# -*- encoding: utf-8 -*-
from __future__ import absolute_import, division, print_function, unicode_literals
import os
import traceback
import warnings
import h2o
from h2o.exceptions import H2OValueError
from h2o.job import H2OJob
from h2o.utils.backward_compatibility import backwards_compatible
from h2o.utils.compatibility import * # NOQA
from h2o.utils.compatibility import viewitems
from h2o.utils.shared_utils import can_use_pandas
from h2o.utils.typechecks import I, assert_is_type
[docs]class ModelBase(backwards_compatible()):
"""Base class for all models."""
def __init__(self):
"""Construct a new model instance."""
super(ModelBase, self).__init__()
self._id = None
self._model_json = None
self._metrics_class = None
self._is_xvalidated = False
self._xval_keys = None
self._parms = {} # internal, for object recycle
self.parms = {} # external
self._estimator_type = "unsupervised"
self._future = False # used by __repr__/show to query job state
self._job = None # used when _future is True
@property
def model_id(self):
"""Model identifier."""
return self._id
@model_id.setter
def model_id(self, newid):
oldid = self._id
self._id = newid
h2o.rapids("(rename '%s' '%s')" % (oldid, newid))
@property
def params(self):
"""
Get the parameters and the actual/default values only.
:returns: A dictionary of parameters used to build this model.
"""
params = {}
for p in self.parms:
params[p] = {"default": self.parms[p]["default_value"],
"actual": self.parms[p]["actual_value"]}
return params
@property
def default_params(self):
"""Dictionary of the default parameters of the model."""
params = {}
for p in self.parms:
params[p] = self.parms[p]["default_value"]
return params
@property
def actual_params(self):
"""Dictionary of actual parameters of the model."""
params_to_select = {"model_id": "name",
"response_column": "column_name",
"training_frame": "name",
"validation_frame": "name"}
params = {}
for p in self.parms:
if p in params_to_select.keys():
params[p] = self.parms[p]["actual_value"].get(params_to_select[p], None)
else:
params[p] = self.parms[p]["actual_value"]
return params
@property
def full_parameters(self):
"""Dictionary of the full specification of all parameters."""
return self.parms
@property
def type(self):
"""The type of model built: ``"classifier"`` or ``"regressor"`` or ``"unsupervised"``"""
return self._estimator_type
def __repr__(self):
# PUBDEV-2278: using <method>? from IPython caused everything to dump
stk = traceback.extract_stack()
if not ("IPython" in stk[-2][0] and "info" == stk[-2][2]):
self.show()
return ""
[docs] def predict_leaf_node_assignment(self, test_data):
"""
Predict on a dataset and return the leaf node assignment (only for tree-based models).
:param H2OFrame test_data: Data on which to make predictions.
:returns: A new H2OFrame of predictions.
"""
if not isinstance(test_data, h2o.H2OFrame): raise ValueError("test_data must be an instance of H2OFrame")
j = h2o.api("POST /3/Predictions/models/%s/frames/%s" % (self.model_id, test_data.frame_id),
data={"leaf_node_assignment": True})
return h2o.get_frame(j["predictions_frame"]["name"])
[docs] def predict(self, test_data):
"""
Predict on a dataset.
:param H2OFrame test_data: Data on which to make predictions.
:returns: A new H2OFrame of predictions.
"""
if not isinstance(test_data, h2o.H2OFrame): raise ValueError("test_data must be an instance of H2OFrame")
j = H2OJob(h2o.api("POST /4/Predictions/models/%s/frames/%s" % (self.model_id, test_data.frame_id)),
self._model_json["algo"] + " prediction")
j.poll()
return h2o.get_frame(j.dest_key)
[docs] def is_cross_validated(self):
"""Return True if the model was cross-validated."""
return self._is_xvalidated
[docs] def xval_keys(self):
"""Return 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.
:returns: 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
def xvals(self):
"""
Return a list of the cross-validated models.
:returns: 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")
if str(layer).isdigit():
j = H2OJob(h2o.api("POST /4/Predictions/models/%s/frames/%s" % (self._id, test_data.frame_id),
data={"deep_features_hidden_layer": layer}), "deepfeatures")
else:
j = H2OJob(h2o.api("POST /4/Predictions/models/%s/frames/%s" % (self._id, test_data.frame_id),
data={"deep_features_hidden_layer_name": layer}), "deepfeatures")
j.poll()
return h2o.get_frame(j.dest_key)
[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
"""
num_weight_matrices = len(self._model_json["output"]["weights"])
if matrix_id not in list(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.
