automatic import of python-skt

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+/skt-0.2.88.tar.gz
diff --git a/python-skt.spec b/python-skt.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-skt
+Version:	0.2.88
+Release:	1
+Summary:	SKT package
+License:	MIT License
+URL:		https://github.com/sktaiflow/skt
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/2d/2f/556e72ec755bbb04509ea3e788e5021dab64258b063b1d626809e0f82642/skt-0.2.88.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-streamz
+Requires:	python3-confluent-kafka
+Requires:	python3-thrift-sasl
+Requires:	python3-hvac
+Requires:	python3-pyhive[hive]
+Requires:	python3-pyarrow
+Requires:	python3-pandas
+Requires:	python3-slackclient
+Requires:	python3-httplib2
+Requires:	python3-click
+Requires:	python3-PyGithub
+Requires:	python3-pycryptodome
+Requires:	python3-tabulate
+Requires:	python3-pandas-gbq
+Requires:	python3-google-cloud-bigquery-storage
+Requires:	python3-grpcio
+Requires:	python3-sqlalchemy
+Requires:	python3-packaging
+Requires:	python3-tqdm
+Requires:	python3-ipywidgets
+Requires:	python3-hmsclient-hive-3
+Requires:	python3-google-cloud-monitoring
+Requires:	python3-redis
+Requires:	python3-pyathena
+Requires:	python3-opensearch-py
+Requires:	python3-requests-aws4auth
+Requires:	python3-bayesian-optimization
+Requires:	python3-catboost
+Requires:	python3-plotnine
+Requires:	python3-shap
+Requires:	python3-gensim
+Requires:	python3-seaborn
+Requires:	python3-scikit-learn
+Requires:	python3-scipy
+Requires:	python3-lifelines
+Requires:	python3-xgboost
+Requires:	python3-lightgbm
+Requires:	python3-implicit
+Requires:	python3-matplotlib
+Requires:	python3-mushroom-rl
+Requires:	python3-pytorch-widedeep
+Requires:	python3-RL-for-reco
+Requires:	python3-LightGBMwithBayesOpt
+Requires:	python3-tensorboardX
+Requires:	python3-torchsummary
+Requires:	python3-pycaret
+Requires:	python3-openpyxl
+Requires:	python3-netcal
+Requires:	python3-haversine
+Requires:	python3-pyfarmhash
+Requires:	python3-mabalgs
+
+%description
+# SKT Package
+
+
+[![Actions Status](https://github.com/sktaiflow/skt/workflows/release/badge.svg)](https://github.com/sktaiflow/skt/actions)
+
+This is highly site dependent package.
+Resources are abstracted into package structure.
+
+
+## Usage
+
+
+Hive metastore
+```python
+from skt.ye import get_hms
+
+c = get_hms()
+c.get_partition_names("db", "table")
+c.close()
+```
+
+
+Hash and unhash
+```python
+from skt.lake import hash_s
+from skt.lake import unhash_s
+
+unhashed_list = ['0000000000']
+hashed_list = hash_s(unhashed_list)
+unhash_s(hashed_list)
+```
+
+
+Execute hive query without fetch result
+```python
+from skt.ye import hive_execute
+hive_execute(ddl_or_ctas_query)
+```
+
+
+Fetch resultset from hive query
+```python
+from skt.ye import hive_get_result
+result_set = hive_get_result(select_query)
+```
+
+
+Get pandas dataframe from hive qeruy resultset
+```python
+from skt.ye import hive_to_pandas
+pandas_df = hive_to_pandas(hive_query)
+```
+
+
+Get pandas dataframe from parquet file in hdfs
+```python
+from skt.ye import parquet_to_pandas
+pandas_df = parquet_to_pandas(hdfs_path)
+```
+
+
+Save pandas dataframe as parquet in hdfs
+```python
+from skt.ye import get_spark
+from skt.ye import pandas_to_parquet
+spark = get_spark()
+pandas_to_parquet(pandas_df, hdfs_path, spark) # we need spark for this operation
