# -*- encoding: utf-8 -*-
from __future__ import absolute_import, division, print_function, unicode_literals
import os
import traceback
import h2o
from h2o.base import Keyed
from h2o.exceptions import H2OValueError
from h2o.job import H2OJob
from h2o.utils.metaclass import BackwardsCompatible, Deprecated as deprecated, h2o_meta
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, assert_satisfies, Enum, is_type
[docs]@BackwardsCompatible(
instance_attrs=dict(
giniCoef=lambda self, *args, **kwargs: self.gini(*args, **kwargs)
)
)
class ModelBase(h2o_meta(Keyed)):
"""Base class for all models."""
def __init__(self):
"""Construct a new model instance."""
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
self._have_pojo = False
self._have_mojo = False
self._start_time = None
self._end_time = None
self._run_time = None
@property
def key(self):
return self._id
@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].get("actual_value") or {}).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
@property
def have_pojo(self):
"""True, if export to POJO is possible"""
return self._have_pojo
@property
def have_mojo(self):
"""True, if export to MOJO is possible"""
return self._have_mojo
@property
def start_time(self):
"""Timestamp (milliseconds since 1970) when the model training was started."""
return self._start_time
@property
def end_time(self):
"""Timestamp (milliseconds since 1970) when the model training was ended."""
return self._end_time
@property
def run_time(self):
"""Model training time in milliseconds"""
return self._run_time
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, type="Path"):
"""
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.
:param Enum type: How to identify the leaf node. Nodes can be either identified by a path from to the root node
of the tree to the node or by H2O's internal node id. One of: ``"Path"``, ``"Node_ID"`` (default: ``"Path"``).
:returns: A new H2OFrame of predictions.
"""
if not isinstance(test_data, h2o.H2OFrame): raise ValueError("test_data must be an instance of H2OFrame")
assert_is_type(type, None, Enum("Path", "Node_ID"))
j = h2o.api("POST /3/Predictions/models/%s/frames/%s" % (self.model_id, test_data.frame_id),
data={"leaf_node_assignment": True, "leaf_node_assignment_type": type})
return h2o.get_frame(j["predictions_frame"]["name"])
[docs] def staged_predict_proba(self, test_data):
"""
Predict class probabilities at each stage of an H2O Model (only GBM models).
The output structure is analogous to the output of function predict_leaf_node_assignment. For each tree t and
class c there will be a column Tt.Cc (eg. T3.C1 for tree 3 and class 1). The value will be the corresponding
predicted probability of this class by combining the raw contributions of trees T1.Cc,..,TtCc. Binomial models
build the trees just for the first class and values in columns Tx.C1 thus correspond to the the probability p0.
:param H2OFrame test_data: Data on which to make predictions.
:returns: A new H2OFrame of staged 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={"predict_staged_proba": True})
return h2o.get_frame(j["predictions_frame"]["name"])
[docs] def predict_contributions(self, test_data):
"""
Predict feature contributions - SHAP values on an H2O Model (only GBM and XGBoost models).
Returned H2OFrame has shape (#rows, #features + 1) - there is a feature contribution column for each input
feature, the last column is the model bias (same value for each row). The sum of the feature contributions
and the bias term is equal to the raw prediction of the model. Raw prediction of tree-based model is the sum
of the predictions of the individual trees before before the inverse link function is applied to get the actual
prediction. For Gaussian distribution the sum of the contributions is equal to the model prediction.
Note: Multinomial classification models are currently not supported.
:param H2OFrame test_data: Data on which to calculate contributions.
:returns: A new H2OFrame made of feature contributions.
"""
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={"predict_contributions": True})
return h2o.get_frame(j["predictions_frame"]["name"])
[docs] def feature_frequencies(self, test_data):
"""
Retrieve the number of occurrences of each feature for given observations
on their respective paths in a tree ensemble model.
Available for GBM, Random Forest and Isolation Forest models.
:param H2OFrame test_data: Data on which to calculate feature frequencies.
:returns: A new H2OFrame made of feature contributions.
"""
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={"feature_frequencies": True})
return h2o.get_frame(j["predictions_frame"]["name"])
[docs] def predict(self, test_data, custom_metric = None, custom_metric_func = None):
"""
Predict on a dataset.
:param H2OFrame test_data: Data on which to make predictions.
