regressor | Generate specs for your rails application | Testing library
kandi X-RAY | regressor Summary
kandi X-RAY | regressor Summary
Regressor is a regression based testing tool. What is regression testing? see here. You can generate specs based on your ActiveRecord models. Made with at Qurasoft.
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Top functions reviewed by kandi - BETA
- Called when the RSpec has been loaded .
- Load an array of models from the model
- Create the regex patterns .
- Create the RSpec file .
- Load the application .
regressor Key Features
regressor Examples and Code Snippets
Lines of Code : 29License : Permissive (MIT License)
Copydef main():
"""
Random Forest Regressor Example using sklearn function.
Boston house price dataset is used to demonstrate the algorithm.
"""
# Load Boston house price dataset
boston = load_boston()
print(boston.keys())
def _add_loss_reduction_transformer(parent, node, full_name, name, logs):
"""Adds a loss_reduction argument if not specified.
Default value for tf.estimator.*Classifier and tf.estimator.*Regressor
loss_reduction argument changed to SUM_OVER_BA def support_vector_regressor(x_train: list, x_test: list, train_user: list) -> float:
"""
Third method: Support vector regressor
svr is quite the same with svm(support vector machine)
it uses the same principles as the SVM for clas Community Discussions
Trending Discussions on regressor
QUESTION
I wanted to make all of the custom transformations I make to my data in a pipe. I thought that I could use it as pipe.fit_transform(X) to transform my X before using it in a model, but I also thought that I'll be able to append to the pipeline model itself and simply use it as one using pipe.steps.append(('model', self.model)).
Unfortunately, after everything was built I've noticed that I'm getting different results when transforming the data and using it directly in a model vs doing everything in one pipeline. Have anyone experienced anything like this?
Adding code:
...ANSWER
Answered 2022-Mar-29 at 18:07The one transformer that stands out to me is data_cat_mix, specifically the count-of-level columns. When applied to train+test, these are consistent (but leaks test information); when applied separately, the values in train will generally be much higher (just from its size being three times larger), so the model doesn't really understand how to treat them in the test set.
QUESTION
I am using random forest regressor as my target values is not categorial. However, the features are.
When I run the algorithm it treats them as continuous variables.
Is there any way to treat them as categorial?
example:
when I try random forest regressor it treats user ID for example as continuous (taking values 1.5 etc.)
The dtype in the data frame is int64.
Could you help me with that?
thanks
here is the code I have tried:
...ANSWER
Answered 2022-Mar-29 at 09:53First of all, RandomForestRegressor only accepts numerical values. So encoding your numerical values to categorical is not a solution because you are not going to be able to train you model.
The way to deal with this type of problem is OneHotEncoder. This function will create one column for every value that you have in the specified feature.
Below there is the example of code:
QUESTION
I am using this architecture (a Masking Layer for varying trajectory lengths that are padded with 0s to maximum length trajectory followed by a LSTM with a dense layer afterwards that outputs 2 values) to build a regressor that predicts 2 values based on a trajectory.
...ANSWER
Answered 2022-Mar-16 at 14:01When weights are random, they contribute to concrete inpute calculations chaotically, and we always get nearly same output. Did you train the model? Looks like not, consider simple MNIST solver output before training:
QUESTION
I have my model and inputs moved on the same device but I still get the runtime error :
...ANSWER
Answered 2022-Feb-27 at 07:14TL;DR use nn.ModuleList instead of a pythonic one to store the hidden layers in Net.
All your hidden layers are stored in a simple pythonic list self.hidden in Net. When you move your model to GPU, using .to(device), pytorch has no way to tell that all the elements of this pythonic list should also be moved to the same device.
however, if you make self.hidden = nn.ModuleLis(), pytorch now knows to treat all elements of this special list as nn.Modules and recursively move them to the same device as Net.
QUESTION
I tried to create stacking regressor to predict multiple output with SVR and Neural network as estimators and final estimator is linear regression.
...ANSWER
Answered 2022-Feb-25 at 00:19Imo the point here is the following. On one side, NN models do support multi-output regression tasks on their own, which might be solved defining an output layer similar to the one you built, namely with a number of nodes equal to the number of outputs (though, with respect to your construction, I would specify a linear activation with activation=None rather than a sigmoid activation).
QUESTION
I am using sklearn and mlxtend.regressor.StackingRegressor to build a stacked regression model.
For example, say I want the following small pipeline:
- A Stacking Regressor with two regressors:
- A pipeline which:
- Performs data imputation
- 1-hot encodes categorical features
- Performs linear regression
- A pipeline which:
- Performs data imputation
- Performs regression using a Decision Tree
- A pipeline which:
Unfortunately this is not possible, because StackingRegressor doesn't accept NaN in its input data.
