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HMM_Framework.py
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HMM_Framework.py
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"""
HMM-Framework: An advanced framework that implements state-of-the-art Hidden Markov Models, mostly for supervised/classification tasks.
"""
import numpy as np
import itertools
from collections import defaultdict
import pandas as pd
import pomegranate as pome # Import it in this manner to avoid conflicts with the 'time' module
import SimpleHOHMM
import time
import random
import copy
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from nltk import ngrams as ngramsgenerator
from sklearn.model_selection import RepeatedStratifiedKFold, StratifiedShuffleSplit
from sklearn import metrics
import Ensemble_Framework
random_state = 22
random.seed(22)
class HMM_Framework:
"""
observations: Observation sequence
state_labels: Hidden State sequence
A: Transition probability matrix
B: Observation probability matrix
pi: Initial probabilities
"""
def __init__(self):
self.state_labels = []
self.observations = []
self.golden_truth = []
self.A = []
self.B = []
self.pi = []
self.text_data = []
self.text_enable = False
self.length = 0
self.architectures = ["A", "B"]
self.selected_architecture = ""
self.models = ["General Mixture Model", "State-emission HMM", "Classic HMM"]
self.selected_model = ""
self.frameworks = ["pome", "hohmm"]
self.selected_framework = ""
self.multivariate = False
self.trained_model = [] # The object that is outputed after training on the current cross validation fold; depends on the framework that was used
self.unique_states = set()
self.unique_states_subset = set()
self.unique_observations_subset = set()
self.state_to_label_mapping = [] # Dict: {"pos": 0, "neg": 1, ...}
self.state_to_label_mapping_rev = [] # Dict: {0: "pos", 1: "neg", ...}
self.observation_to_label_mapping = [] # Dict: {"good": 0, ambitious: 1, ...}
self.hmm_to_label_mapping = {} # Used for Architecture 'B', {Model_0: "pos", Model_1: "neg", ...}
# Evaluation
self.k_fold = 0
self.cross_val_metrics = defaultdict(list) # {Name: [],
# F1-score: [],
# Accuracy: [],
# Metrics_String: [],
# Confusion_Matrix: [],
# Time_Complexity: []}
self.cross_val_prediction_matrix = [] # The final predictions for all the folds of cross validation
self.count_new_oov = [] # Count the number of instances where we encountered out-of-vocabulary new observations
self.count_formula_problems = [] # Count the number of instances where the 'formula' algorithm prediction failed and was forced to perform random guessing
# Stored for future Ensembles
self.ensemble_stored = defaultdict(list) # {Mapping: [],
# Curr_Cross_Val_Golden_Truth: []}
def clean_up(self):
"""
Resets the values which are not needed to their original values, after the entire process is finished.
"""
self.state_labels = []
self.observations = []
self.text_data = []
def reset(self):
"""
Important. Resets some important variables after each cross validation fold.
"""
self.trained_model = []
self.unique_states_subset = set()
self.state_to_label_mapping = []
self.state_to_label_mapping_rev = []
self.observation_to_label_mapping = []
self.hmm_to_label_mapping = {}
def check_input_type(self, state_labels_pandas, observations_pandas, golden_truth_pandas, text_instead_of_sequences, text_enable):
"""
Make sure that the input data are of the correct type: pandas Series. Also perform the assignment to the local variables.
"""
self.text_enable = text_enable
if self.text_enable == False:
if (isinstance(state_labels_pandas, pd.Series) != True) or (isinstance(observations_pandas, pd.Series) != True):
raise ValueError("please make sure that you are inputting the parameters 'state_labels_pandas' and 'observations_pandas' in the form of a pandas Series, i.e. select a column of a DataFrame.")
else:
self.state_labels = copy.deepcopy(state_labels_pandas.values) # or tolist() to get list instead of ndarray; Should be Deep Copy in case the same array is given on both inputs, be careful
self.observations = copy.deepcopy(observations_pandas.values)
else:
if (isinstance(text_instead_of_sequences, pd.Series) != True): # just text parameter
raise ValueError("please make sure that you are inputting the parameter 'text_instead_of_sequences' in the form of a pandas Series, i.e. select a column of a DataFrame.")
else:
self.text_data = copy.deepcopy(text_instead_of_sequences.values)
if len(state_labels_pandas) > 0: # text + state_labels parameters
if (isinstance(state_labels_pandas, pd.Series) != True):
raise ValueError("text parameter was given correctly but please make sure that you are inputting the parameters 'state_labels_pandas' in the form of a pandas Series, i.e. select a column of a DataFrame.")
else:
self.state_labels = copy.deepcopy(state_labels_pandas.values)
if len(observations_pandas) > 0: # text + observations parameters
if (isinstance(observations_pandas, pd.Series) != True):
raise ValueError("text parameter was given correctly but please make sure that you are inputting the parameter 'observations_pandas' in the form of a pandas Series, i.e. select a column of a DataFrame.")
else:
self.observations = copy.deepcopy(observations_pandas.values)
if (isinstance(golden_truth_pandas, pd.Series) != True):
raise ValueError("please make sure that you are inputting the parameter 'golden_truth_pandas' in the form of a pandas Series, i.e. select a column of a DataFrame.")
else:
self.golden_truth = copy.deepcopy(golden_truth_pandas.values)
def verify_and_autodetect(self):
"""
(1) Ensure that the input data are of the same shape.
(2) Automatically detect some basic HMMs that could be useful and suggest them to the user.
(3) Print the approach that the user has selected.
