idx int64 0 252k | question stringlengths 48 5.28k | target stringlengths 5 1.23k |
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237,700 | def monitoring ( line , cell = None ) : parser = datalab . utils . commands . CommandParser ( prog = 'monitoring' , description = ( 'Execute various Monitoring-related operations. Use "%monitoring ' '<command> -h" for help on a specific command.' ) ) list_parser = parser . subcommand ( 'list' , 'List the metrics or res... | Implements the monitoring cell magic for ipython notebooks . |
237,701 | def _render_dataframe ( dataframe ) : data = dataframe . to_dict ( orient = 'records' ) fields = dataframe . columns . tolist ( ) return IPython . core . display . HTML ( datalab . utils . commands . HtmlBuilder . render_table ( data , fields ) ) | Helper to render a dataframe as an HTML table . |
237,702 | def _get_job_status ( line ) : try : args = line . strip ( ) . split ( ) job_name = args [ 0 ] job = None if job_name in _local_jobs : job = _local_jobs [ job_name ] else : raise Exception ( 'invalid job %s' % job_name ) if job is not None : error = '' if job . fatal_error is None else str ( job . fatal_error ) data = ... | magic used as an endpoint for client to get job status . |
237,703 | def updated_on ( self ) : s = self . _info . get ( 'updated' , None ) return dateutil . parser . parse ( s ) if s else None | The updated timestamp of the object as a datetime . datetime . |
237,704 | def delete ( self , wait_for_deletion = True ) : if self . exists ( ) : try : self . _api . objects_delete ( self . _bucket , self . _key ) except Exception as e : raise e if wait_for_deletion : for _ in range ( _MAX_POLL_ATTEMPTS ) : objects = Objects ( self . _bucket , prefix = self . key , delimiter = '/' , context ... | Deletes this object from its bucket . |
237,705 | def metadata ( self ) : if self . _info is None : try : self . _info = self . _api . objects_get ( self . _bucket , self . _key ) except Exception as e : raise e return ObjectMetadata ( self . _info ) if self . _info else None | Retrieves metadata about the object . |
237,706 | def read_stream ( self , start_offset = 0 , byte_count = None ) : try : return self . _api . object_download ( self . _bucket , self . _key , start_offset = start_offset , byte_count = byte_count ) except Exception as e : raise e | Reads the content of this object as text . |
237,707 | def read_lines ( self , max_lines = None ) : if max_lines is None : return self . read_stream ( ) . split ( '\n' ) max_to_read = self . metadata . size bytes_to_read = min ( 100 * max_lines , self . metadata . size ) while True : content = self . read_stream ( byte_count = bytes_to_read ) lines = content . split ( '\n'... | Reads the content of this object as text and return a list of lines up to some max . |
237,708 | def sample_to ( self , count , skip_header_rows , strategy , target ) : if sys . version_info . major > 2 : xrange = range if strategy == 'BIGQUERY' : import datalab . bigquery as bq if not self . path . startswith ( 'gs://' ) : raise Exception ( 'Cannot use BIGQUERY if data is not in GCS' ) federated_table = self . _c... | Sample rows from GCS or local file and save results to target file . |
237,709 | def _ParseExample ( self , example_features , example_feature_lists , entries , index ) : features_seen = set ( ) for feature_list , is_feature in zip ( [ example_features , example_feature_lists ] , [ True , False ] ) : sequence_length = None for feature_name in feature_list : if feature_name not in entries : entries ... | Parses data from an example populating a dictionary of feature values . |
237,710 | def _GetEntries ( self , paths , max_entries , iterator_from_file , is_sequence = False ) : entries = { } index = 0 for filepath in paths : reader = iterator_from_file ( filepath ) for record in reader : if is_sequence : sequence_example = tf . train . SequenceExample . FromString ( record ) self . _ParseExample ( sequ... | Extracts examples into a dictionary of feature values . |
237,711 | def _GetTfRecordEntries ( self , path , max_entries , is_sequence , iterator_options ) : return self . _GetEntries ( [ path ] , max_entries , partial ( tf . python_io . tf_record_iterator , options = iterator_options ) , is_sequence ) | Extracts TFRecord examples into a dictionary of feature values . |
237,712 | def buckets_insert ( self , bucket , project_id = None ) : args = { 'project' : project_id if project_id else self . _project_id } data = { 'name' : bucket } url = Api . _ENDPOINT + ( Api . _BUCKET_PATH % '' ) return datalab . utils . Http . request ( url , args = args , data = data , credentials = self . _credentials ... | Issues a request to create a new bucket . |
