def test_simple_numeric_predictor():
predictor = Predictor(inputs=[NumericInput(dim=30)],
outputs=[Output(dim=1, activation="sigmoid")],
dense_layer_sizes=[30],
dense_activation="relu")
y = predictor.predict(randn(10, 30))
eq_(len(y), 10)
def test_fixed_length_hotshot():
model = Predictor(inputs=SequenceInput(length=9,
variable_length=False,
encoding="onehot"),
outputs=Output(1, activation="sigmoid"))
seqs = ["A" * 9, "L" * 9]
y = model.predict(seqs)
eq_(len(y), 2)
def test_simple_numeric_predictor_named():
predictor = Predictor(
inputs=[NumericInput(name="x", dim=30)],
outputs=[Output(dim=1, name="y", activation="sigmoid")],
hidden_layer_sizes=[30],
hidden_activation="relu")
y = predictor.predict({"x": randn(10, 30)})["y"]
eq_(len(y), 10)
def test_simple_sequence_predictor_named():
predictor = Predictor(
inputs=[SequenceInput(length=4, name="x", variable_length=True)],
outputs=[Output(dim=1, activation="sigmoid", name="y")],
hidden_layer_sizes=[30],
hidden_activation="relu")
y = predictor.predict({"x": ["SFY-"] * 10})["y"]
eq_(len(y), 10)
def test_simple_sequence_predictor():
predictor = Predictor(
inputs=[SequenceInput(length=4, variable_length=True)],
outputs=[Output(dim=1, activation="sigmoid")],
dense_layer_sizes=[30],
dense_activation="relu")
y = predictor.predict(["SFY-"] * 10)
eq_(len(y), 10)
def test_embedding_conv_1_layer():
model = Predictor(inputs=SequenceInput(length=3,
variable_length=False,
conv_filter_sizes={2: 3}),
outputs=Output(1, activation="sigmoid"))
X = ["SAY", "FFQ"]
Y = np.array([True, False])
model.fit(X, Y)
def test_two_input_predictor():
predictor = Predictor(
inputs=[
SequenceInput(length=4, name="x1", variable_length=True),
NumericInput(dim=30, name="x2")
],
outputs=[Output(name="y", dim=1, activation="sigmoid")],
dense_layer_sizes=[30],
dense_activation="relu")
y = predictor.predict({"x1": ["SFY-"] * 10, "x2": randn(10, 30)})["y"]
eq_(len(y), 10)
def test_basic_rnn():
pred = Predictor(inputs=SequenceInput(name="x",
length=4,
variable_length=True,
encoding="embedding",
rnn_layer_sizes=[20],
rnn_type="lstm",
rnn_bidirectional=True),
outputs=Output(dim=1, activation="sigmoid", name="y"))
x = ["SF", "Y", "AALL"]
y = pred.predict({"x": x})["y"]
eq_(len(x), len(y))
found_rnn_layer = any("bidirectional" in layer.name
for layer in pred.model.layers)
assert found_rnn_layer
def test_predictor_output_transform():
predictor = Predictor(inputs=[NumericInput(dim=30, name="x")],
outputs=[
Output(name="y",
dim=1,
activation="sigmoid",
transform=log,
inverse_transform=exp)
],
dense_layer_sizes=[30],
dense_activation="relu")
y = predictor.predict({"x": randn(10, 30)})["y"]
eq_(len(y), 10)
# make sure transformed outputs are within given bounds
assert exp(0.0) <= y.min() <= exp(1.0)
assert exp(0.0) <= y.max() <= exp(1.0)
def make_predictors(widths=[9],
layer_sizes=[8],
n_conv_layers=[2],
conv_dropouts=[0.25],
conv_activation="relu",
global_pooling_batch_normalization=True):
return {(
width, layer_size, n_layers, dropout
): Predictor(inputs=SequenceInput(
name="peptide",
length=45,
add_start_tokens=True,
add_stop_tokens=True,
variable_length=True,
conv_filter_sizes=[1, 3, 5, 7, width, 11
],
n_conv_layers=n_layers,
conv_output_dim=layer_size,
conv_activation=conv_activation,
conv_dropout=dropout,
global_pooling=True,
global_pooling_batch_normalization=global_pooling_batch_normalization),
outputs=Output(1, activation="sigmoid"))
for width in widths for layer_size in layer_sizes
for n_layers in n_conv_layers for dropout in conv_dropouts}
def _make_nn_model(self,
n_features,
dense_layer_sizes=[20],
batch_normalization=True):
return Predictor(inputs=NumericInput(
n_features,
dense_layer_sizes=dense_layer_sizes,
dense_batch_normalization=batch_normalization),
outputs=Output(1, activation="sigmoid"))
def from_dict(cls, d):
# importing locally to avoid adding a significant delay to
# the import of every module
from pepnet import Predictor
model_dicts = d["models"]
model_weights = d.get("model_weights")
models = [Predictor.from_dict(d) for d in model_dicts]
return cls(models=models, model_weights=model_weights)
def test_predictor_on_more_data():
predictor = Predictor(
inputs=[SequenceInput(length=20, name="x", variable_length=True)],
outputs=[Output(dim=1, activation="sigmoid", name="y")],
dense_layer_sizes=[30],
dense_activation="relu")
train_df = synthetic_peptides_by_subsequence(1000)
test_df = synthetic_peptides_by_subsequence(1000)
predictor.fit({"x": train_df.index.values},
train_df.binder.values,
epochs=20)
y_pred = predictor.predict({"x": test_df.index.values})['y']
y_pred = pandas.Series(y_pred, index=test_df.index)
binder_mean_pred = y_pred[test_df.binder].mean()
nonbinder_mean_pred = y_pred[~test_df.binder].mean()
print(binder_mean_pred, nonbinder_mean_pred)
assert binder_mean_pred > nonbinder_mean_pred * 2, (binder_mean_pred,
nonbinder_mean_pred)
def test_model_with_fixed_length_context():
model = Predictor(inputs={
"upstream":
SequenceInput(length=1, variable_length=False),
"downstream":
SequenceInput(length=1, variable_length=False),
"peptide":
SequenceInput(length=3, variable_length=True)
},
outputs=Output(1, activation="sigmoid"))
Y = np.array([True, False, True, False])
input_dict = {
"upstream": ["Q", "A", "L", "I"],
"downstream": ["S"] * 4,
"peptide": ["SYF", "QQ", "C", "GLL"]
}
model.fit(input_dict, Y, epochs=20)
Y_pred = model.predict(input_dict)
assert (Y == (Y_pred > 0.5)).all(), (Y, Y_pred)
def make_model(sufficiently_large_output_names):
mhc = SequenceInput(length=34,
name="mhc",
encoding="index",
variable_length=True,
embedding_dim=20,
embedding_mask_zero=False,
dense_layer_sizes=[64],
dense_batch_normalization=True)
peptide = SequenceInput(
length=50,
name="peptide",
encoding="index",
embedding_dim=20,
embedding_mask_zero=True,
variable_length=True,
conv_filter_sizes=[1, 9, 10],
conv_activation="relu",
conv_output_dim=32,
n_conv_layers=2,
# conv_weight_source=mhc,
global_pooling=True,
global_pooling_batch_normalization=True)
outputs = []
for output_name in sufficiently_large_output_names:
if "IC50" in output_name or "EC50" in output_name:
transform = from_ic50
inverse = to_ic50
activation = "sigmoid"
elif "half life" in output_name:
transform = (lambda x: np.log10(x + 1))
inverse = (lambda x: (10.0**x) - 1)
activation = "relu"
else:
transform = None
inverse = None
activation = "sigmoid"
output = Output(name=output_name,
transform=transform,
inverse_transform=inverse,
activation=activation)
print(output)
outputs.append(output)
return Predictor(inputs=[mhc, peptide],
outputs=outputs,
merge_mode="concat",
dense_layer_sizes=[32],
