Predicting how long an scikit-learn classification will take to run

If you are using IPython, you can consider to use the built-in magic commands such as %time and %timeit

%time - Time execution of a Python statement or expression. The CPU and wall clock times are printed, and the value of the expression (if any) is returned. Note that under Win32, system time is always reported as 0, since it can not be measured.

%timeit - Time execution of a Python statement or expression using the timeit module.

Example:

In [4]: %timeit NMF(n_components=16, tol=1e-2).fit(X)
1 loops, best of 3: 1.7 s per loop

References:

https://ipython.readthedocs.io/en/stable/interactive/magics.html

http://scikit-learn.org/stable/developers/performance.html

There are very specific classes of classifier or regressors that directly report remaining time or progress of your algorithm (number of iterations etc.). Most of this can be turned on by passing verbose=2 (any high number > 1) option to the constructor of individual models. Note: this behavior is according to sklearn-0.14. Earlier versions have a bit different verbose output (still useful though).

The best example of this is ensemble.RandomForestClassifier or ensemble.GradientBoostingClassifier` that print the number of trees built so far and remaining time.

clf = ensemble.GradientBoostingClassifier(verbose=3)
clf.fit(X, y)
Out:
   Iter       Train Loss   Remaining Time
     1           0.0769            0.10s
     ...

Or

clf = ensemble.RandomForestClassifier(verbose=3)
clf.fit(X, y)
Out:
  building tree 1 of 100
  ...

This progress information is fairly useful to estimate the total time.

Then there are other models like SVMs that print the number of optimization iterations completed, but do not directly report the remaining time.

clf = svm.SVC(verbose=2)
clf.fit(X, y)
Out:
   *
    optimization finished, #iter = 1
    obj = -1.802585, rho = 0.000000
    nSV = 2, nBSV = 2
    ...

Models like linear models don't provide such diagnostic information as far as I know.

Check this thread to know more about what the verbosity levels mean: scikit-learn fit remaining time