def plot_overflow():
overflow_models = []
for i in (0.05, 0.1, 0.2, 0.3, 0.4, 0.5):
overflow_models.append(f'CORnet-S_overflow_{i}')
# conn = get_connection()
conn = get_connection('scores_openmind')
result_overflow_models = load_scores(
conn, overflow_models,
['dicarlo.Majaj2015.IT-pls', 'dicarlo.Rajalingham2018-i2n'])
result_base_random = load_scores(
conn, ['CORnet-S_random'],
['dicarlo.Majaj2015.IT-pls', 'dicarlo.Rajalingham2018-i2n'])
result_base = load_scores(
conn, ['CORnet-S'],
['dicarlo.Majaj2015.IT-pls', 'dicarlo.Rajalingham2018-i2n'])
it_data = {}
it_data['overflow_init'] = []
it_data['base_untrained'] = []
it_data['base_trained'] = []
behavior_data = {}
behavior_data['overflow_init'] = []
behavior_data['base_untrained'] = []
behavior_data['base_trained'] = []
labels = []
for i in (0.05, 0.1, 0.2, 0.3, 0.4, 0.5):
labels.append(i)
it_data['overflow_init'].append(
result_overflow_models[f'CORnet-S_overflow_{i}'][0])
it_data['base_untrained'].append(
result_base_random[f'CORnet-S_random'][0])
it_data['base_trained'].append(result_base[f'CORnet-S'][0])
behavior_data['overflow_init'].append(
result_overflow_models[f'CORnet-S_overflow_{i}'][1])
behavior_data['base_untrained'].append(
result_base_random[f'CORnet-S_random'][1])
behavior_data['base_trained'].append(result_base[f'CORnet-S'][1])
print(it_data)
print(behavior_data)
plot_data_base(it_data, 'IT benchmark overflow_initialization', labels,
'Overflow initialization in %', 'Score', [0.0, 0.6])
plot_data_base(behavior_data, 'Behavior benchmark overflow initialization',
labels, 'Overflow initialization in %', 'Score', [0.0, 0.6])
def plot_high_low_nullify_separate():
high_var_models = []
high_var_trained_models = []
low_var_models = []
low_var_trained_models = []
for i in (0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95):
high_var_models.append(f'CORnet-S_high_zero_{i}')
high_var_trained_models.append(f'CORnet-S_trained_high_zero_{i}')
low_var_models.append(f'CORnet-S_low_zero_{i}')
low_var_trained_models.append(f'CORnet-S_trained_low_zero_{i}')
conn = get_connection()
result_high_var = load_scores(
conn, high_var_models,
['dicarlo.Majaj2015.IT-pls', 'dicarlo.Rajalingham2018-i2n'])
result_high_var_trained = load_scores(
conn, high_var_trained_models,
['dicarlo.Majaj2015.IT-pls', 'dicarlo.Rajalingham2018-i2n'])
result_low_var = load_scores(
conn, low_var_models,
['dicarlo.Majaj2015.IT-pls', 'dicarlo.Rajalingham2018-i2n'])
result_low_var_trained = load_scores(
conn, low_var_trained_models,
['dicarlo.Majaj2015.IT-pls', 'dicarlo.Rajalingham2018-i2n'])
result_base_random = load_scores(
conn, ['CORnet-S_random'],
['dicarlo.Majaj2015.IT-pls', 'dicarlo.Rajalingham2018-i2n'])
result_base = load_scores(
conn, ['CORnet-S'],
['dicarlo.Majaj2015.IT-pls', 'dicarlo.Rajalingham2018-i2n'])
it_data = {}
it_data['high_zero'] = []
it_data['low_zero'] = []
# it_data['high_zero_trained'] = []
# it_data['low_zero_trained'] = []
it_data['base'] = []
# it_data['base_trained'] = []
behavior_data = {}
behavior_data['high_zero'] = []
behavior_data['low_zero'] = []
# behavior_data['high_zero_trained'] = []
# behavior_data['low_zero_trained'] = []
behavior_data['base'] = []
# behavior_data['base_trained'] = []
labels = []
