RandomizedSearchCV: All estimators failed to fit

I am currently working on the “French Motor Claims Datasets freMTPL2freq” Kaggle competition (https://www.kaggle.com/floser/french-motor-claims-datasets-fremtpl2freq). Unfortunately I get a “NotFittedError: All estimators failed to fit” error whenever I am using RandomizedSearchCV and I cannot figure out why that is. Any help is much appreciated.

import numpy as np
import statsmodels.api as sm
import scipy.stats as stats
from matplotlib import pyplot as plt
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import mean_poisson_deviance
from sklearn.metrics import mean_squared_error
from sklearn.ensemble import VotingRegressor
from sklearn.ensemble import StackingRegressor
from sklearn.metrics import mean_gamma_deviance
from sklearn.metrics import mean_squared_error
from xgboost import XGBRegressor

data_freq = pd.read_csv('freMTPL2freq.csv')
data_freq['Area'] = data_freq['Area'].str.replace(''','')
data_freq['VehBrand'] = data_freq['VehBrand'].str.replace(''','')
data_freq['VehGas'] = data_freq['VehGas'].str.replace(''','')
data_freq['Region'] = data_freq['Region'].str.replace(''','')

    data_freq['frequency'] = data_freq['ClaimNb'] / data_freq['Exposure']
    y = data_freq['frequency']
    X = data_freq.drop(['frequency', 'ClaimNb', 'IDpol'], axis = 1)
    X_train, X_val, y_train, y_val = train_test_split(X,y, test_size=0.2, shuffle = True, random_state = 42)
    pt_columns = ['VehPower', 'VehAge', 'DrivAge', 'BonusMalus', 'Density']
    cat_columns = ['Area', 'Region', 'VehBrand', 'VehGas']

    from xgboost import XGBRegressor
    ct = ColumnTransformer([('pt', 'passthrough', pt_columns),
                       ('ohe', OneHotEncoder(), cat_columns)])
    pipe_xgbr = Pipeline([('cf_trans', ct),
                      ('ssc', StandardScaler(with_mean = False)),
    ('xgb_regressor', XGBRegressor())
    ])

    param = {'xgb_regressor__n_estimators':[3, 5],
        'xgb_regressor__max_depth':[3, 5, 7],
        'xgb_regressor__learning_rate':[0.1, 0.5],
        'xgb_regressor__colsample_bytree':[0.5, 0.8],
        'xgb_regressor__subsample':[0.5, 0.8]
        }

    rscv = RandomizedSearchCV(pipe_xgbr, param_distributions = param, n_iter = 2, scoring = mean_squared_error, n_jobs = -1, cv = 5, error_score = 'raise')
    rscv.fit(X_train, y_train, xgbr_regressor__sample_weight = X_train['Exposure'])

The first five rows of the original dataframe data_freq look like this:

   IDpol    ClaimNb Exposure    Area    VehPower    VehAge  DrivAge BonusMalus  VehBrand    VehGas  Density Region
0   1.0        1        0.10       D           5         0       55        50        B12    Regular 1217    R82
1   3.0        1        0.77       D           5         0       55        50        B12    Regular 1217    R82
2   5.0        1        0.75       B           6         2       52        50        B12    Diesel  54      R22
3   10.0       1        0.09       B           7         0       46        50        B12    Diesel  76      R72
4   11.0       1        0.84       B           7         0       46        50        B12    Diesel  76      R72

The error I get is as follows:

---------------------------------------------------------------------------
_RemoteTraceback                          Traceback (most recent call last)
_RemoteTraceback: 
"""
Traceback (most recent call last):
  File "C:UsersJananaconda3libsite-packagesjoblibexternalslokyprocess_executor.py", line 418, in _process_worker
    r = call_item()
  File "C:UsersJananaconda3libsite-packagesjoblibexternalslokyprocess_executor.py", line 272, in __call__
    return self.fn(*self.args, **self.kwargs)
  File "C:UsersJananaconda3libsite-packagesjoblib_parallel_backends.py", line 608, in __call__
    return self.func(*args, **kwargs)
  File "C:UsersJananaconda3libsite-packagesjoblibparallel.py", line 256, in __call__
    for func, args, kwargs in self.items]
  File "C:UsersJananaconda3libsite-packagesjoblibparallel.py", line 256, in <listcomp>
    for func, args, kwargs in self.items]
  File "C:UsersJananaconda3libsite-packagessklearnutilsfixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:UsersJananaconda3libsite-packagessklearnmodel_selection_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:UsersJananaconda3libsite-packagessklearnpipeline.py", line 340, in fit
    fit_params_steps = self._check_fit_params(**fit_params)
  File "C:UsersJananaconda3libsite-packagessklearnpipeline.py", line 261, in _check_fit_params
    fit_params_steps[step][param] = pval
KeyError: 'xgbr_regressor'
"""

