def __init__(self, train, test, id_column, y_column_name):
self.train = train
self.test = test
self.y_column_name = y_column_name
self.id_column = id_column
self.data = pd.concat([train, test], ignore_index=True)
self.feature_engineering = FeatureEngineering(train, test, id_column,
y_column_name)
self.feature_selection = FeatureSelection(train, test, id_column,
y_column_name)
def get(argsfselection):
args_list = argsfselection.split(",")
if len(args_list) == 4:
feasel_name = args_list[0]
feasel_arg = args_list[1]
filename = args_list[2]
jfilename = args_list[3]
json_path = currentDirPath + "/jsonfiles/" + jfilename
file_path = currentDirPath + "/csvfiles/" + filename
data = pd.read_csv(file_path)
data_afs = FeatureSelection.selection(data, feasel_name,
feasel_arg)
data_afs.reset_index(inplace=True, drop=True)
data_afs.to_csv(file_path, index=False)
data_t10 = data_afs.head(10)
data_noff = len(data_afs.columns)
with open(json_path) as jcon:
jdata = json.load(jcon)
jdata.update({'FeatureSelection': feasel_name})
with open(json_path, 'w') as j_con:
json.dump(jdata, j_con)
return {
'predata': data_t10.to_json(orient='table'),
'filename': filename,
'numofcol': data_noff,
'jfilename': jfilename
}
elif len(args_list) == 3:
feasel_name = args_list[0]
filename = args_list[1]
jfilename = args_list[2]
json_path = currentDirPath + "/jsonfiles/" + jfilename
file_path = currentDirPath + "/csvfiles/" + filename
data = pd.read_csv(file_path)
data_afs = FeatureSelection.selection(data, feasel_name, "")
data_afs.reset_index(inplace=True, drop=True)
data_afs.to_csv(file_path, index=False)
data_t10 = data_afs.head(10)
data_noff = len(data_afs.columns)
with open(json_path) as jcon:
jdata = json.load(jcon)
jdata.update({'FeatureSelection': feasel_name})
with open(json_path, 'w') as j_con:
json.dump(jdata, j_con)
return {
'predata': data_t10.to_json(orient='table'),
'filename': filename,
'numofcol': data_noff,
'jfilename': jfilename
}
def __main__():
matrix = [[0.7, 0.9, 0.4, 0.6, 1], [0.6, 0.9, 0.3, 0.7, 1],
[0.6, 0.8, 0.3, 0.5, 1], [0.3, 0.5, 0.7, 0.2, 1],
[0.3, 0.4, 0.8, 0.3, 1], [0.4, 0.5, 0.6, 0.3, 1],
[0.9, 0.4, 0.5, 0.9, 0], [0.8, 0.5, 0.4, 0.8, 0],
[0.2, 0.6, 0.7, 1.0, 0], [0.1, 0.7, 0.8, 0.8, 0]]
instance_selection_obj = InstanceSelection(matrix)
instance_selection_obj.apply()
feature_selection_obj = FeatureSelection(
instance_selection_obj.representative_instances_list[0])
feature_selection_obj.apply(instance_selection_obj)
print(instance_selection_obj.representative_instances_list)
print(feature_selection_obj.rep_feature_set)
class PreprocessedData:
"""
This class combines the feature engineering and feature selection
class to one to preprocess the data.
It takes in the cleaned data and the y_column_name(ratings)
"""
def __init__(self, data, y_column_name):
self.data = data
self.y_column_name = y_column_name
self.feature_engineering = FeatureEngineering(data, y_column_name)
self.feature_selection = FeatureSelection(data, y_column_name)
def preprocess_my_data(self, num_of_features_to_select):
"""
This method preprocesses the cleaned data and performs
feature selection to select best n-features
:param num_of_features_to_select: n-best features of the model
:return: Full preprocesse data with n-selected features
"""
self.data = self.feature_engineering.input_rare_categorical()
self.data = self.feature_engineering.encode_categorical_features()
self.data = self.feature_engineering.scale_features()
self.data = self.feature_selection.perform_feature_selection(
num_of_features_to_select)
return self.data
def main():
LOCAL_LOCATION_X = "../Data/fpkm_normalized.csv"
LOCAL_LOCATION_Y = "../Data/disease.csv"
print("Loading Data...")
features = pd.read_csv(LOCAL_LOCATION_X, header=None)
labels = pd.read_csv(LOCAL_LOCATION_Y, header=None)
print("Data Loaded!")
feature_selection = FeatureSelection('mRMR', 5, features, labels)
feature_indices = feature_selection.get_best_features()
# feature_indices = [4929, 5345, 16381, 13656]
print("Features has been selected!")
selected_features = features[feature_indices]
labels = pd.DataFrame(modify_output(labels))
labels = pd.DataFrame(keras.utils.to_categorical(labels, num_classes=34))
run(0, selected_features, labels, False)
def setUp(self):
self.X = np.array([[i,i+1,i+2,i+3, i+4] for i in range(0, 100, 10)])
self.y = np.array([n*10 + 1 for n in range(10)])
self.X_sub = np.array([[31,32,33,34,35],[41,42,43,44,45]])
self.featureSelection = FeatureSelection(lower_is_better = True, method=None,
X=self.X, y=self.y, X_sub=self.X_sub,
clf=RandomForestRegressor(n_estimators=2),
score_func=ProblemType.logloss,
problem_type='classification',
col_names = ['A', 'B', 'C', 'D', 'E'])
class TestFeatureSelection(TestCase):
def setUp(self):
self.X = np.array([[i,i+1,i+2,i+3, i+4] for i in range(0, 100, 10)])
self.y = np.array([n*10 + 1 for n in range(10)])
self.X_sub = np.array([[31,32,33,34,35],[41,42,43,44,45]])
self.featureSelection = FeatureSelection(lower_is_better = True, method=None,
X=self.X, y=self.y, X_sub=self.X_sub,
clf=RandomForestRegressor(n_estimators=2),
score_func=ProblemType.logloss,
problem_type='classification',
col_names = ['A', 'B', 'C', 'D', 'E'])
def test_allSelection(self):
X, X_sub = self.featureSelection.allSelection()
self.assertEqual(X.shape[1], 5, 'number of columns of X is not 5!')
self.assertEqual(X_sub.shape[1], 5, 'number of columns of X_sub is not 5!')
