autodataprepnode properties

Auto Data Prep node iconThe Auto Data Prep (ADP) node can analyze your data and identify fixes, screen out fields that are problematic or not likely to be useful, derive new attributes when appropriate, and improve performance through intelligent screening and sampling techniques. You can use the node in fully automated fashion, allowing the node to choose and apply fixes, or you can preview the changes before they are made and accept, reject, or amend them as desired.

Example

node = stream.create("autodataprep", "My node")
node.setPropertyValue("objective", "Balanced")
node.setPropertyValue("excluded_fields", "Filter")
node.setPropertyValue("prepare_dates_and_times", True)
node.setPropertyValue("compute_time_until_date", True)
node.setPropertyValue("reference_date", "Today")
node.setPropertyValue("units_for_date_durations", "Automatic")
Table 1. autodataprepnode properties
autodataprepnode properties Data type Property description
objective Balanced
Speed
Accuracy
Custom
 
custom_fields flag If true, allows you to specify target, input, and other fields for the current node. If false, the current settings from an upstream Type node are used.
target field Specifies a single target field.
inputs [field1 ... fieldN] Input or predictor fields used by the model.
use_frequency flag  
frequency_field field  
use_weight flag  
weight_field field  
excluded_fields Filter
None
 
if_fields_do_not_match StopExecution
ClearAnalysis
 
prepare_dates_and_times flag Control access to all the date and time fields
compute_time_until_date flag  
reference_date Today
Fixed
 
fixed_date date  
units_for_date_durations Automatic
Fixed
 
fixed_date_units Years
Months
Days
 
compute_time_until_time flag  
reference_time CurrentTime
Fixed
 
fixed_time time  
units_for_time_durations Automatic
Fixed
 
fixed_time_units Hours
Minutes
Seconds
 
extract_year_from_date flag  
extract_month_from_date flag  
extract_day_from_date flag  
extract_hour_from_time flag  
extract_minute_from_time flag  
extract_second_from_time flag  
exclude_low_quality_inputs flag  
exclude_too_many_missing flag  
maximum_percentage_missing number  
exclude_too_many_categories flag  
maximum_number_categories number  
exclude_if_large_category flag  
maximum_percentage_category number  
prepare_inputs_and_target flag  
adjust_type_inputs flag  
adjust_type_target flag  
reorder_nominal_inputs flag  
reorder_nominal_target flag  
replace_outliers_inputs flag  
replace_outliers_target flag  
replace_missing_continuous_inputs flag  
replace_missing_continuous_target flag  
replace_missing_nominal_inputs flag  
replace_missing_nominal_target flag  
replace_missing_ordinal_inputs flag  
replace_missing_ordinal_target flag  
maximum_values_for_ordinal number  
minimum_values_for_continuous number  
outlier_cutoff_value number  
outlier_method Replace
Delete
 
rescale_continuous_inputs flag  
rescaling_method MinMax
ZScore
 
min_max_minimum number  
min_max_maximum number  
z_score_final_mean number  
z_score_final_sd number  
rescale_continuous_target flag  
target_final_mean number  
target_final_sd number  
transform_select_input_fields flag  
maximize_association_with_target flag  
p_value_for_merging number  
merge_ordinal_features flag  
merge_nominal_features flag  
minimum_cases_in_category number  
bin_continuous_fields flag  
p_value_for_binning number  
perform_feature_selection flag  
p_value_for_selection number  
perform_feature_construction flag  
transformed_target_name_extension string  
transformed_inputs_name_extension string  
constructed_features_root_name string  
years_duration_ name_extension string  
months_duration_ name_extension string  
days_duration_ name_extension string  
hours_duration_ name_extension string  
minutes_duration_ name_extension string  
seconds_duration_ name_extension string  
year_cyclical_name_extension string  
month_cyclical_name_extension string  
day_cyclical_name_extension string  
hour_cyclical_name_extension string  
minute_cyclical_name_extension string  
second_cyclical_name_extension string