INSTRUCTION stringlengths 1 46.3k | RESPONSE stringlengths 75 80.2k |
|---|---|
Generate a path from egg_base back to '.' where the
setup script resides and ensure that path points to the
setup path from $install_dir/$egg_path. | def _resolve_setup_path(egg_base, install_dir, egg_path):
"""
Generate a path from egg_base back to '.' where the
setup script resides and ensure that path points to the
setup path from $install_dir/$egg_path.
"""
path_to_setup = egg_base.replace(os.sep, '/').rstrip('/')
... |
Just like 'imp.find_module()', but with package support | def find_module(module, paths=None):
"""Just like 'imp.find_module()', but with package support"""
parts = module.split('.')
while parts:
part = parts.pop(0)
f, path, (suffix, mode, kind) = info = imp.find_module(part, paths)
if kind == PKG_DIRECTORY:
parts = parts or ... |
Find 'module' by searching 'paths', and extract 'symbol'
Return 'None' if 'module' does not exist on 'paths', or it does not define
'symbol'. If the module defines 'symbol' as a constant, return the
constant. Otherwise, return 'default'. | def get_module_constant(module, symbol, default=-1, paths=None):
"""Find 'module' by searching 'paths', and extract 'symbol'
Return 'None' if 'module' does not exist on 'paths', or it does not define
'symbol'. If the module defines 'symbol' as a constant, return the
constant. Otherwise, return 'defau... |
Extract the constant value of 'symbol' from 'code'
If the name 'symbol' is bound to a constant value by the Python code
object 'code', return that value. If 'symbol' is bound to an expression,
return 'default'. Otherwise, return 'None'.
Return value is based on the first assignment to 'symbol'. 'sy... | def extract_constant(code, symbol, default=-1):
"""Extract the constant value of 'symbol' from 'code'
If the name 'symbol' is bound to a constant value by the Python code
object 'code', return that value. If 'symbol' is bound to an expression,
return 'default'. Otherwise, return 'None'.
Return v... |
Patch the globals to remove the objects not available on some platforms.
XXX it'd be better to test assertions about bytecode instead. | def _update_globals():
"""
Patch the globals to remove the objects not available on some platforms.
XXX it'd be better to test assertions about bytecode instead.
"""
if not sys.platform.startswith('java') and sys.platform != 'cli':
return
incompatible = 'extract_constant', 'get_module_... |
Return full package/distribution name, w/version | def full_name(self):
"""Return full package/distribution name, w/version"""
if self.requested_version is not None:
return '%s-%s' % (self.name, self.requested_version)
return self.name |
Is 'version' sufficiently up-to-date? | def version_ok(self, version):
"""Is 'version' sufficiently up-to-date?"""
return self.attribute is None or self.format is None or \
str(version) != "unknown" and version >= self.requested_version |
Get version number of installed module, 'None', or 'default'
Search 'paths' for module. If not found, return 'None'. If found,
return the extracted version attribute, or 'default' if no version
attribute was specified, or the value cannot be determined without
importing the module. T... | def get_version(self, paths=None, default="unknown"):
"""Get version number of installed module, 'None', or 'default'
Search 'paths' for module. If not found, return 'None'. If found,
return the extracted version attribute, or 'default' if no version
attribute was specified, or the va... |
Return true if dependency is present and up-to-date on 'paths | def is_current(self, paths=None):
"""Return true if dependency is present and up-to-date on 'paths'"""
version = self.get_version(paths)
if version is None:
return False
return self.version_ok(version) |
Return sorted list of all package namespaces | def _get_all_ns_packages(self):
"""Return sorted list of all package namespaces"""
pkgs = self.distribution.namespace_packages or []
return sorted(flatten(map(self._pkg_names, pkgs))) |
Given a namespace package, yield the components of that
package.
>>> names = Installer._pkg_names('a.b.c')
>>> set(names) == set(['a', 'a.b', 'a.b.c'])
True | def _pkg_names(pkg):
"""
Given a namespace package, yield the components of that
package.
>>> names = Installer._pkg_names('a.b.c')
>>> set(names) == set(['a', 'a.b', 'a.b.c'])
True
"""
parts = pkg.split('.')
while parts:
yield '.'.joi... |
Return an existing CA bundle path, or None | def find_ca_bundle():
"""Return an existing CA bundle path, or None"""
extant_cert_paths = filter(os.path.isfile, cert_paths)
return (
get_win_certfile()
or next(extant_cert_paths, None)
or _certifi_where()
) |
Rewrite imports in packaging to redirect to vendored copies. | def rewrite_packaging(pkg_files, new_root):
"""
Rewrite imports in packaging to redirect to vendored copies.
"""
for file in pkg_files.glob('*.py'):
text = file.text()
text = re.sub(r' (pyparsing|six)', rf' {new_root}.\1', text)
file.write_text(text) |
Convert POS sequence to our coarse system, formatted as a string. | def coarse_tag_str(pos_seq):
"""Convert POS sequence to our coarse system, formatted as a string."""
global tag2coarse
tags = [tag2coarse.get(tag, 'O') for tag in pos_seq]
return ''.join(tags) |
The "GreedyFSA" method in Handler et al. 2016.
