Source code for asreview.analysis.analysis

# Copyright 2019-2020 The ASReview Authors. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import itertools
import os
from pathlib import Path

import numpy as np
from scipy import stats

from asreview.state.utils import states_from_dir, state_from_file
from asreview.analysis.statistics import _get_labeled_order
from asreview.analysis.statistics import _get_limits
from asreview.analysis.statistics import _find_inclusions
from asreview.analysis.statistics import _get_last_proba_order

[docs]class Analysis(): """Analysis object to do statistical analysis on state files.""" def __init__(self, states, key=None): """Class to analyse state files. Arguments --------- states: list, BaseLogger Either a list of states (opened files) or a single state. key: str Give a name to the analysis. """ if isinstance(states, list): states = {i: state for i, state in enumerate(states)} # Sometimes an extra dataset is present in the state_file(s). # These signify not the labels on which the model was trained, but the # ones that were included in the end (or some other intermediate step. self.final_labels = None self.labels = None self.empty = True if states is None: return self.key = key self.states = states self.num_runs = len(self.states) if self.num_runs == 0: return self._first_file = list(self.states.keys())[0] self.labels = self.states[self._first_file].get('labels') try: self.final_labels = self.states[self._first_file].get( 'final_labels') except KeyError: pass self.empty = False self.inc_found = {}
[docs] @classmethod def from_dir(cls, data_dir, prefix="", key=None): """Create an Analysis object from a directory. Arguments --------- data_dir: str Directory to read the state files from. prefix: str Only assume files starting with this prefix are state files. Ignore all other files. key: str Name for the analysis object. """ if key is None: key = os.path.basename(os.path.normpath(data_dir)) states = states_from_dir(data_dir, prefix=prefix) analysis_inst = cls(states, key=key) if analysis_inst.empty: return None analysis_inst.data_dir = data_dir return analysis_inst
[docs] @classmethod def from_file(cls, data_fp, key=None): """Create an Analysis object from a file. Arguments --------- data_fp: str Path to state file to analyse. key: str Name for analysis object. """ if key is None: key = os.path.basename(os.path.normpath(data_fp)) state = state_from_file(data_fp) analysis_inst = cls(state, key=key) if analysis_inst.empty: return None analysis_inst.data_path = data_fp return analysis_inst
[docs] @classmethod def from_path(cls, data_path, prefix="", key=None): """Create an Analysis object from either a file or a directory.""" if Path(data_path).is_file(): return cls.from_file(data_path, key=key) return cls.from_dir(data_path, prefix, key=key)
[docs] def inclusions_found(self, result_format="fraction", final_labels=False, **kwargs): """Get the number of inclusions at each point in time. Caching is used to prevent multiple calls being expensive. Arguments --------- result_format: str The format % or # of the returned values. final_labels: bool If true, use the final_labels instead of labels for analysis. Returns ------- tuple: Three numpy arrays with x, y, error_bar. """ if final_labels: labels = self.final_labels else: labels = self.labels fl = final_labels if fl not in self.inc_found: # Compute the comclusions if not found in cache. self.inc_found[fl] = {} avg, err, iai, ninit = self._get_inc_found(labels=labels, **kwargs) self.inc_found[fl]["avg"] = avg self.inc_found[fl]["err"] = err self.inc_found[fl]["inc_after_init"] = iai self.inc_found[fl]["n_initial"] = ninit dx = 0 dy = 0 x_norm = len(labels) - self.inc_found[fl]["n_initial"] y_norm = self.inc_found[fl]["inc_after_init"] if result_format == "percentage": x_norm /= 100 y_norm /= 100 elif result_format == "number": x_norm /= len(labels) y_norm /= self.inc_found[fl]["inc_after_init"] norm_xr = (np.arange(1, len(self.inc_found[fl]["avg"]) + 1) - dx) / x_norm norm_yr = (np.array(self.inc_found[fl]["avg"]) - dy) / y_norm norm_y_err = np.array(self.inc_found[fl]["err"]) / y_norm return norm_xr, norm_yr, norm_y_err
def _get_inc_found(self, labels=False): """Get the number of inclusions (without formatting).""" inclusions_found = [] for state in self.states.values(): inclusions, inc_after_init, n_initial = _find_inclusions( state, labels) inclusions_found.append(inclusions) inc_found_avg = [] inc_found_err = [] for i_instance in itertools.count(): cur_vals = [] for i_file in range(self.num_runs): try: cur_vals.append(inclusions_found[i_file][i_instance]) except IndexError: pass if len(cur_vals) == 0: break if len(cur_vals) == 1: err = cur_vals[0] else: err = stats.sem(cur_vals) avg = np.mean(cur_vals) inc_found_avg.append(avg) inc_found_err.append(err) if self.num_runs == 1: inc_found_err = np.zeros(len(inc_found_err)) return inc_found_avg, inc_found_err, inc_after_init, n_initial
