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_prepare.py
472 lines (385 loc) · 18.9 KB
/
_prepare.py
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"""
pyLDAvis Prepare
===============
Main transformation functions for preparing LDAdata to the visualization's data structures
"""
from __future__ import absolute_import
from past.builtins import basestring
from collections import namedtuple
import json
import logging
from joblib import Parallel, delayed, cpu_count
import numpy as np
import pandas as pd
from scipy.stats import entropy
from scipy.spatial.distance import pdist, squareform
from .utils import NumPyEncoder
try:
from sklearn.manifold import MDS, TSNE
sklearn_present = True
except ImportError:
sklearn_present = False
def __num_dist_rows__(array, ndigits=2):
return array.shape[0] - int((pd.DataFrame(array).sum(axis=1) < 0.999).sum())
class ValidationError(ValueError):
pass
def _input_check(topic_term_dists, doc_topic_dists, doc_lengths, vocab, term_frequency):
ttds = topic_term_dists.shape
dtds = doc_topic_dists.shape
errors = []
def err(msg):
errors.append(msg)
if dtds[1] != ttds[0]:
err_msg = ('Number of rows of topic_term_dists does not match number of columns of '
'doc_topic_dists; both should be equal to the number of topics in the model.')
err(err_msg)
if len(doc_lengths) != dtds[0]:
err_msg = ('Length of doc_lengths not equal to the number of rows in doc_topic_dists;'
'both should be equal to the number of documents in the data.')
err(err_msg)
W = len(vocab)
if ttds[1] != W:
err_msg = ('Number of terms in vocabulary does not match the number of columns of '
'topic_term_dists (where each row of topic_term_dists is a probability '
'distribution of terms for a given topic)')
err(err_msg)
if len(term_frequency) != W:
err_msg = ('Length of term_frequency not equal to the number of terms in the '
'number of terms in the vocabulary (len of vocab)')
err(err_msg)
if __num_dist_rows__(topic_term_dists) != ttds[0]:
err('Not all rows (distributions) in topic_term_dists sum to 1.')
if __num_dist_rows__(doc_topic_dists) != dtds[0]:
err('Not all rows (distributions) in doc_topic_dists sum to 1.')
if len(errors) > 0:
return errors
def _input_validate(*args):
res = _input_check(*args)
if res:
raise ValidationError('\n' + '\n'.join([' * ' + s for s in res]))
def _jensen_shannon(_P, _Q):
_M = 0.5 * (_P + _Q)
return 0.5 * (entropy(_P, _M) + entropy(_Q, _M))
def _pcoa(pair_dists, n_components=2):
"""Principal Coordinate Analysis,
aka Classical Multidimensional Scaling
"""
# code referenced from skbio.stats.ordination.pcoa
# https://github.com/biocore/scikit-bio/blob/0.5.0/skbio/stats/ordination/_principal_coordinate_analysis.py
# pairwise distance matrix is assumed symmetric
pair_dists = np.asarray(pair_dists, np.float64)
# perform SVD on double centred distance matrix
n = pair_dists.shape[0]
H = np.eye(n) - np.ones((n, n)) / n
B = - H.dot(pair_dists ** 2).dot(H) / 2
eigvals, eigvecs = np.linalg.eig(B)
# Take first n_components of eigenvalues and eigenvectors
# sorted in decreasing order
ix = eigvals.argsort()[::-1][:n_components]
eigvals = eigvals[ix]
eigvecs = eigvecs[:, ix]
# replace any remaining negative eigenvalues and associated eigenvectors with zeroes
# at least 1 eigenvalue must be zero
eigvals[np.isclose(eigvals, 0)] = 0
if np.any(eigvals < 0):
ix_neg = eigvals < 0
eigvals[ix_neg] = np.zeros(eigvals[ix_neg].shape)
eigvecs[:, ix_neg] = np.zeros(eigvecs[:, ix_neg].shape)
return np.sqrt(eigvals) * eigvecs
def js_PCoA(distributions):
"""Dimension reduction via Jensen-Shannon Divergence & Principal Coordinate Analysis
(aka Classical Multidimensional Scaling)
Parameters
----------
distributions : array-like, shape (`n_dists`, `k`)
Matrix of distributions probabilities.
Returns
-------
pcoa : array, shape (`n_dists`, 2)
"""
dist_matrix = squareform(pdist(distributions, metric=_jensen_shannon))
return _pcoa(dist_matrix)
def js_MMDS(distributions, **kwargs):
"""Dimension reduction via Jensen-Shannon Divergence & Metric Multidimensional Scaling
Parameters
----------
distributions : array-like, shape (`n_dists`, `k`)
Matrix of distributions probabilities.
**kwargs : Keyword argument to be passed to `sklearn.manifold.MDS()`
Returns
-------
mmds : array, shape (`n_dists`, 2)
"""
dist_matrix = squareform(pdist(distributions, metric=_jensen_shannon))
model = MDS(n_components=2, random_state=0, dissimilarity='precomputed', **kwargs)
return model.fit_transform(dist_matrix)
def js_TSNE(distributions, **kwargs):
"""Dimension reduction via Jensen-Shannon Divergence & t-distributed Stochastic Neighbor Embedding
Parameters
----------
distributions : array-like, shape (`n_dists`, `k`)
Matrix of distributions probabilities.
**kwargs : Keyword argument to be passed to `sklearn.manifold.TSNE()`
Returns
-------
tsne : array, shape (`n_dists`, 2)
"""
dist_matrix = squareform(pdist(distributions, metric=_jensen_shannon))
model = TSNE(n_components=2, random_state=0, metric='precomputed', **kwargs)
return model.fit_transform(dist_matrix)
def _df_with_names(data, index_name, columns_name):
if type(data) == pd.DataFrame:
# we want our index to be numbered
df = pd.DataFrame(data.values)
else:
df = pd.DataFrame(data)
df.index.name = index_name
df.columns.name = columns_name
return df
def _series_with_name(data, name):
if type(data) == pd.Series:
data.name = name
# ensures a numeric index
return data.reset_index()[name]
else:
return pd.Series(data, name=name)
def _topic_coordinates(mds, topic_term_dists, topic_proportion, start_index=1):
K = topic_term_dists.shape[0]
mds_res = mds(topic_term_dists)
assert mds_res.shape == (K, 2)
mds_df = pd.DataFrame({'x': mds_res[:, 0], 'y': mds_res[:, 1],
'topics': range(start_index, K + start_index),
'cluster': 1, 'Freq': topic_proportion * 100})
# note: cluster (should?) be deprecated soon. See: https://github.com/cpsievert/LDAvis/issues/26
return mds_df
def _chunks(l, n):
""" Yield successive n-sized chunks from l.
"""
for i in range(0, len(l), n):
yield l[i:i + n]
def _job_chunks(l, n_jobs):
n_chunks = n_jobs
if n_jobs < 0:
# so, have n chunks if we are using all n cores/cpus
n_chunks = cpu_count() + 1 - n_jobs
return _chunks(l, n_chunks)
def _find_relevance(log_ttd, log_lift, R, lambda_):
relevance = lambda_ * log_ttd + (1 - lambda_) * log_lift
return relevance.T.apply(lambda topic: topic.nlargest(R).index)
def _find_relevance_chunks(log_ttd, log_lift, R, lambda_seq):
return pd.concat([_find_relevance(log_ttd, log_lift, R, l) for l in lambda_seq])
def _topic_info(topic_term_dists, topic_proportion, term_frequency, term_topic_freq,
vocab, lambda_step, R, n_jobs, start_index=1):
# marginal distribution over terms (width of blue bars)
term_proportion = term_frequency / term_frequency.sum()
# compute the distinctiveness and saliency of the terms:
# this determines the R terms that are displayed when no topic is selected
tt_sum = topic_term_dists.sum()
topic_given_term = pd.eval("topic_term_dists / tt_sum")
log_1 = np.log(pd.eval("(topic_given_term.T / topic_proportion)"))
kernel = pd.eval("topic_given_term * log_1.T")
distinctiveness = kernel.sum()
saliency = term_proportion * distinctiveness
# Order the terms for the "default" view by decreasing saliency:
default_term_info = pd.DataFrame({
'saliency': saliency,
'Term': vocab,
'Freq': term_frequency,
'Total': term_frequency,
'Category': 'Default'})
default_term_info = default_term_info.sort_values(
by='saliency', ascending=False).head(R).drop('saliency', 1)
# Rounding Freq and Total to integer values to match LDAvis code:
default_term_info['Freq'] = np.floor(default_term_info['Freq'])
default_term_info['Total'] = np.floor(default_term_info['Total'])
ranks = np.arange(R, 0, -1)
default_term_info['logprob'] = default_term_info['loglift'] = ranks
default_term_info = default_term_info.reindex(columns=[
"Term", "Freq", "Total", "Category", "logprob", "loglift"
])
# compute relevance and top terms for each topic
log_lift = np.log(pd.eval("topic_term_dists / term_proportion")).astype("float64")
log_ttd = np.log(topic_term_dists).astype("float64")
lambda_seq = np.arange(0, 1 + lambda_step, lambda_step)
def topic_top_term_df(tup):
new_topic_id, (original_topic_id, topic_terms) = tup
term_ix = topic_terms.unique()
df = pd.DataFrame({'Term': vocab[term_ix],
'Freq': term_topic_freq.loc[original_topic_id, term_ix],
'Total': term_frequency[term_ix],
'Category': 'Topic%d' % new_topic_id,
'logprob': log_ttd.loc[original_topic_id, term_ix].round(4),
'loglift': log_lift.loc[original_topic_id, term_ix].round(4),
})
return df.reindex(columns=[
"Term", "Freq", "Total", "Category", "logprob", "loglift"
])
top_terms = pd.concat(Parallel(n_jobs=n_jobs)
(delayed(_find_relevance_chunks)(log_ttd, log_lift, R, ls)
for ls in _job_chunks(lambda_seq, n_jobs)))
topic_dfs = map(topic_top_term_df, enumerate(top_terms.T.iterrows(), start_index))
return pd.concat([default_term_info] + list(topic_dfs))
def _token_table(topic_info, term_topic_freq, vocab, term_frequency, start_index=1):
