H2O Module¶

h2o – module for using H2O services.

(please add description).

h2o.connect(server=None, url=None, ip=None, port=None, https=None, verify_ssl_certificates=None, auth=None, proxy=None, cluster_name=None, verbose=True)[source]¶

Connect to an existing H2O server, remote or local.

There are two ways to connect to a server: either pass a server parameter containing an instance of an H2OLocalServer, or specify ip and port of the server that you want to connect to.

Parameters:

server – An H2OLocalServer instance to connect to (optional). url – Full URL of the server to connect to (can be used instead of ip + port + https). ip – The ip address (or host name) of the server where H2O is running. port – Port number that H2O service is listening to. https – Set to True to connect via https:// instead of http://. verify_ssl_certificates – When using https, setting this to False will disable SSL certificates verification. auth – Either a (username, password) pair for basic authentication, or one of the requests.auth authenticator objects. proxy – Proxy server address. cluster_name – Name of the H2O cluster to connect to. This option is used from Steam only. verbose – Set to False to disable printing connection status messages.

h2o.init(url=None, ip=None, port=None, https=None, insecure=False, username=None, password=None, cluster_name=None, proxy=None, start_h2o=True, nthreads=-1, ice_root=None, enable_assertions=True, max_mem_size=None, min_mem_size=None, strict_version_check=True, **kwargs)[source]¶

Attempt to connect to a local server, or if not successful start a new server and connect to it.

Parameters:	url – ip – port – https – insecure – username – password – cluster_name – proxy – start_h2o – nthreads – ice_root – enable_assertions – max_mem_size – min_mem_size – strict_version_check – kwargs – (all other deprecated attributes)
Returns:	nothing

h2o.api(endpoint, data=None, json=None, filename=None)[source]¶

Perform a REST API request to a previously connected server.

h2o.connection()[source]¶

Return current H2OConnection handler.

h2o.upload_file(path, destination_frame=u'', header=(-1, 0, 1), sep=u'', col_names=None, col_types=None, na_strings=None)[source]¶

Upload a dataset at the path given from the local machine to the H2O cluster.

Does a single-threaded push to H2O. Also see import_file().

Parameters:

path : str A path specifying the location of the data to upload. destination_frame : str, optional The unique hex key assigned to the imported file. If none is given, a key will automatically be generated. header : int, optional -1 means the first line is data, 0 means guess, 1 means first line is header. sep : str, optional The field separator character. Values on each line of the file are separated by this character. If sep = “”, the parser will automatically detect the separator. col_names : list, optional A list of column names for the file. col_types : list or dict, optional A list of types or a dictionary of column names to types to specify whether columns should be forced to a certain type upon import parsing. If a list, the types for elements that are None will be guessed. The possible types a column may have are “unknown” - this will force the column to be parsed as all NA “uuid” - the values in the column must be true UUID or will be parsed as NA “string” - force the column to be parsed as a string “numeric” - force the column to be parsed as numeric. H2O will handle the compression of the numeric data in the optimal manner. “enum” - force the column to be parsed as a categorical column. “time” - force the column to be parsed as a time column. H2O will attempt to parse the following list of date time formats date - “yyyy-MM-dd”, “yyyy MM dd”, “dd-MMM-yy”, “dd MMM yy” time - “HH:mm:ss”, “HH:mm:ss:SSS”, “HH:mm:ss:SSSnnnnnn”, “HH.mm.ss” “HH.mm.ss.SSS”, “HH.mm.ss.SSSnnnnnn” Times can also contain “AM” or “PM”. na_strings : list or dict, optional A list of strings, or a list of lists of strings (one list per column), or a dictionary of column names to strings which are to be interpreted as missing values.

Returns:

A new H2OFrame instance.

Examples

>> h2o.upload_file(path=”/path/to/local/data”, destination_frame=”my_local_data”)

h2o.lazy_import(path)[source]¶

Import a single file or collection of files.

