參考：作者的Jupyter Notebook
Chapter 2 – End-to-end Machine Learning project
- 生成圖片并保存
from __future__ import division, print_function, unicode_literals import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import os np.random.seed(42) mpl.rc('axes', labelsize=14) mpl.rc('xtick', labelsize=12) mpl.rc('ytick', labelsize=12) # Where to save the figures PROJECT_ROOT_DIR = "images" CHAPTER_ID = "traininglinearmodels" def save_fig(fig_id, tight_layout=True): path = os.path.join(PROJECT_ROOT_DIR, CHAPTER_ID, fig_id + ".png") print("Saving figure", fig_id) if tight_layout: plt.tight_layout() plt.savefig(path, format='png', dpi=600)
線性回歸
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生成一些線性資料來測驗這個公式（標準方程）
import numpy as np X = 2 * np.random.rand(100, 1) y = 4 + 3 * X + np.random.randn(100, 1) plt.plot(X, y, "b.") plt.xlabel("$x_1$", fontsize=18) plt.ylabel("$y$", rotation=0, fontsize=18) plt.axis([0, 2, 0, 15]) #save_fig("generated_data_plot") -
使用NumPy的線性代數模塊（np.linalg）中的inv（）函式來對矩陣求逆，并用dot（）方法計算矩陣的內積：
X_b = np.c_[np.ones((100, 1)), X] # add x0 = 1 to each instance theta_best = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y) #print(theta_best) -
可以用*做出預測：
X_new = np.array([[0], [2]]) X_new_b = np.c_[np.ones((2, 1)), X_new] # add x0 = 1 to each instance y_predict = X_new_b.dot(theta_best) #print(y_predict) #繪制模型的預測結果 plt.plot(X_new, y_predict, "r-") plt.plot(X, y, "b.") plt.axis([0, 2, 0, 15]) plt.plot(X_new, y_predict, "r-", linewidth=2, label="Predictions") plt.plot(X, y, "b.") plt.xlabel("$x_1$", fontsize=18) plt.ylabel("$y$", rotation=0, fontsize=18) plt.legend(loc="upper left", fontsize=14) plt.axis([0, 2, 0, 15]) #save_fig("linear_model_predictions") #plt.show() -
Scikit-Learn的等效代碼如下所示
from sklearn.linear_model import LinearRegression lin_reg = LinearRegression() lin_reg.fit(X, y) print(lin_reg.intercept_, lin_reg.coef_) print(lin_reg.predict(X_new)) theta_best_svd, residuals, rank, s = np.linalg.lstsq(X_b, y, rcond=1e-6) print(theta_best_svd) print(np.linalg.pinv(X_b).dot(y))
梯度下降
梯度下降的中心思想就是迭代地調整引數從而使成本函式最小化，如果學習率太低，演算法需要經過大量迭代才能收斂，這將耗費很長時間如果學習率太高，那你可能會越過山谷直接到達山的另一邊，甚至有可能比之前的起點還要高，這會導致演算法發散，值越來越大，最后無法找到好的解決方案
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批量梯度下降:3個公式，這個演算法的快速實作：
eta = 0.1 n_iterations = 1000 m = 100 theta = np.random.randn(2,1) for iteration in range(n_iterations): gradients = 2/m * X_b.T.dot(X_b.dot(theta) - y) theta = theta - eta * gradients #print(theta) #print(X_new_b.dot(theta)) -
分別使用三種不同的學習率時，梯度下降的前十步:
theta_path_bgd = [] def plot_gradient_descent(theta, eta, theta_path=None): m = len(X_b) plt.plot(X, y, "b.") n_iterations = 1000 for iteration in range(n_iterations): if iteration < 10: y_predict = X_new_b.dot(theta) style = "b-" if iteration > 0 else "r--" plt.plot(X_new, y_predict, style) gradients = 2/m * X_b.T.dot(X_b.dot(theta) - y) theta = theta - eta * gradients if theta_path is not None: theta_path.append(theta) plt.xlabel("$x_1$", fontsize=18) plt.axis([0, 