for i in (0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95):
labels.append(i)
# it_data['high_zero'].append(result_high_var[f'CORnet-S_high_zero_{i}'][0])
it_data['high_zero'].append(
result_high_var_trained[f'CORnet-S_trained_high_zero_{i}'][0])
# it_data['low_zero'].append(result_low_var[f'CORnet-S_low_zero_{i}'][0])
it_data['low_zero'].append(
result_low_var_trained[f'CORnet-S_trained_low_zero_{i}'][0])
# it_data['base'].append(result_base_random[f'CORnet-S_random'][0])
it_data['base'].append(result_base[f'CORnet-S'][0])
# behavior_data['high_zero'].append(result_high_var[f'CORnet-S_high_zero_{i}'][1])
behavior_data['high_zero'].append(
result_high_var_trained[f'CORnet-S_trained_high_zero_{i}'][1])
# behavior_data['low_zero'].append(result_low_var[f'CORnet-S_low_zero_{i}'][1])
behavior_data['low_zero'].append(
result_low_var_trained[f'CORnet-S_trained_low_zero_{i}'][1])
# behavior_data['base'].append(result_base_random[f'CORnet-S_random'][1])
behavior_data['base'].append(result_base[f'CORnet-S'][1])
plot_data_base(it_data,
'IT Benchmark Zero Trained',
labels,
'Zero values in %',
'Score', [0.0, 0.6],
base_line=result_base[f'CORnet-S'][0])
plot_data_base(behavior_data,
'Behavior Benchmark Zero Trained',
labels,
'Zero values in %',
'Score', [0.0, 0.6],
base_line=result_base[f'CORnet-S'][1])
def plot_single_layer_perturbation():
norm_dist_models = []
jumbler_models = []
fixed_models = []
fixed_small_models = []
for i in range(1, 18):
norm_dist_models.append(f'CORnet-S_norm_dist_L{i}')
jumbler_models.append(f'CORnet-S_jumbler_L{i}')
fixed_models.append(f'CORnet-S_fixed_value_L{i}')
fixed_small_models.append(f'CORnet-S_fixed_value_small_L{i}')
conn = get_connection()
result_norm = load_scores(
conn, norm_dist_models,
['dicarlo.Majaj2015.IT-pls', 'dicarlo.Rajalingham2018-i2n'])
result_jumbler = load_scores(
conn, jumbler_models,
['dicarlo.Majaj2015.IT-pls', 'dicarlo.Rajalingham2018-i2n'])
result_fixed = load_scores(
conn, fixed_models,
['dicarlo.Majaj2015.IT-pls', 'dicarlo.Rajalingham2018-i2n'])
result_base = load_scores(
conn, ['CORnet-S'],
['dicarlo.Majaj2015.IT-pls', 'dicarlo.Rajalingham2018-i2n'])
result_fixed_small = load_scores(
conn, fixed_small_models,
['dicarlo.Majaj2015.IT-pls', 'dicarlo.Rajalingham2018-i2n'])
it_data = {}
it_data['norm_dist'] = []
it_data['jumbler'] = []
it_data['fixed_value'] = []
it_data['fixed_small_value'] = []
it_data['base'] = []
behavior_data = {}
behavior_data['norm_dist'] = []
behavior_data['jumbler'] = []
behavior_data['fixed_value'] = []
behavior_data['fixed_small_value'] = []
behavior_data['base'] = []
labels = []
for i in range(1, 18):
labels.append(f'L{i}')
it_data['norm_dist'].append(result_norm[f'CORnet-S_norm_dist_L{i}'][0])
it_data['jumbler'].append(result_jumbler[f'CORnet-S_jumbler_L{i}'][0])
it_data['fixed_value'].append(
result_fixed[f'CORnet-S_fixed_value_L{i}'][0])
it_data['base'].append(result_base[f'CORnet-S'][0])
it_data['fixed_small_value'].append(
result_fixed_small[f'CORnet-S_fixed_value_small_L{i}'][0])
behavior_data['norm_dist'].append(
result_norm[f'CORnet-S_norm_dist_L{i}'][1])
behavior_data['jumbler'].append(
result_jumbler[f'CORnet-S_jumbler_L{i}'][1])
behavior_data['fixed_value'].append(
result_fixed[f'CORnet-S_fixed_value_L{i}'][1])
