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| # stuff specifically for the sklearn logic | |
| from typing import Mapping | |
| from functools import partial, reduce | |
| import operator | |
| from itertools import product | |
| import argparse | |
| ############################################################################### | |
| # A grid search convenience class | |
| ############################################################################### | |
| class ParameterGrid: | |
| """logic YOINKED from sklearn <3 | |
| def worth just using the lib itself, or something fancier in future for | |
| efficient sampling etc. It's implemented as an iterator interface but thats | |
| probs not necessary""" | |
| def __init__(self, params): | |
| # we may want to product a few sets of parameters | |
| # independently of eachother, so expects a List[Mapping] | |
| if isinstance(params, Mapping): | |
| self.params = [params] | |
| else: | |
| self.params = params | |
| # removed all checking code soooo make sure your | |
| # param dict is already nice and conforming | |
| def __iter__(self): | |
| """Iterate over the points in the grid. | |
| Returns | |
| ------- | |
| params : iterator over dict of str to any | |
| Yields dictionaries mapping each estimator parameter to one of its | |
| allowed values. | |
| """ | |
| for p in self.params: | |
| # Always sort the keys of a dictionary, for reproducibility | |
| items = sorted(p.items()) | |
| if not items: | |
| yield {} | |
| else: | |
| keys, values = zip(*items) | |
| for v in product(*values): | |
| params = dict(zip(keys, v)) | |
| yield params | |
| def __len__(self): | |
| """Number of points on the grid.""" | |
| # Product function that can handle iterables (np.product can't). | |
| product = partial(reduce, operator.mul) | |
| return sum( | |
| product(len(v) for v in p.values()) if p else 1 for p in self.params | |
| ) | |
| ############################################################################### | |
| # little "oneliner" reduce thingy that turns your shallow dict into | |
| # the list [k1, v1, k2, v2, k3, v3 ...] | |
| # and optionally "k1 v1 k2 v2 k3 v3" | |
| def flatten_dict(dict, to_string=False, sep=" "): | |
| flat_dict = reduce(operator.iconcat,dict.items() , []) | |
| if to_string: | |
| try: | |
| return sep.join([str(elm) for elm in flat_dict]) | |
| except: | |
| raise ValueError(f'Error converting dict={flat_dict} to whitespace joined string') | |
| else: | |
| return flat_dict | |
| def str2bool(v): | |
| if isinstance(v, bool): | |
| return v | |
| if v.lower() in ('yes', 'true', 't', 'y', '1'): | |
| return True | |
| elif v.lower() in ('no', 'false', 'f', 'n', '0'): | |
| return False | |
| else: | |
| raise argparse.ArgumentTypeError('Boolean value expected.') |