:returns: 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 list(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 normmul(self):
"""Normalization/Standardization multipliers for numeric predictors."""
return self._model_json["output"]["normmul"]
[docs] def normsub(self):
"""Normalization/Standardization offsets for numeric predictors."""
return self._model_json["output"]["normsub"]
[docs] def respmul(self):
"""Normalization/Standardization multipliers for numeric response."""
return self._model_json["output"]["normrespmul"]
[docs] def respsub(self):
"""Normalization/Standardization offsets for numeric response."""
return self._model_json["output"]["normrespsub"]
[docs] def catoffsets(self):
"""Categorical offsets for one-hot encoding."""
return self._model_json["output"]["catoffsets"]
[docs] def scoring_history(self):
"""
Retrieve Model Score History.
:returns: The score history as an H2OTwoDimTable or a Pandas DataFrame.
"""
model = self._model_json["output"]
if "scoring_history" in model and model["scoring_history"] is not None:
return model["scoring_history"].as_data_frame()
print("No score history for this model")
[docs] def cross_validation_metrics_summary(self):
"""
Retrieve Cross-Validation Metrics Summary.
:returns: The cross-validation metrics summary as an H2OTwoDimTable
"""
model = self._model_json["output"]
if "cross_validation_metrics_summary" in model and model["cross_validation_metrics_summary"] is not None:
return model["cross_validation_metrics_summary"]
print("No cross-validation metrics summary for this model")
[docs] def summary(self):
"""Print a detailed summary of the model."""
model = self._model_json["output"]
if "model_summary" in model and model["model_summary"] is not None:
return model["model_summary"]
print("No model summary for this model")
[docs] def show(self):
"""Print innards of model, without regards to type."""
if self._future:
self._job.poll_once()
return
if self._model_json is None:
print("No model trained yet")
return
if self.model_id is None:
print("This H2OEstimator has been removed.")
return
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()
xms = model["cross_validation_metrics_summary"]
if xms: xms.show()
if "scoring_history" in model and model["scoring_history"]:
model["scoring_history"].show()
if "variable_importances" in model and model["variable_importances"]:
model["variable_importances"].show()
[docs] def varimp(self, use_pandas=False):
"""
Pretty print the variable importances, or return them in a list.
:param use_pandas: If True, then the variable importances will be returned as a pandas data frame.
:returns: A list or Pandas DataFrame.
"""
model = self._model_json["output"]
if "variable_importances" in list(model.keys()) and model["variable_importances"]:
vals = model["variable_importances"].cell_values
header = model["variable_importances"].col_header
if use_pandas and can_use_pandas():
import pandas
return pandas.DataFrame(vals, columns=header)
else:
return vals
else:
print("Warning: This model doesn't have variable importances")
[docs] def residual_deviance(self, train=False, valid=False, xval=None):
"""
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.
:returns: Return the residual deviance, or None if it is not present.
"""
if xval: raise H2OValueError("Cross-validation metrics are not available.")
if not train and not valid: train = True
if train and valid: train = True
if train:
return self._model_json["output"]["training_metrics"].residual_deviance()
else:
return 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 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.
:returns: Return the residual dof, or None if it is not present.
"""
if xval: raise H2OValueError("Cross-validation metrics are not available.")
if not train and not valid: train = True
if train and valid: train = True
if train:
return self._model_json["output"]["training_metrics"].residual_degrees_of_freedom()
else:
return 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 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.
:returns: Return the null deviance, or None if it is not present.
"""
if xval: raise H2OValueError("Cross-validation metrics are not available.")
if not train and not valid: train = True
if train and valid: train = True
if train:
return self._model_json["output"]["training_metrics"].null_deviance()
else:
return 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 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.
:returns: Return the null dof, or None if it is not present.
"""
if xval: raise H2OValueError("Cross-validation metrics are not available.")
if not train and not valid: train = True
if train and valid: train = True
if train:
return self._model_json["output"]["training_metrics"].null_degrees_of_freedom()
else:
return self._model_json["output"]["validation_metrics"].null_degrees_of_freedom()
[docs] def pprint_coef(self):
"""Pretty print the coefficents table (includes normalized coefficients)."""
print(self._model_json["output"]["coefficients_table"]) # will return None if no coefs!
