automatic import of python-eland

3 files changed, 336 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..fc48944 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/eland-8.7.0.tar.gz
diff --git a/python-eland.spec b/python-eland.spec
new file mode 100644
index 0000000..8b96dc5
--- /dev/null
+++ b/python-eland.spec
@@ -0,0 +1,334 @@
+%global _empty_manifest_terminate_build 0
+Name:		python-eland
+Version:	8.7.0
+Release:	1
+Summary:	Python Client and Toolkit for DataFrames, Big Data, Machine Learning and ETL in Elasticsearch
+License:	Apache-2.0
+URL:		https://github.com/elastic/eland
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/23/45/416493ab2ee3a7865ffa43bc17a9ac1833fd55752437b9a099d1653ae0b3/eland-8.7.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-elasticsearch
+Requires:	python3-pandas
+Requires:	python3-matplotlib
+Requires:	python3-numpy
+Requires:	python3-torch
+Requires:	python3-lightgbm
+Requires:	python3-xgboost
+Requires:	python3-sentence-transformers
+Requires:	python3-transformers[torch]
+Requires:	python3-scikit-learn
+Requires:	python3-lightgbm
+Requires:	python3-torch
+Requires:	python3-sentence-transformers
+Requires:	python3-transformers[torch]
+Requires:	python3-scikit-learn
+Requires:	python3-xgboost
+
+%description
+ 0   AvgTicketPrice      13059 non-null  float64       
+ 1   Cancelled           13059 non-null  bool          
+ 2   Carrier             13059 non-null  object        
+ 24  OriginWeather       13059 non-null  object        
+ 25  dayOfWeek           13059 non-null  int64         
+ 26  timestamp           13059 non-null  datetime64[ns]
+dtypes: bool(2), datetime64[ns](1), float64(5), int64(2), object(17)
+memory usage: 80.0 bytes
+Elasticsearch storage usage: 5.043 MB
+# Filtering of rows using comparisons
+>>> df[(df.Carrier=="Kibana Airlines") & (df.AvgTicketPrice > 900.0) & (df.Cancelled == True)].head()
+     AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp
+8        960.869736       True  ...         0 2018-01-01 12:09:35
+26       975.812632       True  ...         0 2018-01-01 15:38:32
+311      946.358410       True  ...         0 2018-01-01 11:51:12
+651      975.383864       True  ...         2 2018-01-03 21:13:17
+950      907.836523       True  ...         2 2018-01-03 05:14:51
+[5 rows x 27 columns]
+# Running aggregations across an index
+>>> df[['DistanceKilometers', 'AvgTicketPrice']].aggregate(['sum', 'min', 'std'])
+     DistanceKilometers  AvgTicketPrice
+sum        9.261629e+07    8.204365e+06
+min        0.000000e+00    1.000205e+02
+std        4.578263e+03    2.663867e+02
+```
+## Machine Learning in Eland
+### Regression and classification
+Eland allows transforming trained regression and classification models from scikit-learn, XGBoost, and LightGBM
+libraries to be serialized and used as an inference model in Elasticsearch.
+➤ [Eland Machine Learning API documentation](https://eland.readthedocs.io/en/latest/reference/ml.html)
+➤ [Read more about Machine Learning in Elasticsearch](https://www.elastic.co/guide/en/machine-learning/current/ml-getting-started.html)
+```python
+>>> from xgboost import XGBClassifier
+>>> from eland.ml import MLModel
+# Train and exercise an XGBoost ML model locally
+>>> xgb_model = XGBClassifier(booster="gbtree")
+>>> xgb_model.fit(training_data[0], training_data[1])
+>>> xgb_model.predict(training_data[0])
+[0 1 1 0 1 0 0 0 1 0]
+# Import the model into Elasticsearch
+>>> es_model = MLModel.import_model(
+    es_client="localhost:9200",
+    model_id="xgb-classifier",
+    model=xgb_model,
+    feature_names=["f0", "f1", "f2", "f3", "f4"],
+)
+# Exercise the ML model in Elasticsearch with the training data
+>>> es_model.predict(training_data[0])
+[0 1 1 0 1 0 0 0 1 0]
+```
+### NLP with PyTorch
+For NLP tasks, Eland allows importing PyTorch trained BERT models into Elasticsearch. Models can be either plain PyTorch
+models, or supported [transformers](https://huggingface.co/transformers) models from the
+[Hugging Face model hub](https://huggingface.co/models).
