def train_and_predict(x_train, y_train, x_test, x_val, y_val):
""" Interface to train and test the new/improved decision tree.
This function is an interface for training and testing the new/improved
decision tree classifier.
x_train and y_train should be used to train your classifier, while
x_test should be used to test your classifier.
x_val and y_val may optionally be used as the validation dataset.
You can just ignore x_val and y_val if you do not need a validation dataset.
Args:
x_train (numpy.ndarray): Training instances, numpy array of shape (N, K)
N is the number of instances
K is the number of attributes
y_train (numpy.ndarray): Class labels, numpy array of shape (N, )
Each element in y is a str
x_test (numpy.ndarray): Test instances, numpy array of shape (M, K)
M is the number of test instances
K is the number of attributes
x_val (numpy.ndarray): Validation instances, numpy array of shape (L, K)
L is the number of validation instances
K is the number of attributes
y_val (numpy.ndarray): Class labels of validation set, numpy array of shape (L, )
"""
#######################################################################
# ** TASK 4.2: COMPLETE THIS FUNCTION **
#######################################################################
# TODO: Train new classifier
forest = RandomForestClassifier()
# Forest is trained on the best hyperparameter set
forest.update_hyperparameters(feature_sel=True,
cross_val=False,
max_tree_depth=13,
min_sample_size=2,
num_trees=20)
forest.fit(x_train, y_train)
# set up an empty (M, ) numpy array to store the predicted labels
# feel free to change this if needed
# TODO: Make predictions on x_test using new classifier
predictions = forest.predict(x_test)
# return result on best classifier option
# remember to change this if you rename the variable
return predictions
import reader
import numpy as np
from random_forest import RandomForestClassifier
import metrics
x_train, y_train = reader.read_from_csv("data/train_full.txt")
x_val, y_val = reader.read_from_csv("data/validation.txt")
x_test, y_test = reader.read_from_csv("data/test.txt")
forest = RandomForestClassifier()
"""
# 4.2.3 starts here
forest.update_hyperparameters(num_trees=10, k_fold=10, cross_val=True)
forest.fit(x_train, y_train)
pred_val = forest.predict(x_val)
acc_val_1 = metrics.accuracy(pred_val, y_val)
print(f"10 tree Random Forest Validation Accuracy, ",
f"without feature selection or sampling: {acc_val_1}")
forest.update_hyperparameters(feature_sel=True, cross_val=True)
forest.fit(x_train, y_train)
pred_val = forest.predict(x_val)
acc_val_2 = metrics.accuracy(pred_val, y_val)
print(f"10 tree Random Forest Validation Accuracy, ",
f"with feature selection: {acc_val_2}")
forest.update_hyperparameters(feature_sel=False, cross_val=False)
forest.fit(x_train, y_train)