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
<ipython-input-68-0c1886d1e985> in <module>
----> 1 rscv.fit(X_train, y_train, xgbr_regressor__sample_weight = X_train['Exposure'])
      2 #pipe_xgbr.fit(X_train, y_train)
      3 #X_train.describe(include = 'all')

~anaconda3libsite-packagessklearnutilsvalidation.py in inner_f(*args, **kwargs)
     61             extra_args = len(args) - len(all_args)
     62             if extra_args <= 0:
---> 63                 return f(*args, **kwargs)
     64 
     65             # extra_args > 0

~anaconda3libsite-packagessklearnmodel_selection_search.py in fit(self, X, y, groups, **fit_params)
    839                 return results
    840 
--> 841             self._run_search(evaluate_candidates)
    842 
    843             # multimetric is determined here because in the case of a callable

~anaconda3libsite-packagessklearnmodel_selection_search.py in _run_search(self, evaluate_candidates)
   1633         evaluate_candidates(ParameterSampler(
   1634             self.param_distributions, self.n_iter,
-> 1635             random_state=self.random_state))

~anaconda3libsite-packagessklearnmodel_selection_search.py in evaluate_candidates(candidate_params, cv, more_results)
    807                                    (split_idx, (train, test)) in product(
    808                                    enumerate(candidate_params),
--> 809                                    enumerate(cv.split(X, y, groups))))
    810 
    811                 if len(out) < 1:

~anaconda3libsite-packagesjoblibparallel.py in __call__(self, iterable)
   1015 
   1016             with self._backend.retrieval_context():
-> 1017                 self.retrieve()
   1018             # Make sure that we get a last message telling us we are done
   1019             elapsed_time = time.time() - self._start_time

~anaconda3libsite-packagesjoblibparallel.py in retrieve(self)
    907             try:
    908                 if getattr(self._backend, 'supports_timeout', False):
--> 909                     self._output.extend(job.get(timeout=self.timeout))
    910                 else:
    911                     self._output.extend(job.get())

~anaconda3libsite-packagesjoblib_parallel_backends.py in wrap_future_result(future, timeout)
    560         AsyncResults.get from multiprocessing."""
    561         try:
--> 562             return future.result(timeout=timeout)
    563         except LokyTimeoutError:
    564             raise TimeoutError()

~anaconda3libconcurrentfutures_base.py in result(self, timeout)
    433                 raise CancelledError()
    434             elif self._state == FINISHED:
--> 435                 return self.__get_result()
    436             else:
    437                 raise TimeoutError()

~anaconda3libconcurrentfutures_base.py in __get_result(self)
    382     def __get_result(self):
    383         if self._exception:
--> 384             raise self._exception
    385         else:
    386             return self._result

KeyError: 'xgbr_regressor'

I also tried running fit without the sample_weight parameter. In this case the error changes to:

