From 930be463c8d65bfcf24c64acdb71b1db976385e5 Mon Sep 17 00:00:00 2001 From: kraken_university Date: Tue, 27 Sep 2022 16:46:54 +0000 Subject: [PATCH] Commit before v0.7 --- docs/source/conf.py | 3 +- docs/source/formal.rst | 23 ++++++++++ docs/source/help.rst | 2 +- docs/source/index.rst | 1 + docs/source/pdfs/BW_for_GoHMM.pdf | Bin 0 -> 227400 bytes docs/source/pdfs/BW_for_HMM.pdf | Bin 0 -> 216172 bytes docs/source/pdfs/BW_for_MC.pdf | Bin 0 -> 210316 bytes docs/source/pdfs/BW_for_MDP.pdf | Bin 0 -> 212358 bytes docs/source/pdfs/BW_for_MGoHMM.pdf | Bin 0 -> 237383 bytes docs/source/tuto.rst | 24 ++++++++--- examples/03-mdps.py | 18 +++++++- jajapy/base/BW.py | 53 +++++++++++++++++++---- jajapy/base/Set.py | 12 ++++++ jajapy/ctmc/BW_CTMC.py | 13 ++++-- jajapy/ctmc/MM_CTMC_Composition.py | 13 ++++-- jajapy/gohmm/BW_GOHMM.py | 39 +++++++++-------- jajapy/hmm/BW_HMM.py | 57 +++++++++++-------------- jajapy/mc/BW_MC.py | 49 ++++++++++----------- jajapy/mdp/BW_MDP.py | 14 ++++-- jajapy/mgohmm/BW_MGOHMM.py | 45 ++++++++++--------- jajapy/with_stormpy/model_converter.py | 8 +++- setup.py | 2 +- 22 files changed, 247 insertions(+), 129 deletions(-) create mode 100644 docs/source/formal.rst create mode 100644 docs/source/pdfs/BW_for_GoHMM.pdf create mode 100644 docs/source/pdfs/BW_for_HMM.pdf create mode 100644 docs/source/pdfs/BW_for_MC.pdf create mode 100644 docs/source/pdfs/BW_for_MDP.pdf create mode 100644 docs/source/pdfs/BW_for_MGoHMM.pdf diff --git a/docs/source/conf.py b/docs/source/conf.py index 7655edb..fbce31e 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -32,7 +32,8 @@ # ones. extensions = [ 'sphinx.ext.autodoc', - 'sphinx.ext.autosummary' + 'sphinx.ext.autosummary', + 'sphinx.ext.intersphinx' ] autoclass_content = 'both' # Add any paths that contain templates here, relative to this directory. diff --git a/docs/source/formal.rst b/docs/source/formal.rst new file mode 100644 index 0000000..4285fc4 --- /dev/null +++ b/docs/source/formal.rst @@ -0,0 +1,23 @@ +Formalism +========= +This page contains the formal presentation of most of the learning algorithm and models implemented in Jajapy. + +* HMM + * Baum-Welch for HMMs :download:`pdf ` + * A Tutorial on HMMs and Selected Applications in Speech Recognition (L.R. Rabiner) :download:`pdf ` + +* MC + * Baum-Welch for MCs :download:`pdf ` + * Learning Stochastic Regular Grammars by Means of STate Merging Method (R. Carrasco and J. Oncina) :download:`pdf ` + +* MDP + * Baum-Welch for MDPs :download:`pdf ` + * Active Learning of Markov Decision Processes using Baum-Welch algorithm (G. Bacci et al.) :download:`pdf ` + * Learning Deterministic Probabilistic Automata from a Model Checking Perspective (H. Mao et al.) :download:`pdf ` + +* GoHMM + * Baum-Welch for GoHMMs :download:`pdf ` + * A New Improved Baum-Welch Algorithm for Unsupervised Learning for Continuous-Time HMM Using Spark :download:`pdf ` + +* MGoHMM + * Baum-Welch for MGoHMMs :download:`pdf ` diff --git a/docs/source/help.rst b/docs/source/help.rst index 59a45e0..831f9ea 100644 --- a/docs/source/help.rst +++ b/docs/source/help.rst @@ -123,7 +123,7 @@ To learn a model with jajapy, you need to have a training set. And to evaluate i these sets are given, but one can imagine a situation you have the original model. Then you first need to generate the sets and then you can use them. -Once a model is learnt, we can directly translate it to a *stormpy sparse model* and model check some properties (see the examples :ref:`stormpy-example`). +Once a model is learnt, we can directly translate it to a *stormpy sparse model* and model check some properties (see the examples :ref:`_stormpy-example`). 5. 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zrgH2TS)dxO*fQ!L-_k$8nSXQZ`9J>>Pxi!++~#@5D`CGt+&cVChy}d}iYxqeHAa5b zKD*(9WWPP>|87lq)gIP*O}g8K)$he-qz>c`y@_(`@DxC?0#|Irr#|Xk6aJ~k^4=SK z0oJa^w-WlPXZRE#vjXS*GeY$gz;Xd*u?oj($hVUDsR#8GK(h*$X2fR{{uyibB-GUM z2=Xoafu;YuN3VS{?~D=JTX^@Oe-%in#B9@w|4AE8<7LMvE12E0GqKmlRx-}#-(&;Z z|HuX*dt-e^GaGAReMe(_a$!zJdPYWi4n|f6W)?icf|8pk4Dex_9^&L$ejP>!I|G%lMv`no<9{;R_@_ExU=GR1{y#2V+0E7%pH5xT_|N~= zAmcO8|8%1Jx6SH=jSX##jOF$1tsL;Vxqt48jir;7wFCZ-ep%k$*vQP#@#hvXF#g2C zr&Gh%!e_u|`uS<&(hq6d(pt&m0HO3%&^r2LM1_3D)fYL`_ +:download:`python file ` In this example, we will: @@ -105,7 +105,7 @@ If ``quality`` is positive then we are overfitting. 