logistic-regression | A simple implementation of logisitic regression in Java | Testing library
kandi X-RAY | logistic-regression Summary
kandi X-RAY | logistic-regression Summary
A simple implementation of logisitic regression in Java.
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- Classifies a dataset
- Read a dataset from a file
- Trains the model
- Classify the sigmoid function
- Compute sigmoid
logistic-regression Key Features
logistic-regression Examples and Code Snippets
Lines of Code : 155
License : No License
Copydef main():
Xtrain, Xtest, Ytrain, Ytest = get_normalized_data()
print("Performing logistic regression...")
N, D = Xtrain.shape
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
# 1. full
W = np.random.rand def fit(self, X, Y, learning_rate=0.01, mu=0.99, epochs=30, batch_sz=100):
# cast to float32
learning_rate = np.float32(learning_rate)
mu = np.float32(mu)
N, D = X.shape
K = len(set(Y))
self.hidden_la def benchmark_full():
Xtrain, Xtest, Ytrain, Ytest = get_normalized_data()
print("Performing logistic regression...")
# lr = LogisticRegression(solver='lbfgs')
# convert Ytrain and Ytest to (N x K) matrices of indicator variables
Community Discussions
Trending Discussions on logistic-regression
QUESTION
I have looked into the example on this website: https://scipython.com/blog/plotting-the-decision-boundary-of-a-logistic-regression-model/
I understand how they plot the decision boundary for a linear feature vector. But how would I plot the decision boundary if I apply
...ANSWER
Answered 2022-Apr-08 at 10:39The output of your PolyCoefficients function is a 4th order polynomial made up of:
QUESTION
As a continuation from this question, I want to run many logistic regression equations at once and then note if a group was significantly different from a reference group. This solution works, but it only works when I'm not missing values. Being that my data has 100 equations, it's bound to have missing values, so rather than this solution failing when it hits an error, how can I program it to skip the instances that throw an error?
Here's a modified dataset that's missing cases:
...ANSWER
Answered 2022-Apr-04 at 21:18One option would be purrr::safely which allows to take care of errors. To this end I use a helper function glm_safe which wraps your glm call inside purrr::safely. glm_safe will return a list with two elements, result and error. In case everything works fine result will contain the model object, while element is NULL. In case of an error the error message is stored in error and result will be NULL. To use the results in your pipeline we have to extract the result elements which could be achieved via transpose(reg)$result.
QUESTION
I am not an expert on logistic regression, but I thought when solving it using lgfgs it was doing optimization, finding local minima for the objective function. But every time I run it using scikit-learn, it is returning the same results, even when I feed it a different random state.
Below is code that reproduces my issue.
First set up the problem by generating data ...ANSWER
Answered 2022-Apr-01 at 19:34First, let me put in the answer what got this closed as duplicate earlier: a logistic regression problem (without perfect separation) has a global optimum, and so there are no local optima to get stuck in with different random seeds. If the solver converges satisfactorily, it will do so on the global optimum. So the only time random_state can have any effect is when the solver fails to converge.
Now, the documentation for LogisticRegression's parameter random_state states:
Used when
solver== ‘sag’, ‘saga’ or ‘liblinear’ to shuffle the data. [...]
So for your code, with solver='lbfgs', indeed there is no expected effect.
It's not too hard to make sag and saga fail to converge, and with different random_states to end at different solutions; to make it easier, set max_iter=1. liblinear apparently does not use the random_state unless solving the dual, so also setting dual=True admits different solutions. I found that thanks to this comment on a github issue (the rest of the issue may be worth reading for more background).
QUESTION
I am trying to understand how this function works and the mathematics behind it. Does decision_function() in scikitlearn give us log odds? The function return values ranging from minus infinity to infinity and it seems like 0 is the threshold for prediction when we are using decision_function() whereas the threshold is 0.5 when we are using predict_proba(). This is exactly the relationship between probability and log odds Geeksforgeeks.
I couldn't see anything about that in the documentation but the function behaves like log-likelihood I think. am I right?
...ANSWER
Answered 2022-Mar-06 at 22:14Decision function is nothing but the value of (as you can see in the source)
QUESTION
As the title suggests. I'm playing around with a Twitter bot that scrapes rss feeds and tweets the title of the article and a link.