[docs] def coef(self):
"""
Return the coefficients which can be applied to the non-standardized data.
Note: standardize = True by default, if set to False then coef() return the coefficients which are fit directly.
"""
tbl = self._model_json["output"]["coefficients_table"]
if tbl is None:
return None
return {name: coef for name, coef in zip(tbl["names"], tbl["coefficients"])}
[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.
"""
tbl = self._model_json["output"]["coefficients_table"]
if tbl is None:
return None
return {name: coef for name, coef in zip(tbl["names"], tbl["standardized_coefficients"])}
[docs] def r2(self, train=False, valid=False, xval=False):
"""
Return the R squared for this regression model.
Will return R^2 for GLM Models and will return NaN otherwise.
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 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 squared for this regression model.
"""
tm = ModelBase._get_metrics(self, train, valid, xval)
m = {}
for k, v in viewitems(tm):
m[k] = None if v is None else v.r2()
return list(m.values())[0] if len(m) == 1 else m
[docs] def mse(self, train=False, valid=False, xval=False):
"""
Get the Mean Square Error.
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.
"""
tm = ModelBase._get_metrics(self, train, valid, xval)
m = {}
for k, v in viewitems(tm):
m[k] = None if v is None else v.mse()
return list(m.values())[0] if len(m) == 1 else m
[docs] def rmse(self, train=False, valid=False, xval=False):
"""
Get the Root Mean Square Error.
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 RMSE value for the training data.
:param bool valid: If valid is True, then return the RMSE value for the validation data.
:param bool xval: If xval is True, then return the RMSE value for the cross validation data.
:returns: The RMSE for this regression model.
"""
tm = ModelBase._get_metrics(self, train, valid, xval)
m = {}
for k, v in viewitems(tm):
m[k] = None if v is None else v.rmse()
return list(m.values())[0] if len(m) == 1 else m
[docs] def mae(self, train=False, valid=False, xval=False):
"""
Get the Mean Absolute Error.
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 MAE value for the training data.
:param bool valid: If valid is True, then return the MAE value for the validation data.
:param bool xval: If xval is True, then return the MAE value for the cross validation data.
:returns: The MAE for this regression model.
"""
tm = ModelBase._get_metrics(self, train, valid, xval)
m = {}
for k, v in viewitems(tm):
m[k] = None if v is None else v.mae()
return list(m.values())[0] if len(m) == 1 else m
[docs] def rmsle(self, train=False, valid=False, xval=False):
"""
Get the Root Mean Squared Logarithmic Error.
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 RMSLE value for the training data.
:param bool valid: If valid is True, then return the RMSLE value for the validation data.
:param bool xval: If xval is True, then return the RMSLE value for the cross validation data.
:returns: The RMSLE for this regression model.
"""
tm = ModelBase._get_metrics(self, train, valid, xval)
m = {}
for k, v in viewitems(tm): m[k] = None if v is None else v.rmsle()
return list(m.values())[0] if len(m) == 1 else m
[docs] def logloss(self, train=False, valid=False, xval=False):
"""
Get the Log Loss.
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 regression model.
"""
tm = ModelBase._get_metrics(self, train, valid, xval)
m = {}
for k, v in viewitems(tm): m[k] = None if v is None else v.logloss()
return list(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.
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.
"""
tm = ModelBase._get_metrics(self, train, valid, xval)
m = {}
for k, v in viewitems(tm): m[k] = None if v is None else v.mean_residual_deviance()
return list(m.values())[0] if len(m) == 1 else m
[docs] def auc(self, train=False, valid=False, xval=False):
"""
Get the AUC (Area Under 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 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.
"""
tm = ModelBase._get_metrics(self, train, valid, xval)
m = {}
for k, v in viewitems(tm): m[k] = None if v is None else v.auc()
return list(m.values())[0] if len(m) == 1 else m
[docs] def aic(self, train=False, valid=False, xval=False):
"""
Get the AIC (Akaike Information Criterium).
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.
"""
tm = ModelBase._get_metrics(self, train, valid, xval)
m = {}
for k, v in viewitems(tm): m[k] = None if v is None else v.aic()
return list(m.values())[0] if len(m) == 1 else m
[docs] def gini(self, train=False, valid=False, xval=False):
"""
Get the Gini coefficient.
