【发布时间】：2018-10-05 10:22:11
【问题描述】：
我是机器学习领域的新手，我已经使用 ScikitLearn 库构建和训练了一个机器学习模型。它在 Jupyter 笔记本中运行良好，但是当我将此模型部署到 Google Cloud ML 并尝试使用 Python 提供服务时脚本，它会引发错误。
这是我的模型代码中的一个 sn-p：
更新：
from sklearn.metrics import classification_report, accuracy_score
from sklearn.ensemble import IsolationForest
from sklearn.neighbors import LocalOutlierFactor
# define a random state
state = 1
classifiers = {
"Isolation Forest": IsolationForest(max_samples=len(X),
contamination=outlier_fraction,
random_state=state),
# "Local Outlier Factor": LocalOutlierFactor(
# n_neighbors = 20,
# contamination = outlier_fraction)
}
import pickle
# fit the model
n_outliers = len(Fraud)
for i, (clf_name, clf) in enumerate(classifiers.items()):
# fit te data and tag outliers
if clf_name == "Local Outlier Factor":
y_pred = clf.fit_predict(X)
print("LOF executed")
scores_pred = clf.negative_outlier_factor_
# Export the classifier to a file
with open('model.pkl', 'wb') as model_file:
pickle.dump(clf, model_file)
else:
clf.fit(X)
scores_pred = clf.decision_function(X)
y_pred = clf.predict(X)
print("IF executed")
# Export the classifier to a file
with open('model.pkl', 'wb') as model_file:
pickle.dump(clf, model_file)
# Reshape the prediction values to 0 for valid and 1 for fraudulent
y_pred[y_pred == 1] = 0
y_pred[y_pred == -1] = 1
n_errors = (y_pred != Y).sum()
# run classification metrics
print('{}:{}'.format(clf_name, n_errors))
print(accuracy_score(Y, y_pred ))
print(classification_report(Y, y_pred ))
这是 Jupyter Notebook 中的输出：
隔离森林：7
0.93
precision recall f1-score support 0 0.97 0.96 0.96 94 1 0.43 0.50 0.46 6 avg / total 0.94 0.93 0.93 100
我已将此模型部署到 Google Cloud ML-Engine，然后尝试使用以下 python 脚本提供它：
import os
from googleapiclient import discovery
from oauth2client.service_account import ServiceAccountCredentials
credentials = ServiceAccountCredentials.from_json_keyfile_name('Machine Learning 001-dafe42dfb46f.json')
PROJECT_ID = "machine-learning-001-201312"
VERSION_NAME = "v1"
MODEL_NAME = "mlfd"
service = discovery.build('ml', 'v1', credentials=credentials)
name = 'projects/{}/models/{}'.format(PROJECT_ID, MODEL_NAME)
name += '/versions/{}'.format(VERSION_NAME)
data = [[265580, 7, 68728, 8.36, 4.76, 84.12, 79.36, 3346, 1, 11.99, 1.14,655012, 0.65, 258374, 0, 84.12] ]
response = service.projects().predict(
name=name,
body={'instances': data}
).execute()
if 'error' in response:
print (response['error'])
else:
online_results = response['predictions']
print(online_results)
这是该脚本的输出：
预测失败：sklearn 预测期间出现异常：“LocalOutlierFactor”对象没有“预测”属性
【问题讨论】：
-
你上面显示的python脚本，不要在任何地方使用
LocalOutlierFactor。您确定在 Google 上使用的是相同的脚本吗？ -
为什么要更改代码？只需在 google ml 上发布内容即可。
-
google ml 仍然返回相同的错误。
标签： python scikit-learn google-cloud-platform google-cloud-ml