idx int64 0 252k | question stringlengths 48 5.28k | target stringlengths 5 1.23k |
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237,900 | def _get_schema ( name ) : item = datalab . utils . commands . get_notebook_item ( name ) if not item : item = _get_table ( name ) if isinstance ( item , datalab . bigquery . Schema ) : return item if hasattr ( item , 'schema' ) and isinstance ( item . schema , datalab . bigquery . _schema . Schema ) : return item . sc... | Given a variable or table name get the Schema if it exists . |
237,901 | def _render_table ( data , fields = None ) : return IPython . core . display . HTML ( datalab . utils . commands . HtmlBuilder . render_table ( data , fields ) ) | Helper to render a list of dictionaries as an HTML display object . |
237,902 | def _datasets_line ( args ) : filter_ = args [ 'filter' ] if args [ 'filter' ] else '*' return _render_list ( [ str ( dataset ) for dataset in datalab . bigquery . Datasets ( args [ 'project' ] ) if fnmatch . fnmatch ( str ( dataset ) , filter_ ) ] ) | Implements the BigQuery datasets magic used to display datasets in a project . |
237,903 | def _tables_line ( args ) : filter_ = args [ 'filter' ] if args [ 'filter' ] else '*' if args [ 'dataset' ] : if args [ 'project' ] is None : datasets = [ datalab . bigquery . Dataset ( args [ 'dataset' ] ) ] else : datasets = [ datalab . bigquery . Dataset ( ( args [ 'project' ] , args [ 'dataset' ] ) ) ] else : datas... | Implements the BigQuery tables magic used to display tables in a dataset . |
237,904 | def _extract_line ( args ) : name = args [ 'source' ] source = datalab . utils . commands . get_notebook_item ( name ) if not source : source = _get_table ( name ) if not source : raise Exception ( 'No source named %s found' % name ) elif isinstance ( source , datalab . bigquery . Table ) and not source . exists ( ) : ... | Implements the BigQuery extract magic used to extract table data to GCS . |
237,905 | def bigquery ( line , cell = None ) : namespace = { } if line . find ( '$' ) >= 0 : namespace = datalab . utils . commands . notebook_environment ( ) return datalab . utils . commands . handle_magic_line ( line , cell , _bigquery_parser , namespace = namespace ) | Implements the bigquery cell magic for ipython notebooks . |
237,906 | def table ( name = None , mode = 'create' , use_cache = True , priority = 'interactive' , allow_large_results = False ) : output = QueryOutput ( ) output . _output_type = 'table' output . _table_name = name output . _table_mode = mode output . _use_cache = use_cache output . _priority = priority output . _allow_large_r... | Construct a query output object where the result is a table |
237,907 | def file ( path , format = 'csv' , csv_delimiter = ',' , csv_header = True , compress = False , use_cache = True ) : output = QueryOutput ( ) output . _output_type = 'file' output . _file_path = path output . _file_format = format output . _csv_delimiter = csv_delimiter output . _csv_header = csv_header output . _compr... | Construct a query output object where the result is either a local file or a GCS path |
237,908 | def dataframe ( start_row = 0 , max_rows = None , use_cache = True ) : output = QueryOutput ( ) output . _output_type = 'dataframe' output . _dataframe_start_row = start_row output . _dataframe_max_rows = max_rows output . _use_cache = use_cache return output | Construct a query output object where the result is a dataframe |
237,909 | def list ( ) : running_list = [ ] parser = argparse . ArgumentParser ( ) parser . add_argument ( '--logdir' ) parser . add_argument ( '--port' ) for p in psutil . process_iter ( ) : if p . name ( ) != 'tensorboard' or p . status ( ) == psutil . STATUS_ZOMBIE : continue cmd_args = p . cmdline ( ) del cmd_args [ 0 : 2 ] ... | List running TensorBoard instances . |
237,910 | def start ( logdir ) : if logdir . startswith ( 'gs://' ) : datalab . storage . _api . Api . verify_permitted_to_read ( logdir ) port = datalab . utils . pick_unused_port ( ) args = [ 'tensorboard' , '--logdir=' + logdir , '--port=' + str ( port ) ] p = subprocess . Popen ( args ) retry = 10 while ( retry > 0 ) : if da... | Start a TensorBoard instance . |
237,911 | def stop ( pid ) : if psutil . pid_exists ( pid ) : try : p = psutil . Process ( pid ) p . kill ( ) except Exception : pass | Shut down a specific process . |