2. An example with MC: random restart ------------------------------------- -`python file `_ +:download:`python file ` This time we will try to learn the `Reber grammar `_. @@ -224,7 +224,7 @@ for a better readability. 3. An example with MDP: active learning --------------------------------------- -`python file `_ +:download:`python file ` Here, we will learn a MDP representing the following grid world: @@ -249,7 +249,7 @@ First we create the original model. def modelMDP_gridworld(): alphabet = ['S','M','G','C','W',"done"] actions = list("nsew") - nb_states = 12 + nb_states = 9 s0 = ja.MDP_state({'n': [(0,'W',1.0)], 's': [(3,'M',0.6),(4,'G',0.4)], 'e': [(1,'M',0.6),(4,'G',0.4)], @@ -332,10 +332,24 @@ scheduler with probability 0.25. .. _stormpy-example: +Now, one could ask for the scheduler which minimizes the number of step before reaching our objective, +the bottom-right state. For this, we can use stormpy: + +.. code-block:: python + + # MODEL CHECKING + #--------------- + storm_model = ja.modeltoStorm(output_model) + properties = stormpy.parse_properties("Rmax=? [ F \"done\" ]") + result = stormpy.check_model_sparse(storm_model, properties[0], extract_scheduler=True) + scheduler = result.scheduler + print(result) + + 4. An advanced example with MC and model checking ------------------------------------------------- -`python file `_ +:download:`python file ` .. image:: pictures/knuthdie.png diff --git a/examples/03-mdps.py b/examples/03-mdps.py index 17abdf4..000828f 100644 --- a/examples/03-mdps.py +++ b/examples/03-mdps.py @@ -1,10 +1,11 @@ import jajapy as ja from numpy import array +import stormpy def modelMDP_gridworld(): alphabet = ['S','M','G','C','W',"done"] actions = list("nsew") - nb_states = 12 + nb_states = 9 s0 = ja.MDP_state({'n': [(0,'W',1.0)], 's': [(3,'M',0.6),(4,'G',0.4)], 'e': [(1,'M',0.6),(4,'G',0.4)], @@ -56,7 +57,7 @@ def modelMDP_gridworld(): def example_3(): original_model = modelMDP_gridworld() # SETS GENERATION - #------------------------ + #---------------- # We generate 1000 sequences of 10 observations for each set scheduler = ja.UniformScheduler(original_model.getActions()) training_set = original_model.generateSet(1000,10,scheduler) @@ -73,6 +74,19 @@ def example_3(): print(output_model) print(output_quality) + # MODEL CHECKING + #--------------- + storm_model = ja.modeltoStorm(output_model) + print(storm_model) + formula_str = "Rmax=? [ F \"done\" ]" + properties = stormpy.parse_properties(formula_str) + result = stormpy.check_model_sparse(storm_model, properties[0], extract_scheduler=True) + scheduler = result.scheduler + print(result) + for state in storm_model.states: + choice = scheduler.get_choice(state) + action = choice.get_deterministic_choice() + print("In state {} choose action {}".format(state, output_model.actions[action])) if __name__ == '__main__': example_3() \ No newline at end of file diff --git a/jajapy/base/BW.py b/jajapy/base/BW.py index 1c0e7bc..ce1b5d7 100644 --- a/jajapy/base/BW.py +++ b/jajapy/base/BW.py @@ -1,6 +1,6 @@ from sys import platform from multiprocessing import cpu_count, Pool -from numpy import array, dot, append, zeros, ones +from numpy import array, dot, append, zeros, ones, longdouble, float64 from datetime import datetime from .Set import Set @@ -34,7 +34,7 @@ def h_tau(self,s1: int,s2: int,obs: str) -> float: """ return self.h.tau(s1,s2,obs) - def computeAlphas(self,sequence: list) -> array: + def computeAlphas(self, sequence: list, dtype=False) -> array: """ Compute the alpha values for ``sequence`` under the current BW hypothesis. @@ -43,6 +43,9 @@ def computeAlphas(self,sequence: list) -> array: ---------- sequence : list of str sequence of observations. + dtype : numpy.scalar + If it set, the output will be a numpy array of this type, + otherwise it is a numpy array of float64. Returns ------- @@ -53,13 +56,17 @@ def computeAlphas(self,sequence: list) -> array: init_arr = self.h.initial_state zero_arr = zeros(shape=(len_seq*self.nb_states,)) alpha_matrix = append(init_arr,zero_arr).reshape(len_seq+1,self.nb_states) + if dtype != False: + alpha_matrix = alpha_matrix.astype(dtype) + else: + dtype = float64 for k in range(len_seq): for s in range(self.nb_states): - p = array([self.h_tau(ss,s,sequence[k]) for ss in range(self.nb_states)]) + p = array([self.h_tau(ss,s,sequence[k]) for ss in