237,713 | def objects_delete ( self , bucket , key ) : url = Api . _ENDPOINT + ( Api . _OBJECT_PATH % ( bucket , Api . _escape_key ( key ) ) ) datalab . utils . Http . request ( url , method = 'DELETE' , credentials = self . _credentials , raw_response = True ) | Deletes the specified object . |
237,714 | def list ( self , pattern = '*' ) : if self . _descriptors is None : self . _descriptors = self . _client . list_metric_descriptors ( filter_string = self . _filter_string , type_prefix = self . _type_prefix ) return [ metric for metric in self . _descriptors if fnmatch . fnmatch ( metric . type , pattern ) ] | Returns a list of metric descriptors that match the filters . |
237,715 | def _from_dataframe ( dataframe , default_type = 'STRING' ) : type_mapping = { 'i' : 'INTEGER' , 'b' : 'BOOLEAN' , 'f' : 'FLOAT' , 'O' : 'STRING' , 'S' : 'STRING' , 'U' : 'STRING' , 'M' : 'TIMESTAMP' } fields = [ ] for column_name , dtype in dataframe . dtypes . iteritems ( ) : fields . append ( { 'name' : column_name ... | Infer a BigQuery table schema from a Pandas dataframe . Note that if you don t explicitly set the types of the columns in the dataframe they may be of a type that forces coercion to STRING so even though the fields in the dataframe themselves may be numeric the type in the derived schema may not be . Hence it is pruden... |
237,716 | def _from_dict_record ( data ) : return [ Schema . _get_field_entry ( name , value ) for name , value in list ( data . items ( ) ) ] | Infer a BigQuery table schema from a dictionary . If the dictionary has entries that are in turn OrderedDicts these will be turned into RECORD types . Ideally this will be an OrderedDict but it is not required . |
237,717 | def _from_list_record ( data ) : return [ Schema . _get_field_entry ( 'Column%d' % ( i + 1 ) , value ) for i , value in enumerate ( data ) ] | Infer a BigQuery table schema from a list of values . |
237,718 | def _from_record ( data ) : if isinstance ( data , dict ) : return Schema . _from_dict_record ( data ) elif isinstance ( data , list ) : return Schema . _from_list_record ( data ) else : raise Exception ( 'Cannot create a schema from record %s' % str ( data ) ) | Infer a BigQuery table schema from a list of fields or a dictionary . The typeof the elements is used . For a list the field names are simply Column1 Column2 etc . |
237,719 | def create_args ( line , namespace ) : args = [ ] for arg in shlex . split ( line ) : if not arg : continue if arg [ 0 ] == '$' : var_name = arg [ 1 : ] if var_name in namespace : args . append ( ( namespace [ var_name ] ) ) else : raise Exception ( 'Undefined variable referenced in command line: %s' % arg ) else : arg... | Expand any meta - variable references in the argument list . |
237,720 | def parse ( self , line , namespace = None ) : try : if namespace is None : ipy = IPython . get_ipython ( ) namespace = ipy . user_ns args = CommandParser . create_args ( line , namespace ) return self . parse_args ( args ) except Exception as e : print ( str ( e ) ) return None | Parses a line into a dictionary of arguments expanding meta - variables from a namespace . |
237,721 | def subcommand ( self , name , help ) : if self . _subcommands is None : self . _subcommands = self . add_subparsers ( help = 'commands' ) return self . _subcommands . add_parser ( name , description = help , help = help ) | Creates a parser for a sub - command . |
237,722 | def render_text ( text , preformatted = False ) : return IPython . core . display . HTML ( _html . HtmlBuilder . render_text ( text , preformatted ) ) | Return text formatted as a HTML |
237,723 | def _get_cols ( fields , schema ) : typemap = { 'STRING' : 'string' , 'INT64' : 'number' , 'INTEGER' : 'number' , 'FLOAT' : 'number' , 'FLOAT64' : 'number' , 'BOOL' : 'boolean' , 'BOOLEAN' : 'boolean' , 'DATE' : 'date' , 'TIME' : 'timeofday' , 'DATETIME' : 'datetime' , 'TIMESTAMP' : 'datetime' } cols = [ ] for col in f... | Get column metadata for Google Charts based on field list and schema . |
237,724 | def _get_data_from_empty_list ( source , fields = '*' , first_row = 0 , count = - 1 , schema = None ) : fields = get_field_list ( fields , schema ) return { 'cols' : _get_cols ( fields , schema ) , 'rows' : [ ] } , 0 | Helper function for _get_data that handles empty lists . |
237,725 | def _get_data_from_table ( source , fields = '*' , first_row = 0 , count = - 1 , schema = None ) : if not source . exists ( ) : return _get_data_from_empty_list ( source , fields , first_row , count ) if schema is None : schema = source . schema fields = get_field_list ( fields , schema ) gen = source . range ( first_r... | Helper function for _get_data that handles BQ Tables . |