"""
# Verify shape validity
if self.text_enable == False:
if len(self.state_labels) == 0 or len(self.observations) == 0:
raise ValueError("one or both of the input containers appear to be empty.")
elif len(self.state_labels) != len(self.observations):
raise ValueError("the first input container is of length " + str(len(self.state_labels)) + " while the second is of length " + str(len(self.observations)) + ".")
else:
for i in range(len(self.state_labels)):
if len(self.state_labels[i]) != len(self.observations[i]):
raise ValueError("on row with index " + str(i) + ", the sequence of the first container is of size " + str(len(self.state_labels[i])) + " while the one of the second is of size " + str(len(self.observations[i])) + ".")
else:
if len(self.text_data) == 0:
raise ValueError("the text input container appears to be empty.")
if len(self.state_labels) > 0:
if len(self.text_data) != len(self.state_labels):
raise ValueError("you want to use the first input container but it is of length " + str(len(self.state_labels)) + " while the text one is of length " + str(len(self.text_data)) + ".")
if len(self.observations) > 0:
if len(self.text_data) != len(self.observations):
raise ValueError("you want to use the second input container but it is of length " + str(len(self.observations)) + " while the text one is of length " + str(len(self.text_data)) + ".")
if len(self.golden_truth) != len(self.observations):
raise ValueError("the golden truth list is of length " + str(len(self.golden_truth)) + " while the observation container is of length " + str(len(self.observations)) + ".")
if self.selected_architecture not in self.architectures:
raise ValueError("selected architecture does not exist.")
if self.selected_model not in self.models:
raise ValueError("selected model does not exist.")
if self.selected_framework not in self.frameworks:
raise ValueError("selected framework does not exist.")
# Attempt to automatically detect some HMMs that are a good fit for the input data
# only the first and last row of the data are used in this process
if self.text_enable == False:
if (len(set(self.state_labels[0])) == 1) and (len(set(self.state_labels[-1]))): # General Mixture Model Detection
print("(Supervised Training Autodetect): The labels seem to remain constant, consider using the General Mixture Model.")
elif (self.state_labels[0] == self.observations[0]) and (self.state_labels[-1] == self.observations[-1]): # State-emission Model Detection
print("(Supervised Training Autodetect): The states seem to emit themselves as observations, consider using the State-emission HMM.")
else:
print("(Supervised Training Autodetect): This appears to be a generic task, consider using Architecture B with any HMM.")
print("Selected Architecture:", self.selected_architecture, "| Selected Model:", self.selected_model, "\n")
else:
print("You have opted to use additional text_data, this will require some kind of custom implementation from a scientific paper. Selected Architecture:", self.selected_architecture, "| Selected Model:", self.selected_model)
def check_architecture_selection(self, architecture_b_algorithm):
"""
Perform some checks regarding the selected architecture and the input data.
"""
if self.selected_architecture == "A":
print("Since you selected architecture 'A', you are probably not utilizing the actual truth labels of the training set and the state sequences of the test set, in this supervised task.")
element_1 = set(self.golden_truth)
element_2 = self.unique_states
if element_1 != element_2 and self.selected_model == "State-emission HMM":
raise ValueError("you have selected architecture 'A' and 'State-emission HMM' but the number of unique states is " + str(len(element_2)) + " while the number of unique truth labels is " + str(len(element_1)) + "; consider using architecture 'B'.")
elif self.selected_architecture == "B":
if architecture_b_algorithm == "forward":
print("You have selected architecture 'B', the purely classification-based approach!. The 'forward' algorithm has some shortcomings which will be printed at the end, in this supervised task. Consider using 'formula'.")
elif architecture_b_algorithm == "formula":
print("You have selected architecture 'B', the purely classification-based approach!. The 'formula' algorithm is the ideal choice for supervised tasks.")
def check_shape(self, container_1, container_2):
"""
Given two containers, checks whether their contents are of exact same shape
"""
if len(container_1) != len(container_2):
return False
for i in range(len(container_1)):
if len(container_1[i]) != len(container_2[i]):
return False
return True
def set_unique_states(self):
"""
Find all unique state labels that occur in the entire dataset.
"""
if (len(self.state_labels) > 0):
for seq in self.state_labels:
self.unique_states.update(set(seq))
else:
raise ValueError("couldn't find states, the state container appears to be empty.")
def set_unique_states_subset(self, subset):
"""
Find all unique state labels that occur in a specific subset.
"""
self.unique_states_subset = set() # Reset it
if (len(subset) > 0):
for seq in subset:
self.unique_states_subset.update(set(seq))
else:
raise ValueError("couldn't find states, the state container appears to be empty.")
def set_unique_observations_subset(self, subset):
"""
Find all unique observations that occur in a specific subset.
"""
self.unique_observations_subset = set() # Reset it
if (len(subset) > 0):
for seq in subset:
self.unique_observations_subset.update(set(seq))
else:
raise ValueError("couldn't find observations, the state container appears to be empty.")
def create_state_to_label_mapping(self):
"""
Maps the strings of states (e.g. 'pos') from the input sequence, to indices 1,2,...n that correspond to the states/matrix that the training produces.
The mapping depends on the framework that was chosen, e.g. Pomegranate uses them in a simple sorted manner.
"""
if self.selected_architecture == "A":
self.state_to_label_mapping = {} # Simple Dict, list of Dicts is not needed
self.state_to_label_mapping_rev = {}
if self.selected_framework == "pome":
for i, unique_s in enumerate(sorted(list(self.unique_states_subset))):
self.state_to_label_mapping[unique_s] = i
elif self.selected_framework == "hohmm":
for i, unique_s in enumerate(self.trained_model.get_parameters()["all_states"]):
self.state_to_label_mapping[unique_s] = i
self.state_to_label_mapping_rev = {v: k for k, v in self.state_to_label_mapping.items()} # Reverse the mapping so we end up with {0: "pos", 1: "neg",...}
elif self.selected_architecture == "B":
for current_model in self.trained_model:
temp_mapper = {}
temp_mapper_rev = {}
if self.selected_framework == "pome":
for i, unique_s in enumerate(sorted(list(self.unique_states_subset))):
temp_mapper[unique_s] = i
elif self.selected_framework == "hohmm":
for i, unique_s in enumerate(current_model.get_parameters()["all_states"]):
temp_mapper[unique_s] = i
temp_mapper_rev = {v: k for k, v in temp_mapper.items()} # Reverse the mapping so we end up with {0: "pos", 1: "neg",...}
self.state_to_label_mapping.append(temp_mapper)
self.state_to_label_mapping_rev.append(temp_mapper_rev)
def create_observation_to_label_mapping(self):
"""
Maps the strings of observations (e.g. 'good') from the input sequence, to indices 1,2,...n that correspond to the matrix that the training produces.
The mapping depends on the framework that was chosen.
