L2-regularization-ridge-regression-course/task6.py at main · SENATOROVAI/L2-regularization-ridge-regression-course · GitHub

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# %% [markdown]
# # Регуляризация

# %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import time
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# %% [markdown]
# ## Подготовка данных

# %%
boston = pd.read_csv('/content/boston.csv')

X = boston[['LSTAT', 'RM', 'PTRATIO', 'INDUS']]
y = boston.MEDV

X_train, X_test, y_train, y_test = train_test_split(X, y,
                                                    test_size = 0.3,
                                                    random_state = 42)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# %% [markdown]
# ## Линейная регрессия

# %% [markdown]
# ### Ordinary Least Squares

# %%
from sklearn.linear_model import LinearRegression

ols = LinearRegression()
ols.fit(X_train, y_train)

y_pred_train = ols.predict(X_train)
y_pred_test = ols.predict(X_test)

from sklearn.metrics import root_mean_squared_error

print('train: ' + str(root_mean_squared_error(y_train, y_pred_train)))
print('test: ' + str(root_mean_squared_error(y_test, y_pred_test)))

# %% [markdown]
# ### Ridge Regression (L2 regularization)

# %% [markdown]
# #### Нормальные уравнения

# %% [markdown]
# собственный класс

# %%
class RidgeReg():

  def __init__(self, alpha = 1.0):

    self.alpha = alpha
    self.thetas = None

  def fit(self, x, y):

    x = x.copy()
    x = self.add_ones(x)
    I = np.identity(x.shape[1])
    I[0][0] = 0

    self.thetas = np.linalg.inv(x.T.dot(x) + self.alpha * I).dot(x.T).dot(y)

  def predict(self, x):

    x = x.copy()
    x = self.add_ones(x)
    return np.dot(x, self.thetas)

  def add_ones(self, x):
    return np.c_[np.ones((len(x), 1)), x]

# %%
ridge = RidgeReg(alpha = 10)

ridge.fit(X_train, y_train)

y_pred_train = ridge.predict(X_train)
y_pred_test = ridge.predict(X_test)

print('train: ' + str(root_mean_squared_error(y_train, y_pred_train)))
print('test: ' + str(root_mean_squared_error(y_test, y_pred_test)))

# %% [markdown]
# класс sklearn

# %%
from sklearn.linear_model import Ridge

ridge = Ridge(alpha = 10)

ridge.fit(X_train, y_train)

y_pred_train = ridge.predict(X_train)
y_pred_test = ridge.predict(X_test)

print('train: ' + str(root_mean_squared_error(y_train, y_pred_train)))
print('test: ' + str(root_mean_squared_error(y_test, y_pred_test)))

# %%
features = X.columns

plt.figure(figsize = (5, 5))
plt.plot(features, ridge.coef_, alpha = 0.7, linestyle = 'none' , marker = '*', markersize = 5, color = 'red', label = r'Ridge; $\alpha = 10$', zorder = 7)
plt.plot(features, ols.coef_, alpha = 0.4, linestyle = 'none', marker = 'o', markersize = 7, color = 'green', label = 'Linear Regression')
plt.xticks(rotation = 0)
plt.legend()
plt.show()