idx int64 0 63k | question stringlengths 61 4.03k | target stringlengths 6 1.23k |
|---|---|---|
61,000 | def get_by_symbol ( self , symbol : str ) -> Commodity : assert isinstance ( symbol , str ) query = ( self . currencies_query . filter ( Commodity . mnemonic == symbol ) ) return query . one ( ) | Loads currency by symbol |
61,001 | def import_fx_rates ( self , rates : List [ PriceModel ] ) : have_new_rates = False base_currency = self . get_default_currency ( ) for rate in rates : assert isinstance ( rate , PriceModel ) currency = self . get_by_symbol ( rate . symbol ) amount = rate . value has_rate = currency . prices . filter ( Price . date == ... | Imports the given prices into database . Write operation! |
61,002 | def __get_default_currency ( self ) : if sys . platform == "win32" : def_curr = self . book [ "default-currency" ] = self . __get_default_currency_windows ( ) else : def_curr = self . book [ "default-currency" ] = self . __get_locale_currency ( ) return def_curr | Read the default currency from GnuCash preferences |
61,003 | def __get_registry_key ( self , key ) : import winreg root = winreg . OpenKey ( winreg . HKEY_CURRENT_USER , r'SOFTWARE\GSettings\org\gnucash\general' , 0 , winreg . KEY_READ ) [ pathname , regtype ] = ( winreg . QueryValueEx ( root , key ) ) winreg . CloseKey ( root ) return pathname | Read currency from windows registry |
61,004 | def get_for_accounts ( self , accounts : List [ Account ] ) : account_ids = [ acc . guid for acc in accounts ] query = ( self . query . filter ( Split . account_guid . in_ ( account_ids ) ) ) splits = query . all ( ) return splits | Get all splits for the given accounts |
61,005 | def __get_model_for_portfolio_value ( input_model : PortfolioValueInputModel ) -> PortfolioValueViewModel : result = PortfolioValueViewModel ( ) result . filter = input_model ref_datum = Datum ( ) ref_datum . from_datetime ( input_model . as_of_date ) ref_date = ref_datum . end_of_day ( ) result . stock_rows = [ ] with... | loads the data for portfolio value |
61,006 | def __load_settings ( self ) : file_path = self . file_path try : self . data = json . load ( open ( file_path ) ) except FileNotFoundError : print ( "Could not load" , file_path ) | Load settings from . json file |
61,007 | def file_exists ( self ) -> bool : cfg_path = self . file_path assert cfg_path return path . isfile ( cfg_path ) | Check if the settings file exists or not |
61,008 | def save ( self ) : content = self . dumps ( ) fileutils . save_text_to_file ( content , self . file_path ) | Saves the settings contents |
61,009 | def database_path ( self ) : filename = self . database_filename db_path = ":memory:" if filename == ":memory:" else ( path . abspath ( path . join ( __file__ , "../.." , ".." , "data" , filename ) ) ) return db_path | Full database path . Includes the default location + the database filename . |
61,010 | def file_path ( self ) -> str : user_dir = self . __get_user_path ( ) file_path = path . abspath ( path . join ( user_dir , self . FILENAME ) ) return file_path | Settings file absolute path |
61,011 | def dumps ( self ) -> str : return json . dumps ( self . data , sort_keys = True , indent = 4 ) | Dumps the json content as a string |
61,012 | def __copy_template ( self ) : import shutil template_filename = "settings.json.template" template_path = path . abspath ( path . join ( __file__ , ".." , ".." , "config" , template_filename ) ) settings_path = self . file_path shutil . copyfile ( template_path , settings_path ) self . __ensure_file_exists ( ) | Copy the settings template into the user s directory |
61,013 | def is_not_empty ( self , value , strict = False ) : value = stringify ( value ) if value is not None : return self . shout ( 'Value %r is empty' , strict , value ) | if value is not empty |
61,014 | def is_numeric ( self , value , strict = False ) : value = stringify ( value ) if value is not None : if value . isnumeric ( ) : return self . shout ( 'value %r is not numeric' , strict , value ) | if value is numeric |
