معرفی شرکت ها

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

توضیحات

Feature election group of classes calculate the importance of features based on the Shap library for the classification and regression problem Only works with randomforest models for efficiency or gradient boosting models. DFwrapper - remove multicollinearity and outliers

ویژگی	مقدار
سیستم عامل	OS Independent
نام فایل	SHFS-0.1.5
نام	SHFS
نسخه کتابخانه	0.1.5
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Artem, et al.
ایمیل نویسنده	artysolomko@gmail.com
آدرس صفحه اصلی	https://github.com/ArtyKrafty/featureselectors
آدرس اینترنتی	https://pypi.org/project/SHFS/
مجوز	-

<p align="center"><img src="https://i.ibb.co/ZXSk6jG/machine-learning-1920x1180.jpg" alt="machine-learning-1920x1180"></p> Library consist of two groups of Classes - Feature selectors and DFwrapper to have a deal with outliers and correlation 1. Feature selection group The FeatureSelection calculates the importance of features based on the `Shap` library for a classification problem. Only works with trees for better efficiency or models based on gradient boosting. It is a priority to use such models as: Catboost - does not require handling of `NaN` and categories. works with `sklearn` NOTE: If your import is failing due to a missing package, you can manually install dependencies using either !pip or !apt. !pip install shap !pip install phik https://pypi.org/project/SHFS/ FeatureSelectionClf - for classification FeatureSelectionRegression - for regression FeatureSelectionUniversal - for both classification and regression tasks Quick start: [Collab](https://colab.research.google.com/drive/1eP6qZmxcTcsKgjLL7u_pHaM5sZc8346N?usp=sharing) and [Tutorial](https://nbviewer.org/github/ArtyKrafty/featureselectors/blob/main/Tutorial/Tutorials_ipynb_.ipynb) Parametrs. ___ `estimator` : Supervised learning with the fit method will allow you to retrieve and select indices. the most important features. n_features_to_select: int, default = None. The number of features to select, the default is None. columns: List, default = None. The list of attributes of the initial set, the default is None. Methods ___ fit - trains and identifies the most important features tranform - changes the original set and returns the selected attributes get_index - Returns the selected indexes attributes only for FeatureSelectionClf and FeatureSelectionRegression: plot_values - plotting shap values _estimator_type - @property method get_feature_importance - Returns DataFrame FI Note ___ Nan / Inf are allowed in case they are accepted by the fit method model Example use for classification ___ cols = list(X_train.columns) cat_features = list(X_train_cat.select_dtypes(include=['object', 'category']).columns) num_features = list(X_train_cat.select_dtypes(exclude=['object', 'category']).columns) estimator = CatBoostClassifier(**params_cat) selector = FeatureSelectionClf(estimator, n_features_to_select=3, columns=cols) preprocessor = ColumnTransformer ( transformers = [ ('std_scaler' , StandardScaler() , num_features) , ('cat' , OrdinalEncoder() , cat_features), ] ) pipe = Pipeline(steps= [ ('preprocessor', preprocessor), ('selector', selector) ] ) X_train_prep = pipe.fit_transform(X_train) Example without Pipeline cols = list(X_train.columns) estimator = CatBoostClassifier(**params_cat) selector = FeatureSelectionClf(estimator, n_features_to_select=3, columns=cols) X = selector.fit(X_train_prep, y_train) 2. DFwrapper DFwrapper - remove multicollinearity and outliers from Pandas DataFrame Usage example ---------- 1. Collinearity cleaner = DFwrapper() new_df = cleaner.wrap_corr(df) 2. Outliers. Rough cleaning cleaner = DFwrapper(low=.05, high=.95) cleaned = cleaner.quantile_cleaner(df, cols_to_clean) 2. Outliers. Finer cleaning cleaner = DFwrapper(koeff=1.5) cleaned = cleaner.frame_irq(df, cols_to_clean)

نیازمندی

مقدار	نام
-	pandas
-	numpy
==1.0.1	scikit-learn

زبان مورد نیاز

مقدار	نام
>=3.6	Python

نحوه نصب

نصب پکیج whl SHFS-0.1.5:

pip install SHFS-0.1.5.whl

نصب پکیج tar.gz SHFS-0.1.5:

pip install SHFS-0.1.5.tar.gz