:param custom_metric: custom evaluation function defined as class reference, the class get uploaded
into the cluster
:param custom_metric_func: custom evaluation function reference, e.g, result of upload_custom_metric
:returns: A new H2OFrame of predictions.
"""
# Upload evaluation function into DKV
if custom_metric:
assert_satisfies(custom_metric_func, custom_metric_func is None,
"The argument 'eval_func_ref' cannot be specified when eval_func is specified, ")
eval_func_ref = h2o.upload_custom_metric(custom_metric)
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), data = {'custom_metric_func': custom_metric_func}),
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 detach(self):
self._id = None
[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 training_model_metrics(self):
"""
Return training model metrics for any model.
"""
return self._model_json["output"]["training_metrics"]._metric_json
[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 ntrees_actual(self):
"""
Returns actual number of trees in a tree model. If early stopping enabled, GBM can reset the ntrees value.
In this case, the actual ntrees value is less than the original ntrees value a user set before
building the model.
Type: ``float``
"""
tree_algos = ['gbm', 'drf', 'isolationforest', 'xgboost']
if self._model_json["algo"] in tree_algos:
return self.summary()['number_of_trees'][0]
print("No actual number of trees 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)
print()
summary = self.summary()
if summary:
print(summary)
# 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 bool 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 self.algo=='glm' or "variable_importances" in list(model.keys()) and model["variable_importances"]:
if self.algo=='glm':
tempvals = model["standardized_coefficient_magnitudes"].cell_values
maxVal = 0
sum=0
for item in tempvals:
sum=sum+item[1]
if item[1]>maxVal:
maxVal = item[1]
vals = []
for item in tempvals:
tempT = (item[0], item[1], item[1]/maxVal, item[1]/sum)
vals.append(tempT)
header = ["variable", "relative_importance", "scaled_importance", "percentage"]
else:
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.
"""
if self._model_json["output"]['model_category']=="Multinomial":
return self._fillMultinomialDict(False)
else:
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.
"""
if self._model_json["output"]["model_category"]=="Multinomial":
return self._fillMultinomialDict(True)
else:
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"])}
def _fillMultinomialDict(self, standardize=False):
tbl = self._model_json["output"]["coefficients_table_multinomials_with_class_names"]
if tbl is None:
return None
coeff_dict = {} # contains coefficient names
coeffNames = tbl["names"]
all_col_header = tbl.col_header
startIndex = 1
endIndex = int((len(all_col_header)-1)/2+1)
if standardize:
startIndex = int((len(all_col_header)-1)/2+1) # start index for standardized coefficients
endIndex = len(all_col_header)
for nameIndex in list(range(startIndex, endIndex)):
coeffList = tbl[all_col_header[nameIndex]]
t1Dict = {name: coef for name, coef in zip(coeffNames, coeffList)}
coeff_dict[all_col_header[nameIndex]]=t1Dict
return coeff_dict
[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):
if not(v == None) and not(is_type(v, h2o.model.metrics_base.H2OBinomialModelMetrics)):
raise H2OValueError("auc() is only available for Binomial classifiers.")
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 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.
"""
tm = ModelBase._get_metrics(self, train, valid, xval)
m = {}
for k, v in viewitems(tm):
if v is not None and not is_type(v, h2o.model.metrics_base.H2OBinomialModelMetrics):
raise H2OValueError("aucpr() is only available for Binomial classifiers.")
m[k] = None if v is None else v.aucpr()
return list(m.values())[0] if len(m) == 1 else m
[docs] @deprecated(replaced_by=aucpr)
def pr_auc(self, train=False, valid=False, xval=False):
pass
[docs] def download_model(self, path=""):
"""
Download an H2O Model object to disk.
:param model: The model object to download.
:param path: a path to the directory where the model should be saved.
:returns: the path of the downloaded model
"""
assert_is_type(path, str)
return h2o.download_model(self, path)
[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 not self.have_mojo:
raise H2OValueError("Export to MOJO not supported")
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)
if not self.have_mojo:
raise H2OValueError("Export to MOJO not supported")
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 `iterations`
timestep = "iterations"
if metric == "AUTO":
metric = "objective" # this includes the negative log likelihood and the penalties.
elif metric not in ("negative_log_likelihood", "objective"):
raise H2OValueError("for GLM, metric must be one of: negative_log_likelihood, objective")
plt.xlabel(timestep)
plt.ylabel(metric)
plt.title("Validation Scoring History")
style = "b-" if len(scoring_history[timestep]) > 1 else "bx"
plt.plot(scoring_history[timestep], scoring_history[metric], style)
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=None, destination_key=None, nbins=20, weight_column=None,
plot=True, plot_stddev = True, figsize=(7, 10), server=False, include_na=False, user_splits=None,
col_pairs_2dpdp=None, save_to_file=None, row_index=None):
"""
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. If you enable add_missing_NA, the returned length will be nbin+1.
