def test_q_calculation(self):
dataRetriever = DataRetriever("../Datasets/metadata.json")
dataRetriever.retrieveData("breastCancer")
naiveBayes = NaiveBayes(dataRetriever.getDataSet(),
dataRetriever.getDataClass())
#seperatedByClass = naiveBayes.calculateQ()
#print(seperatedByClass)
self.assertEqual(
dataRetriever.getDataMenu(),
["breastCancer", "glass", "iris", "soybeanSmall", "vote"],
"should return list of data sets")
def testKMeans(self):
data = DataRetriever("../Datasets/metadata.json")
data.retrieveData("computerHardware")
kValue = 15
t = Timer()
t.start()
mediods = KMediods(data.getDataSet(), data.getDataClass(),
data.getDescreteAttributes(),
data.getContinuousAttributes(),
data.getPredictionType(), kValue, 100)
t.stop()
print(f"Time: {t}")
print(mediods)
mediods.to_csv('kmedoids.csv', index=False)
maxItter = 100
kValue = 78
# These are only used for image segmentation and abalone
# frac = .25
# random_state = 69
# kValue = m.floor(frac * kValue)
dataSetUnNormalized = data.getDataSet()
# dataSetUnNormalized[data.getDataClass()] = np.log(dataSetUnNormalized[data.getDataClass()] + 0.001) // This is for Forest Fires
sn = StandardNormalizer(dataSetUnNormalized[data.getContinuousAttributes()])
dataSetUnNormalized[data.getContinuousAttributes()] = sn.train_fit()
dataSetNormalized = dataSetUnNormalized
# dataSetNormalized = dataSetNormalized.sample(frac=frac, random_state=random_state)
# dataSetNormalized = dataSetNormalized.reset_index()
# dataSetNormalized = dataSetNormalized.drop(["idNumber"], axis=1) #// For Glass
medoids = KMediods(dataSetNormalized, data.getDataClass(),
data.getDescreteAttributes(),
data.getContinuousAttributes(), data.getPredictionType(),
kValue, maxItter)
medoids.to_csv('./CSVOutput/' + "normalized" + dataSetName +
'MedoidsClustered.csv',
index=False)
print(f"CSV for " + dataSetName + " has been created!")
# This line is used to normalize the data for Forest Fires
# dataset[dataRetriever.getDataClass()] = np.log(dataset[dataRetriever.getDataClass()]+0.1)
maxIter = 1
learning_rate = 1e-3
batch_size = 0.01
metrics = []
fold = 0
test_set = test_set.reset_index(drop=True)
train_set = train_set.reset_index(drop=True)
ohe = OneHotEncoder()
discrete_attr = dataRetriever.getDescreteAttributes()
if dataRetriever.getDataClass() in discrete_attr:
discrete_attr.remove(dataRetriever.getDataClass())
train_set = ohe.train_fit(train_set, discrete_attr)
test_set = ohe.fit(test_set)
# Normalize Data
sn = StandardNormalizer(train_set[dataRetriever.getContinuousAttributes()])
train_set[dataRetriever.getContinuousAttributes()] = sn.train_fit()
test_set[dataRetriever.getContinuousAttributes()] = sn.fit(test_set[dataRetriever.getContinuousAttributes()])
# Train network and change architecture in respect to data set
nn = NeuralNetwork(train_set, 2, [6,16], dataRetriever.getPredictionType(), dataRetriever.getDataClass())
fitness_matrix, average_fitness = nn._particle_swarm_optimize(70, max_iter=500)
maxItter = 100
kValue = 1330
# These are only used for image segmentation and abalone
frac = .25
random_state = 69
kValue = m.floor(frac * kValue)
dataSetUnNormalized = data.getDataSet()
# dataSetUnNormalized[data.getDataClass()] = np.log(dataSetUnNormalized[data.getDataClass()] + 0.001) // This is for Forest Fires
sn = StandardNormalizer(dataSetUnNormalized[data.getContinuousAttributes()])
dataSetUnNormalized[data.getContinuousAttributes()] = sn.train_fit()
dataSetNormalized = dataSetUnNormalized
dataSetNormalized = dataSetNormalized.sample(frac=frac,
random_state=random_state)
dataSetNormalized = dataSetNormalized.reset_index()
# dataSetNormalized = dataSetNormalized.drop(["idNumber"], axis=1) // For Glass
centroids = KMeans(dataSetNormalized, data.getDataClass(),
data.getDescreteAttributes(),
data.getContinuousAttributes(), data.getPredictionType(),
kValue, maxItter)
centroids.to_csv('./CSVOutput/' + "normalized" + dataSetName +
'KMeansClustered.csv',
index=False)
print(f"CSV for " + dataSetName + " has been created!")
