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Release 2.3.8
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| Original file line number | Diff line number | Diff line change |
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| ''' | ||
| StochSS is a platform for simulating biochemical systems | ||
| Copyright (C) 2019-2021 StochSS developers. | ||
| This program is free software: you can redistribute it and/or modify | ||
| it under the terms of the GNU General Public License as published by | ||
| the Free Software Foundation, either version 3 of the License, or | ||
| (at your option) any later version. | ||
| This program is distributed in the hope that it will be useful, | ||
| but WITHOUT ANY WARRANTY; without even the implied warranty of | ||
| MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | ||
| GNU General Public License for more details. | ||
| You should have received a copy of the GNU General Public License | ||
| along with this program. If not, see <http://www.gnu.org/licenses/>. | ||
| ''' | ||
|
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| from nbformat import v4 as nbf | ||
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| from .stochss_notebook import StochSSNotebook | ||
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| class StochSSSciopeNotebook(StochSSNotebook): | ||
| ''' | ||
| ################################################################################################ | ||
| StochSS sciope notebook object | ||
| ################################################################################################ | ||
| ''' | ||
| def __init__(self, path, new=False, models=None, settings=None): | ||
| '''Intitialize a sciope notebook object and | ||
| if its new create it on the users file system. | ||
| Attributes | ||
| ---------- | ||
| path : str | ||
| Path to the notebook''' | ||
| super().__init__(path=path, new=new, models=models, settings=settings) | ||
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| @classmethod | ||
| def __create_me_expres_cells(cls): | ||
| # res conf cell | ||
| param_conf = "met.data.configurations['listOfParameters'] = " | ||
| param_conf += "list(model.listOfParameters.keys())" | ||
| rconf_strs = ["# First lets add some appropiate information about the model and features", | ||
| param_conf, | ||
| "met.data.configurations['listOfSpecies'] = list(model.listOfSpecies.keys())", | ||
| "met.data.configurations['listOfSummaries'] = met.summaries.features", | ||
| "met.data.configurations['timepoints'] = model.tspan"] | ||
| # met explore cell | ||
| mtexp_strs = ["# Here we use UMAP for dimension reduction", "met.explore(dr_method='umap')"] | ||
| # supervised train cell | ||
| sptrn_strs = ["from sciope.models.label_propagation import LPModel", | ||
| "# here lets use the dimension reduction embedding as input data", | ||
| "data = met.dr_model.embedding_", "", | ||
| "model_lp = LPModel()", "# train using basinhopping", | ||
| "model_lp.train(data, met.data.user_labels, min_=0.01, max_=10, niter=50)"] | ||
| # map label cell | ||
| cmt_str = "# just to vislualize the result we will map the label distribution " | ||
| cmt_str += "to the user_labels\n# (will enable us to see the LP model output " | ||
| cmt_str += "when using method 'explore')" | ||
| mplbl_strs = [cmt_str, "user_labels = np.copy(met.data.user_labels)", | ||
| "# takes the label corresponding to index 0", | ||
| "met.data.user_labels = model_lp.model.label_distributions_[:, 0]"] | ||
| cells = [nbf.new_markdown_cell("## Explore the result"), | ||
| nbf.new_code_cell("\n".join(rconf_strs)), | ||
| nbf.new_code_cell("\n".join(mtexp_strs)), | ||
| nbf.new_code_cell("\n".join(sptrn_strs)), | ||
| nbf.new_code_cell("\n".join(mplbl_strs)), | ||
| nbf.new_code_cell("met.explore(dr_method='umap')"), | ||
| nbf.new_code_cell("met.data.user_labels = user_labels")] | ||
| return cells | ||
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| def __create_me_setup_cells(self): | ||
| spec_of_interest = list(self.model.get_all_species().keys()) | ||
