def fit(self, rows):
"""
Build the tree.
Rules of recursion: 1) Believe that it works. 2) Start by checking
for the base case (no further information gain). 3) Prepare for
giant stack traces.
"""
# Try partitioing the dataset on each of the unique attribute,
# calculate the information gain,
# and return the question that produces the highest gain.
gain, question = find_best_split(rows)
# Base case: no further info gain
# Since we can ask no further questions,
# we'll return a leaf.
if gain == 0:
return Leaf(rows)
# If we reach here, we have found a useful feature / value
# to partition on.
true_rows, false_rows = partition(rows, question)
# Recursively build the true branch.
true_branch = self.fit(true_rows)
# Recursively build the false branch.
false_branch = self.fit(false_rows)
# Return a Question node.
# This records the best feature / value to ask at this point,
self.root = Decision_Node(question, true_branch, false_branch)
def print_tree(self, rows, head, spacing=""):
"""
A tree printing function.
PARAMETERS
==========
rows: list
A list of lists to store the dataset.
head: list
A list to store the headings of the
columns of the dataset.
spacing: String
To store and update the spaces to
print the tree in an organised manner.
RETURNS
=======
None
"""
# Try partitioning the dataset on each of the unique attribute,
# calculate the gini impurity,
# and return the question that produces the least gini impurity.
gain, question = find_best_split(rows, head)
# Base case: we've reached a leaf
if gain == 0:
print(spacing + "Predict", class_counts(rows, len(rows[0])-1))
return
# If we reach here, we have found a useful feature / value
# to partition on.
true_rows, false_rows = partition(rows, question)
# Print the question at this node
print(spacing + str(question))
# Call this function recursively on the true branch
print(spacing + '--> True:')
self.print_tree(true_rows, head, spacing + " ")
# Call this function recursively on the false branch
print(spacing + '--> False:')
self.print_tree(false_rows, head, spacing + " ")
def classify(self, rows, head, prediction_val):
"""
A function to make predictions of
the subsets of the dataset.
PARAMETERS
==========
rows: list
A list of lists to store the subsets
of the dataset.
head: list
A list to store the headings of the
columns of the subset of the dataset.
prediction_val: dictionary
A dictionary to update and return the
predictions of the subsets of the
dataset.
RETURNS
=======
prediction_val
Dictionary to return the predictions
corresponding to the subsets of the
dataset.
"""
N = len(rows[0])
# Finding random indexes for columns
# to collect random samples of the dataset.
indexcol = []
for j in range(0, 5):
r = np.random.randint(0, N-2)
if r not in indexcol:
indexcol.append(r)
row = []
for j in rows:
L = []
for k in indexcol:
L.append(j[k])
row.append(L)
# add last column to the random sample so created.
for j in range(0, len(row)):
row[j].append(rows[j][N-1])
rows = row
# Try partitioning the dataset on each of the unique attribute,
# calculate the gini impurity,
# and return the question that produces the least gini impurity.
gain, question = find_best_split(rows, head)
# Base case: we've reached a leaf
if gain == 0:
# Get the predictions of the current set of rows.
p = class_counts(rows, len(rows[0])-1)
for d in prediction_val:
for j in p:
if d == j:
# update the predictions to be returned.
prediction_val[d] = prediction_val[d] + p[j]
return prediction_val
# If we reach here, we have found a useful feature / value
# to partition on.
true_rows, false_rows = partition(rows, question)
# Recursively build the true branch.
self.classify(true_rows, head, prediction_val)
# Recursively build the false branch.
self.classify(false_rows, head, prediction_val)
# Return the dictionary of the predictions
# at the end of the recursion.
return prediction_val
