In [7]:
%matplotlib inline
%pylab inline
pylab.rcParams['figure.figsize'] = (12, 7)
from pop_rel_5_statsmodel import *
Populating the interactive namespace from numpy and matplotlib
In [8]:
run_model_with(base_playlist_dummy, '../data/pop5_play_dummy.feather')
OLS Regression Results
==============================================================================
Dep. Variable: log_streams R-squared: 0.718
Model: OLS Adj. R-squared: 0.658
Method: Least Squares F-statistic: 11.96
Date: Fri, 21 Sep 2018 Prob (F-statistic): 1.18e-188
Time: 15:50:15 Log-Likelihood: -2502.4
No. Observations: 1344 AIC: 5479.
Df Residuals: 1107 BIC: 6712.
Df Model: 236
Covariance Type: nonrobust
==========================================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------------------
const 7.7781 0.145 53.778 0.000 7.494 8.062
0EVzFau2kULH9e5dwK2OSI -0.0658 0.703 -0.094 0.925 -1.445 1.313
0JyJ4Nt68jBV0sJUErDlmx -0.2151 0.512 -0.420 0.675 -1.221 0.790
0NCspsyf0OS4BsPgGhkQXM 0.3087 0.468 0.659 0.510 -0.610 1.228
0RKnf4gm6sb7iosMZyyW1s -0.4714 0.823 -0.573 0.567 -2.086 1.144
0eeKZp7perkDfgJQt4pc6I 0.0989 0.443 0.223 0.823 -0.771 0.969
10S981NoNhgn9tdUq9jNFE -0.4937 0.707 -0.699 0.485 -1.880 0.893
1Hu8ON3GQpC1HxeYqyzD1M 0.2620 0.589 0.445 0.657 -0.894 1.418
1T2NZYhVC8SqqJfdPxuHNd -4.163e-14 2.17e-14 -1.919 0.055 -8.42e-14 9.31e-16
1g4LsUsgYBvR1AiwN16SoI -2.1955 0.189 -11.610 0.000 -2.567 -1.824
20tce2ASDrStpxljpEDSDI -3.1121 0.709 -4.387 0.000 -4.504 -1.720
2QEXsFWUdk2DsJxhThUFwH -0.6484 0.366 -1.773 0.077 -1.366 0.069
2VlzkJvSAd3PnLzo6llbL8 -0.0204 0.412 -0.049 0.961 -0.829 0.788
2X5BPknxexv0e7DdDKUj0C 4.435e-14 2.64e-14 1.682 0.093 -7.38e-15 9.61e-14
2advhj7iNxHv32Ix2vpVX8 -0.8872 0.497 -1.784 0.075 -1.863 0.089
2iMjNpXB1KzwxEryqITNut -0.3076 0.889 -0.346 0.729 -2.051 1.436
2imbTn8YAeXVwvBhOyXUR0 -0.8063 1.910 -0.422 0.673 -4.554 2.942
2qTeRwnwFquJUKrAFWnolb 2.8367 1.724 1.646 0.100 -0.546 6.219
2tj1iOLK9Uwef7ooLgJMtJ -1.0859 0.489 -2.219 0.027 -2.046 -0.126
307SnTaQdp4wWjQuOU5ivP 5.4137 1.219 4.440 0.000 3.021 7.806
33yjWFG5onxZu3HdIzO1Zu 0.9727 0.622 1.564 0.118 -0.248 2.193
37i9dQZEVXbINTEnbFeb8d -0.6784 1.760 -0.385 0.700 -4.132 2.776
37i9dQZEVXbIP3c3fqVrJY 0.0743 4.149 0.018 0.986 -8.067 8.216
37i9dQZEVXbIPOivNiyjjS 2.4487 1.541 1.589 0.112 -0.575 5.472
37i9dQZEVXbIPWwFssbupI 0.4496 1.256 0.358 0.721 -2.016 2.915
37i9dQZEVXbIQnj7RRhdSX -0.5194 1.222 -0.425 0.671 -2.918 1.879
37i9dQZEVXbIUY6VUoboP4 1.6629 1.275 1.304 0.193 -0.839 4.165
37i9dQZEVXbIVYVBNw9D5K 1.5367 0.899 1.709 0.088 -0.227 3.301
37i9dQZEVXbJ7gPAehey5W 2.0745 1.751 1.185 0.236 -1.361 5.510
37i9dQZEVXbJNSeeHswcKB -0.4983 1.821 -0.274 0.784 -4.071 3.074
37i9dQZEVXbJPcfkRz0wJ0 0.5099 3.798 0.134 0.893 -6.942 7.961