---------------------------------------------------------------------------
_RemoteTraceback                          Traceback (most recent call last)
_RemoteTraceback: 
"""
Traceback (most recent call last):
  File "C:UsersJananaconda3libsite-packagesjoblibexternalslokyprocess_executor.py", line 418, in _process_worker
    r = call_item()
  File "C:UsersJananaconda3libsite-packagesjoblibexternalslokyprocess_executor.py", line 272, in __call__
    return self.fn(*self.args, **self.kwargs)
  File "C:UsersJananaconda3libsite-packagesjoblib_parallel_backends.py", line 608, in __call__
    return self.func(*args, **kwargs)
  File "C:UsersJananaconda3libsite-packagesjoblibparallel.py", line 256, in __call__
    for func, args, kwargs in self.items]
  File "C:UsersJananaconda3libsite-packagesjoblibparallel.py", line 256, in <listcomp>
    for func, args, kwargs in self.items]
  File "C:UsersJananaconda3libsite-packagessklearnutilsfixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:UsersJananaconda3libsite-packagessklearnmodel_selection_validation.py", line 625, in _fit_and_score
    test_scores = _score(estimator, X_test, y_test, scorer, error_score)
  File "C:UsersJananaconda3libsite-packagessklearnmodel_selection_validation.py", line 687, in _score
    scores = scorer(estimator, X_test, y_test)
  File "C:UsersJananaconda3libsite-packagessklearnutilsvalidation.py", line 74, in inner_f
    return f(**kwargs)
  File "C:UsersJananaconda3libsite-packagessklearnmetrics_regression.py", line 336, in mean_squared_error
    y_true, y_pred, multioutput)
  File "C:UsersJananaconda3libsite-packagessklearnmetrics_regression.py", line 88, in _check_reg_targets
    check_consistent_length(y_true, y_pred)
  File "C:UsersJananaconda3libsite-packagessklearnutilsvalidation.py", line 316, in check_consistent_length
    lengths = [_num_samples(X) for X in arrays if X is not None]
  File "C:UsersJananaconda3libsite-packagessklearnutilsvalidation.py", line 316, in <listcomp>
    lengths = [_num_samples(X) for X in arrays if X is not None]
  File "C:UsersJananaconda3libsite-packagessklearnutilsvalidation.py", line 249, in _num_samples
    raise TypeError(message)
TypeError: Expected sequence or array-like, got <class 'sklearn.pipeline.Pipeline'>
"""

The above exception was the direct cause of the following exception:

TypeError                                 Traceback (most recent call last)
<ipython-input-69-a9be9cc5df4a> in <module>
----> 1 rscv.fit(X_train, y_train)#, xgbr_regressor__sample_weight = X_train['Exposure'])
      2 #pipe_xgbr.fit(X_train, y_train)
      3 #X_train.describe(include = 'all')

~anaconda3libsite-packagessklearnutilsvalidation.py in inner_f(*args, **kwargs)
     61             extra_args = len(args) - len(all_args)
     62             if extra_args <= 0:
---> 63                 return f(*args, **kwargs)
     64 
     65             # extra_args > 0

~anaconda3libsite-packagessklearnmodel_selection_search.py in fit(self, X, y, groups, **fit_params)
    839                 return results
    840 
--> 841             self._run_search(evaluate_candidates)
    842 
    843             # multimetric is determined here because in the case of a callable

~anaconda3libsite-packagessklearnmodel_selection_search.py in _run_search(self, evaluate_candidates)
   1633         evaluate_candidates(ParameterSampler(
   1634             self.param_distributions, self.n_iter,
-> 1635             random_state=self.random_state))

~anaconda3libsite-packagessklearnmodel_selection_search.py in evaluate_candidates(candidate_params, cv, more_results)
    807                                    (split_idx, (train, test)) in product(
    808                                    enumerate(candidate_params),
--> 809                                    enumerate(cv.split(X, y, groups))))
    810 
    811                 if len(out) < 1:

~anaconda3libsite-packagesjoblibparallel.py in __call__(self, iterable)
   1015 
   1016             with self._backend.retrieval_context():
-> 1017                 self.retrieve()
   1018             # Make sure that we get a last message telling us we are done
   1019             elapsed_time = time.time() - self._start_time

~anaconda3libsite-packagesjoblibparallel.py in retrieve(self)
    907             try:
    908                 if getattr(self._backend, 'supports_timeout', False):
--> 909                     self._output.extend(job.get(timeout=self.timeout))
    910                 else:
    911                     self._output.extend(job.get())

~anaconda3libsite-packagesjoblib_parallel_backends.py in wrap_future_result(future, timeout)
    560         AsyncResults.get from multiprocessing."""
    561         try:
--> 562             return future.result(timeout=timeout)
    563         except LokyTimeoutError:
    564             raise TimeoutError()

