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jingyimei pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/madlib.git
The following commit(s) were added to refs/heads/master by this push:
new b52a961 SVD: Minor error message change
b52a961 is described below
commit b52a961cee185bc955302bbf8117beb5c91cb260
Author: hpandeycodeit <[email protected]>
AuthorDate: Fri Jan 18 15:36:23 2019 -0800
SVD: Minor error message change
Closes #347
---
src/ports/postgres/modules/linalg/svd.py_in | 84 ++++++++++++++++++-----------
1 file changed, 53 insertions(+), 31 deletions(-)
diff --git a/src/ports/postgres/modules/linalg/svd.py_in
b/src/ports/postgres/modules/linalg/svd.py_in
index 3534479..894fae2 100644
--- a/src/ports/postgres/modules/linalg/svd.py_in
+++ b/src/ports/postgres/modules/linalg/svd.py_in
@@ -40,6 +40,7 @@ actual_lanczos_iterations = 0
# ------------------------------------------------------------------------
+
def svd(schema_madlib, source_table, output_table_prefix,
row_id, k, nIterations, result_summary_table=None):
"""
@@ -70,8 +71,8 @@ def svd(schema_madlib, source_table, output_table_prefix,
plpy.execute("set client_min_messages to error")
_validate_args(schema_madlib, source_table, output_table_prefix,
- k, row_id, nIterations, col_id=None, val_id=None,
- result_summary_table=result_summary_table)
+ k, row_id, nIterations, col_id=None, val_id=None,
+ result_summary_table=result_summary_table)
# Make sure that the input table has row_id and row_vec
# if not then make a copy of the source table and change column names
source_table_copy = "pg_temp." + unique_string() + "_2"
@@ -81,17 +82,17 @@ def svd(schema_madlib, source_table, output_table_prefix,
if(need_new_column_names):
source_table = source_table_copy
- (new_source_table, bd_pref)=_svd_upper_wrap(schema_madlib, source_table,
- output_table_prefix, row_id,k, nIterations, result_summary_table)
+ (new_source_table, bd_pref) = _svd_upper_wrap(schema_madlib, source_table,
+ output_table_prefix, row_id, k, nIterations, result_summary_table)
_svd_lower_wrap(schema_madlib, new_source_table, output_table_prefix,
- row_id, k, nIterations, bd_pref)
+ row_id, k, nIterations, bd_pref)
if result_summary_table:
t1 = time.time()
[row_dim, col_dim] = get_dims(source_table,
- {'row': 'row_id', 'col': 'col_id', 'val': 'row_vec'})
+ {'row': 'row_id', 'col': 'col_id',
'val': 'row_vec'})
arguments = {'schema_madlib': schema_madlib,
'source_table': source_table,
'matrix_u': add_postfix(output_table_prefix, "_u"),
@@ -136,7 +137,8 @@ def _validate_args(schema_madlib, source_table,
output_table_prefix, k,
if k <= 0:
plpy.error("SVD error: k must be a positive integer!")
- # output tables are created from the prefix by appending _u, _s, and _v as
suffixes
+ # output tables are created from the prefix by appending _u, _s, and _v as
+ # suffixes
if output_table_prefix:
output_table_names = [add_postfix(output_table_prefix, i)
for i in ('_u', '_s', '_v')]
@@ -290,7 +292,13 @@ def svd_block(schema_madlib, source_table,
output_table_prefix,
add_postfix(output_table_prefix, "_v"))
plpy.execute("DROP TABLE IF EXISTS {0}".format(x_trans))
else:
- _svd(schema_madlib, source_table, output_table_prefix, k, nIterations,
True)
+ _svd(
+ schema_madlib,
+ source_table,
+ output_table_prefix,
+ k,
+ nIterations,
+ True)
if result_summary_table:
t1 = time.time()
@@ -479,7 +487,7 @@ def svd_sparse_native(schema_madlib, source_table,
output_table_prefix,
def _svd_upper_wrap(schema_madlib, source_table, output_table_prefix,
- row_id, k, nIterations, result_summary_table):
+ row_id, k, nIterations, result_summary_table):
"""
Compute the Singular Value Decomposition of a sparse matrix
@@ -506,7 +514,7 @@ def _svd_upper_wrap(schema_madlib, source_table,
output_table_prefix,
(source_table,bd_pref)
"""
[row_dim, col_dim] = get_dims(source_table,
- {'row': 'row_id', 'col': 'col_id', 'val': 'row_vec'})
+ {'row': 'row_id', 'col': 'col_id', 'val':
'row_vec'})
validate_dense(source_table,
{'row': 'row_id', 'val': 'row_vec'},
check_col=False, row_dim=row_dim)
@@ -525,21 +533,32 @@ def _svd_upper_wrap(schema_madlib, source_table,
output_table_prefix,
'{x_trans}', 'row=row_id, val=row_vec')
""".format(schema_madlib=schema_madlib,
source_table=source_table, x_trans=x_trans))
- bd_pref = _svd_upper(schema_madlib, x_trans, output_table_prefix, k,
nIterations, is_block=False)
+ bd_pref = _svd_upper(
+ schema_madlib,
+ x_trans,
+ output_table_prefix,
+ k,
+ nIterations,
+ is_block=False)
- return (x_trans,bd_pref)
+ return (x_trans, bd_pref)
else:
- bd_pref = _svd_upper(schema_madlib, source_table, output_table_prefix,
k, nIterations, is_block=False)
+ bd_pref = _svd_upper(
+ schema_madlib,
+ source_table,
+ output_table_prefix,
