JorgeVB/code_unlearning_dataset · Datasets at Hugging Face

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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=Tru...
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.prior...
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()]....
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 occu...
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...
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 ------- 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 ...
Parameters ---------- term_dict: dict {metadataname: [term1, term2, ....], ...} Returns ------- None def check_topic_model_string_format(term_dict): ''' Parameters ---------- term_dict: dict {metadataname: [term1, term2, ....], ...} Returns ------- None ''' if ...
Inserts dictionary of meta data terms into object. Parameters ---------- term_dict: dict {metadataname: [term1, term2, ....], ...} Returns ------- self: ScatterChart def inject_metadata_term_lists(self, term_dict): ''' Inserts dictionary of meta data te...
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 ---------- term_dict: dict {metadataname: str: 'explanation to insert', ...} ...
Inject custom x and y coordinates for each term into chart. Parameters ---------- x_coords: array-like positions on x-axis \in [0,1] y_coords: array-like positions on y-axis \in [0,1] rescale_x: lambda list[0,1]: list[0,1], default identity Re...
: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: ret...
Outdated. MPLD3 drawing. Parameters ---------- category num_top_words_to_annotate words_to_annotate scores transform Returns ------- pd.DataFrame, html of fgure def draw(self, category, num_top_words_to_annotat...
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 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 occ...
Parameters ---------- tdm: TermDocMatrix category: str, category name Returns ------- pd.DataFrame(['coef', 'p-val']) def get_scores_and_p_values(self, tdm, category): ''' Parameters ---------- tdm: TermDocMatrix category: str, category name Returns ------- pd.DataFrame(['coef', 'p-val'])...
>>> 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 ------- csr_matrix that is stripped of columns ...
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
Parameters ---------- doc, Spacy Docs Returns ------- Counter noun chunk -> count def get_feats(self, doc): ''' Parameters ---------- doc, Spacy Docs Returns ------- Counter noun chunk -> count ''' return Counter([str(c).lower() for c in doc.noun_chunks])
Parameters ---------- nltk_tokenizer : nltk.tokenize.* instance (e.g., nltk.TreebankWordTokenizer()) Returns ------- Doc of tweets Notes ------- Requires NLTK to be installed def nltk_tokenzier_factory(nltk_tokenizer): ''' Parameters ---------- nltk_tokenizer : nltk.tokenize.* instance (e.g., nltk.Treeba...
Specifies fixed set of embeddings :param embeddings: array-like, sparse or dense, shape should be (embedding size, # terms) :return: EmbeddingsResolver def set_embeddings(self, embeddings): ''' Specifies fixed set of embeddings :param embeddings: array-like, sparse or dense, sha...
:param projection_model: sklearn unsupervised model (e.g., PCA) by default the recommended model is umap.UMAP, which requires UMAP in to be installed :return: array, shape (num dimension, vocab size) def project(self, projection_model=None): ''' :param projection_model: sklearn unsuper...
Parameters ---------- terms : list or None If terms is list, make these the seed terms for the topoics If none, use the first 30 terms in get_scaled_f_scores_vs_background num_terms_per_topic : int, default 10 Use this many terms per topic scorer : TermScorer Implements get_scores, default is RankDi...
Returns ------- pd.DataFrame def get_ranks(self): ''' Returns ------- pd.DataFrame ''' if self._use_non_text_features: return self._term_doc_matrix.get_metadata_freq_df() else: return self._term_doc_matrix.get_term_freq_df()
Generate a TermDocMatrix from data in parameters. Returns ---------- term_doc_matrix : TermDocMatrix The object that this factory class builds. def build(self): """Generate a TermDocMatrix from data in parameters. Returns ---------- term_doc_m...
Entity types to exclude from feature construction. Terms matching specificed entities, instead of labeled by their lower case orthographic form or lemma, will be labeled by their entity type. Parameters ---------- entity_types : set of entity types outputted by spaCy '...
Parameters ---------- category_doc_iter : iterator of (string category name, spacy.tokens.doc.Doc) pairs Returns ---------- t : TermDocMatrix def _build_from_category_spacy_doc_iter(self, category_doc_iter): ''' Parameters ---------- category_doc...
Parameters ---------- raw_text, uncleaned text for parsing out features Returns ------- csr_matrix, feature matrix def feats_from_doc(self, raw_text): ''' Parameters ---------- raw_text, uncleaned text for parsing out features Returns ...
Parameters ---------- num_terms, int Returns ------- dict def get_lexicons(self, num_terms=10): ''' Parameters ---------- num_terms, int Returns ------- dict ''' return {k: v.index[:num_terms] ...
Parameters ---------- term_doc_matrix : TermDocMatrix Term document matrix object to compact Returns ------- New term doc matrix def compact(self, term_doc_matrix): ''' Parameters ---------- term_doc_matrix : TermDocMatrix ...
Parameters ---------- term_doc_matrix : TermDocMatrix Term document matrix object to compact Returns ------- TermDocMatrix def compact(self, term_doc_matrix): ''' Parameters ---------- term_doc_matrix : TermDocMatrix Term d...
Parameters ---------- doc, Spacy Docs Returns ------- Counter (unigram, bigram) -> count def get_feats(self, doc): ''' Parameters ---------- doc, Spacy Docs Returns ------- Counter (unigram, bigram) -> count ''' ngram_counter = Counter() for sent in doc.sents: ngrams = self.phrases[s...
Parameters ---------- corpus: Corpus for phrase augmentation Returns ------- New ParsedCorpus containing unigrams in corpus and new phrases def add_phrases(self, corpus): ''' Parameters ---------- corpus: Corpus for phrase augmentation Returns ------- New ParsedCorpus containing unigrams in c...
Parameters ---------- epochs : int Number of epochs to train for. Default is 2000. training_iterations : int Number of times to repeat training process. Default is training_iterations. Returns ------- A trained word2vec model. def train(self, epochs=2000, training_iterations=5): ''' Parameters...
Trains passive aggressive classifier def passive_aggressive_train(self): '''Trains passive aggressive classifier ''' self._clf = PassiveAggressiveClassifier(n_iter=50, C=0.2, n_jobs=-1, random_state=0) self._clf.fit(self._term_doc_matrix._X, self._term_doc_matrix._y) y_dist = self._clf.decision_function(sel...
Builds Depoyed Classifier def build(self): '''Builds Depoyed Classifier ''' if self._clf is None: raise NeedToTrainExceptionBeforeDeployingException() return DeployedClassifier(self._category, self._term_doc_matrix._category_idx_store, self._term_doc_m...
Parameters ---------- term_to_index_dict: term -> idx dictionary Returns ------- IndexStore def build(term_to_index_dict): ''' Parameters ---------- term_to_index_dict: term -> idx dictionary Returns ------- IndexStore ''' idxstore = IndexStore() idxstore._val2i = term_to_index_dict i...
Returns ------- Corpus def build(self): ''' Returns ------- Corpus ''' constructor_kwargs = self._get_build_kwargs() if type(self.raw_texts) == list: constructor_kwargs['raw_texts'] = np.array(self.raw_texts) else: constructor_kwargs['raw_texts'] = self.raw_texts return Corpus(**constructor...
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 occu...
Parameters ---------- term_doc_matrix : TermDocMatrix Term document matrix object to compact Returns ------- New term doc matrix def compact(self, term_doc_matrix): ''' Parameters ---------- term_doc_matrix : TermDocMatrix Term document matrix object to compact Returns ------- New term ...
Returns ------- pd.Series, all raw documents def get_texts(self): ''' Returns ------- pd.Series, all raw documents ''' if sys.version_info[0] == 2: return self._df[self._parsed_col] return self._df[self._parsed_col].apply(str)
Returns a dataframe indexed on the number of groups a term occured in. Parameters ---------- group_col Returns ------- pd.DataFrame def term_group_freq_df(self, group_col): # type: (str) -> pd.DataFrame ''' Returns a dataframe indexed on the num...
Compute variance from :param X: :return: def sparse_var(X): ''' Compute variance from :param X: :return: ''' Xc = X.copy() Xc.data **= 2 return np.array(Xc.mean(axis=0) - np.power(X.mean(axis=0), 2))[0]
Specify the category to score. Optionally, score against a specific set of categories. def set_categories(self, category_name, not_category_names=[], neutral_category_names=[]): ''' Specify the category to score. Optionally, score aga...
In this case, parameters a and b aren't used, since this information is taken directly from the corpus categories. Returns ------- def get_t_statistics(self): ''' In this case, parameters a and b aren't used, since this information is taken directly from the corpus cate...
Parameters ---------- X_factory mX_factory category_idx_store df parse_pipeline term_idx_store metadata_idx_store y Returns ------- CorpusDF def _apply_pipeline_and_get_build_instance(self, X_factory, mX_fa...
Parameters ---------- background def set_background_corpus(self, background): ''' Parameters ---------- background ''' if issubclass(type(background), TermDocMatrixWithoutCategories): self._background_corpus = pd.DataFrame(background ...
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...
Returns ------- List of dicts. One dict for each document, keys are metadata, values are counts def list_extra_features(self): ''' Returns ------- List of dicts. One dict for each document, keys are metadata, values are counts ''' return FeatureLister(s...
Returns ------- A new TermDocumentMatrix consisting of only terms which occur at least minimum_term_count. def remove_infrequent_words(self, minimum_term_count, term_ranker=AbsoluteFrequencyRanker): ''' Returns ------- A new TermDocumentMatrix consisting of only terms wh...
Returns ------- A new TermDocumentMatrix consisting of only terms in the current TermDocumentMatrix that aren't spaCy entity tags. Note: Used if entity types are censored using FeatsFromSpacyDoc(tag_types_to_censor=...). def remove_entity_tags(self): ''' Returns ...
Non destructive term removal. Parameters ---------- terms : list list of terms to remove ignore_absences : bool, False by default if term does not appear, don't raise an error, just move on. Returns ------- TermDocMatrix, new object with ...
Parameters ---------- threshold: int Minimum number of documents term should appear in to be kept Returns ------- TermDocMatrix, new object with terms removed. def remove_terms_used_in_less_than_num_docs(self, threshold): ''' Parameters -----...
