import numpy as np
import pandas as pd
from sklearn.metrics import r2_score
data_train = pd.read_csv('/datasets/train_data_n.csv')
features_train = data_train.drop(['target'], axis=1)
target_train = data_train['target']
data_test = pd.read_csv('/datasets/test_data_n.csv')
features_test = data_test.drop(['target'], axis=1)
target_test = data_test['target']
class SGDLinearRegression:
def __init__(self, step_size, epochs, batch_size, reg_weight):
self.step_size = step_size
self.epochs = epochs
self.batch_size = batch_size
self.reg_weight = reg_weight
def fit(self, train_features, train_target):
X = np.concatenate((np.ones((train_features.shape[0], 1)), train_features), axis=1)
y = train_target
w = np.zeros(X.shape[1])
for _ in range(self.epochs):
batches_count = X.shape[0] // self.batch_size
for i in range(batches_count):
begin = i * self.batch_size
end = (i + 1) * self.batch_size
X_batch = X[begin:end, :]
y_batch = y[begin:end]
gradient = 2 * X_batch.T.dot(X_batch.dot(w) - y_batch) / X_batch.shape[0]
# копируем вектор w, чтобы его не менять
reg = 2 * w.copy()
# < напишите код здесь >
gradient += self.reg_weight * reg
w -= self.step_size * gradient
self.w = w[1:]
self.w0 = w[0]
def predict(self, test_features):
return test_features.dot(self.w) + self.w0
# Чтобы сравнить гребневую регрессию с линейной, начнём с
# веса регуляризации, равного 0. Затем добавим
# обучение с его различными значениями.
print("Регуляризация:", 0.0)
model = SGDLinearRegression(0.01, 10, 100, 0.0)
model.fit(features_train, target_train)
pred_train = model.predict(features_train)
pred_test = model.predict(features_test)
print(r2_score(target_train, pred_train).round(5))
print(r2_score(target_test, pred_test).round(5))
print("Регуляризация:", 0.1)
model = SGDLinearRegression(0.01, 10, 100, 0.1)
model.fit(features_train, target_train)
pred_train = model.predict(features_train)
pred_test = model.predict(features_test)
print(r2_score(target_train, pred_train).round(5))
print(r2_score(target_test, pred_test).round(5))
print("Регуляризация:", 1.0)
model = SGDLinearRegression(0.01, 10, 100, 1.0)
model.fit(features_train, target_train)
pred_train = model.predict(features_train)
pred_test = model.predict(features_test)
print(r2_score(target_train, pred_train).round(5))
print(r2_score(target_test, pred_test).round(5))
print("Регуляризация:", 10.0)
model = SGDLinearRegression(0.01, 10, 100, 10.0)
model.fit(features_train, target_train)
pred_train = model.predict(features_train)
pred_test = model.predict(features_test)
print(r2_score(target_train, pred_train).round(5))
print(r2_score(target_test, pred_test).round(5))