def test_forwardsSelection(self):
X, X_sub = self.featureSelection.forwardsSelection()
self.assertTrue(X is not None)
self.assertTrue(X_sub is not None)
def test_backwardsSelection(self):
X, X_sub = self.featureSelection.forwardsSelection()
self.assertTrue(X is not None)
self.assertTrue(X_sub is not None)
def test_featureImportancesSelection(self):
X, X_sub = self.featureSelection.featureImportancesSelection(total_importance=0.95)
self.assertTrue(X is not None)
self.assertTrue(X_sub is not None)
def test_randomSubsetSelection(self):
X, X_sub = self.featureSelection.randomSubsetSelection(percent=0.4)
self.assertEqual(X.shape[1], 2, 'number of columns of X is not 2!')
self.assertEqual(X_sub.shape[1], 2, 'number of columns of X_sub is not 2!')
def test_featureExtractionFromActualDataset(self):
dataLoader = TrainTestDataLoader('../data/rossmann/train_100.csv', '../data/rossmann/test_100.csv', train_labels_column='Sales', test_ids_column='Id')
dataLoader.cleanData(max_onehot_limit=200)
X, X_sub, y = dataLoader.getTrainTestData()
featureSelection = FeatureSelection(lower_is_better=True, method='all', X=X, y=y, clf=LogisticRegressionCV(), problem_type='classification')
class ProcessedData:
def __init__(self, train, test, id_column, y_column_name):
self.train = train
self.test = test
self.y_column_name = y_column_name
self.id_column = id_column
self.data = pd.concat([self.train, self.test], ignore_index=True)
self.feature_engineering = FeatureEngineering(self.train, self.test,
self.id_column,
self.y_column_name)
self.feature_selection = FeatureSelection(self.train, self.test,
self.id_column,
self.y_column_name)
def preprocess_my_data(self, num_of_features_to_select):
self.data = self.feature_engineering.scale_features()
self.data = self.feature_selection.perform_feature_selection(
num_of_features_to_select)
return self.data
class PreprocessedData:
def __init__(self, train, test, id_column, y_column_name):
self.train = train
self.test = test
self.y_column_name = y_column_name
self.id_column = id_column
self.data = pd.concat([train, test], ignore_index=True)
self.feature_engineering = FeatureEngineering(train, test, id_column,
y_column_name)
self.feature_selection = FeatureSelection(train, test, id_column,
y_column_name)
def preprocess_my_data(self, num_of_features_to_select):
self.data = self.feature_engineering.fill_na_categorical()
self.data = self.feature_engineering.fill_na_numerical()
self.data = self.feature_engineering.input_rare_categorical()
self.data = self.feature_engineering.label_encoder()
self.data = self.feature_engineering.get_scale_features()
self.data = self.feature_selection.perform_feature_selection(
num_of_features_to_select)
return self.data
def __init__(self, data, y_column_name):
self.data = data
self.y_column_name = y_column_name
self.feature_engineering = FeatureEngineering(data, y_column_name)
self.feature_selection = FeatureSelection(data, y_column_name)
def __init__(
self,
static_imputation_model_list,
temporal_imputation_model_list,
static_feature_selection_model_list,
temporal_feature_selection_model_list,
prediction_model_list,
dataset_training,
dataset_testing,
task,
metric_name,
metric_parameters,
):
self.dataset_testing = dataset_testing
self.dataset_training = dataset_training
self.static_imputation_model_list = static_imputation_model_list
self.temporal_imputation_model_list = temporal_imputation_model_list
self.static_feature_selection_model_list = static_feature_selection_model_list
self.temporal_feature_selection_model_list = temporal_feature_selection_model_list
self.prediction_model_list = prediction_model_list
# imputation
static_imputation_list = [
imputation.Imputation(imputation_model_name=x, data_type="static")
for x in static_imputation_model_list
]
temporal_imputation_list = [
imputation.Imputation(imputation_model_name=x,
data_type="temporal")
for x in temporal_imputation_model_list
]
# feature selection
static_feature_selection_list = []
for x in static_feature_selection_model_list:
# Select relevant features
static_feature_selection = FeatureSelection(
feature_selection_model_name=x[0],
feature_type="static",
feature_number=x[1],
task=task,
metric_name=metric_name,
metric_parameters=metric_parameters,
)
static_feature_selection_list.append(static_feature_selection)
temporal_feature_selection_list = []
for x in temporal_feature_selection_model_list:
# Select relevant features
temporal_feature_selection = FeatureSelection(
feature_selection_model_name=x[0],
feature_type="temporal",
feature_number=x[1],
task=task,
metric_name=metric_name,
metric_parameters=metric_parameters,
)
temporal_feature_selection_list.append(temporal_feature_selection)
# prediction
pred_class_list = []
# Set predictive model
model_name_list = prediction_model_list
for model_name in model_name_list:
# Set model parameters
model_parameters = {
"h_dim": 100,
"n_layer": 2,
"n_head": 2,
"batch_size": 128,
"epoch": 2,
"model_type": model_name,
"learning_rate": 0.001,
"static_mode": "Concatenate",
"time_mode": "Concatenate",
"verbose": False,
}
# Train the predictive model
pred_class = prediction(model_name, model_parameters, task)
pred_class_list.append(pred_class)
self.pred_class_list = pred_class_list
self.temporal_feature_selection_list = temporal_feature_selection_list
self.static_feature_selection_list = static_feature_selection_list
self.temporal_imputation_list = temporal_imputation_list
self.static_imputation_list = static_imputation_list
self.domain = [
{
"name": "static_imputation",
"type": "discrete",
"domain": list(range(len(static_imputation_list)))
},
{
"name": "temporal_imputation",
"type": "discrete",
"domain": list(range(len(temporal_imputation_list)))
},
{
"name": "static_feature_selection",
"type": "discrete",
"domain": list(range(len(static_feature_selection_list))),
},
{
"name": "temporal_feature_selection",
"type": "discrete",
"domain": list(range(len(temporal_feature_selection_list))),
},
{
"name": "pred_class",
"type": "discrete",
"domain": list(range(len(pred_class_list)))
},
]
self.myBopt = BayesianOptimization(f=self.f, domain=self.domain)
from feature_selection import FeatureSelection
from config import Configure
import pandas as pd
print('\n Aplicando algoritmo de seleção de parâmetros')
settings = Configure()
settings.set_fs_params()
settings.set_pre_processing_params()
pp_params = settings.pre_processing_params
fs_params = settings.feature_selection_params
df1 = pd.read_csv(settings.pf1_folder)
df2 = pd.read_csv(settings.pf2_folder)
df3 = pd.read_csv(settings.pf3_folder)
mkt = pd.read_csv(settings.mkt_folder)
fs = FeatureSelection(mkt, df1, df2, df3, pp_params, fs_params)
values, features = fs.feature_selection_algorithm(m='RFECV')
columns = features.values[features.values != 'Unnamed: 0']
mkt = mkt[columns]
MAX_ITERATIONS = int(sys.argv[1])
for size in range(MAX_ITERATIONS-1,MAX_ITERATIONS):
np.random.shuffle(data)
test_data = data[:size]
#Representative Instance Selection
start_time1 = time.time()
InstanceSelector = InstanceSelection(test_data)
InstanceSelector.apply()
end_time1 = time.time()
algo1_time = end_time1-start_time1
#Feature Selection
start_time2 = time.time()
feature_selection_obj = FeatureSelection(InstanceSelector.representative_instances_list[0])
feature_selection_obj.apply(InstanceSelector)
end_time2 = time.time()
algo2_time = end_time2-start_time2
# print("Algo 1 time : ",algo1_time)
# print("Algo 2 time : ",algo2_time)
representative_instances = InstanceSelector.representative_instances_list
# print("Instance set : ",representative_instances)
feature_set = list(feature_selection_obj.rep_feature_set)
# print("Feature set : ",feature_set)
# time_taken[size] = end_time-start_time
# print(len(InstanceSelector.representative_instances_list[0]))
print("{:27s} |{:8s}|{:8s}|{:8s}|{:8s}".format("Model","Accuracy","Precision","Recall","F1-score"))
print("-"*65)
def main(args):
'''Main function for active sensing.
