def topics(self, axis: Subplot) -> BarContainer:
"""Plot the relative importance of the individual topics.
Parameters
----------
axis: Subplot
The matplotlib axis to plot into.
Returns
-------
BarContainer
The container for the bars plotted into the given axis.
"""
colors = [f'C{color}' for color in self.__topic_range]
axis.set(xlabel='Topic', ylabel='Importance')
axis.set_title('Topic distribution')
return axis.bar(self.__topic_range, self.__topics, color=colors,
tick_label=self.__topic_range)
def convergence(self, axis: Subplot) -> List[Line2D]:
"""Plot the convergence of the PLSA run.
The quantity to be minimized is the Kullback-Leibler divergence
between the original document-word matrix and its approximation
given by the (conditional) PLSA factorization.
Parameters
----------
axis: Subplot
The matplotlib axis to plot into.
Returns
-------
list of Line2D
The line object plotted into the given axis.
"""
axis.set(xlabel='Iteration', ylabel='Kullback-Leibler divergence')
return axis.plot(self.__convergence)
def topics_in_doc(self, i_doc: int, axis: Subplot) -> BarContainer:
"""Plot the relative weights of topics in a given document.
Parameters
----------
i_doc: int
Index of the document to plot. Numbering starts at 0.
axis: Subplot
The matplotlib axis to plot into.
Returns
-------
BarContainer
The container for the bars plotted into the given axis.
"""
colors = [f'C{color}' for color in self.__topic_range]
axis.set(xlabel='Topic', ylabel='Importance', title=f'Document {i_doc}')
return axis.bar(self.__topic_range, self.__topic_given_doc[i_doc],
color=colors, tick_label=self.__topic_range)
def prediction(self, doc: str, axis: Subplot) -> BarContainer:
"""Plot the predicted relative weights of topics in a new document.
Parameters
----------
doc: str
A new document given as a single string.
axis: Subplot
The matplotlib axis to plot into.
Returns
-------
BarContainer
The container for the bars plotted into the given axis.
"""
colors = [f'C{color}' for color in self.__topic_range]
prediction, n_unknown_words, _ = self.__predict(doc)
axis.set(xlabel='Topic', ylabel='Importance')
axis.set_title(f'Number of unknown words: {n_unknown_words}')
return axis.bar(self.__topic_range, prediction, color=colors,
tick_label=self.__topic_range)
