Memory error when I am trying to use Gridsearchcv

Question

I am working on a customized estimator that should be passed in gridsearchcv function in sklearn. I have created the estimator now but run into a memory error. In the following code, you will see some constants like 'KxRange[0]' or arrays like retain_rate. They are just pre-defined with some random values in them. Here is my code:

# sklearn grid search
from sklearn.model_selection import GridSearchCV
# import the base estimator
from sklearn.base import BaseEstimator, RegressorMixin
# define my own estimator
class MyEstimator(BaseEstimator,RegressorMixin):
    # define constructor
    #  possible tau: int/float
    #  other parameters: array of int/floats, length 9
    def __init__(self, tau=0, \
                       K1=K1Range[0], K2=K2Range[0], K3=K3Range[0], K4=K4Range[0], K5=K5Range[0], K6=K6Range[0], K7=K7Range[0], K8=K8Range[0], K9=K9Range[0], \
                       S1=0, S2=0, S3=0, S4=0, S5=0, S6=0, S7=0, S8=0, S9=0, \
                       alpha1=1, alpha2=1, alpha3=1, alpha4=1, alpha5=1, alpha6=1, alpha7=1, alpha8=1, alpha9=1, \
                       beta1=1, beta2=1, beta3=1, beta4=1, beta5=1, beta6=1, beta7=1, beta8=1, beta9=1):
        # initialize parameters
        self.tau = tau
        self.K1 = K1
        self.K2 = K2
        self.K3 = K3
        self.K4 = K4
        self.K5 = K5
        self.K6 = K6
        self.K7 = K7
        self.K8 = K8
        self.K9 = K9
        self.S1 = S1
        self.S2 = S2
        self.S3 = S3
        self.S4 = S4
        self.S5 = S5
        self.S6 = S6
        self.S7 = S7
        self.S8 = S8
        self.S9 = S9
        self.alpha1 = alpha1
        self.alpha2 = alpha2
        self.alpha3 = alpha3
        self.alpha4 = alpha4
        self.alpha5 = alpha5
        self.alpha6 = alpha6
        self.alpha7 = alpha7
        self.alpha8 = alpha8
        self.alpha9 = alpha9
        self.beta1 = beta1
        self.beta2 = beta2
        self.beta3 = beta3
        self.beta4 = beta4
        self.beta5 = beta5
        self.beta6 = beta6
        self.beta7 = beta7
        self.beta8 = beta8
        self.beta9 = beta9
    # to fit the model
    def fit(self, X,y=None):
        # define the mu vector
        self.mu_ = np.ones((N))
        # define lag weights
        lag_weights = np.ones((max_lag))
        # define retain_rate
        retain_rate = np.array([alpha1, alpha2, alpha3, alpha4, alpha5, alpha6, alpha7, alpha8, alpha9])
        # define cum_effect, set to a random value
        cum_effect = 1
        # define cum_effects_hill
        cum_effects_hill = np.ones((N, num_media))
        # parameter transformation
        for nn in range(N):
            for m in range(num_media):
                for l in range(max_lag): 
                    lag_weights[l] = retain_rate[m]**l
                cum_effect = Adstock(X[nn][m], lag_weights)
            cum_effects_hill[nn][m] = Hill(cum_effect, ec[m], slope[m])
            self.mu_[nn] = tau + np.dot(cum_effects_hill[nn], beta_medias)
        return self
    # the predict function
    def predict(self, X, y=None):
        # try to get the mu_ argument. If it does not exist, we throw an error
        try:
            getattr(self, "mu_")
        except AttributeError:
            raise RuntimeError("You must train classifer before predicting data!")
        return self.mu_
    # the score function
    def score(self, X, y):
        # calculate the MSE
        return np.dot(y - self.predict(X), y - self.predict(X))/len(X)

The following is like the "main" function

# initiliaze estimator
t = MyEstimator()
# parameter grid
             # tau  
param_grid = {'tau': [100,200], \
             # K
              'K1': [K1Range[0], K1Range[1]], 'K2' : [K2Range[0], K2Range[1]], 'K3': [K3Range[0], K3Range[1]], 'K4' : [K4Range[0], K4Range[1]], 'K5' : [K5Range[0], K5Range[1]], 'K6' : [K6Range[0], K6Range[1]], 'K7' : [K7Range[0], K7Range[1]], 'K8': [K8Range[0], K8Range[1]], 'K9': [K9Range[0], K9Range[1]], \
             # S
              'S1': [1, 100], 'S2': [1, 100], 'S3': [1, 100], 'S4': [1, 100], 'S5': [1, 100], 'S6': [1, 100], 'S7': [1, 100], 'S8': [1, 100], 'S9': [1, 100], \
             # alpha
              'alpha1': [0.1, 0.5], 'alpha2': [0.1, 0.5], 'alpha3': [0.1, 0.5], 'alpha4': [0.1, 0.5], 'alpha5': [0.1, 0.5], 'alpha6': [0.1, 0.5], 'alpha7': [0.1, 0.5], 'alpha8': [0.1, 0.5], 'alpha9': [0.1, 0.5], \
             # beta
              'beta1': [100,200], 'beta2': [100,200], 'beta3': [100,200], 'beta4': [100,200], 'beta5': [100,200], 'beta6': [100,200], 'beta7': [100,200], 'beta8': [100,200], 'beta9': [100,200]}
#
clf = GridSearchCV(t, param_grid)
clf.fit(X_media, actual_sales)
#clf.predict(X_media)

This is error message:

MemoryError                               Traceback (most recent call last)
 in 
     14 #
     15 clf = GridSearchCV(t, param_grid)
---> 16 clf.fit(X_media, actual_sales)
     17 #clf.predict(X_media)

~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
     71                           FutureWarning)
     72         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 73         return f(**kwargs)
     74     return inner_f
     75 

~\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in fit(self, X, y, groups, **fit_params)
    734                 return results
    735 
--> 736             self._run_search(evaluate_candidates)
    737 
    738         # For multi-metric evaluation, store the best_index_, best_params_ and

~\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in _run_search(self, evaluate_candidates)
   1186     def _run_search(self, evaluate_candidates):
   1187         """Search all candidates in param_grid"""
-> 1188         evaluate_candidates(ParameterGrid(self.param_grid))
   1189 
   1190 

~\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in evaluate_candidates(candidate_params)
    698 
    699             def evaluate_candidates(candidate_params):
--> 700                 candidate_params = list(candidate_params)
    701                 n_candidates = len(candidate_params)
    702 

MemoryError:

Can anyone tell me how to fix this error? Or is there any problem with my code? Thanks!

Memory error when I am trying to use Gridsearchcv

Answers (1)

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