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):
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 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")