def __init__(self,
train_task_dict_list,
val_task_dict_list=None,
test_task_dict_list=None,
print_per_task=False,
optimize_labels=None,
verbose=True,
num_cross_folds=5,
val_type=DEFAULT_VAL_TYPE,
accuracy_logged_every_n=1000,
accuracy_output_every_n=1000):
self.train_tasks = copy.deepcopy(train_task_dict_list)
self.val_tasks = copy.deepcopy(val_task_dict_list)
self.test_tasks = copy.deepcopy(test_task_dict_list)
self.n_tasks = len(self.train_tasks)
self.print_per_task = print_per_task
self.task_training_order = self.generateNewTrainingOrder()
self.verbose = verbose
self.optimize_labels = optimize_labels
self.loss_func = tfnet.getSoftmaxLoss
for i in range(self.n_tasks):
self.train_tasks[i]['Y'] = tfnet.changeLabelsToOneHotEncoding(
self.train_tasks[i]['Y'])
if self.val_tasks is not None:
self.val_tasks[i]['Y'] = tfnet.changeLabelsToOneHotEncoding(
self.val_tasks[i]['Y'])
if self.test_tasks is not None:
self.test_tasks[i]['Y'] = tfnet.changeLabelsToOneHotEncoding(
self.test_tasks[i]['Y'])
if self.print_per_task:
self.optimize_labels = []
for i in range(self.n_tasks):
self.optimize_labels.append(self.train_tasks[i]['Name'])
self.initializeStoredTrainingMetrics()
#the following code supports performing cross validation:
self.val_type = val_type
if val_type == 'cross':
assert (self.val_tasks is not None)
self.num_cross_folds = num_cross_folds
print "Generating cross validation sets for each ppt"
for i in range(self.n_tasks):
self.train_tasks[i]['crossVal_X'], self.train_tasks[i][
'crossVal_y'] = helper.generateCrossValSet(
self.train_tasks[i]['X'],
self.train_tasks[i]['Y'],
self.val_tasks[i]['X'],
self.val_tasks[i]['Y'],
self.num_cross_folds,
verbose=False)
self.input_size = helper.calculateNumFeatsInTaskList(self.train_tasks)
self.output_size = np.shape(self.train_tasks[0]['Y'])[1]
print "OUTPUT SIZE IS CALCULATED TO BE:", self.output_size
#parameters that can be tuned
self.l2_beta = 5e-4
self.initial_learning_rate = 0.0001
self.decay = True
self.batch_size = 10
self.decay_steps = 1000
self.decay_rate = 0.95
self.optimizer = tf.train.AdamOptimizer #can also be tf.train.AdagradOptimizer or tf.train.GradientDescentOptimizer
self.dropout = True
#network structure and running stuff
self.hidden_sizes_shared = [1024]
self.hidden_size_task = 10
self.connection_types_shared = ['full']
self.connection_types_task = ['full', 'full']
self.n_steps = 4001
self.accuracy_logged_every_n = accuracy_logged_every_n
self.accuracy_output_every_n = accuracy_output_every_n
# Tensorflow graph computation
self.graph = None
self.session = None
self.saver = None
#Note: for now you can only have one layer of weights that is unique to the task (hidden_size_task is a scalar)
#TODO: improve this later
self.task_w1 = None
self.task_b1 = None
self.task_w2 = None
self.task_b2 = None
def __init__(self,
file_prefix,
users_as_tasks,
user_clusters=True,
eta_filename=None,
regularizers=REGULARIZERS,
tolerance=.0001,
max_iter=100,
val_type=VALIDATION_TYPE,
c_vals=C_VALS,
beta_vals=B_VALS,
v_vals=V_VALS,
kernels=KERNELS,
print_iters=False,
optimize_labels=None,
cont=False,
test_run=False,
results_path=DEFAULT_RESULTS_PATH,
figures_path=DEFAULT_FIGURES_PATH,
datasets_path=DEFAULT_DATASETS_PATH,
etas_path=DEFAULT_ETAS_PATH,
num_cross_folds=DEFAULT_NUM_CROSS_FOLDS,
drop20=False,
test_csv_filename=None):
self.results_path = results_path
self.figures_path = figures_path
self.datasets_path = datasets_path
self.etas_path = etas_path
self.file_prefix = file_prefix
self.cont = cont
self.val_type = val_type
self.users_as_tasks = users_as_tasks
self.cluster_users = user_clusters
self.drop20 = drop20
if test_csv_filename is not None:
self.test_csv_filename = self.datasets_path + test_csv_filename
else:
self.test_csv_filename = None
self.save_prefix = self.getSavePrefix(file_prefix, replace=cont)
self.test_tasks = helper.loadPickledTaskList(datasets_path,
file_prefix,
"Test",
fix_y=True)
self.train_tasks = helper.loadPickledTaskList(datasets_path,
file_prefix,
"Train",
fix_y=True)
if self.val_type != 'cross':
self.val_tasks = helper.loadPickledTaskList(datasets_path,
file_prefix,
