def __init__(self, dbname, runMode = 'all' ):
"""create an instance of a PEContext object associated with the SQLite
database.
dbname: name of SQLite database
test: If True run against the test set, otherwise run against the training set
"""
# Define queries to select data from the SQLite database
# this gets the reports we will process
self.query1 = '''SELECT id,impression FROM pesubject'''
# get the consensus labels for the disease and quality states
self.query2 = '''SELECT diseaseState,qualityState,CDS,diseaseUncertainty,CQS FROM consensus_states WHERE psid==?'''
# get the userannotations from the three raters we are using for this
# study
self.query3 = """SELECT usrname,diseaseState,qualityState,qualitylimit,usrname FROM userannotations where psid_id == ? AND (usrname=='aaron' OR usrname=='hyun' OR usrname=='sean')"""
# create query to get temporal annotations
self.query4 = """SELECT usrname FROM AND (usrname == 'jd' OR usrname =='sean' OR usrname=='peter')"""
self.conn = sqlite.connect(dbname)
self.cursor = self.conn.cursor()
self.cursor.execute(self.query1)
# get the training set reports. The first 20 reports were used to train
# Aaron and Hyun and so can be skipped
reports = self.cursor.fetchall()
if( runMode == 'full' ):
self.reports = reports[:]
elif( 'test' ):
self.reports = reports[270:]
else:
self.reports = (reports[20:])[:250]
print "number of reports to process",len(self.reports)
raw_input('continue')
t = time.localtime()
d = datetime.datetime(t[0],t[1],t[2])
# create dictionaries to store the agreement matricies for the disease,
# quality and uncertainty states
self.usMatrix = {('Yes','Yes'):0,('Yes','No'):0,('No','Yes'):0,('No','No'):0}
self.dsMatrix = {('Pos','Pos'):0,('Pos','Neg'):0,('Neg','Pos'):0,('Neg','Neg'):0}
self.qsMatrix = {('Diagnostic','Diagnostic'):0,('Diagnostic','Not Diagnostic'):0,
('Not Diagnostic','Diagnostic'):0,('Not Diagnostic','Not Diagnostic'):0}
self.hsMatrix = {('Old','Old'):0,('Old','New'):0,('New','Old'):0,('New','New'):0}
# create context objects for each of the questions we want to be answering
self.context = {"disease":pycontext.pycontext(),
"quality":pycontext.pycontext(),
"quality2":pycontext.pycontext()}
# Create files for storing problem cases
suffix = runMode
self.f1 = open(dbname+".ds%s.txt"%suffix,'w')
self.f2 = open(dbname+".qs%s.txt"%suffix,'w')
self.f3 = open(dbname+".us%s.txt"%suffix,'w')
self.f4 = open(dbname+".all%s.txt"%suffix,"w")
self.f5 = open(dbname+".hs%s.txt"%suffix,"w")
self.f6 = open(dbname+".disagreements%s.pckle"%suffix,"wb")
rsltsDB = dbname+".OriginalResults.db"
if( os.path.exists(rsltsDB) ):
os.remove(rsltsDB)
self.resultsConn = sqlite.connect(rsltsDB)
self.resultsCursor = self.resultsConn.cursor()
self.resultsCursor.execute("""CREATE TABLE results (
id INT PRIMARY KEY,
disease TEXT,
uncertainty TEXT,
historical TEXT,
quality TEXT)""")
# Create the itemData object to store the modifiers for the analysis
# starts with definitions defined in pyConText and then adds
# definitions specific for peFinder
self.modifiers = {"disease":itemData.itemData()}
self.modifiers["disease"].extend(pseudoNegations)
self.modifiers["disease"].extend(definiteNegations)
self.modifiers["disease"].extend(probableNegations)
self.modifiers["disease"].extend(probables)
self.modifiers["disease"].extend(definites)
self.modifiers["disease"].extend(indications)
self.modifiers["disease"].extend(historicals)
self.modifiers["disease"].extend(conjugates)
# Create a seperate itemData for the quality modifiers
self.modifiers["quality"] = itemData.itemData()
self.modifiers["quality"].extend(pseudoNegations)
self.modifiers["quality"].extend(definiteNegations)
self.modifiers["quality"].extend(probableNegations)
self.modifiers["quality"].extend(probables)
self.modifiers["quality"].extend(historicals)
self.modifiers["quality"].extend(conjugates)
self.modifiers["quality"].extend(qualities)
self.modifiers["quality"].extend([['limited dataset compliant','EXCLUSION','','']])
# Quality targets
self.targets = {"disease":peItems}
self.targets["quality"] = itemData.itemData()
#self.targets["quality"].extend(qualities)
self.targets["quality"].extend(examFeatures)
self.targets["quality"].extend([['limited dataset compliant','EXCLUSION','','']])
self.targets["quality2"] = itemData.itemData()
self.targets["quality2"].extend(artifacts)
self.temporalCount = 0
self.breakCases = set([320])
self.models = {}
self.problemCases = []
self.disagreements = {"ds":[],"qs":[],"us":[],"hs":[]}
def __init__(self, dbname, outfile):
"""create an instance of a PEContext object associated with the SQLite
database.
