import logging
import warnings
from numbers import Integral
from typing import Optional
import numpy as np
import scipy.stats
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.exceptions import NotFittedError
from sklearn.utils.validation import check_is_fitted
from tsbootstrap.utils.types import BlockCompressorTypes
from tsbootstrap.utils.validate import (
validate_blocks,
validate_integers,
validate_literal_type,
)
logger = logging.getLogger("tsbootstrap")
try:
from dtaidistance import dtw_ndim # type: ignore
# dtaidistance does not compile for Python 3.10 and 3.11
dtaidistance_installed = True
except ImportError:
dtaidistance_installed = False
[docs]
class BlockCompressor:
"""
BlockCompressor class provides the functionality to compress blocks of data using different techniques.
Methods
-------
__init__(method: BlockCompressorTypes = "middle", apply_pca_flag: bool = False, pca: Optional[PCA] = None, random_seed: Optional[Integral] = None) -> None
Initialize the BlockCompressor instance.
_pca_compression(block: np.ndarray, summary: np.ndarray) -> np.ndarray
Summarize a block of data using PCA.
_summarize_block(block: np.ndarray) -> np.ndarray
Summarize a block using a specified method.
summarize_blocks(blocks) -> np.ndarray
Summarize each block in the input list of blocks using the specified method.
"""
[docs]
def __init__(
self,
method: BlockCompressorTypes = "middle",
apply_pca_flag: bool = False,
pca: Optional[PCA] = None,
random_seed: Optional[Integral] = None,
):
"""
Initialize the BlockCompressor with the selected method, PCA flag, PCA instance, and random seed.
Parameters
----------
method : BlockCompressorTypes, optional
The method to use for summarizing the blocks. Default is "middle".
apply_pca_flag : bool, optional
Whether to apply Principal Component Analysis (PCA) for dimensionality reduction. Default is False.
pca : sklearn.decomposition.PCA, optional
PCA instance, with `n_components` set to 1. If not provided, a default PCA instance is used. Default is None.
random_seed : Integral, optional
The seed for the random number generator. Default is None.
"""
self.method = method
self.apply_pca_flag = apply_pca_flag
self.pca = pca
self.random_seed = random_seed
if self.method in ["mean", "median"] and self.apply_pca_flag:
warnings.warn(
"PCA compression is not recommended for 'mean' or 'median' methods.",
stacklevel=2,
)
# once scikit-base object:
# set python_dependencies tag depending on method
# if method is "kmedoids"
# "scikit-learn-extra" (due to MKedoids)
# import name is sklearn_extra
# if method is "kmedians"
# "pyclustering" (due to KMedians)
@property
def method(self) -> str:
"""Getter for method."""
return self._method
@method.setter
def method(self, value: str) -> None:
"""
Setter for method. Performs validation on assignment.
Parameters
----------
value : str
The method to use for summarizing the blocks.
"""
self._method = self._validate_method(value)
def _validate_method(self, method: str) -> str:
"""
Validate and correct the method.
Parameters
----------
method : str
The method to use for summarizing the blocks.
Returns
-------
str
The validated method.
Raises
------
ValueError
If the method is not one of the BlockCompressorTypes.
"""
validate_literal_type(method, BlockCompressorTypes)
return method.lower()
@property
def apply_pca_flag(self) -> bool:
"""Getter for apply_pca_flag."""
return self._apply_pca_flag
@apply_pca_flag.setter
def apply_pca_flag(self, value: bool) -> None:
"""
Setter for apply_pca_flag. Performs validation on assignment.
Parameters
----------
value : bool
Whether to apply PCA or not.
"""
if not isinstance(value, bool):
raise TypeError("apply_pca_flag must be a boolean")
self._apply_pca_flag = value
@property
def pca(self) -> PCA:
"""Getter for pca."""
return self._pca
@pca.setter
def pca(self, value: Optional[PCA]) -> None:
"""
Setter for pca. Performs validation on assignment.
Parameters
----------
value : Optional[PCA]
The PCA instance to use.
"""
if value is not None:
if not isinstance(value, PCA):
raise TypeError(
"pca must be a sklearn.decomposition.PCA instance"
)
elif value.n_components != 1: # type: ignore
raise ValueError(
"The provided PCA object must have n_components set to 1 for compression."
)
self._pca = value
else:
self._pca = PCA(n_components=1)
@property
def random_seed(self):
return self._random_seed
@random_seed.setter
def random_seed(self, value: Optional[Integral]) -> None:
"""
Setter for rng. Performs validation on assignment.
