def transform(self, x=None):
if x is None:
x, y = load_svmlight(self._test_input_path)
x = self._model.transform(x)
save_svmlight(x, y, self._test_output_path)
else:
transformed_x = self._model.transform(x)
return transformed_x
def transform(self, x=None):
if not x:
x, _ = load_svmlight(self._test_input_path)
transformed_x = self._model.predict_proba(x)
save_numpy_txt(transformed_x, self._output_path)
else:
transformed_x = self._model.predict_proba(x)
return transformed_x
def fit_transform(self):
self._model = KNeighborsClassifier(n_neighbors=self._n_neighbors,
weights=self._weights,
algorithm=self._algorithm,
leaf_size=self._leaf_size)
x, y = load_svmlight(self._input_path)
self._model.fit(x, y)
scores = self._model.predict_proba(x)
save_numpy_txt(scores, self._output_path)
def fit_transform(self):
self._model = RandomForestClassifier(n_estimators=self._n_estimator,
criterion=self._criterion,
max_depth=self._max_depth,
max_features=self._max_features)
x, y = load_svmlight(self._input_path)
self._model.fit(x, y)
scores = self._model.predict_proba(x)
save_numpy_txt(scores, self._output_path)
def transform(self, x=None):
if x is None:
_x, y = load_svmlight(self._test_input_path)
conf_x = self._model.decision_function(_x)
predicted_x = self._model.predict(_x)
res = np.vstack((y, conf_x, predicted_x)).T
save_numpy_txt(res, self._test_output_path)
else:
transformed_x = self._model.decision_function(x)
return transformed_x
def run(self):
x, y = load_svmlight(self._input_path)
kf = cross_validation.KFold(len(y), n_folds=self.k)
for out_path, (__, test_index) in zip(self._output_path, kf):
portion_x, portion_y = x[test_index, :], y[test_index]
save_svmlight(portion_x, portion_y, out_path)
def fit_transform(self):
self._model = MinMaxScaler()
x, y = load_svmlight(self.input_path)
x = x.toarray()
x = self._model.fit_transform(x, y)
save_svmlight(x, y, self._output_path)
def fit_transform(self):
self._model = SelectPercentile(f_classif, self._percentile)
x, y = load_svmlight(self.input_path)
x = self._model.fit_transform(x, y)
save_svmlight(x, y, self._output_path)
def fit_transform(self):
self._model = VarianceThreshold(threshold=self._threshold)
x, y = load_svmlight(self.input_path)
x = self._model.fit_transform(x, y)
save_svmlight(x, y, self._output_path)
def fit_transform(self):
self._model = LinearSVC(C=self._c)
x, y = load_svmlight(self.input_path)
self._model.fit(x, y)
scores = self._model.decision_function(x)
save_numpy_txt(scores, self.output_path)