Parameters ------- stoplist : list, optional Returns ------- A new TermDocumentMatrix consisting of only unigrams in the current TermDocumentMatrix. def get_stoplisted_unigram_corpus(self, stoplist=None): ''' Parameters ------- stoplist : list, o...
Parameters ------- stoplist : list of lower-cased words, optional Returns ------- A new TermDocumentMatrix consisting of only unigrams in the current TermDocumentMatrix. def get_stoplisted_unigram_corpus_and_custom(self, custom_s...
Returns a list of document lengths in words Returns ------- np.array def get_doc_lengths(self): ''' Returns a list of document lengths in words Returns ------- np.array ''' idx_to_delete_list = self._build_term_index_list(True, self._get...
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 ...
Returns ------- dict def term_doc_lists(self): ''' Returns ------- dict ''' doc_ids = self._X.transpose().tolil().rows terms = self._term_idx_store.values() return dict(zip(terms, doc_ids))
Parameters ---------- term_ranker : TermRanker Returns ------- pd.Dataframe def apply_ranker(self, term_ranker, use_non_text_features): ''' Parameters ---------- term_ranker : TermRanker Returns ------- pd.Dataframe ...
:param doc_names: array-like[str], document names of reach document :return: Corpus-like object with doc names as metadata. If two documents share the same name (doc number) will be appended to their names. def add_doc_names_as_metadata(self, doc_names): ''' :param doc_names: array-like...
Returns a new corpus with a the metadata matrix and index store integrated. :param metadata_matrix: scipy.sparse matrix (# docs, # metadata) :param meta_index_store: IndexStore of metadata values :return: TermDocMatrixWithoutCategories def add_metadata(self, metadata_matrix, meta_index_store):...
Parameters ---------- doc, Spacy Docs Returns ------- Counter (unigram, bigram) -> count def get_feats(self, doc): ''' Parameters ---------- doc, Spacy Docs Returns ------- Counter (unigram, bigram) -> count ''' ngram_counter = Counter() for sent in doc.sents: unigrams = self._get_un...
Computes Mann Whitney corrected p, z-values. Falls back to normal approximation when numerical limits are reached. :param correction_method: str or None, correction method from statsmodels.stats.multitest.multipletests 'fdr_bh' is recommended. :return: pd.DataFrame def get_score_df(self, cor...
Parameters ---------- term_doc_matrix: TermDocMatrix or descendant scores: array-like Same length as number of terms in TermDocMatrix. num_term_to_keep: int, default=4000. Should be> 0. Number of terms to keep. Will keep between num_terms_to_keep/2 and num_terms_to_keep. Returns ------- TermDocMatr...
Parameters ---------- term_doc_matrix: TermDocMatrix or descendant scores: array-like Same length as number of terms in TermDocMatrix. num_term_to_keep: int, default=4000. Should be> 0. Number of terms to keep. Will keep between num_terms_to_keep/2 and num_terms_to_keep. Returns ------- set, terms ...
In this case, args aren't used, since this information is taken directly from the corpus categories. Returns ------- np.array, scores def get_scores(self, *args): ''' In this case, args aren't used, since this information is taken directly from the corpus categories. Returns ------- np.array, sco...
Imputes p-values from the Z-scores of `ScaledFScore` scores. Assuming incorrectly that the scaled f-scores are normally distributed. 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. Retu...
Returns a projection of the categories :param term_doc_mat: a TermDocMatrix :return: CategoryProjection def project(self, term_doc_mat, x_dim=0, y_dim=1): ''' Returns a projection of the categories :param term_doc_mat: a TermDocMatrix :return: CategoryProjection ...
Returns a projection of the :param term_doc_mat: a TermDocMatrix :return: CategoryProjection def project_with_metadata(self, term_doc_mat, x_dim=0, y_dim=1): ''' Returns a projection of the :param term_doc_mat: a TermDocMatrix :return: CategoryProjection ''' ...
In this case, parameters a and b aren't used, since this information is taken directly from the corpus categories. Returns ------- def get_scores(self, *args): ''' In this case, parameters a and b aren't used, since this information is taken directly from the corpus categories. Returns ------- '''...
Class decorator that ensures support for the special C{__cmp__} method. On Python 2 this does nothing. On Python 3, C{__eq__}, C{__lt__}, etc. methods are added to the class, relying on C{__cmp__} to implement their comparisons. def comparable(klass): """ Class decorator that ensures support for th...
Decorator to validate all the arguments to function are of the type of calling class for passed operator def validate_class_type_arguments(operator): """ Decorator to validate all the arguments to function are of the type of calling class for passed operator """ def inner(function): d...
Decorator to validate the <type> of arguments in the calling function are of the `param_type` class. if `param_type` is None, uses `param_type` as the class where it is used. Note: Use this decorator on the functions of the class. def validate_arguments_type_of_function(param_type=None): """ Deco...
Returns the time offset from UTC accounting for DST Keyword Arguments: time_struct {time.struct_time} -- the struct time for which to return the UTC offset. If None, use current local time. def utc_offset(time_struct=None)...
