【Scikit-learn】糖尿病患者のデータセットでElasticNetモデルのオプションを検討［Python］

＜セル１＞

from sklearn.datasets import load_diabetes
import pandas as pd

diabetes = load_diabetes()

df = pd.DataFrame(diabetes.data, columns=diabetes.feature_names)

df["target"] = diabetes.target

df

実行結果

＜セル２＞

x = df.loc[:, ["bmi", "s5", "bp", "s4", "s3"]]
y = df.loc[:, "target"]

実行結果

＜セル３＞

from sklearn.model_selection import train_test_split
from sklearn.linear_model import ElasticNet
from sklearn.metrics import r2_score
import numpy as np

trial = 100

pred_en_score = []; pred_en_train_score = []

for i in range(trial):
    x_train_ori, x_test_ori, y_train_ori, y_test_ori = train_test_split(x, y, test_size=0.2, train_size=0.8)
    
    model_en = ElasticNet()
    model_en.fit(x_train_ori, y_train_ori)
    pred_en_ori = model_en.predict(x_test_ori)
    pred_en_score.append(r2_score(y_test_ori, pred_en_ori))
    
    pred_en_ori_train = model_en.predict(x_train_ori)
    pred_en_train_score.append(r2_score(y_train_ori, pred_en_ori_train))
    
pred_en_ave = np.average(np.array(pred_en_score))
pred_en_train_ave = np.average(np.array(pred_en_train_score))
    
print(pred_en_ave, pred_en_train_ave)

実行結果
-0.00840863612850006 0.0077880822083276876

＜セル４＞

from matplotlib import pyplot as plt

fig=plt.figure(figsize=(8,6))
plt.clf()

plt.scatter(y_test_ori, pred_en_ori)

実行結果

＜セル５＞

help(ElasticNet())

実行結果
Help on ElasticNet in module sklearn.linear_model._coordinate_descent object:

class ElasticNet(sklearn.base.MultiOutputMixin, sklearn.base.RegressorMixin, sklearn.linear_model._base.LinearModel)
 |  ElasticNet(alpha=1.0, *, l1_ratio=0.5, fit_intercept=True, normalize=False, precompute=False, max_iter=1000, copy_X=True, tol=0.0001, warm_start=False, positive=False, random_state=None, selection='cyclic')
 |  
 |  Linear regression with combined L1 and L2 priors as regularizer.
 |  
 |  Minimizes the objective function::
 |  
 |          1 / (2 * n_samples) * ||y - Xw||^2_2
 |          + alpha * l1_ratio * ||w||_1
 |          + 0.5 * alpha * (1 - l1_ratio) * ||w||^2_2
 |  
 |  If you are interested in controlling the L1 and L2 penalty
 |  separately, keep in mind that this is equivalent to::
 |  
 |          a * L1 + b * L2
（中略）
 |  Parameters
 |  ----------
 |  alpha : float, default=1.0
 |      Constant that multiplies the penalty terms. Defaults to 1.0.
 |      See the notes for the exact mathematical meaning of this
 |      parameter. ``alpha = 0`` is equivalent to an ordinary least square,
 |      solved by the :class:`LinearRegression` object. For numerical
 |      reasons, using ``alpha = 0`` with the ``Lasso`` object is not advised.
 |      Given this, you should use the :class:`LinearRegression` object.
 |  
 |  l1_ratio : float, default=0.5
 |      The ElasticNet mixing parameter, with ``0 <= l1_ratio <= 1``. For
 |      ``l1_ratio = 0`` the penalty is an L2 penalty. ``For l1_ratio = 1`` it
 |      is an L1 penalty.  For ``0 < l1_ratio < 1``, the penalty is a
 |      combination of L1 and L2.
 |  
 |  fit_intercept : bool, default=True
 |      Whether the intercept should be estimated or not. If ``False``, the
 |      data is assumed to be already centered.
 |  
 |  normalize : bool, default=False
 |      This parameter is ignored when ``fit_intercept`` is set to False.
 |      If True, the regressors X will be normalized before regression by
 |      subtracting the mean and dividing by the l2-norm.
 |      If you wish to standardize, please use
 |      :class:`sklearn.preprocessing.StandardScaler` before calling ``fit``
 |      on an estimator with ``normalize=False``.
 |  
 |  precompute : bool or array-like of shape (n_features, n_features),                 default=False
 |      Whether to use a precomputed Gram matrix to speed up
 |      calculations. The Gram matrix can also be passed as argument.
 |      For sparse input this option is always ``True`` to preserve sparsity.
 |  
 |  max_iter : int, default=1000
 |      The maximum number of iterations
 |  
 |  copy_X : bool, default=True
 |      If ``True``, X will be copied; else, it may be overwritten.
 |  
 |  tol : float, default=1e-4
 |      The tolerance for the optimization: if the updates are
 |      smaller than ``tol``, the optimization code checks the
 |      dual gap for optimality and continues until it is smaller
 |      than ``tol``.
 |  
 |  warm_start : bool, default=False
 |      When set to ``True``, reuse the solution of the previous call to fit as
 |      initialization, otherwise, just erase the previous solution.
 |      See :term:`the Glossary <warm_start>`.
 |  
 |  positive : bool, default=False
 |      When set to ``True``, forces the coefficients to be positive.
 |  
 |  random_state : int, RandomState instance, default=None
 |      The seed of the pseudo random number generator that selects a random
 |      feature to update. Used when ``selection`` == 'random'.
 |      Pass an int for reproducible output across multiple function calls.
 |      See :term:`Glossary <random_state>`.
 |  
 |  selection : {'cyclic', 'random'}, default='cyclic'
 |      If set to 'random', a random coefficient is updated every iteration
 |      rather than looping over features sequentially by default. This
 |      (setting to 'random') often leads to significantly faster convergence
 |      especially when tol is higher than 1e-4.

