def _pdi_gs_full_r1(method, xM_l, yV, X_concat=None, mode="Ridge", expension=False, n_folds=20, alphas_log=(-3, 2, (2 - (-3)) * 2 + 1)): if mode == "Ridge": xM = np.concatenate(xM_l, axis=1) gs = jgrid.gs_Ridge(xM, yV, alphas_log, n_folds=n_folds) elif mode == "BIKE_Ridge": # print "BIKE_Ridge mode is working now." A_l = xM_l gs = jgrid.gs_BIKE_Ridge(A_l, yV, alphas_log=alphas_log, X_concat=X_concat, n_folds=n_folds) else: print("Mode {} is not supported.".format(mode)) # gs.grid_scores_ if expension: pdi = pdi_gs(method, gs.grid_scores_, expension=expension) else: pdi = pdi_gs(method, gs.grid_scores_) pdi.plot(kind='line', x='alpha', y='mean(r2)', yerr='std(r2)', logx=True) plt.ylabel(r"E[$r^2$]") return pdi
def grid_BIKE2(pdr, alphas_log, y_id = 'Solubility_log_mol_l'): print "BIKE with (A+B)+W" xM1 = jpd.pd_get_xM( pdr, radius=6, nBits=4096) xM2 = jpd.pd_get_xM_MACCSkeys( pdr) yV = jpd.pd_get_yV( pdr, y_id = y_id) #A1 = jpyx.calc_tm_sim_M( xM1) #A2 = jpyx.calc_tm_sim_M( xM2) #A = np.concatenate( ( A1, A2), axis = 1) xM = np.concatenate( ( xM1, xM2), axis = 1) A = jpyx.calc_tm_sim_M( xM1) print A.shape molw_l = jchem.rdkit_molwt( pdr.SMILES.tolist()) print np.shape( molw_l) A_molw = jchem.add_new_descriptor( A, molw_l) print A_molw.shape gs = jgrid.gs_Ridge( A_molw, yV, alphas_log=alphas_log) jutil.show_gs_alpha( gs.grid_scores_) jgrid.cv( 'Ridge', A_molw, yV, alpha = gs.best_params_['alpha']) return gs
def set_pdi_d_full(pdi_d, method, xM_l, yV): xM = np.concatenate(xM_l, axis = 1) gs = jgrid.gs_Ridge(xM, yV, (-3, 2, 10), n_folds=20) # gs.grid_scores_ set_pdi_d(pdi_d, method, gs.grid_scores_) pdi_d[ method].plot(kind ='line', x='alpha', y='mean(r2)', yerr='std(r2)', logx=True) plt.ylabel(r"E[$r^2$]") return pdi_d[method]
def _pdi_gs_full_r0( method, xM_l, yV, expension = False): xM = np.concatenate( xM_l, axis = 1) gs = jgrid.gs_Ridge( xM, yV, (-3, 2, 10), n_folds=20) # gs.grid_scores_ if expension: pdi = pdi_gs( method, gs.grid_scores_, expension = expension) else: pdi = pdi_gs( pdi_d, method, gs.grid_scores_) pdi.plot( kind ='line', x = 'alpha', y = 'mean(r2)', yerr = 'std(r2)', logx = True) plt.ylabel( r"E[$r^2$]") return pdi
def grid_MLR_B(pdr, alphas_log, y_id = 'Solubility_log_mol_l'): print "MLR with B" xM2 = jpd.pd_get_xM_MACCSkeys( pdr) xM_molw = xM2 yV = jpd.pd_get_yV( pdr, y_id = y_id) gs = jgrid.gs_Ridge( xM_molw, yV, alphas_log=alphas_log) jutil.show_gs_alpha( gs.grid_scores_) jgrid.cv( 'Ridge', xM_molw, yV, alpha = gs.best_params_['alpha']) return gs
def grid_MLR_A(pdr, alphas_log, y_id = 'Solubility_log_mol_l'): print "MLR with A" xM1 = jpd.pd_get_xM( pdr, radius=6, nBits=4096) xM_molw = xM1 yV = jpd.pd_get_yV( pdr, y_id = y_id) gs = jgrid.gs_Ridge( xM_molw, yV, alphas_log=alphas_log) jutil.show_gs_alpha( gs.grid_scores_) jgrid.cv( 'Ridge', xM_molw, yV, alpha = gs.best_params_['alpha']) return gs
def set_pdi_d_full(pdi_d, method, xM_l, yV): xM = np.concatenate(xM_l, axis=1) gs = jgrid.gs_Ridge(xM, yV, (-3, 2, 10), n_folds=20) # gs.grid_scores_ set_pdi_d(pdi_d, method, gs.grid_scores_) pdi_d[method].plot(kind='line', x='alpha', y='mean(r2)', yerr='std(r2)', logx=True) plt.ylabel(r"E[$r^2$]") return pdi_d[method]
def _pdi_gs_full_r0(method, xM_l, yV, expension=False): xM = np.concatenate(xM_l, axis=1) gs = jgrid.gs_Ridge(xM, yV, (-3, 2, 10), n_folds=20) # gs.grid_scores_ if expension: pdi = pdi_gs(method, gs.grid_scores_, expension=expension) else: pdi = pdi_gs(pdi_d, method, gs.grid_scores_) pdi.plot(kind='line', x='alpha', y='mean(r2)', yerr='std(r2)', logx=True) plt.ylabel(r"E[$r^2$]") return pdi
def _pdi_gs_full_r0( method, xM_l, yV, X_concat = None, mode = "Ridge", expension = False, n_folds=20): if mode == "Ridge": xM = np.concatenate( xM_l, axis = 1) gs = jgrid.gs_Ridge( xM, yV, (-3, 2, 12), n_folds=n_folds) elif mode == "BIKE_Ridge": # print "BIKE_Ridge mode is working now." A_l = xM_l gs = jgrid.gs_BIKE_Ridge( A_l, yV, alphas_log=(-3, 2, 12), X_concat = X_concat, n_folds=n_folds) else: print("Mode {} is not supported.".format( mode)) # gs.grid_scores_ if expension: pdi = pdi_gs( method, gs.grid_scores_, expension = expension) else: pdi = pdi_gs( method, gs.grid_scores_) pdi.plot( kind ='line', x = 'alpha', y = 'mean(r2)', yerr = 'std(r2)', logx = True) plt.ylabel( r"E[$r^2$]") return pdi