237,726 | def replace_vars ( config , env ) : if isinstance ( config , dict ) : for k , v in list ( config . items ( ) ) : if isinstance ( v , dict ) or isinstance ( v , list ) or isinstance ( v , tuple ) : replace_vars ( v , env ) elif isinstance ( v , basestring ) : config [ k ] = expand_var ( v , env ) elif isinstance ( confi... | Replace variable references in config using the supplied env dictionary . |
237,727 | def parse_config ( config , env , as_dict = True ) : if config is None : return None stripped = config . strip ( ) if len ( stripped ) == 0 : config = { } elif stripped [ 0 ] == '{' : config = json . loads ( config ) else : config = yaml . load ( config ) if as_dict : config = dict ( config ) replace_vars ( config , en... | Parse a config from a magic cell body . This could be JSON or YAML . We turn it into a Python dictionary then recursively replace any variable references using the supplied env dictionary . |
237,728 | def validate_config ( config , required_keys , optional_keys = None ) : if optional_keys is None : optional_keys = [ ] if not isinstance ( config , dict ) : raise Exception ( 'config is not dict type' ) invalid_keys = set ( config ) - set ( required_keys + optional_keys ) if len ( invalid_keys ) > 0 : raise Exception (... | Validate a config dictionary to make sure it includes all required keys and does not include any unexpected keys . |
237,729 | def validate_config_must_have ( config , required_keys ) : missing_keys = set ( required_keys ) - set ( config ) if len ( missing_keys ) > 0 : raise Exception ( 'Invalid config with missing keys "%s"' % ', ' . join ( missing_keys ) ) | Validate a config dictionary to make sure it has all of the specified keys |
237,730 | def validate_config_has_one_of ( config , one_of_keys ) : intersection = set ( config ) . intersection ( one_of_keys ) if len ( intersection ) > 1 : raise Exception ( 'Only one of the values in "%s" is needed' % ', ' . join ( intersection ) ) if len ( intersection ) == 0 : raise Exception ( 'One of the values in "%s" i... | Validate a config dictionary to make sure it has one and only one key in one_of_keys . |
237,731 | def validate_config_value ( value , possible_values ) : if value not in possible_values : raise Exception ( 'Invalid config value "%s". Possible values are ' '%s' % ( value , ', ' . join ( e for e in possible_values ) ) ) | Validate a config value to make sure it is one of the possible values . |
237,732 | def validate_gcs_path ( path , require_object ) : bucket , key = datalab . storage . _bucket . parse_name ( path ) if bucket is None : raise Exception ( 'Invalid GCS path "%s"' % path ) if require_object and key is None : raise Exception ( 'It appears the GCS path "%s" is a bucket path but not an object path' % path ) | Check whether a given path is a valid GCS path . |
237,733 | def profile_df ( df ) : return IPython . core . display . HTML ( pandas_profiling . ProfileReport ( df ) . html . replace ( 'bootstrap' , 'nonexistent' ) ) | Generate a profile of data in a dataframe . |
237,734 | def _package_to_staging ( staging_package_url ) : import google . datalab . ml as ml package_root = os . path . abspath ( os . path . join ( os . path . dirname ( __file__ ) , '../../' ) ) setup_path = os . path . abspath ( os . path . join ( os . path . dirname ( __file__ ) , 'master_setup.py' ) ) tar_gz_path = os . p... | Repackage this package from local installed location and copy it to GCS . |
237,735 | def analyze ( output_dir , dataset , cloud = False , project_id = None ) : job = analyze_async ( output_dir = output_dir , dataset = dataset , cloud = cloud , project_id = project_id ) job . wait ( ) print ( 'Analyze: ' + str ( job . state ) ) | Blocking version of analyze_async . See documentation of analyze_async . |
237,736 | def analyze_async ( output_dir , dataset , cloud = False , project_id = None ) : import google . datalab . utils as du with warnings . catch_warnings ( ) : warnings . simplefilter ( "ignore" ) fn = lambda : _analyze ( output_dir , dataset , cloud , project_id ) return du . LambdaJob ( fn , job_id = None ) | Analyze data locally or in the cloud with BigQuery . |
237,737 | def cloud_train ( train_dataset , eval_dataset , analysis_dir , output_dir , features , model_type , max_steps , num_epochs , train_batch_size , eval_batch_size , min_eval_frequency , top_n , layer_sizes , learning_rate , epsilon , job_name , job_name_prefix , config ) : import google . datalab . ml as ml if len ( trai... | Train model using CloudML . |
237,738 | def predict ( data , training_dir = None , model_name = None , model_version = None , cloud = False ) : if cloud : if not model_version or not model_name : raise ValueError ( 'model_version or model_name is not set' ) if training_dir : raise ValueError ( 'training_dir not needed when cloud is True' ) with warnings . ca... | Runs prediction locally or on the cloud . |
237,739 | def local_predict ( training_dir , data ) : from . prediction import predict as predict_module tmp_dir = tempfile . mkdtemp ( ) _ , input_file_path = tempfile . mkstemp ( dir = tmp_dir , suffix = '.csv' , prefix = 'input' ) try : if isinstance ( data , pd . DataFrame ) : data . to_csv ( input_file_path , header = False... | Runs local prediction on the prediction graph . |
237,740 | def cloud_predict ( model_name , model_version , data ) : import google . datalab . ml as ml if isinstance ( data , pd . DataFrame ) : string_buffer = io . StringIO ( ) data . to_csv ( string_buffer , header = None , index = False ) input_data = string_buffer . getvalue ( ) . split ( '\n' ) input_data = [ line for line... | Use Online prediction . |
237,741 | def batch_predict ( training_dir , prediction_input_file , output_dir , mode , batch_size = 16 , shard_files = True , output_format = 'csv' , cloud = False ) : job = batch_predict_async ( training_dir = training_dir , prediction_input_file = prediction_input_file , output_dir = output_dir , mode = mode , batch_size = b... | Blocking versoin of batch_predict . |
237,742 | def batch_predict_async ( training_dir , prediction_input_file , output_dir , mode , batch_size = 16 , shard_files = True , output_format = 'csv' , cloud = False ) : import google . datalab . utils as du with warnings . catch_warnings ( ) : warnings . simplefilter ( "ignore" ) if cloud : runner_results = cloud_batch_pr... | Local and cloud batch prediction . |
237,743 | def make_prediction_pipeline ( pipeline , args ) : predicted_values , errors = ( pipeline | 'Read CSV Files' >> beam . io . ReadFromText ( str ( args . predict_data ) , strip_trailing_newlines = True ) | 'Batch Input' >> beam . ParDo ( EmitAsBatchDoFn ( args . batch_size ) ) | 'Run TF Graph on Batches' >> beam . ParDo ... | Builds the prediction pipeline . |
237,744 | def process ( self , element ) : import collections import apache_beam as beam num_in_batch = 0 try : assert self . _session is not None feed_dict = collections . defaultdict ( list ) for line in element : if line . endswith ( '\n' ) : line = line [ : - 1 ] feed_dict [ self . _input_alias_map . values ( ) [ 0 ] ] . app... | Run batch prediciton on a TF graph . |
237,745 | def encode ( self , tf_graph_predictions ) : row = [ ] for col in self . _header : row . append ( str ( tf_graph_predictions [ col ] ) ) return ',' . join ( row ) | Encodes the graph json prediction into csv . |
237,746 | def as_dataframe ( self , max_rows = None ) : max_rows = len ( self . _timeseries_list ) if max_rows is None else max_rows headers = [ { 'resource' : ts . resource . _asdict ( ) , 'metric' : ts . metric . _asdict ( ) } for ts in self . _timeseries_list [ : max_rows ] ] if not headers : return pandas . DataFrame ( ) dat... | Creates a pandas dataframe from the query metadata . |
237,747 | def get_train_eval_files ( input_dir ) : data_dir = _get_latest_data_dir ( input_dir ) train_pattern = os . path . join ( data_dir , 'train*.tfrecord.gz' ) eval_pattern = os . path . join ( data_dir , 'eval*.tfrecord.gz' ) train_files = file_io . get_matching_files ( train_pattern ) eval_files = file_io . get_matching_... | Get preprocessed training and eval files . |
237,748 | def get_labels ( input_dir ) : data_dir = _get_latest_data_dir ( input_dir ) labels_file = os . path . join ( data_dir , 'labels' ) with file_io . FileIO ( labels_file , 'r' ) as f : labels = f . read ( ) . rstrip ( ) . split ( '\n' ) return labels | Get a list of labels from preprocessed output dir . |
237,749 | def override_if_not_in_args ( flag , argument , args ) : if flag not in args : args . extend ( [ flag , argument ] ) | Checks if flags is in args and if not it adds the flag to args . |
237,750 | def loss ( loss_value ) : total_loss = tf . Variable ( 0.0 , False ) loss_count = tf . Variable ( 0 , False ) total_loss_update = tf . assign_add ( total_loss , loss_value ) loss_count_update = tf . assign_add ( loss_count , 1 ) loss_op = total_loss / tf . cast ( loss_count , tf . float32 ) return [ total_loss_update ,... | Calculates aggregated mean loss . |
237,751 | def accuracy ( logits , labels ) : is_correct = tf . nn . in_top_k ( logits , labels , 1 ) correct = tf . reduce_sum ( tf . cast ( is_correct , tf . int32 ) ) incorrect = tf . reduce_sum ( tf . cast ( tf . logical_not ( is_correct ) , tf . int32 ) ) correct_count = tf . Variable ( 0 , False ) incorrect_count = tf . Var... | Calculates aggregated accuracy . |