61,015 | def is_integer ( self , value , strict = False ) : if value is not None : if isinstance ( value , numbers . Number ) : return value = stringify ( value ) if value is not None and value . isnumeric ( ) : return self . shout ( 'value %r is not an integer' , strict , value ) | if value is an integer |
61,016 | def match_date ( self , value , strict = False ) : value = stringify ( value ) try : parse ( value ) except Exception : self . shout ( 'Value %r is not a valid date' , strict , value ) | if value is a date |
61,017 | def match_regexp ( self , value , q , strict = False ) : value = stringify ( value ) mr = re . compile ( q ) if value is not None : if mr . match ( value ) : return self . shout ( '%r not matching the regexp %r' , strict , value , q ) | if value matches a regexp q |
61,018 | def has_length ( self , value , q , strict = False ) : value = stringify ( value ) if value is not None : if len ( value ) == q : return self . shout ( 'Value %r not matching length %r' , strict , value , q ) | if value has a length of q |
61,019 | def must_contain ( self , value , q , strict = False ) : if value is not None : if value . find ( q ) != - 1 : return self . shout ( 'Value %r does not contain %r' , strict , value , q ) | if value must contain q |
61,020 | def extract ( context , data ) : with context . http . rehash ( data ) as result : file_path = result . file_path content_type = result . content_type extract_dir = random_filename ( context . work_path ) if content_type in ZIP_MIME_TYPES : extracted_files = extract_zip ( file_path , extract_dir ) elif content_type in ... | Extract a compressed file |
61,021 | def size ( cls , crawler ) : key = make_key ( 'queue_pending' , crawler ) return unpack_int ( conn . get ( key ) ) | Total operations pending for this crawler |
61,022 | def read_word ( image , whitelist = None , chars = None , spaces = False ) : from tesserocr import PyTessBaseAPI api = PyTessBaseAPI ( ) api . SetPageSegMode ( 8 ) if whitelist is not None : api . SetVariable ( "tessedit_char_whitelist" , whitelist ) api . SetImage ( image ) api . Recognize ( ) guess = api . GetUTF8Tex... | OCR a single word from an image . Useful for captchas . Image should be pre - processed to remove noise etc . |
61,023 | def read_char ( image , whitelist = None ) : from tesserocr import PyTessBaseAPI api = PyTessBaseAPI ( ) api . SetPageSegMode ( 10 ) if whitelist is not None : api . SetVariable ( "tessedit_char_whitelist" , whitelist ) api . SetImage ( image ) api . Recognize ( ) return api . GetUTF8Text ( ) . strip ( ) | OCR a single character from an image . Useful for captchas . |
61,024 | def get ( self , name , default = None ) : value = self . params . get ( name , default ) if isinstance ( value , str ) : value = os . path . expandvars ( value ) return value | Get a configuration value and expand environment variables . |
61,025 | def emit ( self , rule = 'pass' , stage = None , data = { } , delay = None , optional = False ) : if stage is None : stage = self . stage . handlers . get ( rule ) if optional and stage is None : return if stage is None or stage not in self . crawler . stages : self . log . info ( "No next stage: %s (%s)" % ( stage , r... | Invoke the next stage either based on a handling rule or by calling the pass rule by default . |
61,026 | def recurse ( self , data = { } , delay = None ) : return self . emit ( stage = self . stage . name , data = data , delay = delay ) | Have a stage invoke itself with a modified set of arguments . |
61,027 | def execute ( self , data ) : if Crawl . is_aborted ( self . crawler , self . run_id ) : return try : Crawl . operation_start ( self . crawler , self . stage , self . run_id ) self . log . info ( '[%s->%s(%s)]: %s' , self . crawler . name , self . stage . name , self . stage . method_name , self . run_id ) return self ... | Execute the crawler and create a database record of having done so . |