+```bash
+$ eland_import_hub_model \
+  --url http://localhost:9200/ \
+  --hub-model-id elastic/distilbert-base-cased-finetuned-conll03-english \
+  --task-type ner \
+  --start
+```
+```python
+>>> import elasticsearch
+>>> from pathlib import Path
+>>> from eland.ml.pytorch import PyTorchModel
+>>> from eland.ml.pytorch.transformers import TransformerModel
+# Load a Hugging Face transformers model directly from the model hub
+>>> tm = TransformerModel("elastic/distilbert-base-cased-finetuned-conll03-english", "ner")
+Downloading: 100%|██████████| 257/257 [00:00<00:00, 108kB/s]
+Downloading: 100%|██████████| 954/954 [00:00<00:00, 372kB/s]
+Downloading: 100%|██████████| 208k/208k [00:00<00:00, 668kB/s] 
+Downloading: 100%|██████████| 112/112 [00:00<00:00, 43.9kB/s]
+Downloading: 100%|██████████| 249M/249M [00:23<00:00, 11.2MB/s]
+# Export the model in a TorchScrpt representation which Elasticsearch uses
+>>> tmp_path = "models"
+>>> Path(tmp_path).mkdir(parents=True, exist_ok=True)
+>>> model_path, config, vocab_path = tm.save(tmp_path)
+# Import model into Elasticsearch
+>>> es = elasticsearch.Elasticsearch("http://elastic:mlqa_admin@localhost:9200", timeout=300)  # 5 minute timeout
+>>> ptm = PyTorchModel(es, tm.elasticsearch_model_id())
+>>> ptm.import_model(model_path=model_path, config_path=None, vocab_path=vocab_path, config=config)
+100%|██████████| 63/63 [00:12<00:00,  5.02it/s]
+```
+
+%package -n python3-eland
+Summary:	Python Client and Toolkit for DataFrames, Big Data, Machine Learning and ETL in Elasticsearch
+Provides:	python-eland
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-eland
+ 0   AvgTicketPrice      13059 non-null  float64       
+ 1   Cancelled           13059 non-null  bool          
+ 2   Carrier             13059 non-null  object        
+ 24  OriginWeather       13059 non-null  object        
+ 25  dayOfWeek           13059 non-null  int64         
+ 26  timestamp           13059 non-null  datetime64[ns]
+dtypes: bool(2), datetime64[ns](1), float64(5), int64(2), object(17)
+memory usage: 80.0 bytes
+Elasticsearch storage usage: 5.043 MB
+# Filtering of rows using comparisons
+>>> df[(df.Carrier=="Kibana Airlines") & (df.AvgTicketPrice > 900.0) & (df.Cancelled == True)].head()
+     AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp
+8        960.869736       True  ...         0 2018-01-01 12:09:35
+26       975.812632       True  ...         0 2018-01-01 15:38:32
+311      946.358410       True  ...         0 2018-01-01 11:51:12
+651      975.383864       True  ...         2 2018-01-03 21:13:17
+950      907.836523       True  ...         2 2018-01-03 05:14:51
+[5 rows x 27 columns]
+# Running aggregations across an index
+>>> df[['DistanceKilometers', 'AvgTicketPrice']].aggregate(['sum', 'min', 'std'])
+     DistanceKilometers  AvgTicketPrice
+sum        9.261629e+07    8.204365e+06
+min        0.000000e+00    1.000205e+02
+std        4.578263e+03    2.663867e+02
+```
+## Machine Learning in Eland
+### Regression and classification
+Eland allows transforming trained regression and classification models from scikit-learn, XGBoost, and LightGBM
+libraries to be serialized and used as an inference model in Elasticsearch.