37i9dQZEVXbJVi45MafAu0 6.492e-14 2.99e-14 2.174 0.030 6.32e-15 1.24e-13
37i9dQZEVXbJWZV7aRNQck 4.9726 1.743 2.852 0.004 1.552 8.393
37i9dQZEVXbJWuzDrTxbKS -1.2533 3.537 -0.354 0.723 -8.194 5.687
37i9dQZEVXbJajpaXyaKll -1.0003 0.733 -1.364 0.173 -2.439 0.439
37i9dQZEVXbJcpVBLdFV7m -0.4116 0.601 -0.684 0.494 -1.592 0.768
37i9dQZEVXbJfdy5b0KP7W 0.4451 0.840 0.530 0.596 -1.202 2.092
37i9dQZEVXbJiZcmkrIHGU -0.6301 1.723 -0.366 0.715 -4.011 2.751
37i9dQZEVXbJiyhoAPEfMK 1.3663 2.413 0.566 0.571 -3.369 6.102
37i9dQZEVXbJlM6nvL1nD1 1.5453 0.894 1.729 0.084 -0.208 3.299
37i9dQZEVXbJlfUljuZExa -10.6298 4.039 -2.632 0.009 -18.556 -2.704
37i9dQZEVXbJmRv5TqJW16 1.4348 0.865 1.659 0.097 -0.262 3.132
37i9dQZEVXbJp9wcIM9Eo5 0.4451 0.840 0.530 0.596 -1.202 2.092
37i9dQZEVXbJpRQ294oZ9N -0.3946 1.105 -0.357 0.721 -2.563 1.774
37i9dQZEVXbJqdarpmTJDL -0.0501 2.427 -0.021 0.984 -4.812 4.711
37i9dQZEVXbJqfMFK4d691 3.4947 1.412 2.475 0.013 0.724 6.266
37i9dQZEVXbJs8e2vk15a8 -0.4116 0.601 -0.684 0.494 -1.592 0.768
37i9dQZEVXbJv2Mvelmc3I 1.6629 1.275 1.304 0.193 -0.839 4.165
37i9dQZEVXbJvfa0Yxg7E7 -0.4704 1.620 -0.290 0.772 -3.649 2.708
37i9dQZEVXbJx9hUtTN0Sj -0.4116 0.601 -0.684 0.494 -1.592 0.768
37i9dQZEVXbK3Iy2zvpfp4 0.3099 1.826 0.170 0.865 -3.272 3.892
37i9dQZEVXbK4KA2JSuft7 -0.4116 0.601 -0.684 0.494 -1.592 0.768
37i9dQZEVXbK4gjvS1FjPY 16.0639 6.266 2.564 0.010 3.769 28.359
37i9dQZEVXbKAbrMR8uuf7 -0.6369 1.010 -0.630 0.529 -2.619 1.346
37i9dQZEVXbKCF6dqVpDkS -0.0824 0.618 -0.133 0.894 -1.294 1.129
37i9dQZEVXbKHoaIcElSSA -0.4116 0.601 -0.684 0.494 -1.592 0.768
37i9dQZEVXbKIVTPX9a2Sb -0.5836 0.889 -0.657 0.511 -2.327 1.160
37i9dQZEVXbKM896FDX8L1 -8.1350 3.300 -2.465 0.014 -14.610 -1.660
37i9dQZEVXbKMzVsSGQ49S -1.1881 0.654 -1.816 0.070 -2.472 0.096
37i9dQZEVXbKNHh6NIXu36 -0.1370 2.427 -0.056 0.955 -4.898 4.624
37i9dQZEVXbKOefHPXPMyf 0.8345 1.719 0.486 0.627 -2.538 4.207
37i9dQZEVXbKXQ4mDTEBXq 0.0089 2.427 0.004 0.997 -4.753 4.770
37i9dQZEVXbKXd6qahcpCg -0.6787 1.216 -0.558 0.577 -3.065 1.708
37i9dQZEVXbKbvcwe5owJ1 -0.2869 1.718 -0.167 0.867 -3.658 3.085
37i9dQZEVXbKfIuOAZrk7G -0.4116 0.601 -0.684 0.494 -1.592 0.768
37i9dQZEVXbKgCVIE0PTOD -0.5887 0.629 -0.936 0.350 -1.823 0.646
37i9dQZEVXbKj23U1GF4IR 1.5665 1.180 1.328 0.184 -0.748 3.881
37i9dQZEVXbKpV6RVDTWcZ -4.942e-14 2.1e-14 -2.351 0.019 -9.07e-14 -8.18e-15
37i9dQZEVXbKrooeK9WSFF -1.5003 1.718 -0.873 0.383 -4.871 1.871
37i9dQZEVXbKuaTI1Z1Afx 0.0943 0.892 0.106 0.916 -1.656 1.845
37i9dQZEVXbKxYYIUIgn7V -0.4116 0.601 -0.684 0.494 -1.592 0.768
37i9dQZEVXbKyJS56d1pgi 6.8712 3.834 1.792 0.073 -0.652 14.394
37i9dQZEVXbKypXHVwk1f0 1.0338 0.620 1.666 0.096 -0.184 2.251
37i9dQZEVXbL0GavIqMTeb 1.9012 1.601 1.187 0.235 -1.241 5.043
37i9dQZEVXbL3DLHfQeDmV -2.7799 2.039 -1.363 0.173 -6.781 1.221
