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Methods defined here:
- __init__(self, data=None, time_column=None, time_out=None, click_lag=None, emit_null=False, accumulate=None, data_sequence_column=None, data_partition_column=None, data_order_column=None)
- DESCRIPTION:
The Sessionize function maps each click in a session to a unique
session identifier. A session is defined as a sequence of clicks by
one user that are separated by at most n seconds.
The function is useful both for sessionization and for detecting web
crawler (bot) activity. It is typically used to understand user browsing
behavior on a web site.
Note: This function is available only when teradataml is connected to
Vantage 1.1 or later versions.
PARAMETERS:
data:
Required Argument.
Specifies the input teradataml DataFrame.
data_partition_column:
Required Argument.
Specifies Partition By columns for "data".
Values to this argument can be provided as a list, if multiple columns
are used for partition.
Types: str OR list of Strings (str)
data_order_column:
Required Argument.
Specifies Order By columns for "data".
Values to this argument can be provided as a list, if multiple columns
are used for ordering.
Types: str OR list of Strings (str)
time_column:
Required Argument.
Specifies the name of the input column that contains the click
times.
Note: The time_column must also be an data_order_column.
Types: str
time_out:
Required Argument.
Specifies the number of seconds at which the session times out.
If time_out seconds elapse after a click, then the next click
starts a new session.
Types: float
click_lag:
Optional Argument.
Specifies the minimum number of seconds between clicks for the
session user to be considered human. If clicks are more frequent,
indicating that the user is a "bot," the function ignores the
session. The click_lag must be less than time_out.
Types: float
emit_null:
Optional Argument.
Specifies whether to output rows that have None values in their
session id and rapid fire columns, even if their time_column
has a None value.
Default Value: False
Types: bool
accumulate:
Optional Argument.
Specifies the name of the columns in the input teradataml DataFrame
to be copied to the output teradataml DataFrame.
Note: "accumulate" is only available when teradataml is connected to Vantage 1.3 or later.
Types: str OR list of Strings (str)
data_sequence_column:
Optional Argument.
Specifies the list of column(s) that uniquely identifies each row of
the input argument "data". The argument is used to ensure
deterministic results for functions which produce results that vary
from run to run.
Types: str OR list of Strings (str)
RETURNS:
Instance of Sessionize.
Output teradataml DataFrames can be accessed using attribute
references, such as SessionizeObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException
EXAMPLES:
# Load the data to run the example.
load_example_data("Sessionize","sessionize_table")
# Create teradataml DataFram object.
sessionize_table = DataFrame.from_table("sessionize_table")
# Example 1 - This example maps each click in a session to a unique session identifer,
# which uses input table web clickstream data recorded as user navigates through a web site
# based on events — view, click, and so on which are recorded with a timestamp.
sessionize_out = Sessionize(data = sessionize_table,
data_partition_column = ["partition_id"],
data_order_column = ["clicktime"],
time_column = "clicktime",
time_out = 60.0,
click_lag = 0.2
)
# Print the result DataFrame
print(sessionize_out.result)
- __repr__(self)
- Returns the string representation for a Sessionize class instance.
- get_build_time(self)
- Function to return the build time of the algorithm in seconds.
When model object is created using retrieve_model(), then the value returned is
as saved in the Model Catalog.
- get_prediction_type(self)
- Function to return the Prediction type of the algorithm.
When model object is created using retrieve_model(), then the value returned is
as saved in the Model Catalog.
- get_target_column(self)
- Function to return the Target Column of the algorithm.
When model object is created using retrieve_model(), then the value returned is
as saved in the Model Catalog.
- show_query(self)
- Function to return the underlying SQL query.
When model object is created using retrieve_model(), then None is returned.
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