In [1]:

    

import numpy as np
import wisps
import splat
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix,accuracy_score, precision_score
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.model_selection import train_test_split, KFold,RepeatedKFold, GridSearchCV

import seaborn as sns
import pandas as pd
%matplotlib inline


    

In [2]:

    

comb=pd.read_hdf(wisps.COMBINED_PHOTO_SPECTRO_FILE, key='new_stars')


    

In [3]:

    

comb=comb.dropna(how='all')


    

In [4]:

    

comb=comb.loc[(comb[wisps.INDEX_NAMES]).dropna().index]


    

In [5]:

    

comb.shape


    

    
Out[5]:





(254264, 26)

In [6]:

    

train_df=pd.read_pickle(wisps.LIBRARIES+'/training_set.pkl').reset_index(drop=True)
                                                                           
pred_df=wisps.Annotator.reformat_table(comb).reset_index(drop=True)


    

In [7]:

    

pred_df=pred_df.drop_duplicates(subset='grism_id')


    

In [8]:

    

pred_df.shape, train_df.shape


    

    
Out[8]:





((254264, 26), (9728, 26))

In [9]:

    

def apply_scale(x):
    ##put features on a log scale
    #replace nans
    y=np.log10(x)
    if np.isnan(y) or np.isinf(y):
        y=np.random.uniform(-99, -98)
    return y

def create_labels(row):
    #use multiclass system
    label=0
    if row.label ==0.:
        label=0
    if (row.label==1) & (row.spt <20):
        label=1
    if (row.label==1) & np.logical_and(row.spt >=20, row.spt<30):
        label=2
    if (row.label==1) & np.logical_and(row.spt >=30, row.spt<45):
        label=3
    return label


    

In [10]:

    

pred_df['grism_id']=pred_df.grism_id.apply(lambda x: x.lower())


    

In [11]:

    

#features=wisps.INDEX_NAMES
features=np.concatenate([['snr2','f_test', 'line_chi', 'spex_chi'], wisps.INDEX_NAMES])
#features=['snr2','snr1', 'snr3', 'snr4', 'f_test']


    

In [12]:

    

pred_df=pred_df[pred_df.snr2>3.]
train_df['spt']=train_df.spt.apply(wisps.make_spt_number)
pred_df['spt']=pred_df.spt.apply(wisps.make_spt_number)


    

In [13]:

    

labels=train_df.apply(create_labels, axis=1).values


    

In [14]:

    

train_df[features]=(train_df[features]).applymap(apply_scale)
pred_df[features]=(pred_df[features]).applymap(apply_scale)


    

In [15]:

    

scaler = MinMaxScaler(feature_range=(-100, 100))
scaler.fit(train_df[features])
X=scaler.transform(train_df[features])
y=labels


    

In [16]:

    

#scale the data set to predict for the prediction set
pred_set=scaler.transform(pred_df[features])


    

In [17]:

    

class_weigths={0:1., 1:40/10000, 2:1/10000, 3:5/10000}


    

In [18]:

    

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5,  random_state=np.random.randint(1000))


    

In [19]:

    

np.linspace(10, 1000, 10, dtype=int)


    

    
Out[19]:





array([  10,  120,  230,  340,  450,  560,  670,  780,  890, 1000])

In [20]:

    

import dask


    

In [21]:

    

from dask.distributed import Client, progress
client = Client(processes=False, threads_per_worker=4,
                n_workers=1, memory_limit='2GB',  silence_logs='error')
client


    

    
Out[21]:







Client

  
Scheduler: inproc://192.168.0.2/38684/1
  
Dashboard: http://192.168.0.2:8787/status



Cluster

  
Workers: 1
  
Cores: 4
  
Memory: 2.00 GB

In [22]:

    

import warnings
warnings.filterwarnings('ignore')


    

In [23]:

    

np.linspace(2, 10, 10, dtype=int)


    

    
Out[23]:





array([ 2,  2,  3,  4,  5,  6,  7,  8,  9, 10])

In [28]:

    

#do some grid search, use dask to make it faster?
parameters = {'n_estimators': np.linspace(10, 1000, 10, dtype=int),
              'min_samples_split': np.linspace(1, 10, 10, dtype=int)}
#random forest
mrf= RandomForestClassifier()

#initialize grid search object

clf = GridSearchCV(mrf, parameters, refit=False, verbose=2, scoring='precision_macro')
#fit the training data
clf.fit(X, y)


    

    




Fitting 5 folds for each of 100 candidates, totalling 500 fits
[CV] min_samples_split=1, n_estimators=10 ............................
[CV] ............. min_samples_split=1, n_estimators=10, total=   0.0s
[CV] min_samples_split=1, n_estimators=10 ............................
[CV] ............. min_samples_split=1, n_estimators=10, total=   0.0s
[CV] min_samples_split=1, n_estimators=10 ............................
[CV] ............. min_samples_split=1, n_estimators=10, total=   0.0s
[CV] min_samples_split=1, n_estimators=10 ............................
[CV] ............. min_samples_split=1, n_estimators=10, total=   0.0s
[CV] min_samples_split=1, n_estimators=10 ............................
[CV] ............. min_samples_split=1, n_estimators=10, total=   0.0s
[CV] min_samples_split=1, n_estimators=120 ...........................
[CV] ............ min_samples_split=1, n_estimators=120, total=   0.1s
[CV] min_samples_split=1, n_estimators=120 ...........................






    




[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s






    




[CV] ............ min_samples_split=1, n_estimators=120, total=   0.1s
[CV] min_samples_split=1, n_estimators=120 ...........................
[CV] ............ min_samples_split=1, n_estimators=120, total=   0.1s
[CV] min_samples_split=1, n_estimators=120 ...........................
[CV] ............ min_samples_split=1, n_estimators=120, total=   0.1s
[CV] min_samples_split=1, n_estimators=120 ...........................
[CV] ............ min_samples_split=1, n_estimators=120, total=   0.1s
[CV] min_samples_split=1, n_estimators=230 ...........................
[CV] ............ min_samples_split=1, n_estimators=230, total=   0.1s
[CV] min_samples_split=1, n_estimators=230 ...........................
[CV] ............ min_samples_split=1, n_estimators=230, total=   0.1s
[CV] min_samples_split=1, n_estimators=230 ...........................
[CV] ............ min_samples_split=1, n_estimators=230, total=   0.1s
[CV] min_samples_split=1, n_estimators=230 ...........................
[CV] ............ min_samples_split=1, n_estimators=230, total=   0.1s
[CV] min_samples_split=1, n_estimators=230 ...........................
[CV] ............ min_samples_split=1, n_estimators=230, total=   0.1s
[CV] min_samples_split=1, n_estimators=340 ...........................
[CV] ............ min_samples_split=1, n_estimators=340, total=   0.3s
[CV] min_samples_split=1, n_estimators=340 ...........................
[CV] ............ min_samples_split=1, n_estimators=340, total=   0.2s
[CV] min_samples_split=1, n_estimators=340 ...........................
[CV] ............ min_samples_split=1, n_estimators=340, total=   0.2s
[CV] min_samples_split=1, n_estimators=340 ...........................
[CV] ............ min_samples_split=1, n_estimators=340, total=   0.2s
[CV] min_samples_split=1, n_estimators=340 ...........................
[CV] ............ min_samples_split=1, n_estimators=340, total=   0.2s
[CV] min_samples_split=1, n_estimators=450 ...........................
[CV] ............ min_samples_split=1, n_estimators=450, total=   0.3s
[CV] min_samples_split=1, n_estimators=450 ...........................
[CV] ............ min_samples_split=1, n_estimators=450, total=   0.2s
[CV] min_samples_split=1, n_estimators=450 ...........................
[CV] ............ min_samples_split=1, n_estimators=450, total=   0.3s
[CV] min_samples_split=1, n_estimators=450 ...........................
[CV] ............ min_samples_split=1, n_estimators=450, total=   0.3s
[CV] min_samples_split=1, n_estimators=450 ...........................
[CV] ............ min_samples_split=1, n_estimators=450, total=   0.3s
[CV] min_samples_split=1, n_estimators=560 ...........................
[CV] ............ min_samples_split=1, n_estimators=560, total=   0.3s
[CV] min_samples_split=1, n_estimators=560 ...........................
[CV] ............ min_samples_split=1, n_estimators=560, total=   0.4s
[CV] min_samples_split=1, n_estimators=560 ...........................
[CV] ............ min_samples_split=1, n_estimators=560, total=   0.3s
[CV] min_samples_split=1, n_estimators=560 ...........................
[CV] ............ min_samples_split=1, n_estimators=560, total=   0.3s
[CV] min_samples_split=1, n_estimators=560 ...........................
[CV] ............ min_samples_split=1, n_estimators=560, total=   0.4s
[CV] min_samples_split=1, n_estimators=670 ...........................
[CV] ............ min_samples_split=1, n_estimators=670, total=   0.5s
[CV] min_samples_split=1, n_estimators=670 ...........................
[CV] ............ min_samples_split=1, n_estimators=670, total=   0.4s
[CV] min_samples_split=1, n_estimators=670 ...........................
[CV] ............ min_samples_split=1, n_estimators=670, total=   0.4s
[CV] min_samples_split=1, n_estimators=670 ...........................
[CV] ............ min_samples_split=1, n_estimators=670, total=   0.4s
[CV] min_samples_split=1, n_estimators=670 ...........................
[CV] ............ min_samples_split=1, n_estimators=670, total=   0.5s
[CV] min_samples_split=1, n_estimators=780 ...........................
[CV] ............ min_samples_split=1, n_estimators=780, total=   0.6s
[CV] min_samples_split=1, n_estimators=780 ...........................
[CV] ............ min_samples_split=1, n_estimators=780, total=   0.4s
[CV] min_samples_split=1, n_estimators=780 ...........................
[CV] ............ min_samples_split=1, n_estimators=780, total=   0.4s
[CV] min_samples_split=1, n_estimators=780 ...........................
[CV] ............ min_samples_split=1, n_estimators=780, total=   0.4s
[CV] min_samples_split=1, n_estimators=780 ...........................
[CV] ............ min_samples_split=1, n_estimators=780, total=   0.4s
[CV] min_samples_split=1, n_estimators=890 ...........................
[CV] ............ min_samples_split=1, n_estimators=890, total=   0.4s
[CV] min_samples_split=1, n_estimators=890 ...........................
[CV] ............ min_samples_split=1, n_estimators=890, total=   1.2s
[CV] min_samples_split=1, n_estimators=890 ...........................
[CV] ............ min_samples_split=1, n_estimators=890, total=   0.4s
[CV] min_samples_split=1, n_estimators=890 ...........................
[CV] ............ min_samples_split=1, n_estimators=890, total=   0.4s
[CV] min_samples_split=1, n_estimators=890 ...........................
[CV] ............ min_samples_split=1, n_estimators=890, total=   0.4s
[CV] min_samples_split=1, n_estimators=1000 ..........................
[CV] ........... min_samples_split=1, n_estimators=1000, total=   0.5s
[CV] min_samples_split=1, n_estimators=1000 ..........................
[CV] ........... min_samples_split=1, n_estimators=1000, total=   0.5s
[CV] min_samples_split=1, n_estimators=1000 ..........................
[CV] ........... min_samples_split=1, n_estimators=1000, total=   0.5s
[CV] min_samples_split=1, n_estimators=1000 ..........................
[CV] ........... min_samples_split=1, n_estimators=1000, total=   0.5s
[CV] min_samples_split=1, n_estimators=1000 ..........................
[CV] ........... min_samples_split=1, n_estimators=1000, total=   0.5s
[CV] min_samples_split=2, n_estimators=10 ............................
[CV] ............. min_samples_split=2, n_estimators=10, total=   0.3s
[CV] min_samples_split=2, n_estimators=10 ............................
[CV] ............. min_samples_split=2, n_estimators=10, total=   0.2s
[CV] min_samples_split=2, n_estimators=10 ............................
[CV] ............. min_samples_split=2, n_estimators=10, total=   0.3s
[CV] min_samples_split=2, n_estimators=10 ............................
[CV] ............. min_samples_split=2, n_estimators=10, total=   0.2s
[CV] min_samples_split=2, n_estimators=10 ............................
[CV] ............. min_samples_split=2, n_estimators=10, total=   0.2s
[CV] min_samples_split=2, n_estimators=120 ...........................
[CV] ............ min_samples_split=2, n_estimators=120, total=   2.2s
[CV] min_samples_split=2, n_estimators=120 ...........................
[CV] ............ min_samples_split=2, n_estimators=120, total=   2.6s
[CV] min_samples_split=2, n_estimators=120 ...........................
[CV] ............ min_samples_split=2, n_estimators=120, total=   3.1s
[CV] min_samples_split=2, n_estimators=120 ...........................
[CV] ............ min_samples_split=5, n_estimators=780, total=26.8min