:param weight_column: A string denoting which column of data should be used as the weight column.
: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: Specify whether to activate matplotlib "server" mode. In this case, the plots are saved to a file instead of being rendered.
:param include_na: A boolean specifying whether missing value should be included in the Feature values.
:param user_splits: a dictionary containing column names as key and user defined split values as value in a list.
:param col_pairs_2dpdp: list containing pairs of column names for 2D pdp
:param save_to_file: Fully qualified name to an image file the resulting plot should be saved to, e.g. '/home/user/pdpplot.png'. The 'png' postfix might be omitted. If the file already exists, it will be overridden. Plot is only saved if plot = True.
:param row_index: Row for which partial dependence will be calculated instead of the whole input frame.
: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")
num_1dpdp = 0
num_2dpdp = 0
if not(cols==None):
assert_is_type(cols, [str])
num_1dpdp = len(cols)
if not(col_pairs_2dpdp==None):
assert_is_type(col_pairs_2dpdp, [[str, str]])
num_2dpdp=len(col_pairs_2dpdp)
if (cols==None) and (col_pairs_2dpdp==None):
raise ValueError("must specify either cols or col_pairs_2dpd to generate partial dependency plots")
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
if not(cols==None):
for xi in cols:
if xi not in data.names:
raise H2OValueError("Column %s does not exist in the training frame" % xi)
if not(col_pairs_2dpdp==None):
for oneP in col_pairs_2dpdp:
if oneP[0] not in data.names:
raise H2OValueError("Column %s does not exist in the training frame" % oneP[0])
if oneP[1] not in data.names:
raise H2OValueError("Column %s does not exist in the training frame" % oneP[1])
if oneP[0]==oneP[1]:
raise H2OValueError("2D pdp must be with different columns.")
if isinstance(weight_column, int) and not (weight_column == -1):
raise H2OValueError("Weight column should be a column name in your data frame.")
elif isinstance(weight_column, str): # index is a name
if weight_column not in data.names:
raise H2OValueError("Column %s does not exist in the data frame" % weight_column)
weight_column = data.names.index(weight_column)
if row_index:
if not isinstance(row_index, int):
raise H2OValueError("Row index should be of type int.")
else:
row_index = -1
kwargs = {}
kwargs["cols"] = cols
kwargs["model_id"] = self.model_id
kwargs["frame_id"] = data.frame_id
kwargs["nbins"] = nbins
kwargs["destination_key"] = destination_key
kwargs["weight_column_index"] = weight_column
kwargs["add_missing_na"] = include_na
kwargs["row_index"] = row_index
kwargs["col_pairs_2dpdp"] = col_pairs_2dpdp
self.__generate_user_splits(user_splits, data, kwargs)
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
self.__generate_partial_plots(num_1dpdp, num_2dpdp, plot, server, pps, figsize, col_pairs_2dpdp, data, nbins,
kwargs["user_cols"], kwargs["num_user_splits"], plot_stddev, cols, save_to_file)
return pps
def __generate_user_splits(self, user_splits, data, kwargs):
# extract user defined split points from dict user_splits into an integer array of column indices
# and a double array of user define values for the corresponding columns
if not(user_splits == None) and (len(user_splits) > 0):
if not(isinstance(user_splits, dict)):
raise H2OValueError("user_splits must be a Python dict.")
else:
user_cols = []
user_values = []
user_num_splits = []
data_ncol = data.ncol
column_names = data.names
for colKey,val in user_splits.items():
if is_type(colKey, str) and colKey in column_names:
user_cols.append(colKey)
elif isinstance(colKey, int) and colKey < data_ncol:
user_cols.append(column_names[colKey])
else:
raise H2OValueError("column names/indices used in user_splits are not valid. They "
"should be chosen from the columns of your data set.")