+spark.stop()
+```
+
+
+Work with spark
+```python
+from skt.ye import get_spark
+spark = get_spark()
+# do with spark session
+spark.stop()
+```
+
+
+Work with spark-bigquery-connector
+```python
+# SELECT
+from skt.gcp import bq_table_to_pandas 
+pandas_df = bq_table_to_pandas("dataset", "table_name", ["col_1", "col_2"], "2020-01-01", "svc_mgmt_num is not null")
+# INSERT 
+from skt.gcp import pandas_to_bq_table
+pandas_to_bq_table(pandas_df, "dataset", "table_name", "2020-03-01")
+```
+
+
+Send slack message
+```python
+from skt.ye import slack_send
+text = 'Hello'
+username = 'airflow'
+channel = '#leavemealone'
+slack_send(text=text, username=username, channel=channel)
+# Send dataframe as text
+df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]})
+slack_send(text=df, username=username, channel=channel, dataframe=True)
+```
+
+
+Get bigquery client
+```python
+from skt.gcp import get_bigquery_client
+bq = get_bigquery_client()
+bq.query(query)
+```
+
+IPython BigQuery Magic
+```python
+from skt.gcp import import_bigquery_ipython_magic
+
+import_bigquery_ipython_magic()
+
+query_params = {
+    "p_1": "v_1",
+    "dataset": "mnoai",
+}
+```
+```python
+%% bq --params $query_params
+
+SELECT c_1 
+FROM {dataset}.user_logs
+WHERE c_1 = @p_1
+```
+
+Access MLS
+```python
+from skt.mls import set_model_name
+from skt.mls import get_recent_model_path
+from skt.ye import get_pkl_from_hdfs
+
+set_model_name(COMM_DB, params)
+path = get_recent_model_path(COMM_DB, model_key)
+model = get_pkl_from_hdfs(f'{path})
+```
+
+MLS Model Registry (Upload model_binary(model.tar.gz) / model_meta(model.json) to AWS S3 from YE)
+```python
+from skt.mls import save_model
+
+# model object generated by LightGBM or XGBoost
+model
+
+# model name
+model_name = 'sample_model'
+# model version
+model_version = 'v1'
+# AWS ENV in 'stg / prd / dev' (default is 'stg')
+aws_env = 'stg'
+# List of features used in ML Model in string type (only for XGBoost model_type)
+feature_list = ['feature_1', 'feature_2', 'feature_3']
+# Force to overwrite model files on S3 if exists (default is False)
+force = False 
+
+save_model(model, model_name, model_version, aws_env, force)
+```
+
+
+MLS *meta_table* & *meta_table_item* related methods
+```python
+from skt.mls import get_meta_table
+from skt.mls import create_meta_table_item
+from skt.mls import update_meta_table_item
+from skt.mls import get_meta_table_item
+from skt.mls import meta_table_to_pandas
+from skt.mls import pandas_to_meta_table
+
+# Get a meta_table info
+get_meta_table(meta_table_name, aws_env, edd)
+# Create a meta_item
+create_meta_table_item(meta_table_name, item_name, item_dict, aws_env, edd)
+# Update a meta_item
+update_meta_table_item(meta_table_name, item_name, item_dict, aws_env, edd)
+# Get a meta_item
+get_meta_table_item(meta_table_name, item_name, aws_env, edd)
+# Get a meta_table as pandas dataframe
+meta_table_to_pandas(meta_table_name, aws_env, edd)
+# Update pandas dataframe to meta_table
+pandas_to_meta_table(method, meta_table_name, dataframe, key, values, aws_env, edd)
+
+
+# For the detal, use ?{method} to get detailed info (ex. ?get_meta_table)
+# For the user of EDD, must set edd=True
+```
+
+MLS *model_meta* related methods  
+(*Need to set *user* for the ml_model)
+```python
+from skt.mls import get_ml_model
+from skt.mls import create_meta_table_item