"""
if self.selected_architecture == "A":
self.observation_to_label_mapping = {} # Simple Dict, list of Dicts is not needed
if self.selected_framework == "pome":
if len(self.trained_model.states) > 2: # None-start and None-end always exist
temp_dict = self.trained_model.states[0].distribution.parameters[0] # Just the 1st no need for more
for i, unique_o in enumerate(temp_dict.keys()):
self.observation_to_label_mapping[unique_o] = i
else:
raise ValueError("observations index to label mapping failed, the state trained object appears to be empty.")
else:
gets_obs = self.trained_model.get_parameters()["all_obs"]
if len(gets_obs) > 0:
for i, unique_o in enumerate(gets_obs):
self.observation_to_label_mapping[unique_o] = i
else:
raise ValueError("observations index to label mapping failed, the observation trained object appears to be empty.")
elif self.selected_architecture == "B":
for current_model in self.trained_model:
temp_mapper = {}
if self.selected_framework == "pome":
if len(current_model.states) > 2: # None-start and None-end always exist
temp_dict = current_model.states[0].distribution.parameters[0] # Just the 1st no need for more
for i, unique_o in enumerate(temp_dict.keys()):
temp_mapper[unique_o] = i
else:
raise ValueError("observations index to label mapping failed, the state trained object appears to be empty.")
else:
gets_obs = current_model.get_parameters()["all_obs"]
if len(gets_obs) > 0:
for i, unique_o in enumerate(gets_obs):
temp_mapper[unique_o] = i
else:
raise ValueError("observations index to label mapping failed, the observation trained object appears to be empty.")
self.observation_to_label_mapping.append(temp_mapper)
def create_hmm_to_label_mapping(self, unique_golden_truths):
"""
Maps each model that is created, when using Architecture 'B', to the subset of data that it was trained on, e.g. data with "pos" label.
"""
if self.selected_architecture == "B":
for i, unique_s in enumerate(unique_golden_truths):
self.hmm_to_label_mapping[i] = unique_s
else:
raise ValueError("model to label mapping failed, please select architecture='B'.")
def print_probability_parameters(self):
"""
Prints the probability matrices of the trained Hidden Markov Model.
"""
print("# Transition probability matrix. One for each model that was trained.")
print(self.A)
print("# Observation probability matrix. One for each model that was trained.")
#xxx = list(self.B[0][0, :])
#second_smallest_value = sorted(list(set(xxx)))[1] # Good way to find a value for formula_magic_smoothing, use with hohmm
#print(second_smallest_value)
print(self.B)
print("# Initial probabilities. One for each model that was trained.")
print(self.pi)
def build(self, architecture, model, framework, k_fold, boosting=False,
state_labels_pandas=[], observations_pandas=[], golden_truth_pandas=[],
text_instead_of_sequences=[], text_enable=False,
n_grams=1, n_target="", n_prev_flag=False, n_dummy_flag=False,
pome_algorithm="baum-welch", pome_verbose=False, pome_njobs=1, pome_smoothing_trans=0.0, pome_smoothing_obs=0.0,
pome_algorithm_t="map",
hohmm_high_order=1, hohmm_smoothing=0.0, hohmm_synthesize=False,
architecture_b_algorithm="formula", formula_magic_smoothing=0.0
):
"""
The main function of the framework. Execution starts from here.
Parameters:
architecture: string denoting a choice by the user.
model: string denoting a choice by the user.
framework: string denoting a choice by the user.
k_fold: the number of folds to be used in the stratified cross-validation. 3 Possible mode: (1) <int> 0, disables cross-validation and enables an 80-20 split.
(2) <int> 1-n, enables cross-validation with as many folds as the number's value.
(3) <ndarray>, disables cross-validation and enables a split according to this array's indices.
boosting: a boolean value that decides whether boosting (Ensemble) will be used during training.
state_labels_pandas: pandas Series that contains the data the known sequence of state labels.
observations_pandas: pandas Series that contains the data that will be used as observations.
golden_truth_pandas: pandas Series that contains the actual truth labels of all instances.
# (Placeholder for Future Implementations) #
text_instead_of_sequences: a completely different operating mode, where the user inputs text documents;
an example would be words to be used for a multivariate (multi-observation) HMM.
in this scenario the first two arguments don't have to be used.
text_enable: enables the use of the 'text_instead_of_sequences' parameter.
# #
n_grams: n-gram order
n_target: a string that sets the container to be used, "states", "obs" or "both".
n_prev_flag: a boolean value that decides the behavior when a sequence is shorter than the n-gram order.
'True' enables the calculation of those shorter n-grams, leading to more unique states/observations.
'False' disables it and returns an empty list for such cases.
n_dummy_flag: a boolean value that decides whether the length of the sequence should be maintained with the help of a dummy set.
e.g. on a State-emission HMM, set it to 'False' since both the states and observations get shortened.
However, in other scenarios where only one of the two is affected, it will end up with a shorter length per sequence.
pome_algorithm: refers to a setting for Pomegranate training, can be either "baum-welch", "viterbi" or "labeled".
pome_verbose: refers to a setting for Pomegranate training, can be either True or False.
pome_njobs: refers to a setting for Pomegranate training, a value different than 1 enables parallelization.
pome_smoothing_trans: refers to a setting for Pomegranate training, adds the given float to all state transitions.
pome_smoothing_obs: refers to a setting for Pomegranate training, adds the given float to observations.
pome_algorithm_t: refers to a setting for the prediction phase, can be either "map" or "viterbi".
hohmm_high_order: refers to the order of the HMM for HOHMM training; on Pomegranate high-order can be achieved through the n-gram settings.
hohmm_smoothing: refers to a setting for HOHMM training, adds the given float to both state transitions and observations.
hohmm_synthesize: refers to a setting for HOHMM training, ensures to generate all permutations of states; avoids OOV and ensures model is fully ergodic.
architecture_b_algorithm: refers to a setting for training of any framework when using architecture="B", can be either "forward", "formula"".
formula_magic_smoothing: refers to a setting for Architecture 'B', where problematic observations (such as out-of-vocabulary values) in the prediction phase are 'treated' in an intelligent way.
