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- DataikuPredict(modeldata=None, newdata=None, accumulate=None, model_output_fields=None, overwrite_cached_models=False, is_debug=False, **generic_arguments)
- DESCRIPTION:
The DataikuPredict() function is used to score data in Vantage
with a model that has been created outside Vantage and exported
to Vantage using Dataiku format.
PARAMETERS:
modeldata:
Required Argument.
Specifies the model teradataml DataFrame to be used for
scoring.
Types: teradataml DataFrame
newdata:
Required Argument.
Specifies the input teradataml DataFrame that contains
the data to be scored.
Types: teradataml DataFrame
accumulate:
Required Argument.
Specifies the name(s) of input teradataml DataFrame column(s) to
copy to the output. By default, the function copies all input
teradataml DataFrame columns to the output.
Types: str OR list of Strings (str) OR Feature OR list of Features
model_output_fields:
Optional Argument.
Specifies the columns of the json output that the user wants
to specify as individual columns instead of the entire json
report.
Types: str OR list of Strings (str)
overwrite_cached_models:
Optional Argument.
Specifies the model name that needs to be removed from the cache.
If a model is loaded into the memory of the node fits in the cache,
it stays in the cache until being evicted to make space for another
model that needs to be loaded. Therefore, a model can remain in the
cache even after completion of function call. Other queries that
use the same model can use it, saving the cost of reloading it
into memory. User may overwrite a cached model only when it has been
updated, to make sure that the Predict function uses the updated
model instead of the cached model.
Note:
Do not use the "overwrite_cached_models" argument except when trying
to replace a previously cached model. This applies to any model type
(PMML, H2O Open Source, DAI, ONNX, and Dataiku). Using this argument
in other cases, including in concurrent queries or multiple times
within a short period of time, may lead to an OOM error from garbage
collection not being fast enough.
Permitted Values:
'current_cached_model', '*', 'true', 't', 'yes', '1', 'false', 'f', 'no',
'n', or '0'.
Default Values: "false"
Types: bool
is_debug:
Optional Argument.
Specifies whether debug statements are added to a trace table or not.
When set to True, debug statements are added to a trace table that must
be created beforehand.
Notes:
* Only available with BYOM version 3.00.00.02 and later.
* To save logs for debugging, user can create an error log by using
the is_debug=True parameter in the predict functions.
A database trace table is used to collect this information which
does impact performance of the function, so using small data input
sizes is recommended.
* To generate this log, user must do the following:
1. Create a global trace table with columns vproc_ID BYTE(2),
Sequence INTEGER, Trace_Output VARCHAR(31000)
2. Turn on session function tracing:
SET SESSION FUNCTION TRACE USING '' FOR TABLE <trace_table_name_created_in_step_1>;
3. Execute function with "is_debug" set to True.
4. Debug information is logged to the table created in step 1.
5. To turn off the logging, either disconnect from the session or
run following SQL:
SET SESSION FUNCTION TRACE OFF;
The trace table is temporary and the information is deleted if user
logs off from the session. If long term persistence is necessary,
user can copy the table to a permanent table before leaving the
session.
Default Value: False
Types: bool
**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 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) or PartitionKind
* "<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 SQL Engine function supports, else an
exception is raised.
RETURNS:
Instance of DataikuPredict.
Output teradataml DataFrame can be accessed using attribute
references, such as DataikuPredictObj.<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 error if not supported on the Vantage
# user is connected to.
# 4. To execute BYOM functions, set 'configure.byom_install_location' to the
# database name where BYOM functions are installed.
# Import required libraries / functions.
import os, teradataml
from teradataml import get_connection, DataFrame
from teradataml import save_byom, retrieve_byom, load_example_data
from teradataml import configure, display_analytic_functions, execute_sql
# Load example data.
load_example_data("byom", "iris_test")
# Create teradataml DataFrame objects.
iris_test = DataFrame.from_table("iris_test")
# Set install location of BYOM functions.
configure.byom_install_location = "mldb"
# Check the list of available analytic functions.
display_analytic_functions(type="BYOM")
# Load model file into Vantage.
model_file = os.path.join(os.path.dirname(teradataml.__file__), "data",
"models", "dataiku_iris_data_ann_thin")
save_byom("dataiku_iris_data_ann_thin", model_file, "byom_models")
# Retrieve model.
modeldata = retrieve_byom("dataiku_iris_data_ann_thin", table_name="byom_models")
# Example 1: Score data in Vantage with a model that has
# been created outside the Vantage by removing all the
# all cached models.
DataikuPredict_out_1 = DataikuPredict(newdata=iris_test,
modeldata=modeldata,
accumulate=['id', 'sepal_length', 'petal_length'],
overwrite_cached_models="*")
# Print the results.
print(DataikuPredict_out_1.result)
# Example 2: Example to show case the trace table usage using
# is_debug=True.
# Create the trace table.
crt_tbl_query = 'CREATE GLOBAL TEMPORARY TRACE TABLE BYOM_Trace (vproc_ID BYTE(2) ,Sequence INTEGER ,Trace_Output VARCHAR(31000) CHARACTER SET LATIN NOT CASESPECIFIC) ON COMMIT PRESERVE ROWS;'
execute_sql(crt_tbl_query)
# Turn on the session function.
execute_sql("SET SESSION FUNCTION TRACE USING '' FOR TABLE BYOM_Trace;")
# Execute the DataikuPredict() function using is_debug=True.
DataikuPredict_out_2 = DataikuPredict(newdata=iris_test,
modeldata=modeldata,
accumulate=['id', 'sepal_length', 'petal_length'],
overwrite_cached_models="*",
is_debug=True)
# Print the results.
print(DataikuPredict_out_2.result)
# View the trace table information.
trace_df = DataFrame.from_table("BYOM_Trace")
print(trace_df)
# Turn off the session function
execute_sql("SET SESSION FUNCTION TRACE OFF;")
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