lasso | Advanced JavaScript module bundler , asset pipeline | Build Tool library
kandi X-RAY | lasso Summary
kandi X-RAY | lasso Summary
Advanced JavaScript module bundler, asset pipeline and optimizer
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Top functions reviewed by kandi - BETA
- Create a new mongoose instance
- Load configuration from a file
- Walk the manifest tree .
- Initialize a new Stream .
- Resolves a Lasso .
- Promis readable stream
- Build the bundle tree
- Creates a readStream for a given deprecationContext .
- Create a read stream for a given bundle .
- Read a value from a stream
lasso Key Features
lasso Examples and Code Snippets
Lines of Code : 8License : Permissive (MIT)
Copyinterface LassoHandlerOptions {
polygon?: L.PolylineOptions,
intersect?: boolean;
}
const lasso = L.lasso(map, options);
yourCustomButton.addEventListener('click', () => {
lasso.enable();
});
var lasso = d3.lasso(); // creates a new lasso
lasso.items(d3.selectAll("circle")); // sets all circles on the page to be lasso-able
lasso.hoverSelect(true); // allows hovering of elements for selection during lassoing
lasso.closePathSelect(true); var lasso = d3.lasso()
.items(d3.selectAll("circle")) // Create a lasso and provide it some target elements
.targetArea(de.select("#myLassoRect")); // Sets the drag area for the lasso on the rectangle #myLassoRect
d3.s from sklearn.linear_model import Lasso
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline, Pipeline
Pipeline([
('PolynomialFeatures', PolynomialFeatures(include_bias=False)),
('Lasso',#Importing libraries.
import numpy as np
import pandas as pd
import random
import matplotlib.pyplot as plt
from sklearn.linear_model import Lasso
#Define input array with angles from 60deg to 300deg converted to radians
x = np.array([i*n// Lasso functions
var lasso_start = function() {
lasso.items()
.attr("r", 8) // reset size
.classed("not_possible", true)
.classed("selected", false);
};
var lasso_draw = function() {
// Style the possible dots
lasso.p
import * as d3lasso from 'd3-lasso';
declare var d3;
export class UsercomponentComponent implements OnInit {
ngOnInit() {
this.getLasso();
}
getLasso() {
var data = new Array(100).fill(null).map(m=>[Math.random(),Math.randomfrom sklearn.linear_model import Lasso
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
def l1_regularization(x):
...
Lasso (x)
...return x
def l2_regularization(x):
...
var lasso = d3.lasso()
.targetArea(svg)
var circles = svg.selectAll("circle")...
var lasso = d3.lasso()
.items(circles)
.selected {
fill: steelblue;
}
selected.data().map(....
var selected = lasso.selectedItems()...
path {
fill: #ccc;
opacity: 0.4;
stroke: black;
stroke-width: 2px;
}
Community Discussions
Trending Discussions on lasso
QUESTION
I have multiple datasets with different lengths. I want to apply a correlation function to delete correlated variables with 98%. How can I use a loop to apply a correlation function on multiple datasets in the same time and store the variables selected in new dataframes?
How can I also use lasso regression on multiple datasets, also using loop functions? Thank you
...ANSWER
Answered 2022-Jan-10 at 15:52Here's one way (of several) to do this:
QUESTION
I am trying to replicate my lambda function into my pipeline
...ANSWER
Answered 2022-Jan-09 at 07:22The first issue is actually independent from the ColumnTransformer usage and it is due to a bug in method transform's implementation in your HealthyAttributeAdder class.
In order to get a consistent result you should modify line
QUESTION
I am new to Python. I am trying to practice basic regularization by following along with a DataCamp exercise using this CSV: https://assets.datacamp.com/production/repositories/628/datasets/a7e65287ebb197b1267b5042955f27502ec65f31/gm_2008_region.csv
...ANSWER
Answered 2021-Nov-24 at 09:45When you set Lasso(..normalize=True) the normalization is different from that in StandardScaler(). It divides by the l2-norm instead of the standard deviation. If you read the help page:
normalize bool, default=False This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use StandardScaler before calling fit on an estimator with normalize=False.
Deprecated since version 1.0: normalize was deprecated in version 1.0 and will be removed in 1.2.
It is also touched upon in this post. Since it will be deprecated, I think it's better to just use the StandardScaler normalization. You can see it's reproducible as long as you scale it in the same way:
QUESTION
I am trying code from this page. I ran up to the part LR (tf-idf) and got the similar results
After that I decided to try GridSearchCV. My questions below:
1)
...ANSWER
Answered 2021-Dec-09 at 23:12You end up with the error with precision because some of your penalization is too strong for this model, if you check the results, you get 0 for f1 score when C = 0.001 and C = 0.01
QUESTION
Anti-closing preamble: I have read the question "difference between penalty and loss parameters in Sklearn LinearSVC library" but I find the answer there not to be specific enough. Therefore, I’m reformulating the question:
I am familiar with SVM theory and I’m experimenting with LinearSVC class in Python. However, the documentation is not quite clear regarding the meaning of penalty and loss parameters. I recon that loss refers to the penalty for points violating the margin (usually denoted by the Greek letter xi or zeta in the objective function), while penalty is the norm of the vector determining the class boundary, usually denoted by w. Can anyone confirm or deny this?
If my guess is right, then penalty = 'l1' would lead to minimisation of the L1-norm of the vector w, like in LASSO regression. How does this relate to the maximum-margin idea of the SVM? Can anyone point me to a publication regarding this question? In the original paper describing LIBLINEAR I could not find any reference to L1 penalty.
Also, if my guess is right, why doesn't LinearSVC support the combination of penalty='l2' and loss='hinge' (the standard combination in SVC) when dual=False? When trying it, I get the
...ValueError: Unsupported set of arguments
ANSWER
Answered 2021-Nov-18 at 18:08Though very late, I'll try to give my answer. According to the doc, here's the considered primal optimization problem for LinearSVC:
,phi being the Identity matrix, given that LinearSVC only solves linear problems.
Effectively, this is just one of the possible problems that LinearSVC admits (it is the L2-regularized, L1-loss in the terms of the LIBLINEAR paper) and not the default one (which is the L2-regularized, L2-loss).
The LIBLINEAR paper gives a more general formulation for what concerns what's referred to as loss in Chapter 2, then it further elaborates also on what's referred to as penalty within the Appendix (A2+A4).
Basically, it states that LIBLINEAR is meant to solve the following unconstrained optimization pb with different loss functions xi(w;x,y) (which are hinge and squared_hinge); the default setting of the model in LIBLINEAR does not consider the bias term, that's why you won't see any reference to b from now on (there are many posts on SO on this).
- ,
hingeor L1-loss - ,
squared_hingeor L2-loss.
For what concerns the penalty, basically this represents the norm of the vector w used. The appendix elaborates on the different problems:
- L2-regularized, L1-loss (
penalty='l2',loss='hinge'): - L2-regularized, L2-loss (
penalty='l2',loss='squared_hinge'), default inLinearSVC: - L1-regularized, L2-loss (
penalty='l1',loss='squared_hinge'):
Instead, as stated within the documentation, LinearSVC does not support the combination of penalty='l1' and loss='hinge'. As far as I see the paper does not specify why, but I found a possible answer here (within the answer by Arun Iyer).
Eventually, effectively the combination of penalty='l2', loss='hinge', dual=False is not supported as specified in here (it is just not implemented in LIBLINEAR) or here; not sure whether that's the case, but within the LIBLINEAR paper from Appendix B onwards it is specified the optimization pb that's solved (which in the case of L2-regularized, L1-loss seems to be the dual).
For a theoretical discussion on SVC pbs in general, I found that chapter really useful; it shows how the minimization of the norm of w relates to the idea of the maximum-margin.
QUESTION
I've been trying to fit a system of differential equations to some data I have and there are 18 parameters to fit, however ideally some of these parameters should be zero/go to zero. While googling this one thing I came across was building DE layers into neural networks, and I have found a few Github repos with Julia code examples, however I am new to both Julia and Neural ODEs. In particular, I have been modifying the code from this example:
https://computationalmindset.com/en/neural-networks/experiments-with-neural-odes-in-julia.html
Differences: I have a system of 3 DEs, not 2, I have 18 parameters, and I import two CSVs with data to fit that instead of generate a toy dataset to fit.
My dilemma: while goolging I came across LASSO/L1 regularization and hope that by adding an L1 penalty to the cost function, that I can "zero out" some of the parameters. The problem is I don't understand how to modify the cost function to incorporate it. My loss function right now is just
...ANSWER
Answered 2021-Nov-10 at 22:54I've been messing with this, and looking at some other NODE implementations (this one in particular) and have adjusted my cost function so that it is:
QUESTION
I'm trying to use R's caret and glmnet packages to run LASSO to determine the best predictors for a binary outcome of interest.
I get all the way to checking the trained model's performance (pulling root mean squared error and R-squared values from the predictions), and I get the following error:
Error in cor(obs, pred, use = ifelse(na.rm, "complete.obs", "everything")) : 'x' must be numeric
Will anyone please help me figure out why my code is throwing this error? How can I successfully pull the RMSE and R^2 values?
The example code below throws the same error. I'm including all my steps, so you can see how I'm thinking through the LASSO regression. If you want to skip to the end, the final chunk is the problem.
...ANSWER
Answered 2021-Oct-18 at 19:32This happens just because RMSE and R-squared are meaningless for factor outcomes. You have to use caret::confusionMatrix or convert factor to integer (not a so good option in my opinion):
QUESTION
I can match each row with each diciotnary key but I am wondering if there's a way I can get the related value (string) in a different column as well.
...ANSWER
Answered 2021-Oct-15 at 12:58Use DataFrame.stack with convert first level to column by reset_index, so possible join values in GroupBy.agg, for unique values in order is used dict.fromkeys trick:
QUESTION
I want to loop ridge & lasso for 100 times to get 100 mse and mspe. My final goal is draw a boxplot to compare those 100 values. I made one regression model but I don't know how to repeat this model. How could I get the values and boxplots?
...ANSWER
Answered 2021-Oct-04 at 08:11You can try the following:
QUESTION
I want use AIC & BIC to select the parameter alpha for lasso. However sklearn only has LassoLarsIC to do this which does not accept sparse matrix and thus does not fit my case. As a result I decide to use GridSearchCV and create a customized scorer. Below is my try:
ANSWER
Answered 2021-Oct-05 at 16:34The output of make_scorer (and the expected form of a scoring method for a grid search) is a callable with signature estimator, X, y; you should skip make_scorer and define such a callable directly. Then you can use the estimator's fitted attribute coefs_ directly. (The greater_is_better=False option of make_scorer just negates the score, so you should probably define this alternate custom scorer as negative BIC.)
Note however that in a GridSearchCV, you'll always be computing the score on the test folds, which deviates from the intention behind BIC.
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