Source code for SALib.analyze.ff

Created on 30 Jun 2015

@author: will2

import numpy as np
from . import common_args

import pandas as pd
from types import MethodType

from SALib.util import read_param_file, ResultDict
from SALib.sample.ff import generate_contrast, extend_bounds

[docs] def analyze(problem, X, Y, second_order=False, print_to_console=False, seed=None): """Perform a fractional factorial analysis Returns a dictionary with keys 'ME' (main effect) and 'IE' (interaction effect). The techniques bulks out the number of parameters with dummy parameters to the nearest 2**n. Any results involving dummy parameters could indicate a problem with the model runs. Notes ----- Compatible with: `ff` : :func:`SALib.sample.ff.sample` Examples -------- >>> X = sample(problem) >>> Y = X[:, 0] + (0.1 * X[:, 1]) + ((1.2 * X[:, 2]) * (0.2 + X[:, 0])) >>> analyze(problem, X, Y, second_order=True, print_to_console=True) Parameters ---------- problem: dict The problem definition X: numpy.matrix The NumPy matrix containing the model inputs Y: numpy.array The NumPy array containing the model outputs second_order: bool, default=False Include interaction effects print_to_console: bool, default=False Print results directly to console seed : int Seed to generate a random number Returns ------- Si: dict A dictionary of sensitivity indices, including main effects ``ME``, and interaction effects ``IE`` (if ``second_order`` is True) References ---------- 1. Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S., 2008. Global Sensitivity Analysis: The Primer. Wiley, West Sussex, U.K. """ if seed: np.random.seed(seed) problem = extend_bounds(problem) num_vars = problem["num_vars"] X = generate_contrast(problem) main_effect = (1.0 / (2 * num_vars)) *, X) Si = ResultDict((k, [None] * num_vars) for k in ["names", "ME"]) Si["ME"] = main_effect Si["names"] = problem["names"] if second_order: interaction_names, interaction_effects = interactions(problem, Y) Si["interaction_names"] = interaction_names Si["IE"] = interaction_effects Si.to_df = MethodType(to_df, Si) if print_to_console: for S in Si.to_df(): print(S) return Si
[docs] def to_df(self): """Conversion method to Pandas DataFrame. To be attached to ResultDict. Returns ------- main_effect, inter_effect: tuple A tuple of DataFrames for main effects and interaction effects. The second element (for interactions) will be `None` if not available. """ names = self["names"] main_effect = self["ME"] interactions = self.get("IE", None) inter_effect = None if interactions: interaction_names = self.get("interaction_names") names = [name for name in names if not isinstance(name, list)] inter_effect = pd.DataFrame({"IE": interactions}, index=interaction_names) main_effect = pd.DataFrame({"ME": main_effect}, index=names) return main_effect, inter_effect
[docs] def interactions(problem, Y): """Computes the second order effects Computes the second order effects (interactions) between all combinations of pairs of input factors Parameters ---------- problem: dict The problem definition Y: numpy.array The NumPy array containing the model outputs Returns ------- ie_names: list The names of the interaction pairs IE: list The sensitivity indices for the pairwise interactions """ names = problem["names"] num_vars = problem["num_vars"] X = generate_contrast(problem) ie_names = [] IE = [] for col in range(X.shape[1]): for col_2 in range(col): x = X[:, col] * X[:, col_2] var_names = (names[col_2], names[col]) ie_names.append(var_names) IE.append((1.0 / (2 * num_vars)) *, x)) return ie_names, IE
[docs] def cli_parse(parser): parser.add_argument( "-X", "--model-input-file", type=str, required=True, default=None, help="Model input file", ) parser.add_argument( "--max-order", type=int, required=False, default=2, choices=[1, 2], help="Maximum order of sensitivity \ indices to calculate", ) return parser
[docs] def cli_action(args): problem = read_param_file(args.paramfile) Y = np.loadtxt( args.model_output_file, delimiter=args.delimiter, usecols=(args.column,) ) X = np.loadtxt(args.model_input_file, delimiter=args.delimiter, ndmin=2) if len(X.shape) == 1: X = X.reshape((len(X), 1)) analyze(problem, X, Y, (args.max_order == 2), print_to_console=True, seed=args.seed)
if __name__ == "__main__": common_args.run_cli(cli_parse, cli_action)