Code and documentation for my solution (51th place) for the Kaggle Melbourne University AES/MathWorks/NIH Seizure Prediction challenge : https://www.kaggle.com/solomonk
https://www.kaggle.com/c/melbourne-university-seizure-prediction
%reset -f
%matplotlib inline
import numpy as np
import pandas as pd
import pymc3 as pm
import seaborn as sns
import os,sys,inspect
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(currentdir)
dirToInclude=parentdir +'/features/'
sys.path.insert(0,dirToInclude)
import IeegConsts
from IeegConsts import *
from IeegFeatures import *
import pandas
import numpy as np
import pandas as pd
from sklearn import cross_validation
from sklearn import metrics
from sklearn.metrics import roc_auc_score, log_loss, roc_auc_score, roc_curve, auc
from sklearn.cross_validation import StratifiedKFold, ShuffleSplit, cross_val_score, train_test_split
import matplotlib.pyplot as plt
import theano.tensor as tt
%matplotlib inline
np.set_printoptions(precision=4, threshold=10000, linewidth=100, edgeitems=999, suppress=True)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.width', 100)
pd.set_option('expand_frame_repr', False)
pd.set_option('precision', 6)
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 12, 4
train_dir=TRAIN_DATA_FOLDER_IN_ALL
test_dir=TEST_DATA_FOLDER_IN_ALL
ieegFeatures= IeegFeatures(train_dir, True)
df_cols_train=ieegFeatures.ieegGenCols()
print(len(df_cols_train))
# F_NAME_TRAIN= TRAIN_FEAT_BASE + TRAIN_PREFIX_ALL +'-feat_TRAIN_df.hdf'
# X_df_train=pandas.read_hdf(F_NAME_TRAIN, engine='python')
X_df_train= pd.read_hdf(TRAIN_FEAT_BASE + TRAIN_PREFIX_ALL
+ 'X_df_train.hdf', 'data',format='fixed',complib='blosc',complevel=9)
# X_df_train.drop('Unnamed: 0', axis=1, inplace=True)
n=16
last_cols=list()
for i in range(1, n_psd + 1):
last_cols.append('psd_{}'.format(i))
for i in range(1, 16 + 1):
last_cols.append('var_{}'.format(i))
for i in range(1, 16 + 1):
last_cols.append('kurt_{}'.format(i))
for i in range(1, n_corr_coeff + 1):
last_cols.append('corcoef_{}'.format(i))
for i in range(1, n + 1):
last_cols.append('hurst_{}'.format(i))
# for i in range(1, n_plv+ 1):
# last_cols.append('plv_{}'.format(i))
# for i in range(1, n + 1):
# last_cols.append('mean_{}'.format(i))
# for i in range(1, n + 1):
# last_cols.append('median_{}'.format(i))
# for i in range(1, n + 1):
# last_cols.append('std_{}'.format(i))
X_df_train_SINGLE=X_df_train
X_df_train_SINGLE.drop('id', axis=1, inplace=True)
X_df_train_SINGLE.drop('file', axis=1, inplace=True)
X_df_train_SINGLE.drop('patient_id', axis=1, inplace=True)
X_df_train_SINGLE = X_df_train_SINGLE.loc[X_df_train_SINGLE['file_size'] > 100000]
X_df_train_SINGLE.drop('file_size', axis=1, inplace=True)
X_df_train_SINGLE.drop('sequence_id', axis=1, inplace=True)
X_df_train_SINGLE.drop('segment', axis=1, inplace=True)
answers_1_SINGLE = list (X_df_train_SINGLE[singleResponseVariable].values)
X_df_train_SINGLE = X_df_train_SINGLE.drop(singleResponseVariable, axis=1)
X_df_train_SINGLE=X_df_train_SINGLE[last_cols]
X_df_train_SINGLE=X_df_train_SINGLE.apply(lambda x: pandas.to_numeric(x, errors='ignore'))
X_df_train_SINGLE.head(5)
Starting:ieegFeatures:2017-02-11 22:08:18.068192 Cols:1239 1239
