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|
%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 <Python_Bot@openeuler.org> - 0.38.1-1
- Package Spec generated
|