237,912 | def build_graph ( self ) : import tensorflow as tf input_jpeg = tf . placeholder ( tf . string , shape = None ) image = tf . image . decode_jpeg ( input_jpeg , channels = self . CHANNELS ) image = tf . expand_dims ( image , 0 ) image = tf . image . convert_image_dtype ( image , dtype = tf . float32 ) image = tf . image... | Forms the core by building a wrapper around the inception graph . |
237,913 | def restore_from_checkpoint ( self , checkpoint_path ) : import tensorflow as tf all_vars = tf . contrib . slim . get_variables_to_restore ( exclude = [ 'InceptionV3/AuxLogits' , 'InceptionV3/Logits' , 'global_step' ] ) saver = tf . train . Saver ( all_vars ) saver . restore ( self . tf_session , checkpoint_path ) | To restore inception model variables from the checkpoint file . |
237,914 | def calculate_embedding ( self , batch_image_bytes ) : return self . tf_session . run ( self . embedding , feed_dict = { self . input_jpeg : batch_image_bytes } ) | Get the embeddings for a given JPEG image . |
237,915 | def add_final_training_ops ( self , embeddings , all_labels_count , bottleneck_tensor_size , hidden_layer_size = BOTTLENECK_TENSOR_SIZE / 4 , dropout_keep_prob = None ) : with tf . name_scope ( 'input' ) : bottleneck_input = tf . placeholder_with_default ( embeddings , shape = [ None , bottleneck_tensor_size ] , name =... | Adds a new softmax and fully - connected layer for training . |
237,916 | def build_inception_graph ( self ) : image_str_tensor = tf . placeholder ( tf . string , shape = [ None ] ) image = tf . map_fn ( _util . decode_and_resize , image_str_tensor , back_prop = False , dtype = tf . uint8 ) image = tf . image . convert_image_dtype ( image , dtype = tf . float32 ) image = tf . subtract ( imag... | Builds an inception graph and add the necessary input & output tensors . |
237,917 | def build_graph ( self , data_paths , batch_size , graph_mod ) : tensors = GraphReferences ( ) is_training = graph_mod == GraphMod . TRAIN if data_paths : _ , tensors . examples = _util . read_examples ( data_paths , batch_size , shuffle = is_training , num_epochs = None if is_training else 2 ) else : tensors . example... | Builds generic graph for training or eval . |
237,918 | def restore_from_checkpoint ( self , session , inception_checkpoint_file , trained_checkpoint_file ) : inception_exclude_scopes = [ 'InceptionV3/AuxLogits' , 'InceptionV3/Logits' , 'global_step' , 'final_ops' ] reader = tf . train . NewCheckpointReader ( inception_checkpoint_file ) var_to_shape_map = reader . get_varia... | To restore model variables from the checkpoint file . |
237,919 | def build_prediction_graph ( self ) : tensors = self . build_graph ( None , 1 , GraphMod . PREDICT ) keys_placeholder = tf . placeholder ( tf . string , shape = [ None ] ) inputs = { 'key' : keys_placeholder , 'image_bytes' : tensors . input_jpeg } keys = tf . identity ( keys_placeholder ) labels = self . labels + [ 'U... | Builds prediction graph and registers appropriate endpoints . |
237,920 | def export ( self , last_checkpoint , output_dir ) : logging . info ( 'Exporting prediction graph to %s' , output_dir ) with tf . Session ( graph = tf . Graph ( ) ) as sess : inputs , outputs = self . build_prediction_graph ( ) signature_def_map = { 'serving_default' : signature_def_utils . predict_signature_def ( inpu... | Builds a prediction graph and xports the model . |
237,921 | def format_metric_values ( self , metric_values ) : loss_str = 'N/A' accuracy_str = 'N/A' try : loss_str = 'loss: %.3f' % metric_values [ 0 ] accuracy_str = 'accuracy: %.3f' % metric_values [ 1 ] except ( TypeError , IndexError ) : pass return '%s, %s' % ( loss_str , accuracy_str ) | Formats metric values - used for logging purpose . |
237,922 | def package_and_copy ( package_root_dir , setup_py , output_tar_path ) : if not output_tar_path . startswith ( 'gs://' ) : raise ValueError ( 'output_tar_path needs to be a GCS path.' ) if not os . path . isfile ( setup_py ) : raise ValueError ( 'Supplied file "%s" does not exist.' % setup_py ) dest_setup_py = os . pat... | Repackage an CloudML package and copy it to a staging dir . |
237,923 | def read_file_to_string ( path ) : bytes_string = tf . gfile . Open ( path , 'r' ) . read ( ) return dlutils . python_portable_string ( bytes_string ) | Read a file into a string . |
237,924 | def _date ( val , offset = None ) : if val is None : return val if val == '' or val == 'now' : when = datetime . datetime . utcnow ( ) elif val == 'today' : dt = datetime . datetime . utcnow ( ) when = datetime . datetime ( dt . year , dt . month , dt . day ) elif val == 'yesterday' : dt = datetime . datetime . utcnow ... | A special pseudo - type for pipeline arguments . |