For some reason when I run the below code it runs without errors but doesn't retrieve the url link. Any suggestions are gratefully recieved.
...ANSWER
Answered 2022-Feb-05 at 22:40Try this?
QUESTION
Background and my thought process:
I wanted to see if I could utilize logistic regression to create a hypothesis function that could predict recessions in the US economy by looking at a date and its corresponding leading economic indicators. Leading economic indicators are known to be good predictors of the economy.
To do this, I got data from OECD on the composite leading (economic) indicators from January, 1970 to July, 2021 in addition to finding when recessions occurred from 1970 to 2021. The formatted data that I use for training can be found further below.
Knowing the relationship between a recession and the Date/LEI wouldn't be a simple linear relationship, I decided to make more parameters for each datapoint so I could fit a polynominal equation to the data. Thus, each datapoint has the following parameters: Date, LEI, LEI^2, LEI^3, LEI^4, and LEI^5.
The Problem:
When I attempt to train my hypothesis function, I get a very strange cost history that seems to indicate that I either did not implement my cost function correctly or that my gradient descent was implemented incorrectly. Below is the imagine of my cost history:
I have tried implementing the suggestions from this post to fix my cost history, as originally I had the same NaN and Inf issues described in the post. While the suggestions helped me fix the NaN and Inf issues, I couldn't find anything to help me fix my cost function once it started oscillating. Some of the other fixes I've tried are adjusting the learning rate, double checking my cost and gradient descent, and introducing more parameters for datapoints (to see if a higher-degree polynominal equation would help).
My Code
The main file is predictor.m.
ANSWER
Answered 2022-Feb-02 at 05:07The problem you're running into here is your gradient descent function.
In particular, while you correctly calculate the cost portion (aka, (hTheta - Y) or (sigmoid(X * Theta') - Y) ), you do not calculate the derivative of the cost correctly; in Theta = Theta - (sum((sigmoid(X * Theta') - Y) .* X)), the .*X is not correct.
The derivative is equivalent to the cost of each datapoint (found in the vector hTheta - Y) multiplied by their corresponding parameter j, for every parameter. For more information, check out this article.
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
I am trying to fit the Doc2Vec method in a dataframe which the first column has the texts, and the second one the label (author). I have found this article https://towardsdatascience.com/multi-class-text-classification-with-doc2vec-logistic-regression-9da9947b43f4, which is really helpful. However, I am stuck at how to build a model
...ANSWER
Answered 2021-Aug-09 at 09:03i found this
im not sure about Doc2Vec
but this error in python is about module name
This error statement TypeError: 'module' object is not callable is raised as you are being confused about the Class name and Module name. The problem is in the import line . You are importing a module, not a class. This happend because the module name and class name have the same name .
If you have a class MyClass in a file called MyClass.py , then you should write:
QUESTION
I want to try all regression algorithms on my dataset and choose a best. I decide to start from Linear Regression. But i get some error. I tried to do scaling but also get another error.
Here is my code:
...ANSWER
Answered 2021-Apr-15 at 14:38You're using LogisticRegression, which is a special case of Linear Regression used for categorical dependent variables.
This is not necessarily wrong, as you might intend to do so, but that means you need sufficient data per category and enough iterations for the model to converge (which your error points out, it hasn't done).
I suspect, however, that what you intended to use is LinearRegression (used for continuous dependent variables) from sklearn library.
QUESTION
I am using Python to predict values and getting many warnings like:
Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( C:\Users\ASMGX\anaconda3\lib\site-packages\sklearn\linear_model_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
this prevents me from seeing the my own printed results.
Is there any way I can stop these warnings from showing?
...ANSWER
Answered 2021-Apr-04 at 05:52You can use the warnings-module to temporarily suppress warnings. Either all warnings or specific warnings.
In this case scikit-learn is raising a ConvergenceWarning so I suggest suppressing exactly that type of warning. That warning-class is located in sklearn.exceptions.ConvergenceWarning so import it beforehand and use the context-manager catch_warnings and the function simplefilter to ignore the warning, i.e. not print it to the screen:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install logistic-regression
You can use logistic-regression like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the logistic-regression component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
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