range(self.nb_states)],dtype=dtype) alpha_matrix[k+1,s] = dot(alpha_matrix[k],p) return alpha_matrix.T - def computeBetas(self,sequence: list) -> array: + def computeBetas(self, sequence: list, dtype=False) -> array: """ Compute the beta values for ``sequence`` under the current BW hypothesis. @@ -68,6 +75,9 @@ def computeBetas(self,sequence: list) -> array: ---------- sequence : list of str sequence of observations. + dtype : numpy.scalar + If it set, the output will be a numpy array of this type, + otherwise it is a numpy array of float64. Returns ------- @@ -78,9 +88,13 @@ def computeBetas(self,sequence: list) -> array: init_arr = ones(self.nb_states) zero_arr = zeros(shape=(len_seq*self.nb_states,)) beta_matrix = append(zero_arr,init_arr).reshape(len_seq+1,self.nb_states) + if dtype != False: + beta_matrix = beta_matrix.astype(dtype) + else: + dtype = float64 for k in range(len(sequence)-1,-1,-1): for s in range(self.nb_states): - p = array([self.h_tau(s,ss,sequence[k]) for ss in range(self.nb_states)]) + p = array([self.h_tau(s,ss,sequence[k]) for ss in range(self.nb_states)],dtype=dtype) beta_matrix[k,s] = dot(beta_matrix[k+1],p) return beta_matrix.T @@ -101,8 +115,17 @@ def _runProcesses(self,training_set): return [res.get() for res in tasks if res.get() != False] else: return [self._processWork(seq, times) for seq,times in zip(training_set.sequences,training_set.times)] + + def _endPrint(self,it,rt,ll): + print() + print("---------------------------------------------") + print("Learning finished") + print("Iterations:\t ",it) + print("Running time:\t ",rt) + print("---------------------------------------------") + print() - def fit(self,training_set: Set,initial_model,output_file: str,epsilon: float, pp: str): + def fit(self,training_set: Set,initial_model,output_file: str,epsilon: float, max_it: int, pp: str, verbose: bool): """ Fits the model according to ``traces``. @@ -120,21 +143,28 @@ def fit(self,training_set: Set,initial_model,output_file: str,epsilon: float, pp loglikelihood of the training set under the two last hypothesis is lower than ``epsilon``. The lower this value the better the output, but the longer the running time. + max_it: int + Maximal number of iterations. The algorithm will stop after `max_it` + iterations. + Default is infinity. pp : str Will be printed at each iteration. + verbose: bool + Print or not a small recap at the end of the learning. Returns ------- Model fitted model. """ + start_time = datetime.now() self.h = initial_model self.nb_states = self.h.nb_states counter = 0 prevloglikelihood = 10 nb_traces = sum(training_set.times) - while True: + while counter < max_it: print(pp, datetime.now(),counter, prevloglikelihood/nb_traces,end='\r') temp = self._runProcesses(training_set) self.hhat, currentloglikelihood = self._generateHhat(temp) @@ -144,8 +174,13 @@ def fit(self,training_set: Set,initial_model,output_file: str,epsilon: float, pp break else: prevloglikelihood = currentloglikelihood - + + running_time = datetime.now()-start_time + if output_file: self.h.save(output_file) - print() + + if verbose: + self._endPrint(counter,running_time,currentloglikelihood) + return self.h diff --git a/jajapy/base/Set.py b/jajapy/base/Set.py index 3394bf4..d482b05 100644 --- a/jajapy/base/Set.py +++ b/jajapy/base/Set.py @@ -224,3 +224,15 @@ def loadSet(file_path: str) -> Set: f.close() return Set(res_set[0],res_set[1],from_MDP) +def setFromList(l: list) -> Set: + """ + Convert a list of sequences of observations to a set. + + Parameters + ---------- + l : list + list of sequences of observations. + """ + s = Set([],[]) + s.setFromList(l) + return s \ No newline at end of file diff --git a/jajapy/ctmc/BW_CTMC.py b/jajapy/ctmc/BW_CTMC.py index 9602dbd..cce2b0b 100644 --- a/jajapy/ctmc/BW_CTMC.py +++ b/jajapy/ctmc/BW_CTMC.py @@ -1,7 +1,7 @@ from .CTMC import * from ..base.BW import BW from ..base.Set import Set -from numpy import array, zeros, dot, append, ones, log +from numpy import array, zeros, dot, append, ones, log, inf class BW_CTMC(BW): @@ -148,7 +148,7 @@ def computeBetas_nontimed(self,sequence: list) -> array: def fit(self, traces: Set, initial_model: CTMC=None, nb_states: int=None, random_initial_state: bool=False, min_exit_rate_time : int=1.0, max_exit_rate_time: int=10.0, self_loop: bool = True, - output_file: str=None, epsilon: float=0.01, pp: str=''): + output_file: str=None, epsilon: float=0.01, max_it: int= inf, pp: str='', verbose: bool = True): """ Fits the model according to ``traces``. @@ -185,8 +185,15 @@ def fit(self, traces: Set, initial_model: CTMC=None, nb_states: int=None, loglikelihood of the training set under the two last hypothesis is lower than ``epsilon``. The lower this value the better the output, but the longer the running time. By default 0.01. + max_it: int + Maximal number of iterations. The algorithm will stop after `max_it` + iterations. + Default is infinity. pp : str, optional Will be printed at each iteration. By default '' + verbose: bool, optional + Print or not a small recap at the end of the learning. + Default is True. Returns ------- @@ -202,7 +209,7 @@ def fit(self, traces: Set, initial_model: CTMC=None, nb_states: int=None, min_exit_rate_time, max_exit_rate_time, self_loop, random_initial_state) self.alphabet = initial_model.getAlphabet() - return super().fit(traces, initial_model, output_file, epsilon, pp) + return super().fit(traces, initial_model, output_file, epsilon, max_it, pp, verbose) def splitTime(self,sequence: list) -> tuple: diff --git a/jajapy/ctmc/MM_CTMC_Composition.py b/jajapy/ctmc/MM_CTMC_Composition.py index 11cfa55..a8eb7df 100644 --- a/jajapy/ctmc/MM_CTMC_Composition.py +++ b/jajapy/ctmc/MM_CTMC_Composition.py @@ -11,8 +11,8 @@ def fit(self, traces: Set, initial_model_1: CTMC=None, nb_states_1: int=None, max_exit_rate_time_1: int=10.0, initial_model_2: CTMC=None, nb_states_2: int=None, random_initial_state_2: bool=False, min_exit_rate_time_2 : int=1.0, max_exit_rate_time_2: int=10.0, - output_file: str=None, epsilon: float=0.01, pp: str='', - to_update: int=None): + output_file: str=None, epsilon: float=0.01, max_it: int= inf, pp: str='', + to_update: int=None, verbose: bool = True): """ Fits the model according to ``traces``. @@ -64,6 +64,10 @@ def fit(self, traces: Set, initial_model_1: CTMC=None, nb_states_1: int=None, loglikelihood of the training set under the two last hypothesis is lower than ``epsilon``. The lower this value the better the output, but the longer the running time. By default 0.01. + max_it: int + Maximal number of iterations. The algorithm will stop after `max_it` + iterations. + Default is infinity. pp : str, optional Will be printed at each iteration. By default ''. to_update: int, optional @@ -71,6 +75,9 @@ def fit(self, traces: Set, initial_model_1: CTMC=None, nb_states_1: int=None, If set to 2 only the second hypothesis will be updated, If set to None the two hypothesis will be updated. Default is None. + verbose: bool, optional + Print or not a small recap at the end of the learning. + Default is True. Returns ------- @@ -100,7 +107,7 @@ def fit(self, traces: Set, initial_model_1: CTMC=None, nb_states_1: int=None, self.alphabets = [None,self.hs[1].getAlphabet(),self.hs[2].getAlphabet()] self.disjoints_alphabet = len(set(self.alphabets[1]).intersection(set(self.alphabets[2]))) == 0 self.to_update = to_update - super().fit(traces, initial_model, output_file, epsilon, pp) + super().fit(traces, initial_model, output_file, epsilon, max_it, pp, verbose) if output_file: self.hs[1].save(output_file+"_1.txt") self.hs[2].save(output_file+"_2.txt") diff --git a/jajapy/gohmm/BW_GOHMM.py b/jajapy/gohmm/BW_GOHMM.py index fd4cb4e..cb4d57a 100644 --- a/jajapy/gohmm/BW_GOHMM.py +++ b/jajapy/gohmm/BW_GOHMM.py @@ -1,7 +1,7 @@ -from .GOHMM import * +from .GOHMM import GOHMM, GOHMM_random from ..base.BW import * from ..base.Set import Set -from numpy import log +from numpy import log, inf, newaxis, sqrt, stack class BW_GOHMM(BW): @@ -15,8 +15,8 @@ def __init__(self): def fit(self, traces: Set, initial_model: GOHMM=None, nb_states: int=None, random_initial_state: bool=False, output_file: str=None, - epsilon: float=0.01, - pp: str=''): + epsilon: float=0.01, max_it: int= inf, + pp: str='', verbose: bool = True): """ Fits the model according to ``traces``. @@ -44,8 +44,15 @@ def fit(self, traces: Set, initial_model: GOHMM=None, nb_states: int=None, loglikelihood of the training set under the two last hypothesis is lower than ``epsilon``. The lower this value the better the output, but the longer the running time. By default 0.01. + max_it: int + Maximal number of iterations. The algorithm will stop after `max_it` + iterations. + Default is infinity. pp : str, optional Will