Questions tagged [pipelines]
A pipeline is a sequence of functions (or the equivalent thereof), composed so that the output of one is input for the next, in order to create a compound transformation. Famously, a shell pipeline looks like "command | command2 | command3" (but use the tag "pipe" for this). It's also used in computer architecture to define a sequence of serial stages that execute in parallel over elements being fed into a pipe, in order to increase the overall throughput.
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Sklearn pipelines, applying transformations to feature engineered columns in previous steps
I am working on a sklearn pipeline for my binary classification problem. The pipeline should perform typical things like downcasting data types, scaling numerical values, one-hot and ordinal encoding ...
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Memory Error when loading a txt file for an ML model
I am trying to run the Python code below:
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Creating new features as linear combination of others as part of a scikit-learn pipeline?
I have a number of raw features that go into a scikit-learn model. I've already got a number of preprocessing steps (such as PolynomialFeatures) that creates additional features as part of my pipeline....
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Optimization of the entire model development process
I want to perform a global optimization of the entire model development pipeline. I have several stages of development, each of which can be performed automatically: preprocessing, removal of outliers/...
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How do SciKit-Learn Pipelines pass Data between Steps?
I would like to create some Custom Transformers and incorporate them in a SciKit-Learn Pipeline.
I'd like to pass more than just a Dataframe between the transformer ...
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Does Scikit-Learn's OneHotEncoder make all Columns Categorical?
I've been using Scikit-Learn's OneHotEncoder to turn categorical data into binary columns, however, it seems that fitting ...
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How to solve mismatched code dependency issues between two or more different ML models/structures/frameworks?
I'm fairly new to machine learning. Currently I am trying to build a pipeline that uses two established NLP models. One is BERT that is fairly easy to load and quite structured. Another is FLAIR which ...
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How to make a pipeline for Videos Dataset for TensorFlow [Sequence Matters] & train Model Effectively with Low Memory System
I am working on a Deep Learning project and I am facing an issue with the size of the dataset. I want to make a pipeline for video dataset [Sequence Matters]. Because if I try the load the whole ...
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Combining sklearn pipelines with different output shape
As part of a data preprocessing step, I'm trying to create a "master pipeline" from two separate pipelines, one for numerical features and one for datetime features. The numerical pipeline ...
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Sklearn pipeline and custom transformers to remove specific value from columns
I'm trying to use sklearn pipelines and custom transformers to do outlier removal. What I want to do is identify outliers using an IQR-filter, set the outlier values to 'OUTLIER' (not NaN), and then ...
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Data type conversion as part of sklearn pipeline
I'm currently learning and implementing sklearn pipelines. As an early step in the pipeline, I want to convert some data types that are wrong from the import (primarily object to numerical and object ...
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Storing meta information from preprocessing step
Let's say I have a preprocessing step which removes some columns, replaces rare categories with the keyword "Other" and so on. All of that is done based on an initial data analysis.
Now, I ...
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Most common transformations in an sklearn pipeline?
I'm wondering if there is a list anywhere of the most commonly used transformations that people use in a machine learning pipeline - particularly in Python and scikit-learn.
Additionally, are there ...
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SkLearn DecisionTree doesn't include numerical variables after one hot encoding pipeline
I'm trying to fit a dataframe with SkLearn DecisionTree with the following code. But I get a error Length of feature_names, 9 does not match number of features, 8. ...
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Error getting prediction explanation using shap_values when using scikit-learn pipeline?
I am building an NLP model to predict language type (C/C++/C#/Python...) for a given code.
Now I need to provide an explanation for my model prediction. For example the following user_input is written ...
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When I use imblearn pipeline instead of sklearn pipeline all textual features disappear. Any solution?
This is my code below, I need to use SMOTENC to balance the dataset, which means I have to use the pipeline from the imblearn library. However, it does not recognize the CountVectorizer features
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Need an example of a custom class whose instance is fed to sklearn Pipeline / make_pipeline to use with GridSearchCV
According to sklearn.pipeline.Pipeline documentation, the class whose instance is a pipeline element should implement fit() and transform(). I managed to create a custom class that has these methods ...
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Can anyone tell me why is my pipeline wrong?
I am trying to build a pipeline in order to perform GridSearchCV to find the best parameters. I already split the data into train and validation and have the following code:
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I cannot work out the benefits of a pipeline over a linear sequence of code instructions
I've looked at quite a number of how to 'create a pipeline' instructions, but I have yet to see an explanation of the benefits over what I am showing below.
To keep my example code agnostic I'll use ...
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Dynamic creation of sklearn pipeline
I am trying to create an automatic pipeline builder functionality that takes into account a large set of conditions such as the existence of missing values, the scale of numerical features, etc., and ...
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How to retrain sklearn pipeline with new data?
I have trained and saved a data processing pipeline and an LGBM regressor on 3 months of historical data. Now I know that I can retrain the LGBM regressor on new data every day by passing my trained ...
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Retrieving the ordinal encoding of a variable after it's placed in a pipeline/columntransformer
I am applying ordinal encoding to a dataset through a column transformer - how can I retrieve the ordinal encoding of a feature (e.g. Area)?