Returns token position spans of valid ngrams. | def extract_finditer(pos_seq, regex=SimpleNP):
"""The "GreedyFSA" method in Handler et al. 2016.
Returns token position spans of valid ngrams."""
ss = coarse_tag_str(pos_seq)
def gen():
for m in re.finditer(regex, ss):
yield (m.start(), m.end())
return list(gen()) |
The "FilterFSA" method in Handler et al. 2016.
Returns token position spans of valid ngrams. | def extract_ngram_filter(pos_seq, regex=SimpleNP, minlen=1, maxlen=8):
"""The "FilterFSA" method in Handler et al. 2016.
Returns token position spans of valid ngrams."""
ss = coarse_tag_str(pos_seq)
def gen():
for s in xrange(len(ss)):
for n in xrange(minlen, 1 + min(maxlen, len(ss) - s)):
e = s + n
s... |
The 'JK' method in Handler et al. 2016.
Returns token positions of valid ngrams. | def extract_JK(pos_seq):
"""The 'JK' method in Handler et al. 2016.
Returns token positions of valid ngrams."""
def find_ngrams(input_list, num_):
'''get ngrams of len n from input list'''
return zip(*[input_list[i:] for i in range(num_)])
# copied from M and S chp 5'''
patterns = set(['AN', 'NN', 'AAN', 'AN... |
Give a text (or POS tag sequence), return the phrases matching the given
grammar. Works on documents or sentences.
Returns a dict with one or more keys with the phrase information.
text: the text of the document. If supplied, we will try to POS tag it.
You can also do your own tokenzation and/or tagging and sup... | def get_phrases(text=None, tokens=None, postags=None, tagger='nltk', grammar='SimpleNP', regex=None, minlen=2, maxlen=8,
output='counts'):
"""Give a text (or POS tag sequence), return the phrases matching the given
grammar. Works on documents or sentences.
Returns a dict with one or more keys with t... |
take input text and return tokens w/ part of speech tags using NLTK | def tag_text(self, text):
'''take input text and return tokens w/ part of speech tags using NLTK'''
# putting import here instead of top of file b.c. not all will have nltk installed
sents = self.sent_detector.tokenize(text) # TODO: this will fail on some unicode chars. I think assumes ascii
word_pos_pairs = ... |
Constructs the term doc matrix.
Returns
-------
scattertext.ParsedCorpus.ParsedCorpus | def build(self):
'''Constructs the term doc matrix.
Returns
-------
scattertext.ParsedCorpus.ParsedCorpus
'''
self._y = self._get_y_and_populate_category_idx_store()
self._df.apply(self._add_to_x_factory, axis=1)
self._X = self._X_factory.set_last_row_idx(len(self._y) - 1).get_csr_matrix()
self._mX =... |
Combines documents together that are in the same domain
Parameters
----------
doc_domains : array-like
Returns
-------
scipy.sparse.csr_matrix | def get_new_term_doc_mat(self, doc_domains):
'''
Combines documents together that are in the same domain
Parameters
----------
doc_domains : array-like
Returns
-------
scipy.sparse.csr_matrix
'''
assert len(doc_domains) == self.term_doc_matrix.get_num_docs()
doc_domain_set = set(doc_domains)
... |
Constructs the term doc matrix.
Returns
-------
scattertext.ParsedCorpus.ParsedCorpus | def build(self):
'''Constructs the term doc matrix.
Returns
-------
scattertext.ParsedCorpus.ParsedCorpus
'''
self._y = self._get_y_and_populate_category_idx_store()
self._df.apply(self._add_to_x_factory, axis=1)
self._X = self._X_factory.set_last_row_idx(len(self._y)-1).get_csr_matrix()
self._mX = s... |
Returns
-------
A pd.DataFrame consisting of unigram term counts of words occurring
in the TermDocumentMatrix and their corresponding background corpus
counts. The dataframe has two columns, corpus and background.
>>> corpus.get_unigram_corpus.get_term_and_background_counts()
corpus b... | def get_term_and_background_counts(self):
'''
Returns
-------
A pd.DataFrame consisting of unigram term counts of words occurring
in the TermDocumentMatrix and their corresponding background corpus
counts. The dataframe has two columns, corpus and background.
>>> corpus.get_unigram_corpus.get_term_and... |
Returns
-------
np.array
Integer document indices | def get_doc_indices(self):
'''
Returns
-------
np.array
Integer document indices
'''
if self._document_category_df is None:
return pd.np.array([])
categories_d = {d: i for i, d in enumerate(self.get_categories())}
return self._document_category_df.category.apply(categories_d.get).values |
Returns
-------
np.array
Texts | def get_texts(self):
'''
Returns
-------
np.array
Texts
'''
if self._document_category_df is None:
return pd.np.array([])
return self._document_category_df.text.values |
Parameters
----------
scatterchartdata : ScatterChartData
Returns
-------
pd.DataFrame | def get_term_category_frequencies(self, scatterchartdata):
'''
Parameters
----------
scatterchartdata : ScatterChartData
Returns
-------
pd.DataFrame
'''
df = self.term_category_freq_df.rename(
columns={c: c + ' freq' for c in self.term_category_freq_df}
)
df.index.name = 'term'
return df |
Parameters
----------
term_doc_matrix : TermDocMatrix
Returns
-------
TermDocMatrix pmi-filterd term doc matrix | def filter(self, term_doc_matrix):
'''
Parameters
----------
term_doc_matrix : TermDocMatrix
Returns
-------
TermDocMatrix pmi-filterd term doc matrix
'''
df = term_doc_matrix.get_term_freq_df()
if len(df) == 0:
return term_doc_matrix
low_pmi_bigrams = get_low_pmi_bigrams(self._threshold_coef... |
Returns html code of visualization.
