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Methods defined here:
- __init__(self, data=None, text_column=None, delimiter='[\\s]+', grams=None, overlapping=True, to_lower_case=True, punctuation='`~#^&*()-', reset='.,?!', total_gram_count=False, total_count_column='totalcnt', accumulate=None, n_gram_column='ngram', num_grams_column='n', frequency_column='frequency', data_order_column=None)
- DESCRIPTION:
The NGramSplitter function tokenizes (splits) an input stream of text and
outputs n multigrams (called n-grams) based on the specified
delimiter and reset parameters. NGramSplitter provides more flexibility than
standard tokenization when performing text analysis. Many two-word
phrases carry important meaning (for example, "machine learning")
that unigrams (single-word tokens) do not capture. This, combined
with additional analytical techniques, can be useful for performing
sentiment analysis, topic identification and document classification.
Note: This function is only available when teradataml is connected
to Vantage 1.1 or later versions.
PARAMETERS:
data:
Required Argument.
Specifies input teradataml DataFrame, where each row contains a document
to be tokenized. The input teradataml DataFrame can have additional rows,
some or all of which the function returns in the output table.
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)
text_column:
Required Argument.
Specifies the name of the column that contains the input text. The column
must have a SQL string data type.
Types: str
delimiter:
Optional Argument.
Specifies a character or string that separates words in the input text. The
default value is the set of all whitespace characters which includes
the characters for space, tab, newline, carriage return and some others.
Default Value: "[\s]+"
Types: str
grams:
Required Argument.
Specifies a list of integers or ranges of integers that specify the length, in
words, of each n-gram (that is, the value of n). A range of values has
the syntax integer1 - integer2, where integer1 <= integer2. The values
of n, integer1, and integer2 must be positive.
Types: str OR list of strs
overlapping:
Optional Argument.
Specifies whether the function allows overlapping n-grams.
When this value is "True", each word in each sentence starts an n-gram, if
enough words follow it (in the same sentence) to form a whole n-gram of the
specified size. For information on sentences, see the description of the
reset argument.
Default Value: True
Types: bool
to_lower_case:
Optional Argument.
Specifies whether the function converts all letters in the input text
to lowercase.
Default Value: True
Types: bool
punctuation:
Optional Argument.
Specifies the punctuation characters for the function to remove before
evaluating the input text.
Default Value: "`~#^&*()-"
Types: str
reset:
Optional Argument.
Specifies the character or string that ends a sentence.
At the end of a sentence, the function discards any partial n-grams and
searches for the next n-gram at the beginning of the next sentence.
An n-gram cannot span two sentences.
Default Value: ".,?!"
Types: str
total_gram_count:
Optional Argument.
Specifies whether the function returns the total number of n-grams in the
document (that is, in the row). If you specify "True", then the name of the
returned column is specified by the total_count_column argument.
Note: The total number of n-grams is not necessarily the number of unique n-grams.
Default Value: False
Types: bool
total_count_column:
Optional Argument.
Specifies the name of the column to return if the value of the total_gram_count
argument is "True".
Default Value: "totalcnt"
Types: str
accumulate:
Optional Argument.
Specifies the names of the columns to return for each n-gram. These columns
cannot have the same names as those specified by the arguments n_gram_column,
num_grams_column, and total_count_column. By default, the function
returns all input columns for each n-gram.
Types: str OR list of Strings (str)
n_gram_column:
Optional Argument.
Specifies the name of the column that is to contain the generated n-grams.
Default Value: "ngram"
Types: str
num_grams_column:
Optional Argument.
Specifies the name of the column that is to contain the length of n-gram (in
words).
Default Value: "n"
Types: str
frequency_column:
Optional Argument.
Specifies the name of the column that is to contain the count of each unique
n-gram (that is, the number of times that each unique n-gram appears
in the document).
Default Value: "frequency"
Types: str
RETURNS:
Instance of NgramSplitter.
Output teradataml DataFrames can be accessed using attribute
references, such as NgramSplitterObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException
EXAMPLES:
# Load example data.
load_example_data("NGrams","paragraphs_input")
# Create teradataml DataFrame
paragraphs_input = DataFrame.from_table("paragraphs_input")
# Example 1
# Creates output for tokenized data on grams values
NGramSplitter_out1 = NGramSplitter(data=paragraphs_input,
text_column='paratext',
delimiter = " ",
grams = "4-6",
overlapping=True,
to_lower_case=True,
total_gram_count=True,
accumulate=['paraid','paratopic']
)
# Print the result DataFrame
print(NGramSplitter_out1.result)
# Example 2
# Creates total count column with default column totalcnt if total_gram_count is specified as False
NGramSplitter_out2 = NGramSplitter(data = paragraphs_input,
text_column='paratext',
delimiter = " ",
grams = "4-6",
overlapping=False,
to_lower_case=True,
total_gram_count=False,
accumulate=['paraid','paratopic']
)
# Print the result DataFrame
print(NGramSplitter_out2.result)
- __repr__(self)
- Returns the string representation for a NgramSplitter 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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