%global _empty_manifest_terminate_build 0
Name: python-datawig
Version: 0.2.0
Release: 1
Summary: Imputation for tables with missing values
License: Apache License 2.0
URL: https://github.com/awslabs/datawig
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/ad/a9/855c39f27e07c5727fede305f2dc4ce04137f974b5b07c7da8f7b13a72dc/datawig-0.2.0.tar.gz
BuildArch: noarch
%description
[](https://badge.fury.io/py/datawig.svg)
[](https://github.com/awslabs/datawig/blob/master/LICENSE)
[](https://github.com/awslabs/datawig/issues)
[](https://travis-ci.org/awslabs/datawig)
DataWig learns Machine Learning models to impute missing values in tables.
See our user-guide and extended documentation [here](https://datawig.readthedocs.io/en/latest).
## Installation
### CPU
```bash
pip3 install datawig
```
### GPU
If you want to run DataWig on a GPU you need to make sure your version of Apache MXNet Incubating contains the GPU bindings.
Depending on your version of CUDA, you can do this by running the following:
```bash
wget https://raw.githubusercontent.com/awslabs/datawig/master/requirements/requirements.gpu-cu${CUDA_VERSION}.txt
pip install datawig --no-deps -r requirements.gpu-cu${CUDA_VERSION}.txt
rm requirements.gpu-cu${CUDA_VERSION}.txt
```
where `${CUDA_VERSION}` can be `75` (7.5), `80` (8.0), `90` (9.0), or `91` (9.1).
## Running DataWig
The DataWig API expects your data as a [pandas DataFrame](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html). Here is an example of how the dataframe might look:
|Product Type | Description | Size | Color |
|-------------|-----------------------|------|-------|
| Shoe | Ideal for Running | 12UK | Black |
| SDCards | Best SDCard ever ... | 8GB | Blue |
| Dress | This **yellow** dress | M | **?** |
### Quickstart Example
For most use cases, the `SimpleImputer` class is the best starting point. For convenience there is the function [SimpleImputer.complete](https://datawig.readthedocs.io/en/latest/source/API.html#datawig.simple_imputer.SimpleImputer.complete) that takes a DataFrame and fits an imputation model for each column with missing values, with all other columns as inputs:
```python
import datawig, numpy
# generate some data with simple nonlinear dependency
df = datawig.utils.generate_df_numeric()
# mask 10% of the values
df_with_missing = df.mask(numpy.random.rand(*df.shape) > .9)
# impute missing values
df_with_missing_imputed = datawig.SimpleImputer.complete(df_with_missing)
```
You can also impute values in specific columns only (called `output_column` below) using values in other columns (called `input_columns` below). DataWig currently supports imputation of categorical columns and numeric columns.
### Imputation of categorical columns
```python
import datawig
df = datawig.utils.generate_df_string( num_samples=200,
data_column_name='sentences',
label_column_name='label')
df_train, df_test = datawig.utils.random_split(df)
#Initialize a SimpleImputer model
imputer = datawig.SimpleImputer(
input_columns=['sentences'], # column(s) containing information about the column we want to impute
output_column='label', # the column we'd like to impute values for
output_path = 'imputer_model' # stores model data and metrics
)
#Fit an imputer model on the train data
imputer.fit(train_df=df_train)
#Impute missing values and return original dataframe with predictions
imputed = imputer.predict(df_test)
```
### Imputation of numerical columns
```python
import datawig
df = datawig.utils.generate_df_numeric( num_samples=200,
data_column_name='x',
label_column_name='y')
df_train, df_test = datawig.utils.random_split(df)
#Initialize a SimpleImputer model
imputer = datawig.SimpleImputer(
input_columns=['x'], # column(s) containing information about the column we want to impute
output_column='y', # the column we'd like to impute values for
output_path = 'imputer_model' # stores model data and metrics
)
#Fit an imputer model on the train data
imputer.fit(train_df=df_train, num_epochs=50)
#Impute missing values and return original dataframe with predictions
imputed = imputer.predict(df_test)
```
In order to have more control over the types of models and preprocessings, the `Imputer` class allows directly specifying all relevant model features and parameters.
For details on usage, refer to the provided [examples](./examples).
### Acknowledgments
Thanks to [David Greenberg](https://github.com/dgreenberg) for the package name.
### Building documentation
```bash
git clone git@github.com:awslabs/datawig.git
cd datawig/docs
make html
open _build/html/index.html
```
### Executing Tests
Clone the repository from git and set up virtualenv in the root dir of the package:
```
python3 -m venv venv
```
Install the package from local sources:
```
./venv/bin/pip install -e .
```
Run tests:
```
./venv/bin/pip install -r requirements/requirements.dev.txt
./venv/bin/python -m pytest
```
### Updating PyPi distribution
Before updating, increment the version in setup.py.
