from main import mglearn, train_test_split, plt, np
from sklearn.svm import SVC
X, y = mglearn.tools.make_handcrafted_dataset()
svm = SVC(kernel='rbf', C=10, gamma=0.1).fit(X, y)
mglearn.plots.plot_2d_separator(svm, X, eps=.5)
mglearn.discrete_scatter(X[:, 0], X[:, 1], y)
fig, axes = plt.subplots(3, 3, figsize=(15, 10))
for ax, C in zip(axes, [-1, 0, 3]):
for a, gamma in zip(ax, range(-1, 2)):
mglearn.plots.plot_svm(log_C=C, log_gamma=gamma, ax=a)
axes[0, 0].legend(["Class 0", "Class 1", "sv class 0", "sv class 1"],
ncol=4,
loc=(.9, 1.2))
sv = svm.support_vectors_
sv_labels = svm.dual_coef_.ravel() > 0
mglearn.discrete_scatter(sv[:, 0],
sv[:, 1],
sv_labels,
s=15,
markeredgewidth=3)
plt.xlabel("Feature 0")
plt.ylabel("Feature 1")
plt.show()
from main import mglearn, np, plt, train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_moons
X, y = make_moons(n_samples=100, noise=0.25, random_state=3)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
stratify=y,
random_state=0)
forest = RandomForestClassifier(n_estimators=5, random_state=2)
forest.fit(X_train, y_train)
fig, axes = plt.subplots(2, 3, figsize=(20, 10))
for i, (ax, tree) in enumerate(zip(axes.ravel(), forest.estimators_)):
ax.set_title("Tree {}".format(i))
mglearn.plots.plot_tree_partition(X_train, y_train, tree, ax=ax)
mglearn.plots.plot_2d_separator(forest,
X_train,
fill=True,
ax=axes[-1, -1],
alpha=.4)
axes[-1, -1].set_title("Random Forest")
mglearn.discrete_scatter(X_train[:, 0], X_train[:, 1], y_train)
plt.show()
from main import mglearn, plt, np
from sklearn.decomposition import NMF
from sklearn.decomposition import PCA
S = mglearn.datasets.make_signals()
# plt.figure(figsize=(6,1))
# plt.plot(S, '-')
# plt.xlabel("Time")
# plt.ylabel("Signal")
A = np.random.RandomState(0).uniform(size=(100, 3))
X = np.dot(S, A.T)
print("{}".format(X.shape))
nmf = NMF(n_components=3, random_state=42)
S_ = nmf.fit_transform(X)
print("{}".format(S_.shape))
pca = PCA(n_components=3)
H = pca.fit_transform(X)
models = [X, S, S_, H]
names = ['X', 'S', "S_", "H"]
fix, axes = plt.subplots(4, figsize=(15, 8), subplot_kw={'xticks': (), 'yticks': ()})
for model, name, ax in zip(models, names, axes):
ax.set_title(name)
ax.plot(model[:, :3], '-')
plt.show()
# plt.show()
X, y = mglearn.datasets.make_wave(n_samples=40)
# waveデータセットを訓練セットとテストセットに分ける
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
# 実行
reg = KNeighborsRegressor(n_neighbors=3)
reg.fit(X_train, y_train)
# 結果
print("Test set predictions: \n {}".format(reg.predict(X_train)))
print("Test set R^2: \n {:.2f}".format(reg.score(X_test, y_test)))
fig, axes = plt.subplots(1, 3, figsize=(15, 4))
line = np.linspace(-3, 3, 1000).reshape(-1, 1)
for n_neighbors, ax in zip([1, 3, 9], axes):
reg = KNeighborsRegressor(n_neighbors=n_neighbors)
reg.fit(X_train, y_train)
ax.plot(line, reg.predict(line))
ax.plot(X_train, y_train, '^', c=mglearn.cm2(0), markersize=8)
ax.plot(X_test, y_test, 'v', c=mglearn.cm2(1), markersize=8)
ax.set_title(
"{} neighbar(s)\n train score: {:.2f} test score: {:.2f}".format(
n_neighbors, reg.score(X_train, y_train),
reg.score(X_test, y_test)))
ax.set_xlabel("Feature")
ax.set_ylabel("Target")
from main import mglearn, train_test_split, plt
from sklearn.linear_model import LogisticRegression
from sklearn.svm import LinearSVC
X, y = mglearn.datasets.make_forge()
fig, axes = plt.subplots(1, 2, figsize=(10, 3))
for model, ax in zip([LinearSVC(), LogisticRegression()], axes):
clf = model.fit(X, y)
mglearn.plots.plot_2d_separator(clf,
X,
fill=False,
eps=0.5,
ax=ax,
alpha=.7)
mglearn.discrete_scatter(X[:, 0], X[:, 1], y, ax=ax)
ax.set_title("{}".format(clf.__class__.__name__))
ax.set_xlabel("Feature 0")
ax.set_ylabel("Feature 1")
axes[0].legend()
plt.show()