鸢尾花分类_2

Python 3 解法, 执行用时: 1152ms, 内存消耗: 524288KB, 提交时间: 2022-07-27

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score,roc_auc_score,accuracy_score
from sklearn.tree import DecisionTreeClassifier

def transform_three2two_cate():
    data = datasets.load_iris()
    #其中data特征数据的key为data，标签数据的key为target
    #需要取出原来的特征数据和标签数据，移除标签为2的label和特征数据，返回值new_feat为numpy.ndarray格式特征数据，new_label为对应的numpy.ndarray格式label数据
    #需要注意特征和标签的顺序一致性，否则数据集将混乱
    #code start here
    index_arr = np.where(data.target == 2)[0]
    new_feat = np.delete(data.data, index_arr, 0)
    new_label = np.delete(data.target, index_arr)

    #code end here
    return new_feat,new_label

def train_and_evaluate():
    data_X,data_Y = transform_three2two_cate()
    train_x,test_x,train_y,test_y = train_test_split(data_X,data_Y,test_size = 0.2)
    #已经划分好训练集和测试集，接下来请实现对数据的训练
    #code start here
    
    estimator = DecisionTreeClassifier()
    estimator.fit(train_x, train_y)
    y_predict = estimator.predict(test_x)
    



    #code end here
    #注意模型预测的label需要定义为 y_predict，格式为list或numpy.ndarray
    print(accuracy_score(y_predict,test_y))

if __name__ == "__main__":
    train_and_evaluate()
    #要求执行train_and_evaluate()后输出为：
    #1、{0,1}，代表数据label为0和1
    #2、测试集上的准确率分数，要求>0.95

Python 3 解法, 执行用时: 1153ms, 内存消耗: 524288KB, 提交时间: 2022-07-05

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score,roc_auc_score,accuracy_score
from sklearn.tree import DecisionTreeClassifier

def transform_three2two_cate():
    data = datasets.load_iris()
    #其中data特征数据的key为data，标签数据的key为target
    #需要取出原来的特征数据和标签数据，移除标签为2的label和特征数据，返回值new_feat为numpy.ndarray格式特征数据，new_label为对应的numpy.ndarray格式label数据
    #需要注意特征和标签的顺序一致性，否则数据集将混乱
    #code start here
    index2=np.where(np.array(data.target)==2)
    new_feat = np.delete(data.data, index2, axis = 0)
    new_label = np.delete(data.target, index2)

    #code end here
    return new_feat,new_label

def train_and_evaluate():
    data_X,data_Y = transform_three2two_cate()
    train_x,test_x,train_y,test_y = train_test_split(data_X,data_Y,test_size = 0.2)
    #已经划分好训练集和测试集，接下来请实现对数据的训练
    #code start here
    dtc =DecisionTreeClassifier(max_depth=3)                         # 建立决策树模型
    dtc.fit(train_x,train_y)                                         # 训练模型
    y_predict = dtc.predict(test_x)    
    #输出{0，1}
    

#     data_X,tar_Y = transform_three2two_cate()

#     s=[]
#     for i in tar_Y:
#         if i not in s:
#             s.append(i)

#     for j in range(len(s)):
#         if j>0:
#             a=a+','+str(j)
#         else:
#             a=str(j)
#     print('{{{}}}'.format(a))
    #code end here
    #注意模型预测的label需要定义为 y_predict，格式为list或numpy.ndarray
    print(accuracy_score(y_predict,test_y))

if __name__ == "__main__":
    train_and_evaluate()
    #要求执行train_and_evaluate()后输出为：
    #1、{0,1}，代表数据label为0和1
    #2、测试集上的准确率分数，要求>0.95