"""
self.selected_architecture = architecture
self.selected_model = model
self.selected_framework = framework
self.k_fold = k_fold
self.check_input_type(state_labels_pandas, observations_pandas, golden_truth_pandas, text_instead_of_sequences, text_enable)
self.verify_and_autodetect()
if text_enable == False:
self.length = len(self.observations)
else:
self.length = len(self.text_data)
self.convert_to_ngrams_wrapper(n=n_grams, target=n_target, prev_flag=n_prev_flag, dummy_flag=n_dummy_flag, hohmm_check=(hohmm_high_order, architecture_b_algorithm))
self.set_unique_states()
self.check_architecture_selection(architecture_b_algorithm)
if isinstance(k_fold, int):
if k_fold != 0: # Mode 2
cross_val = RepeatedStratifiedKFold(n_splits=k_fold, n_repeats=1, random_state=random_state)
else: # Mode 1, Cross Validation is Disabled
cross_val = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=random_state)
elif isinstance(k_fold, np.ndarray): # Mode 3, Cross Validation is Disabled
select = np.in1d(range(self.golden_truth.shape[0]), k_fold)
train_index = select
test_index = ~select
if True: # Used only with IMDb dataset and with the Artificial Approach
#for train_index, test_index in cross_val.split(self.observations, self.golden_truth):
state_train, obs_train, y_train = self.state_labels[train_index], self.observations[train_index], self.golden_truth[train_index] # Needs to be ndarray<list>, not list<list>
state_test, obs_test, y_test = self.state_labels[test_index], self.observations[test_index], self.golden_truth[test_index]
# # Debug
# print("\nStates Train:", state_train[0:3])
# print("\nObs Train:", obs_train[0:3])
# print("\nGolden Truth Train:", y_train[0:3])
# print("\nLENGTH Train:", len(state_train), len(obs_train), len(y_train))
# print("\nStates Test:", state_test[0:3])
# print("\nObs Test:", obs_test[0:3])
# print("\nGolden Truth Test:", y_test[0:3])
# print("\nLENGTH Test:", len(state_test), len(obs_test), len(y_test))
# #quit()
if boosting == True:
self.boosting_wrapper(state_train, obs_train, y_train, state_test, obs_test, y_test, pome_algorithm, pome_verbose, pome_njobs, pome_smoothing_trans, pome_smoothing_obs, \
hohmm_high_order, hohmm_smoothing, hohmm_synthesize, pome_algorithm_t, architecture_b_algorithm, formula_magic_smoothing)
#else:
#weights = None
#print(weights)
#quit()
weights = np.ones(len(state_train)) # Equivalent to weights = None but actually correct
time_counter = time.time()
# Training Phase
self.set_unique_states_subset(state_train)
self.set_unique_observations_subset(obs_train)
self.train(state_train, obs_train, y_train, pome_algorithm, pome_verbose, pome_njobs, pome_smoothing_trans, pome_smoothing_obs, hohmm_high_order, hohmm_smoothing, hohmm_synthesize, weights)
# Prediction Phase
predict = self.predict(state_test, obs_test, pome_algorithm_t, architecture_b_algorithm, formula_magic_smoothing)
self.result_metrics(y_test, predict, time_counter)
# Store data for future Ensembles and then reset everything
if self.selected_architecture == "A":
self.ensemble_stored["Mapping"].append(self.state_to_label_mapping_rev)
elif self.selected_architecture == "B":
self.ensemble_stored["Mapping"].append(self.hmm_to_label_mapping)
self.ensemble_stored["Curr_Cross_Val_Golden_Truth"].append(y_test)
self.reset()
self.verbose_final(pome_algorithm, pome_algorithm_t, architecture_b_algorithm)
self.clean_up()
def train(self, state_train, obs_train, y_train, pome_algorithm, pome_verbose, pome_njobs, pome_smoothing_trans, pome_smoothing_obs, hohmm_high_order, hohmm_smoothing, hohmm_synthesize, weights):
"""
Train a set of models using k-fold cross-validation
"""
if self.selected_framework == 'pome':
if pome_algorithm not in ["baum-welch", "viterbi", "labeled"]:
raise ValueError("please set the 'pome_algorithm' parameter to one of the following: 'baum-welch', 'viterbi', 'labeled'.")
if pome_algorithm == "labeled":
print("--Warning: The simple counting 'labeled' algorithm is riddled with bugs and the training is going to go completely wrong, consider using 'baum-welch'.")
if pome_njobs != 1:
print("--Warning: the 'pome_njobs' parameter is not set to 1, which means parallelization/batch learning is enabled. Training speed will increase tremendously but accuracy might drop.")
if self.selected_architecture == "A":
self._train_pome_archit_a(state_train, obs_train, None, pome_algorithm, pome_verbose, pome_njobs, pome_smoothing_trans, pome_smoothing_obs, weights)
self.create_state_to_label_mapping()
self.create_observation_to_label_mapping()
self.pome_object_to_matrices_archit_a() # Assign to the local parameters
elif self.selected_architecture == "B":
self._train_pome_archit_b(state_train, obs_train, y_train, pome_algorithm, pome_verbose, pome_njobs, pome_smoothing_trans, pome_smoothing_obs, weights)
self.create_state_to_label_mapping()
self.create_observation_to_label_mapping()
self.pome_object_to_matrices_archit_b() # Assign to the local parameters
elif self.selected_framework == 'hohmm':
if self.selected_architecture == "A":
self._train_hohmm_archit_a(state_train, obs_train, None, hohmm_high_order, hohmm_smoothing, hohmm_synthesize)
self.create_state_to_label_mapping()
self.create_observation_to_label_mapping()
self.hohmm_object_to_matrices_archit_a() # Assign to the local parameters
elif self.selected_architecture == "B":
self._train_hohmm_archit_b(state_train, obs_train, y_train, hohmm_high_order, hohmm_smoothing, hohmm_synthesize) # Local parameters remain empty in this scenario
self.create_state_to_label_mapping()
self.create_observation_to_label_mapping()
self.hohmm_object_to_matrices_archit_b() # Assign to the local parameters
def _train_pome_archit_a(self, state_train, obs_train, _, pome_algorithm, pome_verbose, pome_njobs, pome_smoothing_trans, pome_smoothing_obs, weights):
"""
Train a Hidden Markov Model using the Pomegranate framework as a baseline.