def run_driver(current_data_set,
mutation_rate=0.5,
maxIter=1000,
batch_size=0.6,
population_size=110,
network_architecture=[15],
pb_actor=None):
cost_func = {
"breastCancer": "bin_cross",
"glass": "log_cosh",
"soybeanSmall": "log_cosh",
"abalone": "log_cosh",
"forestFires": "log_cosh",
"computerHardware": "log_cosh"
}
title_text = r"""
______ __ _ ___ __ _ __ __
/ ____/___ ____ ___ / /_ (_)_____ / / / /____ _ ____ _____ (_)/ /_ / /_ ____ ___ _____
/ / __ / _ \ / __ \ / _ \ / __// // ___/ / /| / / // __ `// __ \ / ___// // __// __ \ / __ `__ \ / ___/
/ /_/ // __// / / // __// /_ / // /__ / ___ / / // /_/ // /_/ // / / // /_ / / / // / / / / /(__ )
\____/ \___//_/ /_/ \___/ \__//_/ \___/ /_/ |_//_/ \__, / \____//_/ /_/ \__//_/ /_//_/ /_/ /_//____/
/____/
"""
output_json = {}
# ====================== Adjustable Variables ==============================
# current_data_set = "abalone"
# mutation_rate = .5
# maxIter = 10
# batch_size = .6
# population_size = 110
# network_architecture = []
# ===========================================================================
output_json["parameters"] = {
"mutation_rate": mutation_rate,
"population_size": population_size,
"network_architecture": network_architecture,
"cost_func": cost_func[current_data_set],
"maxIter": maxIter,
"batch_size": batch_size
}
# ================ Data pre-processing =================================================
dataRetriever = DataRetriever("../../Datasets/metadata.json")
dataRetriever.retrieveData(current_data_set)
dataset = dataRetriever.getDataSet().dropna()
discrete_attr = dataRetriever.getDescreteAttributes()
cont_attributes = dataRetriever.getContinuousAttributes()
# This line is used to normalize the data for Forest Fires
if current_data_set == "forestFires":
discrete_attr.remove('month')
discrete_attr.remove('day')
dataset['month'] = (pd.to_datetime(dataset.month,
format='%b').dt.month) - 1
dataset["day"] = dataset['day'].apply(
lambda x: list(calendar.day_abbr).index(x.capitalize()))
dataset["month_sin"] = np.sin(dataset['month'])
dataset["month_cos"] = np.sin(dataset['month'])
dataset["day_sin"] = np.sin(dataset['day'])
dataset["day_cos"] = np.sin(dataset['day'])
dataset = dataset.drop('day', axis=1)
dataset = dataset.drop('month', axis=1)
cont_attributes.append('month_sin')
cont_attributes.append('month_cos')
cont_attributes.append('day_sin')
cont_attributes.append('day_cos')
dataset[dataRetriever.getDataClass()] = np.log(
dataset[dataRetriever.getDataClass()] + 0.000001)
elif current_data_set == "computerHardware":
discrete_attr.remove('venderName')
discrete_attr.remove('modelName')
dataset = dataset.drop('venderName', axis=1)
dataset = dataset.drop('modelName', axis=1)
dataset = dataset.reset_index(drop=True)
if dataRetriever.getDataClass() in discrete_attr:
discrete_attr.remove(dataRetriever.getDataClass())
# ======================= Train Neural Network ================
print(title_text)
fold = 0
metrics = []
for test_set, train_set in KFolds(dataset, 10):
fold += 1
fitness_file = f"../DataDump/GA/{current_data_set}_layer{len(network_architecture)}_fold{fold}_fitness.csv"
output_file = f"../DataDump/GA/{current_data_set}_layer{len(network_architecture)}_fold{fold}_output.csv"
metrics.append(
multiprocess_func.remote(test_set,
train_set,
fold,
fitness_file,
output_file,
dataRetriever,
cost_func[current_data_set],
current_data_set,
mutation_rate,
maxIter,
batch_size,
population_size,
network_architecture,
pb_actor=None))
metrics = ray.get(metrics)
print(metrics)
print("Average Performance: ", np.asarray(metrics).mean())
output_json["Metrics"] = metrics
