Source code for asreview.query_strategies.mixed

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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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import logging
from math import floor

import numpy as np
from scipy.sparse import issparse
from scipy.sparse import vstack

from asreview.query_strategies.base import BaseQueryStrategy
from asreview.query_strategies.utils import get_query_model
from asreview.utils import get_random_state


def interleave(n_samples, n_strat_1, random_state):
    """Interleave the order of the samples of two different strategies.

    While the decisions of which indices to sample are made one after
    the other, it is nicer if the order of the actual samples is mixed up.
    Instead of mixing it the easy way, I decided it should be as nice as
    possible.

    Parameters
    ----------
    n_samples: int
        Total number of samples to mix.
    n_strat_1: int
        Number of samples of the first strategy.
    random_state: int, numpy.RandomState
        RNG.

    Returns
    -------
    numpy.array:
        Order of samples, [0, n_samples).
    """
    n_strat_2 = n_samples - n_strat_1

    # Determine which of the strategies has more samples.
    if n_strat_1 >= n_strat_2:
        max_idx = np.arange(n_strat_1)
        min_idx = n_strat_1 + np.arange(n_strat_2)
    else:
        max_idx = n_strat_1 + np.arange(n_strat_2)
        min_idx = np.arange(n_strat_1)

    # Insert the strategy with less samples at these positions.
    insert_positions = np.sort(random_state.choice(
        np.arange(len(max_idx)), len(min_idx), replace=False))

    # Actually do the inserts.
    new_positions = np.zeros(n_samples, dtype=int)
    i_strat_min = 0
    for i_strat_max in range(len(max_idx)):
        new_positions[i_strat_max+i_strat_min] = max_idx[i_strat_max]
        if (i_strat_min < len(min_idx)
                and insert_positions[i_strat_min] == i_strat_max):
            new_positions[i_strat_min+i_strat_max+1] = min_idx[i_strat_min]
            i_strat_min += 1
    return new_positions


[docs]class MixedQuery(BaseQueryStrategy): """Class for mixed query strategy. The idea is to use two different query strategies at the same time with a ratio of one to the other. """ def __init__(self, strategy_1="max", strategy_2="random", mix_ratio=0.95, random_state=None, **kwargs): """Initialize the Mixed query strategy Arguments --------- strategy_1: str Name of the first query strategy. strategy_2: str Name of the second query strategy. mix_ratio: float Portion of queries done by the first strategy. So a mix_ratio of 0.95 means that 95% of the time query strategy 1 is used and 5% of the time query strategy 2. **kwargs: dict Keyword arguments for the two strategy. To specify which of the strategies the argument is for, prepend with the name of the query strategy and an underscore, e.g. 'max_' for maximal sampling. """ super(MixedQuery, self).__init__() kwargs_1 = {} kwargs_2 = {} for key, value in kwargs.items(): if key.startswith(strategy_1): new_key = key[len(strategy_1)+1:] kwargs_1[new_key] = value elif key.starts_with(strategy_2): new_key = key[len(strategy_2)+1:] kwargs_2[new_key] = value else: logging.warn(f"Key {key} is being ignored for the mixed " "({strategy_1}, {strategy_2}) query strategy.") self.strategy_1 = strategy_1 self.strategy_2 = strategy_2 self.query_model1 = get_query_model(strategy_1, **kwargs_1) self.query_model2 = get_query_model(strategy_2, **kwargs_2) self._random_state = get_random_state(random_state) if "random_state" in self.query_model1.default_param: self.query_model1 = get_query_model(strategy_1, **kwargs_1, random_state=self._random_state ) if "random_state" in self.query_model2.default_param: self.query_model2 = get_query_model(strategy_2, **kwargs_2, random_state=self._random_state ) self.mix_ratio = mix_ratio def query(self, X, classifier, pool_idx=None, n_instances=1, shared={}): n_samples = X.shape[0] if pool_idx is None: pool_idx = np.arange(n_samples) # Split the number of instances for the query strategies. n_instances_1 = floor(n_instances*self.mix_ratio) leftovers = n_instances*self.mix_ratio-n_instances_1 if self._random_state.random_sample() < leftovers: n_instances_1 += 1 n_instances_2 = n_instances-n_instances_1 # Perform the query with strategy 1. query_idx_1, X_1 = self.query_model1.query( X, classifier, pool_idx=pool_idx, n_instances=n_instances_1, shared=shared) # Remove the query indices from the pool. train_idx = np.delete(np.arange(n_samples), pool_idx, axis=0) train_idx = np.append(train_idx, query_idx_1) new_pool_idx = np.delete(np.arange(n_samples), train_idx, axis=0) # Perform the query with strategy 2. query_idx_2, X_2 = self.query_model2.query( X, classifier, pool_idx=new_pool_idx, n_instances=n_instances_2, shared=shared) query_idx = np.append(query_idx_1, query_idx_2) if n_instances_1 == 0: X = X_2 elif n_instances_2 == 0: X = X_1 else: if issparse(X_1) and issparse(X_2): X = vstack([X_1, X_2]).tocsr() else: X = np.concatenate((X_1, X_2), axis=0) # Remix the two strategies without changing the order within. new_order = interleave(len(query_idx), len(query_idx_1), self._random_state) return query_idx[new_order], X[new_order] def full_hyper_space(self): from hyperopt import hp space_1, choices_1 = self.query_model1.hyper_space() space_2, choices_2 = self.query_model2.hyper_space() parameter_space = {} hyper_choices = {} for key, value in space_1.items(): new_key = "qry_" + self.strategy_1 + key[4:] parameter_space[new_key] = value hyper_choices[new_key] = choices_1[key] for key, value in space_2.items(): new_key = "qry_" + self.strategy_2 + key[4:] parameter_space[new_key] = value hyper_choices[new_key] = choices_2[key] parameter_space["qry_mix_ratio"] = hp.uniform( "qry_mix_ratio", 0, 1) return parameter_space, hyper_choices @property def name(self): return "_".join([self.strategy_1, self.strategy_2])