Parameters:	path – A path to a data file (remote or local).

h2o.import_file(path=None, destination_frame=u'', parse=True, header=(-1, 0, 1), sep=u'', col_names=None, col_types=None, na_strings=None)[source]¶

Have H2O import a dataset into memory. The path to the data must be a valid path for each node in the H2O cluster. If some node in the H2O cluster cannot see the file, then an exception will be thrown by the H2O cluster. Does a parallel/distributed multi-threaded pull of the data. Also see upload_file.

Parameters:

path : str | list(str) A path specifying the location of the data to import. destination_frame : str, optional The unique hex key assigned to the imported file. If none is given, a key will automatically be generated. parse : bool, optional A logical value indicating whether the file should be parsed after import. header : int, optional -1 means the first line is data, 0 means guess, 1 means first line is header. sep : str, optional The field separator character. Values on each line of the file are separated by this character. If sep = “”, the parser will automatically detect the separator. col_names : list, optional A list of column names for the file. col_types : list or dict, optional A list of types or a dictionary of column names to types to specify whether columns should be forced to a certain type upon import parsing. If a list, the types for elements that are None will be guessed. The possible types a column may have are: “unknown” - this will force the column to be parsed as all NA “uuid” - the values in the column must be true UUID or will be parsed as NA “string” - force the column to be parsed as a string “numeric” - force the column to be parsed as numeric. H2O will handle the compression of the numeric data in the optimal manner. “enum” - force the column to be parsed as a categorical column. “time” - force the column to be parsed as a time column. H2O will attempt to parse the following list of date time formats date - “yyyy-MM-dd”, “yyyy MM dd”, “dd-MMM-yy”, “dd MMM yy” time - “HH:mm:ss”, “HH:mm:ss:SSS”, “HH:mm:ss:SSSnnnnnn”, “HH.mm.ss” “HH.mm.ss.SSS”, “HH.mm.ss.SSSnnnnnn” Times can also contain “AM” or “PM”. na_strings : list or dict, optional A list of strings, or a list of lists of strings (one list per column), or a dictionary of column names to strings which are to be interpreted as missing values.

Returns:

A new H2OFrame instance.

h2o.import_sql_table(connection_url, table, username, password, columns=None, optimize=None)[source]¶

Import SQL table to H2OFrame in memory. Assumes that the SQL table is not being updated and is stable. Runs multiple SELECT SQL queries concurrently for parallel ingestion. Be sure to start the h2o.jar in the terminal with your downloaded JDBC driver in the classpath:

java -cp <path_to_h2o_jar>:<path_to_jdbc_driver_jar> water.H2OApp

Also see h2o.import_sql_select. Currently supported SQL databases are MySQL, PostgreSQL, and MariaDB. Support for Oracle 12g and Microsoft SQL Server is forthcoming.

Parameters:

connection_url : str URL of the SQL database connection as specified by the Java Database Connectivity (JDBC) Driver. For example, “jdbc:mysql://localhost:3306/menagerie?&useSSL=false“ table : str Name of SQL table username : str Username for SQL server password : str Password for SQL server columns : list of strings, optional A list of column names to import from SQL table. Default is to import all columns. optimize : bool, optional, default is True Optimize import of SQL table for faster imports. Experimental.

Returns:

H2OFrame containing data of specified SQL table

Examples

>> conn_url = “jdbc:mysql://172.16.2.178:3306/ingestSQL?&useSSL=false” >> table = “citibike20k” >> username = “root” >> password = “abc123” >> my_citibike_data = h2o.import_sql_table(conn_url, table, username, password)

h2o.import_sql_select(connection_url, select_query, username, password, optimize=None)[source]¶

Imports the SQL table that is the result of the specified SQL query to H2OFrame in memory.

Creates a temporary SQL table from the specified sql_query. Runs multiple SELECT SQL queries on the temporary table concurrently for parallel ingestion, then drops the table. Be sure to start the h2o.jar in the terminal with your downloaded JDBC driver in the classpath:

java -cp <path_to_h2o_jar>:<path_to_jdbc_driver_jar> water.H2OApp

Also see h2o.import_sql_table. Currently supported SQL databases are MySQL, PostgreSQL, and MariaDB. Support for Oracle 12g and Microsoft SQL Server is forthcoming.

