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Interacting with the framework

KxSystems/automl

There are two primary methods of interacting with this framework:

  1. Apply a function and manipulate models within a q process
  2. With command-line arguments and customized configuration

Run within a q process

The top-level functions in the repository are:

.automl Top-level functions

Generate, retrieve, delete models fit Apply AutoML to provided features and associated targets getModel Retrieve a previously fit AutoML model deleteModels Delete model/s

Generate configuration newConfig Generate a new JSON parameter file for use with .automl.fit

Updates updateIgnoreWarnings Update print warning severity level updateLogging Update logging state updatePrinting Update printing state

You can call .automl.fit with arguments to suit a specific use case. The functions listed above cover a wide range of options. You can also extend them.

Advanced section

The examples following outline the most basic applications of AutoML: non-timeseries-specific machine-learning examples, and timeseries examples which use the FRESH algorithm and NLP Library.

Model prediction

The AutoML library contains no explicit predict function callable as a standalone entity. Instead, predictions are made based on the output of a previously fit model. As for .automl.fit and .automl.getModel, there are two methods by which such models can be made available to a user.

  1. As the output of an in process run of the AutoML framework.
  2. By retrieving the model information and its associated prediction function from disk.

In each case the output is a dictionary containing the predict function required to make predictions based on newly-retrieved data. Below are example invocations. For simplicity, any unnecessary text which would normally be printed to screen is ignored.

q)trainingFeatures:([]1000?1f;asc 1000?1f)
q)trainingTargets:desc 1000?1f
q)testingFeatures:([]100?1f;100?1f)

q)// Fit a regression model within the current process
q)fitModel:.automl.fit[trainingFeatures;trainingTargets;`normal;`reg;::]
q)fitModel
modelInfo| `startDate`startTime`featureExtractionType`problemType`saveO..
predict  | {[config;features]
  original_print:utils.printing;
  utils.printi..

q)// Predict targets for the testing features
q)show fitPredictions:fitModel.predict[testingFeatures]
0.7963151 0.734172 0.9847206 0.9817364 0.9709857 0.2008781 0.9781675 0...

q)// Retrieve the same model from disk (latest fit model)
q)retrievedModel:.automl.getModel[`startDate`startTime!(.z.D;.z.t)]
q)retrievedModel
modelInfo| `startDate`startTime`featureExtractionType`problemType`saveO..
predict  | {[config;features]
  original_print:utils.printing;
  utils.printi..

q)// Predict targets for the testing features
q)show retrievedPredictions:retrievedModel.predict[testingFeatures]
0.7963151 0.734172 0.9847206 0.9817364 0.9709857 0.2008781 0.9781675 0...

q)// Show that both methods are the same
q)fitPredictions~retrievedPredictions
1b

.automl.deleteModels

Delete a model/set of models from disk

.automl.deleteModels modelDetails

Where modelDetails is a dictionary containing information related to previously fit models to facilitate models being deleted from disk, returns null on successful invocation, otherwise errors with an appropriate response

Options for modelDetails:

  • A startDate and startTime to denote the dates/times to be deleted: either an exact match or a regex string matching appropriate saved model dates/times
  • In the case of a model saved according to a specified name models can be deleted individually by passing in an exact match denoting the model name or a regex string where multiple models are to be deleted.
q)// Delete a single dated/timed model
q)modelDetails:`startDate`startTime!(2020.08.01;14:10:10.100)
q).automl.deleteModels[modelDetails]

q)// Delete all models on a specific date any time between 4pm and 5pm
q)modelDetails:`startDate`startTime!(2020.08.01;"16:*")
q).automl.deleteModels[modelDetails]

q)// Delete all models for dates within a certain range
q)modelDetails:`startDate`startTime!("2020.08.0[1-9]";"*")
q).automl.deleteModels[modelDetails]

q)// Attempt to delete a model that does not exist
q)modelDetails:`startDate`startTime!(2000.01.01;10:10:10.100)
q).automl.deleteModels[modelDetails]
'startDate provided was not present within the list of available dates

q)// Delete a model based on its exact name
q)modelDetails:enlist[`savedModelName]!enlist "testModel"
q).automl.deleteModels[modelDetails]

q)// Delete a set of models matching an appropriate regex string
q)modelDetails:enlist[`savedModelName]!enlist "test*"
q).automl.deleteModels[modelDetails]

q)// Attempt to delete a named model that does not exist
q)modelDetails:enlist[`savedModelName]!enlist "myModel"
q).automl.deleteModels[modelDetails]
'No files matching the user provided savedModelName were found for deletion

.automl.fit

Apply AutoML to provided features and associated targets

.automl.fit[features;target;ftype;ptype;params]

Where

  • features is an unkeyed tabular feature data or a dictionary outlining how to retrieve the data in accordance with .ml.i.loaddset
  • target is target vector of any type or a dictionary outlining how to retrieve the target vector in accordance with .ml.i.loaddset
  • ftype is the feature-extraction type as a symbol (`nlp, `normal, or `fresh)
  • ptype is the problem type as a symbol (`reg or `class)
  • params is one of
  • Path to a JSON configuration file, either relative to the working directory or in code/customization/configuration/customConfig
  • Dictionary of non-default behaviors
  • Generic null (::) – run AutoML with default parameters

returns the configuration produced within the current run of AutoML along with a prediction function which can be used to make predictions using the best model produced.

The default setup saves the following items from an individual run:

  1. The best model, saved as a HDF5 file, or ‘pickled’ byte object.
  2. A saved report indicating the procedure taken and scores achieved.
  3. A saved binary-encoded dictionary denoting the procedure to be taken for reproducing results, running on new data and outlining all important information relating to a run.
  4. Results from each step of the pipeline saved to the generated report.
  5. On application NLP techniques a word2vec model is saved outlining the text to numerical mapping for a specific run.

The following examples demonstrate how to apply data in various use cases to .automl.fit. Note that while only one example is shown for each feature-extraction type, datasets with binary-classification, multi-classification and regression targets can all be used in each case. The terminal output is shown here only for the last example.

// Non-time series (normal) regression example table
features:([]asc 100?0t;100?1f;desc 100?0b;100?1f;asc 100?1f)
// Regression target
target:asc 100?1f
// Feature extraction type
featExtractType:`normal
// Problem type
problemType:`reg
// Use default system parameters
params:(::)
// Run example
.automl.fit[features;target;featExtractType;problemType;params]

// Non-time series (normal) multi-classification example table
features:([]100?1f;100?1f)
// Multi-classification target
target:100?5
// Feature extraction type
featExtractType:`normal
// Problem type
problemType:`class
// Use default system parameters
params:(::)
// Run example
.automl.fit[features;target;featExtractType;problemType;params]

// NLP binary-classification example table
features:([]100?1f;asc 100?("Testing the application of nlp";"With different characters"))
// Binary-classification target
target:asc 100?0b
// Feature extraction type
featExtractType:`nlp
// Problem type
ptype:`class
// Use default system parameters
params:(::)
// Run example
.automl.fit[features;target;featExtractType;ptype;params]

// FRESH regression example table
features:([]5000?100?0p;asc 5000?1f;5000?1f;desc 5000?10f;5000?0b)
// Regression target
target:desc 100?1f
// Feature extraction type
featExtractType:`fresh
// Problem type
problemType:`reg
// Use default system parameters
params:(::)

// Run example
.automl.fit[features;target;featExtractType;problemType;params]

Executing node: automlConfig
Executing node: configuration
Executing node: targetDataConfig
Executing node: targetData
Executing node: featureDataConfig
Executing node: featureData
Executing node: dataCheck
Executing node: featureDescription

The following is a breakdown of information for each of the relevant columns in the dataset

  | count unique mean      std       min          max       type
--| ---------------------------------------------------------------
x1| 5000  5000   0.5004232 0.2908372 0.0001313207 0.999641  numeric
x2| 5000  5000   0.4967023 0.2897377 0.0007908894 0.9998165 numeric
x3| 5000  5000   5.036043  2.904289  0.002741043  9.998293  numeric
x | 5000  100    ::        ::        ::           ::        time
x4| 5000  2      ::        ::        ::           ::        boolean

Executing node: dataPreprocessing

Data preprocessing complete, starting feature creation

Executing node: featureCreation
Executing node: labelEncode
Executing node: featureSignificance

Total number of significant features being passed to the models = 214

Executing node: trainTestSplit
Executing node: modelGeneration
Executing node: selectModels

Starting initial model selection - allow ample time for large datasets

Executing node: runModels

Scores for all models using .ml.mse

RandomForestRegressor    | 0.04202918
GradientBoostingRegressor| 0.04534999
Lasso                    | 0.04583557
KNeighborsRegressor      | 0.04822146
AdaBoostRegressor        | 0.05129247
LinearRegression         | 0.4422226
MLPRegressor             | 848.683

Best scoring model = RandomForestRegressor

Executing node: optimizeModels

Continuing to hyperparameter search and final model fitting on testing set

Best model fitting now complete - final score on testing set = 0.2106325

Executing node: predictParams
Executing node: preprocParams
Executing node: pathConstruct
Executing node: saveGraph

Saving down graphs to automl/outputs/dateTimeModels/2020.12.17/run_14.57.20.206/images/

Executing node: saveReport

Saving down procedure report to automl/outputs/dateTimeModels/2020.12.17/run_14.57.20.206/report/

Executing node: saveMeta

Saving down model parameters to automl/outputs/dateTimeModels/2020.12.17/run_14.57.20.206/config/

Executing node: saveModels

Saving down model to automl/outputs/dateTimeModels/2020.12.17/run_14.57.20.206/models/

modelInfo| `startDate`startTime`featureExtractionType`problemType`saveOption`..
predict  | {[config;features]
  original_print:utils.printing;
  utils.printi..

.automl.getModel

Retrieve a previously fit AutoML model to use for prediction

.automl.getModel modelDetails

Where modelDetails is a dictionary containing information related to a previously fit model to facilitate model retrieval from disk, returns relevant model metadata and the prediction function associated with the model.

Options for modelDetails:

  • Provide a startDate and startTime to retrieve the closest prevailing model i.e. nearest model before this time
  • In the case of a model saved according to a specified name, retrieve this by providing a savedModelName
q)// Persisted model at a specific date/time
q)modelDetails:`startDate`startTime!(2020.12.17;14:57:20.206)
q)// Retrieve model
q).automl.getModel[modelDetails]
modelInfo| `modelLib`modelFunc`startDate`startTime`featureExt..
predict  | {[config;features]
  original_print:utils.printing;
  utils.printi..

q)// Retrieve the most recent saved model
q)modelDetails:`startDate`startTime(.z.D;.z.t)
q).automl.getModel[modelDetails]
modelInfo| `modelLib`modelFunc`startDate`startTime`featureExt..
predict  | {[config;features]
  original_print:utils.printing;
  utils.printi..

q)// Retrieve the earliest model saved
q)modelDetails:`startDate`startTime("d"$0;"t"$0)
q).automl.getModel[modelDetails]
modelInfo| `modelLib`modelFunc`startDate`startTime`featureExt..
predict  | {[config;features]
  original_print:utils.printing;
  utils.printi..

q)// Retrieve a model based on a name associated with the model
q)modelDetails:enlist[`savedModelName]!enlist "testModel"
q).automl.getModel[modelDetails]
modelInfo| `modelLib`modelFunc`startDate`startTime`featureExt..
predict  | {[config;features]
  original_print:utils.printing;
  utils.printi..

.automl.newConfig

Generate a new JSON parameter file for use with .automl.fit

.automl.newConfig fileName

Where fileName is the name of a new JSON configuration file as a string, symbol or symbolic file handle, in code/customization/configuration saves a copy of default.json to customConfig/fileName and returns generic null.

q)// Path where new JSON configuration file will be saved
q)configPath:hsym`$.automl.path,"/code/customization/configuration/customConfig/"
q)// Check files present in directory at present
q)key configPath
`symbol$()
q)// Generate new configuration file called "newConfigFile"
q).automl.newConfig[`newConfigFile]
q)// Check files present in directory - new configuration file has been generated
q)key configPath
,`newConfigFile

.automl.updateIgnoreWarnings

Update print warning severity level

.automl.updateIgnoreWarnings warningLevel

Where warningLevel is 0j, 1j or 2j, updates .automl.utils.ignoreWarnings and returns null.

Warning levels:

0   ignore warnings completely and continue evaluation
1   alert user a warning was flagged and continue
2   exit evaluation of AutoML, telling the user why 

q)// Exit pipeline on error
q).automl.updateIgnoreWarnings 2
q)// Fit AutoML
q).automl.fit[features;target;featExtractType;problemType;params]
Executing node: automlConfig
Executing node: configuration
Executing node: targetDataConfig
Executing node: targetData
Executing node: featureDataConfig
Executing node: featureData
Executing node: dataCheck
Error: The savePath chosen already exists, this run will be exited

q)// Highlight warnings
q).automl.updateIgnoreWarnings 1
q)// Fit AutoML
q).automl.fit[features;target;featExtractType;problemType;params]
Executing node: automlConfig
Executing node: configuration
Executing node: targetDataConfig
Executing node: targetData
Executing node: featureDataConfig
Executing node: featureData
Executing node: dataCheck

The savePath chosen already exists and will be overwritten

Executing node: featureDescription
..

q)// Ignore warnings
q).automl.updateIgnoreWarnings 0
q)// Fit AutoML
q).automl.fit[features;target;featExtractType;problemType;params]
Executing node: automlConfig
Executing node: configuration
Executing node: targetDataConfig
Executing node: targetData
Executing node: featureDataConfig
Executing node: featureData
Executing node: dataCheck
Executing node: featureDescription
..

.automl.updateLogging

Toggle logging state

.automl.updateLogging[]

Toggles the flag .automl.utils.logging and returns null.

.automl.utils.logging is a boolean: whether to print statements from .automl.fit to a log file. Its default value is 0b.

.automl.updatePrinting

Toggle printing state

.automl.updatePrinting[]

Toggles the flag .automl.utils.printing and returns null.

.automl.utils.printing is a boolean: whether to print statements to the console. Its default value is 1b.

Run from the command line

You may wish to run the AutoML framework from the command line:

  • to overwrite the default parameters of a process running AutoML such that each run uses these parameters
  • when running the entirety of the framework in a ‘one-shot’ manner, fitting a model and saving it to disk and exiting the process immediately

Both of the above require custom JSON files, in particular a customized version of default.json. Use .automl.newConfig to generate a named custom version of the default.json file. When editing it follow these instructions.

In the examples the custom JSON files used can be in either of two locations:

  • Within folder code/customization/configuration/customConfig relative to .automl.path
  • Relative to the working directory

Overwriting default parameters

Command to run with a custom configuration:

$ q automl.q -config newConfig.json

In the example following, a custom JSON file myConfig.json in folder code/customization/configuration/customConfig sets the testing set size to 0.3 and modifies the target limit to 1000.

First, start AutoML in a q process and display defaults.

$ q automl.q
q).automl.loadfile`:init.q
q).automl.paramDict[`general;`testingSize`targetLimit]
0.2
10000
q)\\

Next, start AutoML using the new configuration file

$ q automl.q -config myConfig.json
q).automl.loadfile`:init.q
q).automl.paramDict[`general;`testingSize`targetLimit]
0.3
1000

Full run from command line

The following is the command line input used when running the entirety of .automl.fit from command line.

$ q automl.q -config newConfig.json -run