def load_lasagne_feedforward_nn(nn_dir, epoch):
init_data_pkl = nn_dir + "/init_data/init_data.pkl"
params = proc.load_obj(init_data_pkl)
#Kludge, take this out in the future
if not params.get("max_struc_start_idx", False):
params["max_struc_start_idx"] = None
if not params.get("max_struc_width", False):
params["max_struc_width"] = None
if not params.get("aa_feats", False):
params["aa_feats"] = False
if not params.get("nonnegative", False):
params["nonnegative"] = False
X_tr, _, X_te, _ = proc.load_lasagne_data(
params["gene_len_fname"],
params["gene_seq_fname"],
params["tr_codons_fname"],
params["te_codons_fname"],
params["outputs_fname"],
rel_cod_idxs=params["rel_cod_idxs"],
rel_nt_idxs=params["rel_nt_idxs"],
rel_struc_idxs=params["rel_struc_idxs"],
struc_fname=params["struc_fname"],
max_struc_start_idx=params["max_struc_start_idx"],
max_struc_width=params["max_struc_width"],
aa_feats=params["aa_feats"],
filter_pct=params["filter_pct"])
my_nn = lasagnenn.FeedforwardMLP(
X_tr,
params["y_tr"],
X_te,
params["y_te"],
name=params["name"],
out_dir=params["out_dir"],
learning_rate=params["learning_rate"],
update_method=params["update_method"],
widths=params["widths"],
nonlinearity=params["nonlinearity"],
input_drop_rate=params["input_drop_rate"],
hidden_drop_rate=params["hidden_drop_rate"],
num_outputs=params["num_outputs"],
momentum=params["momentum"],
batch_size=params["batch_size"],
nonnegative=params["nonnegative"],
reloaded=True)
my_nn.unpickle_epoch(epoch)
return my_nn
def load_lasagne_adjacency_nn(nn_dir, epoch):
init_data_pkl = nn_dir + "/init_data/init_data.pkl"
params = proc.load_obj(init_data_pkl)
try:
max_struc_start_idx = params["max_struc_start_idx"],
except KeyError:
max_struc_start_idx = None
try:
max_struc_width = params["max_struc_width"],
except KeyError:
max_struc_width = None
X_tr, y_tr, X_te, y_te = proc.load_lasagne_data(
params["gene_len_fname"],
params["gene_seq_fname"],
params["tr_codons_fname"],
params["te_codons_fname"],
params["outputs_fname"],
rel_cod_idxs=params["rel_cod_idxs"],
rel_nt_idxs=params["rel_nt_idxs"],
rel_struc_idxs=params["rel_struc_idxs"],
struc_fname=params["struc_fname"],
max_struc_start_idx=max_struc_start_idx,
max_struc_width=max_struc_width,
filter_pct=params["filter_pct"])
my_nn = lasagnenn.AdjacencyMLP(X_tr,
y_tr,
X_te,
y_te,
name=params["name"],
out_dir=params["out_dir"],
rel_cod_idxs=params["rel_cod_idxs"],
cod_adj_idxs=params["cod_adj_idxs"],
rel_nt_idxs=params["rel_nt_idxs"],
nt_adj_idxs=params["nt_adj_idxs"],
learning_rate=params["learning_rate"],
update_method=params["update_method"],
widths=params["widths"],
nonlinearity=params["nonlinearity"],
input_drop_rate=params["input_drop_rate"],
hidden_drop_rate=params["hidden_drop_rate"],
num_outputs=params["num_outputs"],
momentum=params["momentum"],
batch_size=params["batch_size"],
reloaded=True)
my_nn.unpickle_epoch(epoch)
return my_nn
def make_lasagne_feedforward_nn(name,
expt_dir,
gene_seq_fname,
gene_len_fname,
tr_codons_fname,
te_codons_fname,
outputs_fname,
rel_cod_idxs=[],
rel_nt_idxs=[],
nonlinearity="tanh",
widths=[200],
input_drop_rate=0,
hidden_drop_rate=0,
num_outputs=1,
update_method="sgd",
filter_max=False,
filter_test=False,
filter_pct=False,
rel_struc_idxs=False,
struc_fname=False,
max_struc_start_idx=None,
max_struc_width=None,
aa_feats=False,
learning_rate=0.01,
lr_decay=16,
momentum=0.9,
batch_size=500,
log_y=False,
scaled_psct=0,
raw_psct=False,
loss_fn="L2",
drop_zeros=False,
nonnegative=True):
"""
Sets up neural network model directory,
initializes neural network model,
saves initial parameters,
and returns neural network model
Args:
name (str) - name of neural network model
expt_dir (str) - name of experiment directory
gene_seq_fname (str) - name of transcriptome fasta file
gene_len_fname (str) - name of gene lengths file
tr_codons_fname (str) - name of training set codons file
te_codons_fname (str) - name of test set codons file
outputs_fname (str) - name of outputs file
rel_cod_idxs (list of ints) - indices of codon features in model
rel_nt_idxs (list of ints) - indices of nucleotide features in model
nonlinearity (str) - name of nonlinearity fn [tanh|rectify|linear]
widths (list of ints) - # of units in each hidden layer, in order
input_drop_rate (float) - dropout rate for inputs
hidden_drop_rate (float) - dropout rate for hidden unit
num_outputs (int) - number of units in output layer
update_method (str) - name of update method [sgd|momentum|nesterov]
NOTE: more arg descriptions here
Returns:
my_nn (lasagnenn.FeedforwardMLP) - neural network object
"""
# Initialize neural network directories
setup_lasagne_nn(name, expt_dir)
# Load neural network data matrices
X_tr, y_tr, X_te, y_te = proc.load_lasagne_data(
gene_len_fname,
gene_seq_fname,
tr_codons_fname,
te_codons_fname,
outputs_fname,
rel_cod_idxs=rel_cod_idxs,
rel_nt_idxs=rel_nt_idxs,
rel_struc_idxs=rel_struc_idxs,
struc_fname=struc_fname,
max_struc_start_idx=max_struc_start_idx,
max_struc_width=max_struc_width,
aa_feats=aa_feats,
filter_max=filter_max,
filter_pct=filter_pct,
filter_test=filter_test)
# NOTE: Should I remove this?
# Handle log transformation of y values
if log_y:
#Must have either a scaled psct to add, or a raw psct that has already
#been put in the counts_by_codon when making the outputs file
# Maybe change this scheme for raw pscts in the future?
if scaled_psct <= 0 and not raw_psct and not drop_zeros:
raise ValueError("Pseudocount must be >= 0 for log y")
if scaled_psct > 0:
y_tr = np.log(y_tr + scaled_psct)
y_te = np.log(y_te + scaled_psct)
if (not scaled_psct > 0) and raw_psct:
y_tr = np.log(y_tr)
y_te = np.log(y_te)
if ((not scaled_psct > 0) and not raw_psct) and drop_zeros:
positive = (y_tr > 0).ravel()
y_tr = y_tr[positive]
X_tr = X_tr[positive]
# Save initial parameters
out_dir = expt_dir + "/lasagne_nn"
_, _, _, params = inspect.getargvalues(inspect.currentframe())
del params["X_tr"]
del params["X_te"]
proc.pickle_obj(params,
out_dir + "/{0}/init_data/init_data.pkl".format(name))
# Make neural network object
my_nn = lasagnenn.FeedforwardMLP(X_tr,
y_tr,
X_te,
y_te,
name=name,
out_dir=out_dir,
learning_rate=learning_rate,
lr_decay=lr_decay,
update_method=update_method,
widths=widths,
nonlinearity=nonlinearity,
input_drop_rate=input_drop_rate,
hidden_drop_rate=hidden_drop_rate,
num_outputs=num_outputs,
momentum=momentum,
batch_size=batch_size,
loss_fn=loss_fn,
nonnegative=nonnegative)
# Return neural network object
return my_nn