AttributeError: module 'os' has no attribute 'statvfs'を解決したい！

前提・実現したいこと

エラーを直したい

発生している問題・エラーメッセージ

AttributeError                            Traceback (most recent call last)
<ipython-input-3-8055b91220f9> in <module>
     97 x_tes = np.reshape(X_test, (-1,3))
     98 
---> 99 r1.fit(x_tra, Y_train)
    100 predicted_labels_bs = r1.predict(x_tes)
    101 if r1.best_estimator_.score(x_tra,Y_train) < 0.95:

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in fit(self, X, y, groups, **fit_params)
    689                                     verbose=self.verbose)
    690         results_container = [{}]
--> 691         with parallel:
    692             all_candidate_params = []
    693             all_out = []

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __enter__(self)
    660     def __enter__(self):
    661         self._managed_backend = True
--> 662         self._initialize_backend()
    663         return self
    664 

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in _initialize_backend(self)
    671         try:
    672             n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,
--> 673                                              **self._backend_args)
    674             if self.timeout is not None and not self._backend.supports_timeout:
    675                 warnings.warn(

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in configure(self, n_jobs, parallel, prefer, require, idle_worker_timeout, **memmappingexecutor_args)
    474             n_jobs, timeout=idle_worker_timeout,
    475             initializer=self.limit_clib_threads,
--> 476             **memmappingexecutor_args)
    477         self.parallel = parallel
    478         return n_jobs

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\executor.py in get_memmapping_executor(n_jobs, timeout, initializer, initargs, **backend_args)
     29     id_executor = random.randint(0, int(1e10))
     30     job_reducers, result_reducers, temp_folder = get_memmapping_reducers(
---> 31         id_executor, **backend_args)
     32     _executor = get_reusable_executor(n_jobs, job_reducers=job_reducers,
     33                                       result_reducers=result_reducers,

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\_memmapping_reducer.py in get_memmapping_reducers(pool_id, forward_reducers, backward_reducers, temp_folder, max_nbytes, mmap_mode, verbose, prewarm, **kwargs)
    383         os.getpid(), pool_id)
    384     pool_folder, use_shared_mem = _get_temp_dir(pool_folder_name,
--> 385                                                 temp_folder)
    386 
    387     # Register the garbage collector at program exit in case caller forgets

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\_memmapping_reducer.py in _get_temp_dir(pool_folder_name, temp_folder)
    156         if os.path.exists(SYSTEM_SHARED_MEM_FS):
    157             try:
--> 158                 shm_stats = os.statvfs(SYSTEM_SHARED_MEM_FS)
    159                 available_nbytes = shm_stats.f_bsize * shm_stats.f_bavail
    160                 if available_nbytes > SYSTEM_SHARED_MEM_FS_MIN_SIZE:

AttributeError: module 'os' has no attribute 'statvfs'

というのが最後の行のr1.fitに出ます.

該当のソースコード

%matplotlib inline
import schedule
import time
from time import sleep
from light_progress.commandline import ProgressBar
from sshtunnel import SSHTunnelForwarder
import MySQLdb
import pandas as pd
import numpy as np
from sklearn.svm import SVC
from sklearn.metrics import r2_score
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import ExtraTreesRegressor
import mglearn
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn import linear_model
import datetime
def j():
    j = 0
def k():
    k = 0

server = SSHTunnelForwarder(
('xxx', xx),
ssh_username="xxx",
ssh_password="xxx",
remote_bind_address=('127.0.0.1', 3306)
)
server.start()
cnn = MySQLdb.connect(host='127.0.0.1',
                              port = server.local_bind_port,
                              db='xxx',
                              user='root',
                              passwd='xxx',        
                              charset='utf8')
sql = 'SELECT * FROM xxx ORDER BY xxx DESC LIMIT 30';
sql10 = 'SELECT * FROM xxx';       
try:
    with cnn.cursor() as cursor:
            cursor.execute(sql)
            cursor.execute(sql10)
            cnn.commit()
            
except:
                        pass            
first = 5000 
abc = 1
efg = 31 
i = np.array([range(abc,efg,1)])
I = np.array(range(abc,efg,1))
x16 = first-0.3876*i
select_sql1 = 'select measured_value_13 from t_trend2 ORDER BY occur_date DESC LIMIT 30';
select_sql2 = 'select measured_value_14 from t_trend2 ORDER BY occur_date DESC LIMIT 30';
select_sql3 = 'select measured_value_27 from t_trend2 ORDER BY occur_date DESC LIMIT 30'; 
select_sql4 = 'select measured_value_39 from t_trend2 ORDER BY occur_date DESC LIMIT 30'; 
try:
    with cnn.cursor() as cursor:
            cursor.execute(select_sql1)
            x25 = cursor.fetchmany(30)
            cursor.execute(select_sql2)
            x26 = cursor.fetchmany(30)
            cursor.execute(select_sql3)
            x18 = cursor.fetchmany(30)            
            cursor.execute(select_sql4)
            x100 = cursor.fetchmany(30)  
            cnn.commit()
except:
        pass
x25= np.array([x25])
x26= np.array([x26])
x18= np.array([x18])
x100= np.array(x100)
x13=x25 - x26
XX1=I  
XX = np.array([[x13],[x18],[XX1]])
Y = x16
X = XX.T      
X_train, X_test, Y_train, Y_test = train_test_split(X,Y,train_size = 0.7)        
        
search_params = {
    'n_estimators'      : [5, 10, 20, 30, 50, 100, 300, 400, 500,600,1000],
    'max_features'      : [0.1],
    'random_state'      : [2525],
    'n_jobs'            : [2],
    'min_samples_split' : [3, 5, 10, 15, 20, 25, 30, 40, 50, 100],
    'max_depth'         : [3, 5, 10, 15, 20, 25, 30, 40, 50, 100,1000,1500,10000]
}
 
r1 = GridSearchCV(
    RandomForestRegressor(),
    search_params,
    cv = 2,
    n_jobs = 2,
    verbose=True
)
x_tra = np.reshape(X_train, (-1,3)) 
x_tes = np.reshape(X_test, (-1,3)) 

r1.fit(x_tra, Y_train)