def find_genes_CX(drug, test_tcga_expr):
tf.keras.backend.clear_session()
attr_ind = []
for seed in range(1, 11):
exp = CXPlain.load('gene_finding/results/CX_ind1/%s/seed%d/explainer' %
(drug, seed),
relpath=True)
attr = exp.explain(test_tcga_expr.values)
attr = pd.DataFrame(attr,
index=test_tcga_expr.index,
columns=dataset.hgnc)
attr_ind.append(attr)
all_attr = pd.DataFrame(columns=['seed', 'sample'] + list(dataset.hgnc))
i = 0
for sample in attr_ind[0].index:
for seed in range(10):
all_attr.loc[i] = [seed, sample] + list(attr_ind[seed].loc[sample])
i += 1
print('boat')
boat = borda_of_all_tuples(all_attr)
print('seed then sample')
seed_then_sample = sample_borda_of_seed_bordas(all_attr,
test_tcga_expr.index)
print('sample then seed')
sample_then_seed = seed_borda_of_sample_bordas(all_attr, range(10))
return boat, seed_then_sample, sample_then_seed
def test_imdb_padded_valid(self):
num_samples = 32
num_words = 1024
(x_train, y_train), (x_test, y_test) = TestUtil.get_imdb(word_dictionary_size=num_words,
num_subsamples=num_samples)
explained_model = RandomForestClassifier(n_estimators=64, max_depth=5, random_state=1)
counter = CountVectoriser(num_words)
tfidf_transformer = TfidfTransformer()
explained_model = Pipeline([('counts', counter),
('tfidf', tfidf_transformer),
('model', explained_model)])
explained_model.fit(x_train, y_train)
model_builder = RNNModelBuilder(embedding_size=num_words, with_embedding=True,
num_layers=2, num_units=32, activation="relu", p_dropout=0.2, verbose=0,
batch_size=32, learning_rate=0.001, num_epochs=2, early_stopping_patience=128)
masking_operation = WordDropMasking()
loss = binary_crossentropy
explainer = CXPlain(explained_model, model_builder, masking_operation, loss)
x_train = pad_sequences(x_train, padding="post", truncating="post", dtype=int)
x_test = pad_sequences(x_test, padding="post", truncating="post", dtype=int, maxlen=x_train.shape[1])
explainer.fit(x_train, y_train)
eval_score = explainer.score(x_test, y_test)
train_score = explainer.get_last_fit_score()
median = explainer.predict(x_test)
self.assertTrue(median.shape == x_test.shape)
def test_boston_housing_valid(self):
(x_train, y_train), (x_test, y_test) = TestUtil.get_boston_housing()
explained_model = RandomForestRegressor(n_estimators=64,
max_depth=5,
random_state=1)
explained_model.fit(x_train, y_train)
model_builder = MLPModelBuilder(num_layers=2,
num_units=32,
activation="relu",
p_dropout=0.2,
verbose=0,
batch_size=32,
learning_rate=0.001,
num_epochs=2,
early_stopping_patience=128)
masking_operation = ZeroMasking()
loss = mean_squared_error
explainer = CXPlain(explained_model, model_builder, masking_operation,
loss)
explainer.fit(x_train, y_train)
self.assertEqual(explainer.prediction_model.output_shape,
(None, np.prod(x_test.shape[1:])))
eval_score = explainer.score(x_test, y_test)
train_score = explainer.get_last_fit_score()
median = explainer.predict(x_test)
self.assertTrue(median.shape == x_test.shape)
def test_time_series_valid(self):
num_samples = 1024
fixed_length = 99
(x_train,
y_train), (x_test, y_test) = TestUtil.get_random_fixed_length_dataset(
num_samples=num_samples, fixed_length=fixed_length)
model_builder = RNNModelBuilder(with_embedding=False,
num_layers=2,
num_units=32,
activation="relu",
p_dropout=0.2,
verbose=0,
batch_size=32,
learning_rate=0.001,
num_epochs=2,
early_stopping_patience=128)
explained_model = MLPClassifier()
explained_model.fit(x_train.reshape((-1, np.prod(x_train.shape[1:]))),
y_train)
masking_operation = ZeroMasking()
loss = binary_crossentropy
explainer = CXPlain(explained_model,
model_builder,
masking_operation,
loss,
flatten_for_explained_model=True)
explainer.fit(x_train, y_train)
eval_score = explainer.score(x_test, y_test)
train_score = explainer.get_last_fit_score()
median = explainer.predict(x_test)
self.assertTrue(median.shape == x_test.shape)
def test_mnist_unet_valid(self):