Parameters:

connection_url : str URL of the SQL database connection as specified by the Java Database Connectivity (JDBC) Driver. For example, “jdbc:mysql://localhost:3306/menagerie?&useSSL=false“ select_query : str SQL query starting with SELECT that returns rows from one or more database tables. username : str Username for SQL server password : str Password for SQL server optimize : bool, optional, default is True Optimize import of SQL table for faster imports. Experimental.

Returns:

H2OFrame containing data of specified SQL select query

Examples

>>> conn_url = "jdbc:mysql://172.16.2.178:3306/ingestSQL?&useSSL=false"
>>> select_query = "SELECT bikeid from citibike20k"
>>> username = "root"
>>> password = "abc123"
>>> my_citibike_data = h2o.import_sql_select(conn_url, select_query, username, password)

h2o.parse_setup(raw_frames, destination_frame=u'', header=(-1, 0, 1), separator=u'', column_names=None, column_types=None, na_strings=None)[source]¶

During parse setup, the H2O cluster will make several guesses about the attributes of the data. This method allows a user to perform corrective measures by updating the returning dictionary from this method. This dictionary is then fed into parse_raw to produce the H2OFrame instance.

Parameters:

raw_frames : H2OFrame A collection of imported file frames destination_frame : str, optional The unique hex key assigned to the imported file. If none is given, a key will automatically be generated. parse : bool, optional A logical value indicating whether the file should be parsed after import. header : int, optional -1 means the first line is data, 0 means guess, 1 means first line is header. sep : str, optional The field separator character. Values on each line of the file are separated by this character. If sep = “”, the parser will automatically detect the separator. col_names : list, optional A list of column names for the file. col_types : list or dict, optional A list of types or a dictionary of column names to types to specify whether columns should be forced to a certain type upon import parsing. If a list, the types for elements that are None will be guessed. The possible types a column may have are: “unknown” - this will force the column to be parsed as all NA “uuid” - the values in the column must be true UUID or will be parsed as NA “string” - force the column to be parsed as a string “numeric” - force the column to be parsed as numeric. H2O will handle the compression of the numeric data in the optimal manner. “enum” - force the column to be parsed as a categorical column. “time” - force the column to be parsed as a time column. H2O will attempt to parse the following list of date time formats date - “yyyy-MM-dd”, “yyyy MM dd”, “dd-MMM-yy”, “dd MMM yy” time - “HH:mm:ss”, “HH:mm:ss:SSS”, “HH:mm:ss:SSSnnnnnn”, “HH.mm.ss” “HH.mm.ss.SSS”, “HH.mm.ss.SSSnnnnnn” Times can also contain “AM” or “PM”. na_strings : list or dict, optional A list of strings, or a list of lists of strings (one list per column), or a dictionary of column names to strings which are to be interpreted as missing values.

Returns:

A dictionary is returned containing all of the guesses made by the H2O back end.

h2o.parse_raw(setup, id=None, first_line_is_header=(-1, 0, 1))[source]¶

Used in conjunction with lazy_import and parse_setup in order to make alterations before parsing.

Parameters:	setup : dict Result of h2o.parse_setup id : str, optional An id for the frame. first_line_is_header : int, optional -1,0,1 if the first line is to be used as the header
Returns:	H2OFrame

h2o.get_model(model_id)[source]¶

Return the specified model.

Parameters:	model_id : str The model identification in h2o
Returns:	Subclass of H2OEstimator

h2o.get_grid(grid_id)[source]¶

Return the specified grid.

Parameters:	grid_id : str The grid identification in h2o
Returns:	H2OGridSearch instance

h2o.get_frame(frame_id)[source]¶

Obtain a handle to the frame in H2O with the frame_id key.

Returns:	H2OFrame

h2o.show_progress()[source]¶

Enable the progress bar. (Progress bar is enabled by default).

h2o.no_progress()[source]¶

Disable the progress bar from flushing to stdout. The completed progress bar is printed when a job is complete so as to demarcate a log file.

h2o.log_and_echo(message)[source]¶

Log a message on the server-side logs This is helpful when running several pieces of work one after the other on a single H2O cluster and you want to make a notation in the H2O server side log where one piece of work ends and the next piece of work begins.

