def apply(df, config, header, dataset_features):
debug = config['debug']
#------------------------
rows = df.shape[0]
columns = df.shape[1]
final_predictions = pd.DataFrame(np.zeros([rows, 1]),
columns=['prediction'])
worksheet = df.copy()
worksheet['weight'] = 1 #/ rows
tmp_df = df.copy()
tmp_df['Decision'] = worksheet['weight'] * tmp_df[
'Decision'] #normal distribution
for i in range(0, 1):
root = 1
file = "outputs/rules/rules_" + str(i) + ".py"
if debug == False: functions.createFile(file, header)
#print(tmp_df)
Training.buildDecisionTree(tmp_df, root, file, config,
dataset_features)
#print(final_predictions)
"""for row, instance in final_predictions.iterrows():
def apply(df, config, header, dataset_features):
debug = config['debug']
num_of_trees = config['num_of_trees']
for i in range(0, num_of_trees):
subset = df.sample(frac=1 / num_of_trees)
root = 1
file = "outputs/rules/rule_" + str(i) + ".py"
if debug == False:
functions.createFile(file, header)
Training.buildDecisionTree(subset, root, file, config,
dataset_features)
def fit(df, config):
target_label = df.columns[len(df.columns) - 1]
if target_label != 'Decision':
print("Expected: Decision, Existing: ", target_label)
raise ValueError(
'Please confirm that name of the target column is "Decision" and it is put to the right in pandas data frame'
)
#------------------------
#initialize params and folders
config = functions.initializeParams(config)
functions.initializeFolders()
#------------------------
algorithm = config['algorithm']
valid_algorithms = ['ID3', 'C4.5', 'CART', 'Regression']
if algorithm not in valid_algorithms:
raise ValueError('Invalid algorithm passed. You passed ', algorithm,
" but valid algorithms are ", valid_algorithms)
#------------------------
enableRandomForest = config['enableRandomForest']
num_of_trees = config['num_of_trees']
enableMultitasking = config['enableMultitasking']
enableGBM = config['enableGBM']
epochs = config['epochs']
learning_rate = config['learning_rate']
enableAdaboost = config['enableAdaboost']
#------------------------
raw_df = df.copy()
num_of_rows = df.shape[0]
num_of_columns = df.shape[1]
if algorithm == 'Regression':
if df['Decision'].dtypes == 'object':
raise ValueError(
'Regression trees cannot be applied for nominal target values! You can either change the algorithm or data set.'
)
if df['Decision'].dtypes != 'object': #this must be regression tree even if it is not mentioned in algorithm
algorithm = 'Regression'
config['algorithm'] = 'Regression'
global_stdev = df['Decision'].std(ddof=0)
if enableGBM == True:
print("Gradient Boosting Machines...")
algorithm = 'Regression'
config['algorithm'] = 'Regression'
if enableAdaboost == True:
for j in range(0, num_of_columns):
column_name = df.columns[j]
if df[column_name].dtypes == 'object':
raise ValueError(
'Adaboost must be run on numeric data set for both features and target'
)
#-------------------------
print(algorithm, " tree is going to be built...")
dataset_features = dict(
) #initialize a dictionary. this is going to be used to check features numeric or nominal. numeric features should be transformed to nominal values based on scales.
