idx int64 0 63k | question stringlengths 61 4.03k | target stringlengths 6 1.23k |
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8,400 | def assert_tz_offset ( tz ) : tz_offset = get_tz_offset ( tz ) system_offset = get_system_offset ( ) if tz_offset != system_offset : msg = ( 'Timezone offset does not match system offset: {0} != {1}. ' 'Please, check your config files.' ) . format ( tz_offset , system_offset ) raise ValueError ( msg ) | Assert that system s timezone offset equals to the timezone offset found . |
8,401 | def resources ( self ) : if self . _resources_objects is not None : return self . _resources_objects resources = self . parent . parent . resources resources = [ resources [ name ] for name in self . _resources ] self . _resources_objects = resources return resources | Return all the resources which this function refer to . |
8,402 | def update ( self ) : if self . _owner_changed : self . update_owner ( self . owner ) self . _resources = [ res . name for res in self . resources ] return self . parent . update ( self ) | Update this function . |
8,403 | def table_creator ( func ) : def method ( self , table_name , ** kwargs ) : if self . odps . exist_table ( table_name ) : return if kwargs . get ( 'project' , self . odps . project ) != self . odps . project : tunnel = TableTunnel ( self . odps , project = kwargs [ 'project' ] ) else : tunnel = self . tunnel func ( sel... | Decorator for table creating method |
8,404 | def confusion_matrix ( df , col_true = None , col_pred = None ) : if not col_pred : col_pred = get_field_name_by_role ( df , FieldRole . PREDICTED_CLASS ) return _run_cm_node ( df , col_true , col_pred ) [ 0 ] | Compute confusion matrix of a predicted DataFrame . |
8,405 | def accuracy_score ( df , col_true = None , col_pred = None , normalize = True ) : if not col_pred : col_pred = get_field_name_by_role ( df , FieldRole . PREDICTED_CLASS ) mat , _ = _run_cm_node ( df , col_true , col_pred ) if np is not None : acc_count = np . sum ( np . diag ( mat ) ) if not normalize : return acc_cou... | Compute accuracy of a predicted DataFrame . |
8,406 | def fbeta_score ( df , col_true = None , col_pred = 'precision_result' , beta = 1.0 , pos_label = 1 , average = None ) : r if not col_pred : col_pred = get_field_name_by_role ( df , FieldRole . PREDICTED_CLASS ) mat , label_list = _run_cm_node ( df , col_true , col_pred ) class_dict = dict ( ( label , idx ) for idx , l... | r Compute f - beta score of a predicted DataFrame . f - beta is defined as |
8,407 | def f1_score ( df , col_true = None , col_pred = 'precision_result' , pos_label = 1 , average = None ) : r if not col_pred : col_pred = get_field_name_by_role ( df , FieldRole . PREDICTED_CLASS ) return fbeta_score ( df , col_true , col_pred , pos_label = pos_label , average = average ) | r Compute f - 1 score of a predicted DataFrame . f - 1 is defined as |
8,408 | def average_precision_score ( df , col_true = None , col_pred = None , col_scores = None , pos_label = 1 ) : if not col_pred : col_pred = get_field_name_by_role ( df , FieldRole . PREDICTED_CLASS ) if not col_scores : col_scores = get_field_name_by_role ( df , FieldRole . PREDICTED_SCORE ) thresh , tp , fn , tn , fp = ... | Compute average precision score i . e . the area under precision - recall curve . Note that this method will trigger the defined flow to execute . |
8,409 | def _var_uint64_byte_size_no_tag ( uint64 ) : if uint64 > UINT64_MAX : raise errors . EncodeError ( 'Value out of range: %d' % uint64 ) bytes = 1 while uint64 > 0x7f : bytes += 1 uint64 >>= 7 return bytes | Returns the bytes required to serialize a single varint . uint64 must be unsigned . |
8,410 | def train ( self , * args , ** kwargs ) : objs = self . _do_transform ( * args , ** kwargs ) obj_list = [ objs , ] if not isinstance ( objs , Iterable ) else objs for obj in obj_list : if not isinstance ( obj , ODPSModelExpr ) : continue for meta in [ 'predictor' , 'recommender' ] : if meta not in self . _metas : conti... | Perform training on a DataFrame . The label field is specified by the label_field method . |
8,411 | def wait ( fs , timeout = None , return_when = ALL_COMPLETED ) : with _AcquireFutures ( fs ) : done = set ( f for f in fs if f . _state in [ CANCELLED_AND_NOTIFIED , FINISHED ] ) not_done = set ( fs ) - done if ( return_when == FIRST_COMPLETED ) and done : return DoneAndNotDoneFutures ( done , not_done ) elif ( return_... | Wait for the futures in the given sequence to complete . |
8,412 | def cancel ( self ) : with self . _condition : if self . _state in [ RUNNING , FINISHED ] : return False if self . _state in [ CANCELLED , CANCELLED_AND_NOTIFIED ] : return True self . _state = CANCELLED self . _condition . notify_all ( ) self . _invoke_callbacks ( ) return True | Cancel the future if possible . |
8,413 | def add_done_callback ( self , fn ) : with self . _condition : if self . _state not in [ CANCELLED , CANCELLED_AND_NOTIFIED , FINISHED ] : self . _done_callbacks . append ( fn ) return fn ( self ) | Attaches a callable that will be called when the future finishes . |
8,414 | def set_exception_info ( self , exception , traceback ) : with self . _condition : self . _exception = exception self . _traceback = traceback self . _state = FINISHED for waiter in self . _waiters : waiter . add_exception ( self ) self . _condition . notify_all ( ) self . _invoke_callbacks ( ) | Sets the result of the future as being the given exception and traceback . |
8,415 | def persist ( self , name , project = None , drop_model = False , ** kwargs ) : return super ( ODPSModelExpr , self ) . persist ( name , project = project , drop_model = drop_model , ** kwargs ) | Persist the execution into a new model . |
8,416 | def quantile ( expr , prob = None , ** kw ) : prob = kw . get ( '_prob' , prob ) output_type = _stats_type ( expr ) if isinstance ( prob , ( list , set ) ) and not isinstance ( expr , GroupBy ) : output_type = types . List ( output_type ) return _reduction ( expr , Quantile , output_type , _prob = prob ) | Percentile value . |
8,417 | def nunique ( expr ) : output_type = types . int64 if isinstance ( expr , SequenceExpr ) : return NUnique ( _value_type = output_type , _inputs = [ expr ] ) elif isinstance ( expr , SequenceGroupBy ) : return GroupedNUnique ( _data_type = output_type , _inputs = [ expr . to_column ( ) ] , _grouped = expr . input ) elif... | The distinct count . |
8,418 | def cat ( expr , others = None , sep = None , na_rep = None ) : if others is not None : from . strings import _cat as cat_str return cat_str ( expr , others , sep = sep , na_rep = na_rep ) return _cat ( expr , sep = sep , na_rep = na_rep ) | Concatenate strings in sequence with given separator |
8,419 | def moment ( expr , order , central = False ) : if not isinstance ( order , six . integer_types ) : raise ValueError ( 'Only integer-ordered moments are supported.' ) if order < 0 : raise ValueError ( 'Only non-negative orders are supported.' ) output_type = _stats_type ( expr ) return _reduction ( expr , Moment , outp... | Calculate the n - th order moment of the sequence |
8,420 | def stop ( self ) : instance_status = Instance . InstanceStatus ( status = 'Terminated' ) xml_content = instance_status . serialize ( ) headers = { 'Content-Type' : 'application/xml' } self . _client . put ( self . resource ( ) , xml_content , headers = headers ) | Stop this instance . |
8,421 | def get_task_results ( self ) : results = self . get_task_results_without_format ( ) if options . tunnel . string_as_binary : return compat . OrderedDict ( [ ( k , bytes ( result ) ) for k , result in six . iteritems ( results ) ] ) else : return compat . OrderedDict ( [ ( k , str ( result ) ) for k , result in six . i... | Get all the task results . |
8,422 | def get_task_summary ( self , task_name ) : params = { 'instancesummary' : '' , 'taskname' : task_name } resp = self . _client . get ( self . resource ( ) , params = params ) map_reduce = resp . json ( ) . get ( 'Instance' ) if map_reduce : json_summary = map_reduce . get ( 'JsonSummary' ) if json_summary : summary = I... | Get a task s summary mostly used for MapReduce . |
8,423 | def get_task_statuses ( self ) : params = { 'taskstatus' : '' } resp = self . _client . get ( self . resource ( ) , params = params ) self . parse ( self . _client , resp , obj = self ) return dict ( [ ( task . name , task ) for task in self . _tasks ] ) | Get all tasks statuses |
8,424 | def get_task_cost ( self , task_name ) : summary = self . get_task_summary ( task_name ) if summary is None : return None if 'Cost' in summary : task_cost = summary [ 'Cost' ] cpu_cost = task_cost . get ( 'CPU' ) memory = task_cost . get ( 'Memory' ) input_size = task_cost . get ( 'Input' ) return Instance . TaskCost (... | Get task cost |
8,425 | def get_task_info ( self , task_name , key ) : params = OrderedDict ( [ ( 'info' , '' ) , ( 'taskname' , task_name ) , ( 'key' , key ) ] ) resp = self . _client . get ( self . resource ( ) , params = params ) return resp . text | Get task related information . |
8,426 | def put_task_info ( self , task_name , key , value ) : params = OrderedDict ( [ ( 'info' , '' ) , ( 'taskname' , task_name ) ] ) headers = { 'Content-Type' : 'application/xml' } body = self . TaskInfo ( key = key , value = value ) . serialize ( ) self . _client . put ( self . resource ( ) , params = params , headers = ... | Put information into a task . |
8,427 | def get_task_quota ( self , task_name ) : params = OrderedDict ( [ ( 'instancequota' , '' ) , ( 'taskname' , task_name ) ] ) resp = self . _client . get ( self . resource ( ) , params = params ) return json . loads ( resp . text ) | Get queueing info of the task . Note that time between two calls should larger than 30 seconds otherwise empty dict is returned . |
8,428 | def get_sql_task_cost ( self ) : resp = self . get_task_result ( self . get_task_names ( ) [ 0 ] ) cost = json . loads ( resp ) sql_cost = cost [ 'Cost' ] [ 'SQL' ] udf_num = sql_cost . get ( 'UDF' ) complexity = sql_cost . get ( 'Complexity' ) input_size = sql_cost . get ( 'Input' ) return Instance . SQLCost ( udf_num... | Get cost information of the sql task . Including input data size number of UDF Complexity of the sql task |
8,429 | def is_terminated ( self , retry = False ) : retry_num = options . retry_times while retry_num > 0 : try : return self . status == Instance . Status . TERMINATED except ( errors . InternalServerError , errors . RequestTimeTooSkewed ) : retry_num -= 1 if not retry or retry_num <= 0 : raise | If this instance has finished or not . |
8,430 | def is_successful ( self , retry = False ) : if not self . is_terminated ( retry = retry ) : return False retry_num = options . retry_times while retry_num > 0 : try : statuses = self . get_task_statuses ( ) return all ( task . status == Instance . Task . TaskStatus . SUCCESS for task in statuses . values ( ) ) except ... | If the instance runs successfully . |
8,431 | def wait_for_completion ( self , interval = 1 ) : while not self . is_terminated ( retry = True ) : try : time . sleep ( interval ) except KeyboardInterrupt : break | Wait for the instance to complete and neglect the consequence . |
8,432 | def wait_for_success ( self , interval = 1 ) : self . wait_for_completion ( interval = interval ) if not self . is_successful ( retry = True ) : for task_name , task in six . iteritems ( self . get_task_statuses ( ) ) : exc = None if task . status == Instance . Task . TaskStatus . FAILED : exc = errors . parse_instance... | Wait for instance to complete and check if the instance is successful . |
8,433 | def get_task_progress ( self , task_name ) : params = { 'instanceprogress' : task_name , 'taskname' : task_name } resp = self . _client . get ( self . resource ( ) , params = params ) return Instance . Task . TaskProgress . parse ( self . _client , resp ) | Get task s current progress |
8,434 | def get_task_detail ( self , task_name ) : def _get_detail ( ) : from . . compat import json params = { 'instancedetail' : '' , 'taskname' : task_name } resp = self . _client . get ( self . resource ( ) , params = params ) return json . loads ( resp . text if six . PY3 else resp . content , object_pairs_hook = OrderedD... | Get task s detail |
8,435 | def get_task_detail2 ( self , task_name ) : from . . compat import json params = { 'detail' : '' , 'taskname' : task_name } resp = self . _client . get ( self . resource ( ) , params = params ) res = resp . text if six . PY3 else resp . content try : return json . loads ( res , object_pairs_hook = OrderedDict ) except ... | Get task s detail v2 |
