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tidymodels / discrim

Wrappers for discriminant analysis and naive Bayes models for use with the parsnip package
https://discrim.tidymodels.org
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Naive Bayes models don't allow for user-specified priors (as opposed to the function in the underlying klaR package) #40

Closed deschen1 closed 2 years ago

deschen1 commented 2 years ago

?dsicrim::naive_Bayes shows that in the tidymodels framework, you can only pass two parameters to the function: smoothness and Laplace. However, the underlying function in klaR::NaiveBayes does allow for another user-specified parameter, namely:

prior the prior probabilities of class membership. If unspecified, the class proportions for the training set are used. If present, the probabilities should be specified in the order of the factor levels.

I either don't understand how to set this parameter in the tidymodels framework or it is not possible to set this parameter, in which case it would be important to allow setting this parameter.

juliasilge commented 2 years ago

You can set this model parameter as an engine argument:

library(discrim)
#> Loading required package: parsnip

parabolic_prior <- runif(n = 500)

klar_mod <-
    naive_Bayes() %>%
    set_engine("klaR", prior = parabolic_prior) 

translate(klar_mod)
#> Naive Bayes Model Specification (classification)
#> 
#> Engine-Specific Arguments:
#>   prior = parabolic_prior
#> 
#> Computational engine: klaR 
#> 
#> Model fit template:
#> discrim::klar_bayes_wrapper(x = missing_arg(), y = missing_arg(), 
#>     prior = parabolic_prior, usekernel = TRUE)

