给出“ScikitLearn”对象的LocalOutlierFactor模型没有属性“预测”错误
我是机器学习世界的新手，我用ScikitLearn library.It构建并训练了一个ml模型，它在木星笔记本上运行得非常好，但是当我将这个模型部署到Google并尝试使用Python脚本提供服务时，它会抛出一个错误。
下面是我的模型代码片段：
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 ))这是木星笔记本的输出：
隔离林:7 0.93 精确召回F1-得分支持0 0.97 0.96 0.96 94 1 0.43 0.50 0.46 6每克/总计0.94 0.93 0.93 100
我已经将这个模型部署到中，然后尝试使用以下python脚本提供服务：
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)下面是这个脚本的输出：
预测失败:学习过程中的异常预测：“LocalOutlierFactor”对象没有属性“预测”
回答 4
Stack Overflow用户
发布于 2018-04-25 04:39:39
LocalOutlierFactor没有predict方法，但只有私有_predict方法。以下是消息来源的理由。
def _predict(self, X=None):
"""Predict the labels (1 inlier, -1 outlier) of X according to LOF.
If X is None, returns the same as fit_predict(X_train).
This method allows to generalize prediction to new observations (not
in the training set). As LOF originally does not deal with new data,
this method is kept private.https://github.com/scikit-learn/scikit-learn/blob/a24c8b46/sklearn/neighbors/lof.py#L200
Stack Overflow用户
发布于 2018-04-25 05:24:08
看起来这可能是Python版本的问题(虽然我不清楚为什么scikit在Python 2和Python 3中的行为有所不同)。我能够在本地--在同一台机器上--验证我的Python 2安装是否在Python 3成功时复制了上面的错误(两者都在使用sci学习0.19.1)。
解决方案是在部署模型时指定python版本(注意最后一行，如果省略，默认为"2.7")：
gcloud beta ml-engine versions create $VERSION_NAME \
--model $MODEL_NAME --origin $DEPLOYMENT_SOURCE \
--runtime-version="1.5" --framework $FRAMEWORK
--python-version="3.5"Stack Overflow用户
发布于 2018-04-25 07:17:22
令人惊讶的是，问题是runtime version，当您重新创建模型版本时，它将得到解决，如下所示：
gcloud beta ml-engine versions create $VERSION_NAME --model $MODEL_NAME --origin $DEPLOYMENT_SOURCE --runtime-version="1.6" --framework $FRAMEWORK --python-version="3.5"使用运行时版本1.6而不是1.5，至少将其转换为运行模型。
https://stackoverflow.com/questions/50013828
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