num_subsamples = 100
(x_train, y_train), (x_test, y_test) = TestUtil.get_mnist(flattened=False, num_subsamples=num_subsamples)
explained_model = MLPClassifier(solver='lbfgs', alpha=1e-5,
hidden_layer_sizes=(64, 32), random_state=1)
explained_model.fit(x_train.reshape((len(x_train), -1)), y_train)
masking_operation = ZeroMasking()
loss = categorical_crossentropy
downsample_factors = [(2, 2), (4, 4), (4, 7), (7, 4), (7, 7)]
with_bns = [True if i % 2 == 0 else False for i in range(len(downsample_factors))]
for downsample_factor, with_bn in zip(downsample_factors, with_bns):
model_builder = UNetModelBuilder(downsample_factor, num_layers=2, num_units=64, activation="relu",
p_dropout=0.2, verbose=0, batch_size=256, learning_rate=0.001,
num_epochs=2, early_stopping_patience=128, with_bn=with_bn)
explainer = CXPlain(explained_model, model_builder, masking_operation, loss,
downsample_factors=downsample_factor, flatten_for_explained_model=True)
explainer.fit(x_train, y_train)
eval_score = explainer.score(x_test, y_test)
train_score = explainer.get_last_fit_score()
median = explainer.predict(x_test)
self.assertTrue(median.shape == x_test.shape)
def test_mnist_unet_with_shape_valid(self):
num_subsamples = 100
(x_train,
y_train), (x_test,
y_test) = TestUtil.get_mnist(flattened=False,
num_subsamples=num_subsamples)
explained_model_builder = MLPModelBuilder(num_layers=2,
num_units=64,
activation="relu",
p_dropout=0.2,
verbose=0,
batch_size=256,
learning_rate=0.001,
num_epochs=2,
early_stopping_patience=128)
input_shape = x_train.shape[1:]
input_layer = Input(shape=input_shape)
last_layer = Flatten()(input_layer)
last_layer = explained_model_builder.build(last_layer)
last_layer = Dense(y_train.shape[-1], activation="softmax")(last_layer)
explained_model = Model(input_layer, last_layer)
explained_model.compile(loss="categorical_crossentropy",
optimizer="adam")
explained_model.fit(x_train, y_train)
masking_operation = ZeroMasking()
loss = categorical_crossentropy
downsample_factors = [(2, 2), (4, 4), (4, 7), (7, 4), (7, 7)]
with_bns = [
True if i % 2 == 0 else False
for i in range(len(downsample_factors))
]
for downsample_factor, with_bn in zip(downsample_factors, with_bns):
model_builder = UNetModelBuilder(downsample_factor,
num_layers=2,
num_units=64,
activation="relu",
p_dropout=0.2,
verbose=0,
batch_size=256,
learning_rate=0.001,
num_epochs=2,
early_stopping_patience=128,
with_bn=with_bn)
explainer = CXPlain(explained_model,
model_builder,
masking_operation,
loss,
downsample_factors=downsample_factor)
explainer.fit(x_train, y_train)
eval_score = explainer.score(x_test, y_test)
train_score = explainer.get_last_fit_score()
median = explainer.predict(x_test)
self.assertTrue(median.shape == x_test.shape)
def test_mnist_valid(self):
num_subsamples = 100
(x_train,
y_train), (x_test,
y_test) = TestUtil.get_mnist(flattened=False,
num_subsamples=num_subsamples)
explained_model = MLPClassifier(solver='lbfgs',
alpha=1e-5,
hidden_layer_sizes=(64, 32),
random_state=1)
explained_model.fit(x_train.reshape((len(x_train), -1)), y_train)
model_builder = MLPModelBuilder(num_layers=2,
num_units=64,
activation="relu",
p_dropout=0.2,
verbose=0,
batch_size=256,
learning_rate=0.001,
num_epochs=3,
early_stopping_patience=128)
masking_operation = ZeroMasking()
loss = categorical_crossentropy
downsample_factors = [(2, 2), (4, 4), (4, 7), (7, 4), (7, 7)]
for downsample_factor in downsample_factors:
explainer = CXPlain(explained_model,
model_builder,
masking_operation,
loss,
num_models=2,
downsample_factors=downsample_factor,
flatten_for_explained_model=True)
explainer.fit(x_train, y_train)
eval_score = explainer.score(x_test, y_test)
train_score = explainer.get_last_fit_score()
median, confidence = explainer.predict(x_test,
confidence_level=0.95)
self.assertTrue(median.shape == x_test.shape)
self.assertTrue(confidence.shape == x_test.shape[:-1] + (2, ))
