{"id":13738463,"url":"https://github.com/bigmlcom/bigmler","last_synced_at":"2025-04-14T15:28:14.944Z","repository":{"id":5354171,"uuid":"6540086","full_name":"bigmlcom/bigmler","owner":"bigmlcom","description":"A higher-level API to BigML's API","archived":false,"fork":false,"pushed_at":"2025-03-26T23:59:02.000Z","size":20759,"stargazers_count":75,"open_issues_count":1,"forks_count":37,"subscribers_count":14,"default_branch":"master","last_synced_at":"2025-03-27T00:28:11.243Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/bigmlcom.png","metadata":{"files":{"readme":"README.rst","changelog":"HISTORY.rst","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2012-11-05T06:17:03.000Z","updated_at":"2025-03-10T12:19:47.000Z","dependencies_parsed_at":"2023-01-13T16:23:13.419Z","dependency_job_id":"1f74fda4-9544-49c6-ba22-980e4d8b5eca","html_url":"https://github.com/bigmlcom/bigmler","commit_stats":null,"previous_names":[],"tags_count":192,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bigmlcom%2Fbigmler","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bigmlcom%2Fbigmler/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bigmlcom%2Fbigmler/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bigmlcom%2Fbigmler/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/bigmlcom","download_url":"https://codeload.github.com/bigmlcom/bigmler/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248905869,"owners_count":21181068,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-03T03:02:23.181Z","updated_at":"2025-04-14T15:28:14.936Z","avatar_url":"https://github.com/bigmlcom.png","language":"Python","funding_links":[],"categories":["Python"],"sub_categories":[],"readme":"BigMLer - A command-line tool for BigML's API\n=============================================\n\nBigMLer makes `BigML \u003chttps://bigml.com\u003e`_ even easier.\n\nBigMLer wraps `BigML's API Python bindings \u003chttp://bigml.readthedocs.org\u003e`_  to\noffer a high-level command-line script to easily create and publish datasets\nand models, create ensembles,\nmake local predictions from multiple models, and simplify many other machine\nlearning tasks. For additional information, see\nthe\n`full documentation for BigMLer on Read the Docs \u003chttp://bigmler.readthedocs.org\u003e`_.\n\nBigMLer is open sourced under the `Apache License, Version\n2.0 \u003chttp://www.apache.org/licenses/LICENSE-2.0.html\u003e`_.\n\nRequirements\n============\n\nBigMLer needs Python 3.8 or higher versions to work.\nCompatibility with Python 2.X was discontinued in version 3.27.2.\n\nBigMLer requires `bigml 9.8.3 \u003chttps://github.com/bigmlcom/python\u003e`_  or\nhigher, that contains the bindings providing support to use the ``BigML``\nplatform to create, update, get and delete resources,\nbut also to produce local predictions using the\nmodels created in ``BigML``. Most of them will be actionable with the basic\ninstallation, but some additional dependencies are needed\nto use local ``Topic Models`` to produce ``Topic Distributions``. These can\nbe installed using:\n\n.. code-block:: bash\n\n    pip install bigmler[topics]\n\nThe bindings also support local predictions for models generated from images.\nTo use these models, an additional set of libraries needs to be installed\nusing:\n\n.. code-block:: bash\n\n    pip install bigmler[images]\n\nThe external libraries used in this case exist for the majority of recent\nOperating System versions. Still, some of them might need especific\ncompiler versions or dlls, so their installation may require an additional\nsetup effort and will not be supported by default.\n\nThe full set of libraries can be installed using\n\n.. code-block:: bash\n\n    pip install bigmler[full]\n\n\nBigMLer Installation\n====================\n\nTo install the latest stable release with\n`pip \u003chttp://www.pip-installer.org/\u003e`_\n\n.. code-block:: bash\n\n    $ pip install bigmler\n\nYou can also install the development version of bigmler directly\nfrom the Git repository\n\n.. code-block:: bash\n\n    $ pip install -e git://github.com/bigmlcom/bigmler.git#egg=bigmler\n\nFor a detailed description of install instructions on Windows see the\n:ref:bigmler-on-windows section.\n\nSupport for local Topic Distributions (Topic Models' predictions)\nand local predictions for datasets that include Images will only be\navailable as extras, because the libraries used for that are not\nusually available in all Operating Systems. If you need to support those,\nplease check the `Installation Extras \u003c#installation-extras\u003e`_ section.