237,925 | def _make_string_formatter ( f , offset = None ) : format = f delta = offset return lambda v : time . strftime ( format , ( _date ( v , delta ) ) . timetuple ( ) ) | A closure - izer for string arguments that include a format and possibly an offset . |
237,926 | def _make_table_formatter ( f , offset = None ) : format = f delta = offset return lambda v : _resolve_table ( v , format , delta ) | A closure - izer for table arguments that include a format and possibly an offset . |
237,927 | def _arguments ( code , module ) : arg_parser = CommandParser . create ( '' ) try : builtins = { 'source' : _table , 'datestring' : _datestring } env = { } env . update ( builtins ) exec ( code , env ) for key in env : if key in builtins or key [ 0 ] == '_' : continue val = env [ key ] key = '--%s' % key if isinstance ... | Define pipeline arguments . |
237,928 | def _split_cell ( cell , module ) : lines = cell . split ( '\n' ) code = None last_def = - 1 name = None define_wild_re = re . compile ( '^DEFINE\s+.*$' , re . IGNORECASE ) define_re = re . compile ( '^DEFINE\s+QUERY\s+([A-Z]\w*)\s*?(.*)$' , re . IGNORECASE ) select_re = re . compile ( '^SELECT\s*.*$' , re . IGNORECASE... | Split a hybrid %%sql cell into the Python code and the queries . |
237,929 | def sql_cell ( args , cell ) : name = args [ 'module' ] if args [ 'module' ] else '_sql_cell' module = imp . new_module ( name ) query = _split_cell ( cell , module ) ipy = IPython . get_ipython ( ) if not args [ 'module' ] : if query : return datalab . bigquery . Query ( query , values = ipy . user_ns ) . execute ( di... | Implements the SQL cell magic for ipython notebooks . |
237,930 | def get_reader_input_fn ( train_config , preprocess_output_dir , model_type , data_paths , batch_size , shuffle , num_epochs = None ) : def get_input_features ( ) : _ , examples = util . read_examples ( input_files = data_paths , batch_size = batch_size , shuffle = shuffle , num_epochs = num_epochs ) features = util . ... | Builds input layer for training . |
237,931 | def main ( argv = None ) : args = parse_arguments ( sys . argv if argv is None else argv ) tf . logging . set_verbosity ( tf . logging . INFO ) learn_runner . run ( experiment_fn = get_experiment_fn ( args ) , output_dir = args . job_dir ) | Run a Tensorflow model on the Iris dataset . |
237,932 | def sd ( line , cell = None ) : parser = google . datalab . utils . commands . CommandParser ( prog = '%sd' , description = ( 'Execute various Stackdriver related operations. Use "%sd ' '<stackdriver_product> -h" for help on a specific Stackdriver product.' ) ) _create_monitoring_subparser ( parser ) return google . da... | Implements the stackdriver cell magic for ipython notebooks . |
237,933 | def make_prediction_output_tensors ( args , features , input_ops , model_fn_ops , keep_target ) : target_name = feature_transforms . get_target_name ( features ) key_names = get_key_names ( features ) outputs = { } outputs . update ( { key_name : tf . squeeze ( input_ops . features [ key_name ] ) for key_name in key_na... | Makes the final prediction output layer . |
237,934 | def read_vocab ( args , column_name ) : vocab_path = os . path . join ( args . analysis , feature_transforms . VOCAB_ANALYSIS_FILE % column_name ) if not file_io . file_exists ( vocab_path ) : return [ ] vocab , _ = feature_transforms . read_vocab_file ( vocab_path ) return vocab | Reads a vocab file if it exists . |
237,935 | def get_item ( env , name , default = None ) : for key in name . split ( '.' ) : if isinstance ( env , dict ) and key in env : env = env [ key ] elif isinstance ( env , types . ModuleType ) and key in env . __dict__ : env = env . __dict__ [ key ] else : return default return env | Get an item from a dictionary handling nested lookups with dotted notation . |
237,936 | def predict ( model_dir , images ) : results = _tf_predict ( model_dir , images ) predicted_and_scores = [ ( predicted , label_scores [ list ( labels ) . index ( predicted ) ] ) for predicted , labels , label_scores in results ] return predicted_and_scores | Local instant prediction . |
237,937 | def configure_pipeline ( p , dataset , model_dir , output_csv , output_bq_table ) : data = _util . get_sources_from_dataset ( p , dataset , 'predict' ) if len ( dataset . schema ) == 2 : output_schema = [ { 'name' : 'image_url' , 'type' : 'STRING' } , { 'name' : 'target' , 'type' : 'STRING' } , { 'name' : 'predicted' ,... | Configures a dataflow pipeline for batch prediction . |