Parameters
----------
corpus : Corpus
Corpus to use.
category : str
Name of category column as it appears in original data frame.
category_name : str
Name of category to use. E.g., "5-star reviews."
Optional, defaults to category ... | def produce_scattertext_explorer(corpus,
category,
category_name=None,
not_category_name=None,
protocol='https',
pmi_threshold_coefficient=DEFAULT_MINIMUM_... |
Returns html code of visualization.
Parameters
----------
term_doc_matrix : TermDocMatrix
Corpus to use
category : str
name of category column
category_name: str
name of category to mine for
not_category_name: str
name of everything that isn't in category
pro... | def produce_scattertext_html(term_doc_matrix,
category,
category_name,
not_category_name,
protocol='https',
minimum_term_frequency=DEFAULT_MINIMUM_TERM_FREQUENCY,
... |
Parameters
----------
corpus : Corpus
Corpus to use.
category : str
Name of category column as it appears in original data frame.
category_name : str
Name of category to use. E.g., "5-star reviews."
not_category_name : str
Name of ... | def word_similarity_explorer_gensim(corpus,
category,
target_term,
category_name=None,
not_category_name=None,
word2vec=None,
... |
Parameters
----------
corpus : Corpus
Corpus to use.
category : str
Name of category column as it appears in original data frame.
category_name : str
Name of category to use. E.g., "5-star reviews."
not_category_name : str
Name of everything that isn't in category. ... | def word_similarity_explorer(corpus,
category,
category_name,
not_category_name,
target_term,
nlp=None,
alpha=0.01,
m... |
Produces a Monroe et al. style visualization, with the x-axis being the log frequency
Parameters
----------
corpus : Corpus
Corpus to use.
category : str
Name of category column as it appears in original data frame.
category_name : str or None
Name of category to use. E.g.,... | def produce_frequency_explorer(corpus,
category,
category_name=None,
not_category_name=None,
term_ranker=termranking.AbsoluteFrequencyRanker,
alpha=0.01,
... |
Produces a semiotic square visualization.
Parameters
----------
semiotic_square : SemioticSquare
The basis of the visualization
x_label : str
The x-axis label in the scatter plot. Relationship between `category_a` and `category_b`.
y_label
The y-axis label in the scatter pl... | def produce_semiotic_square_explorer(semiotic_square,
x_label,
y_label,
category_name=None,
not_category_name=None,
neutral_category_na... |
Produces a semiotic square visualization.
Parameters
----------
four_square : FourSquare
The basis of the visualization
x_label : str
The x-axis label in the scatter plot. Relationship between `category_a` and `category_b`.
y_label
The y-axis label in the scatter plot. Rel... | def produce_four_square_explorer(four_square,
x_label=None,
y_label=None,
a_category_name=None,
b_category_name=None,
not_a_category_name=None,
... |
Produces a semiotic square visualization.
Parameters
----------
four_square : FourSquareAxes
The basis of the visualization
x_label : str
The x-axis label in the scatter plot. Relationship between `category_a` and `category_b`.
y_label
The y-axis label in the scatter plot. ... | def produce_four_square_axes_explorer(four_square_axes,
x_label=None,
y_label=None,
num_terms_semiotic_square=None,
get_tooltip_content=None,
... |
Parameters
----------
corpus : ParsedCorpus
It is highly recommended to use a stoplisted, unigram corpus-- `corpus.get_stoplisted_unigram_corpus()`
category : str
word2vec_model : Word2Vec
A gensim word2vec model. A default model will be used instead. See Word2VecFromParsedCorpus for th... | def produce_pca_explorer(corpus,
category,
word2vec_model=None,
projection_model=None,
embeddings=None,
projection=None,
term_acceptance_re=re.compile('[a-z]{3,}'),
... |
Parameters
----------
corpus : Corpus
It is highly recommended to use a stoplisted, unigram corpus-- `corpus.get_stoplisted_unigram_corpus()`
category : str
category_name : str
not_category_name : str
not_categories : list
characteristic_scorer : CharacteristicScorer
term_ranker
... | def produce_characteristic_explorer(corpus,
category,
category_name=None,
not_category_name=None,
not_categories=None,
characteristic_scorer... |
Parameters
----------
corpus : Corpus
Corpus to use.
category : str
Name of category column as it appears in original data frame.
category_name : str
Name of category to use. E.g., "5-star reviews."
not_category_name : str
Name of everything that isn't in category. ... | def sparse_explorer(corpus,
category,
scores,
category_name=None,
not_category_name=None,
**kwargs):
'''
Parameters
----------
corpus : Corpus
Corpus to use.
category : str
Name of cat... |
Parameters
----------
values: [term, ...]