Returns a MayaDT instance for the human moment specified. Powered by dateparser. Useful for scraping websites. Examples: 'next week', 'now', 'tomorrow', '300 years ago', 'August 14, 2015' Keyword Arguments: string -- string to be parsed timezone -- timezone referenced from (defaul...
Returns a MayaDT instance for the machine-produced moment specified. Powered by pendulum. Accepts most known formats. Useful for working with data. Keyword Arguments: string -- string to be parsed timezone -- timezone referenced from (default: 'UTC') day_first -- if true, the first...
Returns `datetime.timedelta` object for the passed duration. Keyword Arguments: duration -- `datetime.timedelta` object or seconds in `int` format. def _seconds_or_timedelta(duration): """Returns `datetime.timedelta` object for the passed duration. Keyword Arguments: duration -- `datetime...
Yields MayaDT objects between the start and end MayaDTs given, at a given interval (seconds or timedelta). def intervals(start, end, interval): """ Yields MayaDT objects between the start and end MayaDTs given, at a given interval (seconds or timedelta). """ interval = _seconds_or_timedelta(int...
Returns a new MayaDT object with the given offsets. def add(self, kwargs): """Returns a new MayaDT object with the given offsets.""" return self.from_datetime( pendulum.instance(self.datetime()).add(kwargs) )
Returns a new MayaDT object with the given offsets. def subtract(self, kwargs): """Returns a new MayaDT object with the given offsets.""" return self.from_datetime( pendulum.instance(self.datetime()).subtract(kwargs) )
Returns a new MayaDT object modified by the given instruction. Powered by snaptime. See https://github.com/zartstrom/snaptime for a complete documentation about the snaptime instructions. def snap(self, instruction): """ Returns a new MayaDT object modified by the given instruction. ...
Returns the name of the local timezone. def local_timezone(self): """Returns the name of the local timezone.""" if self._local_tz.zone in pytz.all_timezones: return self._local_tz.zone return self.timezone
Converts a datetime into an epoch. def __dt_to_epoch(dt): """Converts a datetime into an epoch.""" # Assume UTC if no datetime is provided. if dt.tzinfo is None: dt = dt.replace(tzinfo=pytz.utc) epoch_start = Datetime(*MayaDT.__EPOCH_START, tzinfo=pytz.timezone('UTC')) ...
Returns MayaDT instance from a 9-tuple struct It's assumed to be from gmtime(). def from_struct(klass, struct, timezone=pytz.UTC): """Returns MayaDT instance from a 9-tuple struct It's assumed to be from gmtime(). """ struct_time = time.mktime(struct) - utc_offset(struct) ...
Returns a timezone-aware datetime... Defaulting to UTC (as it should). Keyword Arguments: to_timezone {str} -- timezone to convert to (default: None/UTC) naive {bool} -- if True, the tzinfo is simply dropped (default: False) def datetime(self, to_tim...
Returns an ISO 8601 representation of the MayaDT. def iso8601(self): """Returns an ISO 8601 representation of the MayaDT.""" # Get a timezone-naive datetime. dt = self.datetime(naive=True) return '{}Z'.format(dt.isoformat())
Returns human slang representation of date. Keyword Arguments: locale -- locale to translate to, e.g. 'fr' for french. (default: 'en' - English) def slang_date(self, locale="en"): """"Returns human slang representation of date. Keyword Arguments: ...
Returns human slang representation of time. Keyword Arguments: locale -- locale to translate to, e.g. 'fr' for french. (default: 'en' - English) def slang_time(self, locale="en"): """"Returns human slang representation of time. Keyword Arguments: ...
Use sigmoid function to choose a delta that will help smoothly steer from current angle to target angle. def angvel(target, current, scale): '''Use sigmoid function to choose a delta that will help smoothly steer from current angle to target angle.''' delta = target - current while delta < -180: de...
Steer towards the target pitch/yaw, return True when within the given tolerance threshold. def pointTo(agent_host, ob, target_pitch, target_yaw, threshold): '''Steer towards the target pitch/yaw, return True when within the given tolerance threshold.''' pitch = ob.get(u'Pitch', 0) yaw = ob.get(u'Yaw', 0) ...
Download Malmo from github and optionaly build the Minecraft Mod. Args: branch: optional branch to clone. Default is release version. buildMod: don't build the Mod unless build arg is given as True. Returns: The path for the Malmo Minecraft mod. def download(branch=None, buildMod=False)...
Launch Malmo Minecraft Mod in one or more clients from the Minecraft directory on the (optionally) given ports. Args: ports: an optionsl list of ports to start minecraft clients on. Defaults to a single Minecraft client on port 10000. wait_timeout: optional time in secon...
Set the MAMLMO_XSD_PATH environment variable in current process. def set_malmo_xsd_path(): """Set the MAMLMO_XSD_PATH environment variable in current process.""" os.environ["MALMO_XSD_PATH"] = str(pathlib.Path(malmo_install_dir + "/Schemas").absolute()) print(os.environ["MALMO_XSD_PATH"])
Return the index in arr of the closest float value to val. def indexOfClosest( arr, val ): '''Return the index in arr of the closest float value to val.''' i_closest = None for i,v in enumerate(arr): d = math.fabs( v - val ) if i_closest == None or d < d_closest: i_closest = i ...