237,938 | def sampling_query ( sql , context , fields = None , count = 5 , sampling = None , udfs = None , data_sources = None ) : return Query ( _sampling . Sampling . sampling_query ( sql , fields , count , sampling ) , context = context , udfs = udfs , data_sources = data_sources ) | Returns a sampling Query for the SQL object . |
237,939 | def results ( self , use_cache = True , dialect = None , billing_tier = None ) : if not use_cache or ( self . _results is None ) : self . execute ( use_cache = use_cache , dialect = dialect , billing_tier = billing_tier ) return self . _results . results | Retrieves table of results for the query . May block if the query must be executed first . |
237,940 | def extract ( self , storage_uris , format = 'csv' , csv_delimiter = ',' , csv_header = True , compress = False , use_cache = True , dialect = None , billing_tier = None ) : return self . results ( use_cache = use_cache , dialect = dialect , billing_tier = billing_tier ) . extract ( storage_uris , format = format , csv... | Exports the query results to GCS . |
237,941 | def to_dataframe ( self , start_row = 0 , max_rows = None , use_cache = True , dialect = None , billing_tier = None ) : return self . results ( use_cache = use_cache , dialect = dialect , billing_tier = billing_tier ) . to_dataframe ( start_row = start_row , max_rows = max_rows ) | Exports the query results to a Pandas dataframe . |
237,942 | def sample ( self , count = 5 , fields = None , sampling = None , use_cache = True , dialect = None , billing_tier = None ) : return Query . sampling_query ( self . _sql , self . _context , count = count , fields = fields , sampling = sampling , udfs = self . _udfs , data_sources = self . _data_sources ) . results ( us... | Retrieves a sampling of rows for the query . |
237,943 | def execute ( self , table_name = None , table_mode = 'create' , use_cache = True , priority = 'interactive' , allow_large_results = False , dialect = None , billing_tier = None ) : job = self . execute_async ( table_name = table_name , table_mode = table_mode , use_cache = use_cache , priority = priority , allow_large... | Initiate the query blocking until complete and then return the results . |
237,944 | def to_view ( self , view_name ) : from . import _view return _view . View ( view_name , self . _context ) . create ( self . _sql ) | Create a View from this Query . |
237,945 | def format_help ( self ) : if not self . _cell_args : return super ( CommandParser , self ) . format_help ( ) else : epilog = self . epilog self . epilog = None orig_help = super ( CommandParser , self ) . format_help ( ) cell_args_help = '\nCell args:\n\n' for cell_arg , v in six . iteritems ( self . _cell_args ) : re... | Override help doc to add cell args . |
237,946 | def _get_subparsers ( self ) : subparsers = [ ] for action in self . _actions : if isinstance ( action , argparse . _SubParsersAction ) : for _ , subparser in action . choices . items ( ) : subparsers . append ( subparser ) ret = subparsers for sp in subparsers : ret += sp . _get_subparsers ( ) return ret | Recursively get subparsers . |
237,947 | def _get_subparser_line_args ( self , subparser_prog ) : subparsers = self . _get_subparsers ( ) for subparser in subparsers : if subparser_prog == subparser . prog : args_to_parse = [ ] for action in subparser . _actions : if action . option_strings : for argname in action . option_strings : if argname . startswith ( ... | Get line args of a specified subparser by its prog . |
237,948 | def _get_subparser_cell_args ( self , subparser_prog ) : subparsers = self . _get_subparsers ( ) for subparser in subparsers : if subparser_prog == subparser . prog : return subparser . _cell_args return None | Get cell args of a specified subparser by its prog . |
237,949 | def add_cell_argument ( self , name , help , required = False ) : for action in self . _actions : if action . dest == name : raise ValueError ( 'Arg "%s" was added by add_argument already.' % name ) self . _cell_args [ name ] = { 'required' : required , 'help' : help } | Add a cell only argument . |