8,436 | def get_logview_address ( self , hours = None ) : hours = hours or options . log_view_hours project = self . project url = '%s/authorization' % project . resource ( ) policy = { 'expires_in_hours' : hours , 'policy' : { 'Statement' : [ { 'Action' : [ 'odps:Read' ] , 'Effect' : 'Allow' , 'Resource' : 'acs:odps:*:project... | Get logview address of the instance object by hours . |
8,437 | def open_reader ( self , * args , ** kwargs ) : use_tunnel = kwargs . get ( 'use_tunnel' , kwargs . get ( 'tunnel' ) ) auto_fallback_result = use_tunnel is None if use_tunnel is None : use_tunnel = options . tunnel . use_instance_tunnel result_fallback_errors = ( errors . InvalidProjectTable , errors . InvalidArgument ... | Open the reader to read records from the result of the instance . If tunnel is True instance tunnel will be used . Otherwise conventional routine will be used . If instance tunnel is not available and tunnel is not specified the method will fall back to the conventional routine . Note that the number of records returne... |
8,438 | def in_qtconsole ( ) : try : ip = get_ipython ( ) front_end = ( ip . config . get ( 'KernelApp' , { } ) . get ( 'parent_appname' , "" ) or ip . config . get ( 'IPKernelApp' , { } ) . get ( 'parent_appname' , "" ) ) if 'qtconsole' in front_end . lower ( ) : return True except : return False return False | check if we re inside an IPython qtconsole |
8,439 | def isatty ( file ) : if ( multiprocessing . current_process ( ) . name != 'MainProcess' or threading . current_thread ( ) . getName ( ) != 'MainThread' ) : return False if hasattr ( file , 'isatty' ) : return file . isatty ( ) elif ( OutStream is not None and isinstance ( file , ( OutStream , IPythonIOStream ) ) and (... | Returns True if file is a tty . |
8,440 | def color_print ( * args , ** kwargs ) : file = kwargs . get ( 'file' , _get_stdout ( ) ) end = kwargs . get ( 'end' , '\n' ) write = file . write if isatty ( file ) and options . console . use_color : for i in range ( 0 , len ( args ) , 2 ) : msg = args [ i ] if i + 1 == len ( args ) : color = '' else : color = args [... | Prints colors and styles to the terminal uses ANSI escape sequences . |
8,441 | def human_time ( seconds ) : units = [ ( 'y' , 60 * 60 * 24 * 7 * 52 ) , ( 'w' , 60 * 60 * 24 * 7 ) , ( 'd' , 60 * 60 * 24 ) , ( 'h' , 60 * 60 ) , ( 'm' , 60 ) , ( 's' , 1 ) , ] seconds = int ( seconds ) if seconds < 60 : return ' {0:2d}s' . format ( seconds ) for i in range ( len ( units ) - 1 ) : unit1 , limit1 = u... | Returns a human - friendly time string that is always exactly 6 characters long . |
8,442 | def human_file_size ( size ) : suffixes = ' kMGTPEZY' if size == 0 : num_scale = 0 else : num_scale = int ( math . floor ( math . log ( size ) / math . log ( 1000 ) ) ) num_scale = max ( num_scale , 0 ) if num_scale >= len ( suffixes ) : suffix = '?' else : suffix = suffixes [ num_scale ] num_scale = int ( math . pow (... | Returns a human - friendly string representing a file size that is 2 - 4 characters long . |
8,443 | def update ( self , value = None ) : if value is None : value = self . _current_value + 1 self . _current_value = value if self . _ipython_widget : try : self . _update_ipython_widget ( value ) except RuntimeError : pass else : self . _update_console ( value ) | Update progress bar via the console or notebook accordingly . |
8,444 | def map ( cls , function , items , multiprocess = False , file = None ) : results = [ ] if file is None : file = _get_stdout ( ) with cls ( len ( items ) , file = file ) as bar : step_size = max ( 200 , bar . _bar_length ) steps = max ( int ( float ( len ( items ) ) / step_size ) , 1 ) if not multiprocess : for i , ite... | Does a map operation while displaying a progress bar with percentage complete . |
8,445 | def hll_count ( expr , error_rate = 0.01 , splitter = None ) : with open ( os . path . join ( path , 'lib' , 'hll.py' ) ) as hll_file : local = { } six . exec_ ( hll_file . read ( ) , local ) HyperLogLog = local [ 'HyperLogLog' ] return expr . agg ( HyperLogLog , rtype = types . int64 , args = ( error_rate , splitter )... | Calculate HyperLogLog count |
8,446 | def bloomfilter ( collection , on , column , capacity = 3000 , error_rate = 0.01 ) : if not isinstance ( column , Column ) : raise TypeError ( 'bloomfilter can only filter on the column of a collection' ) with open ( os . path . join ( path , 'lib' , 'bloomfilter.py' ) ) as bloomfilter_file : local = { } six . exec_ ( ... | Filter collection on the on sequence by BloomFilter built by column |