    




distributed.client - ERROR - Error in callback <function DaskDistributedBackend.apply_async.<locals>.callback_wrapper at 0x1a5032c440> of <Future: cancelled, type: builtins.list, key: _parallel_build_trees-batch-969183dc7713416f87a0398f1b2a8ef5>:
Traceback (most recent call last):
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 285, in execute_callback
    fn(fut)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/joblib/_dask.py", line 260, in callback_wrapper
    result = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 222, in result
    raise result
concurrent.futures._base.CancelledError: _parallel_build_trees-batch-969183dc7713416f87a0398f1b2a8ef5
distributed.client - ERROR - Error in callback <function DaskDistributedBackend.apply_async.<locals>.callback_wrapper at 0x1b0ec71440> of <Future: cancelled, key: _parallel_build_trees-batch-889faa4206c64b38be3f976b1936b462>:
Traceback (most recent call last):
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 285, in execute_callback
    fn(fut)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/joblib/_dask.py", line 260, in callback_wrapper
    result = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 222, in result
    raise result
concurrent.futures._base.CancelledError: _parallel_build_trees-batch-889faa4206c64b38be3f976b1936b462
distributed.client - ERROR - Error in callback <function DaskDistributedBackend.apply_async.<locals>.callback_wrapper at 0x1b13563710> of <Future: cancelled, key: _parallel_build_trees-batch-d31607e039ed4958adedc4391561a8e2>:
Traceback (most recent call last):
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 285, in execute_callback
    fn(fut)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/joblib/_dask.py", line 260, in callback_wrapper
    result = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 222, in result
    raise result
concurrent.futures._base.CancelledError: _parallel_build_trees-batch-d31607e039ed4958adedc4391561a8e2
distributed.client - ERROR - Error in callback <function DaskDistributedBackend.apply_async.<locals>.callback_wrapper at 0x1b111acf80> of <Future: cancelled, key: _parallel_build_trees-batch-77f6b8953a264c28b364d7eac7010eea>:
Traceback (most recent call last):
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 285, in execute_callback
    fn(fut)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/joblib/_dask.py", line 260, in callback_wrapper
    result = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 222, in result
    raise result
concurrent.futures._base.CancelledError: _parallel_build_trees-batch-77f6b8953a264c28b364d7eac7010eea
distributed.client - ERROR - Error in callback <function DaskDistributedBackend.apply_async.<locals>.callback_wrapper at 0x1b11807950> of <Future: cancelled, key: _parallel_build_trees-batch-4ef9cc99d331460694a56b2c84b59b36>:
Traceback (most recent call last):
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 285, in execute_callback
    fn(fut)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/joblib/_dask.py", line 260, in callback_wrapper
    result = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 222, in result
    raise result
concurrent.futures._base.CancelledError: _parallel_build_trees-batch-4ef9cc99d331460694a56b2c84b59b36
distributed.client - ERROR - Error in callback <function DaskDistributedBackend.apply_async.<locals>.callback_wrapper at 0x1b3a4637a0> of <Future: cancelled, key: _parallel_build_trees-batch-ed54963ba9c0495095f63a2ded321bc5>:
Traceback (most recent call last):
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 285, in execute_callback
    fn(fut)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/joblib/_dask.py", line 260, in callback_wrapper
    result = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 222, in result
    raise result
concurrent.futures._base.CancelledError: _parallel_build_trees-batch-ed54963ba9c0495095f63a2ded321bc5
distributed.client - ERROR - Error in callback <function DaskDistributedBackend.apply_async.<locals>.callback_wrapper at 0x1b10d7f7a0> of <Future: cancelled, key: _parallel_build_trees-batch-20ab4a25d16c408a997c7d9da134b005>:
Traceback (most recent call last):
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 285, in execute_callback
    fn(fut)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/joblib/_dask.py", line 260, in callback_wrapper
    result = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 222, in result
    raise result
concurrent.futures._base.CancelledError: _parallel_build_trees-batch-20ab4a25d16c408a997c7d9da134b005






    




[CV] ............ min_samples_split=2, n_estimators=120, total=   8.2s
[CV] min_samples_split=2, n_estimators=120 ...........................
[CV] ............ min_samples_split=2, n_estimators=120, total=   3.0s
[CV] min_samples_split=2, n_estimators=230 ...........................
[CV] ............ min_samples_split=2, n_estimators=230, total=   8.9s
[CV] min_samples_split=2, n_estimators=230 ...........................
[CV] ............ min_samples_split=2, n_estimators=230, total=   6.5s
[CV] min_samples_split=2, n_estimators=230 ...........................
[CV] ............ min_samples_split=2, n_estimators=230, total=   6.5s
[CV] min_samples_split=2, n_estimators=230 ...........................
[CV] ............ min_samples_split=2, n_estimators=230, total=   5.8s
[CV] min_samples_split=2, n_estimators=230 ...........................
[CV] ............ min_samples_split=2, n_estimators=230, total=   4.2s
[CV] min_samples_split=2, n_estimators=340 ...........................
[CV] ............ min_samples_split=2, n_estimators=340, total=   6.2s
[CV] min_samples_split=2, n_estimators=340 ...........................
[CV] ............ min_samples_split=2, n_estimators=340, total=   7.6s
[CV] min_samples_split=2, n_estimators=340 ...........................
[CV] ............ min_samples_split=2, n_estimators=340, total=   6.7s
[CV] min_samples_split=2, n_estimators=340 ...........................
[CV] ............ min_samples_split=2, n_estimators=340, total=   7.2s
[CV] min_samples_split=2, n_estimators=340 ...........................
[CV] ............ min_samples_split=2, n_estimators=340, total=   7.1s
[CV] min_samples_split=2, n_estimators=450 ...........................
[CV] ............ min_samples_split=2, n_estimators=450, total=   9.9s
[CV] min_samples_split=2, n_estimators=450 ...........................
[CV] ............ min_samples_split=2, n_estimators=450, total=   8.1s
[CV] min_samples_split=2, n_estimators=450 ...........................
[CV] ............ min_samples_split=2, n_estimators=450, total=   9.0s
[CV] min_samples_split=2, n_estimators=450 ...........................
[CV] ............ min_samples_split=2, n_estimators=450, total=   8.1s
[CV] min_samples_split=2, n_estimators=450 ...........................
[CV] ............ min_samples_split=2, n_estimators=450, total=   8.5s
[CV] min_samples_split=2, n_estimators=560 ...........................
[CV] ............ min_samples_split=2, n_estimators=560, total=  10.9s
[CV] min_samples_split=2, n_estimators=560 ...........................
[CV] ............ min_samples_split=2, n_estimators=560, total=  10.0s
[CV] min_samples_split=2, n_estimators=560 ...........................
[CV] ............ min_samples_split=2, n_estimators=560, total=  10.7s
[CV] min_samples_split=2, n_estimators=560 ...........................
[CV] ............ min_samples_split=2, n_estimators=560, total=  10.6s
[CV] min_samples_split=2, n_estimators=560 ...........................
[CV] ............ min_samples_split=2, n_estimators=560, total=  10.1s
[CV] min_samples_split=2, n_estimators=670 ...........................
[CV] ............ min_samples_split=2, n_estimators=670, total=  11.8s
[CV] min_samples_split=2, n_estimators=670 ...........................
[CV] ............ min_samples_split=2, n_estimators=670, total=  12.5s
[CV] min_samples_split=2, n_estimators=670 ...........................
[CV] ............ min_samples_split=2, n_estimators=670, total=  12.6s
[CV] min_samples_split=2, n_estimators=670 ...........................
[CV] ............ min_samples_split=2, n_estimators=670, total=  12.6s
[CV] min_samples_split=2, n_estimators=670 ...........................
[CV] ............ min_samples_split=2, n_estimators=670, total=  13.0s
[CV] min_samples_split=2, n_estimators=780 ...........................
[CV] ............ min_samples_split=2, n_estimators=780, total=  18.6s
[CV] min_samples_split=2, n_estimators=780 ...........................
[CV] ............ min_samples_split=2, n_estimators=780, total=  17.8s
[CV] min_samples_split=2, n_estimators=780 ...........................
[CV] ............ min_samples_split=2, n_estimators=780, total=  24.9s
[CV] min_samples_split=2, n_estimators=780 ...........................
[CV] ............ min_samples_split=2, n_estimators=780, total=  25.5s
[CV] min_samples_split=2, n_estimators=780 ...........................
[CV] ............ min_samples_split=2, n_estimators=780, total=  20.8s
[CV] min_samples_split=2, n_estimators=890 ...........................






    




distributed.client - ERROR - Error in callback <function DaskDistributedBackend.apply_async.<locals>.callback_wrapper at 0x1b1127ce60> of <Future: finished, type: builtins.list, key: _parallel_build_trees-batch-218da332986f4017aa21386a4f7b15bc>:
Traceback (most recent call last):
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 285, in execute_callback
    fn(fut)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/joblib/_dask.py", line 260, in callback_wrapper
    result = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 217, in result
    result = self.client.sync(self._result, callback_timeout=timeout, raiseit=False)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 780, in sync
    self.loop, func, *args, callback_timeout=callback_timeout, **kwargs
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/utils.py", line 348, in sync
    raise exc.with_traceback(tb)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/utils.py", line 332, in f
    result[0] = yield future
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/tornado/gen.py", line 735, in run
    value = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 242, in _result
    result = await self.client._gather([self])
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 1781, in _gather
    response = await future
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 1822, in _gather_remote
    who_has, rpc=self.rpc, close=False
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/utils_comm.py", line 87, in gather_from_workers
    response.update(r["data"])
KeyError: 'data'
distributed.client - ERROR - Error in callback <function DaskDistributedBackend.apply_async.<locals>.callback_wrapper at 0x1b0ff1cf80> of <Future: finished, type: builtins.list, key: _parallel_build_trees-batch-c02d92a302b641c5a1d3500a415e18d8>:
Traceback (most recent call last):
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 285, in execute_callback
    fn(fut)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/joblib/_dask.py", line 260, in callback_wrapper
    result = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 217, in result
    result = self.client.sync(self._result, callback_timeout=timeout, raiseit=False)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 780, in sync
    self.loop, func, *args, callback_timeout=callback_timeout, **kwargs
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/utils.py", line 348, in sync
    raise exc.with_traceback(tb)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/utils.py", line 332, in f
    result[0] = yield future
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/tornado/gen.py", line 735, in run
    value = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 242, in _result
    result = await self.client._gather([self])
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 1781, in _gather
    response = await future
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 1822, in _gather_remote
    who_has, rpc=self.rpc, close=False
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/utils_comm.py", line 87, in gather_from_workers
    response.update(r["data"])
KeyError: 'data'
distributed.client - ERROR - Error in callback <function DaskDistributedBackend.apply_async.<locals>.callback_wrapper at 0x1b0ea6e8c0> of <Future: finished, type: builtins.list, key: _parallel_build_trees-batch-f14d9d1f6658471ab8a6ff6513483a3a>:
Traceback (most recent call last):
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 285, in execute_callback
    fn(fut)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/joblib/_dask.py", line 260, in callback_wrapper
    result = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 217, in result
    result = self.client.sync(self._result, callback_timeout=timeout, raiseit=False)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 780, in sync
    self.loop, func, *args, callback_timeout=callback_timeout, **kwargs
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/utils.py", line 348, in sync
    raise exc.with_traceback(tb)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/utils.py", line 332, in f
    result[0] = yield future
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/tornado/gen.py", line 735, in run
    value = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 242, in _result
    result = await self.client._gather([self])
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 1781, in _gather
    response = await future
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 1822, in _gather_remote
    who_has, rpc=self.rpc, close=False
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/utils_comm.py", line 87, in gather_from_workers
    response.update(r["data"])
KeyError: 'data'
distributed.client - ERROR - Error in callback <function DaskDistributedBackend.apply_async.<locals>.callback_wrapper at 0x1b0f62c440> of <Future: finished, type: builtins.list, key: _parallel_build_trees-batch-d618750918e041fc9e3e7e40ee70111f>:
Traceback (most recent call last):
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 285, in execute_callback
    fn(fut)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/joblib/_dask.py", line 260, in callback_wrapper
    result = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 217, in result
    result = self.client.sync(self._result, callback_timeout=timeout, raiseit=False)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 780, in sync
    self.loop, func, *args, callback_timeout=callback_timeout, **kwargs
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/utils.py", line 348, in sync
    raise exc.with_traceback(tb)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/utils.py", line 332, in f
    result[0] = yield future
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/tornado/gen.py", line 735, in run
    value = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 242, in _result
    result = await self.client._gather([self])
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 1781, in _gather
    response = await future
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 1822, in _gather_remote
    who_has, rpc=self.rpc, close=False
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/utils_comm.py", line 87, in gather_from_workers
    response.update(r["data"])
KeyError: 'data'
distributed.client - ERROR - Error in callback <function DaskDistributedBackend.apply_async.<locals>.callback_wrapper at 0x1b11ec1cb0> of <Future: finished, type: builtins.list, key: _parallel_build_trees-batch-e2613388b0a94861aa5ed808435df863>:
Traceback (most recent call last):
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 285, in execute_callback
    fn(fut)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/joblib/_dask.py", line 260, in callback_wrapper
    result = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 217, in result
    result = self.client.sync(self._result, callback_timeout=timeout, raiseit=False)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 780, in sync
    self.loop, func, *args, callback_timeout=callback_timeout, **kwargs
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/utils.py", line 348, in sync
    raise exc.with_traceback(tb)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/utils.py", line 332, in f
    result[0] = yield future
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/tornado/gen.py", line 735, in run
    value = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 242, in _result
    result = await self.client._gather([self])
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 1781, in _gather
    response = await future
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 1822, in _gather_remote
    who_has, rpc=self.rpc, close=False
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/utils_comm.py", line 87, in gather_from_workers
    response.update(r["data"])
KeyError: 'data'