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 this binomial model.
"""
tm = ModelBase._get_metrics(self, train, valid, xval)
m = {}
for k, v in viewitems(tm): m[k] = None if v is None else v.gini()
return list(m.values())[0] if len(m) == 1 else m
[docs] def download_pojo(self, path="", get_genmodel_jar=False, genmodel_name=""):
"""
Download the POJO for this model to the directory specified by path.
If path is an empty string, then dump the output to screen.
:param path: An absolute path to the directory where POJO should be saved.
:param get_genmodel_jar: if True, then also download h2o-genmodel.jar and store it in folder ``path``.
:param genmodel_name Custom name of genmodel jar
:returns: name of the POJO file written.
"""
assert_is_type(path, str)
assert_is_type(get_genmodel_jar, bool)
path = path.rstrip("/")
return h2o.download_pojo(self, path, get_jar=get_genmodel_jar, jar_name=genmodel_name)
[docs] def download_mojo(self, path=".", get_genmodel_jar=False, genmodel_name=""):
"""
Download the model in MOJO format.
:param path: the path where MOJO file should be saved.
:param get_genmodel_jar: if True, then also download h2o-genmodel.jar and store it in folder ``path``.
:param genmodel_name Custom name of genmodel jar
:returns: name of the MOJO file written.
"""
assert_is_type(path, str)
assert_is_type(get_genmodel_jar, bool)
if self.algo not in {"drf", "gbm", "deepwater", "xgboost", "glrm", "glm", "word2vec"}:
raise H2OValueError("MOJOs are currently supported for Distributed Random Forest, "
"Gradient Boosting Machine, XGBoost, Deep Water, GLM, GLRM and word2vec models only.")
if get_genmodel_jar:
if genmodel_name == "":
h2o.api("GET /3/h2o-genmodel.jar", save_to=os.path.join(path, "h2o-genmodel.jar"))
else:
h2o.api("GET /3/h2o-genmodel.jar", save_to=os.path.join(path, genmodel_name))
return h2o.api("GET /3/Models/%s/mojo" % self.model_id, save_to=path)
[docs] def save_mojo(self, path="", force=False):
"""
Save an H2O Model as MOJO (Model Object, Optimized) to disk.
:param model: The model object to save.
:param path: a path to save the model at (hdfs, s3, local)
:param force: if True overwrite destination directory in case it exists, or throw exception if set to False.
:returns str: the path of the saved model
"""
assert_is_type(path, str)
assert_is_type(force, bool)
path = os.path.join(os.getcwd() if path == "" else path, self.model_id + ".zip")
return h2o.api("GET /99/Models.mojo/%s" % self.model_id, data={"dir": path, "force": force})["dir"]
[docs] def save_model_details(self, path="", force=False):
"""
Save Model Details of an H2O Model in JSON Format to disk.
:param model: The model object to save.
:param path: a path to save the model details at (hdfs, s3, local)
:param force: if True overwrite destination directory in case it exists, or throw exception if set to False.
:returns str: the path of the saved model details
"""
assert_is_type(path, str)
assert_is_type(force, bool)
path = os.path.join(os.getcwd() if path == "" else path, self.model_id + ".json")
return h2o.api("GET /99/Models/%s/json" % self.model_id, data={"dir": path, "force": force})["dir"]
@staticmethod
def _get_metrics(o, train, valid, xval):
# noinspection PyProtectedMember
output = o._model_json["output"]
metrics = {}
if train: metrics["train"] = output["training_metrics"]
if valid: metrics["valid"] = output["validation_metrics"]
if xval: metrics["xval"] = output["cross_validation_metrics"]
if len(metrics) == 0: metrics["train"] = output["training_metrics"]
return metrics
# Delete from cluster as model goes out of scope
# def __del__(self):
# h2o.remove(self._id)
def _plot(self, timestep, metric, server=False):
plt = _get_matplotlib_pyplot(server)
if not plt: return
scoring_history = self.scoring_history()
