255 lines
7.1 KiB
Cython
255 lines
7.1 KiB
Cython
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# Optimized inner loop of load_svmlight_file.
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#
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# Authors: Mathieu Blondel <mathieu@mblondel.org>
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# Lars Buitinck
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# Olivier Grisel <olivier.grisel@ensta.org>
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# License: BSD 3 clause
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import array
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from cpython cimport array
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cimport cython
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from libc.string cimport strchr
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import numpy as np
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cdef bytes COMMA = u','.encode('ascii')
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cdef bytes COLON = u':'.encode('ascii')
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def _load_svmlight_file(f, dtype, bint multilabel, bint zero_based,
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bint query_id, long long offset, long long length):
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cdef array.array data, indices, indptr
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cdef bytes line
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cdef char *hash_ptr
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cdef char *line_cstr
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cdef int idx, prev_idx
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cdef Py_ssize_t i
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cdef bytes qid_prefix = b'qid'
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cdef Py_ssize_t n_features
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cdef long long offset_max = offset + length if length > 0 else -1
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# Special-case float32 but use float64 for everything else;
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# the Python code will do further conversions.
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if dtype == np.float32:
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data = array.array("f")
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else:
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dtype = np.float64
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data = array.array("d")
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indices = array.array("q")
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indptr = array.array("q", [0])
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query = np.arange(0, dtype=np.int64)
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if multilabel:
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labels = []
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else:
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labels = array.array("d")
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if offset > 0:
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f.seek(offset)
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# drop the current line that might be truncated and is to be
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# fetched by another call
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f.readline()
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for line in f:
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# skip comments
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line_cstr = line
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hash_ptr = strchr(line_cstr, 35) # ASCII value of '#' is 35
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if hash_ptr != NULL:
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line = line[:hash_ptr - line_cstr]
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line_parts = line.split()
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if len(line_parts) == 0:
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continue
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target, features = line_parts[0], line_parts[1:]
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if multilabel:
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if COLON in target:
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target, features = [], line_parts[0:]
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else:
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target = [float(y) for y in target.split(COMMA)]
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target.sort()
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labels.append(tuple(target))
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else:
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array.resize_smart(labels, len(labels) + 1)
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labels[len(labels) - 1] = float(target)
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prev_idx = -1
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n_features = len(features)
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if n_features and features[0].startswith(qid_prefix):
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_, value = features[0].split(COLON, 1)
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if query_id:
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query.resize(len(query) + 1)
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query[len(query) - 1] = np.int64(value)
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features.pop(0)
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n_features -= 1
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for i in range(0, n_features):
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idx_s, value = features[i].split(COLON, 1)
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idx = int(idx_s)
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if idx < 0 or not zero_based and idx == 0:
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raise ValueError(
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"Invalid index %d in SVMlight/LibSVM data file." % idx)
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if idx <= prev_idx:
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raise ValueError("Feature indices in SVMlight/LibSVM data "
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"file should be sorted and unique.")
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array.resize_smart(indices, len(indices) + 1)
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indices[len(indices) - 1] = idx
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array.resize_smart(data, len(data) + 1)
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data[len(data) - 1] = float(value)
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prev_idx = idx
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# increment index pointer array size
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array.resize_smart(indptr, len(indptr) + 1)
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indptr[len(indptr) - 1] = len(data)
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if offset_max != -1 and f.tell() > offset_max:
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# Stop here and let another call deal with the following.
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break
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return (dtype, data, indices, indptr, labels, query)
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# Two fused types are defined to be able to
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# use all possible combinations of parameters.
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ctypedef fused int_or_float:
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cython.integral
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cython.floating
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signed long long
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ctypedef fused double_or_longlong:
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double
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signed long long
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ctypedef fused int_or_longlong:
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cython.integral
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signed long long
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def get_dense_row_string(
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const int_or_float[:, :] X,
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Py_ssize_t[:] x_inds,
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double_or_longlong[:] x_vals,
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Py_ssize_t row,
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str value_pattern,
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bint one_based,
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):
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cdef:
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Py_ssize_t row_length = X.shape[1]
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Py_ssize_t x_nz_used = 0
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Py_ssize_t k
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int_or_float val
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for k in range(row_length):
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val = X[row, k]
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if val == 0:
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continue
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x_inds[x_nz_used] = k
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x_vals[x_nz_used] = <double_or_longlong> val
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x_nz_used += 1
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reprs = [
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value_pattern % (x_inds[i] + one_based, x_vals[i])
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for i in range(x_nz_used)
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]
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return " ".join(reprs)
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def get_sparse_row_string(
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int_or_float[:] X_data,
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int[:] X_indptr,
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int[:] X_indices,
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Py_ssize_t row,
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str value_pattern,
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bint one_based,
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):
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cdef:
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Py_ssize_t row_start = X_indptr[row]
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Py_ssize_t row_end = X_indptr[row+1]
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reprs = [
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value_pattern % (X_indices[i] + one_based, X_data[i])
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for i in range(row_start, row_end)
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]
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return " ".join(reprs)
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def _dump_svmlight_file(
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X,
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y,
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f,
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bint multilabel,
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bint one_based,
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int_or_longlong[:] query_id,
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bint X_is_sp,
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bint y_is_sp,
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):
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cdef bint X_is_integral
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cdef bint query_id_is_not_empty = query_id.size > 0
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X_is_integral = X.dtype.kind == "i"
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if X_is_integral:
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value_pattern = "%d:%d"
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else:
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value_pattern = "%d:%.16g"
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if y.dtype.kind == "i":
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label_pattern = "%d"
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else:
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label_pattern = "%.16g"
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line_pattern = "%s"
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if query_id_is_not_empty:
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line_pattern += " qid:%d"
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line_pattern += " %s\n"
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cdef:
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Py_ssize_t num_labels = y.shape[1]
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Py_ssize_t x_len = X.shape[0]
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Py_ssize_t row_length = X.shape[1]
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Py_ssize_t i
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Py_ssize_t j
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Py_ssize_t col_start
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Py_ssize_t col_end
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Py_ssize_t[:] x_inds = np.empty(row_length, dtype=np.intp)
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signed long long[:] x_vals_int
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double[:] x_vals_float
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if not X_is_sp:
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if X_is_integral:
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x_vals_int = np.zeros(row_length, dtype=np.longlong)
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else:
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x_vals_float = np.zeros(row_length, dtype=np.float64)
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for i in range(x_len):
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if not X_is_sp:
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if X_is_integral:
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s = get_dense_row_string(X, x_inds, x_vals_int, i, value_pattern, one_based)
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else:
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s = get_dense_row_string(X, x_inds, x_vals_float, i, value_pattern, one_based)
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else:
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s = get_sparse_row_string(X.data, X.indptr, X.indices, i, value_pattern, one_based)
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if multilabel:
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if y_is_sp:
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col_start = y.indptr[i]
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col_end = y.indptr[i+1]
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labels_str = ','.join(tuple(label_pattern % y.indices[j] for j in range(col_start, col_end) if y.data[j] != 0))
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else:
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labels_str = ','.join(label_pattern % j for j in range(num_labels) if y[i, j] != 0)
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else:
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if y_is_sp:
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labels_str = label_pattern % y.data[i]
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else:
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labels_str = label_pattern % y[i, 0]
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if query_id_is_not_empty:
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feat = (labels_str, query_id[i], s)
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else:
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feat = (labels_str, s)
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f.write((line_pattern % feat).encode("utf-8"))
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