pycaret-nightly==0.26

pycaret/classification.py

@@ -2715,7 +2715,7 @@ def create_model(estimator = None,
             size_bytes = Path('Trained Model.pkl').stat().st_size
             size_kb = np.round(size_bytes/1000, 2)
             mlflow.set_tag("Size KB", size_kb)
-            #os.remove('Trained Model.pkl')
+            os.remove('Trained Model.pkl')
 
     progress.value += 1
 
@@ -3667,7 +3667,7 @@ def plot_model(estimator,
         visualizer.score(X_test, y_test)
         progress.value += 1
         clear_output()
-        if save or log_plots_param:
+        if save:
             if system:
                 visualizer.show(outpath="AUC.png")
             else:
@@ -3721,7 +3721,7 @@ def plot_model(estimator,
         visualizer.score(X_test, y_test)
         progress.value += 1
         clear_output()
-        if save or log_plots_param:
+        if save:
             if system:
                 visualizer.show(outpath="Confusion Matrix.png")
             else:
@@ -4027,7 +4027,7 @@ def plot_model(estimator,
         plt.ylabel('Features')
         progress.value += 1
         clear_output()
-        if save or log_plots_param:
+        if save:
             if system:
                 plt.savefig("Feature Importance.png")
             else:

pycaret/regression.py

@@ -7988,7 +7988,7 @@ def plot_model(estimator,
         visualizer.score(X_test, y_test)  # Evaluate the model on the test data
         progress.value += 1
         clear_output()
-        if save or log_plots_param:
+        if save:
             if system:
                 visualizer.show(outpath="Residuals.png")
             else:
@@ -8006,7 +8006,7 @@ def plot_model(estimator,
         visualizer.score(X_test, y_test)
         progress.value += 1
         clear_output()
-        if save or log_plots_param:
+        if save:
             if system:
                 visualizer.show(outpath="Prediction Error.png")
             else:
@@ -8194,7 +8194,7 @@ def plot_model(estimator,
         plt.ylabel('Features') 
         progress.value += 1
         clear_output()
-        if save or log_plots_param:
+        if save:
             if system:
                 plt.savefig("Feature Importance.png")
             else:

pycaret/utils.py

@@ -2,7 +2,7 @@
 # Author: Moez Ali <moez.ali@queensu.ca>
 # License: MIT
 
-version_ = "pycaret-nightly-0.25"
+version_ = "pycaret-nightly-0.26"
 
 def version():
     print(version_)

setup.py

@@ -13,7 +13,7 @@ with open('requirements.txt') as f:
 
 setup(
     name="pycaret-nightly",
-    version="0.25",
+    version="0.26",
     description="Nightly build of PyCaret - An open source, low-code machine learning library in Python.",
     long_description=readme(),
     long_description_content_type="text/markdown",