# Separate functionality for GLM since its output is different from other algos
if self._model_json["algo"] == "glm":
# GLM has only one timestep option, which is `iteration`
timestep = "iteration"
if metric == "AUTO":
metric = "log_likelihood"
elif metric not in ("log_likelihood", "objective"):
raise H2OValueError("for GLM, metric must be one of: log_likelihood, objective")
plt.xlabel(timestep)
plt.ylabel(metric)
plt.title("Validation Scoring History")
plt.plot(scoring_history[timestep], scoring_history[metric])
elif self._model_json["algo"] in ("deeplearning", "deepwater", "xgboost", "drf", "gbm"):
# Set timestep
if self._model_json["algo"] in ("gbm", "drf", "xgboost"):
assert_is_type(timestep, "AUTO", "duration", "number_of_trees")
if timestep == "AUTO":
timestep = "number_of_trees"
else: # self._model_json["algo"] == "deeplearning":
# Delete first row of DL scoring history since it contains NAs & NaNs
if scoring_history["samples"][0] == 0:
scoring_history = scoring_history[1:]
assert_is_type(timestep, "AUTO", "epochs", "samples", "duration")
if timestep == "AUTO":
timestep = "epochs"
training_metric = "training_{}".format(metric)
validation_metric = "validation_{}".format(metric)
if timestep == "duration":
dur_colname = "duration_{}".format(scoring_history["duration"][1].split()[1])
scoring_history[dur_colname] = [str(x).split()[0] for x in scoring_history["duration"]]
timestep = dur_colname
if can_use_pandas():
valid = validation_metric in list(scoring_history)
ylim = (scoring_history[[training_metric, validation_metric]].min().min(),
scoring_history[[training_metric, validation_metric]].max().max()) if valid \
else (scoring_history[training_metric].min(), scoring_history[training_metric].max())
else:
valid = validation_metric in scoring_history.col_header
ylim = (min(min(scoring_history[[training_metric, validation_metric]])),
max(max(scoring_history[[training_metric, validation_metric]]))) if valid \
else (min(scoring_history[training_metric]), max(scoring_history[training_metric]))
if ylim[0] == ylim[1]: ylim = (0, 1)
if valid: # Training and validation scoring history
plt.xlabel(timestep)
plt.ylabel(metric)
plt.title("Scoring History")
plt.ylim(ylim)
plt.plot(scoring_history[timestep], scoring_history[training_metric], label="Training")
plt.plot(scoring_history[timestep], scoring_history[validation_metric], color="orange",
label="Validation")
plt.legend()
else: # Training scoring history only
plt.xlabel(timestep)
plt.ylabel(training_metric)
plt.title("Training Scoring History")
plt.ylim(ylim)
plt.plot(scoring_history[timestep], scoring_history[training_metric])
else: # algo is not glm, deeplearning, drf, gbm, xgboost
raise H2OValueError("Plotting not implemented for this type of model")
if not server: plt.show()
[docs] def partial_plot(self, data, cols, destination_key=None, nbins=20, plot=True, plot_stddev = True, figsize=(7, 10), server=False):
"""
Create partial dependence plot which gives a graphical depiction of the marginal effect of a variable on the
response. The effect of a variable is measured in change in the mean response.
:param H2OFrame data: An H2OFrame object used for scoring and constructing the plot.
:param cols: Feature(s) for which partial dependence will be calculated.
:param destination_key: An key reference to the created partial dependence tables in H2O.
:param nbins: Number of bins used. For categorical columns make sure the number of bins exceed the level count.
:param plot: A boolean specifying whether to plot partial dependence table.
:param plot_stddev: A boolean specifying whether to add std err to partial dependence plot.
:param figsize: Dimension/size of the returning plots, adjust to fit your output cells.
:param server: ?
:returns: Plot and list of calculated mean response tables for each feature requested.
