def __init__(self, merge_leaf_func, criterion, max_depth=None):
self.tree_ = None
self.feature_importances_ = None
self.output_shape_ = None
self._tree_builder = TreeBuilder(merge_leaf_func=merge_leaf_func,
criterion=criterion,
max_depth=max_depth)
def main():
tree = TreeBuilder()
tree.build_tree(tree.file, tree.root)
print("Behavior tree loaded...")
tree.root.print()
print()
behavior = input("Event: ('q' to exit): ")
while behavior is not 'q':
search = Search()
# DEPTH FIRST
response = search.depth_first_recursive(behavior, tree.root)
# BREADTH FIRST
#response = search.breadth_first(behavior, tree.root)
print("Response = {}".format(response))
behavior = input("Event: ('q' to exit): ")
def main():
tree = TreeBuilder()
tree.build_tree(tree.file, tree.root)
print("Behavior tree loaded...")
tree.root.print()
print()
behavior = input("Event: ('q' to exit): ")
while behavior is not 'q':
search = Search()
# DEPTH FIRST
response = search.depth_first_recursive(behavior, tree.root)
# BREADTH FIRST
#response = search.breadth_first(behavior, tree.root)
print("Response = {}".format(response))
behavior = input("Event: ('q' to exit): ")
def main():
output_file = "conjugation_tree.json"
tree_order = None
if len(sys.argv) == 1:
print("Please include required input file")
exit()
if len(sys.argv) >= 2:
try:
f = open(sys.argv[1], 'r')
f.close()
except OSError:
print("Input file '", sys.argv[1], "' not found")
exit()
input_file = sys.argv[1]
if not input_file.endswith(".csv"):
print(
"Input file must be a comma separated file and have the .csv extension"
)
exit()
if len(sys.argv) >= 3:
output_file = sys.argv[2]
if not output_file.endswith(".json"):
print(
"Selected output file must be a json file (i.e. must have the .json extension)"
)
exit()
if len(sys.argv) == 4:
tree_order = sys.argv[3]
if not tree_order.endswith(".csv"):
print(
"Selected tree order file must be a csv file (i.e. must have the .csv extension)"
)
exit()
try:
f = open(tree_order, 'r')
f.close()
except OSError:
print("Tree order file '", tree_order, "' not found")
exit()
tree_order_file = open(tree_order, 'r')
tree_order = tree_order_file.readline().split(',')
c2d = CSVtoDict(input_file=input_file, order=tree_order)
dict_list, attr_dict_list = c2d.execute()
tb = TreeBuilder(dict_list, output_file, tree_order)
ob = OptionBuilder(attr_dict_list, tree_order)
from tree_builder import TreeBuilder, DecisionNode, TreeClassifer
data = [['slashdot', 'USA', 'yes', 18, 'None'],
['google', 'France', 'yes', 23, 'Premium'],
['digg', 'USA', 'yes', 24, 'Basic'],
['kiwitobes', 'France', 'yes', 23, 'Basic'],
['google', 'UK', 'no', 21, 'Premium'],
['(direct)', 'New Zealand', 'no', 12, 'None'],
['(direct)', 'UK', 'no', 21, 'Basic'],
['google', 'USA', 'no', 24, 'Premium'],
['slashdot', 'France', 'yes', 19, 'None'],
['digg', 'USA', 'no', 18, 'None'], ['google', 'UK', 'no', 18, 'None'],
['kiwitobes', 'UK', 'no', 19, 'None'],
['digg', 'New Zealand', 'yes', 12, 'Basic'],
['slashdot', 'UK', 'no', 21, 'None'],
['google', 'UK', 'yes', 18, 'Basic'],
['kiwitobes', 'France', 'yes', 19, 'Basic']]
if __name__ == "__main__":
tree_builder = TreeBuilder()
tree = tree_builder.recursive_build_tree(data)
# tree_builder.print_tree(tree)
tree_classifier = TreeClassifer()
print(
tree_classifier.classify(['kiwitobes', 'France', 'yes', 19, 'Basic'],
tree))
fileObject.close()
# function to create a file and store the data in the file
def picklemaker(filename, objectname):
# open the file for writing
fileObject = open(filename, 'wb')
# this writes the object a to the
# file named 'testfile'
pickle.dump(objectname, fileObject)
# here we close the fileObject
fileObject.close()
if __name__ == "__main__":
try:
tree_builder = pickleloader("tree_builder_pickled")
except FileNotFoundError:
tree_builder = TreeBuilder()
try:
tree_builder.build_word_tree(word, context, 3)
except (KeyboardInterrupt, Exception):
pass
filename = "tree_builder_pickled"
picklemaker(filename, tree_builder)
class Tree(BaseEstimator, metaclass=ABCMeta):
""" Base class for the tree estimators.
Arguments
---------
merge_leaf_func: function,
The method of merging elements in leave to scalar.
criterion: string,
The name of criterion to split data.
max_depth: integer,
The maximal depth of tree to build.
