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import numpy as np
from PRNN_utils import tags2sentence, check_conditions
class PRNNSigmoid():
def __init__(self, seed=15):
np.random.seed(seed) # Set the seed
self.params = np.random.normal(0, 1, size=10)
self.w = np.random.normal(0, 1, size=1)
def step(self, x, threshold=0.5):
out = (x>threshold).astype('float')
return out
def sigmoid(self, x, max_val=10):
x = np.clip(x, -max_val, max_val)
out = 1 / (1 + np.exp(-x))
return out
def sigmoid_dash(self, x):
sig_x = self.sigmoid(x)
out = sig_x*(1-sig_x)
return out
def forward(self, x, h):
'''
Process x(t) and h(t-1) ie Single pass of RNN
Parameters:
x:np.array = [-upper_input_1-hot- -1 -lower_input_1-hot-]
h:float {0,1}
Returns:
out(float): Sigmoid(params_trans x(t) + w h(t-1))
'''
c_t = np.dot(self.params, x) + self.w*h # p_trans x(t) + w h(t-1)
h_t = self.sigmoid(c_t)
return c_t, h_t
def process_seq(self, sequence, h_0=0.0):
"""
Process the whole sequence
Parameters:
sequence List[List]
Returns:
list of hidden states
"""
hidden_states = [h_0]
C_states = [0] #Assume c(0) is 0
h_tminus1 = h_0
# Sequentially process the
for x_t in sequence:
c_t, h_t = self.forward(x=x_t, h=h_tminus1)
hidden_states.append(h_t[0]) # Just extract the numerical value
C_states.append(c_t[0])
h_tminus1 = h_t
C = np.array(C_states).reshape(-1)
H = np.array(hidden_states).reshape(-1)
return C, H
def predict_tags(self, sequence):
''''
Predict Tags {0,1} using step function
The op is [[y_cap(1), y_cap(2), .... y_cap(T)]]
Each y_cap(i) is either 0 or 1
'''
C, H = self.process_seq(sequence)
out = self.step(H).reshape(-1)[1:]
return out
def process_batch(self, batch):
"""
Processes a batch of sequences throught the model(rnn)
Parameters:
batch (dtaframe) : containint the field <pos_tags>
Oututput
outputs list[numpy_array] : Output of each sequence through RNN, hidden state
"""
H_outputs = []
C_outputs = []
for _, row in batch.iterrows():
x = tags2sentence(row.pos_tags)
C, H = self.process_seq(x)
H = H.reshape(-1)
C = C.reshape(-1)
C_outputs.append(C)
H_outputs.append(H)
return C_outputs, H_outputs
def view_params(self):
'''
prints perceptron parameters along with names
'''
print("PERCEPTRON PARAMETERS")
print(f"Vcap : {self.params[0]}" , end = ' | ')
print(f"Vnn : {self.params[1]}" , end = ' | ')
print(f"Vdt : {self.params[2]}" , end = ' | ')
print(f"Vjj : {self.params[3]}" , end = ' | ')
print(f"Vot : {self.params[4]}" )
print(f"T [Theta] : {self.params[5]}" , end = ' | ')
print(f"Wnn : {self.params[6]}" , end = ' | ')
print(f"Wdt : {self.params[7]}" , end = ' | ')
print(f"Wjj : {self.params[8]}" , end = ' | ')
print(f"Wot : {self.params[9]}")
print(f"W : {self.w[0]}")
def set_perfect_params(self):
'''
Params are of the form
params = [Vcap, Vnn, Vdt, Vjj, Vot, [T]Theta, Wnn, Wdt, Wjj, Wot]
'''
print("RESETTING TO PERFECT PARAMETERS \n")
self.params = np.array([1.5, .3, .1, .2, 2.5, 1.2, .3, 1.3, .2, 2.0])
self.w[0] = 0.1
self.view_params()
def gradient_descent_step(self, grad_p, grad_w, lr=0.05):
'''
Updates the self. parama and self.w according to the fradient descent rule
Parameters:
grad_p : numpy array (10,)
grad_w : sigle float
'''
self.params = self.params - lr*grad_p
self.w = self.w - lr*grad_w
def batch_CE_loss(self, batch):
"""
Processes a batch of sequences and calculates the ReLU loss
Parameters:
batch (dtaframe) : containint the field <pos_tags> and <chunk_tags>
Oututput
Total Loss Relu
"""
total_loss = 0
for _, row in batch.iterrows():
sent_pos_tags = row.pos_tags
X = tags2sentence(sent_pos_tags)
_, H = self.process_seq(X)
H = H[1:] # Exclude h(0)
sent_tags = row.chunk_tags
Y = np.array(sent_tags)
loss = -(Y*np.log(H) + (1-Y)*np.log(1-H))
loss = loss.mean()
total_loss += loss
total_loss = total_loss/len(batch)
return total_loss
def batch_accuracy(self, batch):
correct = 0 # Predictions that match
total = 0 # Total predictions
for _, row in batch.iterrows():
sent_pos_tags = row.pos_tags
x = tags2sentence(sent_pos_tags)
sent_tags = row.chunk_tags
y_target = np.array(sent_tags)
y_pred = self.predict_tags(x)
correct += np.sum(y_pred == y_target)
total += len(y_pred)
acc = (correct/total)*100 # Accuracy in percentage
return acc
def batch_sentence_accuracy(self, batch):
match = 0
for _, row in batch.iterrows():
sent_pos_tags = row.pos_tags
x = tags2sentence(sent_pos_tags)
sent_tags = row.chunk_tags
y_target = np.array(sent_tags)
y_pred = self.predict_tags(x)
if np.array_equal(y_pred, y_target):
match +=1
sent_acc = (match/len(batch))*100
return sent_acc
def set_parameter(self, Vcap=1.5, Vnn=.3, Vdt=.1, Vjj=.2, Vot=2.5, T=1.2, Wnn=.3, Wdt=1.3, Wjj=.2, Wot=2.0, W=.10):
self.params[0] = Vcap
self.params[1] = Vnn
self.params[2] = Vdt
self.params[3] = Vjj
self.params[4] = Vot
self.params[5] = T
self.params[6] = Wnn
self.params[7] = Wdt
self.params[8] = Wjj
self.params[9] = Wot
self.w[0] = W
def does_RNN_satisfy_conditions(self):
"""
Checks whether the RNN satisfies the inequality conditions
"""
check_conditions(Vcap = np.round(self.params[0],4),
Vnn = np.round(self.params[1],4),
Vdt = np.round(self.params[2],4),
Vjj = np.round(self.params[3],4),
Vot = np.round(self.params[4],4),
T = np.round(self.params[5],4),
Wnn = np.round(self.params[6],4),
Wdt = np.round(self.params[7],4),
Wjj = np.round(self.params[8],4),
Wot = np.round(self.params[9],4),
W = np.round(self.w[0],4), verbose=True)
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