+➤ [Eland Machine Learning API documentation](https://eland.readthedocs.io/en/latest/reference/ml.html)
+➤ [Read more about Machine Learning in Elasticsearch](https://www.elastic.co/guide/en/machine-learning/current/ml-getting-started.html)
+```python
+>>> from xgboost import XGBClassifier
+>>> from eland.ml import MLModel
+# Train and exercise an XGBoost ML model locally
+>>> xgb_model = XGBClassifier(booster="gbtree")
+>>> xgb_model.fit(training_data[0], training_data[1])
+>>> xgb_model.predict(training_data[0])
+[0 1 1 0 1 0 0 0 1 0]
+# Import the model into Elasticsearch
+>>> es_model = MLModel.import_model(
+    es_client="localhost:9200",
+    model_id="xgb-classifier",
+    model=xgb_model,
+    feature_names=["f0", "f1", "f2", "f3", "f4"],
+)
+# Exercise the ML model in Elasticsearch with the training data
+>>> es_model.predict(training_data[0])
+[0 1 1 0 1 0 0 0 1 0]
+```
+### NLP with PyTorch
+For NLP tasks, Eland allows importing PyTorch trained BERT models into Elasticsearch. Models can be either plain PyTorch
+models, or supported [transformers](https://huggingface.co/transformers) models from the
+[Hugging Face model hub](https://huggingface.co/models).
+```bash
+$ eland_import_hub_model \
+  --url http://localhost:9200/ \
+  --hub-model-id elastic/distilbert-base-cased-finetuned-conll03-english \
+  --task-type ner \
+  --start
+```
+```python
+>>> import elasticsearch
+>>> from pathlib import Path
+>>> from eland.ml.pytorch import PyTorchModel
+>>> from eland.ml.pytorch.transformers import TransformerModel
+# Load a Hugging Face transformers model directly from the model hub
+>>> tm = TransformerModel("elastic/distilbert-base-cased-finetuned-conll03-english", "ner")
+Downloading: 100%|██████████| 257/257 [00:00<00:00, 108kB/s]
+Downloading: 100%|██████████| 954/954 [00:00<00:00, 372kB/s]
+Downloading: 100%|██████████| 208k/208k [00:00<00:00, 668kB/s] 
+Downloading: 100%|██████████| 112/112 [00:00<00:00, 43.9kB/s]
+Downloading: 100%|██████████| 249M/249M [00:23<00:00, 11.2MB/s]
+# Export the model in a TorchScrpt representation which Elasticsearch uses
+>>> tmp_path = "models"
+>>> Path(tmp_path).mkdir(parents=True, exist_ok=True)
+>>> model_path, config, vocab_path = tm.save(tmp_path)
+# Import model into Elasticsearch
+>>> es = elasticsearch.Elasticsearch("http://elastic:mlqa_admin@localhost:9200", timeout=300)  # 5 minute timeout
+>>> ptm = PyTorchModel(es, tm.elasticsearch_model_id())
+>>> ptm.import_model(model_path=model_path, config_path=None, vocab_path=vocab_path, config=config)
+100%|██████████| 63/63 [00:12<00:00,  5.02it/s]
+```
+
+%package help
+Summary:	Development documents and examples for eland
+Provides:	python3-eland-doc
+%description help
+ 0   AvgTicketPrice      13059 non-null  float64       
+ 1   Cancelled           13059 non-null  bool          
+ 2   Carrier             13059 non-null  object        
+ 24  OriginWeather       13059 non-null  object        
+ 25  dayOfWeek           13059 non-null  int64         
+ 26  timestamp           13059 non-null  datetime64[ns]
+dtypes: bool(2), datetime64[ns](1), float64(5), int64(2), object(17)
+memory usage: 80.0 bytes
+Elasticsearch storage usage: 5.043 MB
+# Filtering of rows using comparisons
+>>> df[(df.Carrier=="Kibana Airlines") & (df.AvgTicketPrice > 900.0) & (df.Cancelled == True)].head()
+     AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp
+8        960.869736       True  ...         0 2018-01-01 12:09:35
+26       975.812632       True  ...         0 2018-01-01 15:38:32
+311      946.358410       True  ...         0 2018-01-01 11:51:12
+651      975.383864       True  ...         2 2018-01-03 21:13:17
+950      907.836523       True  ...         2 2018-01-03 05:14:51
+[5 rows x 27 columns]
+# Running aggregations across an index
+>>> df[['DistanceKilometers', 'AvgTicketPrice']].aggregate(['sum', 'min', 'std'])
+     DistanceKilometers  AvgTicketPrice
+sum        9.261629e+07    8.204365e+06
+min        0.000000e+00    1.000205e+02
+std        4.578263e+03    2.663867e+02
+```
+## Machine Learning in Eland
+### Regression and classification
+Eland allows transforming trained regression and classification models from scikit-learn, XGBoost, and LightGBM
+libraries to be serialized and used as an inference model in Elasticsearch.
