def run(config):
'''
Runs the main code of the program. Checks for mandatory parameters, opens
input files and performs the learning steps.
'''
# check if the mandatory parameters are set in the config file
x_train_path = config.get("x_train", None)
if not x_train_path:
msg = "'x_train' option not found in the configuration file. \
The training dataset is mandatory."
raise Exception(msg)
y_train_path = config.get("y_train", None)
if not y_train_path:
msg = "'y_train' option not found in the configuration file. \
The training dataset is mandatory."
raise Exception(msg)
learning = config.get("learning", None)
if not learning:
msg = "'learning' option not found. At least one \
learning method must be set."
raise Exception(msg)
# checks for the optional parameters
x_test_path = config.get("x_test", None)
y_test_path = config.get("y_test", None)
separator = config.get("separator", DEFAULT_SEP)
labels_path = config.get("labels", None)
scale = config.get("scale", True)
log.info("Opening input files ...")
log.debug("X_train: %s" % x_train_path)
log.debug("y_train: %s" % y_train_path)
log.debug("X_test: %s" % x_test_path)
log.debug("y_test_path: %s" % y_test_path)
# open feature and response files
X_train, y_train, X_test, y_test, labels = \
open_datasets(x_train_path, y_train_path, x_test_path,
y_test_path, separator, labels_path)
X_eval = None
x_eval_path = config.get("eval_input", None)
if x_eval_path:
X_eval = open_eval_datasets(x_eval_path, separator, X_train.shape[1])
if scale:
# preprocess and execute mean removal
if X_eval is None:
X_train, X_test = scale_datasets(X_train, X_test)
else:
X_train, X_test, X_eval = scale_datasets_with_eval(X_train, X_test, X_eval)
# fits training data and predicts the test set using the trained model
y_hat = fit_predict(config, X_train, y_train, X_test, y_test, config.get("ref_thd", None),
X_eval=X_eval, eval_output=config.get("eval_output", None), eval_threshold=config.get("eval_threshold", None))
def run(config):
'''
Runs the main code of the program. Checks for mandatory parameters, opens
input files and performs the learning steps.
'''
# check if the mandatory parameters are set in the config file
x_train_path = config.get("x_train", None)
if not x_train_path:
msg = "'x_train' option not found in the configuration file. \
The training dataset is mandatory."
raise Exception(msg)
y_train_path = config.get("y_train", None)
if not y_train_path:
msg = "'y_train' option not found in the configuration file. \
The training dataset is mandatory."
raise Exception(msg)
learning = config.get("learning", None)
if not learning:
msg = "'learning' option not found. At least one \
learning method must be set."
raise Exception(msg)
# checks for the optional parameters
x_test_path = config.get("x_test", None)
y_test_path = config.get("y_test", None)
separator = config.get("separator", DEFAULT_SEP)
labels_path = config.get("labels", None)
scale = config.get("scale", True)
log.info("Opening input files ...")
log.debug("X_train: %s" % x_train_path)
log.debug("y_train: %s" % y_train_path)
log.debug("X_test: %s" % x_test_path)
log.debug("y_test_path: %s" % y_test_path)
# open feature and response files
X_train, y_train, X_test, y_test, labels = \
open_datasets(x_train_path, y_train_path, x_test_path,
y_test_path, separator, labels_path)
if scale:
# preprocess and execute mean removal
X_train, X_test = scale_datasets(X_train, X_test)
# fits training data and predicts the test set using the trained model
y_hat = fit_predict(config, X_train, y_train, X_test, y_test,
config.get("ref_thd", None))
def run(config):
'''
Runs the main code of the program. Checks for mandatory parameters, opens
input files and performs the learning steps.
'''
# check if the mandatory parameters are set in the config file
x_train_path = config.get("x_train")
y_train_path = config.get("y_train")
learning = config.get("learning")
assert x_train_path
assert y_train_path
assert learning
# checks for the optional parameters
x_test_path = config.get("x_test", None)
y_test_path = config.get("y_test", None)
separator = config.get("separator", '\t')
labels_path = config.get("labels", None)
scale = config.get("scale", True)
print("Opening input files ...")
print("X_train: %s" % x_train_path)
print("y_train: %s" % y_train_path)
print("X_test: %s" % x_test_path)
print("y_test_path: %s" % y_test_path)
# open feature and response files
X_train, y_train, X_test, y_test, labels = \
open_datasets(x_train_path, y_train_path, x_test_path,
y_test_path, separator, labels_path)
if scale:
# preprocess and execute mean removal
X_train, X_test = scale_datasets(X_train, X_test)
# fits training data and predicts the test set using the trained model
y_hat = fit_predict(config, X_train, y_train, X_test, y_test)
def run(config):
'''
Runs the main code of the program. Checks for mandatory parameters, opens
input files and performs the learning steps.
'''
# check if the mandatory parameters are set in the config file
x_train_path = config.get("x_train", None)
if not x_train_path:
msg = "'x_train' option not found in the configuration file. \
The training dataset is mandatory."
raise Exception(msg)
y_train_path = config.get("y_train", None)
if not y_train_path:
msg = "'y_train' option not found in the configuration file. \
The training dataset is mandatory."
raise Exception(msg)
learning = config.get("learning", None)
if not learning:
msg = "'learning' option not found. At least one \
learning method must be set."
raise Exception(msg)
# checks for the optional parameters
x_test_path = config.get("x_test", None)
y_test_path = config.get("y_test", None)
separator = config.get("separator", DEFAULT_SEP)
labels_path = config.get("labels", None)
log.info("Opening input files ...")
# open feature and response files
X_train, y_train, X_test, y_test, labels = open_datasets(x_train_path, y_train_path, x_test_path, y_test_path, separator, labels_path, tostring=True)
log.info("Opened input files.")
# fits training data and predicts the test set using the trained model
y_hat = fit_predict(config, X_train, y_train, X_test, y_test, config.get("ref_thd", None))
def run(config):
'''
Runs the main code of the program. Checks for mandatory parameters, opens
input files and performs the learning steps.
'''
# check if the mandatory parameters are set in the config file
x_train_path = config.get("x_train", None)
if not x_train_path:
msg = "'x_train' option not found in the configuration file. \
The training dataset is mandatory."
raise Exception(msg)
y_train_path = config.get("y_train", None)
if not y_train_path:
msg = "'y_train' option not found in the configuration file. \
The training dataset is mandatory."
raise Exception(msg)
learning = config.get("learning", None)
if not learning:
msg = "'learning' option not found. At least one \
learning method must be set."
raise Exception(msg)
# checks for the optional parameters
x_test_path = config.get("x_test", None)
y_test_path = config.get("y_test", None)
# output file
output_file_path = config.get("output", None)
separator = config.get("separator", DEFAULT_SEP)
labels_path = config.get("labels", None)
scale = config.get("scale", True)
log.debug("Opening input files ...")
log.debug("X_train: %s" % x_train_path)
log.debug("y_train: %s" % y_train_path)
log.debug("X_test: %s" % x_test_path)
log.debug("y_test_path: %s" % y_test_path)
# open feature and response files
X_train, y_train, X_test, y_test, labels = \
open_datasets(x_train_path, y_train_path, x_test_path,
y_test_path, separator, labels_path)
if scale:
# preprocess and execute mean removal
X_train, X_test = scale_datasets(X_train, X_test)
predict_only = config.get("predict_only", False)
if predict_only:
feats_lines = []
line = raw_input()
X_test = read_features_test(line, '\t')
y_test = 2
log.debug(X_test)
y_hat = fit_predict_only(config, X_test, y_test)
else:
# fits training data and predicts the test set using the trained model
y_hat = fit_predict(config, X_train, y_train, X_test, y_test)
def run(config):
'''
Runs the main code of the program. Checks for mandatory parameters, opens
input files and performs the learning steps.
'''
# check if the mandatory parameters are set in the config file
x_train_path = config.get("x_train", None)
if not x_train_path:
msg = "'x_train' option not found in the configuration file. \
The training dataset is mandatory."
raise Exception(msg)
y_train_path = config.get("y_train", None)
if not y_train_path:
msg = "'y_train' option not found in the configuration file. \
The training dataset is mandatory."
raise Exception(msg)
learning = config.get("learning", None)
if not learning:
msg = "'learning' option not found. At least one \
learning method must be set."
raise Exception(msg)
# checks for the optional parameters
x_test_path = config.get("x_test", None)
y_test_path = config.get("y_test", None)
# output file
output_file_path = config.get("output", None)
separator = config.get("separator", DEFAULT_SEP)
labels_path = config.get("labels", None)
scale = config.get("scale", True)
log.debug("Opening input files ...")
log.debug("X_train: %s" % x_train_path)
log.debug("y_train: %s" % y_train_path)
log.debug("X_test: %s" % x_test_path)
log.debug("y_test_path: %s" % y_test_path)
# open feature and response files
X_train, y_train, X_test, y_test, labels = \
open_datasets(x_train_path, y_train_path, x_test_path,
y_test_path, separator, labels_path)
if scale:
# preprocess and execute mean removal
X_train, X_test = scale_datasets(X_train, X_test)
predict_only = config.get("predict_only", False)
if predict_only:
feats_lines = []
line = raw_input()
toks = tuple(line.strip().split('\t'))
cols = []
for t in toks:
if t != '':
try:
cols.append(float(t))
except ValueError as e:
log.error("error")
#log.error("%s line %s: %s" % (e, line_num, t))
feats_lines.append(cols)
X_test = np.asarray(feats_lines)
y_test = 2
log.debug(X_test)
y_hat = fit_predict_only(config, X_test, y_test)
else:
# fits training data and predicts the test set using the trained model
y_hat = fit_predict(config, X_train, y_train, X_test, y_test)
