Source code for pharmpy.modeling.common

"""Common modeling pipeline elements
:meta private:
"""

import importlib
import re
import warnings
from pathlib import Path
from typing import Dict, Literal, Optional, Union

import pharmpy.config as config
from pharmpy.basic import Expr, TSymbol
from pharmpy.deps import pandas
from pharmpy.internals.fs.path import normalize_user_given_path
from pharmpy.model import (
    CompartmentalSystem,
    CompartmentalSystemBuilder,
    JointNormalDistribution,
    Model,
    NormalDistribution,
    Parameter,
    Parameters,
    RandomVariables,
)


[docs] def read_model(path: Union[str, Path]): """Read model from file Parameters ---------- path : str or Path Path to model Returns ------- Model Read model object Example ------- >>> from pharmpy.modeling import read_model >>> model = read_model("/home/run1.mod") # doctest: +SKIP See also -------- read_model_from_database : Read model from database read_model_from_string : Read model from string """ path = normalize_user_given_path(path) model = Model.parse_model(path) return model
[docs] def read_model_from_string(code: str): """Read model from the model code in a string Parameters ---------- code : str Model code to read Returns ------- Model Pharmpy model object Example ------- >>> from pharmpy.modeling import read_model_from_string >>> s = '''$PROBLEM ... $INPUT ID DV TIME ... $DATA file.csv ... $PRED ... Y=THETA(1)+ETA(1)+ERR(1) ... $THETA 1 ... $OMEGA 0.1 ... $SIGMA 1 ... $ESTIMATION METHOD=1''' >>> read_model_from_string(s) # doctest:+ELLIPSIS <...> See also -------- read_model : Read model from file read_model_from_database : Read model from database """ model = Model.parse_model_from_string(code) return model
[docs] def write_model(model: Model, path: Union[str, Path] = '', force: bool = True): """Write model code to file Parameters ---------- model : Model Pharmpy model path : str Destination path force : bool Force overwrite, default is True Returns ------- Model Pharmpy model object Example ------- >>> from pharmpy.modeling import load_example_model, write_model >>> model = load_example_model("pheno") >>> write_model(model) # doctest: +SKIP """ path = normalize_user_given_path(path) if not path or path.is_dir(): try: filename = f'{model.name}{model.filename_extension}' except AttributeError: raise ValueError( 'Cannot name model file as no path argument was supplied and the ' 'model has no name.' ) path = path / filename new_name = None else: # Set new name given filename, but after we've checked for existence new_name = path.stem if not force and path.exists(): raise FileExistsError(f'File {path} already exists.') if new_name: model = model.replace(name=new_name) model = model.write_files(path=path, force=force) if not force and path.exists(): raise FileExistsError(f'Cannot overwrite model at {path} with "force" not set') with open(path, 'w', encoding='latin-1') as fp: fp.write(model.model_code) return model
[docs] def convert_model(model: Model, to_format: Literal['generic', 'nlmixr', 'nonmem', 'rxode']): """Convert model to other format Note that the operation is not done inplace. Parameters ---------- model : Model Model to convert to_format : {'generic', 'nlmixr', 'nonmem', 'rxode'} Name of format to convert into. Currently supported 'generic', 'nlmixr', 'nonmem', and 'rxode' Returns ------- Model New model object with new underlying model format Example ------- >>> from pharmpy.modeling import load_example_model, convert_model >>> model = load_example_model("pheno") >>> converted_model = convert_model(model, "nlmixr") # doctest: +SKIP """ supported = ['generic', 'nlmixr', 'nonmem', 'rxode'] if to_format not in supported: raise ValueError(f"Unknown format {to_format}: supported formats are f{supported}") module_name = 'pharmpy.model.external.' + to_format module = importlib.import_module(module_name) new = module.convert_model(model) return new
[docs] def get_model_code(model: Model): """Get the model code of the underlying model language Parameters ---------- model : Model Pharmpy model Returns ------- str Model code Examples -------- >>> from pharmpy.modeling import get_model_code, load_example_model >>> model = load_example_model("pheno") >>> get_model_code(model) # doctest: +SKIP """ return model.model_code
[docs] def set_name(model: Model, new_name: str): """Set name of model object Parameters ---------- model : Model Pharmpy model new_name : str New name of model Returns ------- Model Pharmpy model object Example ------- >>> from pharmpy.modeling import set_name, load_example_model >>> model = load_example_model("pheno") >>> model.name 'pheno' >>> model = set_name(model, "run2") >>> model.name 'run2' """ model = model.replace(name=new_name) return model
[docs] def bump_model_number(model: Model, path: Optional[Union[str, Path]] = None): """If the model name ends in a number increase it If path is set increase the number until no file exists with the same name in path. If model name does not end in a number do nothing. Parameters ---------- model : Model Pharmpy model object path : Path in which to find next unique number Default is to not look for files. Returns ------- Model Pharmpy model object Examples -------- >>> from pharmpy.modeling import bump_model_number, load_example_model >>> model = load_example_model("pheno") >>> model = model.replace(name="run2") >>> model = bump_model_number(model) >>> model.name 'run3' """ name = model.name m = re.search(r'(.*?)(\d+)$', name) if m: stem = m.group(1) n = int(m.group(2)) if path is None: new_name = f'{stem}{n + 1}' else: path = normalize_user_given_path(path) while True: n += 1 new_name = f'{stem}{n}' new_path = (path / new_name).with_suffix(model.filename_extension) if not new_path.exists(): break model = model.replace(name=new_name) return model
[docs] def load_example_model(name: str): """Load an example model Load an example model from models built into Pharmpy Parameters ---------- name : str Name of the model. Currently available models are "pheno" and "pheno_linear" Returns ------- Model Loaded model object Example ------- >>> from pharmpy.modeling import load_example_model >>> model = load_example_model("pheno") >>> model.statements BTIME = {TIME for AMT > 0 TAD = -BTIME + TIME TVCL = PTVCL⋅WGT TVV = PTVV⋅WGT ⎧TVV⋅(THETA₃ + 1) for APGR < 5 TVV = ⎩ TVV otherwise ETA₁ CL = TVCL⋅ℯ ETA₂ V = TVV⋅ℯ S₁ = V Bolus(AMT, admid=1) → CENTRAL ┌───────┐ │CENTRAL│──CL/V→ └───────┘ A_CENTRAL(t) ──────────── F = S₁ W = F Y = EPS₁⋅W + F IPRED = F IRES = DV - IPRED IRES ──── IWRES = W """ available = ('moxo', 'pheno', 'pheno_linear') if name not in available: raise ValueError(f'Unknown example model {name}. Available examples: {available}') path = Path(__file__).resolve().parent.parent / 'internals' / 'example_models' / (name + '.mod') model = read_model(path) return model
[docs] def get_model_covariates(model: Model, strings: bool = False): """List of covariates used in model A covariate in the model is here defined to be a data item affecting the model prediction excluding dosing items that are not used in model code. Parameters ---------- model : Model Pharmpy model strings : bool Return strings instead of symbols? False (default) will give symbols Returns ------- list Covariate symbols or names Examples -------- >>> from pharmpy.modeling import load_example_model, get_model_covariates >>> model = load_example_model("pheno") >>> get_model_covariates(model) [APGR, WGT] >>> get_model_covariates(model, strings=True) ['APGR', 'WGT'] """ datasymbs = {Expr.symbol(s) for s in model.datainfo.names} odes = model.statements.ode_system # Consider statements that are dependencies of the ode system and y if odes: dose_comp = odes.dosing_compartments[0] cb = CompartmentalSystemBuilder(odes) cb.set_dose(dose_comp, None) cs = CompartmentalSystem(cb) statements = model.statements.before_odes + cs + model.statements.after_odes ode_deps = statements.dependencies(cs) else: ode_deps = set() # FIXME: This should be handled for all DVs first_dv = list(model.dependent_variables.keys())[0] y = model.statements.find_assignment(first_dv) y_deps = model.statements.error.dependencies(y) covs = datasymbs.intersection(ode_deps | y_deps) # Disallow ID from being a covariate covs = covs - {Expr.symbol(model.datainfo.id_column.name)} covs = list(covs) covs = sorted(covs, key=lambda x: x.name) # sort to make order deterministic if strings: covs = [str(x) for x in covs] return covs
[docs] def get_config_path(): r"""Returns path to the user config path Returns ------- str or None Path to user config or None if file does not exist Example ------- >>> from pharmpy.modeling import get_config_path >>> get_config_path() # doctest: +SKIP """ if config.user_config_file_enabled(): env_path = config.env_config_path() if env_path is not None: return str(env_path.resolve()) else: config_path = config.user_config_path() if config_path.exists(): return str(config_path.resolve()) else: warnings.warn(f'Cannot find config path {config_path}') return None else: warnings.warn('User config file is disabled') return None
