In [1]:

    

import glob

import numpy as np
import pandas as pd

from grafting_classifier import GraftingClassifier
from sklearn.linear_model import SGDClassifier
from ogfs_classifier import OGFSClassifier
from osfs_classifier import OSFSClassifier
from dpp_classifier import DPPClassifier
from dpp_classifier_mitra import DPPClassifier as DPPClassifier2
from dpp_classifier_ogfs import DPPClassifier as DPPClassifier3

from sklearn.metrics import log_loss, accuracy_score

#import dask.dataframe as dd
#import dask.array as da


    

In [2]:

    

class_train = glob.glob("NIPS/*_train.csv")
print(class_train)


    

    




['NIPS\\arcene_train.csv', 'NIPS\\dexter_train.csv', 'NIPS\\dorothea_train.csv', 'NIPS\\gisette_train.csv', 'NIPS\\madelon_train.csv']

In [3]:

    

def train_label(fname):
    targetname = fname.replace(".csv", ".labels")
    return pd.read_csv(targetname)


    

In [4]:

    

def get_performance(mod, fpath, mod_name):
    train1 = pd.read_csv(fpath).fillna(0)
    y = np.array(train_label(fpath)).flatten()
    
    # simulate streaming...
    # try splitting into groups of ~10,
    # if there is no splits, try ~5.
    train1_cols = np.array_split(range(train1.shape[1]), int(train1.shape[1]/10.0) + 1)
    if len(train1_cols) == 1:
        train1_cols = np.array_split(range(train1.shape[1]), int(train1.shape[1]/5.0) + 1)
    all_cols = []

    #mod = GraftingClassifier(max_iter=5)
    if mod_name == 'Base':
        mod.fit(train1, y)
        results = {'accuracy': accuracy_score(y, mod.predict(train1)), 
               'logloss': log_loss(y, mod.predict_proba(train1)), 
               'feat_dim': mod.coef_.flatten().shape}
        return results
    
    # lets normalise the dataset...
    train1 = (train1 - train1.mean())/(np.maximum(train1.std(), 1))
    for idx, collist in enumerate(train1_cols):
        if idx == 0:
            column_list = list(np.array(list(train1.columns))[collist])
            mod.fit(train1[column_list], y)
            all_cols.extend(list(collist))
        else:
            all_cols.extend(list(collist))
            column_list = list(np.array(list(train1.columns))[all_cols])
            mod.partial_fit(train1[column_list], y)
        
        # debugging
        print_cond = True if idx % int((len(train1_cols)/10)+1) == 0 else False
        if mod_name in ['Fast_OSFS', 'DPP', 'DPP3', 'OGFS'] and print_cond:
            print("\tmodel: {}, iter: {}".format(mod_name, idx))
        
        # for fast osfs
    if mod_name == 'Fast_OSFS':
        mod._redundancy(train1, y, mode='all')
    
    results = {'accuracy': accuracy_score(y, mod.predict(train1)), 
               'logloss': log_loss(y, mod.predict_proba(train1)), 
               'feat_dim': mod.coef_.flatten().shape}
    return results


    

In [5]:

    

def create_models():
    return [
    ('Grafting', GraftingClassifier(max_iter=5, random_state=42)), 
    #('DPP', DPPClassifier(max_iter=5, random_state=42)), 
    #('DPP2', DPPClassifier2(max_iter=5, random_state=42)),
    #('DPP3', DPPClassifier3(max_iter=5, random_state=42)),
    #('OGFS', OGFSClassifier(max_iter=5, random_state=42)),
    #('OSFS', OSFSClassifier(max_iter=5, random_state=42, fast_osfs=False)),
    ('Fast_OSFS', OSFSClassifier(max_iter=5, random_state=42)),
    ('Base', SGDClassifier(loss='log', max_iter=5, random_state=42))
]


    

In [6]:

    