    




[CV] ............ min_samples_split=2, n_estimators=890, total=  40.0s
[CV] min_samples_split=2, n_estimators=890 ...........................
[CV] ............ min_samples_split=2, n_estimators=890, total=  30.4s
[CV] min_samples_split=2, n_estimators=890 ...........................
[CV] ............ min_samples_split=2, n_estimators=890, total=  27.4s
[CV] min_samples_split=2, n_estimators=890 ...........................
[CV] ............ min_samples_split=2, n_estimators=890, total=  22.7s
[CV] min_samples_split=2, n_estimators=890 ...........................
[CV] ............ min_samples_split=2, n_estimators=890, total=  26.3s
[CV] min_samples_split=2, n_estimators=1000 ..........................
[CV] ........... min_samples_split=2, n_estimators=1000, total=  24.1s
[CV] min_samples_split=2, n_estimators=1000 ..........................
[CV] ........... min_samples_split=2, n_estimators=1000, total=  24.9s
[CV] min_samples_split=2, n_estimators=1000 ..........................
[CV] ........... min_samples_split=2, n_estimators=1000, total=  26.5s
[CV] min_samples_split=2, n_estimators=1000 ..........................
[CV] ........... min_samples_split=2, n_estimators=1000, total=  27.4s
[CV] min_samples_split=2, n_estimators=1000 ..........................
[CV] ........... min_samples_split=2, n_estimators=1000, total=  24.1s
[CV] min_samples_split=3, n_estimators=10 ............................
[CV] ............. min_samples_split=3, n_estimators=10, total=   0.3s
[CV] min_samples_split=3, n_estimators=10 ............................
[CV] ............. min_samples_split=3, n_estimators=10, total=   0.5s
[CV] min_samples_split=3, n_estimators=10 ............................
[CV] ............. min_samples_split=3, n_estimators=10, total=   0.4s
[CV] min_samples_split=3, n_estimators=10 ............................
[CV] ............. min_samples_split=3, n_estimators=10, total=   0.4s
[CV] min_samples_split=3, n_estimators=10 ............................
[CV] ............. min_samples_split=3, n_estimators=10, total=   0.2s
[CV] min_samples_split=3, n_estimators=120 ...........................
[CV] ............ min_samples_split=3, n_estimators=120, total=   4.2s
[CV] min_samples_split=3, n_estimators=120 ...........................
[CV] ............ min_samples_split=3, n_estimators=120, total=   6.0s
[CV] min_samples_split=3, n_estimators=120 ...........................
[CV] ............ min_samples_split=3, n_estimators=120, total=   5.0s
[CV] min_samples_split=3, n_estimators=120 ...........................
[CV] ............ min_samples_split=3, n_estimators=120, total=   2.6s
[CV] min_samples_split=3, n_estimators=120 ...........................
[CV] ............ min_samples_split=3, n_estimators=120, total=   4.3s
[CV] min_samples_split=3, n_estimators=230 ...........................
[CV] ............ min_samples_split=3, n_estimators=230, total=   5.9s
[CV] min_samples_split=3, n_estimators=230 ...........................
[CV] ............ min_samples_split=3, n_estimators=230, total=   6.6s
[CV] min_samples_split=3, n_estimators=230 ...........................
[CV] ............ min_samples_split=3, n_estimators=230, total=   5.7s
[CV] min_samples_split=3, n_estimators=230 ...........................
[CV] ............ min_samples_split=3, n_estimators=230, total=   8.4s
[CV] min_samples_split=3, n_estimators=230 ...........................
[CV] ............ min_samples_split=3, n_estimators=230, total=   4.7s
[CV] min_samples_split=3, n_estimators=340 ...........................
[CV] ............ min_samples_split=3, n_estimators=340, total=   7.6s
[CV] min_samples_split=3, n_estimators=340 ...........................
[CV] ............ min_samples_split=3, n_estimators=340, total=  10.1s
[CV] min_samples_split=3, n_estimators=340 ...........................
[CV] ............ min_samples_split=3, n_estimators=340, total=   9.3s
[CV] min_samples_split=3, n_estimators=340 ...........................
[CV] ............ min_samples_split=3, n_estimators=340, total=   9.4s
[CV] min_samples_split=3, n_estimators=340 ...........................
[CV] ............ min_samples_split=3, n_estimators=340, total=   7.6s
[CV] min_samples_split=3, n_estimators=450 ...........................
[CV] ............ min_samples_split=3, n_estimators=450, total=  11.8s
[CV] min_samples_split=3, n_estimators=450 ...........................
[CV] ............ min_samples_split=3, n_estimators=450, total=  14.7s
[CV] min_samples_split=3, n_estimators=450 ...........................
[CV] ............ min_samples_split=3, n_estimators=450, total=  11.8s
[CV] min_samples_split=3, n_estimators=450 ...........................
[CV] ............ min_samples_split=3, n_estimators=450, total=  11.8s
[CV] min_samples_split=3, n_estimators=450 ...........................
[CV] ............ min_samples_split=3, n_estimators=450, total=  13.4s
[CV] min_samples_split=3, n_estimators=560 ...........................






    




distributed.client - ERROR - Error in callback <function DaskDistributedBackend.apply_async.<locals>.callback_wrapper at 0x1b409e07a0> of <Future: finished, type: builtins.list, key: _parallel_build_trees-batch-7b87c5eb72c943eda410cb6f4e4b7ea0>:
Traceback (most recent call last):
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 285, in execute_callback
    fn(fut)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/joblib/_dask.py", line 260, in callback_wrapper
    result = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 217, in result
    result = self.client.sync(self._result, callback_timeout=timeout, raiseit=False)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 780, in sync
    self.loop, func, *args, callback_timeout=callback_timeout, **kwargs
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/utils.py", line 348, in sync
    raise exc.with_traceback(tb)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/utils.py", line 332, in f
    result[0] = yield future
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/tornado/gen.py", line 735, in run
    value = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 242, in _result
    result = await self.client._gather([self])
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 1781, in _gather
    response = await future
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 1822, in _gather_remote
    who_has, rpc=self.rpc, close=False
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/utils_comm.py", line 87, in gather_from_workers
    response.update(r["data"])
KeyError: 'data'
distributed.scheduler - ERROR - Couldn't gather keys {'_parallel_build_trees-batch-4bbc7e924f8a418890e399016bdde2f3': []} state: [None] workers: []
NoneType: None
distributed.scheduler - ERROR - Workers don't have promised key: [], _parallel_build_trees-batch-4bbc7e924f8a418890e399016bdde2f3
NoneType: None
distributed.client - ERROR - Error in callback <function DaskDistributedBackend.apply_async.<locals>.callback_wrapper at 0x1b27536950> of <Future: cancelled, type: builtins.list, key: _parallel_build_trees-batch-4bbc7e924f8a418890e399016bdde2f3>:
Traceback (most recent call last):
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 285, in execute_callback
    fn(fut)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/joblib/_dask.py", line 260, in callback_wrapper
    result = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 217, in result
    result = self.client.sync(self._result, callback_timeout=timeout, raiseit=False)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 780, in sync
    self.loop, func, *args, callback_timeout=callback_timeout, **kwargs
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/utils.py", line 348, in sync
    raise exc.with_traceback(tb)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/utils.py", line 332, in f
    result[0] = yield future
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/tornado/gen.py", line 735, in run
    value = future.result()
concurrent.futures._base.CancelledError






    




[CV] ............ min_samples_split=5, n_estimators=670, total=40.8min
[CV] ............ min_samples_split=5, n_estimators=670, total=40.5min






    




distributed.client - ERROR - Error in callback <function DaskDistributedBackend.apply_async.<locals>.callback_wrapper at 0x1b10c5f5f0> of <Future: cancelled, type: builtins.list, key: _parallel_build_trees-batch-0eecfb4a6331457ca7fedc777e3fac25>:
Traceback (most recent call last):
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 285, in execute_callback
    fn(fut)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/joblib/_dask.py", line 260, in callback_wrapper
    result = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 222, in result
    raise result
concurrent.futures._base.CancelledError: _parallel_build_trees-batch-0eecfb4a6331457ca7fedc777e3fac25
distributed.client - ERROR - Error in callback <function DaskDistributedBackend.apply_async.<locals>.callback_wrapper at 0x1a3f583c20> of <Future: cancelled, type: builtins.list, key: _parallel_build_trees-batch-eaef3112704a4fdbad4cc9771b25328d>:
Traceback (most recent call last):
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 285, in execute_callback
    fn(fut)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/joblib/_dask.py", line 260, in callback_wrapper
    result = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 222, in result
    raise result
concurrent.futures._base.CancelledError: _parallel_build_trees-batch-eaef3112704a4fdbad4cc9771b25328d
distributed.client - ERROR - Error in callback <function DaskDistributedBackend.apply_async.<locals>.callback_wrapper at 0x1b10c57200> of <Future: cancelled, type: builtins.list, key: _parallel_build_trees-batch-919ff7b591b44310bebb604918c02b04>:
Traceback (most recent call last):
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 285, in execute_callback
    fn(fut)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/joblib/_dask.py", line 260, in callback_wrapper
    result = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 222, in result
    raise result
concurrent.futures._base.CancelledError: _parallel_build_trees-batch-919ff7b591b44310bebb604918c02b04
distributed.client - ERROR - Error in callback <function DaskDistributedBackend.apply_async.<locals>.callback_wrapper at 0x1b401f4dd0> of <Future: cancelled, key: _parallel_build_trees-batch-c3ff6e6fbe0145b1a32ca1c77748b2d4>:
Traceback (most recent call last):
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 285, in execute_callback
    fn(fut)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/joblib/_dask.py", line 260, in callback_wrapper
    result = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 222, in result
    raise result
concurrent.futures._base.CancelledError: _parallel_build_trees-batch-c3ff6e6fbe0145b1a32ca1c77748b2d4
distributed.client - ERROR - Error in callback <function DaskDistributedBackend.apply_async.<locals>.callback_wrapper at 0x1b0f1f6c20> of <Future: cancelled, key: _parallel_build_trees-batch-1496ab9456644704ba0fd5d3cb6ba94d>:
Traceback (most recent call last):
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 285, in execute_callback
    fn(fut)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/joblib/_dask.py", line 260, in callback_wrapper
    result = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 222, in result
    raise result
concurrent.futures._base.CancelledError: _parallel_build_trees-batch-1496ab9456644704ba0fd5d3cb6ba94d
distributed.client - ERROR - Error in callback <function DaskDistributedBackend.apply_async.<locals>.callback_wrapper at 0x1b2f653dd0> of <Future: cancelled, key: _parallel_build_trees-batch-0417776c6bc84f7bb7c95f4ea5a0c1cb>:
Traceback (most recent call last):
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 285, in execute_callback
    fn(fut)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/joblib/_dask.py", line 260, in callback_wrapper
    result = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 222, in result
    raise result
concurrent.futures._base.CancelledError: _parallel_build_trees-batch-0417776c6bc84f7bb7c95f4ea5a0c1cb
distributed.client - ERROR - Error in callback <function DaskDistributedBackend.apply_async.<locals>.callback_wrapper at 0x1ad2ea9050> of <Future: cancelled, key: _parallel_build_trees-batch-da294c43ddf9454bb1d358ef9e7f3a05>:
Traceback (most recent call last):
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 285, in execute_callback
    fn(fut)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/joblib/_dask.py", line 260, in callback_wrapper
    result = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 222, in result
    raise result
concurrent.futures._base.CancelledError: _parallel_build_trees-batch-da294c43ddf9454bb1d358ef9e7f3a05
distributed.client - ERROR - Error in callback <function DaskDistributedBackend.apply_async.<locals>.callback_wrapper at 0x1b361b54d0> of <Future: cancelled, key: _parallel_build_trees-batch-5b39ac9af47b40219bbe6ef94c2e276c>:
Traceback (most recent call last):
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 285, in execute_callback
    fn(fut)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/joblib/_dask.py", line 260, in callback_wrapper
    result = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 222, in result
    raise result
concurrent.futures._base.CancelledError: _parallel_build_trees-batch-5b39ac9af47b40219bbe6ef94c2e276c
distributed.client - ERROR - Error in callback <function DaskDistributedBackend.apply_async.<locals>.callback_wrapper at 0x1b2b116290> of <Future: cancelled, key: _parallel_build_trees-batch-1a9c3f77ccb14f3e9a6eeff4fc7488ad>:
Traceback (most recent call last):
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 285, in execute_callback
    fn(fut)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/joblib/_dask.py", line 260, in callback_wrapper
    result = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 222, in result
    raise result
concurrent.futures._base.CancelledError: _parallel_build_trees-batch-1a9c3f77ccb14f3e9a6eeff4fc7488ad
distributed.client - ERROR - Error in callback <function DaskDistributedBackend.apply_async.<locals>.callback_wrapper at 0x1b39dd0440> of <Future: cancelled, key: _parallel_build_trees-batch-88171e75d0414151963503172d6d778a>:
Traceback (most recent call last):
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 285, in execute_callback
    fn(fut)
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/joblib/_dask.py", line 260, in callback_wrapper
    result = future.result()
  File "/Users/caganze/anaconda3/lib/python3.7/site-packages/distributed/client.py", line 222, in result
    raise result
concurrent.futures._base.CancelledError: _parallel_build_trees-batch-88171e75d0414151963503172d6d778a






