def compute_idf_label1(aids_list, daid2_label):
"""
One of our idf extensions
Example:
>>> from ibeis.algo.hots.smk.smk_index import * # NOQA
>>> from ibeis.algo.hots.smk import smk_debug
>>> ibs, annots_df, daids, qaids, invindex, wx2_idxs, qparams = smk_debug.testdata_raw_internals1()
>>> wx_series = np.arange(len(invindex.words))
>>> idx2_aid = invindex.idx2_daid
>>> daid2_label = invindex.daid2_label
>>> _ = helper_idf_wordgroup(wx2_idxs, idx2_aid, wx_series)
>>> idxs_list, aids_list = _
>>> wx2_idf = compute_idf_label1(wx_series, wx2_idxs, idx2_aid, daids)
"""
nWords = len(aids_list)
# Computes our novel label idf weight
lblindex_list = np.array(ut.tuples_to_unique_scalars(daid2_label.values()))
#daid2_lblindex = dict(zip(daid_list, lblindex_list))
unique_lblindexes, groupxs = clustertool.group_indices(lblindex_list)
daid_list = np.array(daid2_label.keys())
daids_list = [daid_list.take(xs) for xs in groupxs]
daid2_wxs = ut.ddict(list)
for wx, daids in enumerate(aids_list):
for daid in daids:
daid2_wxs[daid].append(wx)
lblindex2_daids = list(zip(unique_lblindexes, daids_list))
nLabels = len(unique_lblindexes)
pcntLblsWithWord = np.zeros(nWords, np.float64)
# Get num times word appears for eachlabel
for lblindex, daids in lblindex2_daids:
nWordsWithLabel = np.zeros(nWords)
for daid in daids:
wxs = daid2_wxs[daid]
nWordsWithLabel[wxs] += 1
pcntLblsWithWord += (1 - nWordsWithLabel.astype(np.float64) / len(daids))
# Labels for each word
idf_list = np.log(np.divide(nLabels, np.add(pcntLblsWithWord, 1),
dtype=hstypes.FLOAT_TYPE),
dtype=hstypes.FLOAT_TYPE)
return idf_list
def compute_idf_label1(aids_list, daid2_label):
"""
One of our idf extensions
Example:
>>> from ibeis.algo.hots.smk.smk_index import * # NOQA
>>> from ibeis.algo.hots.smk import smk_debug
>>> ibs, annots_df, daids, qaids, invindex, wx2_idxs, qparams = smk_debug.testdata_raw_internals1()
>>> wx_series = np.arange(len(invindex.words))
>>> idx2_aid = invindex.idx2_daid
>>> daid2_label = invindex.daid2_label
>>> _ = helper_idf_wordgroup(wx2_idxs, idx2_aid, wx_series)
>>> idxs_list, aids_list = _
>>> wx2_idf = compute_idf_label1(wx_series, wx2_idxs, idx2_aid, daids)
"""
nWords = len(aids_list)
# Computes our novel label idf weight
lblindex_list = np.array(ut.tuples_to_unique_scalars(daid2_label.values()))
#daid2_lblindex = dict(zip(daid_list, lblindex_list))
unique_lblindexes, groupxs = clustertool.group_indices(lblindex_list)
daid_list = np.array(daid2_label.keys())
daids_list = [daid_list.take(xs) for xs in groupxs]
daid2_wxs = ut.ddict(list)
for wx, daids in enumerate(aids_list):
for daid in daids:
daid2_wxs[daid].append(wx)
lblindex2_daids = list(zip(unique_lblindexes, daids_list))
nLabels = len(unique_lblindexes)
pcntLblsWithWord = np.zeros(nWords, np.float64)
# Get num times word appears for eachlabel
for lblindex, daids in lblindex2_daids:
nWordsWithLabel = np.zeros(nWords)
for daid in daids:
wxs = daid2_wxs[daid]
nWordsWithLabel[wxs] += 1
pcntLblsWithWord += (1 - nWordsWithLabel.astype(np.float64) / len(daids))
# Labels for each word
idf_list = np.log(np.divide(nLabels, np.add(pcntLblsWithWord, 1),
dtype=hstypes.FLOAT_TYPE),
dtype=hstypes.FLOAT_TYPE)
return idf_list
def compute_negentropy_names(aids_list, daid2_label):
r"""
One of our idf extensions
Word weighting based on the negative entropy over all names of p(n_i | word)
Args:
aids_list (list of aids):
daid2_label (dict from daid to label):
Returns:
negentropy_list (ndarray[float32]): idf-like weighting for each word based on the negative entropy
Example:
>>> # SLOW_DOCTEST
>>> from ibeis.algo.hots.smk.smk_index import * # NOQA
>>> from ibeis.algo.hots.smk import smk_debug
>>> ibs, annots_df, daids, qaids, invindex, wx2_idxs, qparams = smk_debug.testdata_raw_internals1()
>>> wx_series = np.arange(len(invindex.words))
>>> idx2_aid = invindex.idx2_daid
>>> daid2_label = invindex.daid2_label
>>> _ = helper_idf_wordgroup(wx2_idxs, idx2_aid, wx_series)
>>> idxs_list, aids_list = _
Math::
p(n_i | \word) = \sum_{\lbl \in L_i} p(\lbl | \word)
p(\lbl | \word) = \frac{p(\word | \lbl) p(\lbl)}{p(\word)}
p(\word) = \sum_{\lbl' \in L} p(\word | \lbl') p(\lbl')
p(\word | \lbl) = NumAnnotOfLabelWithWord / NumAnnotWithLabel =
\frac{\sum_{\X \in \DB_\lbl} b(\word, \X)}{\card{\DB_\lbl}}
h(n_i | word) = -\sum_{i=1}^N p(n_i | \word) \log p(n_i | \word)
word_weight = log(N) - h(n | word)
CommandLine:
python dev.py -t smk2 --allgt --db GZ_ALL
python dev.py -t smk5 --allgt --db GZ_ALL
Auto:
python -c "import utool as ut; ut.print_auto_docstr('ibeis.algo.hots.smk.smk_index', 'compute_negentropy_names')"
"""
nWords = len(aids_list)
# --- LABEL MEMBERS w.r.t daids ---
# compute mapping from label to daids
# Translate tuples into scalars for efficiency
label_list = list(daid2_label.values())
lblindex_list = np.array(ut.tuples_to_unique_scalars(label_list))
#daid2_lblindex = dict(zip(daid_list, lblindex_list))
unique_lblindexes, groupxs = clustertool.group_indices(lblindex_list)
daid_list = np.array(daid2_label.keys())
daids_list = [daid_list.take(xs) for xs in groupxs]
# --- DAID MEMBERS w.r.t. words ---
# compute mapping from daid to word indexes
# finds all the words that belong to an annotation
daid2_wxs = ut.ddict(list)
for wx, _daids in enumerate(aids_list):
for daid in _daids:
daid2_wxs[daid].append(wx)
# --- \Pr(\word \given \lbl) for each label ---
# Compute the number of annotations in a label with the word vs
# the number of annotations in the label
lblindex2_daids = list(zip(unique_lblindexes, daids_list))
# Get num times word appears for each label
probWordGivenLabel_list = []
for lblindex, _daids in lblindex2_daids:
nAnnotOfLabelWithWord = np.zeros(nWords, dtype=np.int32)
for daid in _daids:
wxs = np.unique(daid2_wxs[daid])
nAnnotOfLabelWithWord[wxs] += 1
probWordGivenLabel = nAnnotOfLabelWithWord.astype(np.float64) / len(_daids)
probWordGivenLabel_list.append(probWordGivenLabel)
# (nLabels, nWords)
probWordGivenLabel_arr = np.array(probWordGivenLabel_list)
# --- \Pr(\lbl \given \word) ---
# compute partition function that approximates probability of a word
# (1, nWords)
probWord = probWordGivenLabel_arr.sum(axis=0)
probWord.shape = (1, probWord.size)
# (nLabels, nWords)
probLabelGivenWord_arr = (probWordGivenLabel_arr / probWord)
# --- \Pr(\name \given \lbl) ---
# get names for each unique label
nid_list = np.array([label_list[xs[0]][0] for xs in groupxs])
unique_nids, groupxs_ = clustertool.group_indices(nid_list)
# (nNames, nWords)
# add a little wiggle room
eps = 1E-9
# http://stackoverflow.com/questions/872544/precision-of-floating-point
#epsilon = 2^(E-52) % For a 64-bit float (double precision)
#epsilon = 2^(E-23) % For a 32-bit float (single precision)
#epsilon = 2^(E-10) % For a 16-bit float (half precision)
probNameGivenWord = eps + (1.0 - eps) * np.array([probLabelGivenWord_arr.take(xs, axis=0).sum(axis=0) for xs in groupxs_])
logProbNameGivenWord = np.log(probNameGivenWord)
wordNameEntropy = -(probNameGivenWord * logProbNameGivenWord).sum(0)
# Compute negative entropy for weights
nNames = len(nid_list)
negentropy_list = np.log(nNames) - wordNameEntropy
return negentropy_list
def compute_negentropy_names(aids_list, daid2_label):
r"""
One of our idf extensions
Word weighting based on the negative entropy over all names of p(n_i | word)
Args:
aids_list (list of aids):
daid2_label (dict from daid to label):
Returns:
negentropy_list (ndarray[float32]): idf-like weighting for each word based on the negative entropy
Example:
>>> # SLOW_DOCTEST
>>> from ibeis.algo.hots.smk.smk_index import * # NOQA
>>> from ibeis.algo.hots.smk import smk_debug
>>> ibs, annots_df, daids, qaids, invindex, wx2_idxs, qparams = smk_debug.testdata_raw_internals1()
>>> wx_series = np.arange(len(invindex.words))
>>> idx2_aid = invindex.idx2_daid
>>> daid2_label = invindex.daid2_label
>>> _ = helper_idf_wordgroup(wx2_idxs, idx2_aid, wx_series)
>>> idxs_list, aids_list = _
Math::
p(n_i | \word) = \sum_{\lbl \in L_i} p(\lbl | \word)
p(\lbl | \word) = \frac{p(\word | \lbl) p(\lbl)}{p(\word)}
p(\word) = \sum_{\lbl' \in L} p(\word | \lbl') p(\lbl')
p(\word | \lbl) = NumAnnotOfLabelWithWord / NumAnnotWithLabel =
\frac{\sum_{\X \in \DB_\lbl} b(\word, \X)}{\card{\DB_\lbl}}
h(n_i | word) = -\sum_{i=1}^N p(n_i | \word) \log p(n_i | \word)
word_weight = log(N) - h(n | word)
CommandLine:
python dev.py -t smk2 --allgt --db GZ_ALL
python dev.py -t smk5 --allgt --db GZ_ALL
Auto:
python -c "import utool as ut; ut.print_auto_docstr('ibeis.algo.hots.smk.smk_index', 'compute_negentropy_names')"
"""
nWords = len(aids_list)
# --- LABEL MEMBERS w.r.t daids ---
# compute mapping from label to daids
# Translate tuples into scalars for efficiency
label_list = list(daid2_label.values())
lblindex_list = np.array(ut.tuples_to_unique_scalars(label_list))
#daid2_lblindex = dict(zip(daid_list, lblindex_list))
unique_lblindexes, groupxs = clustertool.group_indices(lblindex_list)
daid_list = np.array(daid2_label.keys())
daids_list = [daid_list.take(xs) for xs in groupxs]
# --- DAID MEMBERS w.r.t. words ---
# compute mapping from daid to word indexes
# finds all the words that belong to an annotation
daid2_wxs = ut.ddict(list)
for wx, _daids in enumerate(aids_list):
for daid in _daids:
daid2_wxs[daid].append(wx)
# --- \Pr(\word \given \lbl) for each label ---
# Compute the number of annotations in a label with the word vs
# the number of annotations in the label
lblindex2_daids = list(zip(unique_lblindexes, daids_list))
# Get num times word appears for each label
probWordGivenLabel_list = []
for lblindex, _daids in lblindex2_daids:
nAnnotOfLabelWithWord = np.zeros(nWords, dtype=np.int32)
for daid in _daids:
wxs = np.unique(daid2_wxs[daid])
nAnnotOfLabelWithWord[wxs] += 1
probWordGivenLabel = nAnnotOfLabelWithWord.astype(np.float64) / len(_daids)
probWordGivenLabel_list.append(probWordGivenLabel)
# (nLabels, nWords)
probWordGivenLabel_arr = np.array(probWordGivenLabel_list)
# --- \Pr(\lbl \given \word) ---
# compute partition function that approximates probability of a word
# (1, nWords)
probWord = probWordGivenLabel_arr.sum(axis=0)
probWord.shape = (1, probWord.size)
# (nLabels, nWords)
probLabelGivenWord_arr = (probWordGivenLabel_arr / probWord)
# --- \Pr(\name \given \lbl) ---
# get names for each unique label
nid_list = np.array([label_list[xs[0]][0] for xs in groupxs])
unique_nids, groupxs_ = clustertool.group_indices(nid_list)
# (nNames, nWords)
# add a little wiggle room
eps = 1E-9
# http://stackoverflow.com/questions/872544/precision-of-floating-point
#epsilon = 2^(E-52) # For a 64-bit float (double precision)
#epsilon = 2^(E-23) # For a 32-bit float (single precision)
#epsilon = 2^(E-10) # For a 16-bit float (half precision)
probNameGivenWord = eps + (1.0 - eps) * np.array([probLabelGivenWord_arr.take(xs, axis=0).sum(axis=0) for xs in groupxs_])
logProbNameGivenWord = np.log(probNameGivenWord)
wordNameEntropy = -(probNameGivenWord * logProbNameGivenWord).sum(0)
# Compute negative entropy for weights
nNames = len(nid_list)
negentropy_list = np.log(nNames) - wordNameEntropy
return negentropy_list