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RegressDeepLearning.py
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import tensorflow as tf
from tensorflow import keras
from keras.callbacks import ModelCheckpoint
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten, Conv2D, MaxPooling2D
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from matplotlib import pyplot as plt
import seaborn as sb
import pandas as pd
import numpy as np
import sklearn
import warnings
warnings.filterwarnings('ignore')
warnings.filterwarnings('ignore', category=DeprecationWarning)
from xgboost import XGBRegressor
from math import sqrt
import operator
class NNRegress():
def __init__(self):
self.datasetPaths = np.array(["../Data/four_value_function.csv", "../Data/six_value_function.csv"])
self.architectureFilePaths = "../Architectures/list.txt"
self.lossFunc = 'mse'
self.optimizer = 'adam'
self.metrics = ['mse', 'mae']
self.validationSplit = 0.2
self.numEphocs = np.array([50,150])
self.batchSize = np.array([32, 128])
self.validationSplit = 0.2 # The parameters used in Keras in training (cross-validation)
self.splitTrainTestPercentage = 0.9 # 1 data = 0.9 training + 0.1 test
def prepData(self, fullFileName, scale = 1.0):
self.df = pd.read_csv(fullFileName)
if (scale < 1.0):
self.df = self.df.sample(frac=scale).reset_index(drop = True)
numOftraining = int(self.df.shape[0] * self.splitTrainTestPercentage)
self.train = self.df[:numOftraining]
self.train_target = self.train['obj'] # self.train_target will be training Y
self.train.drop(['obj'], axis=1, inplace=True) # self.train will be training X
self.test = self.df[numOftraining:]
self.test_target = self.test['obj'] # self.test will be test X
self.test.drop(['obj'], axis=1, inplace=True) # self.test will be test Y
self.result = np.ndarray( shape=(self.test_target.shape[0],1), dtype = float)
self.result = np.insert(self.result, 1, np.round(self.test_target,2), axis = 1)
self.result = np.delete(self.result, 0, axis = 1)
print('done\n')
#self.result = np.insert(self.result, 1, self.test_target, axis=1)
#self.result = np.insert(self.result, 1, np.zeros(self.result.shape[0]), axis=1)
#print('done twice!\n')
def buildModel(self, fullFileName): #fullFileName refers to the architecture file
architecture = pd.read_csv(fullFileName.strip())
self.NN_model = Sequential()
# The input layer :
layerSpec = np.array(architecture.iloc[0]) # Each row of architeture defines the properties of that layer
if (layerSpec[1] == 'Dense'):
self.NN_model.add(
Dense(layerSpec[2], activation=layerSpec[3], kernel_initializer=layerSpec[4],
input_dim=self.train.shape[1])
)
else:
if (layerSpec[1] == 'Conv2D'):
self.NN_model.add(
Conv2D(layerSpec[2], activation=layerSpec[3], kernel_initializer=layerSpec[4],
input_dim=self.train.shape[1])
)
# All other layers
for i in range(0, architecture.shape[0]):
layerSpec = architecture.iloc[i] # Each row of architeture defines the properties of that layer
if (layerSpec[1] == 'Dense'):
self.NN_model.add(
Dense(layerSpec[2], activation=layerSpec[3], kernel_initializer=layerSpec[4])
)
else:
if(layerSpec[1] == 'Conv2D'):
self.NN_model.add(
Conv2D(layerSpec[2], activation=layerSpec[3], kernel_initializer=layerSpec[4])
)
# Compile the network :
self.NN_model.compile(loss = self.lossFunc, optimizer = self.optimizer, metrics = self.metrics)
self.NN_model.summary()
def fitModel(self, nEpochs, batchSize):
self.history = self.NN_model.fit(self.train, self.train_target, epochs = nEpochs,
batch_size=batchSize, validation_split = self.validationSplit)
self.score = self.NN_model.evaluate(self.test, self.test_target, batch_size = batchSize)
self.scoreCollection= np.append(self.scoreCollection, self.score)
#else:
# self.scoreCollection = np.insert(self.scoreCollection, self.scoreCollection.shape[1], self.score, axis = 1)
def testModel(self):
test_predictions = self.NN_model.predict(self.test).flatten()
self.result = np.insert(self.result, self.result.shape[1], test_predictions, axis=1)
self.result[:,self.result.shape[1]-1] = np.round(self.result[:,self.result.shape[1]-1], 2)
print('done\n')
def parse(self):
file1 = open(self.architectureFilePaths, 'r')
allArchitecPaths = file1.readlines()
for i in range(0, self.datasetPaths.shape[0]):
datasetPath = self.datasetPaths[i]
modelNo = -1
memo = [' ' for m in range(0, self.datasetPaths.size * self.numEphocs.size * np.size(allArchitecPaths)) ]
# this will be used to store the memo indicating the info in each column of self.result
self.scoreCollection = np.empty(shape=(0,0))#.array([], dtype=float) # This will store vectors of scores (which are the output of evaluate function in Keras)
if (i == 0):
self.prepData(datasetPath)
else:
self.prepData(datasetPath, 0.1)
for nEpochs in self.numEphocs:
for batchSize in self.batchSize:
for architecPath in allArchitecPaths:
modelNo = modelNo + 1
self.buildModel(architecPath)
self.fitModel(nEpochs, batchSize)
self.testModel()
str1 = "architecture, " + architecPath.strip() + ", nEpochs, " + str(nEpochs) + ", batch_size, ", str(batchSize)
memo[modelNo] = str1
strPredictionsFileName = "../Output/linregPredict" + "_" + str(i) + ".txt"
np.savetxt(strPredictionsFileName, self.result, fmt='%1.2f', delimiter=",")
print("One dataset is done.\n")
numOfSubArrays = np.size(self.score)
numOfElementsInEachSubArray = np.size(self.scoreCollection) // np.size(self.score)
self.scoreCollection.reshape(numOfSubArrays, numOfElementsInEachSubArray)
strScoresFileName = "../Output/linregScores" + "_" + str(i) + ".txt"
np.savetxt(strScoresFileName, self.scoreCollection, fmt='%1.3f', delimiter=",")
strMemoFileName = "../Output/linregMemo" + "_" + str(i) + ".txt"
np.savetxt(strMemoFileName, memo, delimiter=";", fmt="%s")
print("One dataset is done.\n")
print("One dataset is done.\n")
print("Over!\n")
def main():
obj = NNRegress()
obj.parse()
if __name__ == "__main__":
main()
# sr = SAMPLEREGRESS()
# sr.run()