+from skt.mls import update_meta_table_item
+
+# Get a ml_model
+get_ml_model(user, model_name, model_version, aws_env, edd)
+# Get a model_meta of ml_model
+get_ml_model_meta(user, model_name, model_version, aws_env, edd)
+# Update or Create meta_item(s)
+update_ml_model_meta(user, model_name, model_version, model_meta_dict, aws_env, edd)
+
+# For the detal, use ?{method} to get detailed info (ex. ?get_ml_model)
+# For the user of EDD, must set edd=True
+```
+
+
+
+Use NES CLI
+```bas
+nes input_notebook_url -p k1 v1 -p k2 v2 -p k3 v3
+```
+
+
+Use github util
+```python
+from skt.ye import get_github_util
+g = get_github_util
+# query graphql
+res = g.query_gql(graph_ql)
+# get file in github repository
+byte_object = g.download_from_git(github_url_path)
+```
+
+
+## Installation
+
+```sh
+$ pip install skt --upgrade
+```
+
+If you would like to install submodules for AIR
+
+```sh
+$ pip install skt[air] --upgrade
+```
+
+## Develop
+
+Create issue first and follow the GitHub flow
+https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/github-flow
+
+
+# AIPS EDA tools
+
+## OVERVIEW
+
+- **Modeling EDA** 시 활용할 수 있는 기능의 공통 module
+- **Modules**
+    - 1) EDA (Nuemric / Categorical variable)
+<br>
+<br>
+
+## 1) EDA
+#### 1. Numeric variable EDA
+- **def** *numeric_eda_plot*
+
+```
+    Numeric feature에 대한 EDA Plot function
+
+    Args. :
+        - df           :   Pandas DataFrame 형태의 EDA대상 데이터
+        - feature_list :   EDA 대상 feature list (df의 columns)
+        - label_col    :   Label(or Hue) column
+        - cols         :   Multi-plot 시 grid column 개수 (row 개수는 feature_list에 따라 자동으로 결정 됨)
+        - n_samples    :   Label 별 sampling 할 개수 (default = -1(전수 데이터로 EDA할 경우))
+        - plot_type    :   density or box (default = 'density')
+        - stat_yn      :   기초 통계량 출력여부 (mean / min / max / 1q / 3q) (default : False)
+        - figsize      :   (default : (7,4))
+
+    Returns : 
+        matplotlib.pyplot object
+
+    Example : 
+        fig = numeric_eda_plot(df, ['age'], 'answer', cols = 1, n_samples = 10000, plot_type='density', stat_yn=True, figsize = (7,4))
+        fig
+
+        if want to Save the EDA images,
+        fig.savefig('filename')
+```
+
+
+#### 2. Categorical variable EDA
+- **def** *categorical_eda_plot*
+
+```
+    Categorical feature에 대한 EDA Plot function
+
+    Args. :
+        - df           :   Pandas DataFrame 형태의 EDA대상 데이터
+        - feature_list :   EDA 대상 feature list (df의 columns)
+        - label_col    :   Label(or Hue) column
+        - cols         :   Multi-plot 시 grid column 개수 (row 개수는 feature_list에 따라 자동으로 결정 됨)
+        - n_samples    :   Label 별 sampling 할 개수 (default = -1(전수 데이터로 EDA할 경우))
+        - figsize      :   (default : (7,4))
+
+    Returns : 
+        matplotlib.pyplot object
+
+
+    Example : 
+        Example : 
+        fig = categorical_eda_plot(df, ['sex_cd'], 'answer', cols = 1, n_samples = 10000, figsize = (7,4))
+        fig
+
+        if want to Save the EDA images,
+        fig.savefig('filename')
+
+```
+
+
+
+
+%package -n python3-skt
+Summary:	SKT package
+Provides:	python-skt
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-skt
+# SKT Package
+
+
+[![Actions Status](https://github.com/sktaiflow/skt/workflows/release/badge.svg)](https://github.com/sktaiflow/skt/actions)
+
+This is highly site dependent package.
+Resources are abstracted into package structure.