"""
if not isinstance(data, h2o.H2OFrame): raise ValueError("data must be an instance of H2OFrame")
assert_is_type(cols, [str])
assert_is_type(destination_key, None, str)
assert_is_type(nbins, int)
assert_is_type(plot, bool)
assert_is_type(figsize, (int, int))
# Check cols specified exist in frame data
for xi in cols:
if xi not in data.names:
raise H2OValueError("Column %s does not exist in the training frame" % xi)
kwargs = {}
kwargs["cols"] = cols
kwargs["model_id"] = self.model_id
kwargs["frame_id"] = data.frame_id
kwargs["nbins"] = nbins
kwargs["destination_key"] = destination_key
json = H2OJob(h2o.api("POST /3/PartialDependence/", data=kwargs), job_type="PartialDependencePlot").poll()
json = h2o.api("GET /3/PartialDependence/%s" % json.dest_key)
# Extract partial dependence data from json response
pps = json["partial_dependence_data"]
# Plot partial dependence plots using matplotlib
if plot:
plt = _get_matplotlib_pyplot(server)
if not plt: return
fig, axs = plt.subplots(len(cols), squeeze=False, figsize=figsize)
for i, pp in enumerate(pps):
# Check weather column was categorical or numeric
col = cols[i]
cat = data[col].isfactor()[0]
upper = [a + b for a, b in zip(pp[1], pp[2]) ]
lower = [a - b for a, b in zip(pp[1], pp[2]) ]
if cat:
labels = pp[0]
x = range(len(labels))
y = pp[1]
axs[i, 0].plot(x, y, "ro")
if plot_stddev:
axs[i, 0].plot(x, lower, 'b--')
axs[i, 0].plot(x, upper, 'b--')
axs[i, 0].set_ylim(min(lower) - 0.1*abs(min(lower)), max(upper) + 0.1*abs(max(upper)))
axs[i, 0].set_xticks(x)
axs[i, 0].set_xticklabels(labels)
axs[i, 0].margins(0.2)
else:
x = pp[0]
y = pp[1]
axs[i, 0].plot(x, y, "r-")
if plot_stddev:
axs[i, 0].plot(x, lower, 'b--')
axs[i, 0].plot(x, upper, 'b--')
axs[i, 0].set_xlim(min(x), max(x))
axs[i, 0].set_ylim(min(lower) - 0.1*abs(min(lower)), max(upper) + 0.1*abs(max(upper)))
axs[i, 0].set_title("Partial Dependence Plot For {}".format(col))
axs[i, 0].set_xlabel(pp.col_header[0])
axs[i, 0].set_ylabel(pp.col_header[1])
axs[i, 0].xaxis.grid()
axs[i, 0].yaxis.grid()
if len(col) > 1:
fig.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
return pps
[docs] def varimp_plot(self, num_of_features=None, server=False):
"""
Plot the variable importance for a trained model.
:param num_of_features: the number of features shown in the plot (default is 10 or all if less than 10).
:param server: ?
:returns: None.
"""
assert_is_type(num_of_features, None, int)
assert_is_type(server, bool)
plt = _get_matplotlib_pyplot(server)
if not plt: return
# check if the model is a glm
if self._model_json["algo"] == "glm":
# print statement to used std_coef_plot(), and use std_coef_plt instead
print("Variable importance does not apply to GLM. Will use std_coef_plot() instead.")
self.std_coef_plot(num_of_features)
return
# get the variable importances as a list of tuples, do not use pandas dataframe
importances = self.varimp(use_pandas=False)
# features labels correspond to the first value of each tuple in the importances list
feature_labels = [tup[0] for tup in importances]
# relative importances correspond to the first value of each tuple in the importances list
scaled_importances = [tup[2] for tup in importances]
# specify bar centers on the y axis, but flip the order so largest bar appears at top
pos = range(len(feature_labels))[::-1]
# specify the bar lengths
val = scaled_importances
# # check that num_of_features is an integer
# if num_of_features is None:
# num_of_features = len(val)
# default to 10 or less features if num_of_features is not specified
if num_of_features is None:
num_of_features = min(len(val), 10)
fig, ax = plt.subplots(1, 1, figsize=(14, 10))
# create separate plot for the case where num_of_features == 1
if num_of_features == 1:
plt.barh(pos[0:num_of_features], val[0:num_of_features], align="center",
height=0.8, color="#1F77B4", edgecolor="none")
# Hide the right and top spines, color others grey
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_color("#7B7B7B")
ax.spines["left"].set_color("#7B7B7B")
# Only show ticks on the left and bottom spines
ax.yaxis.set_ticks_position("left")
ax.xaxis.set_ticks_position("bottom")
plt.yticks(pos[0:num_of_features], feature_labels[0:num_of_features])
ax.margins(y=0.5)
else:
plt.barh(pos[0:num_of_features], val[0:num_of_features], align="center",
height=0.8, color="#1F77B4", edgecolor="none")
# Hide the right and top spines, color others grey
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_color("#7B7B7B")
ax.spines["left"].set_color("#7B7B7B")
# Only show ticks on the left and bottom spines
ax.yaxis.set_ticks_position("left")
ax.xaxis.set_ticks_position("bottom")
plt.yticks(pos[0:num_of_features], feature_labels[0:num_of_features])
plt.ylim([min(pos[0:num_of_features])- 1, max(pos[0:num_of_features])+1])
# ax.margins(y=0.5)
# check which algorithm was used to select right plot title
if self._model_json["algo"] == "gbm":
plt.title("Variable Importance: H2O GBM", fontsize=20)
if not server: plt.show()
elif self._model_json["algo"] == "drf":
plt.title("Variable Importance: H2O DRF", fontsize=20)
if not server: plt.show()
elif self._model_json["algo"] == "xgboost":
plt.title("Variable Importance: H2O XGBoost", fontsize=20)
if not server: plt.show()
# if H2ODeepLearningEstimator has variable_importances == True
elif self._model_json["algo"] == "deeplearning":
plt.title("Variable Importance: H2O Deep Learning", fontsize=20)
if not server: plt.show()
else:
raise H2OValueError("A variable importances plot is not implemented for this type of model")
[docs] def std_coef_plot(self, num_of_features=None, server=False):
"""
Plot a GLM model"s standardized coefficient magnitudes.
:param num_of_features: the number of features shown in the plot.
:param server: ?
:returns: None.
"""
assert_is_type(num_of_features, None, I(int, lambda x: x > 0))
# check that model is a glm
if self._model_json["algo"] != "glm":
raise H2OValueError("This function is available for GLM models only")
plt = _get_matplotlib_pyplot(server)
if not plt: return
# get unsorted tuple of labels and coefficients
unsorted_norm_coef = self.coef_norm().items()
# drop intercept value then sort tuples by the coefficient"s absolute value
drop_intercept = [tup for tup in unsorted_norm_coef if tup[0] != "Intercept"]
norm_coef = sorted(drop_intercept, key=lambda x: abs(x[1]), reverse=True)
signage = []
for element in norm_coef:
# if positive including zero, color blue, else color orange (use same colors as Flow)
if element[1] >= 0:
signage.append("#1F77B4") # blue
else:
signage.append("#FF7F0E") # dark orange
# get feature labels and their corresponding magnitudes
feature_labels = [tup[0] for tup in norm_coef]
norm_coef_magn = [abs(tup[1]) for tup in norm_coef]
# specify bar centers on the y axis, but flip the order so largest bar appears at top
pos = range(len(feature_labels))[::-1]
# specify the bar lengths
val = norm_coef_magn
# check number of features, default is all the features
if num_of_features is None:
num_of_features = len(val)
# plot horizontal plot
fig, ax = plt.subplots(1, 1, figsize=(14, 10))
# create separate plot for the case where num_of_features = 1
if num_of_features == 1:
plt.barh(pos[0], val[0],
align="center", height=0.8, color=signage[0], edgecolor="none")
# Hide the right and top spines, color others grey
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_color("#7B7B7B")
ax.spines["left"].set_color("#7B7B7B")
# Only show ticks on the left and bottom spines
ax.yaxis.set_ticks_position("left")
ax.xaxis.set_ticks_position("bottom")
plt.yticks([0], feature_labels[0])
ax.margins(y=0.5)
else:
plt.barh(pos[0:num_of_features], val[0:num_of_features],
align="center", height=0.8, color=signage[0:num_of_features], edgecolor="none")
# Hide the right and top spines, color others grey
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_color("#7B7B7B")
ax.spines["left"].set_color("#7B7B7B")
# Only show ticks on the left and bottom spines
ax.yaxis.set_ticks_position("left")
ax.xaxis.set_ticks_position("bottom")
plt.yticks(pos[0:num_of_features], feature_labels[0:num_of_features])
ax.margins(y=0.05)
# generate custom fake lines that will be used as legend entries:
# check if positive and negative values exist
# if positive create positive legend
if "#1F77B4" in signage[0:num_of_features] and "#FF7F0E" not in signage[0:num_of_features]:
color_ids = {"Positive": "#1F77B4"}
markers = [plt.Line2D([0, 0], [0, 0], color=color, marker="s", linestyle="")
for color in signage[0:num_of_features]]
lgnd = plt.legend(markers, color_ids, numpoints=1, loc="best", frameon=False, fontsize=13)
lgnd.legendHandles[0]._legmarker.set_markersize(10)
# if neg create neg legend
elif "#FF7F0E" in signage[0:num_of_features] and "#1F77B4" not in signage[0:num_of_features]:
color_ids = {"Negative": "#FF7F0E"}
markers = [plt.Line2D([0, 0], [0, 0], color=color, marker="s", linestyle="")
for color in set(signage[0:num_of_features])]
lgnd = plt.legend(markers, color_ids, numpoints=1, loc="best", frameon=False, fontsize=13)
lgnd.legendHandles[0]._legmarker.set_markersize(10)
# if both provide both colors in legend
else:
color_ids = {"Positive": "#1F77B4", "Negative": "#FF7F0E"}