Args:
- data loading parameters:
- data_names: mimic, ward, cf
- preprocess parameters:
- normalization: minmax, standard, None
- one_hot_encoding: input features that need to be one-hot encoded
- problem: 'one-shot' or 'online'
- 'one-shot': one time prediction at the end of the time-series
- 'online': preditcion at every time stamps of the time-series
- max_seq_len: maximum sequence length after padding
- label_name: the column name for the label(s)
- treatment: the column name for treatments
- imputation parameters:
- static_imputation_model: mean, median, mice, missforest, knn, gain
- temporal_imputation_model: mean, median, linear, quadratic, cubic, spline, mrnn, tgain
- feature selection parameters:
- feature_selection_model: greedy-addtion, greedy-deletion, recursive-addition, recursive-deletion, None
- feature_number: selected featuer number
- active_sensing_model_parameters:
- active_sensing_model_name: asac, deepsensing
- model_name: rnn, lstm, gru
- model_parameters: network parameters such as numer of layers
- h_dim: hidden dimensions
- n_layer: layer number
- n_head: head number (only for transformer model)
- batch_size: number of samples in mini-batch
- epochs: number of epochs
- learning_rate: learning rate
- static_mode: how to utilize static features (concatenate or None)
- time_mode: how to utilize time information (concatenate or None)
- task: classification or regression
'''
#%% Step 0: Set basic parameters
metric_parameters = {
'problem': args.problem,
'label_name': [args.label_name]
}
#%% Step 1: Upload Dataset
# File names
data_directory = '../datasets/data/' + args.data_name + '/' + args.data_name + '_'
data_loader_training = CSVLoader(
static_file=data_directory + 'static_train_data.csv.gz',
temporal_file=data_directory + 'temporal_train_data_eav.csv.gz')
data_loader_testing = CSVLoader(
static_file=data_directory + 'static_test_data.csv.gz',
temporal_file=data_directory + 'temporal_test_data_eav.csv.gz')
dataset_training = data_loader_training.load()
dataset_testing = data_loader_testing.load()
print('Finish data loading.')
#%% Step 2: Preprocess Dataset
# (0) filter out negative values (Automatically)
negative_filter = FilterNegative()
# (1) one-hot encode categorical features
onehot_encoder = OneHotEncoder(
one_hot_encoding_features=[args.one_hot_encoding])
# (2) Normalize features: 3 options (minmax, standard, none)
normalizer = Normalizer(args.normalization)
filter_pipeline = PipelineComposer(negative_filter, onehot_encoder,
normalizer)
dataset_training = filter_pipeline.fit_transform(dataset_training)
dataset_testing = filter_pipeline.transform(dataset_testing)
print('Finish preprocessing.')
#%% Step 3: Define Problem
problem_maker = ProblemMaker(problem=args.problem,
label=[args.label_name],
max_seq_len=args.max_seq_len,
treatment=args.treatment)
dataset_training = problem_maker.fit_transform(dataset_training)
dataset_testing = problem_maker.fit_transform(dataset_testing)
print('Finish defining problem.')
#%% Step 4: Impute Dataset
static_imputation = Imputation(
imputation_model_name=args.static_imputation_model, data_type='static')
temporal_imputation = Imputation(
imputation_model_name=args.temporal_imputation_model,
data_type='temporal')
imputation_pipeline = PipelineComposer(static_imputation,
temporal_imputation)
dataset_training = imputation_pipeline.fit_transform(dataset_training)
dataset_testing = imputation_pipeline.transform(dataset_testing)
print('Finish imputation.')
#%% Step 5: Feature selection (4 options)
static_feature_selection = \
FeatureSelection(feature_selection_model_name = args.static_feature_selection_model,
feature_type = 'static', feature_number = args.static_feature_selection_number,
task = args.task, metric_name = args.metric_name,
metric_parameters = metric_parameters)
temporal_feature_selection = \
FeatureSelection(feature_selection_model_name = args.temporal_feature_selection_model,
feature_type = 'temporal', feature_number = args.temporal_feature_selection_number,
task = args.task, metric_name = args.metric_name,
metric_parameters = metric_parameters)
feature_selection_pipeline = PipelineComposer(static_feature_selection,
temporal_feature_selection)
dataset_training = feature_selection_pipeline.fit_transform(
dataset_training)
dataset_testing = feature_selection_pipeline.transform(dataset_testing)
print('Finish feature selection.')
#%% Step 6: Fit and Predict (6 options)
# Set predictor model parameters
model_parameters = {
'h_dim': args.h_dim,
'n_layer': args.n_layer,
'batch_size': args.batch_size,
'epoch': args.epochs,
'model_type': args.model_name,
'learning_rate': args.learning_rate,
'static_mode': args.static_mode,
'time_mode': args.time_mode,
'verbose': True
}
# Set the validation data for best model saving
dataset_training.train_val_test_split(prob_val=0.2, prob_test=0.0)
active_sensing_class = active_sensing(args.active_sensing_model_name,
model_parameters, args.task)
active_sensing_class.fit(dataset_training)
test_s_hat = active_sensing_class.predict(dataset_testing)
print('Finish original predictor model training and testing.')
#%% Step 7: Visualize Results
idx = np.random.permutation(len(test_s_hat))[:2]
# Visualize the output
print('Future Measurements Recommendation')
print_interpretation(test_s_hat[idx], dataset_testing.feature_name,
metric_parameters, model_parameters)
return
from feature_selection import FeatureSelection
import pandas as pd
from config.config_writer import ConfigWriter
from utils import run_utils
from config import config_reader
df = pd.read_csv('data_test/iris.csv')
fs = FeatureSelection(df)
labels = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']
directory = 'data_test/best_exporter_test'
config_test_file = 'data_test/iris_config_test.ini'
checkpoints = {'1532434574': {'accuracy': 1.0, 'loss': 0.153, 'step': '42'}}
dict_types = {
'sepal_length': 'numerical',
'sepal_width': 'numerical',
'petal_length': 'numerical',
'petal_width': 'numerical',
'class': 'categorical'
}
sfeatures = {
'sepal_length': '5.8',
'sepal_width': '3.0',
'petal_length': '4.35',
'petal_width': '1.3'
}
categories = [
'numerical', 'numerical', 'numerical', 'numerical', 'categorical'
]
def test_get_html_types():
def fs(df):
return FeatureSelection(df)
def main(args):
"""Main function for AutoML in time-series predictions.
Args:
- data loading parameters:
- data_names: mimic, ward, cf
- preprocess parameters:
- normalization: minmax, standard, None
- one_hot_encoding: input features that need to be one-hot encoded
- problem: 'one-shot' or 'online'
- 'one-shot': one time prediction at the end of the time-series
- 'online': preditcion at every time stamps of the time-series
- max_seq_len: maximum sequence length after padding
- label_name: the column name for the label(s)
- treatment: the column name for treatments
- imputation parameters:
- static_imputation_model: mean, median, mice, missforest, knn, gain
- temporal_imputation_model: mean, median, linear, quadratic, cubic, spline, mrnn, tgain
- feature selection parameters:
- feature_selection_model: greedy-addition, greedy-deletion, recursive-addition, recursive-deletion, None
- feature_number: selected featuer number
- predictor_parameters:
- epochs: number of epochs
- bo_itr: bayesian optimization iterations
- static_mode: how to utilize static features (concatenate or None)
- time_mode: how to utilize time information (concatenate or None)
- task: classification or regression
- metric_name: auc, apr, mae, mse
"""
#%% Step 0: Set basic parameters
metric_sets = [args.metric_name]
metric_parameters = {
"problem": args.problem,
"label_name": [args.label_name]
}
#%% Step 1: Upload Dataset
# File names
data_directory = "../datasets/data/" + args.data_name + "/" + args.data_name + "_"
data_loader_training = CSVLoader(
static_file=data_directory + "static_train_data.csv.gz",
temporal_file=data_directory + "temporal_train_data_eav.csv.gz",
)
data_loader_testing = CSVLoader(
static_file=data_directory + "static_test_data.csv.gz",
temporal_file=data_directory + "temporal_test_data_eav.csv.gz",
)
dataset_training = data_loader_training.load()
dataset_testing = data_loader_testing.load()
print("Finish data loading.")
#%% Step 2: Preprocess Dataset
# (0) filter out negative values (Automatically)
negative_filter = FilterNegative()
# (1) one-hot encode categorical features
onehot_encoder = OneHotEncoder(
one_hot_encoding_features=[args.one_hot_encoding])
# (2) Normalize features: 3 options (minmax, standard, none)
normalizer = Normalizer(args.normalization)
filter_pipeline = PipelineComposer(negative_filter, onehot_encoder,
normalizer)
dataset_training = filter_pipeline.fit_transform(dataset_training)
dataset_testing = filter_pipeline.transform(dataset_testing)
print("Finish preprocessing.")
#%% Step 3: Define Problem
problem_maker = ProblemMaker(problem=args.problem,
label=[args.label_name],
max_seq_len=args.max_seq_len,
treatment=args.treatment)
dataset_training = problem_maker.fit_transform(dataset_training)
dataset_testing = problem_maker.fit_transform(dataset_testing)
print("Finish defining problem.")
#%% Step 4: Impute Dataset
static_imputation = Imputation(
imputation_model_name=args.static_imputation_model, data_type="static")
temporal_imputation = Imputation(
imputation_model_name=args.temporal_imputation_model,
data_type="temporal")
imputation_pipeline = PipelineComposer(static_imputation,
temporal_imputation)
dataset_training = imputation_pipeline.fit_transform(dataset_training)
dataset_testing = imputation_pipeline.transform(dataset_testing)
print("Finish imputation.")
#%% Step 5: Feature selection (4 options)
static_feature_selection = FeatureSelection(
feature_selection_model_name=args.static_feature_selection_model,
feature_type="static",
feature_number=args.static_feature_selection_number,
task=args.task,
metric_name=args.metric_name,
metric_parameters=metric_parameters,
)
temporal_feature_selection = FeatureSelection(
feature_selection_model_name=args.temporal_feature_selection_model,
feature_type="temporal",
feature_number=args.temporal_feature_selection_number,
task=args.task,
metric_name=args.metric_name,
metric_parameters=metric_parameters,
)
feature_selection_pipeline = PipelineComposer(static_feature_selection,
temporal_feature_selection)
dataset_training = feature_selection_pipeline.fit_transform(
dataset_training)
dataset_testing = feature_selection_pipeline.transform(dataset_testing)
print("Finish feature selection.")
#%% Step 6: Bayesian Optimization
## Model define
# RNN model
rnn_parameters = {
"model_type": "lstm",
"epoch": args.epochs,
"static_mode": args.static_mode,
"time_mode": args.time_mode,
"verbose": False,
}
general_rnn = GeneralRNN(task=args.task)
general_rnn.set_params(**rnn_parameters)
# CNN model
cnn_parameters = {
"epoch": args.epochs,
"static_mode": args.static_mode,
"time_mode": args.time_mode,
"verbose": False,
}
temp_cnn = TemporalCNN(task=args.task)
temp_cnn.set_params(**cnn_parameters)
# Transformer
transformer = TransformerPredictor(task=args.task,
epoch=args.epochs,
static_mode=args.static_mode,
time_mode=args.time_mode)
# Attention model
attn_parameters = {
"model_type": "lstm",
"epoch": args.epochs,
"static_mode": args.static_mode,
"time_mode": args.time_mode,
"verbose": False,
}
attn = Attention(task=args.task)
attn.set_params(**attn_parameters)
# model_class_list = [general_rnn, attn, temp_cnn, transformer]
model_class_list = [general_rnn, attn]
# train_validate split
dataset_training.train_val_test_split(prob_val=0.2, prob_test=0.1)
# Bayesian Optimization Start
metric = BOMetric(metric="auc", fold=0, split="test")
ens_model_list = []
# Run BO for each model class
for m in model_class_list:
BO_model = automl.model.AutoTS(dataset_training,
m,
metric,
model_path="tmp/")
models, bo_score = BO_model.training_loop(num_iter=args.bo_itr)
auto_ens_model = AutoEnsemble(models, bo_score)
ens_model_list.append(auto_ens_model)
# Load all ensemble models
for ens in ens_model_list:
for m in ens.models:
m.load_model(BO_model.model_path + "/" + m.model_id + ".h5")
# Stacking algorithm
stacking_ens_model = StackingEnsemble(ens_model_list)
stacking_ens_model.fit(dataset_training, fold=0, train_split="val")
# Prediction
assert not dataset_testing.is_validation_defined
test_y_hat = stacking_ens_model.predict(dataset_testing, test_split="test")
test_y = dataset_testing.label
print("Finish AutoML model training and testing.")
