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
zeenk-tql
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