Sends a message to H2O for logging. Generally used for debugging purposes.

Parameters:	message – (str)message to write to the log.

:return None

h2o.remove(x)[source]¶

Remove object(s) from H2O.

Parameters:	x : H2OFrame, H2OEstimator, or string, or a list/tuple of those things. The object(s) or unique id(s) pointing to the object(s) to be removed.

h2o.remove_all()[source]¶

Remove all objects from H2O.

h2o.rapids(expr)[source]¶

Execute a Rapids expression.

Parameters:	expr : str The rapids expression (ascii string).
Returns:	The JSON response (as a python dictionary) of the Rapids execution

h2o.ls()[source]¶

List keys on an H2O Cluster.

h2o.frame(frame_id, exclude=u'')[source]¶

Retrieve metadata for an id that points to a Frame.

Parameters:	frame_id : str A pointer to a Frame in H2O. :returns: dict containing the frame meta-information.

h2o.frames()[source]¶

Retrieve all the Frames.

Returns:	Meta information on the frames

h2o.download_pojo(model, path=u'', get_jar=True)[source]¶

Download the POJO for this model to the directory specified by path (no trailing slash!). If path is “”, then dump to screen.

Parameters:	model : H2OModel Retrieve this model’s scoring POJO. path : str An absolute path to the directory where POJO should be saved. get_jar : bool Retrieve the h2o-genmodel.jar also.

h2o.download_csv(data, filename)[source]¶

Download an H2O data set to a CSV file on the local disk.

Warning: Files located on the H2O server may be very large! Make sure you have enough hard drive space to accommodate the entire file.

Parameters:	data : H2OFrame An H2OFrame object to be downloaded. filename : str A string indicating the name that the CSV file should be should be saved to.

h2o.download_all_logs(dirname=u'.', filename=None)[source]¶

Download H2O Log Files to Disk

Parameters:	dirname : str, optional A character string indicating the directory that the log file should be saved in. filename : str, optional A string indicating the name that the CSV file should be
Returns:	Path of logs written.

h2o.save_model(model, path=u'', force=False)[source]¶

Save an H2O Model Object to Disk.

Parameters:	model : H2OModel The model object to save. path : str A path to save the model at (hdfs, s3, local) force : bool Overwrite destination directory in case it exists or throw exception if set to false.
Returns:	The path of the saved model (string)

h2o.load_model(path)[source]¶

Load a saved H2O model from disk.

Parameters:	path : str The full path of the H2O Model to be imported.
Returns:	H2OEstimator object

Examples

>> path = h2o.save_mode(my_model,dir=my_path) >> h2o.load_model(path)

h2o.export_file(frame, path, force=False)[source]¶

Export a given H2OFrame to a path on the machine this python session is currently connected to. To view the current session, call h2o.cluster_info().

Parameters:	frame : H2OFrame The Frame to save to disk. path : str The path to the save point on disk. force : bool Overwrite any preexisting file with the same path

h2o.cluster_status()[source]¶

This is possibly confusing because this can come back without warning, but if a user tries to do any remoteSend, they will get a “cloud sick warning” Retrieve information on the status of the cluster running H2O.

h2o.cluster_info()[source]¶

Display the current H2O cluster information.

h2o.shutdown(prompt=True)[source]¶

Shut down the specified instance. All data will be lost. This method checks if H2O is running at the specified IP address and port, and if it is, shuts down that H2O instance.

Parameters:	prompt – (bool) A logical value indicating whether to prompt the user before shutting down the H2O server.

h2o.create_frame(id=None, rows=10000, cols=10, randomize=True, value=0, real_range=100, categorical_fraction=0.2, factors=100, integer_fraction=0.2, integer_range=100, binary_fraction=0.1, binary_ones_fraction=0.02, time_fraction=0, string_fraction=0, missing_fraction=0.01, response_factors=2, has_response=False, seed=None, seed_for_column_types=None)[source]¶

Data Frame Creation in H2O.