# Flatten predictions for iteration below.
median = median.reshape((len(x_test), -1))
confidence = confidence.reshape((len(x_test), -1, 2))
for sample_idx in range(len(x_test)):
for feature_idx in range(len(x_test[sample_idx])):
self.assertTrue(confidence[sample_idx][feature_idx][0] <=
median[sample_idx][feature_idx] <=
confidence[sample_idx][feature_idx][1])
self.assertTrue(
confidence[sample_idx][feature_idx][0] >= 0)
self.assertTrue(
confidence[sample_idx][feature_idx][1] >= 0)
def test_mnist_confidence_levels_valid(self):
num_subsamples = 100
(x_train,
y_train), (x_test,
y_test) = TestUtil.get_mnist(flattened=False,
num_subsamples=num_subsamples)
explained_model = MLPClassifier(solver='lbfgs',
alpha=1e-5,
hidden_layer_sizes=(64, 32),
random_state=1)
explained_model.fit(x_train.reshape((len(x_train), -1)), y_train)
model_builder = MLPModelBuilder(num_layers=2,
num_units=64,
activation="relu",
p_dropout=0.2,
verbose=0,
batch_size=256,
learning_rate=0.001,
num_epochs=3,
early_stopping_patience=128)
masking_operation = ZeroMasking()
loss = categorical_crossentropy
confidence_levels = [0.0, 1.0, 1.01, -0.01]
for confidence_level in confidence_levels:
downsample_factor = (2, 2)
explainer = CXPlain(explained_model,
model_builder,
masking_operation,
loss,
num_models=2,
downsample_factors=downsample_factor,
flatten_for_explained_model=True)
explainer.fit(x_train, y_train)
with self.assertRaises(ValueError):
_ = explainer.predict(x_test,
confidence_level=confidence_level)
def test_boston_housing_no_fit_invalid(self):
(x_train, y_train), (x_test, y_test) = TestUtil.get_boston_housing()
explained_model = RandomForestRegressor(n_estimators=64,
max_depth=5,
random_state=1)
explained_model.fit(x_train, y_train)
model_builder = MLPModelBuilder(num_layers=2,
num_units=32,
activation="relu",
p_dropout=0.2,
verbose=0,
batch_size=32,
learning_rate=0.001,
num_epochs=2,
early_stopping_patience=128)
masking_operation = ZeroMasking()
loss = mean_squared_error
explainer = CXPlain(explained_model, model_builder, masking_operation,
loss)
with self.assertRaises(AssertionError):
explainer.predict(x_test, y_test)
with self.assertRaises(AssertionError):
explainer.score(x_test, y_test)
def test_boston_housing_confidence_level_invalid(self):
(x_train, y_train), (x_test, y_test) = TestUtil.get_boston_housing()
explained_model = RandomForestRegressor(n_estimators=64,
max_depth=5,
random_state=1)
explained_model.fit(x_train, y_train)
model_builder = MLPModelBuilder(num_layers=2,
num_units=32,
activation="relu",
p_dropout=0.2,
verbose=0,
batch_size=32,
learning_rate=0.001,
num_epochs=3,
early_stopping_patience=128)
masking_operation = ZeroMasking()
loss = mean_squared_error
num_models = 2
explainer = CXPlain(explained_model,
model_builder,
masking_operation,
loss,
num_models=num_models)
explainer.fit(x_train, y_train)
invalid_confidence_levels = [1.01, -0.5, -0.01]
for confidence_level in invalid_confidence_levels:
with self.assertRaises(ValueError):
explainer.predict(x_test, confidence_level=confidence_level)
def test_nlp_not_padded_invalid(self):
num_words = 1024
(x_train, y_train), (_, _) = TestUtil.get_random_variable_length_dataset(max_value=num_words)
explained_model = RandomForestClassifier(n_estimators=64, max_depth=5, random_state=1)
counter = CountVectoriser(num_words)
tfidf_transformer = TfidfTransformer()
explained_model = Pipeline([('counts', counter),
('tfidf', tfidf_transformer),
('model', explained_model)])
explained_model.fit(x_train, y_train)
model_builder = RNNModelBuilder(embedding_size=num_words, with_embedding=True,
num_layers=2, num_units=32, activation="relu", p_dropout=0.2, verbose=0,
batch_size=32, learning_rate=0.001, num_epochs=2, early_stopping_patience=128)
masking_operation = WordDropMasking()
loss = binary_crossentropy
explainer = CXPlain(explained_model, model_builder, masking_operation, loss)
with self.assertRaises(ValueError):
explainer.fit(x_train, y_train)
def test_boston_housing_valid(self):
(x_train, y_train), (x_test, y_test) = TestUtil.get_boston_housing()
explained_model = RandomForestRegressor(n_estimators=64,
max_depth=5,
random_state=1)
explained_model.fit(x_train, y_train)
model_builder = MLPModelBuilder(num_layers=2,
num_units=32,
activation="relu",
p_dropout=0.2,
verbose=0,
batch_size=32,
learning_rate=0.001,
num_epochs=3,
early_stopping_patience=128)
masking_operation = ZeroMasking()
loss = mean_squared_error
for num_models in [2, 5, 10]:
explainer = CXPlain(explained_model,
model_builder,
masking_operation,
loss,
num_models=num_models)
explainer.fit(x_train, y_train)
eval_score = explainer.score(x_test, y_test)
train_score = explainer.get_last_fit_score()
median, confidence = explainer.predict(x_test,
confidence_level=0.95)
self.assertTrue(median.shape == x_test.shape)
self.assertTrue(confidence.shape == x_test.shape + (2, ))
# Flatten predictions for iteration below.
median = median.reshape((len(x_test), -1))
confidence = confidence.reshape((len(x_test), -1, 2))
for sample_idx in range(len(x_test)):
for feature_idx in range(len(x_test[sample_idx])):
self.assertTrue(confidence[sample_idx][feature_idx][0] <=
median[sample_idx][feature_idx] <=
confidence[sample_idx][feature_idx][1])
self.assertTrue(
confidence[sample_idx][feature_idx][0] >= 0)
self.assertTrue(
confidence[sample_idx][feature_idx][1] >= 0)
def find_genes_CX(drug, model, gdsc_expr, gdsc_dr, test_tcga_expr):
print('obtaining masked data...')
# masked_data, list_of_baselines = get_masked_data_for_CXPlain(model, gdsc_expr, pathway_matrix)
masked_data, list_of_baselines = get_masked_data_for_CXPlain(model, gdsc_expr, pathway_matrix, subtract_mean=True)
lb = np.concatenate(list_of_baselines).reshape(len(gdsc_expr), -1)
lb = pd.DataFrame(lb, index=gdsc_expr.index, columns=pathway_names)
lb.to_csv(res_dir + drug + '/baselines.csv')
# print(lb.shape)
# exit()
print('obtained masked data...')