8,447 | def cumsum ( expr , sort = None , ascending = True , unique = False , preceding = None , following = None ) : if expr . _data_type == types . boolean : output_type = types . int64 else : output_type = expr . _data_type return _cumulative_op ( expr , CumSum , sort = sort , ascending = ascending , unique = unique , prece... | Calculate cumulative summation of a sequence expression . |
8,448 | def cummax ( expr , sort = None , ascending = True , unique = False , preceding = None , following = None ) : return _cumulative_op ( expr , CumMax , sort = sort , ascending = ascending , unique = unique , preceding = preceding , following = following ) | Calculate cumulative maximum of a sequence expression . |
8,449 | def cummin ( expr , sort = None , ascending = True , unique = False , preceding = None , following = None ) : return _cumulative_op ( expr , CumMin , sort = sort , ascending = ascending , unique = unique , preceding = preceding , following = following ) | Calculate cumulative minimum of a sequence expression . |
8,450 | def cummean ( expr , sort = None , ascending = True , unique = False , preceding = None , following = None ) : data_type = _stats_type ( expr ) return _cumulative_op ( expr , CumMean , sort = sort , ascending = ascending , unique = unique , preceding = preceding , following = following , data_type = data_type ) | Calculate cumulative mean of a sequence expression . |
8,451 | def cummedian ( expr , sort = None , ascending = True , unique = False , preceding = None , following = None ) : data_type = _stats_type ( expr ) return _cumulative_op ( expr , CumMedian , sort = sort , ascending = ascending , unique = unique , preceding = preceding , following = following , data_type = data_type ) | Calculate cumulative median of a sequence expression . |
8,452 | def cumcount ( expr , sort = None , ascending = True , unique = False , preceding = None , following = None ) : data_type = types . int64 return _cumulative_op ( expr , CumCount , sort = sort , ascending = ascending , unique = unique , preceding = preceding , following = following , data_type = data_type ) | Calculate cumulative count of a sequence expression . |
8,453 | def cumstd ( expr , sort = None , ascending = True , unique = False , preceding = None , following = None ) : data_type = _stats_type ( expr ) return _cumulative_op ( expr , CumStd , sort = sort , ascending = ascending , unique = unique , preceding = preceding , following = following , data_type = data_type ) | Calculate cumulative standard deviation of a sequence expression . |
8,454 | def nth_value ( expr , nth , skip_nulls = False , sort = None , ascending = True ) : return _cumulative_op ( expr , NthValue , data_type = expr . _data_type , sort = sort , ascending = ascending , _nth = nth , _skip_nulls = skip_nulls ) | Get nth value of a grouped and sorted expression . |
8,455 | def rank ( expr , sort = None , ascending = True ) : return _rank_op ( expr , Rank , types . int64 , sort = sort , ascending = ascending ) | Calculate rank of a sequence expression . |
8,456 | def dense_rank ( expr , sort = None , ascending = True ) : return _rank_op ( expr , DenseRank , types . int64 , sort = sort , ascending = ascending ) | Calculate dense rank of a sequence expression . |
8,457 | def percent_rank ( expr , sort = None , ascending = True ) : return _rank_op ( expr , PercentRank , types . float64 , sort = sort , ascending = ascending ) | Calculate percentage rank of a sequence expression . |
8,458 | def row_number ( expr , sort = None , ascending = True ) : return _rank_op ( expr , RowNumber , types . int64 , sort = sort , ascending = ascending ) | Calculate row number of a sequence expression . |
8,459 | def qcut ( expr , bins , labels = False , sort = None , ascending = True ) : if labels is None or labels : raise NotImplementedError ( 'Showing bins or customizing labels not supported' ) return _rank_op ( expr , QCut , types . int64 , sort = sort , ascending = ascending , _bins = bins ) | Get quantile - based bin indices of every element of a grouped and sorted expression . The indices of bins start from 0 . If cuts are not of equal sizes extra items will be appended into the first group . |
