How to resize the image of the tree using sklearn tree and export_graph_viz within a Jupyter Notebook












1















I am using export_graph_viz to visualize a decision tree but the image spreads out of view in my Jupyter Notebook.



If this was a pyplot figure I would use the command plt.figure(figsize = (12,7)) to constrain the visualization. But in this case I donot know how to proceed.



Below is a snapshot of my Jupyter Notebook and what I see:



enter image description here



Your advice will be appreciated.






python scikit-learn tree jupyter-notebook graphviz







share|improve this question



























    1















    I am using export_graph_viz to visualize a decision tree but the image spreads out of view in my Jupyter Notebook.



    If this was a pyplot figure I would use the command plt.figure(figsize = (12,7)) to constrain the visualization. But in this case I donot know how to proceed.



    Below is a snapshot of my Jupyter Notebook and what I see:



    enter image description here



    Your advice will be appreciated.






    python scikit-learn tree jupyter-notebook graphviz







    share|improve this question

























      1












      1








      1








      I am using export_graph_viz to visualize a decision tree but the image spreads out of view in my Jupyter Notebook.



      If this was a pyplot figure I would use the command plt.figure(figsize = (12,7)) to constrain the visualization. But in this case I donot know how to proceed.



      Below is a snapshot of my Jupyter Notebook and what I see:



      enter image description here



      Your advice will be appreciated.






      python scikit-learn tree jupyter-notebook graphviz







      share|improve this question














      I am using export_graph_viz to visualize a decision tree but the image spreads out of view in my Jupyter Notebook.



      If this was a pyplot figure I would use the command plt.figure(figsize = (12,7)) to constrain the visualization. But in this case I donot know how to proceed.



      Below is a snapshot of my Jupyter Notebook and what I see:



      enter image description here



      Your advice will be appreciated.






      python scikit-learn tree jupyter-notebook graphviz




      python scikit-learn tree jupyter-notebook graphviz






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Nov 20 '18 at 10:55









      user8270077user8270077

      745311




      745311
























          1 Answer
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          You can save the visualized tree to a file and then show it with pyplot.



          Example:



          import matplotlib.pyplot as plt
          
import pydotplus
          
import matplotlib.image as mpimg
          
import io
          

          
from sklearn.externals.six import StringIO
          
from sklearn.tree import export_graphviz
          

          
dot_data = io.StringIO()
          

          
export_graphviz(clf, out_file=dot_data, rounded=True, filled=True)
          

          
filename = "tree.png"
          
pydotplus.graph_from_dot_data(dot_data.getvalue()).write_png(filename)
          

          
plt.figure(figsize=(12,12))
          
img = mpimg.imread(filename)
          
imgplot = plt.imshow(img)
          

          
plt.show()


          Result:
          enter image description here






          share|improve this answer

























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            1 Answer
            1






            active

            oldest

            votes








            1 Answer
            1






            active

            oldest

            votes









            active

            oldest

            votes






            active

            oldest

            votes









            1














            You can save the visualized tree to a file and then show it with pyplot.



            Example:



            import matplotlib.pyplot as plt
            
import pydotplus
            
import matplotlib.image as mpimg
            
import io
            

            
from sklearn.externals.six import StringIO
            
from sklearn.tree import export_graphviz
            

            
dot_data = io.StringIO()
            

            
export_graphviz(clf, out_file=dot_data, rounded=True, filled=True)
            

            
filename = "tree.png"
            
pydotplus.graph_from_dot_data(dot_data.getvalue()).write_png(filename)
            

            
plt.figure(figsize=(12,12))
            
img = mpimg.imread(filename)
            
imgplot = plt.imshow(img)
            

            
plt.show()


            Result:
            enter image description here






            share|improve this answer






























              1














              You can save the visualized tree to a file and then show it with pyplot.



              Example:



              import matplotlib.pyplot as plt
              
import pydotplus
              
import matplotlib.image as mpimg
              
import io
              

              
from sklearn.externals.six import StringIO
              
from sklearn.tree import export_graphviz
              

              
dot_data = io.StringIO()
              

              
export_graphviz(clf, out_file=dot_data, rounded=True, filled=True)
              

              
filename = "tree.png"
              
pydotplus.graph_from_dot_data(dot_data.getvalue()).write_png(filename)
              

              
plt.figure(figsize=(12,12))
              
img = mpimg.imread(filename)
              
imgplot = plt.imshow(img)
              

              
plt.show()


              Result:
              enter image description here






              share|improve this answer




























                1












                1








                1







                You can save the visualized tree to a file and then show it with pyplot.



                Example:



                import matplotlib.pyplot as plt
                
import pydotplus
                
import matplotlib.image as mpimg
                
import io
                

                
from sklearn.externals.six import StringIO
                
from sklearn.tree import export_graphviz
                

                
dot_data = io.StringIO()
                

                
export_graphviz(clf, out_file=dot_data, rounded=True, filled=True)
                

                
filename = "tree.png"
                
pydotplus.graph_from_dot_data(dot_data.getvalue()).write_png(filename)
                

                
plt.figure(figsize=(12,12))
                
img = mpimg.imread(filename)
                
imgplot = plt.imshow(img)
                

                
plt.show()


                Result:
                enter image description here






                share|improve this answer















                You can save the visualized tree to a file and then show it with pyplot.



                Example:



                import matplotlib.pyplot as plt
                
import pydotplus
                
import matplotlib.image as mpimg
                
import io
                

                
from sklearn.externals.six import StringIO
                
from sklearn.tree import export_graphviz
                

                
dot_data = io.StringIO()
                

                
export_graphviz(clf, out_file=dot_data, rounded=True, filled=True)
                

                
filename = "tree.png"
                
pydotplus.graph_from_dot_data(dot_data.getvalue()).write_png(filename)
                

                
plt.figure(figsize=(12,12))
                
img = mpimg.imread(filename)
                
imgplot = plt.imshow(img)
                

                
plt.show()


                Result:
                enter image description here







                share|improve this answer














                share|improve this answer



                share|improve this answer








                edited Nov 21 '18 at 1:19

























                answered Nov 20 '18 at 19:28









                runcoderunruncoderun

                37437




                37437
































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