#!/usr/bin/env python """ Classification Using Hidden Markov Model ======================================== This is a demonstration using the implemented forward algorithm in the context of a hidden Markov model to classify multiple targets. We will attempt to classify 3 targets in an undefined region. """ # %% # All Stone Soup imports will be given in order of usage. from datetime import datetime, timedelta import numpy as np from stonesoup.models.transition.categorical import MarkovianTransitionModel from stonesoup.types.groundtruth import CategoricalGroundTruthState from stonesoup.types.groundtruth import GroundTruthPath # %% # Ground Truth # ^^^^^^^^^^^^ # The targets may take one of two discrete hidden classes: 'bike', and 'car'. # A target may be able to transition from one class to another (this could be considered as a # person switching from riding a bike to driving a car and vice versa). # This behaviour will be modelled in the transition matrix of the # :class:`~.MarkovianTransitionModel`. This transition matrix is a Markov process matrix, whereby # it is assumed that the state of a target is wholly dependent on its previous state, and nothing # else. # # A :class:`~.CategoricalState` class is used to store information on the classification/category # of the targets. The state vector will define a categorical distribution over the 2 possible # classes, whereby each component defines the probability that a target is of the corresponding # class. For example, the state vector (0.2, 0.8), with category names ('bike', 'car') # indicates that a target has a 20% probability of being class 'bike' and an 80% probability of # being class 'car' etc. # It does not make sense to have a true target being a distribution over the possible classes, and # therefore the true categorical states will have binary state vectors indicating a specific class # (i.e. a '1' at one state vector index, and '0's elsewhere). This can be considered as stating # there is a 100% probability that the target is of a particular class. We specify that there # should be noise when functioning our transition model in order to sample the resultant # distribution and receive this binary vector. # The :class:`~.CategoricalGroundTruthState` class inherits directly from the base # :class:`~.CategoricalState` class. # # The category and timings for one of the ground truth paths will be printed. transition_matrix = np.array([[0.8, 0.2], # P(bike | bike), P(bike | car) [0.4, 0.6]]) # P(car | bike), P(car | car) category_transition = MarkovianTransitionModel(transition_matrix=transition_matrix) start = datetime.now() hidden_classes = ['bike', 'car'] # Generating ground truth ground_truths = list() for i in range(1, 4): # 4 targets state_vector = np.zeros(2) # create a vector with 2 zeroes state_vector[np.random.choice(2, 1, p=[1 / 2, 1 / 2])] = 1 # pick a random class out of the 2 ground_truth_state = CategoricalGroundTruthState(state_vector, timestamp=start, categories=hidden_classes) ground_truth = GroundTruthPath([ground_truth_state], id=f"GT{i}") for _ in range(10): new_vector = category_transition.function(ground_truth[-1], noise=True, time_interval=timedelta(seconds=1)) new_state = CategoricalGroundTruthState( new_vector, timestamp=ground_truth[-1].timestamp + timedelta(seconds=1), categories=hidden_classes ) ground_truth.append(new_state) ground_truths.append(ground_truth) for states in np.vstack(ground_truths).T: print(f"{states[0].timestamp:%H:%M:%S}", end="") for state in states: print(f" -- {state.category}", end="") print() # %% # Measurement # ^^^^^^^^^^^ # Using a Hidden Markov model, it is assumed the true class of a target cannot be directly # observed (hence 'hidden'), and instead observations that are dependent on this class are taken. # In this instance, observations of the targets' sizes are taken ('small', 'medium' or 'large'). # The relationship between true class and observed size is modelled by the `emission matrix` of the # :class:`~.MarkovianMeasurementModel`, which is used by the :class:`~.HMMSensor` to # provide :class:`~.CategoricalDetection` types. # We will model this such that a 'bike' has a very small chance of being observed as a 'big' # target etc. from stonesoup.models.measurement.categorical import MarkovianMeasurementModel from stonesoup.sensor.categorical import HMMSensor E = np.array([[0.8, 0.1], # P(small | bike), P(small | car) [0.19, 0.3], # P(medium | bike), P(medium | car) [0.01, 0.6]]) # P(large | bike), P(large | car) model = MarkovianMeasurementModel(emission_matrix=E, measurement_categories=['small', 'medium', 'large']) eo = HMMSensor(measurement_model=model) # Generating measurements measurements = list() for index, states in enumerate(np.vstack(ground_truths).T): if index == 5: measurements_at_time = set() # Give tracker chance to use prediction instead else: measurements_at_time = eo.measure(states) timestamp = next(iter(states)).timestamp