import tensorflow as tf
tf.compat.v1.disable_v2_behavior()
tf.keras.backend.clear_session()
tf.random.set_seed(SEED)
from tensorflow.python.keras.losses import mean_squared_error as loss
from cxplain import CXPlain
from cxplain.backend.model_builders.custom_mlp import CustomMLPModelBuilder
# from cxplain.backend.masking.zero_masking import FastZeroMasking
n_pathways = len(pathway_names)
model_builder = CustomMLPModelBuilder(num_layers=2, num_units=512, batch_size=16, learning_rate=0.001, n_feature_groups=n_pathways)
# masking_operation = FastZeroMasking()
print(gdsc_expr.values.shape, gdsc_dr.values.shape)
print("Fitting CXPlain model")
explainer = CXPlain(model, model_builder, None, loss, num_models=3)
explainer.fit(gdsc_expr.values, gdsc_dr.values, masked_data=masked_data)
print("Attributing using CXPlain")
attr,_ = explainer.explain_groups(test_tcga_expr.values)
print('attr')
attr = pd.DataFrame(attr, index=test_tcga_expr.index, columns=pathway_names)
borda = get_ranked_list(attr, k=n_pathways)
attr_mean = list(np.abs(attr).mean(axis=0).nlargest(n_pathways).index)
out = pd.DataFrame(columns=['borda', 'mean'])
out['borda'] = borda
out['mean'] = attr_mean
out.to_csv(res_dir + drug + '/pathways.csv', index=False)
if not os.path.exists(res_dir + drug + '/explainer/'):
os.mkdir(res_dir + drug + '/explainer/')
explainer.save(res_dir + drug + '/explainer/', custom_model_saver=None)
def find_genes_CX(drug, model, meta, gdsc_expr, gdsc_dr, test_tcga_expr,
save_dir):
torch.manual_seed(SEED)
np.random.seed(SEED)
print('obtaining masked data...')
masked_data = get_masked_data_for_CXPlain(model, gdsc_expr)
print('obtained masked data...')
# get_masked_data_for_CXPlain(model, test_tcga_expr)
import tensorflow as tf
tf.compat.v1.disable_v2_behavior()
tf.keras.backend.clear_session()
tf.random.set_seed(SEED)
from tensorflow.python.keras.losses import mean_squared_error as loss
from cxplain import MLPModelBuilder, CXPlain
# from cxplain.backend.masking.zero_masking import FastZeroMasking
model_builder = MLPModelBuilder(num_layers=2,
num_units=512,
batch_size=8,
learning_rate=0.001)
# masking_operation = FastZeroMasking()
print(gdsc_expr.values.shape, gdsc_dr.values.shape)
print("Fitting CXPlain model")
explainer = CXPlain(model, model_builder, None, loss)
explainer.fit(gdsc_expr.values, gdsc_dr.values, masked_data=masked_data)
print("Attributing using CXPlain")
attr = explainer.explain(test_tcga_expr.values)
attr = pd.DataFrame(attr, index=test_tcga_expr.index, columns=dataset.hgnc)
borda = get_ranked_list(attr)
attr_mean = list(np.abs(attr).mean(axis=0).nlargest(200).index)
out = pd.DataFrame(columns=['borda', 'mean'])
out['borda'] = borda
out['mean'] = attr_mean
out.to_csv(save_dir + '/genes.csv', index=False)
if not os.path.exists(save_dir + '/explainer/'):
os.mkdir(save_dir + '/explainer/')
explainer.save(save_dir + '/explainer/')
def find_genes_CX(drug, model, gdsc_expr, gdsc_dr, test_tcga_expr):
print('obtaining precalculating omegas...')
loss = torch.nn.MSELoss(reduction='none')
# idx = gdsc_expr.index[:10]
# gdsc_expr = gdsc_expr.loc[idx]
# gdsc_dr = gdsc_dr[idx]
# omegas, lb = precalculate_omegas(model, gdsc_expr, gdsc_dr, pathway_matrix, loss)
# omegas, lb = precalculate_omegas_scaled(model, gdsc_expr, gdsc_dr, pathway_matrix, loss)
# omegas, lb = precalculate_omegas(model, gdsc_expr, gdsc_dr, pathway_matrix, loss,mode='scaled-difference')
# omegas, lb = precalculate_omegas(model, gdsc_expr, gdsc_dr, pathway_matrix, loss, mode='delta-of-delta')
omegas, lb = precalculate_omegas(model,
gdsc_expr,
gdsc_dr,
pathway_matrix,
loss,
mode='delta-scaled')
if lb is not None:
lb = pd.DataFrame(lb, index=gdsc_expr.index, columns=pathway_names)
lb.to_csv(res_dir + drug + '/baselines.csv')
print('obtained masked data...')
import tensorflow as tf
tf.compat.v1.disable_v2_behavior()
tf.keras.backend.clear_session()
tf.random.set_seed(SEED)
from tensorflow.python.keras.losses import mean_squared_error as loss
from cxplain import CXPlain
from cxplain.backend.model_builders.custom_mlp_precalc import CustomMLPModelBuilder
# from cxplain.backend.masking.zero_masking import FastZeroMasking
n_pathways = len(pathway_names)
model_builder = CustomMLPModelBuilder(num_layers=2,
num_units=512,
batch_size=16,
learning_rate=0.001,
n_feature_groups=n_pathways)
# masking_operation = FastZeroMasking()
print(gdsc_expr.values.shape, gdsc_dr.values.shape)
print("Fitting CXPlain model")
explainer = CXPlain(model, model_builder, None, loss, num_models=3)
explainer.fit_precalc(gdsc_expr.values, gdsc_dr.values, omega=omegas)
print("Attributing using CXPlain")
attr, _ = explainer.explain_groups(test_tcga_expr.values)
print('attr')
attr = pd.DataFrame(attr,
index=test_tcga_expr.index,
columns=pathway_names)
borda = get_ranked_list(attr, k=n_pathways)
attr_mean = list(np.abs(attr).mean(axis=0).nlargest(n_pathways).index)
out = pd.DataFrame(columns=['borda', 'mean'])
out['borda'] = borda
out['mean'] = attr_mean
out.to_csv(res_dir + drug + '/pathways.csv', index=False)
if not os.path.exists(res_dir + drug + '/explainer/'):
os.mkdir(res_dir + drug + '/explainer/')
explainer.save(res_dir + drug + '/explainer/', custom_model_saver=None)
model(masked_sample).unsqueeze(0).detach().numpy())
masked_outs = np.concatenate(list_of_masked_outs)
return (x, y_pred, masked_outs)
model_builder = CustomMLPModelBuilder(num_layers=2,
num_units=32,
batch_size=32,
learning_rate=0.001,
n_feature_groups=10)
# masking_operation = ZeroMasking()
# masking_operation = FastZeroMasking()
k = get_masked_data_for_CXPlain(model, x[:200])
explainer = CXPlain(model, model_builder, None,
loss) #,downsample_factors=(5,))
explainer.fit(x[:200], y[:200], masked_data=k)
attributions = explainer.explain_groups(x[200:])
attr = pd.DataFrame(attributions, index=range(200, 300))
# plt.plot(range(50), np.abs(attr).mean(axis=0), label='attr')
# #plt.plot(range(50), np.abs(mult).mean(axis=0), label='mult')
# plt.show()
print(attr.shape)
print(attr.loc[200])
df = pd.DataFrame(x[200:], index=range(200, 300))
idx = df.loc[np.abs(df[20]) < 0.1].loc[np.abs(df[40]) > 1].index[0]
# plt.plot(range(50), np.abs(attr.loc[idx]), label='attr')
def test_boston_housing_load_save_valid(self):
(x_train, y_train), (x_test, y_test) = TestUtil.get_boston_housing()
explained_model = RandomForestRegressor(n_estimators=64,
max_depth=5,
random_state=1)
explained_model.fit(x_train, y_train)
model_builder = MLPModelBuilder(num_layers=2,
num_units=32,
activation="relu",
p_dropout=0.2,
verbose=0,
batch_size=32,
learning_rate=0.001,
num_epochs=2,
early_stopping_patience=128)
masking_operation = ZeroMasking()
loss = mean_squared_error
num_models_settings = [1, 2]
for num_models in num_models_settings:
explainer = CXPlain(explained_model,
model_builder,
masking_operation,
loss,
num_models=num_models)
explainer.fit(x_train, y_train)
median_1 = explainer.predict(x_test)
tmp_dir_name = tempfile.mkdtemp()
explainer.save(tmp_dir_name)
with self.assertRaises(ValueError):
explainer.save(tmp_dir_name, overwrite=False)
explainer.save(tmp_dir_name, overwrite=True)
explainer.load(tmp_dir_name)
median_2 = explainer.predict(x_test)
self.assertTrue(np.array_equal(median_1, median_2))
shutil.rmtree(tmp_dir_name) # Cleanup.
