neuraxle

问题 I updated Neuraxle to the latest version (3.4). I noticed the whole auto_ml.py was redone. I checked the documentation but there is nothing about it. On git it seems method RandomSearch() was replaced a long time ago by AutoML() method. However the parameters are different. Does somebody knows how can I channel Boston Housing example pipeline to automatic parameter search in latest Neuraxle version (3.4)? import numpy as np from sklearn.cluster import KMeans from sklearn.datasets import load

问题 I typically get PCA loadings like this: pca = PCA(n_components=2) X_t = pca.fit(X).transform(X) loadings = pca.components_ If I run PCA using a scikit-learn pipline ... from sklearn.pipeline import Pipeline pipeline = Pipeline(steps=[ ('scaling',StandardScaler()), ('pca',PCA(n_components=2)) ]) X_t=pipeline.fit_transform(X) ... is it possible to get the loadings? Simply trying loadings = pipeline.components_ fails: AttributeError: 'Pipeline' object has no attribute 'components_' Thanks! (Also

问题 I could not find a built-in function in Python to generate a log uniform distribution given a min and max value (the R equivalent is here), something like: loguni[n, exp(min), exp(max), base] that returns n log uniformly distributed in the range exp(min) and exp(max). The closest I found though was numpy.random.uniform. 回答1: From http://ecolego.facilia.se/ecolego/show/Log-Uniform%20Distribution: In a loguniform distribution, the logtransformed random variable is assumed to be uniformly