2, 0, 15]) plt.title(r"$\eta = {}$".format(eta), fontsize=16) np.random.seed(42) theta = np.random.randn(2,1) # random initialization plt.figure(figsize=(10,4)) plt.subplot(131); plot_gradient_descent(theta, eta=0.02) plt.ylabel("$y$", rotation=0, fontsize=18) plt.subplot(132); plot_gradient_descent(theta, eta=0.1, theta_path=theta_path_bgd) plt.subplot(133); plot_gradient_descent(theta, eta=0.5) #save_fig("gradient_descent_plot") #plt.show() -
下面這段代碼使用了一個簡單的學習計劃實作隨機梯度下降：
theta_path_sgd = [] m = len(X_b) np.random.seed(42) n_epochs = 50 t0, t1 = 5, 50 # learning schedule hyperparameters def learning_schedule(t): return t0 / (t + t1) theta = np.random.randn(2,1) # random initialization for epoch in range(n_epochs): for i in range(m): if epoch == 0 and i < 20: # not shown in the book y_predict = X_new_b.dot(theta) # not shown style = "b-" if i > 0 else "r--" # not shown plt.plot(X_new, y_predict, style) # not shown random_index = np.random.randint(m) xi = X_b[random_index:random_index+1] yi = y[random_index:random_index+1] gradients = 2 * xi.T.dot(xi.dot(theta) - yi) eta = learning_schedule(epoch * m + i) theta = theta - eta * gradients theta_path_sgd.append(theta) # not shown plt.plot(X, y, "b.") # not shown plt.xlabel("$x_1$", fontsize=18) # not shown plt.ylabel("$y$", rotation=0, fontsize=18) # not shown plt.axis([0, 2, 0, 15]) # not shown #save_fig("sgd_plot") # not shown #plt.show() from sklearn.linear_model import SGDRegressor sgd_reg = SGDRegressor(n_iter=50, penalty=None, eta0=0.1) sgd_reg.fit(X, y.ravel()) print(sgd_reg.intercept_, sgd_reg.coef_) -
小批量梯度下降
theta_path_bgd = [] theta_path_sgd = [] theta_path_mgd = [] m = 100 n_iterations = 50 minibatch_size = 20 np.random.seed(42) theta = np.random.randn(2,1) # random initialization t0, t1 = 200, 1000 def learning_schedule(t): return t0 / (t + t1) t = 0 for epoch in range(n_iterations): shuffled_indices = np.random.permutation(m) X_b_shuffled = X_b[shuffled_indices] y_shuffled = y[shuffled_indices] for i in range(0, m, minibatch_size): t += 1 xi = X_b_shuffled[i:i+minibatch_size] yi = y_shuffled[i:i+minibatch_size] gradients = 2/minibatch_size * xi.T.dot(xi.dot(theta) - yi) eta = learning_schedule(t) theta = theta - eta * gradients theta_path_mgd.append(theta) theta_path_bgd = np.array(theta_path_bgd) theta_path_sgd = np.array(theta_path_sgd) theta_path_mgd = np.array(theta_path_mgd) plt.figure(figsize=(7,4)) plt.plot(theta_path_sgd[:, 0], theta_path_sgd[:, 1], "r-s", linewidth=1, label="Stochastic") plt.plot(theta_path_mgd[:, 0], theta_path_mgd[:, 1], "g-+", linewidth=2, label="Mini-batch") plt.plot(theta_path_bgd[:, 0], theta_path_bgd[:, 1], "b-o", linewidth=3, label="Batch") plt.legend(loc="upper left", fontsize=16) plt.xlabel(r"$\theta_0$", fontsize=20) plt.ylabel(r"$\theta_1$ ", fontsize=20, rotation=0) plt.axis([2.5, 4.5, 2.3, 3.9]) save_fig("gradient_descent_paths_plot") plt.show()
多項式回歸
