BigMLer makes BigML <https://bigml.com>_ even easier.
BigMLer wraps BigML's API Python bindings <http://bigml.readthedocs.org>_ 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 <http://bigmler.readthedocs.org>_.
BigMLer is open sourced under the Apache License, Version 2.0 <http://www.apache.org/licenses/LICENSE-2.0.html>_.
BigMLer needs Python 3.8 or higher versions to work. Compatibility with Python 2.X was discontinued in version 3.27.2.
BigMLer requires bigml 9.7.1 <https://github.com/bigmlcom/python>_ or
higher, that contains the bindings providing support to use the BigML
platform to create, update, get and delete resources,
but also to produce local predictions using the
models created in BigML. Most of them will be actionable with the basic
installation, but some additional dependencies are needed
to use local Topic Models to produce Topic Distributions. These can
be installed using:
.. code-block:: bash
pip install bigmler[topics]
The bindings also support local predictions for models generated from images. To use these models, an additional set of libraries needs to be installed using:
.. code-block:: bash
pip install bigmler[images]
The external libraries used in this case exist for the majority of recent Operating System versions. Still, some of them might need especific compiler versions or dlls, so their installation may require an additional setup effort and will not be supported by default.
The full set of libraries can be installed using
.. code-block:: bash
pip install bigmler[full]
To install the latest stable release with
pip <http://www.pip-installer.org/>_
.. code-block:: bash
$ pip install bigmler
You can also install the development version of bigmler directly from the Git repository
.. code-block:: bash
$ pip install -e git://github.com/bigmlcom/bigmler.git#egg=bigmler
For a detailed description of install instructions on Windows see the :ref:bigmler-on-windows section.
Support for local Topic Distributions (Topic Models' predictions)
and local predictions for datasets that include Images will only be
available as extras, because the libraries used for that are not
usually available in all Operating Systems. If you need to support those,
please check the Installation Extras <#installation-extras>_ section.
Local Topic Distributions support can be installed using:
.. code-block:: bash
pip install bigmler[topics]
Images local predictions support can be installed using:
.. code-block:: bash
pip install bigmler[images]
The full set of features can be installed using:
.. code-block:: bash
pip install bigmler[full]
WARNING: Mind that installing these extras can require some extra work, as explained in the :ref:requirements section.
All the requests to BigML.io must be authenticated using your username
and API key <https://bigml.com/account/apikey>_ 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
.. code-block:: bash
export BIGML_USERNAME=myusername
export BIGML_API_KEY=ae579e7e53fb9abd646a6ff8aa99d4afe83ac291
Otherwise, you can initialize directly when running the BigMLer script as follows
.. code-block:: bash
bigmler --train data/iris.csv --username myusername \
--api-key ae579e7e53fb9abd646a6ff8aa99d4afe83ac291
For a detailed description of authentication instructions on Windows see the
BigMLer on Windows <#bigmler-on-windows>_ section.
To install BigMLer on Windows environments, you'll need Python installed.
The code has been tested with Python 3.10 and you can create a conda
environment with that Python version or download it from Python for Windows <http://www.python.org/download/>_ and install it. In the latter case, you'll
also need too install the pip tool to install BigMLer.
To install pip, first you need to open your command terminal window
(write cmd in
the input field that appears when you click on Start and hit enter).
Then you can follow the steps described, for example, in this guide <https://monovm.com/blog/how-to-install-pip-on-windows-linux/#How-to-install-PIP-on-Windows?-[A-Step-by-Step-Guide]>_
to install its latest version.
And finally, to install BigMLer in its basic capacities, just type
.. code-block:: bash
python -m pip install bigmler
and BigMLer should be installed in your computer or conda environment. Then issuing
.. code-block:: bash
bigmler --version
should show BigMLer version information.
Extensions of BigMLer to use images are usually not available in Windows. The libraries needed for those models are not available usually for that operating system. If your Machine Learning project involves images, we recommend that you choose a Linux based operating system.
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
.. code-block:: bash
setx BIGML_USERNAME myusername
setx BIGML_API_KEY ae579e7e53fb9abd646a6ff8aa99d4afe83ac291
Note that setx will not change the environment variables of your actual
console, so you will need to open a new one to start using them.
Also, you can instruct BigMLer to work in BigML's Sandbox
environment by using the parameter ---dev
.. code-block:: bash
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
.. code-block:: bash
bigmler --help
Alternatively you can just call bigmler as follows
.. code-block:: bash
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 <http://bigmler.readthedocs.org>_ if
you are not familiar with BigML.
You can create a new model just with
.. code-block:: bash
bigmler --train data/iris.csv
If you check your dashboard at BigML <https://bigml.com/dashboard>_, you will
see a new source, dataset, and model. Isn't it magic?
You can generate predictions for a test set using
.. code-block:: bash
bigmler --train data/iris.csv --test data/test_iris.csv
You can also specify a file name to save the newly created predictions
.. code-block:: bash
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
.. code-block:: bash
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
.. code-block:: bash
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
.. code-block:: bash
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
.. code-block:: bash
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.
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.
The tests will be run using pytest <https://docs.pytest.org/en/7.2.x/>_.
You'll need to set up your authentication
via environment variables, as explained in the authentication section.
Also some of the tests need other environment
variables like BIGML_ORGANIZATION to test calls when used by Organization
members and BIGML_EXTERNAL_CONN_HOST, BIGML_EXTERNAL_CONN_PORT,
BIGML_EXTERNAL_CONN_DB, BIGML_EXTERNAL_CONN_USER,
BIGML_EXTERNAL_CONN_PWD and BIGML_EXTERNAL_CONN_SOURCE
in order to test external data connectors.
With that in place, you can run the test suite simply by issuing
.. code-block:: bash
$ pytest
For additional information, see
the full documentation for BigMLer on Read the Docs <http://bigmler.readthedocs.org>_.
Please report problems and bugs to our BigML.io issue tracker <https://github.com/bigmlcom/io/issues>_.
Discussions about the different bindings take place in the general
BigML mailing list <http://groups.google.com/group/bigml>_.
.. :changelog:
5.9.1 (2024-06-13)
- Fixing issues with Readthedocs templates.
5.9.0 (2024-06-12)
5.8.1 (2024-05-31)
- Fixing Pascal VOC to BigML-COCO translator.
5.8.0 (2024-04-05)
5.7.0 (2023-12-14)
- Upgrading underlying bindings version and updating API query string
separator.
5.6.1 (2023-11-14)
5.6.0 (2023-11-10)
- Adding `exclude-types` flag to bigmler delete subcommand to provide types
of resources to be excluded from deletion using `--resource-types`.
- Improving deletion filter order to ensure that composed objects, like
ensembles or clusters, are deleted first.
5.5.1 (2023-10-27)
5.5.0 (2023-04-12)
- Improving delete capabilities and updating the underlying bindings.
5.4.0 (2023-01-10)
cluster to match
remote header.5.3.0 (2022-10-21)
- Updating underlying bindings version.
- Fixing bigmler delete query for synchronous resources.
5.2.0 (2022-09-30)
5.1.0 (2022-09-21)
- Upgrading dependencies.
- Improving bigmler delete subcommand.
- Moving the default output directory to `.bigmler_outputs` in the current
working directory.
5.0.0 (2022-03-26)
4.1.1 (2021-05-04)
- Changing the ascii used in output to console to avoid problems with
Windows encodings.
- Updating copyright
4.1.0 (2020-09-11)
4.0.0 (2020-08-01)
- Python 3 only version. Deprecating Python 2.X compatibility.
3.27.2 (2020-07-15)
3.27.1 (2020-05-26)
- Refactoring resources management code.
3.27.0 (2020-05-26)
3.26.3 (2020-04-17)
- Fixing local deepnet predictions headers for regressions.