if data[colKey].isfactor()[0] or data[colKey].isnumeric()[0]: # replace enum string with actual value
nVal = len(val)
if data[colKey].isfactor()[0]:
domains = data[colKey].levels()[0]
numVal = [0]*nVal
for ind in range(nVal):
if (val[ind] in domains):
numVal[ind] = domains.index(val[ind])
else:
raise H2OValueError("Illegal enum value {0} encountered. To include missing"
" values in your feature values, set include_na to "
"True".format(val[ind]))
user_values.extend(numVal)
else:
user_values.extend(val)
user_num_splits.append(nVal)
else:
raise H2OValueError("Partial dependency plots are generated for numerical and categorical "
"columns only.")
kwargs["user_cols"] = user_cols
kwargs["user_splits"] = user_values
kwargs["num_user_splits"] = user_num_splits
else:
kwargs["user_cols"] = None
kwargs["user_splits"] = None
kwargs["num_user_splits"] = None
def __generate_partial_plots(self, num_1dpdp, num_2dpdp, plot, server, pps, figsize, col_pairs_2dpdp, data, nbins,
user_cols, user_num_splits, plot_stddev, cols, save_to_file):
# Plot partial dependence plots using matplotlib
totFig = num_1dpdp+num_2dpdp
if plot and totFig>0: # plot 1d pdp for now
plt = _get_matplotlib_pyplot(server)
if not plt: return pps
import matplotlib.gridspec as gridspec
fig = plt.figure(figsize=figsize)
gxs = gridspec.GridSpec(totFig, 1)
if num_2dpdp>0: # 2d pdp requested
axes3D = _get_mplot3d_pyplot("2D partial plots")
cm = _get_matplotlib_cm("2D partial plots")
figPlotted = False # indicated number of figures plotted
for i, pp in enumerate(pps):
if (i >= num_1dpdp): # plot 2D pdp
if (axes3D==None) or (cm==None) or (plt==None): # quit if cannot find toolbox
break
figPlotted = self.__plot_2dpdp(fig, col_pairs_2dpdp, gxs, num_1dpdp, data, pp, nbins, user_cols,
user_num_splits, plot_stddev, cm, i)
else: # plot 1D pdp
figPlotted = self.__plot_1dpdp(cols, i, data, pp, fig, gxs, plot_stddev)
if figPlotted:
fig.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
else:
print("No partial plot is generated and/or saved. You may be missing toolboxes like "
"mpl_toolkits.mplot3d, matplotlib")
if (save_to_file is not None) and figPlotted: # only save when a figure is actually plotted
plt.savefig(save_to_file)
def __plot_2dpdp(self, fig, col_pairs_2dpdp, gxs, num_1dpdp, data, pp, nbins, user_cols, user_num_splits, plot_stddev, cm, i):
ax = fig.add_subplot(gxs[i], projection='3d')
colPairs = col_pairs_2dpdp[i-num_1dpdp]
x = self.__grabValues(pp, 0, data, colPairs[0], ax) # change to numpy 2d_array
y = self.__grabValues(pp, 1, data, colPairs[1], ax)
X,Y,Z = self.__predFor3D(x,y,pp[2], colPairs, nbins, user_cols, user_num_splits)
zupper = [a + b for a, b in zip(pp[2], pp[3]) ] # pp[1] is mean, pp[2] is std
zlower = [a - b for a, b in zip(pp[2], pp[3]) ]
_,_,Zupper = self.__predFor3D(x,y,zupper, colPairs, nbins, user_cols, user_num_splits)
_,_,Zlower = self.__predFor3D(x,y,zlower, colPairs, nbins, user_cols, user_num_splits)
ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,linewidth=1, antialiased=False, alpha=0.5, edgecolor='k')
if plot_stddev:
ax.plot_surface(X, Y, Zupper, cmap=cm.coolwarm,linewidth=0.2, antialiased=False, alpha=0.3, edgecolor='y')
ax.plot_surface(X, Y, Zlower, cmap=cm.coolwarm,linewidth=0.2, antialiased=False, alpha=0.3, edgecolor='g')
ax.set_xlabel(colPairs[0])
ax.set_xlim(min(x), max(x))
ax.set_ylabel(colPairs[1])
ax.set_ylim(min(y), max(y))
ax.set_zlim(min([min(zupper), min(zlower), min(pp[2])]), max([max(zupper), max(zlower), max(pp[2])]))
ax.set_zlabel('Partial dependence')
titles = '2D partial dependence plot for '+colPairs[0] + ' and '+colPairs[1]
ax.set_title(titles)
return True
def __plot_1dpdp(self, cols, i, data, pp, fig, gxs, plot_stddev):
col = cols[i]
cat = data[col].isfactor()[0]
upper = [a + b for a, b in zip(pp[1], pp[2]) ] # pp[1] is mean, pp[2] is std
lower = [a - b for a, b in zip(pp[1], pp[2]) ]
axs = fig.add_subplot(gxs[i])
self.__setAxs1D(axs, upper, lower, plot_stddev, cat, pp, 0, col) # setup graph, axis, labels and ...
