In [1]:
# 导入头文件
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC

import matplotlib.pyplot as plt
In [2]:
data = pd.read_csv('data.csv')
np.array(data['y'])
Out[2]:
array([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, 0, 0, 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, 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, 1, 1, 1, 1])
In [3]:
# 分解数据中的
X = np.array(data[['x1', 'x2']])
y = np.array(data['y'])
In [4]:
plt.scatter(X[:,0],X[:,1],c=y)
Out[4]:
<matplotlib.collections.PathCollection at 0x1a1a217828>
In [5]:
# Logistic Regression Classifier
lg_classifier = LogisticRegression()
lg_classifier.fit(X,y)

/Users/weixu/AI/tool/anaconda3/envs/py3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
  FutureWarning)
Out[5]:
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='warn',
          n_jobs=None, penalty='l2', random_state=None, solver='warn',
          tol=0.0001, verbose=0, warm_start=False)
In [6]:
y_p = lg_classifier.predict(X)
In [7]:
plt.scatter(X[:,0],X[:,1],c=y_p)
Out[7]:
<matplotlib.collections.PathCollection at 0x1a1c28bc88>
In [8]:
Dt_classifier = DecisionTreeClassifier()
Dt_classifier.fit(X, y)
Out[8]:
DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=None,
            splitter='best')
In [9]:
y_Dp = Dt_classifier.predict(X)
plt.scatter(X[:,0],X[:,1],c=y_Dp)
Out[9]:
<matplotlib.collections.PathCollection at 0x1a1c4b9208>
In [10]:
svc_classifier = SVC()
svc_classifier.fit(X, y)

/Users/weixu/AI/tool/anaconda3/envs/py3/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.
  "avoid this warning.", FutureWarning)
Out[10]:
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',
  kernel='rbf', max_iter=-1, probability=False, random_state=None,
  shrinking=True, tol=0.001, verbose=False)
In [11]:
y_sp = svc_classifier.predict(X)
plt.scatter(X[:,0],X[:,1],c=y_sp)
Out[11]:
<matplotlib.collections.PathCollection at 0x1a1c3cb518>
In [19]:
svc_classifier1 = SVC(kernel = 'poly', degree=8)
svc_classifier1.fit(X, y)
y_sp1 = svc_classifier1.predict(X)
plt.scatter(X[:,0],X[:,1],c=y_sp1)

/Users/weixu/AI/tool/anaconda3/envs/py3/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.
  "avoid this warning.", FutureWarning)
Out[19]:
<matplotlib.collections.PathCollection at 0x1a1cb20198>
In [20]:
svc_classifier1 = SVC(kernel = 'rbf', gamma=200)
svc_classifier1.fit(X, y)
y_sp1 = svc_classifier1.predict(X)
plt.scatter(X[:,0],X[:,1],c=y_sp1)
Out[20]:
<matplotlib.collections.PathCollection at 0x1a1cc95048>
In [21]:
svc_classifier1 = SVC(kernel = 'linear')
svc_classifier1.fit(X, y)
y_sp1 = svc_classifier1.predict(X)
plt.scatter(X[:,0],X[:,1],c=y_sp1)
Out[21]:
<matplotlib.collections.PathCollection at 0x1a1cd4aeb8>