header = "def findDecision("
header = header + "obj"
header = header + "): #"
num_of_columns = df.shape[1] - 1
for i in range(0, num_of_columns):
column_name = df.columns[i]
dataset_features[column_name] = df[column_name].dtypes
header = header + "obj[" + str(i) + "]: " + column_name
if i != num_of_columns - 1:
header = header + ", "
header = header + "\n"
#------------------------
begin = time.time()
trees = []
alphas = []
if enableAdaboost == True:
trees, alphas = adaboost.apply(df, config, header, dataset_features)
elif enableGBM == True:
if df['Decision'].dtypes == 'object': #transform classification problem to regression
trees, alphas = gbm.classifier(df, config, header,
dataset_features)
classification = True
else: #regression
trees = gbm.regressor(df, config, header, dataset_features)
classification = False
elif enableRandomForest == True:
trees = randomforest.apply(df, config, header, dataset_features)
else: #regular decision tree building
root = 1
file = "outputs/rules/rules.py"
functions.createFile(file, header)
trees = Training.buildDecisionTree(df, root, file, config,
dataset_features)
print("finished in ", time.time() - begin, " seconds")
obj = {"trees": trees, "alphas": alphas, "config": config}
return obj
def apply(df, config, header, dataset_features):
models = []
alphas = []
initializeAlphaFile()
num_of_weak_classifier = config['num_of_weak_classifier']
#------------------------
rows = df.shape[0]
columns = df.shape[1]
final_predictions = pd.DataFrame(np.zeros([rows, 1]),
columns=['prediction'])
worksheet = df.copy()
worksheet['Weight'] = 1 / rows #uniform distribution initially
final_predictions = pd.DataFrame(np.zeros((df.shape[0], 2)),
columns=['Prediction', 'Actual'])
final_predictions['Actual'] = df['Decision']
#for i in range(0, num_of_weak_classifier):
pbar = tqdm(range(0, num_of_weak_classifier), desc='Adaboosting')
for i in pbar:
worksheet['Decision'] = worksheet['Weight'] * worksheet['Decision']
root = 1
file = "outputs/rules/rules_" + str(i) + ".py"
functions.createFile(file, header)
#print(worksheet)
Training.buildDecisionTree(worksheet.drop(columns=['Weight']), root,
file, config, dataset_features)
#---------------------------------------
moduleName = "outputs/rules/rules_" + str(i)
fp, pathname, description = imp.find_module(moduleName)
myrules = imp.load_module(moduleName, fp, pathname, description)
models.append(myrules)
#---------------------------------------
df['Epoch'] = i
worksheet['Prediction'] = df.apply(findPrediction, axis=1)
df = df.drop(columns=['Epoch'])
#---------------------------------------
worksheet['Actual'] = df['Decision']
worksheet['Loss'] = abs(worksheet['Actual'] -
worksheet['Prediction']) / 2
worksheet[
'Weight_Times_Loss'] = worksheet['Loss'] * worksheet['Weight']
epsilon = worksheet['Weight_Times_Loss'].sum()
alpha = math.log(
(1 - epsilon) /
epsilon) / 2 #use alpha to update weights in the next round
alphas.append(alpha)
#-----------------------------
#store alpha
addEpochAlpha(i, alpha)
#-----------------------------
worksheet['Alpha'] = alpha
worksheet['New_Weights'] = worksheet['Weight'] * (
-alpha * worksheet['Actual'] * worksheet['Prediction']).apply(
math.exp)
#normalize
worksheet['New_Weights'] = worksheet['New_Weights'] / worksheet[
'New_Weights'].sum()
worksheet['Weight'] = worksheet['New_Weights']
worksheet['Decision'] = df['Decision']
final_predictions['Prediction'] = final_predictions[
'Prediction'] + worksheet['Alpha'] * worksheet['Prediction']
#print(final_predictions)
worksheet = worksheet.drop(columns=[
'New_Weights', 'Prediction', 'Actual', 'Loss', 'Weight_Times_Loss',
'Alpha'
])
mae = (np.abs(final_predictions['Prediction'].apply(functions.sign) -
final_predictions['Actual']) /
2).sum() / final_predictions.shape[0]
#print(mae)
pbar.set_description("Epoch %d. Loss: %d. Process: " % (i + 1, mae))
#------------------------------
final_predictions['Prediction'] = final_predictions['Prediction'].apply(
functions.sign)
final_predictions['Absolute_Error'] = np.abs(
final_predictions['Actual'] - final_predictions['Prediction']) / 2
#print(final_predictions)
mae = final_predictions['Absolute_Error'].sum(
) / final_predictions.shape[0]
print("Loss (MAE) found ", mae, " with ", num_of_weak_classifier,
' weak classifiers')
return models, alphas
def initializeAlphaFile():
file = "outputs/rules/alphas.py"
header = "def findAlpha(epoch):\n"
functions.createFile(file, header)
def apply(df, config, header, dataset_features):
models = []
num_of_trees = config['num_of_trees']
pbar = tqdm(range(0, num_of_trees), desc='Bagging')
for i in pbar:
#for i in range(0, num_of_trees):
pbar.set_description("Sub decision tree %d is processing" % (i + 1))
subset = df.sample(frac=1 / num_of_trees)
root = 1
moduleName = "outputs/rules/rule_" + str(i)
file = moduleName + ".py"
functions.createFile(file, header)
Training.buildDecisionTree(subset, root, file, config,
dataset_features)
#--------------------------------
fp, pathname, description = imp.find_module(moduleName)
myrules = imp.load_module(moduleName, fp, pathname, description)
models.append(myrules)
#-------------------------------
#check regression or classification
if df['Decision'].dtypes == 'object': problem_type = 'classification'
else: problem_type = 'regression'
actual_values = df['Decision'].values
num_of_features = df.shape[1] - 1 #discard Decision
number_of_instances = df.shape[0]
global_predictions = []
#if classification get the max number of prediction
if problem_type == 'classification':
for i in range(0, num_of_trees):
moduleName = "outputs/rules/rule_" + str(i)
fp, pathname, description = imp.find_module(moduleName)
myrules = imp.load_module(moduleName, fp, pathname, description)
predictions = []
for index, instance in df.iterrows():
params = []
for j in range(0, num_of_features):
params.append(instance[j])
#index row, i th column
prediction = myrules.findDecision(params)
predictions.append(prediction)
#print(i,"th tree prediction: ",prediction)
#print(predictions)
global_predictions.append(predictions)
#-------------------------------
classified = 0
for index, instance in df.iterrows():
actual = actual_values[index]
predictions = []
for i in range(0, num_of_trees):
prediction = global_predictions[i][index]
if prediction != None: #why None exists in some cases?