- Fixing output for bigml reify into Python when using multidatasets.
3.26.2 (2020-04-15)
3.26.1 (2020-03-16)
- Fixing bug in the --output option when no directory is used in the file path.
3.26.0 (2020-01-26)
3.25.0 (2019-11-27)
- Dependency version bump.
3.24.0 (2019-09-20)
3.23.1 (2019-08-08)
- Changing the underlying Python bindings version to fix local linear
regression predictions when `scipy` is not installed.
3.23.0 (2019-06-26)
3.22.0 (2019-05-14)
- Adding bigmler fusion subcommand.
3.21.1 (2019-05-06)
3.21.0 (2019-04-16)
- Adding bigmler pca subcommand.
3.20.0 (2019-04-06)
3.19.0 (2019-04-05)
- Adding bigmler dataset subcommand to allow generating datasets using
transformations.
3.18.8 (2019-04-04)
3.18.7 (2019-01-09)
- Fixing bigmler retrain for models based on transformed datasets.
3.18.6 (2019-01-07)
3.18.5 (2018-12-12)
- Fixing bigmler retrain and execute-related subcommands for organizations.
- Fixing bigmler retrain when using a model-tag.
3.18.4 (2018-11-07)
3.18.3 (2018-10-16)
- Fixing bigmler reify subcommand for multidatasets.
3.18.2 (2018-10-12)
3.18.1 (2018-09-19)
- Updating underlying bindings version.
- Updating reify library.
- Improving bigmler retrain to allow remote sources
3.18.0 (2018-05-23)
3.17.0 (2018-01-30)
- Adding support for organizations.
3.16.0 (2018-01-23)
3.15.2 (2018-01-10)
- Fixing bug in remote predictions with models and ensembles when --no-batch
was used.
3.15.1 (2017-12-26)
3.15.0 (2017-12-21)
- Adding the bigmler retrain command to retrain modeling resources with
incremental data.
3.14.1 (2017-11-28)
3.14.0 (2017-11-22)
- Adding the --operating-point option for models, ensembles and logistic
regressions.
3.13.2 (2017-11-10)
3.13.1 (2017-11-05)
- Extending bigmler export to generate the code for the models in an ensemble.
- Fixing code generation in bigmler export for models with missings.
3.13.0 (2017-07-22)
3.12.0 (2017-07-22)
- Adding bigmler timeseries subcommand to create time series models and
forecasts.
- Solving issues in cross-validation due to new evaluation formats.
3.11.2 (2017-06-16)
3.11.1 (2017-05-25)
- Fixing bug in bigmler export when non-ascii characters are used in a model.
3.11.0 (2017-05-16)
3.10.3 (2017-04-21)
- Fixing bug: Adapting to changes in the structure of evaluations that caused
cross-validation failure.
3.10.2 (2017-04-13)
3.10.1 (2017-03-25)
- Adding --embed-libs and --embedded-libraries to bigmler whizzml and
bigmler execute subcommands to embed the libraries'
code in the scripts.
3.10.0 (2017-03-21)
3.9.3 (2017-03-08)
- Fixing bug in bigmler whizzml when using --username and --api-key.
3.9.2 (2017-02-16)
3.9.1 (2017-01-04)
- Fixing bug in bigmler: --evaluation-attributes were not used.
- Fixing bug in bigmler: --threshold and --class were not used.
- Fixing bug in bigmler topic-model: adding --topic-model-attributes.
3.9.0 (2016-12-03)
3.8.7 (2016-11-04)
- Fixing bug in bigmler commands when using samples to create different model
types.
3.8.6 (2016-10-25)
3.8.5 (2016-10-20)
- Fixing bug in bigmler commands when using local predictions form development
mode resources.
3.8.4 (2016-10-13)
3.8.3 (2016-09-30)
- Fixing bug in logistic regression evaluation.
- Adding --balance-fields flag to bigmler logistic-regression.
- Refactoring and style changes.
- Adding the logistic regression options to documentation.
