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magfft_lstm_classify.py
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magfft_lstm_classify.py
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import numpy as np
import tflearn
import numpy as np
import scipy.fftpack as spfft
import tensorflow as tf
import scipy.io as sio
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
from scipy.signal import blackman
import sys
import operator
from collections import OrderedDict
from numpy import linalg as la
import os
'''
Directory containing dump files with names "<technology-type>_<count>.npy"
e.g. lte_1.npy, lte_2.npy, gsm_1.npy...
This can be easily generated using the labeling tool over the api
'''
mydir="../tech_dumps/"
files = []
for file in os.listdir(mydir):
if file.endswith(".npy"):
files.append(os.path.join(mydir, file))
print files
labels={}
lfiles={}
count=0
for f in files:
fname = f.split("/")[-1]
if not labels.has_key(fname.split("_")[0]):
labels[fname.split("_")[0]]=count
lfiles[fname.split("_")[0]]=[]
count+=1
lfiles[fname.split("_")[0]].append(f)
labels = OrderedDict(sorted(labels.items(), key=operator.itemgetter(1)))
print labels
print lfiles
num_labels = len(labels)
nsamples = 0
for f in files:
dta = np.load(f)
if nsamples < dta.shape[1]:
nsamples = dta.shape[1]
datatype = "float32"
train_data = np.zeros(nsamples,dtype=datatype)
test_data = np.zeros(nsamples,dtype=datatype)
train_labels = np.zeros(num_labels)
test_labels = np.zeros(num_labels)
def setup_data():
global train_data, train_labels, valid_data, valid_labels, test_data, test_labels
for key in labels.keys():
print("--"*50)
for f in lfiles[key]:
dta = np.load(f)
res = np.zeros((dta.shape[0],nsamples))
#append zeros
res[:,:dta.shape[1]] = dta
train_cnt = dta.shape[0]/2
test_cnt = dta.shape[0]/2
train_data = np.vstack((train_data,res[0:train_cnt]))
dummy_labels = np.zeros((train_cnt, len(labels)))
dummy_labels[:, labels[key]] = 1
train_labels = np.vstack((train_labels,dummy_labels))
print("Training data: Generation done for:", key)
test_data = np.vstack((test_data,res[train_cnt:train_cnt+test_cnt]))
dummy_labels = np.zeros((test_cnt, len(labels)))
dummy_labels[:, labels[key]] = 1
test_labels = np.vstack((test_labels,dummy_labels))
print("Testing data: Generation done for:", key)
train_data = np.delete(train_data,0,0)
test_data = np.delete(test_data,0,0)
train_labels = np.delete(train_labels,0,0)
test_labels = np.delete(test_labels,0,0)
setup_data()
Y_train = train_labels
Y_test = test_labels
print train_data.shape
print test_data.shape
def lnorm(X_train):
print "Pad:", X_train.shape
for i in range(X_train.shape[0]):
X_train[i,:] = X_train[i,:]/la.norm(X_train[i,:],2)
return X_train
train_data = lnorm(train_data)
test_data = lnorm(test_data)
#out0 = (out0-np.mean(out0))/np.std(out0)
X_train = np.reshape(train_data,(-1,nsamples,1))
X_test = np.reshape(test_data,(-1,nsamples,1))
def getFontColor(value):
if np.isnan(value):
return "black"
elif value < 0.2:
return "black"
else:
return "white"
def getConfusionMatrixPlot(true_labels, predicted_labels):
# Compute confusion matrix
cm = confusion_matrix(true_labels, predicted_labels)
cm_norm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
cm_norm = np.nan_to_num(cm_norm)
cm = np.round(cm_norm,2)
print(cm)
# create figure
fig = plt.figure()
plt.clf()
ax = fig.add_subplot(111)
ax.set_aspect(1)
ax.set_xlabel('Predicted label')
ax.set_ylabel('True label')
res = ax.imshow(cm, cmap=plt.cm.binary,
interpolation='nearest', vmin=0, vmax=1)
# add color bar
plt.colorbar(res)
# annotate confusion entries
width = len(cm)
height = len(cm[0])
for x in xrange(width):
for y in xrange(height):
ax.annotate(str(cm[x][y]), xy=(y, x), horizontalalignment='center',
verticalalignment='center', color=getFontColor(cm[x][y]))
# add genres as ticks
alphabet = labels.keys()
plt.xticks(range(width), alphabet[:width], rotation=30)
plt.yticks(range(height), alphabet[:height])
return plt
class MonitorCallback(tflearn.callbacks.Callback):
def __init__(self, model):
self.model = model
self.accuracy = 0.0
def on_epoch_end(self, training_state):
print "accuracy1:", training_state.global_acc
print "accuracy2:", training_state.val_acc
if self.accuracy<training_state.val_acc:
self.accuracy = training_state.val_acc
print "Model saved:", self.accuracy
self.model.save('lstm_tech_classify.tfl')
print("--"*50)
print("Training data:",X_train.shape)
print("Training labels:",Y_train.shape)
print("Testing data",X_test.shape)
print("Testing labels",Y_test.shape)
print("--"*50)
network = tflearn.input_data(shape=[None, nsamples, 1],name="inp")
network = tflearn.lstm(network, 128, dynamic=True)
network = tflearn.fully_connected(network, len(labels), activation='softmax',name="out")
network = tflearn.regression(network, optimizer='adam',
loss='categorical_crossentropy',
learning_rate=0.001)
model = tflearn.DNN(network,tensorboard_verbose=2)
monitorCallback = MonitorCallback(model)
model.fit(X_train, Y_train, n_epoch=8, shuffle=True,show_metric=True, batch_size=100,validation_set=(X_test,Y_test), run_id='lstm_tech', callbacks=monitorCallback)
model.save('../models/lstm_tech_classify.tfl')
classes = labels.keys()
def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues, labels=[]):
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(labels))
plt.xticks(tick_marks, labels, rotation=45)
plt.yticks(tick_marks, labels)
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
test_X_i = X_test
test_Y_i = Y_test
# estimate classes
test_Y_i_hat = np.array(model.predict(test_X_i))
width = 4.1
height = width / 1.618
plt.figure(figsize=(width, height))
plt = getConfusionMatrixPlot(np.argmax(test_Y_i, 1), np.argmax(test_Y_i_hat, 1))
plt.gcf().subplots_adjust(bottom=0.15)
plt.savefig("./confmat_tech.pdf")