This is even if its regressors know how to handle NaN, as it would be in my case where the regressors are actually pipelines which perform data imputation.
However, this is not a problem: I can just move data imputation outside the stacked regressor. Now my pipeline looks like this:
- Perform data imputation
- Apply a Stacking Regressor with two regressors:
- A pipeline which:
- 1-hot encodes categorical features
- Standardises numerical features
- Performs linear regression
- An
sklearn.tree.DecisionTreeRegressor.
- A pipeline which:
One might try to implement it as follows (the entire minimal working example in this gist, with comments):
...ANSWER
Answered 2022-Feb-18 at 21:31Imo the issue has to be ascribed to StackingRegressor. Actually, I am not an expert on its usage and still I have not explored its source code, but I've found this sklearn issue - #16473 which seems implying that << the concatenation [of regressors and meta_regressors] does not preserve dataframe >> (though this is referred to sklearn StackingRegressor instance, rather than on mlxtend one).
Indeed, have a look at what happens once you replace it with your sr_linear pipeline:
QUESTION
I have a very large dataset (10m observations but reduced to 14 essential variables), and am following the following thread: Cluster bootstrapped standard errors in R for plm functions
My codes after loading libraries are:
...ANSWER
Answered 2022-Feb-11 at 14:37Thanks for your question!
fwildclusterboot::boottest() only supports estimation of OLS models, so running a Poisson regression should in fact throw an error in boottest(). I will have to add an error condition for that :)
The error that you observe
QUESTION
I want to iterate over different matrix blocks based on an index variable. You can think of it as how you would compute the individual contributions to the loglikelihood of the different individuals on a model that uses panel data. That being said, I want it to be as fast as it can be.
I've already read some questions related to it. But none of them answer my question directly. For example, What is the recommended way to iterate a matrix over rows? shows ways to run over the WHOLE bunch of rows not iterating over blocks of rows. Additionally, Julia: loop over rows of matrix (or not) is also about how to iterate over every row again and not over blocks of them.
So here is my question. Say you have X, which is a 2x9 matrix and an id variable that indexes the individuals in the sample. I want to iterate over them to construct my loglikelihood contributions as fast as possible. I did it here just by slicing the matrix using booleans, but this seems relatively inefficient given I am for each individual checking the entire vector to see if they match or not.
ANSWER
Answered 2022-Feb-02 at 15:26First, I would recommend you to use vectors instead of matrices:
QUESTION
How should :param n_jobs: be used when both the random forest estimator for multioutput regressor and the multioutput regressor itself both have it? For example, is it better to not specify n_jobs for the estimator, but set n_jobs for the multioutput regressor? Several examples are shown below:
ANSWER
Answered 2022-Jan-25 at 02:25Since RandomForestRegressor has 'native' multioutput support (no need for the multioutput wrapper), I instead looked at the KNeighborsRegressor and LightGBM which have an inner n_jobs argument and about which I had the same question.
Running on a Ryzen 5950X (Linux) and Intel 11800H (Windows), both with n_jobs = 8, I found consistent results:
- With low Y dimensionality (say, 1 - 10 targets) it doesn't matter much where n_jobs goes, it finishes quickly regardless. Initializing multiprocessing has a ~1 second overhead, but joblib will reuse existing pools by default, speeding things up.
- With high dimensionality (say > 20) placing n_jobs only in the
MultiOutputRegressorwith KNN receiving n_jobs=1 is 10x faster at 160 dimensions/targets. - Using
with joblib.parallel_backend("loky", n_jobs=your_n_jobs):was equally fast and conveniently sets the n_jobs for all sklearn things inside. This is the easy option. RegressorChainis fast enough at low dimensionality but gets ridiculously slow (500x slower vs Multioutput) with 160 dimensions for KNeighbors (I would stick toLightGBMfor use with theRegressorChainwhich performs better).- With
LightGBM,MultiOutputRegressoronly setting n_jobs was again faster than the inner n_jobs, but the difference was much smaller (5950x Linux the difference was 3x, 11800H Windows only 1.2x).
Since the full code gets a bit long, here is a partial sample that gets most of it:
QUESTION
Does CatBoost regressor have a method to predict the probabilities of each prediction? I see one for CatBoost classifier (https://catboost.ai/en/docs/concepts/python-reference_catboostclassifier_predict_proba) but not for regressor.
...ANSWER
Answered 2022-Jan-21 at 06:47There is no predict_proba method in the Catboost regressor, but you can specify the output type when you call predict on the trained model.
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