print "Current fold is %d" % fid
train_idx = list(set(range(nsamples)) - set(predict_idx))
# extract predictions using RF on static
print " Extracting predictions on static data with RF..."
rf = RandomForestClassifier(n_estimators=nestimators)
rf.fit(static_all[train_idx], labels_all[train_idx])
predictions_all_rf[predict_idx] = rf.predict_log_proba(static_all[predict_idx])
predictions_all_rf[predictions_all_rf == -inf] = np.min(predictions_all_rf[predictions_all_rf != -inf])
# extract predictions using HMM on dynamic
print " Extracting predictions on dynamic data with HMM..."
hmmcl = HMMClassifier()
model_pos, model_neg = hmmcl.train(nhmmstates, nhmmiter, hmmcovtype,
dynamic_all[train_idx], labels_all[train_idx])
predictions_all_hmm[predict_idx] = hmmcl.predict_log_proba(model_pos, model_neg, dynamic_all[predict_idx])
ratios_all_hmm[predict_idx] = hmmcl.pos_neg_ratios(model_pos, model_neg, dynamic_all[predict_idx])
# extract predictions using LSTM on dynamic
print " Extracting predictions on dynamic data with LSTM..."
lstmcl = LSTMClassifier(lstmsize, lstmdropout, lstmoptim, lstmnepochs)
model_pos, model_neg = lstmcl.train(dynamic_all[train_idx], labels_all[train_idx])
mse_pos, mse_neg = lstmcl.predict_mse(model_pos, model_neg, dynamic_all[predict_idx])
predictions_all_lstm[predict_idx] = np.vstack((mse_pos, mse_neg)).T
ratios_all_lstm[predict_idx] = lstmcl.pos_neg_ratios(model_pos, model_neg, dynamic_all[predict_idx])
#
# Prepare combined datasets for the future experiments
#
# extract predictions using RF on static
rf = RandomForestClassifier(n_estimators=nestimators)
rf.fit(trainA_static, trainA_labels)
predictions_trainB_rf = rf.predict_log_proba(trainB_static)
predictions_trainB_rf[predictions_trainB_rf == -inf] = np.min(
predictions_trainB_rf[predictions_trainB_rf != -inf])
predictions_test_rf = rf.predict_log_proba(test_static)
predictions_test_rf[predictions_test_rf == -inf] = np.min(
predictions_test_rf[predictions_test_rf != -inf])
# extract predictions using HMM on dynamic
hmmcl = HMMClassifier()
model_pos, model_neg = hmmcl.train(nhmmstates, nhmmiter, hmmcovtype,
trainA_dynamic, trainA_labels)
predictions_trainB_hmm = hmmcl.predict_log_proba(model_pos, model_neg,
trainB_dynamic)
ratios_trainB_hmm = hmmcl.pos_neg_ratios(model_pos, model_neg, trainB_dynamic)
predictions_test_hmm = hmmcl.predict_log_proba(model_pos, model_neg,
test_dynamic)
ratios_test_hmm = hmmcl.pos_neg_ratios(model_pos, model_neg, test_dynamic)
# extract predictions using LSTM on dynamic
lstmcl = LSTMClassifier(lstmsize, lstmdropout, lstmoptim, lstmnepochs,
lstmbatchsize)
model_pos, model_neg = lstmcl.train(trainA_dynamic, trainA_labels)
mse_pos, mse_neg = lstmcl.predict_mse(model_pos, model_neg, trainB_dynamic)
predictions_trainB_lstm = np.vstack((mse_pos, mse_neg)).T
ratios_trainB_lstm = lstmcl.pos_neg_ratios(model_pos, model_neg,
trainB_dynamic)
mse_pos, mse_neg = lstmcl.predict_mse(model_pos, model_neg, test_dynamic)
predictions_test_lstm = np.vstack((mse_pos, mse_neg)).T
print "Current fold is %d" % fid
train_idx = list(set(range(nsamples)) - set(predict_idx))
# extract predictions using RF on static
print " Extracting predictions on static data with RF..."
rf = RandomForestClassifier(n_estimators=nestimators)
rf.fit(static_all[train_idx], labels_all[train_idx])
predictions_all_rf[predict_idx] = rf.predict_log_proba(static_all[predict_idx])
predictions_all_rf[predictions_all_rf == -inf] = np.min(predictions_all_rf[predictions_all_rf != -inf])
# extract predictions using HMM on dynamic
print " Extracting predictions on dynamic data with HMM..."
hmmcl = HMMClassifier()
model_pos, model_neg = hmmcl.train(nhmmstates, nhmmiter, hmmcovtype,
dynamic_all[train_idx], labels_all[train_idx])
predictions_all_hmm[predict_idx] = hmmcl.predict_log_proba(model_pos, model_neg, dynamic_all[predict_idx])
ratios_all_hmm[predict_idx] = hmmcl.pos_neg_ratios(model_pos, model_neg, dynamic_all[predict_idx])
# extract predictions using LSTM on dynamic
print " Extracting predictions on dynamic data with LSTM..."
lstmcl = LSTMClassifier(lstmsize, lstmdropout, lstmoptim, lstmnepochs, lstmbatchsize)
model_pos, model_neg = lstmcl.train(dynamic_all[train_idx], labels_all[train_idx])
mse_pos, mse_neg = lstmcl.predict_mse(model_pos, model_neg, dynamic_all[predict_idx])
predictions_all_lstm[predict_idx] = np.vstack((mse_pos, mse_neg)).T
ratios_all_lstm[predict_idx] = lstmcl.pos_neg_ratios(model_pos, model_neg, dynamic_all[predict_idx])
#
# Prepare combined datasets for the future experiments
#
# Train enrichment models on trainA # print 'Training enrichment models...' # extract predictions using RF on static rf = RandomForestClassifier(n_estimators=nestimators) rf.fit(trainA_static, trainA_labels) predictions_trainB_rf = rf.predict_log_proba(trainB_static) predictions_trainB_rf[predictions_trainB_rf == -inf] = np.min(predictions_trainB_rf[predictions_trainB_rf != -inf]) predictions_test_rf = rf.predict_log_proba(test_static) predictions_test_rf[predictions_test_rf == -inf] = np.min(predictions_test_rf[predictions_test_rf != -inf]) # extract predictions using HMM on dynamic hmmcl = HMMClassifier() model_pos, model_neg = hmmcl.train(nhmmstates, nhmmiter, hmmcovtype, trainA_dynamic, trainA_labels) predictions_trainB_hmm = hmmcl.predict_log_proba(model_pos, model_neg, trainB_dynamic) ratios_trainB_hmm = hmmcl.pos_neg_ratios(model_pos, model_neg, trainB_dynamic) predictions_test_hmm = hmmcl.predict_log_proba(model_pos, model_neg, test_dynamic) ratios_test_hmm = hmmcl.pos_neg_ratios(model_pos, model_neg, test_dynamic) # extract predictions using LSTM on dynamic lstmcl = LSTMClassifier(lstmsize, lstmdropout, lstmoptim, lstmnepochs, lstmbatchsize) model_pos, model_neg = lstmcl.train(trainA_dynamic, trainA_labels) mse_pos, mse_neg = lstmcl.predict_mse(model_pos, model_neg, trainB_dynamic) predictions_trainB_lstm = np.vstack((mse_pos, mse_neg)).T ratios_trainB_lstm = lstmcl.pos_neg_ratios(model_pos, model_neg, trainB_dynamic) mse_pos, mse_neg = lstmcl.predict_mse(model_pos, model_neg, test_dynamic) predictions_test_lstm = np.vstack((mse_pos, mse_neg)).T ratios_test_lstm = lstmcl.pos_neg_ratios(model_pos, model_neg, test_dynamic)
