poetic.predictor module¶

Making poetic predictions using models.

The predictor module provides interfaces for poetry predictions with the Predictor class. To make prediction, an instance of the Predictor is needed with the necessary model and gensim dictionary is needed.

Examples

The most common use case for the predictor module is with its default settings. To make a prediction with string, below is an example:

import poetic

pred = poetic.Predictor()
result = pred.predict("This is an example.")

To make a prediction with a text file, use the following codes:

import poetic

pred = poetic.Predictor()
result = pred.predict_file("<PATH>")

class poetic.predictor.Predictor(model: Optional[tensorflow.keras.Model] = None, dict: Optional[gensim.corpora.dictionary.Dictionary] = None, force_download_assets: Optional[bool] = False)¶

Bases: object

The Predictor() class processes and predicts inputs for poetic scores. It can be used as the single interface of the package with other modules built as helpers.

Parameters

model (tensorflow.keras.Model, optional) – A pre-trained keras model. The default model will be loaded if no model is supplied. If a custom model is supplied, a custom gensim dictionary is recommended for it to work correctly although not strictly enforced.
dict (gensim.corpora.dictionary.Dictionary, optional) – Gensim dictionary for word IDs. If nothing is supplied, the default dictionary will be loaded. The default dictionary will be required for the the default model to work correctly although it is not strictly enforced.
force_download_assets (bool, optional) – Wheher to download assets (the default models) without asking/user input.

model¶

The pre-trained keras model.

Type: tensorflow.keras.Model

dict¶

Gensim dictionary for word IDs.

Type: gensim.corpora.dictionary.Dictionary

force_download_assets¶

Wheher to download assets without asking.

Type: bool

predict(input: str) → poetic.predictor.Predictions ¶

Predict poetic score from string.

Parameters: input (str) – Text content to be predicted.
Returns: A Predictions object with predicted scores of the given input.
Return type: Predictions
Raises: poetic.exceptions.InputLengthError – Error for processing input length of zero.

predict_file(path: str) → poetic.predictor.Predictions ¶

Predict poetic score from file.

This method essentially loads the text file into a string of text and then calls the predict method.

Parameters: path (str) – The path to the text file.
Returns: A Predictions object with predicted scores of the given input.
Return type: Predictions
Raises: poetic.exceptions.InputLengthError – Error for processing empty file, resulting in input length of zero.

preprocess(input: str) → numpy.ndarray¶

Preprocess inputs: tokenize, to lower, and padding.

Parameters: input (str) – Text either in a single string or a list of strings.
Returns: A 2-d numpy array of processed inputs.
Return type: numpy.ndarray
Raises: poetic.exceptions.InputLengthError – Error for processing input length of zero.

tokenize(input: str) → List[List[str]]¶

Tokenize text input into sentences and then words.

Parameters: input (str) – A string or list of strings of text.
Returns: A 2-d list of tokenized words.
Return type: list(str)

word_id(input: List[List[str]]) → List[List[int]]¶

Convert tokenized words to word IDs using a gensim dictionary.

Parameters: input (list) – A 2-d list of tokenized words.
Returns: A 2-d list of word ids.
Return type: list