predictions.append(prediction)
predictions = np.array(predictions)
unique_values = np.unique(predictions)
if unique_values.shape[0] == 1:
prediction = unique_values[0]
else:
counts = []
for unique in unique_values:
count = 0
for j in predictions:
if unique == j:
count = count + 1
counts.append(count)
#print("unique: ",unique_values)
#print("counts: ",counts)
prediction = None
if len(counts) > 0:
max_index = np.argmax(np.array(counts))
prediction = unique_values[max_index]
#print(index,". actual: ",actual," - prediction: ", prediction)
if actual == prediction:
classified = classified + 1
print("Accuracy: ", 100 * classified / number_of_instances, "% on ",
number_of_instances, " instances")
return models
def fit(df, config):
target_label = df.columns[len(df.columns) - 1]
if target_label != 'Decision':
print("Expected: Decision, Existing: ", target_label)
raise ValueError('Lỗi dữ liệu, hãy chuyển dữ liệu về đúng định dạng!')
#------------------------
#initialize params and folders
config = functions.initializeParams(config)
functions.initializeFolders()
algorithm = config['algorithm']
RandomForest = config['RandomForest']
num_of_trees = config['num_of_trees']
#------------------------
raw_df = df.copy()
num_of_rows = df.shape[0]
num_of_columns = df.shape[1]
if algorithm == 'Regression':
if df['Decision'].dtypes == 'object':
raise ValueError(
'Lỗi dữ liệu khi chạy kết quả dạng Regression Tree')
if df['Decision'].dtypes != 'object':
algorithm = 'Regression'
config['algorithm'] = 'Regression'
global_stdev = df['Decision'].std(ddof=0)
#-------------------------
print(algorithm, ": Đang tiến hành tạo cây quyết định...")
dataset_features = dict() # dictionary
header = "def findDecision("
header = header + "obj"
header = header + "): #"
num_of_columns = df.shape[1] - 1
for i in range(0, num_of_columns):
column_name = df.columns[i]
dataset_features[column_name] = df[column_name].dtypes
header = header + "obj[" + str(i) + "]: " + column_name
if i != num_of_columns - 1:
header = header + ", "
header = header + "\n"
#------------------------
begin = time.time()
trees = []
alphas = []
if RandomForest == True:
trees = randomforest.apply(df, config, header, dataset_features)
else:
root = 1
file = "outputs/rules/rules.py"
functions.createFile(file, header)
trees = Training.buildDecisionTree(df, root, file, config,
dataset_features)
print("Thuật toán chạy hoàn thành trong: ", time.time() - begin, " giây")
obj = {"trees": trees, "alphas": alphas, "config": config}
return obj
def fit(df, config):
#config parameters
debug = config['debug']
algorithm = config['algorithm']
enableRandomForest = config['enableRandomForest']
num_of_trees = config['num_of_trees']
enableMultitasking = config['enableMultitasking']
enableGBM = config['enableGBM']
epochs = config['epochs']
learning_rate = config['learning_rate']
enableAdaboost = config['enableAdaboost']
#------------------------
if algorithm == 'Regression':
if df['Decision'].dtypes == 'object':
raise ValueError(
'Regression trees cannot be applied for nominal target values! You can either change the algorithm or data set.'