61,028 | def skip_incremental ( self , * criteria ) : if not self . incremental : return False key = make_key ( * criteria ) if key is None : return False if self . check_tag ( key ) : return True self . set_tag ( key , None ) return False | Perform an incremental check on a set of criteria . |
61,029 | def store_data ( self , data , encoding = 'utf-8' ) : path = random_filename ( self . work_path ) try : with open ( path , 'wb' ) as fh : if isinstance ( data , str ) : data = data . encode ( encoding ) if data is not None : fh . write ( data ) return self . store_file ( path ) finally : try : os . unlink ( path ) exce... | Put the given content into a file possibly encoding it as UTF - 8 in the process . |
61,030 | def check_due ( self ) : if self . disabled : return False if self . is_running : return False if self . delta is None : return False last_run = self . last_run if last_run is None : return True now = datetime . utcnow ( ) if now > last_run + self . delta : return True return False | Check if the last execution of this crawler is older than the scheduled interval . |
61,031 | def flush ( self ) : Queue . flush ( self ) Event . delete ( self ) Crawl . flush ( self ) | Delete all run - time data generated by this crawler . |
61,032 | def run ( self , incremental = None , run_id = None ) : state = { 'crawler' : self . name , 'run_id' : run_id , 'incremental' : settings . INCREMENTAL } if incremental is not None : state [ 'incremental' ] = incremental self . cancel ( ) Event . delete ( self ) Queue . queue ( self . init_stage , state , { } ) | Queue the execution of a particular crawler . |
61,033 | def fetch ( context , data ) : url = data . get ( 'url' ) attempt = data . pop ( 'retry_attempt' , 1 ) try : result = context . http . get ( url , lazy = True ) rules = context . get ( 'rules' , { 'match_all' : { } } ) if not Rule . get_rule ( rules ) . apply ( result ) : context . log . info ( 'Fetch skip: %r' , resul... | Do an HTTP GET on the url specified in the inbound data . |
61,034 | def dav_index ( context , data ) : url = data . get ( 'url' ) result = context . http . request ( 'PROPFIND' , url ) for resp in result . xml . findall ( './{DAV:}response' ) : href = resp . findtext ( './{DAV:}href' ) if href is None : continue rurl = urljoin ( url , href ) rdata = data . copy ( ) rdata [ 'url' ] = ru... | List files in a WebDAV directory . |
61,035 | def session ( context , data ) : context . http . reset ( ) user = context . get ( 'user' ) password = context . get ( 'password' ) if user is not None and password is not None : context . http . session . auth = ( user , password ) user_agent = context . get ( 'user_agent' ) if user_agent is not None : context . http ... | Set some HTTP parameters for all subsequent requests . |
61,036 | def save ( cls , crawler , stage , level , run_id , error = None , message = None ) : event = { 'stage' : stage . name , 'level' : level , 'timestamp' : pack_now ( ) , 'error' : error , 'message' : message } data = dump_json ( event ) conn . lpush ( make_key ( crawler , "events" ) , data ) conn . lpush ( make_key ( cra... | Create an event possibly based on an exception . |
61,037 | def get_stage_events ( cls , crawler , stage_name , start , end , level = None ) : key = make_key ( crawler , "events" , stage_name , level ) return cls . event_list ( key , start , end ) | events from a particular stage |
61,038 | def get_run_events ( cls , crawler , run_id , start , end , level = None ) : key = make_key ( crawler , "events" , run_id , level ) return cls . event_list ( key , start , end ) | Events from a particular run |
61,039 | def soviet_checksum ( code ) : def sum_digits ( code , offset = 1 ) : total = 0 for digit , index in zip ( code [ : 7 ] , count ( offset ) ) : total += int ( digit ) * index summed = ( total / 11 * 11 ) return total - summed check = sum_digits ( code , 1 ) if check == 10 : check = sum_digits ( code , 3 ) if check == 10... | Courtesy of Sir Vlad Lavrov . |
61,040 | def search_results_total ( html , xpath , check , delimiter ) : for container in html . findall ( xpath ) : if check in container . findtext ( '.' ) : text = container . findtext ( '.' ) . split ( delimiter ) total = int ( text [ - 1 ] . strip ( ) ) return total | Get the total number of results from the DOM of a search index . |