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=Tru...

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.prior...

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()]....

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 occu...

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...

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 ------- 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 ...

Parameters ---------- term_dict: dict {metadataname: [term1, term2, ....], ...} Returns ------- None def check_topic_model_string_format(term_dict): ''' Parameters ---------- term_dict: dict {metadataname: [term1, term2, ....], ...} Returns ------- None ''' if ...

Inserts dictionary of meta data terms into object. Parameters ---------- term_dict: dict {metadataname: [term1, term2, ....], ...} Returns ------- self: ScatterChart def inject_metadata_term_lists(self, term_dict): ''' Inserts dictionary of meta data te...

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 ---------- term_dict: dict {metadataname: str: 'explanation to insert', ...} ...

Inject custom x and y coordinates for each term into chart. Parameters ---------- x_coords: array-like positions on x-axis \in [0,1] y_coords: array-like positions on y-axis \in [0,1] rescale_x: lambda list[0,1]: list[0,1], default identity Re...

: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: ret...

Outdated. MPLD3 drawing. Parameters ---------- category num_top_words_to_annotate words_to_annotate scores transform Returns ------- pd.DataFrame, html of fgure def draw(self, category, num_top_words_to_annotat...

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 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 occ...

Parameters ---------- tdm: TermDocMatrix category: str, category name Returns ------- pd.DataFrame(['coef', 'p-val']) def get_scores_and_p_values(self, tdm, category): ''' Parameters ---------- tdm: TermDocMatrix category: str, category name Returns ------- pd.DataFrame(['coef', 'p-val'])...

>>> 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 ------- csr_matrix that is stripped of columns ...

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

Parameters ---------- doc, Spacy Docs Returns ------- Counter noun chunk -> count def get_feats(self, doc): ''' Parameters ---------- doc, Spacy Docs Returns ------- Counter noun chunk -> count ''' return Counter([str(c).lower() for c in doc.noun_chunks])

Parameters ---------- nltk_tokenizer : nltk.tokenize.* instance (e.g., nltk.TreebankWordTokenizer()) Returns ------- Doc of tweets Notes ------- Requires NLTK to be installed def nltk_tokenzier_factory(nltk_tokenizer): ''' Parameters ---------- nltk_tokenizer : nltk.tokenize.* instance (e.g., nltk.Treeba...

Specifies fixed set of embeddings :param embeddings: array-like, sparse or dense, shape should be (embedding size, # terms) :return: EmbeddingsResolver def set_embeddings(self, embeddings): ''' Specifies fixed set of embeddings :param embeddings: array-like, sparse or dense, sha...

:param projection_model: sklearn unsupervised model (e.g., PCA) by default the recommended model is umap.UMAP, which requires UMAP in to be installed :return: array, shape (num dimension, vocab size) def project(self, projection_model=None): ''' :param projection_model: sklearn unsuper...

Parameters ---------- terms : list or None If terms is list, make these the seed terms for the topoics If none, use the first 30 terms in get_scaled_f_scores_vs_background num_terms_per_topic : int, default 10 Use this many terms per topic scorer : TermScorer Implements get_scores, default is RankDi...

Returns ------- pd.DataFrame def get_ranks(self): ''' Returns ------- pd.DataFrame ''' if self._use_non_text_features: return self._term_doc_matrix.get_metadata_freq_df() else: return self._term_doc_matrix.get_term_freq_df()

Generate a TermDocMatrix from data in parameters. Returns ---------- term_doc_matrix : TermDocMatrix The object that this factory class builds. def build(self): """Generate a TermDocMatrix from data in parameters. Returns ---------- term_doc_m...

Entity types to exclude from feature construction. Terms matching specificed entities, instead of labeled by their lower case orthographic form or lemma, will be labeled by their entity type. Parameters ---------- entity_types : set of entity types outputted by spaCy '...

Parameters ---------- category_doc_iter : iterator of (string category name, spacy.tokens.doc.Doc) pairs Returns ---------- t : TermDocMatrix def _build_from_category_spacy_doc_iter(self, category_doc_iter): ''' Parameters ---------- category_doc...

Parameters ---------- raw_text, uncleaned text for parsing out features Returns ------- csr_matrix, feature matrix def feats_from_doc(self, raw_text): ''' Parameters ---------- raw_text, uncleaned text for parsing out features Returns ...

Parameters ---------- num_terms, int Returns ------- dict def get_lexicons(self, num_terms=10): ''' Parameters ---------- num_terms, int Returns ------- dict ''' return {k: v.index[:num_terms] ...

Parameters ---------- term_doc_matrix : TermDocMatrix Term document matrix object to compact Returns ------- New term doc matrix def compact(self, term_doc_matrix): ''' Parameters ---------- term_doc_matrix : TermDocMatrix ...

Parameters ---------- term_doc_matrix : TermDocMatrix Term document matrix object to compact Returns ------- TermDocMatrix def compact(self, term_doc_matrix): ''' Parameters ---------- term_doc_matrix : TermDocMatrix Term d...