Returns
-------
IndexStore | def build(values):
'''
Parameters
----------
values: [term, ...]
Returns
-------
IndexStore
'''
idxstore = IndexStore()
idxstore._i2val = list(values)
idxstore._val2i = {term:i for i,term in enumerate(values)}
idxstore._next_i = len(values)
return idxstore |
Parameters
-------
term_doc_matrix : TermDocMatrix
Returns
-------
New term doc matrix | def compact(self, term_doc_matrix):
'''
Parameters
-------
term_doc_matrix : TermDocMatrix
Returns
-------
New term doc matrix
'''
tdf = self.term_ranker(term_doc_matrix).get_ranks()
tdf_sum = tdf.sum(axis=0)
tdf_portions = tdf / tdf_sum
threshold = np.max(self.term_count / tdf_sum)
terms_to_... |
median = np.median(cat_scores)
scores = np.zeros(len(cat_scores)).astype(np.float)
scores[cat_scores > median] = cat_scores[cat_scores > median]
not_cat_mask = cat_scores < median if median != 0 else cat_scores <= median
scores[not_cat_mask] = -not_cat_scores[not_cat_mask] | def balance_scores_and_dont_scale(cat_scores, not_cat_scores):
'''
median = np.median(cat_scores)
scores = np.zeros(len(cat_scores)).astype(np.float)
scores[cat_scores > median] = cat_scores[cat_scores > median]
not_cat_mask = cat_scores < median if median != 0 else cat_scores <= median
scores[not_cat_mask]... |
Parameters
----------
metadata : (array like or None)
Returns
-------
{'labels':[], 'texts': []} or {'labels':[], 'texts': [], 'meta': []} | def get_labels_and_texts(self, metadata=None):
'''
Parameters
----------
metadata : (array like or None)
Returns
-------
{'labels':[], 'texts': []} or {'labels':[], 'texts': [], 'meta': []}
'''
to_ret = {'categories': self._corpus.get_categories(), 'labels': [], 'texts': []}
labels = self._corpus._... |
Parameters
----------
cat_word_counts : np.array
category counts
not_cat_word_counts : np.array
not category counts
Returns
-------
np.array
scores | def get_scores(self, cat_word_counts, not_cat_word_counts):
'''
Parameters
----------
cat_word_counts : np.array
category counts
not_cat_word_counts : np.array
not category counts
Returns
-------
np.array
scores
'''
cat_scores = self.get_scores_for_category(cat_word_counts,
... |
Parameters
----------
cat_word_counts : np.array
category counts
not_cat_word_counts : np.array
not category counts
Returns
-------
np.array
scores | def get_scores_for_category(self, cat_word_counts, not_cat_word_counts):
'''
Parameters
----------
cat_word_counts : np.array
category counts
not_cat_word_counts : np.array
not category counts
Returns
-------
np.array
scores
'''
beta = self.beta_
assert len(cat_word_counts) == len(not_ca... |
Parameters
----------
X : np.array
Array of word counts, shape (N, 2) where N is the vocab size. X[:,0] is the
positive class, while X[:,1] is the negative class. None by default
Returns
-------
np.array of p-values | def get_p_vals(self, X):
'''
Parameters
----------
X : np.array
Array of word counts, shape (N, 2) where N is the vocab size. X[:,0] is the
positive class, while X[:,1] is the negative class. None by default
Returns
-------
np.array of p-values
'''
z_scores = self.get_scores(X[:, 0], X[:, 1])... |
Computes unbalanced scaled-fscores
Parameters
----------
category : str
category name to score
scaler_algo : str
Function that scales an array to a range \in [0 and 1]. Use 'percentile', 'normcdf'. Default normcdf
beta : float
Beta in (1+B^2) * (Scale(P(w|c)) * Scale(P(c|w)))/(B^2*Scale(P(w|c)) + Sca... | def get_scores_for_category(cat_word_counts, not_cat_word_counts,
scaler_algo=DEFAULT_SCALER_ALGO, beta=DEFAULT_BETA):
''' Computes unbalanced scaled-fscores
Parameters
----------
category : str
category name to score
scaler_algo : str
Function that scales an array to a rang... |
Parameters
----------
text, str
Returns
-------
List of 5.0-compliant emojis that occur in text. | def extract_emoji(text):
'''
Parameters
----------
text, str
Returns
-------
List of 5.0-compliant emojis that occur in text.