Parameters
----------
value : Generator
The random number generator to use.
"""
if value is not None:
if not isinstance(value, Integral):
raise TypeError(
"The random number generator must be an integer."
)
else:
if value < 0 or value >= 2**32:
raise ValueError(
"The random seed must be a non-negative integer less than 2**32."
)
else:
self._random_seed = value
else:
self._random_seed = None
[docs]
def _pca_compression(
self, block: np.ndarray, summary: np.ndarray
) -> np.ndarray:
"""Compress the block using PCA.
The method fits a PCA instance to the block and transforms it to a lower dimension.
If the PCA instance has already been fitted, only the transformation is performed.
Parameters
----------
block : np.ndarray
The block to compress.
Returns
-------
np.ndarray
The compressed block.
"""
# Check if the PCA instance has already been fitted
try:
check_is_fitted(self.pca)
except NotFittedError:
self.pca.fit(block)
transformed_summary = self.pca.transform(summary)
return transformed_summary
[docs]
def _summarize_block(self, block: np.ndarray) -> np.ndarray:
"""
Helper method to summarize a block using a specified method.
The available methods are 'first', 'middle', 'last', 'mean', 'median',
'mode', 'kmeans', 'kmedians', 'kmedoids'.
Parameters
----------
block : np.ndarray
A 2D numpy array representing a block of data.
Returns
-------
np.ndarray
A 1D numpy array representing the summarized block.
Raises
------
ValueError
If the specified method is not recognized.
"""
# Mapping of methods to corresponding functions
summarization_methods = {
"first": lambda x: x[0],
"middle": lambda x: x[len(x) // 2],
"last": lambda x: x[-1],
"mean": lambda x: x.mean(axis=0),
"median": lambda x: np.median(x, axis=0),
"mode": lambda x: scipy.stats.mode(x, axis=0, keepdims=True)[0][0],
"kmeans": self._kmeans_compression,
"kmedians": self._kmedians_compression,
"kmedoids": self._kmedoids_compression,
}
method = summarization_methods.get(self.method)
if method is None:
raise ValueError(
f"Method '{self.method}' is not recognized. Please select one of {list(summarization_methods.keys())}."
)
summary = method(block)
summary = np.array(summary).reshape(1, -1)
summary = (
self._pca_compression(block, summary)
if self.apply_pca_flag
else summary
)
return summary
# Additional private methods to handle kmeans, kmedians, and kmedoids
def _kmeans_compression(self, block: np.ndarray) -> np.ndarray:
"""
Helper method to compress a block using k-means clustering.
Parameters
----------
block : np.ndarray
A 2D numpy array representing a block of data.
Returns
-------
np.ndarray
A 1D numpy array representing the compressed block.
Notes
-----
This method uses the scikit-learn implementation of k-means clustering.
"""
return (
KMeans(n_clusters=1, random_state=self.random_seed, n_init="auto") # type: ignore
.fit(block)
.cluster_centers_[0]
)
def _kmedians_compression(self, block: np.ndarray) -> np.ndarray:
"""
Helper method to compress a block using k-medians clustering.
Parameters
----------
block : np.ndarray
A 2D numpy array representing a block of data.
Returns
-------
np.ndarray
A 1D numpy array representing the compressed block.
Notes
-----
This method uses the scipy implementation of k-medians clustering.
"""
from pyclustering.cluster.kmedians import kmedians # type: ignore
rng = np.random.default_rng(self.random_seed) # type: ignore
initial_centers = rng.choice(block.flatten(), size=(1, block.shape[1]))
kmedians_instance = kmedians(block, initial_centers)
kmedians_instance.process()
return kmedians_instance.get_medians()[0] # type: ignore
def _kmedoids_compression(self, block: np.ndarray) -> np.ndarray:
"""
Helper method to compress a block using k-medoids clustering.
Parameters
----------
block : np.ndarray
A 2D numpy array representing a block of data.
Returns
-------
np.ndarray
A 1D numpy array representing the compressed block.
Notes
-----
This method uses the scikit-learn-extra implementation of k-medoids clustering.
"""
from sklearn_extra.cluster import KMedoids
return (
KMedoids(n_clusters=1, random_state=self.random_seed) # type: ignore
.fit(block)
.cluster_centers_[0]
)
[docs]
def summarize_blocks(self, blocks) -> np.ndarray:
"""
Summarize each block in the input list of blocks using the specified method.
