class Subset(SubsetBase):
K_FEATURES = 10
def __init__(self, indices, data_handler=None, data=None):
self.indices = indices
self.labels = None
if data_handler:
self.data_handler = data_handler
else:
self.data_handler = DataHandler(data)
def get_size(self):
"""
Returns the size of the subset i.e the number
of rows in the subset
"""
return len(self.indices)
def purity(self):
"""
Determines the "purity" of the subset by calculating
the gini index of the data
"""
return self.data_handler.gini_index(self.indices)
def majority_label(self):
"""
Returns the mode of the all the labels in the subset
"""
labels = self.data_handler.get_freq(self.indices)
# Loop frequency hash and find the mode
majority, count = None, -1
for label, value in labels.iteritems():
if value > count:
majority = label
return majority
def split(self):
"""
Returns a tuple of arrays of (feature, values, subsets)
given the feature to split on.
"""
n, f = self.data_handler.get_shape()
# Selects k features without replacement
features = random.sample(range(1, f), self.K_FEATURES)
# Calculate the gini index of k different splits
splits = {}
for feature in features:
(gini, threshold) = self.data_handler.test_split(self.indices, feature)
splits[feature] = {"threshold": threshold, "gini": gini}
# Finds the optimal split from all the splits above
best_feature, threshold, min_gini = None, None, 100
for feature, results in splits.iteritems():
if results["gini"] < min_gini:
best_feature, threshold, min_gini = feature, results["threshold"], results["gini"]
# Split the subset
subset_left, subset_right = self.get_subsets(best_feature, threshold)
return best_feature, threshold, subset_left, subset_right
def get_subsets(self, feature, threshold):
"""
Splits the current subset into two based on the
input feature and threshold
"""
left_indices, right_indices = self.data_handler.split(self.indices, feature, threshold)
left_subset = Subset(left_indices, data_handler=self.data_handler)
right_subset = Subset(right_indices, data_handler=self.data_handler)
return left_subset, right_subset
class Subset(SubsetBase):
K_FEATURES = 10
def __init__(self, indices, data_handler=None, data=None):
self.indices = indices
self.labels = None
if data_handler:
self.data_handler = data_handler
else:
self.data_handler = DataHandler(data)
def get_size(self):
"""
Returns the size of the subset i.e the number
of rows in the subset
"""
return len(self.indices)
def purity(self):
"""
Determines the "purity" of the subset by calculating
the gini index of the data
"""
return self.data_handler.gini_index(self.indices)
def majority_label(self):
"""
Returns the mode of the all the labels in the subset
"""
labels = self.data_handler.get_freq(self.indices)
#Loop frequency hash and find the mode
majority, count = None, -1
for label, value in labels.iteritems():
if value > count:
majority = label
return majority
def split(self):
"""
Returns a tuple of arrays of (feature, values, subsets)
given the feature to split on.
"""
n, f = self.data_handler.get_shape()
# Selects k features without replacement
features = random.sample(range(1, f), self.K_FEATURES)
# Calculate the gini index of k different splits
splits = {}
for feature in features:
(gini,
threshold) = self.data_handler.test_split(self.indices, feature)
splits[feature] = {'threshold': threshold, 'gini': gini}
# Finds the optimal split from all the splits above
best_feature, threshold, min_gini = None, None, 100
for feature, results in splits.iteritems():
if results['gini'] < min_gini:
best_feature, threshold, min_gini = feature, results[
'threshold'], results['gini']
#Split the subset
subset_left, subset_right = self.get_subsets(best_feature, threshold)
return best_feature, threshold, subset_left, subset_right
def get_subsets(self, feature, threshold):
"""
Splits the current subset into two based on the
input feature and threshold
"""
left_indices, right_indices = self.data_handler.split(
self.indices, feature, threshold)
left_subset = Subset(left_indices, data_handler=self.data_handler)
right_subset = Subset(right_indices, data_handler=self.data_handler)
return left_subset, right_subset
