def load_get_coefficients(config_learning, config_data):
output = open(os.path.expanduser(config_data.get("Learner", "models")) + "/" + "coefficients.txt", "w")
feature_names = FeatureExtractor.get_features_from_config_file_unsorted(config_data)
combination_methods = FeatureExtractor.get_combinations_from_config_file_unsorted(config_data)
learning_config = config_learning.get("learning", None)
method_name = learning_config.get("method", None)
estimator = joblib.load(os.path.expanduser(config_data.get("Learner", "models")) + "/" + method_name + ".pkl")
coefficients = estimator.coef_
feature_list = []
for i, feature_name in enumerate(feature_names):
if combination_methods[i] == 'both':
feature_list.append(feature_name)
feature_list.append(feature_name)
else:
feature_list.append(feature_name)
for i, name in enumerate(feature_list):
output.write(name + "\t" + str(coefficients[0][i]) + "\n")
output.close()
def recursive_feature_elimination_cv(config_learning, config_data):
output = open(os.path.expanduser(config_data.get("Learner", "models")) + "/" + "feature_ranks.txt", "w")
feature_names = FeatureExtractor.get_features_from_config_file_unsorted(config_data)
combination_methods = FeatureExtractor.get_combinations_from_config_file_unsorted(config_data)
x_train = read_features_file(config_learning.get('x_train'), '\t')
y_train = read_reference_file(config_learning.get('y_train'), '\t')
x_test = read_features_file(config_learning.get('x_test'), '\t')
estimator, scorers = learn_model.set_learning_method(config_learning, x_train, y_train)
scale = config_learning.get("scale", True)
if scale:
x_train, x_test = scale_datasets(x_train, x_test)
rfecv = RFECV(estimator=estimator, step=1, cv=StratifiedKFold(y_train, 2), scoring='accuracy')
rfecv.fit(x_train, y_train)
feature_list = []
for i, feature_name in enumerate(feature_names):
if combination_methods[i] == 'both':
feature_list.append(feature_name)
feature_list.append(feature_name)
else:
feature_list.append(feature_name)
for i, name in enumerate(feature_list):
output.write(name + "\t" + str(rfecv.ranking_[i]) + "\n")
output.close()
predictions = rfecv.predict(x_test)
return predictions
def average_feature_values():
config_path = os.getcwd() + "/" + "config" + "/" + "wmt.cfg"
config = ConfigParser()
config.readfp(open(config_path))
my_dir = os.path.expanduser("~/Dropbox/experiments_fluency/test_learn_to_rank")
feature_file = my_dir + "/" + "x_newstest2015.cobalt_comb_min_fluency_features_all.cs-en.tsv"
feature_names = FeatureExtractor.get_features_from_config_file_unsorted(config)
strategies = FeatureExtractor.get_combinations_from_config_file(config)
feature_values = read_features_file(feature_file, "\t")
averages = np.mean(feature_values, axis=0)
feature_list = []
for i, feature_name in enumerate(feature_names):
# if strategies[i] == 'both':
# feature_list.append(feature_name)
# feature_list.append(feature_name)
# else:
feature_list.append(feature_name)
for i, name in enumerate(feature_list):
print(name + "\t" + str(averages[i]))