'''
found_emojis = []
len_text = len(text)
i = 0
while i < len_text:
cur_char = ord(text[i])
try:
VALID_EMOJIS[cur_char]
except:
i += 1
continue
found = False
for... |
Returns
-------
altair.Chart | def make_chart(self):
'''
Returns
-------
altair.Chart
'''
task_df = self.get_task_df()
import altair as alt
chart = alt.Chart(task_df).mark_bar().encode(
x='start',
x2='end',
y='term',
)
return chart |
Returns
------- | def get_temporal_score_df(self):
'''
Returns
-------
'''
scoredf = {}
tdf = self.term_ranker(self.corpus).get_ranks()
for cat in sorted(self.corpus.get_categories()):
if cat >= self.starting_time_step:
negative_categories = self._get_negative_categories(cat, tdf)
scores = self.term_scorer.get_... |
Returns
------- | def get_task_df(self):
'''
Returns
-------
'''
term_time_df = self._get_term_time_df()
terms_to_include = (
term_time_df
.groupby('term')['top']
.sum()
.sort_values(ascending=False)
.iloc[:self.num_terms_to_include].index
)
task_df = (
term_time_df[term_time_df.term.isin(terms_to_... |
Parameters
----------
ngram, str or unicode, string to search for
Returns
-------
pd.DataFrame, {self._parsed_col: <matching texts>, self._category_col: <corresponding categories>, ...} | def search(self, ngram):
'''
Parameters
----------
ngram, str or unicode, string to search for
Returns
-------
pd.DataFrame, {self._parsed_col: <matching texts>, self._category_col: <corresponding categories>, ...}
'''
mask = self._document_index... |
Parameters
----------
df : A data frame from, e.g., get_term_freq_df : pd.DataFrame
positive_category : str
The positive category name.
term_significance : TermSignificance
A TermSignificance instance from which to extract p-values. | def get_p_vals(df, positive_category, term_significance):
'''
Parameters
----------
df : A data frame from, e.g., get_term_freq_df : pd.DataFrame
positive_category : str
The positive category name.
term_significance : TermSignificance
A TermSignificance instance from which to extract p-values.
'''
df_pos = ... |
:return: term freq matrix or metadata freq matrix | def get_X(self):
'''
:return: term freq matrix or metadata freq matrix
'''
if self._use_non_text_features:
return self._term_doc_matrix._mX
else:
return self._term_doc_matrix._X |
!!! not working | def scale_neg_1_to_1_with_zero_mean_log_abs_max(v):
'''
!!! not working
'''
df = pd.DataFrame({'v':v,
'sign': (v > 0) * 2 - 1})
df['lg'] = np.log(np.abs(v)) / np.log(1.96)
df['exclude'] = (np.isinf(df.lg) | np.isneginf(df.lg))
for mask in [(df['sign'] == -1) & (df['exclude'] == False),
... |
Constructs the term doc matrix.
Returns
-------
TermDocMatrix | def build(self):
'''Constructs the term doc matrix.
Returns
-------
TermDocMatrix
'''
X_factory, mX_factory, category_idx_store, term_idx_store, metadata_idx_store, y \
= self._init_term_doc_matrix_variables()
parse_pipeline = ParsePipelineFactory(se... |
Constructs the term doc matrix.
Returns
-------
TermDocMatrix | def build(self):
'''Constructs the term doc matrix.
Returns
-------
TermDocMatrix
'''
X_factory = CSRMatrixFactory()
mX_factory = CSRMatrixFactory()
term_idx_store = IndexStore()
metadata_idx_store = IndexStore()
parse_pipeline = ParsePi... |
Parameters
----------
corpus
Returns
-------
float, pd.Series
float: point on x-axis at even characteristicness
pd.Series: term -> value between 0 and 1, sorted by score in a descending manner
Background scores from corpus | def get_scores(self, corpus):
'''
Parameters
----------
corpus
Returns
-------
float, pd.Series
float: point on x-axis at even characteristicness
pd.Series: term -> value between 0 and 1, sorted by score in a descending manner
Background scores from corpus
'''
term_ranks = self.term_ranker(corp... |
Returns
-------
str, the html file representation | def to_html(self):
'''
Returns
-------
str, the html file representation
'''
javascript_to_insert = '\n'.join([
PackedDataUtils.full_content_of_javascript_files(),
self.category_scatterplot_structure._visualization_data.to_javascript('getCategoryD... |
Parameters
----------
corpus, ParsedCorpus
Returns
-------
iter: [sentence1word1, ...], [sentence2word1, ...] | def get_sentences(corpus):
'''
Parameters
----------
corpus, ParsedCorpus
Returns
-------
iter: [sentence1word1, ...], [sentence2word1, ...]
'''
assert isinstance(corpus, ParsedCorpus)
return itertools.chain(*[[[corpus._term_idx_store.getidxstrict(t.lower_) for t in sent
... |
Gets list of terms to display that have some interesting diachronic variation.
Returns
-------
pd.DataFrame
e.g.,
term variable frequency trending
2 in 200310 1.0 0.000000
19 for 200310 1.0 0.000000
20 to 200311 1.0 0.000000 | def get_display_dataframe(self):
'''
Gets list of terms to display that have some interesting diachronic variation.