#%% Step 7: Visualize Results
idx = np.random.permutation(len(test_y_hat))[:2]
# Evaluate predictor model
result = Metrics(metric_sets,
metric_parameters).evaluate(test_y, test_y_hat)
print("Finish predictor model evaluation.")
# Visualize the output
# (1) Performance
print("Overall performance")
print_performance(result, metric_sets, metric_parameters)
# (2) Predictions
print("Each prediction")
print_prediction(test_y_hat[idx], metric_parameters)
return
def main(args):
'''Main function for individual treatment effect estimation.
Args:
- data loading parameters:
- data_names: mimic, ward, cf, mimic_antibiotics
- preprocess parameters:
- normalization: minmax, standard, None
- one_hot_encoding: input features that need to be one-hot encoded
- problem: 'online'
- 'online': preiction at every time stamps of the time-series
- max_seq_len: maximum sequence length after padding
- label_name: the column name for the label(s)
- treatment: the column name for treatments
- imputation parameters:
- static_imputation_model: mean, median, mice, missforest, knn, gain
- temporal_imputation_model: mean, median, linear, quadratic, cubic, spline, mrnn, tgain
- feature selection parameters:
- feature_selection_model: greedy-addtion, greedy-deletion, recursive-addition, recursive-deletion, None
- feature_number: selected featuer number
- treatment effects model parameters:
- model_name: CRN, RMSN, GANITE
Each model has different types of hyperparameters that need to be set.
- Parameters needed for the Counterfactual Recurrent Network (CRN):
- hyperparameters for encoder:
- rnn_hidden_units: hidden dimensions in the LSTM unit
- rnn_keep_prob: keep probability used for variational dropout in the LSTM unit
- br_size: size of the balancing representation
- fc_hidden_units: hidden dimensions of the fully connected layers used for treatment classifier and predictor
- batch_size: number of samples in mini-batch
- num_epochs: number of epochs
- learning_rate: learning rate
- max_alpha: alpha controls the trade-off between building tratment invariant representations (domain
discrimination) and being able to predict outcomes (outcome prediction); during training, CRN uses an
exponentially increasing schedule for alpha from 0 to max_alpha.
- hyperparameters for decoder:
- the decoder requires the same hyperparameters as the encoder with the exception of the rnn_hidden_units
which is set to be equal to the br_size of the encoder
- Parameters for Recurrent Marginal Structural Networks (RMSN):
- hyperparameters for encoder:
- dropout_rate: dropout probability used for variational
- rnn_hidden_units: hidden dimensions in the LSTM unit
- batch_size: number of samples in mini-batch
- num_epochs: number of epochs
- learning_rate: learning rate
- max_norm: max gradient norm used for gradient clipping during training
- hyperparameters for decoder:
- the decoder requires the same hyperparameters as the encoder.
- model_dir: directory where the model is saved
- model_name: name of the saved model
- Parameters for GANITE:
- batch size: number of samples in mini-batch
- alpha: parameter trading off between discriminator loss and supervised loss for the generator training
- learning_rate: learning rate
- hidden_units: hidden dimensions of the fully connected layers used in the networks
- stack_dim: number of timesteps to stack
All models have the following common parameters:
- static_mode: how to utilize static features (concatenate or None)
- time_mode: how to utilize time information (concatenate or None)
- taks: 'classification' or 'regression'
- metric_name: auc, apr, mae, mse (used for factual prediction)
- patient id: patient for which counterfactual trajectories are computed
- timestep: timestep in patient trajectory for estimating counterfactuals
'''
# %% Step 0: Set basic parameters
metric_sets = [args.metric_name]
metric_parameters = {
'problem': args.problem,
'label_name': [args.label_name]
}
# %% Step 1: Upload Dataset
# File names
data_directory = '../datasets/data/' + args.data_name + '/' + args.data_name + '_'
data_loader_training = CSVLoader(
static_file=data_directory + 'static_train_data.csv.gz',
temporal_file=data_directory + 'temporal_train_data_eav.csv.gz')
data_loader_testing = CSVLoader(
static_file=data_directory + 'static_test_data.csv.gz',
temporal_file=data_directory + 'temporal_test_data_eav.csv.gz')
dataset_training = data_loader_training.load()
dataset_testing = data_loader_testing.load()
print('Finish data loading.')
# %% Step 2: Preprocess Dataset
# (0) filter out negative values (Automatically)
negative_filter = FilterNegative()
# (1) one-hot encode categorical features
onehot_encoder = OneHotEncoder(
one_hot_encoding_features=[args.one_hot_encoding])
# (2) Normalize features: 3 options (minmax, standard, none)
normalizer = Normalizer(args.normalization)
filter_pipeline = PipelineComposer(negative_filter, onehot_encoder,
normalizer)
dataset_training = filter_pipeline.fit_transform(dataset_training)
dataset_testing = filter_pipeline.transform(dataset_testing)
print('Finish preprocessing.')
# %% Step 3: Define Problem
problem_maker = ProblemMaker(problem=args.problem,
label=[args.label_name],
max_seq_len=args.max_seq_len,
treatment=[args.treatment])
dataset_training = problem_maker.fit_transform(dataset_training)
dataset_testing = problem_maker.fit_transform(dataset_testing)
print('Finish defining problem.')
# %% Step 4: Impute Dataset
static_imputation = Imputation(
imputation_model_name=args.static_imputation_model, data_type='static')
temporal_imputation = Imputation(
imputation_model_name=args.temporal_imputation_model,
data_type='temporal')
imputation_pipeline = PipelineComposer(static_imputation,
temporal_imputation)
dataset_training = imputation_pipeline.fit_transform(dataset_training)
dataset_testing = imputation_pipeline.transform(dataset_testing)
print('Finish imputation.')