Architecture A is used, which is the traditional approach where a single HMM is built; even if it looks like it, it is not really suited for classification tasks.
"""
pome_HMM = pome.HiddenMarkovModel.from_samples(pome.DiscreteDistribution, n_components=len(self.unique_states_subset), X=obs_train, labels=state_train, weights=weights, \
algorithm=pome_algorithm, end_state=False, transition_pseudocount=pome_smoothing_trans, emission_pseudocount=pome_smoothing_obs, \
max_iterations=1, state_names=sorted(list(self.unique_states_subset)), \
verbose=pome_verbose, n_jobs=pome_njobs \
)
self.trained_model = pome_HMM
def _train_pome_archit_b(self, state_train, obs_train, y_train, pome_algorithm, pome_verbose, pome_njobs, pome_smoothing_trans, pome_smoothing_obs, weights):
"""
Train a Hidden Markov Model using the Pomegranate framework as a baseline.
Architecture B is used, where multiple HMMs are built, in a purely classification-based approach.
"""
unique_golden_truths = np.unique(y_train)
self.create_hmm_to_label_mapping(unique_golden_truths)
index_sets = [np.where(i == y_train) for i in unique_golden_truths]
for j in index_sets:
state_new, obs_new = self.validate_architecture_b_consistency(state_train[j], obs_train[j]) # Vital validation and transformation of the subsets, to avoid inconsistent number of states and observations across subsets
pome_HMM = pome.HiddenMarkovModel.from_samples(pome.DiscreteDistribution, n_components=len(self.unique_states_subset), X=obs_new, labels=state_new, weights=weights[j], \
algorithm=pome_algorithm, end_state=False, transition_pseudocount=pome_smoothing_trans, emission_pseudocount=pome_smoothing_obs, \
max_iterations=1, state_names=sorted(list(self.unique_states_subset)), \
verbose=pome_verbose, n_jobs=pome_njobs \
)
self.trained_model.append(pome_HMM) # In this scenario, we want to store a list of trained models, not just 1.
def _train_hohmm_archit_a(self, state_train, obs_train, _, hohmm_high_order, hohmm_smoothing, hohmm_synthesize):
"""
Train a Hidden Markov Model using the HOHMM framework as a baseline.
Architecture A is used, which is the traditional approach where a single HMM is built; even if it looks like it, it is not really suited for classification tasks.
"""
_hohmm_builder = SimpleHOHMM.HiddenMarkovModelBuilder()
_hohmm_builder.add_batch_training_examples(list(obs_train), list(state_train)) # The builder does not accept objects of type ndarray<list>
_trained_hohmm = _hohmm_builder.build(highest_order=hohmm_high_order, k_smoothing=hohmm_smoothing, synthesize_states=hohmm_synthesize, include_pi=True)
self.trained_model = _trained_hohmm
def _train_hohmm_archit_b(self, state_train, obs_train, y_train, hohmm_high_order, hohmm_smoothing, hohmm_synthesize):
"""
Train a Hidden Markov Model using the HOHMM framework as a baseline.
Architecture B is used, where multiple HMMs are built, in a purely classification-based approach.
"""
unique_golden_truths = np.unique(y_train)
self.create_hmm_to_label_mapping(unique_golden_truths)
index_sets = [np.where(i == y_train) for i in unique_golden_truths]
for j in index_sets:
state_new, obs_new = self.validate_architecture_b_consistency(state_train[j], obs_train[j]) # Vital validation and transformation of the subsets, to avoid inconsistent number of states and observations across subsets
_hohmm_builder = SimpleHOHMM.HiddenMarkovModelBuilder()
_hohmm_builder.add_batch_training_examples(list(obs_new), list(state_new)) # The builder does not accept objects of type ndarray<list>
_trained_hohmm = _hohmm_builder.build(highest_order=hohmm_high_order, k_smoothing=hohmm_smoothing, synthesize_states=hohmm_synthesize, include_pi=True)
self.trained_model.append(_trained_hohmm)
def predict(self, state_test, obs_test, pome_algorithm_t, architecture_b_algorithm, formula_magic_smoothing):
"""
Perform predictions on new sequences.
"""
if self.selected_framework == 'pome':
if self.selected_architecture == "A":
if pome_algorithm_t in ["map", "viterbi"]:
return(self._predict_pome_archit_a(None, obs_test, pome_algorithm_t))
else:
raise ValueError("please set the 'pome_algorithm_t' parameter to one of the following: 'map', 'viterbi'.")
elif self.selected_architecture == "B":
if architecture_b_algorithm in ["forward", "formula"]:
return(self._predict_pome_archit_b(state_test, obs_test, architecture_b_algorithm, formula_magic_smoothing))
else:
raise ValueError("please set the 'architecture_b_algorithm' parameter to one of the following: 'forward', 'formula'.")
elif self.selected_framework == 'hohmm':
if self.selected_architecture == "A":
return(self._predict_hohmm_archit_a(obs_test))
elif self.selected_architecture == "B":
if architecture_b_algorithm in ["forward", "formula"]:
return(self._predict_hohmm_archit_b(state_test, obs_test, architecture_b_algorithm, formula_magic_smoothing))
else:
raise ValueError("please set the 'architecture_b_algorithm' parameter to one of the following: 'forward', 'formula'.")
def _predict_pome_archit_a(self, _, obs_test, pome_algorithm_t):
"""
Performs the prediction phase when the Hidden Markov Model is based on the Pomegranate framework.
Architecture A is used, which is the traditional approach where a single HMM is built; even if it looks like it, it is not really suited for classification tasks.
