Metadata-Version: 1.1
Name: analyzefit
Version: 0.3.3
Summary: Performs analysis of the fit of a model.
Home-page: https://github.com/wsmorgan/analyzefit
Author: Wiley S Morgan
Author-email: wsmorgan@gmail.com
License: MIT
Description: [![Build Status](https://travis-ci.org/wsmorgan/analyzefit.svg?branch=master)](https://travis-ci.org/wsmorgan/analyzefit)[![Coverage Status](https://coveralls.io/repos/github/wsmorgan/analyzefit/badge.svg?branch=master)](https://coveralls.io/github/wsmorgan/analyzefit?branch=master)
        
        # analyzefit
        
        Analyze fit is a python package that performs standard analysis on the
        fit of a regression model. The analysis class validate method will
        create a residuals vs fitted plot, a quantile plot, a spread location
        plot, and a leverage plot for the model provided as well as print the
        accuracy scores for any metric the user likes. For example:
        
        ![alt_text](../master/support/images/validation.png)
        
        If a detailed plot is desired then the plots can also be generated
        individually using the methods res_vs_fit, quantile, spread_loc, and
        leverage respectively. By default when the plots are created
        individually they are rendered in an interactive inverontment using
        the bokeh plotting package. For example:
        
        ![alt text](../master/support/images/interactive.pdf)
        
        This allows the user to determine which points the model is failing to
        predict.
        
        Full API Documentation available at: [github pages](https://wsmorgan.github.io/analysefit/).
        
        ## Installing the code
        
        To install analyzefit you may either pip install:
        
        ```
        pip install analyzefit
        ```
        
        or clone this repository and install manually:
        
        ```
        python setup.py install
        ```
        
        # Validating a Model
        
        To use analyze fit simply pass the feature matrix, target values, and
        the model to the analysis class then call the validate method, (or any
        other plotting method). For example:
        
        ```
        import pandas as pd
        import numpy as np
        from sklearn.linear_model import LinearRegression
        from sklearn.metrics import mean_squared_error, r2_score
        
        df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data', header=None,sep="\s+")
        df.columns = ["CRIM","ZN","INDUS","CHAS","NOX","RM","AGE","DIS","RAD","TAX","PTRATIO","B","LSTAT","MEDV"]
        X = df.iloc[:,:-1].values
        y = df[["MEDV"]].values
        X_train, X_test,y_train,y_test = train_test_split(X,y, test_size=0.3,random_state=0)
        slr = LinearRegression()
        slr.fit(X_train,y_train)
        
        an = analyze.analysis(X_train, y_train, slr)
        an.validate()
        
        an.validate(X=X_test, y=y_test, metrics=[mean_squared_error, r2_score)
        
        an.res_vs_fit()
        
        an.quantile()
        
        an.spread_loc()
        
        an.leverage()
        ```
        
        ## Python Packages Used
        
        - numpy
        
        - matplotlib
        
        - bokeh
        
        - sklearn
        
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