dbname: name of SQLite database
"""
# Define queries to select data from the SQLite database
# this gets the reports we will process
self.query1 = '''SELECT id,impression FROM pesubject'''
self.conn = sqlite.connect(dbname)
self.cursor = self.conn.cursor()
self.cursor.execute(self.query1)
self.reports = self.cursor.fetchall()
#conn = sqlite.connect("nx_o_context_differences.db")
#curs = conn.cursor()
#curs.execute("""select id from disease_diff""")
#es = curs.fetchall()
#errors = [e[0] for e in es]
#
#tmp = []
#for r in self.reports:
# if r[0] in errors:
# print "removed"
# tmp.append(r)
#self.reports = tmp
print "number of reports to process",len(self.reports)
t = time.localtime()
d = datetime.datetime(t[0],t[1],t[2])
# create context objects for each of the questions we want to be answering
self.context = {"disease":pycontext.pycontext(),
"quality":pycontext.pycontext(),
"quality2":pycontext.pycontext()}
rsltsDB = outfile
if( os.path.exists(rsltsDB) ):
os.remove(rsltsDB)
self.resultsConn = sqlite.connect(rsltsDB)
self.resultsCursor = self.resultsConn.cursor()
self.resultsCursor.execute("""CREATE TABLE results (
id INT PRIMARY KEY,
disease TEXT,
uncertainty TEXT,
historical TEXT,
quality TEXT)""")
# Create the itemData object to store the modifiers for the analysis
# starts with definitions defined in pyConText and then adds
# definitions specific for peFinder
self.modifiers = {"disease":itemData.itemData()}
self.modifiers["disease"].extend(pseudoNegations)
self.modifiers["disease"].extend(definiteNegations)
self.modifiers["disease"].extend(probableNegations)
self.modifiers["disease"].extend(probables)
self.modifiers["disease"].extend(definites)
self.modifiers["disease"].extend(indications)
self.modifiers["disease"].extend(historicals)
self.modifiers["disease"].extend(conjugates)
# Create a seperate itemData for the quality modifiers
self.modifiers["quality"] = itemData.itemData()
self.modifiers["quality"].extend(pseudoNegations)
self.modifiers["quality"].extend(definiteNegations)
self.modifiers["quality"].extend(probableNegations)
self.modifiers["quality"].extend(probables)
self.modifiers["quality"].extend(historicals)
self.modifiers["quality"].extend(conjugates)
self.modifiers["quality"].extend(qualities)
self.modifiers["quality"].extend([['limited dataset compliant','EXCLUSION','','']])
# Quality targets
self.targets = {"disease":peItems}
self.targets["quality"] = itemData.itemData()
#self.targets["quality"].extend(qualities)
self.targets["quality"].extend(examFeatures)
self.targets["quality"].extend([['limited dataset compliant','EXCLUSION','','']])
self.targets["quality2"] = itemData.itemData()
self.targets["quality2"].extend(artifacts)
self.temporalCount = 0
self.models = {}
def __init__(self, dbname, runMode='all'):
"""create an instance of a PEContext object associated with the SQLite
database.
dbname: name of SQLite database
test: If True run against the test set, otherwise run against the training set
"""
# Define queries to select data from the SQLite database
# this gets the reports we will process
self.query1 = '''SELECT id,impression FROM pesubject'''
# get the consensus labels for the disease and quality states
self.query2 = '''SELECT diseaseState,qualityState,CDS,diseaseUncertainty,CQS FROM consensus_states WHERE psid==?'''