237,950 | def parse ( self , line , cell , namespace = None ) : if namespace is None : ipy = IPython . get_ipython ( ) namespace = ipy . user_ns args = CommandParser . create_args ( line , namespace ) sub_parsers_progs = [ x . prog for x in self . _get_subparsers ( ) ] matched_progs = [ ] for prog in sub_parsers_progs : match = ... | Parses a line and cell into a dictionary of arguments expanding variables from a namespace . |
237,951 | def _glob_events_files ( self , paths , recursive ) : event_files = [ ] for path in paths : dirs = tf . gfile . Glob ( path ) dirs = filter ( lambda x : tf . gfile . IsDirectory ( x ) , dirs ) for dir in dirs : if recursive : dir_files_pair = [ ( root , filenames ) for root , _ , filenames in tf . gfile . Walk ( dir ) ... | Find all tf events files under a list of paths recursively . |
237,952 | def list_events ( self ) : event_dir_dict = collections . defaultdict ( set ) for event_file in self . _glob_events_files ( self . _paths , recursive = True ) : dir = os . path . dirname ( event_file ) try : for record in tf_record . tf_record_iterator ( event_file ) : event = event_pb2 . Event . FromString ( record ) ... | List all scalar events in the directory . |
237,953 | def from_storage ( source , source_format = 'csv' , csv_options = None , ignore_unknown_values = False , max_bad_records = 0 , compressed = False , schema = None ) : result = FederatedTable ( ) if source_format == 'csv' : result . _bq_source_format = 'CSV' if csv_options is None : csv_options = _csv_options . CSVOption... | Create an external table for a GCS object . |
237,954 | def get_query_parameters ( args , cell_body , date_time = datetime . datetime . now ( ) ) : env = google . datalab . utils . commands . notebook_environment ( ) config = google . datalab . utils . commands . parse_config ( cell_body , env = env , as_dict = False ) sql = args [ 'query' ] if sql is None : raise Exception... | Extract query parameters from cell body if provided Also validates the cell body schema using jsonschema to catch errors before sending the http request . This validation isn t complete however ; it does not validate recursive schemas but it acts as a good filter against most simple schemas |
237,955 | def _udf_cell ( args , cell_body ) : udf_name = args [ 'name' ] if not udf_name : raise Exception ( 'Declaration must be of the form %%bq udf --name <variable name>' ) param_pattern = r'^\s*\/\/\s*@param\s+([<>\w]+)\s+([<>\w,\s]+)\s*$' returns_pattern = r'^\s*\/\/\s*@returns\s+([<>\w,\s]+)\s*$' import_pattern = r'^\s*\... | Implements the Bigquery udf cell magic for ipython notebooks . |
237,956 | def _datasource_cell ( args , cell_body ) : name = args [ 'name' ] paths = args [ 'paths' ] data_format = ( args [ 'format' ] or 'CSV' ) . lower ( ) compressed = args [ 'compressed' ] or False record = google . datalab . utils . commands . parse_config ( cell_body , google . datalab . utils . commands . notebook_enviro... | Implements the BigQuery datasource cell magic for ipython notebooks . |
237,957 | def _query_cell ( args , cell_body ) : name = args [ 'name' ] udfs = args [ 'udfs' ] datasources = args [ 'datasources' ] subqueries = args [ 'subqueries' ] query = bigquery . Query ( cell_body , env = IPython . get_ipython ( ) . user_ns , udfs = udfs , data_sources = datasources , subqueries = subqueries ) if name is ... | Implements the BigQuery cell magic for used to build SQL objects . |
237,958 | def _get_table ( name ) : item = google . datalab . utils . commands . get_notebook_item ( name ) if isinstance ( item , bigquery . Table ) : return item try : return _existing_table_cache [ name ] except KeyError : table = bigquery . Table ( name ) if table . exists ( ) : _existing_table_cache [ name ] = table return ... | Given a variable or table name get a Table if it exists . |
237,959 | def _render_list ( data ) : return IPython . core . display . HTML ( google . datalab . utils . commands . HtmlBuilder . render_list ( data ) ) | Helper to render a list of objects as an HTML list object . |
237,960 | def _dataset_line ( args ) : if args [ 'command' ] == 'list' : filter_ = args [ 'filter' ] if args [ 'filter' ] else '*' context = google . datalab . Context . default ( ) if args [ 'project' ] : context = google . datalab . Context ( args [ 'project' ] , context . credentials ) return _render_list ( [ str ( dataset ) ... | Implements the BigQuery dataset magic subcommand used to operate on datasets |
