verticapy.machine_learning.vertica.preprocessing.CountVectorizer¶
- class verticapy.machine_learning.vertica.preprocessing.CountVectorizer(name: str = None, overwrite_model: bool = False, 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.
Deprecated since version version: 1.0.0.
This feature is deprecated and it will be removed on VerticaPy version 1.1.0. Please use:
TfidfVectorizerinstead.Parameters¶
- name: str, optional
Name of the model.
- overwrite_model: bool, optional
If set to
True, training a model with the same name as an existing model overwrites the existing model.- lowercase: bool, optional
Converts all the elements to lowercase before processing.
- max_df: float, optional
Keeps the words that represent less than this float in the total dictionary distribution.
- min_df: float, optional
Keeps the words that represent more than this float in the total dictionary distribution.
- max_features: int, optional
Keeps only the top words of the dictionary.
- ignore_special: bool, optional
Ignores all the special characters when building the dictionary.
- max_text_size: int, optional
The maximum size of the column that concatenates all of the text columns during fitting.
Attributes¶
Many attributes are created during the fitting phase.
- vocabulary_: numpy.array
Model’s final vocabulary.
- stop_words_: numpy.array
Model’s stop words. It corresponds to the words ignored by the model.
- n_errors_: int
Number of errors during the model’s computation.
Note
All attributes can be accessed using the
get_attributes()method.- __init__(name: str = None, overwrite_model: bool = False, 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) None¶
Must be overridden in the child class
Methods
__init__([name, overwrite_model, lowercase, ...])Must be overridden in the child class
contour([nbins, chart])Draws the model's contour plot.
deploySQL([_return_main_table])Returns the SQL code needed to deploy the model.
does_model_exists(name[, raise_error, ...])Checks whether the model is stored in the Vertica database.
drop()Drops the model from the Vertica database.
export_models(name, path[, kind])Exports machine learning models.
fit(input_relation[, X, return_report])Trains the model.
get_attributes([attr_name])Returns the model attributes.
get_match_index(x, col_list[, str_check])Returns the matching index.
Returns the parameters of the model.
get_plotting_lib([class_name, chart, ...])Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
get_vertica_attributes([attr_name])Returns the model Vertica attributes.
import_models(path[, schema, kind])Imports machine learning models.
register(registered_name[, raise_error])Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
set_params([parameters])Sets the parameters of the model.
Summarizes the model.
to_binary(path)Exports the model to the Vertica Binary format.
to_pmml(path)Exports the model to PMML.
to_python([return_proba, ...])Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
to_sql([X, return_proba, ...])Returns the SQL code needed to deploy the model without using built-in Vertica functions.
to_tf(path)Exports the model to the Frozen Graph format (TensorFlow).
Creates a vDataFrame of the model.
Attributes