cross validation + decision trees in sklearn - machine-learning

Question

Attempting to create a decision tree with cross validation using sklearn and panads.
My question is in the code below, the cross validation splits the data, which i then use for both training and testing. I will be attempting to find the best depth of the tree by recreating it n times with different max depths set. In using cross validation should i instead be using k folds CV and if so how would I use that within the code I have?
import numpy as np
import pandas as pd
from sklearn import tree
from sklearn import cross_validation
features = ["fLength", "fWidth", "fSize", "fConc", "fConc1", "fAsym", "fM3Long", "fM3Trans", "fAlpha", "fDist", "class"]
df = pd.read_csv('magic04.data',header=None,names=features)
df['class'] = df['class'].map({'g':0,'h':1})
x = df[features[:-1]]
y = df['class']
x_train,x_test,y_train,y_test = cross_validation.train_test_split(x,y,test_size=0.4,random_state=0)
depth = []
for i in range(3,20):
clf = tree.DecisionTreeClassifier(max_depth=i)
clf = clf.fit(x_train,y_train)
depth.append((i,clf.score(x_test,y_test)))
print depth
here is a link to the data that i am using in case that helps anyone. https://archive.ics.uci.edu/ml/datasets/MAGIC+Gamma+Telescope

Answer 1

In your code you are creating a static training-test split. If you want to select the best depth by cross-validation you can use sklearn.cross_validation.cross_val_score inside the for loop.
You can read sklearn's documentation for more information.
Here is an update of your code with CV:
import numpy as np
import pandas as pd
from sklearn import tree
from sklearn.cross_validation import cross_val_score
from pprint import pprint
features = ["fLength", "fWidth", "fSize", "fConc", "fConc1", "fAsym", "fM3Long", "fM3Trans", "fAlpha", "fDist", "class"]
df = pd.read_csv('magic04.data',header=None,names=features)
df['class'] = df['class'].map({'g':0,'h':1})
x = df[features[:-1]]
y = df['class']
# x_train,x_test,y_train,y_test = cross_validation.train_test_split(x,y,test_size=0.4,random_state=0)
depth = []
for i in range(3,20):
clf = tree.DecisionTreeClassifier(max_depth=i)
# Perform 7-fold cross validation
scores = cross_val_score(estimator=clf, X=x, y=y, cv=7, n_jobs=4)
depth.append((i,scores.mean()))
print(depth)
Alternatively, you can use sklearn.grid_search.GridSearchCV and not write the for loop yourself, especially if you want to optimize for more than one hyper-parameter.
import numpy as np
import pandas as pd
from sklearn import tree
from sklearn.model_selection import GridSearchCV
features = ["fLength", "fWidth", "fSize", "fConc", "fConc1", "fAsym", "fM3Long", "fM3Trans", "fAlpha", "fDist", "class"]
df = pd.read_csv('magic04.data',header=None,names=features)
df['class'] = df['class'].map({'g':0,'h':1})
x = df[features[:-1]]
y = df['class']
parameters = {'max_depth':range(3,20)}
clf = GridSearchCV(tree.DecisionTreeClassifier(), parameters, n_jobs=4)
clf.fit(X=x, y=y)
tree_model = clf.best_estimator_
print (clf.best_score_, clf.best_params_)
Edit: changed how GridSearchCV is imported to accommodate learn2day's comment.