| | |
- Apriori(data=None, target_column=None, id_column=None, partition_columns=None, max_len=2, delimiter=',', is_dense_input=False, patterns_or_rules=None, support=0.01, **generic_arguments)
- DESCRIPTION:
The Apriori() function finds patterns and calculates different statistical metrics to
understand the influence of the occurrence of a set of items on others.
PARAMETERS:
data:
Required Argument.
Specifies the input teradataml DataFrame.
Types: teradataml DataFrame
target_column:
Required Argument.
Specifies the input teradataml DataFrame column which contains the data to filter.
Types: str
id_column:
Optional Argument.
Specifies the name of the column that uniquely groups the items that are purchased together.
Applicable only when `is_dense_input` is False.
Types: str
partition_columns:
Optional Argument.
Specifies the column name(s) in the "data" to partition the input.
Types: str or list of str
max_len:
Optional Argument.
Specifies the maximum number of items in the item set.
"max_len" must be greater than or equal to 1 and less than or equal to 20.
Default Value: 2
Types: int
delimiter:
Optional Argument, Required when "is_dense_input" is set to True.
Specifies a character or string that separates words in the input text.
Default Value: ","
Types: str
is_dense_input:
Optional Argument.
Specifies whether input data is in dense format or not.
When set to True, function considers the data is in dense format.
Otherwise function considers data is not in dense format.
Default Value: False
Types: bool
patterns_or_rules:
Optional Argument.
Specifies whether to emit PATTERNS or RULES as output.
Permitted Values: "PATTERNS", "RULES"
Types: str
support:
Optional Argument.
Specifies the support value (minimum occurrence threshold) of the itemset.
Default Value: 0.01
Types: float
**generic_arguments:
Optional Argument.
Specifies the generic keyword arguments SQLE functions accept. Below are the generic
keyword arguments:
persist:
Optional Argument.
Specifies whether to persist the results of the function in a table or not.
When set to True, results are persisted in a table; otherwise, results are
garbage collected at the end of the session.
Default Value: False
Types: bool
volatile:
Optional Argument.
Specifies whether to put the results of the function in a volatile table or not.
When set to True, results are stored in a volatile table; otherwise not.
Default Value: False
Types: bool
Function allows the user to partition, hash, order or local order the input
data. These generic arguments are available for each argument that accepts
teradataml DataFrame as input and can be accessed as:
* "<input_data_arg_name>_partition_column" accepts str or list of str (Strings)
* "<input_data_arg_name>_hash_column" accepts str or list of str (Strings)
* "<input_data_arg_name>_order_column" accepts str or list of str (Strings)
* "local_order_<input_data_arg_name>" accepts boolean
Note:
These generic arguments are supported by teradataml if the underlying SQLE Engine
function supports, else an exception is raised.
RETURNS:
Instance of Apriori.
Output teradataml DataFrames can be accessed using attribute references, such as AprioriObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException, TypeError, ValueError
EXAMPLES:
# Notes:
# 1. Get the connection to Vantage to execute the function.
# 2. One must import the required functions mentioned in the example from teradataml.
# 3. Function will raise an error if not supported on the Vantage user is connected to.
# Load the example data.
load_example_data("apriori", ["trans_dense","trans_sparse"])
# Create teradataml DataFrame objects.
dense_table = DataFrame.from_table("trans_dense")
sparse_table = DataFrame.from_table("trans_sparse")
# Check the list of available analytic functions.
display_analytic_functions()
# Import function Apriori.
from teradataml import Apriori
# Example 1: Find patterns in the input data with DENSE DATA, PARTITION, RULES .
Apriori_out = Apriori(data=dense_table, target_column="item",
partition_columns=["location"], max_len=2,
patterns_or_rules="rules", support=0.01)
# Print the result DataFrame.
print(Apriori_out.result)
# Example 2: Find patterns in the input data with SPARSE DATA, NO PARTITIONS, PATTERNS.
Apriori_out = Apriori(data=sparse_table, target_column="item",
id_column="tranid", max_len=3)
# Print the result DataFrame.
print(Apriori_out.result)
|