61,041 | def search_results_last_url ( html , xpath , label ) : for container in html . findall ( xpath ) : if container . text_content ( ) . strip ( ) == label : return container . find ( './/a' ) . get ( 'href' ) | Get the URL of the last button in a search results listing . |
61,042 | def op_count ( cls , crawler , stage = None ) : if stage : total_ops = conn . get ( make_key ( crawler , stage ) ) else : total_ops = conn . get ( make_key ( crawler , "total_ops" ) ) return unpack_int ( total_ops ) | Total operations performed for this crawler |
61,043 | def index ( ) : crawlers = [ ] for crawler in manager : data = Event . get_counts ( crawler ) data [ 'last_active' ] = crawler . last_run data [ 'total_ops' ] = crawler . op_count data [ 'running' ] = crawler . is_running data [ 'crawler' ] = crawler crawlers . append ( data ) return render_template ( 'index.html' , cr... | Generate a list of all crawlers alphabetically with op counts . |
61,044 | def clean_html ( context , data ) : doc = _get_html_document ( context , data ) if doc is None : context . emit ( data = data ) return remove_paths = context . params . get ( 'remove_paths' ) for path in ensure_list ( remove_paths ) : for el in doc . findall ( path ) : el . drop_tree ( ) html_text = html . tostring ( d... | Clean an HTML DOM and store the changed version . |
61,045 | def execute ( cls , stage , state , data , next_allowed_exec_time = None ) : try : context = Context . from_state ( state , stage ) now = datetime . utcnow ( ) if next_allowed_exec_time and now < next_allowed_exec_time : Queue . queue ( stage , state , data , delay = next_allowed_exec_time ) elif context . crawler . di... | Execute the operation rate limiting allowing . |
61,046 | def _recursive_upsert ( context , params , data ) : children = params . get ( "children" , { } ) nested_calls = [ ] for child_params in children : key = child_params . get ( "key" ) child_data_list = ensure_list ( data . pop ( key ) ) if isinstance ( child_data_list , dict ) : child_data_list = [ child_data_list ] if n... | Insert or update nested dicts recursively into db tables |
61,047 | def db ( context , data ) : table = context . params . get ( "table" , context . crawler . name ) params = context . params params [ "table" ] = table _recursive_upsert ( context , params , data ) | Insert or update data as a row into specified db table |
61,048 | def cli ( debug , cache , incremental ) : settings . HTTP_CACHE = cache settings . INCREMENTAL = incremental settings . DEBUG = debug if settings . DEBUG : logging . basicConfig ( level = logging . DEBUG ) else : logging . basicConfig ( level = logging . INFO ) init_memorious ( ) | Crawler framework for documents and structured scrapers . |
61,049 | def run ( crawler ) : crawler = get_crawler ( crawler ) crawler . run ( ) if is_sync_mode ( ) : TaskRunner . run_sync ( ) | Run a specified crawler . |
61,050 | def index ( ) : crawler_list = [ ] for crawler in manager : is_due = 'yes' if crawler . check_due ( ) else 'no' if crawler . disabled : is_due = 'off' crawler_list . append ( [ crawler . name , crawler . description , crawler . schedule , is_due , Queue . size ( crawler ) ] ) headers = [ 'Name' , 'Description' , 'Sched... | List the available crawlers . |
61,051 | def scheduled ( wait = False ) : manager . run_scheduled ( ) while wait : manager . run_scheduled ( ) time . sleep ( settings . SCHEDULER_INTERVAL ) | Run crawlers that are due . |
61,052 | def _get_directory_path ( context ) : path = os . path . join ( settings . BASE_PATH , 'store' ) path = context . params . get ( 'path' , path ) path = os . path . join ( path , context . crawler . name ) path = os . path . abspath ( os . path . expandvars ( path ) ) try : os . makedirs ( path ) except Exception : pass... | Get the storage path fro the output . |
