Python run Exemples

Exemple #1

Fichier : paper_miniboone_itr.py Projet : yakawa/defragTrees

    np.random.seed(seed + t)
    idx = np.random.permutation(num)
    df1 = df.ix[idx[:m1], :]
    df2 = df.ix[idx[m1:m2], :]

    # save
    dirname2 = '%s/result_%02d' % (dirname, t)
    if not os.path.exists(dirname2):
        os.mkdir(dirname2)
    trfile = '%s/%s_train_%02d.csv' % (dirname2, prefix, t)
    tefile = '%s/%s_test_%02d.csv' % (dirname2, prefix, t)
    df1.to_csv(trfile, header=None, index=False)
    df2.to_csv(tefile, header=None, index=False)

# demo_R
Kmax = 10
restart = 20
njobs = 10
maxitr = 3000
tol = 1e-2
treenum = 100
paper_sub_itr.run(prefix,
                  Kmax,
                  restart,
                  trial,
                  treenum=treenum,
                  modeltype='classification',
                  maxitr=maxitr,
                  tol=tol,
                  njobs=njobs)

Exemple #2

for t in range(trial):
    
    # data - train
    np.random.seed(seed + t)
    Xtr = np.random.rand(num, dim)
    ytr = np.zeros(num)
    ytr = np.logical_xor(Xtr[:, 0] > 0.5, Xtr[:, 1] > 0.5)
    ytr = np.logical_xor(ytr, np.random.rand(num) > b)
    
    # data - test
    Xte = np.random.rand(num, dim)
    yte = np.zeros(num)
    yte = np.logical_xor(Xte[:, 0] > 0.5, Xte[:, 1] > 0.5)
    yte = np.logical_xor(yte, np.random.rand(num) > b)
    
    # save
    dirname2 = '%s/result_%02d' % (dirname, t)
    if not os.path.exists(dirname2):
        os.mkdir(dirname2)
    trfile = '%s/%s_train_%02d.csv' % (dirname2, prefix, t)
    tefile = '%s/%s_test_%02d.csv' % (dirname2, prefix, t)
    np.savetxt(trfile, np.c_[Xtr, ytr], delimiter=',')
    np.savetxt(tefile, np.c_[Xte, yte], delimiter=',')

# demo_R
Kmax = 10
restart = 20
njobs = 4
treenum = 100
paper_sub_itr.run(prefix, Kmax, restart, trial, treenum=treenum, modeltype='classification', njobs=njobs, rftype='SL')

Exemple #3

Fichier : paper_energy_itr.py Projet : ascillitoe/defragTrees

    os.mkdir(dirname)
for t in range(trial):

    # data - train & test
    np.random.seed(seed + t)
    idx = np.random.permutation(num)
    df1 = df.iloc[idx[:m1], :]
    df2 = df.iloc[idx[m1:m2], :]

    # save
    dirname2 = '%s/result_%02d' % (dirname, t)
    if not os.path.exists(dirname2):
        os.mkdir(dirname2)
    trfile = '%s/%s_train_%02d.csv' % (dirname2, prefix, t)
    tefile = '%s/%s_test_%02d.csv' % (dirname2, prefix, t)
    df1.to_csv(trfile, header=None, index=False)
    df2.to_csv(tefile, header=None, index=False)

# demo_R
Kmax = 10
restart = 200
njobs = 4
treenum = 100
paper_sub_itr.run(prefix,
                  Kmax,
                  restart,
                  trial,
                  treenum=treenum,
                  modeltype='regression',
                  njobs=njobs)

Exemple #4

Fichier : paper_higgs_itr.py Projet : julianhatwell/defragTrees

if not os.path.exists('./result/'):
    os.mkdir('./result/')
dirname = './result/result_%s_itr' % (prefix,)
if not os.path.exists(dirname):
    os.mkdir(dirname)
for t in range(trial):
    
    # data - train & test
    np.random.seed(seed + t)
    idx = np.random.permutation(num)
    df1 = df.ix[idx[:m1], :]
    df2 = df.ix[idx[m1:m2], :]
    
    # save
    dirname2 = '%s/result_%02d' % (dirname, t)
    if not os.path.exists(dirname2):
        os.mkdir(dirname2)
    trfile = '%s/%s_train_%02d.csv' % (dirname2, prefix, t)
    tefile = '%s/%s_test_%02d.csv' % (dirname2, prefix, t)
    df1.to_csv(trfile, header=None, index=False)
    df2.to_csv(tefile, header=None, index=False)

# demo_R
Kmax = 10
restart = 20
njobs = 10
maxitr = 3000
tol = 1e-2
treenum = 100
paper_sub_itr.run(prefix, Kmax, restart, trial, treenum=treenum, modeltype='classification', maxitr=maxitr, tol=tol, njobs=njobs)

Exemple #5

Fichier : paper_energy_itr.py Projet : julianhatwell/defragTrees

# data
if not os.path.exists('./result/'):
    os.mkdir('./result/')
dirname = './result/result_%s_itr' % (prefix,)
if not os.path.exists(dirname):
    os.mkdir(dirname)
for t in range(trial):
    
    # data - train & test
    np.random.seed(seed + t)
    idx = np.random.permutation(num)
    df1 = df.ix[idx[:m1], :]
    df2 = df.ix[idx[m1:m2], :]
    
    # save
    dirname2 = '%s/result_%02d' % (dirname, t)
    if not os.path.exists(dirname2):
        os.mkdir(dirname2)
    trfile = '%s/%s_train_%02d.csv' % (dirname2, prefix, t)
    tefile = '%s/%s_test_%02d.csv' % (dirname2, prefix, t)
    df1.to_csv(trfile, header=None, index=False)
    df2.to_csv(tefile, header=None, index=False)

# demo_R
Kmax = 10
restart = 200
njobs = 10
treenum = 100
paper_sub_itr.run(prefix, Kmax, restart, trial, treenum=treenum, modeltype='regression', njobs=njobs)