\n\nInstallation Extras\n===================\n\nLocal Topic Distributions support can be installed using:\n\n.. code-block:: bash\n\n    pip install bigmler[topics]\n\nImages local predictions support can be installed using:\n\n.. code-block:: bash\n\n    pip install bigmler[images]\n\nThe full set of features can be installed using:\n\n.. code-block:: bash\n\n    pip install bigmler[full]\n\n\nWARNING: Mind that installing these extras can require some extra work, as\nexplained in the :ref:requirements section.\n\nBigML Authentication\n====================\n\nAll the requests to BigML.io must be authenticated using your username\nand `API key \u003chttps://bigml.com/account/apikey\u003e`_ and are always\ntransmitted over HTTPS.\n\nBigML module will look for your username and API key in the environment\nvariables ``BIGML_USERNAME`` and ``BIGML_API_KEY`` respectively. You can\nadd the following lines to your ``.bashrc`` or ``.bash_profile`` to set\nthose variables automatically when you log in\n\n.. code-block:: bash\n\n    export BIGML_USERNAME=myusername\n    export BIGML_API_KEY=ae579e7e53fb9abd646a6ff8aa99d4afe83ac291\n\nOtherwise, you can initialize directly when running the BigMLer\nscript as follows\n\n.. code-block:: bash\n\n    bigmler --train data/iris.csv --username myusername \\\n            --api-key ae579e7e53fb9abd646a6ff8aa99d4afe83ac291\n\nFor a detailed description of authentication instructions on Windows see the\n`BigMLer on Windows \u003c#bigmler-on-windows\u003e`_ section.\n\n\nBigMLer on Windows\n==================\n\nTo install BigMLer on Windows environments, you'll need Python installed.\nThe code has been tested with Python 3.10 and you can create a *conda*\nenvironment with that Python version or download it from `Python for Windows\n\u003chttp://www.python.org/download/\u003e`_ and install it. In the latter case, you'll\nalso need too install the ``pip`` tool to install BigMLer.\n\nTo install ``pip``, first you need to open your command terminal window\n(write ``cmd`` in\nthe input field that appears when you click on ``Start`` and hit ``enter``).\nThen you can follow the steps described, for example, in this `guide\n\u003chttps://monovm.com/blog/how-to-install-pip-on-windows-linux/#How-to-install-PIP-on-Windows?-[A-Step-by-Step-Guide]\u003e`_\nto install its latest version.\n\nAnd finally, to install BigMLer in its basic capacities, just type\n\n.. code-block:: bash\n\n    python -m pip install bigmler\n\nand BigMLer should be installed in your computer or conda environment. Then\nissuing\n\n.. code-block:: bash\n\n    bigmler --version\n\nshould show BigMLer version information.\n\nExtensions of BigMLer to use images are usually not available in Windows.\nThe libraries needed for those models are not available usually for that\noperating system. If your Machine Learning project involves images, we\nrecommend that you choose a Linux based operating system.\n\nFinally, to start using BigMLer to handle your BigML resources, you need to\nset your credentials in BigML for authentication. If you want them to be\npermanently stored in your system, use\n\n.. code-block:: bash\n\n    setx BIGML_USERNAME myusername\n    setx BIGML_API_KEY ae579e7e53fb9abd646a6ff8aa99d4afe83ac291\n\nNote that ``setx`` will not change the environment variables of your actual\nconsole, so you will need to open a new one to start using them.\n\n\nBigML Development Mode\n======================\n\nAlso, you can instruct BigMLer to work in BigML's Sandbox\nenvironment by using the parameter ``---dev``\n\n.. code-block:: bash\n\n    bigmler --train data/iris.csv --dev\n\nUsing the development flag you can run tasks under 1 MB without spending any of\nyour BigML credits.\n\nUsing BigMLer\n=============\n\nTo run BigMLer you can use the console script directly. The `--help` option will\ndescribe all the available options\n\n.. code-block:: bash\n\n    bigmler --help\n\nAlternatively you can just call bigmler as follows\n\n.. code-block:: bash\n\n    python bigmler.py --help\n\nThis will display the full list of optional arguments. You can read a brief\nexplanation for each option below.\n\nQuick Start\n===========\n\nLet's see some basic usage examples. Check the `installation` and `authentication`\nsections in `BigMLer on Read the Docs \u003chttp://bigmler.readthedocs.org\u003e`_ if\nyou are not familiar with BigML.\n\nBasics\n------\n\nYou can create a new model just with\n\n.. code-block:: bash\n\n    bigmler --train data/iris.csv\n\nIf you check your `dashboard at BigML \u003chttps://bigml.com/dashboard\u003e`_, you will\nsee a new source, dataset, and model. Isn't it magic?\n\nYou can generate predictions for a test set using\n\n.. code-block:: bash\n\n    bigmler --train data/iris.csv --test data/test_iris.csv\n\nYou can also specify a file name to save the newly created predictions\n\n.. code-block:: bash\n\n    bigmler --train data/iris.csv --test data/test_iris.csv --output predictions\n\nIf you do not specify the path to an output file, BigMLer will auto-generate\none for you under a\nnew directory named after the current date and time\n(e.g., `MonNov1212_174715/predictions.csv`).\nWith ``--prediction-info``\nflag set to ``brief`` only the prediction result will be stored (default is\n``normal`` and includes confidence information).\n\nA different ``objective field`` (the field that you want to predict) can\nbe selected using\n\n.. code-block:: bash\n\n    bigmler --train data/iris.csv  \\\n            --test data/test_iris.csv \\\n            --objective 'sepal length'\n\nIf you do not explicitly specify an objective field, BigML will\ndefault to the last\ncolumn in your dataset.\n\nAlso, if your test file uses a particular field separator for its data,\nyou can tell BigMLer using ``--test-separator``.\nFor example, if your test file uses the tab character as field separator the\ncall should be like\n\n.. code-block:: bash\n\n    bigmler --train data/iris.csv --test data/test_iris.tsv \\\n            --test-separator '\\t'\n\nIf you don't provide a file name for your training source, BigMLer will try to\nread it from the standard input\n\n.. code-block:: bash\n\n    cat data/iris.csv | bigmler --train\n\nBigMLer will try to use the locale of the model both to create a new source\n(if ``--train`` flag is used) and to interpret test data. In case\nit fails, it will try ``en_US.UTF-8``\nor ``English_United States.1252`` and a warning message will be printed.\nIf you want to change this behaviour you can specify your preferred locale\n\n.. code-block:: bash\n\n    bigmler --train data/iris.csv --test data/test_iris.csv \\\n    --locale \"English_United States.1252\"\n\nIf you check your working directory you will see that BigMLer creates a file\nwith the\nmodel ids that have been generated (e.g., FriNov0912_223645/models).\nThis file is handy if then you want to use those model ids to generate local\npredictions. BigMLer also creates a file with the dataset id that has been\ngenerated (e.g., TueNov1312_003451/dataset) and another one summarizing\nthe steps taken in the session progress: ``bigmler_sessions``. You can also\nstore a copy of every created or retrieved resource in your output directory\n(e.g., TueNov1312_003451/model_50c23e5e035d07305a00004f) by setting the flag\n``--store``.\n\nPrior Versions Compatibility Issues\n-----------------------------------\n\nBigMLer will accept flags written with underscore as word separator like\n``--clear_logs`` for compatibility with prior versions. Also ``--field-names``\nis accepted, although the more complete ``--field-attributes`` flag is\npreferred. ``--stat_pruning`` and ``--no_stat_pruning`` are discontinued\nand their effects can be achived by setting the actual ``--pruning`` flag\nto ``statistical`` or ``no-pruning`` values respectively.\n\nRunning the Tests\n-----------------\n\nThe tests will be run using `pytest \u003chttps://docs.pytest.org/en/7.2.x/\u003e`_.\nYou'll need to set up your authentication\nvia environment variables, as explained in the authentication section.\nAlso some of the tests need other environment\nvariables like ``BIGML_ORGANIZATION`` to test calls when used by Organization\nmembers and ``BIGML_EXTERNAL_CONN_HOST``, ``BIGML_EXTERNAL_CONN_PORT``,\n``BIGML_EXTERNAL_CONN_DB``, ``BIGML_EXTERNAL_CONN_USER``,\n``BIGML_EXTERNAL_CONN_PWD`` and ``BIGML_EXTERNAL_CONN_SOURCE``\nin order to test external data connectors.\n\nWith that in place, you can run the test suite simply by issuing\n\n.. code-block:: bash\n\n    $ pytest\n\nAdditional Information\n----------------------\n\nFor additional information, see\nthe `full documentation for BigMLer on Read the Docs \u003chttp://bigmler.readthedocs.org\u003e`_.\n\n\nSupport\n=======\n\nPlease report problems and bugs to our `BigML.io issue\ntracker \u003chttps://github.com/bigmlcom/io/issues\u003e`_.\n\nDiscussions about the different bindings take place in the general\n`BigML mailing list \u003chttp://groups.google.com/group/bigml\u003e`_.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbigmlcom%2Fbigmler","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbigmlcom%2Fbigmler","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbigmlcom%2Fbigmler/lists"}