reg metric updated

README.md

@@ -1013,15 +1013,16 @@ Attribute | Description
 
 ### ROC plot
 
-Receiver operating characteristic (ROC) curve for visualizing classification performance
-
 `latest update v1.0.4`
 
 `bioinfokit.visuz.stat.roc(fpr, tpr, c_line_style, c_line_color, c_line_width, diag_line, diag_line_style, 
     diag_line_width, diag_line_color, auc, shade_auc, shade_auc_color, axxlabel, axylabel, axtickfontsize, 
     axtickfontname, axlabelfontsize, axlabelfontname, plotlegend, legendpos, legendanchor, legendcols, legendfontsize,
     legendlabelframe, legend_columnspacing, dim, show, figtype, figname, r, ylm)`
 
+Receiver operating characteristic (ROC) curve for visualizing classification performance
+
+
 Parameters | Description
 ------------ | -------------
 `fpr` | Increasing false positive rates obtained from `sklearn.metrics.roc_curve` [list][default:None]
@@ -1063,6 +1064,26 @@ ROC plot image in same directory (roc.png)
 <a href="https://reneshbedre.github.io/blog/logit.html#prediction-of-test-dataset-using-fitted-model" target="_blank">Working example</a>
 
 
+### Regression metrics
+
+Calculate Root Mean Square Error (RMSE), Mean squared error (MSE), Mean absolute error (MAE), and Mean absolute percent 
+error (MAPE) from regression fit
+
+`latest update v1.0.8`
+
+`bioinfokit.analys.stat.reg_metric(y, yhat, resid)`
+
+Parameters | Description
+------------ | -------------
+`y` |  Original values for dependent variable [numpy array] [default: None]
+`yhat` | Predicted values from regression [numpy array] [default: None]
+`resid` | Regression residuals  [numpy array][default: None]
+
+Returns:
+
+Pandas dataframe with values for RMSE, MSE, MAE, and MAPE
+
+<a href="https://www.reneshbedre.com/blog/linear-regression.html" target="_blank">Working example</a>
 
 
 

bioinfokit/__init__.py

@@ -1,5 +1,5 @@
 name = "bioinfokit"
-__version__ = "1.0.7"
+__version__ = "1.0.8"
 __author__ = "Renesh Bedre"
 
 

bioinfokit/analys.py

@@ -1449,7 +1449,6 @@ def reg_metric(self, y=None, yhat=None, resid=None):
         mae = np.mean(np.abs(y - yhat))
         # Mean absolute percentage error (MAPE)
         mape = np.mean(np.abs((y - yhat) / y))
-        print(rmse, mae, mse, mape)
         self.reg_metric_df = pd.DataFrame({'Metrics':
                           ['Root Mean Square Error (RMSE)', 'Mean Squared Error (MSE)', 'Mean Absolute Error (MAE)',
                            'Mean Absolute Percentage Error (MAPE)'], 'Value': [rmse, mse, mae, mape]}).round(4)
@@ -2677,6 +2676,8 @@ def __init__(self, data=None):
             self.data = pd.read_csv("https://reneshbedre.github.io/assets/posts/logit/wdbc_test.csv")
         elif data=='plant_richness':
             self.data = pd.read_csv('https://reneshbedre.github.io/assets/posts/reg/plant_richness_data_mlr.txt', sep='\t')
+        elif data=='plant_richness_lr':
+            self.data = pd.read_csv('https://reneshbedre.github.io/assets/posts/reg/plant_richness_data_lr.txt', sep='\t')
         else:
             print("Error: Provide correct parameter for data\n")