Parameters
----------
blocks : List[np.ndarray]
List of numpy arrays representing the blocks to be summarized.
Returns
-------
np.ndarray
Numpy array containing the summarized blocks.
Example
-------
>>> compressor = BlockCompressor(method='middle')
>>> blocks = [np.array([1, 2, 3]), np.array([4, 5, 6])]
>>> summarized_blocks = compressor.summarize_blocks(blocks)
>>> summarized_blocks
array([2, 5])
"""
"""
Summarize each block in the input list of blocks using the specified method.
Parameters
----------
blocks : List[np.ndarray]
A list of 2D NumPy arrays, each representing a block of data.
Returns
-------
np.ndarray
A 2D NumPy array of shape (len(blocks), num_features==blocks[0].shape[1]) with each row containing the summarized element for the corresponding input block.
"""
# Validate input blocks
validate_blocks(blocks)
# Preallocate an empty array of the correct size
num_blocks = len(blocks)
num_features = blocks[0].shape[1]
summaries = np.empty((num_blocks, num_features))
# Fill the array in a loop
for i, block in enumerate(blocks):
summaries[i] = self._summarize_block(block)
return summaries
[docs]
@classmethod
def get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
----------
parameter_set : str, default="default"
Name of the set of test parameters to return, for use in tests. If no
special parameters are defined for a value, will return `"default"` set.
Returns
-------
params : dict or list of dict, default = {}
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
`create_test_instance` uses the first (or only) dictionary in `params`
"""
from skbase.utils.dependencies import _check_soft_dependencies
methods = [
"first",
"middle",
"last",
"mean",
"mode",
"median",
"kmeans",
]
if _check_soft_dependencies("scikit-learn-extra", severity="none"):
methods.append("kmedoids")
if _check_soft_dependencies("pyclustering", severity="none"):
methods.append("kmedians")
return [{"method": method} for method in methods]
[docs]
class MarkovTransitionMatrixCalculator:
"""
MarkovTransitionMatrixCalculator class provides the functionality to calculate the transition matrix for a set of data blocks based on their DTW distances between consecutive blocks.
The transition matrix is normalized to obtain transition probabilities.
The underlying assumption is that the data blocks are generated from a Markov chain.
In other words, the next block is generated based on the current block and not on any previous blocks.
Methods
-------
__init__() -> None
Initialize the MarkovTransitionMatrixCalculator instance.
_calculate_dtw_distances(blocks, eps: float = 1e-5) -> np.ndarray
Calculate the DTW distances between all pairs of blocks.
calculate_transition_probabilities(blocks) -> np.ndarray
Calculate the transition probability matrix based on DTW distances between all pairs of blocks.
Examples
--------
>>> calculator = MarkovTransitionMatrixCalculator()
>>> blocks = [np.random.rand(10, 5) for _ in range(50)]
>>> transition_matrix = calculator.calculate_transition_probabilities(blocks)
"""
_tags = {"python_dependencies": "hmmlearn>=0.3.0"}
[docs]
@staticmethod
def _calculate_dtw_distances(blocks, eps: float = 1e-5) -> np.ndarray:
"""
Calculate the DTW distances between all pairs of blocks. A small constant epsilon is added to every distance to ensure that there is always a non-zero probability of remaining in the same state.
Parameters
----------
blocks : List[np.ndarray]
A list of numpy arrays, each of shape (num_timestamps, num_features), representing the time series data blocks.
eps : float
A small constant to be added to the DTW distances to ensure non-zero probabilities.
Returns
-------
np.ndarray
A matrix of DTW distances of shape (len(blocks), len(blocks)).
"""
validate_blocks(blocks)
num_blocks = len(blocks)
# Compute pairwise DTW distances between all pairs of blocks
distances = np.zeros((num_blocks, num_blocks))
for i in range(num_blocks):
for j in range(i, num_blocks):
dist = dtw_ndim.distance(blocks[i], blocks[j]) + eps # type: ignore
distances[i, j] = dist
distances[j, i] = dist
# Add a small constant to the diagonal to allow remaining in the same state
np.fill_diagonal(distances, eps)
return distances
[docs]
@staticmethod
def calculate_transition_probabilities(
blocks,
) -> np.ndarray:
"""
Calculate the transition probability matrix based on DTW distances between all pairs of blocks.
