Delete utils.py

build/lib/pycaret/utils.py

-# Module: Utility
-# Author: Moez Ali <moez.ali@queensu.ca>
-# License: MIT
-
-version_ = "2.0"
-
-def version():
-    print(version_)
-
-def __version__():
-    return version_
-
-def check_metric(actual, prediction, metric, round=4):
-    
-    """
-    Function to evaluate classification and regression metrics.
-    """
-    
-    #general dependencies
-    import numpy as np
-
-    #metric calculation starts here
-    
-    if metric == 'Accuracy':
-        
-        from sklearn import metrics
-        result = metrics.accuracy_score(actual,prediction)
-        result = result.round(round)
-        
-    elif metric == 'Recall':
-        
-        from sklearn import metrics
-        result = metrics.recall_score(actual,prediction)
-        result = result.round(round)
-        
-    elif metric == 'Precision':
-        
-        from sklearn import metrics
-        result = metrics.precision_score(actual,prediction)
-        result = result.round(round)
-        
-    elif metric == 'F1':
-        
-        from sklearn import metrics
-        result = metrics.f1_score(actual,prediction)
-        result = result.round(round)
-        
-    elif metric == 'Kappa':
-        
-        from sklearn import metrics
-        result = metrics.cohen_kappa_score(actual,prediction)
-        result = result.round(round)
-       
-    elif metric == 'AUC':
-        
-        from sklearn import metrics
-        result = metrics.roc_auc_score(actual,prediction)
-        result = result.round(round)
-        
-    elif metric == 'MCC':
-        
-        from sklearn import metrics
-        result = metrics.matthews_corrcoef(actual,prediction)
-        result = result.round(round)
-
-    elif metric == 'MAE':
-
-        from sklearn import metrics
-        result = metrics.mean_absolute_error(actual,prediction)
-        result = result.round(round)
-        
-    elif metric == 'MSE':
-
-        from sklearn import metrics
-        result = metrics.mean_squared_error(actual,prediction)
-        result = result.round(round)        
-        
-    elif metric == 'RMSE':
-
-        from sklearn import metrics
-        result = metrics.mean_squared_error(actual,prediction)
-        result = np.sqrt(result)
-        result = result.round(round)     
-        
-    elif metric == 'R2':
-
-        from sklearn import metrics
-        result = metrics.r2_score(actual,prediction)
-        result = result.round(round)    
-        
-    elif metric == 'RMSLE':
-
-        result = np.sqrt(np.mean(np.power(np.log(np.array(abs(prediction))+1) - np.log(np.array(abs(actual))+1), 2)))
-        result = result.round(round)
-
-    elif metric == 'MAPE':
-
-        mask = actual != 0
-        result = (np.fabs(actual - prediction)/actual)[mask].mean()
-        result = result.round(round)
-       
-    return result
-
-
-def enable_colab():
-    
-    """
-    Function to render plotly visuals in colab.
-    """
-    
-    def configure_plotly_browser_state():
-        
-        import IPython
-        display(IPython.core.display.HTML('''
-            <script src="/static/components/requirejs/require.js"></script>
-            <script>
-              requirejs.config({
-                paths: {
-                  base: '/static/base',
-                  plotly: 'https://cdn.plot.ly/plotly-latest.min.js?noext',
-                },
-              });
-            </script>
-            '''))
-  
-    import IPython
-    IPython.get_ipython().events.register('pre_run_cell', configure_plotly_browser_state)
-    print('Colab mode activated.')
\ No newline at end of file