3.8.2 (2016-09-23)
3.8.1 (2016-07-06)
- Adding types of deletable resources to bigmler delete. Adding option
--execution-only to avoid deleting the output resources of an
execution when the execution is deleted.
- Fixing bug: directory structure in bigmler whizzml was wrong when components
were found in metadata.
- Upgrading the underlying Python bindings version.
3.8.0 (2016-07-04)
3.7.1 (2016-06-27)
- Adding new --field-codings option to bigmler logisitic-regression
subcommand.
- Changing underlying bindings version
3.7.0 (2016-06-03)
3.6.4 (2016-04-08)
- Fixing bug: the --predictions-csv flag in the bigmler analyze command did
not work with ensembles (--number-of-models > 1)
3.6.3 (2016-04-04)
3.6.2 (2016-04-01)
- Improving bigmler analyze --features CSV output to reflect the best fields
set found at each step.
3.6.1 (2016-03-14)
3.6.0 (2016-03-08)
- Adding subcommand bigmler logistic-regression.
- Changing tests to adapt to backend random numbers changes.
3.5.4 (2016-02-09)
3.5.3 (2016-02-07)
- Adding links to docs and changing tests to adapt bigmler reify
to new automatically generated names for resources.
3.5.2 (2016-01-01)
3.5.1 (2015-12-26)
- Adding docs for association discovery.
3.5.0 (2015-12-24)
3.4.0 (2015-12-21)
- Adding bigmler project subcommand for project creation and update.
3.3.9 (2015-12-19)
3.3.8 (2015-11-24)
- Fixing bug: simplifying bigmler reify output for datasets created from
batch resources.
- Allowing column numbers as keys for fields structures in
--source-attributes, --dataset-attributes, etc
3.3.7 (2015-11-18)
3.3.6 (2015-11-16)
- Fixing bug: Report title for feature analysis was not shown.
3.3.5 (2015-11-15)
3.3.4 (2015-11-10)
- Fixing bug: bigmler cluster did not use the --prediction-fields option.
3.3.3 (2015-11-04)
developers documentation <https://bigml.com/developers/status_codes#sc_resource_status_code_summary>_.3.3.2 (2015-10-31)
- Fixing bug: bigmler reify failed for dataset generated from batch
predictions, batch centroids or batch anomaly scores.
3.3.1 (2015-10-15)
3.3.0 (2015-09-14)
- Adding new bigmler analyze option, --random-fields to analyze performance of
random forests chaging the number of random candidates.
3.2.1 (2015-09-05)
3.2.0 (2015-08-23)
- Adding bigmler reify subcommand to script the resource creation.
3.1.1 (2015-08-16)
3.1.0 (2015-08-05)
- Adding bigmler report subcommand to generate reports for cross-validation
results in bigmler analyze.
3.0.5 (2015-07-30)
3.0.4 (2015-07-22)
- Fixing bug: bigmler analyze --features could not analyze phi for a user-given
category because the metric is called phi_coefficient.
- Modifying the output of bigmler analyze --features and --nodes to include
the command to generate the best performing model and the command to
clean all the generated resources.
3.0.3 (2015-07-01)
3.0.2 (2015-06-24)
- Adding the --project-tag option to bigmler delete.
- Fixing that the --test-dataset and related options can be used in model
evaluation.
- Fixing bug: bigmler anomalies for datasets with more than 1000 fields failed.
3.0.1 (2015-06-12)
3.0.0 (2015-04-25)
- Supporting Python 3 and changing the test suite to nose.
- Adding --cluster-models option to generate the models related to
cluster datasets.
2.2.0 (2015-04-15)
2.1.0 (2015-04-10)
- Adding --median flag to use median instead of mean in single models'
predictions.
- Updating underlying BigML python bindings' version to 4.0.2 (Python 3
compatible).
2.0.1 (2015-04-09)
2.0.0 (2015-03-26)
- Fixing docs formatting errors.