#ex_dat = class_train[0]
#print(ex_dat, pd.read_csv(ex_dat).shape)
#models = create_models()
#for nm, mod in models:
#    print(nm, get_performance(mod, ex_dat, mod_name=nm))


    

In [7]:

    

ex_dat = class_train[1]
print(ex_dat, pd.read_csv(ex_dat).shape)
models = create_models()
for nm, mod in models:
    print(nm, get_performance(mod, ex_dat, mod_name=nm))


    

    




NIPS\dexter_train.csv (300, 20000)
Grafting {'accuracy': 1.0, 'logloss': 0.0040602029275617216, 'feat_dim': (159,)}






    




C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 2 4 6] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide
  f = msb / msw
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 2 3 4 5 7 8 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 3 4 5 6 7 8] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 3 4 5 6 7] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 3 6 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 3 6 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 2 3 4 5 7 8] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 2 6 7 8] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 2 3 5] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 2 6 7 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [2 7 8] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [3 5 6 8] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 2 3 6 7 8] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 3 4 5 7 8] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 3 4 5 6 7 8 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 2 3 4 5 6 8] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [2 6 7 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 4 5 6 8 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 4 6 8 9] are constant.
  UserWarning)






    




	model: Fast_OSFS, iter: 0






    




C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 2 4 5 6 7 8 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [5 6 7 8 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 2 3 5 6] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 2 3 5 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 2 3 5 7 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 2 3 5 7 8 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 2 5 8] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 2 3 5 6] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 2 3 4 6 7 8] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 2 3 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 5 8 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [3 4 5 6 7 8 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [5 6 7 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 2 3 4 5 7 8 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 2 3 4 5 6 7] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 6 9] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 2 3 4 6 7 8] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 2 4 5 7 8] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [2 3 4 5 9] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 2 3 4 5 6 8 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 3 6 7 8 9] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 2 3 4 5 6 8 9] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [2 3 5 9] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [3 4 8 9] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 2 4 5 6 7 8 9] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 4 6 7 8 9] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 2 4 6 7 8] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 2 4 5 6 9] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 4 6 7 9] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 3 4 5 8 9] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 2 6 7] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 4 6 9] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 3 5 7 9] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 7 9] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 2 3 4 5 6 9] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 3 4 6 8] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 3 4 6 7 9] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 2 4 5 6 8 9] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 4 8 9] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 3 5 7 8] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 2 4 5 8] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [5 7 9] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [4 8 9] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 2 5 6 7 8] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [2 6 7 8 9] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 2 3 4 6 9] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 3 4 5] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 6 7 8 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 2 4 5 7] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 2 3 4 5 7 8] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 2 3 5 6 7 8 9] are constant.
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [2 3 4 6 7 8 9] are constant.
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	model: Fast_OSFS, iter: 201






    




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	model: Fast_OSFS, iter: 402






    




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	model: Fast_OSFS, iter: 603






    




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	model: Fast_OSFS, iter: 804






    




C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [2 3 4 5 6 9] are constant.
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	model: Fast_OSFS, iter: 1005






    




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	model: Fast_OSFS, iter: 1206






    




C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [2 4 5 7 8] are constant.
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	model: Fast_OSFS, iter: 1407






    




C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 2 3 4 5 8 9] are constant.
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	model: Fast_OSFS, iter: 1608






    




C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 6 7 9] are constant.
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  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [2 4 6 7 8 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [3 4 5 6 7 8] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 2 3 8] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 5 7 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [2 5 6 7 8] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [3 4 5 6] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 3 4 5 6 8] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [3 4 8] are constant.
  UserWarning)






    




	model: Fast_OSFS, iter: 1809






    




C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 4 5 7 8] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 4 6 7 8] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 3 4] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 2 3 4 5 7] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 5 6 8] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 2 4 6] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 3 6] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 2 3 4 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 4 5 6] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 3 5 8] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [3 5 6 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 4 5 8] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [2 3 4 7 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 4 5] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 3 7] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 5 7] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 5 6 7] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 4 5 8 9] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 2 4 7] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 3 4] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 1 4 8] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 2 4 6 7] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [0 2 7] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 2 5 6 7] are constant.
  UserWarning)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [1 2 3 6 8] are constant.
  UserWarning)