# last, to compute the areas of the circles when a term is highlighted
# we must gather all unique terms that could show up (for every combination
# of topic and value of lambda) and compute its distribution over topics.
# term-topic frequency table of unique terms across all topics and all values of lambda
term_ix = topic_info.index.unique()
term_ix = np.sort(term_ix)
top_topic_terms_freq = term_topic_freq[term_ix]
# use the new ordering for the topics
K = len(term_topic_freq)
top_topic_terms_freq.index = range(start_index, K + start_index)
top_topic_terms_freq.index.name = 'Topic'
# we filter to Freq >= 0.5 to avoid sending too much data to the browser
token_table = pd.DataFrame({'Freq': top_topic_terms_freq.unstack()})\
.reset_index().set_index('term').query('Freq >= 0.5')
token_table['Freq'] = token_table['Freq'].round()
token_table['Term'] = vocab[token_table.index.values].values
# Normalize token frequencies:
token_table['Freq'] = token_table.Freq / term_frequency[token_table.index]
return token_table.sort_values(by=['Term', 'Topic'])
def prepare(topic_term_dists, doc_topic_dists, doc_lengths, vocab, term_frequency,
R=30, lambda_step=0.01, mds=js_PCoA, n_jobs=-1,
plot_opts=None, sort_topics=True, start_index=1):
"""Transforms the topic model distributions and related corpus data into
the data structures needed for the visualization.
Parameters
----------
topic_term_dists : array-like, shape (`n_topics`, `n_terms`)
Matrix of topic-term probabilities. Where `n_terms` is `len(vocab)`.
doc_topic_dists : array-like, shape (`n_docs`, `n_topics`)
Matrix of document-topic probabilities.
doc_lengths : array-like, shape `n_docs`
The length of each document, i.e. the number of words in each document.
The order of the numbers should be consistent with the ordering of the
docs in `doc_topic_dists`.
vocab : array-like, shape `n_terms`
List of all the words in the corpus used to train the model.
term_frequency : array-like, shape `n_terms`
The count of each particular term over the entire corpus. The ordering
of these counts should correspond with `vocab` and `topic_term_dists`.
R : int
The number of terms to display in the barcharts of the visualization.
Default is 30. Recommended to be roughly between 10 and 50.
lambda_step : float, between 0 and 1
Determines the interstep distance in the grid of lambda values over
which to iterate when computing relevance.
Default is 0.01. Recommended to be between 0.01 and 0.1.
mds : function or a string representation of function
A function that takes `topic_term_dists` as an input and outputs a
`n_topics` by `2` distance matrix. The output approximates the distance
between topics. See :func:`js_PCoA` for details on the default function.
A string representation currently accepts `pcoa` (or upper case variant),
`mmds` (or upper case variant) and `tsne` (or upper case variant),
if `sklearn` package is installed for the latter two.
n_jobs : int
The number of cores to be used to do the computations. The regular
joblib conventions are followed so `-1`, which is the default, will
use all cores.
plot_opts : dict, with keys 'xlab' and `ylab`
Dictionary of plotting options, right now only used for the axis labels.
sort_topics : sort topics by topic proportion (percentage of tokens covered). Set to false to
to keep original topic order.
start_index: how to number topics for prepared data. Defaults to one-based indexing.
Set to 0 for zero-based indexing.
Returns
-------
prepared_data : PreparedData
A named tuple containing all the data structures required to create
the visualization. To be passed on to functions like :func:`display`.
This named tuple can be represented as json or a python dictionary.
There is a helper function 'sorted_terms' that can be used to get
the terms of a topic using lambda to rank their relevance.
Notes
-----
This implements the method of `Sievert, C. and Shirley, K. (2014):
LDAvis: A Method for Visualizing and Interpreting Topics, ACL Workshop on
Interactive Language Learning, Visualization, and Interfaces.`
http://nlp.stanford.edu/events/illvi2014/papers/sievert-illvi2014.pdf
See Also
--------
:func:`save_json`: save json representation of a figure to file
:func:`save_html` : save html representation of a figure to file
:func:`show` : launch a local server and show a figure in a browser
:func:`display` : embed figure within the IPython notebook
:func:`enable_notebook` : automatically embed visualizations in IPython notebook
"""
if plot_opts is None:
plot_opts = {'xlab': 'PC1', 'ylab': 'PC2'}
# parse mds
if isinstance(mds, basestring):
mds = mds.lower()
if mds == 'pcoa':
mds = js_PCoA
elif mds in ('mmds', 'tsne'):
if sklearn_present:
mds_opts = {'mmds': js_MMDS, 'tsne': js_TSNE}
mds = mds_opts[mds]
else:
logging.warning('sklearn not present, switch to PCoA')
mds = js_PCoA
else:
logging.warning('Unknown mds `%s`, switch to PCoA' % mds)
mds = js_PCoA
# Conceptually, the items in `topic_term_dists` end up as individual rows in the
# DataFrame, but we can speed up ingestion by treating them as columns and
# transposing at the end. (This is especially true when the number of terms far
# exceeds the number of topics.)
topic_term_dist_cols = [
pd.Series(topic_term_dist, dtype="float64")
for topic_term_dist in topic_term_dists
]
topic_term_dists = pd.concat(topic_term_dist_cols, axis=1).T
topic_term_dists = _df_with_names(topic_term_dists, 'topic', 'term')
doc_topic_dists = _df_with_names(doc_topic_dists, 'doc', 'topic')
term_frequency = _series_with_name(term_frequency, 'term_frequency')
doc_lengths = _series_with_name(doc_lengths, 'doc_length')
vocab = _series_with_name(vocab, 'vocab')
_input_validate(topic_term_dists, doc_topic_dists, doc_lengths, vocab, term_frequency)
R = min(R, len(vocab))
topic_freq = doc_topic_dists.mul(doc_lengths, axis="index").sum()
# topic_freq = np.dot(doc_topic_dists.T, doc_lengths)
if (sort_topics):
topic_proportion = (topic_freq / topic_freq.sum()).sort_values(ascending=False)
else:
topic_proportion = (topic_freq / topic_freq.sum())
topic_order = topic_proportion.index
# reorder all data based on new ordering of topics
topic_freq = topic_freq[topic_order]
topic_term_dists = topic_term_dists.iloc[topic_order]
# Unused: doc_topic_dists = doc_topic_dists[topic_order]
# token counts for each term-topic combination (widths of red bars)
term_topic_freq = (topic_term_dists.T * topic_freq).T
# Quick fix for red bar width bug. We calculate the
# term frequencies internally, using the topic term distributions and the
# topic frequencies, rather than using the user-supplied term frequencies.
# For a detailed discussion, see: https://github.com/cpsievert/LDAvis/pull/41
term_frequency = np.sum(term_topic_freq, axis=0)
topic_info = _topic_info(topic_term_dists, topic_proportion,
term_frequency, term_topic_freq, vocab, lambda_step, R,
n_jobs, start_index)
token_table = _token_table(topic_info, term_topic_freq, vocab, term_frequency, start_index)
topic_coordinates = _topic_coordinates(mds, topic_term_dists, topic_proportion, start_index)
client_topic_order = [x + start_index for x in topic_order]
return PreparedData(topic_coordinates, topic_info,
token_table, R, lambda_step, plot_opts, client_topic_order)
class PreparedData(namedtuple('PreparedData', ['topic_coordinates', 'topic_info', 'token_table',
'R', 'lambda_step', 'plot_opts', 'topic_order'])):
def sorted_terms(self, topic=1, _lambda=1):
"""Returns a dataframe using _lambda to calculate term relevance of a given topic."""
tdf = pd.DataFrame(self.topic_info[self.topic_info.Category == 'Topic' + str(topic)])
if _lambda < 0 or _lambda > 1:
_lambda = 1
stdf = tdf.assign(relevance=_lambda * tdf['logprob'] + (1 - _lambda) * tdf['loglift'])
return stdf.sort_values('relevance', ascending=False)
def to_dict(self):
return {'mdsDat': self.topic_coordinates.to_dict(orient='list'),
'tinfo': self.topic_info.to_dict(orient='list'),
'token.table': self.token_table.to_dict(orient='list'),
'R': self.R,
'lambda.step': self.lambda_step,
'plot.opts': self.plot_opts,
'topic.order': self.topic_order}
def to_json(self):
return json.dumps(self.to_dict(), cls=NumPyEncoder)