+
+
+## Usage
+
+
+Hive metastore
+```python
+from skt.ye import get_hms
+
+c = get_hms()
+c.get_partition_names("db", "table")
+c.close()
+```
+
+
+Hash and unhash
+```python
+from skt.lake import hash_s
+from skt.lake import unhash_s
+
+unhashed_list = ['0000000000']
+hashed_list = hash_s(unhashed_list)
+unhash_s(hashed_list)
+```
+
+
+Execute hive query without fetch result
+```python
+from skt.ye import hive_execute
+hive_execute(ddl_or_ctas_query)
+```
+
+
+Fetch resultset from hive query
+```python
+from skt.ye import hive_get_result
+result_set = hive_get_result(select_query)
+```
+
+
+Get pandas dataframe from hive qeruy resultset
+```python
+from skt.ye import hive_to_pandas
+pandas_df = hive_to_pandas(hive_query)
+```
+
+
+Get pandas dataframe from parquet file in hdfs
+```python
+from skt.ye import parquet_to_pandas
+pandas_df = parquet_to_pandas(hdfs_path)
+```
+
+
+Save pandas dataframe as parquet in hdfs
+```python
+from skt.ye import get_spark
+from skt.ye import pandas_to_parquet
+spark = get_spark()
+pandas_to_parquet(pandas_df, hdfs_path, spark) # we need spark for this operation
+spark.stop()
+```
+
+
+Work with spark
+```python
+from skt.ye import get_spark
+spark = get_spark()
+# do with spark session
+spark.stop()
+```
+
+
+Work with spark-bigquery-connector
+```python
+# SELECT
+from skt.gcp import bq_table_to_pandas 
+pandas_df = bq_table_to_pandas("dataset", "table_name", ["col_1", "col_2"], "2020-01-01", "svc_mgmt_num is not null")
+# INSERT 
+from skt.gcp import pandas_to_bq_table
+pandas_to_bq_table(pandas_df, "dataset", "table_name", "2020-03-01")
+```
+
+
+Send slack message
+```python
+from skt.ye import slack_send
+text = 'Hello'
+username = 'airflow'
+channel = '#leavemealone'
+slack_send(text=text, username=username, channel=channel)
+# Send dataframe as text
+df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]})
+slack_send(text=df, username=username, channel=channel, dataframe=True)
+```
+
+
+Get bigquery client
+```python
+from skt.gcp import get_bigquery_client
+bq = get_bigquery_client()
+bq.query(query)
+```
+
+IPython BigQuery Magic
+```python
+from skt.gcp import import_bigquery_ipython_magic
+
+import_bigquery_ipython_magic()
+
+query_params = {
+    "p_1": "v_1",
+    "dataset": "mnoai",
+}
+```
+```python
+%% bq --params $query_params
+
+SELECT c_1 
+FROM {dataset}.user_logs
+WHERE c_1 = @p_1
+```
+
+Access MLS
+```python
+from skt.mls import set_model_name
+from skt.mls import get_recent_model_path
+from skt.ye import get_pkl_from_hdfs
+
+set_model_name(COMM_DB, params)
+path = get_recent_model_path(COMM_DB, model_key)
+model = get_pkl_from_hdfs(f'{path})
+```
+
+MLS Model Registry (Upload model_binary(model.tar.gz) / model_meta(model.json) to AWS S3 from YE)
+```python
+from skt.mls import save_model
+
+# model object generated by LightGBM or XGBoost
+model
+
+# model name
+model_name = 'sample_model'
+# model version
+model_version = 'v1'
+# AWS ENV in 'stg / prd / dev' (default is 'stg')
+aws_env = 'stg'
+# List of features used in ML Model in string type (only for XGBoost model_type)
+feature_list = ['feature_1', 'feature_2', 'feature_3']
+# Force to overwrite model files on S3 if exists (default is False)
+force = False 
+
+save_model(model, model_name, model_version, aws_env, force)
+```
+
+
+MLS *meta_table* & *meta_table_item* related methods
+```python
+from skt.mls import get_meta_table
+from skt.mls import create_meta_table_item
+from skt.mls import update_meta_table_item
+from skt.mls import get_meta_table_item
+from skt.mls import meta_table_to_pandas
+from skt.mls import pandas_to_meta_table
+
+# Get a meta_table info
+get_meta_table(meta_table_name, aws_env, edd)
+# Create a meta_item
+create_meta_table_item(meta_table_name, item_name, item_dict, aws_env, edd)
+# Update a meta_item
+update_meta_table_item(meta_table_name, item_name, item_dict, aws_env, edd)
+# Get a meta_item
