python-3.x - 如何获取用户输入并将其传递给预测模型

Question

我有一个数据框，我在其上构建了预测模型。将数据分为训练和测试，我使用了Randomforest分类器。

现在，用户传递一个新数据，该数据需要通过这个模型并给出结果。

这是一个文本数据，下面是数据框:

Description          Category
Rejoin this domain   Network
Laptop crashed       Hardware
Installation Error   Software

代码:

############### Feature extraction ##############
countvec = CountVectorizer()
counts = countvec.fit_transform(read_data['Description'])
df = pd.DataFrame(counts.toarray())
df.columns = countvec.get_feature_names()
print(df)

########## Join with original data ##############
df = read_data.join(df)
a = list(df.columns.values)

########## Creating the dependent variable class for "Category" variable ###########
factor = pd.factorize(df['Category'])
df.Category = factor[0]
definitions = factor[1]
print(df.Category.head())
print(definitions)

########## Creating the dependent variable class for "Description" variable ###########
factor = pd.factorize(df['Description'])
df.Description = factor[0]
definitions_1 = factor[1]
print(df.Description.head())
print(definitions_1)

######### Split into Train and Test data #######################
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.80, random_state = 21)

############# Random forest classification model #########################
classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 42)
classifier.fit(X_train, y_train)

######### Predicting the Test set results ##############
y_pred = classifier.predict(X_test)

#####Reverse factorize (converting y_pred from 0s,1s and 2s to original class for "Category" ###############
reversefactor = dict(zip(range(3),definitions))
y_test = np.vectorize(reversefactor.get)(y_test)
y_pred = np.vectorize(reversefactor.get)(y_pred)

#####Reverse factorize (converting y_pred from 0s,1s and 2s to original class for "Description" ###############
reversefactor = dict(zip(range(53),definitions_1))
X_test = np.vectorize(reversefactor.get)(X_test)

Answer 1

如果您只想对用户的数据进行预测，那么我只需加载包含用户数据的新 csv(或其他格式)(确保列与原始训练数据集中的列相同，减去依赖项)显然是变量)，您可以为您的任务提取预测:

user_df = pd.read_csv("user_data.csv")

#insert a preprocessing step if needed to make sure user_df is identical to the original dataset

new_predictions = classifier.predict(user_df)