# get the userannotations from the three raters we are using for this
# study
self.query3 = """SELECT usrname,diseaseState,qualityState,qualitylimit,usrname FROM userannotations where psid_id == ? AND (usrname=='aaron' OR usrname=='hyun' OR usrname=='sean')"""
# create query to get temporal annotations
self.query4 = """SELECT usrname FROM AND (usrname == 'jd' OR usrname =='sean' OR usrname=='peter')"""
self.conn = sqlite.connect(dbname)
self.cursor = self.conn.cursor()
self.cursor.execute(self.query1)
# get the training set reports. The first 20 reports were used to train
# Aaron and Hyun and so can be skipped
reports = self.cursor.fetchall()
if (runMode == 'full'):
self.reports = reports[:]
elif ('test'):
self.reports = reports[270:]
else:
self.reports = (reports[20:])[:250]
print "number of reports to process", len(self.reports)
raw_input('continue')
t = time.localtime()
d = datetime.datetime(t[0], t[1], t[2])
# create dictionaries to store the agreement matricies for the disease,
# quality and uncertainty states
self.usMatrix = {
('Yes', 'Yes'): 0,
('Yes', 'No'): 0,
('No', 'Yes'): 0,
('No', 'No'): 0
}
self.dsMatrix = {
('Pos', 'Pos'): 0,
('Pos', 'Neg'): 0,
('Neg', 'Pos'): 0,
('Neg', 'Neg'): 0
}
self.qsMatrix = {
('Diagnostic', 'Diagnostic'): 0,
('Diagnostic', 'Not Diagnostic'): 0,
('Not Diagnostic', 'Diagnostic'): 0,
('Not Diagnostic', 'Not Diagnostic'): 0
}
self.hsMatrix = {
('Old', 'Old'): 0,
('Old', 'New'): 0,
('New', 'Old'): 0,
('New', 'New'): 0
}
# create context objects for each of the questions we want to be answering
self.context = {
"disease": pycontext.pycontext(),
"quality": pycontext.pycontext(),
"quality2": pycontext.pycontext()
}
# Create files for storing problem cases
suffix = runMode
self.f1 = open(dbname + ".ds%s.txt" % suffix, 'w')
self.f2 = open(dbname + ".qs%s.txt" % suffix, 'w')
self.f3 = open(dbname + ".us%s.txt" % suffix, 'w')
self.f4 = open(dbname + ".all%s.txt" % suffix, "w")
self.f5 = open(dbname + ".hs%s.txt" % suffix, "w")
self.f6 = open(dbname + ".disagreements%s.pckle" % suffix, "wb")
rsltsDB = dbname + ".OriginalResults.db"
if (os.path.exists(rsltsDB)):
os.remove(rsltsDB)
self.resultsConn = sqlite.connect(rsltsDB)
self.resultsCursor = self.resultsConn.cursor()
self.resultsCursor.execute("""CREATE TABLE results (
id INT PRIMARY KEY,
disease TEXT,
uncertainty TEXT,
historical TEXT,
quality TEXT)""")
# Create the itemData object to store the modifiers for the analysis
# starts with definitions defined in pyConText and then adds
# definitions specific for peFinder
self.modifiers = {"disease": itemData.itemData()}
self.modifiers["disease"].extend(pseudoNegations)
self.modifiers["disease"].extend(definiteNegations)
self.modifiers["disease"].extend(probableNegations)
self.modifiers["disease"].extend(probables)
self.modifiers["disease"].extend(definites)
self.modifiers["disease"].extend(indications)
self.modifiers["disease"].extend(historicals)
self.modifiers["disease"].extend(conjugates)
# Create a seperate itemData for the quality modifiers
self.modifiers["quality"] = itemData.itemData()
self.modifiers["quality"].extend(pseudoNegations)
self.modifiers["quality"].extend(definiteNegations)
self.modifiers["quality"].extend(probableNegations)
self.modifiers["quality"].extend(probables)
self.modifiers["quality"].extend(historicals)
self.modifiers["quality"].extend(conjugates)
self.modifiers["quality"].extend(qualities)
self.modifiers["quality"].extend(
[['limited dataset compliant', 'EXCLUSION', '', '']])
# Quality targets
self.targets = {"disease": peItems}
self.targets["quality"] = itemData.itemData()
#self.targets["quality"].extend(qualities)
self.targets["quality"].extend(examFeatures)
self.targets["quality"].extend(
[['limited dataset compliant', 'EXCLUSION', '', '']])
self.targets["quality2"] = itemData.itemData()
self.targets["quality2"].extend(artifacts)
self.temporalCount = 0
self.breakCases = set([320])
self.models = {}
self.problemCases = []
self.disagreements = {"ds": [], "qs": [], "us": [], "hs": []}
def __init__(self, dbname, outfile):
"""create an instance of a PEContext object associated with the SQLite
database.