[docs] def create_config_template(): r"""Create a basic config file template If a configuration file already exists it will not be overwritten Example ------- >>> from pharmpy.modeling import create_config_template >>> create_config_template() # doctest: +SKIP """ template = r"""[pharmpy.plugins.nonmem] ;default_nonmem_path=""" if config.user_config_file_enabled(): path = config.user_config_path() if path is not None: if not path.exists(): path.parent.mkdir(parents=True) with open(path, 'w') as fp: print(template, file=fp) else: warnings.warn('Config file already exists') else: warnings.warn('User config file is disabled')
[docs] def remove_unused_parameters_and_rvs(model: Model): """Remove any parameters and rvs that are not used in the model statements Parameters ---------- model : Model Pharmpy model object Returns ------- Model Pharmpy model object """ new_rvs, new_params = _get_unused_parameters_and_rvs( model.statements, model.parameters, model.random_variables ) model = model.replace(random_variables=new_rvs, parameters=new_params) return model.update_source()
def _get_unused_parameters_and_rvs(statements, parameters, random_variables): symbols = statements.free_symbols # Find unused rvs needing unjoining to_unjoin = [] for dist in random_variables: if isinstance(dist, JointNormalDistribution): names = dist.names for i, name in enumerate(names): params = dist.variance[i, :].free_symbols symb = Expr.symbol(name) if symb not in symbols and symbols.isdisjoint(params): to_unjoin.append(name) rvs = random_variables.unjoin(to_unjoin) new_dists = [] for dist in rvs: if isinstance(dist, NormalDistribution): if not symbols.isdisjoint(dist.free_symbols): new_dists.append(dist) else: new_dists.append(dist) new_rvs = RandomVariables(tuple(new_dists), rvs._eta_levels, rvs._epsilon_levels) new_params = [] for p in parameters: symb = p.symbol if symb in symbols or symb in new_rvs.free_symbols or (p.fix and p.init == 0): new_params.append(p) return new_rvs, Parameters.create(new_params)
[docs] def rename_symbols(model: Model, new_names: Dict[TSymbol, TSymbol]): """Rename symbols in the model Make sure that no name clash occur. Parameters ---------- model : Model Pharmpy model object new_names : dict From old name or symbol to new name or symbol Returns ------- Model Pharmpy model object """ d = {Expr(key): Expr(val) for key, val in new_names.items()} new = [] for p in model.parameters: if p.symbol in d: newparam = Parameter( name=d[p.symbol].name, init=p.init, lower=p.lower, upper=p.upper, fix=p.fix ) else: newparam = p new.append(newparam) model = model.replace( parameters=Parameters.create(new), statements=model.statements.subs(d), random_variables=model.random_variables.subs(d), ) return model.update_source()
# FIXME: Only handles parameters, statements and random_variables and no clashes and circular renaming
[docs] def filter_dataset(model: Model, expr: str): """Filter dataset according to expr and return a model with the filtered dataset. Example: "DVID == 1" will filter the dataset so that only the rows with DVID = 1 remain. Parameters ---------- model : Model Pharmpy model object expr : str expression for dataset query Returns ------- Model Pharmpy model object Example ------- >>> from pharmpy.modeling import * >>> model = load_example_model("pheno") >>> model.dataset ID TIME AMT WGT APGR DV FA1 FA2 0 1 0.0 25.0 1.4 7.0 0.0 1.0 1.0 1 1 2.0 0.0 1.4 7.0 17.3 0.0 0.0 2 1 12.5 3.5 1.4 7.0 0.0 1.0 1.0 3 1 24.5 3.5 1.4 7.0 0.0 1.0 1.0 4 1 37.0 3.5 1.4 7.0 0.0 1.0 1.0 .. .. ... ... ... ... ... ... ... 739 59 108.3 3.0 1.1 6.0 0.0 1.0 1.0 740 59 120.5 3.0 1.1 6.0 0.0 1.0 1.0 741 59 132.3 3.0 1.1 6.0 0.0 1.0 1.0 742 59 144.8 3.0 1.1 6.0 0.0 1.0 1.0 743 59 146.8 0.0 1.1 6.0 40.2 0.0 0.0 <BLANKLINE> [744 rows x 8 columns] >>> model = filter_dataset(model, 'WGT < 1.4') >>> model.dataset ID TIME AMT WGT APGR DV FA1 FA2 42 4 0.0 18.6 0.9 6.0 0.0 1.0 1.0 43 4 1.8 0.0 0.9 6.0 20.8 0.0 0.0 44 4 12.0 2.3 0.9 6.0 0.0 1.0 1.0 45 4 24.3 2.3 0.9 6.0 0.0 1.0 1.0 46 4 35.8 2.3 0.9 6.0 0.0 1.0 1.0 .. .. ... ... ... ... ... ... ... 739 59 108.3 3.0 1.1 6.0 0.0 1.0 1.0 740 59 120.5 3.0 1.1 6.0 0.0 1.0 1.0 741 59 132.3 3.0 1.1 6.0 0.0 1.0 1.0 742 59 144.8 3.0 1.1 6.0 0.0 1.0 1.0 743 59 146.8 0.0 1.1 6.0 40.2 0.0 0.0 <BLANKLINE> [400 rows x 8 columns] """ original_dataset = model.dataset try: new_dataset = original_dataset.query(expr) new_model = model.replace( dataset=new_dataset, description=model.description + ". Filtered dataset.", name=model.name + "_filtered", ) except pandas.errors.UndefinedVariableError as e: raise ValueError(f'The expression `{expr}` is invalid: {e}') return new_model