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| import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import datetime
from sklearn.model_selection import KFold
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from graphviz import Digraph
from sklearn.tree import export_graphviz, DecisionTreeClassifier
import graphviz
class dataprocess:
def __init__(self, data_path):
self.threshold = 130000
self.data = pd.read_csv(data_path).values
self.GPA = np.array(self.data[:, 0])
self.Internships = np.array(self.data[:, 1])
self.Competitions = np.array(self.data[:, 2])
self.Penalties = np.array(self.data[:, 3])
self.Nationality = np.array(self.data[:, 4])
self.Salary = np.array(self.data[:, 5])
self.Salary_Classification = np.where(self.Salary > self.threshold, 1, 0)
self.C = np.array([self.data[100 * i:100 * (i + 1), :] for i in range(5)])
self.Nationality_onehot = self.one_hot_encoder(self.Nationality)
self.batchs = [1, 4, 8, 16]
self.weights = None
self.feature = None
self.beta = None
def Visualization(self):
fig = plt.figure(figsize=(16, 12))
ax1 = fig.add_subplot(221)
ax1.hist(self.Salary, bins=15, color='#f59311', alpha=0.5, edgecolor='k')
ax1.set_title('Salary')
ax1.set_xlabel('Salary')
ax1.set_ylabel('Frequency')
ax2 = fig.add_subplot(222)
ax2.scatter(self.GPA, self.Salary, color='#f59311', alpha=0.5, edgecolor='k')
ax2.set_title('GPA vs Salary')
ax2.set_xlabel('GPA')
ax2.set_ylabel('Salary')
ax3 = fig.add_subplot(223)
ax3.scatter(self.Internships, self.Salary, color='#f59311', alpha=0.5, edgecolor='k')
ax3.set_title('Internships vs Salary')
ax3.set_xlabel('Internships')
ax3.set_ylabel('Salary')
ax4 = fig.add_subplot(224)
ax4.scatter(self.Competitions, self.Salary, color='#f59311', alpha=0.5, edgecolor='k')
ax4.set_title('Competitions vs Salary')
ax4.set_xlabel('Competitions')
ax4.set_ylabel('Salary')
plt.show()
fig = plt.figure(figsize=(16, 12))
ax5 = fig.add_subplot(231)
ax5.scatter(self.Penalties, self.Salary, color='#f59311', alpha=0.5, edgecolor='k')
ax5.set_title('Penalties vs Salary')
ax5.set_xlabel('Penalties')
ax5.set_ylabel('Salary')
ax6 = fig.add_subplot(232)
ax6.scatter(self.Nationality, self.Salary, color='#f59311', alpha=0.5, edgecolor='k')
ax6.set_title('Nationality vs Salary')
ax6.set_xlabel('Nationality')
ax6.set_ylabel('Salary')
plt.show()
def average(self, C, K=None):
return np.mean(C, axis=0)
def variance(self, C, K=None):
return np.var(C, axis=0)
def one_hot_encoder(self, Nationality):
unique_categories = np.unique(Nationality)
category_to_index = {category: index for index, category in enumerate(unique_categories)}
Nationality_onehot = np.zeros((len(Nationality), len(unique_categories)))
for i, category in enumerate(Nationality):
Nationality_onehot[i, category_to_index[category]] = 1
return Nationality_onehot
def standardization(self, C, average, variance, k):
return (C - average) / variance
def Least_Square(self, train_data, train_label, test_data, test_label):
X_transpose = np.transpose(train_data)
XTX = np.dot(X_transpose, train_data)
XTX_inv = np.linalg.inv(XTX)
XTy = np.dot(X_transpose, train_label)
self.beta = np.dot(XTX_inv, XTy)
train_predictions = np.dot(train_data, self.beta)
test_predictions = np.dot(test_data, self.beta)
train_mse = np.mean((train_predictions - train_label) ** 2)
test_mse = np.mean((test_predictions - test_label) ** 2)
return self.beta, train_mse, test_mse
def task3_dataprocess(self, i):
GPA_average = self.average(np.delete(self.GPA, slice(100 * i, 100 * (i + 1))), i)
GPA_variance = self.variance(np.delete(self.GPA, slice(100 * i, 100 * (i + 1))), i)
GPA = self.standardization(self.GPA, GPA_average, GPA_variance, i)
