def update_loss_with_prior(loss_matrix,
prior_params,
prior_motifreg,
prior_regreg,
iteration,
best_split_regulator=None):
new_loss = loss_matrix
# Apply motif-regulator prior
if prior_motifreg is not None:
ones_mat = np.ones(prior_motifreg.data.shape)
loss_decrease = ones_mat - loss_matrix
motifreg_multiplier = prior_motifreg.data * \
prior_params.prior_constant * \
np.power(prior_params.prior_decay_rate, iteration) + 1
loss_decrease_weighted = util.element_mult(loss_decrease,
motifreg_multiplier)
new_loss = ones_mat - loss_decrease_weighted
# Apply regulator-regulator prior
if prior_regreg is not None:
assert best_split_regulator is not None
# Get prior for adding on to that regulator another regulator that interacts with that
if best_split_regulator == 'root':
return new_loss
reg_row = prior_regreg.data[prior_regreg.row_labels ==
best_split_regulator, ]
reg_mat = np.vstack([reg_row for el in xrange(new_loss.shape[0])])
regreg_multiplier = reg_mat * prior_params.prior_constant * \
np.power(prior_params.prior_decay_rate, iteration) + 1
new_loss = util.element_mult(new_loss, regreg_multiplier)
return new_loss
def find_rule_weights(leaf_training_examples, example_weights, ones_mat,
holdout, y, x1, x2):
"""
Find rule weights, and return an object store containing them.
"""
# log('find_rule_weights start')
w_temp = util.element_mult(example_weights, leaf_training_examples)
# log('weights element-wise')
w_pos = util.element_mult(w_temp, holdout.ind_train_up)
# log('weights element-wise')
w_neg = util.element_mult(w_temp, holdout.ind_train_down)
# log('weights element-wise')
x2_pos = x2.element_mult(x2.data > 0)
# log('x2 element-wise')
x2_neg = abs(x2.element_mult(x2.data < 0))
# log('x2 element-wise')
x1wpos = x1.matrix_mult(w_pos)
# log('x1 weights dot')
x1wneg = x1.matrix_mult(w_neg)
# log('x1 weights dot')
w_up_regup = util.matrix_mult(x1wpos, x2_pos)
# log('x1w x2 dot')
w_up_regdown = util.matrix_mult(x1wpos, x2_neg)
# log('x1w x2 dot')
w_down_regup = util.matrix_mult(x1wneg, x2_pos)
# log('x1w x2 dot')
w_down_regdown = util.matrix_mult(x1wneg, x2_neg)
# log('x1w x2 dot')
w_zero_regup = ones_mat - w_up_regup - w_down_regup
# log('weights subtraction')
w_zero_regdown = ones_mat - w_up_regdown - w_down_regdown
# log('weights subtraction')
return ObjectStore(w_up_regup, w_up_regdown, w_down_regup, w_down_regdown,
w_zero_regup, w_zero_regdown, w_pos, w_neg)
def get_index_mat_dict_enh_prom(index_mat_enh, index_math_prom):
index_mat_dict = {}
index_mat_dict['enh_up'] = util.element_mult(index_mat_enh, y.data == 1)
index_mat_dict['enh_down'] = util.element_mult(index_mat_enh, y.data == -1)
index_mat_dict['prom_up'] = util.element_mult(index_mat_prom, y.data == 1)
index_mat_dict['prom_down'] = util.element_mult(index_mat_prom,
y.data == -1)
return index_mat_dict
def get_hierarchy_index(hier_node, hierarchy, training_index, tree):
"""Mask training index with hierarchy node if there is a hierarchy
"""
if hierarchy is None:
return training_index
cells = hierarchy.subtree_nodes[hier_node]
cell_matrix = np.zeros(training_index.shape, dtype=bool)
cell_matrix[:, cells] = True
if tree.sparse:
leaf_training_examples = util.element_mult(training_index,
csr_matrix(cell_matrix))
else:
leaf_training_examples = util.element_mult(training_index, cell_matrix)