psd_1 | psd_2 | psd_3 | psd_4 | psd_5 | psd_6 | psd_7 | psd_8 | psd_9 | psd_10 | psd_11 | psd_12 | psd_13 | psd_14 | psd_15 | psd_16 | psd_17 | psd_18 | psd_19 | psd_20 | psd_21 | psd_22 | psd_23 | psd_24 | psd_25 | psd_26 | psd_27 | psd_28 | psd_29 | psd_30 | psd_31 | psd_32 | psd_33 | psd_34 | psd_35 | psd_36 | psd_37 | psd_38 | psd_39 | psd_40 | psd_41 | psd_42 | psd_43 | psd_44 | psd_45 | psd_46 | psd_47 | psd_48 | psd_49 | psd_50 | psd_51 | psd_52 | psd_53 | psd_54 | psd_55 | psd_56 | psd_57 | psd_58 | psd_59 | psd_60 | psd_61 | psd_62 | psd_63 | psd_64 | psd_65 | psd_66 | psd_67 | psd_68 | psd_69 | psd_70 | psd_71 | psd_72 | psd_73 | psd_74 | psd_75 | psd_76 | psd_77 | psd_78 | psd_79 | psd_80 | psd_81 | psd_82 | psd_83 | psd_84 | psd_85 | psd_86 | psd_87 | psd_88 | psd_89 | psd_90 | psd_91 | psd_92 | psd_93 | psd_94 | psd_95 | psd_96 | psd_97 | psd_98 | psd_99 | psd_100 | psd_101 | psd_102 | psd_103 | psd_104 | psd_105 | psd_106 | psd_107 | psd_108 | psd_109 | psd_110 | psd_111 | psd_112 | psd_113 | psd_114 | psd_115 | psd_116 | psd_117 | psd_118 | psd_119 | psd_120 | psd_121 | psd_122 | psd_123 | psd_124 | psd_125 | psd_126 | psd_127 | psd_128 | psd_129 | psd_130 | psd_131 | psd_132 | psd_133 | psd_134 | psd_135 | psd_136 | psd_137 | psd_138 | psd_139 | psd_140 | psd_141 | psd_142 | psd_143 | psd_144 | psd_145 | psd_146 | psd_147 | psd_148 | psd_149 | psd_150 | psd_151 | psd_152 | psd_153 | psd_154 | psd_155 | psd_156 | psd_157 | psd_158 | psd_159 | psd_160 | psd_161 | psd_162 | psd_163 | psd_164 | psd_165 | psd_166 | psd_167 | psd_168 | psd_169 | psd_170 | psd_171 | psd_172 | psd_173 | psd_174 | psd_175 | psd_176 | psd_177 | psd_178 | psd_179 | psd_180 | psd_181 | psd_182 | psd_183 | psd_184 | psd_185 | psd_186 | psd_187 | psd_188 | psd_189 | psd_190 | psd_191 | psd_192 | var_1 | var_2 | var_3 | var_4 | var_5 | var_6 | var_7 | var_8 | var_9 | var_10 | var_11 | var_12 | var_13 | var_14 | var_15 | var_16 | kurt_1 | kurt_2 | kurt_3 | kurt_4 | kurt_5 | kurt_6 | kurt_7 | kurt_8 | kurt_9 | kurt_10 | kurt_11 | kurt_12 | kurt_13 | kurt_14 | kurt_15 | kurt_16 | corcoef_1 | corcoef_2 | corcoef_3 | corcoef_4 | corcoef_5 | corcoef_6 | corcoef_7 | corcoef_8 | corcoef_9 | corcoef_10 | corcoef_11 | corcoef_12 | corcoef_13 | corcoef_14 | corcoef_15 | corcoef_16 | corcoef_17 | corcoef_18 | corcoef_19 | corcoef_20 | corcoef_21 | corcoef_22 | corcoef_23 | corcoef_24 | corcoef_25 | corcoef_26 | corcoef_27 | corcoef_28 | corcoef_29 | corcoef_30 | corcoef_31 | corcoef_32 | corcoef_33 | corcoef_34 | corcoef_35 | corcoef_36 | corcoef_37 | corcoef_38 | corcoef_39 | corcoef_40 | corcoef_41 | corcoef_42 | corcoef_43 | corcoef_44 | corcoef_45 | corcoef_46 | corcoef_47 | corcoef_48 | corcoef_49 | corcoef_50 | corcoef_51 | corcoef_52 | corcoef_53 | corcoef_54 | corcoef_55 | corcoef_56 | corcoef_57 | corcoef_58 | corcoef_59 | corcoef_60 | corcoef_61 | corcoef_62 | corcoef_63 | corcoef_64 | corcoef_65 | corcoef_66 | corcoef_67 | corcoef_68 | corcoef_69 | corcoef_70 | corcoef_71 | corcoef_72 | corcoef_73 | corcoef_74 | corcoef_75 | corcoef_76 | corcoef_77 | corcoef_78 | corcoef_79 | corcoef_80 | corcoef_81 | corcoef_82 | corcoef_83 | corcoef_84 | corcoef_85 | corcoef_86 | corcoef_87 | corcoef_88 | corcoef_89 | corcoef_90 | corcoef_91 | corcoef_92 | corcoef_93 | corcoef_94 | corcoef_95 | corcoef_96 | corcoef_97 | corcoef_98 | corcoef_99 | corcoef_100 | corcoef_101 | corcoef_102 | corcoef_103 | corcoef_104 | corcoef_105 | corcoef_106 | corcoef_107 | corcoef_108 | corcoef_109 | corcoef_110 | corcoef_111 | corcoef_112 | corcoef_113 | corcoef_114 | corcoef_115 | corcoef_116 | corcoef_117 | corcoef_118 | corcoef_119 | corcoef_120 | hurst_1 | hurst_2 | hurst_3 | hurst_4 | hurst_5 | hurst_6 | hurst_7 | hurst_8 | hurst_9 | hurst_10 | hurst_11 | hurst_12 | hurst_13 | hurst_14 | hurst_15 | hurst_16 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | -0.406786 | -0.506428 | -0.550631 | -0.602230 | -0.117925 | -0.258156 | -0.292530 | -0.310629 | -0.464966 | -2.681048 | -0.672553 | -0.735941 | -0.558759 | -0.644890 | -0.688039 | -0.669862 | -0.510144 | -0.598658 | -0.646677 | -0.670835 | -0.129998 | -0.251415 | -0.323000 | -0.227784 | -0.639318 | -2.760449 | -0.665167 | -0.659974 | -0.590483 | -0.588708 | -0.647818 | -0.715215 | -0.629282 | -0.647044 | -0.621452 | -0.675702 | -0.426132 | -0.442531 | -0.483979 | -0.434602 | -0.750417 | -2.766089 | -0.708479 | -0.729438 | -0.629134 | -0.464072 | -0.620423 | -0.708196 | -0.707625 | -0.711356 | -0.642788 | -0.672846 | -0.478605 | -0.479591 | -0.520513 | -0.456928 | -0.790443 | -2.769022 | -0.732106 | -0.730806 | -0.646176 | -0.473156 | -0.634019 | -0.703660 | -0.763965 | -0.765547 | -0.680584 | -0.695172 | -0.550166 | -0.547159 | -0.539212 | -0.476176 | -0.818632 | -2.770506 | -0.785050 | -0.764124 | -0.638319 | -0.486804 | -0.654367 | -0.688834 | -0.905803 | -0.866233 | -0.841806 | -0.849490 | -0.760451 | -0.696508 | -0.704839 | -0.673899 | -0.937149 | -2.788111 | -0.922047 | -0.953263 | -0.767587 | -0.691095 | -0.780723 | -0.759457 | -1.181694 | -1.112317 | -1.080482 | -1.044585 | -0.970119 | -0.848156 | -0.901840 | -0.927819 | -1.146751 | -2.809118 | -1.091821 | -1.153706 | -0.992040 | -0.950499 | -0.943695 | -0.874682 | -1.422331 | -1.389611 | -1.338669 | -1.274514 | -1.122098 | -1.014736 | -1.053481 | -1.121061 | -1.423202 | -2.849218 | -1.344742 | -1.423175 | -1.194762 | -1.215790 | -1.194936 | -1.162727 | 0.615413 | 0.465645 | 0.394319 | 0.333944 | 0.734827 | 0.507590 | 0.499734 | 0.471931 | 0.564459 | 0.117440 | 0.326137 | 0.306081 | 0.306713 | 0.156820 | 0.166570 | 0.143397 | 0.512056 | 0.373415 | 0.298273 | 0.265338 | 0.722755 | 0.514332 | 0.469264 | 0.554776 | 0.390108 | 0.038039 | 0.333523 | 0.382048 | 0.274988 | 0.213003 | 0.206791 | 0.098044 | 0.330776 | 0.269807 | 0.294938 | 0.250845 | 0.369017 | 0.274044 | 0.281546 | 0.327668 | 0.246239 | 0.030195 | 0.253611 | 0.295587 | 0.231769 | 0.326421 | 0.217546 | 0.114852 | -0.181280 | -0.103608 | -0.003335 | -0.014493 | -0.353738 | -0.240287 | -0.187718 | -0.227108 | -0.143868 | -0.007843 | -0.079912 | -0.086461 | -0.043219 | 0.113418 | 0.010754 | 0.016808 | 1330.445710 | 1053.480839 | 1012.823856 | 882.276599 | 5324.886292 | 3438.669283 | 2822.905996 | 3031.208239 | 949.112785 | 0.365631 | 707.979865 | 596.306308 | 1072.018943 | 1306.998679 | 937.363732 | 935.186251 | 2.666569 | 6.968982 | 3.161490 | 1.738240 | 1.444126 | 18.737641 | 33.201227 | 40.104324 | 3.765897 | 30.769902 | 1.565525 | 1.709295 | 2.556811 | 3.083199 | 1.464667 | 2.148079 | 0.178701 | 0.032578 | -0.023397 | -0.152288 | -0.219400 | -0.201431 | -0.190916 | 0.204805 | 0.006099 | 0.081204 | 0.049197 | -0.009776 | -0.070802 | -0.063187 | -0.074768 | 0.220537 | 0.081216 | -0.179154 | -0.259667 | -0.271336 | -0.228816 | 0.135671 | 0.116379 | 0.205116 | 0.137675 | -0.019435 | -0.069349 | -0.026827 | -0.071847 | 0.210955 | -0.159451 | -0.245452 | -0.239983 | -0.167325 | 0.007277 | 0.011734 | 0.167109 | 0.104715 | -0.002921 | -0.062695 | -0.046157 | -0.062296 | -0.169828 | -0.228330 | -0.229710 | -0.120309 | -0.001547 | 0.064140 | 0.139054 | 0.209108 | 0.004949 | -0.047816 | -0.013022 | -0.013767 | -0.016553 | -0.148324 | -0.176761 | -0.166190 | -0.022829 | -0.180159 | -0.186908 | -0.120251 | -0.150716 | -0.152404 | -0.142461 | 0.354104 | 0.136804 | -0.234881 | -0.070225 | -0.273721 | -0.272838 | -0.240637 | -0.232917 | -0.275701 | -0.222007 | 0.337664 | -0.211292 | -0.057565 | -0.260903 | -0.268919 | -0.218403 | -0.237076 | -0.278634 | -0.220479 | -0.198198 | -0.036452 | -0.208113 | -0.195315 | -0.202388 | -0.227254 | -0.264204 | -0.178708 | -0.049883 | 0.126748 | 0.116390 | 0.055399 | -0.036598 | -0.025073 | -0.024066 | -0.049266 | 0.031814 | 0.016276 | 0.028395 | 0.088016 | 0.058297 | 0.276228 | 0.031344 | -0.044703 | -0.039266 | 0.000437 | 0.044788 | -0.014861 | 0.011477 | 0.072533 | 0.106350 | 0.118652 | 0.088373 | 0.493077 | 0.194405 | 0.354023 | 0.474543 | 0.344651 | 0.312167 | 0.568701 | 0.411647 | 0.388849 | 0.264564 | 0.256256 | 0.495332 | 0.611394 | 0.347852 | 0.331746 | 0.501788 | 0.473050 | 0.391432 | 0.359969 |
