def main(argv=None):
logger = log.get_logger(__name__)
if argv == None:
argv = sys.argv[1:]
args = __parse_arguments(argv)
df = pd.DataFrame.from_csv(args.data_location)
x = df[df.columns[:-1]]
y = df["class"]
y = pd.DataFrame(y, columns=['class'])
#encoder = prep.LabelEncoder()
#y = pd.DataFrame(encoder.fit_transform(y), columns=["class"])
#print("Classes %s", str(encoder.classes_))
x_train, x_test, y_train, y_test = cv.train_test_split(x,
y,
test_size=0.3,
stratify=y.values)
config = [
{
"C":[0.1, 0.5, 1, 2, 4, 8, 10, 100],
"kernel": ["linear"],
"probability": [True]
},
{
"C":[0.1, 0.5, 1, 2, 4, 8, 10, 100],
"kernel": ["poly"],
"degree": [2, 3, 4, 5],
"coef0": [0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 2, 4, 8, 16],
"probability": [True],
},
{
"C":[0.1, 0.5, 1, 2, 4, 8, 10, 100],
"kernel": ["sigmoid"],
"probability": [True],
},
{
"C":[0.1, 0.5, 1, 2, 4, 8, 10, 100],
"kernel": ["rbf"],
"probability": [True],
}
]
logger.info("training")
if args.config is not None:
with open(args.config) as config_file:
config = json.load(config_file)
model = train_model(x_train, y_train, config)
logger.info("trained")
y_predict = model.predict(x_test)
logger.info("true: %s predicted: %s", str(y_test["class"].values), str(y_predict))
score = metrics.f1_score(y_test, y_predict,
average="macro")
print("Simple score {0}".format(model.score(x_test, y_test)))
print("F1 score {0}".format(score))
model = train_model(x, y, model.best_params_)
joblib.dump(model, args.output)
def train_model(x, y, model_parameters, model_cls=svm.SVC):
"""Trains a model on a *training set*.
Parameters:
*training_set* should be a dataframe-like object
*args* should either be a dict, or a list of dicts. If a dict is provided,
it will be used for initializing a model. If a list is provided,
the gridsearch will be run on those grids.
*model_cls* should be a sklearn class for a desired classifier. Default
is SVC.
Returns a *sklearn* model."""
logger = log.get_logger(__name__)
if isinstance(model_parameters, list):
leave_p_out = cv.LeavePOut(len(x), 4)
model = grid_search.GridSearchCV(model_cls(),
model_parameters,
cv=3, n_jobs=4,
scoring="f1_macro",
)
model.fit(x.values, y["class"].values)
print("Ideal parameters {0}".format(model.best_params_))
else:
model = model_cls(**model_parameters)
model.fit(x.values, y["class"].values)
return model