8,460 | def cume_dist ( expr , sort = None , ascending = True ) : return _rank_op ( expr , CumeDist , types . float64 , sort = sort , ascending = ascending ) | Calculate cumulative ratio of a sequence expression . |
8,461 | def lag ( expr , offset , default = None , sort = None , ascending = True ) : return _shift_op ( expr , Lag , offset , default = default , sort = sort , ascending = ascending ) | Get value in the row offset rows prior to the current row . |
8,462 | def lead ( expr , offset , default = None , sort = None , ascending = True ) : return _shift_op ( expr , Lead , offset , default = default , sort = sort , ascending = ascending ) | Get value in the row offset rows after to the current row . |
8,463 | def get_model ( self ) : url = self . resource ( ) params = { 'data' : '' } resp = self . _client . get ( url , params = params ) return resp . text | Get PMML text of the current model . Note that model file obtained via this method might be incomplete due to size limitations . |
8,464 | def parse_response ( resp ) : host_id , msg , code = None , None , None try : content = resp . content root = ET . fromstring ( content ) code = root . find ( './Code' ) . text msg = root . find ( './Message' ) . text request_id = root . find ( './RequestId' ) . text host_id = root . find ( './HostId' ) . text except E... | Parses the content of response and returns an exception object . |
8,465 | def throw_if_parsable ( resp ) : e = None try : e = parse_response ( resp ) except : LOG . debug ( utils . stringify_expt ( ) ) if e is not None : raise e if resp . status_code == 404 : raise NoSuchObject ( 'No such object.' ) else : text = resp . text if six . PY3 else resp . content if text : raise ODPSError ( text ,... | Try to parse the content of the response and raise an exception if neccessary . |
8,466 | def merge_data ( * data_frames , ** kwargs ) : from . specialized import build_merge_expr from . . utils import ML_ARG_PREFIX if len ( data_frames ) <= 1 : raise ValueError ( 'Count of DataFrames should be at least 2.' ) norm_data_pairs = [ ] df_tuple = collections . namedtuple ( 'MergeTuple' , 'df cols exclude' ) for ... | Merge DataFrames by column . Number of rows in tables must be the same . |
8,467 | def exclude_fields ( self , * args ) : if not args : raise ValueError ( "Field list cannot be None." ) new_df = copy_df ( self ) fields = _render_field_set ( args ) self . _assert_ml_fields_valid ( * fields ) new_df . _perform_operation ( op . ExcludeFieldsOperation ( fields ) ) return new_df | Exclude one or more fields from feature fields . |
8,468 | def select_features ( self , * args , ** kwargs ) : if not args : raise ValueError ( "Field list cannot be empty." ) augment = kwargs . get ( 'add' , False ) fields = _render_field_set ( args ) self . _assert_ml_fields_valid ( * fields ) return _batch_change_roles ( self , fields , FieldRole . FEATURE , augment ) | Select one or more fields as feature fields . |
8,469 | def weight_field ( self , f ) : if f is None : raise ValueError ( "Field name cannot be None." ) self . _assert_ml_fields_valid ( f ) return _change_singleton_roles ( self , { f : FieldRole . WEIGHT } , clear_feature = True ) | Select one field as the weight field . |
8,470 | def label_field ( self , f ) : if f is None : raise ValueError ( "Label field name cannot be None." ) self . _assert_ml_fields_valid ( f ) return _change_singleton_roles ( self , { _get_field_name ( f ) : FieldRole . LABEL } , clear_feature = True ) | Select one field as the label field . |
8,471 | def continuous ( self , * args ) : new_df = copy_df ( self ) fields = _render_field_set ( args ) self . _assert_ml_fields_valid ( * fields ) new_df . _perform_operation ( op . FieldContinuityOperation ( dict ( ( _get_field_name ( f ) , True ) for f in fields ) ) ) return new_df | Set fields to be continuous . |
8,472 | def discrete ( self , * args ) : new_df = copy_df ( self ) fields = _render_field_set ( args ) self . _assert_ml_fields_valid ( * fields ) new_df . _perform_operation ( op . FieldContinuityOperation ( dict ( ( _get_field_name ( f ) , False ) for f in fields ) ) ) return new_df | Set fields to be discrete . |