237,961 | def _table_cell ( args , cell_body ) : if args [ 'command' ] == 'list' : filter_ = args [ 'filter' ] if args [ 'filter' ] else '*' if args [ 'dataset' ] : if args [ 'project' ] is None : datasets = [ bigquery . Dataset ( args [ 'dataset' ] ) ] else : context = google . datalab . Context ( args [ 'project' ] , google . ... | Implements the BigQuery table magic subcommand used to operate on tables |
237,962 | def _extract_cell ( args , cell_body ) : env = google . datalab . utils . commands . notebook_environment ( ) config = google . datalab . utils . commands . parse_config ( cell_body , env , False ) or { } parameters = config . get ( 'parameters' ) if args [ 'table' ] : table = google . datalab . bigquery . Query . reso... | Implements the BigQuery extract magic used to extract query or table data to GCS . |
237,963 | def bq ( line , cell = None ) : return google . datalab . utils . commands . handle_magic_line ( line , cell , _bigquery_parser ) | Implements the bq cell magic for ipython notebooks . |
237,964 | def _table_viewer ( table , rows_per_page = 25 , fields = None ) : if not table . exists ( ) : raise Exception ( 'Table %s does not exist' % table . full_name ) if not table . is_listable ( ) : return "Done" _HTML_TEMPLATE = u if fields is None : fields = google . datalab . utils . commands . get_field_list ( fields , ... | Return a table viewer . |
237,965 | def _build_js ( inputs , outputs , name , implementation , support_code ) : input_fields = json . dumps ( [ f [ 0 ] for f in inputs ] ) output_fields = [ { 'name' : f [ 0 ] , 'type' : f [ 1 ] } for f in outputs ] output_fields = json . dumps ( output_fields , sort_keys = True ) if support_code is None : support_code = ... | Creates a BigQuery SQL UDF javascript object . |
237,966 | def sampling_query ( sql , fields = None , count = 5 , sampling = None ) : if sampling is None : sampling = Sampling . default ( count = count , fields = fields ) return sampling ( sql ) | Returns a sampling query for the SQL object . |
237,967 | def _remove_nonascii ( self , df ) : df_copy = df . copy ( deep = True ) for col in df_copy . columns : if ( df_copy [ col ] . dtype == np . dtype ( 'O' ) ) : df_copy [ col ] = df [ col ] . apply ( lambda x : re . sub ( r'[^\x00-\x7f]' , r'' , x ) if isinstance ( x , six . string_types ) else x ) return df_copy | Make copy and remove non - ascii characters from it . |
237,968 | def plot ( self , data ) : import IPython if not isinstance ( data , dict ) or not all ( isinstance ( v , pd . DataFrame ) for v in data . values ( ) ) : raise ValueError ( 'Expect a dictionary where the values are all dataframes.' ) gfsg = GenericFeatureStatisticsGenerator ( ) data = [ { 'name' : k , 'table' : self . ... | Plots an overview in a list of dataframes |
237,969 | def plot ( self , data , height = 1000 , render_large_data = False ) : import IPython if not isinstance ( data , pd . DataFrame ) : raise ValueError ( 'Expect a DataFrame.' ) if ( len ( data ) > 10000 and not render_large_data ) : raise ValueError ( 'Facets dive may not work well with more than 10000 rows. ' + 'Reduce ... | Plots a detail view of data . |
237,970 | def DtypeToType ( self , dtype ) : if dtype . char in np . typecodes [ 'AllFloat' ] : return self . fs_proto . FLOAT elif ( dtype . char in np . typecodes [ 'AllInteger' ] or dtype == np . bool or np . issubdtype ( dtype , np . datetime64 ) or np . issubdtype ( dtype , np . timedelta64 ) ) : return self . fs_proto . IN... | Converts a Numpy dtype to the FeatureNameStatistics . Type proto enum . |
237,971 | def NdarrayToEntry ( self , x ) : row_counts = [ ] for row in x : try : rc = np . count_nonzero ( ~ np . isnan ( row ) ) if rc != 0 : row_counts . append ( rc ) except TypeError : try : row_counts . append ( row . size ) except AttributeError : row_counts . append ( 1 ) data_type = self . DtypeToType ( x . dtype ) conv... | Converts an ndarray to the Entry format . |
237,972 | def serving_from_csv_input ( train_config , args , keep_target ) : examples = tf . placeholder ( dtype = tf . string , shape = ( None , ) , name = 'csv_input_string' ) features = parse_example_tensor ( examples = examples , train_config = train_config , keep_target = keep_target ) if keep_target : target = features . p... | Read the input features from a placeholder csv string tensor . |
237,973 | def parse_example_tensor ( examples , train_config , keep_target ) : csv_header = [ ] if keep_target : csv_header = train_config [ 'csv_header' ] else : csv_header = [ name for name in train_config [ 'csv_header' ] if name != train_config [ 'target_column' ] ] record_defaults = [ [ train_config [ 'csv_defaults' ] [ nam... | Read the csv files . |
