def test_experiment(self):
output_filename_header = FileLoader.create_output_file()
time.sleep(1)
loaded_algorithm_combinations = FileLoader.read_csv_file("../Datasets/test.csv")
file_path = "../Datasets/HIV_37_Samples/MergedDataset.csv"
loaded_data = FileLoader.load_file(file_path)
# feature_eliminator = SelectKBest(f_regression,k=k_value)
print (loaded_algorithm_combinations[0])
output_filename = FileLoader.create_output_file()
for i in range(0, 80):
normalizer = self.getnormalizer(loaded_algorithm_combinations[i][0])
feature_eliminator = self.getfeature_eliminator(loaded_algorithm_combinations[i][1])
the_model = self.get_model(loaded_algorithm_combinations[i][2])
print "taking ", type(normalizer).__name__, "and feature selector ", type(
feature_eliminator
).__name__, "model", type(the_model).__name__
FileLoader.write_model_in_file(
output_filename_header,
type(normalizer).__name__,
type(feature_eliminator).__name__,
type(the_model).__name__,
"",
"",
"",
"",
"",
)
the_data_manager = DataManager(feature_eliminator, normalizer=normalizer)
the_data_manager.set_data(loaded_data)
the_data_manager.split_data(test_split=0.15, train_split=0.70)
exp = Experiment(the_data_manager, the_model)
exp.run_experiment()
# arr_selected = feature_eliminator.get_support(indices=True)
# if(exp.get_r2(SplitTypes.Train) > 0 and exp.get_r2(SplitTypes.Valid) > 0 and exp.get_r2(SplitTypes.Test) > 0):
FileLoader.write_model_in_file(
output_filename,
type(normalizer).__name__,
type(feature_eliminator).__name__,
type(the_model).__name__,
"",
exp.fitness_matrix[0],
exp.get_r2(SplitTypes.Train),
exp.get_r2(SplitTypes.Valid),
exp.get_r2(SplitTypes.Test),
)
linear_model.BayesianRidge()
# ,svm.SVR()
# ,RandomForestRegressor()
# , LinearRegression()
]
normalizers = [
None
# , StandardScaler()
# ,NumpyNormalizer(), ScikitNormalizer()
# , MinMaxScaler() ,Binarizer()
# ,Imputer(), KernelCenterer()
# ,Normalizer()
]
output_filename = FileLoader.create_output_file()
for normalizer in normalizers:
for feature_eliminator in feature_eliminators:
# for k_value in range(5, 20):
# for k_value in range(13, 14):
for the_model in the_models:
print(
"taking care of ",
type(normalizer).__name__,
"and feature selector ",
type(feature_eliminator).__name__,
"model",
type(the_model).__name__,
)