Parameters
----------
blocks : List[np.ndarray]
A list of numpy arrays, each of shape (num_timestamps, num_features), representing the time series data blocks.
Returns
-------
np.ndarray
A transition probability matrix of shape (len(blocks), len(blocks)).
"""
distances = MarkovTransitionMatrixCalculator._calculate_dtw_distances(
blocks
)
num_blocks = len(blocks)
# Normalize the distances to obtain transition probabilities
transition_probabilities = np.zeros((num_blocks, num_blocks))
for i in range(num_blocks):
total_distance = np.sum(distances[i, :])
if total_distance > 0:
transition_probabilities[i, :] = (
distances[i, :] / total_distance
)
else:
# Case when all blocks are identical, assign uniform probabilities
transition_probabilities[i, :] = 1 / num_blocks
return transition_probabilities
[docs]
class MarkovSampler:
"""
A class for sampling from a Markov chain with given transition probabilities.
This class allows for the combination of block-based bootstrapping and Hidden Markov Model (HMM) fitting.
Attributes
----------
transition_matrix_calculator : MarkovTransitionMatrixCalculator
An instance of MarkovTransitionMatrixCalculator to calculate transition probabilities.
block_compressor : BlockCompressor
An instance of BlockCompressor to perform block summarization/compression.
Methods
-------
__init__(method: str = "mean", apply_pca_flag: bool = False, pca: Optional[PCA] = None, n_iter_hmm: Integral = 100, n_fits_hmm: Integral = 10, blocks_as_hidden_states_flag: bool = False, random_seed: Optional[Integral] = None) -> None
Initialize the MarkovSampler instance.
_validate_n_states(n_states: Integral, blocks) -> Integral
Validate the number of states.
_validate_n_iter_hmm(n_iter_hmm: Integral) -> Integral
Validate the number of iterations for the HMM.
_validate_n_fits_hmm(n_fits_hmm: Integral) -> Integral
Validate the number of fits for the HMM.
_validate_blocks_as_hidden_states_flag(blocks_as_hidden_states_flag: bool) -> bool
Validate the blocks_as_hidden_states_flag.
_validate_random_seed(random_seed: Optional[Integral]) -> Optional[Integral]
Validate the random seed.
fit_hidden_markov_model(blocks, n_states: Integral = 5) -> hmm.GaussianHMM
Fit a Hidden Markov Model (HMM) to the input blocks.
fit(blocks, n_states: Integral = 5) -> MarkovSampler
Fit the MarkovSampler instance to the input blocks.
sample(blocks, n_states: Integral = 5) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]
Sample from the MarkovSampler instance.
Examples
--------
>>> sampler = MarkovSampler(n_iter_hmm=200, n_fits_hmm=20)
>>> blocks = [np.random.rand(10, 5) for _ in range(50)]
>>> start_probs, trans_probs, centers, covariances, assignments = sampler.sample(blocks, n_states=5, blocks_as_hidden_states_flag=True)
"""
[docs]
def __init__(
self,
method: BlockCompressorTypes = "middle",
apply_pca_flag: bool = False,
pca: Optional[PCA] = None,
n_iter_hmm: Integral = 100, # type: ignore
n_fits_hmm: Integral = 10, # type: ignore
blocks_as_hidden_states_flag: bool = False,
random_seed: Optional[Integral] = None,
):
"""
Initialize the MarkovSampler instance.
Parameters
----------
method : str, optional
The method to use for summarizing the blocks. Default is "middle".
apply_pca_flag : bool, optional
Whether to apply Principal Component Analysis (PCA) for dimensionality reduction. Default is False.
pca : sklearn.decomposition.PCA, optional
An instance of sklearn's PCA class, with `n_components` set to 1. If not provided, a default PCA instance will be used.
n_iter_hmm : Integral, optional
The number of iterations to run the HMM for. Default is 100.
n_fits_hmm : Integral, optional
The number of times to fit the HMM. Default is 10.
blocks_as_hidden_states_flag : bool, optional
If True, each block will be used as a hidden state for the HMM (i.e., n_states = len(blocks)).
If False, the blocks are interpreted as separate sequences of data and the HMM is initialized with uniform transition probabilities. Default is False.
random_seed : Integral, optional
The seed for the random number generator. Default is None (no fixed seed).
Notes
-----
The MarkovSampler class uses the dtaidistance package for calculating DTW distances between blocks. This package is not available for Python 3.10 and 3.11. If you are using Python 3.10 or 3.11, the MarkovSampler class will automatically set the blocks_as_hidden_states_flag to False.
