These are typically 100-1000x faster than Python for-loops.įor example: floatTable = combined_campaign_tables.astype('f8')Ĭonversion_rate = floatTable.ixĬlose_rate = floatTable. You should replace your for-loops with vectorized operations. You should convert each column from your dataframe to floats and store these as individual variables (as they're only being looked up each time, no need to convert them from floats each time the function is called). Without the data I can't definitively identify performance bottlenecks, but it looks to me like your functions are very inefficient. If you really have 200 model parameters, then you might need to consider moving to a distributed platform running on a cluster.īut first, you should try to fix your code. In general, the cost to optimize will grow exponentially with more model parameters. So, if it takes a long time to evaluate the objective function and/or constraints, then it will take a LONG time to optimize. Each constraint also must be evaluated many times as well for each model parameter. The key is to remember that nonlinear optimizers rely on evaluating the objective function to minimize many times for each iteration to calculate numerical partial derivatives for each model parameter. Res = minimize(profit_model, x0, constraints=cons)Īdw_bids = pd.DataFrame(data=bids, index=None, columns=, dtype=None, copy=False).fillna(0)` X0 = np.arange(len(combined_campaign_tables)) cost_constraint = 2000įor i in range(0, len(combined_campaign_tables), 1):Ĭlicks = (x * float(combined_campaign_tables.ix)) + float(Ĭombined_campaign_tables.ix)Ĭombined_campaign_tables.ix) * float(Ĭombined_campaign_tables.ix)) - (clicks * x))Ĭlicks = ((x * float(combined_campaign_tables.ix)) + float(Ĭombined_campaign_tables.ix))Ĭons = ()
Scipy optimize minimize code#
The last thing I want to do is take the lazy way out and post my code but I am hoping it answers questions rather than raising more. Thanks for reading.Įdit: Thank you for all your comments. Any reading materials on where to go from where would be greatly appreciated. Any dataframe over 200 rows takes several hours. I do not know whether it this problem is related to the previous one.We're using scipy.optimize (minimize) currently to optimize our Cost-per-Click bids in Adwords but as we add more campaigns the optimization problem essentially never finishes. It usually start decrease at the second or the third iteration, even through 'ABNORMAL_TERMINATION_IN_LNSRCH' does not occurs. I also find that the log-likelihood does not monotonically increase: # Convergence !!! # What are the possible reasons that I should investigate? What does it mean by "rounding error dominate computation"? But here I do not provide gradient function because I am using 'approx_grad'. Someone said it occurs often because the objective and gradient functions do not match. I know that it means the minimum can be be reached in this iteration. (Most get 'CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL' or 'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'). I do not get this warning every time, but sometimes. Subspace minimization time 0.000E+00 seconds. Possible causes: 1 error in function or gradient evaluation Line search cannot locate an adequate point after 20 functionĪnd gradient evaluations. Projg = norm of the final projected gradient Nact = number of active bounds at final generalized Cauchy point Tnint = total number of segments explored during Cauchy searches
Scipy optimize minimize how to#
You'll learn how to install SciPy using Anaconda or pip and see some of its modules. Tnf = total number of function evaluations In this tutorial, you'll learn about the SciPy ecosystem and how it differs from the SciPy library. Information dictionary: Īt X0 0 variables are exactly at the boundsĪt iterate 0 f= 1.14462D-07 |proj g|= 3.51719D-05 sigma_sp_new, func_val, info_dict = fmin_l_bfgs_b(func_to_minimize, self.sigma_vector,Īrgs=(self.w_vectors, Y, X, E_step_results),Īpprox_grad=True, bounds=, factr=1e02, pgtol=1e-05, epsilon=1e-08)īut sometimes I got a warning 'ABNORMAL_TERMINATION_IN_LNSRCH' in the information dictionary: func_to_minimize value = 1.14462324063e-07 The means of mixture distributions are modeled by regressions whose weights have to be optimized using EM algorithm. I am using _l_bfgs_b to solve a gaussian mixture problem. _l_bfgs_b returns 'ABNORMAL_TERMINATION_IN_LNSRCH'
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