ohjeah / sparsereg Goto Github PK

Make sure that all score methods of all models satisfy the sklearn convention for scores of "bigger is better".

Hi, I downloaded and try to run the sindy file from Example folder.
I have the most updated python and sklearn.
But I got this error message showing up:
ModuleNotFoundError: No module named 'sklearn.linear_model.base'

Does anyone have the same issues? Or are there any ways to solve this?

for SimpleBase with same input feature ProductBase should never be built.

• model
  • base
  • efs
  • ffx
  • sindy
• utils
• preprocessing

make sindy model a full sklearn pipeline:

• transformer to get derivatives from data
• variable sparse regression class
• multioutput wrapper

• rational model wrapper
• intercept_
• strategy pruning
• path_wise learning
• timeout just use max_iter

Proposal of features for sindy class:

• Symbolic features as default w/ params
• Time-series data input option
• Provide xdot for fit option

I am training a group lasso model with time series data, which means rho=0 in my model. In every month, I train the model with the data in the past 24 months. The parameter alpha is very stable, around 0.0005. However, in several months, when I move ahead by one month, which means that I replace the data of month t-24 with the data of the latest month, the model do not converge. In this case, I need a far more larger parameters, which is aroung 0.002 to make the model converge. I am wondering why this happens. Because I only replace a small portion(less than 5%) of the training data, but the parameters change sharply. This may cause instability of the model, as in the previous month, I may choose 30 features, however in the current month, I can only choose 16 features.

Traceback (most recent call last):
File "D:\Program Files\Python\Python36\lib\site-packages\IPython\core\interactiveshell.py", line 2963, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "", line 4, in
model = model.fit(p, y)
File "D:\Program Files\Python\Python36\lib\site-packages\sparsereg\model\group_lasso.py", line 33, in fit
max_iter=self.max_iter, rtol=self.tol)
File "D:\Program Files\Python\Python36\lib\site-packages\sparsereg\vendor\group_lasso\group_lasso.py", line 92, in sparse_group_lasso
delta = linalg.norm(tmp - w_new[group])
File "D:\Program Files\Python\Python36\lib\site-packages\scipy\linalg\misc.py", line 137, in norm
a = np.asarray_chkfinite(a)
File "D:\Program Files\Python\Python36\lib\site-packages\numpy\lib\function_base.py", line 1233, in asarray_chkfinite
"array must not contain infs or NaNs")
ValueError: array must not contain infs or NaNs

• model
  • base
  • efs
  • ffx
  • sindy
• utils
• preprocessing

>>> import sparsereg
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Python27\lib\site-packages\sparsereg\__init__.py", line 1, in <module>
    from sparsereg.model import *
ImportError: No module named model
>>>

This error occurs in Python 2.7.11 and Anaconda Python 3.6, both on Windows 10 64-bit. Any help resolving this one?

Expected Behavior

example\sindy.py to print score and equations.

Actual Behavior

TypeError: 'tuple' object does not support item assignment

Screenshot of traceback

http://imgur.com/a/s2Ph3

Steps to Reproduce the Problem

In ipython console

1. runfile('../sparsereg-master/example/sindy.py', wdir='../sparsereg-master/example')

In powershell

1. cd to '../sparsereg-master/example/
2. python sindy.py

In cmd

1. cd to '../sparsereg-master/example/
2. python sindy.py

Specifications

• Version: 0.6.3
• Platform: Windows 10
• Subsystem: ipython, powershell, and cmd

Some small functionality edits for wrapper class:

• score with param y
• n_features in model
• tests

• Use scientific notation for numbers
• truncate numbers
• Use space for multiplication

Following conventions for the objective functions in the ElasticNet are used:
sklearn: of = 1/2N C + \alpha \mu P_1 + 0.5 \alpha (1-\mu) P_2

McConaughy: of = C + \lambda \rho P_1 + \lambda(1-\rho) P_2

here, C is the (squared) two norm of the residuals and P_1 and P_2 are the regularization term.
McConaughy probably also means a factor of 1/N in front of the C, otherwise the amount of regularization would scale with the number of features which doesn't make any sense.

Assuming this factor, the following formulae should be applied when mapping the regularization parameters from sparseregs interface to that of sklearn.

\alpha = \lambda ( 1-\rho/2)
\mu = \rho/(2-\rho)