JorgeVB/code_unlearning_dataset · Datasets at Hugging Face

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Replace self.files with only safe paths Because some owners of FileList manipulate the underlying ``files`` attribute directly, this method must be called to repair those paths. def _repair(self): """ Replace self.files with only safe paths Because some owners of FileL...
Write the file list in 'self.filelist' to the manifest file named by 'self.manifest'. def write_manifest(self): """ Write the file list in 'self.filelist' to the manifest file named by 'self.manifest'. """ self.filelist._repair() # Now _repairs should encodabili...
Find all files under revision control def walk_revctrl(dirname=''): """Find all files under revision control""" for ep in pkg_resources.iter_entry_points('setuptools.file_finders'): for item in ep.load()(dirname): yield item
In a context, remove and restore os.link if it exists def _remove_os_link(): """ In a context, remove and restore os.link if it exists """ class NoValue: pass orig_val = getattr(os, 'link', NoValue) try: del os.link except Exception: ...
getting python files def _add_defaults_python(self): """getting python files""" if self.distribution.has_pure_modules(): build_py = self.get_finalized_command('build_py') self.filelist.extend(build_py.get_source_files()) # This functionality is incompatible with incl...
Read the manifest file (named by 'self.manifest') and use it to fill in 'self.filelist', the list of files to include in the source distribution. def read_manifest(self): """Read the manifest file (named by 'self.manifest') and use it to fill in 'self.filelist', the list of files to inc...
Checks if license_file' is configured and adds it to 'self.filelist' if the value contains a valid path. def check_license(self): """Checks if license_file' is configured and adds it to 'self.filelist' if the value contains a valid path. """ opts = self.distribution.get_option_...
Return a collections.Sized collections.Container of paths to be excluded for single_version_externally_managed installations. def get_exclusions(self): """ Return a collections.Sized collections.Container of paths to be excluded for single_version_externally_managed installations. ...
Given a package name and exclusion path within that package, compute the full exclusion path. def _exclude_pkg_path(self, pkg, exclusion_path): """ Given a package name and exclusion path within that package, compute the full exclusion path. """ parts = pkg.split('.') + ...
Get namespace packages (list) but only for single_version_externally_managed installations and empty otherwise. def _get_SVEM_NSPs(self): """ Get namespace packages (list) but only for single_version_externally_managed installations and empty otherwise. """ # TODO: is it...
Generate file paths to be excluded for namespace packages (bytecode cache files). def _gen_exclusion_paths(): """ Generate file paths to be excluded for namespace packages (bytecode cache files). """ # always exclude the package module itself yield '__init__.py' ...
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 resi...
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) ...
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): ...
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...
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. ...
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...
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. >>> na...
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', tex...
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):...
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...
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 ran...
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...
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 ...
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...
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...
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...
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...
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_cate...
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( colu...
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...
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 ...
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...
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 ...
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. ...
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.,...
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...
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...
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. ...
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...
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...
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 ...
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. ...
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...
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)} idxs...
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 {'l...
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...
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...
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 cou...
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 w...
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) * (Sc...
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...
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_ch...
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 = se...
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....
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, st...
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 --...
: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'] ...
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 \ =...
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 = ...
Parameters ---------- corpus, ParsedCorpus Returns ------- iter: [sentence1word1, ...], [sentence2word1, ...] def get_sentences(corpus): ''' Parameters ---------- corpus, ParsedCorpus Returns ------- iter: [sentence1word1, ...], [sentence2word1, ...] ''' assert isinstance(corpus, ParsedCo...
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 Retur...
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_sc...
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...
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) ...
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_count...
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`. ...
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, {'te...
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 ---...
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()), d...
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()), ...
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_categori...
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 Ret...
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...
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...
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 ...
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 ...
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...
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, ...
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()): ''' Com...
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 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...
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...
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 = se...
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() ...
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_pe...
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 scatterchart...
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...
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 t...
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...
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 ...

Replace self.files with only safe paths Because some owners of FileList manipulate the underlying ``files`` attribute directly, this method must be called to repair those paths. def _repair(self): """ Replace self.files with only safe paths Because some owners of FileL...

Write the file list in 'self.filelist' to the manifest file named by 'self.manifest'. def write_manifest(self): """ Write the file list in 'self.filelist' to the manifest file named by 'self.manifest'. """ self.filelist._repair() # Now _repairs should encodabili...

Find all files under revision control def walk_revctrl(dirname=''): """Find all files under revision control""" for ep in pkg_resources.iter_entry_points('setuptools.file_finders'): for item in ep.load()(dirname): yield item

In a context, remove and restore os.link if it exists def _remove_os_link(): """ In a context, remove and restore os.link if it exists """ class NoValue: pass orig_val = getattr(os, 'link', NoValue) try: del os.link except Exception: ...

getting python files def _add_defaults_python(self): """getting python files""" if self.distribution.has_pure_modules(): build_py = self.get_finalized_command('build_py') self.filelist.extend(build_py.get_source_files()) # This functionality is incompatible with incl...

Read the manifest file (named by 'self.manifest') and use it to fill in 'self.filelist', the list of files to include in the source distribution. def read_manifest(self): """Read the manifest file (named by 'self.manifest') and use it to fill in 'self.filelist', the list of files to inc...

Checks if license_file' is configured and adds it to 'self.filelist' if the value contains a valid path. def check_license(self): """Checks if license_file' is configured and adds it to 'self.filelist' if the value contains a valid path. """ opts = self.distribution.get_option_...

Return a collections.Sized collections.Container of paths to be excluded for single_version_externally_managed installations. def get_exclusions(self): """ Return a collections.Sized collections.Container of paths to be excluded for single_version_externally_managed installations. ...

Given a package name and exclusion path within that package, compute the full exclusion path. def _exclude_pkg_path(self, pkg, exclusion_path): """ Given a package name and exclusion path within that package, compute the full exclusion path. """ parts = pkg.split('.') + ...

Get namespace packages (list) but only for single_version_externally_managed installations and empty otherwise. def _get_SVEM_NSPs(self): """ Get namespace packages (list) but only for single_version_externally_managed installations and empty otherwise. """ # TODO: is it...

Generate file paths to be excluded for namespace packages (bytecode cache files). def _gen_exclusion_paths(): """ Generate file paths to be excluded for namespace packages (bytecode cache files). """ # always exclude the package module itself yield '__init__.py' ...

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 resi...

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) ...

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): ...

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...

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. ...

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...

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. >>> na...

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', tex...

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):...

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...

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 ran...

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...

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 ...

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...

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...

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...

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...

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_cate...

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( colu...

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...

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 ...

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...

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 ...

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. ...

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.,...

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...

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...

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. ...

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...

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 ...

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. ...

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...

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)} idxs...

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 {'l...

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...

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...

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 cou...

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 w...

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) * (Sc...

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...

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_ch...

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 = se...

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....

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, st...

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 --...

: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'] ...

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 \ =...

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 = ...

Parameters ---------- corpus, ParsedCorpus Returns ------- iter: [sentence1word1, ...], [sentence2word1, ...] def get_sentences(corpus): ''' Parameters ---------- corpus, ParsedCorpus Returns ------- iter: [sentence1word1, ...], [sentence2word1, ...] ''' assert isinstance(corpus, ParsedCo...

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 Retur...

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_sc...

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...

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) ...

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_count...

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`. ...

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, {'te...

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 ---...

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()), d...

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()), ...

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_categori...

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 Ret...

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...

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...

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 ...

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 ...

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...

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, ...

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()): ''' Com...

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 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...

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...

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 = se...

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() ...

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_pe...

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 scatterchart...

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...

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 t...

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...

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 ...