"""
self.method = method
self.apply_pca_flag = apply_pca_flag
self.pca = pca
self.n_iter_hmm = n_iter_hmm
self.n_fits_hmm = n_fits_hmm
self.blocks_as_hidden_states_flag = blocks_as_hidden_states_flag
self.random_seed = random_seed
if self.blocks_as_hidden_states_flag and not dtaidistance_installed:
warnings.warn(
"blocks_as_hidden_states_flag requires the 'dtaidistance' package, "
"which is not available on Python 3.10 and 3.11. The blocks_as_hidden_states_flag "
"will be set to False.",
stacklevel=2,
)
self.blocks_as_hidden_states_flag = False
self.transition_matrix_calculator = MarkovTransitionMatrixCalculator()
self.block_compressor = BlockCompressor(
apply_pca_flag=self.apply_pca_flag,
pca=self.pca,
random_seed=self.random_seed,
method=self.method,
)
self.model = None
self.X = None
@property
def n_iter_hmm(self) -> Integral:
"""Getter for n_iter_hmm."""
return self._n_iter_hmm
@n_iter_hmm.setter
def n_iter_hmm(self, value: Integral) -> None:
"""
Setter for n_iter_hmm. Performs validation on assignment.
Parameters
----------
value : Integral
The number of iterations to run the HMM for.
"""
validate_integers(value, min_value=1) # type: ignore
self._n_iter_hmm = value
@property
def n_fits_hmm(self) -> Integral:
"""Getter for n_fits_hmm."""
return self._n_fits_hmm
@n_fits_hmm.setter
def n_fits_hmm(self, value: Integral) -> None:
"""
Setter for n_fits_hmm. Performs validation on assignment.
Parameters
----------
value : Integral
The number of times to fit the HMM.
"""
validate_integers(value, min_value=1) # type: ignore
self._n_fits_hmm = value
@property
def blocks_as_hidden_states_flag(self) -> bool:
"""Getter for blocks_as_hidden_states_flag."""
return self._blocks_as_hidden_states_flag
@blocks_as_hidden_states_flag.setter
def blocks_as_hidden_states_flag(self, value: bool) -> None:
"""
Setter for blocks_as_hidden_states_flag. Performs validation on assignment.
Parameters
----------
value : bool
Whether to use the blocks as hidden states for the HMM.
"""
if not isinstance(value, bool):
raise TypeError("blocks_as_hidden_states_flag must be a boolean")
self._blocks_as_hidden_states_flag = value
@property
def random_seed(self):
"""Getter for random_seed."""
return self._random_seed
@random_seed.setter
def random_seed(self, value: Optional[Integral]) -> None:
"""
Setter for rng. Performs validation on assignment.
Parameters
----------
value : Generator
The random number generator to use.
"""
if value is not None:
if not isinstance(value, Integral):
raise TypeError(
"The random number generator must be an integer."
)
else:
if value < 0 or value >= 2**32:
raise ValueError(
"The random seed must be a non-negative integer less than 2**32."
)
else:
self._random_seed = value
else:
self._random_seed = None
[docs]
def fit_hidden_markov_model(
self,
X: np.ndarray,
n_states: Integral = 5, # type: ignore
transmat_init: Optional[np.ndarray] = None,
means_init: Optional[np.ndarray] = None,
lengths: Optional[np.ndarray] = None,
):
"""
Fit a Gaussian Hidden Markov Model on the input data.
Parameters
----------
X : np.ndarray
A 2D NumPy array, where each row represents a summarized block of data.
n_states : Integral, optional
The number of states in the hidden Markov model. By default 5.
Returns
-------
hmm.GaussianHMM
The trained Gaussian Hidden Markov Model.
"""
self._validate_fit_hidden_markov_model_inputs(
X, n_states, transmat_init, means_init
)
best_score = -np.inf
best_hmm_model = None
for idx in range(self.n_fits_hmm):
hmm_model = self._initialize_hmm_model(
n_states, transmat_init, means_init, idx # type: ignore
)
try:
hmm_model.fit(X, lengths=lengths)
except ValueError:
continue
score = hmm_model.score(X, lengths=lengths)
if score > best_score:
best_hmm_model = hmm_model
best_score = score
if best_hmm_model is None:
raise RuntimeError(
"All fitting attempts failed. Check your input data and model parameters."
