parsnip | A modern XML library for Android and Java | Parser library
kandi X-RAY | parsnip Summary
kandi X-RAY | parsnip Summary
A modern XML library for Android and Java.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Parses an instance of the class
- Skips next events
- Returns the field binding with the given name and namespace
- Returns the field binding for the given name and namespace
- Create an adapter for the given type
- Create an adapter method from an object
- Create an adapter method from an adapter
- Find and return an adapter for the given type
- Read tweets from an input stream
- Extract the PC data from a node
- Unmarshall the tweets
- Unmarshalls a node
- Get the element type of a collection type
- Get the generic super type
- Resolve the given type using the given context
- Initializes the activity
- Set the progress listener
- Serializes this tag into the given serializer
- Initializes the declared namespaces
- Read Tweets from input stream
- Invoked afterTaskExecute
- Returns a string representation of the statistics
- Returns an array with the key and value pairs of the given context type
- Converts a value to a media type
- Waits for the tweets
- Creates a factory for the given raw type
parsnip Key Features
parsnip Examples and Code Snippets
class BlackjackHand {
@SerializedName("HiddenCard")
public final Card hiddenCard;
@SerializedName("VisibleCard")
public final List visibleCards;
...
}
class Card {
public final char rank;
public final Suit suit;
...
}
enum Suit {
class BlackjackHand {
public final Card hiddenCard;
public final List visibleCard;
...
}
class Card {
public final char rank;
public final Suit suit;
...
}
enum Suit {
CLUBS, DIAMONDS, HEARTS, SPADES;
}
Copyright 2015 Evan Tatarka
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unle
Community Discussions
Trending Discussions on parsnip
QUESTION
I am trying to run a loop which takes different columns of a dataset as the dependent variable and remaining variables as the independent variables and run the lm command. Here's my code
...ANSWER
Answered 2022-Mar-24 at 17:53We could change the line of fit
with
QUESTION
Here's a simple modelling workflow using the palmerpenguins dataset:
...ANSWER
Answered 2022-Mar-23 at 20:49When you use
last_fit()
you fit to the training data and evaluate on the testing data. If you look at the output oflast_fit()
, the metrics and predictions are from the testing data, while the fitted workflow was trained using the training data. You can read more about using the test set.You have surfaced a bug in how we handle tuning engine-specific arguments in parsnip extension packages. I know this is inconvenient for you, but thank you for the report!
QUESTION
Requesting your help or expert opinion on a parallelization issue I am facing.
I regularly run an Xgboost classifier model on a rather large dataset (dim(train_data) = 357,401 x 281, dims after recipe prep() are 147,304 x 1159 ) for a multiclass prediction. In base R the model runs in just over 4 hours using registerDoParallel(using all 24 cores of my server). I am now trying to run it in the Tidymodels environment, however, I am yet to find a robust parallelization option to tune the grid.
I attempted the following parallelization options within tidymodels. All of them seem to work on a smaller subsample (eg 20% data), but options 1-4 fail when I run the entire dataset, mostly due to memory allocation issues.
- makePSOCKcluster(), library(doParallel)
- registerDoFuture(), library(doFuture)
- doMC::registerDoMC()
- plan(cluster, workers), doFuture, parallel
- registerDoParallel(), library(doParallel)
- future::plan(multisession), library(furrr)
Option 5 (doParallel) has worked with 100% data in the tidymodel environment, however, it takes 4-6 hours to tune the grid. I would request your attention to option 6 (future/ furrr), this appeared to be the most efficient of all methods I tried. This method however worked only once (successful code included below, please note I have incorporated a racing method and stopping grid into the tuning).
...ANSWER
Answered 2022-Mar-19 at 04:55Apparently, in tidymodels code, the parallelization happens internally, and there is no need to use furrr/future to do manual parallel computation. Moreover, the above code may be syntactically incorrect. For a more detailed explanation of why this is please see this post by mattwarkentin in the R Studio community forum.
QUESTION
ANSWER
Answered 2022-Mar-15 at 17:41As mentioned in the comment above, you can pass engine-specific arguments like penalty.factor
in set_engine()
:
QUESTION
I'm unable to deploy a tidymodel with vetiver and get a prediction when the model includes a variable with role as ID in the recipe. See the following error in the image:
{ "error": "500 - Internal server error", "message": "Error: The following required columns are missing: 'Fake_ID'.\n" }
The code for the dummy example is below. Do I need to remove the ID-variable from both the model and recipe to make the Plumber API work?
...ANSWER
Answered 2022-Mar-11 at 14:46As of today, vetiver looks for the "mold" workflows::extract_mold(rf_fit)
and only get the predictors out to create the ptype. But then when you predict from a workflow, it does require all the variables, including non-predictors. If you have trained a model with non-predictors, as of today you can make the API work by passing in a custom ptype
:
QUESTION
I want to use purrr::map_* functions to extract info from multiple models involving linear regression method. I am first creating some random dataset. The dataset has three dependent variables, and one independent variable.
...ANSWER
Answered 2022-Jan-20 at 08:40The list_tidymodels
needs to be created with list()
and not with c()
.
QUESTION
WHAT I WANT: I'm trying to fit a GAM model for classification using tidymodels
on a given data.
SO FAR: I'm able to fit a logit model.
...ANSWER
Answered 2022-Jan-12 at 23:47This problem has been fixed in the developmental version of {parsnip} (>0.1.7). You can install it by running remotes::install_github("tidymodels/parsnip")
.
QUESTION
I'm new to tidymodels but apparently the step_pca()
arguments such as nom_comp
or threshold
are not being implemented when being trained. as in example below, I'm still getting 4 component despite setting nom_comp = 2
.
ANSWER
Answered 2022-Jan-11 at 14:56If you bake
the recipe it seems to work as intended but I don't know what you aim to achieve afterward.
QUESTION
I have a monthly (Jan - Dec) data set for weather and crop yield. This data is collected for multiple years (2002 - 2019). My aim is to obtain bootstrapped slope coefficient of the affect of temperature in each month on yield gap. In bootstrapping, I want to block the year information in a way that the function should randomly sample data from a specific year in each bootstrap rather than choosing rows from mixed years.
I read some blogs and tried different methods but I am not confident about those. I tried to disect the bootstrapped splits to ensure if I am doing it correctly but I was not.
Here is the starting code:
...ANSWER
Answered 2022-Jan-08 at 04:19We don't currently have support for grouped or blocked bootstrapping; we are tracking interest in more group-based methods here.
If you want to create a resampling scheme that holds out whole groups of data, you might check out group_vfold_cv()
(maybe together with nested_cv()
?) to see if it fits your needs in the meantime. It results in a resampling scheme that looks like this:
QUESTION
I want to use xgboost
for a classification problem, and two predictors (out of several) are binary columns that also happen to have some missing values. Before fitting a model with xgboost
, I want to replace those missing values by imputing the mode in each binary column.
My problem is that I want to do this imputation as part of a tidymodels
"recipe". That is, not using typical data wrangling procedures such as dplyr
/tidyr
/data.table
, etc. Doing the imputation within a recipe should guard against "information leakage".
Although the recipes
package provides many step_*()
functions that are designed for data preprocessing, I could not find a way to do the desired imputation by mode on numeric binary columns. While there is a function called step_impute_mode()
, it accepts only nominal variables (i.e., of class factor
or character
). But I need my binary columns to remain numeric so they could be passed to the xgboost
engine.
Consider the following toy example. I took it from this reference page and changed the data a bit to reflect the problem.
create toy data
...ANSWER
Answered 2021-Dec-25 at 07:37Credit to user @gus who answered here:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install parsnip
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page