dense_activation="tanh",
dense_batch_normalization=True)
def test_predictor_weights_all_ones():
predictor = Predictor(inputs=[
SequenceInput(length=2, variable_length=False, encoding="onehot")
],
outputs=[Output(dim=1, activation="sigmoid")])
weights = predictor.get_weights()
for w in weights:
w.fill(1)
predictor.set_weights(weights)
predictor2 = Predictor.from_json(predictor.to_json())
for w in predictor2.get_weights():
assert (w == np.ones_like(w)).all(), "Expected %s to be all 1s" % (w, )
def make_predictors(widths=[9],
layer_sizes=[16],
n_conv_layers=[2],
conv_dropouts=[0]):
return {(width, layer_size, n_layers, dropout):
Predictor(inputs=SequenceInput(name="peptide",
length=22,
variable_length=True,
conv_filter_sizes=[1, 3, width],
n_conv_layers=n_layers,
conv_output_dim=layer_size,
conv_dropout=dropout,
global_pooling=True),
outputs=Output(1, activation="sigmoid"))
for width in widths for layer_size in layer_sizes
for n_layers in n_conv_layers for dropout in conv_dropouts}
def make_predictors(
widths=[8, 9, 10],
layer_sizes=[4, 16, 32],
n_conv_layers=[1, 2],
conv_dropouts=[0, 0.25]):
return {
"width=%d, layer_size=%d, n_layers=%d, conv=%0.2f" % (
width, layer_size, n_layers, dropout): Predictor(
inputs=SequenceInput(
name="peptide",
length=22,
variable_length=True,
conv_filter_sizes=[width],
n_conv_layers=n_layers,
conv_output_dim=layer_size,
conv_dropout=dropout,
global_pooling=True),
outputs=Output(1, activation="sigmoid"))
for width in widths
for layer_size in layer_sizes
for n_layers in n_conv_layers
for dropout in conv_dropouts
}
def make_predictor(conv_filter_sizes,
conv_dropout=0.25,
conv_activation="relu",
global_pooling_batch_normalization=True,
dense_layer_sizes=[],
dense_dropout=0.25,
dense_activation="relu"):
return Predictor(inputs=SequenceInput(
name="peptide",
length=MAX_LENGTH,
add_start_tokens=True,
add_stop_tokens=True,
variable_length=True,
embedding_dim=24,
conv_filter_sizes=conv_filter_sizes,
conv_activation=conv_activation,
conv_dropout=conv_dropout,
global_pooling=True,
global_pooling_batch_normalization=global_pooling_batch_normalization),
outputs=Output(1,
activation="sigmoid",
dense_layer_sizes=dense_layer_sizes,
dense_activation="relu",
dense_dropout=dense_dropout))
def make_model(output_names):
mhc = SequenceInput(
length=34,
name="mhc",
encoding="index",
variable_length=True,
embedding_dim=32,
embedding_mask_zero=False,
dense_layer_sizes=[32],
dense_activation="tanh",
dense_batch_normalization=MERGE_BATCH_NORMALIZATION,
dense_dropout=MERGE_DROPOUT)
peptide = SequenceInput(
length=45,
name="peptide",
encoding="index",
add_start_tokens=True,
add_stop_tokens=True,
embedding_dim=32,
embedding_mask_zero=True,
variable_length=True,
conv_filter_sizes=[9],
conv_activation="relu",
conv_output_dim=32,
conv_dropout=CONV_DROPOUT,
conv_batch_normalization=CONV_BATCH_NORMALIZATION,
n_conv_layers=2,
# conv_weight_source=mhc,
global_pooling=True,
global_pooling_batch_normalization=True,
global_pooling_dropout=0.25,
dense_layer_sizes=[32],
dense_activation="sigmoid",
dense_batch_normalization=MERGE_BATCH_NORMALIZATION,
dense_dropout=MERGE_DROPOUT)
outputs = []
for output_name in output_names:
if "IC50" in output_name or "EC50" in output_name:
transform = from_ic50
inverse = to_ic50
activation = "sigmoid"
elif "half life" in output_name:
transform = (lambda x: np.log10(x + 1))
inverse = (lambda x: (10.0 ** x) - 1)
activation = "relu"
else:
transform = None
inverse = None
activation = "sigmoid"
output = Output(
name=output_name,
transform=transform,
inverse_transform=inverse,
activation=activation,
loss=LOSS)
print(output)
outputs.append(output)
return Predictor(
inputs=[mhc, peptide],
outputs=outputs,
merge_mode=MERGE,
training_metrics=["accuracy"])
def test_discrete_input_with_str_tokens():
pred = Predictor(inputs=DiscreteInput(choices=["x", "y", "z"],
embedding_dim=2),
outputs=Output(1, "sigmoid"))
pred.fit(["x", "x", "y", "z"], [0, 0, 0.5, 1.0], epochs=20)
assert pred.predict(["x"]) < pred.predict(["z"])
def make_predictors():
return {
"pool":
Predictor(inputs=SequenceInput(name="peptide",
length=22,
variable_length=True,
global_pooling=True),
outputs=Output(1, activation="sigmoid")),
"rnn":
Predictor(inputs=SequenceInput(name="peptide",
length=22,
variable_length=True,
rnn_layer_sizes=[32]),
outputs=Output(1, activation="sigmoid")),
"rnn2":
Predictor(inputs=SequenceInput(name="peptide",
length=22,
variable_length=True,
rnn_layer_sizes=[32, 32]),
outputs=Output(1, activation="sigmoid")),
"conv-pool":
Predictor(inputs=SequenceInput(name="peptide",
length=22,
variable_length=True,
conv_filter_sizes=[9],
conv_output_dim=16,
conv_dropout=0.1,
global_pooling=True),
outputs=Output(1, activation="sigmoid")),
"conv2-pool":
Predictor(inputs=SequenceInput(name="peptide",
length=22,
variable_length=True,
conv_filter_sizes=[9],
n_conv_layers=2,
conv_output_dim=16,
conv_dropout=0.1,
global_pooling=True),
outputs=Output(1, activation="sigmoid")),
"conv-rnn":
Predictor(inputs=SequenceInput(name="peptide",
length=22,
variable_length=True,
conv_filter_sizes=[9],
conv_output_dim=16,
conv_dropout=0.1,
rnn_layer_sizes=[32]),
outputs=Output(1, activation="sigmoid")),
"multiconv-pool":
Predictor(inputs=SequenceInput(name="peptide",
length=22,
variable_length=True,
conv_filter_sizes=[3, 9],
conv_output_dim=16,
conv_dropout=0.1,
global_pooling=True),
outputs=Output(1, activation="sigmoid")),
"multiconv2-pool":
Predictor(inputs=SequenceInput(name="peptide",
length=22,
variable_length=True,
conv_filter_sizes=[3, 9],
n_conv_layers=2,
conv_output_dim=16,
conv_dropout=0.1,
global_pooling=True),
outputs=Output(1, activation="sigmoid")),
"multiconv-rnn":
Predictor(inputs=SequenceInput(name="peptide",
length=22,
variable_length=True,
conv_filter_sizes=[3, 9],
conv_output_dim=16,
conv_dropout=0.1,
rnn_layer_sizes=[32]),
outputs=Output(1, activation="sigmoid")),
"multiconv2-rnn":
Predictor(inputs=SequenceInput(name="peptide",
length=22,
variable_length=True,
conv_filter_sizes=[3, 9],
n_conv_layers=2,
conv_output_dim=16,
conv_dropout=0.1,
rnn_layer_sizes=[32]),
outputs=Output(1, activation="sigmoid")),
"multiconv2-rnn2":
Predictor(inputs=SequenceInput(name="peptide",
length=22,
variable_length=True,
conv_filter_sizes=[3, 9],
n_conv_layers=2,
conv_output_dim=16,
conv_dropout=0.1,
rnn_layer_sizes=[32, 32]),
outputs=Output(1, activation="sigmoid"))
}
def test_predictor_json_identity():
predictor = Predictor(inputs=[
SequenceInput(length=2, variable_length=False, encoding="onehot")
],
outputs=[Output(dim=1, activation="sigmoid")])
eq_(predictor, Predictor.from_json(predictor.to_json()))