~anaconda3libconcurrentfutures_base.py in result(self, timeout)
    433                 raise CancelledError()
    434             elif self._state == FINISHED:
--> 435                 return self.__get_result()
    436             else:
    437                 raise TimeoutError()

~anaconda3libconcurrentfutures_base.py in __get_result(self)
    382     def __get_result(self):
    383         if self._exception:
--> 384             raise self._exception
    385         else:
    386             return self._result

TypeError: Expected sequence or array-like, got <class 'sklearn.pipeline.Pipeline'>

When setting verbose = 10 and n_jobs = 1 the following error message shows up:

Fitting 5 folds for each of 2 candidates, totalling 10 fits
[CV 1/5; 1/2] START xgb_regressor__colsample_bytree=0.5, xgb_regressor__learning_rate=0.5, xgb_regressor__max_depth=5, xgb_regressor__n_estimators=5, xgb_regressor__subsample=0.5
C:UsersJananaconda3libsite-packagessklearnutilsvalidation.py:72: FutureWarning: Pass sample_weight=406477    1.0
393150    0.0
252885    0.0
260652    0.0
661256    0.0
         ... 
154663    0.0
398414    0.0
42890     0.0
640774    0.0
114446    0.0
Name: frequency, Length: 108482, dtype: float64 as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error
  "will result in an error", FutureWarning)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-84-74435f74c470> in <module>
----> 1 rscv.fit(X_train, y_train, xgb_regressor__sample_weight = X_train['Exposure'])
      2 #pipe_xgbr.fit(X_train, y_train)
      3 #X_train.describe(include = 'all')

~anaconda3libsite-packagessklearnutilsvalidation.py in inner_f(*args, **kwargs)
     61             extra_args = len(args) - len(all_args)
     62             if extra_args <= 0:
---> 63                 return f(*args, **kwargs)
     64 
     65             # extra_args > 0

~anaconda3libsite-packagessklearnmodel_selection_search.py in fit(self, X, y, groups, **fit_params)
    839                 return results
    840 
--> 841             self._run_search(evaluate_candidates)
    842 
    843             # multimetric is determined here because in the case of a callable

~anaconda3libsite-packagessklearnmodel_selection_search.py in _run_search(self, evaluate_candidates)
   1633         evaluate_candidates(ParameterSampler(
   1634             self.param_distributions, self.n_iter,
-> 1635             random_state=self.random_state))

~anaconda3libsite-packagessklearnmodel_selection_search.py in evaluate_candidates(candidate_params, cv, more_results)
    807                                    (split_idx, (train, test)) in product(
    808                                    enumerate(candidate_params),
--> 809                                    enumerate(cv.split(X, y, groups))))
    810 
    811                 if len(out) < 1:

~anaconda3libsite-packagesjoblibparallel.py in __call__(self, iterable)
   1002             # remaining jobs.
   1003             self._iterating = False
-> 1004             if self.dispatch_one_batch(iterator):
   1005                 self._iterating = self._original_iterator is not None
   1006 

~anaconda3libsite-packagesjoblibparallel.py in dispatch_one_batch(self, iterator)
    833                 return False
    834             else:
--> 835                 self._dispatch(tasks)
    836                 return True
    837 

~anaconda3libsite-packagesjoblibparallel.py in _dispatch(self, batch)
    752         with self._lock:
    753             job_idx = len(self._jobs)
--> 754             job = self._backend.apply_async(batch, callback=cb)
    755             # A job can complete so quickly than its callback is
    756             # called before we get here, causing self._jobs to

~anaconda3libsite-packagesjoblib_parallel_backends.py in apply_async(self, func, callback)
    207     def apply_async(self, func, callback=None):
    208         """Schedule a func to be run"""
--> 209         result = ImmediateResult(func)
    210         if callback:
    211             callback(result)

~anaconda3libsite-packagesjoblib_parallel_backends.py in __init__(self, batch)
    588         # Don't delay the application, to avoid keeping the input
    589         # arguments in memory
--> 590         self.results = batch()
    591 
    592     def get(self):

~anaconda3libsite-packagesjoblibparallel.py in __call__(self)
    254         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    255             return [func(*args, **kwargs)
--> 256                     for func, args, kwargs in self.items]
    257 
    258     def __len__(self):

~anaconda3libsite-packagesjoblibparallel.py in <listcomp>(.0)
    254         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    255             return [func(*args, **kwargs)
--> 256                     for func, args, kwargs in self.items]
    257 
    258     def __len__(self):

~anaconda3libsite-packagessklearnutilsfixes.py in __call__(self, *args, **kwargs)
    220     def __call__(self, *args, **kwargs):
    221         with config_context(**self.config):
--> 222             return self.function(*args, **kwargs)

~anaconda3libsite-packagessklearnmodel_selection_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)
    623 
    624         fit_time = time.time() - start_time
--> 625         test_scores = _score(estimator, X_test, y_test, scorer, error_score)
    626         score_time = time.time() - start_time - fit_time
    627         if return_train_score:

~anaconda3libsite-packagessklearnmodel_selection_validation.py in _score(estimator, X_test, y_test, scorer, error_score)
    685             scores = scorer(estimator, X_test)
    686         else:
--> 687             scores = scorer(estimator, X_test, y_test)
    688     except Exception:
    689         if error_score == 'raise':

~anaconda3libsite-packagessklearnutilsvalidation.py in inner_f(*args, **kwargs)
     72                           "will result in an error", FutureWarning)
     73             kwargs.update(zip(sig.parameters, args))
---> 74             return f(**kwargs)
     75         return inner_f
     76 

~anaconda3libsite-packagessklearnmetrics_regression.py in mean_squared_error(y_true, y_pred, sample_weight, multioutput, squared)
    334     """
    335     y_type, y_true, y_pred, multioutput = _check_reg_targets(
--> 336         y_true, y_pred, multioutput)
    337     check_consistent_length(y_true, y_pred, sample_weight)
    338     output_errors = np.average((y_true - y_pred) ** 2, axis=0,

~anaconda3libsite-packagessklearnmetrics_regression.py in _check_reg_targets(y_true, y_pred, multioutput, dtype)
     86         the dtype argument passed to check_array.
     87     """
---> 88     check_consistent_length(y_true, y_pred)
     89     y_true = check_array(y_true, ensure_2d=False, dtype=dtype)
     90     y_pred = check_array(y_pred, ensure_2d=False, dtype=dtype)

~anaconda3libsite-packagessklearnutilsvalidation.py in check_consistent_length(*arrays)
    314     """
    315 
--> 316     lengths = [_num_samples(X) for X in arrays if X is not None]
    317     uniques = np.unique(lengths)
    318     if len(uniques) > 1:

~anaconda3libsite-packagessklearnutilsvalidation.py in <listcomp>(.0)
    314     """
    315 
--> 316     lengths = [_num_samples(X) for X in arrays if X is not None]
    317     uniques = np.unique(lengths)
    318     if len(uniques) > 1:

~anaconda3libsite-packagessklearnutilsvalidation.py in _num_samples(x)
    247     if hasattr(x, 'fit') and callable(x.fit):
    248         # Don't get num_samples from an ensembles length!
--> 249         raise TypeError(message)
    250 
    251     if not hasattr(x, '__len__') and not hasattr(x, 'shape'):

TypeError: Expected sequence or array-like, got <class 'sklearn.pipeline.Pipeline'>

Answer

Wow, that was a mess of a traceback, but I think I’ve finally found it. You set scoring=mean_squared_error, and should instead use scoring="neg_mean_squared_error".

The metric function mean_squared_error has signature (y_true, y_pred, *, <kwargs>), whereas the scorer obtained by using the string "neg_mean_squared_error" has signature (estimator, X_test, y_test). So in the traceback, where you see

--> 687             scores = scorer(estimator, X_test, y_test)

it is calling mean_squared_error with y_true=estimator, y_test=X_test, and sample_weight=y_test (the first kwarg, and hence the FutureWarning about specifying keyword arguments as positional). Going deeper into the traceback, we see a check that the shapes of y_true and y_pred are compatible, but it thinks the former is your pipeline object (and hence the final error message)!