237,752 | def check_dataset ( dataset , mode ) : names = [ x [ 'name' ] for x in dataset . schema ] types = [ x [ 'type' ] for x in dataset . schema ] if mode == 'train' : if ( set ( [ 'image_url' , 'label' ] ) != set ( names ) or any ( t != 'STRING' for t in types ) ) : raise ValueError ( 'Invalid dataset. Expect only "image_ur... | Validate we have a good dataset . |
237,753 | def get_sources_from_dataset ( p , dataset , mode ) : import apache_beam as beam import csv from google . datalab . ml import CsvDataSet , BigQueryDataSet check_dataset ( dataset , mode ) if type ( dataset ) is CsvDataSet : source_list = [ ] for ii , input_path in enumerate ( dataset . files ) : source_list . append ( ... | get pcollection from dataset . |
237,754 | def decode_and_resize ( image_str_tensor ) : height = 299 width = 299 channels = 3 image = tf . image . decode_jpeg ( image_str_tensor , channels = channels ) image = tf . expand_dims ( image , 0 ) image = tf . image . resize_bilinear ( image , [ height , width ] , align_corners = False ) image = tf . squeeze ( image ,... | Decodes jpeg string resizes it and returns a uint8 tensor . |
237,755 | def resize_image ( image_str_tensor ) : image = decode_and_resize ( image_str_tensor ) image = tf . image . encode_jpeg ( image , quality = 100 ) return image | Decodes jpeg string resizes it and re - encode it to jpeg . |
237,756 | def load_images ( image_files , resize = True ) : images = [ ] for image_file in image_files : with file_io . FileIO ( image_file , 'r' ) as ff : images . append ( ff . read ( ) ) if resize is False : return images image_str_tensor = tf . placeholder ( tf . string , shape = [ None ] ) image = tf . map_fn ( resize_image... | Load images from files and optionally resize it . |
237,757 | def process_prediction_results ( results , show_image ) : import pandas as pd if ( is_in_IPython ( ) and show_image is True ) : import IPython for image_url , image , label_and_score in results : IPython . display . display_html ( '<p style="font-size:28px">%s(%.5f)</p>' % label_and_score , raw = True ) IPython . displ... | Create DataFrames out of prediction results and display images in IPython if requested . |
237,758 | def repackage_to_staging ( output_path ) : import google . datalab . ml as ml package_root = os . path . join ( os . path . dirname ( __file__ ) , '../../../' ) setup_py = os . path . join ( os . path . dirname ( __file__ ) , 'setup.py' ) staging_package_url = os . path . join ( output_path , 'staging' , 'image_classif... | Repackage it from local installed location and copy it to GCS . |
237,759 | def generate_airflow_spec ( name , pipeline_spec ) : task_definitions = '' up_steam_statements = '' parameters = pipeline_spec . get ( 'parameters' ) for ( task_id , task_details ) in sorted ( pipeline_spec [ 'tasks' ] . items ( ) ) : task_def = PipelineGenerator . _get_operator_definition ( task_id , task_details , pa... | Gets the airflow python spec for the Pipeline object . |
237,760 | def _get_dependency_definition ( task_id , dependencies ) : set_upstream_statements = '' for dependency in dependencies : set_upstream_statements = set_upstream_statements + '{0}.set_upstream({1})' . format ( task_id , dependency ) + '\n' return set_upstream_statements | Internal helper collects all the dependencies of the task and returns the Airflow equivalent python sytax for specifying them . |
237,761 | def _get_operator_class_name ( task_detail_type ) : task_type_to_operator_prefix_mapping = { 'pydatalab.bq.execute' : ( 'Execute' , 'google.datalab.contrib.bigquery.operators._bq_execute_operator' ) , 'pydatalab.bq.extract' : ( 'Extract' , 'google.datalab.contrib.bigquery.operators._bq_extract_operator' ) , 'pydatalab.... | Internal helper gets the name of the Airflow operator class . We maintain this in a map so this method really returns the enum name concatenated with the string Operator . |
237,762 | def _get_operator_param_name_and_values ( operator_class_name , task_details ) : operator_task_details = task_details . copy ( ) if 'type' in operator_task_details . keys ( ) : del operator_task_details [ 'type' ] if 'up_stream' in operator_task_details . keys ( ) : del operator_task_details [ 'up_stream' ] if ( operat... | Internal helper gets the name of the python parameter for the Airflow operator class . In some cases we do not expose the airflow parameter name in its native form but choose to expose a name that s more standard for Datalab or one that s more friendly . For example Airflow s BigQueryOperator uses bql for the query str... |
