NLP

pycaret.nlp.setup(data, target=None, custom_stopwords=None, html=True, session_id=None, log_experiment=False, experiment_name=None, log_plots=False, log_data=False, verbose=True)

This function initializes the training environment and creates the transformation pipeline. Setup function must be called before executing any other function. It takes one mandatory parameter only: data. All the other parameters are optional.

Example

>>> from pycaret.datasets import get_data
>>> kiva = get_data('kiva')
>>> from pycaret.nlp import *
>>> exp_name = setup(data = kiva, target = 'en')
data: pandas.Dataframe or list

pandas.Dataframe with shape (n_samples, n_features) or a list.

target: str

When data is pandas.Dataframe, name of column containing text.

custom_stopwords: list, default = None

List of stopwords.

html: bool, default = True

When set to False, prevents runtime display of monitor. This must be set to False when the environment does not support IPython. For example, command line terminal, Databricks Notebook, Spyder and other similar IDEs.

session_id: int, default = None

Controls the randomness of experiment. It is equivalent to ‘random_state’ in scikit-learn. When None, a pseudo random number is generated. This can be used for later reproducibility of the entire experiment.

log_experiment: bool, default = False

When set to True, all metrics and parameters are logged on the MLFlow server.

experiment_name: str, default = None

Name of the experiment for logging. Ignored when log_experiment is not True.

log_plots: bool or list, default = False

When set to True, certain plots are logged automatically in the MLFlow server.

log_data: bool, default = False

When set to True, dataset is logged on the MLflow server as a csv file. Ignored when log_experiment is not True.

verbose: bool, default = True

When set to False, Information grid is not printed.

Returns

Global variables that can be changed using the set_config function.

Warning

  • pycaret.nlp requires following language models:

    python -m spacy download en_core_web_sm python -m textblob.download_corpora

pycaret.nlp.create_model(model=None, multi_core=False, num_topics=None, verbose=True, system=True, **kwargs)

This function trains a given topic model. All the available models can be accessed using the models function.

Example

>>> from pycaret.datasets import get_data
>>> kiva = get_data('kiva')
>>> from pycaret.nlp import *
>>> exp_name = setup(data = kiva, target = 'en')
>>> lda = create_model('lda')
model: str, default = None

Models available in the model library (ID - Name):

  • ‘lda’ - Latent Dirichlet Allocation

  • ‘lsi’ - Latent Semantic Indexing

  • ‘hdp’ - Hierarchical Dirichlet Process

  • ‘rp’ - Random Projections

  • ‘nmf’ - Non-Negative Matrix Factorization

multi_core: bool, default = False

True would utilize all CPU cores to parallelize and speed up model training. Ignored when model is not ‘lda’.

num_topics: int, default = 4

Number of topics to be created. If None, default is set to 4.

verbose: bool, default = True

Status update is not printed when verbose is set to False.

system: bool, default = True

Must remain True all times. Only to be changed by internal functions.

**kwargs:

Additional keyword arguments to pass to the estimator.

Returns

Trained Model

pycaret.nlp.assign_model(model, verbose=True)

This function assigns topic labels to the dataset for a given model.

Example

>>> from pycaret.datasets import get_data
>>> kiva = get_data('kiva')
>>> from pycaret.nlp import *
>>> exp_name = setup(data = kiva, target = 'en')
>>> lda = create_model('lda')
>>> lda_df = assign_model(lda)
model: trained model object, default = None

Trained model object

verbose: bool, default = True

Status update is not printed when verbose is set to False.

Returns

pandas.DataFrame

pycaret.nlp.plot_model(model=None, plot='frequency', topic_num=None, save=False, system=True)

This function takes a trained model object (optional) and returns a plot based on the inferred dataset by internally calling assign_model before generating a plot. Where a model parameter is not passed, a plot on the entire dataset will be returned instead of one at the topic level. As such, plot_model can be used with or without model. All plots with a model parameter passed as a trained model object will return a plot based on the first topic i.e. ‘Topic 0’. This can be changed using the topic_num param.

Example

>>> from pycaret.datasets import get_data
>>> kiva = get_data('kiva')
>>> from pycaret.nlp import *
>>> exp = setup(data = kiva, target = 'en')
>>> lda = create_model('lda')
>>> plot_model(lda, plot = 'frequency')
model: object, default = none

Trained Model Object

plot: str, default = ‘frequency’

List of available plots (ID - Name):

  • Word Token Frequency - ‘frequency’

  • Word Distribution Plot - ‘distribution’

  • Bigram Frequency Plot - ‘bigram’

  • Trigram Frequency Plot - ‘trigram’

  • Sentiment Polarity Plot - ‘sentiment’

  • Part of Speech Frequency - ‘pos’

  • t-SNE (3d) Dimension Plot - ‘tsne’

  • Topic Model (pyLDAvis) - ‘topic_model’

  • Topic Infer Distribution - ‘topic_distribution’

  • Wordcloud - ‘wordcloud’

  • UMAP Dimensionality Plot - ‘umap’

topic_numstr, default = None

Topic number to be passed as a string. If set to None, default generation will be on ‘Topic 0’

save: bool, default = False

Plot is saved as png file in local directory when save parameter set to True.

system: bool, default = True

Must remain True all times. Only to be changed by internal functions.

