zeenk-tql
  • Overview
    • What is TQL?
    • Installation
      • Obtaining a license key
      • Prerequisites
      • Installing
    • Where To Next?
      • Quick Start
        • Table of Contents
        • Starting the TQL Backend
        • Importing TQL
        • Creating Timelines
        • Introduction to Queries
        • The TQL Expression Language
        • Building a Machine Learning Dataset
        • Training A Machine Learning Model
        • Model Deployment
        • Making Predictions
        • What Next?
      • Key Concepts and Architecture
        • The Timeline
        • Executing Queries against Timelines
      • Creating Timelines
        • Creating A TimeSeries
        • Validating A TimeSeries
        • Loading A TimeSeries into Pandas or Spark
        • Getting an Example TimeSeries Row
        • Visualizing A TimeSeries
        • Analyzing A TimeSeries
        • Creating Another TimeSeries
        • Creating a TimeSeries from an External SQL Store
        • Listing All Available Projects
        • Loading a Project Definition
        • Using A Project In A TQL Query
      • Expression Language
        • Literals
        • Event Attributes
        • Booleans and Logical Operators
        • TQL Functions
        • Function Reference
        • Mathematical Operators
        • Probabilibity Distributions
        • Generating Random Numbers
        • Hashing
        • String Manipulation
        • Date/Time Manipulation
        • Array Manipulation
        • Array Manipulation of Timeline Events
        • Local Variables and Multi-Statement Expressions
        • The PREDICT Function
        • The SET_PROPERTY Function
        • Comments
        • User Defined Functions (UDFs)
        • Compile and Runtime Error Handling
        • Expression Language Cheat Sheet
        • Where To Next?
      • Writing Queries
        • Query Syntax
        • Query Columns
        • Expression Types
        • Selecting Event Attributes
        • The Select Wildcard Operator
        • The Validation Operator
        • The Timeline Limit Operator
        • The Where Operator
        • The From Timelines Operator
        • The Submit Operator
        • The Format Operator for Spark ResultSets
        • Column
        • FeatureColumn
        • Filtering on Columns
        • Using event_metadata() and event_time()
        • The Partition By Operator
        • The External Timelines Operator
        • The Downsample By Operator
        • The Options Operator
        • The *_var Operators
        • The Opportunities Operator
        • The Union or Sampling Operator
        • User-Defined Functions
      • Query Options
        • The drop_constant_feature_columns Option
        • The numerical_feature_epsilon Option
      • Using ResultSets
        • Loading ResultSet Data
        • ResultSet Partitions
        • Retrieving Column Names
        • Row Count
        • Positive row count
        • Get Query
        • Get ID
        • Load
        • Refresh
        • Metrics
      • User Defined Functions
        • Table-level UDFs
        • Query-level UDFs
        • Inline expression UDFs
      • Training ML Models
        • Inspect The bid TimeSeries
        • Create a Training ResultSet
        • Estimating a Model
        • Summarize the Model Training Session
        • Publish the Model into TQL:
        • Make Model Predictions with TQL
      • Jupyter Extensions
        • Installation
        • Debugger
        • Query Visualizer
      • Configuring TQL
        • The TQL Configuration File
        • Modifying the Configuration
        • Configuration Sections
      • Scaling TQL
        • TQL on Spark
        • Downsampling ResultSets
        • Optimizing Computation with Scoped Variables
      • Python API Reference
        • zeenk-tql
        • zeenk-causmos
        • zeenk-data-simulator
      • Expression Language Reference
        • Built-ins
        • UDFs
  • Quick Start
    • Table of Contents
    • Starting the TQL Backend
    • Importing TQL
    • Creating Timelines
    • Introduction to Queries
      • Creating and executing queries
      • Interactive Query Execution in Jupyter
      • The TQL Data Processing Model
    • The TQL Expression Language
      • Timeline Expressions
    • Building a Machine Learning Dataset
    • Training A Machine Learning Model
    • Model Deployment
    • Making Predictions
      • Batch Predictions
      • Real-Time Predictions
      • Prediction As A Service
    • What Next?
  • Key Concepts and Architecture
    • The Timeline
      • The TimeSeries
      • The Project
      • Building Timelines
      • The Timeline Data Structure
    • Executing Queries against Timelines
  • Creating Timelines
    • Creating A TimeSeries
    • Validating A TimeSeries
    • Loading A TimeSeries into Pandas or Spark
    • Getting an Example TimeSeries Row
    • Visualizing A TimeSeries
    • Analyzing A TimeSeries
    • Creating Another TimeSeries
    • Creating a TimeSeries from an External SQL Store
    • Listing All Available Projects
    • Loading a Project Definition
    • Using A Project In A TQL Query
  • Expression Language
    • Literals
    • Event Attributes
    • Booleans and Logical Operators
    • TQL Functions
    • Function Reference
    • Mathematical Operators
    • Probabilibity Distributions
    • Generating Random Numbers
    • Hashing
    • String Manipulation
    • Date/Time Manipulation
    • Array Manipulation
    • Array Manipulation of Timeline Events
    • Local Variables and Multi-Statement Expressions
    • The PREDICT Function
    • The SET_PROPERTY Function
    • Comments
    • User Defined Functions (UDFs)
    • Compile and Runtime Error Handling
    • Expression Language Cheat Sheet
    • Where To Next?
  • Writing Queries
    • Query Syntax
    • Query Columns
    • Expression Types
    • Selecting Event Attributes
    • The Select Wildcard Operator
      • The wildcard operator selects all event attributes.
      • The wildcard operator can also select a subset of event attributes.
    • The Validation Operator
    • The Timeline Limit Operator
    • The Where Operator
    • The From Timelines Operator
    • The Submit Operator
    • The Format Operator for Spark ResultSets
    • Column
    • FeatureColumn
    • Filtering on Columns
    • Using event_metadata() and event_time()
    • The Partition By Operator
    • The External Timelines Operator
    • The Downsample By Operator
    • The Options Operator
    • The *_var Operators
    • The Opportunities Operator
    • The Union or Sampling Operator
    • User-Defined Functions
  • Query Options
    • The drop_constant_feature_columns Option
    • The numerical_feature_epsilon Option
  • Using ResultSets
    • Loading ResultSet Data
      • Loading Data as a Python List
      • Loading Data as a Pandas Dataframe
      • Loading Data as a Spark Dataframe
    • ResultSet Partitions
      • Default Partition
      • Partition Names
      • Partitions
      • Partition
    • Retrieving Column Names
    • Row Count
    • Positive row count
    • Get Query
    • Get ID
    • Load
    • Refresh
    • Metrics
  • User Defined Functions
    • Table-level UDFs
      • Defining UDF function source strings
      • UDF Validation
      • Uploading a UDF to a table/project
      • Looking up Table-level UDFs
      • Deleting a UDF
      • List all existing UDFs on a table
    • Query-level UDFs
    • Inline expression UDFs
  • Training ML Models
    • Inspect The bid TimeSeries
    • Create a Training ResultSet
    • Estimating a Model
    • Summarize the Model Training Session
    • Publish the Model into TQL:
    • Make Model Predictions with TQL
  • Jupyter Extensions
    • Installation
    • Debugger
      • The debugger extension will be displayed with the current expression value:
      • Hovering over a keyword will display its documentation:
      • There is auto-completion for keywords, variables, and functions:
        • You can restore the expression value to the value stored in the notebook cell by right-clicking in the editor and selecting Restore Expression.
      • Running Tests
      • Code Stepper
      • Basic Usage
    • Query Visualizer
  • Configuring TQL
    • The TQL Configuration File
    • Modifying the Configuration
    • Configuration Sections
      • FileSystem
        • Amazon S3 Filesystem
        • Google GCS Filesystem:
      • PySpark
      • Icarus
      • Database
  • Scaling TQL
    • TQL on Spark
      • Submitting Queries to a Spark Cluster
      • Spark Resultsets
      • Retrieving Asynchronous Queries and ResultSets
      • Connecting to an external Spark Cluster
      • Reading and Writing To Cloud Storage
    • Downsampling ResultSets
      • Simple Downsampling
      • Downsampling By Attribute
      • Using the downsample_by() operator
      • Using the timeline_sample_rate() operator
    • Optimizing Computation with Scoped Variables
  • Python API Reference
    • zeenk-tql
      • Subpackages
        • tql.modeling package
      • Submodules
      • tql.column module
      • tql.column_utils module
      • tql.columnset module
      • tql.demo_projects module
      • tql.expression_debugger module
      • tql.expression_utils module
      • tql.function_doc module
      • tql.opportunity_conf module
      • tql.query module
      • tql.query_templates module
      • tql.resultset module
      • tql.sampling module
      • tql.timelines module
      • tql.timeseries module
      • tql.udf module
      • tql.validation module
      • tql.visualizer module
    • zeenk-causmos
      • Subpackages
        • causmos.tests package
      • Submodules
      • causmos.causmos_model module
      • causmos.event_group module
      • causmos.globals module
      • causmos.validation module
    • zeenk-data-simulator
      • Subpackages
        • lethe.configs package
        • lethe.tests package
      • Submodules
      • lethe.config module
      • lethe.config_objects module
      • lethe.core module
      • lethe.lethe module
      • lethe.storage module
      • lethe.util module
  • Expression Language Reference
    • Built-ins
    • UDFs
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