pycaret-nightly==0.33

pycaret/anomaly.py

@@ -3565,12 +3565,33 @@ def get_outliers(data,
     else:
         ignore_features_pass = ignore_features
     
-    global X, data_, seed, n_jobs_param, logging_param
+    global X, data_, seed, n_jobs_param, logging_param, logger
     
     n_jobs_param = n_jobs
 
     logging_param = False
 
+    import logging
+
+    logger = logging.getLogger('logs')
+    logger.setLevel(logging.DEBUG)
+    
+    # create console handler and set level to debug
+    if logger.hasHandlers():
+        logger.handlers.clear()
+    
+    ch = logging.FileHandler('logs.log')
+    ch.setLevel(logging.DEBUG)
+
+    # create formatter
+    formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')
+
+    # add formatter to ch
+    ch.setFormatter(formatter)
+
+    # add ch to logger
+    logger.addHandler(ch)
+    
     data_ = data.copy()
     
     seed = 99

pycaret/utils.py

@@ -2,7 +2,7 @@
 # Author: Moez Ali <moez.ali@queensu.ca>
 # License: MIT
 
-version_ = "pycaret-nightly-0.32"
+version_ = "pycaret-nightly-0.33"
 
 def version():
     print(version_)
@@ -21,73 +21,79 @@ def check_metric(actual, prediction, metric, round=4):
 
     #metric calculation starts here
     
-    if metric == 'accuracy':
+    if metric == 'Accuracy':
         
         from sklearn import metrics
         result = metrics.accuracy_score(actual,prediction)
         result = result.round(round)
         
-    elif metric == 'recall':
+    elif metric == 'Recall':
         
         from sklearn import metrics
         result = metrics.recall_score(actual,prediction)
         result = result.round(round)
         
-    elif metric == 'precision':
+    elif metric == 'Precision':
         
         from sklearn import metrics
         result = metrics.precision_score(actual,prediction)
         result = result.round(round)
         
-    elif metric == 'f1':
+    elif metric == 'F1':
         
         from sklearn import metrics
         result = metrics.f1_score(actual,prediction)
         result = result.round(round)
         
-    elif metric == 'kappa':
+    elif metric == 'Kappa':
         
         from sklearn import metrics
         result = metrics.cohen_kappa_score(actual,prediction)
         result = result.round(round)
        
-    elif metric == 'auc':
+    elif metric == 'AUC':
         
         from sklearn import metrics
         result = metrics.roc_auc_score(actual,prediction)
         result = result.round(round)
         
-    elif metric == 'mae':
+    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':
+    elif metric == 'MSE':
 
         from sklearn import metrics
         result = metrics.mean_squared_error(actual,prediction)
         result = result.round(round)        
         
-    elif metric == 'rmse':
+    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':
+    elif metric == 'R2':
 
         from sklearn import metrics
         result = metrics.r2_score(actual,prediction)
         result = result.round(round)    
         
-    elif metric == 'rmsle':
+    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':
+    elif metric == 'MAPE':
 
         mask = actual != 0
         result = (np.fabs(actual - prediction)/actual)[mask].mean()

setup.py

@@ -13,7 +13,7 @@ with open('requirements.txt') as f:
 
 setup(
     name="pycaret-nightly",
-    version="0.32",
+    version="0.33",
     description="Nightly build of PyCaret - An open source, low-code machine learning library in Python.",
     long_description=readme(),
     long_description_content_type="text/markdown",