| # Wrapper cell | ||
| sim_str = "simulator = wrapper.get_simulator(gillespy_model=model, " | ||
| sim_str += f"run_settings=settings, species_of_interest={spec_of_interest})" | ||
| sim_strs = ["from sciope.utilities.gillespy2 import wrapper", | ||
| "settings = configure_simulation()", sim_str, | ||
| "expression_array = wrapper.get_parameter_expression_array(model)"] | ||
| # Dask cell | ||
| dask_strs = ["from dask.distributed import Client", "", "c = Client()"] | ||
| # lhc cell | ||
| lhc_str = "lhc = latin_hypercube_sampling.LatinHypercube(" | ||
| lhc_str += "xmin=expression_array, xmax=expression_array*3)" | ||
| lhc_strs = ["from sciope.designs import latin_hypercube_sampling", | ||
| "from sciope.utilities.summarystats.auto_tsfresh import SummariesTSFRESH", "", | ||
| lhc_str, "lhc.generate_array(1000) # creates a LHD of size 1000", "", | ||
| "# will use default minimal set of features", | ||
| "summary_stats = SummariesTSFRESH()"] | ||
| # stochmet cell | ||
| ism_strs = ["from sciope.stochmet.stochmet import StochMET", "", | ||
| "met = StochMET(simulator, lhc, summary_stats)"] | ||
| cells = [nbf.new_markdown_cell("## Define simulator function (using gillespy2 wrapper)"), | ||
| nbf.new_code_cell("\n".join(sim_strs)), | ||
| nbf.new_markdown_cell("## Start local cluster using dask client"), | ||
| nbf.new_code_cell("\n".join(dask_strs)), | ||
| nbf.new_markdown_cell("## Define parameter sampler/design and summary statistics"), | ||
| nbf.new_code_cell("\n".join(lhc_strs)), | ||
| nbf.new_markdown_cell("## Initiate StochMET"), | ||
| nbf.new_code_cell("\n".join(ism_strs))] | ||
| return cells | ||
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| def __create_mi_setup_cells(self): | ||
| pad = " " | ||
| priors = ["from sciope.utilities.priors import uniform_prior", "", | ||
| "# take default from mode 1 as reference", | ||
| "default_param = np.array(list(model.listOfParameters.items()))[:, 1]", | ||
| "", "bound = []", "for exp in default_param:", | ||
| f"{pad}bound.append(float(exp.expression))", "", "# Set the bounds", | ||
| "bound = np.array(bound)", "dmin = bound * 0.1", "dmax = bound * 2.0", | ||
| "", "# Here we use uniform prior", | ||
| "uni_prior = uniform_prior.UniformPrior(dmin, dmax)"] | ||
| stat_dist = ["from sciope.utilities.summarystats import auto_tsfresh", | ||
| "from sciope.utilities.distancefunctions import naive_squared", "", | ||
| "# Function to generate summary statistics", | ||
| "summ_func = auto_tsfresh.SummariesTSFRESH()", "", | ||
| "# Distance", "ns = naive_squared.NaiveSquaredDistance()"] | ||
| cells = [nbf.new_markdown_cell("## Define prior distribution"), | ||
| nbf.new_code_cell("\n".join(priors)), | ||
| nbf.new_markdown_cell("## Define simulator"), | ||
| self.__create_mi_simulator_cell(), | ||
| nbf.new_markdown_cell("## Define summary statistics and distance function"), | ||
| nbf.new_code_cell("\n".join(stat_dist))] | ||
| return cells | ||
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| def __create_mi_simulator_cell(self): | ||
| pad = " " | ||
| comment = f"{pad}# params - array, need to have the same order as model.listOfParameters" | ||
| loop = f"{pad}for e, pname in enumerate(model.listOfParameters.keys()):" | ||
| if self.settings['solver'] == "SSACSolver": | ||
| comment += "\n"+ pad +"variables = {}" | ||
| func_def = "def get_variables(params, model):" | ||
| body = f"{pad*2}variables[pname] = params[e]" | ||
| return_str = f"{pad}return variables" | ||
| call = f"{pad}variables = get_variables(params, model)" | ||
| run = f"{pad}res = model.run(**kwargs, variables=variables)" | ||
| else: | ||
| func_def = "def set_model_parameters(params, model):" | ||
| body = f"{pad*2}model.get_parameter(pname).set_expression(params[e])" | ||
| return_str = f"{pad}return model" | ||
| call = f"{pad}model_update = set_model_parameters(params, model)" | ||
| run = f"{pad}res = model_update.run(**kwargs)" | ||
| sim_strs = [func_def, comment, loop, body, return_str, ""] | ||
| simulator = ["# Here we use the GillesPy2 Solver", "def simulator(params, model):", | ||
