is not None
@staticmethod
def _check_targets(y_actual, y_predicted):
"""
Check that y_actual and y_predicted have the same length.
:param y_actual: An H2OFrame
:param y_predicted: An H2OFrame
:return: None
"""
if len(y_actual) != len(y_predicted):
raise ValueError("Row mismatch: [{},{}]".format(len(y_actual),len(y_predicted)))