1 | -0.428051 | -0.566576 | -0.635108 | -0.672799 | -0.256426 | -0.372712 | -0.317646 | -0.404852 | -0.479488 | -2.690905 | -0.720872 | -0.778956 | -0.560340 | -0.700978 | -0.691731 | -0.715577 | -0.417979 | -0.647573 | -0.697937 | -0.670199 | -0.292538 | -0.322548 | -0.347250 | -0.277774 | -0.667934 | -2.740056 | -0.661574 | -0.690016 | -0.623905 | -0.589920 | -0.679789 | -0.771444 | -0.612797 | -0.640917 | -0.636664 | -0.640541 | -0.502826 | -0.474716 | -0.480880 | -0.469672 | -0.725687 | -2.745536 | -0.664675 | -0.692026 | -0.589230 | -0.488217 | -0.596600 | -0.682200 | -0.709069 | -0.710412 | -0.686045 | -0.677144 | -0.565640 | -0.513876 | -0.526703 | -0.516816 | -0.774404 | -2.758830 | -0.715076 | -0.711834 | -0.596229 | -0.430723 | -0.572233 | -0.684438 | -0.776716 | -0.806387 | -0.763084 | -0.727851 | -0.639602 | -0.570051 | -0.592727 | -0.567083 | -0.814626 | -2.772359 | -0.767065 | -0.751058 | -0.622363 | -0.450963 | -0.600960 | -0.682608 | -0.919301 | -0.930214 | -0.913473 | -0.894954 | -0.852568 | -0.710257 | -0.763854 | -0.761351 | -0.947070 | -2.782325 | -0.932717 | -0.959682 | -0.763956 | -0.695249 | -0.787622 | -0.767490 | -1.227845 | -1.222355 | -1.169036 | -1.106684 | -1.059154 | -0.901919 | -0.944014 | -0.952874 | -1.192869 | -2.814676 | -1.129484 | -1.171308 | -0.982528 | -0.906194 | -0.977814 | -0.920432 | -1.466409 | -1.442010 | -1.393084 | -1.318627 | -1.200591 | -1.060743 | -1.056533 | -1.169781 | -1.446816 | -2.839578 | -1.372801 | -1.427087 | -1.210371 | -1.217941 | -1.219325 | -1.220321 | 0.618679 | 0.485593 | 0.387613 | 0.315243 | 0.687265 | 0.422889 | 0.527013 | 0.441787 | 0.573319 | 0.107294 | 0.299179 | 0.273774 | 0.299294 | 0.087059 | 0.180658 | 0.121686 | 0.628751 | 0.404596 | 0.324784 | 0.317843 | 0.651154 | 0.473052 | 0.497408 | 0.568865 | 0.384873 | 0.058143 | 0.358477 | 0.362714 | 0.235729 | 0.198118 | 0.192600 | 0.065819 | 0.359662 | 0.336351 | 0.327431 | 0.306036 | 0.377840 | 0.275827 | 0.311445 | 0.330987 | 0.284924 | 0.039459 | 0.307191 | 0.332190 | 0.254153 | 0.318847 | 0.273614 | 0.154859 | -0.269089 | -0.068246 | 0.002647 | -0.011806 | -0.273313 | -0.197225 | -0.185963 | -0.237878 | -0.099950 | -0.018684 | -0.051286 | -0.030524 | 0.018424 | 0.120729 | 0.081014 | 0.089040 | 1452.257995 | 899.164212 | 823.909800 | 802.319827 | 3459.216856 | 2657.810182 | 2670.563703 | 2588.839317 | 919.439123 | 0.347581 | 731.671687 | 661.900286 | 1423.068986 | 1568.033791 | 1143.074468 | 879.163979 | 3.047194 | 7.240983 | 4.372952 | 2.604562 | 2.639642 | 21.375989 | 16.903039 | 19.801985 | 4.652647 | 35.961740 | 2.177266 | 2.657048 | 4.372113 | 3.978509 | 1.861551 | 1.916273 | 0.158984 | 0.006843 | -0.020018 | -0.114797 | -0.187727 | -0.183429 | -0.153786 | 0.175157 | 0.010284 | 0.052897 | -0.014611 | -0.064157 | -0.117036 | -0.133033 | -0.121117 | 0.199643 | 0.116175 | -0.163561 | -0.226921 | -0.222678 | -0.194723 | 0.107515 | 0.126857 | 0.184516 | 0.111269 | -0.061203 | -0.125231 | -0.091346 | -0.106738 | 0.258751 | -0.143910 | -0.213731 | -0.205557 | -0.162809 | 0.005357 | 0.008838 | 0.167182 | 0.112151 | -0.043900 | -0.113255 | -0.071328 | -0.098273 | -0.141045 | -0.211931 | -0.213553 | -0.140393 | -0.014006 | 0.060842 | 0.151790 | 0.248345 | -0.035642 | -0.116842 | -0.094239 | -0.068171 | 0.047842 | -0.159501 | -0.132093 | -0.119789 | -0.039888 | -0.155188 | -0.133624 | -0.096723 | -0.129859 | -0.148303 | -0.128713 | 0.266662 | 0.050960 | -0.196714 | -0.059094 | -0.228505 | -0.223246 | -0.177460 | -0.188110 | -0.210657 | -0.183980 | 0.248465 | -0.184424 | -0.047883 | -0.198102 | -0.205756 | -0.154796 | -0.195820 | -0.221138 | -0.193138 | -0.157629 | -0.034454 | -0.178085 | -0.166659 | -0.135418 | -0.194642 | -0.207960 | -0.151013 | -0.056442 | 0.073630 | 0.055497 | -0.007204 | -0.085205 | -0.104699 | -0.048605 | -0.043361 | 0.032085 | 0.024517 | 0.010900 | 0.068934 | 0.041464 | 0.238677 | -0.037642 | -0.121946 | -0.120850 | -0.050691 | -0.025828 | -0.101934 | -0.091659 | -0.011054 | -0.006813 | -0.002555 | 0.026854 | 0.464664 | 0.188227 | 0.363075 | 0.502679 | 0.401603 | 0.414200 | 0.488810 | 0.622529 | 0.485241 | 0.571458 | 0.406541 | 0.615575 | 0.687594 | 0.409496 | 0.531784 | 0.434593 | 0.765483 | 0.494393 | 0.491168 |
2 | -0.438473 | -0.388276 | -0.560804 | -0.462901 | -0.261924 | -0.412540 | -0.530027 | -0.607979 | -0.674493 | -0.713967 | -0.710172 | -0.723321 | -0.598466 | -0.773537 | -0.741306 | -0.744617 | -0.648960 | -0.595146 | -0.648578 | -0.649621 | -0.392382 | -0.425151 | -0.550321 | -0.508957 | -0.794122 | -0.822105 | -0.802749 | -0.803596 | -0.627584 | -0.527127 | -0.652850 | -0.811482 | -0.559530 | -0.478087 | -0.490636 | -0.533479 | -0.510440 | -0.583750 | -0.647979 | -0.686542 | -0.791141 | -0.825084 | -0.751450 | -0.687138 | -0.624905 | -0.485618 | -0.654331 | -0.784637 | -0.654161 | -0.568703 | -0.505873 | -0.532767 | -0.559091 | -0.647034 | -0.714812 | -0.753031 | -0.823205 | -0.820382 | -0.738886 | -0.652164 | -0.659866 | -0.503283 | -0.688057 | -0.788743 | -0.785134 | -0.680696 | -0.549041 | -0.554762 | -0.613011 | -0.687526 | -0.759987 | -0.774439 | -0.868717 | -0.823551 | -0.744845 | -0.680407 | -0.620970 | -0.451936 | -0.653109 | -0.771946 | -0.877637 | -0.784635 | -0.765896 | -0.777874 | -0.786298 | -0.849431 | -0.915044 | -0.947412 | -0.949799 | -0.922733 | -0.860079 | -0.859587 | -0.610814 | -0.514078 | -0.701109 | -0.777309 | -1.178028 | -1.095816 | -1.054415 | -1.014244 | -0.986959 | -0.985453 | -1.088277 | -1.190281 | -1.230308 | -1.236988 | -1.154182 | -1.145834 | -0.998370 | -0.907247 | -1.004930 | -0.979935 | -1.428880 | -1.364952 | -1.297947 | -1.237569 | -1.054132 | -1.113059 | -1.231489 | -1.363413 | -1.510361 | -1.500478 | -1.358628 | -1.392567 | -1.255594 | -1.165068 | -1.221994 | -1.208271 | 0.563889 | 0.524655 | 0.325820 | 0.417272 | 0.613218 | 0.499598 | 0.463052 | 0.444107 | 0.393296 | 0.338068 | 0.272525 | 0.256221 | 0.164258 | -0.105926 | 0.085670 | 0.122293 | 0.353402 | 0.317786 | 0.238046 | 0.230551 | 0.482760 | 0.486987 | 0.442759 | 0.543129 | 0.273668 | 0.229931 | 0.179948 | 0.175946 | 0.135140 | 0.140484 | 0.174126 | 0.055428 | 0.344518 | 0.345249 | 0.367767 | 0.336182 | 0.316437 | 0.279592 | 0.292698 | 0.323815 | 0.239590 | 0.227718 | 0.234561 | 0.295782 | 0.139791 | 0.199161 | 0.173257 | 0.088665 | -0.008884 | 0.027464 | 0.129721 | 0.105631 | -0.166323 | -0.207395 | -0.150061 | -0.219314 | -0.034078 | -0.002212 | 0.054614 | 0.119837 | 0.004651 | 0.058676 | -0.000870 | 0.033237 | 2656.218991 | 1535.532773 | 1214.674003 | 1946.472638 | 2661.030179 | 1732.844809 | 1066.889208 | 944.611286 | 583.431453 | 584.678465 | 725.139566 | 639.098452 | 1373.657623 | 1883.493652 | 1005.484401 | 742.213566 | 0.749912 | 7.486165 | 4.812114 | 1.285146 | 1.231140 | 1.518840 | 3.409387 | 2.827718 | 1.183922 | 2.380808 | 1.954354 | 2.532612 | 1.832488 | 3.483692 | 0.901819 | 1.961327 | -0.029160 | -0.061747 | 0.192742 | -0.160926 | -0.184992 | -0.210974 | -0.170999 | 0.025638 | -0.085231 | -0.148064 | -0.124346 | -0.094917 | -0.140349 | -0.138500 | -0.221177 | 0.112207 | -0.040638 | -0.157796 | -0.182545 | -0.193836 | -0.146527 | 0.006995 | 0.032771 | 0.040653 | 0.003278 | -0.069760 | -0.129927 | -0.113316 | -0.130260 | 0.138613 | -0.159326 | -0.173303 | -0.164666 | -0.097393 | -0.094210 | -0.000567 | 0.048494 | 0.012564 | -0.073928 | -0.149313 | -0.140981 | -0.147593 | -0.185277 | -0.184865 | -0.220040 | -0.124713 | -0.162265 | -0.127757 | -0.102602 | 0.031571 | -0.103355 | -0.147154 | -0.119620 | -0.221497 | 0.144652 | 0.033266 | -0.054917 | -0.135020 | -0.158212 | -0.138838 | -0.159000 | -0.072380 | -0.100799 | -0.133940 | -0.097326 | 0.390705 | 0.037903 | -0.146170 | -0.167650 | -0.160586 | -0.154301 | -0.103053 | -0.120547 | -0.140514 | -0.103375 | 0.286846 | -0.082819 | -0.128839 | -0.101643 | -0.114714 | -0.064995 | -0.130834 | -0.150770 | -0.070582 | -0.038292 | -0.076110 | -0.050995 | -0.027173 | -0.020764 | -0.123348 | -0.118128 | -0.025818 | 0.279122 | 0.071281 | 0.072518 | 0.022651 | -0.117495 | -0.123846 | -0.025361 | 0.341524 | 0.142416 | -0.019938 | -0.140631 | -0.158554 | -0.051268 | 0.197225 | -0.030536 | -0.145669 | -0.177705 | -0.043935 | -0.011806 | -0.141013 | -0.146454 | -0.034084 | -0.167929 | -0.096414 | -0.004234 | 0.441489 | 0.178188 | 0.407435 | 0.236761 | 0.419352 | 0.262515 | 0.232381 | 0.196593 | 0.279796 | 0.205667 | 0.184826 | 0.258989 | 0.246480 | 0.192713 | 0.360385 | 0.268366 | 0.271937 | 0.228565 | 0.213197 |
3 | -0.111201 | -0.041987 | -0.313406 | -0.228682 | -0.245675 | -0.030617 | -0.180695 | -0.312091 | -0.233526 | 0.009591 | -0.279750 | -0.301803 | -0.203881 | -0.209715 | -0.205457 | -0.254544 | -0.330172 | -0.221672 | -0.476177 | -0.453048 | -0.550985 | -0.417036 | -0.528471 | -0.600747 | -0.413666 | -0.398714 | -0.542718 | -0.581673 | -0.472075 | -0.445944 | -0.471322 | -0.551943 | -0.662568 | -0.692448 | -0.868319 | -0.821175 | -0.896454 | -0.761788 | -0.864188 | -0.902398 | -0.738648 | -0.756345 | -0.805154 | -0.824320 | -0.664092 | -0.679323 | -0.750961 | -0.830958 | -0.697661 | -0.752281 | -0.928897 | -0.873393 | -0.946171 | -0.814563 | -0.931151 | -0.953419 | -0.780086 | -0.816081 | -0.868454 | -0.895429 | -0.729425 | -0.741979 | -0.815945 | -0.901900 | -0.733869 | -0.810772 | -0.962738 | -0.927516 | -1.014153 | -0.868144 | -0.992215 | -0.999176 | -0.828851 | -0.885969 | -0.915254 | -0.965826 | -0.822768 | -0.819871 | -0.876483 | -0.970698 | -0.972177 | -1.062594 | -1.156289 | -1.116091 | -1.288333 | -1.179984 | -1.264226 | -1.247310 | -1.083451 | -1.085929 | -1.178520 | -1.229726 | -1.104440 | -1.064691 | -1.102844 | -1.237834 | -1.339655 | -1.480199 | -1.520626 | -1.407563 | -1.617144 | -1.543949 | -1.609962 | -1.547593 | -1.381859 | -1.360007 | -1.522461 | -1.622398 | -1.390270 | -1.358110 | -1.394830 | -1.581343 | -1.565151 | -1.734503 | -1.791662 | -1.650791 | -1.810555 | -1.802295 | -1.858161 | -1.760692 | -1.490321 | -1.535764 | -1.744210 | -1.874748 | -1.571948 | -1.623168 | -1.601572 | -1.777521 | 1.006954 | 1.181037 | 0.987916 | 1.009138 | 1.176661 | 1.294287 | 1.222866 | 1.059909 | 0.973988 | 1.211287 | 1.037546 | 1.081314 | 1.020372 | 0.977363 | 1.019290 | 1.121932 | 0.787984 | 1.001352 | 0.825146 | 0.784772 | 0.871351 | 0.907869 | 0.875091 | 0.771252 | 0.793848 | 0.802983 | 0.774579 | 0.801444 | 0.752178 | 0.741134 | 0.753425 | 0.824533 | 0.421398 | 0.475432 | 0.388355 | 0.366721 | 0.471007 | 0.513187 | 0.480061 | 0.423903 | 0.426102 | 0.385358 | 0.460572 | 0.493782 | 0.488029 | 0.443142 | 0.415544 | 0.481244 | -0.366585 | -0.525920 | -0.436791 | -0.418051 | -0.400344 | -0.394682 | -0.395030 | -0.347350 | -0.367746 | -0.417625 | -0.314007 | -0.307662 | -0.264148 | -0.297992 | -0.337881 | -0.343289 | 3788.115611 | 5446.828809 | 1691.364780 | 2308.655231 | 1710.880902 | 4515.610336 | 2227.635213 | 1331.450809 | 2275.862726 | 5136.964324 | 1586.126659 | 1376.815749 | 2241.370570 | 2339.214348 | 2251.594604 | 1757.943299 | 2.040104 | 2.225392 | 0.739700 | 1.147698 | 1.072190 | 2.500653 | 0.793764 | 0.409903 | 0.840943 | 0.632733 | 0.553273 | 0.550425 | 1.570896 | 0.646849 | 0.687921 | 0.347896 | 0.642901 | 0.274792 | -0.066169 | -0.233990 | -0.180758 | -0.177053 | -0.112920 | -0.219090 | -0.108098 | -0.101637 | -0.229437 | -0.227107 | -0.255041 | -0.275551 | -0.379040 | 0.510787 | 0.016690 | -0.255037 | -0.158896 | -0.062680 | -0.035647 | -0.296388 | -0.283800 | -0.070270 | -0.272400 | -0.276680 | -0.331240 | -0.361764 | -0.382152 | 0.062699 | -0.176201 | -0.132981 | 0.018451 | 0.205976 | -0.257594 | -0.189507 | -0.105161 | -0.189763 | -0.243678 | -0.291848 | -0.318517 | -0.311132 | -0.156172 | -0.054545 | 0.038934 | 0.077202 | -0.246424 | -0.152501 | -0.072676 | 0.078642 | -0.233161 | -0.176286 | -0.099320 | 0.110287 | 0.631025 | 0.419345 | -0.078530 | -0.174900 | -0.115546 | -0.258702 | -0.215993 | -0.184341 | -0.108651 | -0.054307 | -0.029026 | 0.545307 | -0.039585 | -0.259957 | -0.179956 | -0.283438 | -0.225450 | -0.314569 | -0.247017 | -0.201914 | -0.125843 | 0.273577 | -0.276813 | -0.282466 | -0.278067 | -0.230892 | -0.315863 | -0.279530 | -0.253046 | -0.112160 | -0.188416 | -0.163674 | -0.048980 | 0.062526 | -0.156347 | -0.209395 | -0.182854 | 0.011055 | 0.427245 | 0.177390 | 0.088372 | 0.268092 | 0.100422 | -0.017090 | -0.070929 | -0.021371 | 0.031837 | -0.091824 | -0.026530 | 0.056613 | -0.251510 | 0.477342 | 0.044892 | -0.102662 | -0.161943 | 0.237846 | 0.030722 | -0.154910 | -0.114277 | 0.472173 | 0.476516 | 0.306272 | 0.260913 | 0.709682 | 0.116640 | 0.170123 | 0.335260 | 0.291863 | 0.326536 | 0.257931 | 0.469382 | 0.175357 | 0.220069 | 0.398464 | 0.263109 | 0.274553 | 0.245795 | 0.289799 | 0.272079 | 0.321547 | 0.246082 | 0.324690 |
4 | -0.770331 | -0.584485 | -0.502479 | -0.454269 | -0.553190 | -0.636482 | -0.795933 | -0.820530 | -0.787236 | -0.671795 | -0.796073 | -0.793696 | -0.679834 | -0.628853 | -0.670575 | -0.794874 | -0.928855 | -0.881467 | -0.786080 | -0.976289 | -0.934422 | -1.029379 | -1.072448 | -1.145465 | -1.016527 | -0.975320 | -1.063246 | -1.115494 | -0.958634 | -0.902233 | -0.986790 | -1.188340 | -1.047846 | -1.020852 | -1.043965 | -1.165375 | -1.101895 | -1.233633 | -1.199967 | -1.297162 | -1.037072 | -1.144614 | -1.268083 | -1.374482 | -1.146901 | -1.116260 | -1.229882 | -1.395987 | -1.084449 | -1.059271 | -1.090673 | -1.209574 | -1.136210 | -1.260735 | -1.212954 | -1.316720 | -1.116572 | -1.194359 | -1.319347 | -1.425663 | -1.177851 | -1.173634 | -1.270851 | -1.439431 | -1.101896 | -1.073788 | -1.113964 | -1.211854 | -1.131726 | -1.244887 | -1.215078 | -1.339792 | -1.171134 | -1.210906 | -1.361289 | -1.462588 | -1.211765 | -1.203163 | -1.298599 | -1.474930 | -0.923572 | -1.080045 | -1.095923 | -1.285582 | -1.134799 | -1.274666 | -1.171211 | -1.363859 | -1.139129 | -1.235459 | -1.429862 | -1.517777 | -1.294699 | -1.278452 | -1.367044 | -1.531927 | -0.899367 | -1.100151 | -0.985105 | -1.557288 | -1.298830 | -1.503771 | -1.473174 | -1.561458 | -1.428877 | -1.340960 | -1.552326 | -1.626884 | -1.371516 | -1.371110 | -1.431025 | -1.634748 | -1.281053 | -1.315199 | -1.357246 | -1.756855 | -1.486056 | -1.726523 | -1.678698 | -1.748864 | -1.621325 | -1.442570 | -1.695904 | -1.850529 | -1.549643 | -1.467518 | -1.627333 | -1.829001 | 0.140970 | 0.505496 | 0.534510 | 0.946256 | 0.655926 | 0.737801 | 0.500527 | 0.630986 | 0.473039 | 0.613219 | 0.690718 | 0.775217 | 0.651577 | 0.693462 | 0.727282 | 0.785428 | -0.017554 | 0.208515 | 0.250909 | 0.424236 | 0.274694 | 0.344904 | 0.224013 | 0.306051 | 0.243748 | 0.309694 | 0.423545 | 0.453419 | 0.372777 | 0.420082 | 0.411067 | 0.391962 | -0.162730 | 0.043468 | -0.040567 | 0.212531 | 0.092561 | 0.135059 | 0.089004 | 0.133562 | 0.161324 | 0.108517 | 0.174601 | 0.152609 | 0.153288 | 0.164773 | 0.134974 | 0.146634 | -0.145176 | -0.165047 | -0.291476 | -0.211705 | -0.182133 | -0.209845 | -0.135009 | -0.172489 | -0.082424 | -0.201176 | -0.248945 | -0.300811 | -0.219489 | -0.255309 | -0.276093 | -0.245328 | 511.591893 | 717.759756 | 1305.924328 | 2609.476978 | 470.554463 | 300.968428 | 200.910476 | 164.800668 | 232.670311 | 353.349813 | 185.055518 | 163.490757 | 327.573035 | 412.032264 | 270.711882 | 147.534822 | 1.996694 | 0.228024 | 0.450064 | -0.254879 | 0.542542 | 1.087637 | 0.792145 | 1.881766 | 1.114670 | 0.304191 | 1.542368 | 0.757896 | 1.059704 | 0.859168 | 2.562184 | 0.349247 | 0.030753 | -0.024299 | -0.145883 | -0.127290 | -0.178721 | -0.186140 | -0.145188 | 0.021860 | 0.015256 | -0.026582 | -0.087564 | -0.020789 | -0.033702 | -0.080075 | -0.124249 | -0.320325 | 0.248216 | -0.056074 | -0.059749 | -0.080378 | -0.013758 | 0.015854 | -0.313705 | -0.134662 | -0.250060 | -0.123679 | -0.293216 | -0.207075 | -0.225572 | -0.473004 | -0.000651 | -0.194882 | -0.098219 | -0.236044 | -0.178006 | 0.137542 | -0.019509 | 0.158804 | -0.074843 | 0.072406 | 0.119957 | 0.102874 | -0.164157 | 0.035398 | -0.130333 | 0.026446 | -0.028035 | -0.372921 | -0.191441 | -0.358372 | -0.231236 | -0.294606 | -0.297595 | -0.263418 | 0.393418 | 0.083894 | -0.097702 | -0.102853 | -0.121281 | -0.099799 | -0.083224 | -0.132804 | -0.160676 | -0.118069 | -0.146844 | 0.321721 | 0.107047 | -0.091751 | -0.202066 | -0.117705 | -0.134136 | -0.173693 | -0.222429 | -0.202671 | -0.117897 | 0.502476 | -0.154050 | -0.136416 | -0.045410 | 0.040682 | -0.139774 | -0.169549 | -0.097835 | 0.093651 | -0.101272 | -0.115679 | -0.014920 | 0.094516 | -0.141819 | -0.142176 | -0.142847 | 0.135922 | 0.136635 | 0.067097 | -0.102046 | 0.079685 | -0.058121 | -0.077385 | -0.178405 | 0.142252 | 0.097217 | 0.088642 | 0.179863 | 0.101274 | 0.058654 | -0.056705 | 0.032192 | 0.021425 | 0.084679 | 0.061646 | 0.076973 | 0.121898 | 0.093407 | 0.485742 | 0.242453 | 0.087940 | -0.063777 | 0.188335 | 0.040613 | 0.212929 | 0.435515 | 0.292176 | 0.311181 | 0.236584 | 0.269141 | 0.365435 | 0.430709 | 0.268305 | 0.350697 | 0.300376 | 0.470037 | 0.158063 | 0.384128 | 0.254672 | 0.270050 | 0.276992 |
y=answers_1_SINGLE
X=X_df_train_SINGLE
# Normalize variables
X_norm = (X - X.mean())/X.std()
lr_best_params = {'penalty': 'l2', 'C': 100, 'solver': 'newton-cg', 'fit_intercept': False}
lr = LogisticRegression(**lr_best_params)
lr.fit(X_norm, y)
#Store LR coeefs
lr_coeefs=lr.coef_
k = (X_df_train_SINGLE.shape[1])
with pm.Model() as logistic_model:
μ = pm.Normal('μ', 0, sd=10)
b = pm.Laplace('b', 0.0, b=0.1, shape=k)
p = pm.math.invlogit(μ + tt.dot(X_norm, b))
likelihood = pm.Bernoulli('likelihood', p, observed=y)
niter=3000
with logistic_model:
trace_logistic_model = pm.sample(niter, n_init=50000)
Auto-assigning NUTS sampler... Initializing NUTS using advi... Average ELBO = -8,546.2: 100%|██████████| 50000/50000 [02:06<00:00, 396.60it/s] Finished [100%]: Average ELBO = -7,615.1 100%|██████████| 3000/3000 [1:00:51<00:00, 1.08s/it]
ax = pm.traceplot(trace_logistic_model[-1000:], figsize=(12,len(trace_logistic_model.varnames)*1.5),
lines={k: v['mean'] for k, v in pm.df_summary(trace_logistic_model[-1000:]).iterrows()})