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TFIDF

text.TfidfVectorizer([name, ...])

[Beta Version] Create tfidf representation of documents.

Methods:

TfidfVectorizer.contour([nbins, chart])	Draws the model's contour plot.
TfidfVectorizer.deploySQL([X])	Returns the SQL code needed to deploy the model.
TfidfVectorizer.does_model_exists(name[, ...])	Checks whether the model is stored in the Vertica database.
TfidfVectorizer.drop()	Drops the model from the Vertica database.
TfidfVectorizer.export_models(name, path[, kind])	Exports machine learning models.
TfidfVectorizer.fit(input_relation, index, x)	Applies basic pre-processing.
TfidfVectorizer.get_attributes([attr_name])	Returns the model attributes.
TfidfVectorizer.get_match_index(x, col_list)	Returns the matching index.
TfidfVectorizer.get_params()	Returns the parameters of the model.
TfidfVectorizer.get_plotting_lib([...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
TfidfVectorizer.get_vertica_attributes([...])	Returns the model Vertica attributes.
TfidfVectorizer.import_models(path[, ...])	Imports machine learning models.
TfidfVectorizer.register(registered_name[, ...])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
TfidfVectorizer.set_params([parameters])	Sets the parameters of the model.
TfidfVectorizer.summarize()	Summarizes the model.
TfidfVectorizer.to_pmml(path)	Exports the model to PMML.
TfidfVectorizer.to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
TfidfVectorizer.to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
TfidfVectorizer.to_tf(path)	Exports the model to the Frozen Graph format (TensorFlow).
TfidfVectorizer.transform(vdf, index, x[, pivot])	Transforms input data to tf-idf representation.

Attributes:

TfidfVectorizer.object_type

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Count Vectorizor (Beta)

preprocessing.CountVectorizer([name, ...])

Creates a Text Index that counts the occurences of each word in the data.

Methods:

CountVectorizer.contour([nbins, chart])	Draws the model's contour plot.
CountVectorizer.deploySQL([_return_main_table])	Returns the SQL code needed to deploy the model.
CountVectorizer.does_model_exists(name[, ...])	Checks whether the model is stored in the Vertica database.
CountVectorizer.drop()	Drops the model from the Vertica database.
CountVectorizer.export_models(name, path[, kind])	Exports machine learning models.
CountVectorizer.fit(input_relation[, X, ...])	Trains the model.
CountVectorizer.get_attributes([attr_name])	Returns the model attributes.
CountVectorizer.get_match_index(x, col_list)	Returns the matching index.
CountVectorizer.get_params()	Returns the parameters of the model.
CountVectorizer.get_plotting_lib([...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
CountVectorizer.get_vertica_attributes([...])	Returns the model Vertica attributes.
CountVectorizer.import_models(path[, ...])	Imports machine learning models.
CountVectorizer.register(registered_name[, ...])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
CountVectorizer.set_params([parameters])	Sets the parameters of the model.
CountVectorizer.summarize()	Summarizes the model.
CountVectorizer.to_pmml(path)	Exports the model to PMML.
CountVectorizer.to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
CountVectorizer.to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
CountVectorizer.to_tf(path)	Exports the model to the Frozen Graph format (TensorFlow).
CountVectorizer.transform()	Creates a vDataFrame of the model.

Attributes:

CountVectorizer.object_type