8,473 | def roles ( self , clear_features = True , ** field_roles ) : field_roles = dict ( ( k , v . name if isinstance ( v , SequenceExpr ) else v ) for k , v in six . iteritems ( field_roles ) ) self . _assert_ml_fields_valid ( * list ( six . itervalues ( field_roles ) ) ) field_roles = dict ( ( _get_field_name ( f ) , MLFie... | Set roles of fields |
8,474 | def split ( self , frac ) : from . . import preprocess split_obj = getattr ( preprocess , '_Split' ) ( fraction = frac ) return split_obj . transform ( self ) | Split the DataFrame into two DataFrames with certain ratio . |
8,475 | def append_id ( self , id_col_name = 'append_id' , cols = None ) : from . . import preprocess if id_col_name in self . schema : raise ValueError ( 'ID column collides with existing columns.' ) append_id_obj = getattr ( preprocess , '_AppendID' ) ( id_col = id_col_name , selected_cols = cols ) return append_id_obj . tra... | Append an ID column to current DataFrame . |
8,476 | def continuous ( self ) : field_name = self . name new_df = copy_df ( self ) new_df . _perform_operation ( op . FieldContinuityOperation ( { field_name : True } ) ) return new_df | Set sequence to be continuous . |
8,477 | def discrete ( self ) : field_name = self . name new_df = copy_df ( self ) new_df . _perform_operation ( op . FieldContinuityOperation ( { field_name : False } ) ) return new_df | Set sequence to be discrete . |
8,478 | def role ( self , role_name ) : field_name = self . name field_roles = { field_name : MLField . translate_role_name ( role_name ) } if field_roles : return _change_singleton_roles ( self , field_roles , True ) else : return self | Set role of current column |
8,479 | def _init ( self , * args , ** kwargs ) : self . _init_attr ( '_deps' , None ) self . _init_attr ( '_ban_optimize' , False ) self . _init_attr ( '_engine' , None ) self . _init_attr ( '_Expr__execution' , None ) self . _init_attr ( '_need_cache' , False ) self . _init_attr ( '_mem_cache' , False ) if '_id' not in kwarg... | _deps is used for common dependencies . When a expr depend on other exprs and the expr is not calculated from the others the _deps are specified to identify the dependencies . |
8,480 | def compile ( self ) : from . . engines import get_default_engine engine = get_default_engine ( self ) return engine . compile ( self ) | Compile this expression into an ODPS SQL |
8,481 | def persist ( self , name , partitions = None , partition = None , lifecycle = None , project = None , ** kwargs ) : if lifecycle is None and options . lifecycle is not None : lifecycle = options . lifecycle if not name . startswith ( TEMP_TABLE_PREFIX ) else options . temp_lifecycle return self . _handle_delay_call ( ... | Persist the execution into a new table . If partitions not specified will create a new table without partitions if the table does not exist and insert the SQL result into it . If partitions are specified they will be the partition fields of the new table . If partition is specified the data will be inserted into the ex... |
8,482 | def query ( self , expr ) : from . query import CollectionVisitor if not isinstance ( expr , six . string_types ) : raise ValueError ( 'expr must be a string' ) frame = sys . _getframe ( 2 ) . f_locals try : env = frame . copy ( ) finally : del frame visitor = CollectionVisitor ( self , env ) predicate = visitor . eval... | Query the data with a boolean expression . |
8,483 | def filter ( self , * predicates ) : predicates = self . _get_fields ( predicates ) predicate = reduce ( operator . and_ , predicates ) return FilterCollectionExpr ( self , predicate , _schema = self . _schema ) | Filter the data by predicates |
8,484 | def select ( self , * fields , ** kw ) : if len ( fields ) == 1 and isinstance ( fields [ 0 ] , list ) : fields = fields [ 0 ] else : fields = list ( fields ) if kw : def handle ( it ) : it = self . _defunc ( it ) if not isinstance ( it , Expr ) : it = Scalar ( it ) return it fields . extend ( [ handle ( f ) . rename (... | Projection columns . Remember to avoid column names conflict . |
8,485 | def exclude ( self , * fields ) : if len ( fields ) == 1 and isinstance ( fields [ 0 ] , list ) : exclude_fields = fields [ 0 ] else : exclude_fields = list ( fields ) exclude_fields = [ self . _defunc ( it ) for it in exclude_fields ] exclude_fields = [ field . name if not isinstance ( field , six . string_types ) els... | Projection columns which not included in the fields |
