8.3 Shared Settings

These settings are common to all of the OML4Py machine learning classes.

The following table lists the settings that are shared by all OML4Py models.

Table 8-1 Shared Model Settings

Setting Name	Setting Value	Description
ODMS_DETAILS	ODMS_ENABLE ODMS_DISABLE	Helps to control model size in the database. Model details can consume significant disk space, especially for partitioned models. The default value is ODMS_ENABLE. If the setting value is ODMS_ENABLE, then model detail tables and views are created along with the model. You can query the model details using SQL. If the value is ODMS_DISABLE, then model detail tables are not created and tables relevant to model details are also not created. The reduction in the space depends on the algorithm. Model size reduction can be on the order of 10x .
ODMS_MAX_PARTITIONS	1 < value <= 1000000	Controls the maximum number of partitions allowed for a partitioned model. The default is 1000.
ODMS_MISSING_VALUE_TREATMENT	ODMS_MISSING_VALUE_AUTO ODMS_MISSING_VALUE_MEAN_MODE ODMS_MISSING_VALUE_DELETE_ROW	Indicates how to treat missing values in the training data. This setting does not affect the scoring data. The default value is ODMS_MISSING_VALUE_AUTO. ODMS_MISSING_VALUE_MEAN_MODE replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. ODMS_MISSING_VALUE_AUTO performs different strategies for different algorithms. When ODMS_MISSING_VALUE_TREATMENT is set to ODMS_MISSING_VALUE_DELETE_ROW, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly. The value ODMS_MISSING_VALUE_DELETE_ROW is applicable to all algorithms.
ODMS_PARTITION_BUILD_TYPE	ODMS_PARTITION_BUILD_INTRA ODMS_PARTITION_BUILD_INTER ODMS_PARTITION_BUILD_HYBRID	Controls the parallel building of partitioned models. ODMS_PARTITION_BUILD_INTRA builds each partition in parallel using all slaves. ODMS_PARTITION_BUILD_INTER builds each partition entirely in a single slave, but multiple partitions may be built at the same time because multiple slaves are active. ODMS_PARTITION_BUILD_HYBRID combines the other two types and is recommended for most situations to adapt to dynamic environments. This is the default value.
ODMS_PARTITION_COLUMNS	Comma separated list of machine learning attributes	Requests the building of a partitioned model. The setting value is a comma-separated list of the machine learning attributes to be used to determine the in-list partition key values. These attributes are taken from the input columns, unless an XFORM_LIST parameter is passed to the model. If XFORM_LIST parameter is passed to the model, then the attributes are taken from the attributes produced by these transformations.
ODMS_TABLESPACE_NAME	tablespace_name	Specifies the tablespace in which to store the model. If you explicitly set this to the name of a tablespace (for which you have sufficient quota), then the specified tablespace storage creates the resulting model content. If you do not provide this setting, then the your default tablespace creates the resulting model content.
ODMS_SAMPLE_SIZE	0 < value	Determines how many rows to sample (approximately). You can use this setting only if ODMS_SAMPLING is enabled. The default value is system determined.
ODMS_SAMPLING	ODMS_SAMPLING_ENABLE ODMS_SAMPLING_DISABLE	Allows the user to request sampling of the build data. The default is ODMS_SAMPLING_DISABLE.
ODMS_TEXT_MAX_FEATURES	1 <= value	The maximum number of distinct features, across all text attributes, to use from a document set passed to the model. The default is 3000. An oml.esa model has the default value of 300000.
ODMS_TEXT_MIN_DOCUMENTS	Non-negative value	This text processing setting controls how many documents a token needs to appear in to be used as a feature. The default is 1. An oml.esa model has the default value of 3.
ODMS_TEXT_POLICY_NAME	The name of an Oracle Text POLICY created using CTX_DDL.CREATE_POLICY.	Affects how individual tokens are extracted from unstructured text. For details about CTX_DDL.CREATE_POLICY, see Oracle Text Reference.
PREP_AUTO	PREP_AUTO_ON PREP_AUTO_OFF	This data preparation setting enables fully automated data preparation. The default is PREP_AUTO_ON.
PREP_SCALE_2DNUM	pPREP_SCALE_STDDEV PREP_SCALE_RANGE	This data preparation setting enables scaling data preparation for two-dimensional numeric columns. PREP_AUTO must be OFF for this setting to take effect. The following are the possible values: PREP_SCALE_STDDEV: A request to divide the column values by the standard deviation of the column and is often provided together with PREP_SHIFT_MEAN to yield z-score normalization. PREP_SCALE_RANGE: A request to divide the column values by the range of values and is often provided together with PREP_SHIFT_MIN to yield a range of [0,1].
PREP_SCALE_NNUM	PREP_SCALE_MAXABS	This data preparation setting enables scaling data preparation for nested numeric columns. PREP_AUTO must be OFF for this setting to take effect. If specified, then the valid value for this setting is PREP_SCALE_MAXABS, which yields data in the range of [-1,1].
PREP_SHIFT_2DNUM	PREP_SHIFT_MEAN PREP_SHIFT_MIN	This data preparation setting enables centering data preparation for two-dimensional numeric columns. PREP_AUTO must be OFF for this setting to take effect. The following are the possible values: PREP_SHIFT_MEAN: Results in subtracting the average of the column from each value. PREP_SHIFT_MIN: Results in subtracting the minimum of the column from each value.