Python get_peptide_predictor_dump_file_path示例

示例#1

from sklearn.svm import SVC
from mlxtend.classifier import EnsembleClassifier

from asap import train_window_classifier, PeptidePredictor, FeatureSelectionPipeline

from cleavepred import util
from cleavepred import project_paths
from cleavepred.common import window_extraction_params

### Parse arguments ###

advanced = util.parse_bool(sys.argv[1])

if len(sys.argv) > 2:
    if sys.argv[2].lower() == 'auto':
        predictor_dump_path = project_paths.get_peptide_predictor_dump_file_path(
            advanced)
    else:
        predictor_dump_path = sys.argv[2]
else:
    predictor_dump_path = None

### Configuration ###

# We use NeuroPred's dataset for training/validation of our predictors.
project_paths.dataset_name = 'neuropred'

ensemble_classifiers = [
    LogisticRegressionCV(Cs=16, n_jobs=-2, class_weight='auto'),
    RandomForestClassifier(n_estimators=250,
                           bootstrap=True,
                           criterion='gini',

示例#2

文件： test_classifier.py 项目： icemansina/asap

def open_files():
    global predictor_dump_file, windows_file
    predictor_dump_file = open(
        project_paths.get_peptide_predictor_dump_file_path(advanced), 'rb')
    windows_file = open(project_paths.get_window_features_file_path(advanced),
                        'rb')

示例#3

文件： test_classifier.py 项目： ouceduxzk/asap

def open_files():
    global predictor_dump_file, windows_file
    predictor_dump_file = open(project_paths.get_peptide_predictor_dump_file_path(advanced), 'rb')
    windows_file = open(project_paths.get_window_features_file_path(advanced), 'rb')

示例#4

文件： train_classifier.py 项目： ouceduxzk/asap

from sklearn.svm import SVC
from mlxtend.classifier import EnsembleClassifier

from asap import train_window_classifier, PeptidePredictor, FeatureSelectionPipeline

from cleavepred import util
from cleavepred import project_paths
from cleavepred.common import window_extraction_params

### Parse arguments ###

advanced = util.parse_bool(sys.argv[1])

if len(sys.argv) > 2:
    if sys.argv[2].lower() == 'auto':
        predictor_dump_path = project_paths.get_peptide_predictor_dump_file_path(advanced)
    else:
        predictor_dump_path = sys.argv[2]
else:
    predictor_dump_path = None

### Configuration ###

# We use NeuroPred's dataset for training/validation of our predictors.
project_paths.dataset_name = 'neuropred'

ensemble_classifiers = [
    LogisticRegressionCV(Cs = 16, n_jobs = -2, class_weight = 'auto'),
    RandomForestClassifier(n_estimators = 250, bootstrap = True, criterion = 'gini', n_jobs = -2, class_weight = 'auto'),
    SVC(kernel = 'rbf', C = 3.798, probability = True, cache_size = 2400, class_weight = 'auto'),
]