Example #1
0
    def fit(self, X, Y):
        """Fits linear model on data points. Warning: serializing the classifier object won't save the model; specifying a model file is necessary.

        Parameters
        ----------
        X: numpy array, shape = [n_samples, n_features]
            Training vectors, where n_samples is the number of samples and n_features is the number of features.
        Y: numpy array
            Target Values.
        """
        self._dim = X.shape[1]
        data = make_svmlight(X, Y).name
        argv = map(str, self.fit_argv)
        argv += ["--training_file", data]
        self._default(argv)
Example #2
0
    def predict(self, X, Y=None):
        """Returns model prediction of data points.

        Parameters
        ----------
        X: numpy array, shape = [n_samples, n_features]
            Testing vectors, where n_samples is the number of samples and n_features is the number of features.
        Y: Not used for computation, optional
        """
        assert self._dim == X.shape[1]
        data = make_svmlight(X, Y).name
        results_file = tempfile().name
        argv = map(str, self.pred_argv)
        argv += ["--test_file", data]
        argv += ["--results_file", results_file]
        self._default(argv)
        Y = read_csv(results_file, sep='\t', header=None, index_col=False)
        return Y.as_matrix()[:, 0]
Example #3
0
 def _preprocess(self, X):
     X = make_matrix(X)
     temporary_file = make_svmlight(X).name
     self._dim = str(X.shape[1])
     self._data = temporary_file