def main(use_censored=USE_CENSORED, use_similarity=USE_SIMILARITY, grid_results_folder='rnn_grid_search_no_censored'):
if not use_recorded:
x_for_deep, y_for_deep, x_for_deep_censored, y_for_deep_censored, censored_data, not_censored, otu_after_pca_wo_taxonomy, OtuMf = prep_data(n_components)
else:
x_for_deep = pickle.load(open(os.path.join(SCRIPT_DIR, "x_for_deep.p"), "rb"))
y_for_deep = pickle.load(open(os.path.join(SCRIPT_DIR, "y_for_deep.p"), "rb"))
x_for_deep_censored = pickle.load(open(os.path.join(SCRIPT_DIR, "x_for_deep_censored.p"), "rb"))
y_for_deep_censored = pickle.load(open(os.path.join(SCRIPT_DIR, "y_for_deep_censored.p"), "rb"))
censored_data = pickle.load(open(os.path.join(SCRIPT_DIR, "censored_data.p"), "rb"))
not_censored = pickle.load(open(os.path.join(SCRIPT_DIR, "not_censored.p"), "rb"))
otu_after_pca_wo_taxonomy = pickle.load(open(os.path.join(SCRIPT_DIR, "otu_after_pca_wo_taxonomy.p"), "rb"))
OtuMf = pickle.load(open(os.path.join(SCRIPT_DIR, "OtuMf.p"), "rb"))
if record_inputs:
pickle.dump(x_for_deep, open(os.path.join(SCRIPT_DIR, "x_for_deep.p"), "wb"))
pickle.dump(y_for_deep, open(os.path.join(SCRIPT_DIR, "y_for_deep.p"), "wb"))
pickle.dump(x_for_deep_censored, open(os.path.join(SCRIPT_DIR, "x_for_deep_censored.p"), "wb"))
pickle.dump(y_for_deep_censored, open(os.path.join(SCRIPT_DIR, "y_for_deep_censored.p"), "wb"))
pickle.dump(censored_data, open(os.path.join(SCRIPT_DIR, "censored_data.p"), "wb"))
pickle.dump(not_censored, open(os.path.join(SCRIPT_DIR, "not_censored.p"), "wb"))
pickle.dump(otu_after_pca_wo_taxonomy, open(os.path.join(SCRIPT_DIR, "otu_after_pca_wo_taxonomy.p"), "wb"))
pickle.dump(OtuMf, open(os.path.join(SCRIPT_DIR, "OtuMf.p"), "wb"))
if use_similarity:
betas_list = [1, 10, 100]
else:
betas_list = [None] # just a list of one element so that the for loop will run only once
for beta in betas_list:
censored_mse_fraction_factor = None
if use_censored:
y_for_deep_censored['mse_coeff'] = 0
if use_similarity:
censored_mse_fraction_factor = 2
##### Similarity algo ####
not_censored_for_similarity = not_censored.join(otu_after_pca_wo_taxonomy)
censored_data_with_time = compute_time_for_censored_using_similarity_matrix(not_censored_for_similarity,
censored_data,
n_components,
OtuMf,
otu_after_pca_wo_taxonomy,
beta=beta,
remove_outliers=True,
th_value=None)
# combine the x_censored and the syntethic time
x_for_deep_censored['time_for_the_event'][censored_data_with_time['time_for_the_event'].index] = \
censored_data_with_time['time_for_the_event']
y_for_deep_censored['delta_time'][censored_data_with_time['time_for_the_event'].index] = censored_data_with_time['time_for_the_event']
# change the MSE coeff for the last sample of censored (its just prep, the actual value will be set within the algo)
y_for_deep_censored['mse_coeff'][censored_data_with_time['time_for_the_event'].index] = 'last_censored'
##### END Similarity algo ####
starting_col = np.argwhere(x_for_deep.columns == 0).tolist()[0][0]
X = x_for_deep.iloc[:, starting_col:starting_col + n_components]
X['groupby'] = x_for_deep['PatientNumber210119']
y = y_for_deep # ['delta_time']
starting_col = np.argwhere(x_for_deep_censored.columns == 0).tolist()[0][0]