61,053 | def directory ( context , data ) : with context . http . rehash ( data ) as result : if not result . ok : return content_hash = data . get ( 'content_hash' ) if content_hash is None : context . emit_warning ( "No content hash in data." ) return path = _get_directory_path ( context ) file_name = data . get ( 'file_name'... | Store the collected files to a given directory . |
61,054 | def seed ( context , data ) : for key in ( 'url' , 'urls' ) : for url in ensure_list ( context . params . get ( key ) ) : url = url % data context . emit ( data = { 'url' : url } ) | Initialize a crawler with a set of seed URLs . |
61,055 | def enumerate ( context , data ) : items = ensure_list ( context . params . get ( 'items' ) ) for item in items : data [ 'item' ] = item context . emit ( data = data ) | Iterate through a set of items and emit each one of them . |
61,056 | def sequence ( context , data ) : number = data . get ( 'number' , context . params . get ( 'start' , 1 ) ) stop = context . params . get ( 'stop' ) step = context . params . get ( 'step' , 1 ) delay = context . params . get ( 'delay' ) prefix = context . params . get ( 'tag' ) while True : tag = None if prefix is None... | Generate a sequence of numbers . |
61,057 | def fetch ( self ) : if self . _file_path is not None : return self . _file_path temp_path = self . context . work_path if self . _content_hash is not None : self . _file_path = storage . load_file ( self . _content_hash , temp_path = temp_path ) return self . _file_path if self . response is not None : self . _file_pa... | Lazily trigger download of the data when requested . |
61,058 | def make_key ( * criteria ) : criteria = [ stringify ( c ) for c in criteria ] criteria = [ c for c in criteria if c is not None ] if len ( criteria ) : return ':' . join ( criteria ) | Make a string key out of many criteria . |
61,059 | def random_filename ( path = None ) : filename = uuid4 ( ) . hex if path is not None : filename = os . path . join ( path , filename ) return filename | Make a UUID - based file name which is extremely unlikely to exist already . |
61,060 | def sample_vMF ( mu , kappa , num_samples ) : dim = len ( mu ) result = np . zeros ( ( num_samples , dim ) ) for nn in range ( num_samples ) : w = _sample_weight ( kappa , dim ) v = _sample_orthonormal_to ( mu ) result [ nn , : ] = v * np . sqrt ( 1. - w ** 2 ) + w * mu return result | Generate num_samples N - dimensional samples from von Mises Fisher distribution around center mu \ in R^N with concentration kappa . |
61,061 | def _sample_weight ( kappa , dim ) : dim = dim - 1 b = dim / ( np . sqrt ( 4. * kappa ** 2 + dim ** 2 ) + 2 * kappa ) x = ( 1. - b ) / ( 1. + b ) c = kappa * x + dim * np . log ( 1 - x ** 2 ) while True : z = np . random . beta ( dim / 2. , dim / 2. ) w = ( 1. - ( 1. + b ) * z ) / ( 1. - ( 1. - b ) * z ) u = np . rando... | Rejection sampling scheme for sampling distance from center on surface of the sphere . |
61,062 | def _sample_orthonormal_to ( mu ) : v = np . random . randn ( mu . shape [ 0 ] ) proj_mu_v = mu * np . dot ( mu , v ) / np . linalg . norm ( mu ) orthto = v - proj_mu_v return orthto / np . linalg . norm ( orthto ) | Sample point on sphere orthogonal to mu . |
61,063 | def _spherical_kmeans_single_lloyd ( X , n_clusters , sample_weight = None , max_iter = 300 , init = "k-means++" , verbose = False , x_squared_norms = None , random_state = None , tol = 1e-4 , precompute_distances = True , ) : random_state = check_random_state ( random_state ) sample_weight = _check_sample_weight ( X ,... | Modified from sklearn . cluster . k_means_ . k_means_single_lloyd . |
61,064 | def spherical_k_means ( X , n_clusters , sample_weight = None , init = "k-means++" , n_init = 10 , max_iter = 300 , verbose = False , tol = 1e-4 , random_state = None , copy_x = True , n_jobs = 1 , algorithm = "auto" , return_n_iter = False , ) : if n_init <= 0 : raise ValueError ( "Invalid number of initializations." ... | Modified from sklearn . cluster . k_means_ . k_means . |