| call, "", run, f"{pad}res = res.to_array()", | ||
| f"{pad}tot_res = np.asarray([x.T for x in res]) # reshape to (N, S, T)", | ||
| f"{pad}# should not contain timepoints", f"{pad}tot_res = tot_res[:, 1:, :]", | ||
| "", f"{pad}return tot_res", ""] | ||
| sim_strs.extend(simulator) | ||
| sim2_com = "# Wrapper, simulator function to abc should should only take one argument " | ||
| sim2_com += "(the parameter point)" | ||
| simulator2 = [sim2_com, "def simulator2(x):", f"{pad}return simulator(x, model=model)"] | ||
| sim_strs.extend(simulator2) | ||
| return nbf.new_code_cell("\n".join(sim_strs)) | ||
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| def create_me_notebook(self): | ||
| '''Create a model exploration jupiter notebook for a StochSS model/workflow | ||
| Attributes | ||
| ----------''' | ||
| self.nb_type = self.MODEL_EXPLORATION | ||
| self.settings['solver'] = self.get_gillespy2_solver_name() | ||
| cells = [nbf.new_code_cell("%matplotlib notebook")] | ||
| cells.extend(self.create_common_cells()) | ||
| cells.append(nbf.new_markdown_cell("# Model Exploration")) | ||
| cells.extend(self.__create_me_setup_cells()) | ||
| cells.extend([nbf.new_markdown_cell("## Run parameter sweep"), | ||
| nbf.new_code_cell("met.compute(n_points=500, chunk_size=10)")]) | ||
| cells.extend(self.__create_me_expres_cells()) | ||
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| message = self.write_notebook_file(cells=cells) | ||
| return {"Message":message, "FilePath":self.get_path(), "File":self.get_file()} | ||
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| def create_mi_notebook(self): | ||
| '''Create a model inference jupiter notebook for a StochSS model/workflow | ||
| Attributes | ||
| ----------''' | ||
| self.nb_type = self.MODEL_INFERENCE | ||
| self.settings['solver'] = self.get_gillespy2_solver_name() | ||
| cells = [nbf.new_code_cell("%load_ext autoreload\n%autoreload 2")] | ||
| cells.extend(self.create_common_cells()) | ||
| imports = "from tsfresh.feature_extraction.settings import MinimalFCParameters" | ||
| fd_header = "## Generate some fixed(observed) data based on default parameters of the model" | ||
| fd_str = "kwargs = configure_simulation()\nfixed_data = model.run(**kwargs)" | ||
| rshp_strs = ["# Reshape the data and remove timepoints array", | ||
| "fixed_data = fixed_data.to_array()", | ||
| "fixed_data = np.asarray([x.T for x in fixed_data])", | ||
| "fixed_data = fixed_data[:, 1:, :]"] | ||
| cells.extend([nbf.new_markdown_cell("# Model Inference"), | ||
| nbf.new_code_cell(imports), nbf.new_markdown_cell(fd_header), | ||
| nbf.new_code_cell(fd_str), nbf.new_code_cell("\n".join(rshp_strs))]) | ||
| cells.extend(self.__create_mi_setup_cells()) | ||
| # abc cell | ||
| abc_str = "from sciope.inference.abc_inference import ABC\n\n" | ||
| abc_str += "abc = ABC(fixed_data, sim=simulator2, prior_function=uni_prior, " | ||
| abc_str += "summaries_function=summ_func.compute, distance_function=ns)" | ||
| # compute fixed mean cell | ||
| fm_str = "# First compute the fixed(observed) mean\nabc.compute_fixed_mean(chunk_size=2)" | ||
| # run model inference cell | ||
| rmi_str = "res = abc.infer(num_samples=100, batch_size=10, chunk_size=2)" | ||
| # absolute error cell | ||
| abse_str = "from sklearn.metrics import mean_absolute_error\n\n" | ||
| abse_str += "mae_inference = mean_absolute_error(bound, abc.results['inferred_parameters'])" | ||
| cells.extend([nbf.new_markdown_cell("## Start local cluster using dask client"), | ||
| nbf.new_code_cell("from dask.distributed import Client\n\nc = Client()"), | ||
| nbf.new_markdown_cell("## Start abc instance"), | ||
| nbf.new_code_cell(abc_str), nbf.new_code_cell(fm_str), | ||
| nbf.new_code_cell(rmi_str), nbf.new_code_cell(abse_str)]) | ||
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| message = self.write_notebook_file(cells=cells) | ||
| return {"Message":message, "FilePath":self.get_path(), "File":self.get_file()} |
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