tf.Graph.as_graph_def(sess.graph)) for epoch in range(PARAM_NUM_EPOCH): #print "Training in epoch: ", epoch #log_file_object.write("Training in epoch: "+str(epoch)+"\r\n") #for start, end in zip(range(0, len(trX), 128), range(128, len(trX), 128)): sess.run(train_op, feed_dict={X: trX, Y: trY}) cost_log = sess.run(cost, feed_dict={X: trX, Y: trY}) log_file_object.write(str(cost_log) + "\r\n") log_file_object.flush() predict_result = sess.run(predict_op, feed_dict={X: teX, Y: teY}) statistic_file_object.write("Training in epoch: " + str(epoch) + "\r\n") statistic_file_object.write("R2 : \r\n") R2 = data_loader.R2(np.array(predict_result), teY) statistic_file_object.write(str(R2) + "\r\n") statistic_file_object.flush() # predict option #batch_xs, batch_ys = drag_data.test.next_batch(128) predict_result_temp = sess.run(predict_op, feed_dict={X: teX, Y: teY}) #print predict_result predict_result = [] for pred in predict_result_temp: predict_result.append(pred[0]) # write file statistic_file_object.write("\r\nFinal : \r\n") statistic_file_object.write("True Activities: \r\n")
import numpy as np import cPickle as pickle import data_loader data_file_train = "/Users/peter/Documents/Work/data/drag_design/NK1_training_disguised.csv" data_file_test = "/Users/peter/Documents/Work/data/drag_design/NK1_test_disguised.csv" drag_data = data_loader.read_data_sets(data_file_train, data_file_test, 1000) trX, trY, teX, teY = drag_data.train.descriptors, drag_data.train.activities, drag_data.test.descriptors, drag_data.test.activities # Random Forest Classifier def random_forest_regressor(train_x, train_y): from sklearn.ensemble import RandomForestRegressor model = RandomForestRegressor(n_estimators=200) model.fit(train_x, train_y) return model if __name__ == '__main__': num_train, num_feat = trX.shape num_test, num_feat = teX.shape model = random_forest_regressor(trX, trY) predict = model.predict(teX) #accuracy = metrics.accuracy_score(teY, predict) R2 = data_loader.R2(np.array(predict), teY) print "R : ", R2