+➤ [Eland Machine Learning API documentation](https://eland.readthedocs.io/en/latest/reference/ml.html)
+➤ [Read more about Machine Learning in Elasticsearch](https://www.elastic.co/guide/en/machine-learning/current/ml-getting-started.html)
+```python
+>>> from xgboost import XGBClassifier
+>>> from eland.ml import MLModel
+# Train and exercise an XGBoost ML model locally
+>>> xgb_model = XGBClassifier(booster="gbtree")
+>>> xgb_model.fit(training_data[0], training_data[1])
+>>> xgb_model.predict(training_data[0])
+[0 1 1 0 1 0 0 0 1 0]
+# Import the model into Elasticsearch
+>>> es_model = MLModel.import_model(
+    es_client="localhost:9200",
+    model_id="xgb-classifier",
+    model=xgb_model,
+    feature_names=["f0", "f1", "f2", "f3", "f4"],
+)
+# Exercise the ML model in Elasticsearch with the training data
+>>> es_model.predict(training_data[0])
+[0 1 1 0 1 0 0 0 1 0]
+```
+### NLP with PyTorch
+For NLP tasks, Eland allows importing PyTorch trained BERT models into Elasticsearch. Models can be either plain PyTorch
+models, or supported [transformers](https://huggingface.co/transformers) models from the
+[Hugging Face model hub](https://huggingface.co/models).
+```bash
+$ eland_import_hub_model \
+  --url http://localhost:9200/ \
+  --hub-model-id elastic/distilbert-base-cased-finetuned-conll03-english \
+  --task-type ner \
+  --start
+```
+```python
+>>> import elasticsearch
+>>> from pathlib import Path
+>>> from eland.ml.pytorch import PyTorchModel
+>>> from eland.ml.pytorch.transformers import TransformerModel
+# Load a Hugging Face transformers model directly from the model hub
+>>> tm = TransformerModel("elastic/distilbert-base-cased-finetuned-conll03-english", "ner")
+Downloading: 100%|██████████| 257/257 [00:00<00:00, 108kB/s]
+Downloading: 100%|██████████| 954/954 [00:00<00:00, 372kB/s]
+Downloading: 100%|██████████| 208k/208k [00:00<00:00, 668kB/s] 
+Downloading: 100%|██████████| 112/112 [00:00<00:00, 43.9kB/s]
+Downloading: 100%|██████████| 249M/249M [00:23<00:00, 11.2MB/s]
+# Export the model in a TorchScrpt representation which Elasticsearch uses
+>>> tmp_path = "models"
+>>> Path(tmp_path).mkdir(parents=True, exist_ok=True)
+>>> model_path, config, vocab_path = tm.save(tmp_path)
+# Import model into Elasticsearch
+>>> es = elasticsearch.Elasticsearch("http://elastic:mlqa_admin@localhost:9200", timeout=300)  # 5 minute timeout
+>>> ptm = PyTorchModel(es, tm.elasticsearch_model_id())
+>>> ptm.import_model(model_path=model_path, config_path=None, vocab_path=vocab_path, config=config)
+100%|██████████| 63/63 [00:12<00:00,  5.02it/s]
+```
+
+%prep
+%autosetup -n eland-8.7.0
+
+%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-eland -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 8.7.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..8cc5926
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+ab5de7d7fe8c4beed20fb8ada30d4afb  eland-8.7.0.tar.gz