- Adding --to-dataset and --no-csv flags causing batch predictions,
batch centroids and batch anomaly scores to be stored in a new remote
dataset and not in a local CSV respectively.
- Adding the sample subcommand to generate samples from datasets
1.15.6 (2015-01-28)
1.15.5 (2015-01-20)
- Fixing bug: Failed field retrieval for batch predictions starting from
source or dataset test data.
1.15.4 (2015-01-15)
1.15.3 (2014-12-26)
- Adding --optimize-category in bigmler analyze subcommands to select
the category whose evaluations will be optimized.
1.15.2 (2014-12-17)
1.15.1 (2014-12-15)
- Fixing bug: ensembles' evaluations failed when using the ensemble id.
- Fixing bug: bigmler analyze lacked model configuration options (weight-field,
objective-fields, pruning, model-attributes...)
1.15.0 (2014-12-06)
1.14.6 (2014-11-26)
- Adding the --model-file, --cluster-file, --anomaly-file and --ensemble-file
to produce entirely local predictions.
- Fixing bug: the bigmler delete subcommand was not using the --anomaly-tag,
--anomaly-score-tag and --batch-anomaly-score-tag options.
- Fixing bug: the --no-test-header flag was not working.
1.14.5 (2014-11-14)
1.14.4 (2014-11-10)
- Adding the capability of creating a model/cluster/anomaly and its
corresponding batch prediction from a train/test split using --test-split.
1.14.3 (2014-11-10)
1.14.2 (2014-10-30)
- Refactoring predictions code, improving some cases performance and memory
usage.
- Adding the --fast option to speed prediction by not storing partial results
in files.
- Adding the --optimize option to the bigmler analyze --features command.
1.14.1 (2014-10-23)
1.14.0 (2014-10-19)
- Adding bigmler anomaly as new subcommand to generate anomaly detectors,
anomaly scores and batch anomaly scores.
1.13.3 (2014-10-13)
1.13.2 (2014-10-05)
- Fixing bug: --source-attributes and --dataset-attributes where not updated.
1.13.1 (2014-09-22)
1.13.0 (2014-09-10)
- Adding the new --missing-splits flag to control if missing values are
included in tree branches.
1.12.4 (2014-08-03)
1.12.3 (2014-07-30)
- Fixing bug: handling stdout writes of unicodes on Windows.
1.12.2 (2014-07-29)
1.12.1 (2014-07-25)
- Fixing bug when many models are evaluated in k-fold cross-validations. The
create evaluation could fail when called with a non-finished model.
1.12.0 (2014-07-15)
1.11.0 (2014-07-11)
- Adding the --to-csv option to export datasets to a CSV file.
1.10.0 (2014-07-11)
1.9.2 (2014-07-07)
- Fixing bug for the --delete flag. Cluster, centroids and batch centroids
could not be deleted.
1.9.1 (2014-07-02)
1.9.0 (2014-07-02)
- Adding cluster subcommand to generate clusters and centroid predictions.
1.8.12 (2014-06-10)
1.8.11 (2014-05-30)
- Fixing bug for --delete flag, broken by last fix.
1.8.10 (2014-05-29)
1.8.9 (2014-05-28)
- Fixing bug when utf8 characters were used in command lines.
1.8.8 (2014-05-27)
1.8.7 (2014-05-23)
- Fixing bug for analyze. User-given objective field was changed when using
filtered datasets.
1.8.6 (2014-05-22)
1.8.5 (2014-05-19)
- Docs update and test change to adapt to backend node threshold changes.
1.8.4 (2014-05-07)
1.8.3 (2014-05-06)
- Setting dependency of new python bindings version 1.3.1.
1.8.2 (2014-05-06)
1.8.1 (2014-05-04)
- Changing the Gazibit report for shared resources to include the model
shared url in embedded format.
- Fixing bug: train and tests data could not be read from stdin.