# %% Step 5: Feature selection (4 options)
static_feature_selection = \
FeatureSelection(feature_selection_model_name=args.static_feature_selection_model,
feature_type='static', feature_number=args.static_feature_selection_number,
task=args.task, metric_name=args.metric_name,
metric_parameters=metric_parameters)
temporal_feature_selection = \
FeatureSelection(feature_selection_model_name=args.temporal_feature_selection_model,
feature_type='temporal', feature_number=args.temporal_feature_selection_number,
task=args.task, metric_name=args.metric_name,
metric_parameters=metric_parameters)
feature_selection_pipeline = PipelineComposer(static_feature_selection,
temporal_feature_selection)
dataset_training = feature_selection_pipeline.fit_transform(
dataset_training)
dataset_testing = feature_selection_pipeline.transform(dataset_testing)
print('Finish feature selection.')
# %% Step 6: Fit treatment effects (3 options)
# Set the validation data for best model saving
dataset_training.train_val_test_split(prob_val=0.2, prob_test=0.0)
# Set the treatment effects model
model_name = args.model_name
# Set treatment effects model parameters
if model_name == 'CRN':
model_parameters = {
'encoder_rnn_hidden_units': args.crn_encoder_rnn_hidden_units,
'encoder_br_size': args.crn_encoder_br_size,
'encoder_fc_hidden_units': args.crn_encoder_fc_hidden_units,
'encoder_learning_rate': args.crn_encoder_learning_rate,
'encoder_batch_size': args.crn_encoder_batch_size,
'encoder_keep_prob': args.crn_encoder_keep_prob,
'encoder_num_epochs': args.crn_encoder_num_epochs,
'encoder_max_alpha': args.crn_encoder_max_alpha,
'decoder_br_size': args.crn_decoder_br_size,
'decoder_fc_hidden_units': args.crn_decoder_fc_hidden_units,
'decoder_learning_rate': args.crn_decoder_learning_rate,
'decoder_batch_size': args.crn_decoder_batch_size,
'decoder_keep_prob': args.crn_decoder_keep_prob,
'decoder_num_epochs': args.crn_decoder_num_epochs,
'decoder_max_alpha': args.crn_decoder_max_alpha,
'projection_horizon': args.projection_horizon,
'static_mode': args.static_mode,
'time_mode': args.time_mode
}
treatment_model = treatment_effects_model(model_name,
model_parameters,
task='classification')
treatment_model.fit(dataset_training)
elif model_name == 'RMSN':
hyperparams_encoder_iptw = {
'dropout_rate': args.rmsn_encoder_dropout_rate,
'memory_multiplier': args.rmsn_encoder_memory_multiplier,
'num_epochs': args.rmsn_encoder_num_epochs,
'batch_size': args.rmsn_encoder_batch_size,
'learning_rate': args.rmsn_encoder_learning_rate,
'max_norm': args.rmsn_encoder_max_norm
}
hyperparams_decoder_iptw = {
'dropout_rate': args.rmsn_decoder_dropout_rate,
'memory_multiplier': args.rmsn_decoder_memory_multiplier,
'num_epochs': args.rmsn_decoder_num_epochs,
'batch_size': args.rmsn_decoder_batch_size,
'learning_rate': args.rmsn_decoder_learning_rate,
'max_norm': args.rmsn_decoder_max_norm
}
model_parameters = {
'hyperparams_encoder_iptw': hyperparams_encoder_iptw,
'hyperparams_decoder_iptw': hyperparams_decoder_iptw,
'model_dir': args.rmsn_model_dir,
'model_name': args.rmsn_model_name,
'static_mode': args.static_mode,
'time_mode': args.time_mode
}
treatment_model = treatment_effects_model(model_name,
model_parameters,
task='classification')
treatment_model.fit(dataset_training,
projection_horizon=args.projection_horizon)
elif model_name == 'GANITE':
hyperparams = {
'batch_size': args.ganite_batch_size,
'alpha': args.ganite_alpha,
'hidden_dims': args.ganite_hidden_dims,
'learning_rate': args.ganite_learning_rate
}
model_parameters = {
'hyperparams': hyperparams,
'stack_dim': args.ganite_stack_dim,
'static_mode': args.static_mode,
'time_mode': args.time_mode
}
treatment_model = treatment_effects_model(model_name,
model_parameters,
task='classification')
treatment_model.fit(dataset_training)
test_y_hat = treatment_model.predict(dataset_testing)
print('Finish treatment effects model training and testing.')
# %% Step 9: Visualize Results
# Evaluate predictor model
result = Metrics(metric_sets,
metric_parameters).evaluate(dataset_testing.label,
test_y_hat)
print('Finish predictor model evaluation.')
# Visualize the output
# (1) Performance on estimating factual outcomes
print('Overall performance on estimating factual outcomes')
print_performance(result, metric_sets, metric_parameters)
# (2) Counterfactual trajectories
print('Counterfactual trajectories')
if model_name in ['CRN', 'RMSN']:
# Predict and visualize counterfactuals for the sequence of treatments indicated by the user
# through the treatment_options. The lengths of each sequence of treatments needs to be projection_horizon + 1.
treatment_options = np.array([[[1], [1], [1], [1], [1], [0]],
[[0], [0], [0], [0], [1], [1]]])
history, counterfactual_traj = treatment_model.predict_counterfactual_trajectories(
dataset=dataset_testing,
patient_id=args.patient_id,
timestep=args.timestep,
treatment_options=treatment_options)
print_counterfactual_predictions(
patient_history=history,
treatment_options=treatment_options,
counterfactual_predictions=counterfactual_traj)
return
def assign_category(self, df):
fs = FeatureSelection(df)
self.set('fs', fs)
category_list, unique_values, default_list, frequent_values2frequency = fs.assign_category(
self.get('config_file'), df)
return category_list, unique_values, default_list, frequent_values2frequency
from utils import feature_util
import pytest
from feature_selection import FeatureSelection
import pandas as pd
from config.config_writer import ConfigWriter
from config import config_reader
from utils import preprocessing
file = 'data_test/iris.csv'
config_test_file = 'data_test/iris_config_test.ini'
df = pd.read_csv('data_test/iris.csv')
fs = FeatureSelection(df)
df_range = pd.read_csv('data_test/dataset.csv')
fs_range = FeatureSelection(df_range)
categories = ['numerical', 'numerical', 'numerical', 'numerical', 'categorical']
unique_values = [-1, -1, -1, -1, 3]
default_list = {'sepal_length': 5.8, 'sepal_width': 3.0, 'petal_length': 4.35, 'petal_width': 1.3,
'class': 'Iris-setosa'}
frequent_values2frequency = {'sepal_length': (5.0, 10), 'sepal_width': (3.0, 26), 'petal_length': (1.5, 14),
'petal_width': (0.2, 28), 'class': ('Iris-setosa', 50)}
SAMPLE_DATA_SIZE = 5
data = preprocessing.insert_data(df, categories, unique_values, default_list, frequent_values2frequency,
SAMPLE_DATA_SIZE)
data.Category = categories
def test_already_order_reorder_request():
features = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'class']
categories = ['numerical', 'numerical', 'numerical', 'numerical', 'categorical']
def main(args):
'''Main function for AutoML in time-series predictions.
