9.8 Association Rules
The oml.ar class implements the Apriori algorithm to find frequent itemsets and association rules, all as part of an association model object.
The Apriori algorithm is efficient and scales well with respect to the number of transactions, number of items, and number of itemsets and rules produced.
Use the oml.ar class to identify frequent itemsets within large volumes of transactional data, such as in market basket analysis. The results of an association model are the rules that identify patterns of association within the data.
An association rule identifies a pattern in the data in which the appearance of a set of items in a transactional record implies another set of items. The groups of items used to form rules must pass a minimum threshold according to how often they occur (the support of the rule) and how often the consequent follows the antecedent (the confidence of the rule). Association models generate all rules that have support and confidence greater than user-specified thresholds.
Oracle Machine Learning does not support the scoring operation for association modeling.
For information on the oml.ar class attributes and methods, invoke help(oml.ar) or see Oracle Machine Learning for Python API Reference.
Settings for an Association Rules Model
The following table lists the settings applicable to association rules models.
Table 9-3 Association Rules Models Settings
| Setting Name | Setting Value | Description |
|---|---|---|
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0<ASSO_ABS_ERROR≤MAX(ASSO_MIN_SUPPORT, ASSO_MIN_CONFIDENCE) |
Specifies the absolute error for the association rules sampling. A smaller value of |
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Specifies the columns to aggregate. It is a comma separated list of strings containing the names of the columns for aggregation. The number of columns in the list must be <= 10. You can set An item value is not mandatory. The default value is For each item, you may supply several columns to aggregate. However, doing so requires more memory to buffer the extra data and also affects performance because of the larger input data set and increased operations. |
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Sets Including Rules for the antecedent: it is a comma separated list of strings, at least one of which must appear in the antecedent part of each reported association rule. The default value is |
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Sets Excluding Rules for the antecedent: it is a comma separated list of strings, none of which can appear in the antecedent part of each reported association rule. The default value is |
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0≤ ASSO_CONF_LEVEL ≤ 1 |
Specifies the confidence level for an association rules sample. A larger value of |
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Sets Including Rules for the consequent: it is a comma separated list of strings, at least one of which must appear in the consequent part of each reported association rule. The default value is |
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Sets Excluding Rules for the consequent: it is a comma separated list of strings, none of which can appear in the consequent part of a reported association rule. You can use the excluding rule to reduce the data that must be stored, but you may be required to build extra models for executing different Including or Excluding Rules. The default value is |
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Sets Excluding Rules applied for each association rule: it is a comma separated list of strings that cannot appear in an association rule. No rule can contain any item in the list. The default value is |
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Sets Including Rules applied for each association rule: it is a comma separated list of strings, at least one of which must appear in each reported association rule, either as antecedent or as consequent The default value |
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Maximum rule length for association rules. The default value is |
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Minimum confidence for association rules. The default value is |
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Sets the Minimum Reverse Confidence that each rule should satisfy. The Reverse Confidence of a rule is defined as the number of transactions in which the rule occurs divided by the number of transactions in which the consequent occurs. The value is real number between 0 and 1. The default value is |
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Minimum support for association rules. The default value is |
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Minimum absolute support that each rule must satisfy. The value must be an integer. The default value is |
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ODMS_ITEM_ID_COLUMN_NAME |
column_name |
The name of a column that contains the items in a transaction. When you specify this setting, the algorithm expects the data to be presented in native transactional format, consisting of two columns:
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ODMS_ITEM_VALUE_COLUMN_ NAME |
column_name |
The name of a column that contains a value associated with each item in a transaction. Use this setting only when you have specified a value for If you also use
If The Item Value column may specify information such as the number of items (for example, three apples) or the type of the item (for example, macintosh apples). |
See Also:
Example 9-8 Using the oml.ar Class
This example uses methods of the oml.ar class.