GPA_test = GPA[100 * i:100 * (i + 1)]
GPA = np.delete(GPA, slice(100 * i, 100 * (i + 1)), axis=0)
Interships_test = self.Internships[100 * i:100 * (i + 1)]
Interships = np.delete(self.Internships, slice(100 * i, 100 * (i + 1)), axis=0)
Competitions_test = self.Competitions[100 * i:100 * (i + 1)]
Competitions = np.delete(self.Competitions, slice(100 * i, 100 * (i + 1)), axis=0)
Penalties_test = self.Penalties[100 * i:100 * (i + 1)]
Penalties = np.delete(self.Penalties, slice(100 * i, 100 * (i + 1)), axis=0)
Nationality_onehot = self.Nationality_onehot
Nationality_test = Nationality_onehot[100 * i:100 * (i + 1)]
Nationality = np.delete(Nationality_onehot, slice(100 * i, 100 * (i + 1)), axis=0)
Salary = self.Salary_Classification
Salary_test = Salary[100 * i:100 * (i + 1)]
Salary = np.delete(Salary, slice(100 * i, 100 * (i + 1)), axis=0)
train_data = np.concatenate((GPA[:, np.newaxis], Interships[:, np.newaxis], Competitions[:, np.newaxis],
Penalties[:, np.newaxis], Nationality), axis=1).astype(float)
train_label = Salary.astype(int)
test_data = np.concatenate(
(GPA_test[:, np.newaxis], Interships_test[:, np.newaxis], Competitions_test[:, np.newaxis],
Penalties_test[:, np.newaxis], Nationality_test), axis=1).astype(float)
test_label = Salary_test.astype(float)
return train_data, train_label, test_data, test_label
def pre_dataprocess(self, i):
GPA_average = self.average(np.delete(self.GPA, slice(100 * i, 100 * (i + 1))), i)
GPA_variance = self.variance(np.delete(self.GPA, slice(100 * i, 100 * (i + 1))), i)
GPA = self.standardization(self.GPA, GPA_average, GPA_variance, i)
GPA_test = GPA[100 * i:100 * (i + 1)]
GPA = np.delete(GPA, slice(100 * i, 100 * (i + 1)), axis=0)
Interships_test = self.Internships[100 * i:100 * (i + 1)]
Interships = np.delete(self.Internships, slice(100 * i, 100 * (i + 1)), axis=0)
Competitions_test = self.Competitions[100 * i:100 * (i + 1)]
Competitions = np.delete(self.Competitions, slice(100 * i, 100 * (i + 1)), axis=0)
Penalties_test = self.Penalties[100 * i:100 * (i + 1)]
Penalties = np.delete(self.Penalties, slice(100 * i, 100 * (i + 1)), axis=0)
Nationality_onehot = self.Nationality_onehot
Nationality_test = Nationality_onehot[100 * i:100 * (i + 1)]
Nationality = np.delete(Nationality_onehot, slice(100 * i, 100 * (i + 1)), axis=0)
Salary_average = self.average(np.delete(self.Salary, slice(100 * i, 100 * (i + 1))), i)
Salary_variance = self.variance(np.delete(self.Salary, slice(100 * i, 100 * (i + 1))), i)
Salary = self.standardization(self.Salary, Salary_average, Salary_variance, i)
Salary_test = Salary[100 * i:100 * (i + 1)]
Salary = np.delete(Salary, slice(100 * i, 100 * (i + 1)), axis=0)
train_data = np.concatenate((GPA[:, np.newaxis], Interships[:, np.newaxis], Competitions[:, np.newaxis],
Penalties[:, np.newaxis], Nationality), axis=1).astype(float)
train_label = Salary.astype(float)
test_data = np.concatenate(
(GPA_test[:, np.newaxis], Interships_test[:, np.newaxis], Competitions_test[:, np.newaxis],
Penalties_test[:, np.newaxis], Nationality_test), axis=1).astype(float)
test_label = Salary_test.astype(float)
return train_data, train_label, test_data, test_label
def K_fold(self, k=5):
beta_array, train_mse_array, test_mse_array = [], [], []
for i in range(k):
train_data, train_label, test_data, test_label = self.pre_dataprocess(i)
beta, train_mse, test_mse = self.Least_Square(train_data, train_label, test_data, test_label)
beta_array.append(beta)
train_mse_array.append(train_mse)
test_mse_array.append(test_mse)
print("############################################")
print('my K-fold function:')
print(f"beta: {np.mean(beta_array)} + {np.var(beta_array)}")