Parameters ---------- doc, Spacy Docs Returns ------- Counter (unigram, bigram) -> count def get_feats(self, doc): ''' Parameters ---------- doc, Spacy Docs Returns ------- Counter (unigram, bigram) -> count ''' ngram_counter = Counter() for sent in doc.sents: ngrams = self.phrases[s...

Parameters ---------- corpus: Corpus for phrase augmentation Returns ------- New ParsedCorpus containing unigrams in corpus and new phrases def add_phrases(self, corpus): ''' Parameters ---------- corpus: Corpus for phrase augmentation Returns ------- New ParsedCorpus containing unigrams in c...

Parameters ---------- epochs : int Number of epochs to train for. Default is 2000. training_iterations : int Number of times to repeat training process. Default is training_iterations. Returns ------- A trained word2vec model. def train(self, epochs=2000, training_iterations=5): ''' Parameters...

Trains passive aggressive classifier def passive_aggressive_train(self): '''Trains passive aggressive classifier ''' self._clf = PassiveAggressiveClassifier(n_iter=50, C=0.2, n_jobs=-1, random_state=0) self._clf.fit(self._term_doc_matrix._X, self._term_doc_matrix._y) y_dist = self._clf.decision_function(sel...

Builds Depoyed Classifier def build(self): '''Builds Depoyed Classifier ''' if self._clf is None: raise NeedToTrainExceptionBeforeDeployingException() return DeployedClassifier(self._category, self._term_doc_matrix._category_idx_store, self._term_doc_m...

Parameters ---------- term_to_index_dict: term -> idx dictionary Returns ------- IndexStore def build(term_to_index_dict): ''' Parameters ---------- term_to_index_dict: term -> idx dictionary Returns ------- IndexStore ''' idxstore = IndexStore() idxstore._val2i = term_to_index_dict i...

Returns ------- Corpus def build(self): ''' Returns ------- Corpus ''' constructor_kwargs = self._get_build_kwargs() if type(self.raw_texts) == list: constructor_kwargs['raw_texts'] = np.array(self.raw_texts) else: constructor_kwargs['raw_texts'] = self.raw_texts return Corpus(**constructor...

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 occu...

Parameters ---------- term_doc_matrix : TermDocMatrix Term document matrix object to compact Returns ------- New term doc matrix def compact(self, term_doc_matrix): ''' Parameters ---------- term_doc_matrix : TermDocMatrix Term document matrix object to compact Returns ------- New term ...

Returns ------- pd.Series, all raw documents def get_texts(self): ''' Returns ------- pd.Series, all raw documents ''' if sys.version_info[0] == 2: return self._df[self._parsed_col] return self._df[self._parsed_col].apply(str)

Returns a dataframe indexed on the number of groups a term occured in. Parameters ---------- group_col Returns ------- pd.DataFrame def term_group_freq_df(self, group_col): # type: (str) -> pd.DataFrame ''' Returns a dataframe indexed on the num...

Compute variance from :param X: :return: def sparse_var(X): ''' Compute variance from :param X: :return: ''' Xc = X.copy() Xc.data **= 2 return np.array(Xc.mean(axis=0) - np.power(X.mean(axis=0), 2))[0]

Specify the category to score. Optionally, score against a specific set of categories. def set_categories(self, category_name, not_category_names=[], neutral_category_names=[]): ''' Specify the category to score. Optionally, score aga...

In this case, parameters a and b aren't used, since this information is taken directly from the corpus categories. Returns ------- def get_t_statistics(self): ''' In this case, parameters a and b aren't used, since this information is taken directly from the corpus cate...

Parameters ---------- X_factory mX_factory category_idx_store df parse_pipeline term_idx_store metadata_idx_store y Returns ------- CorpusDF def _apply_pipeline_and_get_build_instance(self, X_factory, mX_fa...

Parameters ---------- background def set_background_corpus(self, background): ''' Parameters ---------- background ''' if issubclass(type(background), TermDocMatrixWithoutCategories): self._background_corpus = pd.DataFrame(background ...

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...

Returns ------- List of dicts. One dict for each document, keys are metadata, values are counts def list_extra_features(self): ''' Returns ------- List of dicts. One dict for each document, keys are metadata, values are counts ''' return FeatureLister(s...

Returns ------- A new TermDocumentMatrix consisting of only terms which occur at least minimum_term_count. def remove_infrequent_words(self, minimum_term_count, term_ranker=AbsoluteFrequencyRanker): ''' Returns ------- A new TermDocumentMatrix consisting of only terms wh...

Returns ------- A new TermDocumentMatrix consisting of only terms in the current TermDocumentMatrix that aren't spaCy entity tags. Note: Used if entity types are censored using FeatsFromSpacyDoc(tag_types_to_censor=...). def remove_entity_tags(self): ''' Returns ...

Non destructive term removal. Parameters ---------- terms : list list of terms to remove ignore_absences : bool, False by default if term does not appear, don't raise an error, just move on. Returns ------- TermDocMatrix, new object with ...

Parameters ---------- threshold: int Minimum number of documents term should appear in to be kept Returns ------- TermDocMatrix, new object with terms removed. def remove_terms_used_in_less_than_num_docs(self, threshold): ''' Parameters -----...

