Python make_train_test示例

示例#1

文件： macau_sgvb.py 项目： jaak-s/chemblnet

                    type=str,
                    help="matrix",
                    default="chembl-IC50-346targets.mm")
args = parser.parse_args()

import tensorflow as tf
import scipy.io
import numpy as np
import chemblnet as cn
import chemblnet.vbutils as vb

label = scipy.io.mmread(args.y)
X = scipy.io.mmread(args.side).tocsr()
# 109, 167, 168, 204, 214, 215

Ytrain, Ytest = cn.make_train_test(label, 0.2, seed=123456)
Ytrain = Ytrain.tocsr()
Ytest = Ytest.tocsr()
Nfeat = X.shape[1]
Ncomp = Ytrain.shape[0]
Nprot = Ytrain.shape[1]
print("St. deviation:   %f" % np.std(Ytest.data))

# learning parameters
Y_prec = 5.0
h1_size = 32

batch_size = 256
lrate = 1e-1
lrate_decay = 0.1
lrate_jump = 1.2

示例#2

                    type=str,
                    help="Network model",
                    choices=["mlp"],
                    default="mlp")

args = parser.parse_args()

import tensorflow as tf
import scipy.io
import numpy as np
import chemblnet as cn

label = scipy.io.mmread(args.y)
X = scipy.io.mmread(args.side).tocsr()

Ytrain, Ytest = cn.make_train_test(label, 0.2)
Ytrain = Ytrain.tocsr()
Ytest = Ytest.tocsr()

Nfeat = X.shape[1]
Nprot = Ytrain.shape[1]
Ncmpd = Ytrain.shape[0]

batch_size = args.batch_size
h_size = args.hsize
reg = args.reg
res_reg = 3e-3
lrate = 0.001
lrate_decay = 0.1  #0.986
lrate_min = 3e-5
epsilon = 1e-5

示例#3

文件： deep_macau.py 项目： jaak-s/chemblnet

                    choices = ["main", "linear", "non_linear_z", "residual", "residual2", "relu"],
                    default = "main")
parser.add_argument("--save",  type=str,   help="filename to save the model to (default)", default = None)

args = parser.parse_args()

import tensorflow as tf
import scipy.io
import numpy as np
import chemblnet as cn
from scipy.sparse import hstack

label = scipy.io.mmread(args.y)
X     = scipy.io.mmread(args.side).tocsr()

Ytrain, Ytest = cn.make_train_test(label, args.test_ratio)
Ytrain = Ytrain.tocsr()
Ytest  = Ytest.tocsr()

Nfeat  = X.shape[1]
Nprot  = Ytrain.shape[1]
Ncmpd  = Ytrain.shape[0]

batch_size = args.batch_size
h_size     = args.hsize
reg        = args.reg
zreg       = args.zreg
dropout    = args.dropout
res_reg    = 3e-3
lrate      = 0.001
lrate_decay = 0.1 #0.986