updated pycaret-nightly==0.17

pycaret/classification.py

@@ -1833,7 +1833,7 @@ def create_model(estimator = None,
     ------------
     This function creates a model and scores it using Stratified Cross Validation. 
     The output prints a score grid that shows Accuracy, AUC, Recall, Precision, 
-    F1 and Kappa by fold (default = 10 Fold). 
+    F1, Kappa and MCC by fold (default = 10 Fold). 
 
     This function returns a trained model object. 
 
@@ -9902,7 +9902,10 @@ def predict_model(estimator,
     
     estimator = deepcopy(estimator) #lookout for an alternate of deepcopy()
     
-    clear_output()
+    try:
+        clear_output()
+    except:
+        pass
     
     if type(estimator) is str:
         if platform == 'aws':

pycaret/nlp.py

@@ -136,17 +136,13 @@ def setup(data,
         if type(session_id) is not int:
             sys.exit('(Type Error): session_id parameter must be an integer.')  
             
-    #chcek if spacy is loaded 
+    #check if spacy is loaded 
     try:
         import spacy
         sp = spacy.load('en_core_web_sm', disable=['parser', 'ner'])
     except:
         sys.exit('(Type Error): spacy english model is not yet downloaded. See the documentation of setup to see installation guide.')
     
-    #silent
-    if type(silent) is not bool:
-        sys.exit("(Type Error): silent parameter only accepts True or False. ")
-        
     #html
     if type(html) is not bool:
         sys.exit('(Type Error): html parameter only accepts True or False.')
@@ -485,6 +481,7 @@ def setup(data,
             update_display(monitor, display_id = 'monitor')
     
     nlp = spacy.load('en_core_web_sm', disable=['parser', 'ner'])
+    nlp.max_length=3000000 #increasing text length to 3000000 from default of 1000000
     allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']
     
     text_step7 = []

pycaret/regression.py

@@ -8785,7 +8785,10 @@ def predict_model(estimator,
     
     estimator = deepcopy(estimator)
 
-    clear_output()
+    try:
+        clear_output()
+    except:
+        pass
     
     if type(estimator) is str:
         if platform == 'aws':

pycaret/utils.py

@@ -3,7 +3,7 @@
 # License: MIT
 
 def version():
-    print("pycaret-nightly-0.16")
+    print("pycaret-nightly-0.17")
 
 
 def check_metric(actual, prediction, metric, round=4):

setup.py

@@ -13,7 +13,7 @@ with open('requirements.txt') as f:
 
 setup(
     name="pycaret-nightly",
-    version="0.16",
+    version="0.17",
     description="Nightly build of PyCaret - An open source, low-code machine learning library in Python.",
     long_description=readme(),
     long_description_content_type="text/markdown",