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基于簡單的二次方程（注：二次方程的形式為y=ax2+bx+c）制造一些非線性資料（添加隨機噪聲)
import numpy as np import numpy.random as rnd np.random.seed(42) m = 100 X = 6 * np.random.rand(m, 1) - 3 y = 0.5 * X**2 + X + 2 + np.random.randn(m, 1) plt.plot(X, y, "b.") plt.xlabel("$x_1$", fontsize=18) plt.ylabel("$y$", rotation=0, fontsize=18) plt.axis([-3, 3, 0, 10]) save_fig("quadratic_data_plot") plt.show() -
使用Scikit-Learn的PolynomialFeatures類來對訓練資料進行轉換
from sklearn.preprocessing import PolynomialFeatures poly_features = PolynomialFeatures(degree=2, include_bias=False) X_poly = poly_features.fit_transform(X) #print(X[0]) #print(X_poly[0]) -
對這個擴展后的訓練集匹配一個LinearRegression模型
from sklearn.linear_model import LinearRegression lin_reg = LinearRegression() lin_reg.fit(X_poly, y) #print(lin_reg.intercept_, lin_reg.coef_) X_new=np.linspace(-3, 3, 100).reshape(100, 1) X_new_poly = poly_features.transform(X_new) y_new = lin_reg.predict(X_new_poly) plt.plot(X, y, "b.") plt.plot(X_new, y_new, "r-", linewidth=2, label="Predictions") plt.xlabel("$x_1$", fontsize=18) plt.ylabel("$y$", rotation=0, fontsize=18) plt.legend(loc="upper left", fontsize=14) plt.axis([-3, 3, 0, 10]) #save_fig("quadratic_predictions_plot(多項式回歸模型預測)") #plt.show()
學習曲線
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高階多項式回歸
from sklearn.preprocessing import StandardScaler from sklearn.pipeline import Pipeline for style, width, degree in (("g-", 1, 300), ("b--", 2, 2), ("r-+", 2, 1)): polybig_features = PolynomialFeatures(degree=degree, include_bias=False) std_scaler = StandardScaler() lin_reg = LinearRegression() polynomial_regression = Pipeline([ ("poly_features", polybig_features), ("std_scaler", std_scaler), ("lin_reg", lin_reg), ]) polynomial_regression.fit(X, y) y_newbig = polynomial_regression.predict(X_new) plt.plot(X_new, y_newbig, style, label=str(degree), linewidth=width) plt.plot(X, y, "b.", linewidth=3) plt.legend(loc="upper left") plt.xlabel("$x_1$", fontsize=18) plt.ylabel("$y$", rotation=0, fontsize=18) plt.axis([-3, 3, 0, 10]) #save_fig("high_degree_polynomials_plot(高階多項式回歸)") #plt.show() -
純線性回歸模型學習曲線
from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split def plot_learning_curves(model, X, y): X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=10) train_errors, val_errors = [], [] for m in range(1, len(X_train)): model.fit(X_train[:m], y_train[:m]) y_train_predict = model.predict(X_train[:m]) y_val_predict = model.predict(X_val) train_errors.append(mean_squared_error(y_train[:m], y_train_predict)) val_errors.append(mean_squared_error(y_val, y_val_predict)) plt.plot(np.sqrt(train_errors), "r-+", linewidth=2, label="train") plt.plot(np.sqrt(val_errors), "b-", linewidth=3, label="val") plt.legend(loc="upper right", fontsize=14) # not shown in the book plt.xlabel("Training set size", fontsize=14) # not shown plt.ylabel("RMSE", fontsize=14) # not shown ''' ''' lin_reg = LinearRegression() plot_learning_curves(lin_reg, X, y) plt.axis([0, 80, 0, 3]) # not shown in the book #save_fig("underfitting_learning_curves_plot(學習曲線)") # not shown #plt.show() # not shown -