"""
def __init__(self, merge_leaf_func, criterion, max_depth=None):
self.tree_ = None
self.feature_importances_ = None
self.output_shape_ = None
self._tree_builder = TreeBuilder(merge_leaf_func=merge_leaf_func,
criterion=criterion,
max_depth=max_depth)
def fit(self, X, y):
""" Build tree with respect to training data.
Parameters
----------
X : array, shape (n_samples, n_features)
Training data
y : array, shape (n_samples,)
Target values.
"""
self.output_shape_ = y.shape
self.tree_ = self._tree_builder.build(X, y)
self.feature_importances_ = self._tree_builder.feature_importances_
# TODO: To refactor method to avoid using recursion.
def _walk_tree(self, node, data, indexes):
""" Passes data to the leaves of the tree and returns the node labels. """
if node.leaf_value is not None:
return np.zeros(shape=(data.shape[0])) + node.leaf_value, indexes
data_left, indexes_left, data_right, indexes_right = \
node.splitter.split(data, indexes)
labels_left, indexes_left = self._walk_tree(node.left_node, data_left,
indexes_left)
labels_right, indexes_right = self._walk_tree(node.right_node,
data_right,
indexes_right)
labels = np.concatenate([labels_left, labels_right])
indexes = np.concatenate([indexes_left, indexes_right])
return labels, indexes
def predict(self, X):
"""Predict labels
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Samples.
Returns
-------
y : array, shape (n_samples,)
Returns predicted values.
"""
labels, indexes = self._walk_tree(self.tree_, X,
np.arange(0, X.shape[0]))
return labels[np.argsort(indexes)].reshape(-1, *self.output_shape_[1:])
def fit_predict(self, X, y):
self.fit(X, y)
return self.predict(X)
def build_tree(self):
tree_builder = TreeBuilder(self)
self._tree_file_path = tree_builder.build()
return self._tree_file_path
def main():
tree_order = None
app_order = None
conjugation_order = None
if len(sys.argv) == 1:
print("Please include required input file")
exit()
if len(sys.argv) >= 2:
try:
f = open(sys.argv[1], 'r')
f.close()
except OSError:
print("Input file '", sys.argv[1], "' not found")
exit()
input_file = sys.argv[1]
if not input_file.endswith(".csv"):
print(
"Input file must be a comma separated file and have the .csv extension"
)
exit()
if len(sys.argv) >= 3:
tree_order = sys.argv[2]
if not tree_order.endswith(".csv"):
print(
"Selected tree order file must be a csv file (i.e. must have the .csv extension)"
)
exit()
try:
f = open(tree_order, 'r')
f.close()
except OSError:
print("Order file '", tree_order, "' not found")
exit()
tree_order_file = open(tree_order, 'r')
orders = tree_order_file.readlines()
tree_order = orders[0].strip().split(',')
tree_order = [x.strip().lower() for x in tree_order]
if len(orders) >= 2:
conjugation_order = orders[1].strip().split(',')
conjugation_order = [x.strip().lower() for x in conjugation_order]
print("Progress [= ]", end='\r')
makeToolTips(tree_order)
c2d = CSVtoDict(input_file=input_file, order=tree_order)
dict_list, attr_dict_list = c2d.execute()
print("Progress [=> ]", end='\r')
tb = TreeBuilder(dict_list, "category_tree.json", tree_order)
print("Progress [==> ]", end='\r')
ob = OptionBuilder(attr_dict_list, tree_order)
print("Progress [===> ]", end='\r')
c2d = CSVtoDict(input_file=input_file, order=conjugation_order)
dict_list, attr_dict_list = c2d.execute()
print("Progress [====> ]", end='\r')
tb = TreeBuilder(dict_list, "conjugation.json", conjugation_order)
print("Progress [=====>]", end='\r')
with open('JSON/conjugation.json', 'r') as f:
f_str = f.read()
d = json.loads(f_str)
l = [conjugation_order, d]
f.close()
with open('JSON/conjugation.json', 'w') as f:
json.dump(l, f, indent=4)
f.close()
print("Progress [======]", end='\r')
print("Complete. See JSON folder.")
TESTS.runtests()
# doc 3 is copy-pasted directly from Excel from a structure where element names are in # columns A, B, C, D and element values are in column F doc3 = """ World Continent Europe Country France Capital Paris Country Germany Capital Berlin """ # logger.debug(tree_struct) # load doc 1 tr1 = TreeBuilder(DOC_STRUCT_SYNTAX1) tree_root = tr1.load(doc1) logging.info(tree_root.print()) # load doc 2 tr2 = TreeBuilder(DOC_STRUCT_SYNTAX2) tree_root2 = tr2.load(doc2) logging.info(tree_root2.print()) logging.info(tree_root2.xmlify()) # load doc 3 tr3 = TreeBuilder(DOC_STRUCT_SYNTAX3) tree_root3 = tr3.load(doc3) logging.info(tree_root3.print()) logging.info(tree_root3.xmlify())