237,974 | def get_estimator ( output_dir , train_config , args ) : target_name = train_config [ 'target_column' ] if is_classification_model ( args . model_type ) and target_name not in train_config [ 'categorical_columns' ] : raise ValueError ( 'When using a classification model, the target must be a ' 'categorical variable.' )... | Returns a tf learn estimator . |
237,975 | def preprocess_input ( features , target , train_config , preprocess_output_dir , model_type ) : target_name = train_config [ 'target_column' ] key_name = train_config [ 'key_column' ] with tf . name_scope ( 'numerical_feature_preprocess' ) : if train_config [ 'numerical_columns' ] : numerical_analysis_file = os . path... | Perform some transformations after reading in the input tensors . |
237,976 | def _scale_tensor ( tensor , range_min , range_max , scale_min , scale_max ) : if range_min == range_max : return tensor float_tensor = tf . to_float ( tensor ) scaled_tensor = tf . divide ( ( tf . subtract ( float_tensor , range_min ) * tf . constant ( float ( scale_max - scale_min ) ) ) , tf . constant ( float ( rang... | Scale a tensor to scale_min to scale_max . |
237,977 | def _tflearn_features ( train_config , args ) : feature_columns = [ ] target_name = train_config [ 'target_column' ] key_name = train_config [ 'key_column' ] for name in train_config [ 'numerical_columns' ] : if name != target_name and name != key_name : feature_columns . append ( tf . contrib . layers . real_valued_co... | Builds the tf . learn feature list . |
237,978 | def get_vocabulary ( preprocess_output_dir , name ) : vocab_file = os . path . join ( preprocess_output_dir , CATEGORICAL_ANALYSIS % name ) if not file_io . file_exists ( vocab_file ) : raise ValueError ( 'File %s not found in %s' % ( CATEGORICAL_ANALYSIS % name , preprocess_output_dir ) ) labels = python_portable_stri... | Loads the vocabulary file as a list of strings . |
237,979 | def validate_metadata ( train_config ) : if len ( train_config [ 'csv_header' ] ) != len ( train_config [ 'csv_defaults' ] ) : raise ValueError ( 'Unequal number of columns in input features file and ' 'schema file.' ) sorted_columns = sorted ( train_config [ 'csv_header' ] + [ train_config [ 'target_column' ] ] ) sort... | Perform some checks that the trainig config is correct . |
237,980 | def get_default_id ( credentials = None ) : project_id = _utils . get_project_id ( ) if project_id is None : projects , _ = Projects ( credentials ) . _retrieve_projects ( None , 2 ) if len ( projects ) == 1 : project_id = projects [ 0 ] . id return project_id | Get default project id . |
237,981 | def init_app ( state ) : app = state . app app . config . setdefault ( 'SPLIT_ALLOW_MULTIPLE_EXPERIMENTS' , False ) app . config . setdefault ( 'SPLIT_DB_FAILOVER' , False ) app . config . setdefault ( 'SPLIT_IGNORE_IP_ADDRESSES' , [ ] ) app . config . setdefault ( 'SPLIT_ROBOT_REGEX' , r ) app . jinja_env . globals . ... | Prepare the Flask application for Flask - Split . |
237,982 | def finished ( experiment_name , reset = True ) : if _exclude_visitor ( ) : return redis = _get_redis_connection ( ) try : experiment = Experiment . find ( redis , experiment_name ) if not experiment : return alternative_name = _get_session ( ) . get ( experiment . key ) if alternative_name : split_finished = set ( ses... | Track a conversion . |
237,983 | def _is_robot ( ) : robot_regex = current_app . config [ 'SPLIT_ROBOT_REGEX' ] user_agent = request . headers . get ( 'User-Agent' , '' ) return re . search ( robot_regex , user_agent , flags = re . VERBOSE ) | Return True if the current visitor is a robot or spider or False otherwise . |
237,984 | def start_time ( self ) : t = self . redis . hget ( 'experiment_start_times' , self . name ) if t : return datetime . strptime ( t , '%Y-%m-%dT%H:%M:%S' ) | The start time of this experiment . |
237,985 | def reset ( self ) : for alternative in self . alternatives : alternative . reset ( ) self . reset_winner ( ) self . increment_version ( ) | Delete all data for this experiment . |
237,986 | def delete ( self ) : for alternative in self . alternatives : alternative . delete ( ) self . reset_winner ( ) self . redis . srem ( 'experiments' , self . name ) self . redis . delete ( self . name ) self . increment_version ( ) | Delete this experiment and all its data . |