markers = [plt.Line2D([0, 0], [0, 0], color=color, marker="s", linestyle="")
for color in set(signage[0:num_of_features])]
lgnd = plt.legend(markers, color_ids, numpoints=1, loc="best", frameon=False, fontsize=13)
lgnd.legendHandles[0]._legmarker.set_markersize(10)
lgnd.legendHandles[1]._legmarker.set_markersize(10)
# Hide the right and top spines, color others grey
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_color("#7B7B7B")
ax.spines["left"].set_color("#7B7B7B")
# Only show ticks on the left and bottom spines
# ax.yaxis.set_ticks_position("left")
# ax.xaxis.set_ticks_position("bottom")
plt.yticks(pos[0:num_of_features], feature_labels[0:num_of_features])
plt.tick_params(axis="x", which="minor", bottom="off", top="off", labelbottom="off")
plt.title("Standardized Coef. Magnitudes: H2O GLM", fontsize=20)
# plt.axis("tight")
# show plot
if not server: plt.show()
@staticmethod
def _check_targets(y_actual, y_predicted):
"""Check that y_actual and y_predicted have the same length.
:param H2OFrame y_actual:
:param H2OFrame y_predicted:
:returns: None
"""
if len(y_actual) != len(y_predicted):
raise ValueError("Row mismatch: [{},{}]".format(len(y_actual), len(y_predicted)))
[docs] def cross_validation_models(self):
"""
Obtain a list of cross-validation models.
:returns: list of H2OModel objects.
"""
cvmodels = self._model_json["output"]["cross_validation_models"]
if cvmodels is None: return None
m = []
for p in cvmodels: m.append(h2o.get_model(p["name"]))
return m
[docs] def cross_validation_predictions(self):
"""
Obtain the (out-of-sample) holdout predictions of all cross-validation models on their holdout data.
Note that the predictions are expanded to the full number of rows of the training data, with 0 fill-in.
:returns: list of H2OFrame objects.
"""
preds = self._model_json["output"]["cross_validation_predictions"]
if preds is None: return None
m = []
for p in preds: m.append(h2o.get_frame(p["name"]))
return m
[docs] def cross_validation_holdout_predictions(self):
"""
Obtain the (out-of-sample) holdout predictions of all cross-validation models on the training data.
This is equivalent to summing up all H2OFrames returned by cross_validation_predictions.
:returns: H2OFrame
"""
preds = self._model_json["output"]["cross_validation_holdout_predictions_frame_id"]
if preds is None: return None
return h2o.get_frame(preds["name"])
[docs] def cross_validation_fold_assignment(self):
"""
Obtain the cross-validation fold assignment for all rows in the training data.
:returns: H2OFrame
"""
fid = self._model_json["output"]["cross_validation_fold_assignment_frame_id"]
if fid is None: return None
return h2o.get_frame(fid["name"])
[docs] def rotation(self):
"""
Obtain the rotations (eigenvectors) for a PCA model
:return: H2OFrame
"""
if self._model_json["algo"] != "pca":
raise H2OValueError("This function is available for PCA models only")
return self._model_json["output"]["eigenvectors"]
[docs] def score_history(self):
"""DEPRECATED. Use :meth:`scoring_history` instead."""
return self.scoring_history()
# Deprecated functions; left here for backward compatibility
_bcim = {
"giniCoef": lambda self, *args, **kwargs: self.gini(*args, **kwargs),
}
def _get_matplotlib_pyplot(server):
try:
# noinspection PyUnresolvedReferences
import matplotlib
if server: matplotlib.use("Agg", warn=False)
# noinspection PyUnresolvedReferences
import matplotlib.pyplot as plt
return plt
except ImportError:
print("`matplotlib` library is required for this function!")
return None