%global _empty_manifest_terminate_build 0 Name: python-Amplo Version: 0.17.0 Release: 1 Summary: Fully automated end to end machine learning pipeline License: GNU General Public License v3 (GPLv3) URL: https://github.com/nielsuit227/AutoML Source0: https://mirrors.nju.edu.cn/pypi/web/packages/4a/40/f178bed9ff3276ccb073ca265efd1672b8901bcb6a16dedd489f8ebf1e84/Amplo-0.17.0.tar.gz BuildArch: noarch Requires: python3-azure-core Requires: python3-azure-storage-blob Requires: python3-catboost Requires: python3-cleanlab Requires: python3-colorlog Requires: python3-joblib Requires: python3-lightgbm Requires: python3-numba Requires: python3-numpy Requires: python3-optuna Requires: python3-pandas Requires: python3-polars Requires: python3-pyarrow Requires: python3-pytest Requires: python3-pywavelets Requires: python3-requests Requires: python3-scikit-learn Requires: python3-scipy Requires: python3-setuptools Requires: python3-shap Requires: python3-tqdm Requires: python3-xgboost Requires: python3-flake8 Requires: python3-mypy Requires: python3-types-chardet Requires: python3-types-colorama Requires: python3-types-decorator Requires: python3-types-psycopg2 Requires: python3-types-Pygments Requires: python3-types-PyMySQL Requires: python3-types-python-dateutil Requires: python3-types-pytz Requires: python3-types-redis Requires: python3-types-requests Requires: python3-types-setuptools Requires: python3-types-six Requires: python3-types-urllib3 %description # Amplo - AutoML (for Machine Data) [![image](https://img.shields.io/pypi/v/amplo.svg)](https://pypi.python.org/pypi/amplo) [![PyPI - License](https://img.shields.io/pypi/l/virtualenv?style=flat-square)](https://opensource.org/licenses/MIT) ![](https://img.shields.io/badge/python-%3E%3D3.9%2C%3C4.0-blue) ![](https://tokei.rs/b1/github/nielsuit227/automl) ![](https://img.shields.io/pypi/dm/amplo) Welcome to the Automated Machine Learning package `amplo`. Amplo's AutoML is designed specifically for machine data and works very well with tabular time series data (especially unbalanced classification!). Though this is a standalone Python package, Amplo's AutoML is also available on Amplo's Smart Maintenance Platform. With a graphical user interface and various data connectors, it is the ideal place for service engineers to get started on Predictive. Amplo's AutoML Pipeline contains the entire Machine Learning development cycle, including exploratory data analysis, data cleaning, feature extraction, feature selection, model selection, hyperparameter optimization, stacking, version control, production-ready models and documentation. It comes with additional tools such as interval analysers, drift detectors, data quality checks, etc. ## 1. Downloading Amplo The easiest way is to install our Python package through [PyPi](https://pypi.org/project/amplo/): ```bash pip install amplo ``` ## 2. Usage Usage is very simple with Amplo's AutoML Pipeline. ```python from amplo import Pipeline from sklearn.datasets import make_classification from sklearn.datasets import make_regression x, y = make_classification() pipeline = Pipeline() pipeline.fit(x, y) yp = pipeline.predict_proba(x) x, y = make_regression() pipeline = Pipeline() pipeline.fit(x, y) yp = pipeline.predict(x) ``` ## 3. Amplo AutoML Features ### Interval Analyser ```python from amplo.automl import IntervalAnalyser ``` Interval Analyser for Log file classification. When log files have to be classified, and there is not enough data for time series methods (such as LSTMs, ROCKET or Weasel, Boss, etc.), one needs to fall back to classical machine learning models which work better with lower samples. This raises the problem of which samples to classify. You shouldn't just simply classify on every sample and accumulate, that may greatly disrupt classification performance. Therefore, we introduce this interval analyser. By using an approximate K-Nearest Neighbors algorithm, one can estimate the strength of correlation for every sample inside a log. Using this allows for better interval selection for classical machine learning models. To use this interval analyser, make sure that your logs are located in a folder of their class, with one parent folder with all classes, e.g.: ```text +-- Parent Folder | +-- Class_1 | +-- Log_1.* | +-- Log_2.* | +-- Class_2 | +-- Log_3.* ``` ### Data Processing ```python from amplo.automl import DataProcessor ``` Automated Data Cleaning: - Infers & converts data types (integer, floats, categorical, datetime) - Reformats column names - Removes duplicates columns and rows - Handles missing values by: - Removing columns - Removing rows - Interpolating - Filling with zero's - Removes outliers using: - Clipping - Z-score - Quantiles - Removes constant columns ### Feature Processing ```python from amplo.automl import FeatureProcessor ``` Automatically extracts and selects features. Removes Co-Linear Features. Included Feature Extraction algorithms: - Multiplicative Features - Dividing Features - Additive Features - Subtractive Features - Trigonometric Features - K-Means Features - Lagged Features - Differencing Features - Inverse Features - Datetime Features Included Feature Selection algorithms: - Random Forest Feature Importance (Threshold and Increment) - Predictive Power Score ### Sequencing ```python from amplo.automl import Sequencer ``` For time series regression problems, it is often useful to