Args:
- data loading parameters:
- data_names: mimic, ward, cf
- preprocess parameters:
- normalization: minmax, standard, None
- one_hot_encoding: input features that need to be one-hot encoded
- problem: 'one-shot' or 'online'
- 'one-shot': one time prediction at the end of the time-series
- 'online': preditcion at every time stamps of the time-series
- max_seq_len: maximum sequence length after padding
- label_name: the column name for the label(s)
- treatment: the column name for treatments
- imputation parameters:
- static_imputation_model: mean, median, mice, missforest, knn, gain
- temporal_imputation_model: mean, median, linear, quadratic, cubic, spline, mrnn, tgain
- feature selection parameters:
- feature_selection_model: greedy-addtion, greedy-deletion, recursive-addition, recursive-deletion, None
- feature_number: selected featuer number
- predictor_parameters:
- epochs: number of epochs
- bo_itr: bayesian optimization iterations
- static_mode: how to utilize static features (concatenate or None)
- time_mode: how to utilize time information (concatenate or None)
- task: classification or regression
- metric_name: auc, apr, mae, mse
'''
#%% Step 0: Set basic parameters
metric_sets = [args.metric_name]
metric_parameters = {
'problem': args.problem,
'label_name': [args.label_name]
}
#%% Step 1: Upload Dataset
# File names
data_directory = '../datasets/data/' + args.data_name + '/' + args.data_name + '_'
data_loader_training = CSVLoader(
static_file=data_directory + 'static_train_data.csv.gz',
temporal_file=data_directory + 'temporal_train_data_eav.csv.gz')
data_loader_testing = CSVLoader(
static_file=data_directory + 'static_test_data.csv.gz',
temporal_file=data_directory + 'temporal_test_data_eav.csv.gz')
dataset_training = data_loader_training.load()
dataset_testing = data_loader_testing.load()
print('Finish data loading.')
#%% Step 2: Preprocess Dataset
# (0) filter out negative values (Automatically)
negative_filter = FilterNegative()
# (1) one-hot encode categorical features
onehot_encoder = OneHotEncoder(
one_hot_encoding_features=[args.one_hot_encoding])
# (2) Normalize features: 3 options (minmax, standard, none)
normalizer = Normalizer(args.normalization)
filter_pipeline = PipelineComposer(negative_filter, onehot_encoder,
normalizer)
dataset_training = filter_pipeline.fit_transform(dataset_training)
dataset_testing = filter_pipeline.transform(dataset_testing)
print('Finish preprocessing.')
#%% Step 3: Define Problem
problem_maker = ProblemMaker(problem=args.problem,
label=[args.label_name],
max_seq_len=args.max_seq_len,
treatment=[args.treatment])
dataset_training = problem_maker.fit_transform(dataset_training)
dataset_testing = problem_maker.fit_transform(dataset_testing)
print('Finish defining problem.')
#%% Step 4: Impute Dataset
static_imputation = Imputation(
imputation_model_name=args.static_imputation_model, data_type='static')
temporal_imputation = Imputation(
imputation_model_name=args.temporal_imputation_model,
data_type='temporal')
imputation_pipeline = PipelineComposer(static_imputation,
temporal_imputation)
dataset_training = imputation_pipeline.fit_transform(dataset_training)
dataset_testing = imputation_pipeline.transform(dataset_testing)
print('Finish imputation.')
#%% Step 5: Feature selection (4 options)
static_feature_selection = \
FeatureSelection(feature_selection_model_name = args.static_feature_selection_model,
feature_type = 'static', feature_number = args.static_feature_selection_number,
task = args.task, metric_name = args.metric_name,
metric_parameters = metric_parameters)
temporal_feature_selection = \
FeatureSelection(feature_selection_model_name = args.temporal_feature_selection_model,
feature_type = 'temporal', feature_number = args.temporal_feature_selection_number,
task = args.task, metric_name = args.metric_name,
metric_parameters = metric_parameters)
feature_selection_pipeline = PipelineComposer(static_feature_selection,
temporal_feature_selection)
dataset_training = feature_selection_pipeline.fit_transform(
dataset_training)
dataset_testing = feature_selection_pipeline.transform(dataset_testing)
print('Finish feature selection.')
#%% Step 6: Bayesian Optimization
## Model define
model_parameters = {
'projection_horizon': 5,
'static_mode': 'concatenate',
'time_mode': 'concatenate'
}
crn_model = CRN_Model(task=args.task)
crn_model.set_params(**model_parameters)
model_class = crn_model
# train_validate split
dataset_training.train_val_test_split(prob_val=0.2, prob_test=0.2)
# Bayesian Optimization Start
metric = BOMetric(metric='auc', fold=0, split='test')
# Run BO for selected model class
BO_model = AutoTS(dataset_training, model_class, metric)
models, bo_score = BO_model.training_loop(num_iter=2)
auto_ens_model = AutoEnsemble(models, bo_score)
# Prediction
assert not dataset_testing.is_validation_defined
test_y_hat = auto_ens_model.predict(dataset_testing, test_split='test')
test_y = dataset_testing.label
print('Finish AutoML model training and testing.')
#%% Step 7: Visualize Results
idx = np.random.permutation(len(test_y_hat))[:2]
# Evaluate predictor model
result = Metrics(metric_sets,
metric_parameters).evaluate(test_y, test_y_hat)
print('Finish predictor model evaluation.')
# Visualize the output
# (1) Performance
print('Overall performance')
print_performance(result, metric_sets, metric_parameters)
# (2) Predictions
print('Each prediction')
print_prediction(test_y_hat[idx], metric_parameters)
return
def test_log(log_info):
logging.info("testing foo")
logAssert(log_info == 'foo', "foo is not foo")
t = ThreadHandler()
username = '******'
config_file = 'data_test/iris_config.ini'
port = '5000'
target = 'class'
file = 'data_test/iris.csv'
config_test_file = 'data_test/iris_config_test.ini'
df = pd.read_csv('data_test/iris.csv')
fs = FeatureSelection(df)
categories = [
'numerical', 'numerical', 'numerical', 'numerical', 'categorical'
]
unique_values = [-1, -1, -1, -1, 3]
default_list = {
'sepal_length': 5.8,
'sepal_width': 3.0,
'petal_length': 4.35,
'petal_width': 1.3,
'class': 'Iris-setosa'
}
frequent_values2frequency = {
'sepal_length': (5.0, 10),
'sepal_width': (3.0, 26),
def main(args):
"""Main function for time-series prediction.