X_train_censored = x_for_deep_censored.iloc[:, starting_col:starting_col + n_components]
X_train_censored['groupby'] = x_for_deep_censored['PatientNumber210119']
y_train_censored = y_for_deep_censored
number_samples_censored = y_train_censored.shape[0]
print(f'Number of censored subjects: {number_samples_censored}')
# remove outliers
before_removal = y.shape[0]
std = y['delta_time'].values.std()
th = std * 5
outlier_mask = y['delta_time'] < th
y = y.loc[outlier_mask]
X = X.loc[outlier_mask]
after_removal = y.shape[0]
print(f'{before_removal-after_removal} outlier/s were removed')
stats_input(y, y_train_censored)
PLOT_INPUT_TO_NN_STATS = False
if PLOT_INPUT_TO_NN_STATS:
plt.hist(y['delta_time'].values, bins=150)
b = y['delta_time'].values.copy()
b.sort()
med = b[int(len(b)/2)]
std = y['delta_time'].values.std()
mean = y['delta_time'].values.mean()
plt.title(f'STD={std}, MED={med}, Mean={mean}')
epochs_list = [20, 100, 1000]#['MAX', 20, 100, 1000]
mse_factor_list = [0.1, 10, 1000] # np.arange(0.005, 1, 0.005)
if not use_similarity:
if not use_censored:
mse_factor_list = [1]
X_train_censored = None
y_train_censored = None
l2_lambda_list = [1, 20]
#np.logspace(0, 2, 5) # 0.01, 0.1, 1, 10, 100
number_layers_list = [1, 2, 3]
number_neurons_per_layer_list = [20, 50]
l2_lambda_list = [0.1, 1, 10, 100]
dropout_list = [0, 0.2, 0.6] # np.arange(0, 0.8, 0.1)
epochs_list = [1000]
number_layers_list = [1, 2, 3]
number_neurons_per_layer_list = [10, 30]
train_res, test_res = time_series_analysis_rnn(X, y,
n_components,
l2_lambda_list,
dropout_list,
mse_factor_list,
number_layers_list,
number_neurons_per_layer_list,
epochs_list,
cross_val_number=5,
X_train_censored=X_train_censored,
y_train_censored=y_train_censored,
record=RECORD,
grid_search_dir=grid_results_folder,
beta_for_similarity=beta,
censored_mse_fraction_factor=censored_mse_fraction_factor,
early_stop_fraction=None, #0.05,
min_epochs=30)
total_num_of_configs = len(dropout_list) * \
len(l2_lambda_list) * \
len(number_layers_list) * \
len(number_neurons_per_layer_list) * \
len(betas_list)
print(f'Total number of configuration that were checked: {total_num_of_configs}')
def main(use_similarity=USE_SIMILARITY,
grid_results_folder='grid_search_xgboost_with_censored'):
if not use_recorded:
x_for_deep, y_for_deep, x_for_deep_censored, y_for_deep_censored, censored_data, not_censored, otu_after_pca_wo_taxonomy, OtuMf = prep_data(
n_components)
else:
x_for_deep = pickle.load(
open(os.path.join(SCRIPT_DIR, "x_for_deep.p"), "rb"))
y_for_deep = pickle.load(
open(os.path.join(SCRIPT_DIR, "y_for_deep.p"), "rb"))
x_for_deep_censored = pickle.load(
open(os.path.join(SCRIPT_DIR, "x_for_deep_censored.p"), "rb"))
y_for_deep_censored = pickle.load(
open(os.path.join(SCRIPT_DIR, "y_for_deep_censored.p"), "rb"))
censored_data = pickle.load(
open(os.path.join(SCRIPT_DIR, "censored_data.p"), "rb"))
not_censored = pickle.load(
open(os.path.join(SCRIPT_DIR, "not_censored.p"), "rb"))
otu_after_pca_wo_taxonomy = pickle.load(
open(os.path.join(SCRIPT_DIR, "otu_after_pca_wo_taxonomy.p"),
"rb"))
OtuMf = pickle.load(open(os.path.join(SCRIPT_DIR, "OtuMf.p"), "rb"))