return True
# change x, y, z to be 2-D numpy arrays in order to plot it.
# note that, x stays at one value for the duration of y value changes.
def __predFor3D(self, x, y, z, colPairs, nbins, user_cols, user_num_splits):
# deal with y axis first
np = _get_numpy("2D partial plots")
if np==None:
print("Numpy not found. Cannot plot 2D partial plots.")
ycol = colPairs[1]
nBins = nbins
if user_cols is not None and ycol in user_cols:
ind = user_cols.index(ycol)
nBins = user_num_splits[ind]
nrow = int(len(x)/nBins)
X = np.transpose(np.array(x).reshape(nrow, nBins))
Y = np.transpose(np.array(y).reshape(nrow, nBins))
Z = np.transpose(np.array(z).reshape(nrow, nBins))
return X,Y,Z
def __grabValues(self, pp, index, data, col, axs):
cat = data[col].isfactor()[0]
if cat:
labels = pp[index]
uniqueL =list(set(labels))
x = range(len(uniqueL))
xlab = [None]*len(uniqueL)
for ind in range(len(uniqueL)):
xlab[ind] = labels[labels.index(uniqueL[ind])]
# replace string enum labels with integer values
xext = [None]*len(labels)
for ind in range(len(labels)):
xext[ind] = labels.index(labels[ind])
if index==0: # x-axis
axs.set_xticks(x)
axs.set_xticklabels(xlab)
else: # y-axis
axs.set_yticks(x)
axs.set_yticklabels(labels)
axs.margins(0.2)
return xext
else:
return pp[index]
def __setAxs1D(self, axs, upper, lower, plot_stddev, cat, pp, pp_start_index, col):
if cat:
labels = pp[pp_start_index] # 1d pdp, this is 0
x = range(len(labels))
y = pp[pp_start_index+1]
axs.plot(x, y, "ro")
if plot_stddev:
axs.plot(x, lower, 'b--')
axs.plot(x, upper, 'b--')
axs.set_ylim(min(lower) - 0.1*abs(min(lower)), max(upper) + 0.1*abs(max(upper)))
axs.set_xticks(x)
axs.set_xticklabels(labels)
axs.margins(0.2)
else:
x = pp[pp_start_index]
y = pp[pp_start_index+1]
axs.plot(x, y, "r-")
if plot_stddev:
axs.plot(x, lower, 'b--')
axs.plot(x, upper, 'b--')
axs.set_xlim(min(x), max(x))
axs.set_ylim(min(lower) - 0.1*abs(min(lower)), max(upper) + 0.1*abs(max(upper)))
axs.set_title("Partial Dependence Plot For {}".format(col))
axs.set_xlabel(pp.col_header[pp_start_index])
axs.set_ylabel(pp.col_header[pp_start_index+1])
axs.xaxis.grid()
axs.yaxis.grid()
[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
# 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(None, 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()
elif self._model_json["algo"] == "glm":
plt.title("Variable Importance: H2O GLM", 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(None, 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(None, 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",)
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",)
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", "Negative")
markers = [plt.Line2D([0, 0], [0, 0], color=color, marker="s", linestyle="")
for color in ['#1F77B4', '#FF7F0E']] # blue should always be positive, orange negative
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()
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
def _get_mplot3d_pyplot(functionName):
try:
# noinspection PyUnresolvedReferences
from mpl_toolkits.mplot3d import Axes3D
return Axes3D
except ImportError:
print("`mpl_toolkits.mplot3d` library is required for function {0}!".format(functionName))
return None
def _get_numpy(functionName):
try:
import numpy as np
return np
except ImportError:
print("`numpy` library is required for function {0}!".format(functionName))
return None
def _get_matplotlib_cm(functionName):
try:
from matplotlib import cm
return cm
except ImportError:
print('matplotlib library is required for 3D plots for function {0}'.format(functionName))
return None