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utils.py
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| from collections import deque | |||
| from keras import backend as K | |||
| from keras.callbacks import ModelCheckpoint | |||
| import warnings | |||
| import pandas as pd | |||
| from xml.etree import ElementTree as ET | |||
| BIOLOGICAL_PROCESS = 'GO:0008150' | |||
| MOLECULAR_FUNCTION = 'GO:0003674' | |||
| CELLULAR_COMPONENT = 'GO:0005575' | |||
| FUNC_DICT = { | |||
| 'cc': CELLULAR_COMPONENT, | |||
| 'mf': MOLECULAR_FUNCTION, | |||
| 'bp': BIOLOGICAL_PROCESS} | |||
| EXP_CODES = set(['EXP', 'IDA', 'IPI', 'IMP', 'IGI', 'IEP', 'TAS', 'IC']) | |||
| def get_ipro(): | |||
| ipro = dict() | |||
| tree = ET.parse('data/interpro.xml') | |||
| root = tree.getroot() | |||
| for child in root: | |||
| if child.tag != 'interpro': | |||
| continue | |||
| ipro_id = child.attrib['id'] | |||
| name = child.find('name').text | |||
| ipro[ipro_id] = { | |||
| 'id': ipro_id, | |||
| 'name': name, | |||
| 'children': list(), 'parents': list()} | |||
| parents = child.find('parent_list') | |||
| if parents: | |||
| for parent in parents: | |||
| ipro[ipro_id]['parents'].append(parent.attrib['ipr_ref']) | |||
| children = child.find('child_list') | |||
| if children: | |||
| for ch in children: | |||
| ipro[ipro_id]['children'].append(ch.attrib['ipr_ref']) | |||
| return ipro | |||
| def get_ipro_anchestors(ipro, ipro_id): | |||
| ipro_set = set() | |||
| q = deque() | |||
| q.append(ipro_id) | |||
| while(len(q) > 0): | |||
| i_id = q.popleft() | |||
| ipro_set.add(i_id) | |||
| if ipro[i_id]['parents']: | |||
| for parent_id in ipro[i_id]['parents']: | |||
| if parent_id in ipro: | |||
| q.append(parent_id) | |||
| return ipro_set | |||
| def get_gene_ontology(filename='go.obo'): | |||
| # Reading Gene Ontology from OBO Formatted file | |||
| go = dict() | |||
| obj = None | |||
| with open('data/' + filename, 'r') as f: | |||
| for line in f: | |||
| line = line.strip() | |||
| if not line: | |||
| continue | |||
| if line == '[Term]': | |||
| if obj is not None: | |||
| go[obj['id']] = obj | |||
| obj = dict() | |||
| obj['is_a'] = list() | |||
| obj['part_of'] = list() | |||
| obj['regulates'] = list() | |||
| obj['is_obsolete'] = False | |||
| continue | |||
| elif line == '[Typedef]': | |||
| obj = None | |||
| else: | |||
| if obj is None: | |||
| continue | |||
| l = line.split(": ") | |||
| if l[0] == 'id': | |||
| obj['id'] = l[1] | |||
| elif l[0] == 'is_a': | |||
| obj['is_a'].append(l[1].split(' ! ')[0]) | |||
| elif l[0] == 'name': | |||
| obj['name'] = l[1] | |||
| elif l[0] == 'is_obsolete' and l[1] == 'true': | |||
| obj['is_obsolete'] = True | |||
| if obj is not None: | |||
| go[obj['id']] = obj | |||
| for go_id in go.keys(): | |||
| if go[go_id]['is_obsolete']: | |||
| del go[go_id] | |||
| for go_id, val in go.iteritems(): | |||
| if 'children' not in val: | |||
| val['children'] = set() | |||
| for p_id in val['is_a']: | |||
| if p_id in go: | |||
| if 'children' not in go[p_id]: | |||
| go[p_id]['children'] = set() | |||
| go[p_id]['children'].add(go_id) | |||
| return go | |||
| def get_anchestors(go, go_id): | |||
| go_set = set() | |||
| q = deque() | |||
| q.append(go_id) | |||
| while(len(q) > 0): | |||
| g_id = q.popleft() | |||
| go_set.add(g_id) | |||
| for parent_id in go[g_id]['is_a']: | |||
| if parent_id in go: | |||
| q.append(parent_id) | |||
| return go_set | |||
| def get_parents(go, go_id): | |||
| go_set = set() | |||
| for parent_id in go[go_id]['is_a']: | |||
| if parent_id in go: | |||
| go_set.add(parent_id) | |||
| return go_set | |||
| def get_height(go, go_id): | |||
| height_min = 100000 | |||
| if len(go[go_id]['is_a'])==0: | |||
| height_min = 0 | |||
| else: | |||
| for parent_id in go[go_id]['is_a']: | |||
| if parent_id in go: | |||
| height = get_height(go, parent_id) + 1 | |||
| if height < height_min: | |||
| height_min = height | |||
| return height_min | |||
| def get_go_set(go, go_id): | |||
| go_set = set() | |||
| q = deque() | |||
| q.append(go_id) | |||
| while len(q) > 0: | |||
| g_id = q.popleft() | |||
| go_set.add(g_id) | |||
| for ch_id in go[g_id]['children']: | |||
| q.append(ch_id) | |||
| return go_set | |||
| def save_model_weights(model, filepath): | |||
| if hasattr(model, 'flattened_layers'): | |||
| # Support for legacy Sequential/Merge behavior. | |||
| flattened_layers = model.flattened_layers | |||
| else: | |||
| flattened_layers = model.layers | |||
| l_names = [] | |||
| w_values = [] | |||
| for layer in flattened_layers: | |||
| layer_name = layer.name | |||
| symbolic_weights = layer.weights | |||
| weight_values = K.batch_get_value(symbolic_weights) | |||
| if weight_values: | |||
| l_names.append(layer_name) | |||
| w_values.append(weight_values) | |||
| df = pd.DataFrame({ | |||
| 'layer_names': l_names, | |||
| 'weight_values': w_values}) | |||
| df.to_pickle(filepath) | |||
| def load_model_weights(model, filepath): | |||
| ''' Name-based weight loading | |||
| Layers that have no matching name are skipped. | |||
| ''' | |||
| if hasattr(model, 'flattened_layers'): | |||
| # Support for legacy Sequential/Merge behavior. | |||
| flattened_layers = model.flattened_layers | |||
| else: | |||
| flattened_layers = model.layers | |||
| df = pd.read_pickle(filepath) | |||
| # Reverse index of layer name to list of layers with name. | |||
| index = {} | |||
| for layer in flattened_layers: | |||
| if layer.name: | |||
| index[layer.name] = layer | |||
| # We batch weight value assignments in a single backend call | |||
| # which provides a speedup in TensorFlow. | |||
| weight_value_tuples = [] | |||
| for row in df.iterrows(): | |||
| row = row[1] | |||
| name = row['layer_names'] | |||
| weight_values = row['weight_values'] | |||
| if name in index: | |||
| symbolic_weights = index[name].weights | |||
| if len(weight_values) != len(symbolic_weights): | |||
| raise Exception('Layer named "' + layer.name + | |||
| '") expects ' + str(len(symbolic_weights)) + | |||
| ' weight(s), but the saved weights' + | |||
| ' have ' + str(len(weight_values)) + | |||
| ' element(s).') | |||
| # Set values. | |||
| for i in range(len(weight_values)): | |||
| weight_value_tuples.append( | |||
| (symbolic_weights[i], weight_values[i])) | |||
| K.batch_set_value(weight_value_tuples) | |||
| def f_score(labels, preds): | |||
| preds = K.round(preds) | |||
| tp = K.sum(labels * preds) | |||
| fp = K.sum(preds) - tp | |||
| fn = K.sum(labels) - tp | |||
| p = tp / (tp + fp) | |||
| r = tp / (tp + fn) | |||
| return 2 * p * r / (p + r) | |||
| def filter_specific(go, gos): | |||
| go_set = set() | |||
| for go_id in gos: | |||
| go_set.add(go_id) | |||
| for go_id in gos: | |||
| anchestors = get_anchestors(go, go_id) | |||
| anchestors.discard(go_id) | |||
| go_set -= anchestors | |||
| return list(go_set) | |||
| def read_fasta(lines): | |||
| seqs = list() | |||
| info = list() | |||
| seq = '' | |||
| inf = '' | |||
| for line in lines: | |||
| line = line.strip() | |||
| if line.startswith('>'): | |||
| if seq != '': | |||
| seqs.append(seq) | |||
| info.append(inf) | |||
| seq = '' | |||
| inf = line[1:] | |||
| else: | |||
| seq += line | |||
| seqs.append(seq) | |||
| info.append(inf) | |||
| return info, seqs | |||
| class MyCheckpoint(ModelCheckpoint): | |||
| def on_epoch_end(self, epoch, logs={}): | |||
| filepath = self.filepath.format(epoch=epoch, **logs) | |||
| current = logs.get(self.monitor) | |||
| if current is None: | |||
| warnings.warn('Can save best model only with %s available, ' | |||
| 'skipping.' % (self.monitor), RuntimeWarning) | |||
| else: | |||
| if self.monitor_op(current, self.best): | |||
| if self.verbose > 0: | |||
| print('Epoch %05d: %s improved from %0.5f to %0.5f,' | |||
| ' saving model to %s' | |||
| % (epoch, self.monitor, self.best, | |||
| current, filepath)) | |||
| self.best = current | |||
| save_model_weights(self.model, filepath) | |||
| else: | |||
| if self.verbose > 0: | |||
| print('Epoch %05d: %s did not improve' % | |||
| (epoch, self.monitor)) | |||
| class DataGenerator(object): | |||
| def __init__(self, batch_size, num_outputs): | |||
| self.batch_size = batch_size | |||
| self.num_outputs = num_outputs | |||
| def fit(self, inputs, targets): | |||
| self.start = 0 | |||
| self.inputs = inputs | |||
| self.targets = targets | |||
| self.size = len(self.inputs) | |||
| if isinstance(self.inputs, tuple) or isinstance(self.inputs, list): | |||
| self.size = len(self.inputs[0]) | |||
| self.has_targets = targets is not None | |||
| def __next__(self): | |||
| return self.next() | |||
| def reset(self): | |||
| self.start = 0 | |||
| def next(self): | |||
| if self.start < self.size: | |||
| # output = [] | |||
| # if self.has_targets: | |||
| # labels = self.targets | |||
| # for i in range(self.num_outputs): | |||
| # output.append( | |||
| # labels[self.start:(self.start + self.batch_size), i]) | |||
| if self.has_targets: | |||
| labels = self.targets[self.start:(self.start + self.batch_size), :] | |||
| if isinstance(self.inputs, tuple) or isinstance(self.inputs, list): | |||
| res_inputs = [] | |||
| for inp in self.inputs: | |||
| res_inputs.append( | |||
| inp[self.start:(self.start + self.batch_size)]) | |||
| else: | |||
| res_inputs = self.inputs[self.start:( | |||
| self.start + self.batch_size)] | |||
| self.start += self.batch_size | |||
| if self.has_targets: | |||
| return (res_inputs, labels) | |||
| return res_inputs | |||
| else: | |||
| self.reset() | |||
| return self.next() | |||
| if __name__ == '__main__': | |||
| pass | |||
| get_ipro_xml() | |||
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