コード例 #1
0
def test_knn():
    knn = KNN(n_neighbors=1)
    chip_data1 = {'ORB_a':1, 'ORB_b':1, 'gnd':1}
    chip_data2 = {'ORB_a':-1, 'ORB_b':-1, 'gnd':-1}
    chip1 = Chip(chip_data1, LCT_prefix='ORB')
    chip2 = Chip(chip_data2, LCT_prefix='ORB')
    chips = [chip1, chip2]

    knn.fit(chips)

    chip3 = Chip({'ORB_a':0.9, 'ORB_b':0.9, 'gnd':1}, LCT_prefix='ORB')
    assert knn.predict(chip3) == 1, 'fail: prediction not correct.'
コード例 #2
0
ファイル: basic_testing.py プロジェクト: trela/qikify
 def __init__(self, port = 5000):
     self.port          = port
     self.chips         = []
     
     self.num_predicted    = 0
     self.num_chips_tested = 0
     self.test_escapes     = 0
     self.yield_loss       = 0
     
     self.is_trained    = False
     
     self.knn           = KNN()
     self.c             = Colors()
     
     # ZeroMQ stuff
     self.context = zmq.Context()
     self.socket  = self.context.socket(zmq.REQ)
     
     # hand off the ZeroMQ context and port number to the ViewServerMixin
     # superclass, for logging to view server.
     super(BasicTesting, self).__init__('atesim', self.port+2, self.context)
コード例 #3
0
ファイル: basic_testing.py プロジェクト: trela/qikify
    def __init__(self, port=5000):
        self.port = port
        self.chips = []

        self.num_predicted = 0
        self.num_chips_tested = 0
        self.test_escapes = 0
        self.yield_loss = 0

        self.is_trained = False

        self.knn = KNN()
        self.c = Colors()

        # ZeroMQ stuff
        self.context = zmq.Context()
        self.socket = self.context.socket(zmq.REQ)

        # hand off the ZeroMQ context and port number to the ViewServerMixin
        # superclass, for logging to view server.
        super(BasicTesting, self).__init__("atesim", self.port + 2, self.context)
コード例 #4
0
ファイル: basic_testing.py プロジェクト: trela/qikify
class BasicTesting(ViewServerMixin):
    def __init__(self, port=5000):
        self.port = port
        self.chips = []

        self.num_predicted = 0
        self.num_chips_tested = 0
        self.test_escapes = 0
        self.yield_loss = 0

        self.is_trained = False

        self.knn = KNN()
        self.c = Colors()

        # ZeroMQ stuff
        self.context = zmq.Context()
        self.socket = self.context.socket(zmq.REQ)

        # hand off the ZeroMQ context and port number to the ViewServerMixin
        # superclass, for logging to view server.
        super(BasicTesting, self).__init__("atesim", self.port + 2, self.context)

    def run(self):
        print "Running Basic Machine learning-based testing," + "listening on port %d ..." % self.port
        self.socket.connect("tcp://127.0.0.1:%d" % self.port)
        try:
            while True:
                print "[ Train:     %s %7d %s ] " % (self.c.GREEN, self.num_train_chips, self.c.ENDC),
                print "[ Predicted: %s %7d %s ]" % (self.c.GREEN, self.num_predicted, self.c.ENDC)

                self.get_chip()
                self.update_model()
                self.update_view(self.stats)

        except KeyboardInterrupt:
            print "\nterminating basic ML testing."

    def get_chip(self):
        """Request and retrieve a chip from the ATE simulator.
        """

        self.socket.send("REQ:LCT")
        chip_lct = json.loads(self.socket.recv())
        self.log("REQ:LCT")

        self.socket.send("REQ:gnd")
        chip_gnd = json.loads(self.socket.recv())
        self.log("REQ:gnd")

        self.socket.send("REQ:done")
        assert self.socket.recv() == "RES:ack", "Error: invalid ack from ATE simulator"
        self.log("REQ:done")
        self.current_chip = Chip(LCT=chip_lct, gnd=chip_gnd)
        self.num_chips_tested += 1

    def update_model(self):
        """Add the latest chip to the chip buffer. If we've already collected
        1,000 chips, then we go ahead and train the model.
        """

        if self.num_chips_tested <= 1000:
            self.chips.append(self.current_chip)

        if self.num_chips_tested == 1000:
            print "Training KNN on 1000 chips"
            self.is_trained = True
            self.knn.fit(self.chips)

        if self.num_chips_tested >= 1000:
            time.sleep(0.1)
            self.num_predicted += 1
            chip_gnd_predicted = self.knn.predict(self.current_chip)
            self.log("Actual pf: %d" % self.current_chip.gnd)
            self.log("Pred. pf:  %d" % chip_gnd_predicted)

            # Accounting TE / YL here
            if self.current_chip.gnd == -1 and chip_gnd_predicted == 1:
                self.test_escapes += 1
            if self.current_chip.gnd == 1 and chip_gnd_predicted == -1:
                self.yield_loss += 1

    def log(self, msg):
        # might expand this later
        print "\t-> %s %s %s" % (self.c.RED, msg, self.c.ENDC)

    @property
    def num_train_chips(self):
        return len(self.chips)