import pandas as pd
from sklearn import datasets
import oml
# Load the iris data set and create a pandas.DataFrame for it.
iris = datasets.load_iris()
x = pd.DataFrame(iris.data,
columns = ['Sepal_Length','Sepal_Width',
'Petal_Length','Petal_Width'])
y = pd.DataFrame(list(map(lambda x:
{0: 'setosa', 1: 'versicolor',
2:'virginica'}[x], iris.target)),
columns = ['Species']))
try:
oml.drop('IRIS')
except:
pass
# Create the IRIS database table.
oml_iris = oml.create(pd.concat([x, y], axis=1), table = 'IRIS')
# Create training data.
train_dat = oml.sync(table = 'IRIS')
# Specify settings.
setting = {'asso_min_support':'0.1', 'asso_min_confidence':'0.1'}
# Create an AR model object.
ar_mod = oml.ar(**setting)
# Fit the model according to the training data and parameter
# settings.
ar_mod = ar_mod.fit(train_dat)
# Show details of the model.
ar_modListing for This Example
>>> import pandas as pd
>>> from sklearn import datasets
>>> import oml
>>>
>>> # Load the iris data set and create a pandas.DataFrame for it.
... iris = datasets.load_iris()
>>> x = pd.DataFrame(iris.data,
... columns = ['Sepal_Length','Sepal_Width',
... 'Petal_Length','Petal_Width'])
>>> y = pd.DataFrame(list(map(lambda x:
... {0: 'setosa', 1: 'versicolor',
... 2:'virginica'}[x], iris.target)),
... columns = ['Species'])
>>>
>>> try:
... oml.drop('IRIS')
... except:
... pass
>>>
>>> # Create the IRIS database table.
... oml_iris = oml.create(pd.concat([x, y], axis=1), table = 'IRIS')
>>>
>>> # Create training data.
... train_dat = oml.sync(table = 'IRIS')
>>>
>>> # Specify settings.
... setting = {'asso_min_support':'0.1', 'asso_min_confidence':'0.1'}
>>>
>>> # Create an AR model object.
... ar_mod = oml.ar(**setting)
>>>
>>> # Fit the model according to the training data and parameter
... # settings.
>>> ar_mod = ar_mod.fit(train_dat)
>>>
>>> # Show details of the model.
... ar_mod
Algorithm Name: Association Rules
Mining Function: ASSOCIATION
Settings:
setting name setting value
0 ALGO_NAME ALGO_APRIORI_ASSOCIATION_RULES
1 ASSO_MAX_RULE_LENGTH 4
2 ASSO_MIN_CONFIDENCE 0.1
3 ASSO_MIN_REV_CONFIDENCE 0
4 ASSO_MIN_SUPPORT 0.1
5 ASSO_MIN_SUPPORT_INT 1
6 ODMS_DETAILS ODMS_ENABLE
7 ODMS_MISSING_VALUE_TREATMENT ODMS_MISSING_VALUE_AUTO
8 ODMS_SAMPLING ODMS_SAMPLING_DISABLE
9 PREP_AUTO ON
Global Statistics:
attribute name attribute value
0 ITEMSET_COUNT 6.000000
1 MAX_SUPPORT 0.333333
2 NUM_ROWS 150.000000
3 RULE_COUNT 2.000000
4 TRANSACTION_COUNT 150.000000
Attributes:
Petal_Length
Petal_Width
Sepal_Length
Sepal_Width
Species
Partition: NO
Itemsets:
ITEMSET_ID SUPPORT NUMBER_OF_ITEMS ITEM_NAME ITEM_VALUE
0 1 0.193333 1 Petal_Width .20000000000000001
1 2 0.173333 1 Sepal_Width 3
2 3 0.333333 1 Species setosa
3 4 0.333333 1 Species versicolor
4 5 0.333333 1 Species virginica
5 6 0.193333 2 Petal_Width .20000000000000001
6 6 0.193333 2 Species setosa
Rules:
RULE_ID NUMBER_OF_ITEMS LHS_NAME LHS_VALUE RHS_NAME \
0 1 2 Species setosa Petal_Width
1 2 2 Petal_Width .20000000000000001 Species
RHS_VALUE SUPPORT CONFIDENCE REVCONFIDENCE LIFT
0 None 0.186667 0.58 1.00 3
1 None 0.186667 1.00 0.58 3