"""
predict_length = len(obs_test)
total_states = len(self.unique_states_subset)
predict = [] # The list of label predictions
count_new_oov_local = 0
if pome_algorithm_t == "map":
predict_log_proba_matrix = np.zeros((predict_length, total_states)) # The matrix of log probabilities for each label to be stored
for i in range(predict_length):
if len(obs_test[i]) > 0:
try:
temp_predict = self.trained_model.predict(obs_test[i], algorithm='map')[-1] # We only care about the last prediction
temp_predict_log_proba = self.trained_model.predict_log_proba(obs_test[i])[-1] # Using 'argmax' to not call predict twice is wrong because for random guessing all 3 probabilities are equal
except ValueError: # Prediction failed probably because of out-of-vocabulary value, perform random guessing
count_new_oov_local += 1
temp_predict = random.randint(0, total_states - 1)
temp_predict_log_proba = [np.log(1.0 / total_states)] * total_states # log of base e
else: # Empty sequence, perform random guessing
temp_predict = random.randint(0, total_states - 1)
temp_predict_log_proba = [np.log(1.0 / total_states)] * total_states # log of base e
predict.append(self.state_to_label_mapping_rev[temp_predict])
predict_log_proba_matrix[i,:] = temp_predict_log_proba
self.cross_val_prediction_matrix.append(predict_log_proba_matrix)
self.count_new_oov.append(count_new_oov_local)
elif pome_algorithm_t == "viterbi":
predict_matrix = np.empty((predict_length, 1), dtype=object) # The matrix of predictions to be stored
for i in range(predict_length):
if len(obs_test[i]) > 0:
try:
temp_predict = self.trained_model.predict(obs_test[i], algorithm='viterbi')[-1] # We only care about the last prediction
except ValueError: # Prediction failed, perform random guessing
count_new_oov_local += 1
temp_predict = random.randint(0, total_states - 1)
else: # Prediction would be pointless for an empty sequence
temp_predict = random.randint(0, total_states - 1)
predict.append(self.state_to_label_mapping_rev[temp_predict])
predict_matrix[i] = self.state_to_label_mapping_rev[temp_predict]
self.cross_val_prediction_matrix.append(predict_matrix)
self.count_new_oov.append(count_new_oov_local)
return(predict)
def _predict_pome_archit_b(self, state_test, obs_test, architecture_b_algorithm, formula_magic_smoothing):
"""
Performs the prediction phase when the Hidden Markov Model is based on the Pomegranate framework.
Architecture B is used, where multiple HMMs are built, in a purely classification-based approach. Tries to find the model that was most likely to have generated each of the instances at hand.
"""
predict_length = len(obs_test)
total_models = len(self.hmm_to_label_mapping)
predict = [] # The list of label predictions
predict_log_proba_matrix = np.zeros((predict_length, total_models)) # The matrix of log probabilities for each label to be stored
count_new_oov_local = 0
count_formula_problems_local = 0
if architecture_b_algorithm == "forward":
for i in range(predict_length):
# Debug
print(i)
temp_predict_log_proba = []
for current_model in self.trained_model: # For every trained model
_current_temp_log_proba = current_model.log_probability(np.array(obs_test[i]), check_input=True) # 'check_input'=False breaks functionality completely
# Normalization already performed by Pomegranate
if _current_temp_log_proba == 0.0: # Possibly an out-of-vocabulary new observation, maybe a smarter solution would be normalized(np.log(1.0 / total_states))
print("--Warning: Unstable Prediction") # It is unstable because it can be both out-of-vocabulary and perfect match, can't be sure.
count_new_oov_local += 1
_current_temp_log_proba = np.NINF
temp_predict_log_proba.append(_current_temp_log_proba)
if len(set(temp_predict_log_proba)) == 1: # Ensure that we don't have n equal predictions, where argmax wouldn't work
temp_predict = random.randint(0, total_models - 1)
else:
temp_predict = np.argmax(temp_predict_log_proba)
predict.append(self.hmm_to_label_mapping[temp_predict])
predict_log_proba_matrix[i,:] = temp_predict_log_proba
elif architecture_b_algorithm == "formula":
# Formula: score = π(state1) * ObservProb(o1|state1) * P(state2|state1) * ObservProb(o2|state2) * P(state3|state2) * ... * P(staten|staten-1) * ObservProb(on|staten) , divided by the sequence length to normalize
for k in range(predict_length):
temp_predict_log_proba = []
current_states = state_test[k]
current_observations = obs_test[k]
seq_length = len(current_states)
if len(current_observations) > 0:
for j in range(total_models): # For every trained model, j is the index of current model across all containers
# (1.1) Transition from start to first state (pi)
current_state_index = self.state_to_label_mapping[j][current_states[0]]
current_temp_score = self.pi[j][current_state_index]
# (1.2) Probability of first observation (B)
try:
current_obs_index = self.observation_to_label_mapping[j][current_observations[0]]
current_temp_score *= self.B[j][current_state_index, current_obs_index]
except KeyError:
# Out-of-vocabulary value
count_new_oov_local += 1
current_temp_score *= formula_magic_smoothing
# (2) Everything that is between the first and last
for i in range(1, seq_length): # This line is different between Pomegranate and HOHMM because the latter has a specific behavior for higher orders; it has multidimensional pi
# (2.1)
previous_state_index = self.state_to_label_mapping[j][current_states[i-1]]
current_state_index = self.state_to_label_mapping[j][current_states[i]]
try:
current_obs_index = self.observation_to_label_mapping[j][current_observations[i]]
_obsprob_temp = self.B[j][current_state_index, current_obs_index] # Observations are on columns
except KeyError:
# Out-of-vocabulary value
count_new_oov_local += 1
_obsprob_temp = formula_magic_smoothing
# (2.2)
_trans_prob_temp = self.A[j][previous_state_index, current_state_index]
# Score Update
current_temp_score = current_temp_score * _obsprob_temp * _trans_prob_temp
# Create a vector that contains the scores for all models
temp_predict_log_proba.append(np.log(current_temp_score / float(len(current_observations)))) # divided by the sequence length to normalize
# Comparison
if len(set(temp_predict_log_proba)) == 1: # Ensure that we don't have n equal predictions, where argmax wouldn't work
temp_predict = random.randint(0, total_models - 1)
count_formula_problems_local += 1
else:
temp_predict = np.argmax(temp_predict_log_proba)
else: # Prediction would be pointless for an empty sequence
temp_predict = random.randint(0, total_models - 1)
temp_predict_log_proba = [np.NINF] * total_models # or could do what I do on 'map' algorithm with log
predict.append(self.hmm_to_label_mapping[temp_predict])
predict_log_proba_matrix[k,:] = temp_predict_log_proba
# Debug
# print(temp_predict_log_proba, self.hmm_to_label_mapping)
# print(predict)
# print(predict_log_proba_matrix)
# quit()
self.count_formula_problems.append(count_formula_problems_local)
self.cross_val_prediction_matrix.append(predict_log_proba_matrix)
self.count_new_oov.append(count_new_oov_local)
return(predict)
def _predict_hohmm_archit_a(self, obs_test):
"""
Performs the prediction phase when the Hidden Markov Model is based on the HOHMM framework.