behavior_data['base'].append(result_base[f'CORnet-S'][1])
behavior_data['fixed_small_value'].append(
result_fixed_small[f'CORnet-S_fixed_value_small_L{i}'][1])
plot_data_base(it_data, 'IT Benchmark single layer', labels, 'Conv Layers',
'Score', [0.0, 0.6])
plot_data_base(behavior_data, 'Behavior Benchmark single layer', labels,
'Conv Layers', 'Score', [0.0, 0.6])
def plot_high_low_nullify():
high_var_models = []
high_var_trained_models = []
low_var_models = []
low_var_trained_models = []
for i in (0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95):
high_var_models.append(f'CORnet-S_high_zero_{i}')
high_var_trained_models.append(f'CORnet-S_trained_high_zero_{i}')
low_var_models.append(f'CORnet-S_low_zero_{i}')
low_var_trained_models.append(f'CORnet-S_trained_low_zero_{i}')
# conn = get_connection()
conn = get_connection('scores_openmind')
result_high_var = load_scores(
conn, high_var_models,
['dicarlo.Majaj2015.IT-pls', 'dicarlo.Rajalingham2018-i2n'])
result_high_var_trained = load_scores(
conn, high_var_trained_models,
['dicarlo.Majaj2015.IT-pls', 'dicarlo.Rajalingham2018-i2n'])
result_low_var = load_scores(
conn, low_var_models,
['dicarlo.Majaj2015.IT-pls', 'dicarlo.Rajalingham2018-i2n'])
result_low_var_trained = load_scores(
conn, low_var_trained_models,
['dicarlo.Majaj2015.IT-pls', 'dicarlo.Rajalingham2018-i2n'])
result_base_random = load_scores(
conn, ['CORnet-S_random'],
['dicarlo.Majaj2015.IT-pls', 'dicarlo.Rajalingham2018-i2n'])
result_base = load_scores(
conn, ['CORnet-S'],
['dicarlo.Majaj2015.IT-pls', 'dicarlo.Rajalingham2018-i2n'])
it_data = {
'high_zero_untrained': [],
'low_zero_untrained': [],
'high_zero_trained': [],
'low_zero_trained': [],
'base_untrained': [],
'base_trained': []
}
behavior_data = {
'high_zero_untrained': [],
'low_zero_untrained': [],
'high_zero_trained': [],
'low_zero_trained': [],
'base_untrained': [],
'base_trained': []
}
labels = []
for i in (0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95):
labels.append(i)
it_data['high_zero_untrained'].append(
result_high_var[f'CORnet-S_high_zero_{i}'][0])
it_data['high_zero_trained'].append(
result_high_var_trained[f'CORnet-S_trained_high_zero_{i}'][0])
it_data['low_zero_untrained'].append(
result_low_var[f'CORnet-S_low_zero_{i}'][0])
it_data['low_zero_trained'].append(
result_low_var_trained[f'CORnet-S_trained_low_zero_{i}'][0])
it_data['base_untrained'].append(
result_base_random[f'CORnet-S_random'][0])
it_data['base_trained'].append(result_base[f'CORnet-S'][0])
behavior_data['high_zero_untrained'].append(
result_high_var[f'CORnet-S_high_zero_{i}'][1])
behavior_data['high_zero_trained'].append(
result_high_var_trained[f'CORnet-S_trained_high_zero_{i}'][1])
behavior_data['low_zero_untrained'].append(
result_low_var[f'CORnet-S_low_zero_{i}'][1])
behavior_data['low_zero_trained'].append(
result_low_var_trained[f'CORnet-S_trained_low_zero_{i}'][1])
behavior_data['base_untrained'].append(
result_base_random[f'CORnet-S_random'][1])
behavior_data['base_trained'].append(result_base[f'CORnet-S'][1])
plot_data_base(it_data, 'it_benchmark_zero', labels, 'Zero values in %',
'Score', [0.0, 0.6])
plot_data_base(behavior_data, 'behavior_benchmark_zero', labels,
'Zero values in %', 'Score', [0.0, 0.6])