alueError: Found input variables with inconsistent numbers of samples: [1818794, 79078]

[gaoexingyun]

When I use HyperoptEstimator to train the model, I want to calculate logloss, so I manually set several classifiers to support predict_proba, but it will report errorError: Found input variables with inconsistent numbers of samples: [1818794, 79078].
What is the reason?

Following is my code：

from sklearn.model_selection import cross_val_score
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import log_loss
from hpsklearn import sklearn_RandomForestClassifier,extra_trees,random_forest,knn,svc
from hyperopt import hp
from random import choice
def algorithms(name):
classifiers = [

    random_forest(name + '.random_forest')
]

return choice(classifiers)

kf = StratifiedKFold(n_splits=5,shuffle=True)

use sigmoid kernal to train model

Hyper_logloss = []
for train_index, test_index in kf.split(X,y):
X_train, X_test = X.iloc[train_index], X.iloc[test_index]
y_train, y_test = y.iloc[train_index], y.iloc[test_index]
estim = HyperoptEstimator(classifier=algorithms("clf"),algo=tpe.suggest,loss_fn=log_loss,continuous_loss_fn=True,trial_timeout=3600)
estim.fit(X_train, y_train)

ValueError Traceback (most recent call last)
in
19 y_train, y_test = y.iloc[train_index], y.iloc[test_index]
20 estim = HyperoptEstimator(classifier=algorithms("clf"),algo=tpe.suggest,loss_fn=log_loss,continuous_loss_fn=True,trial_timeout=3600)
---> 21 estim.fit(X_train, y_train)

~/anaconda3/hyperopt-sklearn/hpsklearn/estimator.py in fit(self, X, y, EX_list, valid_size, n_folds, cv_shuffle, warm_start, random_state, weights)
778 increment = min(self.fit_increment,
779 adjusted_max_evals - len(self.trials.trials))
--> 780 fit_iter.send(increment)
781 if filename is not None:
782 with open(filename, 'wb') as dump_file:

~/anaconda3/hyperopt-sklearn/hpsklearn/estimator.py in fit_iter(self, X, y, EX_list, valid_size, n_folds, cv_shuffle, warm_start, random_state, weights, increment)
688 # so we notice them.
689 catch_eval_exceptions=False,
--> 690 return_argmin=False, # -- in case no success so far
691 )
692 else:

~/anaconda3/lib/python3.7/site-packages/hyperopt/fmin.py in fmin(fn, space, algo, max_evals, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar)
386 catch_eval_exceptions=catch_eval_exceptions,
387 return_argmin=return_argmin,
--> 388 show_progressbar=show_progressbar,
389 )
390

~/anaconda3/lib/python3.7/site-packages/hyperopt/base.py in fmin(self, fn, space, algo, max_evals, rstate, verbose, pass_expr_memo_ctrl, catch_eval_exceptions, return_argmin, show_progressbar)
637 catch_eval_exceptions=catch_eval_exceptions,
638 return_argmin=return_argmin,
--> 639 show_progressbar=show_progressbar)
640
641

~/anaconda3/lib/python3.7/site-packages/hyperopt/fmin.py in fmin(fn, space, algo, max_evals, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar)
405 show_progressbar=show_progressbar)
406 rval.catch_eval_exceptions = catch_eval_exceptions
--> 407 rval.exhaust()
408 if return_argmin:
409 return trials.argmin

~/anaconda3/lib/python3.7/site-packages/hyperopt/fmin.py in exhaust(self)
260 def exhaust(self):
261 n_done = len(self.trials)
--> 262 self.run(self.max_evals - n_done, block_until_done=self.asynchronous)
263 self.trials.refresh()
264 return self

~/anaconda3/lib/python3.7/site-packages/hyperopt/fmin.py in run(self, N, block_until_done)
225 else:
226 # -- loop over trials and do the jobs directly
--> 227 self.serial_evaluate()
228
229 try:

~/anaconda3/lib/python3.7/site-packages/hyperopt/fmin.py in serial_evaluate(self, N)
139 ctrl = base.Ctrl(self.trials, current_trial=trial)
140 try:
--> 141 result = self.domain.evaluate(spec, ctrl)
142 except Exception as e:
143 logger.info('job exception: %s' % str(e))

~/anaconda3/lib/python3.7/site-packages/hyperopt/base.py in evaluate(self, config, ctrl, attach_attachments)
842 memo=memo,
843 print_node_on_error=self.rec_eval_print_node_on_error)
--> 844 rval = self.fn(pyll_rval)
845
846 if isinstance(rval, (float, int, np.number)):

~/anaconda3/hyperopt-sklearn/hpsklearn/estimator.py in fn_with_timeout(*args, **kwargs)
651 assert fn_rval[0] in ('raise', 'return')
652 if fn_rval[0] == 'raise':
--> 653 raise fn_rval[1]
654
655 # -- remove potentially large objects from the rval

ValueError: Found input variables with inconsistent numbers of samples: [1818794, 79078]

[lucasmejiall]

I had the same problem. I believe it's this line causing the problem in estimator.py line 333:

if continuous_loss_fn: cv_pred_pool = np.append(cv_pred_pool, learner.predict_proba(XEXval))

It should be:

if continuous_loss_fn: cv_pred_pool = np.append(cv_pred_pool, learner.predict_proba(XEXval)[:, 1])

[jhmenke]

I had the same problem. I believe it's this line causing the problem in estimator.py line 333:

if continuous_loss_fn: cv_pred_pool = np.append(cv_pred_pool, learner.predict_proba(XEXval))

It should be:

if continuous_loss_fn: cv_pred_pool = np.append(cv_pred_pool, learner.predict_proba(XEXval)[:, 1])

What is the purpose of this? You would only select a single class probability per sample from predict_proba

[linehammer]

Sounds like the shapes of your labels and predictions are not in alignment. I faced a similar problem while fitting a regression model . The problem in my case was, Number of rows in X was not equal to number of rows in y. You likely get problems because you remove rows containing nulls in X_train and y_train independent of each other. y_train probably has few, or no nulls and X_train probably has some. So when you remove a row in X_train and the same row is not removed in y_train it will cause your data to be unsynced and have different lenghts. Instead you should remove nulls before you separate X and y.

In most case, x as your feature parameter and y as your predictor. But your feature parameter should not be 1D. So check the shape of x and if it is 1D, then convert it from 1D to 2D.

x.reshape(-1,1)