klar_fit <- fit(klar_mod, class ~ ., data = parabolic)
extract_fit_engine(klar_fit)
#> $apriori
#> grouping
#>            A            B            C            D            E            F 
#> 3.207521e-03 2.695053e-03 3.459401e-03 1.335757e-03 1.362815e-03 1.375149e-03 
#>            G            H            I            J            K            L 
#> 3.295724e-03 2.406432e-03 1.562261e-03 2.996791e-03 3.073184e-03 2.987839e-03 
#>            M            N            O            P            Q            R 
#> 3.877373e-03 2.168203e-03 8.729486e-04 9.223970e-04 3.204783e-03 1.968238e-03 
#>            S            T            U            V            W            X 
#> 1.442091e-03 2.294184e-04 1.773992e-03 2.160879e-03 2.473144e-03 2.122702e-03 
#>            Y            Z           A1           B1           C1           D1 
#> 3.925541e-03 2.182583e-03 9.977355e-04 2.768403e-04 2.254304e-03 3.542463e-03 
#>           E1           F1           G1           H1           I1           J1 
#> 3.368096e-03 1.148833e-03 3.074778e-03 3.695228e-04 4.992874e-04 2.176980e-03 
#>           K1           L1           M1           N1           O1           P1 
#> 1.076547e-03 2.144099e-03 2.878281e-03 3.209691e-03 2.916672e-03 1.448133e-04 
#>           Q1           R1           S1           T1           U1           V1 
#> 2.954593e-04 8.191948e-04 3.760188e-04 2.096368e-03 8.343828e-04 9.707154e-04 
#>           W1           X1           Y1           Z1           A2           B2 
#> 3.326446e-04 2.925917e-03 6.912111e-04 3.771202e-03 1.916487e-03 1.520485e-03 
#>           C2           D2           E2           F2           G2           H2 
#> 2.583644e-03 1.340270e-03 5.142256e-04 5.362677e-04 2.493518e-03 3.372247e-03 
#>           I2           J2           K2           L2           M2           N2 
#> 3.362962e-03 7.061794e-04 9.915764e-04 1.837097e-03 7.207898e-04 3.227596e-03 
#>           O2           P2           Q2           R2           S2           T2 
#> 3.213921e-03 2.263830e-03 6.715523e-04 2.248861e-05 1.524565e-03 1.205318e-03 
#>           U2           V2           W2           X2           Y2           Z2 
#> 2.982570e-03 5.288042e-04 1.066764e-03 2.735724e-04 1.718153e-04 7.209118e-04 
#>           A3           B3           C3           D3           E3           F3 
#> 3.308706e-04 3.055182e-03 3.055942e-03 3.248190e-03 1.314280e-03 3.958522e-03 
#>           G3           H3           I3           J3           K3           L3 
#> 3.355169e-03 2.070646e-03 3.753512e-03 3.975040e-03 7.507036e-05 1.150596e-03 
#>           M3           N3           O3           P3           Q3           R3 
#> 1.579965e-04 1.363579e-03 1.163667e-03 3.453144e-03 2.691497e-03 2.279516e-03 
#>           S3           T3           U3           V3           W3           X3 
#> 1.742588e-03 1.873158e-03 2.740407e-03 2.358533e-03 2.634004e-03 5.048204e-04 
#>           Y3           Z3           A4           B4           C4           D4 
#> 7.638129e-04 2.656089e-04 2.295844e-03 2.683332e-03 2.838174e-03 2.333803e-03 
#>           E4           F4           G4           H4           I4           J4 
#> 1.595495e-03 3.951807e-03 2.909030e-03 2.228941e-03 1.718267e-03 2.628695e-03 
#>           K4           L4           M4           N4           O4           P4 
#> 2.196410e-03 2.402112e-03 2.337697e-03 1.932933e-03 1.569880e-03 3.026867e-03 
#>           Q4           R4           S4           T4           U4           V4 
#> 1.185515e-04 2.497091e-03 3.676663e-03 6.374540e-04 3.651354e-03 5.030583e-04 
#>           W4           X4           Y4           Z4           A5           B5 
#> 2.561106e-03 3.129619e-03 3.419775e-03 3.115500e-03 5.187392e-04 9.502783e-04 