[docs] def wss(self, val=100, x_format="percentage", **kwargs): """Get the WSS (Work Saved Sampled) value. Arguments --------- val: At which recall, between 0 and 100. x_format: Format for position of WSS value in graph. Returns ------- tuple: Tuple consisting of WSS value, x_positions, y_positions of WSS bar. """ norm_xr, norm_yr, _ = self.inclusions_found(result_format="percentage", **kwargs) if x_format == "number": x_return, y_result, _ = self.inclusions_found( result_format="number", **kwargs) y_max = self.inc_found[False]["inc_after_init"] y_coef = y_max / (len(self.labels) - self.inc_found[False]["n_initial"]) else: x_return = norm_xr y_result = norm_yr y_max = 1.0 y_coef = 1.0 for i in range(len(norm_yr)): if norm_yr[i] >= val - 1e-6: return (norm_yr[i] - norm_xr[i], (x_return[i], x_return[i]), (x_return[i] * y_coef, y_result[i])) return (None, None, None)
[docs] def rrf(self, val=10, x_format="percentage", **kwargs): """Get the RRF (Relevant References Found). Arguments --------- val: At which recall, between 0 and 100. x_format: Format for position of RRF value in graph. Returns ------- tuple: Tuple consisting of RRF value, x_positions, y_positions of RRF bar. """ norm_xr, norm_yr, _ = self.inclusions_found(result_format="percentage", **kwargs) if x_format == "number": x_return, y_return, _ = self.inclusions_found( result_format="number", **kwargs) else: x_return = norm_xr y_return = norm_yr for i in range(len(norm_yr)): if norm_xr[i] >= val - 1e-6: return (norm_yr[i], (x_return[i], x_return[i]), (0, y_return[i])) return (None, None, None)
[docs] def avg_time_to_discovery(self, result_format="number"): """Estimate the Time to Discovery (TD) for each paper. Get the best/last estimate on how long it takes to find a paper. Arguments --------- result_format: str Desired output format: "number", "fraction" or "percentage". Returns ------- dict: For each inclusion, key=paper_id, value=avg time. """ labels = self.labels one_labels = np.where(labels == 1)[0] time_results = {label: [] for label in one_labels} # Iterate over all state files for state in self.states.values(): # Get the order in which records were labeled label_order, n = _get_labeled_order(state) # Get the ranking of all papers at the last query proba_order = _get_last_proba_order(state) # Adjust factor, depending on the desired output format if result_format == "percentage": time_mult = 100 / (len(labels) - n) elif result_format == "fraction": time_mult = 1 / (len(labels) - n) else: time_mult = 1 # Get the time to discovery for i_time, idx in enumerate(label_order[n:]): # for all inclusions that were found/labeled if labels[idx] == 1: time_results[idx].append(time_mult * (i_time + 1)) for i_time, idx in enumerate(proba_order): # for all inclusions that weren't found/labeled if labels[idx] == 1 and idx not in label_order[:n]: time_results[idx].append(time_mult * (i_time + len(label_order) + 1)) results = {} # Merge the results of all state files for label, trained_time in time_results.items(): if len(trained_time) > 0: results[label] = np.average(trained_time) return results
[docs] def limits(self, prob_allow_miss=[0.1], result_format="percentage"): """For each query, compute the number of papers for a criterium. A criterium is the average number of papers missed. For example, with 0.1, the criterium is that after reading x papers, there is (about) a 10% chance that one paper is not included. Another example, with 2.0, there are on average 2 papers missed after reading x papers. The value for x is returned for each query and probability by the function. Arguments --------- prob_allow_miss: list, float Sets the criterium for how many papers can be missed. returns ------- dict: One entry, "x_range" with the number of papers read. List, "limits" of results for each probability and at # papers read. """ if not isinstance(prob_allow_miss, list): prob_allow_miss = [prob_allow_miss] state = self.states[self._first_file] n_queries = state.n_queries() results = { "x_range": [], "limits": [[] for _ in range(len(prob_allow_miss))], } n_train = 0 _, n_initial = _get_labeled_order(state) for query_i in range(n_queries): new_limits = _get_limits(self.states, query_i, self.labels, proba_allow_miss=prob_allow_miss) try: new_train_idx = state.get("train_idx", query_i) except KeyError: new_train_idx = None if new_train_idx is not None: n_train = len(new_train_idx) if new_limits is not None: if result_format == "percentage": normalizer = 100 / (len(self.labels) - n_initial) else: normalizer = 1 results["x_range"].append((n_train - n_initial) * normalizer) for i_prob in range(len(prob_allow_miss)): results["limits"][i_prob].append( (new_limits[i_prob] - n_initial) * normalizer) if result_format == "percentage": res_dtype = np.float else: res_dtype = results["x_range"] = np.array(results["x_range"], dtype=res_dtype) for i_prob in range(len(prob_allow_miss)): results["limits"][i_prob] = np.array(results["limits"][i_prob], res_dtype) return results
[docs] def close(self): """Close states.""" for state in self.states.values(): state.close()