Returns

None

Warning

  • ‘pos’ and ‘umap’ plot not available at model level. Hence the model parameter is ignored. The result will always be based on the entire training corpus.

  • ‘topic_model’ plot is based on pyLDAVis implementation. Hence its not available for model = ‘lsi’, ‘rp’ and ‘nmf’.

pycaret.nlp.tune_model(model=None, multi_core=False, supervised_target=None, estimator=None, optimize=None, custom_grid=None, auto_fe=True, fold=10, verbose=True)

This function tunes the num_topics parameter of a given model.

Example

>>> from pycaret.datasets import get_data
>>> kiva = get_data('kiva')
>>> from pycaret.nlp import *
>>> exp_name = setup(data = kiva, target = 'en')
>>> tuned_lda = tune_model(model = 'lda', supervised_target = 'status')
model: str, default = None

Enter ID of the models available in model library (ID - Model):

  • ‘lda’ - Latent Dirichlet Allocation

  • ‘lsi’ - Latent Semantic Indexing

  • ‘hdp’ - Hierarchical Dirichlet Process

  • ‘rp’ - Random Projections

  • ‘nmf’ - Non-Negative Matrix Factorization

multi_core: bool, default = False

True would utilize all CPU cores to parallelize and speed up model training. Ignored when model is not ‘lda’.

supervised_target: str

Name of the target column for supervised learning. If None, the model coherence value is used as the objective function.

estimator: str, default = None
Classification (ID - Name):
  • ‘lr’ - Logistic Regression (Default)

  • ‘knn’ - K Nearest Neighbour

  • ‘nb’ - Naive Bayes

  • ‘dt’ - Decision Tree Classifier

  • ‘svm’ - SVM - Linear Kernel

  • ‘rbfsvm’ - SVM - Radial Kernel

  • ‘gpc’ - Gaussian Process Classifier

  • ‘mlp’ - Multi Level Perceptron

  • ‘ridge’ - Ridge Classifier

  • ‘rf’ - Random Forest Classifier

  • ‘qda’ - Quadratic Discriminant Analysis

  • ‘ada’ - Ada Boost Classifier

  • ‘gbc’ - Gradient Boosting Classifier

  • ‘lda’ - Linear Discriminant Analysis

  • ‘et’ - Extra Trees Classifier

  • ‘xgboost’ - Extreme Gradient Boosting

  • ‘lightgbm’ - Light Gradient Boosting

  • ‘catboost’ - CatBoost Classifier

Regression (ID - Name):
  • ‘lr’ - Linear Regression (Default)

  • ‘lasso’ - Lasso Regression

  • ‘ridge’ - Ridge Regression

  • ‘en’ - Elastic Net

  • ‘lar’ - Least Angle Regression

  • ‘llar’ - Lasso Least Angle Regression

  • ‘omp’ - Orthogonal Matching Pursuit

  • ‘br’ - Bayesian Ridge

  • ‘ard’ - Automatic Relevance Determ.

  • ‘par’ - Passive Aggressive Regressor

  • ‘ransac’ - Random Sample Consensus

  • ‘tr’ - TheilSen Regressor

  • ‘huber’ - Huber Regressor

  • ‘kr’ - Kernel Ridge

  • ‘svm’ - Support Vector Machine

  • ‘knn’ - K Neighbors Regressor

  • ‘dt’ - Decision Tree

  • ‘rf’ - Random Forest

  • ‘et’ - Extra Trees Regressor

  • ‘ada’ - AdaBoost Regressor

  • ‘gbr’ - Gradient Boosting

  • ‘mlp’ - Multi Level Perceptron

  • ‘xgboost’ - Extreme Gradient Boosting

  • ‘lightgbm’ - Light Gradient Boosting

  • ‘catboost’ - CatBoost Regressor

optimize: str, default = None
For Classification tasks:

Accuracy, AUC, Recall, Precision, F1, Kappa (default = ‘Accuracy’)

For Regression tasks:

MAE, MSE, RMSE, R2, RMSLE, MAPE (default = ‘R2’)

custom_grid: list, default = None

By default, a pre-defined number of topics is iterated over to optimize the supervised objective. To overwrite default iteration, pass a list of num_topics to iterate over in custom_grid param.

auto_fe: bool, default = True

Automatic text feature engineering. When set to True, it will generate text based features such as polarity, subjectivity, wordcounts. Ignored when supervised_target is None.

fold: int, default = 10

Number of folds to be used in Kfold CV. Must be at least 2.

verbose: bool, default = True

Status update is not printed when verbose is set to False.

