Y_training = Y[0:200]
X_test = X[200:437]
Y_test = Y[200:437]
dbn = dbn.DBN([X_training.shape[1], 1000, 2], learn_rates=0.3, learn_rate_decays=0.9, epochs=10, verbose=1)
logReg = linear_model.LogisticRegression(C=1e6)
dbn.fit(X_training, Y_training)
logReg.fit(X_training, Y_training)
i = 0
preds, probs = dbn.predict(X_test)
total = 0
correct = 0
for xt in X_test:
dbn_pred, dbn_prob = dbn.predict(xt)
theta = logReg.coef_[0]
intercept = logReg.intercept_
logReg_pred = confidenceLogRegression(theta, xt, intercept)
if float(dbn_prob[0][1]) >= 0.55:
if dbn_pred[0] == Y_test[i]:
correct += 1
preds[i] = 1
total += 1
verbose=1,
)
net1.fit(X_training_Matrix,Y_training_Matrix)
logReg = linear_model.LogisticRegression(C=1e6)
logReg.fit(X_training_Matrix, Y_training_Matrix)
dbn = dbn.DBN(
[X_training.shape[1],1000,2],
learn_rates = 0.3,
learn_rate_decays = 0.9,
epochs = 10,
verbose = 1)
dbn.fit(X_training, Y_training)
net1Preds,net1Probs = net1.predict(X_test_Matrix)
dbnPreds,dbnProbs = dbn.predict(X_test)
logRegPreds = logReg.predict(X_test_Matrix)
print "neural network"
print classification_report(Y_test_Matrix, net1Preds)
print "deep belief network"
print classification_report(Y_test, dbnPreds)
print "logistic regression"
print classification_report(Y_test_Matrix, logRegPreds)
count = 0
i = 0
while i < 237:
if Y_test[i] == net1Preds[i] == logRegPreds[i] == dbnPreds[i]:
count = count + 1
print
mediaFeatureDBNPreds,mediaFeatureDBNProbs = dbn.predict(mediaFeatureTest)
print classification_report(Y_test_Matrix,mediaFeatureDBNPreds)
########################deep belief network with media features###########################################
"""
########################deep belief network with comment features###########################################
commentFeatureTraining = X_training_Matrix[0:200,15:35]
commentFeatureTest = X_test_Matrix[0:237,15:35]
dbn = dbn.DBN(
[commentFeatureTraining.shape[1],1000,2],
learn_rates = 0.1,
learn_rate_decays = 0.9,
epochs = 100,
verbose = 1)
dbn.fit(commentFeatureTraining, Y_training_Matrix)
commentFeatureDBNPreds,commentFeatureDBNProbs = dbn.predict(commentFeatureTest)
print classification_report(Y_test_Matrix,commentFeatureDBNPreds)
########################deep belief network with media features###########################################
##################### THIS IS BELIEF NETWORK ############################################
"""userFeatureTraining = X_training_Matrix[0:200,35:]
logReg = linear_model.LogisticRegression(C=1e6)
logReg.fit(userFeatureTraining, Y_training_Matrix)
print str(X_test_Matrix.shape)+" shape of test matrix"
print "logistic regression using user features"