#>           C5           D5           E5           F5           G5           H5 
#> 3.901620e-03 1.074167e-03 3.398702e-03 3.961134e-03 8.871857e-04 9.508106e-04 
#>           I5           J5           K5           L5           M5           N5 
#> 1.879382e-03 3.486364e-03 2.404413e-03 2.438472e-03 1.397231e-03 3.634301e-03 
#>           O5           P5           Q5           R5           S5           T5 
#> 1.132004e-03 2.620712e-03 2.350576e-03 9.577411e-04 3.144638e-03 1.942067e-03 
#>           U5           V5           W5           X5           Y5           Z5 
#> 1.322441e-03 1.672542e-03 8.563860e-04 1.074983e-03 1.926518e-03 5.664884e-04 
#>           A6           B6           C6           D6           E6           F6 
#> 1.581738e-03 3.290319e-03 1.741133e-06 1.051171e-05 3.685470e-03 5.637002e-04 
#>           G6           H6           I6           J6           K6           L6 
#> 3.635431e-03 9.504956e-04 1.606614e-03 9.855875e-04 1.868993e-03 2.730412e-03 
#>           M6           N6           O6           P6           Q6           R6 
#> 2.310592e-03 1.779248e-03 3.369947e-03 2.388073e-03 9.178999e-04 3.862322e-03 
#>           S6           T6           U6           V6           W6           X6 
#> 1.703395e-03 6.434917e-04 5.615100e-04 2.498916e-03 2.077467e-03 2.310973e-03 
#>           Y6           Z6           A7           B7           C7           D7 
#> 2.548110e-03 2.935822e-03 9.557235e-04 3.528673e-03 7.704509e-04 2.666740e-03 
#>           E7           F7           G7           H7           I7           J7 
#> 1.150308e-03 3.947918e-04 1.325904e-03 2.246099e-03 3.027445e-03 1.780467e-03 
#>           K7           L7           M7           N7           O7           P7 
#> 3.248631e-03 3.712012e-03 4.930125e-04 2.841444e-03 2.763526e-03 2.291103e-03 
#>           Q7           R7           S7           T7           U7           V7 
#> 2.163674e-03 1.215169e-03 7.395904e-05 3.642003e-03 1.425307e-03 8.759486e-04 
#>           W7           X7           Y7           Z7           A8           B8 
#> 1.799866e-03 3.833937e-03 2.823341e-03 3.178708e-03 2.193967e-03 2.805654e-03 
#>           C8           D8           E8           F8           G8           H8 
#> 1.380905e-04 3.645854e-03 2.654857e-03 3.094691e-03 2.426127e-04 3.718206e-03 
#>           I8           J8           K8           L8           M8           N8 
#> 1.091571e-03 1.358437e-03 2.274976e-04 2.489749e-05 1.627188e-03 1.998081e-03 
#>           O8           P8           Q8           R8           S8           T8 
#> 2.329495e-03 7.321020e-04 2.752157e-03 1.351051e-03 2.211469e-03 8.664282e-04 
#>           U8           V8           W8           X8           Y8           Z8 
#> 2.676498e-03 3.029386e-04 3.740669e-03 1.992632e-03 2.686797e-03 2.882932e-04 
#>           A9           B9           C9           D9           E9           F9 
#> 7.708795e-05 3.632686e-03 3.156354e-03 1.409823e-03 3.282137e-03 2.748989e-04 
#>           G9           H9           I9           J9           K9           L9 
#> 2.488166e-04 3.156516e-03 3.100797e-03 2.223196e-03 3.334762e-03 8.242802e-04 
#>           M9           N9           O9           P9           Q9           R9 
#> 1.140551e-03 3.398443e-04 1.394144e-03 1.816333e-03 1.327707e-03 2.347030e-03 
#>           S9           T9           U9           V9           W9           X9 
#> 8.321681e-04 3.191651e-03 3.234813e-03 1.744576e-03 3.409729e-03 3.943094e-03 
#>           Y9           Z9          A10          B10          C10          D10 
#> 1.882837e-03 1.891948e-03 5.769486e-05 2.030490e-03 2.453196e-03 3.750009e-03 
#>          E10          F10          G10          H10          I10          J10 