return leaf_training_examples
def find_rule_process_stumps(tree, holdout, y, x1, x2, hierarchy):
if hierarchy == None:
# Use all examples because no tree
leaf_training_examples = tree.ind_pred_train[0]
# calculate the loss for this leaf
leaf_loss_mat, regulator_sign = find_min_loss(tree,
leaf_training_examples,
holdout, y, x1, x2)
# hierarchy node is just root
hierarchy_node = 0
# # If there is a hierarchy, iterate through possible children
else:
# Keep best loss node of hierarchy
best_loss = float('Inf')
leaf_loss_mat = None
regulator_sign = None
hierarchy_node = None
# Iterate over children (always adding to root)
for hier_node in hierarchy.direct_children[tree.hierarchy_node[0]]:
cells = hierarchy.subtree_nodes[hier_node]
cell_matrix = np.zeros(tree.ind_pred_train[0].shape, dtype=bool)
cell_matrix[:, cells] = True
if tree.sparse:
leaf_training_examples = util.element_mult(
tree.ind_pred_train[0], csr_matrix(cell_matrix))
else:
leaf_training_examples = util.element_mult(
tree.ind_pred_train[0], cell_matrix)
# calculate the loss for this leaf
leaf_loss_mat_cell, regulator_sign_cell = find_min_loss(
tree, leaf_training_examples, holdout, y, x1, x2)
# Update with best loss
if leaf_loss_mat_cell.min() < best_loss:
best_loss = leaf_loss_mat_cell.min()
leaf_loss_mat = leaf_loss_mat_cell
regulator_sign = regulator_sign_cell
hierarchy_node = hier_node
return (0, regulator_sign, 0, leaf_loss_mat)
def get_current_rule(tree, best_split, regulator_sign, loss_best, holdout, y,
x1, x2, hierarchy, hierarchy_node):
motif, regulator = np.where(np.isclose(loss_best, loss_best.min()))
# If multiple rules have the same loss, randomly select one
if len(motif) > 1:
random.seed(1)
choice = random.sample(range(len(motif)), 1)
motif = np.array(motif[choice])
regulator = np.array(regulator[choice])
# Convert to int
if isinstance(motif, int) == False:
motif = int(motif)
if isinstance(regulator, int) == False:
regulator = int(regulator)
# Find indices of where motif and regulator appear
if x2.sparse:
valid_m = np.nonzero(x1.data[motif, :])[1]
valid_r = np.where(x2.data.toarray()[:,
regulator] == regulator_sign)[0]
else:
valid_m = np.nonzero(x1.data[motif, :])[0]
valid_r = np.where(x2.data[:, regulator] == regulator_sign)[0]
# Get joint motif-regulator index - training and testing
if y.sparse:
valid_mat = csr_matrix((y.num_row, y.num_col), dtype=np.bool)
else:
valid_mat = np.zeros((y.num_row, y.num_col), dtype=np.bool)
valid_mat[np.ix_(valid_m, valid_r)] = 1 # XX not efficient
non_hier_training_examples = tree.ind_pred_train[best_split]
training_examples = h.get_hierarchy_index(hierarchy_node, hierarchy,
non_hier_training_examples, tree)
non_hier_testing_examples = tree.ind_pred_test[best_split]
testing_examples = h.get_hierarchy_index(hierarchy_node, hierarchy,
non_hier_testing_examples, tree)
rule_train_index = util.element_mult(valid_mat, training_examples)
rule_test_index = util.element_mult(valid_mat, testing_examples)
return motif, regulator, regulator_sign, rule_train_index, rule_test_index
def extract_feat_affecting_example_set(y, tree, peak_index, condition_index,
ex_by_feat_mat):
# ! check
example_index = peak_index * y.num_col + condition_index
if y.data[peak_index, condition_index] == 0:
print 'There is no change at this feature in this condition. STOP.'