237,763 | def sample ( self , n ) : total = bq . Query ( 'select count(*) from %s' % self . _get_source ( ) ) . execute ( ) . result ( ) [ 0 ] . values ( ) [ 0 ] if n > total : raise ValueError ( 'sample larger than population' ) sampling = bq . Sampling . random ( percent = n * 100.0 / float ( total ) ) if self . _query is not ... | Samples data into a Pandas DataFrame . Note that it calls BigQuery so it will incur cost . |
237,764 | def size ( self ) : import tensorflow as tf if self . _size is None : self . _size = 0 options = tf . python_io . TFRecordOptions ( tf . python_io . TFRecordCompressionType . GZIP ) for tfexample_file in self . files : self . _size += sum ( 1 for x in tf . python_io . tf_record_iterator ( tfexample_file , options = opt... | The number of instances in the data . If the underlying data source changes it may be outdated . |
237,765 | def list ( self , pattern = '*' ) : if self . _group_dict is None : self . _group_dict = collections . OrderedDict ( ( group . id , group ) for group in self . _client . list_groups ( ) ) return [ group for group in self . _group_dict . values ( ) if fnmatch . fnmatch ( group . display_name , pattern ) ] | Returns a list of groups that match the filters . |
237,766 | def as_dataframe ( self , pattern = '*' , max_rows = None ) : data = [ ] for i , group in enumerate ( self . list ( pattern ) ) : if max_rows is not None and i >= max_rows : break parent = self . _group_dict . get ( group . parent_id ) parent_display_name = '' if parent is None else parent . display_name data . append ... | Creates a pandas dataframe from the groups that match the filters . |
237,767 | def _find_recursive_dependencies ( sql , values , code , resolved_vars , resolving_vars = None ) : dependencies = SqlStatement . _get_dependencies ( sql ) for dependency in dependencies : if dependency in resolved_vars : continue dep = datalab . utils . get_item ( values , dependency ) if isinstance ( dep , types . Mod... | Recursive helper method for expanding variables including transitive dependencies . |
237,768 | def format ( sql , args = None ) : resolved_vars = { } code = [ ] SqlStatement . _find_recursive_dependencies ( sql , args , code = code , resolved_vars = resolved_vars ) parts = [ ] for ( escape , placeholder , _ , literal ) in SqlStatement . _get_tokens ( sql ) : if escape : parts . append ( '$' ) elif placeholder : ... | Resolve variable references in a query within an environment . |
237,769 | def _get_dependencies ( sql ) : dependencies = [ ] for ( _ , placeholder , dollar , _ ) in SqlStatement . _get_tokens ( sql ) : if placeholder : variable = placeholder [ 1 : ] if variable not in dependencies : dependencies . append ( variable ) elif dollar : raise Exception ( 'Invalid sql; $ with no following $ or iden... | Return the list of variables referenced in this SQL . |
237,770 | def pymodule ( line , cell = None ) : parser = _commands . CommandParser . create ( 'pymodule' ) parser . add_argument ( '-n' , '--name' , help = 'the name of the python module to create and import' ) parser . set_defaults ( func = _pymodule_cell ) return _utils . handle_magic_line ( line , cell , parser ) | Creates and subsequently auto - imports a python module . |
237,771 | def compare_datetimes ( d1 , d2 ) : if d1 . tzinfo is None or d1 . tzinfo . utcoffset ( d1 ) is None : d1 = d1 . replace ( tzinfo = pytz . UTC ) if d2 . tzinfo is None or d2 . tzinfo . utcoffset ( d2 ) is None : d2 = d2 . replace ( tzinfo = pytz . UTC ) if d1 < d2 : return - 1 elif d1 > d2 : return 1 return 0 | Compares two datetimes safely whether they are timezone - naive or timezone - aware . |
237,772 | def pick_unused_port ( ) : s = socket . socket ( socket . AF_INET , socket . SOCK_STREAM ) s . bind ( ( 'localhost' , 0 ) ) addr , port = s . getsockname ( ) s . close ( ) return port | get an unused port on the VM . |
237,773 | def is_http_running_on ( port ) : try : conn = httplib . HTTPConnection ( '127.0.0.1:' + str ( port ) ) conn . connect ( ) conn . close ( ) return True except Exception : return False | Check if an http server runs on a given port . |
237,774 | def save_project_id ( project_id ) : try : subprocess . call ( [ 'gcloud' , 'config' , 'set' , 'project' , project_id ] ) except : config_file = os . path . join ( get_config_dir ( ) , 'config.json' ) config = { } if os . path . exists ( config_file ) : with open ( config_file ) as f : config = json . loads ( f . read ... | Save project id to config file . |
237,775 | def get_default_project_id ( ) : try : proc = subprocess . Popen ( [ 'gcloud' , 'config' , 'list' , '--format' , 'value(core.project)' ] , stdout = subprocess . PIPE ) stdout , _ = proc . communicate ( ) value = stdout . strip ( ) if proc . poll ( ) == 0 and value : if isinstance ( value , six . string_types ) : return... | Get default project id from config or environment var . |