Returns

Trained Model with optimized num_topics parameter.

Warning

  • Random Projections (‘rp’) and Non Negative Matrix Factorization (‘nmf’) is not available for unsupervised learning. Error is raised when ‘rp’ or ‘nmf’ is passed without supervised_target.

  • Estimators using kernel based methods such as Kernel Ridge Regressor, Automatic Relevance Determinant, Gaussian Process Classifier, Radial Basis Support Vector Machine and Multi Level Perceptron may have longer training times.

pycaret.nlp.evaluate_model(model)

This function displays a user interface for analyzing performance of a trained model. It calls the plot_model function internally.

Example

>>> from pycaret.datasets import get_data
>>> kiva = get_data('kiva')
>>> experiment_name = setup(data = kiva, target = 'en')
>>> lda = create_model('lda')
>>> evaluate_model(lda)
model: object, default = none

A trained model object should be passed.

Returns

None

pycaret.nlp.save_model(model, model_name, verbose=True)

This function saves the trained model object into the current active directory as a pickle file for later use.

Example

>>> from pycaret.datasets import get_data
>>> kiva = get_data('kiva')
>>> experiment_name = setup(data = kiva, target = 'en')
>>> lda = create_model('lda')
>>> save_model(lda, 'saved_lda_model')
model: object

A trained model object should be passed.

model_name: str

Name of pickle file to be passed as a string.

verbose: bool, default = True

When set to False, success message is not printed.

Returns

Tuple of the model object and the filename.

pycaret.nlp.load_model(model_name, verbose=True)

This function loads a previously saved model.

Example

>>> from pycaret.nlp import load_model
>>> saved_lda = load_model('saved_lda_model')
model_name: str

Name of pickle file to be passed as a string.

verbose: bool, default = True

When set to False, success message is not printed.

Returns

Trained Model

pycaret.nlp.models()

Returns table of models available in model library.

Example

>>> from pycaret.nlp import models
>>> all_models = models()
Returns

pandas.DataFrame

pycaret.nlp.get_logs(experiment_name=None, save=False)

Returns a table of experiment logs. Only works when log_experiment is True when initializing the setup function.

Example

>>> from pycaret.datasets import get_data
>>> kiva = get_data('kiva')
>>> from pycaret.nlp import *
>>> exp_name = setup(data = kiva, target = 'en', log_experiment = True)
>>> lda = create_model('lda')
>>> exp_logs = get_logs()
experiment_name: str, default = None

When None current active run is used.

save: bool, default = False

When set to True, csv file is saved in current working directory.

Returns

pandas.DataFrame

pycaret.nlp.get_config(variable)

This function retrieves the global variables created when initializing the setup function. Following variables are accessible:

  • text: Tokenized words as a list with length = # documents

  • data_: pandas.DataFrame containing text after all processing

  • corpus: List containing tuples of id to word mapping

  • id2word: gensim.corpora.dictionary.Dictionary

  • seed: random state set through session_id

  • target_: Name of column containing text. ‘en’ by default.

  • html_param: html_param configured through setup

  • exp_name_log: Name of experiment set through setup

  • logging_param: log_experiment param set through setup

  • log_plots_param: log_plots param set through setup

  • USI: Unique session ID parameter set through setup

Example

>>> from pycaret.datasets import get_data
>>> kiva = get_data('kiva')
>>> from pycaret.nlp import *
>>> exp_name = setup(data = kiva, target = 'en')
>>> text = get_config('text')
Returns

Global variable

pycaret.nlp.set_config(variable, value)

This function resets the global variables. Following variables are accessible:

  • text: Tokenized words as a list with length = # documents

  • data_: pandas.DataFrame containing text after all processing

  • corpus: List containing tuples of id to word mapping

  • id2word: gensim.corpora.dictionary.Dictionary

  • seed: random state set through session_id

  • target_: Name of column containing text. ‘en’ by default.

  • html_param: html_param configured through setup

  • exp_name_log: Name of experiment set through setup

  • logging_param: log_experiment param set through setup

  • log_plots_param: log_plots param set through setup

  • USI: Unique session ID parameter set through setup

Example

>>> from pycaret.datasets import get_data
>>> kiva = get_data('kiva')
>>> from pycaret.nlp import *
>>> exp_name = setup(data = kiva, target = 'en')
>>> set_config('seed', 123)
Returns

None

pycaret.nlp.get_topics(data, text, model=None, num_topics=4)

Callable from any external environment without requiring setup initialization.