pipeline

问题 When running an Azure Powershell task in an Azure DevOps Release Pipeline with system.debug=true, you will get an output similar to this: # anonymized ... 2019-09-05T12:19:41.8983585Z ##[debug]INPUT_CONNECTEDSERVICENAMEARM: '7dd40b2a-1c37-4c0a-803e-9b0044a8b54e' 2019-09-05T12:19:41.9156487Z ##[debug]ENDPOINT_URL_7dd40b2a-1c37-4c0a-803e-9b0044a8b54e: 'https://management.azure.com/' 2019-09-05T12:19:41.9188051Z ##[debug]ENDPOINT_AUTH_7dd40b2a-1c37-4c0a-803e-9b0044a8b54e: '********' 2019-09

问题 I have a C++ project that is compiled and packaged for multiple OS (Linux, Windows, MacOS) as well as multiple CPU architectures (i386, x86_64, arm, Aarch64) For this I'm using Jenkins to grab the source code and run the build script in parallel on each system. It's a simple working solution, since my build script deals with the system differences. Now I'm looking into Gitlab CI/CD, and it has many things I find appealing ( being able to keep the build script as part of the repository, very

问题 I want to get feature names after I fit the pipeline. categorical_features = ['brand', 'category_name', 'sub_category'] categorical_transformer = Pipeline(steps=[ ('imputer', SimpleImputer(strategy='constant', fill_value='missing')), ('onehot', OneHotEncoder(handle_unknown='ignore'))]) numeric_features = ['num1', 'num2', 'num3', 'num4'] numeric_transformer = Pipeline(steps=[ ('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())]) preprocessor = ColumnTransformer(

问题 I want to look into how latest processors differs from standard RISC V implementation (RISC V having 5 stage pipeline - fetch, decode, memory , ALU , Write back) but not able to find how should I start approaching the problem so as to find the current implementation of pipelining at processor I tried referring Intel documentation for i7-4510U documentation but it was not much help 回答1: Haswell's pipeline length is reportedly 14 stages (on a uop-cache hit), 19 stages when fetching from L1i for

问题 EDIT: the question remains the same but the code has changed. I am working on the home credit dataset on Kaggle and specifically on instalment_payment.csv. Following are my custom transformers class Xfrmer_replace1(BaseEstimator, TransformerMixin): """ this transformer does the global repplace within the dataframe replace 365243 spcific to this case study with 0 replace +/-inf , nan with zero """ # constructor def __init__(self): #we are not going to use this self._features = None #Return

问题 EDIT: the question remains the same but the code has changed. I am working on the home credit dataset on Kaggle and specifically on instalment_payment.csv. Following are my custom transformers class Xfrmer_replace1(BaseEstimator, TransformerMixin): """ this transformer does the global repplace within the dataframe replace 365243 spcific to this case study with 0 replace +/-inf , nan with zero """ # constructor def __init__(self): #we are not going to use this self._features = None #Return

问题 EDIT: the question remains the same but the code has changed. I am working on the home credit dataset on Kaggle and specifically on instalment_payment.csv. Following are my custom transformers class Xfrmer_replace1(BaseEstimator, TransformerMixin): """ this transformer does the global repplace within the dataframe replace 365243 spcific to this case study with 0 replace +/-inf , nan with zero """ # constructor def __init__(self): #we are not going to use this self._features = None #Return

问题 I found out how to create input parameters dynamically from this SO answer agent any stages { stage("Release scope") { steps { script { // This list is going to come from a file, and is going to be big. // for example purpose, I am creating a file with 3 items in it. sh "echo \"first\nsecond\nthird\" > ${WORKSPACE}/list" // Load the list into a variable env.LIST = readFile (file: "${WORKSPACE}/list") // Show the select input env.RELEASE_SCOPE = input message: 'User input required', ok:

问题 Consider a situation where a task depends on another through a dynamic dependency: import luigi from luigi import Task, TaskParameter, IntParameter class TaskA(Task): parent = TaskParameter() arg = IntParameter(default=0) def requires(self): return self.parent() def run(self): print(f"task A arg = {self.arg}") class TaskB(Task): arg = IntParameter(default=0) def run(self): print(f"task B arg = {self.arg}") if __name__ == "__main__": luigi.run(["TaskA", "--parent" , "TaskB", "--arg", "1", "-

问题 As part of a research project, I want to select the best combination of preprocessing techniques and textual features that optimize the results of a text classification task. For this, I am using Python 3.6. There are a number of methods to combine features and algorithms, but I want to take full advantage of sklearn's pipelines and test all the different (valid) possibilities using grid search for the ultimate feature combo. My first step was to build a pipeline that looks like the following