#> 1.766363e-03 1.476870e-03 1.908335e-03 3.622259e-03 1.005897e-03 1.675627e-03 
#>          K10          L10          M10          N10          O10          P10 
#> 3.970910e-03 2.617645e-03 3.044043e-03 3.504048e-03 2.965618e-03 1.051254e-03 
#>          Q10          R10          S10          T10          U10          V10 
#> 2.711165e-03 1.561637e-03 1.371222e-03 3.809375e-03 4.373602e-04 1.991711e-03 
#>          W10          X10          Y10          Z10          A11          B11 
#> 9.776146e-04 1.341135e-03 9.334004e-04 1.004496e-03 2.341189e-03 1.297809e-03 
#>          C11          D11          E11          F11          G11          H11 
#> 1.948462e-03 2.104814e-03 9.460605e-04 2.437898e-03 9.312116e-04 3.521280e-04 
#>          I11          J11          K11          L11          M11          N11 
#> 7.312788e-04 2.195445e-03 5.135582e-04 8.899861e-04 1.597343e-04 9.190221e-05 
#>          O11          P11          Q11          R11          S11          T11 
#> 1.894525e-03 3.242980e-03 1.996153e-03 9.322883e-04 2.036889e-03 4.860130e-05 
#>          U11          V11          W11          X11          Y11          Z11 
#> 2.072629e-03 1.711523e-03 1.356078e-03 3.035650e-03 3.211951e-03 2.844104e-03 
#>          A12          B12          C12          D12          E12          F12 
#> 1.806702e-03 2.714680e-04 3.005380e-03 8.357940e-04 2.489160e-03 1.884703e-04 
#>          G12          H12          I12          J12          K12          L12 
#> 2.074581e-03 3.531287e-03 2.183538e-03 2.263683e-03 2.986607e-03 1.410263e-03 
#>          M12          N12          O12          P12          Q12          R12 
#> 2.892095e-03 2.272051e-03 1.531452e-03 2.821978e-03 2.689606e-04 2.560325e-03 
#>          S12          T12          U12          V12          W12          X12 
#> 3.110342e-03 2.759637e-03 4.153661e-04 1.405949e-03 3.154015e-03 3.177067e-03 
#>          Y12          Z12          A13          B13          C13          D13 
#> 2.703580e-03 2.409463e-04 1.773404e-03 5.998131e-04 1.642200e-03 6.461345e-04 
#>          E13          F13          G13          H13          I13          J13 
#> 1.270525e-03 8.803927e-04 1.888394e-03 1.540757e-03 3.206139e-03 3.124661e-03 
#>          K13          L13          M13          N13          O13          P13 
#> 1.416848e-03 9.476741e-04 2.868383e-03 3.303693e-03 2.106207e-03 2.261122e-03 
#>          Q13          R13          S13          T13          U13          V13 
#> 2.184102e-03 2.624679e-03 3.963052e-03 3.847176e-03 1.909073e-03 2.991301e-03 
#>          W13          X13          Y13          Z13          A14          B14 
#> 1.250042e-03 3.710093e-03 2.616418e-03 4.388958e-05 2.803535e-03 1.331416e-03 
#>          C14          D14          E14          F14          G14          H14 
#> 2.941059e-03 8.382726e-04 2.992582e-03 2.461460e-03 2.014082e-04 2.447176e-03 
#>          I14          J14          K14          L14          M14          N14 
#> 1.824252e-03 2.831229e-03 1.292220e-03 2.956398e-03 5.833297e-04 1.981824e-03 
#>          O14          P14          Q14          R14          S14          T14 
#> 5.283911e-04 3.902962e-03 2.348575e-03 4.497686e-04 2.338604e-03 3.958911e-04 
#>          U14          V14          W14          X14          Y14          Z14 
#> 7.689718e-04 3.278419e-03 6.204639e-05 3.011154e-04 2.742257e-03 3.890625e-03 
#>          A15          B15          C15          D15          E15          F15 
#> 1.819818e-03 5.261746e-04 3.037618e-03 2.979417e-03 9.062275e-04 2.830444e-03 
#>          G15          H15          I15          J15          K15          L15 
#> 3.172644e-03 1.749231e-03 2.380304e-03 3.569508e-03 2.587079e-03 2.539407e-03 