output_json["Average"] = np.asarray(metrics, dtype=np.float64).mean()
output_json["Std"] = np.asarray(metrics, dtype=np.float64).std()
with open(
f"../DataDump/GA_{current_data_set}_layer{len(network_architecture)}.json",
'w') as f:
json.dump(output_json, f, indent=4)
from MLAlgorithms.Utils.StandardNormalizer import StandardNormalizer
from MLAlgorithms.Utils.DataRetriever import DataRetriever
from MLAlgorithms.Utils.ClassifierAnalyzer import ClassifierAnalyzer
import numpy as np
import json
dataRetriever = DataRetriever("../Datasets/metadata.json")
dataRetriever.retrieveData("vote")
data = dataRetriever.getDataSet()
data = data.dropna()
data = data.sample(frac=1.0, random_state=93)
data = data.reset_index(drop=True)
# data = data.drop('idNumber', axis=1)
class_col = dataRetriever.getDataClass()
# data[class_col] = np.log(data[class_col] + 0.001)
contAttr = dataRetriever.getContinuousAttributes()
discAttr = dataRetriever.getDescreteAttributes()
predictionType = dataRetriever.getPredictionType()
output_json = {}
iter_num = 0
for test, train in KFolds(data, 5, stratisfied=True, class_col=class_col):
#KFolds doesn't have the capability of returning a validate set
#K is set to desired k/2 and the validate set is half of the test set
sn = StandardNormalizer(train[contAttr])
def network_tuner(*nodes_per_hidden_layer):
"""
This function is used to calcuate the optimal network architecture
The user should input the dataset they would like to operate with and change the performance metric in accordance to the data set type IE regression or classification
"""
MSEs = []
bestNetwork = {}
learning_rate = 0.0001
maxItter = 500
batch_size = .5
dataRetriever = DataRetriever("../Datasets/metadata.json")
dataRetriever.retrieveData("glass")
dataset = dataRetriever.getDataSet().dropna()
dataset = dataset.reset_index(drop=True)
# This line is used to normalize the data for Forest Fires
# dataset[dataRetriever.getDataClass()] = np.log(dataset[dataRetriever.getDataClass()]+0.1)
dataset[dataRetriever.getContinuousAttributes()] = (dataset[dataRetriever.getContinuousAttributes()]-dataset[dataRetriever.getContinuousAttributes()].mean())/dataset[dataRetriever.getContinuousAttributes()].std()
test_set = dataset.sample(frac=0.1, random_state=69)
train_set = dataset.drop(test_set.index)
test_set = test_set.reset_index(drop=True)
train_set = train_set.reset_index(drop=True)
ohe = OneHotEncoder()
discrete_attr = dataRetriever.getDescreteAttributes()
if dataRetriever.getDataClass() in discrete_attr:
discrete_attr.remove(dataRetriever.getDataClass())
datasetEncoded = ohe.train_fit(train_set, dataRetriever.getDescreteAttributes())
testEncoded = ohe.fit(test_set)
output = None
nn = NeuralNetwork(datasetEncoded, 0, [], dataRetriever.getPredictionType(), dataRetriever.getDataClass())
for i in range(maxItter):
# We don't call an inital feedforward because backpropagate starts with a feedforward call
# batch_size represents the number of data points per batch
output = nn._back_propagate(learning_rate=learning_rate, batch_size=batch_size)
final = nn.test(testEncoded.drop(dataRetriever.getDataClass(), axis=1))
output = nn._feed_forward(testEncoded.drop(dataRetriever.getDataClass(), axis=1), testing=True)
actual = testEncoded[dataRetriever.getDataClass()]
## ===================== Classification =================
correct = 0
acc = 0
for i, row in enumerate(final):
if row == actual.iloc[i]: correct += 1
# final = final.reshape(final.shape[0])
# MSE = ((actual-final)**2).mean()
# MSEs.append(MSE)
bestNetwork['network'] = nn
bestNetwork['acc'] = acc
bestNetwork['arc'] = [0]
