A command-line tool for BigML.io, the public BigML API
BigMLer - A command-line tool for BigML’s API
BigMLer makes BigML even easier.
BigMLer wraps BigML’s API Python bindings to offer a high-level command-line script to easily create and publish datasets and models, create ensembles, make local predictions from multiple models, and simplify many other machine learning tasks. For additional information, see the full documentation for BigMLer on Read the Docs.
BigMLer is open sourced under the Apache License, Version 2.0.
Please report problems and bugs to our BigML.io issue tracker.
Python 2.7 is currently supported by BigMLer.
BigMLer requires bigml 1.0 or higher.
To install the latest stable release with pip:
$ pip install bigmler
You can also install the development version of bigmler directly from the Git repository:
$ pip install -e git://github.com/bigmlcom/bigmler.git#egg=bigmler
For a detailed description of install instructions on Windows see the BigMLer on Windows section.
All the requests to BigML.io must be authenticated using your username and API key and are always transmitted over HTTPS.
BigML module will look for your username and API key in the environment variables BIGML_USERNAME and BIGML_API_KEY respectively. You can add the following lines to your .bashrc or .bash_profile to set those variables automatically when you log in:
export BIGML_USERNAME=myusername export BIGML_API_KEY=ae579e7e53fb9abd646a6ff8aa99d4afe83ac291
Otherwise, you can initialize directly when running the BigMLer script as follows:
bigmler --train data/iris.csv --username myusername --api_key ae579e7e53fb9abd646a6ff8aa99d4afe83ac291
For a detailed description of authentication instructions on Windows see the BigMLer on Windows section.
BigMLer on Windows
To install BigMLer on Windows environments, you’ll need Python for Windows (v.2.7.x) installed.
In addition to that, you’ll need the pip tool to install BigMLer. To install pip, first you need to open your command line window (write cmd in the input field that appears when you click on Start and hit enter), download this python file and execute it:
After that, you’ll be able to install pip by typing the following command:
And finally, to install BigMLer, just type:
c:\Python27\Scripts\pip.exe install bigmler
and BigMLer should be installed in your computer. Then issuing:
should show BigMLer version information.
Finally, to start using BigMLer to handle your BigML resources, you need to set your credentials in BigML for authentication. If you want them to be permanently stored in your system, use:
setx BIGML_USERNAME myusername setx BIGML_API_KEY ae579e7e53fb9abd646a6ff8aa99d4afe83ac291
BigML Development Mode
Also, you can instruct BigMLer to work in BigML’s Sandbox environment by using the parameter ---dev:
bigmler --train data/iris.csv --dev
Using the development flag you can run tasks under 1 MB without spending any of your BigML credits.
To run BigMLer you can use the console script directly. The –help option will describe all the available options:
Alternatively you can just call bigmler as follows:
python bigmler.py --help
This will display the full list of optional arguments. You can read a brief explanation for each option below.
Let’s see some basic usage examples. Check the installation and authentication sections in BigMLer on Read the Docs if you are not familiar with BigML.
You can create a new model just with
bigmler --train data/iris.csv
If you check your dashboard at BigML, you will see a new source, dataset, and model. Isn’t it magic?
You can generate predictions for a test set using:
bigmler --train data/iris.csv --test data/test_iris.csv
You can also specify a file name to save the newly created predictions:
bigmler --train data/iris.csv --test data/test_iris.csv --output predictions
If you do not specify the path to an output file, BigMLer will auto-generate one for you under a new directory named after the current date and time (e.g., MonNov1212_174715/predictions.csv). With --prediction-info flag set to brief only the prediction result will be stored (default is normal and includes confidence information).
A different objective field (the field that you want to predict) can be selected using:
bigmler --train data/iris.csv --test data/test_iris.csv --objective 'sepal length'
If you do not explicitly specify an objective field, BigML will default to the last column in your dataset.
Also, if your test file uses a particular field separator for its data, you can tell BigMLer using --test-separator. For example, if your test file uses the tab character as field separator the call should be like:
bigmler --train data/iris.csv --test data/test_iris.tsv \ --test-separator '\t'
If you don’t provide a file name for your training source, BigMLer will try to read it from the standard input:
cat data/iris.csv | bigmler --train
BigMLer will try to use the locale of the model both to create a new source (if --train flag is used) and to interpret test data. In case it fails, it will try en_US.UTF-8 or English_United States.1252 and a warning message will be printed. If you want to change this behaviour you can specify your preferred locale:
bigmler --train data/iris.csv --test data/test_iris.csv \ --locale "English_United States.1252"
If you check your working directory you will see that BigMLer creates a file with the model ids that have been generated (e.g., FriNov0912_223645/models). This file is handy if then you want to use those model ids to generate local predictions. BigMLer also creates a file with the dataset id that has been generated (e.g., TueNov1312_003451/dataset) and another one summarizing the steps taken in the session progress: bigmler_sessions. You can also store a copy of every created or retrieved resource in your output directory (e.g., TueNov1312_003451/model_50c23e5e035d07305a00004f) by setting the flag --store.