return None
# Find the features that have non-zero margin score
# feat_list = (ex_by_feat_mat[example_index,:]!=0).nonzero()[1].tolist()
# Find the features where the index actually overlaps the place
df = csr_matrix(y.data.shape)
# Make one TRUE entry at yo
df[peak_index, condition_index] = True
feat_list = []
for ind in xrange(tree.nsplit):
if util.element_mult(
tree.ind_pred_test[ind] + tree.ind_pred_train[ind],
df).sum() != 0:
feat_list.append(ind)
return feat_list
def get_rule_score_and_indices(rule_bundle, training_examples,
testing_examples, weights_i, rule_weights, tree,
y, x1, x2, holdout, rule_train_index,
rule_test_index):
### ADD IN
if rule_bundle.size == 1:
rule_score = util.calc_score(tree, rule_weights, rule_train_index)
motif_bundle = []
regulator_bundle = []
return rule_score, rule_train_index, rule_test_index
# Get a lock
lock_stable = multiprocessing.Lock()
# Initialize shared data objects
theta_alphas = multiprocessing.RawArray(ctypes.c_double, rule_bundle.size)
bundle_train_pred = multiprocessing.RawArray(ctypes.c_double,
y.num_row * y.num_col)
bundle_test_pred = multiprocessing.RawArray(ctypes.c_double,
y.num_row * y.num_col)
# Store the value of the next rule that needs to be worked on
rule_index_cntr = multiprocessing.Value('i', 0)
# Pack arguments
stable_args = [y, x1, x2, rule_index_cntr, rule_bundle, \
training_examples, testing_examples,
rule_weights.w_pos, rule_weights.w_neg,
(lock_stable, theta_alphas, bundle_train_pred, bundle_test_pred)]
# Fork worker processes, and wait for them to return
fork_and_wait(config.NCPU, return_rule_index, stable_args)
### Get results back into the right format ()
theta_alphas = np.array(theta_alphas)
if y.sparse:
bundle_train_pred = csr_matrix(
np.reshape(np.array(bundle_train_pred), (y.data.shape)))
bundle_test_pred = csr_matrix(
np.reshape(np.array(bundle_test_pred), (y.data.shape)))
else:
bundle_train_pred = np.reshape(np.array(bundle_train_pred),
(y.data.shape))
bundle_test_pred = np.reshape(np.array(bundle_test_pred),
(y.data.shape))
# Calculate theta
min_val = min([abs(a) for a in theta_alphas])
theta = sum([abs(alph) - min_val for alph in theta_alphas]) / 2
# new index is where absolute value greater than theta
new_train_rule_ind = (abs(bundle_train_pred) > theta)
new_test_rule_ind = (abs(bundle_test_pred) > theta)
# calculate W+ and W- for new rule
w_pos = util.element_mult(weights_i, holdout.ind_train_up)
w_neg = util.element_mult(weights_i, holdout.ind_train_down)
w_bundle_pos = util.element_mult(w_pos, new_train_rule_ind)
w_bundle_neg = util.element_mult(w_neg, new_train_rule_ind)
# get score of new rule
rule_bundle_score = 0.5 * np.log(
(w_bundle_pos.sum() + config.TUNING_PARAMS.epsilon) /
(w_bundle_neg.sum() + config.TUNING_PARAMS.epsilon))
return rule_bundle_score, new_train_rule_ind, new_test_rule_ind
def stable_boost_test(tree, rule_train_index, holdout):
w_pos = util.element_mult(tree.weights, holdout.ind_train_up)
w_neg = util.element_mult(tree.weights, holdout.ind_train_down)
test = 0.5*abs(util.element_mult(w_pos, rule_train_index).sum() - \
util.element_mult(w_neg, rule_train_index).sum())
return test
def bundle_rules(tree, y, x1, x2, m, r, reg, best_split, rule_weights,
hierarchy, hierarchy_node):
level = 'VERBOSE' if config.VERBOSE else 'QUIET'
log('starting bundle rules', level=level)
log('best split is {0}'.format(best_split), level=level)
log('calculate A', level=level)
non_hier_training_examples = tree.ind_pred_train[best_split]
training_examples = h.get_hierarchy_index(hierarchy_node, hierarchy,
non_hier_training_examples, tree)
# Get weights based on current training examples (obeying hierarchy)
# (formerly tree.ind_pred_train[best_split])
weights_i = util.element_mult(tree.weights, training_examples)
