我有一个如下所示的数据集:
| "Consignor Code" | "Consignee Code" | "Origin" | "Destination" | "Carrier Code" |
|------------------|------------------|----------|---------------|----------------|
| "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" |
我正在尝试在其上构建我的第一个 ML 模型。为此,我正在使用 scikit-learn。这是我的代码:
#Import the dependencies
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
#Import the dataset (A CSV file)
dataset = pd.read_csv('shipments.csv', header=0, skip_blank_lines=True)
#Drop any rows containing NaN values
dataset.dropna(subset=['Consignor Code', 'Consignee Code',
'Origin', 'Destination', 'Carrier Code'], inplace=True)
#Convert the numeric only cells to strings
dataset['Consignor Code'] = dataset['Consignor Code'].astype('int64')
dataset['Consignee Code'] = dataset['Consignee Code'].astype('int64')
dataset['Carrier Code'] = dataset['Carrier Code'].astype('int64')
#Define our target (What we want to be able to predict)
target = dataset.pop('Destination')
#Convert all our data to numeric values, so we can use the .fit function.
#For that, we use 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']))
#Prepare the dataset.
X_train, X_test, y_train, y_test = train_test_split(
dataset, target, test_size=0.3, random_state=0)
#Prepare the model and .fit it.
model = RandomForestClassifier()
model.fit(X_train, y_train)
#Make a prediction on the test set.
predictions = model.predict(X_test)
#Print the accuracy score.
print("Accuracy score: {}".format(accuracy_score(y_test, predictions)))
现在上面的代码返回:
Accuracy score: 0.7172413793103448
现在我的问题可能很愚蠢 - 但我如何使用我的model
来实际向我展示它对新数据的预测?
考虑下面的新输入,我希望它预测Destination
:
"6408507648","6403601344","DKCPH","","66565231"
如何使用这些数据查询我的模型并获得预测Destination
结果?
在这里,您有一个完整的工作示例,其中包含预测。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拟合新数据,否则你会遇到错误(现在可能会显示,但你会在计算准确度时注意到):
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
#Import the dataset (A CSV file)
#Drop any rows containing NaN values
dataset.dropna(subset=['Consignor Code', 'Consignee Code',
'Origin', 'Destination', 'Carrier Code'], inplace=True)
#Define our target (What we want to be able to predict)
target = dataset.pop('Destination')
#Convert all our data to numeric values, so we can use the .fit function.
#For that, we use 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']))
#Prepare the dataset.
X_train, X_test, y_train, y_test = train_test_split(
dataset, target, test_size=0.3, random_state=42)
#Prepare the model and .fit it.
model = RandomForestClassifier(random_state=42)
model.fit(X_train, y_train)
#Make a prediction on the test set.
predictions = model.predict(X_test)
#Print the accuracy score.
print("Accuracy score: {}".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))
['THBKK']
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