37i9dQZEVXbL3J0k32lWnN 7.3116 1.721 4.248 0.000 3.935 10.688
37i9dQZEVXbLDLOTfCtAUM 1.6629 1.275 1.304 0.193 -0.839 4.165
37i9dQZEVXbLOov4J0GutU 1.6629 1.275 1.304 0.193 -0.839 4.165
37i9dQZEVXbLRQDuF5jeBp -4.9822 2.842 -1.753 0.080 -10.559 0.595
37i9dQZEVXbLRmg3qDbY1H -0.4116 0.601 -0.684 0.494 -1.592 0.768
37i9dQZEVXbLesry2Qw2xS 0.0218 1.566 0.014 0.989 -3.052 3.095
37i9dQZEVXbLiRSasKsNU9 -0.4116 0.601 -0.684 0.494 -1.592 0.768
37i9dQZEVXbLnolsZ8PSNw 5.7843 4.031 1.435 0.152 -2.125 13.694
37i9dQZEVXbLo3yC8XJf1e 1.4355 1.516 0.947 0.344 -1.538 4.409
37i9dQZEVXbLoATJ81JYXz -0.6355 1.173 -0.542 0.588 -2.936 1.666
37i9dQZEVXbLuUZrygauiA -2.4809 1.771 -1.401 0.162 -5.956 0.994
37i9dQZEVXbLuey1EKVv9I -0.4116 0.601 -0.684 0.494 -1.592 0.768
37i9dQZEVXbLwpL8TjsxOG -5.7080 3.004 -1.900 0.058 -11.602 0.187
37i9dQZEVXbLxoIml4MYkT 0.4451 0.840 0.530 0.596 -1.202 2.092
37i9dQZEVXbLy5tBFyQvd4 1.5453 0.894 1.729 0.084 -0.208 3.299
37i9dQZEVXbLzhUVGQUCoe -0.4116 0.601 -0.684 0.494 -1.592 0.768
37i9dQZEVXbM1qaaFAyPLz -0.5887 0.629 -0.936 0.350 -1.823 0.646
37i9dQZEVXbM8SIrkERIYl -8.3462 4.471 -1.867 0.062 -17.119 0.427
37i9dQZEVXbMA8BIYDeMkD -2.2657 1.595 -1.421 0.156 -5.395 0.864
37i9dQZEVXbMBNcyQCfU4w 1.3139 1.217 1.080 0.281 -1.074 3.702
37i9dQZEVXbMBUm3g7j4Kb -1.4789 1.718 -0.861 0.390 -4.850 1.892
37i9dQZEVXbMDoHDwVN2tF 7.4014 4.278 1.730 0.084 -0.993 15.796
37i9dQZEVXbMGnTCc4Vx7v 1.8277 2.104 0.869 0.385 -2.301 5.956
37i9dQZEVXbMHnoaLVkVuk 2.0745 1.751 1.185 0.236 -1.361 5.510
37i9dQZEVXbMIJZxwqzod6 -2.8527 1.160 -2.459 0.014 -5.129 -0.576
37i9dQZEVXbMIO7B1pcKUy -0.3464 1.255 -0.276 0.783 -2.808 2.116
37i9dQZEVXbMJJi3wgRbAy -1.3559 0.553 -2.453 0.014 -2.441 -0.271
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37i9dQZEVXbNBz9cRCSFkY -9.5983 6.033 -1.591 0.112 -21.435 2.238
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37i9dQZF1DWUa8ZRTfalHk 0.0722 0.520 0.139 0.890 -0.948 1.093
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37i9dQZF1DWVPRVPGc4ZVv 1.8796 0.889 2.113 0.035 0.135 3.625
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37i9dQZF1DX0UrRvztWcAU -0.6048 0.725 -0.834 0.405 -2.028 0.818
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37i9dQZF1DX1BNoP9erXlf -1.2258 0.997 -1.230 0.219 -3.181 0.730
37i9dQZF1DX1EYoiDq3BXX 1.7090 0.903 1.892 0.059 -0.063 3.481
37i9dQZF1DX1N5uK98ms5p -0.9081 0.981 -0.926 0.355 -2.832 1.016
37i9dQZF1DX1X23oiQRTB5 0.2809 1.816 0.155 0.877 -3.282 3.844
37i9dQZF1DX1lVhptIYRda 3.7979 3.080 1.233 0.218 -2.246 9.842
37i9dQZF1DX1lp03JVa0o6 -1.6356 1.374 -1.190 0.234 -4.332 1.061
37i9dQZF1DX2A29LI7xHn1 1.4945 0.641 2.331 0.020 0.237 2.752
37i9dQZF1DX2Nc3B70tvx0 5.4886 1.263 4.347 0.000 3.011 7.966
37i9dQZF1DX2RxBh64BHjQ 0.1052 0.482 0.218 0.827 -0.840 1.050
