DecisionTreeRegressor

class hana_ml.algorithms.pal.trees.DecisionTreeRegressor(algorithm='cart', thread_ratio=None, allow_missing_dependent=True, percentage=None, min_records_of_parent=None, min_records_of_leaf=None, max_depth=None, categorical_variable=None, split_threshold=None, use_surrogate=None, model_format=None, output_rules=True, output_confusion_matrix=True, resampling_method=None, fold_num=None, repeat_times=None, evaluation_metric=None, timeout=None, search_strategy=None, random_search_times=None, progress_indicator_id=None, param_values=None, param_range=None)

Decision Tree model for regression.

Parameters

algorithm{'cart'}, optional

Algorithm used to grow a decision tree.

'cart': Classification and Regression tree.

If not specified, defaults to 'cart'.

thread_ratiofloat, optional

Controls the proportion of available threads to use.

The value range is from 0 to 1, where 0 indicates a single thread, and 1 indicates up to all available threads.

Values between 0 and 1 will use that percentage of available threads.

Values outside this range tell PAL to heuristically determine the number of threads to use.

Defaults to -1.

allow_missing_dependentbool, optional

Specifies if a missing target value is allowed.

False: Not allowed. An error occurs if a missing target is present.

True: Allowed. The datum with the missing target is removed.

Defaults to True.

percentagefloat, optional

Specifies the percentage of the input data that will be used to build the tree model.

The rest of the data will be used for pruning.

Defaults to 1.0.

min_records_of_parentint, optional

Specifies the stop condition: if the number of records in one node is less than the specified value, the algorithm stops splitting.

Defaults to 2.

min_records_of_leafint, optional

Promises the minimum number of records in a leaf.

Defaults to 1.

max_depthint, optional

The maximum depth of a tree.

By default it is unlimited.

categorical_variablestr or list of str, optional

Specifies INTEGER column(s) that should be treated as categorical.

The default behavior is:

string: categorical,

integer and float: continuous.

VALID only for integer variables, ignored otherwise.

Default value detected from input data.

split_thresholdfloat, optional

Specifies the stop condition for a node:

CART: The reduction of Gini index or relative MSE of the best split is less than this value.

The smaller the split_threshold value is, the larger a CART tree grows.

Defaults to 1e-5 for CART.

use_surrogatebool, optional

If true, use surrogate split when NULL values are encountered.

Defaults to True.

model_format{'json', 'pmml'}, optional

Specifies the tree model format for store. Case-insensitive.

'json': export model in json format.

'pmml': export model in pmml format.

Defaults to json.

output_rulesbool, optional

If true, output decision rules.

Defaults to True.

resampling_method{'cv', 'bootstrap'}, optional

The resampling method for model evaluation or parameter search. Once set, model evaluation or parameter search is enabled.

No default value.

evaluation_metric{'mae', 'rmse'}, optional

The evaluation metric. Once resampling_method is set, this parameter must be set.

No default value.

fold_numint, optional

The fold number for cross validation.

Valid only and mandatory when resampling_method is set as 'cv'.

No default value.

repeat_timesint, optional

The number of repeated times for model evaluation or parameter search.

Defaults to 1.

timeoutint, optional

The time allocated (in seconds) for program running.

0 indicates unlimited.

Defaults to 0.

search_strategy{'random', 'grid'}, optional

The search strategy for parameters.

If not specified, parameter selection cannot be carried out.

No default value.

random_search_timesint, optional

Specifies the number of search times for random search.

Only valid and mandatory when search_strategy is set as 'random'.

No default value.

progress_indicator_idstr, optional

Sets an ID of progress indicator for model evaluation or parameter selection.

No progress indicator is active if no value is provided.

No default value.

param_valuesdict or ListOfTuples, optional

Specifies values of parameters to be selected.

Input should be a dict or a list of size-two tuples, with key/1st element being the target parameter name, while value/2nd element being the a list of valued for selection.

Only valid when resampling_method and search_strategy are both specified.

Valid Parameters for values specification include :

split_threshold, max_depth, min_records_of_leaf, min_records_of_parent.

No default value.

param_rangedict or ListOfTuples, optional

Specifies ranges of parameters to be selected.

Input should be dict or list of size-two tuples, with key/1st element being the name of the target parameter(in string format), while value/2nd element specifies the range of that parameter with [start, step, end] or [start, end].

Valid only when resampling_method and search_strategy are both specified.

Valid Parameters for range specification include:

split_threshold, max_depth, min_records_of_leaf, min_records_of_parent.

No default value.

Examples

Input dataframe for training:

>>> df1.head(5).collect()
   ID         A         B         C         D      CLASS
 0  1.764052  0.400157  0.978738  2.240893  49.822907
 1  1.867558 -0.977278  0.950088 -0.151357   4.877286
 2 -0.103219  0.410598  0.144044  1.454274  11.914875
 3  0.761038  0.121675  0.443863  0.333674  19.753078
 4  1.494079 -0.205158  0.313068 -0.854096  23.607000

Creating DecisionTreeRegressor instance:

>>>  dtr = DecisionTreeRegressor(algorithm='cart',
...                              min_records_of_parent=2, min_records_of_leaf=1,
...                              thread_ratio=0.4, split_threshold=1e-5,
...                              model_format='pmml', output_rules=True)

Performing fit() on given dataframe:

>>> dtr.fit(df1, key='ID')
>>> dtr.decision_rules_.head(2).collect()
   ROW_INDEX                                      RULES_CONTENT
0          0         (A<-0.495502) && (B<-0.663588) => -85.8762
1          1        (A<-0.495502) && (B>=-0.663588) => -29.9827

