Github user mktal commented on a diff in the pull request:
https://github.com/apache/incubator-madlib/pull/10#discussion_r52060951
--- Diff: src/ports/postgres/modules/svm/kernel_approximation.py_in ---
@@ -0,0 +1,481 @@
+from __future__ import division
+
+import plpy
+
+from utilities.utilities import unique_string
+from utilities.utilities import extract_keyvalue_params
+from utilities.utilities import num_features
+
+from math import sqrt
+from math import pi
+
+
+class GaussianKernelBase(object):
+ """docstring for gaussianKernel"""
+ def __init__(self, gamma, n_components, random_state,
+ random_weights, random_offset, id_col, val_col,
+ orig_data, **kwargs):
+ self.kernel_func = 'gaussian'
+ self.gamma = gamma
+ self.n_components = n_components
+ # int32 seed used by boost::minstd_rand
+ self.random_state = random_state
+ # random operators
+ self.rd_weights = random_weights
+ self.rd_offset = random_offset
+ # val column in random operators
+ self.rd_val = val_col
+ # id column in random operators
+ self.rd_id = id_col
+ self.transformed_table = dict()
+ self.original_table = dict()
+ # indicate whether rd_weights and rd_offset is view or table
+ # store the original data table name if they are view
+ # None if they are table
+ self.orig_data = orig_data
+
+ def clear(self):
+ data_type = 'view' if self.orig_data else 'table'
+ if self.rd_weights:
+ plpy.execute("drop {data_type} if exists {data};".format(
+ data=self.rd_weights,
+ data_type=data_type))
+ if self.rd_offset:
+ plpy.execute("drop {data_type} if exists {data};".format(
+ data=self.rd_offset,
+ data_type=data_type))
+
+ def __del__(self):
+ self.clear()
+
+ def saveAs(self, name):
+ if self.orig_data:
+ plpy.warning("Gaussian Kernel Warning: no need to save."
+ "Original data table exists: {0}"
+ .format(self.orig_data))
+ return
+
+ run_sql = """
+ create table {name} as
+ select
+ {id} as id, {val} as val,
+ 'offsets' as desp
+ from {rd_offset}
+ union
+ select
+ {id} as id, {val} as val,
+ 'weights' as desp
+ from {rd_weights}
+ """.format(name=name,
+ id=self.rd_id,
+ val=self.rd_val,
+ rd_offset=self.rd_offset,
+ rd_weights=self.rd_weights)
+ plpy.execute(run_sql)
+
+ @classmethod
+ def parse_params(cls, kernel_params='', n_features=10):
+ params_default = {
+ 'in_memory': 1,
+ 'gamma': 1/n_features,
+ 'random_state': 1,
+ 'n_components': 2*n_features}
+ params_types = {
+ 'in_memory': int,
+ 'gamma': float,
+ 'random_state': int,
+ 'n_components': int}
+ return extract_keyvalue_params(kernel_params,
+ params_types,
+ params_default)
+
+ @classmethod
+ def create(cls, schema_madlib, n_features, kernel_params):
+ params = cls.parse_params(kernel_params, n_features)
+ in_memory = params.pop('in_memory', True)
+ # according to the 1gb limit on each entry of the table
+ nelems = params['n_components']*n_features
+ if in_memory and nelems <= 1e8:
+ return GaussianKernelInMemory(schema_madlib, **params)
+ else:
+ return GaussianKernel(schema_madlib, **params)
+
+ @classmethod
+ def loadFrom(cls, schema_madlib, data, kernel_params=''):
+ rd_weights = unique_string(desp='random_weights')
+ rd_offset = unique_string(desp='random_offsets')
+ rd_val = unique_string(desp='val')
+ rd_id = unique_string(desp='id')
+ plpy.execute("""
+ drop view if exists {rd_weights};
+ create temp view {rd_weights} as
+ select id as {rd_id}, val as {rd_val} from {data}
+ where desp = 'weights';
+
+ drop view if exists {rd_offset};
+ create temp view {rd_offset} as
+ select id as {rd_id}, val as {rd_val} from {data}
+ where desp = 'offsets';
+ """.format(**locals()))
+ params = cls.parse_params(kernel_params)
+ in_memory = params.pop('in_memory', True)
--- End diff --
I understand. I used **kwargs in the base class but in the derived class I
am trying to make the interface more restrictive such that no unexpected
variables got passed to base class through, for example,
super().__init__(**locals()). Plus, it will be more clear to the caller this
way what are expected to create the object
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