"Val",
fix_y=True)
# print dataset sizes
print "Num train points:", sum([len(t['Y']) for t in self.train_tasks])
if self.val_type != 'cross':
print "Num val points:", sum([len(t['Y']) for t in self.val_tasks])
print "Num test points:", sum([len(t['Y']) for t in self.test_tasks])
if self.val_type != 'cross':
self.initializeMTMKLModel(self.train_tasks)
else:
self.classifier = None
self.n_feats = helper.calculateNumFeatsInTaskList(self.test_tasks)
self.n_tasks = len(self.test_tasks)
if optimize_labels is None:
self.optimize_labels = [
'tomorrow_Group_Happiness_Evening_Label',
'tomorrow_Group_Health_Evening_Label',
'tomorrow_Group_Calmness_Evening_Label'
]
else:
self.optimize_labels = optimize_labels
self.c_vals = c_vals
self.v_vals = v_vals
self.kernels = kernels
self.beta_vals = beta_vals
self.regularizers = regularizers
self.tolerance = tolerance
self.max_iter = max_iter
self.print_iters = print_iters
if test_run:
print "This is only a testing run. Using cheap settings to make it faster"
self.c_vals = [100]
self.beta_vals = [.01]
self.kernels = ['linear']
self.v_vals = [1.0]
self.regularizers = ['L1']
self.max_iter = 1
self.calcNumSettingsDesired()
#storing the results
self.time_sum = 0
if cont:
self.val_results_df = pd.DataFrame.from_csv(self.results_path +
self.save_prefix +
'.csv')
print '\nPrevious validation results df loaded. It has', len(
self.val_results_df), "rows"
self.started_from = len(self.val_results_df)
else:
self.val_results_df = pd.DataFrame()
self.started_from = 0
self.num_cross_folds = num_cross_folds
if self.val_type == 'cross':
helper.generateCrossValPickleFiles(self.datasets_path,
self.file_prefix,
self.num_cross_folds)
def __init__(self,
file_prefix,
users_as_tasks=False,
num_cross_folds=DEFAULT_NUM_CROSS_FOLDS,
cont=False,
results_path=DEFAULT_RESULTS_PATH,
figures_path=DEFAULT_FIGURES_PATH,
datasets_path=DEFAULT_DATASETS_PATH,
test_run=False,
max_iters=DEFAULT_MAX_ITERS,
val_type=DEFAULT_VALIDATION_TYPE,
optimize_labels=None,
test_csv_filename=None):
self.results_path = results_path
self.figures_path = figures_path
self.datasets_path = datasets_path
self.save_prefix = self.getSavePrefix(file_prefix, replace=cont)
self.cont = cont
self.max_iters = max_iters
self.val_type = val_type
self.users_as_tasks = users_as_tasks
self.file_prefix = file_prefix
if test_csv_filename is not None:
self.test_csv_filename = self.datasets_path + test_csv_filename
else:
self.test_csv_filename = None
self.test_tasks = helper.loadPickledTaskList(datasets_path,
file_prefix, "Test")
self.train_tasks = helper.loadPickledTaskList(datasets_path,
file_prefix, "Train")
if self.val_type != 'cross':
self.val_tasks = helper.loadPickledTaskList(
datasets_path, file_prefix, "Val")
self.initializeHBLRModel(self.train_tasks)
else:
self.classifier = None
if users_as_tasks:
self.K = 25
else:
self.K = len(self.test_tasks)
self.n_feats = helper.calculateNumFeatsInTaskList(self.test_tasks)
self.n_tasks = len(self.test_tasks)
if optimize_labels is None:
self.optimize_labels = [
'tomorrow_Group_Happiness_Evening_Label',
'tomorrow_Group_Health_Evening_Label',
'tomorrow_Group_Calmness_Evening_Label'
]
else:
self.optimize_labels = optimize_labels
#parameters that can be tuned
self.tau10s = [10, 1, 0.05, 0.01]
self.tau20s = [1.0, 0.05, 0.01]
self.sigma_multipliers = [.01, 0.1, 1]
self.mu_multipliers = [0.0]
if test_run:
print "This is only a testing run. Using cheap settings to make it faster"
self.K = 2
self.max_iters = 5
self.n_tasks = 2
self.tau10s = [1]
self.tau20s = [.1]
self.sigma_multipliers = [.01]
self.mu_multipliers = [0]
self.calcNumSettingsDesired()
#storing the results
self.time_sum = 0
if cont:
self.val_results_df = pd.DataFrame.from_csv(self.results_path +
self.save_prefix +
'.csv')
print '\nPrevious validation results df loaded. It has', len(
self.val_results_df), "rows"
self.started_from = len(self.val_results_df)
else:
self.val_results_df = pd.DataFrame()
self.started_from = 0
self.num_cross_folds = num_cross_folds
if self.val_type == 'cross':
helper.generateCrossValPickleFiles(self.datasets_path,
self.file_prefix,
self.num_cross_folds)