    




		(300, 268)
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C:\Users\chapm\Documents\GitHub\sklearn-recipes\streaming\osfs_util.py:68: RuntimeWarning: invalid value encountered in double_scalars
  cor = -V_inv[0][1]/(np.sqrt(V_inv[0][0]*V_inv[1][1]))
C:\Users\chapm\Documents\GitHub\sklearn-recipes\streaming\osfs_util.py:30: RuntimeWarning: invalid value encountered in minimum
  cor_m = np.minimum(cor_m, 0.9999)
C:\Users\chapm\Documents\GitHub\sklearn-recipes\streaming\osfs_util.py:31: RuntimeWarning: invalid value encountered in maximum
  cor_m = np.maximum(cor_m, -0.9999)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\scipy\stats\_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
  return (self.a < x) & (x < self.b)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\scipy\stats\_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
  return (self.a < x) & (x < self.b)
C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\scipy\stats\_distn_infrastructure.py:1735: RuntimeWarning: invalid value encountered in greater_equal
  cond2 = (x >= self.b) & cond0






    




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		(300, 100)
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C:\Users\chapm\Documents\GitHub\sklearn-recipes\streaming\osfs_util.py:25: RuntimeWarning: invalid value encountered in sqrt
  d_inv = np.sqrt(np.diag(np.diag(K)))






    




		(300, 98)
		(300, 98)
		(300, 98)
		(300, 98)
		(300, 98)
		(300, 98)
		(300, 98)
		(300, 98)






    




C:\Users\chapm\Documents\GitHub\sklearn-recipes\streaming\osfs_util.py:68: RuntimeWarning: invalid value encountered in sqrt
  cor = -V_inv[0][1]/(np.sqrt(V_inv[0][0]*V_inv[1][1]))
C:\Users\chapm\Documents\GitHub\sklearn-recipes\streaming\osfs_util.py:68: RuntimeWarning: divide by zero encountered in double_scalars
  cor = -V_inv[0][1]/(np.sqrt(V_inv[0][0]*V_inv[1][1]))






    




		(300, 98)
		(300, 98)
		(300, 98)
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\kernel_approximation.py:470: UserWarning: n_components > n_samples. This is not possible.
n_components was set to n_samples, which results in inefficient evaluation of the full kernel.
  warnings.warn("n_components > n_samples. This is not possible.\n"






    




		(300, 97)
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C:\Users\chapm\Anaconda3\envs\skrecipe\lib\site-packages\sklearn\linear_model\base.py:340: RuntimeWarning: overflow encountered in exp
  np.exp(prob, prob)






    




Fast_OSFS {'accuracy': 0.5, 'logloss': 8.9316943511579083, 'feat_dim': (264,)}
Base {'accuracy': 1.0, 'logloss': 9.9920072216264148e-16, 'feat_dim': (20000,)}

In [ ]:

    

#ex_dat = class_train[2]
#print(ex_dat, pd.read_csv(ex_dat).shape)
#models = create_models()
#for nm, mod in models:
#    if nm != 'Base':
#        print(nm, get_performance(mod, ex_dat))
#    else:
#        print(nm, get_performance(mod, ex_dat, base=True))


    

In [ ]:

    

ex_dat = class_train[3]
print(ex_dat, pd.read_csv(ex_dat).shape)
models = create_models()
for nm, mod in models:
    print(nm, get_performance(mod, ex_dat, mod_name=nm))


    

In [ ]:

    

ex_dat = class_train[4]
print(ex_dat, pd.read_csv(ex_dat).shape)
models = create_models()
for nm, mod in models:
    print(nm, get_performance(mod, ex_dat, mod_name=nm))


    

In [ ]: