%global _empty_manifest_terminate_build 0 Name: python-mlmodels Version: 0.38.1 Release: 1 Summary: Generic model API, Model Zoo in Tensorflow, Keras, Pytorch, Hyperparamter search License: Apache Software License URL: https://github.com/arita37/mlmodels Source0: https://mirrors.aliyun.com/pypi/web/packages/8a/69/23f54dc4af5166b555115d1f50b460c2f87462ab44df92d1debfcc3051d7/mlmodels-0.38.1.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-pandas Requires: python3-scipy Requires: python3-scikit-learn Requires: python3-numexpr Requires: python3-sqlalchemy Requires: python3-tensorflow Requires: python3-pytorch Requires: python3-optuna Requires: python3-lightgbm Requires: python3-mlflow %description ### AutoML example in Gluon ([Example notebook](mlmodels/example/gluon_automl.ipynb)) ```python # import library import mlmodels import autogluon as ag #### Define model and data definitions model_uri = "model_gluon.gluon_automl.py" data_pars = {"train": True, "uri_type": "amazon_aws", "dt_name": "Inc"} model_pars = {"model_type": "tabular", "learning_rate": ag.space.Real(1e-4, 1e-2, default=5e-4, log=True), "activation": ag.space.Categorical(*tuple(["relu", "softrelu", "tanh"])), "layers": ag.space.Categorical( *tuple([[100], [1000], [200, 100], [300, 200, 100]])), 'dropout_prob': ag.space.Real(0.0, 0.5, default=0.1), 'num_boost_round': 10, 'num_leaves': ag.space.Int(lower=26, upper=30, default=36) } compute_pars = { "hp_tune": True, "num_epochs": 10, "time_limits": 120, "num_trials": 5, "search_strategy": "skopt" } out_pars = { "out_path": "dataset/" } #### Load Parameters and Train from mlmodels.models import module_load module = module_load( model_uri= model_uri ) # Load file definition model = module.Model(model_pars=model_pars, compute_pars=compute_pars) # Create Model instance model, sess = module.fit(model, data_pars=data_pars, model_pars=model_pars, compute_pars=compute_pars, out_pars=out_pars) #### Inference ypred = module.predict(model, data_pars, compute_pars, out_pars) # predict pipeline %package -n python3-mlmodels Summary: Generic model API, Model Zoo in Tensorflow, Keras, Pytorch, Hyperparamter search Provides: python-mlmodels BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-mlmodels ### AutoML example in Gluon ([Example notebook](mlmodels/example/gluon_automl.ipynb)) ```python # import library import mlmodels import autogluon as ag #### Define model and data definitions model_uri = "model_gluon.gluon_automl.py" data_pars = {"train": True, "uri_type": "amazon_aws", "dt_name": "Inc"} model_pars = {"model_type": "tabular", "learning_rate": ag.space.Real(1e-4, 1e-2, default=5e-4, log=True), "activation": ag.space.Categorical(*tuple(["relu", "softrelu", "tanh"])), "layers": ag.space.Categorical( *tuple([[100], [1000], [200, 100], [300, 200, 100]])), 'dropout_prob': ag.space.Real(0.0, 0.5, default=0.1), 'num_boost_round': 10, 'num_leaves': ag.space.Int(lower=26, upper=30, default=36) } compute_pars = { "hp_tune": True, "num_epochs": 10, "time_limits": 120, "num_trials": 5, "search_strategy": "skopt" } out_pars = { "out_path": "dataset/" } #### Load Parameters and Train from mlmodels.models import module_load module = module_load( model_uri= model_uri ) # Load file definition model = module.Model(model_pars=model_pars, compute_pars=compute_pars) # Create Model instance model, sess = module.fit(model, data_pars=data_pars, model_pars=model_pars, compute_pars=compute_pars, out_pars=out_pars) #### Inference ypred = module.predict(model, data_pars, compute_pars, out_pars) # predict pipeline %package help Summary: Development documents and examples for mlmodels Provides: python3-mlmodels-doc %description help ### AutoML example in Gluon ([Example notebook](mlmodels/example/gluon_automl.ipynb)) ```python # import library import mlmodels import autogluon as ag #### Define model and data definitions model_uri = "model_gluon.gluon_automl.py" data_pars = {"train": True, "uri_type": "amazon_aws", "dt_name": "Inc"} model_pars = {"model_type": "tabular", "learning_rate": ag.space.Real(1e-4, 1e-2, default=5e-4, log=True), "activation": ag.space.Categorical(*tuple(["relu", "softrelu", "tanh"])), "layers": ag.space.Categorical( *tuple([[100], [1000], [200, 100], [300, 200, 100]])), 'dropout_prob': ag.space.Real(0.0, 0.5, default=0.1), 'num_boost_round': 10, 'num_leaves': ag.space.Int(lower=26, upper=30, default=36) } compute_pars = { "hp_tune": True, "num_epochs": 10, "time_limits": 120, "num_trials": 5, "search_strategy": "skopt" } out_pars = { "out_path": "dataset/" } #### Load Parameters and Train from mlmodels.models import module_load module = module_load( model_uri= model_uri ) # Load file definition model = module.Model(model_pars=model_pars, compute_pars=compute_pars) # Create Model instance model, sess = module.fit(model, data_pars=data_pars, model_pars=model_pars, compute_pars=compute_pars, out_pars=out_pars) #### Inference ypred = module.predict(model, data_pars, compute_pars, out_pars) # predict pipeline %prep %autosetup -n mlmodels-0.38.1 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-mlmodels -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Jun 20 2023 Python_Bot - 0.38.1-1 - Package Spec generated