Architecture A is used, which is the traditional approach where a single HMM is built; even if it looks like it, it is not really suited for classification tasks.
"""
predict_length = len(obs_test)
total_states = len(self.unique_states_subset)
predict = [] # The list of label predictions
count_new_oov_local = 0
predict_matrix = np.empty((predict_length, 1), dtype=object) # The matrix of predictions to be stored
for i in range(predict_length):
if len(obs_test[i]) > 0:
try:
temp_predict = self.trained_model.decode(obs_test[i])[-1] # We only care about the last prediction; should be of <list> type for HOHMM to work
except ValueError: # Prediction failed probably because of out-of-vocabulary value, perform random guessing
count_new_oov_local += 1
temp_predict = self.state_to_label_mapping_rev[random.randint(0, total_states - 1)]
else: # Empty sequence, perform random guessing
temp_predict = self.state_to_label_mapping_rev[random.randint(0, total_states - 1)]
predict.append(temp_predict) # This framework outputs the label name not an index
predict_matrix[i] = temp_predict
self.cross_val_prediction_matrix.append(predict_matrix)
self.count_new_oov.append(count_new_oov_local)
return(predict)
def _predict_hohmm_archit_b(self, state_test, obs_test, architecture_b_algorithm, formula_magic_smoothing):
"""
Performs the prediction phase when the Hidden Markov Model is based on the HOHMM framework.
Architecture B is used, where multiple HMMs are built, in a purely classification-based approach. Tries to find the model that was most likely to have generated each of the instances at hand.
"""
predict_length = len(obs_test)
total_models = len(self.hmm_to_label_mapping)
predict = [] # The list of label predictions
predict_log_proba_matrix = np.zeros((predict_length, total_models)) # The matrix of log probabilities for each label to be stored
count_new_oov_local = 0
count_formula_problems_local = 0
if architecture_b_algorithm == "forward":
for i in range(predict_length):
# Debug
print(i)
temp_predict_log_proba = []
for j in range(total_models): # For every trained model
try:
_current_temp_log_proba = np.log(self.trained_model[j].evaluate(obs_test[i])) # Unsure if normalization is performed
except ValueError: # Catches both empty sequences and out-of-vocabulary scenarios
if len(obs_test[i]) > 0:
count_new_oov_local += 1
_current_temp_log_proba = np.NINF
temp_predict_log_proba.append(_current_temp_log_proba)
if len(set(temp_predict_log_proba)) == 1: # Ensure that we don't have n equal predictions, where argmax wouldn't work
temp_predict = random.randint(0, total_models - 1)
else:
temp_predict = np.argmax(temp_predict_log_proba)
predict.append(self.hmm_to_label_mapping[temp_predict])
predict_log_proba_matrix[i,:] = temp_predict_log_proba
elif architecture_b_algorithm == "formula":
# IMPOSSIBLE FOR HIGH-ORDERS BECAUSE (self.B[j]) for HOHMM remains the same for all orders
# detect_high_order = len(self.pi)
# object_hohmm = SimpleHOHMM.HiddenMarkovModelBuilder()
# new_state_test = object_hohmm._make_higher_order_states(state_sequences=state_test, order=detect_high_order)
# if len(current_observations) > 0:
# for j in range(total_models): # For every trained model, j is the index of current model across all containers
# # ! HOHMM takes a different approach than Pomegranate when it comes to pi and higher-order
# current_pi = current_states[0].split('-')
# current_temp_score = 1.0
# for pi_index in range(detect_high_order):
# # (1.1) Transitions from start to one or more states at the start of the sequence (pi), e.g. seq=["pos-neg", "pos-pos", ...], means we should do start->"pos" and start->"pos-neg"
# temp = '-'.join(current_pi[0:pi_index+1])
# current_temp_score *= self.pi[pi_index][temp]
# current_state_index = self.state_to_label_mapping[j][current_states[0]]
# current_obs_index = self.observation_to_label_mapping[j][current_observations[0]]
# # (1.2) Probability of first observation (B)
# current_temp_score *= self.B[j][current_state_index, current_obs_index]
# Formula: score = π(state1) * ObservProb(o1|state1) * P(state2|state1) * ObservProb(o2|state2) * P(state3|state2) * ... * P(staten|staten-1) * ObservProb(on|staten) , divided by the sequence length to normalize
for k in range(predict_length):
# Debug
#print(k)
temp_predict_log_proba = []
current_states = state_test[k]
current_observations = obs_test[k]
seq_length = len(current_states)
if len(current_observations) > 0:
for j in range(total_models): # For every trained model, j is the index of current model across all containers
# (1.1) Transition from start to first state (pi)
current_state_index = self.state_to_label_mapping[j][current_states[0]]
current_temp_score = self.pi[j][0][current_states[0]]
# (1.2) Probability of first observation (B)
try:
current_obs_index = self.observation_to_label_mapping[j][current_observations[0]]
current_temp_score *= self.B[j][current_state_index, current_obs_index]
except KeyError:
# Out-of-vocabulary value
count_new_oov_local += 1
current_temp_score *= formula_magic_smoothing
# (2) Everything that is between the first and last
for i in range(1, seq_length): # This line is different between Pomegranate and HOHMM because the latter has a specific behavior for higher orders; it has multidimensional pi
# (2.1)
previous_state_index = self.state_to_label_mapping[j][current_states[i-1]]
current_state_index = self.state_to_label_mapping[j][current_states[i]]
try:
current_obs_index = self.observation_to_label_mapping[j][current_observations[i]]
_obsprob_temp = self.B[j][current_state_index, current_obs_index] # Observations are on columns
except KeyError:
# Out-of-vocabulary value
count_new_oov_local += 1
_obsprob_temp = formula_magic_smoothing
# (2.2)
_trans_prob_temp = self.A[j][previous_state_index, current_state_index]
# Score Update
current_temp_score = current_temp_score * _obsprob_temp * _trans_prob_temp
# Create a vector that contains the scores for all models
temp_predict_log_proba.append(np.log(current_temp_score / float(len(current_observations)))) # divided by the sequence length to normalize
# Comparison
if len(set(temp_predict_log_proba)) == 1: # Ensure that we don't have n equal predictions, where argmax wouldn't work
temp_predict = random.randint(0, total_models - 1)
count_formula_problems_local += 1
else:
temp_predict = np.argmax(temp_predict_log_proba)
else: # Prediction would be pointless for an empty sequence
temp_predict = random.randint(0, total_models - 1)
temp_predict_log_proba = [np.NINF] * total_models # or could do what I do on 'map' algorithm with log
predict.append(self.hmm_to_label_mapping[temp_predict])
predict_log_proba_matrix[k,:] = temp_predict_log_proba
# Debug
# print(temp_predict_log_proba, self.hmm_to_label_mapping)
# print(predict)
# print(predict_log_proba_matrix)
# quit()
self.count_formula_problems.append(count_formula_problems_local)
self.cross_val_prediction_matrix.append(predict_log_proba_matrix)
self.count_new_oov.append(count_new_oov_local)
return(predict)
def convert_to_ngrams_wrapper(self, n, target, prev_flag, dummy_flag, hohmm_check):
"""
(1) Execute the n-gram conversion on the correct container, as defined by 'target'.