#>          M15          N15          O15          P15          Q15          R15 
#> 3.388413e-03 2.192004e-03 1.260093e-03 4.848261e-05 1.168685e-03 3.949073e-03 
#>          S15          T15          U15          V15          W15          X15 
#> 2.033184e-03 2.078799e-03 2.824646e-03 2.218235e-03 3.976077e-03 2.359002e-03 
#>          Y15          Z15          A16          B16          C16          D16 
#> 2.156881e-03 8.907326e-04 1.452146e-03 1.205966e-03 5.821903e-04 2.990518e-03 
#>          E16          F16          G16          H16          I16          J16 
#> 1.247321e-03 2.453751e-04 7.052024e-04 5.617626e-04 2.349425e-03 5.760638e-04 
#>          K16          L16          M16          N16          O16          P16 
#> 4.928089e-04 2.531901e-03 3.792013e-03 2.036294e-03 1.427397e-03 3.294987e-03 
#>          Q16          R16          S16          T16          U16          V16 
#> 2.043596e-03 3.297109e-03 2.695832e-03 3.973656e-03 2.435720e-03 1.767889e-03 
#>          W16          X16          Y16          Z16          A17          B17 
#> 1.032917e-03 2.183906e-03 3.949251e-03 1.097858e-03 8.954733e-04 3.692270e-03 
#>          C17          D17          E17          F17          G17          H17 
#> 2.629960e-03 3.168847e-03 2.984971e-03 3.290030e-03 1.843927e-03 2.292397e-03 
#>          I17          J17          K17          L17          M17          N17 
#> 1.952749e-03 2.184465e-03 3.714019e-03 2.703430e-03 2.558714e-03 3.189162e-03 
#>          O17          P17          Q17          R17          S17          T17 
#> 9.028320e-04 2.469380e-04 3.884405e-03 3.526266e-03 4.939252e-04 2.453096e-03 
#>          U17          V17          W17          X17          Y17          Z17 
#> 3.830246e-03 1.663255e-03 5.396665e-04 3.158731e-03 1.689583e-03 3.817318e-03 
#>          A18          B18          C18          D18          E18          F18 
#> 2.511907e-03 2.074410e-03 2.891034e-03 2.931509e-03 2.079636e-03 1.074139e-03 
#>          G18          H18          I18          J18          K18          L18 
#> 2.871654e-03 3.044985e-04 1.278433e-03 1.075664e-03 1.861420e-03 2.597110e-04 
#>          M18          N18          O18          P18          Q18          R18 
#> 3.276145e-04 1.218334e-03 3.866326e-03 3.720283e-03 3.440079e-03 7.554153e-04 
#>          S18          T18          U18          V18          W18          X18 
#> 3.109607e-03 1.663954e-03 3.554611e-03 1.254794e-03 5.935748e-04 3.847954e-03 
#>          Y18          Z18          A19          B19          C19          D19 
#> 3.239474e-04 2.217949e-03 2.177681e-03 1.451137e-03 2.173339e-03 3.641407e-03 
#>          E19          F19 
#> 9.693573e-04 3.920396e-03 
#> 
#> $tables
#> $tables$X1
#> $tables$X1$Class1
#> 
#> Call:
#>  density.default(x = xx)
#> 
#> Data: xx (244 obs.); Bandwidth 'bw' = 0.5482
#> 
#>        x                 y            
#>  Min.   :-6.7324   Min.   :3.362e-05  
#>  1st Qu.:-3.4920   1st Qu.:4.496e-03  
#>  Median :-0.2516   Median :5.067e-02  
#>  Mean   :-0.2516   Mean   :7.708e-02  
#>  3rd Qu.: 2.9887   3rd Qu.:1.469e-01  
#>  Max.   : 6.2291   Max.   :2.191e-01  
#> 
#> $tables$X1$Class2
#> 
#> Call:
#>  density.default(x = xx)
#> 
#> Data: xx (256 obs.); Bandwidth 'bw' = 0.2899
#> 
#>        x                 y            
#>  Min.   :-2.6963   Min.   :0.0000788  
#>  1st Qu.:-1.0009   1st Qu.:0.0169058  
#>  Median : 0.6945   Median :0.0969314  
#>  Mean   : 0.6945   Mean   :0.1473106  
#>  3rd Qu.: 2.3899   3rd Qu.:0.2849839  
#>  Max.   : 4.0853   Max.   :0.3862728  
#> 
#> 
#> $tables$X2
#> $tables$X2$Class1
#> 
#> Call:
#>  density.default(x = xx)
#> 
#> Data: xx (244 obs.); Bandwidth 'bw' = 0.565
#> 
#>        x                 y            
#>  Min.   :-7.0274   Min.   :3.263e-05  
#>  1st Qu.:-3.8698   1st Qu.:6.022e-03  
#>  Median :-0.7122   Median :6.869e-02  
#>  Mean   :-0.7122   Mean   :7.909e-02  
#>  3rd Qu.: 2.4455   3rd Qu.:1.370e-01  
#>  Max.   : 5.6031   Max.   :2.305e-01  
#> 
#> $tables$X2$Class2
#> 
#> Call:
#>  density.default(x = xx)
#> 
#> Data: xx (256 obs.); Bandwidth 'bw' = 0.2502
#> 
#>        x                 y           
#>  Min.   :-3.8936   Min.   :0.000071  
#>  1st Qu.:-2.3114   1st Qu.:0.017124  
#>  Median :-0.7293   Median :0.078130  
#>  Mean   :-0.7293   Mean   :0.157859  
#>  3rd Qu.: 0.8528   3rd Qu.:0.321281  
#>  Max.   : 2.4349   Max.   :0.457778  
#> 
#> 
#> 
#> $levels
#> [1] "Class1" "Class2"
#> 
#> $call
#> NaiveBayes.default(x = ~maybe_data_frame(x), grouping = ~y, prior = ~parabolic_prior, 
#>     usekernel = ~TRUE)
#> 
#> $x
#>               X1           X2
#> 1    3.291779227  1.661089277
#> 2    1.465839693  0.413985662
#> 3    1.660986913  0.791470132
#> 4    1.602301296  0.276391100
#> 5    2.165003287  3.165570113
#> 6    1.937023350  3.825806219
#> 7   -0.588201796 -0.977299678
#> 8   -0.951049791  1.398632856
#> 9    0.274694146  0.370449680
#> 10  -1.127505885 -1.139584374
#> 11   0.083512851  0.585492143
#> 12   0.745821425  0.081292964
#> 13  -1.060715487  0.387057362
#> 14   1.302754956  0.792671701
#> 15   0.944543522  1.114287224
#> 16   1.076135505 -0.222358968
#> 17   1.298487103  0.821604828
#> 18   0.007308238 -0.294849155
#> 19   3.142366460  2.481697907
#> 20   0.330853264  0.585366865
#> 21   1.708801067 -0.180636333
#> 22  -1.662765348 -1.110350993
#> 23   1.924382880  1.337187699
#> 24   0.657285651 -0.362667812
#> 25  -0.733024329 -1.079364769
#> 26   1.448472449  2.358216332
#> 27  -2.273260679 -2.399004644
#> 28   1.020688667  1.792856462
#> 29  -1.833272840 -0.647395114
#> 30  -1.036024066  0.239294118
#> 31  -1.156003198 -1.246544372
#> 32  -0.443798490 -0.066780629
#> 33   0.543050473  1.116699157
#> 34   2.190740679  0.392657005
#> 35   0.128980806 -0.009592513
#> 36   0.761390733  0.058057532
#> 37   0.541666718 -0.508048041
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#> 469  0.731945943  2.601451324
#> 470  0.949154453 -1.501503357
#> 471  1.377634090 -0.973988083
#> 472  1.334210176  1.121564398
#> 473  0.356252279 -0.537300858
#> 474  1.893154180  0.832042378
#> 475  1.375584984  0.473146528
#> 476 -0.215837961 -0.060662999
#> 477 -2.018099625 -1.908560675
#> 478 -2.354729381 -2.747584669
#> 479 -2.647713348 -3.233394884
#> 480  1.753117525  3.908154811
#> 481 -1.530881335 -0.899053159
#> 482 -2.474445453 -0.509539689
#> 483  1.156870597  1.725345838
#> 484  0.160303046  2.608394052
#> 485 -1.505025296  0.357159314
#> 486 -1.425345060 -0.284916972
#> 487 -0.294804949 -1.010211898
#> 488 -0.287355095  1.590422374
#> 489  1.907753979  3.755459838
#> 490 -1.855253680 -1.066119931
#> 491  1.411319092 -1.120272504
#> 492  2.857601328  1.271970432
#> 493 -0.413279992 -1.854785981
#> 494 -0.780503761 -1.124171542
#> 495 -0.253169597  0.852680582
#> 496 -0.380574724  1.348168615
#> 497  1.918912506  0.639936914
#> 498  0.289749330  0.822210811
#> 499  0.537026903  0.883583560
#> 500 -1.110597072 -1.546735706
#> 
#> $usekernel
#> [1] TRUE
#> 
#> $varnames
#> [1] "X1" "X2"
#> 
#> attr(,"class")
#> [1] "NaiveBayes"

Created on 2022-01-26 by the reprex package (v2.0.1)

deschen1 commented 2 years ago

Indeed, this works. Thank you!

github-actions[bot] commented 2 years ago

This issue has been automatically locked. If you believe you have found a related problem, please file a new issue (with a reprex: https://reprex.tidyverse.org) and link to this issue.