0.11.0
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getML suite
Importing data
Annotating data
Data model
Feature engineering
Predicting
Hyperparameter optimization
Deployment
API Documentation
Python
Command Line Interface
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getML in one minute
How to use this guide
Installation
Mac
System requirements
Install and run the getML engine and monitor
Install the getML Python API
From PyPI (recommended)
From source
Install the getML-CLI
Uninstall getML
Where to go next
Windows
System requirements
Run the getML engine and monitor
Install the getML Python API
From PyPI (recommended)
From source
Uninstall getML
Where to go next
Linux
System requirements
Install and run the getML engine and monitor
Install the getML Python API
From PyPI (recommended)
From source
Install the getML CLI
Uninstall getML
Where to go next
Remote access
Running the getML suite remotely
Prerequisites
Remote installation
Starting engine and monitor
Login
Running analyses using the Python API
Retrieving results
Stopping engine and monitor
Accessing the getML monitor via the internet
What is accessible and what is not?
Creating and using TLS certificates
Adding an exception in Firefox
Adding an exception in Chrome
Opening the HTTPS port
Getting started
Starting a new project
Data set
Defining the data model
Training a model
Scoring the model
Making predictions
Extracting features
Next steps
Support
User Guide
Tutorials
getML suite
Engine
Starting the engine
Shutting down the engine
Logging
Managing data using projects
Monitor
Projects
Pipelines
Top-level view
Fitted pipelines
Score plots
Deployed pipelines
Pipeline View
ROC curve
Accuracy
Correlations and Importances
Features table
Hyperparameters
Data model
Feature View
Density plot
Average target plot
Data Frames
Top-level view
Data frame view
Column view
Density plot
Relation plot
Database
Table View
Log
Docs
Configuration
Monitor configuration
Engine configuration
Certificates
User Management
Support
Logout
Shutdown
Python API
Connecting to the getML engine
Session management
Lifecycles and synchronization between engine and API
Lifecycle of a
DataFrame
Lifecycle of a
Pipeline
Importing data
Unified import interface
CSV interface
Import from CSV
Export to CSV
Pandas interface
Import from Pandas
Export to Pandas
JSON interface
Import from JSON
Export to JSON
SQLite3 interface
Import from SQLite3
Export to SQLite3
MySQL interface
Import from MySQL
Export to MySQL
MariaDB interface
Import from MariaDB
Export to MariaDB
PostgreSQL interface
Import from PostgreSQL
Export to PostgreSQL
Greenplum interface
Import from Greenplum
Export to Greenplum
ODBC interface
An example: Microsoft SQL Server
Important: Always pass
escape_chars
Import data using ODBC
Export data using ODBC
Data Frames
Handling of NULL values
Annotating data
In short
Roles
A note on reproducibility and efficiency
Join key
Time stamp
Target
Numerical
Categorical
Unused_float
Unused_string
Units
Data model
Tables
The population table
Peripheral tables
Placeholders
Joins
Data schemata
The star schema
The snowflake schema
Time series
Self-joining a single table
Features based on time stamps
Feature engineering
Definition
Design principles
Algorithms
Multirel
Relboost
Best practices
How to make features more interpretable
How to add handcrafted features
Which hyperparameters have the most impact for Multirel?
How to reduce the training time of Multirel
Influence of the data set on the training time
Predicting
Using getML
Using external software
Hyperparameter optimization
Random search
Latin hypercube search
Gaussian hyperparameter search
Deployment
Prerequisites
HTTP(S) Endpoints
Request Format
Time stamp formats in requests
Using an existing
DataFrame
Using data from a database
Transform Endpoint
Predict Endpoint
API Documentation
Python
getml.data
Functions
concat
load_data_frame
list_data_frames
Classes
DataFrame
Placeholder
Submodules
columns
Classes
Aggregation
FloatColumn
StringColumn
VirtualBooleanColumn
VirtualFloatColumn
VirtualStringColumn
roles
Variables
categorical
join_key
numerical
target
time_stamp
unused_float
unused_string
time
Functions
seconds
minutes
hours
days
weeks
milliseconds
microseconds
getml.database
Functions
connect_greenplum
connect_mariadb
connect_mysql
connect_odbc
connect_postgres
connect_sqlite3
copy_table
drop_table
execute
get
get_colnames
list_connections
list_tables
read_csv
read_s3
sniff_csv
sniff_s3
Classes
Connection
getml.dataset
Functions
make_categorical
make_discrete
make_numerical
make_same_units_categorical
make_same_units_numerical
make_snowflake
getml.engine
Functions
delete_project
list_projects
is_alive
set_project
set_s3_access_key_id
set_s3_secret_access_key
shutdown
getml.feature_learning
Classes
MultirelModel
MultirelTimeSeries
RelboostModel
RelboostTimeSeries
getml.hyperopt
Functions
list_hyperopts
load_hyperopt
Classes
GaussianHyperparameterSearch
LatinHypercubeSearch
RandomSearch
Submodules
burn_in
Variables
latin_hypercube
random
kernels
Variables
exp
gauss
matern32
matern52
optimization
Variables
bfgs
nelder_mead
getml.pipeline
Functions
list_pipelines
load
Classes
Features
Metrics
Pipeline
SQLCode
Submodules
scores
Variables
accuracy
auc
cross_entropy
mae
rmse
rsquared
getml.predictors
Classes
LinearRegression
LogisticRegression
XGBoostClassifier
XGBoostRegressor
Command Line Interface