)
if df['Decision'].dtypes != 'object': #this must be regression tree even if it is not mentioned in algorithm
algorithm = 'Regression'
config['algorithm'] = 'Regression'
global_stdev = df['Decision'].std(ddof=0)
if enableGBM == True:
debug = False #gbm needs rules files to iterate
algorithm = 'Regression'
config['algorithm'] = 'Regression'
#-------------------------
print(algorithm, " tree is going to be built...")
dataset_features = dict(
) #initialize a dictionary. this is going to be used to check features numeric or nominal. numeric features should be transformed to nominal values based on scales.
if (True): #header of rules files
header = "def findDecision("
num_of_columns = df.shape[1] - 1
for i in range(0, num_of_columns):
if debug == True:
if i > 0:
header = header + ","
header = header + df.columns[i]
column_name = df.columns[i]
dataset_features[column_name] = df[column_name].dtypes
if debug == False:
header = header + "obj"
header = header + "):\n"
if debug == True:
print(header, end='')
#------------------------
begin = time.time()
if enableAdaboost == True:
adaboost.apply(df, config, header, dataset_features)
elif enableGBM == True:
if df['Decision'].dtypes == 'object': #transform classification problem to regression
gbm.classifier(df, config, header, dataset_features)
else: #regression
gbm.regressor(df, config, header, dataset_features)
elif enableRandomForest == True:
randomforest.apply(df, config, header, dataset_features)
else: #regular decision tree building
root = 1
file = "outputs/rules/rules.py"
if debug == False: functions.createFile(file, header)
Training.buildDecisionTree(df, root, file, config, dataset_features)
print("finished in ", time.time() - begin, " seconds")
def regressor(df, config, header, dataset_features):
models = []
algorithm = config['algorithm']
enableRandomForest = config['enableRandomForest']
num_of_trees = config['num_of_trees']
enableMultitasking = config['enableMultitasking']
enableGBM = config['enableGBM']
epochs = config['epochs']
learning_rate = config['learning_rate']
enableAdaboost = config['enableAdaboost']
#------------------------------
boosted_from = 0
boosted_to = 0
#------------------------------
base_df = df.copy()
#gbm will manipulate actuals. store its raw version.
target_values = base_df['Decision'].values
num_of_instances = target_values.shape[0]
root = 1
file = "outputs/rules/rules0.py"
functions.createFile(file, header)
Training.buildDecisionTree(df, root, file, config,
dataset_features) #generate rules0
df = base_df.copy()
base_df['Boosted_Prediction'] = 0
#------------------------------
pbar = tqdm(range(1, epochs + 1), desc='Boosting')
#for index in range(1,epochs+1):
#for index in tqdm(range(1,epochs+1), desc='Boosting'):
for index in pbar:
#print("epoch ",index," - ",end='')
loss = 0
#run data(i-1) and rules(i-1), save data1
#dynamic import
moduleName = "outputs/rules/rules%s" % (index - 1)
fp, pathname, description = imp.find_module(moduleName)
myrules = imp.load_module(moduleName, fp, pathname,
description) #rules0
models.append(myrules)
new_data_set = "outputs/data/data%s.csv" % (index)
f = open(new_data_set, "w")
#put header in the following file
columns = df.shape[1]
mae = 0
#----------------------------------------
df['Epoch'] = index
df['Prediction'] = df.apply(findPrediction, axis=1)
base_df['Boosted_Prediction'] += df['Prediction']
loss = (base_df['Boosted_Prediction'] -
base_df['Decision']).pow(2).sum()
if index == 1:
boosted_from = loss / num_of_instances
elif index == epochs:
boosted_to = loss / num_of_instances
df['Decision'] = int(learning_rate) * (df['Decision'] -
df['Prediction'])
df = df.drop(columns=['Epoch', 'Prediction'])
#---------------------------------
df.to_csv(new_data_set, index=False)
#data(i) created
#---------------------------------
file = "outputs/rules/rules" + str(index) + ".py"
functions.createFile(file, header)
current_df = df.copy()
Training.buildDecisionTree(df, root, file, config, dataset_features)
df = current_df.copy(
) #numeric features require this restoration to apply findDecision function