多項式回歸模型的學習曲線 10階
polynomial_regression = Pipeline([ ("poly_features", PolynomialFeatures(degree=10, include_bias=False)), ("lin_reg", LinearRegression()), ]) plot_learning_curves(polynomial_regression, X, y) plt.axis([0, 80, 0, 3]) # not shown save_fig("learning_curves_plot(多項式回歸模型的學習曲線)") # not shown plt.show()
正則線性模型
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嶺回歸（也叫作吉洪諾夫正則化）是線性回歸的正則化版
from sklearn.linear_model import Ridge np.random.seed(42) m = 20 X = 3 * np.random.rand(m, 1) y = 1 + 0.5 * X + np.random.randn(m, 1) / 1.5 X_new = np.linspace(0, 3, 100).reshape(100, 1) def plot_model(model_class, polynomial, alphas, **model_kargs): for alpha, style in zip(alphas, ("b-", "g--", "r:")): model = model_class(alpha, **model_kargs) if alpha > 0 else LinearRegression() if polynomial: model = Pipeline([ ("poly_features", PolynomialFeatures(degree=10, include_bias=False)), ("std_scaler", StandardScaler()), ("regul_reg", model), ]) model.fit(X, y) y_new_regul = model.predict(X_new) lw = 2 if alpha > 0 else 1 plt.plot(X_new, y_new_regul, style, linewidth=lw, label=r"$\alpha = {}$".format(alpha)) plt.plot(X, y, "b.", linewidth=3) plt.legend(loc="upper left", fontsize=15) plt.xlabel("$x_1$", fontsize=18) plt.axis([0, 3, 0, 4]) plt.figure(figsize=(8,4)) plt.subplot(121) plot_model(Ridge, polynomial=False, alphas=(0, 10, 100), random_state=42) plt.ylabel("$y$", rotation=0, fontsize=18) plt.subplot(122) plot_model(Ridge, polynomial=True, alphas=(0, 10**-5, 1), random_state=42) save_fig("ridge_regression_plot(嶺回歸)") plt.show() -
使用Scikit-Learn執行閉式解的嶺回歸
from sklearn.linear_model import Ridge ridge_reg = Ridge(alpha=1, solver="cholesky", random_state=42) ridge_reg.fit(X, y) ridge_reg.predict([[1.5]]) ridge_reg = Ridge(alpha=1, solver="sag", random_state=42) ridge_reg.fit(X, y) ridge_reg.predict([[1.5]]) #使用隨機梯度下降 from sklearn.linear_model import SGDRegressor sgd_reg = SGDRegressor(max_iter=50, tol=-np.infty, penalty="l2", random_state=42) sgd_reg.fit(X, y.ravel()) sgd_reg.predict([[1.5]]) -
套索回歸、Lasso回歸.線性回歸的另一種正則化，叫作最小絕對收縮和選擇算子回歸（Least Absolute Shrinkage and Selection Operator Regression，簡稱Lasso回歸，或套索回歸），
from sklearn.linear_model import Lasso plt.figure(figsize=(8,4)) plt.subplot(121) plot_model(Lasso, polynomial=False, alphas=(0, 0.1, 1), random_state=42) plt.ylabel("$y$", rotation=0, fontsize=18) plt.subplot(122) plot_model(Lasso, polynomial=True, alphas=(0, 10**-7, 1), tol=1, random_state=42) #save_fig("lasso_regression_plot(套索回歸Lasso回歸)") #plt.show() -
使用Scikit-Learn的Lasso類的小例子，
from sklearn.linear_model import ElasticNet elastic_net = ElasticNet(alpha=0.1, l1_ratio=0.5, random_state=42) elastic_net.fit(X, y) #print(elastic_net.predict([[1.5]])) -
彈性網路，使用Scikit-Learn的ElasticNet的小例子