be printed at each iteration. By default '' + verbose: bool, optional + Print or not a small recap at the end of the learning. + Default is True. Returns ------- @@ -57,7 +64,7 @@ def fit(self, traces: Set, initial_model: GOHMM=None, nb_states: int=None, print("Either nb_states or initial_model should be set") return initial_model = GOHMM_random(nb_states,random_initial_state) - return super().fit(traces, initial_model, output_file, epsilon, pp) + return super().fit(traces, initial_model, output_file, epsilon, max_it, pp, verbose) def _processWork(self,sequence,times): sequence = array(sequence) @@ -65,12 +72,11 @@ def _processWork(self,sequence,times): beta_matrix = self.computeBetas(sequence) proba_seq = alpha_matrix.T[-1].sum() if proba_seq != 0.0: - den = zeros(self.nb_states) + den = (alpha_matrix.T[:-1]*beta_matrix.T[:-1]*times/proba_seq).sum(axis=0) num_a = zeros(shape=(self.nb_states,self.nb_states)) num_mu = zeros(self.nb_states) num_va = zeros(self.nb_states) for s in range(self.nb_states): - den[s] = dot(alpha_matrix[s][:-1]*beta_matrix[s][:-1],times/proba_seq).sum() num_mu[s] = dot(alpha_matrix[s][:-1]*beta_matrix[s][:-1]*sequence,times/proba_seq).sum() num_va[s] = dot(alpha_matrix[s][:-1]*beta_matrix[s][:-1]*(sequence-self.h.mu(s))**2,times/proba_seq).sum() for ss in range(self.nb_states): @@ -78,13 +84,11 @@ def _processWork(self,sequence,times): num_a[s,ss] = dot(alpha_matrix[s][:-1]*p*beta_matrix[ss][1:],times/proba_seq).sum() num_init = alpha_matrix.T[0]*beta_matrix.T[0]*times/proba_seq return [den,num_a,num_mu,num_va,proba_seq,times,num_init] - print('proba0') return False def _generateHhat(self,temp): - a = zeros(shape=(self.nb_states,self.nb_states)) + num_a = array([i[1] for i in temp]).sum(axis=0) den = array([i[0] for i in temp]).sum(axis=0) - lst_num_a = array([i[1] for i in temp]).T.reshape(self.nb_states*self.nb_states,len(temp)) mu = array([i[2] for i in temp]).sum(axis=0) va = array([i[3] for i in temp]).sum(axis=0) lst_proba=array([i[4] for i in temp]) @@ -93,16 +97,11 @@ def _generateHhat(self,temp): currentloglikelihood = dot(log(lst_proba),lst_times) - matrix = [] - output = [] - for s in range(self.nb_states): - for x in range(self.nb_states): - a[s,x] = lst_num_a[x*self.nb_states+s].sum() - la = list(zip(list(range(self.nb_states)) ,a[s]/den[s])) - l = GOHMM_state(la,(mu[s]/den[s],sqrt(va[s]/den[s])),self.nb_states) - matrix.append(l[0]) - output.append(l[1]) + matrix = num_a/den[:, newaxis] + output_mu = mu/den + output_va = sqrt(va/den) + output = stack((output_mu,output_va),axis=1) initial_state = [init[s]/init.sum() for s in range(self.nb_states)] - return [GOHMM(array(matrix),array(output),initial_state),currentloglikelihood] \ No newline at end of file + return [GOHMM(matrix,output,initial_state),currentloglikelihood] \ No newline at end of file diff --git a/jajapy/hmm/BW_HMM.py b/jajapy/hmm/BW_HMM.py index 50cc17a..df71ec8 100644 --- a/jajapy/hmm/BW_HMM.py +++ b/jajapy/hmm/BW_HMM.py @@ -1,6 +1,6 @@ -from .HMM import HMM, HMM_state, HMM_random +from .HMM import HMM, HMM_random from ..base.BW import * -from numpy import log +from numpy import log, inf, newaxis from ..base.Set import Set class BW_HMM(BW): @@ -14,8 +14,8 @@ def __init__(self): def fit(self, traces: Set, initial_model: HMM=None, nb_states: int=None, random_initial_state: bool=False, output_file: str=None, - epsilon: float=0.01, - pp: str=''): + epsilon: float=0.01, max_it: int= inf, + pp: str='', verbose: bool = True): """ Fits the model according to ``traces``. @@ -43,8 +43,15 @@ def fit(self, traces: Set, initial_model: HMM=None, nb_states: int=None, loglikelihood of the training set under the two last hypothesis is lower than ``epsilon``. The lower this value the better the output, but the longer the running time. By default 0.01. + max_it: int + Maximal number of iterations. The algorithm will stop after `max_it` + iterations. + Default is infinity. pp : str, optional Will be printed at each iteration. By default '' + verbose: bool, optional + Print or not a small recap at the end of the learning. + Default is True. Returns ------- @@ -57,7 +64,7 @@ def fit(self, traces: Set, initial_model: HMM=None, nb_states: int=None, return initial_model = HMM_random(nb_states,traces.getAlphabet(),random_initial_state) self.alphabet = initial_model.getAlphabet() - return