8,486 | def head ( self , n = None , ** kwargs ) : if n is None : n = options . display . max_rows return self . _handle_delay_call ( 'execute' , self , head = n , ** kwargs ) | Return the first n rows . Execute at once . |
8,487 | def tail ( self , n = None , ** kwargs ) : if n is None : n = options . display . max_rows return self . _handle_delay_call ( 'execute' , self , tail = n , ** kwargs ) | Return the last n rows . Execute at once . |
8,488 | def to_pandas ( self , wrap = False , ** kwargs ) : try : import pandas as pd except ImportError : raise DependencyNotInstalledError ( 'to_pandas requires `pandas` library' ) def wrapper ( result ) : res = result . values if wrap : from . . import DataFrame return DataFrame ( res , schema = self . schema ) return res r... | Convert to pandas DataFrame . Execute at once . |
8,489 | def view ( self ) : proxied = get_proxied_expr ( self ) kv = dict ( ( attr , getattr ( proxied , attr ) ) for attr in get_attrs ( proxied ) ) return type ( proxied ) ( ** kv ) | Clone a same collection . useful for self - join . |
8,490 | def to_pandas ( self , wrap = False , ** kwargs ) : try : import pandas as pd except ImportError : raise DependencyNotInstalledError ( 'to_pandas requires for `pandas` library' ) def wrapper ( result ) : df = result . values if wrap : from . . import DataFrame df = DataFrame ( df ) return df [ self . name ] return self... | Convert to pandas Series . Execute at once . |
8,491 | def astype ( self , data_type ) : data_type = types . validate_data_type ( data_type ) if data_type == self . _data_type : return self attr_dict = dict ( ) attr_dict [ '_data_type' ] = data_type attr_dict [ '_source_data_type' ] = self . _source_data_type attr_dict [ '_input' ] = self new_sequence = AsTypedSequenceExpr... | Cast to a new data type . |
8,492 | def explode ( expr , * args , ** kwargs ) : if not isinstance ( expr , Column ) : expr = to_collection ( expr ) [ expr . name ] if isinstance ( expr , SequenceExpr ) : dtype = expr . data_type else : dtype = expr . value_type func_name = 'EXPLODE' if args and isinstance ( args [ 0 ] , ( list , tuple , set ) ) : names =... | Expand list or dict data into multiple rows |
8,493 | def _contains ( expr , value ) : return composite_op ( expr , ListContains , df_types . boolean , _value = _scalar ( value ) ) | Check whether certain value is in the inspected list |
8,494 | def _keys ( expr ) : if isinstance ( expr , SequenceExpr ) : dtype = expr . data_type else : dtype = expr . value_type return composite_op ( expr , DictKeys , df_types . List ( dtype . key_type ) ) | Retrieve keys of a dict |
8,495 | def _values ( expr ) : if isinstance ( expr , SequenceExpr ) : dtype = expr . data_type else : dtype = expr . value_type return composite_op ( expr , DictValues , df_types . List ( dtype . value_type ) ) | Retrieve values of a dict |
8,496 | def mean_squared_error ( df , col_true , col_pred = None ) : if not col_pred : col_pred = get_field_name_by_role ( df , FieldRole . PREDICTED_VALUE ) return _run_evaluation_node ( df , col_true , col_pred ) [ 'mse' ] | Compute mean squared error of a predicted DataFrame . |
8,497 | def mean_absolute_error ( df , col_true , col_pred = None ) : if not col_pred : col_pred = get_field_name_by_role ( df , FieldRole . PREDICTED_VALUE ) return _run_evaluation_node ( df , col_true , col_pred ) [ 'mae' ] | Compute mean absolute error of a predicted DataFrame . |
8,498 | def mean_absolute_percentage_error ( df , col_true , col_pred = None ) : if not col_pred : col_pred = get_field_name_by_role ( df , FieldRole . PREDICTED_VALUE ) return _run_evaluation_node ( df , col_true , col_pred ) [ 'mape' ] | Compute mean absolute percentage error of a predicted DataFrame . |
8,499 | def residual_histogram ( df , col_true , col_pred = None ) : if not col_pred : col_pred = get_field_name_by_role ( df , FieldRole . PREDICTED_VALUE ) return _run_evaluation_node ( df , col_true , col_pred ) [ 'hist' ] | Compute histogram of residuals of a predicted DataFrame . |
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