+ k,
+ nIterations,
+ is_block=False)
return (source_table, bd_pref)
# ------------------------------------------------------------------------
def _svd_upper(schema_madlib, source_table, output_table_prefix,
- k, nIterations, is_block=False,
- row_id=None, col_id=None, val=None):
-
+ k, nIterations, is_block=False,
+ row_id=None, col_id=None, val=None):
"""
Compute the Singular Value Decomposition of a sparse matrix
@@ -656,7 +675,7 @@ def _svd_upper(schema_madlib, source_table,
output_table_prefix,
def _svd_lower_wrap(schema_madlib, source_table, output_table_prefix,
- row_id, k, nIterations, bd_pref, matrix_s=None):
+ row_id, k, nIterations, bd_pref, matrix_s=None):
"""
Compute the Singular Value Decomposition of a sparse matrix
@@ -676,7 +695,7 @@ def _svd_lower_wrap(schema_madlib, source_table,
output_table_prefix,
None
"""
_svd_lower(schema_madlib, source_table, output_table_prefix,
- k, nIterations, bd_pref, False, None,None,None,matrix_s)
+ k, nIterations, bd_pref, False, None, None, None, matrix_s)
if "_xt_3" in source_table and "pg_temp." in source_table:
@@ -686,15 +705,15 @@ def _svd_lower_wrap(schema_madlib, source_table,
output_table_prefix,
add_postfix(output_table_prefix, "_v"))
plpy.execute(
"""DROP TABLE IF EXISTS {source_table}""".format(
- source_table=source_table
+ source_table=source_table
))
return None
# ------------------------------------------------------------------------
def _svd_lower(schema_madlib, source_table, output_table_prefix,
- k, nIterations, bd_pref, is_block=False,
- row_id=None, col_id=None, val=None, matrix_s=None):
+ k, nIterations, bd_pref, is_block=False,
+ row_id=None, col_id=None, val=None, matrix_s=None):
"""
Compute the Singular Value Decomposition of a sparse matrix
@@ -757,7 +776,7 @@ def _svd_lower(schema_madlib, source_table,
output_table_prefix,
svd_bidiagonal=svd_bidiagonal,
k=k))
- #plpy.execute("DROP TABLE if exists {matrix_s_tmp}"
+ # plpy.execute("DROP TABLE if exists {matrix_s_tmp}"
# .format(matrix_s_tmp=matrix_s_tmp))
plpy.execute("DROP TABLE if exists {svd_bidiagonal}"
.format(svd_bidiagonal=svd_bidiagonal))
@@ -796,14 +815,15 @@ def _svd(schema_madlib, source_table, output_table_prefix,
@param val Name of the value column
"""
bd_pref = _svd_upper(schema_madlib, source_table, output_table_prefix, k,
- nIterations, is_block, row_id, col_id, val)
+ nIterations, is_block, row_id, col_id, val)
_svd_lower(schema_madlib, source_table, output_table_prefix, k,
- nIterations, bd_pref, is_block, row_id, col_id, val)
+ nIterations, bd_pref, is_block, row_id, col_id, val)
# ------------------------------------------------------------------------
-def _lanczos_bidiagonalize_create_pq_table(schema_madlib, pq_table_prefix,
col_dim):
+def _lanczos_bidiagonalize_create_pq_table(
+ schema_madlib, pq_table_prefix, col_dim):
"""
Creates and initializes the P and Q (left and right) matrices output of
Lanczos bidiagonalization.
@@ -944,15 +964,16 @@ def lanczos_bidiagonalize(schema_madlib, source_table,
output_table_prefix,
if nIterations is None:
nIterations = min(col_dim, MAX_LANCZOS_STEPS)
elif nIterations < k or nIterations > col_dim:
- plpy.error("SVD error: Number of Lanczos iterations should be"
+ plpy.error("SVD error: n_Iterations should be"
" in the range of [{0}, {1}]".format(k, col_dim))
elif nIterations > MAX_LANCZOS_STEPS:
- plpy.error("SVD error: Number of Lanczos iterations"
+ plpy.error("SVD error: n_Iterations"
" should be less than {0}".format(MAX_LANCZOS_STEPS))
actual_lanczos_iterations = nIterations
pq_table_prefix = "pg_temp." + unique_string() + "_8"
- _lanczos_bidiagonalize_create_pq_table(schema_madlib, pq_table_prefix,
col_dim)
+ _lanczos_bidiagonalize_create_pq_table(
+ schema_madlib, pq_table_prefix, col_dim)
# Transform the matrix
x_trans = "pg_temp." + unique_string() + "_9"
@@ -1070,16 +1091,17 @@ def lanczos_bidiagonalize_sparse(schema_madlib,
source_table,
if nIterations is None:
nIterations = min(col_dim, MAX_LANCZOS_STEPS)
elif nIterations < k or nIterations > col_dim:
- plpy.error("SVD error: Number of Lanczos iterations should be in the"
+ plpy.error("SVD error: n_Iterations should be in the"
"range of [{k}, {col_dim}]".format(k=k, col_dim=col_dim))
elif nIterations > MAX_LANCZOS_STEPS:
- plpy.error("SVD error: Number of Lanczos iterations"
+ plpy.error("SVD error: n_Iterations"
" should be less than {0}".format(MAX_LANCZOS_STEPS))
actual_lanczos_iterations = nIterations
pq_table_prefix = "pg_temp." + unique_string() + "_8"
- _lanczos_bidiagonalize_create_pq_table(schema_madlib, pq_table_prefix,
col_dim)
+ _lanczos_bidiagonalize_create_pq_table(
+ schema_madlib, pq_table_prefix, col_dim)
lanczos_sql = """
{schema_madlib}.__svd_sparse_lanczos_agg(
