Bugfixes

README.rst

@@ -1,5 +1,5 @@
 ===============
-Wordbatch 1.4.5
+Wordbatch 1.4.6
 ===============
 
 Overview

setup.py

@@ -13,7 +13,7 @@
 
 setup(
 	name='Wordbatch',
-	version='1.4.5',
+	version='1.4.6',
 	description='Python library for distributed AI processing pipelines, using swappable scheduler backends',
 	url='https://github.com/anttttti/Wordbatch',
 	author='Antti Puurula',

wordbatch/__init__.py

@@ -1,4 +1,4 @@
 import os
 PACKAGE_DIR = os.path.dirname(os.path.abspath(__file__))
-__version__ = '1.4.5'
+__version__ = '1.4.6'
 

wordbatch/models/ftrl.pyx

@@ -1,14 +1,12 @@
 # cython: boundscheck=False, wraparound=False, cdivision=True
 import numpy as np
-import gzip
 cimport cython
 from cpython cimport array
 import scipy.sparse as ssp
 cimport numpy as np
 from cython.parallel import prange
 from libc.math cimport exp, log, fmax, fmin, sqrt, fabs
 import multiprocessing
-import sys
 import randomgen
 
 np.import_array()
@@ -213,20 +211,20 @@ cdef class FTRL:
 
 	def finalize_model(self):
 		D= self.D
-		indices = np.arange(start=0, stop=D+1, step=1, dtype=np.int32)
-		indptr= np.array([0, D+1], dtype=np.int32)
-		data = np.zeros((D+1,), dtype=np.float64)
+		indices = np.arange(start=0, stop=D, step=1, dtype=np.int32)
+		indptr= np.array([0, D], dtype=np.int32)
+		data = np.zeros(D, dtype=np.float64)
 		self.predict_f(data, indices, indptr, threads= self.threads)
 		del(indices, indptr, data)
-		self.z = None
-		self.n = None
+		self.z = np.zeros(0, dtype=np.float64)
+		self.n = np.zeros(0, dtype=np.float64)
 		self.model_finalized= True
 
 	def __getstate__(self):
 		return (self.alpha, self.beta, self.L1, self.L2, self.e_clip, self.D, self.init, self.seed, self.iters,
 				np.asarray(self.w), np.asarray(self.z), np.asarray(self.n), self.inv_link, self.threads, self.bias_term,
-				self.verbose)
+				self.model_finalized, self.verbose)
 
 	def __setstate__(self, params):
-		(self.alpha, self.beta, self.L1, self.L2, self.e_clip, self.D, self.init, self.seed,
-		 self.iters, self.w, self.z, self.n, self.inv_link, self.threads, self.bias_term, self.verbose)= params
+		(self.alpha, self.beta, self.L1, self.L2, self.e_clip, self.D, self.init, self.seed, self.iters, self.w,
+		self.z, self.n, self.inv_link, self.threads, self.bias_term, self.model_finalized, self.verbose)= params

wordbatch/models/ftrl32.pyx

@@ -1,14 +1,12 @@
 # cython: boundscheck=False, wraparound=False, cdivision=True
 import numpy as np
-import gzip
 cimport cython
 from cpython cimport array
 import scipy.sparse as ssp
 cimport numpy as np
 from cython.parallel import prange
 from libc.math cimport exp, log, fmax, fmin, sqrt, fabs
 import multiprocessing
-import sys
 import randomgen
 
 np.import_array()
@@ -214,20 +212,20 @@ cdef class FTRL32:
 
 	def finalize_model(self):
 		D= self.D
-		indices = np.arange(start=0, stop=D+1, step=1, dtype=np.int32)
-		indptr= np.array([0, D+1], dtype=np.int32)
-		data = np.zeros((D+1,), dtype=np.float64)
+		indices = np.arange(start=0, stop=D, step=1, dtype=np.int32)
+		indptr= np.array([0, D], dtype=np.int32)
+		data = np.zeros(D, dtype=np.float64)
 		self.predict_f(data, indices, indptr, threads= self.threads)
 		del(indices, indptr, data)
-		self.z = None
-		self.n = None
+		self.z = np.zeros(0, dtype=np.float32)
+		self.n = np.zeros(0, dtype=np.float32)
 		self.model_finalized= True
 
 	def __getstate__(self):
 		return (self.alpha, self.beta, self.L1, self.L2, self.e_clip, self.D, self.init, self.seed, self.iters,
 				np.asarray(self.w), np.asarray(self.z), np.asarray(self.n), self.inv_link, self.threads, self.bias_term,
-				self.verbose)
+				self.model_finalized, self.verbose)
 
 	def __setstate__(self, params):
-		(self.alpha, self.beta, self.L1, self.L2, self.e_clip, self.D, self.init, self.seed,
-		 self.iters, self.w, self.z, self.n, self.inv_link, self.threads, self.bias_term, self.verbose)= params
+		(self.alpha, self.beta, self.L1, self.L2, self.e_clip, self.D, self.init, self.seed, self.iters, self.w,
+		self.z, self.n, self.inv_link, self.threads, self.bias_term, self.model_finalized, self.verbose)= params

wordbatch/pipelines/apply.py

@@ -8,9 +8,7 @@
 
 def decorator_apply(func, batcher=None, cache=None, vectorize=None):
 	def wrapper_func(*args, **kwargs):
-		if len(args)>1:  argss= args[1]
-		else:  argss= []
-		return Apply(func, args=argss, kwargs= kwargs, batcher= batcher, cache= cache,
+		return Apply(func, args=args[1:], kwargs= kwargs, batcher= batcher, cache= cache,
 					vectorize= vectorize).transform(args[0])
 	return wrapper_func