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eval_cnssnn.py
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# 重要：预测值为y_hat的argmax索引位置！
import os
import numpy as np
from mxnet import nd
from mxnet.gluon import nn, rnn
from sklearn.metrics import precision_recall_fscore_support, classification_report
CWD = os.getcwd()
MODEL_PARAMS_PATH = os.path.join(CWD, "net_params", "cnssnn", "net_cnssnn_epoch54.params")
WORD_DIMENSION = 100
POS_DIMENSION = 5
DIMENSION = WORD_DIMENSION + 2 * POS_DIMENSION
FIXED_WORD_LENGTH = 60
MAX_ENTITY_DEGREE = 50
ENTITY_DEGREE = MAX_ENTITY_DEGREE + 1
MASK_LENGTH = ENTITY_DEGREE
ENTITY_EDGE_VEC_LENGTH = ENTITY_DEGREE * (WORD_DIMENSION * 2)
VEC_LENGTH = DIMENSION * FIXED_WORD_LENGTH + ENTITY_EDGE_VEC_LENGTH * 2
input_test = np.load('data_test_cnssnn_id.npy')
# input_test = input_test[input_test[:, 0] == PREDICT_VALUE]
x_all = input_test[:, 4:]
y_all = input_test[:, 0]
# y_all[:] = PREDICT_INDEX
print(x_all.shape)
print(y_all.shape)
x_all = x_all.astype(np.float32)
y_all = y_all.astype(np.float32)
x_test = x_all
y_test = y_all
x_test = x_test.astype(np.float32)
y_test = y_test.astype(np.int)
print(x_test.shape, y_test.shape)
class Network(nn.Block):
def __init__(self, **kwargs):
super(Network, self).__init__(**kwargs)
with self.name_scope():
self.gru = rnn.GRU(100, num_layers=1, bidirectional=True)
self.gru_out = nn.Sequential()
self.gru_out.add(nn.MaxPool2D(pool_size=(FIXED_WORD_LENGTH, 1)), )
self.gru_out.add(nn.Flatten())
self.gru_out.add(nn.Activation(activation='relu'))
self.center_att = nn.Sequential()
self.center_att.add(nn.Dense(1, in_units=200, flatten=False,
activation="sigmoid"))
self.center_out = nn.Sequential()
self.center_out.add(nn.Dense(200, activation="relu"))
self.output = nn.Sequential()
self.output.add(nn.Dropout(0.5))
self.output.add(nn.Dense(11))
def forward(self, input_data):
e1_vec_start = FIXED_WORD_LENGTH * DIMENSION
x = input_data[:, :e1_vec_start].reshape(
(input_data.shape[0], FIXED_WORD_LENGTH, DIMENSION)) # (m, 60, 110)
e1neimask = input_data[:, e1_vec_start:e1_vec_start + MASK_LENGTH] # (m, 51)
e1edge = input_data[:, e1_vec_start + MASK_LENGTH:e1_vec_start + MASK_LENGTH + ENTITY_EDGE_VEC_LENGTH].reshape(
(input_data.shape[0], ENTITY_DEGREE, WORD_DIMENSION * 2)) # (m, 51, 200)
e1neigh = e1edge[:, :, :WORD_DIMENSION]
e2_vec_start = e1_vec_start + MASK_LENGTH + ENTITY_EDGE_VEC_LENGTH
e2neimask = input_data[:, e2_vec_start:e2_vec_start + MASK_LENGTH] # (m, 51)
e2edge = input_data[:, e2_vec_start + MASK_LENGTH:e2_vec_start + MASK_LENGTH + ENTITY_EDGE_VEC_LENGTH].reshape(
(input_data.shape[0], ENTITY_DEGREE, WORD_DIMENSION * 2)) # (m, 51,200)
e2neigh = e2edge[:, :, :WORD_DIMENSION]
gru = self.gru
x = nd.transpose(x, axes=(1, 0, 2))
h = gru(x)
ht = nd.transpose(h, axes=(1, 0, 2))
gru_out = self.gru_out
y1 = gru_out(ht.expand_dims(1)) # (m,200)
att = self.center_att
e1edge = nd.tanh(e1edge)
e1g = att(e1edge) # (m,51,1)
e1g = e1g * e1neimask.expand_dims(2)
e1g = nd.softmax(e1g, axis=1)
e1gt = nd.transpose(e1g, axes=(0, 2, 1)) # (m,1,151)
e1n = nd.batch_dot(e1gt, e1neigh) # (m,1,100)
e1n = e1n.reshape((e1n.shape[0], 100)) # (m,100)
e2edge = nd.tanh(e2edge)
e2g = att(e2edge) # (m,51,1)
e2g = e2g * e2neimask.expand_dims(2)
e2g = nd.softmax(e2g, axis=1)
e2gt = nd.transpose(e2g, axes=(0, 2, 1)) # (m,1,151)
e2n = nd.batch_dot(e2gt, e2neigh) # (m,1,100)
e2n = e2n.reshape((e2n.shape[0], 100)) # (m,100)
center_y = nd.concat(e1n, e2n, dim=1) # (m,200)
center_out = self.center_out
center_y = center_out(center_y)
out = self.output
y4 = nd.concat(y1, center_y, dim=1)
y5 = out(y4)
return y5
net = Network()
net.load_params(MODEL_PARAMS_PATH)
print(net)
label_list = y_test.tolist()
y_hat = net(nd.array(x_test))
predict_list = y_hat.argmax(axis=1).asnumpy().astype(np.int).tolist()
print(precision_recall_fscore_support(label_list, predict_list, average='weighted'))
print(classification_report(label_list, predict_list))