Python 3 解法, 执行用时: 1184ms, 内存消耗: 524288KB, 提交时间: 2022-07-16

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score,roc_auc_score,accuracy_score
from sklearn.tree import DecisionTreeClassifier

def transform_three2two_cate():
    data = datasets.load_iris()
    #其中data特征数据的key为data，标签数据的key为target
    #需要取出原来的特征数据和标签数据，移除标签为2的label和特征数据，返回值new_feat为numpy.ndarray格式特征数据，
    #new_label为对应的numpy.ndarray格式label数据
    #需要注意特征和标签的顺序一致性，否则数据集将混乱
    #code start here
    index_2=np.where(np.array(data.target==2))
    new_feat=np.delete(data.data,index_2,0)
    new_label=np.delete(data.target,index_2)
    #code end here
    return new_feat,new_label

def train_and_evaluate():
    data_X,data_Y = transform_three2two_cate()
    train_x,test_x,train_y,test_y = train_test_split(data_X,data_Y,test_size = 0.2)
    #已经划分好训练集和测试集，接下来请实现对数据的训练
    #code start here
    classifier = LogisticRegression(solver='liblinear',C=100)
    classifier.fit(train_x, train_y)
    y_predict=classifier.predict(test_x)
    #code end here
    #注意模型预测的label需要定义为 y_predict，格式为list或numpy.ndarray
    print(accuracy_score(y_predict,test_y))

if __name__ == "__main__":
    train_and_evaluate()
    #要求执行train_and_evaluate()后输出为：
    #1、{0,1}，代表数据label为0和1
    #2、测试集上的准确率分数，要求>0.95

Python 3 解法, 执行用时: 1190ms, 内存消耗: 524288KB, 提交时间: 2022-07-18

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score,roc_auc_score,accuracy_score
from sklearn.tree import DecisionTreeClassifier

def transform_three2two_cate():
    data = datasets.load_iris()
    #其中data特征数据的key为data，标签数据的key为target
    #需要取出原来的特征数据和标签数据，移除标签为2的label和特征数据，返回值new_feat为numpy.ndarray格式特征数据，new_label为对应的numpy.ndarray格式label数据
    #需要注意特征和标签的顺序一致性，否则数据集将混乱
    #code start here
    idx=np.where(np.array(data.target==2))
    new_feat=np.delete(data.data,idx,axis=0)
    new_label=np.delete(data.target,idx)

    #code end here
    return new_feat,new_label

def train_and_evaluate():
    data_X,data_Y = transform_three2two_cate()
    train_x,test_x,train_y,test_y = train_test_split(data_X,data_Y,test_size = 0.2)
    #已经划分好训练集和测试集，接下来请实现对数据的训练
    #code start here
    dtc=DecisionTreeClassifier()
    dtc.fit(train_x,train_y)
    y_predict=dtc.predict(test_x)

    #code end here
    #注意模型预测的label需要定义为 y_predict，格式为list或numpy.ndarray
    print(accuracy_score(y_predict,test_y))

if __name__ == "__main__":
    train_and_evaluate()
    #要求执行train_and_evaluate()后输出为：
    #1、{0,1}，代表数据label为0和1
    #2、测试集上的准确率分数，要求>0.95

Python 3 解法, 执行用时: 1194ms, 内存消耗: 524288KB, 提交时间: 2022-06-24

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score,roc_auc_score,accuracy_score
from sklearn.tree import DecisionTreeClassifier

def transform_three2two_cate():
    data = datasets.load_iris()
    #data导入以后是dict格式，其中特征数据的key为data，标签数据的key为target
    #需要取出原来的特征数据和标签数据，移除标签为2的label和特征数据，返回值new_feat为numpy.ndarray格式特征数据，new_label为对应的numpy.ndarray格式label数据
    #需要注意特征和标签的顺序一致性，否则数据集将混乱
    #code start here
    label = list(data.target)
    two_index = list(label).index(2)
    new_feat = data.data[:two_index]
    new_label = label[:two_index]

    #code end here
    #print(set(new_label))
    return new_feat,new_label

def train_and_evaluate():
    data_X,data_Y = transform_three2two_cate()
    train_x,test_x,train_y,test_y = train_test_split(data_X,data_Y,test_size = 0.2)
    #已经划分好训练集和测试集，接下来请实现对数据的训练
    #code start here
    lr = LogisticRegression(penalty='l2',solver='liblinear',C=0.5,max_iter=1000)
    lr.fit(train_x,train_y)
    y_predict = lr.predict(test_x)


    #code end here
    #注意模型预测的label需要定义为 y_predict，格式为list或numpy.ndarray
    print(accuracy_score(y_predict,test_y))

if __name__ == "__main__":
    train_and_evaluate()
    #要求执行train_and_evaluate()后输出为：
    #1、{0,1}，代表数据label为0和1
    #2、测试集上的准确率分数，要求>0.95

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