1.8.0 (2014-04-29)
Adding the analyze subcommand. The subcommand presents new features,
such as:
--cross-validation that performs k-fold cross-validation,
--features that selects the best features to increase accuracy
(or any other evaluation metric) using a smart search algorithm and
--nodes that selects the node threshold that ensures best accuracy
(or any other evaluation metric) in user defined range of nodes.
1.7.1 (2014-04-21)
- Fixing bug: --no-upload flag was not really used.
1.7.0 (2014-04-20)
1.6.0 (2014-04-18)
- Adding the --shared flag to share the created dataset, model and evaluation.
1.5.1 (2014-04-04)
1.5.0 (2014-03-24)
- Adding --missing-strategy option to allow different prediction strategies
when a missing value is found in a split field. Available for local
predictions, batch predictions and evaluations.
- Adding new --delete options: --newer-than and --older-than to delete lists
of resources according to their creation date.
- Adding --multi-dataset flag to generate a new dataset from a list of
equally structured datasets.
1.4.7 (2014-03-14)
1.4.6 (2014-02-21)
- Bug fixing: dataset objective field is not updated each time --objective
is used, but only if it differs from the existing objective.
1.4.5 (2014-02-04)
other label)
in user_metadata.1.4.4 (2014-02-03)
- Fix when using the combined method in --max-categories models.
The combination function now uses confidence to choose the predicted
category.
- Allowing full content text fields to be also used as --max-categories
objective fields.
- Fix solving objective issues when its column number is zero.
1.4.3 (2014-01-28)
1.4.2 (2014-01-24)
- Fix in local random forests' predictions. Sometimes the fields used in all
the models were not correctly retrieved and some predictions could be
erroneus.
1.4.1 (2014-01-23)
1.4.0 (2014-01-20)
- Adding new --balance option to automatically balance all the classes evenly.
- Adding new --weight-field option to use the field contents as weights for
the instances.
1.3.0 (2014-01-17)
1.2.3 (2014-01-16)
- Fix to transmit --training-separator when creating remote sources.
1.2.2 (2014-01-14)
1.2.1 (2014-01-12)
- Fix for datasets generated using the --new-fields option. The new dataset
was not used in model generation.
1.2.0 (2014-01-09)
1.1.0 (2014-01-08)
- 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.
1.0.1 (2014-01-06)
1.0 (2014-01-02)
- 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.
0.7.2 (2013-12-20)
0.7.1 (2013-12-19)
- Bug fixing: multi-label predictions failed when the --ensembles option
is used to provide the ensemble information
0.7.0 (2013-11-24)
0.6.1 (2013-11-23)
- 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
models.
- Adding pruning to the ensemble creation configuration options
0.6.0 (2013-11-08)
0.5.0 (2013-10-08)
- 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.
0.4.1 (2013-10-02)
0.4.0 (2013-08-15)
- Adding text analysis through the corresponding bindings
0.3.7 (2013-09-17)
0.3.6 (2013-08-21)
- Adding --test-separator flag
0.3.5 (2013-08-16)
0.3.4 (2013-08-09)
- Adding --max-parallel-evaluations flag
- Bug fixing: matching seeds in models and evaluations for cross validation
0.3.3 (2013-08-09)
0.3.2 (2013-07-04)
- 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
0.3.1 (2013-05-14)
0.3.0 (2013-04-30)
- Adding cross-validation feature
- Using user locale to create new resources in BigML
- Adding --ensemble flag to use ensembles in predictions and evaluations
0.2.1 (2013-03-03)
0.2.0 (2012-01-21)
- Evaluations management
- console monitoring of process advance
- resume option
- user defaults
- Refactoring to improve readability
0.1.4 (2012-12-21)
0.1.3 (2012-12-06)
- New flag for locale settings configuration.
- Filtering only finished resources.
0.1.2 (2012-12-06)
0.1.1 (2012-11-07)
- Initial release.
FAQs
A command-line tool for BigML.io, the public BigML API
We found that bigmler demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 3 open source maintainers collaborating on the project.
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