#rules(i) created
loss = loss / num_of_instances
#print("epoch ",index," - loss: ",loss)
#print("loss: ",loss)
pbar.set_description("Epoch %d. Loss: %d. Process: " % (index, loss))
#---------------------------------
print(num_of_instances, " instances are boosted from ", boosted_from,
" to ", boosted_to, " in ", epochs, " epochs")
return models
def classifier(df, config, header, dataset_features):
models = []
print("gradient boosting for classification")
epochs = config['epochs']
temp_df = df.copy()
original_dataset = df.copy()
worksheet = df.copy()
classes = df['Decision'].unique()
boosted_predictions = np.zeros([df.shape[0], len(classes)])
pbar = tqdm(range(0, epochs), desc='Boosting')
#store actual set, we will use this to calculate loss
actual_set = pd.DataFrame(np.zeros([df.shape[0], len(classes)]),
columns=classes)
for i in range(0, len(classes)):
current_class = classes[i]
actual_set[current_class] = np.where(df['Decision'] == current_class,
1, 0)
actual_set = actual_set.values #transform it to numpy array
#for epoch in range(0, epochs):
for epoch in pbar:
for i in range(0, len(classes)):
current_class = classes[i]
if epoch == 0:
temp_df['Decision'] = np.where(df['Decision'] == current_class,
1, 0)
worksheet['Y_' + str(i)] = temp_df['Decision']
else:
temp_df['Decision'] = worksheet['Y-P_' + str(i)]
predictions = []
#change data type for decision column
temp_df[['Decision']].astype('int64')
root = 1
file = "outputs/rules/rules-for-" + current_class + "-round-" + str(
epoch) + ".py"
functions.createFile(file, header)
Training.buildDecisionTree(temp_df, root, file, config,
dataset_features)
#decision rules created
#----------------------------
#dynamic import
moduleName = "outputs/rules/rules-for-" + current_class + "-round-" + str(
epoch)
fp, pathname, description = imp.find_module(moduleName)
myrules = imp.load_module(moduleName, fp, pathname,
description) #rules0
models.append(myrules)
num_of_columns = df.shape[1]
for row, instance in df.iterrows():
features = []
for j in range(0, num_of_columns - 1): #iterate on features
features.append(instance[j])
actual = temp_df.loc[row]['Decision']
prediction = myrules.findDecision(features)
predictions.append(prediction)
#----------------------------
if epoch == 0:
worksheet['F_' + str(i)] = 0
else:
worksheet['F_' + str(i)] = pd.Series(predictions).values
boosted_predictions[:, i] = boosted_predictions[:, i] + worksheet[
'F_' + str(i)].values.astype(np.float32)
#print(boosted_predictions[0:5,:])
worksheet['P_' + str(i)] = 0
#----------------------------
temp_df = df.copy() #restoration
for row, instance in worksheet.iterrows():
f_scores = []
for i in range(0, len(classes)):
f_scores.append(instance['F_' + str(i)])
probabilities = functions.softmax(f_scores)
for j in range(0, len(probabilities)):
instance['P_' + str(j)] = probabilities[j]
worksheet.loc[row] = instance
for i in range(0, len(classes)):
worksheet['Y-P_' +
str(i)] = worksheet['Y_' + str(i)] - worksheet['P_' +
str(i)]
prediction_set = np.zeros([df.shape[0], len(classes)])
for i in range(0, boosted_predictions.shape[0]):
predicted_index = np.argmax(boosted_predictions[i])
prediction_set[i][predicted_index] = 1
#----------------------------
#find loss for this epoch: prediction_set vs actual_set
classified = 0
for i in range(0, actual_set.shape[0]):
actual = np.argmax(actual_set[i])
prediction = np.argmax(prediction_set[i])
#print("actual: ",actual," - prediction: ",prediction)
if actual == prediction:
classified = classified + 1
accuracy = str(100 * classified / actual_set.shape[0]) + "%"
#----------------------------
#print(worksheet.head())
#print("round ",epoch+1)
pbar.set_description("Epoch %d. Accuracy: %s. Process: " %
(epoch + 1, accuracy))
return models, classes
def classifier(df, config, header, dataset_features):
print("gradient boosting for classification")
debug = config['debug']
epochs = config['epochs']
temp_df = df.copy()
original_dataset = df.copy()
worksheet = df.copy()
classes = df['Decision'].unique()
boosted_predictions = np.zeros([df.shape[0], len(classes)])
for epoch in range(0, epochs):
for i in range(0, len(classes)):
current_class = classes[i]
if epoch == 0:
temp_df['Decision'] = np.where(df['Decision'] == current_class,
1, 0)
worksheet['Y_' + str(i)] = temp_df['Decision']
else:
temp_df['Decision'] = worksheet['Y-P_' + str(i)]
predictions = []
#change data type for decision column
temp_df[['Decision']].astype('int64')
root = 1
file = "outputs/rules/rules-for-" + current_class + ".py"
if debug == False: functions.createFile(file, header)
Training.buildDecisionTree(temp_df, root, file, config,
dataset_features)
#decision rules created
#----------------------------
#dynamic import
moduleName = "outputs/rules/rules-for-" + current_class
fp, pathname, description = imp.find_module(moduleName)
myrules = imp.load_module(moduleName, fp, pathname,
description) #rules0
num_of_columns = df.shape[1]
for row, instance in df.iterrows():
features = []
for j in range(0, num_of_columns - 1): #iterate on features
features.append(instance[j])
actual = temp_df.loc[row]['Decision']
prediction = myrules.findDecision(features)
predictions.append(prediction)
#----------------------------
if epoch == 0:
worksheet['F_' + str(i)] = 0
else:
worksheet['F_' + str(i)] = pd.Series(predictions).values
boosted_predictions[:, i] = boosted_predictions[:, i] + worksheet[
'F_' + str(i)].values.astype(np.float32)
worksheet['P_' + str(i)] = 0
#----------------------------
temp_df = df.copy() #restoration
for row, instance in worksheet.iterrows():
f_scores = []
for i in range(0, len(classes)):
f_scores.append(instance['F_' + str(i)])
probabilities = functions.softmax(f_scores)
for j in range(0, len(probabilities)):
instance['P_' + str(j)] = probabilities[j]
worksheet.loc[row] = instance
for i in range(0, len(classes)):
worksheet['Y-P_' +
str(i)] = worksheet['Y_' + str(i)] - worksheet['P_' +
str(i)]
print("round ", epoch + 1)
def regressor(df, config, header, dataset_features):
debug = config['debug']
algorithm = config['algorithm']
enableRandomForest = config['enableRandomForest']
num_of_trees = config['num_of_trees']
enableMultitasking = config['enableMultitasking']
enableGBM = config['enableGBM']
epochs = config['epochs']
learning_rate = config['learning_rate']
enableAdaboost = config['enableAdaboost']
#------------------------------
base_df = df.copy()
root = 1
file = "outputs/rules/rules0.py"
if debug == False:
functions.createFile(file, header)
Training.buildDecisionTree(df, root, file, config,
dataset_features) #generate rules0
df = base_df.copy()
#------------------------------
for index in range(1, epochs):
#run data(i-1) and rules(i-1), save data1
#dynamic import
moduleName = "outputs/rules/rules%s" % (index - 1)
fp, pathname, description = imp.find_module(moduleName)
myrules = imp.load_module(moduleName, fp, pathname,
description) #rules0
new_data_set = "outputs/data/data%s.csv" % (index)
f = open(new_data_set, "w")
#put header in the following file
columns = df.shape[1]
for i, instance in df.iterrows():
params = []
line = ""
for j in range(0, columns - 1):
params.append(instance[j])
if j > 0:
line = line + ","
line = line + str(instance[j])
prediction = int(
myrules.findDecision(params)) #apply rules(i-1) for data(i-1)
actual = instance[columns - 1]
#print(prediction)
#loss was ((actual - prediction)^2) / 2
#partial derivative of loss function with respect to the prediction is prediction - actual
#y' = y' - alpha * gradient = y' - alpha * (prediction - actual) = y' = y' + alpha * (actual - prediction)
#whereas y' is prediction and alpha is learning rate
gradient = int(learning_rate) * (actual - prediction)
instance[columns - 1] = gradient
df.loc[i] = instance
df.to_csv(new_data_set, index=False)
#data(i) created
#---------------------------------
file = "outputs/rules/rules" + str(index) + ".py"
if debug == False:
functions.createFile(file, header)
current_df = df.copy()
Training.buildDecisionTree(df, root, file, config, dataset_features)
df = current_df.copy(
) #numeric features require this restoration to apply findDecision function
