INSTRUCTION stringlengths 1 8.43k | RESPONSE stringlengths 75 104k |
|---|---|
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. | 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. | 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 | 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. | 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. | 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 | 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 | 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 | 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 | 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. | 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. | 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 everything that isn t in category. E. g. Below 5 - star reviews. target_term: str Word or phr... | 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. E. g. Below 5 - star reviews. target_term: str Word or phr... | 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 | 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. | 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. | 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. | 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 the default model. projection_model: s... | def produce_projection_explorer(corpus,
category,
word2vec_model=None,
projection_model=None,
embeddings=None,
term_acceptance_re=re.compile('[a-z]{3,}'),
... |
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 the default model. projection_model: s... | 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 term_scorer term_acceptance_re: SRE_Pattern Re... | 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. E. g. Below 5 - star reviews. scores: np. array Scores to ... | 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 ------- term_doc_matrix: TermDocMatrix | 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_... |
Parameters ---------- values: [ term... ] | 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 ---------- metadata: ( array like or None ) | 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._... |
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 ---------- cat_word_counts: np. array category counts not_cat_word_counts: np. array not category counts | 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 | 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 | 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 balanced scaled f - scores Parameters ---------- cat_word_counts: np. array category counts not_cat_word_counts: np. array not category counts 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_scores(cat_word_counts, not_cat_word_counts,
scaler_algo=DEFAULT_SCALER_ALGO, beta=DEFAULT_BETA):
''' Computes balanced scaled f-scores
Parameters
----------
cat_word_counts : np.array
category counts
not_cat_word_counts : np.array
not category counts
scaler_algo : str
Funct... |
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 )) + S... | 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 | 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 | 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. | 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. | 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 ParsedCorpus | 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
... |
Parameters ---------- 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... |
Gets list of terms to display that have some interesting diachronic variation. | 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 | 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 | 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. d3_scale_chromatic_url: str None by default. URL of d3_scale_chromatic_url to be... | 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 | 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 | 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 | 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. | 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. | 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 | 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 | 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 | 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 | 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 | 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 | 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 )) * Scale ( P ( c|w )))/ ( B^2 * Scale ( P ( w|c )) + Scale ( P ( c|w )))... | 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_probabilities_over_non_zero_i... | 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. | 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 | 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. | 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. | 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 | 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 | 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 ) | 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 | 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 | 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 | 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
) |
Parameters ---------- term_dict: dict { metadataname: [ term1 term2.... ]... } | def check_topic_model_string_format(term_dict):
'''
Parameters
----------
term_dict: dict {metadataname: [term1, term2, ....], ...}
Returns
-------
None
'''
if type(term_dict) != dict:
raise TypeError("Argument for term_dict must be a dict, keyed on strings, and contain a li... |
Inserts dictionary of meta data terms into object. | def inject_metadata_term_lists(self, term_dict):
'''
Inserts dictionary of meta data terms into object.
Parameters
----------
term_dict: dict {metadataname: [term1, term2, ....], ...}
Returns
-------
self: ScatterChart
'''
check_topic_mod... |
Inserts a set of descriptions of meta data terms. These will be displayed below the scatter plot when a meta data term is clicked. All keys in the term dict must occur as meta data. | def inject_metadata_descriptions(self, term_dict):
'''
Inserts a set of descriptions of meta data terms. These will be displayed
below the scatter plot when a meta data term is clicked. All keys in the term dict
must occur as meta data.
Parameters
----------
ter... |
Inject custom x and y coordinates for each term into chart. | def inject_coordinates(self,
x_coords,
y_coords,
rescale_x=None,
rescale_y=None,
original_x=None,
original_y=None):
'''
Inject custom x and y ... |
: param vec: array to jitter: return: array jittered version of arrays | def _add_jitter(self, vec):
"""
:param vec: array to jitter
:return: array, jittered version of arrays
"""
if self.scatterchartdata.jitter == 0 or self.scatterchartdata.jitter is None:
return vec
return vec + np.random.rand(1, len(vec))[0] * self.scatterchartd... |
Outdated. MPLD3 drawing. | def draw(self,
category,
num_top_words_to_annotate=4,
words_to_annotate=[],
scores=None,
transform=percentile_alphabetical):
'''Outdated. MPLD3 drawing.
Parameters
----------
category
num_top_words_to_annotate
... |
Parameters ---------- y_i np. array ( int ) Arrays of word counts of words occurring in positi ve class y_j np. array ( int ) | 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 positi ve class
y_j, np.array(int)
Returns
-------
np.array of z-scores
'''
n_i, n_j = y_i.sum(), y_j.sum()
delta_i_j = (np.log((y_i + 1) / (1. + n_i... |
Parameters ---------- tdm: TermDocMatrix category: str category name | def get_scores_and_p_values(self, tdm, category):
'''
Parameters
----------
tdm: TermDocMatrix
category: str, category name
Returns
-------
pd.DataFrame(['coef', 'p-val'])
'''
X = tdm._X
y = self._make_response_variable_1_or_negative_1(category, tdm)
pX = X / X.sum(axis=1)
ansX = self._anscom... |
>>> a = csr_matrix ( np. array ( [[ 0 1 3 0 1 0 ] [ 0 0 1 0 1 1 ]] ) >>> delete_columns ( a [ 1 2 ] ). todense () matrix ( [[ 0 0 1 0 ] [ 0 0 1 1 ]] ) | def delete_columns(mat, columns_to_delete):
'''
>>> a = csr_matrix(np.array([[0, 1, 3, 0, 1, 0],
[0, 0, 1, 0, 1, 1]])
>>> delete_columns(a, [1,2]).todense()
matrix([[0, 0, 1, 0],
[0, 0, 1, 1]])
Parameters
----------
mat : csr_matrix
columns_to_delete : list[int]
Returns... |
Parameters ---------- param last_col_idx: int number of columns | def set_last_col_idx(self, last_col_idx):
'''
Parameters
----------
param last_col_idx : int
number of columns
'''
assert last_col_idx >= self._max_col
self._max_col = last_col_idx
return self |
Parameters ---------- param last_row_idx: int number of rows | def set_last_row_idx(self, last_row_idx):
'''
Parameters
----------
param last_row_idx : int
number of rows
'''
assert last_row_idx >= self._max_row
self._max_row = last_row_idx
return self |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.