    




[CV] ............ min_samples_split=3, n_estimators=560, total=  18.3s
[CV] min_samples_split=3, n_estimators=560 ...........................
[CV] ............ min_samples_split=5, n_estimators=780, total=40.3min
[CV] ............ min_samples_split=5, n_estimators=780, total=40.1min
[CV] ............ min_samples_split=3, n_estimators=560, total=  38.3s
[CV] min_samples_split=3, n_estimators=560 ...........................
[CV] ............ min_samples_split=3, n_estimators=560, total=  17.8s
[CV] min_samples_split=3, n_estimators=560 ...........................
[CV] ............ min_samples_split=3, n_estimators=560, total=  15.8s
[CV] min_samples_split=3, n_estimators=560 ...........................
[CV] ............ min_samples_split=3, n_estimators=560, total=  12.6s
[CV] min_samples_split=3, n_estimators=670 ...........................
[CV] ............ min_samples_split=3, n_estimators=670, total=  19.8s
[CV] min_samples_split=3, n_estimators=670 ...........................
[CV] ............ min_samples_split=3, n_estimators=670, total=  20.0s
[CV] min_samples_split=3, n_estimators=670 ...........................
[CV] ............ min_samples_split=3, n_estimators=670, total=  14.6s
[CV] min_samples_split=3, n_estimators=670 ...........................
[CV] ............ min_samples_split=3, n_estimators=670, total=  16.6s
[CV] min_samples_split=3, n_estimators=670 ...........................
[CV] ............ min_samples_split=3, n_estimators=670, total=  20.6s
[CV] min_samples_split=3, n_estimators=780 ...........................
[CV] ............ min_samples_split=3, n_estimators=780, total=  21.1s
[CV] min_samples_split=3, n_estimators=780 ...........................
[CV] ............ min_samples_split=3, n_estimators=780, total=  14.6s
[CV] min_samples_split=3, n_estimators=780 ...........................
[CV] ............ min_samples_split=3, n_estimators=780, total=  13.9s
[CV] min_samples_split=3, n_estimators=780 ...........................
[CV] ............ min_samples_split=3, n_estimators=780, total=  13.7s
[CV] min_samples_split=3, n_estimators=780 ...........................
[CV] ............ min_samples_split=3, n_estimators=780, total=  12.7s
[CV] min_samples_split=3, n_estimators=890 ...........................
[CV] ............ min_samples_split=3, n_estimators=890, total=  15.9s
[CV] min_samples_split=3, n_estimators=890 ...........................
[CV] ............ min_samples_split=3, n_estimators=890, total=  15.6s
[CV] min_samples_split=3, n_estimators=890 ...........................
[CV] ............ min_samples_split=3, n_estimators=890, total=  15.3s
[CV] min_samples_split=3, n_estimators=890 ...........................
[CV] ............ min_samples_split=3, n_estimators=890, total=  18.2s
[CV] min_samples_split=3, n_estimators=890 ...........................
[CV] ............ min_samples_split=3, n_estimators=890, total=  27.8s
[CV] min_samples_split=3, n_estimators=1000 ..........................
[CV] ........... min_samples_split=3, n_estimators=1000, total=  24.6s
[CV] min_samples_split=3, n_estimators=1000 ..........................
[CV] ........... min_samples_split=3, n_estimators=1000, total=  21.3s
[CV] min_samples_split=3, n_estimators=1000 ..........................
[CV] ........... min_samples_split=3, n_estimators=1000, total=  24.6s
[CV] min_samples_split=3, n_estimators=1000 ..........................
[CV] ........... min_samples_split=3, n_estimators=1000, total=  23.2s
[CV] min_samples_split=3, n_estimators=1000 ..........................
[CV] ........... min_samples_split=3, n_estimators=1000, total=  21.6s
[CV] min_samples_split=4, n_estimators=10 ............................
[CV] ............. min_samples_split=4, n_estimators=10, total=   0.3s
[CV] min_samples_split=4, n_estimators=10 ............................
[CV] ............. min_samples_split=4, n_estimators=10, total=   0.2s
[CV] min_samples_split=4, n_estimators=10 ............................
[CV] ............. min_samples_split=4, n_estimators=10, total=   0.2s
[CV] min_samples_split=4, n_estimators=10 ............................
[CV] ............. min_samples_split=4, n_estimators=10, total=   0.2s
[CV] min_samples_split=4, n_estimators=10 ............................
[CV] ............. min_samples_split=4, n_estimators=10, total=   0.2s
[CV] min_samples_split=4, n_estimators=120 ...........................
[CV] ............ min_samples_split=4, n_estimators=120, total=   2.6s
[CV] min_samples_split=4, n_estimators=120 ...........................
[CV] ............ min_samples_split=4, n_estimators=120, total=   2.7s
[CV] min_samples_split=4, n_estimators=120 ...........................
[CV] ............ min_samples_split=4, n_estimators=120, total=   2.5s
[CV] min_samples_split=4, n_estimators=120 ...........................
[CV] ............ min_samples_split=4, n_estimators=120, total=   2.7s
[CV] min_samples_split=4, n_estimators=120 ...........................
[CV] ............ min_samples_split=4, n_estimators=120, total=   2.6s
[CV] min_samples_split=4, n_estimators=230 ...........................
[CV] ............ min_samples_split=4, n_estimators=230, total=   5.1s
[CV] min_samples_split=4, n_estimators=230 ...........................
[CV] ............ min_samples_split=4, n_estimators=230, total=   6.5s
[CV] min_samples_split=4, n_estimators=230 ...........................
[CV] ............ min_samples_split=4, n_estimators=230, total=   6.1s
[CV] min_samples_split=4, n_estimators=230 ...........................
[CV] ............ min_samples_split=4, n_estimators=230, total=   5.4s
[CV] min_samples_split=4, n_estimators=230 ...........................
[CV] ............ min_samples_split=4, n_estimators=230, total=   4.3s
[CV] min_samples_split=4, n_estimators=340 ...........................
[CV] ............ min_samples_split=4, n_estimators=340, total=   7.3s
[CV] min_samples_split=4, n_estimators=340 ...........................
[CV] ............ min_samples_split=4, n_estimators=340, total=   6.6s
[CV] min_samples_split=4, n_estimators=340 ...........................
[CV] ............ min_samples_split=4, n_estimators=340, total=   6.8s
[CV] min_samples_split=4, n_estimators=340 ...........................
[CV] ............ min_samples_split=4, n_estimators=340, total=   7.0s
[CV] min_samples_split=4, n_estimators=340 ...........................
[CV] ............ min_samples_split=4, n_estimators=340, total=   6.7s
[CV] min_samples_split=4, n_estimators=450 ...........................
[CV] ............ min_samples_split=4, n_estimators=450, total=   8.7s
[CV] min_samples_split=4, n_estimators=450 ...........................
[CV] ............ min_samples_split=4, n_estimators=450, total=   8.7s
[CV] min_samples_split=4, n_estimators=450 ...........................
[CV] ............ min_samples_split=4, n_estimators=450, total=   8.9s
[CV] min_samples_split=4, n_estimators=450 ...........................
[CV] ............ min_samples_split=4, n_estimators=450, total=   8.7s
[CV] min_samples_split=4, n_estimators=450 ...........................
[CV] ............ min_samples_split=4, n_estimators=450, total=   8.2s
[CV] min_samples_split=4, n_estimators=560 ...........................
[CV] ............ min_samples_split=4, n_estimators=560, total=  11.2s
[CV] min_samples_split=4, n_estimators=560 ...........................
[CV] ............ min_samples_split=4, n_estimators=560, total=  11.0s
[CV] min_samples_split=4, n_estimators=560 ...........................
[CV] ............ min_samples_split=4, n_estimators=560, total=  11.4s
[CV] min_samples_split=4, n_estimators=560 ...........................
[CV] ............ min_samples_split=4, n_estimators=560, total=  11.0s
[CV] min_samples_split=4, n_estimators=560 ...........................
[CV] ............ min_samples_split=4, n_estimators=560, total=  10.7s
[CV] min_samples_split=4, n_estimators=670 ...........................
[CV] ............ min_samples_split=4, n_estimators=670, total=  14.3s
[CV] min_samples_split=4, n_estimators=670 ...........................
[CV] ............ min_samples_split=4, n_estimators=670, total=  13.3s
[CV] min_samples_split=4, n_estimators=670 ...........................
[CV] ............ min_samples_split=4, n_estimators=670, total=  13.4s
[CV] min_samples_split=4, n_estimators=670 ...........................
[CV] ............ min_samples_split=4, n_estimators=670, total=  13.3s
[CV] min_samples_split=4, n_estimators=670 ...........................
[CV] ............ min_samples_split=4, n_estimators=670, total=  13.1s
[CV] min_samples_split=4, n_estimators=780 ...........................
[CV] ............ min_samples_split=4, n_estimators=780, total=  15.7s
[CV] min_samples_split=4, n_estimators=780 ...........................
[CV] ............ min_samples_split=4, n_estimators=780, total=  22.4s
[CV] min_samples_split=4, n_estimators=780 ...........................
[CV] ............ min_samples_split=4, n_estimators=780, total=  15.7s
[CV] min_samples_split=4, n_estimators=780 ...........................
[CV] ............ min_samples_split=4, n_estimators=780, total=  16.3s
[CV] min_samples_split=4, n_estimators=780 ...........................
[CV] ............ min_samples_split=4, n_estimators=780, total=  15.3s
[CV] min_samples_split=4, n_estimators=890 ...........................
[CV] ............ min_samples_split=4, n_estimators=890, total=  17.6s
[CV] min_samples_split=4, n_estimators=890 ...........................
[CV] ............ min_samples_split=4, n_estimators=890, total=  17.6s
[CV] min_samples_split=4, n_estimators=890 ...........................
[CV] ............ min_samples_split=4, n_estimators=890, total=  18.4s
[CV] min_samples_split=4, n_estimators=890 ...........................
[CV] ............ min_samples_split=4, n_estimators=890, total=  18.3s
[CV] min_samples_split=4, n_estimators=890 ...........................
[CV] ............ min_samples_split=4, n_estimators=890, total=  16.4s
[CV] min_samples_split=4, n_estimators=1000 ..........................
[CV] ........... min_samples_split=4, n_estimators=1000, total=  21.0s
[CV] min_samples_split=4, n_estimators=1000 ..........................
[CV] ........... min_samples_split=4, n_estimators=1000, total=  25.8s
[CV] min_samples_split=4, n_estimators=1000 ..........................
[CV] ........... min_samples_split=4, n_estimators=1000, total=  25.8s
[CV] min_samples_split=4, n_estimators=1000 ..........................
[CV] ........... min_samples_split=4, n_estimators=1000, total=  21.8s
[CV] min_samples_split=4, n_estimators=1000 ..........................
[CV] ........... min_samples_split=4, n_estimators=1000, total=  25.6s
[CV] min_samples_split=5, n_estimators=10 ............................
[CV] ............. min_samples_split=5, n_estimators=10, total=   0.4s
[CV] min_samples_split=5, n_estimators=10 ............................
[CV] ............. min_samples_split=5, n_estimators=10, total=   0.6s
[CV] min_samples_split=5, n_estimators=10 ............................
[CV] ............. min_samples_split=5, n_estimators=10, total=   1.8s
[CV] min_samples_split=5, n_estimators=10 ............................
[CV] ............. min_samples_split=5, n_estimators=10, total=   0.9s
[CV] min_samples_split=5, n_estimators=10 ............................
[CV] ............. min_samples_split=5, n_estimators=10, total=   0.8s
[CV] min_samples_split=5, n_estimators=120 ...........................
[CV] ............ min_samples_split=5, n_estimators=120, total=   4.0s
[CV] min_samples_split=5, n_estimators=120 ...........................
[CV] ............ min_samples_split=5, n_estimators=120, total=   2.9s
[CV] min_samples_split=5, n_estimators=120 ...........................
[CV] ............ min_samples_split=5, n_estimators=120, total=   3.6s
[CV] min_samples_split=5, n_estimators=120 ...........................
[CV] ............ min_samples_split=5, n_estimators=120, total=   2.7s
[CV] min_samples_split=5, n_estimators=120 ...........................
[CV] ............ min_samples_split=5, n_estimators=120, total=   2.5s
[CV] min_samples_split=5, n_estimators=230 ...........................
[CV] ............ min_samples_split=5, n_estimators=230, total=   6.8s
[CV] min_samples_split=5, n_estimators=230 ...........................
[CV] ............ min_samples_split=5, n_estimators=230, total=   5.4s
[CV] min_samples_split=5, n_estimators=230 ...........................
[CV] ............ min_samples_split=5, n_estimators=230, total=   6.2s
[CV] min_samples_split=5, n_estimators=230 ...........................
[CV] ............ min_samples_split=5, n_estimators=230, total=   7.2s
[CV] min_samples_split=5, n_estimators=230 ...........................
[CV] ............ min_samples_split=5, n_estimators=230, total=   5.9s
[CV] min_samples_split=5, n_estimators=340 ...........................
[CV] ............ min_samples_split=5, n_estimators=340, total=   7.5s
[CV] min_samples_split=5, n_estimators=340 ...........................
[CV] ............ min_samples_split=5, n_estimators=340, total=   7.1s
[CV] min_samples_split=5, n_estimators=340 ...........................
[CV] ............ min_samples_split=5, n_estimators=340, total=   9.2s
[CV] min_samples_split=5, n_estimators=340 ...........................
