RDTRegressor

class hana_ml.algorithms.pal.trees.RDTRegressor(n_estimators=100, max_features=None, max_depth=None, min_samples_leaf=None, split_threshold=None, calculate_oob=True, random_state=None, thread_ratio=None, allow_missing_dependent=True, categorical_variable=None, sample_fraction=None, compression=None, max_bits=None, quantize_rate=None, fittings_quantization=None, model_format=None)

Random Decision Tree model for regression.

Parameters

n_estimatorsint, optional

Specifies the number of decision trees in the model.

Defaults to 100.

max_featuresint, optional

Specifies the number of randomly selected splitting variables.

Should not be larger than the number of input features.

Defaults to p/3, where p is the number of input features.

max_depthint, optional

The maximum depth of a tree, where -1 means unlimited.

Default to 56.

min_samples_leafint, optional

Specifies the minimum number of records in a leaf.Defaults to 5 for regression.

split_thresholdfloat, optional

Specifies the stop condition: if the improvement value of the best split is less than this value, the tree stops growing.Defaults to 1e-5.

calculate_oobbool, optional

If True, calculate the out-of-bag error.

Defaults to True.

random_stateint, optional

Specifies the seed for random number generator.

0: Uses the current time (in seconds) as the seed.

Others: Uses the specified value as the seed.

Defaults to 0.

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 up to 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 a missing target is removed.

Defaults to True.

categorical_variablestr or list of str, optional

Specifies INTEGER column(s) that should be treated as categorical. The default behavior is: string: categorical, or integer and float: continuous. VALID only for integer variables; omitted otherwise.

Default value detected from input data.

sample_fractionfloat, optional

The fraction of data used for training.

Assume there are n pieces of data, sample fraction is r, then n*r data is selected for training.

Defaults to 1.0.

compressionbool, optional

Specifies if the model is stored in compressed format.

Default value depends on the SAP HANA Version. Please refer to the corresponding documentation of SAP HANA PAL.

max_bitsint, optional

The maximum number of bits to quantize continuous features, equivalent to use \(2^{max\_bits}\) bins.

Must be less than 31.

Valid only when the value of compression is True.

Defaults to 12.

quantize_ratefloat, optional

Quantizes a categorical feature if the largest class frequency of the feature is less than quantize_rate.

Valid only when compression is True.

Defaults to 0.005.

fittings_quantizationint, optional

Indicates whether to quantize fitting values.

Valid only for regression when compression is True.

Defaults to False.

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.

Not effective if compression is True, in which case the model is stored in neither json nor pmml, but compressed format.

Defaults to 'pmml'.

References

Parameters compression, max_bits, quantize_rate and fittings_quantization are for compressing Random Decision Trees regression model, please see Model Compression for more details about this topic.

Examples

Input dataframe for training:

>>> df1.head(5).collect()
   ID         A         B         C         D       CLASS
 0 -0.965679  1.142985 -0.019274 -1.598807  -23.633813
 1  2.249528  1.459918  0.153440 -0.526423  212.532559
 2 -0.631494  1.484386 -0.335236  0.354313   26.342585
 3 -0.967266  1.131867 -0.684957 -1.397419  -62.563666
 4 -1.175179 -0.253179 -0.775074  0.996815 -115.534935

Creating RDTRegressor instance:

>>> rfr = RDTRegressor(random_state=3)

Performing fit() on given dataframe:

>>> rfr.fit(df1, key='ID')
>>> rfr.feature_importances_.collect()
   VARIABLE_NAME  IMPORTANCE
0             A    0.249593
1             B    0.381879
2             C    0.291403
3             D    0.077125

Input dataframe for predicting:

>>> df2.collect()
   ID         A         B         C         D
0   0  1.081277  0.204114  1.220580 -0.750665
1   1  0.524813 -0.012192 -0.418597  2.946886

Performing predict() on given dataframe:

>>> result = rfr.predict(df2, key='ID')
>>> result.collect()
   ID    SCORE  CONFIDENCE
0   0    48.126   62.952884
1   1  -10.9017   73.461039

Input dataframe for scoring:

>>> df3.head(5).collect()
    ID         A         B         C         D       CLASS
  0  1.081277  0.204114  1.220580 -0.750665   139.10170
  1  0.524813 -0.012192 -0.418597  2.946886    52.17203
  2 -0.280871  0.100554 -0.343715 -0.118843   -34.69829
  3 -0.113992 -0.045573  0.957154  0.090350    51.93602
  4  0.287476  1.266895  0.466325 -0.432323   106.63425

Performing score() on given dataframe:

>>> rfr.score(df3, key='ID')
0.6530768858159514

Attributes

model_DataFrame: Trained model content.
feature_importances_DataFrame: The feature importance (the higher, the more important the feature).
oob_error_DataFrame: Out-of-bag error rate or mean squared error for random decision trees up to indexed tree. Set to None if calculate_oob is False.

Methods

`create_model_state`([model, function, ...])	Create PAL model state.
`delete_model_state`([state])	Delete PAL model state.
`fit`(data[, key, features, label, ...])	Train the model on the input data.
`predict`(data[, key, features, verbose, ...])	Predict dependent variable values based on fitted model.
`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)

Train the model on the input data.

Parameters

dataDataFrame

Training data.

keystr, optional

Name of the ID column in data.

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 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.

No default value.

Returns

Fitted object.

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

Predict dependent variable values based on fitted model.

Parameters

dataDataFrame

Independent variable values to predict for.

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 columns.

verbosebool, optional

If true, output all classes and the corresponding confidences for each data point. Only valid classification.

Default to False if not provided.

block_sizeint, optional

The number of rows loaded per time during prediction.

0 indicates load all data at once.

Defaults to 0.

missing_replacementstr, optional

The missing replacement strategy:

'feature_marginalized': marginalise each missing feature out independently.
'instance_marginalized': marginalise all missing features in an instance as a whole corresponding to each category.

Defaults to feature_marginalized.

Returns

DataFrame

DataFrame of score and confidence, structured as follows:

ID column, with same name and type as data's ID column.

SCORE, type DOUBLE, representing the predicted values.

CONFIDENCE, all 0s. It is included due to the fact PAL uses the same table for classification.

score(data, key=None, features=None, label=None, block_size=None, missing_replacement=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 last non-ID column.

block_sizeint, optional

The number of rows loaded per time during prediction.

0 indicates load all data at once.

Defaults to 0.

missing_replacementstr, optional

The missing replacement strategy:

'feature_marginalized': marginalise each missing feature out independently.

'instance_marginalized': marginalise all missing features in an instance as a whole corresponding to each category.

Defaults to feature_marginalized.

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_RANDOM_DECISION_TREES', 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 RDT.

pal_funcnameint or str, optional

PAL function name.

Defaults to 'PAL_RANDOM_DECISION_TREES'.

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 RDTRegressor class also inherits methods from PALBase class, please refer to PAL Base for more details.