pipeline

问题 I am attempting to use a pipeline to feed an ensemble voting classifier as I want the ensemble learner to use models that train on different feature sets. For this purpose, I followed the tutorial available at [1]. Following is the code that I could develop so far. y = df1.index x = preprocessing.scale(df1) phy_features = ['A', 'B', 'C'] phy_transformer = Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())]) phy_processer = ColumnTransformer

问题 I am attempting to use a pipeline to feed an ensemble voting classifier as I want the ensemble learner to use models that train on different feature sets. For this purpose, I followed the tutorial available at [1]. Following is the code that I could develop so far. y = df1.index x = preprocessing.scale(df1) phy_features = ['A', 'B', 'C'] phy_transformer = Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())]) phy_processer = ColumnTransformer

问题 I am trying to stream the data from a mongodb cursor into an s3 file using a nodejs lambda. Following is a snippet of my code. What i observe is that the lambda does not wait for the pipeline to complete and exists it, so the file is not written to s3. But the same works fine if I run it as a standalone node.js script. const logger = require('./logger').logger; let s3Client = require('aws-sdk/clients/s3'); const stream = require('stream'); const util = require('util'); const pipeline = util

问题 I have two environments on azure. One difference between them is only environment variables that came from variable groups. Is it possible to set up group name dynamically for one pipeline instead of set up two pipelines that can map their own group variables? It is an example of my build pipeline trigger: - master - develop jobs: - job: DefineVariableGroups steps: - script: | if [ $(Build.SourceBranch) = 'refs/heads/master' ]; then echo "##vso[task.setvariable variable=group_name_variable

问题 I have a data set with categorical and numerical features on which I want to apply some transformations followed by XGBClassifier. Link to data set : https://www.kaggle.com/blastchar/telco-customer-churn As the transformations are different for the numerical and categorical features, I used sklearn_pandas and its DataFrameMapper. To perform one-hot encoding on the categorical features, I want to use DictVectorizer. But to use DictVectorizer, I first need to convert the dataframe into a dict,

问题 I have a data set with categorical and numerical features on which I want to apply some transformations followed by XGBClassifier. Link to data set : https://www.kaggle.com/blastchar/telco-customer-churn As the transformations are different for the numerical and categorical features, I used sklearn_pandas and its DataFrameMapper. To perform one-hot encoding on the categorical features, I want to use DictVectorizer. But to use DictVectorizer, I first need to convert the dataframe into a dict,

问题 I have a data for a regression task. The independent features( X_train ) are scaled with a standard scaler. Built a Keras sequential model adding hidden layers. Compiled the model. Then fitting the model with model.fit(X_train_scaled, y_train ) Then I saved the model in a .hdf5 file. Now how to include the scaling part inside the saved model, so that the same scaling parameters can be applied to unseen test data. #imported all the libraries for training and evaluating the model X_train, X

问题 I'm trying to implement a pipeline on azure data-factory where a I get access to datas from ODATA. Taken these I implement a pipeline where azure DF copies these on a blob storage. For what concerns access and permissions there is no problem. The issue rises up when I want to get a preview of what I'm going to store inside my blob storage. The reported error is: A supported MIME type could not be found that matches the content type of the response. None of the supported type(s) ... Don't ask

问题 In my classification scheme, there are several steps including: SMOTE (Synthetic Minority Over-sampling Technique) Fisher criteria for feature selection Standardization (Z-score normalisation) SVC (Support Vector Classifier) The main parameters to be tuned in the scheme above are percentile (2.) and hyperparameters for SVC (4.) and I want to go through grid search for tuning. The current solution builds a "partial" pipeline including step 3 and 4 in the scheme clf = Pipeline([('normal'

问题 In my classification scheme, there are several steps including: SMOTE (Synthetic Minority Over-sampling Technique) Fisher criteria for feature selection Standardization (Z-score normalisation) SVC (Support Vector Classifier) The main parameters to be tuned in the scheme above are percentile (2.) and hyperparameters for SVC (4.) and I want to go through grid search for tuning. The current solution builds a "partial" pipeline including step 3 and 4 in the scheme clf = Pipeline([('normal'