Parameters ------- stoplist : list, optional Returns ------- A new TermDocumentMatrix consisting of only unigrams in the current TermDocumentMatrix. def get_stoplisted_unigram_corpus(self, stoplist=None): ''' Parameters ------- stoplist : list, o...

Parameters ------- stoplist : list of lower-cased words, optional Returns ------- A new TermDocumentMatrix consisting of only unigrams in the current TermDocumentMatrix. def get_stoplisted_unigram_corpus_and_custom(self, custom_s...

Returns a list of document lengths in words Returns ------- np.array def get_doc_lengths(self): ''' Returns a list of document lengths in words Returns ------- np.array ''' idx_to_delete_list = self._build_term_index_list(True, self._get...

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 ...

Returns ------- dict def term_doc_lists(self): ''' Returns ------- dict ''' doc_ids = self._X.transpose().tolil().rows terms = self._term_idx_store.values() return dict(zip(terms, doc_ids))

Parameters ---------- term_ranker : TermRanker Returns ------- pd.Dataframe def apply_ranker(self, term_ranker, use_non_text_features): ''' Parameters ---------- term_ranker : TermRanker Returns ------- pd.Dataframe ...

:param doc_names: array-like[str], document names of reach document :return: Corpus-like object with doc names as metadata. If two documents share the same name (doc number) will be appended to their names. def add_doc_names_as_metadata(self, doc_names): ''' :param doc_names: array-like...

Returns a new corpus with a the metadata matrix and index store integrated. :param metadata_matrix: scipy.sparse matrix (# docs, # metadata) :param meta_index_store: IndexStore of metadata values :return: TermDocMatrixWithoutCategories def add_metadata(self, metadata_matrix, meta_index_store):...

Parameters ---------- doc, Spacy Docs Returns ------- Counter (unigram, bigram) -> count def get_feats(self, doc): ''' Parameters ---------- doc, Spacy Docs Returns ------- Counter (unigram, bigram) -> count ''' ngram_counter = Counter() for sent in doc.sents: unigrams = self._get_un...

Computes Mann Whitney corrected p, z-values. Falls back to normal approximation when numerical limits are reached. :param correction_method: str or None, correction method from statsmodels.stats.multitest.multipletests 'fdr_bh' is recommended. :return: pd.DataFrame def get_score_df(self, cor...

Parameters ---------- term_doc_matrix: TermDocMatrix or descendant scores: array-like Same length as number of terms in TermDocMatrix. num_term_to_keep: int, default=4000. Should be> 0. Number of terms to keep. Will keep between num_terms_to_keep/2 and num_terms_to_keep. Returns ------- TermDocMatr...

Parameters ---------- term_doc_matrix: TermDocMatrix or descendant scores: array-like Same length as number of terms in TermDocMatrix. num_term_to_keep: int, default=4000. Should be> 0. Number of terms to keep. Will keep between num_terms_to_keep/2 and num_terms_to_keep. Returns ------- set, terms ...

In this case, args aren't used, since this information is taken directly from the corpus categories. Returns ------- np.array, scores def get_scores(self, *args): ''' In this case, args aren't used, since this information is taken directly from the corpus categories. Returns ------- np.array, sco...

Imputes p-values from the Z-scores of `ScaledFScore` scores. Assuming incorrectly that the scaled f-scores are normally distributed. 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. Retu...

Returns a projection of the categories :param term_doc_mat: a TermDocMatrix :return: CategoryProjection def project(self, term_doc_mat, x_dim=0, y_dim=1): ''' Returns a projection of the categories :param term_doc_mat: a TermDocMatrix :return: CategoryProjection ...

Returns a projection of the :param term_doc_mat: a TermDocMatrix :return: CategoryProjection def project_with_metadata(self, term_doc_mat, x_dim=0, y_dim=1): ''' Returns a projection of the :param term_doc_mat: a TermDocMatrix :return: CategoryProjection ''' ...

In this case, parameters a and b aren't used, since this information is taken directly from the corpus categories. Returns ------- def get_scores(self, *args): ''' In this case, parameters a and b aren't used, since this information is taken directly from the corpus categories. Returns ------- '''...

Class decorator that ensures support for the special C{__cmp__} method. On Python 2 this does nothing. On Python 3, C{__eq__}, C{__lt__}, etc. methods are added to the class, relying on C{__cmp__} to implement their comparisons. def comparable(klass): """ Class decorator that ensures support for th...

Decorator to validate all the arguments to function are of the type of calling class for passed operator def validate_class_type_arguments(operator): """ Decorator to validate all the arguments to function are of the type of calling class for passed operator """ def inner(function): d...

Decorator to validate the <type> of arguments in the calling function are of the `param_type` class. if `param_type` is None, uses `param_type` as the class where it is used. Note: Use this decorator on the functions of the class. def validate_arguments_type_of_function(param_type=None): """ Deco...

Returns the time offset from UTC accounting for DST Keyword Arguments: time_struct {time.struct_time} -- the struct time for which to return the UTC offset. If None, use current local time. def utc_offset(time_struct=None)...

Returns a MayaDT instance for the human moment specified. Powered by dateparser. Useful for scraping websites. Examples: 'next week', 'now', 'tomorrow', '300 years ago', 'August 14, 2015' Keyword Arguments: string -- string to be parsed timezone -- timezone referenced from (defaul...