```
git clone git@github.com:awslabs/datawig.git
cd datawig
# build local distribution for current version
python setup.py sdist
# upload to PyPi
twine upload --skip-existing dist/*
```
%package -n python3-datawig
Summary: Imputation for tables with missing values
Provides: python-datawig
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-datawig
[](https://badge.fury.io/py/datawig.svg)
[](https://github.com/awslabs/datawig/blob/master/LICENSE)
[](https://github.com/awslabs/datawig/issues)
[](https://travis-ci.org/awslabs/datawig)
DataWig learns Machine Learning models to impute missing values in tables.
See our user-guide and extended documentation [here](https://datawig.readthedocs.io/en/latest).
## Installation
### CPU
```bash
pip3 install datawig
```
### GPU
If you want to run DataWig on a GPU you need to make sure your version of Apache MXNet Incubating contains the GPU bindings.
Depending on your version of CUDA, you can do this by running the following:
```bash
wget https://raw.githubusercontent.com/awslabs/datawig/master/requirements/requirements.gpu-cu${CUDA_VERSION}.txt
pip install datawig --no-deps -r requirements.gpu-cu${CUDA_VERSION}.txt
rm requirements.gpu-cu${CUDA_VERSION}.txt
```
where `${CUDA_VERSION}` can be `75` (7.5), `80` (8.0), `90` (9.0), or `91` (9.1).
## Running DataWig
The DataWig API expects your data as a [pandas DataFrame](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html). Here is an example of how the dataframe might look:
|Product Type | Description | Size | Color |
|-------------|-----------------------|------|-------|
| Shoe | Ideal for Running | 12UK | Black |
| SDCards | Best SDCard ever ... | 8GB | Blue |
| Dress | This **yellow** dress | M | **?** |
### Quickstart Example
For most use cases, the `SimpleImputer` class is the best starting point. For convenience there is the function [SimpleImputer.complete](https://datawig.readthedocs.io/en/latest/source/API.html#datawig.simple_imputer.SimpleImputer.complete) that takes a DataFrame and fits an imputation model for each column with missing values, with all other columns as inputs:
```python
import datawig, numpy
# generate some data with simple nonlinear dependency
df = datawig.utils.generate_df_numeric()
# mask 10% of the values
df_with_missing = df.mask(numpy.random.rand(*df.shape) > .9)
# impute missing values
df_with_missing_imputed = datawig.SimpleImputer.complete(df_with_missing)
```
You can also impute values in specific columns only (called `output_column` below) using values in other columns (called `input_columns` below). DataWig currently supports imputation of categorical columns and numeric columns.
### Imputation of categorical columns
```python
import datawig
df = datawig.utils.generate_df_string( num_samples=200,
data_column_name='sentences',
label_column_name='label')
df_train, df_test = datawig.utils.random_split(df)
#Initialize a SimpleImputer model
imputer = datawig.SimpleImputer(
input_columns=['sentences'], # column(s) containing information about the column we want to impute
output_column='label', # the column we'd like to impute values for
output_path = 'imputer_model' # stores model data and metrics
)
#Fit an imputer model on the train data
imputer.fit(train_df=df_train)
#Impute missing values and return original dataframe with predictions
imputed = imputer.predict(df_test)
```
### Imputation of numerical columns
```python
import datawig
df = datawig.utils.generate_df_numeric( num_samples=200,
data_column_name='x',
label_column_name='y')
df_train, df_test = datawig.utils.random_split(df)
#Initialize a SimpleImputer model
imputer = datawig.SimpleImputer(
input_columns=['x'], # column(s) containing information about the column we want to impute
output_column='y', # the column we'd like to impute values for
output_path = 'imputer_model' # stores model data and metrics
)
#Fit an imputer model on the train data
imputer.fit(train_df=df_train, num_epochs=50)
#Impute missing values and return original dataframe with predictions
imputed = imputer.predict(df_test)
```
In order to have more control over the types of models and preprocessings, the `Imputer` class allows directly specifying all relevant model features and parameters.
For details on usage, refer to the provided [examples](./examples).
### Acknowledgments
Thanks to [David Greenberg](https://github.com/dgreenberg) for the package name.
### Building documentation
```bash
git clone git@github.com:awslabs/datawig.git
cd datawig/docs
make html
open _build/html/index.html
```
### Executing Tests
Clone the repository from git and set up virtualenv in the root dir of the package:
```
python3 -m venv venv
```
Install the package from local sources:
```
./venv/bin/pip install -e .
```
Run tests:
```
./venv/bin/pip install -r requirements/requirements.dev.txt
./venv/bin/python -m pytest
```
### Updating PyPi distribution
Before updating, increment the version in setup.py.
```
git clone git@github.com:awslabs/datawig.git
cd datawig
# build local distribution for current version
python setup.py sdist
# upload to PyPi
twine upload --skip-existing dist/*
```
%package help
Summary: Development documents and examples for datawig
Provides: python3-datawig-doc
%description help
[](https://badge.fury.io/py/datawig.svg)
[](https://github.com/awslabs/datawig/blob/master/LICENSE)
[](https://github.com/awslabs/datawig/issues)
[](https://travis-ci.org/awslabs/datawig)
DataWig learns Machine Learning models to impute missing values in tables.
See our user-guide and extended documentation [here](https://datawig.readthedocs.io/en/latest).
## Installation
### CPU
```bash
pip3 install datawig
```
### GPU
If you want to run DataWig on a GPU you need to make sure your version of Apache MXNet Incubating contains the GPU bindings.
Depending on your version of CUDA, you can do this by running the following:
```bash
wget https://raw.githubusercontent.com/awslabs/datawig/master/requirements/requirements.gpu-cu${CUDA_VERSION}.txt
pip install datawig --no-deps -r requirements.gpu-cu${CUDA_VERSION}.txt
rm requirements.gpu-cu${CUDA_VERSION}.txt
```
where `${CUDA_VERSION}` can be `75` (7.5), `80` (8.0), `90` (9.0), or `91` (9.1).
## Running DataWig
The DataWig API expects your data as a [pandas DataFrame](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html). Here is an example of how the dataframe might look:
|Product Type | Description | Size | Color |
|-------------|-----------------------|------|-------|
| Shoe | Ideal for Running | 12UK | Black |
| SDCards | Best SDCard ever ... | 8GB | Blue |
| Dress | This **yellow** dress | M | **?** |
### Quickstart Example
For most use cases, the `SimpleImputer` class is the best starting point. For convenience there is the function [SimpleImputer.complete](https://datawig.readthedocs.io/en/latest/source/API.html#datawig.simple_imputer.SimpleImputer.complete) that takes a DataFrame and fits an imputation model for each column with missing values, with all other columns as inputs:
```python
import datawig, numpy
# generate some data with simple nonlinear dependency
df = datawig.utils.generate_df_numeric()
# mask 10% of the values
df_with_missing = df.mask(numpy.random.rand(*df.shape) > .9)
# impute missing values
df_with_missing_imputed = datawig.SimpleImputer.complete(df_with_missing)
```
You can also impute values in specific columns only (called `output_column` below) using values in other columns (called `input_columns` below). DataWig currently supports imputation of categorical columns and numeric columns.
### Imputation of categorical columns
```python
import datawig
df = datawig.utils.generate_df_string( num_samples=200,
data_column_name='sentences',
label_column_name='label')
df_train, df_test = datawig.utils.random_split(df)
#Initialize a SimpleImputer model
imputer = datawig.SimpleImputer(
input_columns=['sentences'], # column(s) containing information about the column we want to impute
output_column='label', # the column we'd like to impute values for
output_path = 'imputer_model' # stores model data and metrics
)
#Fit an imputer model on the train data
imputer.fit(train_df=df_train)
#Impute missing values and return original dataframe with predictions
imputed = imputer.predict(df_test)
```
### Imputation of numerical columns
```python
import datawig
df = datawig.utils.generate_df_numeric( num_samples=200,
data_column_name='x',
label_column_name='y')
df_train, df_test = datawig.utils.random_split(df)
#Initialize a SimpleImputer model
imputer = datawig.SimpleImputer(
input_columns=['x'], # column(s) containing information about the column we want to impute
output_column='y', # the column we'd like to impute values for
output_path = 'imputer_model' # stores model data and metrics
)
#Fit an imputer model on the train data
imputer.fit(train_df=df_train, num_epochs=50)
#Impute missing values and return original dataframe with predictions
imputed = imputer.predict(df_test)
```
In order to have more control over the types of models and preprocessings, the `Imputer` class allows directly specifying all relevant model features and parameters.
For details on usage, refer to the provided [examples](./examples).
### Acknowledgments
Thanks to [David Greenberg](https://github.com/dgreenberg) for the package name.
### Building documentation
```bash
git clone git@github.com:awslabs/datawig.git
cd datawig/docs
make html
open _build/html/index.html
```
### Executing Tests
Clone the repository from git and set up virtualenv in the root dir of the package:
```
python3 -m venv venv
```
Install the package from local sources:
```
./venv/bin/pip install -e .
```
Run tests:
```
./venv/bin/pip install -r requirements/requirements.dev.txt
./venv/bin/python -m pytest
```
### Updating PyPi distribution
Before updating, increment the version in setup.py.
```
git clone git@github.com:awslabs/datawig.git
cd datawig
# build local distribution for current version
python setup.py sdist
# upload to PyPi
twine upload --skip-existing dist/*
```
%prep
%autosetup -n datawig-0.2.0
%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-datawig -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed May 17 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.0-1
- Package Spec generated