61,065 | def fit ( self , X , y = None , sample_weight = None ) : if self . normalize : X = normalize ( X ) random_state = check_random_state ( self . random_state ) self . cluster_centers_ , self . labels_ , self . inertia_ , self . n_iter_ = spherical_k_means ( X , n_clusters = self . n_clusters , sample_weight = sample_weigh... | Compute k - means clustering . |
61,066 | def _inertia_from_labels ( X , centers , labels ) : n_examples , n_features = X . shape inertia = np . zeros ( ( n_examples , ) ) for ee in range ( n_examples ) : inertia [ ee ] = 1 - X [ ee , : ] . dot ( centers [ int ( labels [ ee ] ) , : ] . T ) return np . sum ( inertia ) | Compute inertia with cosine distance using known labels . |
61,067 | def _labels_inertia ( X , centers ) : n_examples , n_features = X . shape n_clusters , n_features = centers . shape labels = np . zeros ( ( n_examples , ) ) inertia = np . zeros ( ( n_examples , ) ) for ee in range ( n_examples ) : dists = np . zeros ( ( n_clusters , ) ) for cc in range ( n_clusters ) : dists [ cc ] = ... | Compute labels and inertia with cosine distance . |
61,068 | def _S ( kappa , alpha , beta ) : kappa = 1. * np . abs ( kappa ) alpha = 1. * alpha beta = 1. * np . abs ( beta ) a_plus_b = alpha + beta u = np . sqrt ( kappa ** 2 + beta ** 2 ) if alpha == 0 : alpha_scale = 0 else : alpha_scale = alpha * np . log ( ( alpha + u ) / a_plus_b ) return u - beta - alpha_scale | Compute the antiderivative of the Amos - type bound G on the modified Bessel function ratio . |
61,069 | def _init_unit_centers ( X , n_clusters , random_state , init ) : n_examples , n_features = np . shape ( X ) if isinstance ( init , np . ndarray ) : n_init_clusters , n_init_features = init . shape assert n_init_clusters == n_clusters assert n_init_features == n_features centers = init for cc in range ( n_clusters ) : ... | Initializes unit norm centers . |
61,070 | def _expectation ( X , centers , weights , concentrations , posterior_type = "soft" ) : n_examples , n_features = np . shape ( X ) n_clusters , _ = centers . shape if n_features <= 50 : vmf_f = _vmf_log else : vmf_f = _vmf_log_asymptotic f_log = np . zeros ( ( n_clusters , n_examples ) ) for cc in range ( n_clusters ) ... | Compute the log - likelihood of each datapoint being in each cluster . |
61,071 | def _maximization ( X , posterior , force_weights = None ) : n_examples , n_features = X . shape n_clusters , n_examples = posterior . shape concentrations = np . zeros ( ( n_clusters , ) ) centers = np . zeros ( ( n_clusters , n_features ) ) if force_weights is None : weights = np . zeros ( ( n_clusters , ) ) for cc i... | Estimate new centers weights and concentrations from |
61,072 | def _movMF ( X , n_clusters , posterior_type = "soft" , force_weights = None , max_iter = 300 , verbose = False , init = "random-class" , random_state = None , tol = 1e-6 , ) : random_state = check_random_state ( random_state ) n_examples , n_features = np . shape ( X ) centers = _init_unit_centers ( X , n_clusters , r... | Mixture of von Mises Fisher clustering . |
61,073 | def movMF ( X , n_clusters , posterior_type = "soft" , force_weights = None , n_init = 10 , n_jobs = 1 , max_iter = 300 , verbose = False , init = "random-class" , random_state = None , tol = 1e-6 , copy_x = True , ) : if n_init <= 0 : raise ValueError ( "Invalid number of initializations." " n_init=%d must be bigger t... | Wrapper for parallelization of _movMF and running n_init times . |
61,074 | def _check_fit_data ( self , X ) : X = check_array ( X , accept_sparse = "csr" , dtype = [ np . float64 , np . float32 ] ) n_samples , n_features = X . shape if X . shape [ 0 ] < self . n_clusters : raise ValueError ( "n_samples=%d should be >= n_clusters=%d" % ( X . shape [ 0 ] , self . n_clusters ) ) for ee in range ... | Verify that the number of samples given is larger than k |
61,075 | def fit ( self , X , y = None ) : if self . normalize : X = normalize ( X ) self . _check_force_weights ( ) random_state = check_random_state ( self . random_state ) X = self . _check_fit_data ( X ) ( self . cluster_centers_ , self . labels_ , self . inertia_ , self . weights_ , self . concentrations_ , self . posterio... | Compute mixture of von Mises Fisher clustering . |