def test_overwrite_ensemble_model_invalid(self):
(x_train, y_train), (x_test, y_test) = TestUtil.get_boston_housing()
model_builder = MLPModelBuilder()
explained_model = RandomForestRegressor(n_estimators=64,
max_depth=5,
random_state=1)
explained_model.fit(x_train, y_train)
masking_operation = ZeroMasking()
loss = binary_crossentropy
num_models = 5
explainer = CXPlain(explained_model,
model_builder,
masking_operation,
loss,
num_models=num_models)
file_names = [
CXPlain.get_config_file_name(),
CXPlain.get_explained_model_file_name(".pkl"),
CXPlain.get_loss_pkl_file_name(),
CXPlain.get_model_builder_pkl_file_name(),
CXPlain.get_masking_operation_pkl_file_name()
]
# Test with untrained explanation model.
for file_name in file_names:
tmp_dir = TestExplanationModel.make_at_tmp(file_name)
with self.assertRaises(ValueError):
explainer.save(tmp_dir, overwrite=False)
# Test with trained explanation model.
explainer.fit(x_train, y_train)
file_names = [
CXPlain.get_config_file_name(),
CXPlain.get_explained_model_file_name(".pkl"),
CXPlain.get_loss_pkl_file_name(),
CXPlain.get_model_builder_pkl_file_name(),
CXPlain.get_masking_operation_pkl_file_name()
] + [
CXPlain.get_prediction_model_h5_file_name(i)
for i in range(num_models)
]
for file_name in file_names:
tmp_dir = TestExplanationModel.make_at_tmp(file_name)
with self.assertRaises(ValueError):
explainer.save(tmp_dir, overwrite=False)
# _, _, _, test_tcga_expr = dataset.filter_and_normalize_data(drug)
# exp = CXPlain.load('gene_finding/results/%s/%s/explainer'%(folder, drug), custom_model_loader=None, relpath=True)
# attr,_ = exp.explain(test_tcga_expr.values)
# attr = pd.DataFrame(attr, index=test_tcga_expr.index, columns=dataset.genes)
# attr_dict[drug]=attr
attr_dict = {}
conf_dict = {}
for i, drug in enumerate(drugs):
print(drug)
_, _, _, test_tcga_expr = dataset.filter_and_normalize_data(drug)
attr_all = np.zeros((len(test_tcga_expr.index), len(dataset.genes)))
for seed in range(1, 11):
exp = CXPlain.load('gene_finding/results/%s/%s/seed%d/explainer'%(folder, drug, seed), custom_model_loader=None, relpath=True)
attr = exp.explain(test_tcga_expr.values)
attr_all += attr
attr = pd.DataFrame(attr_all/10.0, index=test_tcga_expr.index, columns=dataset.genes)
attr_dict[drug]=attr
fig, axes = plt.subplots(7, 2, figsize=(14, 35))
writer_a = pd.ExcelWriter('gene_finding/results/%s/top_genes_mean_of_means_aggregation.xlsx'%folder, engine='xlsxwriter')
conv = pd.DataFrame(index=dataset.genes, columns=['hgnc'])
conv['hgnc'] = dataset.hgnc
for i, drug in enumerate(drugs):
def cxpl(model_dir, data_dir, results_subdir, random_seed, resolution):
np.random.seed(random_seed)
tf.set_random_seed(np.random.randint(1 << 31))
session_conf = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)
set_session(sess)
# parser config
config_file = model_dir+ "/config.ini"
print("Config File Path:", config_file,flush=True)
assert os.path.isfile(config_file)
cp = ConfigParser()
cp.read(config_file)
output_dir = os.path.join(results_subdir, "classification_results/test")
print("Output Directory:", output_dir,flush=True)
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
# default config
image_dimension = cp["TRAIN"].getint("image_dimension")
gan_resolution = resolution
batch_size = cp["TEST"].getint("batch_size")
use_best_weights = cp["TEST"].getboolean("use_best_weights")
if use_best_weights:
print("** Using BEST weights",flush=True)
model_weights_path = os.path.join(results_subdir, "classification_results/train/best_weights.h5")
else:
print("** Using LAST weights",flush=True)
model_weights_path = os.path.join(results_subdir, "classification_results/train/weights.h5")
print("** DenseNet Input Resolution:", image_dimension, flush=True)
print("** GAN Image Resolution:", gan_resolution, flush=True)
# get test sample count
test_dir = os.path.join(results_subdir, "inference/test")
shutil.copy(test_dir+"/test.csv", output_dir)
# Get class names
class_names = get_class_names(output_dir,"test")
tfrecord_dir_te = os.path.join(data_dir, "test")
test_counts, _ = get_sample_counts(output_dir, "test", class_names)
# get indicies (all of csv file for validation)
print("** test counts:", test_counts, flush=True)
# compute steps
test_steps = int(np.floor(test_counts / batch_size))
print("** test_steps:", test_steps, flush=True)
log2_record = int(np.log2(gan_resolution))
record_file_ending = "*"+ np.str(log2_record)+ ".tfrecords"
print("** resolution ", gan_resolution, " corresponds to ", record_file_ending, " TFRecord file.", flush=True)
# Get Model
# ------------------------------------
input_shape=(image_dimension, image_dimension, 3)
img_input = Input(shape=input_shape)
base_model = DenseNet121(
include_top = False,
weights = None,
input_tensor = img_input,
input_shape = input_shape,
pooling = "avg")
x = base_model.output
predictions = Dense(len(class_names), activation="sigmoid", name="predictions")(x)
model = Model(inputs=img_input, outputs = predictions)
print(" ** load model from:", model_weights_path, flush=True)
model.load_weights(model_weights_path)
# ------------------------------------
print("** load test generator **", flush=True)
test_seq = TFWrapper(
tfrecord_dir=tfrecord_dir_te,
record_file_endings = record_file_ending,
batch_size = batch_size,
model_target_size = (image_dimension, image_dimension),
steps = None,
augment=False,
shuffle=False,
prefetch=True,
repeat=False)
print("** make prediction **", flush=True)
test_seq.initialise()
x_all, y_all = test_seq.get_all_test_data()
print("X-Test Shape:", x_all.shape,flush=True)
print("Y-Test Shape:", y_all.shape,flush=True)
print("----------------------------------------", flush=True)
print("Test Model AUROC", flush=True)
y_pred = model.predict(x_all)
current_auroc = []
for i in range(len(class_names)):
try:
score = roc_auc_score(y_all[:, i], y_pred[:, i])
except ValueError:
score = 0
current_auroc.append(score)
print(i+1,class_names[i],": ", score, flush=True)
mean_auroc = np.mean(current_auroc)
print("Mean auroc: ", mean_auroc,flush=True)
print("----------------------------------------", flush=True)
downscale_factor = 8
num_models_to_use = 3
num_test_images = 100
print("Number of Models to use:", num_models_to_use, flush=True)
print("Number of Test images:", num_test_images, flush=True)
x_tr, y_tr = x_all[num_test_images:], y_all[num_test_images:]
x_te, y_te = x_all[0:num_test_images], y_all[0:num_test_images]
downsample_factors = (downscale_factor,downscale_factor)
print("Downsample Factors:", downsample_factors,flush=True)
model_builder = UNetModelBuilder(downsample_factors, num_layers=2, num_units=8, activation="relu",
p_dropout=0.0, verbose=0, batch_size=32, learning_rate=0.001)
print("Model build done.",flush=True)
masking_operation = ZeroMasking()
loss = categorical_crossentropy
explainer = CXPlain(model, model_builder, masking_operation, loss,
num_models=num_models_to_use, downsample_factors=downsample_factors, flatten_for_explained_model=False)
print("Explainer build done.",flush=True)
explainer.fit(x_tr, y_tr);
print("Explainer fit done.",flush=True)
try:
attr, conf = explainer.explain(x_te, confidence_level=0.80)
np.save(output_dir+"/x_cxpl.npy", x_te)
np.save(output_dir+"/y_cxpl.npy", y_te)
np.save(output_dir+"/attr.npy", attr)
np.save(output_dir+"/conf.npy", conf)
print("Explainer explain done and saved.",flush=True)
except Exception as ef: print(ef,flush=True)