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Methods defined here:
- __init__(self, data=None, rules_data=None, language='en', rules=None, tokenize=False, outputby_tag=False, tag_delimiter=',', accumulate=None, data_sequence_column=None, rules_data_sequence_column=None, data_order_column=None, rules_data_order_column=None)
- DESCRIPTION:
The TextTagger function tags text documents according to user-defined
rules that use text-processing and logical operators.
PARAMETERS:
data:
Required Argument.
The input teradataml DataFrame that contains the texts.
data_order_column:
Optional 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)
rules_data:
Optional Argument.
The input teradataml DataFrame that contains the rules.
rules_data_order_column:
Optional Argument.
Specifies Order By columns for rules_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)
language:
Optional Argument.
Specifies the language of the input text: "en": English (default),
"zh_cn": Simple Chinese, "zh_tw": Traditional Chinese, If Tokenize
specifies "true", then the function uses the value of Language to
create the word tokenizer.
Default Value: "en"
Permitted Values: en, zh_CN, zh_TW
Types: str
rules:
Optional Argument.
Specifies the tag names and tagging rules. Use this argument if and
only if you do not specify a rules table. For information about
defining tagging rules, refer to "Defining Tagging Rules" in function documentation.
Types: str OR list of Strings (str)
tokenize:
Optional Argument.
Specifies whether the function tokenizes the input text before
evaluating the rules and tokenizes the text string parameter in the
rule definition when parsing a rule. If you specify "True", then you
must also specify the Language argument.
Default Value: False
Types: bool
outputby_tag:
Optional Argument.
Specifies whether the function outputs a tuple when a text document
matches multiple tags. which means that one tuple in the output
stands for one document and the matched tags are listed in the output
column tag.
Default Value: False
Types: bool
tag_delimiter:
Optional Argument.
Specifies the delimiter that separates multiple tags in the output
column tag if outputby.tag has the value "False" (the default). The
default value is the comma (,). If outputby.tag has the value "True",
specifying this argument causes an error.
Default Value: ","
Types: str
accumulate:
Optional Argument.
Specifies the names of text teradataml DataFrame columns to copy to
the output table.
Note: Do not use the name "tag" for an accumulate_column, because the
function uses that name for the output teradataml DataFrame column
that contains the tags.
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)
rules_data_sequence_column:
Optional Argument.
Specifies the list of column(s) that uniquely identifies each row of
the input argument "rules_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 TextTagger.
Output teradataml DataFrames can be accessed using attribute
references, such as TextTaggerObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException
EXAMPLES:
# Load the data to run the example.
load_example_data("TextTagger",["text_inputs","rule_inputs"])
# Create teradataml DataFrame objects.
text_inputs = DataFrame("text_inputs")
rule_inputs = DataFrame("rule_inputs")
# Example 1:
# Passing rules through 'rules_data' argument as teradataml dataframe.
result = TextTagger(data=text_inputs,
rules_data=rule_inputs,
accumulate='id',
language='en',
tokenize=False,
outputby_tag=False,
tag_delimiter=',',
data_sequence_column='id',
rules_data_sequence_column='tagname')
# Print the result
print(result.result)
# Example 2:
# Passing rules through 'rules' argument as List of strings
result = TextTagger(data=text_inputs,
accumulate='id',
rules=[
'contain(content,"floods",1,) or contain(content,"tsunamis",1,) AS Natural-Disaster',
'contain(content,"Roger",1,) and contain(content,"Nadal",1,) AS Tennis-Rivalry',
'contain(titles,"Tennis",1,) and contain(content,"Roger",1,) AS Tennis-Greats',
'contain(content,"India",1,) and contain(content,"Pakistan",1,) AS Cricket-Rivalry',
'contain(content,"Australia",1,) and contain(content,"England",1,) AS The-Ashes'],
language='en',
tokenize=False,
outputby_tag=False,
tag_delimiter=',',
data_sequence_column='id')
# Print the result
print(result.result)
# Example 3 - Specify dictionary file in rules argument
result = TextTagger(data = text_inputs,
rules=['dict(content, "keywords.txt", 1,) and equal(titles, "Chennai Floods") AS Natural-Disaster',
'dict(content, "keywords.txt", 2,) and equal(catalog, "sports") AS Great-Sports-Rivalry '],
accumulate = ["id"])
# Print the result
print(result.result)
# Example 4 - Specify superdist in rules argument
result = TextTagger(data = text_inputs,
rules=['superdist(content,"Chennai","floods",sent,,) AS Chennai-Flood-Disaster',
'superdist(content,"Roger","titles",para, "Nadal",para) AS Roger-Champion',
'superdist(content,"Roger","Nadal",para,,) AS Tennis-Rivalry',
'contain(content,regex"[A|a]shes",2,) AS Aus-Eng-Cricket',
'superdist(content,"Australia","won",nw5,,) AS Aus-victory'],
accumulate = ["id"]
)
# Print the result
print(result.result)
- __repr__(self)
- Returns the string representation for a TextTagger 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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