if record_inputs:
pickle.dump(x_for_deep,
open(os.path.join(SCRIPT_DIR, "x_for_deep.p"), "wb"))
pickle.dump(y_for_deep,
open(os.path.join(SCRIPT_DIR, "y_for_deep.p"), "wb"))
pickle.dump(
x_for_deep_censored,
open(os.path.join(SCRIPT_DIR, "x_for_deep_censored.p"), "wb"))
pickle.dump(
y_for_deep_censored,
open(os.path.join(SCRIPT_DIR, "y_for_deep_censored.p"), "wb"))
pickle.dump(censored_data,
open(os.path.join(SCRIPT_DIR, "censored_data.p"), "wb"))
pickle.dump(not_censored,
open(os.path.join(SCRIPT_DIR, "not_censored.p"), "wb"))
pickle.dump(
otu_after_pca_wo_taxonomy,
open(os.path.join(SCRIPT_DIR, "otu_after_pca_wo_taxonomy.p"),
"wb"))
pickle.dump(OtuMf, open(os.path.join(SCRIPT_DIR, "OtuMf.p"), "wb"))
if use_similarity:
betas_list = [1, 10, 100]
else:
betas_list = [
None
] # just a list of one element so that the for loop will run only once
for beta in betas_list:
if use_similarity:
##### Similarity algo ####
not_censored_for_similarity = not_censored.join(
otu_after_pca_wo_taxonomy)
censored_data_with_time = compute_time_for_censored_using_similarity_matrix(
not_censored_for_similarity,
censored_data,
n_components,
OtuMf,
otu_after_pca_wo_taxonomy,
beta=beta,
remove_outliers=True,
th_value=None)
# combine the x_censored and the syntethic time
x_for_deep_censored['time_for_the_event'][censored_data_with_time['time_for_the_event'].index] = \
censored_data_with_time['time_for_the_event']
y_for_deep_censored['delta_time'][
censored_data_with_time['time_for_the_event'].
index] = censored_data_with_time['time_for_the_event']
# change the MSE coeff for the last sample of censored
y_for_deep_censored['mse_coeff'][
censored_data_with_time['time_for_the_event'].index] = 5
##### END Similarity algo ####
starting_col = np.argwhere(x_for_deep.columns == 0).tolist()[0][0]
X = x_for_deep.iloc[:, starting_col:starting_col + n_components]
y = y_for_deep['delta_time']
starting_col = np.argwhere(
x_for_deep_censored.columns == 0).tolist()[0][0]
X_train_censored = x_for_deep_censored.iloc[:,
starting_col:starting_col +
n_components]
y_train_censored = y_for_deep_censored['delta_time']
number_samples_censored = y_train_censored.shape[0]
print(f'Number of censored subjects: {number_samples_censored}')
alpha_list = [0.01, 20, 50, 100]
n_estimators_list = [5, 10, 20]
min_child_weight_list = [0.1, 1, 10, 20]
reg_lambda_list = [0, 10, 20]
max_depth_list = [3, 5, 10]
# alpha_list = [0.01]
# n_estimators_list = [5]
# min_child_weight_list = [0.1]
# reg_lambda_list = [0]
# max_depth_list = [3]
if not use_similarity:
X_train_censored = None
y_train_censored = None
train_res, test_res = time_series_using_xgboost(
X,
y,
alpha_list,
n_estimators_list,
min_child_weight_list,
reg_lambda_list,
max_depth_list,
cross_val_number=5,
X_train_censored=X_train_censored,
y_train_censored=y_train_censored,
record=RECORD,
grid_search_dir=grid_results_folder,
deep_verbose=False,
beta_for_similarity=beta,
use_random_time=True)
total_num_of_configs = len(alpha_list) *\
len(n_estimators_list) *\
len(min_child_weight_list) *\
len(reg_lambda_list) *\
len(max_depth_list) *\
len(betas_list)
print(
f'Total number of configuration that were checked: {total_num_of_configs}'
)