def gabelopsitu(): runevaler("opsitu", 2000, GabelTrainer, GabelTorchEvaler, eval_gabel_ann, networklayers=[70, 50, 20], lr=.03, dropoutrate=0.3, validate_on_k=10, filenamepostfix="gabel")
def esnniris(): runevaler("iris", 200, ESNNSystem, TorchEvaler, eval_dual_ann, networklayers=[40, 10, 2], lr=.02, dropoutrate=0.05, validate_on_k=10, filenamepostfix="esnn")
def gabeliris(): runevaler("iris", 2000, GabelTrainer, GabelTorchEvaler, eval_dual_ann, networklayers=[40, 40, 40], lr=.01, dropoutrate=0.00, validate_on_k=10, filenamepostfix="gabel")
def choprairis(): runevaler("iris", 200, ChopraTrainer, ChopraTorchEvaler, eval_dual_ann, networklayers=[40, 40], lr=.05, dropoutrate=0.05, validate_on_k=10, filenamepostfix="chopra")
def testTorchESNNOPSITU2(datasetname, epochs, eval_on_k, filename): runevaler(datasetname, epochs, ESNNSystem, TorchEvaler, eval_dual_ann, networklayers=[40, 40, 40], lr=.09, dropoutrate=0.5, eval_on_k=eval_on_k, filenamepostfix=filename)
def plotGableOpsitu(lr=0.03, networklayers=[50, 30, 3], dropout=0.05, epochs=200, validate_on_k=10, n=5): model, \ dsl, \ train, \ test, \ evaler, \ best_model, \ losses, \ lossdf = runevaler("opsitu", epochs, GabelTrainer, GabelTorchEvaler, eval_gabel_ann, networklayers=networklayers, lr=lr, dropoutrate=dropout, validate_on_k=validate_on_k, n=n, filenamepostfix="gabel") df1 = makematrixdata(best_model, dsl.getFeatures()[train], dsl.getTargets()[train], 10, type=1) plot2heatmap(df1, 10, annot=True, outputfile="gabel-matrix.pdf") df2 = makematrixdata(model, dsl.getFeatures()[train], dsl.getTargets()[train], 10, type=1) plot2heatmap(df2, 10, annot=True, outputfile="training-gabel-matrix.pdf") return losses, lossdf
def plotCHOPRAOpsitu(lr=0.2, networklayers=[50, 30, 3], dropout=0.05, epochs=200, validate_on_k=10, n=5): model, \ dsl, \ train, \ test, \ evaler, \ best_model, \ losses,\ lossdf = runevaler("opsitu", epochs, ChopraTrainer, ChopraTorchEvaler, eval_dual_ann, networklayers=networklayers, lr=lr, dropoutrate=dropout, validate_on_k=validate_on_k, n=n, filenamepostfix="chopra") df = makematrixdata(best_model, dsl.getFeatures()[train], dsl.getTargets()[train], 10, type=2) plot2heatmap(df, 10, annot=True, outputfile="chopra-matrix.pdf") df = makematrixdata(model, dsl.getFeatures()[train], dsl.getTargets()[train], 10, type=2) plot2heatmap(df, 10, annot=True, outputfile="training-chopra-matrix.pdf") return losses, lossdf
def esnnopsitu(): model, \ test_data, \ test_target, \ evaler,\ best_model = runevaler("opsitu", 200, ESNNSystem, TorchEvaler, eval_dual_ann, networklayers=[70, 20, 3], lr=.02, dropoutrate=0.05, validate_on_k=10, filenamepostfix="esnn") return model, best_model, test_data, test_target
def chopraopsitu(): model, \ test_data, \ test_target, \ evaler, \ best_model = runevaler("opsitu", 2000, ChopraTrainer, ChopraTorchEvaler, eval_dual_ann, networklayers=[30, 10, 2], lr=.01, dropoutrate=0.02, validate_on_k=10, filenamepostfix="chopra") print(model(evaler.x1s[0:1, ], evaler.x1s[0:1, ])) print(model(evaler.x1s[1:2, ], evaler.x1s[1:2, ])) print(model(evaler.x1s[1:2, ], evaler.x1s[0:1, ]))