Returns
-------
pd.DataFrame
e.g.,
term variable frequency trending
2 in 200310 1.0 0.000000
19 for 200310 1.0 0.000000
20 ... |
Parameters
----------
doc, Spacy Doc
Returns
-------
Counter noun chunk -> count | def get_feats(self, doc):
'''
Parameters
----------
doc, Spacy Doc
Returns
-------
Counter noun chunk -> count
'''
ngram_counter = Counter()
for sent in doc.sents:
ngram_counter += _phrase_counts(sent)
return ngram_counter |
Parameters
----------
doc, Spacy Doc
Returns
-------
Counter noun chunk -> count | def get_feats(self, doc):
'''
Parameters
----------
doc, Spacy Doc
Returns
-------
Counter noun chunk -> count
'''
# ngram_counter = phrasemachine.get_phrases(str(doc), tagger='spacy')['counts']
ngram_counter = Counter()
for sent in doc.sents:
unigrams = self._get_unigram_feats(sent)
ngram_... |
Parameters
----------
protocol : str
'http' or 'https' for including external urls
d3_url, str
None by default. The url (or path) of
d3, to be inserted into <script src="..."/>
By default, this is `DEFAULT_D3_URL` declared in `ScatterplotStructure`.
... | def to_html(self,
protocol='http',
d3_url=None,
d3_scale_chromatic_url=None,
html_base=None):
'''
Parameters
----------
protocol : str
'http' or 'https' for including external urls
d3_url, str
None... |
Parameters
----------
ngram str or unicode, string to search for
Returns
-------
pd.DataFrame, {'texts': <matching texts>, 'categories': <corresponding categories>} | def search(self, ngram):
'''
Parameters
----------
ngram str or unicode, string to search for
Returns
-------
pd.DataFrame, {'texts': <matching texts>, 'categories': <corresponding categories>}
'''
mask = self._document_index_mask(ngram)
return pd.DataFrame({
'text': self.get_texts()[mask],
... |
Parameters
-------
label_append : str
Returns
-------
pd.DataFrame indexed on terms, with columns giving frequencies for each | def get_term_freq_df(self, label_append=' freq'):
'''
Parameters
-------
label_append : str
Returns
-------
pd.DataFrame indexed on terms, with columns giving frequencies for each
'''
'''
row = self._row_category_ids()
newX = csr_... |
Returns
-------
np.array with columns as categories and rows as terms | def get_term_freq_mat(self):
'''
Returns
-------
np.array with columns as categories and rows as terms
'''
freq_mat = np.zeros(shape=(self.get_num_terms(), self.get_num_categories()), dtype=int)
for cat_i in range(self.get_num_categories()):
freq_mat[:... |
Returns
-------
np.array with columns as categories and rows as terms | def get_term_count_mat(self):
'''
Returns
-------
np.array with columns as categories and rows as terms
'''
freq_mat = np.zeros(shape=(self.get_num_terms(), self.get_num_categories()), dtype=int)
for cat_i in range(self.get_num_categories()):
X = (self... |
Returns
-------
np.array with columns as categories and rows as terms | def get_metadata_count_mat(self):
'''
Returns
-------
np.array with columns as categories and rows as terms
'''
freq_mat = np.zeros(shape=(self.get_num_metadata(), self.get_num_categories()), dtype=int)
for cat_i in range(self.get_num_categories()):
mX... |
Parameters
-------
label_append : str
Returns
-------
pd.DataFrame indexed on metadata, with columns giving frequencies for each category | def get_metadata_freq_df(self, label_append=' freq'):
'''
Parameters
-------
label_append : str
Returns
-------
pd.DataFrame indexed on metadata, with columns giving frequencies for each category
'''
row = self._row_category_ids_for_meta()
... |
Non destructive category removal.
Parameters
----------
categories : list
list of categories to keep
ignore_absences : bool, False by default
if categories does not appear, don't raise an error, just move on.
Returns
-------
TermDocMatrix... | def keep_only_these_categories(self, categories, ignore_absences=False):
'''
Non destructive category removal.
Parameters
----------
categories : list
list of categories to keep
ignore_absences : bool, False by default
if categories does not appea... |
Non destructive category removal.
Parameters
----------
categories : list
list of categories to remove
ignore_absences : bool, False by default
if categories does not appear, don't raise an error, just move on.
Returns
-------
TermDocMatr... | def remove_categories(self, categories, ignore_absences=False):
'''
Non destructive category removal.
Parameters
----------
categories : list
list of categories to remove
ignore_absences : bool, False by default
if categories does not appear, don'... |
Parameters
----------
idx_to_delete_list, list
Returns
-------
TermDocMatrix | def remove_terms_by_indices(self, idx_to_delete_list):
'''
Parameters
----------
idx_to_delete_list, list
Returns
-------
TermDocMatrix
'''
new_X, new_term_idx_store = self._get_X_after_delete_terms(idx_to_delete_list)
return self._make_ne... |
Computes Rudder score.
Parameters
----------
category : str
category name to score
Returns
-------
np.array | def get_rudder_scores(self, category):
''' Computes Rudder score.
Parameters
----------
category : str
category name to score
Returns
-------
np.array
'''
category_percentiles = self._get_term_percentiles_in_category(category)
... |
Computes l2-penalized logistic regression score.
Parameters
----------
category : str
category name to score
category : str
category name to score
Returns
-------
(coefficient array, accuracy, majority class baseline accuracy) | def get_logistic_regression_coefs_l2(self, category,
clf=RidgeClassifierCV()):
''' Computes l2-penalized logistic regression score.