Args:
- data loading parameters:
- data_names: mimic, ward, cf
- preprocess parameters:
- normalization: minmax, standard, None
- one_hot_encoding: input features that need to be one-hot encoded
- problem: 'one-shot' or 'online'
- 'one-shot': one time prediction at the end of the time-series
- 'online': prediction at every time stamps of the time-series
- max_seq_len: maximum sequence length after padding
- label_name: the column name for the label(s)
- treatment: the column name for treatments
- imputation parameters:
- static_imputation_model: mean, median, mice, missforest, knn, gain
- temporal_imputation_model: mean, median, linear, quadratic, cubic, spline, mrnn, tgain
- feature selection parameters:
- feature_selection_model: greedy-addition, greedy-deletion, recursive-addition, recursive-deletion, None
- feature_number: selected feature number
- predictor_parameters:
- model_name: rnn, gru, lstm, attention, tcn, transformer
- model_parameters: network parameters such as number of layers
- h_dim: hidden dimensions
- n_layer: layer number
- n_head: head number (only for transformer model)
- batch_size: number of samples in mini-batch
- epochs: number of epochs
- learning_rate: learning rate
- static_mode: how to utilize static features (concatenate or None)
- time_mode: how to utilize time information (concatenate or None)
- task: classification or regression
- uncertainty_model_name: uncertainty estimation model name (ensemble)
- interpretation_model_name: interpretation model name (tinvase)
- metric_name: auc, apr, mae, mse
"""
#%% Step 0: Set basic parameters
metric_sets = [args.metric_name]
metric_parameters = {
"problem": args.problem,
"label_name": [args.label_name]
}
#%% Step 1: Upload Dataset
# File names
data_directory = "../datasets/data/" + args.data_name + "/" + args.data_name + "_"
data_loader_training = CSVLoader(
static_file=data_directory + "static_train_data.csv.gz",
temporal_file=data_directory + "temporal_train_data_eav.csv.gz",
)
data_loader_testing = CSVLoader(
static_file=data_directory + "static_test_data.csv.gz",
temporal_file=data_directory + "temporal_test_data_eav.csv.gz",
)
dataset_training = data_loader_training.load()
dataset_testing = data_loader_testing.load()
print("Finish data loading.")
#%% Step 2: Preprocess Dataset
# (0) filter out negative values (Automatically)
negative_filter = FilterNegative()
# (1) one-hot encode categorical features
onehot_encoder = OneHotEncoder(
one_hot_encoding_features=[args.one_hot_encoding])
# (2) Normalize features: 3 options (minmax, standard, none)
normalizer = Normalizer(args.normalization)
filter_pipeline = PipelineComposer(negative_filter, onehot_encoder,
normalizer)
dataset_training = filter_pipeline.fit_transform(dataset_training)
dataset_testing = filter_pipeline.transform(dataset_testing)
print("Finish preprocessing.")
#%% Step 3: Define Problem
problem_maker = ProblemMaker(problem=args.problem,
label=[args.label_name],
max_seq_len=args.max_seq_len,
treatment=args.treatment)
dataset_training = problem_maker.fit_transform(dataset_training)
dataset_testing = problem_maker.fit_transform(dataset_testing)
print("Finish defining problem.")
#%% Step 4: Impute Dataset
static_imputation = Imputation(
imputation_model_name=args.static_imputation_model, data_type="static")
temporal_imputation = Imputation(
imputation_model_name=args.temporal_imputation_model,
data_type="temporal")
imputation_pipeline = PipelineComposer(static_imputation,
temporal_imputation)
dataset_training = imputation_pipeline.fit_transform(dataset_training)
dataset_testing = imputation_pipeline.transform(dataset_testing)
print("Finish imputation.")
#%% Step 5: Feature selection (4 options)
static_feature_selection = FeatureSelection(
feature_selection_model_name=args.static_feature_selection_model,
feature_type="static",
feature_number=args.static_feature_selection_number,
task=args.task,
metric_name=args.metric_name,
metric_parameters=metric_parameters,
)
temporal_feature_selection = FeatureSelection(
feature_selection_model_name=args.temporal_feature_selection_model,
feature_type="temporal",
feature_number=args.temporal_feature_selection_number,
task=args.task,
metric_name=args.metric_name,
metric_parameters=metric_parameters,
)
feature_selection_pipeline = PipelineComposer(static_feature_selection,
temporal_feature_selection)
dataset_training = feature_selection_pipeline.fit_transform(
dataset_training)
dataset_testing = feature_selection_pipeline.transform(dataset_testing)
print("Finish feature selection.")
#%% Step 6: Fit and Predict (6 options)
# Set predictor model parameters
model_parameters = {
"h_dim": args.h_dim,
"n_layer": args.n_layer,
"n_head": args.n_head,
"batch_size": args.batch_size,
"epoch": args.epochs,
"model_type": args.model_name,
"learning_rate": args.learning_rate,
"static_mode": args.static_mode,
"time_mode": args.time_mode,
"verbose": True,
}
# Set the validation data for best model saving
dataset_training.train_val_test_split(prob_val=0.2, prob_test=0.0)
pred_class = prediction(args.model_name, model_parameters, args.task)
pred_class.fit(dataset_training)
test_y_hat = pred_class.predict(dataset_testing)
print("Finish predictor model training and testing.")
#%% Step 7: Estimate Uncertainty (1 option)
uncertainty_model = uncertainty(args.uncertainty_model_name,
model_parameters, pred_class, args.task)
uncertainty_model.fit(dataset_training)
test_ci_hat = uncertainty_model.predict(dataset_testing)
print("Finish uncertainty estimation")
#%% Step 8: Interpret Predictions (1 option)
interpretor = interpretation(args.interpretation_model_name,
model_parameters, pred_class, args.task)
interpretor.fit(dataset_training)
test_s_hat = interpretor.predict(dataset_testing)
print("Finish model interpretation")
#%% Step 9: Visualize Results
idx = np.random.permutation(len(test_y_hat))[:2]
# Evaluate predictor model
result = Metrics(metric_sets,
metric_parameters).evaluate(dataset_testing.label,
test_y_hat)
print("Finish predictor model evaluation.")
# Visualize the output
# (1) Performance
print("Overall performance")
print_performance(result, metric_sets, metric_parameters)
# (2) Predictions
print("Each prediction")
print_prediction(test_y_hat[idx], metric_parameters)
# (3) Uncertainty
print("Uncertainty estimations")
print_uncertainty(test_y_hat[idx], test_ci_hat[idx], metric_parameters)
# (4) Model interpretation
print("Model interpretation")
print_interpretation(test_s_hat[idx], dataset_training.feature_name,
metric_parameters, model_parameters)
return