+get_meta_table_item(meta_table_name, item_name, aws_env, edd)
+# Get a meta_table as pandas dataframe
+meta_table_to_pandas(meta_table_name, aws_env, edd)
+# Update pandas dataframe to meta_table
+pandas_to_meta_table(method, meta_table_name, dataframe, key, values, aws_env, edd)
+
+
+# For the detal, use ?{method} to get detailed info (ex. ?get_meta_table)
+# For the user of EDD, must set edd=True
+```
+
+MLS *model_meta* related methods  
+(*Need to set *user* for the ml_model)
+```python
+from skt.mls import get_ml_model
+from skt.mls import create_meta_table_item
+from skt.mls import update_meta_table_item
+
+# Get a ml_model
+get_ml_model(user, model_name, model_version, aws_env, edd)
+# Get a model_meta of ml_model
+get_ml_model_meta(user, model_name, model_version, aws_env, edd)
+# Update or Create meta_item(s)
+update_ml_model_meta(user, model_name, model_version, model_meta_dict, aws_env, edd)
+
+# For the detal, use ?{method} to get detailed info (ex. ?get_ml_model)
+# For the user of EDD, must set edd=True
+```
+
+
+
+Use NES CLI
+```bas
+nes input_notebook_url -p k1 v1 -p k2 v2 -p k3 v3
+```
+
+
+Use github util
+```python
+from skt.ye import get_github_util
+g = get_github_util
+# query graphql
+res = g.query_gql(graph_ql)
+# get file in github repository
+byte_object = g.download_from_git(github_url_path)
+```
+
+
+## Installation
+
+```sh
+$ pip install skt --upgrade
+```
+
+If you would like to install submodules for AIR
+
+```sh
+$ pip install skt[air] --upgrade
+```
+
+## Develop
+
+Create issue first and follow the GitHub flow
+https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/github-flow
+
+
+# AIPS EDA tools
+
+## OVERVIEW
+
+- **Modeling EDA** 시 활용할 수 있는 기능의 공통 module
+- **Modules**
+    - 1) EDA (Nuemric / Categorical variable)
+<br>
+<br>
+
+## 1) EDA
+#### 1. Numeric variable EDA
+- **def** *numeric_eda_plot*
+
+```
+    Numeric feature에 대한 EDA Plot function
+
+    Args. :
+        - df           :   Pandas DataFrame 형태의 EDA대상 데이터
+        - feature_list :   EDA 대상 feature list (df의 columns)
+        - label_col    :   Label(or Hue) column
+        - cols         :   Multi-plot 시 grid column 개수 (row 개수는 feature_list에 따라 자동으로 결정 됨)
+        - n_samples    :   Label 별 sampling 할 개수 (default = -1(전수 데이터로 EDA할 경우))
+        - plot_type    :   density or box (default = 'density')
+        - stat_yn      :   기초 통계량 출력여부 (mean / min / max / 1q / 3q) (default : False)
+        - figsize      :   (default : (7,4))
+
+    Returns : 
+        matplotlib.pyplot object
+
+    Example : 
+        fig = numeric_eda_plot(df, ['age'], 'answer', cols = 1, n_samples = 10000, plot_type='density', stat_yn=True, figsize = (7,4))
+        fig
+
+        if want to Save the EDA images,
+        fig.savefig('filename')
+```
+
+
+#### 2. Categorical variable EDA
+- **def** *categorical_eda_plot*
+
+```
+    Categorical feature에 대한 EDA Plot function
+
+    Args. :
+        - df           :   Pandas DataFrame 형태의 EDA대상 데이터
+        - feature_list :   EDA 대상 feature list (df의 columns)
+        - label_col    :   Label(or Hue) column
+        - cols         :   Multi-plot 시 grid column 개수 (row 개수는 feature_list에 따라 자동으로 결정 됨)
+        - n_samples    :   Label 별 sampling 할 개수 (default = -1(전수 데이터로 EDA할 경우))
+        - figsize      :   (default : (7,4))
+
+    Returns : 
+        matplotlib.pyplot object
+
+
+    Example : 
+        Example : 
+        fig = categorical_eda_plot(df, ['sex_cd'], 'answer', cols = 1, n_samples = 10000, figsize = (7,4))
+        fig
+
+        if want to Save the EDA images,
+        fig.savefig('filename')
+
+```
+
+
+
+
+%package help
+Summary:	Development documents and examples for skt
+Provides:	python3-skt-doc
+%description help
+# SKT Package
+
+
+[![Actions Status](https://github.com/sktaiflow/skt/workflows/release/badge.svg)](https://github.com/sktaiflow/skt/actions)
+
+This is highly site dependent package.
+Resources are abstracted into package structure.
+
+
+## Usage
+
+
+Hive metastore
+```python
+from skt.ye import get_hms
+
+c = get_hms()
+c.get_partition_names("db", "table")
+c.close()
+```
+
+
+Hash and unhash
+```python
+from skt.lake import hash_s
+from skt.lake import unhash_s
+
+unhashed_list = ['0000000000']
+hashed_list = hash_s(unhashed_list)
+unhash_s(hashed_list)
+```
+
+
+Execute hive query without fetch result
+```python
+from skt.ye import hive_execute
+hive_execute(ddl_or_ctas_query)
+```
+
+
+Fetch resultset from hive query
+```python
+from skt.ye import hive_get_result
+result_set = hive_get_result(select_query)
+```
+
+
+Get pandas dataframe from hive qeruy resultset
+```python
+from skt.ye import hive_to_pandas
+pandas_df = hive_to_pandas(hive_query)
+```
+
+
+Get pandas dataframe from parquet file in hdfs
+```python
+from skt.ye import parquet_to_pandas
+pandas_df = parquet_to_pandas(hdfs_path)
+```
+
+
+Save pandas dataframe as parquet in hdfs
+```python
+from skt.ye import get_spark
+from skt.ye import pandas_to_parquet
+spark = get_spark()
+pandas_to_parquet(pandas_df, hdfs_path, spark) # we need spark for this operation
+spark.stop()
+```
+
+
+Work with spark
+```python
+from skt.ye import get_spark
+spark = get_spark()
+# do with spark session
+spark.stop()
+```
+
+
+Work with spark-bigquery-connector
+```python
+# SELECT
+from skt.gcp import bq_table_to_pandas 
+pandas_df = bq_table_to_pandas("dataset", "table_name", ["col_1", "col_2"], "2020-01-01", "svc_mgmt_num is not null")
+# INSERT 
+from skt.gcp import pandas_to_bq_table
+pandas_to_bq_table(pandas_df, "dataset", "table_name", "2020-03-01")
+```
+
+
+Send slack message
+```python
+from skt.ye import slack_send
+text = 'Hello'
+username = 'airflow'
+channel = '#leavemealone'
+slack_send(text=text, username=username, channel=channel)
+# Send dataframe as text
+df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]})
+slack_send(text=df, username=username, channel=channel, dataframe=True)
+```
+
+
+Get bigquery client
+```python
+from skt.gcp import get_bigquery_client
+bq = get_bigquery_client()
+bq.query(query)
+```
+
+IPython BigQuery Magic
+```python
+from skt.gcp import import_bigquery_ipython_magic
+
+import_bigquery_ipython_magic()
+
+query_params = {
+    "p_1": "v_1",
+    "dataset": "mnoai",
+}
+```
+```python
+%% bq --params $query_params
+
+SELECT c_1 
+FROM {dataset}.user_logs
+WHERE c_1 = @p_1
+```
+
+Access MLS
+```python
+from skt.mls import set_model_name
+from skt.mls import get_recent_model_path
+from skt.ye import get_pkl_from_hdfs
+
+set_model_name(COMM_DB, params)
+path = get_recent_model_path(COMM_DB, model_key)
+model = get_pkl_from_hdfs(f'{path})
+```
+
+MLS Model Registry (Upload model_binary(model.tar.gz) / model_meta(model.json) to AWS S3 from YE)
+```python
+from skt.mls import save_model
+
+# model object generated by LightGBM or XGBoost
+model
+
+# model name
+model_name = 'sample_model'
+# model version
+model_version = 'v1'
+# AWS ENV in 'stg / prd / dev' (default is 'stg')
+aws_env = 'stg'
+# List of features used in ML Model in string type (only for XGBoost model_type)
+feature_list = ['feature_1', 'feature_2', 'feature_3']
+# Force to overwrite model files on S3 if exists (default is False)
+force = False 
+
+save_model(model, model_name, model_version, aws_env, force)
+```
+
+
+MLS *meta_table* & *meta_table_item* related methods
+```python
+from skt.mls import get_meta_table
+from skt.mls import create_meta_table_item
+from skt.mls import update_meta_table_item
+from skt.mls import get_meta_table_item
+from skt.mls import meta_table_to_pandas
+from skt.mls import pandas_to_meta_table
+
+# Get a meta_table info
+get_meta_table(meta_table_name, aws_env, edd)
+# Create a meta_item
+create_meta_table_item(meta_table_name, item_name, item_dict, aws_env, edd)
+# Update a meta_item
+update_meta_table_item(meta_table_name, item_name, item_dict, aws_env, edd)
+# Get a meta_item