measurements.append((timestamp, measurements_at_time)) print(f"{timestamp:%H:%M:%S} -- {[meas.category for meas in measurements_at_time]}") # %% # Tracking Components # ^^^^^^^^^^^^^^^^^^^ # %% # Predictor # --------- # A :class:`~.HMMPredictor` specifically uses :class:`~.MarkovianTransitionModel` types to # predict. from stonesoup.predictor.categorical import HMMPredictor # It would be cheating to use the same transition model as in ground truth generation! transition_matrix = np.array([[0.81, 0.19], # P(bike | bike), P(bike | car) [0.39, 0.61]]) # P(car | bike), P(car | car) category_transition = MarkovianTransitionModel(transition_matrix=transition_matrix) predictor = HMMPredictor(category_transition) # %% # Updater # ------- from stonesoup.updater.categorical import HMMUpdater updater = HMMUpdater() # %% # Hypothesiser # ------------ # A :class:`~.HMMHypothesiser` is used for calculating categorical hypotheses. # It utilises the :class:`~.ObservationAccuracy` measure: a multi-dimensional extension of an # 'accuracy' score, essentially providing a measure of the similarity between two categorical # distributions. from stonesoup.hypothesiser.categorical import HMMHypothesiser hypothesiser = HMMHypothesiser(predictor=predictor, updater=updater) # %% # Data Associator # --------------- # We will use a standard :class:`~.GNNWith2DAssignment` data associator. from stonesoup.dataassociator.neighbour import GNNWith2DAssignment data_associator = GNNWith2DAssignment(hypothesiser) # %% # Prior # ----- # As we are tracking in a categorical state space, we should initiate with a categorical state for # the prior. Equal probability is given to all 3 of the possible hidden classes that a target # might take (the category names are also provided here). from stonesoup.types.state import CategoricalState prior = CategoricalState([1 / 2, 1 / 2], categories=hidden_classes) # %% # Initiator # --------- # For each unassociated detection, a new track will be initiated. In this instance we use a # :class:`~.SimpleCategoricalMeasurementInitiator`, which specifically handles categorical state # priors. from stonesoup.initiator.categorical import SimpleCategoricalMeasurementInitiator initiator = SimpleCategoricalMeasurementInitiator(prior_state=prior, updater=updater) # %% # Deleter # ------- # We can use a standard :class:`~.UpdateTimeStepsDeleter`. from stonesoup.deleter.time import UpdateTimeStepsDeleter deleter = UpdateTimeStepsDeleter(2) # %% # Tracker # ------- # We can use a standard :class:`~.MultiTargetTracker`. from stonesoup.tracker.simple import MultiTargetTracker tracker = MultiTargetTracker(initiator, deleter, measurements, data_associator, updater) # %% # Tracking # ^^^^^^^^ tracks = set() for time, ctracks in tracker: tracks.update(ctracks) print(f"Number of tracks: {len(tracks)}") for track in tracks: certainty = track.state_vector[np.argmax(track.state_vector)][0] * 100 print(f"id: {track.id} -- category: {track.category} -- certainty: {certainty}%") for state in track: _time = state.timestamp.strftime('%H:%M') _type = str(type(state)).replace("class 'stonesoup.types.", "").strip("<>'. ") state_string = f"{_time} -- {_type} -- {state.category}" try: meas_string = f"associated measurement: {state.hypothesis.measurement.category}" except AttributeError: pass else: state_string += f" -- {meas_string}" print(state_string) print() # %% # Metric # ^^^^^^ # Determining tracking accuracy. # In calculating how many targets were classified correctly, only tracks with the highest # classification certainty are considered. In the situation where probabilities are equal, a # random classification is chosen. excess_tracks = len(tracks) - len(ground_truths) # target value = 0 sorted_tracks = sorted(tracks, key=lambda track: track.state_vector[np.argmax(track.state_vector)][0], reverse=True) best_tracks = sorted_tracks[:3] true_classifications = [ground_truth.category for ground_truth in ground_truths] track_classifications = [track.category for track in best_tracks] num_correct_classifications = 0 # target value = num ground truths for true_classification in true_classifications: for i in range(len(track_classifications)): if track_classifications[i] == true_classification: num_correct_classifications += 1 del track_classifications[i] break print(f"Excess tracks: {excess_tracks}") print(f"No. correct classifications: {num_correct_classifications}") # %% # Plotting # ^^^^^^^^ # Plotting the probability that each one of our targets and tracks is a 'bike' will help to # visualise this 2-hidden class problem. # # Dotted lines indicate ground truth probabilities, and solid lines for tracks. import matplotlib.pyplot as plt def plot(path, style): times = list() probs = list() for state in path: times.append(state.timestamp) probs.append(state.state_vector[0]) plt.plot(times, probs, linestyle=style) for truth in ground_truths: plot(truth, '--') for track in tracks: plot(track, '-')