[CV] ............ min_samples_split=5, n_estimators=340, total=  12.2s
[CV] min_samples_split=5, n_estimators=340 ...........................
[CV] ............ min_samples_split=5, n_estimators=340, total=   6.9s
[CV] min_samples_split=5, n_estimators=450 ...........................
[CV] ............ min_samples_split=5, n_estimators=450, total=  11.0s
[CV] min_samples_split=5, n_estimators=450 ...........................
[CV] ............ min_samples_split=5, n_estimators=450, total=   9.8s
[CV] min_samples_split=5, n_estimators=450 ...........................
[CV] ............ min_samples_split=5, n_estimators=450, total=  10.1s
[CV] min_samples_split=5, n_estimators=450 ...........................
[CV] ............ min_samples_split=5, n_estimators=450, total=   9.7s
[CV] min_samples_split=5, n_estimators=450 ...........................
[CV] ............ min_samples_split=5, n_estimators=450, total=  10.8s
[CV] min_samples_split=5, n_estimators=560 ...........................
[CV] ............ min_samples_split=5, n_estimators=560, total=  15.8s
[CV] min_samples_split=5, n_estimators=560 ...........................
[CV] ............ min_samples_split=5, n_estimators=560, total=  14.5s
[CV] min_samples_split=5, n_estimators=560 ...........................
[CV] ............ min_samples_split=5, n_estimators=560, total=  17.5s
[CV] min_samples_split=5, n_estimators=560 ...........................
[CV] ............ min_samples_split=5, n_estimators=560, total=  15.4s
[CV] min_samples_split=5, n_estimators=560 ...........................
[CV] ............ min_samples_split=5, n_estimators=560, total=  12.0s
[CV] min_samples_split=5, n_estimators=670 ...........................
[CV] ............ min_samples_split=5, n_estimators=670, total=  14.0s
[CV] min_samples_split=5, n_estimators=670 ...........................
[CV] ............ min_samples_split=5, n_estimators=670, total=  19.6s
[CV] min_samples_split=5, n_estimators=670 ...........................
[CV] ............ min_samples_split=5, n_estimators=670, total=  15.2s
[CV] min_samples_split=5, n_estimators=670 ...........................
[CV] ............ min_samples_split=5, n_estimators=670, total=  23.4s
[CV] min_samples_split=5, n_estimators=670 ...........................
[CV] ............ min_samples_split=5, n_estimators=670, total=  23.5s
[CV] min_samples_split=5, n_estimators=780 ...........................
[CV] ............ min_samples_split=5, n_estimators=780, total=  21.8s
[CV] min_samples_split=5, n_estimators=780 ...........................
[CV] ............ min_samples_split=5, n_estimators=780, total=  15.6s
[CV] min_samples_split=5, n_estimators=780 ...........................
[CV] ............ min_samples_split=5, n_estimators=780, total=  16.2s
[CV] min_samples_split=5, n_estimators=780 ...........................
[CV] ............ min_samples_split=5, n_estimators=780, total=  27.6s
[CV] min_samples_split=5, n_estimators=780 ...........................
[CV] ............ min_samples_split=5, n_estimators=780, total=  24.6s
[CV] min_samples_split=5, n_estimators=890 ...........................
[CV] ............ min_samples_split=5, n_estimators=890, total=  28.4s
[CV] min_samples_split=5, n_estimators=890 ...........................
[CV] ............ min_samples_split=5, n_estimators=890, total=  26.6s
[CV] min_samples_split=5, n_estimators=890 ...........................
[CV] ............ min_samples_split=5, n_estimators=890, total=  27.1s
[CV] min_samples_split=5, n_estimators=890 ...........................
[CV] ............ min_samples_split=5, n_estimators=890, total=  28.3s
[CV] min_samples_split=5, n_estimators=890 ...........................
[CV] ............ min_samples_split=5, n_estimators=890, total=  30.0s
[CV] min_samples_split=5, n_estimators=1000 ..........................
[CV] ........... min_samples_split=5, n_estimators=1000, total=  27.0s
[CV] min_samples_split=5, n_estimators=1000 ..........................
[CV] ........... min_samples_split=5, n_estimators=1000, total=  21.5s
[CV] min_samples_split=5, n_estimators=1000 ..........................
[CV] ........... min_samples_split=5, n_estimators=1000, total=  19.6s
[CV] min_samples_split=5, n_estimators=1000 ..........................
[CV] ........... min_samples_split=5, n_estimators=1000, total=  19.7s
[CV] min_samples_split=5, n_estimators=1000 ..........................
[CV] ........... min_samples_split=5, n_estimators=1000, total=  19.5s
[CV] min_samples_split=6, n_estimators=10 ............................
[CV] ............. min_samples_split=6, n_estimators=10, total=   0.2s
[CV] min_samples_split=6, n_estimators=10 ............................
[CV] ............. min_samples_split=6, n_estimators=10, total=   0.2s
[CV] min_samples_split=6, n_estimators=10 ............................
[CV] ............. min_samples_split=6, n_estimators=10, total=   0.2s
[CV] min_samples_split=6, n_estimators=10 ............................
[CV] ............. min_samples_split=6, n_estimators=10, total=   0.2s
[CV] min_samples_split=6, n_estimators=10 ............................
[CV] ............. min_samples_split=6, n_estimators=10, total=   0.2s
[CV] min_samples_split=6, n_estimators=120 ...........................
[CV] ............ min_samples_split=6, n_estimators=120, total=   2.5s
[CV] min_samples_split=6, n_estimators=120 ...........................
[CV] ............ min_samples_split=6, n_estimators=120, total=   2.3s
[CV] min_samples_split=6, n_estimators=120 ...........................
[CV] ............ min_samples_split=6, n_estimators=120, total=   2.4s
[CV] min_samples_split=6, n_estimators=120 ...........................
[CV] ............ min_samples_split=6, n_estimators=120, total=   2.3s
[CV] min_samples_split=6, n_estimators=120 ...........................
[CV] ............ min_samples_split=6, n_estimators=120, total=   2.2s
[CV] min_samples_split=6, n_estimators=230 ...........................
[CV] ............ min_samples_split=6, n_estimators=230, total=   4.7s
[CV] min_samples_split=6, n_estimators=230 ...........................
[CV] ............ min_samples_split=6, n_estimators=230, total=   4.8s
[CV] min_samples_split=6, n_estimators=230 ...........................
[CV] ............ min_samples_split=6, n_estimators=230, total=   4.5s
[CV] min_samples_split=6, n_estimators=230 ...........................
[CV] ............ min_samples_split=6, n_estimators=230, total=   4.7s
[CV] min_samples_split=6, n_estimators=230 ...........................
[CV] ............ min_samples_split=6, n_estimators=230, total=   4.4s
[CV] min_samples_split=6, n_estimators=340 ...........................
[CV] ............ min_samples_split=6, n_estimators=340, total=   8.2s
[CV] min_samples_split=6, n_estimators=340 ...........................
[CV] ............ min_samples_split=6, n_estimators=340, total=  11.6s
[CV] min_samples_split=6, n_estimators=340 ...........................
[CV] ............ min_samples_split=6, n_estimators=340, total=   7.6s
[CV] min_samples_split=6, n_estimators=340 ...........................
[CV] ............ min_samples_split=6, n_estimators=340, total=   7.2s
[CV] min_samples_split=6, n_estimators=340 ...........................
[CV] ............ min_samples_split=6, n_estimators=340, total=  11.8s
[CV] min_samples_split=6, n_estimators=450 ...........................
[CV] ............ min_samples_split=6, n_estimators=450, total=  12.4s
[CV] min_samples_split=6, n_estimators=450 ...........................
[CV] ............ min_samples_split=6, n_estimators=450, total=  11.7s
[CV] min_samples_split=6, n_estimators=450 ...........................
[CV] ............ min_samples_split=6, n_estimators=450, total=   8.5s
[CV] min_samples_split=6, n_estimators=450 ...........................
[CV] ............ min_samples_split=6, n_estimators=450, total=  10.7s
[CV] min_samples_split=6, n_estimators=450 ...........................
[CV] ............ min_samples_split=6, n_estimators=450, total=   9.5s
[CV] min_samples_split=6, n_estimators=560 ...........................
[CV] ............ min_samples_split=6, n_estimators=560, total=  16.3s
[CV] min_samples_split=6, n_estimators=560 ...........................
[CV] ............ min_samples_split=6, n_estimators=560, total=  17.3s
[CV] min_samples_split=6, n_estimators=560 ...........................
[CV] ............ min_samples_split=6, n_estimators=560, total=  11.9s
[CV] min_samples_split=6, n_estimators=560 ...........................
[CV] ............ min_samples_split=6, n_estimators=560, total=  11.5s
[CV] min_samples_split=6, n_estimators=560 ...........................
[CV] ............ min_samples_split=6, n_estimators=560, total=  17.2s
[CV] min_samples_split=6, n_estimators=670 ...........................
[CV] ............ min_samples_split=6, n_estimators=670, total=  16.1s
[CV] min_samples_split=6, n_estimators=670 ...........................
[CV] ............ min_samples_split=6, n_estimators=670, total=  18.2s
[CV] min_samples_split=6, n_estimators=670 ...........................
[CV] ............ min_samples_split=6, n_estimators=670, total=  16.3s
[CV] min_samples_split=6, n_estimators=670 ...........................
[CV] ............ min_samples_split=6, n_estimators=670, total=  13.8s
[CV] min_samples_split=6, n_estimators=670 ...........................
[CV] ............ min_samples_split=6, n_estimators=670, total=  13.2s
[CV] min_samples_split=6, n_estimators=780 ...........................
[CV] ............ min_samples_split=6, n_estimators=780, total=  22.9s
[CV] min_samples_split=6, n_estimators=780 ...........................
[CV] ............ min_samples_split=6, n_estimators=780, total=  21.8s
[CV] min_samples_split=6, n_estimators=780 ...........................
[CV] ............ min_samples_split=6, n_estimators=780, total=  17.9s
[CV] min_samples_split=6, n_estimators=780 ...........................
[CV] ............ min_samples_split=6, n_estimators=780, total=  19.9s
[CV] min_samples_split=6, n_estimators=780 ...........................
[CV] ............ min_samples_split=6, n_estimators=780, total=  21.9s
[CV] min_samples_split=6, n_estimators=890 ...........................
[CV] ............ min_samples_split=6, n_estimators=890, total=  22.8s
[CV] min_samples_split=6, n_estimators=890 ...........................
[CV] ............ min_samples_split=6, n_estimators=890, total=  23.9s
[CV] min_samples_split=6, n_estimators=890 ...........................
[CV] ............ min_samples_split=6, n_estimators=890, total=  21.6s
[CV] min_samples_split=6, n_estimators=890 ...........................
[CV] ............ min_samples_split=6, n_estimators=890, total=  17.5s
[CV] min_samples_split=6, n_estimators=890 ...........................
[CV] ............ min_samples_split=6, n_estimators=890, total=  17.9s
[CV] min_samples_split=6, n_estimators=1000 ..........................
[CV] ........... min_samples_split=6, n_estimators=1000, total=  19.1s
[CV] min_samples_split=6, n_estimators=1000 ..........................
[CV] ........... min_samples_split=6, n_estimators=1000, total=  21.1s
[CV] min_samples_split=6, n_estimators=1000 ..........................
[CV] ........... min_samples_split=6, n_estimators=1000, total=  18.7s
[CV] min_samples_split=6, n_estimators=1000 ..........................
[CV] ........... min_samples_split=6, n_estimators=1000, total=  21.1s
[CV] min_samples_split=6, n_estimators=1000 ..........................
[CV] ........... min_samples_split=6, n_estimators=1000, total=  26.1s
[CV] min_samples_split=7, n_estimators=10 ............................
[CV] ............. min_samples_split=7, n_estimators=10, total=   0.6s
[CV] min_samples_split=7, n_estimators=10 ............................
[CV] ............. min_samples_split=7, n_estimators=10, total=   0.4s
[CV] min_samples_split=7, n_estimators=10 ............................
[CV] ............. min_samples_split=7, n_estimators=10, total=   0.4s
[CV] min_samples_split=7, n_estimators=10 ............................
[CV] ............. min_samples_split=7, n_estimators=10, total=   0.5s
[CV] min_samples_split=7, n_estimators=10 ............................
[CV] ............. min_samples_split=7, n_estimators=10, total=   0.2s
[CV] min_samples_split=7, n_estimators=120 ...........................
[CV] ............ min_samples_split=7, n_estimators=120, total=   3.8s
[CV] min_samples_split=7, n_estimators=120 ...........................
[CV] ............ min_samples_split=7, n_estimators=120, total=   2.9s
[CV] min_samples_split=7, n_estimators=120 ...........................
[CV] ............ min_samples_split=7, n_estimators=120, total=   3.9s
[CV] min_samples_split=7, n_estimators=120 ...........................
[CV] ............ min_samples_split=7, n_estimators=120, total=   4.1s
[CV] min_samples_split=7, n_estimators=120 ...........................
[CV] ............ min_samples_split=7, n_estimators=120, total=   3.8s
[CV] min_samples_split=7, n_estimators=230 ...........................
[CV] ............ min_samples_split=7, n_estimators=230, total=   7.0s
[CV] min_samples_split=7, n_estimators=230 ...........................
[CV] ............ min_samples_split=7, n_estimators=230, total=   4.9s