Returns a MayaDT instance for the machine-produced moment specified. Powered by pendulum. Accepts most known formats. Useful for working with data. Keyword Arguments: string -- string to be parsed timezone -- timezone referenced from (default: 'UTC') day_first -- if true, the first...

Returns `datetime.timedelta` object for the passed duration. Keyword Arguments: duration -- `datetime.timedelta` object or seconds in `int` format. def _seconds_or_timedelta(duration): """Returns `datetime.timedelta` object for the passed duration. Keyword Arguments: duration -- `datetime...

Yields MayaDT objects between the start and end MayaDTs given, at a given interval (seconds or timedelta). def intervals(start, end, interval): """ Yields MayaDT objects between the start and end MayaDTs given, at a given interval (seconds or timedelta). """ interval = _seconds_or_timedelta(int...

Returns a new MayaDT object with the given offsets. def add(self, **kwargs): """Returns a new MayaDT object with the given offsets.""" return self.from_datetime( pendulum.instance(self.datetime()).add(**kwargs) )

Returns a new MayaDT object with the given offsets. def subtract(self, **kwargs): """Returns a new MayaDT object with the given offsets.""" return self.from_datetime( pendulum.instance(self.datetime()).subtract(**kwargs) )

Returns a new MayaDT object modified by the given instruction. Powered by snaptime. See https://github.com/zartstrom/snaptime for a complete documentation about the snaptime instructions. def snap(self, instruction): """ Returns a new MayaDT object modified by the given instruction. ...

Returns the name of the local timezone. def local_timezone(self): """Returns the name of the local timezone.""" if self._local_tz.zone in pytz.all_timezones: return self._local_tz.zone return self.timezone

Converts a datetime into an epoch. def __dt_to_epoch(dt): """Converts a datetime into an epoch.""" # Assume UTC if no datetime is provided. if dt.tzinfo is None: dt = dt.replace(tzinfo=pytz.utc) epoch_start = Datetime(*MayaDT.__EPOCH_START, tzinfo=pytz.timezone('UTC')) ...

Returns MayaDT instance from a 9-tuple struct It's assumed to be from gmtime(). def from_struct(klass, struct, timezone=pytz.UTC): """Returns MayaDT instance from a 9-tuple struct It's assumed to be from gmtime(). """ struct_time = time.mktime(struct) - utc_offset(struct) ...

Returns a timezone-aware datetime... Defaulting to UTC (as it should). Keyword Arguments: to_timezone {str} -- timezone to convert to (default: None/UTC) naive {bool} -- if True, the tzinfo is simply dropped (default: False) def datetime(self, to_tim...

Returns an ISO 8601 representation of the MayaDT. def iso8601(self): """Returns an ISO 8601 representation of the MayaDT.""" # Get a timezone-naive datetime. dt = self.datetime(naive=True) return '{}Z'.format(dt.isoformat())

Returns human slang representation of date. Keyword Arguments: locale -- locale to translate to, e.g. 'fr' for french. (default: 'en' - English) def slang_date(self, locale="en"): """"Returns human slang representation of date. Keyword Arguments: ...

Returns human slang representation of time. Keyword Arguments: locale -- locale to translate to, e.g. 'fr' for french. (default: 'en' - English) def slang_time(self, locale="en"): """"Returns human slang representation of time. Keyword Arguments: ...

Use sigmoid function to choose a delta that will help smoothly steer from current angle to target angle. def angvel(target, current, scale): '''Use sigmoid function to choose a delta that will help smoothly steer from current angle to target angle.''' delta = target - current while delta < -180: de...

Steer towards the target pitch/yaw, return True when within the given tolerance threshold. def pointTo(agent_host, ob, target_pitch, target_yaw, threshold): '''Steer towards the target pitch/yaw, return True when within the given tolerance threshold.''' pitch = ob.get(u'Pitch', 0) yaw = ob.get(u'Yaw', 0) ...

Download Malmo from github and optionaly build the Minecraft Mod. Args: branch: optional branch to clone. Default is release version. buildMod: don't build the Mod unless build arg is given as True. Returns: The path for the Malmo Minecraft mod. def download(branch=None, buildMod=False)...

Launch Malmo Minecraft Mod in one or more clients from the Minecraft directory on the (optionally) given ports. Args: ports: an optionsl list of ports to start minecraft clients on. Defaults to a single Minecraft client on port 10000. wait_timeout: optional time in secon...

Set the MAMLMO_XSD_PATH environment variable in current process. def set_malmo_xsd_path(): """Set the MAMLMO_XSD_PATH environment variable in current process.""" os.environ["MALMO_XSD_PATH"] = str(pathlib.Path(malmo_install_dir + "/Schemas").absolute()) print(os.environ["MALMO_XSD_PATH"])

Return the index in arr of the closest float value to val. def indexOfClosest( arr, val ): '''Return the index in arr of the closest float value to val.''' i_closest = None for i,v in enumerate(arr): d = math.fabs( v - val ) if i_closest == None or d < d_closest: i_closest = i ...