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1-online-passive-aggressive.py
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1-online-passive-aggressive.py
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import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split as tts
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import MinMaxScaler, StandardScaler
def load_data(data_file_path):
# using breast cancer WIS consin dataset
data = pd.read_csv(data_file_path,
usecols=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
names=["clump_thickness", "uniformity_of_cell_size", "uniformity_of_cell_shape",
"marginal_adhesion", "single_epi_cell_size", "bare_nuclei", "bland_chro",
"normal_nucleoli", "mitoses", "class"])
data["class"] = data["class"].replace({2: -1, 4: 1})
# add all features to X matrix
# add all outputs vector Y
x = data.iloc[:, 0:9]
y = data.loc[:, "class"]
# normalize data
x_normalized = StandardScaler().fit_transform(x.values)
x = pd.DataFrame(x_normalized)
# add 1 as b(intercept) value for every Xi'th row
x.insert(loc=len(x.columns), column='b', value=1)
# split into training and testing sets
x_train, x_test, y_train, y_test = tts(x, y, test_size=1 / 3)
return x_train, x_test, y_train, y_test
def calculate_loss(w, x_instance, y_instance):
return max(0, (1 - y_instance * np.dot(w, x_instance)))
def calculate_update(loss, x_instance, c, tau):
if tau == 1:
return loss / np.dot(x_instance, x_instance)
elif tau == 2:
return min(c, loss / np.dot(x_instance, x_instance))
else:
aggressiveness_parameter_term = 1 / 2 * c
update_value = loss / (np.dot(x_instance, x_instance) + aggressiveness_parameter_term)
return update_value
def train_model(x, y, num_of_iterations, tau):
# create a weight vector
w = np.zeros(x.shape[1])
w_1 = np.zeros(x.shape[1])
w_2 = np.zeros(x.shape[1])
w_10 = np.zeros(x.shape[1])
# define C(aggressiveness parameter)
c = 1
for iteration in range(0, num_of_iterations):
for index, example_value in enumerate(x):
# pass one example at a time to train the model
loss = calculate_loss(w, example_value, y[index])
update = calculate_update(loss, example_value, c, tau)
w = w + (update * y[index] * example_value)
if iteration == 1:
w_1 = w
if iteration == 2:
w_2 = w
if iteration == 9:
w_10 = w
return w_1, w_2, w_10
def predict_category(w_1, w_2, w_10, x):
y_predicted_1 = []
y_predicted_2 = []
y_predicted_10 = []
for i in range(x.shape[0]):
y_predicted_1.append(np.sign(np.dot(w_1, x.to_numpy()[i])))
y_predicted_2.append(np.sign(np.dot(w_2, x.to_numpy()[i])))
y_predicted_10.append(np.sign(np.dot(w_10, x.to_numpy()[i])))
return y_predicted_1, y_predicted_2, y_predicted_10
def get_prediction_counts(y):
benign_count = 0
malignant_count = 0;
for value in y:
if value == -1:
benign_count += 1
elif value == 1:
malignant_count += 1
return benign_count, malignant_count
x_train, x_test, y_train, y_test = load_data('./data/breast-cancer-wisconsin.data')
w_1, w_2, w_10 = train_model(x_train.to_numpy(), y_train.to_numpy(), 10, 3)
y_test_predicted_1, y_test_predicted_2, y_test_predicted_10 = predict_category(w_1, w_2, w_10, x_test)
y_train_predicted_1, y_train_predicted_2, y_train_predicted_10 = predict_category(w_1, w_2, w_10, x_train)
print("testing accuracy on 1 iteration: " + str(accuracy_score(y_test.to_numpy(), y_test_predicted_1)) +
" training accuracy on 1 iteration: " + str(accuracy_score(y_train.to_numpy(), y_train_predicted_1)))
print("accuracy on 2 iterations: " + str(accuracy_score(y_test.to_numpy(), y_test_predicted_2)) +
" training accuracy on 2 iteration: " + str(accuracy_score(y_train.to_numpy(), y_train_predicted_2)))
print("accuracy on 10 iterations: " + str(accuracy_score(y_test.to_numpy(), y_test_predicted_10)) +
" training accuracy on 10 iteration: " + str(accuracy_score(y_train.to_numpy(), y_train_predicted_10)))
be, mal = get_prediction_counts(y_test_predicted_1)
print("1 iteration\n##############")
print("Benign: " + str(be))
print("Malignant: " + str(mal) + "\n")
be, mal = get_prediction_counts(y_test_predicted_2)
print("2 iterations\n##############\n")
print("Benign: " + str(be))
print("Malignant: " + str(mal) + "\n")
be, mal = get_prediction_counts(y_test_predicted_10)
print("10 iterations\n##############\n")
print("Benign: " + str(be))
print("Malignant: " + str(mal))