from sklearn.linear_model import ElasticNet elastic_net = ElasticNet(alpha=0.1, l1_ratio=0.5, random_state=42) elastic_net.fit(X, y) #print(elastic_net.predict([[1.5]])) -
早期停止法
from sklearn.linear_model import SGDRegressor np.random.seed(42) m = 100 X = 6 * np.random.rand(m, 1) - 3 y = 2 + X + 0.5 * X**2 + np.random.randn(m, 1) X_train, X_val, y_train, y_val = train_test_split(X[:50], y[:50].ravel(), test_size=0.5, random_state=10) poly_scaler = Pipeline([ ("poly_features", PolynomialFeatures(degree=90, include_bias=False)), ("std_scaler", StandardScaler()), ]) X_train_poly_scaled = poly_scaler.fit_transform(X_train) X_val_poly_scaled = poly_scaler.transform(X_val) sgd_reg = SGDRegressor(max_iter=1, tol=-np.infty, penalty=None, eta0=0.0005, warm_start=True, learning_rate="constant", random_state=42) n_epochs = 500 train_errors, val_errors = [], [] for epoch in range(n_epochs): sgd_reg.fit(X_train_poly_scaled, y_train) y_train_predict = sgd_reg.predict(X_train_poly_scaled) y_val_predict = sgd_reg.predict(X_val_poly_scaled) train_errors.append(mean_squared_error(y_train, y_train_predict)) val_errors.append(mean_squared_error(y_val, y_val_predict)) best_epoch = np.argmin(val_errors) best_val_rmse = np.sqrt(val_errors[best_epoch]) plt.annotate('Best model', xy=(best_epoch, best_val_rmse), xytext=(best_epoch, best_val_rmse + 1), ha="center", arrowprops=dict(facecolor='black', shrink=0.05), fontsize=16, ) best_val_rmse -= 0.03 # just to make the graph look better plt.plot([0, n_epochs], [best_val_rmse, best_val_rmse], "k:", linewidth=2) plt.plot(np.sqrt(val_errors), "b-", linewidth=3, label="Validation set") plt.plot(np.sqrt(train_errors), "r--", linewidth=2, label="Training set") plt.legend(loc="upper right", fontsize=14) plt.xlabel("Epoch", fontsize=14) plt.ylabel("RMSE", fontsize=14) save_fig("early_stopping_plot(早期停止法)") plt.show() -
Lasso回歸與嶺回歸
t1a, t1b, t2a, t2b = -1, 3, -1.5, 1.5 # ignoring bias term t1s = np.linspace(t1a, t1b, 500) t2s = np.linspace(t2a, t2b, 500) t1, t2 = np.meshgrid(t1s, t2s) T = np.c_[t1.ravel(), t2.ravel()] Xr = np.array([[-1, 1], [-0.3, -1], [1, 0.1]]) yr = 2 * Xr[:, :1] + 0.5 * Xr[:, 1:] J = (1/len(Xr) * np.sum((T.dot(Xr.T) - yr.T)**2, axis=1)).reshape(t1.shape) N1 = np.linalg.norm(T, ord=1, axis=1).reshape(t1.shape) N2 = np.linalg.norm(T, ord=2, axis=1).reshape(t1.shape) t_min_idx = np.unravel_index(np.argmin(J), J.shape) t1_min, t2_min = t1[t_min_idx], t2[t_min_idx] t_init = np.array([[0.25], [-1]]) def bgd_path(theta, X, y, l1, l2, core = 1, eta = 0.1, n_iterations = 50): path = [theta] for iteration in range(n_iterations): gradients = core * 2/len(X) * X.T.dot(X.dot(theta) - y) + l1 * np.sign(theta) + 2 * l2 * theta theta = theta - eta * gradients path.append(theta) return np.array(path) plt.figure(figsize=(12, 8)) for i, N, l1, l2, title in ((0, N1, 0.5, 0, "Lasso"), (1, N2, 0, 0.1, "Ridge")): JR = J + l1 * N1 + l2 * N2**2 tr_min_idx = np.unravel_index(np.argmin(JR), JR.shape) t1r_min, t2r_min = t1[tr_min_idx], t2[tr_min_idx] levelsJ=(np.exp(np.linspace(0, 1, 20)) - 1) * (np.max(J) - np.min(J)) + np.min(J) levelsJR=(np.exp(np.linspace(0, 1, 20)) - 1) * (np.max(JR) - np.min(JR)) + np.min(JR) levelsN=np.linspace(0, np.max(N), 10) path_J = bgd_path(t_init, Xr, yr, l1=0, l2=0) path_JR = bgd_path(t_init, Xr, yr, l1, l2) path_N = bgd_path(t_init, Xr, yr, np.sign(l1)/3, np.sign(l2), core=0) plt.subplot(221 + i * 2) plt.grid(True) plt.axhline(y=0, color='k') plt.axvline(x=0, color='k') plt.contourf(t1, t2, J, levels=levelsJ, alpha=0.9) plt.contour(t1, t2, N, levels=levelsN) plt.plot(path_J[:, 0], path_J[:, 1], "w-o") plt.plot(path_N[:, 0], path_N[:, 1], "y-^") plt.plot(t1_min, t2_min, "rs") plt.title(r"$\ell_{}$ penalty".format(i + 1), fontsize=16) plt.axis([t1a, t1b, t2a, t2b]) if i == 1: plt.xlabel(r"$\theta_1$", fontsize=20) plt.ylabel(r"$\theta_2$", fontsize=20, rotation=0) plt.subplot(222 + i * 2) plt.grid(True) plt.axhline(y=0, color='k') plt.axvline(x=0, color='k') plt.contourf(t1, t2, JR, levels=levelsJR, alpha=0.9) plt.plot(path_JR[:, 0], path_JR[:, 1], "w-o") plt.plot(t1r_min, t2r_min, "rs") plt.title(title, fontsize=16) plt.axis([t1a, t1b, t2a, t2b]) if i == 1: plt.xlabel(r"$\theta_1$", fontsize=20) save_fig("lasso_vs_ridge_plot") plt.show() -
邏輯回歸
#邏輯函式 t = np.linspace(-10, 10, 100) sig = 1 / (1 + np.exp(-t)) plt.figure(figsize=(9, 3)) plt.plot([-10, 10], [0, 0], "k-") plt.plot([-10, 10], [0.5, 0.5], "k:") plt.plot([-10, 10], [1, 1], "k:") plt.plot([0, 0], [-1.1, 1.1], "k-") plt.plot(t, sig, "b-", linewidth=2, label=r"$\sigma(t) = \frac{1}{1 + e^{-t}}$") plt.xlabel("t") plt.legend(loc="upper left", fontsize=20) plt.axis([-10, 10, -0.1, 1.1]) save_fig("logistic_function_plot") plt.show() -
決策邊界
#創建一個分類器來檢測Virginica鳶尾花， from sklearn import datasets iris = datasets.load_iris() list(iris.keys()) #print(list(iris.keys())) #print(iris.DESCR) X = iris["data"][:, 3:] # petal width y = (iris["target"] == 2).astype(np.int) # 1 if Iris-Virginica, else 0 -
訓練邏輯回歸模型
from sklearn.linear_model import LogisticRegression log_reg = LogisticRegression(solver="liblinear", random_state=42) log_reg.fit(X, y) #精簡版 X_new = np.linspace(0, 3, 1000).reshape(-1, 1) y_proba = log_reg.predict_proba(X_new) plt.plot(X_new, y_proba[:, 1], "g-", linewidth=2, label="Iris-Virginica") plt.plot(X_new, y_proba[:, 0], "b--", linewidth=2, label="Not Iris-Virginica") plt.show() #完整版 X_new = np.linspace(0, 3, 1000).reshape(-1, 1) y_proba = log_reg.predict_proba(X_new) decision_boundary = X_new[y_proba[:, 1] >= 0.5][0] plt.figure(figsize=(8, 3)) plt.plot(X[y==0], y[y==0], "bs") plt.plot(X[y==1], y[y==1], "g^") plt.plot([decision_boundary, decision_boundary], [-1, 2], "k:", linewidth=2) plt.plot(X_new, y_proba[:, 1], "g-", linewidth=2, label="Iris-Virginica") plt.plot(X_new, y_proba[:, 0], "b--", linewidth=2, label="Not Iris-Virginica") plt.text(decision_boundary+0.02, 0.15, "Decision