pipeline

How to do Onehotencoding in Sklearn Pipeline

99封情书 提交于 2019-12-04 08:35:14
问题 I am trying to oneHotEncode the categorical variables of my Pandas dataframe, which includes both categorical and continues variables. I realise this can be done easily with the pandas .get_dummies() function, but I need to use a pipeline so I can generate a PMML-file later on. This is the code to create a mapper. The categorical variables I would like to encode are stored in a list called 'dummies'. from sklearn_pandas import DataFrameMapper from sklearn.preprocessing import OneHotEncoder

Why du or echo pipelining is not working?

删除回忆录丶 提交于 2019-12-04 06:48:20
I'm trying to use du command for every directory in the current one. So I'm trying to use code like this: ls | du -sb But its not working as expected. It outputs only size of current '.' directory and thats all. The same thing is with echo ls | echo Outputs empty line. Why is this happening? FatalError Using a pipe sends the output ( stdout ) of the first command, to stdin (input) of the child process (2nd command). The commands you mentioned don't take any input on stdin . This would work, for example, with cat (and by work, I mean work like cat run with no arguments, and just pass along the

How to fit different inputs into an sklearn Pipeline?

喜你入骨 提交于 2019-12-04 05:19:24
I am using Pipeline from sklearn to classify text. In this example Pipeline I have a TfIDF vectorizer and some custom features wrapped with FeatureUnion and a classifier as the Pipeline steps, I then fit the training data and do the prediction: from sklearn.pipeline import FeatureUnion, Pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC X = ['I am a sentence', 'an example'] Y = [1, 2] X_dev = ['another sentence'] # load custom features and FeatureUnion with Vectorizer features = [] measure_features = MeasureFeatures() # this class includes my

What's the difference between -> and |> in reasonml?

坚强是说给别人听的谎言 提交于 2019-12-04 02:55:50
A period of intense googling provided me with some examples where people use both types of operators in one code, but generally they look just like two ways of doing one thing, they even have the same name tl;dr: The defining difference is that -> pipes to the first argument while |> pipes to the last. That is: x -> f(y, z) <=> f(x, y, z) x |> f(y, z) <=> f(y, z, x) Unfortunately there are some subtleties and implications that makes this a bit more complicated and confusing in practice. Please bear with me as I try to explain the history behind it. Before the age of pipe Before there were any

Require tree in asset pipeline

*爱你&永不变心* 提交于 2019-12-03 15:56:35
I have a folder in my asset pipeline called typefaces. It works without any additions to application.rb . In the directory I have different typeface types, like .eof, .ttf, etc in folders, like this Assets Typefaces Eof ...files Ttf ...files Unless the typefaces are in Assets/typefaces they don't become part of asset pipeline. Asset pipeline doesn't go into the subdirectories. How would I have asset pipeline look beyond assets/typefaces into assets/typefaces/eof, assets/typefaces/ttf etc? In your app/assets/javascripts/application.js file, try putting: //= require_tree ../Typefaces See more:

How to implement SMOTE in cross validation and GridSearchCV

不羁的心 提交于 2019-12-03 12:45:35
问题 I'm relatively new to Python. Can you help me improve my implementation of SMOTE to a proper pipeline? What I want is to apply the over and under sampling on the training set of every k-fold iteration so that the model is trained on a balanced data set and evaluated on the imbalanced left out piece. The problem is that when I do that I cannot use the familiar sklearn interface for evaluation and grid search. Is it possible to make something similar to model_selection.RandomizedSearchCV . My

return coefficients from Pipeline object in sklearn

痴心易碎 提交于 2019-12-03 11:50:50
I've fit a Pipeline object with RandomizedSearchCV pipe_sgd = Pipeline([('scl', StandardScaler()), ('clf', SGDClassifier(n_jobs=-1))]) param_dist_sgd = {'clf__loss': ['log'], 'clf__penalty': [None, 'l1', 'l2', 'elasticnet'], 'clf__alpha': np.linspace(0.15, 0.35), 'clf__n_iter': [3, 5, 7]} sgd_randomized_pipe = RandomizedSearchCV(estimator = pipe_sgd, param_distributions=param_dist_sgd, cv=3, n_iter=30, n_jobs=-1) sgd_randomized_pipe.fit(X_train, y_train) I want to access the coef_ attribute of the best_estimator_ but I'm unable to do that. I've tried accessing coef_ with the code below. sgd

Asset Pipeline/Framework for PHP

随声附和 提交于 2019-12-03 11:48:29
Background I am working on "modernizing" a pre-existing PHP-driven website. This website started out as a static website with a few php methods. It now has a mobile web app, multiple models, and a lot of dynamic content. However, overtime the structure of the app itself hasn't changed much from when it was a largely static site, so now there are include files all over the place, no separation of application/presentation logic, etc etc. It is a mess to work on. So I am reorganizing everything and redeveloping a lot of the pre-existing functionality as we prepare for upcoming upgrades to the

What is a good tool for Build Pipelines?

淺唱寂寞╮ 提交于 2019-12-03 09:40:55
问题 I need a tool that will graphically represent our build pipeline. The below screenshots of ThoughtWorks Go and the Jenkins Pipeline plugin illustrate almost exactly what I want it to look like. The problem is that we already use Jenkins for our builds and deployments, along with a few other custom tools for orchestration type duties. We don't want a pipeline tool to do the builds or deployments itself, it just needs to invoke Jenkins! I tried out Go, and the first thing it asked for is where

Invalid parameter for sklearn estimator pipeline

本秂侑毒 提交于 2019-12-03 09:18:58
问题 I am implementing an example from the O'Reilly book " Introduction to Machine Learning with Python ", using Python 2.7 and sklearn 0.16. The code I am using: pipe = make_pipeline(TfidfVectorizer(), LogisticRegression()) param_grid = {"logisticregression_C": [0.001, 0.01, 0.1, 1, 10, 100], "tfidfvectorizer_ngram_range": [(1,1), (1,2), (1,3)]} grid = GridSearchCV(pipe, param_grid, cv=5) grid.fit(X_train, y_train) print("Best cross-validation score: {:.2f}".format(grid.best_score_)) The error