[CV] min_samples_split=7, n_estimators=230 ...........................
[CV] ............ min_samples_split=7, n_estimators=230, total=   4.7s
[CV] min_samples_split=7, n_estimators=230 ...........................
[CV] ............ min_samples_split=7, n_estimators=230, total=   5.2s
[CV] min_samples_split=7, n_estimators=230 ...........................
[CV] ............ min_samples_split=7, n_estimators=230, total=   4.5s
[CV] min_samples_split=7, n_estimators=340 ...........................
[CV] ............ min_samples_split=7, n_estimators=340, total=   7.2s
[CV] min_samples_split=7, n_estimators=340 ...........................
[CV] ............ min_samples_split=7, n_estimators=340, total=   7.3s
[CV] min_samples_split=7, n_estimators=340 ...........................
[CV] ............ min_samples_split=7, n_estimators=340, total=   7.8s
[CV] min_samples_split=7, n_estimators=340 ...........................
[CV] ............ min_samples_split=7, n_estimators=340, total=   8.3s
[CV] min_samples_split=7, n_estimators=340 ...........................
[CV] ............ min_samples_split=7, n_estimators=340, total=   6.4s
[CV] min_samples_split=7, n_estimators=450 ...........................
[CV] ............ min_samples_split=7, n_estimators=450, total=  10.7s
[CV] min_samples_split=7, n_estimators=450 ...........................
[CV] ............ min_samples_split=7, n_estimators=450, total=  11.9s
[CV] min_samples_split=7, n_estimators=450 ...........................
[CV] ............ min_samples_split=7, n_estimators=450, total=  15.0s
[CV] min_samples_split=7, n_estimators=450 ...........................
[CV] ............ min_samples_split=7, n_estimators=450, total=  16.6s
[CV] min_samples_split=7, n_estimators=450 ...........................
[CV] ............ min_samples_split=7, n_estimators=450, total=  11.4s
[CV] min_samples_split=7, n_estimators=560 ...........................
[CV] ............ min_samples_split=7, n_estimators=560, total=  12.0s
[CV] min_samples_split=7, n_estimators=560 ...........................
[CV] ............ min_samples_split=7, n_estimators=560, total=  15.1s
[CV] min_samples_split=7, n_estimators=560 ...........................
[CV] ............ min_samples_split=7, n_estimators=560, total=  12.0s
[CV] min_samples_split=7, n_estimators=560 ...........................
[CV] ............ min_samples_split=7, n_estimators=560, total=  11.9s
[CV] min_samples_split=7, n_estimators=560 ...........................
[CV] ............ min_samples_split=7, n_estimators=560, total=  10.9s
[CV] min_samples_split=7, n_estimators=670 ...........................
[CV] ............ min_samples_split=7, n_estimators=670, total=  14.3s
[CV] min_samples_split=7, n_estimators=670 ...........................
[CV] ............ min_samples_split=7, n_estimators=670, total=  15.2s
[CV] min_samples_split=7, n_estimators=670 ...........................
[CV] ............ min_samples_split=7, n_estimators=670, total=  22.1s
[CV] min_samples_split=7, n_estimators=670 ...........................
[CV] ............ min_samples_split=7, n_estimators=670, total=  15.5s
[CV] min_samples_split=7, n_estimators=670 ...........................
[CV] ............ min_samples_split=7, n_estimators=670, total=  15.1s
[CV] min_samples_split=7, n_estimators=780 ...........................
[CV] ............ min_samples_split=7, n_estimators=780, total=  24.7s
[CV] min_samples_split=7, n_estimators=780 ...........................
[CV] ............ min_samples_split=7, n_estimators=780, total=  18.0s
[CV] min_samples_split=7, n_estimators=780 ...........................
[CV] ............ min_samples_split=7, n_estimators=780, total=  18.9s
[CV] min_samples_split=7, n_estimators=780 ...........................
[CV] ............ min_samples_split=7, n_estimators=780, total=  22.4s
[CV] min_samples_split=7, n_estimators=780 ...........................
[CV] ............ min_samples_split=7, n_estimators=780, total=  18.1s
[CV] min_samples_split=7, n_estimators=890 ...........................
[CV] ............ min_samples_split=7, n_estimators=890, total=  20.1s
[CV] min_samples_split=7, n_estimators=890 ...........................
[CV] ............ min_samples_split=7, n_estimators=890, total=  19.4s
[CV] min_samples_split=7, n_estimators=890 ...........................
[CV] ............ min_samples_split=7, n_estimators=890, total=  17.5s
[CV] min_samples_split=7, n_estimators=890 ...........................
[CV] ............ min_samples_split=7, n_estimators=890, total=  19.0s
[CV] min_samples_split=7, n_estimators=890 ...........................
[CV] ............ min_samples_split=7, n_estimators=890, total=  17.3s
[CV] min_samples_split=7, n_estimators=1000 ..........................
[CV] ........... min_samples_split=7, n_estimators=1000, total=  22.2s
[CV] min_samples_split=7, n_estimators=1000 ..........................
[CV] ........... min_samples_split=7, n_estimators=1000, total=  22.7s
[CV] min_samples_split=7, n_estimators=1000 ..........................
[CV] ........... min_samples_split=7, n_estimators=1000, total=  21.5s
[CV] min_samples_split=7, n_estimators=1000 ..........................
[CV] ........... min_samples_split=7, n_estimators=1000, total=  20.0s
[CV] min_samples_split=7, n_estimators=1000 ..........................
[CV] ........... min_samples_split=7, n_estimators=1000, total=  19.1s
[CV] min_samples_split=8, n_estimators=10 ............................
[CV] ............. min_samples_split=8, n_estimators=10, total=   0.2s
[CV] min_samples_split=8, n_estimators=10 ............................
[CV] ............. min_samples_split=8, n_estimators=10, total=   0.3s
[CV] min_samples_split=8, n_estimators=10 ............................
[CV] ............. min_samples_split=8, n_estimators=10, total=   0.2s
[CV] min_samples_split=8, n_estimators=10 ............................
[CV] ............. min_samples_split=8, n_estimators=10, total=   0.2s
[CV] min_samples_split=8, n_estimators=10 ............................
[CV] ............. min_samples_split=8, n_estimators=10, total=   0.2s
[CV] min_samples_split=8, n_estimators=120 ...........................
[CV] ............ min_samples_split=8, n_estimators=120, total=   2.4s
[CV] min_samples_split=8, n_estimators=120 ...........................
[CV] ............ min_samples_split=8, n_estimators=120, total=   2.4s
[CV] min_samples_split=8, n_estimators=120 ...........................
[CV] ............ min_samples_split=8, n_estimators=120, total=   2.5s
[CV] min_samples_split=8, n_estimators=120 ...........................
[CV] ............ min_samples_split=8, n_estimators=120, total=   2.4s
[CV] min_samples_split=8, n_estimators=120 ...........................
[CV] ............ min_samples_split=8, n_estimators=120, total=   2.3s
[CV] min_samples_split=8, n_estimators=230 ...........................
[CV] ............ min_samples_split=8, n_estimators=230, total=   4.4s
[CV] min_samples_split=8, n_estimators=230 ...........................
[CV] ............ min_samples_split=8, n_estimators=230, total=   5.3s
[CV] min_samples_split=8, n_estimators=230 ...........................
[CV] ............ min_samples_split=8, n_estimators=230, total=   4.8s
[CV] min_samples_split=8, n_estimators=230 ...........................
[CV] ............ min_samples_split=8, n_estimators=230, total=   4.4s
[CV] min_samples_split=8, n_estimators=230 ...........................
[CV] ............ min_samples_split=8, n_estimators=230, total=   4.4s
[CV] min_samples_split=8, n_estimators=340 ...........................
[CV] ............ min_samples_split=8, n_estimators=340, total=   6.7s
[CV] min_samples_split=8, n_estimators=340 ...........................
[CV] ............ min_samples_split=8, n_estimators=340, total=   6.0s
[CV] min_samples_split=8, n_estimators=340 ...........................
[CV] ............ min_samples_split=8, n_estimators=340, total=   7.0s
[CV] min_samples_split=8, n_estimators=340 ...........................
[CV] ............ min_samples_split=8, n_estimators=340, total=   6.7s
[CV] min_samples_split=8, n_estimators=340 ...........................
[CV] ............ min_samples_split=8, n_estimators=340, total=   6.4s
[CV] min_samples_split=8, n_estimators=450 ...........................
[CV] ............ min_samples_split=8, n_estimators=450, total=  11.6s
[CV] min_samples_split=8, n_estimators=450 ...........................
[CV] ............ min_samples_split=8, n_estimators=450, total=   8.1s
[CV] min_samples_split=8, n_estimators=450 ...........................
[CV] ............ min_samples_split=8, n_estimators=450, total=   8.4s
[CV] min_samples_split=8, n_estimators=450 ...........................
[CV] ............ min_samples_split=8, n_estimators=450, total=   9.9s
[CV] min_samples_split=8, n_estimators=450 ...........................
[CV] ............ min_samples_split=8, n_estimators=450, total=   8.3s
[CV] min_samples_split=8, n_estimators=560 ...........................
[CV] ............ min_samples_split=8, n_estimators=560, total=  11.1s
[CV] min_samples_split=8, n_estimators=560 ...........................
[CV] ............ min_samples_split=8, n_estimators=560, total=  12.1s
[CV] min_samples_split=8, n_estimators=560 ...........................
[CV] ............ min_samples_split=8, n_estimators=560, total=  11.9s
[CV] min_samples_split=8, n_estimators=560 ...........................
[CV] ............ min_samples_split=8, n_estimators=560, total=  14.1s
[CV] min_samples_split=8, n_estimators=560 ...........................
[CV] ............ min_samples_split=8, n_estimators=560, total=  14.9s
[CV] min_samples_split=8, n_estimators=670 ...........................
[CV] ............ min_samples_split=8, n_estimators=670, total=  16.2s
[CV] min_samples_split=8, n_estimators=670 ...........................
[CV] ............ min_samples_split=8, n_estimators=670, total=  16.8s
[CV] min_samples_split=8, n_estimators=670 ...........................
[CV] ............ min_samples_split=8, n_estimators=670, total=  16.5s
[CV] min_samples_split=8, n_estimators=670 ...........................
[CV] ............ min_samples_split=8, n_estimators=670, total=  15.1s
[CV] min_samples_split=8, n_estimators=670 ...........................
[CV] ............ min_samples_split=8, n_estimators=670, total=  15.7s
[CV] min_samples_split=8, n_estimators=780 ...........................
[CV] ............ min_samples_split=8, n_estimators=780, total=  20.6s
[CV] min_samples_split=8, n_estimators=780 ...........................
[CV] ............ min_samples_split=8, n_estimators=780, total=  14.5s
[CV] min_samples_split=8, n_estimators=780 ...........................
[CV] ............ min_samples_split=8, n_estimators=780, total=  18.2s
[CV] min_samples_split=8, n_estimators=780 ...........................
[CV] ............ min_samples_split=8, n_estimators=780, total=  21.0s
[CV] min_samples_split=8, n_estimators=780 ...........................
[CV] ............ min_samples_split=8, n_estimators=780, total=  16.0s
[CV] min_samples_split=8, n_estimators=890 ...........................
[CV] ............ min_samples_split=8, n_estimators=890, total=  21.3s
[CV] min_samples_split=8, n_estimators=890 ...........................
[CV] ............ min_samples_split=8, n_estimators=890, total=  24.1s
[CV] min_samples_split=8, n_estimators=890 ...........................
[CV] ............ min_samples_split=8, n_estimators=890, total=  23.6s
[CV] min_samples_split=8, n_estimators=890 ...........................
[CV] ............ min_samples_split=8, n_estimators=890, total=  29.4s
[CV] min_samples_split=8, n_estimators=890 ...........................
[CV] ............ min_samples_split=8, n_estimators=890, total=  18.4s
[CV] min_samples_split=8, n_estimators=1000 ..........................
[CV] ........... min_samples_split=8, n_estimators=1000, total=  22.0s
[CV] min_samples_split=8, n_estimators=1000 ..........................
[CV] ........... min_samples_split=8, n_estimators=1000, total=  27.8s
[CV] min_samples_split=8, n_estimators=1000 ..........................
[CV] ........... min_samples_split=8, n_estimators=1000, total=  24.3s
[CV] min_samples_split=8, n_estimators=1000 ..........................
[CV] ........... min_samples_split=8, n_estimators=1000, total=  19.8s
[CV] min_samples_split=8, n_estimators=1000 ..........................
[CV] ........... min_samples_split=8, n_estimators=1000, total=  19.8s
[CV] min_samples_split=9, n_estimators=10 ............................
[CV] ............. min_samples_split=9, n_estimators=10, total=   0.2s
[CV] min_samples_split=9, n_estimators=10 ............................
[CV] ............. min_samples_split=9, n_estimators=10, total=   0.3s
[CV] min_samples_split=9, n_estimators=10 ............................
[CV] ............. min_samples_split=9, n_estimators=10, total=   0.4s
[CV] min_samples_split=9, n_estimators=10 ............................
[CV] ............. min_samples_split=9, n_estimators=10, total=   0.3s
[CV] min_samples_split=9, n_estimators=10 ............................
[CV] ............. min_samples_split=9, n_estimators=10, total=   0.3s
[CV] min_samples_split=9, n_estimators=120 ...........................
[CV] ............ min_samples_split=9, n_estimators=120, total=   3.0s