dbname: name of SQLite database
"""
# Define queries to select data from the SQLite database
# this gets the reports we will process
self.query1 = '''SELECT id,impression FROM pesubject'''
self.conn = sqlite.connect(dbname)
self.cursor = self.conn.cursor()
self.cursor.execute(self.query1)
self.reports = self.cursor.fetchall()
#conn = sqlite.connect("nx_o_context_differences.db")
#curs = conn.cursor()
#curs.execute("""select id from disease_diff""")
#es = curs.fetchall()
#errors = [e[0] for e in es]
#
#tmp = []
#for r in self.reports:
# if r[0] in errors:
# print "removed"
# tmp.append(r)
#self.reports = tmp
print "number of reports to process", len(self.reports)
t = time.localtime()
d = datetime.datetime(t[0], t[1], t[2])
# create context objects for each of the questions we want to be answering
self.context = {
"disease": pycontext.pycontext(),
"quality": pycontext.pycontext(),
"quality2": pycontext.pycontext()
}
rsltsDB = outfile
if (os.path.exists(rsltsDB)):
os.remove(rsltsDB)
self.resultsConn = sqlite.connect(rsltsDB)
self.resultsCursor = self.resultsConn.cursor()
self.resultsCursor.execute("""CREATE TABLE results (
id INT PRIMARY KEY,
disease TEXT,
uncertainty TEXT,
historical TEXT,
quality TEXT)""")
# Create the itemData object to store the modifiers for the analysis
# starts with definitions defined in pyConText and then adds
# definitions specific for peFinder
self.modifiers = {"disease": itemData.itemData()}
self.modifiers["disease"].extend(pseudoNegations)
self.modifiers["disease"].extend(definiteNegations)
self.modifiers["disease"].extend(probableNegations)
self.modifiers["disease"].extend(probables)
self.modifiers["disease"].extend(definites)
self.modifiers["disease"].extend(indications)
self.modifiers["disease"].extend(historicals)
self.modifiers["disease"].extend(conjugates)
# Create a seperate itemData for the quality modifiers
self.modifiers["quality"] = itemData.itemData()
self.modifiers["quality"].extend(pseudoNegations)
self.modifiers["quality"].extend(definiteNegations)
self.modifiers["quality"].extend(probableNegations)
self.modifiers["quality"].extend(probables)
self.modifiers["quality"].extend(historicals)
self.modifiers["quality"].extend(conjugates)
self.modifiers["quality"].extend(qualities)
self.modifiers["quality"].extend(
[['limited dataset compliant', 'EXCLUSION', '', '']])
# Quality targets
self.targets = {"disease": peItems}
self.targets["quality"] = itemData.itemData()
#self.targets["quality"].extend(qualities)
self.targets["quality"].extend(examFeatures)
self.targets["quality"].extend(
[['limited dataset compliant', 'EXCLUSION', '', '']])
self.targets["quality2"] = itemData.itemData()
self.targets["quality2"].extend(artifacts)
self.temporalCount = 0
self.models = {}