    @property
    def stats(self):
        """The self.update_view() method from the ViewServerMixin class
        expects a Python object that can be JSONified. This function returns 
        such an object.
        """
        return {
            "name": "basic",
            "datetime": datetime.datetime.utcnow().isoformat(),
            "parms": {
                "num_train_chips": {"desc": "Number of chips in training set", "value": str(self.num_train_chips)},
                "num_predicted": {"desc": "Number of chips predicted with k-NN", "value": str(self.num_predicted)},
                "test_escape": {
                    "desc": "Test escape rate",
                    #'value': '%1.3f%%' % (self.test_escapes * 100.0 / self.num_chips_tested)
                    "value": (self.test_escapes * 100.0 / self.num_chips_tested),
                },
                "yield_loss": {
                    "desc": "Yield loss rate",
                    #'value': '%1.3f%%' % (self.yield_loss * 100.0 / self.num_chips_tested)
                    "value": (self.yield_loss * 100.0 / self.num_chips_tested),
                },
                "is_trained": {"desc": "Model trained", "value": (lambda (x): "Yes" if x else "No")(self.is_trained)},
            },
        }
コード例 #5
0
ファイル: basic_testing.py プロジェクト: trela/qikify
class BasicTesting(ViewServerMixin):
    def __init__(self, port = 5000):
        self.port          = port
        self.chips         = []
        
        self.num_predicted    = 0
        self.num_chips_tested = 0
        self.test_escapes     = 0
        self.yield_loss       = 0
        
        self.is_trained    = False
        
        self.knn           = KNN()
        self.c             = Colors()
        
        # ZeroMQ stuff
        self.context = zmq.Context()
        self.socket  = self.context.socket(zmq.REQ)
        
        # hand off the ZeroMQ context and port number to the ViewServerMixin
        # superclass, for logging to view server.
        super(BasicTesting, self).__init__('atesim', self.port+2, self.context)
        
        
    def run(self):
        print 'Running Basic Machine learning-based testing,' + \
              'listening on port %d ...' % self.port
        self.socket.connect("tcp://127.0.0.1:%d" % self.port)
        try:
            while True:
                print '[ Train:     %s %7d %s ] ' \
                    % (self.c.GREEN, self.num_train_chips, self.c.ENDC),
                print '[ Predicted: %s %7d %s ]' \
                    % (self.c.GREEN, self.num_predicted, self.c.ENDC)
                    
                self.get_chip()
                self.update_model()
                self.update_view(self.stats)
                
        except KeyboardInterrupt:
            print '\nterminating basic ML testing.'


    def get_chip(self):
        """Request and retrieve a chip from the ATE simulator.
        """
        
        self.socket.send('REQ:LCT')
        chip_lct = json.loads(self.socket.recv())
        self.log('REQ:LCT')
        
        self.socket.send('REQ:gnd')
        chip_gnd = json.loads(self.socket.recv())
        self.log('REQ:gnd')

        self.socket.send('REQ:done')
        assert self.socket.recv() == 'RES:ack', \
            'Error: invalid ack from ATE simulator'
        self.log('REQ:done')
        self.current_chip = Chip(LCT=chip_lct, gnd=chip_gnd)
        self.num_chips_tested += 1


    def update_model(self):
        """Add the latest chip to the chip buffer. If we've already collected
        1,000 chips, then we go ahead and train the model.
        """
        
        if self.num_chips_tested <= 1000:
            self.chips.append(self.current_chip)
                 
        if self.num_chips_tested == 1000:
            print "Training KNN on 1000 chips"
            self.is_trained = True
            self.knn.fit(self.chips)
            
        if self.num_chips_tested >= 1000:
            time.sleep(0.1)
            self.num_predicted += 1
            chip_gnd_predicted = self.knn.predict(self.current_chip)
            self.log('Actual pf: %d' % self.current_chip.gnd)
            self.log('Pred. pf:  %d' % chip_gnd_predicted)

            # Accounting TE / YL here
            if self.current_chip.gnd == -1 and chip_gnd_predicted == 1:
                self.test_escapes += 1
            if self.current_chip.gnd == 1 and chip_gnd_predicted == -1:
                self.yield_loss += 1
            
    
    def log(self, msg):
        # might expand this later
        print '\t-> %s %s %s' % ( self.c.RED, msg, self.c.ENDC )


    @property
    def num_train_chips(self):
        return len(self.chips)
    
    @property
    def stats(self):
        """The self.update_view() method from the ViewServerMixin class
        expects a Python object that can be JSONified. This function returns 
        such an object.
        """
        return {
                'name' : 'basic',
                'datetime' : datetime.datetime.utcnow().isoformat(),
                'parms' :   
                    {
                        'num_train_chips' : 
                        {
                            'desc' : 'Number of chips in training set',
                            'value': str(self.num_train_chips)
                        },
                        'num_predicted' : 
                        {
                            'desc' : 'Number of chips predicted with k-NN',
                            'value': str(self.num_predicted)
                        },
                        'test_escape' : 
                        {
                            'desc' : 'Test escape rate',
                            #'value': '%1.3f%%' % (self.test_escapes * 100.0 / self.num_chips_tested)
                            'value': (self.test_escapes * 100.0 / self.num_chips_tested)
                        },
                        'yield_loss' : 
                        {
                            'desc' : 'Yield loss rate',
                            #'value': '%1.3f%%' % (self.yield_loss * 100.0 / self.num_chips_tested)
                            'value': (self.yield_loss * 100.0 / self.num_chips_tested)
                        },
                        'is_trained' : 
                        {
                            'desc' : 'Model trained',
                            'value': (lambda (x): 'Yes' if x else 'No')(self.is_trained)
                        }
                    }
                }