我有一个如下所示的数据集:
| "发货人代码" | "收货人代码" | "起始地" | "目的地" | "承运人代码" |
|------------------|------------------|----------|---------------|----------------|
| "6402106844" | "66903717" | "DKCPH" | "CNPVG" | "6402746387" |
| "6402106844" | "66903717" | "DKCPH" | "CNPVG" | "6402746387" |
| "6402106844" | "6404814143" | "DKCPH" | "CNPVG" | "6402746387" |
| "6402107662" | "66974631" | "DKCPH" | "VNSGN" | "6402746393" |
| "6402107662" | "6404518090" | "DKCPH" | "THBKK" | "6402746393" |
| "6402107662" | "6404518090" | "DKBLL" | "THBKK" | "6402746393" |
| "6408507648" | "6403601344" | "DKCPH" | "USTPA" | "66565231" |
我正在尝试在这个数据集上构建我的第一个机器学习模型。为此，我使用了scikit-learn。这是我使用的代码:
#导入依赖项
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import make_scorer, accuracy_score
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.externals import joblib
from sklearn import preprocessing
import pandas as pd
#导入数据集（一个CSV文件）
dataset = pd.read_csv('shipments.csv', header=0, skip_blank_lines=True)
#删除包含NaN值的行
dataset.dropna(subset=['Consignor Code', 'Consignee Code', 'Origin', 'Destination', 'Carrier Code'], inplace=True)
#将仅包含数字的单元格转换为字符串
dataset['Consignor Code'] = dataset['Consignor Code'].astype('int64')
dataset['Consignee Code'] = dataset['Consignee Code'].astype('int64')
dataset['Carrier Code'] = dataset['Carrier Code'].astype('int64')
#定义我们的目标（我们想要预测的内容）
target = dataset.pop('Destination')
#将所有数据转换为数值，以便我们可以使用.fit函数。
#为此，我们使用LabelEncoder
le = preprocessing.LabelEncoder()
target = le.fit_transform(list(target))
dataset['Origin'] = le.fit_transform(list(dataset['Origin']))
dataset['Consignor Code'] = le.fit_transform(list(dataset['Consignor Code']))
dataset['Consignee Code'] = le.fit_transform(list(dataset['Consignee Code']))
dataset['Carrier Code'] = le.fit_transform(list(dataset['Carrier Code']))
#准备数据集
X_train, X_test, y_train, y_test = train_test_split(dataset, target, test_size=0.3, random_state=0)
#准备模型并进行.fit
model = RandomForestClassifier()
model.fit(X_train, y_train)
#对测试集进行预测
predictions = model.predict(X_test)
#打印准确率得分
print("准确率得分: {}".format(accuracy_score(y_test, predictions)))
上面的代码返回如下结果:
准确率得分: 0.7172413793103448现在我的问题可能很傻 – 但是我如何使用我的model来实际显示它对新数据的预测呢？
考虑下面这个新输入，我想让它预测Destination:
"6408507648","6403601344","DKCPH","","66565231"我如何使用这个数据查询我的模型并得到预测的Destination呢？
回答：
这里有一个包含预测的完整工作示例。最重要的部分是为每个特征定义不同的标签编码器，这样你就可以用相同的编码来拟合新数据，否则你会遇到错误（这些错误可能不会显示，但当你计算准确率时会注意到）:
dataset = pd.DataFrame({'Consignor Code':["6402106844","6402106844","6402106844","6402107662","6402107662","6402107662","6408507648"],
'Consignee Code': ["66903717","66903717","6404814143","66974631","6404518090","6404518090","6403601344"],
'Origin':["DKCPH","DKCPH","DKCPH","DKCPH","DKCPH","DKBLL","DKCPH"],
'Destination':["CNPVG","CNPVG","CNPVG","VNSGN","THBKK","THBKK","USTPA"],
'Carrier Code':["6402746387","6402746387","6402746387","6402746393","6402746393","6402746393","66565231"]})
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import make_scorer, accuracy_score
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.externals import joblib
from sklearn import preprocessing
import pandas as pd
#导入数据集（一个CSV文件）
#删除包含NaN值的行
dataset.dropna(subset=['Consignor Code', 'Consignee Code', 'Origin', 'Destination', 'Carrier Code'], inplace=True)
#定义我们的目标（我们想要预测的内容）
target = dataset.pop('Destination')
#将所有数据转换为数值，以便我们可以使用.fit函数。
#为此，我们使用LabelEncoder
le_origin = preprocessing.LabelEncoder()
le_consignor = preprocessing.LabelEncoder()
le_consignee = preprocessing.LabelEncoder()
le_carrier = preprocessing.LabelEncoder()
le_target = preprocessing.LabelEncoder()
target = le_target.fit_transform(list(target))
dataset['Origin'] = le_origin.fit_transform(list(dataset['Origin']))
dataset['Consignor Code'] = le_consignor.fit_transform(list(dataset['Consignor Code']))
dataset['Consignee Code'] = le_consignee.fit_transform(list(dataset['Consignee Code']))
dataset['Carrier Code'] = le_carrier.fit_transform(list(dataset['Carrier Code']))
#准备数据集
X_train, X_test, y_train, y_test = train_test_split(dataset, target, test_size=0.3, random_state=42)
#准备模型并进行.fit
model = RandomForestClassifier(random_state=42)
model.fit(X_train, y_train)
#对测试集进行预测
predictions = model.predict(X_test)
#打印准确率得分
print("准确率得分: {}".format(accuracy_score(y_test, predictions)))
new_input = ["6408507648","6403601344","DKCPH","66565231"]
fitted_new_input = np.array([le_consignor.transform([new_input[0]])[0],
le_consignee.transform([new_input[1]])[0],
le_origin.transform([new_input[2]])[0],
le_carrier.transform([new_input[3]])[0]])
new_predictions = model.predict(fitted_new_input.reshape(1,-1))
print(le_target.inverse_transform(new_predictions))
最后，你的树预测:
['THBKK']