Question

我是机器学习世界的新手，我使用ScikitLearn库构建并训练了一个ml模型。它在Jupyter笔记本中运行得非常好但是当我将此模型部署到Google Cloud ML并尝试使用它时一个Python脚本，它会抛出一个错误。

这是我的模型代码的片段：

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 ))

以及Jupyter笔记本中的输出：

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)

以下是此脚本的输出：

预测失败：在sklearn预测期间出现异常：＆＃39; LocalOutlierFactor＆＃39;对象没有属性＆＃39;预测＆＃39;

Answer 1

LocalOutlierFactor没有predict方法，只有私有_predict方法。以下是来源的理由。

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

Answer 2

看起来这可能是一个Python版本的东西（虽然我不清楚为什么scikit学习在Python 2和Python 3中表现不同）。我能够在同一台机器上进行本地验证 - 我的Python 2安装会在Python 3成功时重现上述错误（两者都使用sci-kit learn 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＆＃34; 2.7＆＃34;）：

gcloud beta ml-engine versions create $VERSION_NAME \
    --model $MODEL_NAME --origin $DEPLOYMENT_SOURCE \
    --runtime-version="1.5" --framework $FRAMEWORK
    --python-version="3.5"

Answer 3

令人惊讶的是，问题是runtime version，当您重新创建模型版本时，它将被解决：

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 ，至少将其转为正在运行的模型。

Answer 4

我参与了一个看似非常合情合理的项目。我得到了同样的错误。我的问题是if语句中的拼写错误。

此致洛伦茨

ScikitLearn模型给出了LocalOutlierFactor＆＃39;对象没有属性＆＃39;预测＆＃39;错误

4 个答案: