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• getML
  • 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

Tutorials

• Overview
• Loan default prediction
  • Data preparation
    • Setting roles
    • Population table
    • Peripheral tables
    • Setting units
    • Data model
  • Training a Multirel Model
  • Hyperparameter optimization
  • Extracting Features
  • Results
• Consumer Expenditure
  • The Challenge
  • How To Use
  • Staging
    • Staging Data Directly
      • First steps
      • Loading the data
      • Staging EXPD
      • Staging MEMD
      • Staging POPULATION
    • Staging Data Using Pandas
      • Loading the data
      • Staging EXPD
      • Staging POPULATION
      • Loading the data into the getML engine
      • Separating the data
    • Staging Data Using sqlite3
      • Loading the data
      • Staging EXPD
      • Staging POPULATION
      • Loading the data into the engine
      • Separate data
    • Staging Data Using PostgreSQL/Greenplum/Redshift (Linux and macOS only)
      • Setting up PostgreSQL
      • Loading the data
      • Staging EXPD
      • Staging POPULATION
      • Loading the data into the engine
      • Separate data
    • Staging Data Using MySQL/MariaDB
      • Setting up MySQL or MariaDB
      • Loading the data
      • Staging EXPD
      • Staging POPULATION
      • Loading the data into the engine
      • Separate data
  • Training
    • Training a single MultirelModel
      • Getting started
      • Building the data model
      • Building the model
      • Fitting the model
      • Evaluation
      • Retrieving data
    • Hyperparameter optimization for MultirelModel
      • Getting started
      • Building the data model
      • Building the reference model
      • Building the hyperparameter space
      • Fitting
    • Training a single RelboostModel
      • Getting started
      • Building the data model
      • Building the model
      • Fitting the model
      • Evaluation
      • Retrieving data
    • Hyperparameter optimization for RelboostModel
      • Getting started
      • Building the data model
      • Building the reference model
      • Building the hyperparameter space
      • Fitting

User Guide

• getML suite
  • Engine
    • Starting the engine
    • Shutting down the engine
    • Logging
    • Managing data using projects
  • Monitor
    • Projects
    • Models
      • Top-level View
        • Fitted Models
        • Score plots
        • Deployed models
      • Model View
        • ROC curve
        • Accuracy
        • Correlations and Importances
        • Features table
        • Hyperparameters
        • Data model
      • Feature View
        • Frequency plot
        • Average target plot
    • Data Frames
      • Top-level View
      • Data Frame View
      • Column View
        • Frequency 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 models
• Uploading 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
  • 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
  • Bayesian hyperparameter optimization
• 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
      • load_data_frame
      • list_data_frames
    • Classes
      • DataFrame
      • Placeholder
    • Submodules
      • columns
        • Classes
          • FloatColumn
          • StringColumn
      • roles
        • Variables
          • categorical
          • join_key
          • numerical
          • target
          • time_stamp
          • unused_float
          • unused_string
  • getml.database
    • Functions
      • connect_greenplum
      • connect_mariadb
      • connect_mysql
      • connect_postgres
      • connect_sqlite3
      • drop_table
      • execute
      • get
      • get_colnames
      • list_tables
      • read_csv
      • sniff_csv
  • 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
      • shutdown
  • getml.hyperopt
    • Functions
      • _decode_hyperopt
      • list_hyperopts
      • load_hyperopt
    • Classes
      • GaussianHyperparameterSearch
      • LatinHypercubeSearch
      • RandomSearch
  • getml.models
    • Functions
      • list_models
      • load_model
    • Classes
      • MultirelModel
      • RelboostModel
    • Submodules
      • aggregations
        • Variables
          • Avg
          • Count
          • CountDistinct
          • CountMinusCountDistinct
          • Max
          • Median
          • Min
          • Stddev
          • Sum
          • Var
      • scores
        • Variables
          • accuracy
          • auc
          • cross_entropy
          • mae
          • rmse
          • rsquared
      • loss_functions
        • Classes
          • CrossEntropyLoss
          • SquareLoss
  • getml.predictors
    • Classes
      • LinearRegression
      • LogisticRegression
      • XGBoostClassifier
      • XGBoostRegressor
  • Variables
    • port
• Command Line Interface