Module topicnet.cooking_machine.models.example_score

Expand source code 
import numpy as np

from typing import Callable

from .base_score import BaseScore


class ScoreExample(BaseScore):
    """
    Example score that calculates
    average size of topic kernel across all topics.
    We inherit from BaseScore in order to have self.value property and self.update() method
    (the internal logic of TopicNet relies on them)

    """
    def __init__(
            self,
            name: str = None,
            token_threshold: float = 1e-3,
            should_compute: Callable[[int], bool] = None):
        """

        Parameters
        ----------
        name:
            name of the score
        token_threshold : float
            what probabilities to take as token belonging to the topic

        """
        super().__init__(name=name, should_compute=should_compute)

        self.threshold = token_threshold

    def call(self, model, **kwargs):
        """
        Method that calculates the score

        Parameters
        ----------
        model : TopicModel

        Returns
        -------
        score : float
            mean kernel size for all topics in the model

        """
        phi = model.get_phi().values
        score = np.sum((phi > self.threshold).astype('int'), axis=0).mean()

        return score

Classes

class ScoreExample (name: str = None, token_threshold: float = 0.001, should_compute: Callable[[int], bool] = None)

Example score that calculates average size of topic kernel across all topics. We inherit from BaseScore in order to have self.value property and self.update() method (the internal logic of TopicNet relies on them)

Parameters

name:
name of the score
token_threshold : float
what probabilities to take as token belonging to the topic
Expand source code 
class ScoreExample(BaseScore):
    """
    Example score that calculates
    average size of topic kernel across all topics.
    We inherit from BaseScore in order to have self.value property and self.update() method
    (the internal logic of TopicNet relies on them)

    """
    def __init__(
            self,
            name: str = None,
            token_threshold: float = 1e-3,
            should_compute: Callable[[int], bool] = None):
        """

        Parameters
        ----------
        name:
            name of the score
        token_threshold : float
            what probabilities to take as token belonging to the topic

        """
        super().__init__(name=name, should_compute=should_compute)

        self.threshold = token_threshold

    def call(self, model, **kwargs):
        """
        Method that calculates the score

        Parameters
        ----------
        model : TopicModel

        Returns
        -------
        score : float
            mean kernel size for all topics in the model

        """
        phi = model.get_phi().values
        score = np.sum((phi > self.threshold).astype('int'), axis=0).mean()

        return score

Ancestors

Methods

def call(self, model, **kwargs)

Method that calculates the score

Parameters

model : TopicModel
 

Returns

score : float
mean kernel size for all topics in the model
Expand source code 
def call(self, model, **kwargs):
    """
    Method that calculates the score

    Parameters
    ----------
    model : TopicModel

    Returns
    -------
    score : float
        mean kernel size for all topics in the model

    """
    phi = model.get_phi().values
    score = np.sum((phi > self.threshold).astype('int'), axis=0).mean()

    return score

Inherited members