def test_train_test_sizes(self):
#Initialization
dataRetriever = DataRetriever("../Datasets/metadata.json")
dataRetriever.retrieveData("breastCancer")
breastCancer = dataRetriever.getDataSet()
continousAttributes = [
"clumpThickness", "uniformityOfCellSize", "uniformityOfCellShape",
"marginalAdhesion", "singleEpithelialCellSize", "bareNuclei",
"blandChromatin", "normalNucleoli", "mitoses", "class"
]
for test, train in KFolds(breastCancer, 10):
self.assertEqual(len(test) + len(train), len(breastCancer))
def test_train_test_independence(self):
#Initialization
dataRetriever = DataRetriever("../Datasets/metadata.json")
dataRetriever.retrieveData("breastCancer")
breastCancer = dataRetriever.getDataSet()
continousAttributes = [
"clumpThickness", "uniformityOfCellSize", "uniformityOfCellShape",
"marginalAdhesion", "singleEpithelialCellSize", "bareNuclei",
"blandChromatin", "normalNucleoli", "mitoses", "class"
]
for test, train in KFolds(breastCancer, 10):
#https://stackoverflow.com/questions/3170055/test-if-lists-share-any-items-in-python
self.assertFalse(bool(set(test.index) & set(train.index)))
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 test_proper_number_of_folds(self):
#Initialization
dataRetriever = DataRetriever("../Datasets/metadata.json")
dataRetriever.retrieveData("breastCancer")
breastCancer = dataRetriever.getDataSet()
continousAttributes = [
"clumpThickness", "uniformityOfCellSize", "uniformityOfCellShape",
"marginalAdhesion", "singleEpithelialCellSize", "bareNuclei",
"blandChromatin", "normalNucleoli", "mitoses", "class"
]
iterations = 0
for test, train in KFolds(breastCancer, 10):
iterations += 1
self.assertEqual(iterations, 10)
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)
def test_stratisfied(self):
#Initialization
dataRetriever = DataRetriever("../Datasets/metadata.json")
dataRetriever.retrieveData("breastCancer")
breastCancer = dataRetriever.getDataSet()
continousAttributes = [
"clumpThickness", "uniformityOfCellSize", "uniformityOfCellShape",
"marginalAdhesion", "singleEpithelialCellSize", "bareNuclei",
"blandChromatin", "normalNucleoli", "mitoses", "class"
]
iterations = 0
for test, train in KFolds(breastCancer, 10, stratisfied=True):
print("TestLen 2", len(test[test['class'] == 2]), "TestLen 4",
len(test[test['class'] == 4]))
print("TrainLen 2", len(train[train['class'] == 2]), "TrainLen 4",
len(train[train['class'] == 4]))
iterations += 1
def test_test_set_coverage(self):
#Initialization
dataRetriever = DataRetriever("../Datasets/metadata.json")
dataRetriever.retrieveData("breastCancer")
breastCancer = dataRetriever.getDataSet()
continousAttributes = [
"clumpThickness", "uniformityOfCellSize", "uniformityOfCellShape",
"marginalAdhesion", "singleEpithelialCellSize", "bareNuclei",
"blandChromatin", "normalNucleoli", "mitoses", "class"
]
tested_vals = []
#Add a dummy index to the dataset so we can see which rows are selected each test
breastCancer["dummyIndex"] = np.arange(len(breastCancer)) + 1
for test, train in KFolds(breastCancer, 10):
tested_vals.extend(test["dummyIndex"])
self.assertTrue(set(tested_vals) == set(breastCancer["dummyIndex"]))
data = DataRetriever("../Datasets/metadata.json")
dataSetName = "computerHardware"
print(f"Creating CSV for {dataSetName}")
data.retrieveData(dataSetName)
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(),
os.chdir(dname)
happiness = r"""
____ __ _ __ _____ ____ __ _ _ __ _
/ __ \____ ______/ /_(_)____/ /__ / ___/ ______ __________ ___ / __ \____ / /_(_)___ ___ (_)___ ____ _/ /_(_)___ ____
/ /_/ / __ `/ ___/ __/ / ___/ / _ \ \__ \ | /| / / __ `/ ___/ __ `__ \ / / / / __ \/ __/ / __ `__ \/ /_ / / __ `/ __/ / __ \/ __ \
/ ____/ /_/ / / / /_/ / /__/ / __/ ___/ / |/ |/ / /_/ / / / / / / / / / /_/ / /_/ / /_/ / / / / / / / / /_/ /_/ / /_/ / /_/ / / / /
/_/ \__,_/_/ \__/_/\___/_/\___/ /____/|__/|__/\__,_/_/ /_/ /_/ /_/ \____/ .___/\__/_/_/ /_/ /_/_/ /___/\__,_/\__/_/\____/_/ /_/
/_/
"""
print(happiness)
dataRetriever = DataRetriever("../Datasets/metadata.json")
dataRetriever.retrieveData("abalone")
dataset = dataRetriever.getDataSet().dropna()
dataset = dataset.reset_index(drop=True)
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)
# 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
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.KNN.KNearest import KNearestNeighbor, EditedKNN, CondensedKNN
from MLAlgorithms.Utils.KFolds import KFolds
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
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