| | |
- Attribution(data=None, data_optional=None, data_optional2=None, data_optional3=None, data_optional4=None, conversion_data=None, excluding_data=None, optional_data=None, model1_type=None, model2_type=None, event_column=None, timestamp_column=None, **generic_arguments)
- DESCRIPTION:
The Attribution() function is used in web page analysis, where it lets
companies assign weights to pages before certain events, such as
buying a product.
PARAMETERS:
data:
Required Argument.
Specifies the input teradataml DataFrame.
Types: teradataml DataFrame
data_optional:
Optional Argument.
Specifies the click stream data, which the function uses to compute
attributions.
Types: teradataml DataFrame
data_optional2:
Optional Argument.
Specifies the click stream data, which the function uses to compute
attributions.
Types: teradataml DataFrame
data_optional3:
Optional Argument.
Specifies the click stream data, which the function uses to compute
attributions.
Types: teradataml DataFrame
data_optional4:
Optional Argument.
Specifies the click stream data, which the function uses to compute
attributions.
Types: teradataml DataFrame
conversion_data:
Required Argument.
Specifies one varchar column (conversion_events) containing conversion
event values.
Types: teradataml DataFrame
excluding_data:
Optional Argument.
Specifies one varchar column (excluding_events) containing excluding
cause event values.
Types: teradataml DataFrame
optional_data:
Optional Argument.
Specifies one varchar column (optional_events) containing optional
cause event values.
Types: teradataml DataFrame
model1_type:
Required Argument.
Specifies the type and specification of the first model.
For example:
+----+----------------------+
| id | model |
+----+----------------------+
| 0 | SEGMENT_SECONDS |
| | |
| 1 | 6:0.5:UNIFORM:NA |
| | |
| 2 | 8:0.3:LAST_CLICK:NA |
| | |
| 3 | 6:0.2:FIRST_CLICK:NA |
+----+----------------------+
Types: teradataml DataFrame
model2_type:
Optional Argument.
Specifies the type and distributions of the second model.
For example:
+----+--------------------------------+
| id | model |
+----+--------------------------------+
| 0 | SEGMENT_ROWS |
| | |
| 1 | 3:0.5:EXPONENTIAL:0.5,SECOND |
| | |
| 2 | 4:0.3:WEIGHTED:0.4,0.3,0.2,0.1 |
| | |
| 3 | 3:0.2:FIRST_CLICK:NA |
+----+--------------------------------+
Types: teradataml DataFrame
event_column:
Required Argument.
Specifies the name of the input column that contains the clickstream
events.
Types: str
timestamp_column:
Required Argument.
Specifies the name of the input column that contains the timestamps
of the clickstream events.
Types: str
**generic_arguments:
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 table or
not. When set to True, results are persisted in table; otherwise,
results are garbage collected at the end of the session.
Default Value: False
Types: boolean
volatile:
Optional Argument.
Specifies whether to put the results of the function in volatile table
or not. When set to True, results are stored in volatile table,
otherwise not.
Default Value: False
Types: boolean
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 function supports it, else an exception is raised.
RETURNS:
Instance of Attribution.
Output teradataml DataFrames can be accessed using attribute
references, such as AttributionObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException, TypeError, ValueError
EXAMPLES:
# Notes:
# 1. Get the connection to Vantage, before importing the function in user space.
# 2. User can import the function, if it is available on the Vantage user is connected to.
# 3. To check the list of analytic functions available on the Vantage user connected to,
# use "display_analytic_functions()".
# Load the data to run the example
load_example_data("attribution", ["attribution_sample_table1",
"attribution_sample_table2" , "conversion_event_table",
"optional_event_table", "model1_table", "model2_table"])
# Create teradataml DataFrame objects
attribution_sample_table1 = DataFrame.from_table("attribution_sample_table1")
attribution_sample_table2 = DataFrame.from_table("attribution_sample_table2")
conversion_event_table = DataFrame.from_table("conversion_event_table")
optional_event_table = DataFrame.from_table("optional_event_table")
model1_table = DataFrame.from_table("model1_table")
model2_table = DataFrame.from_table("model2_table")
# Check the list of available analytic functions.
display_analytic_functions()
# Import function Attribution.
from teradataml import Attribution
# Example 1: Assign attribution weights to events and channels.
attribution_out = teradataml.Attribution(data=attribution_sample_table1,
data_partition_column="user_id",
data_order_column="time_stamp",
data_optional=attribution_sample_table2,
data_optional_partition_column='user_id',
data_optional_order_column='time_stamp',
event_column="event",
conversion_data=conversion_event_table,
optional_data=optional_event_table,
timestamp_column = "time_stamp",
window_size = "rows:10&seconds:20",
model1_type=model1_table,
model2_type=model2_table)
# Print the results DataFrame.
print(attribution_out.result)
|