61,076 | def transform ( self , X , y = None ) : if self . normalize : X = normalize ( X ) check_is_fitted ( self , "cluster_centers_" ) X = self . _check_test_data ( X ) return self . _transform ( X ) | Transform X to a cluster - distance space . In the new space each dimension is the cosine distance to the cluster centers . Note that even if X is sparse the array returned by transform will typically be dense . |
61,077 | def log_likelihood ( covariance , precision ) : assert covariance . shape == precision . shape dim , _ = precision . shape log_likelihood_ = ( - np . sum ( covariance * precision ) + fast_logdet ( precision ) - dim * np . log ( 2 * np . pi ) ) log_likelihood_ /= 2. return log_likelihood_ | Computes the log - likelihood between the covariance and precision estimate . |
61,078 | def kl_loss ( covariance , precision ) : assert covariance . shape == precision . shape dim , _ = precision . shape logdet_p_dot_c = fast_logdet ( np . dot ( precision , covariance ) ) return 0.5 * ( np . sum ( precision * covariance ) - logdet_p_dot_c - dim ) | Computes the KL divergence between precision estimate and reference covariance . |
61,079 | def ebic ( covariance , precision , n_samples , n_features , gamma = 0 ) : l_theta = - np . sum ( covariance * precision ) + fast_logdet ( precision ) l_theta *= n_features / 2. if np . isinf ( l_theta ) or np . isnan ( l_theta ) : return 1e10 mask = np . abs ( precision . flat ) > np . finfo ( precision . dtype ) . ep... | Extended Bayesian Information Criteria for model selection . |
61,080 | def lattice ( prng , n_features , alpha , random_sign = False , low = 0.3 , high = 0.7 ) : degree = int ( 1 + np . round ( alpha * n_features / 2. ) ) if random_sign : sign_row = - 1.0 * np . ones ( degree ) + 2 * ( prng . uniform ( low = 0 , high = 1 , size = degree ) > .5 ) else : sign_row = - 1.0 * np . ones ( degre... | Returns the adjacency matrix for a lattice network . |
61,081 | def _to_diagonally_dominant ( mat ) : mat += np . diag ( np . sum ( mat != 0 , axis = 1 ) + 0.01 ) return mat | Make matrix unweighted diagonally dominant using the Laplacian . |
61,082 | def _to_diagonally_dominant_weighted ( mat ) : mat += np . diag ( np . sum ( np . abs ( mat ) , axis = 1 ) + 0.01 ) return mat | Make matrix weighted diagonally dominant using the Laplacian . |
61,083 | def _rescale_to_unit_diagonals ( mat ) : d = np . sqrt ( np . diag ( mat ) ) mat /= d mat /= d [ : , np . newaxis ] return mat | Rescale matrix to have unit diagonals . |
61,084 | def create ( self , n_features , alpha ) : n_block_features = int ( np . floor ( 1. * n_features / self . n_blocks ) ) if n_block_features * self . n_blocks != n_features : raise ValueError ( ( "Error: n_features {} not divisible by n_blocks {}." "Use n_features = n_blocks * int" ) . format ( n_features , self . n_bloc... | Build a new graph with block structure . |
61,085 | def _sample_mvn ( n_samples , cov , prng ) : n_features , _ = cov . shape return prng . multivariate_normal ( np . zeros ( n_features ) , cov , size = n_samples ) | Draw a multivariate normal sample from the graph defined by cov . |
61,086 | def _fully_random_weights ( n_features , lam_scale , prng ) : weights = np . zeros ( ( n_features , n_features ) ) n_off_diag = int ( ( n_features ** 2 - n_features ) / 2 ) weights [ np . triu_indices ( n_features , k = 1 ) ] = 0.1 * lam_scale * prng . randn ( n_off_diag ) + ( 0.25 * lam_scale ) weights [ weights < 0 ]... | Generate a symmetric random matrix with zeros along the diagonal . |
61,087 | def _fix_weights ( weight_fun , * args ) : weights = weight_fun ( * args ) return weights if _check_psd ( weights ) : return weights off_diag_sums = np . sum ( weights , axis = 1 ) mod_mat = np . linalg . inv ( np . sqrt ( np . diag ( off_diag_sums ) ) ) return np . dot ( mod_mat , weights , mod_mat ) | Ensure random weight matrix is valid . |