＜セル６ alpha=10＞

trial = 100

pred_en_score = []; pred_en_train_score = []

for i in range(trial):
    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, train_size=0.8)
    
    model_en = ElasticNet(alpha=10)
    model_en.fit(x_train, y_train)
    pred_en = model_en.predict(x_test)
    pred_en_score.append(r2_score(y_test, pred_en))
    
    pred_en_train = model_en.predict(x_train)
    pred_en_train_score.append(r2_score(y_train, pred_en_train))
    
pred_en_ave = np.average(np.array(pred_en_score))
pred_en_train_ave = np.average(np.array(pred_en_train_score))
    
print(pred_en_ave, pred_en_train_ave)

実行結果
-0.015099578097767432 0.0

fig=plt.figure(figsize=(8,6))
plt.clf()

plt.scatter(y_test_ori, pred_en_ori)
plt.scatter(y_test, pred_en)

実行結果

＜セル6 alpha=0.1＞

trial = 100

pred_en_score = []; pred_en_train_score = []

for i in range(trial):
    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, train_size=0.8)
    
    model_en = ElasticNet(alpha=0.1)
    model_en.fit(x_train, y_train)
    pred_en = model_en.predict(x_test)
    pred_en_score.append(r2_score(y_test, pred_en))
    
    pred_en_train = model_en.predict(x_train)
    pred_en_train_score.append(r2_score(y_train, pred_en_train))
    
pred_en_ave = np.average(np.array(pred_en_score))
pred_en_train_ave = np.average(np.array(pred_en_train_score))
    
print(pred_en_ave, pred_en_train_ave)

実行結果
0.07581482098361181 0.08856067834801275

＜セル7 alpha=0.1＞

fig=plt.figure(figsize=(8,6))
plt.clf()

plt.scatter(y_test_ori, pred_en_ori)
plt.scatter(y_test, pred_en)

実行結果

＜セル6 alpha=0.01＞

trial = 100

pred_en_score = []; pred_en_train_score = []

for i in range(trial):
    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, train_size=0.8)
    
    model_en = ElasticNet(alpha=0.01)
    model_en.fit(x_train, y_train)
    pred_en = model_en.predict(x_test)
    pred_en_score.append(r2_score(y_test, pred_en))
    
    pred_en_train = model_en.predict(x_train)
    pred_en_train_score.append(r2_score(y_train, pred_en_train))
    
pred_en_ave = np.average(np.array(pred_en_score))
pred_en_train_ave = np.average(np.array(pred_en_train_score))
    
print(pred_en_ave, pred_en_train_ave)

実行結果
0.3476736634520542 0.3682683840927076

＜セル7＞

fig=plt.figure(figsize=(8,6))
plt.clf()

plt.scatter(y_test_ori, pred_en_ori)
plt.scatter(y_test, pred_en)

実行結果

＜セル8 l1_ratio=0.99＞

trial = 100

pred_en_score = []; pred_en_train_score = []

for i in range(trial):
    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, train_size=0.8)
    
    model_en = ElasticNet(l1_ratio=0.99)
    model_en.fit(x_train, y_train)
    pred_en = model_en.predict(x_test)
    pred_en_score.append(r2_score(y_test, pred_en))
    
    pred_en_train = model_en.predict(x_train)
    pred_en_train_score.append(r2_score(y_train, pred_en_train))
    
pred_en_ave = np.average(np.array(pred_en_score))
pred_en_train_ave = np.average(np.array(pred_en_train_score))
    
print(pred_en_ave, pred_en_train_ave)

実行結果
0.127162151825194 0.14390151640382176

＜セル9 l1_ratio=0.99＞

fig=plt.figure(figsize=(8,6))
plt.clf()

plt.scatter(y_test_ori, pred_en_ori)
plt.scatter(y_test, pred_en)

実行結果

＜セル8 l1_ratio=0.01＞

trial = 100

pred_en_score = []; pred_en_train_score = []

for i in range(trial):
    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, train_size=0.8)
    
    model_en = ElasticNet(l1_ratio=0.01)
    model_en.fit(x_train, y_train)
    pred_en = model_en.predict(x_test)
    pred_en_score.append(r2_score(y_test, pred_en))
    
    pred_en_train = model_en.predict(x_train)
    pred_en_train_score.append(r2_score(y_train, pred_en_train))
    
pred_en_ave = np.average(np.array(pred_en_score))
pred_en_train_ave = np.average(np.array(pred_en_train_score))
    
print(pred_en_ave, pred_en_train_ave)

実行結果
-0.0074562451691631716 0.005409525832601795

＜セル9 l1_ratio=0.01＞

fig=plt.figure(figsize=(8,6))
plt.clf()

plt.scatter(y_test_ori, pred_en_ori)
plt.scatter(y_test, pred_en)

実行結果