Parameters
----------
category : str
category name to score
category : str
categor... |
Computes l1-penalized logistic regression score.
Parameters
----------
category : str
category name to score
Returns
-------
(coefficient array, accuracy, majority class baseline accuracy) | def get_logistic_regression_coefs_l1(self, category,
clf=LassoCV(alphas=[0.1, 0.001],
max_iter=10000,
n_jobs=-1)):
''' Computes l1-penalized logistic regression scor... |
Computes regression score of tdfidf transformed features
Parameters
----------
category : str
category name to score
clf : sklearn regressor
Returns
-------
coefficient array | def get_regression_coefs(self, category, clf=ElasticNet()):
''' Computes regression score of tdfidf transformed features
Parameters
----------
category : str
category name to score
clf : sklearn regressor
Returns
-------
coefficient array
... |
Computes regression score of tdfidf transformed features
Parameters
----------
category : str
category name to score
clf : sklearn regressor
Returns
-------
coefficient array | def get_logreg_coefs(self, category, clf=LogisticRegression()):
''' Computes regression score of tdfidf transformed features
Parameters
----------
category : str
category name to score
clf : sklearn regressor
Returns
-------
coefficient array
... |
Computes scaled-fscores
Parameters
----------
category : str
category name to score
scaler_algo : str
Function that scales an array to a range \in [0 and 1]. Use 'percentile', 'normcdf'. Default.
beta : float
Beta in (1+B^2) * (Scale(P(w|c)) * Sc... | def get_scaled_f_scores(self,
category,
scaler_algo=DEFAULT_SCALER_ALGO,
beta=DEFAULT_BETA):
''' Computes scaled-fscores
Parameters
----------
category : str
category name to score
sca... |
scaler = self._get_scaler_function(scaler_algo)
p_word_given_category = cat_word_counts.astype(np.float64) / cat_word_counts.sum()
p_category_given_word = cat_word_counts.astype(np.float64) / (cat_word_counts + not_cat_word_counts)
scores \
= self._computer_harmoic_mean_of_probabilit... | def _get_scaled_f_score_from_counts(self, cat_word_counts, not_cat_word_counts, scaler_algo,
beta=DEFAULT_BETA):
'''
scaler = self._get_scaler_function(scaler_algo)
p_word_given_category = cat_word_counts.astype(np.float64) / cat_word_counts.sum()
... |
Returns
-------
pd.DataFrame of fisher scores vs background | def get_fisher_scores_vs_background(self):
'''
Returns
-------
pd.DataFrame of fisher scores vs background
'''
df = self.get_term_and_background_counts()
odds_ratio, p_values = self._get_fisher_scores_from_counts(
df['corpus'], df['background'])
... |
Returns
-------
pd.DataFrame of posterior mean scores vs background | def get_posterior_mean_ratio_scores_vs_background(self):
'''
Returns
-------
pd.DataFrame of posterior mean scores vs background
'''
df = self.get_term_and_background_counts()
df['Log Posterior Mean Ratio'] = self._get_posterior_mean_ratio_from_counts(df['cor... |
Returns
-------
pd.DataFrame of rudder scores vs background | def get_rudder_scores_vs_background(self):
'''
Returns
-------
pd.DataFrame of rudder scores vs background
'''
df = self.get_term_and_background_counts()
corpus_percentiles = self._get_percentiles_from_freqs(df['corpus'])
background_percentiles = self._get... |
Applies the ranker in scatterchartdata to term-category frequencies.
Parameters
----------
scatterchartdata : ScatterChartData
Returns
-------
pd.DataFrame | def get_term_category_frequencies(self, scatterchartdata):
'''
Applies the ranker in scatterchartdata to term-category frequencies.
Parameters
----------
scatterchartdata : ScatterChartData
Returns
-------
pd.DataFrame
'''
term_ranker = s... |
Parameters
----------
new_categories : array like
String names of new categories. Length should be equal to number of documents
Returns
-------
TermDocMatrix | def recategorize(self, new_categories):
'''
Parameters
----------
new_categories : array like
String names of new categories. Length should be equal to number of documents
Returns
-------
TermDocMatrix
'''
assert len(new_categories) == sel... |
Returns a TermDocMatrix which is identical to self except the metadata values are now identical to the
categories present.
:return: TermDocMatrix | def use_categories_as_metadata(self):
'''
Returns a TermDocMatrix which is identical to self except the metadata values are now identical to the
categories present.
:return: TermDocMatrix
'''
new_metadata_factory = CSRMatrixFactory()
for i, category_idx in enume... |
Returns a TermDocMatrix which is identical to self except the metadata values are now identical to the
categories present and term-doc-matrix is now the metadata matrix.
:return: TermDocMatrix | def use_categories_as_metadata_and_replace_terms(self):
'''
Returns a TermDocMatrix which is identical to self except the metadata values are now identical to the
categories present and term-doc-matrix is now the metadata matrix.
:return: TermDocMatrix
'''
new_metadata_... |
Returns
-------
pd.DataFrame
I.e.,
>>> convention_df.iloc[0]
category plot
filename subjectivity_html/obj/2002/Abandon.html
text A senior at an elite college (Katie Holmes), a...
movie_name ... | def get_data():
'''
Returns
-------
pd.DataFrame
I.e.,
>>> convention_df.iloc[0]
category plot
filename subjectivity_html/obj/2002/Abandon.html
text A senior at an elite college (Katie Holmes), a...
movie_name ... |
Returns
-------
PriorFactory | def use_general_term_frequencies(self):
'''
Returns
-------
PriorFactory
'''
tdf = self._get_relevant_term_freq()
bg_df = self.term_doc_mat.get_term_and_background_counts()[['background']]
bg_df = pd.merge(tdf,
bg_df,
left_index=True,
right_index=... |
Parameters
----------
pd.Series
term -> frequency
Returns
-------
PriorFactory | def use_custom_term_frequencies(self, custom_term_frequencies):
'''
Parameters
----------
pd.Series
term -> frequency
Returns
-------
PriorFactory
'''
self.priors += custom_term_frequencies.reindex(self.priors.index).fillna(0)
return self |
Returns
-------
PriorFactory | def use_all_categories(self):
'''
Returns
-------
PriorFactory
'''
term_df = self.term_ranker.get_ranks()
self.priors += term_df.sum(axis=1).fillna(0.)
return self |
Returns
-------
PriorFactory | def use_neutral_categories(self):
'''
Returns
-------
PriorFactory
'''
term_df = self.term_ranker.get_ranks()
self.priors += term_df[[c + ' freq' for c in self._get_neutral_categories()]].sum(axis=1)
return self |
Returns
-------
PriorFactory | def drop_neutral_categories_from_corpus(self):
'''
Returns
-------
PriorFactory
'''
neutral_categories = self._get_neutral_categories()
self.term_doc_mat = self.term_doc_mat.remove_categories(neutral_categories)
self._reindex_priors()
return self |
Returns
-------
PriorFactory | def drop_unused_terms(self):
'''
Returns
-------
PriorFactory
'''
self.term_doc_mat = self.term_doc_mat.remove_terms(
set(self.term_doc_mat.get_terms()) - set(self.priors.index)
)
self._reindex_priors()
return self |
Returns
-------
PriorFactory | def drop_zero_priors(self):
'''
Returns
-------
PriorFactory
'''
self.term_doc_mat = self.term_doc_mat.remove_terms(
self.priors[self.priors == 0].index
)
self._reindex_priors()
return self |
Parameters
----------
target_term_doc_mat : TermDocMatrix
Returns
-------
PriorFactory | def align_to_target(self, target_term_doc_mat):
'''
Parameters
----------
target_term_doc_mat : TermDocMatrix
Returns
-------
PriorFactory
'''
self.priors = self.priors[target_term_doc_mat.get_terms()].fillna(0)
return self |
Returns
-------
pd.Series | def get_priors(self):
'''
Returns
-------
pd.Series
'''
priors = self.priors
priors[~np.isfinite(priors)] = 0
priors += self.starting_count
return priors |
Parameters
----------
y_i, np.array(int)
Arrays of word counts of words occurring in positive class
y_j, np.array(int)
Returns
-------
np.array of z-scores | def get_zeta_i_j_given_separate_counts(self, y_i, y_j):
'''
Parameters
----------
y_i, np.array(int)
Arrays of word counts of words occurring in positive class
y_j, np.array(int)
Returns
-------
np.array of z-scores
'''
yp_i = y_i + self.alpha_w
yp_j = y_j + self.alpha_w
np_i = np.sum(yp_i)
... |
Parameters
----------
X : np.array
Array of word counts, shape (N, 2) where N is the vocab size. X[:,0] is the
positive class, while X[:,1] is the negative class. None by default
Returns
-------
np.array of z-scores | def get_zeta_i_j(self, X):
'''
Parameters
----------
X : np.array
Array of word counts, shape (N, 2) where N is the vocab size. X[:,0] is the
positive class, while X[:,1] is the negative class. None by default
Returns
-------
np.array of z-scores
'''
y_i, y_j = X.T[0], X.T[1]
return self.get... |
Same function as get_zeta_i_j_given_separate_counts
Parameters
----------
y_i, np.array(int)
Arrays of word counts of words occurring in positive class
y_j, np.array(int)
Returns
-------
np.array of z-scores | def get_scores(self, y_i, y_j):
'''
Same function as get_zeta_i_j_given_separate_counts
Parameters
----------
y_i, np.array(int)
Arrays of word counts of words occurring in positive class
y_j, np.array(int)
Returns
-------
np.array of z-scores
'''
z_scores = self.get_zeta_i_j_given_separate_c... |
Parameters
-------
term_doc_matrix : TermDocMatrix
Returns
-------
New term doc matrix | def compact(self, term_doc_matrix):
'''
Parameters
-------
term_doc_matrix : TermDocMatrix
Returns
-------
New term doc matrix
'''
count_df = self._get_statistics_dataframe(term_doc_matrix)
return term_doc_matrix.remove_terms(
count_df[count_df['pmi'] < self.minimum_pmi].index
) |
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