print(f"train_mse: {np.mean(train_mse_array)} + {np.var(train_mse_array)}")
print(f"test_mse: {np.mean(test_mse_array)} + {np.var(test_mse_array)}")
x_labels = [f'Run {i + 1}' for i in range(5)]
x = np.arange(len(x_labels))
width = 0.35
fig, ax = plt.subplots()
train_mse_array_scaled = [value * 1e15 for value in train_mse_array]
test_mse_array_scaled = [value * 1e15 for value in test_mse_array]
rects1 = ax.bar(x - width / 2, train_mse_array_scaled, width, label='Training MSE', color='b')
rects2 = ax.bar(x + width / 2, test_mse_array_scaled, width, label='Testing MSE', color='r')
ax.set_ylabel('MSE / e^-15')
ax.set_title('Training and Testing MSE for Each Run')
ax.set_xticks(x)
ax.set_xticklabels(x_labels)
ax.legend()
for rect in rects1:
height = rect.get_height()
ax.annotate(f'{height:.2f}',
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3),
textcoords="offset points",
ha='center', va='bottom')
for rect in rects2:
height = rect.get_height()
ax.annotate(f'{height:.2f}',
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3),
textcoords="offset points",
ha='center', va='bottom')
plt.tight_layout()
plt.show()
kf = KFold(n_splits=5)
model = LinearRegression()
train_mse_list, test_mse_list = [], []
for train_index, test_index in kf.split(train_data):
train_data_kf, test_data_kf = train_data[train_index], train_data[test_index]
train_label_kf, test_label_kf = train_label[train_index], train_label[test_index]
model.fit(train_data_kf, train_label_kf)
train_pred = model.predict(train_data_kf)
test_pred = model.predict(test_data_kf)
train_mse = mean_squared_error(train_label_kf, train_pred)
test_mse = mean_squared_error(test_label_kf, test_pred)
train_mse_list.append(train_mse)
test_mse_list.append(test_mse)
print("############################################")
print('sklearn K-fold function:')
print("beta: ", model.intercept_)
print("train_mse: ", np.mean(train_mse_list))
print("test_mse: ", np.mean(test_mse_list))
print("############################################")
def SGD(self, train_data, train_label, test_data, test_label, learning_rate=0.001, max_iter=100, batch=1):
n_samples, n_features = train_data.shape
weights = np.zeros(n_features)
bias = 0
train_loss_list, test_loss_list = [], []
for epoch in range(max_iter):
for i in range(0, n_samples, batch):
X_batch = train_data[i:i + batch]
y_batch = train_label[i:i + batch]
y_pred = np.dot(X_batch, weights) + bias
gradient_w = -2 * np.dot(X_batch.T, (y_batch - y_pred)) / batch
gradient_b = -2 * np.mean(y_batch - y_pred)
weights -= learning_rate * gradient_w
bias -= learning_rate * gradient_b
train_loss = np.mean((train_label - (np.dot(train_data, weights) + bias)) ** 2) * 1e11
train_loss_list.append(train_loss)
test_loss = np.mean((test_label - (np.dot(test_data, weights) + bias)) ** 2) * 1e11
test_loss_list.append(test_loss)
return weights, bias, train_loss_list, test_loss_list
def mysgd(self, k=5):
batches = [1, 4, 8, 16]
weight_dict, bias_dict, train_loss_dict, test_loss_dict = {}, {}, {}, {}
for batch in batches:
weight_b = []
bias_b = []
train_loss_b = []
test_loss_b = []
for i in range(k):
train_data, train_label, test_data, test_label = self.pre_dataprocess(i)
sgd_weight, sgd_bias, train_loss_mse, test_loss_mse = self.SGD(train_data, train_label, test_data, test_label, learning_rate=0.001, batch=batch)
weight_b.append(sgd_weight)
bias_b.append(sgd_bias)
train_loss_b.append(train_loss_mse)
test_loss_b.append(test_loss_mse)
weight_dict[batch] = np.mean(weight_b, axis=0)
bias_dict[batch] = np.mean(bias_b, axis=0)
train_loss_dict[batch] = np.mean(train_loss_b, axis=0)
test_loss_dict[batch] = np.mean(test_loss_b, axis=0)
fig = plt.figure(figsize=(16, 12))
print("############################################")
print('my SGD function:')
for batch in batches:
print("############################################")
print("batch: ", batch)
print("weight: ", weight_dict[batch])
print("bias: ", bias_dict[batch])
ax = fig.add_subplot(2, 2, batches.index(batch) + 1)
ax.plot(train_loss_dict[batch], label='train_mse')
ax.set_title(f'Batch Size: {batch}')
ax.set_xlabel('Epoch')
ax.set_ylabel('MSE ^ 1e-11')
ax.legend()
print("############################################")
plt.show()
def task1(self):
self.Visualization()
self.K_fold()
def task2(self):
self.mysgd()
class Perceptron:
def __init__(self, learning_rate=0.001, max_iter=100):
self.learning_rate = learning_rate
self.max_iter = max_iter
self.weights = None
self.bias = None
self.dp = dataprocess('salary.csv')
def sigmoid(self, x):
return 1 / (1 + np.exp(-x))
def SGD(self, train_data, train_label, test_data, test_label, learning_rate=0.001, max_iter=100, batch=1):
n_samples, n_features = train_data.shape
weights = np.zeros(n_features)
bias = 0
train_loss_list, test_loss_list = [], []
for epoch in range(max_iter):
for i in range(0, n_samples, batch):
X_batch = train_data[i:i + batch]
y_batch = train_label[i:i + batch]
y_pred = self.sigmoid(np.dot(X_batch, weights) + bias)
gradient_w = -2 * np.dot(X_batch.T, (y_batch - y_pred)) / batch
gradient_b = -2 * np.mean(y_batch - y_pred)
weights -= learning_rate * gradient_w
bias -= learning_rate * gradient_b
y_train_pred = self.sigmoid(np.dot(train_data, weights) + bias)
train_loss = -np.mean(
train_label * np.log(y_train_pred + 1e-9) + (1 - train_label) * np.log(1 - y_train_pred + 1e-9))
train_loss_list.append(train_loss)
y_test_pred = self.sigmoid(np.dot(test_data, weights) + bias)
test_loss = -np.mean(test_label * np.log(y_test_pred + 1e-9) + (1 - test_label) * np.log(1 - y_test_pred + 1e-9))
test_loss_list.append(test_loss)
return weights, bias, train_loss_list, test_loss_list
def train(self, k=5):
weight_list, bias_list,train_mse_list, test_mse_list = [], [], [], []
print('training with Perceptron')
for i in range(k):
train_data, train_label, test_data, test_label = self.dp.task3_dataprocess(i)
weight, bias, train_mse_error, test_mse_error = self.SGD(train_data, train_label, test_data, test_label, learning_rate=0.001, batch=1)
weight_list.append(weight)
bias_list.append(bias)
train_mse_list.append(train_mse_error)
test_mse_list.append(test_mse_error)
print("############################################")
print('Perceptron function:')
print("############################################")
print("weight: ", np.mean(weight_list, axis=0))
print("bias: ", np.mean(bias_list, axis=0))
print("train_mse: ", np.mean(train_mse_list, axis=0))
print("test_mse: ", np.mean(test_mse_list, axis=0))
plt.plot(np.mean(train_mse_list, axis=1), label='train_mse')
plt.xlabel('Epoch')
plt.ylabel('MSE')
plt.legend()
plt.show()
class LogisticRegressionSGD:
def __init__(self, learning_rate=0.001, max_iter=100):
self.learning_rate = learning_rate
self.max_iter = max_iter
self.weights = None
self.bias = None
self.dp = dataprocess('salary.csv')
def sigmoid(self, z):
return 1 / (1 + np.exp(-z))
def SGD(self, train_data, train_label, test_data, test_label, learning_rate=0.001, max_iter=1000, batch=1):
n_samples, n_features = train_data.shape
weights = np.zeros(n_features)
bias = 0
train_loss_list, test_loss_list = [], []
for epoch in range(max_iter):
for i in range(0, n_samples, batch):
X_batch = train_data[i:i + batch]
y_batch = train_label[i:i + batch]
y_pred = np.dot(X_batch, weights) + bias
y_pred = self.sigmoid(y_pred)
gradient_w = -2 * np.dot(X_batch.T, (y_batch - y_pred)) / batch
gradient_b = -2 * np.mean(y_batch - y_pred)
weights -= learning_rate * gradient_w
bias -= learning_rate * gradient_b
train_loss = -np.mean(y_batch * np.log(y_pred + 1e-9) + (1 - y_batch) * np.log(1 - y_pred + 1e-9))
train_loss_list.append(train_loss)
test_pred = self.sigmoid(np.dot(test_data, weights) + bias)
test_loss = -np.mean(test_label * np.log(test_pred + 1e-9) + (1 - test_label) * np.log(1 - test_pred + 1e-9))
test_loss_list.append(test_loss)
return weights, bias, train_loss_list, test_loss_list
def train(self, k=5):
weight_list, bias_list,train_mse_list, test_mse_list = [], [], [], []
print('training Logistic Regression model...')
for i in range(k):
train_data, train_label, test_data, test_label = self.dp.task3_dataprocess(i)
weight, bias, train_mse_error, test_mse_error = self.SGD(train_data, train_label, test_data, test_label, learning_rate=0.01, batch=1)
weight_list.append(weight)
bias_list.append(bias)
train_mse_list.append(train_mse_error)
test_mse_list.append(test_mse_error)
print(test_mse_list)
print("############################################")
print('Logistic Regression function:')
print("############################################")
print("weight: ", np.mean(weight_list, axis=0))
print("bias: ", np.mean(bias_list, axis=0))
print("train_mse: ", np.mean(train_mse_list, axis=0))
print("test_mse: ", np.mean(test_mse_list, axis=0))
plt.plot(np.mean(train_mse_list, axis=0), label='train_mse')
plt.xlabel('Epoch')
plt.ylabel('MSE')
plt.legend()
plt.show()
class DecisionTree:
def __init__(self, epsilon=0.1, data_path='salary.csv'):
self.epsilon = epsilon
self.tree = None
self.dp = dataprocess(data_path)
self.labels = ["GPA", "Internships", "Competitions", "Penalties", "Australia", "U.K.", "U.S."]
def _grow_tree(self, X, y):
num_samples = len(y)
if num_samples < 2:
return {'label': np.argmax(np.bincount(y)), 'num_samples': num_samples, 'loss': self._cross_entropy(y)}
if len(set(y)) == 1:
return {'label': y[0], 'num_samples': num_samples, 'loss': self._cross_entropy(y)}
probabilities = np.bincount(y) / num_samples
if np.max(probabilities) >= (1 - self.epsilon):
return {'label': np.argmax(probabilities), 'num_samples': num_samples, 'loss': self._cross_entropy(y)}
best_feature = self._best_feature_to_split(X, y)
threshold = np.median(X[:, best_feature])
left_indices = X[:, best_feature] <= threshold
right_indices = X[:, best_feature] > threshold
if not np.any(left_indices) or not np.any(right_indices):
return {'label': np.argmax(np.bincount(y)), 'num_samples': num_samples, 'loss': self._cross_entropy(y)}
tree = {
'feature_index': best_feature,
'threshold': threshold,
'children': {
'left': self._grow_tree(X[left_indices], y[left_indices]),
'right': self._grow_tree(X[right_indices], y[right_indices])
},
'num_samples': num_samples,
'loss': self._cross_entropy(y)
}
return tree
left_indices = X[:, best_feature] <= threshold
right_indices = X[:, best_feature] > threshold
tree['children']['left'] = self._grow_tree(X[left_indices], y[left_indices])
tree['children']['right'] = self._grow_tree(X[right_indices], y[right_indices])
return tree
def _cross_entropy(self, y):
probabilities = np.bincount(y) / len(y)
return -np.sum(probabilities[probabilities > 0] * np.log(probabilities[probabilities > 0]))
def _best_feature_to_split(self, X, y):
num_features = X.shape[1]
best_gain = -np.inf
best_feature = -1
for feature in range(num_features):
gain = self._information_gain(X, y, feature)
if gain > best_gain:
best_gain = gain
best_feature = feature
return best_feature
def _information_gain(self, X, y, feature_index):
total_entropy = self._cross_entropy(y)
if len(y) <= 1:
return 0
threshold = np.median(X[:, feature_index])
left_indices = X[:, feature_index] <= threshold
right_indices = X[:, feature_index] > threshold
if not np.any(left_indices) or not np.any(right_indices):
return 0
left_entropy = self._cross_entropy(y[left_indices])
right_entropy = self._cross_entropy(y[right_indices])
weighted_entropy = (
(np.sum(left_indices) / len(y)) * left_entropy +
(np.sum(right_indices) / len(y)) * right_entropy
)
return total_entropy - weighted_entropy
def predict(self, X, tree):
return np.array([self._predict_sample(sample, tree) for sample in X])
def _predict_sample(self, sample, tree):
if tree is None or 'label' in tree:
return tree['label'] if tree is not None else None
feature_value = sample[tree['feature_index']]
if feature_value <= tree['threshold']:
return self._predict_sample(sample, tree['children']['left'])
else:
return self._predict_sample(sample, tree['children']['right'])
def train(self, X, y):
return self._grow_tree(X, y)
def K_fold(self, k=5):
test_acc = 0
train_acc = 0
for i in range(k):
train_data, train_label, test_data, test_label = self.dp.task3_dataprocess(i)
tree = self.train(train_data, train_label)
train_pred = self.predict(train_data, tree)
test_pred = self.predict(test_data, tree)
train_acc += np.sum(train_pred == train_label)
test_acc += np.sum(test_pred == test_label)
train_accuracy = train_acc / (len(train_label) * k)
print(f'Tree decision K-fold train accuracy is: {train_accuracy}')
test_accuracy = test_acc / (len(test_label) * k)
print(f'Tree decision K-fold test accuracy is: {test_accuracy}')
def visualize(self, tree, X):
dot = Digraph()
self._add_nodes_edges(dot, tree, X=X)
dot.render('simple_decision_tree' + str(datetime.datetime.today()), format='png', view=True)
def _add_nodes_edges(self, dot, tree, parent_name=None, X=None):
if tree is None:
return
if 'label' in tree:
return
feature_index = tree['feature_index']
threshold = tree['threshold']
feature_name = self.labels[feature_index]
node_name = f"Node_{parent_name}_{feature_index}"
dot.node(node_name, f"{feature_name} <= {threshold}")
if parent_name:
dot.edge(parent_name, node_name)
self._add_nodes_edges(dot, tree['children']['left'], node_name, X=X)
self._add_nodes_edges(dot, tree['children']['right'], node_name, X=X)
def sklearn_tree(self, X, y):
clf = DecisionTreeClassifier(criterion='entropy', max_depth=5)
clf.fit(X, y)
dot_data = clf.export_graphviz(clf, out_file=None,feature_names=self.labels)
dot = graphviz.Source(dot_data)
dot.view()
class assignment2:
def __init__(self, data_path):
self.data_path = data_path
self.dp = dataprocess(data_path)
self.LR = LogisticRegressionSGD(data_path)
self.p = Perceptron(data_path)
def task1(self):
self.dp.task1()
def task2(self):
self.dp.task2()
def task3(self):
print("############################################")
self.LR.train()
print("############################################")
self.p.train()
print("############################################")
for epsilon in [0.05, 0.1, 0.2]:
tree = DecisionTree(epsilon=epsilon)
tree.K_fold(5)
print("############################################")
if __name__ == '__main__':
data_path = 'salary.csv'
homework = assignment2(data_path)
print("############################################")
print("PROBLEM 1 Regression on Salary")
print("############################################")
homework.task1()
print("PROBLEM 2 Regression with SGD")
print("############################################")
homework.task2()
print("############################################")
print("PROBLEM 3 Classification on Salary")
homework.task3()
print("############################################")
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