from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.pipeline import Pipeline
from sklearn.datasets import make_regression
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_squared_error, r2_score
from operator import itemgetter

import graphviz
from sklearn.tree import export_graphviz
from os import system




X, y = make_regression (n_samples = 1000, n_features =5)

best_tree = DecisionTreeRegressor(criterion='mse', max_depth=20).fit (X,y)
export_graphviz(decision_tree=best_tree.tree_, out_file='noP.dot')
os.system("dot -Tpng noP.dot -o "+ 'noP.png')

>  for i in range(best_tree.tree_.node_count):
>     n_value = best_tree.tree_.
>     if (n_value < 10):
>             #print('do i get here', n_value, best_tree.tree_.children_left[i])
>             best_tree.tree_.children_left[i]=-1
>             best_tree.tree_.children_right[i]=-1