topic_coherence.direct_confirmation_measure – Direct confirmation measure module

`topic_coherence.direct_confirmation_measure` – Direct confirmation measure module¶

This module contains functions to compute direct confirmation on a pair of words or word subsets.

gensim.topic_coherence.direct_confirmation_measure.aggregate_segment_sims(segment_sims, with_std, with_support)¶

Compute various statistics from the segment similarities generated via set pairwise comparisons of top-N word lists for a single topic.

Parameters

segment_sims (iterable of float) – Similarity values to aggregate.
with_std (bool) – Set to True to include standard deviation.
with_support (bool) – Set to True to include number of elements in segment_sims as a statistic in the results returned.

Returns

Tuple with (mean[, std[, support]]).

Return type

(float[, float[, int]])

Examples

>>> from gensim.topic_coherence import direct_confirmation_measure
>>>
>>> segment_sims = [0.2, 0.5, 1., 0.05]
>>> direct_confirmation_measure.aggregate_segment_sims(segment_sims, True, True)
(0.4375, 0.36293077852394939, 4)
>>> direct_confirmation_measure.aggregate_segment_sims(segment_sims, False, False)
0.4375

gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability(segmented_topics, accumulator, with_std=False, with_support=False)¶

Calculate the log-conditional-probability measure which is used by coherence measures such as U_mass. This is defined as $m_{lc}(S_i) = log \frac{P(W', W^{*}) + \epsilon}{P(W^{*})}$ .

Parameters

segmented_topics (list of lists of (int, int)) – Output from the s_one_pre(), s_one_one().
accumulator (InvertedIndexAccumulator) – Word occurrence accumulator from gensim.topic_coherence.probability_estimation.
with_std (bool, optional) – True to also include standard deviation across topic segment sets in addition to the mean coherence for each topic.
with_support (bool, optional) – True to also include support across topic segments. The support is defined as the number of pairwise similarity comparisons were used to compute the overall topic coherence.

Returns

Log conditional probabilities measurement for each topic.

Return type

list of float

Examples

>>> from gensim.topic_coherence import direct_confirmation_measure, text_analysis
>>> from collections import namedtuple
>>>
>>> # Create dictionary
>>> id2token = {1: 'test', 2: 'doc'}
>>> token2id = {v: k for k, v in id2token.items()}
>>> dictionary = namedtuple('Dictionary', 'token2id, id2token')(token2id, id2token)
>>>
>>> # Initialize segmented topics and accumulator
>>> segmentation = [[(1, 2)]]
>>>
>>> accumulator = text_analysis.InvertedIndexAccumulator({1, 2}, dictionary)
>>> accumulator._inverted_index = {0: {2, 3, 4}, 1: {3, 5}}
>>> accumulator._num_docs = 5
>>>
>>> # result should be ~ ln(1 / 2) = -0.693147181
>>> result = direct_confirmation_measure.log_conditional_probability(segmentation, accumulator)[0]

gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure(segmented_topics, accumulator, normalize=False, with_std=False, with_support=False)¶

Compute log ratio measure for segment_topics.

Parameters

segmented_topics (list of lists of (int, int)) – Output from the s_one_pre(), s_one_one().
accumulator (InvertedIndexAccumulator) – Word occurrence accumulator from gensim.topic_coherence.probability_estimation.
normalize (bool, optional) – Details in the “Notes” section.
with_std (bool, optional) – True to also include standard deviation across topic segment sets in addition to the mean coherence for each topic.
with_support (bool, optional) – True to also include support across topic segments. The support is defined as the number of pairwise similarity comparisons were used to compute the overall topic coherence.

Notes

If normalize=False:: Calculate the log-ratio-measure, popularly known as PMI which is used by coherence measures such as c_v. This is defined as $m_{lr}(S_i) = log \frac{P(W', W^{*}) + \epsilon}{P(W') * P(W^{*})}$
If normalize=True:: Calculate the normalized-log-ratio-measure, popularly knowns as NPMI which is used by coherence measures such as c_v. This is defined as $m_{nlr}(S_i) = \frac{m_{lr}(S_i)}{-log(P(W', W^{*}) + \epsilon)}$

Returns: Log ratio measurements for each topic.
Return type: list of float

Examples

>>> from gensim.topic_coherence import direct_confirmation_measure, text_analysis
>>> from collections import namedtuple
>>>
>>> # Create dictionary
>>> id2token = {1: 'test', 2: 'doc'}
>>> token2id = {v: k for k, v in id2token.items()}
>>> dictionary = namedtuple('Dictionary', 'token2id, id2token')(token2id, id2token)
>>>
>>> # Initialize segmented topics and accumulator
>>> segmentation = [[(1, 2)]]
>>>
>>> accumulator = text_analysis.InvertedIndexAccumulator({1, 2}, dictionary)
>>> accumulator._inverted_index = {0: {2, 3, 4}, 1: {3, 5}}
>>> accumulator._num_docs = 5
>>>
>>> # result should be ~ ln{(1 / 5) / [(3 / 5) * (2 / 5)]} = -0.182321557
>>> result = direct_confirmation_measure.log_ratio_measure(segmentation, accumulator)[0]

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topic_coherence.direct_confirmation_measure – Direct confirmation measure module¶

`topic_coherence.direct_confirmation_measure` – Direct confirmation measure module¶