def _build_tree(self, X, y, current_depth=0):
with pyRAPL.Measurement('Build_tree', output=csv_output):
""" Recursive method which builds out the decision tree and splits X and respective y on the feature of X which (based on impurity) best separates the data"""
largest_impurity = 0
best_criteria = None # Feature index and threshold
best_sets = None # Subsets of the data
# Check if expansion of y is needed
if len(np.shape(y)) == 1:
y = np.expand_dims(y, axis=1)
# Add y as last column of X
Xy = np.concatenate((X, y), axis=1)
n_samples, n_features = np.shape(X)
if n_samples >= self.min_samples_split and current_depth <= self.max_depth:
# Calculate the impurity for each feature
for feature_i in range(n_features):
# All values of feature_i
feature_values = np.expand_dims(X[:, feature_i], axis=1)
unique_values = np.unique(feature_values)
# Iterate through all unique values of feature column i and
# calculate the impurity
for threshold in unique_values:
# Divide X and y depending on if the feature value of X at index feature_i
# meets the threshold
Xy1, Xy2 = divide_on_feature(Xy, feature_i, threshold)
if len(Xy1) > 0 and len(Xy2) > 0:
# Select the y-values of the two sets
y1 = Xy1[:, n_features:]
y2 = Xy2[:, n_features:]
# Calculate impurity
impurity = self._impurity_calculation(y, y1, y2)
# If this threshold resulted in a higher information gain than previously
# recorded save the threshold value and the feature
# index
if impurity > largest_impurity:
largest_impurity = impurity
best_criteria = {
"feature_i": feature_i,
"threshold": threshold
}
best_sets = {
"leftX":
Xy1[:, :n_features], # X of left subtree
"lefty":
Xy1[:, n_features:], # y of left subtree
"rightX":
Xy2[:, :n_features], # X of right subtree
"righty":
Xy2[:, n_features:] # y of right subtree
}
if largest_impurity > self.min_impurity:
# Build subtrees for the right and left branches
true_branch = self._build_tree(best_sets["leftX"],
best_sets["lefty"],
current_depth + 1)
false_branch = self._build_tree(best_sets["rightX"],
best_sets["righty"],
current_depth + 1)
return DecisionNode(feature_i=best_criteria["feature_i"],
threshold=best_criteria["threshold"],
true_branch=true_branch,
false_branch=false_branch)
# We're at leaf => determine value
leaf_value = self._leaf_value_calculation(y)
return DecisionNode(value=leaf_value)
csv_output.save()
def _build_tree(self, X, y, current_depth=0):
""" Recursive method which builds out the decision tree and splits X and respective y
on the feature of X which (based on impurity) best separates the data"""
largest_impurity = 0
best_criteria = None # Feature index and threshold
best_sets = None # Subsets of the data
# Check if expansion of y is needed
if len(np.shape(y)) == 1:
y = np.expand_dims(y, axis=1)
# Add y as last column of X
Xy = np.concatenate((X, y), axis=1)
n_samples, n_features = np.shape(X)
if n_samples >= self.min_samples_split and current_depth <= self.max_depth:
# Calculate the impurity for each feature
for feature_i in range(n_features):
# All values of feature_i
feature_values = np.expand_dims(X[:, feature_i], axis=1)
unique_values = np.unique(feature_values)
# Iterate through all unique values of feature column i and
# calculate the impurity
for threshold in unique_values:
# Divide X and y depending on if the feature value of X at index feature_i
# meets the threshold
Xy1, Xy2 = divide_on_feature(Xy, feature_i, threshold)
if len(Xy1) > 0 and len(Xy2) > 0:
# Select the y-values of the two sets
y1 = Xy1[:, n_features:]
y2 = Xy2[:, n_features:]
# Calculate impurity
impurity = self._impurity_calculation(y, y1, y2)
# If this threshold resulted in a higher information gain than previously
# recorded save the threshold value and the feature
# index
if impurity > largest_impurity:
largest_impurity = impurity
best_criteria = {"feature_i": feature_i, "threshold": threshold}
best_sets = {
"leftX": Xy1[:, :n_features], # X of left subtree
"lefty": Xy1[:, n_features:], # y of left subtree
"rightX": Xy2[:, :n_features], # X of right subtree
"righty": Xy2[:, n_features:] # y of right subtree
}
if largest_impurity > self.min_impurity:
# Build subtrees for the right and left branches
true_branch = self._build_tree(best_sets["leftX"], best_sets["lefty"], current_depth + 1)
false_branch = self._build_tree(best_sets["rightX"], best_sets["righty"], current_depth + 1)
return DecisionNode(feature_i=best_criteria["feature_i"], threshold=best_criteria[
"threshold"], true_branch=true_branch, false_branch=false_branch)
# We're at leaf => determine value
leaf_value = self._leaf_value_calculation(y)
return DecisionNode(value=leaf_value)
