statsmodels | Statsmodels : statistical modeling and econometrics in Python | Analytics library

I have a local python project called jive that I would like to use in an another project. My current method of using jive in other projects is to activate the conda env for the project, then move to my jive directory and use python setup.py install. This works fine, and when I use conda list, I see everything installed in the env including jive, with a note that jive was installed using pip.

But what I really want is to do this with full conda. When I want to use jive in another project, I want to just put jive in that projects environment.yml.

So I did the following:

1. write a simple meta.yaml so I could use conda-build to build jive locally
2. build jive with conda build .
3. I looked at the tarball that was produced and it does indeed contain the jive source as expected
4. In my other project, add jive to the dependencies in environment.yml, and add 'local' to the list of channels.
5. create a conda env using that environment.yml.

When I activate the environment and use conda list, it lists all the dependencies including jive, as desired. But when I open python interpreter, I cannot import jive, it says there is no such package. (If use python setup.py install, I can import it.) How can I fix the build/install so that this works?

Here is the meta.yaml, which lives in the jive project top level directory:

Building up the model from a previous post, and the helpful answer, I've subclassed the MLEModel to encapsulate the model. I'd like to allow for two parameters q1 and q2 so that the state noise covariance matrix is generalized as in Sarkka (2013)'s example 4.3 (terms re-arranged for my convention):

I thought I would accomplish this with the update method below, but I'm running into problems with the fit method, as it returns a UFuncTypeError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind'. What am I missing here?

I bought a book which comes with jupyter notebook. In the first chapter, it asks me to install required libraries. It use {sys.executable} -m. I never see it before. what does {sys.executable} and -m do? also why use --user at the end?

typically, I just use ! pip install numpy==1.19.2

Anyone can help me understand it? Thank you!

I am trying to calculate the variance of the coefficients for logistic regression using bootstrap and I am using scikit-learn and statsmodels to compare results. I am using the Default dataset from the ISLR website which can be found in the zip forlder here or here as a plain csv file. I am using the following codes to perform the bootstrap:

Import the Dataset and create the response variable

I would like to plot a sophisticated graph in Julia. The code below is in Julia's version using ggplot.

I have the following problem. I would like to do an in-sample prediction using logit from statsmodels.formula.api.

See my code:

I am new to Jupyter notebook and trying to see the some help about the functions. For example, when I print the help of statsmodels.OLS I got the following plain black and white help.

Are there any python modules that colorize/beautify the help outputs?

For example:

• hightlight parameters names
• highlight the code example in python syntax hightlight
• and so on.

If there are not some modules, what would be the starting point, to colorize the parameters and the python codes?

The example output of help is given below:

I am currently working on the "French Motor Claims Datasets freMTPL2freq" Kaggle competition (https://www.kaggle.com/floser/french-motor-claims-datasets-fremtpl2freq). Unfortunately I get a "NotFittedError: All estimators failed to fit" error whenever I am using RandomizedSearchCV and I cannot figure out why that is. Any help is much appreciated.

There seems to be two methods for OLS fits in python. The Sklearn one and the Statsmodel one. I have a preference for the statsmodel one because it gives the error on the coefficients via the summary() function. However, I would like to use the TransformedTargetRegressor from sklearn to log my target. It would seem that I need to choose between getting the error on my fit coefficients in statsmodel and being able to transform my target in statsmodel. Is there a good way to do both of these at the same time in either system?

In stats model it would be done like this