fix import StringVar from tkinter

data/convert.py

@@ -0,0 +1,144 @@
+
+import json
+
+
+letter_dict = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
+
+domain = ['entity']
+
+
+def num2letter(num):
+    num = int(num)
+    result = ''
+    while True:
+        div = num // 26
+        mod = num % 26
+        result = letter_dict[mod] + result
+        num = div
+        if div == 0:
+            break
+
+    return result
+
+
+def load(file, pred_num):
+    fi = open(file, 'r')
+    preds = []
+    evids = []
+    for line in fi:
+        line = line.strip()
+        linelist = line.split('\t')
+        sub = 'Ent_' + num2letter(linelist[0])
+        pred = 'pred_' + num2letter(linelist[1])
+        obj = 'Ent_' + num2letter(linelist[2])
+
+        pred_inv = 'pred_' + num2letter(int(linelist[1]) + int(pred_num))
+
+        if pred not in preds:
+            preds.append(pred)
+            preds.append(pred_inv)
+
+        evids.append(pred + '(' + sub + ', ' + obj + ')')
+        evids.append(pred_inv + '(' + obj + ', ' + sub + ')')
+
+    fi.close()
+    return preds, evids
+
+
+def load_rules(file):
+    tlogc_rules = json.load(open(file, 'r'))
+
+    formulas = []
+    for hr in tlogc_rules:
+        for rule in tlogc_rules[hr]:
+            formula_str = rule2formula(rule)
+            formulas.append([rule['conf'], formula_str])
+
+    return formulas
+
+
+def load_learnt_rules(file):
+    weighted_rules = json.load(open(file, 'r'))
+    formulas = []
+    for rule in weighted_rules:
+        formula_str = rule2formula(rule)
+        formulas.append([rule['conf'], formula_str])
+
+    return formulas
+
+
+def rule2formula(rule):
+    i = 0
+    formula = ''
+    sub0 = ''
+    obj0 = ''
+    for rel in rule['body_rels']:
+        pred = 'pred_' + num2letter(rel)
+        sub = 'var_' + letter_dict[i]
+        i += 1
+        obj = 'var_' + letter_dict[i]
+        if formula != '':
+            formula += '^'
+        else:
+            sub0 = sub
+        formula += pred + '(' + sub + ', ' + obj + ')'
+        obj0 = obj
+
+    pred = 'pred_' + num2letter(rule['head_rel'])
+    formula += '=>' + pred + '(' + sub0 + ', ' + obj0 + ')'
+    return formula
+
+
+predicates, train = load('icews14/origin/train.del', 230)
+
+preds, test = load('icews14/origin/test.del', 230)
+
+for pred in preds:
+    if pred not in predicates:
+        predicates.append(pred)
+
+for i in range(len(predicates)):
+    predicates[i] = predicates[i] + '(entity, entity)'
+
+# mln with unlearnt weight
+formulas = load_rules('icews14/origin/tlogic_rules.json')
+
+fo = open('icews14/mlns/icews14.mln', 'w')
+fo.write('//Predicates\n')
+for pred in predicates:
+    fo.write(pred + '\n')
+fo.write('\n//Formulas\n')
+for formula in formulas:
+    fo.write(str(formula[0]) + ' ' + formula[1] + '\n')
+
+fo.close()
+
+# mln with learnt weight from MLN from tLogicNet
+formulas = load_learnt_rules('icews14/rule.txt_0.85_1000_0.03_f2.formula')
+
+fo = open('icews14/mlns/learnt.tlogicnet.train.icews14.mln', 'w')
+fo.write('//Predicates\n')
+for pred in predicates:
+    fo.write(pred + '\n')
+fo.write('\n//Formulas\n')
+for formula in formulas:
+    fo.write(str(formula[0]) + ' ' + formula[1] + '\n')
+
+fo.close()
+
+# train data
+fo = open('icews14/dbs/icews14_train.db', 'w')
+for evid in train:
+    fo.write(evid + '\n')
+
+fo.close()
+
+# test data
+fo = open('icews14/dbs/icews14_test.db', 'w')
+for evid in test:
+    fo.write(evid + '\n')
+
+fo.close()
+
+
+

python3/pracmln/mln/base.py

@@ -28,7 +28,7 @@
 
 from ..logic import FirstOrderLogic, FuzzyLogic
 
-import platform
+import platform,json
 from .mrf import MRF
 from .errors import MLNParsingError
 from pyparsing import ParseException
@@ -76,14 +76,17 @@ class MLN(object):
     def __init__(self, logic='FirstOrderLogic', grammar='PRACGrammar', mlnfile=None):
         # instantiate the logic and grammar
         logic_str = '%s("%s", self)' % (logic, grammar)
+        #  logic_str 是一行 python 代码，eval() 可以执行一行代码然后把结果返回给 self.logic
         self.logic = eval(logic_str)
+        #  print(type(self.logic))
         logger.debug('Creating MLN with %s syntax and %s semantics' % (grammar, logic))
-        self._predicates = {} # maps from predicate name to the predicate instance
-        self.domains = {}    # maps from domain names to list of values
-        self._formulas = []   # list of MLNFormula instances
-        self.domain_decls = []
-        self.weights = []
-        self.fixweights = []
+        self._predicates = {}  # maps from predicate name to the predicate instance，类定义为 pracmln.mln.mlnpreds.Predicate
+        self.domains = {}   # maps from domain names to list of values 所谓的 domain（域） 实际上就是定义（类型）空间，
+                            # 比如实体的类型有['演员','电影','导演']，这个就是 KG 的 domains。
+        self._formulas = []   # list of MLNFormula instances, 类定义为 pracmln.logic.fol.FirstOrderLogic.Implication
+        self.domain_decls = []  ## ?
+        self.weights = []  # 权重，初始为 0
+        self.fixweights = []  # 固定权重 True/False
         self.vars = {}
         self._unique_templvars = []
         self._probreqs = []
@@ -92,6 +95,7 @@ def __init__(self, logic='FirstOrderLogic', grammar='PRACGrammar', mlnfile=None)
         if mlnfile is not None:
             MLN.load(mlnfile, logic=logic, grammar=grammar, mln=self)
             return
+
         self.closedWorldPreds = []
         self.formulaGroups = []
         self.templateIdx2GroupIdx = {}
@@ -517,7 +521,8 @@ def iter_formulas_printable(self):
                 yield "%-10.6f\t%s" % (f.weight, fstr(f))
             else:
                 yield "%s\t%s" % (str(f.weight), fstr(f))
-
+
+
     @staticmethod
     def load(files, logic='FirstOrderLogic', grammar='PRACGrammar', mln=None):
         '''
@@ -537,13 +542,15 @@ def load(files, logic='FirstOrderLogic', grammar='PRACGrammar', mln=None):
             for f in files:
                 if isinstance(f, str):
                     p = mlnpath(f)
+                    #  print(p.content), 输入加载的内容，Evidence、Query，Rules
                     if p.project is not None: 
                         projectpath = p.projectloc
                     text += p.content
                 elif isinstance(f, mlnpath):
                     text += f.content
                 else: raise Exception('Unexpected file specification: %s' % str(f))
             dirs = [os.path.dirname(fn) for fn in files]
+            # print(mln)  # MLN self
             return parse_mln(text, searchpaths=dirs, projectpath=projectpath, logic=logic, grammar=grammar, mln=mln)
         raise Exception('No mln files given.')
 
@@ -687,7 +694,7 @@ def parse_mln(text, searchpaths=['.'], projectpath=None, logic='FirstOrderLogic'
                 if m is None:
                     raise MLNParsingError("Variable assigment malformed: %s" % line)
                 mln.vars[m.group(1)] = "%s" % m.group(2).strip()
-                continue                        
+                continue
             # predicate decl or formula with weight
             else:
                 isHard = False

python3/pracmln/mlnlearn.py

@@ -29,6 +29,7 @@
 import fnmatch
 import io
 import pstats
+import sys
 import tkinter.messagebox
 import traceback
 from cProfile import Profile
@@ -361,7 +362,7 @@ def run(self):
         if self.verbose:
             print(('loaded %d database(s).' % len(dbs)))
 
-        watch = StopWatch()
+        watch = StopWatch()  # 一个计时器
 
         if self.verbose:
             confg = dict(self._config)
@@ -375,14 +376,14 @@ def run(self):
         params = dict([(k, getattr(self, k)) for k in (
             'multicore', 'verbose', 'profile', 'ignore_zero_weight_formulas')])
 
-        # for discriminative learning
+        # for discriminative learning todo method 初传化 learner 的时候传的是串，什么时候变成对象的？
         if issubclass(self.method, DiscriminativeLearner):
             if self.discr_preds == QUERY_PREDS:  # use query preds
                 params['qpreds'] = self.qpreds
             elif self.discr_preds == EVIDENCE_PREDS:  # use evidence preds
                 params['epreds'] = self.epreds
 
-        # gaussian prior settings            
+        # gaussian prior settings todo 如果使用先验
         if self.use_prior:
             params['prior_mean'] = self.prior_mean
             params['prior_stdev'] = self.prior_stdev
@@ -400,7 +401,7 @@ def run(self):
         logger.level = eval('logs.%s' % params.get('debug', 'WARNING').upper())
         mlnlearnt = None
         try:
-            # run the learner
+            # run the learner, most expensive part
             mlnlearnt = mln.learn(dbs, self.method, **params)
             if self.verbose:
                 print()

python3/pracmln/mlnquery.py

@@ -30,8 +30,8 @@
 import ntpath
 import traceback
 from tkinter import Frame, BOTH, Label, Button, OptionMenu, IntVar, Checkbutton, \
-    W, E, Entry, messagebox, END, DISABLED, NORMAL, Tk
-from tkinter.filedialog import askopenfilename, asksaveasfilename, StringVar
+    W, E, Entry, messagebox, END, DISABLED, NORMAL, Tk, StringVar
+from tkinter.filedialog import askopenfilename, asksaveasfilename
 
 from dnutils import logs, ifnone, out
 

python3/pracmln/utils/project.py

@@ -295,9 +295,9 @@ def convert(data):
     """
     if isinstance(data, str):
         return str(data)
-    elif isinstance(data, collections.Mapping):
+    elif isinstance(data, collections.abc.Mapping):
         return dict(list(map(convert, iter(data.items()))))
-    elif isinstance(data, collections.Iterable):
+    elif isinstance(data, collections.abc.Iterable):
         return type(data)(list(map(convert, data)))
     else:
         return data
@@ -470,10 +470,12 @@ def content(self):
         """
         path = self.resolve_path()
         if self.project is not None:
+            #  MLNProject
             proj = MLNProject.open(os.path.join(self.resolve_path(), self.project))
             if self.file is None:
                 return proj
             fileext = self.file.split('.')[-1]
+            # print(fileext)  # mln/db
             if fileext == 'mln':
                 mln = proj.mlns.get(self.file)
                 if mln is None: raise Exception('Project %s does not contain and MLN named %s' % (self.project, self.file))