This classifies the data as a decision tree. The decision tree is created but I am not able to view the decision tree.
import numpy as np
from sklearn import linear_model, datasets, tree
import matplotlib.pyplot as plt
iris = datasets.load_iris()
f = open('decision_tree_data.txt')
x_train = []
y_train = []
for line in f:
line = np.asarray(line.split(),dtype = np.float32)
x_train.append(line[:-1])
y_train.append(line[:-1])
x_train = np.asmatrix(x_train)
y_train = np.asmatrix(y_train)
model = tree.DecisionTreeClassifier()
model.fit(x_train,y_train)
from sklearn.externals.six import StringIO
import pydot
from IPython.display import Image
dot_data = StringIO()
tree.export_graphviz(model, out_file=dot_data,
feature_names=iris.feature_names,
class_names=iris.target_names,
filled=True, rounded=True,
special_characters=True)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
Image(graph.create_png())
The function pydot.graph_from_dot_data returns a list in pydot >= 1.2.0 (in contrast to earlier versions of pydot).
The reason was to homogenize the output, which in the past was a list if two graphs were returned, but a graph if a single graph was returned. Such branching is a common source of errors in user code (simple is better than complex [PEP 20]).
The change applies to all functions that call the function dot_parser.parse_dot_data, which now returns a list in all cases.
To address the error, you need to unpack the single graph that you expect:
graph, = pydot.graph_from_dot_data(dot_data.getvalue())
This statement also asserts that a single graph is returned. So if this assumption doesn't hold, and more graphs are returned, this unpacking will catch it. In contrast, graph = (...)[0] won't.
Relevant pydot issues: