CountVectorizer¶

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CountVectorizer(name: str,
                lowercase: bool = True,
                max_df: float = 1.0,
                min_df: float = 0.0,
                max_features: int = -1,
                ignore_special: bool = True,
                max_text_size: int = 2000)

Creates a text index that counts the occurences of each word in the data.

Parameters¶

Name	Type	Optional	Description
name	str	❌	Name of the model to be stored in the database.
lowercase	bool	✓	Converts all the elements to lowercase before processing.
max_df	float	✓	Keeps words less than max_df in the total dictionary distribution.
min_df	float	✓	Keeps words greater than min_df in the total dictionary distribution.
max_features	int	✓	Keeps only the top words of the dictionary.
ignore_special	bool	✓	Ignores all special characters when building the dictionary.
max_text_size	int	✓	The maximum size of the column, which itself is the concatenation of all the text columns during the fitting.

Attributes¶

After the object is created, all parameters become attributes. Additional attributes will be created when fitting the model:

Name	Type	Description
stop_words_	list	Words not added to the vocabulary.
vocabulary_	list	The final vocabulary.
input_relation	str	The training relation.
X	list	List of the predictors.

Methods¶

Name	Description
deploySQL	Returns the SQL code needed to deploy the model.
drop	Drops the model from the Vertica database.
fit	Trains the model.
get_attr	Returns the model attribute.
get_params	Returns the model parameters.
set_params	Sets the parameters of the model.
transform	Creates a vDataFrame of the model.

Example¶

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from verticapy.learn.preprocessing import CountVectorizer
model = CountVectorizer(name = "public.vocabulary")
display(model)

<CountVectorizer>