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CH3 无监督学习与预处理

已有 35 次阅读2021-7-23 16:44 |个人分类:python机器学习

#3.3预处理与缩放
import matplotlib.pyplot as plt
import mglearn

plt.rcParams['image.cmap'] = "gray"
mglearn.plots.plot_scaling()

plt.show()

from sklearn.datasets import load_breast_cancer

from sklearn.model_selection import train_test_split


cancer = load_breast_cancer()

X_train, X_test, y_train, y_test = train_test_split(cancer.data,

                                                    cancer.target,

                                                    random_state=1)

print(X_train.shape)

print(X_test.shape)

输出:

(426, 30)

(143, 30)






from sklearn.preprocessing import MinMaxScaler

from sklearn.datasets import load_breast_cancer

from sklearn.model_selection import train_test_split


cancer = load_breast_cancer()

X_train, X_test, y_train, y_test = train_test_split(cancer.data,


                                                    cancer.target,


                                                    random_state=1)

scaler = MinMaxScaler()

#导入实现预处理的类,并将其实例化

scaler.fit(X_train)

#用fit方法拟合缩放器(scaler)

# transform data

X_train_scaled = scaler.transform(X_train)

MinMaxScaler(copy=True,feature_range=(0,1))

#缩放10倍

# print dataset properties before and after scaling

print("transformed shape: {}".format(X_train_scaled.shape))

print("per-feature minimum before scaling:\n {}".format(X_train.min(axis=0)))

print("per-feature maximum before scaling:\n {}".format(X_train.max(axis=0)))

print("per-feature minimum after scaling:\n {}".format(

    X_train_scaled.min(axis=0)))

print("per-feature maximum after scaling:\n {}".format(

    X_train_scaled.max(axis=0)))

输出:

transformed shape: (426, 30)

per-feature minimum before scaling:

 [6.981e+00 9.710e+00 4.379e+01 1.435e+02 5.263e-02 1.938e-02 0.000e+00

 0.000e+00 1.060e-01 5.024e-02 1.153e-01 3.602e-01 7.570e-01 6.802e+00

 1.713e-03 2.252e-03 0.000e+00 0.000e+00 9.539e-03 8.948e-04 7.930e+00

 1.202e+01 5.041e+01 1.852e+02 7.117e-02 2.729e-02 0.000e+00 0.000e+00

 1.566e-01 5.521e-02]

per-feature maximum before scaling:

 [2.811e+01 3.928e+01 1.885e+02 2.501e+03 1.634e-01 2.867e-01 4.268e-01

 2.012e-01 3.040e-01 9.575e-02 2.873e+00 4.885e+00 2.198e+01 5.422e+02

 3.113e-02 1.354e-01 3.960e-01 5.279e-02 6.146e-02 2.984e-02 3.604e+01

 4.954e+01 2.512e+02 4.254e+03 2.226e-01 9.379e-01 1.170e+00 2.910e-01

 5.774e-01 1.486e-01]

per-feature minimum after scaling:

 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.

 0. 0. 0. 0. 0. 0.]

per-feature maximum after scaling:

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.

 1. 1. 1. 1. 1. 1.]







from sklearn.preprocessing import MinMaxScaler
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

cancer = load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(cancer.data,

                                                    cancer.target,

                                                    random_state=1)
scaler = MinMaxScaler()
scaler.fit(X_train)

# transform test data
X_test_scaled = scaler.transform(X_test)
# print test data properties after scaling
print("per-feature minimum after scaling:\n{}".format(X_test_scaled.min(axis=0)))
print("per-feature maximum after scaling:\n{}".format(X_test_scaled.max(axis=0)))
输出:
per-feature minimum after scaling:
[ 0.0336031   0.0226581   0.03144219  0.01141039  0.14128374  0.04406704
  0.          0.          0.1540404  -0.00615249 -0.00137796  0.00594501
  0.00430665  0.00079567  0.03919502  0.0112206   0.          0.
 -0.03191387  0.00664013  0.02660975  0.05810235  0.02031974  0.00943767
  0.1094235   0.02637792  0.          0.         -0.00023764 -0.00182032]
per-feature maximum after scaling:
[0.9578778  0.81501522 0.95577362 0.89353128 0.81132075 1.21958701
 0.87956888 0.9333996  0.93232323 1.0371347  0.42669616 0.49765736
 0.44117231 0.28371044 0.48703131 0.73863671 0.76717172 0.62928585
 1.33685792 0.39057253 0.89612238 0.79317697 0.84859804 0.74488793
 0.9154725  1.13188961 1.07008547 0.92371134 1.20532319 1.63068851]







from sklearn.datasets import make_blobs
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import mglearn
# make synthetic data
X, _ = make_blobs(n_samples=50, centers=5, random_state=4, cluster_std=2)
# split it into training and test sets
X_train, X_test = train_test_split(X, random_state=5, test_size=.1)

# plot the training and test sets
fig, axes = plt.subplots(1, 3, figsize=(13, 4))
axes[0].scatter(X_train[:, 0], X_train[:, 1],
                c=mglearn.cm2(0), label="Training set", s=60)
axes[0].scatter(X_test[:, 0], X_test[:, 1], marker='^',
                c=mglearn.cm2(1), label="Test set", s=60)
axes[0].legend(loc='upper left')
axes[0].set_title("Original Data")

# scale the data using MinMaxScaler
scaler = MinMaxScaler()
scaler.fit(X_train)
X_train_scaled = scaler.transform(X_train)
X_test_scaled = scaler.transform(X_test)

# visualize the properly scaled data
axes[1].scatter(X_train_scaled[:, 0], X_train_scaled[:, 1],
                c=mglearn.cm2(0), label="Training set", s=60)
axes[1].scatter(X_test_scaled[:, 0], X_test_scaled[:, 1], marker='^',
                c=mglearn.cm2(1), label="Test set", s=60)
axes[1].set_title("Scaled Data")

# rescale the test set separately
# so test set min is 0 and test set max is 1
# DO NOT DO THIS! For illustration purposes only.
test_scaler = MinMaxScaler()
test_scaler.fit(X_test)
X_test_scaled_badly = test_scaler.transform(X_test)

# visualize wrongly scaled data
axes[2].scatter(X_train_scaled[:, 0], X_train_scaled[:, 1],
                c=mglearn.cm2(0), label="training set", s=60)
axes[2].scatter(X_test_scaled_badly[:, 0], X_test_scaled_badly[:, 1],
                marker='^', c=mglearn.cm2(1), label="test set", s=60)
axes[2].set_title("Improperly Scaled Data")

for ax in axes:
    ax.set_xlabel("Feature 0")
    ax.set_ylabel("Feature 1")
    
fig.tight_layout()
plt.show()
输出:





#
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC

cancer = load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(cancer.data,
                                                    cancer.target,
                                                    random_state=0)

scaler = StandardScaler()
# calling fit and transform in sequence (using method chaining)
X_scaled = scaler.fit(X_train).transform(X_train)
# same result, but more efficient computation
X_scaled_d = scaler.fit_transform(X_train)

svm = SVC(C=100)
svm.fit(X_train, y_train)
print("Test set accuracy: {:.2f}".format(svm.score(X_test, y_test)))
输出:
0.94










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