boundary", fontsize=14, color="k", ha="center") plt.arrow(decision_boundary, 0.08, -0.3, 0, head_width=0.05, head_length=0.1, fc='b', ec='b') plt.arrow(decision_boundary, 0.92, 0.3, 0, head_width=0.05, head_length=0.1, fc='g', ec='g') plt.xlabel("Petal width (cm)", fontsize=14) plt.ylabel("Probability", fontsize=14) plt.legend(loc="center left", fontsize=14) plt.axis([0, 3, -0.02, 1.02]) #save_fig("logistic_regression_plot(估算概率和決策邊界)") #plt.show() print(decision_boundary) print(log_reg.predict([[1.7], [1.5]])) -
Softmax回歸 多元邏輯回歸
from sklearn.linear_model import LogisticRegression X = iris["data"][:, (2, 3)] # petal length, petal width y = (iris["target"] == 2).astype(np.int) log_reg = LogisticRegression(solver="liblinear", C=10**10, random_state=42) log_reg.fit(X, y) x0, x1 = np.meshgrid( np.linspace(2.9, 7, 500).reshape(-1, 1), np.linspace(0.8, 2.7, 200).reshape(-1, 1), ) X_new = np.c_[x0.ravel(), x1.ravel()] y_proba = log_reg.predict_proba(X_new) plt.figure(figsize=(10, 4)) plt.plot(X[y==0, 0], X[y==0, 1], "bs") plt.plot(X[y==1, 0], X[y==1, 1], "g^") zz = y_proba[:, 1].reshape(x0.shape) contour = plt.contour(x0, x1, zz, cmap=plt.cm.brg) left_right = np.array([2.9, 7]) boundary = -(log_reg.coef_[0][0] * left_right + log_reg.intercept_[0]) / log_reg.coef_[0][1] plt.clabel(contour, inline=1, fontsize=12) plt.plot(left_right, boundary, "k--", linewidth=3) plt.text(3.5, 1.5, "Not Iris-Virginica", fontsize=14, color="b", ha="center") plt.text(6.5, 2.3, "Iris-Virginica", fontsize=14, color="g", ha="center") plt.xlabel("Petal length", fontsize=14) plt.ylabel("Petal width", fontsize=14) plt.axis([2.9, 7, 0.8, 2.7]) save_fig("logistic_regression_contour_plot") plt.show() X = iris["data"][:, (2, 3)] # petal length, petal width y = iris["target"] softmax_reg = LogisticRegression(multi_,solver="lbfgs", C=10, random_state=42) softmax_reg.fit(X, y) x0, x1 = np.meshgrid( np.linspace(0, 8, 500).reshape(-1, 1), np.linspace(0, 3.5, 200).reshape(-1, 1), ) X_new = np.c_[x0.ravel(), x1.ravel()] y_proba = softmax_reg.predict_proba(X_new) y_predict = softmax_reg.predict(X_new) zz1 = y_proba[:, 1].reshape(x0.shape) zz = y_predict.reshape(x0.shape) plt.figure(figsize=(10, 4)) plt.plot(X[y==2, 0], X[y==2, 1], "g^", label="Iris-Virginica") plt.plot(X[y==1, 0], X[y==1, 1], "bs", label="Iris-Versicolor") plt.plot(X[y==0, 0], X[y==0, 1], "yo", label="Iris-Setosa") from matplotlib.colors import ListedColormap custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0']) plt.contourf(x0, x1, zz, cmap=custom_cmap) contour = plt.contour(x0, x1, zz1, cmap=plt.cm.brg) plt.clabel(contour, inline=1, fontsize=12) plt.xlabel("Petal length", fontsize=14) plt.ylabel("Petal width", fontsize=14) plt.legend(loc="center left", fontsize=14) plt.axis([0, 7, 0, 3.5]) save_fig("softmax_regression_contour_plot") plt.show() print(softmax_reg.predict([[5, 2]])) print(softmax_reg.predict_proba([[5, 2]]))
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標籤：Python