Feb 27, 2024

Rolynominal regression + Lasso regression (다항 회귀)

 Ref. : https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import Lasso
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, mean_squared_error

# Data load
df = pd.read_csv('./data/xxx.csv')

# Target data separation
y = df['y']
X = df.drop('y', axis = 1)

# Split train : test data set = 7:3 and set random seed
X_train, X_test, y_train, y_test = train_test_split(X,
                                                    y,
                                                    test_size = 0.3,
                                                    random_state = 42)


# Define a model structure for a polynomial variable
poly = PolynomialFeatures(degree = 2, include_bias = False)

# Train polynomial variable
poly.fit(X_train)

# Show feature names
poly.get_feature_names_out()
# poly.get_feature_names()
pd.options.display.max_columns = 200

# Change the data frame to the polynomial variables
poly_X_train = pd.DataFrame(poly.transform(X_train),
                            columns = poly.get_feature_names_out())

# poly_X_train = pd.DataFrame(poly.transform(X_train),
#                             columns = poly.get_feature_names())

# Change the test data to the polynomial variables
poly_X_test = poly.transform(X_test)

# Define a model structure
ls = Lasso() # To reduce some low coefficients

# Train model
ls.fit(poly_X_train, y_train)

# Model prediction
ls_pred = ls.predict(poly_X_test)

# Confirm coefficient
ls.coef_

# Error metric
print("Lasso-r2 score:", r2_score(y_test, ls_pred))
print("Lasso-RMSE:", np.sqrt(mean_squared_error(y_test, ls_pred)))

# Plot
plt.figure(figsize=(20, 20))
plt.barh(poly.get_feature_names_out(), ls.coef_)


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