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Does sklearn.pipeline have a single mechanism for cross-validation regardless of model API?
With a single standard interface (sklearn.pipeline) on top of different regressors, how do I use cross-validation?
The example below uses two regressors with different internal cross-validation ...
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Custom vectorizer transformer in sklearn with cross validation
I created a custom transformer class called Vectorizer() that inherits from sklearn's ...
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Explaining the logic behind the pipe_line method for cross-validation of imbalance datasets
Reading the following article: https://kiwidamien.github.io/how-to-do-cross-validation-when-upsampling-data.html
There is an explanation of how to use ...
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How to combine preprocessor/estimator selection with hyperparameter tuning using sklearn pipelines?
I'm aware of how to use sklearn.pipeline.Pipeline() for simple and slightly more complicated use cases alike. I know how to set up pipelines for homogeneous as well ...
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Custom preprocessing using piplines
I have searched a lot for this issue but unfortunately came up with nothing.
Usually in a ML model, during preprocessing, we use Pipelines and ...
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ColumnTransformer worse performance than sklearn pipeline
I have an (unbalanced , binary data) pipeline model consisting of two pipelines (preprocessing and the actual model). Now I wanted to include SimpleImputer into my ...
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Deep Neural Network Model in sklearn Pipeline
Is it possible to add a deep neural network model as the estimator/model in an sklearn Pipeline? or is it only possible for ML models as the estimator.
For example, ...
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Shap after scikit pipeline - feature name
I'm using pipeline to transform data and predict model and I want to apply SHAP after that. However, when I apply it, it returns SHAP chart just fine, but the name of the feature are like feature 1, ...
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How can I get the dataframe after scikit pipeline?
I'm making many data transformations and fitting a model using scikit pipeline, but I need to extract X_train and X_test right after transformations (imputer, encoding, etc) in order to use it for ...
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when I only give command 'fit', my class does 'transform' too
I have created 2 classes, first of which is:
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What can I do when my test and validation scores are good, but the submission is terrible?
This is a very broad question, I understand and I'm totally fine if someone believes it's not appropriate to do it. But it's killing me not to understand this...
Here's the thing, I'm doing a machine ...
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AttributeError: 'numpy.ndarray' object has no attribute 'fit' [closed]
I am relatively new to ML and in the process of learning pipelines.
I am creating a pipeline of custom transformers and get this error:
AttributeError: 'numpy.ndarray' object has no attribute 'fit'. ...
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How to get column names after One Hot Encoding when using Pipelines?
I am using Pipeline and ColumnTransformer to preprocess the data. Basically I am using them to impute null values, scale the ...
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unsupervised anomaly detection for univariate fast frequency time series data?
I have a univariate time series (there is a value for each time sampling) (sampling time: 66.66 micro second, number of samples/sampling time=151) coming from a scala customer
This time series ...
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determining size of batch, time of sending and memory in to send from scala to ML section
I have a time series (sampling time: 66.66 micro second, number of samples/sampling time=151), I would like to determine some anomalies in them, the inputs are made by scala customer message bus.
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Pipelines with categorical and nan values
I am trying a Regression model on a dataset which has categorical and numerical variables along with nan values. I want to use Pipelines for imputation and encoding purposes. Now I have a few ...
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Alternative to EC2 for running ML batch training jobs on AWS
We are building an ML pipeline on AWS, which will obviously require some heavy-compute components including preprocessing and batch training.
Most the the pipeline is on Lambda, but Lambda is known to ...
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Data Pipeline Best Practices
I am looking more for concepts or pointed in the right direction for best practices for a project I am working on. I am currently playing around with Luigi for a data pipeline. I followed the tutorial ...
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TPOT machine learning
I trained a regression TPOT algorithm on Google Colab, where the output of the TPOT process is some boiler plate Python code as shown below.
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Working with others: Tidy data vs Pretty data [closed]
When working with someone whose background and skill level in data work may not be strong, how do you best make the argument for tidy data over "pretty" data?
There are notes of what I want ...
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How to use SMOTE in Stacking in SKLearn?
I have a data set X,y and split them to train and test data.
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Why GridSearchCV returns nan?
I am using gridsearchcv to tune the parameters of my model and I also use pipeline and cross-validation. When I run the model to tune the parameter of XGBoost, it returns ...
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cost-complexity-pruning-path with pipeline
I'm using Kaggle's titanic set. I'm using pieplines and I'm trying to prune my decision tree and for that I want the cost_complexity_pruning_path. The last line of code produces the error:
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How to remove features from a sklearn pipeline after it has already been fitted?
Background:
I have created a basic modeling workflow in sklearn that utilizes sklearn's pipeline object. There are some preprocessing steps within the pipeline, and the last step of the pipeline is to ...
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Data Lineage/Traceability in Pipelines
I want to collect information about: 1) from which single data signals a feature is composed in a ML pipeline and 2) what data preprocessing operations are/were executed on a data signal.
Does anyone ...
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