# # ============================================
# # ============ Compare Acc to Most Common Class
values = test_set[dataRetriever.getDataClass()].value_counts()
# USED FOR CLASSIFICATION
# print(f'Accuracy: {acc}')
# print(f'Max Class Prior: {values.max()/values.sum()}')
# print(f"Class Distribution:\n{values}")
# print("Final: ", final)
# print("Actual: ", list(actual))
# print()
numOfLayer = len(nodes_per_hidden_layer)
print("Number of Hidden Layers: ", numOfLayer)
for layer in range(numOfLayer):
print(f"Layer Number: {layer + 1}")
combinations = list(itertools.product(*nodes_per_hidden_layer[:layer+1]))
for combo in combinations:
output = None
print("Node Combination: ",list(combo))
print(combo)
nn = NeuralNetwork(datasetEncoded, layer, list(combo), dataRetriever.getPredictionType(), dataRetriever.getDataClass())
for i in range(maxItter):
# We don't call an inital feedforward because backpropagate starts with a feedforward call
# batch_size represents the number of data points per batch
output = nn._back_propagate(learning_rate=learning_rate, batch_size=batch_size)
final = nn.test(testEncoded.drop(dataRetriever.getDataClass(), axis=1))
output = nn._feed_forward(testEncoded.drop(dataRetriever.getDataClass(), axis=1), testing=True)
actual = testEncoded[dataRetriever.getDataClass()]
## ===================== Classification =================
correct = 0
acc = 0
for i, row in enumerate(final):
if row == actual.iloc[i]: correct += 1
acc = correct/len(test_set)
# # # ============================================
# # # ============ Compare Acc to Most Common Class
values = test_set[dataRetriever.getDataClass()].value_counts()
# USED FOR CLASSIFICATION
# print(f'Accuracy: {acc}')
# print(f'Max Class Prior: {values.max()/values.sum()}')
# # print(f"Class Distribution:\n{values}")
# print("Final: ", final)
# print("Actual: ", list(actual))
# print()
if acc > bestNetwork['acc']:
bestNetwork['network'] = nn
bestNetwork['acc'] = acc
bestNetwork['arc'] = combo
# final = final.reshape(final.shape[0])
# MSE = ((actual-final)**2).mean()
# MSEs.append(MSE)
# if MSE < bestNetwork['acc']:
# bestNetwork['network'] = nn
# bestNetwork['acc'] = MSE
# bestNetwork['arc'] = combo
return bestNetwork#, MSEs
learning_rate = 1e-3
batch_size = 0.01
metrics = []
fold = 0
# Ten-Fold Cross Validation
for test_set, train_set in KFolds(dataset, 10):
fold += 1
print("Fold Num: ", fold)
# Encode Data
test_set = test_set.reset_index(drop=True)
train_set = train_set.reset_index(drop=True)
ohe = OneHotEncoder()
discrete_attr = dataRetriever.getDescreteAttributes()
if dataRetriever.getDataClass() in discrete_attr:
discrete_attr.remove(dataRetriever.getDataClass())
train_set = ohe.train_fit(train_set, discrete_attr)
test_set = ohe.fit(test_set)
# Normalize Data
sn = StandardNormalizer(train_set[dataRetriever.getContinuousAttributes()])
train_set[dataRetriever.getContinuousAttributes()] = sn.train_fit()
test_set[dataRetriever.getContinuousAttributes()] = sn.fit(
test_set[dataRetriever.getContinuousAttributes()])
# Train network and change architecture in respect to data set
nn = NeuralNetwork(train_set, 2, [2, 2], dataRetriever.getPredictionType(),
dataRetriever.getDataClass())
nn.train(maxIter, learning_rate, batch_size)
print("The Percent of Correct Predictions is {t}%".format(
t=round((t * 100 / len(answers)), 1)))
print("The Percent of Incorrect Predictions is {f}%\n".format(
f=round((f * 100 / len(answers)), 1)))
dataRetriever = DataRetriever("../Datasets/metadata.json")
################################################ Un-Shuffled Data ################################################