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7nnyvG2CHYgiCOrUlh67v7 4.2034 1.074 3.915 0.000 2.097 6.310
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7xom7CO8dOHxx9fnGwwrnO 0.6766 1.327 0.510 0.610 -1.928 3.281
pop_5 0.0976 0.004 25.110 0.000 0.090 0.105
==============================================================================
Omnibus: 40.154 Durbin-Watson: 2.048
Prob(Omnibus): 0.000 Jarque-Bera (JB): 63.929
Skew: 0.263 Prob(JB): 1.31e-14
Kurtosis: 3.930 Cond. No. 4.75e+17
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 9.99e-30. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.
Best model: <statsmodels.regression.linear_model.RegressionResultsWrapper object at 0x7fd93c8427b8>
Training error:
RMSE: 0.0
Rsq: 0.718330580133109
Test error:
RMSE: 0.0
Rsq: 0.5146017952023583
/home/paperspace/anaconda3/envs/whitelist/lib/python3.6/site-packages/pandas/core/indexes/base.py:3772: RuntimeWarning: '<' not supported between instances of 'str' and 'int', sort order is undefined for incomparable objects return this.join(other, how=how, return_indexers=return_indexers) /home/paperspace/anaconda3/envs/whitelist/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance. warnings.warn(message, mplDeprecation, stacklevel=1)
In [9]:
run_model_with(base_model, '../data/pop5_play_dummy.feather')
OLS Regression Results
==============================================================================
Dep. Variable: log_streams R-squared: 0.530
Model: OLS Adj. R-squared: 0.529
Method: Least Squares F-statistic: 1511.
Date: Fri, 21 Sep 2018 Prob (F-statistic): 4.73e-222
Time: 15:50:16 Log-Likelihood: -2847.0
No. Observations: 1344 AIC: 5698.
Df Residuals: 1342 BIC: 5708.
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 7.1808 0.123 58.230 0.000 6.939 7.423
pop_5 0.1170 0.003 38.874 0.000 0.111 0.123
==============================================================================
Omnibus: 64.672 Durbin-Watson: 2.045
Prob(Omnibus): 0.000 Jarque-Bera (JB): 76.012
Skew: 0.512 Prob(JB): 3.12e-17
Kurtosis: 3.555 Cond. No. 92.0
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
Best model: <statsmodels.regression.linear_model.RegressionResultsWrapper object at 0x7fd93c7f2080>
Training error:
RMSE: 0.0
Rsq: 0.5296428445603323
Test error:
RMSE: 0.0
Rsq: 0.5441836626160941
/home/paperspace/anaconda3/envs/whitelist/lib/python3.6/site-packages/pandas/core/indexes/base.py:3772: RuntimeWarning: '<' not supported between instances of 'str' and 'int', sort order is undefined for incomparable objects return this.join(other, how=how, return_indexers=return_indexers) /home/paperspace/anaconda3/envs/whitelist/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance. warnings.warn(message, mplDeprecation, stacklevel=1)