Prior Versions Compatibility Issues
BigMLer will accept flags written with underscore as word separator like --clear_logs for compatibility with prior versions. Also --field-names is accepted, although the more complete --field-attributes flag is preferred. --stat_pruning and --no_stat_pruning are discontinued and their effects can be achived by setting the actual --pruning flag to statistical or no-pruning values respectively.
Running the Tests
To run the tests you will need to install lettuce:
$ pip install lettuce
and set up your authentication via environment variables, as explained above. With that in place, you can run the test suite simply by:
$ cd tests $ lettuce
For additional information, see the full documentation for BigMLer on Read the Docs.
- Fix to transmit –training-separator when creating remote sources.
- Fix for multiple multi-label fields: headers did not match rows contents in some cases.
- Fix for datasets generated using the –new-fields option. The new dataset was not used in model generation.
- Adding –multi-label-fields to provide a comma-separated list of multi-label fields in a file.
- Fix for ensembles’ local predictions when order is used in tie break.
- Fix for duplicated model ids in models file.
- Adding new –node-threshold option to allow node limit in models.
- Adding new –model-attributes option pointing to a JSON file containing model attributes for model creation.
- Fix for missing modules during installation.
- Adding the –max-categories option to handle datasets with a high number of categories.
- Adding the –method combine option to produce predictions with the sets of datasets generated using –max-categories option.
- Fixing problem with –max-categories when the categorical field is not a preferred field of the dataset.
- Changing the –datasets option behaviour: it points to a file where dataset ids are stored, one per line, and now it reads all of them to be used in model and ensemble creation.
- Adding confidence to predictions output in full format
- Bug fixing: multi-label predictions failed when the –ensembles option is used to provide the ensemble information
- Bug fixing: –dataset-price could not be set.
- Adding the threshold combination method to the local ensemble.
- Bug fixing: –model-fields option with absolute field names was not compatible with multi-label classification models.
- Changing resource type checking function.
- Bug fixing: evaluations did not use the given combination method.
- Bug fixing: evaluation of an ensemble had turned into evaluations of its
- Adding pruning to the ensemble creation configuration options
- Changing fields_map column order: previously mapped dataset column number to model column number, now maps model column number to dataset column number.
- Adding evaluations to multi-label models.
- Bug fixing: unicode characters greater than ascii-127 caused crash in multi-label classification
- Adapting to predictions issued by the high performance prediction server and the 0.9.0 version of the python bindings.
- Support for shared models using the same version on python bindings.
- Support for different server names using environment variables.
- Adding ensembles’ predictions for multi-label objective fields
- Bug fixing: in evaluation mode, evaluation for –dataset and –number-of-models > 1 did not select the 20% hold out instances to test the generated ensemble.
- Adding text analysis through the corresponding bindings
- Adding support for multi-label objective fields
- Adding –prediction-headers and –prediction-fields to improve –prediction-info formatting options for the predictions file
- Adding the ability to read –test input data from stdin
- Adding –seed option to generate different splits from a dataset
- Adding –test-separator flag
- Bug fixing: resume crash when remote predictions were not completed
- Bug fixing: Fields object for input data dict building lacked fields
- Bug fixing: test data was repeated in remote prediction function
- Bug fixing: Adding replacement=True as default for ensembles’ creation
- Adding –max-parallel-evaluations flag
- Bug fixing: matching seeds in models and evaluations for cross validation
- Changing –model-fields and –dataset-fields flag to allow adding/removing fields with +/- prefix
- Refactoring local and remote prediction functions
- Adding ‘full data’ option to the –prediction-info flag to join test input data with prediction results in predictions file
- Fixing errors in documentation and adding install for windows info
- Adding new flag to control predictions file information
- Bug fixing: using default sample-rate in ensemble evaluations
- Adding standard deviation to evaluation measures in cross-validation
- Bug fixing: using only-model argument to download fields in models
- Adding delete for ensembles
- Creating ensembles when the number of models is greater than one
- Remote predictions using ensembles
- Adding cross-validation feature
- Using user locale to create new resources in BigML
- Adding –ensemble flag to use ensembles in predictions and evaluations
- Deep refactoring of main resources management
- Fixing bug in batch_predict for no headers test sets
- Fixing bug for wide dataset’s models than need query-string to retrieve all fields
- Fixing bug in test asserts to catch subprocess raise
- Adding default missing tokens to models
- Adding stdin input for –train flag
- Fixing bug when reading descriptions in –field-attributes
- Refactoring to get status from api function
- Adding confidence to combined predictions
- Evaluations management
- console monitoring of process advance
- resume option
- user defaults
- Refactoring to improve readability
- Improved locale management.
- Adds progressive handling for large numbers of models.
- More options in field attributes update feature.
- New flag to combine local existing predictions.
- More methods in local predictions: plurality, confidence weighted.
- New flag for locale settings configuration.
- Filtering only finished resources.
- Fix to ensure windows compatibility.
- Initial release.