Creates a data frame in H2O with real-valued, categorical, integer, and binary columns specified by the user.

Parameters:

id : str A string indicating the destination key. If empty, this will be auto-generated by H2O. rows : int The number of rows of data to generate. cols : int The number of columns of data to generate. Excludes the response column if has_response == True. randomize : bool A logical value indicating whether data values should be randomly generated. This must be TRUE if either categorical_fraction or integer_fraction is non-zero. value : int If randomize == FALSE, then all real-valued entries will be set to this value. real_range : float The range of randomly generated real values. categorical_fraction : float The fraction of total columns that are categorical. factors : int The number of (unique) factor levels in each categorical column. integer_fraction : float The fraction of total columns that are integer-valued. integer_range : list The range of randomly generated integer values. binary_fraction : float The fraction of total columns that are binary-valued. binary_ones_fraction : float The fraction of values in a binary column that are set to 1. time_fraction : float The fraction of randomly created date/time columns string_fraction : float The fraction of randomly created string columns missing_fraction : float The fraction of total entries in the data frame that are set to NA. response_factors : int If has_response == TRUE, then this is the number of factor levels in the response column. has_response : bool A logical value indicating whether an additional response column should be pre-pended to the final H2O data frame. If set to TRUE, the total number of columns will be cols+1. seed : int A seed used to generate random values when randomize = TRUE. seed_for_column_types : int A seed used to generate random column types when randomize = TRUE.

Returns:

H2OFrame

h2o.interaction(data, factors, pairwise, max_factors, min_occurrence, destination_frame=None)[source]¶

Categorical Interaction Feature Creation in H2O. Creates a frame in H2O with n-th order interaction features between categorical columns, as specified by the user.

Parameters:

data : H2OFrame the H2OFrame that holds the target categorical columns. factors : list factors Factor columns (either indices or column names). pairwise : bool Whether to create pairwise interactions between factors (otherwise create one higher-order interaction). Only applicable if there are 3 or more factors. max_factors : int Max. number of factor levels in pair-wise interaction terms (if enforced, one extra catch-all factor will be made) min_occurrence : int Min. occurrence threshold for factor levels in pair-wise interaction terms destination_frame : str A string indicating the destination key. If empty, this will be auto-generated by H2O.

Returns:

H2OFrame

h2o.as_list(data, use_pandas=True)[source]¶

Convert an H2O data object into a python-specific object.

WARNING! This will pull all data local!

If Pandas is available (and use_pandas is True), then pandas will be used to parse the data frame. Otherwise, a list-of-lists populated by character data will be returned (so the types of data will all be str).

Parameters:	data : H2OFrame An H2O data object. use_pandas : bool Try to use pandas for reading in the data.
Returns:	List of list (Rows x Columns).

h2o.set_timezone(tz)[source]¶

Set the Time Zone on the H2O Cloud

Parameters:	tz : str The desired timezone.

h2o.get_timezone()[source]¶

Get the Time Zone on the H2O Cloud

Returns:	The time zone (string)

h2o.list_timezones()[source]¶

Get a list of all the timezones

Returns:	The time zones (as an H2OFrame)

h2o.demo(funcname, interactive=True, echo=True, test=False)[source]¶

H2O built-in demo facility.

Parameters:	funcname – A string that identifies the h2o python function to demonstrate. interactive – If True, the user will be prompted to continue the demonstration after every segment. echo – If True, the python commands that are executed will be displayed. test – If True, h2o.init() will not be called (used for pyunit testing).

Example: >>> import h2o >>> h2o.demo(“gbm”)

h2o.make_metrics(predicted, actual, domain=None, distribution=None)[source]¶

Create Model Metrics from predicted and actual values in H2O.

Params H2OFrame predicted:
	an H2OFrame containing predictions.
Params H2OFrame actuals:
	an H2OFrame containing actual values.
Params domain:	list of response factors for classification.
Params distribution:
	distribution for regression.