237,776 | def _construct_context_for_args ( args ) : global_default_context = google . datalab . Context . default ( ) config = { } for key in global_default_context . config : config [ key ] = global_default_context . config [ key ] billing_tier_arg = args . get ( 'billing' , None ) if billing_tier_arg : config [ 'bigquery_bill... | Construct a new Context for the parsed arguments . |
237,777 | def python_portable_string ( string , encoding = 'utf-8' ) : if isinstance ( string , six . string_types ) : return string if six . PY3 : return string . decode ( encoding ) raise ValueError ( 'Unsupported type %s' % str ( type ( string ) ) ) | Converts bytes into a string type . |
237,778 | def _storage_list_buckets ( project , pattern ) : data = [ { 'Bucket' : 'gs://' + bucket . name , 'Created' : bucket . metadata . created_on } for bucket in datalab . storage . Buckets ( project_id = project ) if fnmatch . fnmatch ( bucket . name , pattern ) ] return datalab . utils . commands . render_dictionary ( dat... | List all storage buckets that match a pattern . |
237,779 | def _storage_list_keys ( bucket , pattern ) : data = [ { 'Name' : item . metadata . name , 'Type' : item . metadata . content_type , 'Size' : item . metadata . size , 'Updated' : item . metadata . updated_on } for item in _storage_get_keys ( bucket , pattern ) ] return datalab . utils . commands . render_dictionary ( d... | List all storage keys in a specified bucket that match a pattern . |
237,780 | def tables_list ( self , dataset_name , max_results = 0 , page_token = None ) : url = Api . _ENDPOINT + ( Api . _TABLES_PATH % ( dataset_name . project_id , dataset_name . dataset_id , '' , '' ) ) args = { } if max_results != 0 : args [ 'maxResults' ] = max_results if page_token is not None : args [ 'pageToken' ] = pag... | Issues a request to retrieve a list of tables . |
237,781 | def _bag_of_words ( x ) : def _bow ( x ) : return tf . SparseTensor ( indices = x . indices , values = tf . to_float ( tf . ones_like ( x . values ) ) , dense_shape = x . dense_shape ) return _bow ( x ) | Computes bag of words weights |
237,782 | def csv_header_and_defaults ( features , schema , stats , keep_target ) : target_name = get_target_name ( features ) if keep_target and not target_name : raise ValueError ( 'Cannot find target transform' ) csv_header = [ ] record_defaults = [ ] for col in schema : if not keep_target and col [ 'name' ] == target_name : ... | Gets csv header and default lists . |
237,783 | def build_csv_serving_tensors_for_transform_step ( analysis_path , features , schema , stats , keep_target ) : csv_header , record_defaults = csv_header_and_defaults ( features , schema , stats , keep_target ) placeholder = tf . placeholder ( dtype = tf . string , shape = ( None , ) , name = 'csv_input_placeholder' ) t... | Builds a serving function starting from raw csv . |
237,784 | def build_csv_serving_tensors_for_training_step ( analysis_path , features , schema , stats , keep_target ) : transformed_features , _ , placeholder_dict = build_csv_serving_tensors_for_transform_step ( analysis_path = analysis_path , features = features , schema = schema , stats = stats , keep_target = keep_target ) t... | Builds a serving function starting from raw csv used at model export time . |
237,785 | def build_csv_transforming_training_input_fn ( schema , features , stats , analysis_output_dir , raw_data_file_pattern , training_batch_size , num_epochs = None , randomize_input = False , min_after_dequeue = 1 , reader_num_threads = 1 , allow_smaller_final_batch = True ) : def raw_training_input_fn ( ) : if isinstance... | Creates training input_fn that reads raw csv data and applies transforms . |
237,786 | def build_tfexample_transfored_training_input_fn ( schema , features , analysis_output_dir , raw_data_file_pattern , training_batch_size , num_epochs = None , randomize_input = False , min_after_dequeue = 1 , reader_num_threads = 1 , allow_smaller_final_batch = True ) : def transformed_training_input_fn ( ) : if isinst... | Creates training input_fn that reads transformed tf . example files . |
237,787 | def image_feature_engineering ( features , feature_tensors_dict ) : engineered_features = { } for name , feature_tensor in six . iteritems ( feature_tensors_dict ) : if name in features and features [ name ] [ 'transform' ] == IMAGE_TRANSFORM : with tf . name_scope ( name , 'Wx_plus_b' ) : hidden = tf . contrib . layer... | Add a hidden layer on image features . |