)
return best_hmm_model
def _validate_fit_hidden_markov_model_inputs(
self,
X: np.ndarray,
n_states: Integral,
transmat_init: Optional[np.ndarray],
means_init: Optional[np.ndarray],
) -> None:
"""
Validate the inputs to fit_hidden_markov_model.
Parameters
----------
X : np.ndarray
A 2D NumPy array, where each row represents a summarized block of data.
n_states : Integral
The number of states in the hidden Markov model.
transmat_init : Optional[np.ndarray]
The initial transition matrix for the HMM.
means_init : Optional[np.ndarray]
The initial means for the HMM.
Raises
------
TypeError
If X is not a NumPy array.
ValueError
If X is not a two-dimensional array.
If n_states is not an integer >= 1.
If the shape of transmat_init is invalid.
If the shape of means_init is invalid.
Returns
-------
None
Notes
-----
This method is called by fit_hidden_markov_model. It is not intended to be called directly.
"""
if X.ndim != 2:
raise ValueError("Input 'X' must be a two-dimensional array.")
if not isinstance(n_states, Integral) or n_states < 1:
raise ValueError("Input 'n_states' must be an integer >= 1.")
if transmat_init is not None:
transmat_init = np.array(transmat_init)
if not isinstance(transmat_init, np.ndarray):
raise TypeError("Input 'transmat_init' must be a NumPy array.")
if transmat_init.shape != (n_states, n_states):
raise ValueError("Invalid shape for initial transition matrix")
if means_init is not None:
means_init = np.array(means_init)
if not isinstance(means_init, np.ndarray):
raise TypeError("Input 'means_init' must be a NumPy array.")
if means_init.shape != (n_states, X.shape[1]):
raise ValueError("Invalid shape for initial means")
def _initialize_hmm_model(
self,
n_states: Integral,
transmat_init: Optional[np.ndarray],
means_init: Optional[np.ndarray],
idx: Integral,
):
"""
Initialize a Gaussian Hidden Markov Model.
Parameters
----------
n_states : Integral
The number of states in the hidden Markov model.
transmat_init : Optional[np.ndarray]
The initial transition matrix for the HMM.
means_init : Optional[np.ndarray]
The initial means for the HMM.
idx : Integral
The index of the current fit.
Returns
-------
hmm.GaussianHMM
The initialized Gaussian Hidden Markov Model.
Notes
-----
This method is called by fit_hidden_markov_model. It is not intended to be called directly.
"""
from hmmlearn import hmm
hmm_model = hmm.GaussianHMM(
n_components=n_states, # type: ignore
covariance_type="full",
n_iter=self.n_iter_hmm, # type: ignore
init_params="stmc",
params="stmc",
random_state=(
self.random_seed + idx if self.random_seed is not None else idx
),
)
if transmat_init is not None:
hmm_model.transmat_ = transmat_init
if means_init is not None:
hmm_model.means_ = means_init
return hmm_model
[docs]
def fit(
self,
blocks,
n_states: Integral = 5, # type: ignore
) -> "MarkovSampler":
"""
Sample from a Markov chain with given transition probabilities.
Parameters
----------
blocks : List[np.ndarray] or np.ndarray
A list of 2D NumPy arrays, each representing a block of data, or a 2D NumPy array, where each row represents a row of raw data.
n_states : Integral, optional
The number of states in the hidden Markov model. Default is 5.
Returns
-------
MarkovSampler
Current instance of the MarkovSampler class, with the model trained.
Examples
--------
>>> blocks = [np.random.rand(10, 5) for _ in range(50)]
>>> sampler.fit(blocks, n_states=5)
"""
X, lengths, n_states = self._prepare_fit_inputs(blocks, n_states)
transmat_init = (
self.transition_matrix_calculator.calculate_transition_probabilities(
blocks
)
if self.blocks_as_hidden_states_flag
else None
)
means_init = (
self.block_compressor.summarize_blocks(blocks)
if self.blocks_as_hidden_states_flag
else None
)
hmm_model = self.fit_hidden_markov_model(
X, n_states, transmat_init, means_init, lengths
)
self.model = hmm_model
self.X = X
return self
# Helper functions for fit
def _prepare_fit_inputs(self, blocks, n_states):
"""
Validate the inputs to fit.
Parameters
----------
blocks : List[np.ndarray] or np.ndarray
A list of 2D NumPy arrays, each representing a block of data, or a 2D NumPy array, where each row represents a row of raw data.
n_states : Integral
The number of states in the hidden Markov model.
