Metadata-Version: 2.1
Name: skestimate
Version: 0.0.1
Summary: fit estimate utility
Home-page: https://github.com/skhabiri/EstimatorPkg
Author: skhabiri
License: UNKNOWN
Description: 
        ## skestimate package
        
        This package is used to fit and get various metrics for a multi class classifier. It is based on scikit-learn library and uses the methods from that library.
        
        fit_est module inside the package has a class named Xest(self, estimator, data, target_label, ts).
        * estimator: A piplined classifier that encodes all the categorical features.
        * data: Raw dataset including the target label
        * target_label: A string type representing the name of the target label
        * ts: A number between 0 and 1 that specifies the test portion of the data.
        
        Below is an example of how to use the class methods on the CoverType data set from UCI repository.
        ```
        $ data = pd.read_csv("https://github.com/skhabiri/PredictiveModeling-CoverType-u2build/blob/master/data/train.csv?raw=true")
        rfc = make_pipeline(
            RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                                   criterion='entropy', max_depth=14, max_features=20,
                                   max_leaf_nodes=None, max_samples=None,
                                   min_impurity_decrease=0.0, min_impurity_split=None,
                                   min_samples_leaf=2, min_samples_split=10,
                                   min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=-1,
                                   oob_score=False, random_state=42, verbose=0,
                                   warm_start=False)
                            )
        xest = skestimate.Xest(rfc, data, "Cover_Type", 0.2)
        ```
        
        For local testing we can use the example() function in fit_est.py.
        ```
        >>> import skestimate	
        >>> myest = skestimate.example()
        >>> myest.xskew(0.9)
        ```
        
        **Available methods associated with Xest class:**
        * xunique():
        Reports counts of unique values in each column of data
        
        * xskew(imb=0.99):   
        Returns a pandas Series of the sorted column with skewness more than imb. imb is between 0 and 1
        
        * xfit():    
        Fits the pipeline estimator and returns fitted estimator, training score, and test score
        
        * xscore(fit=True):   
        Calculates accuracy, recall and precision of a classifier and plots the confusion matrix
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