# This first for loop performs the NaiveBayes algorithm for un-shuffled data
jsonResults1 = {}
for dataSet in dataRetriever.getDataMenu():
dataRetriever.retrieveData(dataSet)
dataClass = dataRetriever.getDataClass()
retrievedData = dataRetriever.getDataSet()
numOfClassValues = len(
retrievedData[dataRetriever.getDataClass()].unique())
method = "macro"
foldNum = 1
jsonResults1[dataSet] = {}
print(f"PRINTING RESULTS FOR THE CONTROL DATASET {dataSet}")
for train, test in KFolds(retrievedData, 10):
trainBin = BinDiscretizer(
train[dataRetriever.getContinuousAttributes()], multi=True)
if __name__ == "__main__":
dataRetriever = DataRetriever("../Datasets/metadata.json")
dataRetriever.retrieveData("computerHardware")
data = dataRetriever.getDataSet()
data = data.dropna()
data = data.reset_index(drop=True)[[
"venderName", "modelName", "myct", "mmin", "mmax", "cach", "chmin",
"chmax", "prp", "erp"
]]
test = data.sample(frac=0.2)
train = data.drop(test.index)
test = test.reset_index(drop=True)
train = train.reset_index(drop=True)
VDM = ValueDifferenceMetric(
data,
unknown_col=dataRetriever.getDataClass(),
prediction_type=dataRetriever.getPredictionType())
start = time.time()
VDM.train()
print(f"Training took: {time.time() - start} seconds")
start = time.time()
VDM.calc_distance_matrix(data["mmin"], data["mmin"])
print(f"Matrix took: {time.time() - start} seconds")
# print(KNN.get_neighbors([5,10]))
# print(KNN.test(augmented=True))
mutation_rate = .5
maxItter = 1000
batch_size = .6
population_size = 110
# ===========================================================================
# ================ Data pre-processing =================================================
dataRetriever = DataRetriever("../Datasets/metadata.json")
dataRetriever.retrieveData(current_data_set)
dataset = dataRetriever.getDataSet().dropna()
discrete_attr = dataRetriever.getDescreteAttributes()
cont_attributes = dataRetriever.getContinuousAttributes()
# This line is used to normalize the data for Forest Fires
if current_data_set == "forestFires":
zeros = dataset[dataset[dataRetriever.getDataClass()] < 1].index
print(len(zeros) / len(dataset))
dataset = dataset.drop(zeros)
discrete_attr.remove('month')
discrete_attr.remove('day')
dataset['month'] = (pd.to_datetime(dataset.month,
format='%b').dt.month) - 1
dataset["day"] = dataset['day'].apply(
lambda x: list(calendar.day_abbr).index(x.capitalize()))
dataset["month_sin"] = np.sin(dataset['month'])
dataset["month_cos"] = np.sin(dataset['month'])
dataset["day_sin"] = np.sin(dataset['day'])
dataset["day_cos"] = np.sin(dataset['day'])
dataset = dataset.drop('day', axis=1)
learning_rate = 1e-3
batch_size = 0.01
metrics = []
fold = 0
# Ten-Fold Cross Validation
for test_set, train_set in KFolds(dataset, 10):
fold += 1
print("Fold Num: ", fold)
# Encode Data
test_set = test_set.reset_index(drop=True)
train_set = train_set.reset_index(drop=True)
ohe = OneHotEncoder()
discrete_attr = dataRetriever.getDescreteAttributes()
if dataRetriever.getDataClass() in discrete_attr:
discrete_attr.remove(dataRetriever.getDataClass())
train_set = ohe.train_fit(train_set, discrete_attr)
test_set = ohe.fit(test_set)
# Normalize Data
sn = StandardNormalizer(train_set[dataRetriever.getContinuousAttributes()])
train_set[dataRetriever.getContinuousAttributes()] = sn.train_fit()
test_set[dataRetriever.getContinuousAttributes()] = sn.fit(
test_set[dataRetriever.getContinuousAttributes()])
# Train network and change architecture in respect to data set
nn = NeuralNetwork(train_set, 0, [], dataRetriever.getPredictionType(),
dataRetriever.getDataClass())
nn.train(maxIter, learning_rate, batch_size)