include multiple previous samples instead of just the latest. This class sequences the data, based on which time steps you want included in the in- and output. This is also very useful when working with tensors, as a tensor can be returned which directly fits into a Recurrent Neural Network. ### Modelling ```python from amplo.automl import Modeller ``` Runs various regression or classification models. Includes: - Scikit's Linear Model - Scikit's Random Forest - Scikit's Bagging - Scikit's GradientBoosting - Scikit's HistGradientBoosting - DMLC's XGBoost - Catboost's Catboost - Microsoft's LightGBM - Stacking Models ### Grid Search ```python from amplo.grid_search import OptunaGridSearch ``` Contains three hyperparameter optimizers with extended predefined model parameters: - Optuna's Tree-Parzen-Estimator %package -n python3-Amplo Summary: Fully automated end to end machine learning pipeline Provides: python-Amplo BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-Amplo # Amplo - AutoML (for Machine Data) [![image](https://img.shields.io/pypi/v/amplo.svg)](https://pypi.python.org/pypi/amplo) [![PyPI - License](https://img.shields.io/pypi/l/virtualenv?style=flat-square)](https://opensource.org/licenses/MIT) ![](https://img.shields.io/badge/python-%3E%3D3.9%2C%3C4.0-blue) ![](https://tokei.rs/b1/github/nielsuit227/automl) ![](https://img.shields.io/pypi/dm/amplo) Welcome to the Automated Machine Learning package `amplo`. Amplo's AutoML is designed specifically for machine data and works very well with tabular time series data (especially unbalanced classification!). Though this is a standalone Python package, Amplo's AutoML is also available on Amplo's Smart Maintenance Platform. With a graphical user interface and various data connectors, it is the ideal place for service engineers to get started on Predictive. Amplo's AutoML Pipeline contains the entire Machine Learning development cycle, including exploratory data analysis, data cleaning, feature extraction, feature selection, model selection, hyperparameter optimization, stacking, version control, production-ready models and documentation. It comes with additional tools such as interval analysers, drift detectors, data quality checks, etc. ## 1. Downloading Amplo The easiest way is to install our Python package through [PyPi](https://pypi.org/project/amplo/): ```bash pip install amplo ``` ## 2. Usage Usage is very simple with Amplo's AutoML Pipeline. ```python from amplo import Pipeline from sklearn.datasets import make_classification from sklearn.datasets import make_regression x, y = make_classification() pipeline = Pipeline() pipeline.fit(x, y) yp = pipeline.predict_proba(x) x, y = make_regression() pipeline = Pipeline() pipeline.fit(x, y) yp = pipeline.predict(x) ``` ## 3. Amplo AutoML Features ### Interval Analyser ```python from amplo.automl import IntervalAnalyser ``` Interval Analyser for Log file classification. When log files have to be classified, and there is not enough data for time series methods (such as LSTMs, ROCKET or Weasel, Boss, etc.), one needs to fall back to classical machine learning models which work better with lower samples. This raises the problem of which samples to classify. You shouldn't just simply classify on every sample and accumulate, that may greatly disrupt classification performance. Therefore, we introduce this interval analyser. By using an approximate K-Nearest Neighbors algorithm, one can estimate the strength of correlation for every sample inside a log. Using this allows for better interval selection for classical machine learning models. To use this interval analyser, make sure that your logs are located in a folder of their class, with one parent folder with all classes, e.g.: ```text +-- Parent Folder | +-- Class_1 | +-- Log_1.* | +-- Log_2.* | +-- Class_2 | +-- Log_3.* ``` ### Data Processing ```python from amplo.automl import DataProcessor ``` Automated Data Cleaning: - Infers & converts data types (integer, floats, categorical, datetime) - Reformats column names - Removes duplicates columns and rows - Handles missing values by: - Removing columns - Removing rows - Interpolating - Filling with zero's - Removes outliers using: - Clipping - Z-score - Quantiles - Removes constant columns ### Feature Processing ```python from amplo.automl import FeatureProcessor ``` Automatically extracts and selects features. Removes Co-Linear Features. Included Feature Extraction algorithms: - Multiplicative Features - Dividing Features - Additive Features - Subtractive Features - Trigonometric Features - K-Means Features - Lagged Features - Differencing Features - Inverse Features - Datetime Features Included Feature Selection algorithms: - Random Forest Feature Importance (Threshold and Increment) - Predictive Power Score ### Sequencing ```python from amplo.automl import Sequencer ``` For time series regression problems, it is often useful to include multiple previous samples instead of just the latest. This class sequences the data, based on which time steps you want included in the in- and output. This is also very useful when working with tensors, as a tensor can be returned which directly fits into a Recurrent Neural Network. ### Modelling ```python from amplo.automl import Modeller ``` Runs various regression or classification models. Includes: - Scikit's Linear Model - Scikit's Random Forest - Scikit's Bagging - Scikit's GradientBoosting - Scikit's HistGradientBoosting - DMLC's XGBoost - Catboost's Catboost - Microsoft's LightGBM - Stacking Models ### Grid Search ```python from amplo.grid_search import OptunaGridSearch ``` Contains three hyperparameter optimizers with extended predefined model parameters: - Optuna's Tree-Parzen-Estimator %package help Summary: Development documents and examples for Amplo Provides: python3-Amplo-doc %description help # Amplo - AutoML (for Machine Data) [![image](https://img.shields.io/pypi/v/amplo.svg)](https://pypi.python.org/pypi/amplo) [![PyPI - License](https://img.shields.io/pypi/l/virtualenv?style=flat-square)](https://opensource.org/licenses/MIT) ![](https://img.shields.io/badge/python-%3E%3D3.9%2C%3C4.0-blue) ![](https://tokei.rs/b1/github/nielsuit227/automl) ![](https://img.shields.io/pypi/dm/amplo) Welcome to the Automated Machine Learning package `amplo`. Amplo's AutoML is designed specifically for machine data and works very well with tabular time series data (especially unbalanced classification!). Though this is a standalone Python package, Amplo's AutoML is also available on Amplo's Smart Maintenance Platform. With a graphical user interface and various data connectors, it is the ideal place for service engineers to get started on Predictive. Amplo's AutoML Pipeline contains the entire Machine Learning development cycle, including exploratory data analysis, data cleaning, feature extraction, feature selection, model selection, hyperparameter optimization, stacking, version control, production-ready models and documentation. It comes with additional tools such as interval analysers, drift detectors, data quality checks, etc. ## 1. Downloading Amplo The easiest way is to install our Python package through [PyPi](https://pypi.org/project/amplo/): ```bash pip install amplo ``` ## 2. Usage Usage is very simple with Amplo's AutoML Pipeline. ```python from amplo import Pipeline from sklearn.datasets import make_classification from sklearn.datasets import make_regression x, y = make_classification() pipeline = Pipeline() pipeline.fit(x, y) yp = pipeline.predict_proba(x) x, y = make_regression() pipeline = Pipeline() pipeline.fit(x, y) yp = pipeline.predict(x) ``` ## 3. Amplo AutoML Features ### Interval Analyser ```python from amplo.automl import IntervalAnalyser ``` Interval Analyser for Log file classification. When log files have to be classified, and there is not enough data for time series methods (such as LSTMs, ROCKET or Weasel, Boss, etc.), one needs to fall back to classical machine learning models which work better with lower samples. This raises the problem of which samples to classify. You shouldn't just simply classify on every sample and accumulate, that may greatly disrupt classification performance. Therefore, we introduce this interval analyser. By using an approximate K-Nearest Neighbors algorithm, one can estimate the strength of correlation for every sample inside a log. Using this allows for better interval selection for classical machine learning models. To use this interval analyser, make sure that your logs are located in a folder of their class, with one parent folder with all classes, e.g.: ```text +-- Parent Folder | +-- Class_1 | +-- Log_1.* | +-- Log_2.* | +-- Class_2 | +-- Log_3.* ``` ### Data Processing ```python from amplo.automl import DataProcessor ``` Automated Data Cleaning: - Infers & converts data types (integer, floats, categorical, datetime) - Reformats column names - Removes duplicates columns and rows - Handles missing values by: - Removing columns - Removing rows - Interpolating - Filling with zero's - Removes outliers using: - Clipping - Z-score - Quantiles - Removes constant columns ### Feature Processing ```python from amplo.automl import FeatureProcessor ``` Automatically extracts and selects features. Removes Co-Linear Features. Included Feature Extraction algorithms: - Multiplicative Features - Dividing Features - Additive Features - Subtractive Features - Trigonometric Features - K-Means Features - Lagged Features - Differencing Features - Inverse Features - Datetime Features Included Feature Selection algorithms: - Random Forest Feature Importance (Threshold and Increment) - Predictive Power Score ### Sequencing ```python from amplo.automl import Sequencer ``` For time series regression problems, it is often useful to include multiple previous samples instead of just the latest. This class sequences the data, based on which time steps you want included in the in- and output. This is also very useful when working with tensors, as a tensor can be returned which directly fits into a Recurrent Neural Network. ### Modelling ```python from amplo.automl import Modeller ``` Runs various regression or classification models. Includes: - Scikit's Linear Model - Scikit's Random Forest - Scikit's Bagging - Scikit's GradientBoosting - Scikit's HistGradientBoosting - DMLC's XGBoost - Catboost's Catboost - Microsoft's LightGBM - Stacking Models ### Grid Search ```python from amplo.grid_search import OptunaGridSearch ``` Contains three hyperparameter optimizers with extended predefined model parameters: - Optuna's Tree-Parzen-Estimator %prep %autosetup -n Amplo-0.17.0 %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-Amplo -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Apr 11 2023 Python_Bot - 0.17.0-1 - Package Spec generated