Input dataframe for predicting:

>>> df2.collect()
   ID         A         B         C         D
 0  1.764052  0.400157  0.978738  2.240893
 1  1.867558 -0.977278  0.950088 -0.151357
 2 -0.103219  0.410598  0.144044  1.454274
 3  0.761038  0.121675  0.443863  0.333674
 4  1.494079 -0.205158  0.313068 -0.854096

Performing predict() on given dataframe:

>>> result = dtr.predict(df2, key='ID')
>>> result.collect()
   ID    SCORE  CONFIDENCE
0   0  49.8229         0.0
1   1  4.87728         0.0
2   2  11.9148         0.0
3   3   19.753         0.0
4   4   23.607         0.0

Input dataframe for scoring:

>>> df3.collect()
   ID         A         B         C         D      CLASS
 0  1.764052  0.400157  0.978738  2.240893  49.822907
 1  1.867558 -0.977278  0.950088 -0.151357   4.877286
 2 -0.103219  0.410598  0.144044  1.454274  11.914875
 3  0.761038  0.121675  0.443863  0.333674  19.753078
 4  1.494079 -0.205158  0.313068 -0.854096  23.607000

Performing score() on given dataframe:

>>> dtr.score(df3, key='ID')
0.9999999999900131

Attributes

model_DataFrame: Trained model content.
decision_rules_DataFrame: Rules for decision tree to make decisions. Set to None if output_rules is False.
stats_DataFrame: Statistics information.
cv_DataFrame: Cross validation information. Only has content when parameter selection is enabled.

Methods

`create_model_state`([model, function, ...])	Create PAL model state.
`delete_model_state`([state])	Delete PAL model state.
`fit`(data[, key, features, label, ...])	Function for building a decision tree classifier.
`predict`(data[, key, features, verbose])	Prediction function for a fitted DecisionTreeClassifier.
`score`(data[, key, features, label])	Returns the coefficient of determination R^2 of the prediction.
`set_model_state`(state)	Set the model state by state information.

fit(data, key=None, features=None, label=None, categorical_variable=None)

Function for building a decision tree classifier.

Parameters

dataDataFrame

Training data.

keystr, optional

Name of the ID column.

If key is not provided, then:

if data is indexed by a single column, then key defaults to that index column;
otherwise, it is assumed that data contains no ID column.

featureslist of str, optional

Names of the feature columns. If features is not provided, it defaults to all non-ID, non-label columns.

labelstr, optional

Name of the dependent variable. Defaults to the name of last non-ID column.

categorical_variablestr or list of str, optional

Specifies INTEGER column(s) that should be treated as categorical.

Other INTEGER columns will be treated as continuous.

Returns

Fitted object.

predict(data, key=None, features=None, verbose=None)

Prediction function for a fitted DecisionTreeClassifier.

Parameters

dataDataFrame

DataFrame containing the data.

keystr, optional

Name of the ID column in data.

Mandatory if data is not indexed, or the index of data contains multiple columns.

Defaults to the single index column of data if not provided.

featureslist of str, optional

Names of the feature columns.

If features is not provided, it defaults to all the non-ID columns.

verbosebool, optional(deprecated)

Specifies whether to output all classes and the corresponding confidences for each data record in data.

Non-effective, reserved only for forward compatibility.

Returns

DataFrame

Predict result, structured as follows:

ID column, with the same name and type as the ID column in data.

SCORE, type NVARCHAR(100), predicted values.

CONFIDENCE, type DOUBLE, all 0s.

score(data, key=None, features=None, label=None)

Returns the coefficient of determination R^2 of the prediction.

Parameters

dataDataFrame

Data on which to assess model performance.

keystr, optional

Name of the ID column.

Mandatory if data is not indexed, or the index of data contains multiple columns.

Defaults to the single index column of data if not provided.

featureslist of str, optional

Names of the feature columns. If features is not provided, it defaults to all non-ID, non-label columns.

labelstr, optional

Name of the dependent variable.

Defaults to the name of the last non-ID column.

Returns

float: The coefficient of determination R^2 of the prediction on the given data.

create_model_state(model=None, function=None, pal_funcname='PAL_DECISION_TREE', state_description=None, force=False)

Create PAL model state.

Parameters

modelDataFrame, optional

Specify the model for AFL state.

Defaults to self.model_.

functionstr, optional

Specify the function in the unified API.

A placeholder parameter, not effective for Decision Tree.

pal_funcnameint or str, optional

PAL function name. Should be a valid PAL procedure name that supports model state.

Defaults to 'PAL_DECISION_TREE'.

state_descriptionstr, optional

Description of the state as model container.

Defaults to None.

forcebool, optional

If True it will delete the existing state.

Defaults to False.

delete_model_state(state=None)

Delete PAL model state.

Parameters

stateDataFrame, optional

Specified the state.

Defaults to self.state.

property fit_hdbprocedure: Returns the generated hdbprocedure for fit.

property predict_hdbprocedure: Returns the generated hdbprocedure for predict.

set_model_state(state)

Set the model state by state information.

Parameters

state: DataFrame or dict

If state is DataFrame, it has the following structure:

NAME: VARCHAR(100), it mush have STATE_ID, HINT, HOST and PORT.

VALUE: VARCHAR(1000), the values according to NAME.

If state is dict, the key must have STATE_ID, HINT, HOST and PORT.

Inherited Methods from PALBase

Besides those methods mentioned above, the DecisionTreeRegressor class also inherits methods from PALBase class, please refer to PAL Base for more details.