super().fit(traces, initial_model, output_file, epsilon, pp) + return super().fit(traces, initial_model, output_file, epsilon, max_it, pp, verbose) def _processWork(self,sequence,times): alpha_matrix = self.computeAlphas(sequence) @@ -65,18 +72,18 @@ def _processWork(self,sequence,times): proba_seq = alpha_matrix.T[-1].sum() if proba_seq != 0.0: #################### - den = zeros(self.nb_states) + den = (alpha_matrix.T[:-1]*beta_matrix.T[:-1]*times/proba_seq).sum(axis=0) #################### num_a = zeros(shape=(self.nb_states,self.nb_states)) num_b = zeros(shape=(self.nb_states,len(self.alphabet))) for s in range(self.nb_states): - den[s] = dot(alpha_matrix[s][:-1]*beta_matrix[s][:-1],times/proba_seq).sum() for ss in range(self.nb_states): p = array([self.h_tau(s,ss,o) for o in sequence]) num_a[s,ss] = dot(alpha_matrix[s][:-1]*p*beta_matrix[ss][1:],times/proba_seq).sum() + temp = alpha_matrix[s][:-1]*beta_matrix[s][:-1]*times/proba_seq for o, obs in enumerate(self.alphabet): arr_dirak = [1.0 if t == obs else 0.0 for t in sequence] - num_b[s,o] = dot(alpha_matrix[s][:-1]*arr_dirak*beta_matrix[s][:-1],times/proba_seq).sum() + num_b[s,o] = (temp*arr_dirak).sum() #################### num_init = alpha_matrix.T[0]*beta_matrix.T[0]*times/proba_seq #################### @@ -84,37 +91,23 @@ def _processWork(self,sequence,times): return False def _generateHhat(self,temp): - den = zeros(shape=(self.nb_states,)) - a = zeros(shape=(self.nb_states,self.nb_states)) - b = zeros(shape=(self.nb_states,len(self.alphabet))) - lst_den = array([i[0] for i in temp]).T - lst_num_a = array([i[1] for i in temp]).T.reshape(self.nb_states*self.nb_states,len(temp)) - lst_num_b = array([i[2] for i in temp]).T.reshape(self.nb_states*len(self.alphabet),len(temp)) + den = array([i[0] for i in temp]).sum(axis=0) + num_a = array([i[1] for i in temp]).sum(axis=0) + num_b = array([i[2] for i in temp]).sum(axis=0) lst_proba=array([i[3] for i in temp]) lst_times=array([i[4] for i in temp]) lst_init =array([i[5] for i in temp]).T currentloglikelihood = dot(log(lst_proba),lst_times) - for s in range(self.nb_states): - den[s] = lst_den[s].sum() - for x in range(self.nb_states): - a[s,x] = lst_num_a[x*self.nb_states+s].sum() - for x in range(len(self.alphabet)): - b[s,x] = lst_num_b[x*self.nb_states+s].sum() + for s in range(len(den)): + if den[s] == 0.0: + den[s] = 1.0 + num_a[s] = self.h.matrix[s] + num_b[s] = self.h.output[s] - new_states_t = [] - new_states_o = [] - for s in range(self.nb_states): - la = a[s]/den[s] - lb = b[s]/den[s] - la = list(zip(range(self.nb_states),la)) - lb = list(zip(self.alphabet,lb)) - l = HMM_state(lb, la, self.alphabet, self.nb_states) - new_states_t.append(l[0]) - new_states_o.append(l[1]) - output = array(new_states_o) - matrix = array(new_states_t) + matrix = num_a/den[:, newaxis] + output = num_b/den[:, newaxis] initial_state = [lst_init[s].sum()/lst_init.sum() for s in range(self.nb_states)] diff --git a/jajapy/mc/BW_MC.py b/jajapy/mc/BW_MC.py index 88afb3c..e33e34b 100644 --- a/jajapy/mc/BW_MC.py +++ b/jajapy/mc/BW_MC.py @@ -1,7 +1,7 @@ -from .MC import * +from .MC import MC, MC_random from ..base.BW import * from ..base.Set import Set -from numpy import log +from numpy import log, newaxis, inf class BW_MC(BW): """ @@ -12,7 +12,7 @@ def __init__(self): def fit(self, traces: Set, initial_model: MC=None, nb_states: int=None, random_initial_state: bool=False, output_file: str=None, - epsilon: float=0.01, pp: str=''): + epsilon: float=0.01, max_it: int = inf, pp: str='', verbose: bool = True): """ Fits the model according to ``traces``. @@ -40,8 +40,15 @@ def fit(self, traces: Set, initial_model: MC=None, nb_states: int=None, loglikelihood of the training set under the two last hypothesis is lower than ``epsilon``. The lower this value the better the output, but the longer the running time. By default 0.01. + max_it: int + Maximal number of iterations. The algorithm will stop after `max_it` + iterations. + Default is infinity. pp : str, optional Will be printed at each iteration. By default '' + verbose: bool, optional + Print or not a small recap at the end of the learning. + Default is True. Returns ------- @@ -54,24 +61,22 @@ def fit(self, traces: Set, initial_model: MC=None, nb_states: int=None, return initial_model = MC_random(nb_states,traces.getAlphabet(),random_initial_state) self.alphabet = initial_model.getAlphabet() - return super().fit(traces, initial_model, output_file, epsilon, pp) + return super().fit(traces, initial_model, output_file, epsilon, max_it, pp, verbose) def _processWork(self,sequence,times): alpha_matrix = self.computeAlphas(sequence) beta_matrix = self.computeBetas(sequence) proba_seq = alpha_matrix.T[-1].sum() if proba_seq != 0.0: - den = zeros(self.nb_states) - num = zeros(shape=(self.nb_states,self.nb_states*len(self.alphabet))) + den = (alpha_matrix.T[:-1]*beta_matrix.T[:-1]*times/proba_seq).sum(axis=0) + num = zeros(shape=(self.nb_states,self.nb_states,len(self.alphabet))) for s in range(self.nb_states): - den[s] = dot(alpha_matrix[s][:-1]*beta_matrix[s][:-1],times/proba_seq).sum() - c = 0 for ss in range(self.nb_states): p = array([self.h_tau(s,ss,o) for o in sequence]) - for obs in self.alphabet: - arr_dirak = [1.0 if t == obs else 0.0 for t in sequence] - num[s,c] = dot(alpha_matrix[s][:-1]*arr_dirak*p*beta_matrix[ss][1:],times/proba_seq).sum() - c += 1 + alph_bet = alpha_matrix[s][:-1]*p*beta_matrix[ss][1:]*times/proba_seq + for o,obs in enumerate(self.alphabet): + arr_dirak = [1 if t == obs else 0 for t in sequence] + num[s,ss,o] = (alph_bet*arr_dirak).sum() #################### num_init = alpha_matrix.T[0]*beta_matrix.T[0]*times/proba_seq #################### @@ -87,22 +92,12 @@ def _generateHhat(self,temp): currentloglikelihood = dot(log(lst_proba),lst_times) - list_sta = [] - for i in range(self.nb_states): - for _ in self.alphabet: - list_sta.append(i) - list_obs = self.alphabet*self.nb_states - new_states = [] + for s in range(len(den)): + if den[s] == 0.0: + den[s] = 1.0 + num[s] = self.h.matrix[s] - new_states = [] - for s in range(self.nb_states): - if den[s] != 0.0: - l = list(zip(list_sta, list_obs, num[s]/den[s])) - l = MC_state(l, self.alphabet, self.nb_states) - else: - l = self.h.matrix[s] - new_states.append(l) - matrix = array(new_states) + matrix = num/den[:, newaxis, newaxis] initial_state = [lst_init[s].sum()/lst_init.sum() for s in range(self.nb_states)] return [MC(matrix,self.alphabet,initial_state),currentloglikelihood] \ No newline at end of file diff --git a/jajapy/mdp/BW_MDP.py b/jajapy/mdp/BW_MDP.py index 501bba9..ea5c7f8 100644 --- a/jajapy/mdp/BW_MDP.py +++ b/jajapy/mdp/BW_MDP.py @@ -2,7 +2,7 @@ from ..base.BW import * from ..base.Set import Set from ..base.tools import normalize -from numpy import array, dot, append, zeros, ones, log +from numpy import array, dot, append, zeros, ones, log, inf NB_PROCESS = 11 @@ -18,7 +18,8 @@ def __init__(self): def fit(self, traces: Set, initial_model: MDP=None, nb_states: int=None, random_initial_state: bool=False, output_file: str=None, - epsilon: float=0.01, pp: str=''): + epsilon: float=0.01, max_it: int= inf, pp: str='', + verbose: bool = True): """ Fits the model according to ``traces``. @@ -46,8 +47,15 @@ def fit(self, traces: Set, initial_model: MDP=None, nb_states: int=None, loglikelihood of the training set under the two last hypothesis is lower than ``epsilon``. The lower this value the better the output, but the longer the running time. By default 0.01. + max_it: int + Maximal number of iterations. The algorithm will stop after `max_it` + iterations. + Default is infinity. pp : str, optional Will be printed at each iteration. By default '' + verbose: bool, optional + Print or not a small recap at the end of the learning. + Default is True. Returns ------- @@ -65,7 +73,7 @@ def fit(self, traces: Set, initial_model: MDP=None, nb_states: int=None, actions = initial_model.getActions() self.alphabet = observations self.actions = actions - return super().fit(traces, initial_model, output_file, epsilon, pp) + return super().fit(traces, initial_model, output_file, epsilon, max_it, pp, verbose) def h_tau(self,s1: int,act: str,s2: int,obs: str) -> float: """ diff --git a/jajapy/mgohmm/BW_MGOHMM.py b/jajapy/mgohmm/BW_MGOHMM.py index 869014e..542fec6 100644 --- a/jajapy/mgohmm/BW_MGOHMM.py +++ b/jajapy/mgohmm/BW_MGOHMM.py @@ -1,9 +1,10 @@ -from .MGOHMM import * -from ..gohmm.BW_GOHMM import * -from numpy import log, sqrt +from .MGOHMM import MGOHMM, MGOHMM_random +from ..base.BW import * +from ..base.Set import Set +from numpy import log, sqrt, inf, newaxis, stack -class BW_MGOHMM(BW_GOHMM): +class BW_MGOHMM(BW): """ class for general Baum-Welch algorithm on MGOHMM. """ @@ -12,7 +13,8 @@ def __init__(self): def fit(self, traces: Set, initial_model: MGOHMM=None, nb_states: int=None, nb_distributions: int=None, random_initial_state: bool=False, - output_file: str=None, epsilon: float=0.01, pp: str=''): + output_file: str=None, epsilon: float=0.01, max_it: int= inf, + pp: str='', verbose: bool = True): """ Fits the model according to ``traces``. @@ -44,8 +46,15 @@ def fit(self, traces: Set, initial_model: MGOHMM=None, nb_states: int=None, loglikelihood of the training set under the two last hypothesis is lower than ``epsilon``. The lower this value the better the output, but the longer the running time. By default 0.01. + max_it: int + Maximal number of iterations. The algorithm will stop after `max_it` + iterations. + Default is infinity. pp : str, optional Will be printed at each iteration. By default '' + verbose: bool, optional + Print or not a small recap at the end of the learning. + Default is True. Returns ------- @@ -62,20 +71,19 @@ def fit(self, traces: Set, initial_model: MGOHMM=None, nb_states: int=None, initial_model = MGOHMM_random(nb_states,nb_distributions, random_initial_state) self.nb_distr = initial_model.nb_distributions - return super().fit(traces, initial_model, output_file, epsilon, pp) + return super().fit(traces, initial_model, output_file, epsilon, max_it, pp, verbose) def _processWork(self,sequence,times): sequence = array(sequence) - alpha_matrix = self.computeAlphas(sequence) - beta_matrix = self.computeBetas(sequence) + alpha_matrix = self.computeAlphas(sequence,longdouble) + beta_matrix = self.computeBetas(sequence,longdouble) proba_seq = alpha_matrix.T[-1].sum() if proba_seq != 0.0: - den = zeros(self.nb_states) + den = (alpha_matrix.T[:-1]*beta_matrix.T[:-1]*times/proba_seq).sum(axis=0) num_a = zeros(shape=(self.nb_states,self.nb_states)) num_mu = zeros(shape=(self.nb_states,self.nb_distr)) num_va = zeros(shape=(self.nb_states,self.nb_distr)) for s in range(self.nb_states): - den[s] = dot(alpha_matrix[s][:-1]*beta_matrix[s][:-1],times/proba_seq).sum() num_mu[s] = dot(alpha_matrix[s][:-1]*beta_matrix[s][:-1]*sequence.T,times/proba_seq).sum(axis=1) num_va[s] = dot(alpha_matrix[s][:-1]*beta_matrix[s][:-1]*(sequence-self.h.mu(s)).T**2,times/proba_seq).sum(axis=1) for ss in range(self.nb_states): @@ -97,18 +105,13 @@ def _generateHhat(self,temp): currentloglikelihood = dot(log(lst_proba),lst_times) - mu = (mu.T/den).T - va = sqrt((va.T/den).T) - a = (a.T /den).T + output_mu = (mu.T/den).T + output_va = sqrt((va.T/den).T) - matrix = [] - output = [] - for s in range(self.nb_states): - la = list(zip(list(range(self.nb_states)),a[s].tolist())) - l = MGOHMM_state(la,array([mu[s],va[s]]).T.tolist(),self.nb_states) - matrix.append(l[0]) - output.append(l[1]) + matrix = a/den[:, newaxis] + output = stack((output_mu,output_va),axis=2) + initial_state = [init[s]/init.sum() for s in range(self.nb_states)] - return [MGOHMM(array(matrix),array(output),initial_state),currentloglikelihood] \ No newline at end of file + return [MGOHMM(matrix,output,initial_state),currentloglikelihood] \ No newline at end of file diff --git a/jajapy/with_stormpy/model_converter.py b/jajapy/with_stormpy/model_converter.py index 62a1009..1ed4f4b 100644 --- a/jajapy/with_stormpy/model_converter.py +++ b/jajapy/with_stormpy/model_converter.py @@ -108,8 +108,14 @@ def _MDPtoStorm(h): choice_labeling.add_label(a) for ia,a in enumerate(h.getActions()): choice_labeling.add_label_to_choice(a,ia) + + reward_models = {} + action_reward = [-1.0 for _ in range(len(transition_matrix))] + reward_models["nb_executed_actions"] = st.SparseRewardModel(optional_state_action_reward_vector = action_reward) + components = st.SparseModelComponents(transition_matrix=transition_matrix, - state_labeling=state_labeling) + state_labeling=state_labeling, + reward_models=reward_models) components.choice_labeling = choice_labeling mdp = st.storage.SparseMdp(components) return mdp diff --git a/setup.py b/setup.py index feb74ce..3412cd4 100644 --- a/setup.py +++ b/setup.py @@ -13,7 +13,7 @@ packages=find_packages(), install_requires=['numpy', 'scipy'], long_description_content_type="text/markdown", - version='0.5.1', + version='0.7', url="", description='Baum-Welch for all kind of Markov model', author='RaphaĆ«l Reynouard',

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