[CV] min_samples_split=9, n_estimators=120 ...........................
[CV] ............ min_samples_split=9, n_estimators=120, total=   2.6s
[CV] min_samples_split=9, n_estimators=120 ...........................
[CV] ............ min_samples_split=9, n_estimators=120, total=   2.9s
[CV] min_samples_split=9, n_estimators=120 ...........................
[CV] ............ min_samples_split=9, n_estimators=120, total=   2.4s
[CV] min_samples_split=9, n_estimators=120 ...........................
[CV] ............ min_samples_split=9, n_estimators=120, total=   2.5s
[CV] min_samples_split=9, n_estimators=230 ...........................
[CV] ............ min_samples_split=9, n_estimators=230, total=   5.3s
[CV] min_samples_split=9, n_estimators=230 ...........................
[CV] ............ min_samples_split=9, n_estimators=230, total=   6.1s
[CV] min_samples_split=9, n_estimators=230 ...........................
[CV] ............ min_samples_split=9, n_estimators=230, total=   5.2s
[CV] min_samples_split=9, n_estimators=230 ...........................
[CV] ............ min_samples_split=9, n_estimators=230, total=   5.0s
[CV] min_samples_split=9, n_estimators=230 ...........................
[CV] ............ min_samples_split=9, n_estimators=230, total=   5.0s
[CV] min_samples_split=9, n_estimators=340 ...........................
[CV] ............ min_samples_split=9, n_estimators=340, total=   7.5s
[CV] min_samples_split=9, n_estimators=340 ...........................
[CV] ............ min_samples_split=9, n_estimators=340, total=   7.3s
[CV] min_samples_split=9, n_estimators=340 ...........................
[CV] ............ min_samples_split=9, n_estimators=340, total=   6.8s
[CV] min_samples_split=9, n_estimators=340 ...........................
[CV] ............ min_samples_split=9, n_estimators=340, total=   6.8s
[CV] min_samples_split=9, n_estimators=340 ...........................
[CV] ............ min_samples_split=9, n_estimators=340, total=   7.8s
[CV] min_samples_split=9, n_estimators=450 ...........................
[CV] ............ min_samples_split=9, n_estimators=450, total=   9.5s
[CV] min_samples_split=9, n_estimators=450 ...........................
[CV] ............ min_samples_split=9, n_estimators=450, total=  11.7s
[CV] min_samples_split=9, n_estimators=450 ...........................
[CV] ............ min_samples_split=9, n_estimators=450, total=  10.2s
[CV] min_samples_split=9, n_estimators=450 ...........................
[CV] ............ min_samples_split=9, n_estimators=450, total=  10.9s
[CV] min_samples_split=9, n_estimators=450 ...........................
[CV] ............ min_samples_split=9, n_estimators=450, total=   8.6s
[CV] min_samples_split=9, n_estimators=560 ...........................
[CV] ............ min_samples_split=9, n_estimators=560, total=  14.3s
[CV] min_samples_split=9, n_estimators=560 ...........................
[CV] ............ min_samples_split=9, n_estimators=560, total=  13.7s
[CV] min_samples_split=9, n_estimators=560 ...........................
[CV] ............ min_samples_split=9, n_estimators=560, total=  15.9s
[CV] min_samples_split=9, n_estimators=560 ...........................
[CV] ............ min_samples_split=9, n_estimators=560, total=  13.4s
[CV] min_samples_split=9, n_estimators=560 ...........................
[CV] ............ min_samples_split=9, n_estimators=560, total=  14.5s
[CV] min_samples_split=9, n_estimators=670 ...........................
[CV] ............ min_samples_split=9, n_estimators=670, total=  14.4s
[CV] min_samples_split=9, n_estimators=670 ...........................
[CV] ............ min_samples_split=9, n_estimators=670, total=  18.6s
[CV] min_samples_split=9, n_estimators=670 ...........................
[CV] ............ min_samples_split=9, n_estimators=670, total=  15.7s
[CV] min_samples_split=9, n_estimators=670 ...........................
[CV] ............ min_samples_split=9, n_estimators=670, total=  14.2s
[CV] min_samples_split=9, n_estimators=670 ...........................
[CV] ............ min_samples_split=9, n_estimators=670, total=  12.2s
[CV] min_samples_split=9, n_estimators=780 ...........................
[CV] ............ min_samples_split=9, n_estimators=780, total=  15.3s
[CV] min_samples_split=9, n_estimators=780 ...........................
[CV] ............ min_samples_split=9, n_estimators=780, total=  28.0s
[CV] min_samples_split=9, n_estimators=780 ...........................
[CV] ............ min_samples_split=9, n_estimators=780, total=  21.3s
[CV] min_samples_split=9, n_estimators=780 ...........................
[CV] ............ min_samples_split=9, n_estimators=780, total=  14.1s
[CV] min_samples_split=9, n_estimators=780 ...........................
[CV] ............ min_samples_split=9, n_estimators=780, total=  16.9s
[CV] min_samples_split=9, n_estimators=890 ...........................
[CV] ............ min_samples_split=9, n_estimators=890, total=  18.0s
[CV] min_samples_split=9, n_estimators=890 ...........................
[CV] ............ min_samples_split=9, n_estimators=890, total=  20.5s
[CV] min_samples_split=9, n_estimators=890 ...........................
[CV] ............ min_samples_split=9, n_estimators=890, total=  19.8s
[CV] min_samples_split=9, n_estimators=890 ...........................
[CV] ............ min_samples_split=9, n_estimators=890, total=  18.3s
[CV] min_samples_split=9, n_estimators=890 ...........................
[CV] ............ min_samples_split=9, n_estimators=890, total=  17.5s
[CV] min_samples_split=9, n_estimators=1000 ..........................
[CV] ........... min_samples_split=9, n_estimators=1000, total=  20.2s
[CV] min_samples_split=9, n_estimators=1000 ..........................
[CV] ........... min_samples_split=9, n_estimators=1000, total=  21.5s
[CV] min_samples_split=9, n_estimators=1000 ..........................
[CV] ........... min_samples_split=9, n_estimators=1000, total=  26.8s
[CV] min_samples_split=9, n_estimators=1000 ..........................
[CV] ........... min_samples_split=9, n_estimators=1000, total=  24.8s
[CV] min_samples_split=9, n_estimators=1000 ..........................
[CV] ........... min_samples_split=9, n_estimators=1000, total=  22.9s
[CV] min_samples_split=10, n_estimators=10 ...........................
[CV] ............ min_samples_split=10, n_estimators=10, total=   0.2s
[CV] min_samples_split=10, n_estimators=10 ...........................
[CV] ............ min_samples_split=10, n_estimators=10, total=   0.2s
[CV] min_samples_split=10, n_estimators=10 ...........................
[CV] ............ min_samples_split=10, n_estimators=10, total=   0.2s
[CV] min_samples_split=10, n_estimators=10 ...........................
[CV] ............ min_samples_split=10, n_estimators=10, total=   0.2s
[CV] min_samples_split=10, n_estimators=10 ...........................
[CV] ............ min_samples_split=10, n_estimators=10, total=   0.2s
[CV] min_samples_split=10, n_estimators=120 ..........................
[CV] ........... min_samples_split=10, n_estimators=120, total=   3.4s
[CV] min_samples_split=10, n_estimators=120 ..........................
[CV] ........... min_samples_split=10, n_estimators=120, total=   2.3s
[CV] min_samples_split=10, n_estimators=120 ..........................
[CV] ........... min_samples_split=10, n_estimators=120, total=   2.6s
[CV] min_samples_split=10, n_estimators=120 ..........................
[CV] ........... min_samples_split=10, n_estimators=120, total=   2.2s
[CV] min_samples_split=10, n_estimators=120 ..........................
[CV] ........... min_samples_split=10, n_estimators=120, total=   2.1s
[CV] min_samples_split=10, n_estimators=230 ..........................
[CV] ........... min_samples_split=10, n_estimators=230, total=   4.7s
[CV] min_samples_split=10, n_estimators=230 ..........................
[CV] ........... min_samples_split=10, n_estimators=230, total=   4.3s
[CV] min_samples_split=10, n_estimators=230 ..........................
[CV] ........... min_samples_split=10, n_estimators=230, total=   4.4s
[CV] min_samples_split=10, n_estimators=230 ..........................
[CV] ........... min_samples_split=10, n_estimators=230, total=   4.4s
[CV] min_samples_split=10, n_estimators=230 ..........................
[CV] ........... min_samples_split=10, n_estimators=230, total=   5.2s
[CV] min_samples_split=10, n_estimators=340 ..........................
[CV] ........... min_samples_split=10, n_estimators=340, total=   8.1s
[CV] min_samples_split=10, n_estimators=340 ..........................
[CV] ........... min_samples_split=10, n_estimators=340, total=   9.5s
[CV] min_samples_split=10, n_estimators=340 ..........................
[CV] ........... min_samples_split=10, n_estimators=340, total=   6.7s
[CV] min_samples_split=10, n_estimators=340 ..........................
[CV] ........... min_samples_split=10, n_estimators=340, total=   9.6s
[CV] min_samples_split=10, n_estimators=340 ..........................
[CV] ........... min_samples_split=10, n_estimators=340, total=   7.8s
[CV] min_samples_split=10, n_estimators=450 ..........................
[CV] ........... min_samples_split=10, n_estimators=450, total=  10.4s
[CV] min_samples_split=10, n_estimators=450 ..........................
[CV] ........... min_samples_split=10, n_estimators=450, total=  13.2s
[CV] min_samples_split=10, n_estimators=450 ..........................
[CV] ........... min_samples_split=10, n_estimators=450, total=  11.6s
[CV] min_samples_split=10, n_estimators=450 ..........................
[CV] ........... min_samples_split=10, n_estimators=450, total=   8.9s
[CV] min_samples_split=10, n_estimators=450 ..........................
[CV] ........... min_samples_split=10, n_estimators=450, total=   8.9s
[CV] min_samples_split=10, n_estimators=560 ..........................
[CV] ........... min_samples_split=10, n_estimators=560, total=  12.6s
[CV] min_samples_split=10, n_estimators=560 ..........................
[CV] ........... min_samples_split=10, n_estimators=560, total=  11.8s
[CV] min_samples_split=10, n_estimators=560 ..........................
[CV] ........... min_samples_split=10, n_estimators=560, total=  10.9s
[CV] min_samples_split=10, n_estimators=560 ..........................
[CV] ........... min_samples_split=10, n_estimators=560, total=  13.6s
[CV] min_samples_split=10, n_estimators=560 ..........................
[CV] ........... min_samples_split=10, n_estimators=560, total=  10.2s
[CV] min_samples_split=10, n_estimators=670 ..........................
[CV] ........... min_samples_split=10, n_estimators=670, total=  15.1s
[CV] min_samples_split=10, n_estimators=670 ..........................
[CV] ........... min_samples_split=10, n_estimators=670, total=  20.6s
[CV] min_samples_split=10, n_estimators=670 ..........................
[CV] ........... min_samples_split=10, n_estimators=670, total=  18.5s
[CV] min_samples_split=10, n_estimators=670 ..........................
[CV] ........... min_samples_split=10, n_estimators=670, total=  18.0s
[CV] min_samples_split=10, n_estimators=670 ..........................
[CV] ........... min_samples_split=10, n_estimators=670, total=  18.3s
[CV] min_samples_split=10, n_estimators=780 ..........................
[CV] ........... min_samples_split=10, n_estimators=780, total=  14.9s
[CV] min_samples_split=10, n_estimators=780 ..........................
[CV] ........... min_samples_split=10, n_estimators=780, total=  20.3s
[CV] min_samples_split=10, n_estimators=780 ..........................
[CV] ........... min_samples_split=10, n_estimators=780, total=  19.9s
[CV] min_samples_split=10, n_estimators=780 ..........................
[CV] ........... min_samples_split=10, n_estimators=780, total=  19.9s
[CV] min_samples_split=10, n_estimators=780 ..........................
[CV] ........... min_samples_split=10, n_estimators=780, total=  18.8s
[CV] min_samples_split=10, n_estimators=890 ..........................
[CV] ........... min_samples_split=10, n_estimators=890, total=  22.2s
[CV] min_samples_split=10, n_estimators=890 ..........................
[CV] ........... min_samples_split=10, n_estimators=890, total=  24.3s
[CV] min_samples_split=10, n_estimators=890 ..........................
[CV] ........... min_samples_split=10, n_estimators=890, total=  20.2s
[CV] min_samples_split=10, n_estimators=890 ..........................
[CV] ........... min_samples_split=10, n_estimators=890, total=  25.0s
[CV] min_samples_split=10, n_estimators=890 ..........................
[CV] ........... min_samples_split=10, n_estimators=890, total=  26.0s
[CV] min_samples_split=10, n_estimators=1000 .........................
[CV] .......... min_samples_split=10, n_estimators=1000, total=  25.3s
[CV] min_samples_split=10, n_estimators=1000 .........................
[CV] .......... min_samples_split=10, n_estimators=1000, total=  23.5s
[CV] min_samples_split=10, n_estimators=1000 .........................
[CV] .......... min_samples_split=10, n_estimators=1000, total=  23.4s
[CV] min_samples_split=10, n_estimators=1000 .........................
[CV] .......... min_samples_split=10, n_estimators=1000, total=  23.3s
[CV] min_samples_split=10, n_estimators=1000 .........................
[CV] .......... min_samples_split=10, n_estimators=1000, total=  27.0s






    




[Parallel(n_jobs=1)]: Done 500 out of 500 | elapsed: 91.1min finished






    
Out[28]:





GridSearchCV(cv=None, error_score=nan,
             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,
                                              class_weight=None,
                                              criterion='gini', max_depth=None,
                                              max_features='auto',
                                              max_leaf_nodes=None,
                                              max_samples=None,
                                              min_impurity_decrease=0.0,
                                              min_impurity_split=None,
                                              min_samples_leaf=1,
                                              min_samples_split=2,
                                              min_weight_fraction_leaf=0.0,
                                              n_estimators=100, n_jobs=None,
                                              oob_score=False,
                                              random_state=None, verbose=0,
                                              warm_start=False),
             iid='deprecated', n_jobs=None,
             param_grid={'min_samples_split': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10]),
                         'n_estimators': array([  10,  120,  230,  340,  450,  560,  670,  780,  890, 1000])},
             pre_dispatch='2*n_jobs', refit=False, return_train_score=False,
             scoring='precision_macro', verbose=2)

In [33]:

    

ressc=pd.DataFrame(clf.cv_results_)


    

In [34]:

    

plt.plot(ressc['mean_test_score'])


    

    
Out[34]:





[<matplotlib.lines.Line2D at 0x1b398b9550>]






    

In [35]:

    

clf.best_score_, clf.best_params_


    

    
Out[35]:





(0.9699100581040024, {'min_samples_split': 3, 'n_estimators': 670})

In [ ]:

    




    

In [75]:

    

rf = RandomForestClassifier(n_estimators=670, min_samples_split=3, verbose=True,bootstrap=True, n_jobs=-1, 
                            class_weight=class_weigths, criterion='entropy',  random_state=np.random.randint(1000), 
                            warm_start=False)
rf.fit(X_train, y_train)
pred_labels = rf.predict(X_test)
model_accuracy = accuracy_score(y_test, pred_labels)


    

    




[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.
[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:    1.1s
[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed:    2.5s
[Parallel(n_jobs=-1)]: Done 670 out of 670 | elapsed:    3.8s finished
[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.
[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s
[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:    0.1s
[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:    0.2s
[Parallel(n_jobs=4)]: Done 670 out of 670 | elapsed:    0.3s finished

In [76]:

    

print ('accuracy score {}'.format(model_accuracy))
classes=['non-UCD', 'M7-L0', 'L', 'T']
cm = pd.DataFrame(confusion_matrix(y_test, pred_labels), 
                  columns=classes, index=classes)


    

    




accuracy score 0.9769736842105263

In [77]:

    

precision_score(y_test, pred_labels, average='macro')


    

    
Out[77]:





0.9647710976400707

In [78]:

    

#create a table a confusion matrix

fig, ax=plt.subplots(figsize=(8, 6))

#matr=(cm/cm.sum()).applymap(lambda x: np.round(x, 2)).values
matr=cm.values
im = ax.imshow(matr, cmap='Blues')

# We want to show all ticks...
ax.set_xticks(np.arange(len(classes)))
ax.set_yticks(np.arange(len(classes)))
# ... and label them with the respective list entries
ax.set_xticklabels(classes)
ax.set_yticklabels(classes)

ax.set_xlabel('True Label', fontsize=18)
ax.set_ylabel('Predicted Label', fontsize=18)
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=0, ha="right",
         rotation_mode="anchor")

# Loop over data dimensions and create text annotations.
for i in range(len(classes)):
    for j in range(len(classes)):
        text = ax.text(j, i, matr[i, j], ha="center", va="center", color="k", fontsize=18)
        
ax.set_xlim([-0.5, 3.5])
ax.set_ylim([3.5, -0.5])
plt.tight_layout()
plt.savefig(wisps.OUTPUT_FIGURES+'/confusion_matrix.pdf')


    

In [79]:

    

rlabels=rf.predict(pred_set)


    

    




[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.
[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s
[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:    0.6s
[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:    1.3s
[Parallel(n_jobs=4)]: Done 670 out of 670 | elapsed:    2.1s finished

In [80]:

    

len(rlabels[rlabels>0])


    

    
Out[80]:





168

In [42]:

    

cands=pd.read_pickle(wisps.OUTPUT_FILES+'/true_spectra_cands.pkl')


    

In [43]:

    

cands['grism_id']=cands.grism_id.apply(lambda x: x.lower())
cands['spt']=[x.spectral_type for x in cands.spectra]


    

In [44]:

    

len(cands), len( pred_df[pred_df.grism_id.isin(cands.grism_id.values)])


    

    
Out[44]:





(364, 362)

In [45]:

    

strs=wisps.datasets['stars']


    

In [46]:

    

#cands[~ cands.grism_id.isin(pred_df.grism_id.values) ]


    

In [47]:

    

cands[~ cands.grism_id.isin(strs.grism_id.values) ]


    

    
Out[47]:







  

    

      

      
grism_id
      
spectra
      
spt
      
spt_unc
      
distance
      
RA
      
DEC
      
snr1
      
pointing
      
designation
    
  
  

  

In [48]:

    

true=(pred_df[(rlabels>0) & pred_df.grism_id.isin(cands.grism_id.values)])#.drop_duplicates(subset='grism_id')
truep=len(true)
ps=len(rlabels[rlabels>0])


    

In [49]:

    

len(true[true.spt.between(17,19)]), len(true[true.spt.between(20,29)]), len(true[true.spt.between(30,40)])


    

    
Out[49]:





(40, 11, 5)

In [50]:

    

'FP rate {}'.format((ps-truep)/ps)


    

    
Out[50]:





'FP rate 0.42011834319526625'

In [51]:

    

rf_dict={'classifier': rf,
            'sclr':scaler,
            'feats':features}


    

In [52]:

    

import pickle


    

In [53]:

    

#save the random forest
output_file=wisps.OUTPUT_FILES+'/random_forest_classifier.pkl'
with open(output_file, 'wb') as file:
    pickle.dump(rf_dict,file)


    

In [54]:

    

sv_df=pred_df[(rlabels>0)]


    

In [55]:

    

sv_df.to_pickle(wisps.LIBRARIES+'/labelled_by_rf.pkl')


    

In [56]:

    

slbyids=pd.read_pickle(wisps.OUTPUT_FILES+'/selected_by_indices.pkl')


    

In [57]:

    

#slbyids


    

In [58]:

    

len(sv_df[(sv_df.grism_id.isin(slbyids.grism_id)) & (sv_df.grism_id.isin(cands.grism_id))])


    

    
Out[58]:





23

In [59]:

    

len(sv_df[( ~sv_df.grism_id.isin(slbyids.grism_id)) & (sv_df.grism_id.isin(cands.grism_id))])


    

    
Out[59]:





75

In [60]:

    

len(slbyids[( ~slbyids.grism_id.isin(sv_df.grism_id)) & (slbyids.grism_id.isin(cands.grism_id))])


    

    
Out[60]:





58

In [61]:

    

pred_df.shape


    

    
Out[61]:





(67450, 26)

In [62]:

    

not_in=cands[~cands.grism_id.isin(comb.grism_id)]


    

In [63]:

    

(not_in[['spt', 'grism_id']]).values


    

    
Out[63]:





array([[array([16.,  1.]), 'goodsn-36-g141_24221'],
       [array([17.,  0.]), 'goodsn-36-g141_22694']], dtype=object)

In [64]:

    

s=wisps.Source(filename='goodss-04-G141_17402', is_ucd=True)


    

In [65]:

    

#low=pd.read_pickle(wisps.LIBRARIES+'/lowsnr_obejcts.pkl')


    

In [66]:

    

#high=pd.read_pickle(wisps.LIBRARIES+'/highsnr_obejcts.pkl')


    

In [67]:

    

#comb=pd.concat([low, high], ignore_index=True)


    

In [68]:

    

#comb['grism_id']=comb.grism_id.apply(lambda x: str(x).lower())


    

In [69]:

    

#comb.to_hdf(wisps.COMBINED_PHOTO_SPECTRO_FILE, key='new_stars')


    

In [70]:

    

#comb.loc[578]


    

In [71]:

    

comb.shape


    

    
Out[71]:





(254264, 26)

In [82]:

    

s=wisps.Source(filename='goodss-28-G141_14876')


    

In [83]:

    

s.plot()


    

In [142]:

    

files=glob.glob('/users/caganze/desktop/ATMO_2020_models/evolutionary_tracks/ATMO_CEQ/MKO_WISE_IRAC/*')


    

In [143]:

    

dframes=[]


    

In [144]:

    

for f in tqdm(files):
    dframes.append(ascii.read(f).to_pandas())


    

    




100%|██████████| 76/76 [00:00<00:00, 83.94it/s]

In [145]:

    

df=pd.concat(dframes).rename(columns='Teff')


    

In [147]:

    

df.columns=[x.lower() for x in df.columns]


    

In [149]:

    

df=df.rename(columns={'teff':'temperature'})


    

In [150]:

    

df.to_csv('/users/caganze/research/wisps/evmodels/phillips2020.csv')


    

In [ ]: