def train(self, a_train_data, a_dev_data=None, a_n_y=-1,
a_i=-1, a_train_out=None, a_dev_out=None):
"""Method for training the model.
Args:
a_train_data (tuple[list, dict]):
list of training JSON data
a_dev_data (tuple[list, dict] or None):
list of development JSON data
a_n_y (int):
number of distinct classes
a_i (int):
row index for the output predictions
a_train_out (np.array or None):
predictions for the training set
a_dev_out (np.array or None):
predictions for the training set
Returns:
void:
Note:
updates ``a_train_out`` and ``a_dev_out`` in place
"""
self.n_y = a_n_y
x_train, y_train = self._generate_ts(a_train_data)
x_dev, y_dev = self._generate_ts(a_dev_data)
# determine cross-validation and grid-search strategy and fit the model
if self._gs:
if a_dev_data is None or not a_dev_data[0]:
cv = StratifiedKFold(y_train, n_folds=NFOLDS, shuffle=True)
else:
cv = self._devset_cv(y_train, len(y_dev), NFOLDS)
x_train = x_train + x_dev
y_train = y_train + y_dev
scorer = make_scorer(f1_score, average="macro")
self._model = GridSearchCV(self._model, self.PARAM_GRID,
scoring=scorer,
cv=cv, n_jobs=self.N_JOBS, verbose=1)
self._model.fit([el[-1] for el in x_train], y_train)
# output best hyper-parameters
if self._gs:
print("Best params:", repr(self._model.best_params_),
file=sys.stderr)
if a_i >= 0:
if a_train_out is not None:
if self._gs and a_dev_data and a_dev_data[0]:
x_train = x_train[:-len(x_dev)]
for i, x_i in x_train:
self._predict(x_i, a_train_out[i], a_i)
if a_dev_out is not None:
for i, x_i in x_dev:
self._predict(x_i, a_dev_out[i], a_i)
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