61,088 | def _fit ( indexed_params , penalization , lam , lam_perturb , lam_scale_ , estimator , penalty_name , subsample , bootstrap , prng , X = None , ) : index = indexed_params if isinstance ( X , np . ndarray ) : local_X = X else : local_X = X . value n_samples , n_features = local_X . shape prec_is_real = False while not ... | Wrapper function outside of instance for fitting a single model average trial . |
61,089 | def _spark_map ( fun , indexed_param_grid , sc , seed , X_bc ) : def _wrap_random_state ( split_index , partition ) : prng = np . random . RandomState ( seed + split_index ) yield map ( partial ( fun , prng = prng , X = X_bc ) , partition ) par_param_grid = sc . parallelize ( indexed_param_grid ) indexed_results = par_... | We cannot pass a RandomState instance to each spark worker since it will behave identically across partitions . Instead we explictly handle the partitions with a newly seeded instance . |
61,090 | def quic_graph_lasso_ebic_manual ( X , gamma = 0 ) : print ( "QuicGraphicalLasso (manual EBIC) with:" ) print ( " mode: path" ) print ( " gamma: {}" . format ( gamma ) ) model = QuicGraphicalLasso ( lam = 1.0 , mode = "path" , init_method = "cov" , path = np . logspace ( np . log10 ( 0.01 ) , np . log10 ( 1.0 ) , n... | Run QuicGraphicalLasso with mode = path and gamma ; use EBIC criteria for model selection . |
61,091 | def quic_graph_lasso_ebic ( X , gamma = 0 ) : print ( "QuicGraphicalLassoEBIC with:" ) print ( " mode: path" ) print ( " gamma: {}" . format ( gamma ) ) model = QuicGraphicalLassoEBIC ( lam = 1.0 , init_method = "cov" , gamma = gamma ) model . fit ( X ) print ( " len(path lams): {}" . format ( len ( model . path_... | Run QuicGraphicalLassoEBIC with gamma . |
61,092 | def empirical ( X ) : print ( "Empirical" ) cov = np . dot ( X . T , X ) / n_samples return cov , np . linalg . inv ( cov ) | Compute empirical covariance as baseline estimator . |
61,093 | def sk_ledoit_wolf ( X ) : print ( "Ledoit-Wolf (sklearn)" ) lw_cov_ , _ = ledoit_wolf ( X ) lw_prec_ = np . linalg . inv ( lw_cov_ ) return lw_cov_ , lw_prec_ | Estimate inverse covariance via scikit - learn ledoit_wolf function . |
61,094 | def _nonzero_intersection ( m , m_hat ) : n_features , _ = m . shape m_no_diag = m . copy ( ) m_no_diag [ np . diag_indices ( n_features ) ] = 0 m_hat_no_diag = m_hat . copy ( ) m_hat_no_diag [ np . diag_indices ( n_features ) ] = 0 m_hat_nnz = len ( np . nonzero ( m_hat_no_diag . flat ) [ 0 ] ) m_nnz = len ( np . nonz... | Count the number of nonzeros in and between m and m_hat . |
61,095 | def support_false_positive_count ( m , m_hat ) : m_nnz , m_hat_nnz , intersection_nnz = _nonzero_intersection ( m , m_hat ) return int ( ( m_hat_nnz - intersection_nnz ) / 2.0 ) | Count the number of false positive support elements in m_hat in one triangle not including the diagonal . |
61,096 | def support_false_negative_count ( m , m_hat ) : m_nnz , m_hat_nnz , intersection_nnz = _nonzero_intersection ( m , m_hat ) return int ( ( m_nnz - intersection_nnz ) / 2.0 ) | Count the number of false negative support elements in m_hat in one triangle not including the diagonal . |
61,097 | def support_difference_count ( m , m_hat ) : m_nnz , m_hat_nnz , intersection_nnz = _nonzero_intersection ( m , m_hat ) return int ( ( m_nnz + m_hat_nnz - ( 2 * intersection_nnz ) ) / 2.0 ) | Count the number of different elements in the support in one triangle not including the diagonal . |
61,098 | def has_exact_support ( m , m_hat ) : m_nnz , m_hat_nnz , intersection_nnz = _nonzero_intersection ( m , m_hat ) return int ( ( m_nnz + m_hat_nnz - ( 2 * intersection_nnz ) ) == 0 ) | Returns 1 if support_difference_count is zero 0 else . |
61,099 | def has_approx_support ( m , m_hat , prob = 0.01 ) : m_nz = np . flatnonzero ( np . triu ( m , 1 ) ) m_hat_nz = np . flatnonzero ( np . triu ( m_hat , 1 ) ) upper_diagonal_mask = np . flatnonzero ( np . triu ( np . ones ( m . shape ) , 1 ) ) not_m_nz = np . setdiff1d ( upper_diagonal_mask , m_nz ) intersection = np . i... | Returns 1 if model selection error is less than or equal to prob rate 0 else . |
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