diff options
Diffstat (limited to 'python/pyspark/mllib/linalg.py')
-rw-r--r-- | python/pyspark/mllib/linalg.py | 110 |
1 files changed, 104 insertions, 6 deletions
diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py index 51c1490b16..a80320c52d 100644 --- a/python/pyspark/mllib/linalg.py +++ b/python/pyspark/mllib/linalg.py @@ -640,6 +640,15 @@ class Matrix(object): """ raise NotImplementedError + @staticmethod + def _convert_to_array(array_like, dtype): + """ + Convert Matrix attributes which are array-like or buffer to array. + """ + if isinstance(array_like, basestring): + return np.frombuffer(array_like, dtype=dtype) + return np.asarray(array_like, dtype=dtype) + class DenseMatrix(Matrix): """ @@ -647,13 +656,8 @@ class DenseMatrix(Matrix): """ def __init__(self, numRows, numCols, values): Matrix.__init__(self, numRows, numCols) - if isinstance(values, basestring): - values = np.frombuffer(values, dtype=np.float64) - elif not isinstance(values, np.ndarray): - values = np.array(values, dtype=np.float64) + values = self._convert_to_array(values, np.float64) assert len(values) == numRows * numCols - if values.dtype != np.float64: - values.astype(np.float64) self.values = values def __reduce__(self): @@ -670,6 +674,17 @@ class DenseMatrix(Matrix): """ return self.values.reshape((self.numRows, self.numCols), order='F') + def toSparse(self): + """Convert to SparseMatrix""" + indices = np.nonzero(self.values)[0] + colCounts = np.bincount(indices / self.numRows) + colPtrs = np.cumsum(np.hstack( + (0, colCounts, np.zeros(self.numCols - colCounts.size)))) + values = self.values[indices] + rowIndices = indices % self.numRows + + return SparseMatrix(self.numRows, self.numCols, colPtrs, rowIndices, values) + def __getitem__(self, indices): i, j = indices if i < 0 or i >= self.numRows: @@ -687,6 +702,82 @@ class DenseMatrix(Matrix): all(self.values == other.values)) +class SparseMatrix(Matrix): + """Sparse Matrix stored in CSC format.""" + def __init__(self, numRows, numCols, colPtrs, rowIndices, values, + isTransposed=False): + Matrix.__init__(self, numRows, numCols) + self.isTransposed = isTransposed + self.colPtrs = self._convert_to_array(colPtrs, np.int32) + self.rowIndices = self._convert_to_array(rowIndices, np.int32) + self.values = self._convert_to_array(values, np.float64) + + if self.isTransposed: + if self.colPtrs.size != numRows + 1: + raise ValueError("Expected colPtrs of size %d, got %d." + % (numRows + 1, self.colPtrs.size)) + else: + if self.colPtrs.size != numCols + 1: + raise ValueError("Expected colPtrs of size %d, got %d." + % (numCols + 1, self.colPtrs.size)) + if self.rowIndices.size != self.values.size: + raise ValueError("Expected rowIndices of length %d, got %d." + % (self.rowIndices.size, self.values.size)) + + def __reduce__(self): + return SparseMatrix, ( + self.numRows, self.numCols, self.colPtrs.tostring(), + self.rowIndices.tostring(), self.values.tostring(), + self.isTransposed) + + def __getitem__(self, indices): + i, j = indices + if i < 0 or i >= self.numRows: + raise ValueError("Row index %d is out of range [0, %d)" + % (i, self.numRows)) + if j < 0 or j >= self.numCols: + raise ValueError("Column index %d is out of range [0, %d)" + % (j, self.numCols)) + + # If a CSR matrix is given, then the row index should be searched + # for in ColPtrs, and the column index should be searched for in the + # corresponding slice obtained from rowIndices. + if self.isTransposed: + j, i = i, j + + colStart = self.colPtrs[j] + colEnd = self.colPtrs[j + 1] + nz = self.rowIndices[colStart: colEnd] + ind = np.searchsorted(nz, i) + colStart + if ind < colEnd and self.rowIndices[ind] == i: + return self.values[ind] + else: + return 0.0 + + def toArray(self): + """ + Return an numpy.ndarray + """ + A = np.zeros((self.numRows, self.numCols), dtype=np.float64, order='F') + for k in xrange(self.colPtrs.size - 1): + startptr = self.colPtrs[k] + endptr = self.colPtrs[k + 1] + if self.isTransposed: + A[k, self.rowIndices[startptr:endptr]] = self.values[startptr:endptr] + else: + A[self.rowIndices[startptr:endptr], k] = self.values[startptr:endptr] + return A + + def toDense(self): + densevals = np.reshape( + self.toArray(), (self.numRows * self.numCols), order='F') + return DenseMatrix(self.numRows, self.numCols, densevals) + + # TODO: More efficient implementation: + def __eq__(self, other): + return np.all(self.toArray == other.toArray) + + class Matrices(object): @staticmethod def dense(numRows, numCols, values): @@ -695,6 +786,13 @@ class Matrices(object): """ return DenseMatrix(numRows, numCols, values) + @staticmethod + def sparse(numRows, numCols, colPtrs, rowIndices, values): + """ + Create a SparseMatrix + """ + return SparseMatrix(numRows, numCols, colPtrs, rowIndices, values) + def _test(): import doctest |