# SYMM DIFF - calculate weights and weights squared of best loss_rule (A)
if reg == 1:
a_val = rule_weights.w_up_regup[m,r]+ \
rule_weights.w_down_regup[m,r]
elif reg == -1:
a_val = rule_weights.w_up_regdown[m,r]+ \
rule_weights.w_down_regdown[m,r]
# Allocate matrix of weight value
if y.sparse:
a_weights = csr_matrix(a_val *
np.ones(shape=rule_weights.w_down_regup.shape))
else:
a_weights = a_val * np.ones(shape=rule_weights.w_down_regup.shape)
## Calculate weights and weights square of all the other rules (B)
# W+ + W- from find_rule()
log('calculate B', level=level)
b_weights_regup = rule_weights.w_up_regup+ \
rule_weights.w_down_regup
b_weights_regdown = rule_weights.w_up_regdown+ \
rule_weights.w_down_regdown
## Calculate intersection of A and B (A union B)
# Allocate matrix with best rule in repeated m matrix, and best rule in repeated r matrix
log('calculate A+B', level=level)
if y.sparse:
reg_vec = (x2.data[:, r] == reg)
else:
reg_vec = np.reshape((x2.data[:, r] == reg), (x2.num_row, 1))
# Multiply best rule times all other rules
log('best rule times others', level=level)
x1_intersect = util.element_mult(x1.data[m, :], x1.data)
x2_up = x2.element_mult(x2.data > 0)
x2_down = abs(x2.element_mult(x2.data < 0))
x2_intersect_regup = util.element_mult(reg_vec, x2_up)
x2_intersect_regdown = util.element_mult(reg_vec, x2_down)
# Get weights for intersection
x1_intersect_weights = util.matrix_mult(x1_intersect, weights_i)
log('intersection weights', level=level)
ab_weights_regup = util.matrix_mult(
x1_intersect_weights, x2_intersect_regup) # PRE-FILTER weights
ab_weights_regdown = util.matrix_mult(
x1_intersect_weights, x2_intersect_regdown) # PRE-FILTER weights
# Get symmetric difference in weights
log('symmetric diff', level=level)
symm_diff_w_regup = a_weights + b_weights_regup - 2 * ab_weights_regup
symm_diff_w_regdown = a_weights + b_weights_regdown - 2 * ab_weights_regdown
## Calculate threshold for stabilization
log('get threshold', level=level)
if y.sparse:
bundle_thresh = util.calc_sqrt_sum_sqr_sqr_sums(weights_i.data)
else:
bundle_thresh = util.calc_sqrt_sum_sqr_sqr_sums(weights_i.ravel())
## If large bundle, but hard cap on number of rules in bundle:
log('test bundle size', level=level)
test_big_bundle = (symm_diff_w_regup < \
config.TUNING_PARAMS.eta_1*bundle_thresh).sum() + \
(symm_diff_w_regdown < \
config.TUNING_PARAMS.eta_1*bundle_thresh).sum() \
> config.TUNING_PARAMS.bundle_max
# If large bundle cap at max bundle size
if test_big_bundle:
log('cap bundle', level=level)
print "=" * 80
print 'large bundle - cap at {0}'.format(
config.TUNING_PARAMS.bundle_max)
### Get rule bundles
if y.sparse:
rule_bundle_regup = np.where(symm_diff_w_regup.todense().ravel(
).argsort().argsort().reshape(symm_diff_w_regup.toarray().shape) <
config.TUNING_PARAMS.bundle_max / 2)
rule_bundle_regdown = np.where(symm_diff_w_regdown.todense().ravel(
).argsort().argsort().reshape(symm_diff_w_regup.toarray().shape) <
config.TUNING_PARAMS.bundle_max / 2)
else:
rule_bundle_regup = np.where(symm_diff_w_regup.ravel().argsort(
).argsort().reshape(symm_diff_w_regup.shape) <
config.TUNING_PARAMS.bundle_max / 2)
rule_bundle_regdown = np.where(symm_diff_w_regdown.ravel().argsort(
).argsort().reshape(symm_diff_w_regup.shape) <
config.TUNING_PARAMS.bundle_max / 2)
rule_bundle_regup_motifs = rule_bundle_regup[0].tolist(
) # Keeping min loss rule
rule_bundle_regup_regs = rule_bundle_regup[1].tolist()
rule_bundle_regdown_motifs = rule_bundle_regdown[0].tolist()
rule_bundle_regdown_regs = rule_bundle_regdown[1].tolist()
# Otherwise take all bundled rules
else:
log('keep bundle', level=level)
rule_bundle_regup = (symm_diff_w_regup < \
config.TUNING_PARAMS.eta_1*bundle_thresh).nonzero()
rule_bundle_regdown = (symm_diff_w_regdown < \
config.TUNING_PARAMS.eta_1*bundle_thresh).nonzero()
rule_bundle_regup_motifs = rule_bundle_regup[0].tolist(
) # Keeping min loss rule
rule_bundle_regup_regs = rule_bundle_regup[1].tolist()
rule_bundle_regdown_motifs = rule_bundle_regdown[0].tolist()
rule_bundle_regdown_regs = rule_bundle_regdown[1].tolist()
# # Investigate large bundles
# if len(rule_bundle_regup_motifs)+len(rule_bundle_regdown_motifs) > 40:
# pdb.set_trace()
# Print names of x1/x2 features that are bundled
rule_bundle_motifs = x1.row_labels[ \
rule_bundle_regup_motifs+rule_bundle_regdown_motifs]
rule_bundle_regs = x2.col_labels[ \
rule_bundle_regup_regs+rule_bundle_regdown_regs]
# Return list where first element is bundle where reg_up and second is where reg_down
return BundleStore(rule_bundle_regup_motifs, rule_bundle_regup_regs,
rule_bundle_regdown_motifs, rule_bundle_regdown_regs)
r_h = (rule_bundle.rule_bundle_regup_regs +
rule_bundle.rule_bundle_regdown_regs)[rule_index]
reg_h = (
[+1] * len(rule_bundle.rule_bundle_regup_motifs) +
[-1] * len(rule_bundle.rule_bundle_regdown_motifs))[rule_index]
if x1.sparse:
valid_m_h = np.nonzero(x1.data[m_h, :])[1]
valid_r_h = np.where(x2.data.toarray()[:, r_h] == reg_h)[0]
else:
valid_m_h = np.nonzero(x1.data[m_h, :])[0]
valid_r_h = np.where(x2.data[:, r_h] == reg_h)[0]
# Calculate the loss for this leaf
valid_mat_h[np.ix_(valid_m_h, valid_r_h)] = 1
rule_train_index_h = util.element_mult(valid_mat_h,
best_split_train_index)
rule_test_index_h = util.element_mult(valid_mat_h,
best_split_test_index)
rule_score_h = 0.5 * np.log(
(util.element_mult(w_pos, rule_train_index_h).sum() +
config.TUNING_PARAMS.epsilon) /
(util.element_mult(w_neg, rule_train_index_h).sum() +
config.TUNING_PARAMS.epsilon))
# print rule_index
# Update current predictions
with lock_stable:
# Add current rule to training and testing sets
current_bundle_train_pred = np.reshape(np.array(bundle_train_pred),
best_loss = float('Inf')
leaf_loss_mat = None
regulator_sign = None
hierarchy_node = None
# Iterate over children
for hier_node in hierarchy.direct_children[
tree.hierarchy_node[leaf_index]]:
cells = hierarchy.subtree_nodes[hier_node]
cell_matrix = np.zeros(tree.ind_pred_train[leaf_index].shape,
dtype=bool)
cell_matrix[:, cells] = True
if tree.sparse:
leaf_training_examples = util.element_mult(
tree.ind_pred_train[leaf_index],
csr_matrix(cell_matrix))
else:
leaf_training_examples = util.element_mult(
tree.ind_pred_train[leaf_index], cell_matrix)
# calculate the loss for this leaf
leaf_loss_mat_cell, regulator_sign_cell = find_min_loss(
tree, leaf_training_examples, holdout, y, x1, x2)
# Update with best loss
if leaf_loss_mat_cell.min() < best_loss:
best_loss = leaf_loss_mat_cell.min()
leaf_loss_mat = leaf_loss_mat_cell
regulator_sign = regulator_sign_cell
hierarchy_node = hier_node
def find_next_decision_node(tree, holdout, y, x1, x2, hierarchy, iteration):
level = 'VERBOSE' if config.VERBOSE else 'QUIET'
## Calculate loss at all search nodes
## (will search across current hier node and direct children to find best hier node)
log('Find rule process', level=level)
best_split, regulator_sign, hierarchy_node, loss_best = find_rule_processes(
tree, holdout, y, x1, x2, hierarchy)
# Update loss with prior
if config.TUNING_PARAMS.use_prior:
log('Update loss with prior', level=level)
best_split_regulator = util.get_best_split_regulator(
tree, x2, best_split)
loss_best = prior.update_loss_with_prior(loss_best, prior.PRIOR_PARAMS,
prior.prior_motifreg,
prior.prior_regreg, iteration,
best_split_regulator)
log('Find rule weights', level=level)
# Mask training/testing examples by using only hierarchy children
non_hier_training_examples = tree.ind_pred_train[best_split]
training_examples = h.get_hierarchy_index(hierarchy_node, hierarchy,
non_hier_training_examples, tree)
non_hier_testing_examples = tree.ind_pred_test[best_split]
testing_examples = h.get_hierarchy_index(hierarchy_node, hierarchy,
non_hier_testing_examples, tree)
# Get rule weights for the best split
rule_weights = find_rule_weights(training_examples, tree.weights,
tree.ones_mat, holdout, y, x1, x2)
# Get current rule, no stabilization
# rule_train_index/rule_test_index restricted to hierarchy
log('Get current rule', level=level)
(motif, regulator, regulator_sign, rule_train_index,
rule_test_index) = get_current_rule(tree, best_split, regulator_sign,
loss_best, holdout, y, x1, x2,
hierarchy, hierarchy_node)
if config.TUNING_PARAMS.use_stable:
log('Starting stabilization', level=level)
# Store current training weights
weights_i = util.element_mult(tree.weights, training_examples)
# Test if stabilization criterion is met
log('Stabilization test', level=level)
stable_test = stabilize.stable_boost_test(tree, rule_train_index,
holdout)
stable_thresh = stabilize.stable_boost_thresh(tree, y, weights_i)
# If stabilization criterion met, then we want to find a bundle of
# correlated rules to use as a single node
if stable_test >= config.TUNING_PARAMS.eta_2 * stable_thresh:
log('Stabilization criterion applies', level='VERBOSE')
# Get rules that are bundled together
log('Getting rule bundle', level=level)
bundle = stabilize.bundle_rules(tree, y, x1, x2, motif, regulator,
regulator_sign, best_split,
rule_weights, hierarchy,
hierarchy_node)
# rule score is the direction and magnitude of the prediciton update
# for the rule given by rule_weights and rule_train_index
log('Updating scores and indices with bundle', level=level)
(rule_score, rule_train_index,
rule_test_index) = stabilize.get_rule_score_and_indices(
bundle, training_examples, testing_examples, weights_i,
rule_weights, tree, y, x1, x2, holdout, rule_train_index,
rule_test_index)
# Add bundled rules to bundle
log('Adding bundles to rule', level=level)
motif_bundle = bundle.rule_bundle_regup_motifs + bundle.rule_bundle_regdown_motifs
regulator_bundle = bundle.rule_bundle_regup_regs + bundle.rule_bundle_regdown_regs
else:
# rule score is the direction and magnitude of the prediciton update
# for the rule given by rule_weights and rule_train_index
log('Updating rule without stabilization', level=level)
rule_score = calc_score(tree, rule_weights, rule_train_index)
motif_bundle = []
regulator_bundle = []
# If no stabilization
else:
# rule score is the direction and magnitude of the prediciton update
# for the rule given by rule_weights and rule_train_index
log('Updating rule without stabilization', level=level)
rule_score = calc_score(tree, rule_weights, rule_train_index)
motif_bundle = []
regulator_bundle = []
log('Adding above motifs/regs', level=level)
above_motifs = tree.above_motifs[best_split] + np.unique(
tree.bundle_x1[best_split] + [tree.split_x1[best_split]]).tolist()
above_regs = tree.above_regs[best_split] + np.unique(
tree.bundle_x2[best_split] + [tree.split_x2[best_split]]).tolist()
return (motif, regulator, best_split, hierarchy_node, motif_bundle,
regulator_bundle, rule_train_index, rule_test_index, rule_score,
above_motifs, above_regs)
def get_index_mat_dict(index_mat):
index_mat_dict = {}
index_mat_dict['all_up'] = util.element_mult(index_mat, y.data == 1)
index_mat_dict['all_down'] = util.element_mult(index_mat, y.data == -1)
return index_mat_dict