237,987 | def _get_redis_connection ( ) : url = current_app . config . get ( 'REDIS_URL' , 'redis://localhost:6379' ) return redis . from_url ( url , decode_responses = True ) | Return a Redis connection based on the Flask application s configuration . |
237,988 | def set_experiment_winner ( experiment ) : redis = _get_redis_connection ( ) experiment = Experiment . find ( redis , experiment ) if experiment : alternative_name = request . form . get ( 'alternative' ) alternative = Alternative ( redis , alternative_name , experiment . name ) if alternative . name in experiment . al... | Mark an alternative as the winner of the experiment . |
237,989 | def reset_experiment ( experiment ) : redis = _get_redis_connection ( ) experiment = Experiment . find ( redis , experiment ) if experiment : experiment . reset ( ) return redirect ( url_for ( '.index' ) ) | Delete all data for an experiment . |
237,990 | def delete_experiment ( experiment ) : redis = _get_redis_connection ( ) experiment = Experiment . find ( redis , experiment ) if experiment : experiment . delete ( ) return redirect ( url_for ( '.index' ) ) | Delete an experiment and all its data . |
237,991 | def _get_ipaddress ( node ) : if "ipaddress" not in node : with settings ( hide ( 'stdout' ) , warn_only = True ) : output = sudo ( 'ohai -l warn ipaddress' ) if output . succeeded : try : node [ 'ipaddress' ] = json . loads ( output ) [ 0 ] except ValueError : abort ( "Could not parse ohai's output for ipaddress" ":\n... | Adds the ipaddress attribute to the given node object if not already present and it is correctly given by ohai Returns True if ipaddress is added False otherwise |
237,992 | def sync_node ( node ) : if node . get ( 'dummy' ) or 'dummy' in node . get ( 'tags' , [ ] ) : lib . print_header ( "Skipping dummy: {0}" . format ( env . host ) ) return False current_node = lib . get_node ( node [ 'name' ] ) solo . configure ( current_node ) ipaddress = _get_ipaddress ( node ) filepath = save_config ... | Builds synchronizes and configures a node . It also injects the ipaddress to the node s config file if not already existent . |
237,993 | def build_dct ( dic , keys , value ) : key = keys . pop ( 0 ) if len ( keys ) : dic . setdefault ( key , { } ) build_dct ( dic [ key ] , keys , value ) else : if value == "false" : value = False elif value == "true" : value = True dic [ key ] = deepcopy ( value ) | Builds a dictionary with arbitrary depth out of a key list |
237,994 | def update_dct ( dic1 , dic2 ) : for key , val in dic2 . items ( ) : if isinstance ( val , dict ) : dic1 . setdefault ( key , { } ) update_dct ( dic1 [ key ] , val ) else : dic1 [ key ] = val | Merges two dictionaries recursively dic2 will have preference over dic1 |
237,995 | def _add_merged_attributes ( node , all_recipes , all_roles ) : attributes = { } for recipe in node [ 'recipes' ] : found = False for r in all_recipes : if recipe == r [ 'name' ] : found = True for attr in r [ 'attributes' ] : if r [ 'attributes' ] [ attr ] . get ( 'type' ) == "hash" : value = { } else : value = r [ 'a... | Merges attributes from cookbooks node and roles |
237,996 | def build_node_data_bag ( ) : nodes = lib . get_nodes ( ) node_data_bag_path = os . path . join ( 'data_bags' , 'node' ) remove_local_node_data_bag ( ) os . makedirs ( node_data_bag_path ) all_recipes = lib . get_recipes ( ) all_roles = lib . get_roles ( ) for node in nodes : node [ 'id' ] = node [ 'name' ] . replace (... | Builds one node data bag item per file found in the nodes directory |
237,997 | def remove_local_node_data_bag ( ) : node_data_bag_path = os . path . join ( 'data_bags' , 'node' ) if os . path . exists ( node_data_bag_path ) : shutil . rmtree ( node_data_bag_path ) | Removes generated node data_bag locally |
237,998 | def ensure_berksfile_cookbooks_are_installed ( ) : msg = "Vendoring cookbooks from Berksfile {0} to directory {1}..." print ( msg . format ( env . berksfile , env . berksfile_cookbooks_directory ) ) run_vendor = True cookbooks_dir = env . berksfile_cookbooks_directory berksfile_lock_path = cookbooks_dir + '/Berksfile.l... | Run berks vendor to berksfile cookbooks directory |
237,999 | def _remove_remote_node_data_bag ( ) : node_data_bag_path = os . path . join ( env . node_work_path , 'data_bags' , 'node' ) if exists ( node_data_bag_path ) : sudo ( "rm -rf {0}" . format ( node_data_bag_path ) ) | Removes generated node data_bag from the remote node |
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