(2) Perform some input data validation checks.
"""
if self.selected_framework == "hohmm":
if hohmm_check[0] > 1 and hohmm_check[1] == "formula":
raise ValueError("the selected 'hohmm' framework combined with 'formula' algorithm has not been implemented for high-order HMMs.")
if n < 2:
print("N-gram conversion is disabled.")
return None
if target not in ["states", "obs", "both"]:
raise ValueError("invalid selection of target for the n-gram process.")
if self.selected_framework != "pome":
if target != "obs":
raise ValueError("you should be attempting to perform n-grams on the states only when using the 'pome' framework.")
if target == "states":
self._convert_to_ngrams(n, "states", prev_flag, dummy_flag)
elif target == "obs":
self._convert_to_ngrams(n, "obs", prev_flag, dummy_flag)
elif target == "both":
self._convert_to_ngrams(n, "states", prev_flag, dummy_flag)
self._convert_to_ngrams(n, "obs", prev_flag, dummy_flag)
print("N-gram conversion to", n, "\b-gram was sucessful. Container type remained as ndarray<list>.")
if self.check_shape(self.state_labels, self.observations) == False:
print("--Warning: one of the containers is now shorter than the other on the y axis, consider using the flags or using 'both' as target.")
self._ngrams_balance_shape()
def _convert_to_ngrams(self, n, target, prev_flag, dummy_flag):
"""
Convert the contents of a single container to an n-gram representation.
Parameters:
n: n-gram order.
container: the container of data, on which to perform the conversion.
target: a string setting that can take the following values, "states", "obs" or "both".
prev_flag: a boolean value that decides the behavior when a sequence is shorter than the n-gram order.
'True' enables the calculation of those shorter n-grams, leading to more unique states/observations.
'False' disables it and returns an empty list for such cases.
dummy_flag: a boolean value that decides whether the length of the sequence should be maintained with the help of a dummy set.
e.g. on a State-emission HMM, set it to 'False' since both the states and observations get shortened.
However, in other scenarios where only one of the two is affected, it will end up with a shorter length per sequence.
"""
if target == "states":
container = self.state_labels # Shallow copy not Deep, be careful
elif target == "obs":
container = self.observations
if (len(container) > 0):
ngrams_temp = []
for seq in container:
seq_deep = copy.deepcopy(seq) # Insert is going to be used, better make a deep copy
current_seq = list()
if len(seq_deep) >= n:
if dummy_flag == True:
for i in range(n-1): # Append one or more dummies at the start of the sequence
seq_deep.insert(i, "dummy"+str(i))
for grams in ngramsgenerator(seq_deep, n):
current_seq.append("".join(grams))
elif prev_flag == True:
if dummy_flag == True:
for i in range(len(seq_deep)-1):
seq_deep.insert(i, "dummy"+str(i))
for grams in ngramsgenerator(seq_deep, len(seq_deep)):
current_seq.append("".join(grams))
ngrams_temp.append(current_seq)
if target == "states":
self.state_labels = np.array(ngrams_temp)
elif target == "obs":
self.observations = np.array(ngrams_temp)
else:
raise ValueError("n-gram conversion failed, the input container appears to be empty.")
def _ngrams_balance_shape(self):
"""
Executed after the n-gram process is completed, if the state and observation containers of the class are not of the same exact shape.
(1) If a row is empty on one container, wipes it on the other. Caused by prev_flag=False.
(2) If a row is shorter on one container, removes the first elements on the other. Caused by dummy_flag=False. This is probably not the best idea, but we are all consenting adults here.
"""
count_wipe = 0
count_shorten = 0
for i in range(self.length):
length_1 = len(self.state_labels[i])
length_2 = len(self.observations[i])
if length_1 == 0:
self.observations[i] = []
count_wipe += 1
elif length_2 == 0:
self.state_labels[i] = []
count_wipe += 1
length_1 = len(self.state_labels[i])
length_2 = len(self.observations[i])
if length_1 > length_2:
self.state_labels[i] = self.state_labels[i][-length_2:] # Remove the first elements of the list
count_shorten += 1
elif length_2 > length_1:
self.observations[i] = self.observations[i][-length_1:] # Remove the first elements of the list
count_shorten += 1
if count_wipe > 0:
print("---Wiped", count_wipe, "rows/instances. Caused by prev_flag=False.")
if count_shorten > 0: