#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#-----------------------------------------------------------------------------
# Copyright (c) 2023, IBM Corp.
# All rights reserved.
#
# Distributed under the terms of the BSD Simplified License.
#
# The full license is in the LICENSE file, distributed with this software.
#-----------------------------------------------------------------------------
"""
Regression trees are decision trees adapted to the regression task, which store
numeric target attribute values instead of class labels in leaves, and use
appropriately modified split selection and stop criteria.
As with decision trees, regression tree nodes decompose the data into subsets,
and regression tree leaves correspond to sufficiently small or sufficiently
uniform subsets. Splits are selected to decrease the dispersion of target
attribute values, so that they can be reasonably well predicted by their mean
values at leaves. The resulting model is piecewise-constant, with fixed
predicted values assigned to regions to which the domain is decomposed by
the tree structure.
"""
from typing import List
from nzpyida.frame import IdaDataFrame
from nzpyida.base import IdaDataBase
from nzpyida.analytics.predictive.regression import Regression
from nzpyida.analytics.utils import map_to_props, q
[docs]
class DecisionTreeRegressor(Regression):
"""
Decision tree based regressor
"""
def __init__(self, idadb: IdaDataBase, model_name: str):
"""
Creates the regressor class.
Parameters
----------
idada : IdaDataBase
database connector
model_name : str
model name - if it exists in the database, it will be used, otherwise
it must be trained using fit() function before prediction or scoring is called.
"""
super().__init__(idadb, model_name)
self.fit_proc = 'REGTREE'
self.predict_proc = 'PREDICT_REGTREE'
self.target_column_in_output = idadb.to_def_case("CLASS")
self.has_print_proc = True
[docs]
def fit(self, in_df: IdaDataFrame, target_column: str, id_column: str=None,
in_columns: List[str]=None, col_def_type: str=None, col_def_role: str=None,
col_properties_table: str=None, eval_measure: str=None, min_improve: float=0.1,
min_split: int=50, max_depth: int=10, val_table: str=None, qmeasure: str=None,
statistics: str=None):
"""
This function creates a regression tree model based on provided data and
store it in a database.
Parameters
----------
in_df : IdaDataFrame
the input data frame
target_column : str
the input table column representing the prediction target, definition of multitargets
can be processed by 'incolumn' parameter and column properties.
id_column : str, optional
the input table column identifying a unique instance id - if skipped,
the input data frame indexer must be set and will be used as an instance id
nominal_colums : str, optional
the input table nominal columns, if any, separated by a semi-colon (;).
Parameter 'nominalCols' is deprecated please use 'incolumn' intead.
in_columns : List[str], optional
the list of input table columns with special properties.
Each column is followed by one or several of the following properties:
its type: ':nom' (for nominal), ':cont' (for continuous).
Per default, all numerical types are con-tinuous, other types are nominal.
its role: ':id', ':target', ':input', ':ignore'.
(Remark: ':objweight' is unsupported, i.e. ':objweight' same as ':ignore').
(Remark: ':colweight(<wgt>)' is unsupported, i.e. ':colweight(<wgt>)' same
as ':colweight(1)' same as ':input').
If the parameter is undefined, all columns of the input table have default properties.
col_def_type : str, optional
default type of the input table columns. Allowed values are 'nom' and 'cont'.
If the parameter is undefined, all numeric columns are considered continuous,
other columns nominal.
col_def_role : str, optional
default role of the input table columns. Allowed values are 'input' and 'ignore'.
If the parameter is undefined, all columns are considered 'input' columns.
col_properties_table : str, optional
the input table where column properties for the input table columns are stored.
The format of this table is the output format of stored procedure
nza..COLUMN_PROPERTIES().
If the parameter is undefined, the input table column properties will be
detected automatically.
(Remark: colPropertiesTable with "COLROLE" column with value 'objweight' is
unsupported, i.e. same as 'ignore')
(Remark: colPropertiesTable with "COLWEIGHT" column with value '<wgt>' is unsup-ported,
i.e. same as '1')
eval_measure : str, optional
the split evaluation measure. Allowed values are: variance.
min_improve : float, optional
the minimum improvement of the split evaluation measure required
min_split : int, optional
the minimum number of instances per tree node that can be split
max_depth : int, optional
the maximum number of tree levels (including leaves)
val_table : str, optional
the input table containing the validation dataset.
If this parameter is undefined, no pruning will be performed.
qmeasure : str, optional
the quality measure for pruning the tree. Allowed values are: mse, r2.
statistics : str, optional
flags indicating which statistics to collect.
Allowed values are: none, columns, values:n, all.
If statistics=none, no statistics are collected.
If statistics=columns, statistics on the input table columns like mean value
are collected.
If statistics=values:n with n a positive number, statistics about the columns
and the column val-ues are collected. Up to <n> column value statistics
are collected:
If a nominal column contains more than <n> values, only the <n> most frequent
column stat-istics are kept.
If a numeric column contains more than <n> values, the values will be discretized
and the stat-istics will be collected on the discretized values.
Indicating statistics=all is equal to statistics=values:100.
"""
params = {
'id': q(id_column),
'target': q(target_column),
'incolumn': q(in_columns),
'coldeftype': col_def_type,
'coldefrole': col_def_role,
'colpropertiestable': col_properties_table,
'eval': eval_measure,
'minimprove': min_improve,
'minsplit': min_split,
'maxdepth': max_depth,
'valtable': val_table,
'qmeasure': qmeasure,
'statistics': statistics
}
self._fit(in_df=in_df, params=params)
[docs]
def predict(self, in_df: IdaDataFrame, out_table: str=None,
id_column: str=None, variance: bool=False):
"""
Makes predictions based on this model. The model must exist.
Parameters
----------
in_df : IdaDataFrame
the input data frame to predict
out_table : str, optional
the output table where the predictions will be stored
id_column : str, optional
the input table column identifying a unique instance id
Default: id column used to build the model
variance : bool, optional
a flag indicating whether the variance of the predictions should be included
into the output table
Returns
-------
IdaDataFrame
the data frame containing row identifiers and predicted target values
"""
params = {
'id': q(id_column),
'var': variance
}
return self._predict(in_df=in_df, params=params, out_table=out_table)