237,788 | def read_vocab_file ( file_path ) : with file_io . FileIO ( file_path , 'r' ) as f : vocab_pd = pd . read_csv ( f , header = None , names = [ 'vocab' , 'count' ] , dtype = str , na_filter = False ) vocab = vocab_pd [ 'vocab' ] . tolist ( ) ex_count = vocab_pd [ 'count' ] . astype ( int ) . tolist ( ) return vocab , ex_... | Reads a vocab file to memeory . |
237,789 | def _to_query_json ( self ) : json = { 'compression' : 'GZIP' if self . _compressed else 'NONE' , 'ignoreUnknownValues' : self . _ignore_unknown_values , 'maxBadRecords' : self . _max_bad_records , 'sourceFormat' : self . _bq_source_format , 'sourceUris' : self . _source , } if self . _source_format == 'csv' and self .... | Return the table as a dictionary to be used as JSON in a query job . |
237,790 | def load_ipython_extension ( shell ) : def _request ( self , uri , method = "GET" , body = None , headers = None , redirections = _httplib2 . DEFAULT_MAX_REDIRECTS , connection_type = None ) : if headers is None : headers = { } headers [ 'user-agent' ] = 'GoogleCloudDataLab/1.0' return _orig_request ( self , uri , meth... | Called when the extension is loaded . |
237,791 | def _get_sql_args ( parser , args = None ) : overrides = None if args is None : tokens = [ ] elif isinstance ( args , basestring ) : command_line = ' ' . join ( args . split ( '\n' ) ) tokens = shlex . split ( command_line ) elif isinstance ( args , dict ) : overrides = args tokens = [ ] else : tokens = args args = { }... | Parse a set of %%sql arguments or get the default value of the arguments . |
237,792 | def get_sql_statement_with_environment ( item , args = None ) : if isinstance ( item , basestring ) : item = _sql_statement . SqlStatement ( item ) elif not isinstance ( item , _sql_statement . SqlStatement ) : item = SqlModule . get_default_query_from_module ( item ) if not item : raise Exception ( 'Expected a SQL sta... | Given a SQLStatement string or module plus command line args or a dictionary return a SqlStatement and final dictionary for variable resolution . |
237,793 | def expand ( sql , args = None ) : sql , args = SqlModule . get_sql_statement_with_environment ( sql , args ) return _sql_statement . SqlStatement . format ( sql . _sql , args ) | Expand a SqlStatement query string or SqlModule with a set of arguments . |
237,794 | def parse_dataset_name ( name , project_id = None ) : _project_id = _dataset_id = None if isinstance ( name , basestring ) : m = re . match ( _ABS_DATASET_NAME_PATTERN , name , re . IGNORECASE ) if m is not None : _project_id , _dataset_id = m . groups ( ) else : m = re . match ( _REL_DATASET_NAME_PATTERN , name ) if m... | Parses a dataset name into its individual parts . |
237,795 | def parse_table_name ( name , project_id = None , dataset_id = None ) : _project_id = _dataset_id = _table_id = _decorator = None if isinstance ( name , basestring ) : m = re . match ( _ABS_TABLE_NAME_PATTERN , name , re . IGNORECASE ) if m is not None : _project_id , _dataset_id , _table_id , _decorator = m . groups (... | Parses a table name into its individual parts . |
237,796 | def _make_text_predict_fn ( self , labels , instance , column_to_explain ) : def _predict_fn ( perturbed_text ) : predict_input = [ ] for x in perturbed_text : instance_copy = dict ( instance ) instance_copy [ column_to_explain ] = x predict_input . append ( instance_copy ) df = _local_predict . get_prediction_results ... | Create a predict_fn that can be used by LIME text explainer . |
237,797 | def _make_image_predict_fn ( self , labels , instance , column_to_explain ) : def _predict_fn ( perturbed_image ) : predict_input = [ ] for x in perturbed_image : instance_copy = dict ( instance ) instance_copy [ column_to_explain ] = Image . fromarray ( x ) predict_input . append ( instance_copy ) df = _local_predict ... | Create a predict_fn that can be used by LIME image explainer . |
237,798 | def _get_unique_categories ( self , df ) : categories = [ ] for col in self . _categorical_columns : categocial = pd . Categorical ( df [ col ] ) col_categories = list ( map ( str , categocial . categories ) ) col_categories . append ( '_UNKNOWN' ) categories . append ( col_categories ) return categories | Get all categories for each categorical columns from training data . |
237,799 | def _preprocess_data_for_tabular_explain ( self , df , categories ) : df = df . copy ( ) for col in list ( df . columns ) : if col not in ( self . _categorical_columns + self . _numeric_columns ) : del df [ col ] for col_name , col_categories in zip ( self . _categorical_columns , categories ) : df [ col_name ] = df [ ... | Get preprocessed training set in numpy array and categorical names from raw training data . |
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