Raises
------
TypeError
If blocks is not a list of NumPy arrays or a NumPy array.
ValueError
If blocks is a list of NumPy arrays and any of the arrays are not two-dimensional.
If blocks is a list of NumPy arrays and any of the arrays are empty.
If blocks is a list of NumPy arrays and any of the arrays have zero columns.
If blocks is a list of NumPy arrays and any of the arrays have zero rows.
If blocks is a list of NumPy arrays and any of the arrays have different numbers of columns.
If blocks is a list of NumPy arrays and any of the arrays have different numbers of rows.
If blocks is a NumPy array and it is not two-dimensional.
If blocks is a NumPy array and it is empty.
If blocks is a NumPy array and it has zero columns.
If blocks is a NumPy array and it has zero rows.
If blocks is a NumPy array and it has different numbers of columns.
If blocks is a NumPy array and it has different numbers of rows.
If n_states is not an integer >= 1.
If n_states is greater than the number of rows in blocks.
Returns
-------
Tuple[np.ndarray, Optional[np.ndarray], Integral]
A tuple containing the input data, the lengths of the blocks (if applicable), and the number of states.
"""
if isinstance(blocks, list):
validate_blocks(blocks)
X = np.concatenate(blocks, axis=0)
lengths = np.array([len(block) for block in blocks])
if self.blocks_as_hidden_states_flag:
n_states = len(blocks)
if min(lengths) < 10:
raise ValueError(
f"Input 'X' must have at least {n_states * 10} points to fit a {n_states}-state HMM."
)
logger.debug(
f"Using {len(blocks)} blocks as 'n_states', since 'blocks_as_hidden_states_flag' is True. Ignoring user-provided 'n_states' parameter."
)
lengths = None
else:
self._validate_single_block_input(blocks)
X = blocks
lengths = None
if not isinstance(n_states, Integral) or n_states < 1:
raise ValueError("Input 'n_states' must be an integer >= 1.")
if n_states > X.shape[0]: # type: ignore
raise ValueError(
f"Input 'X' must have at least {n_states} points to fit a {n_states}-state HMM."
)
return X, lengths, n_states
def _validate_single_block_input(self, blocks: np.ndarray):
"""
Validate the input to fit when a single block is provided.
Parameters
----------
blocks : np.ndarray
A 2D NumPy array, where each row represents a row of raw data.
Raises
------
TypeError
If blocks is not a NumPy array.
ValueError
If blocks is not a two-dimensional array.
If blocks is empty.
If blocks has zero columns.
If blocks has zero rows.
Returns
-------
None
"""
if not isinstance(blocks, np.ndarray):
raise TypeError(
"Input 'blocks' must be a list of NumPy arrays or a NumPy array."
)
if blocks.ndim != 2 or blocks.shape[0] == 0 or blocks.shape[1] == 0:
raise ValueError(
"Input 'blocks' must be a non-empty two-dimensional array."
)
[docs]
def sample(
self,
X: Optional[np.ndarray] = None,
random_seed: Optional[Integral] = None,
):
"""
Sample from a Markov chain with given transition probabilities.
Parameters
----------
X : Optional[np.ndarray]
A 2D NumPy array, where each row represents a summarized block of data. If not provided, the model will be sampled using the data used to fit the model.
random_seed : Optional[Integral]
The seed for the random number generator. If not provided, the random seed used to fit the model will be used.
Returns
-------
Tuple[np.ndarray, np.ndarray]
A tuple containing the start probabilities and transition probabilities of the Markov chain.
"""
# Check if the model is already fitted
check_is_fitted(self, ["model"]) # type: ignore
if X is None:
X = self.X
if random_seed is None:
random_seed = self.random_seed
return self.model.sample(X.shape[0], random_state=random_seed) # type: ignore
def __repr__(self) -> str:
return f"BlockCompressor(method='{self.method}', apply_pca_flag={self.apply_pca_flag}, pca={self.pca}, random_seed={self.random_seed})"
def __str__(self) -> str:
return f"BlockCompressor using method '{self.method}' with PCA flag {self.apply_pca_flag} and random seed {self.random_seed}"
def __eq__(self, other: object) -> bool:
if isinstance(other, BlockCompressor):
return (
self.method == other.method
and self.apply_pca_flag == other.apply_pca_flag
and self.pca == other.pca
and self.random_seed == other.random_seed
)
return False