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data_management.py
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data_management.py
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from sklearn.utils import shuffle
import params
from tmfg_bootstrapped import *
class DataManager:
def __init__(self, dataset_id, seed):
self.dataset_id = dataset_id
self.seed = seed
np.random.seed(self.seed)
#self.root_folder = None
self.X = None
self.y = None
self.X_train = None
self.y_train = None
self.X_val = None
self.y_val = None
self.X_test = None
self.y_test = None
self.__download_open_ml_dataset()
self.__customize_data()
self.numerical_features = None
self.categorical_features = None
def __get_feature_types(self, dataset):
feature_types = dataset.features
feature_names = feature_types.keys()
numerical_features = []
categorical_features = []
for feature_name in feature_names:
if feature_types[feature_name].data_type == 'numeric':
numerical_features.append(feature_name)
else:
categorical_features.append(feature_name)
return numerical_features, categorical_features
def __download_open_ml_dataset(self):
#self.root_folder = f'./Homological_FS/HCNN_Classifier/Dataset_{self.dataset_id}/Seed_{self.seed}/openml/'
#generate_folder_structure(self.root_folder)
#shutil.rmtree(self.root_folder)
self.X = pd.read_csv(f'./data/{self.dataset_id}/X.csv')
self.y = pd.read_csv(f'./data/{self.dataset_id}/y.csv')
if params.SHUFFLE_DATA_BEFORE_SPLITTING:
self.X, self.y = shuffle(self.X, self.y)
self.X.reset_index(drop=True, inplace=True)
self.X = self.X.to_numpy()
self.y = self.y.to_numpy()
'''while True:
try:
shutil.rmtree(self.root_folder)
openml.config.cache_directory = os.path.expanduser(self.root_folder)
dataset = open_data.get_dataset(self.dataset_id)
self.X, self.y, categorical_indicator, attribute_names = dataset.get_data(dataset_format="dataframe", target=dataset.default_target_attribute)
shutil.rmtree(self.root_folder)
break
except:
try:
shutil.rmtree(self.root_folder)
openml.config.cache_directory = os.path.expanduser(self.root_folder)
task = openml.tasks.get_task(self.dataset_id) # download the OpenML task
dataset = task.get_dataset()
print(dataset)
self.X, self.y, categorical_indicator, attribute_names = dataset.get_data(dataset_format="dataframe", target=dataset.default_target_attribute)
break
except:
time.sleep(30)
print(f'Downloading error for dataset {self.dataset_id}. Trying again in 30 secs...')
continue
label_encoder = LabelEncoder()
self.y = label_encoder.fit_transform(np.array(self.y).ravel())
columns_names = []
for n, i in enumerate(self.X.columns):
columns_names.append(n)
self.X.columns = columns_names
if params.SHUFFLE_DATA_BEFORE_SPLITTING:
self.X, self.y = shuffle(self.X, self.y)
self.X.reset_index(drop=True, inplace=True)
self.X = self.X.to_numpy()
mask = ~np.any(np.isnan(self.X), axis=1)
self.X = self.X[mask]
self.y = self.y[mask]'''
def __customize_data(self):
upper_bound_train_test = int(len(self.X) * params.TEST_PERCENTAGE)
upper_bound_train_val = int((len(self.X) - upper_bound_train_test) * params.VALIDATION_PERCENTAGE)
# Isolate numerical features.
'''self.X = self.X[self.numerical_features]
self.X.reset_index(drop=True, inplace=True)
self.X.columns = np.arange(0, self.X.shape[1]).tolist()'''
# Isolate categorical features.
'''self.X_cat = self.X[self.categorical_features]
self.X_cat.reset_index(drop=True, inplace=True)
self.X_cat.columns = np.arange(0, self.X_cat.shape[1]).tolist()'''
self.X_train = self.X[:-upper_bound_train_test]
self.y_train = self.y[:-upper_bound_train_test]
self.X_val = self.X_train[-upper_bound_train_val:]
self.y_val = self.y_train[-upper_bound_train_val:]
self.X_train = self.X_train[:-upper_bound_train_val]
self.y_train = self.y_train[:-upper_bound_train_val]
self.X_test = self.X[-upper_bound_train_test:]
self.y_test = self.y[-upper_bound_train_test:]
self.X_train = pd.DataFrame(self.X_train)
self.X_val = pd.DataFrame(self.X_val)
self.X_test = pd.DataFrame(self.X_test)
def get_data(self):
return self.X_train, self.X_val, self.X_test, self.y_train, self.y_val, self.y_test
class HomologicalDataManager:
def __init__(self, X_train, X_val, X_test, tmfg_iterations, tmfg_confidence, tmfg_similarity, seed):
self.X_train = X_train
self.X_val = X_val
self.X_test = X_test
self.tmfg_iterations = tmfg_iterations
self.tmfg_confidence = tmfg_confidence
self.tmfg_similarity = tmfg_similarity
self.seed = seed
self.__get_stat_robust_tmfg()
def __get_stat_robust_tmfg(self):
cliques, separators, original_tmfg, _, adjacency_matrix = TMFG_Bootstrapped(df=self.X_train,
correlation_type=self.tmfg_similarity,
number_of_repetitions=self.tmfg_iterations,
confidence_level=self.tmfg_confidence,
parallel=True,
seed=self.seed).compute_tmfg_bootstrapping()
c = nx.degree_centrality(adjacency_matrix)
keys = np.array(list(c.keys()))
values = np.array(list(c.values()))
nodes_list = sorted(list(keys[values != 0]))
self.X_train = self.X_train[nodes_list]
self.X_val = self.X_val[nodes_list]
self.X_test = self.X_test[nodes_list]
self.number_selected_features = len(nodes_list)
def get_homological_data(self):
return self.X_train, self.X_val, self.X_test, self.number_selected_features