+get_meta_table_item(meta_table_name, item_name, aws_env, edd)
+# Get a meta_table as pandas dataframe
+meta_table_to_pandas(meta_table_name, aws_env, edd)
+# Update pandas dataframe to meta_table
+pandas_to_meta_table(method, meta_table_name, dataframe, key, values, aws_env, edd)
+
+
+# For the detal, use ?{method} to get detailed info (ex. ?get_meta_table)
+# For the user of EDD, must set edd=True
+```
+
+MLS *model_meta* related methods  
+(*Need to set *user* for the ml_model)
+```python
+from skt.mls import get_ml_model
+from skt.mls import create_meta_table_item
+from skt.mls import update_meta_table_item
+
+# Get a ml_model
+get_ml_model(user, model_name, model_version, aws_env, edd)
+# Get a model_meta of ml_model
+get_ml_model_meta(user, model_name, model_version, aws_env, edd)
+# Update or Create meta_item(s)
+update_ml_model_meta(user, model_name, model_version, model_meta_dict, aws_env, edd)
+
+# For the detal, use ?{method} to get detailed info (ex. ?get_ml_model)
+# For the user of EDD, must set edd=True
+```
+
+
+
+Use NES CLI
+```bas
+nes input_notebook_url -p k1 v1 -p k2 v2 -p k3 v3
+```
+
+
+Use github util
+```python
+from skt.ye import get_github_util
+g = get_github_util
+# query graphql
+res = g.query_gql(graph_ql)
+# get file in github repository
+byte_object = g.download_from_git(github_url_path)
+```
+
+
+## Installation
+
+```sh
+$ pip install skt --upgrade
+```
+
+If you would like to install submodules for AIR
+
+```sh
+$ pip install skt[air] --upgrade
+```
+
+## Develop
+
+Create issue first and follow the GitHub flow
+https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/github-flow
+
+
+# AIPS EDA tools
+
+## OVERVIEW
+
+- **Modeling EDA** 시 활용할 수 있는 기능의 공통 module
+- **Modules**
+    - 1) EDA (Nuemric / Categorical variable)
+<br>
+<br>
+
+## 1) EDA
+#### 1. Numeric variable EDA
+- **def** *numeric_eda_plot*
+
+```
+    Numeric feature에 대한 EDA Plot function
+
+    Args. :
+        - df           :   Pandas DataFrame 형태의 EDA대상 데이터
+        - feature_list :   EDA 대상 feature list (df의 columns)
+        - label_col    :   Label(or Hue) column
+        - cols         :   Multi-plot 시 grid column 개수 (row 개수는 feature_list에 따라 자동으로 결정 됨)
+        - n_samples    :   Label 별 sampling 할 개수 (default = -1(전수 데이터로 EDA할 경우))
+        - plot_type    :   density or box (default = 'density')
+        - stat_yn      :   기초 통계량 출력여부 (mean / min / max / 1q / 3q) (default : False)
+        - figsize      :   (default : (7,4))
+
+    Returns : 
+        matplotlib.pyplot object
+
+    Example : 
+        fig = numeric_eda_plot(df, ['age'], 'answer', cols = 1, n_samples = 10000, plot_type='density', stat_yn=True, figsize = (7,4))
+        fig
+
+        if want to Save the EDA images,
+        fig.savefig('filename')
+```
+
+
+#### 2. Categorical variable EDA
+- **def** *categorical_eda_plot*
+
+```
+    Categorical feature에 대한 EDA Plot function
+
+    Args. :
+        - df           :   Pandas DataFrame 형태의 EDA대상 데이터
+        - feature_list :   EDA 대상 feature list (df의 columns)
+        - label_col    :   Label(or Hue) column
+        - cols         :   Multi-plot 시 grid column 개수 (row 개수는 feature_list에 따라 자동으로 결정 됨)
+        - n_samples    :   Label 별 sampling 할 개수 (default = -1(전수 데이터로 EDA할 경우))
+        - figsize      :   (default : (7,4))
+
+    Returns : 
+        matplotlib.pyplot object
+
+
+    Example : 
+        Example : 
+        fig = categorical_eda_plot(df, ['sex_cd'], 'answer', cols = 1, n_samples = 10000, figsize = (7,4))
+        fig
+
+        if want to Save the EDA images,
+        fig.savefig('filename')
+
+```
+
+
+
+
+%prep
+%autosetup -n skt-0.2.88
+
+%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-skt -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.88-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..5756700
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+dd2e65bdd461afe28ef8db6c3fb9210d  skt-0.2.88.tar.gz