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experimentalloaderdualrecall.py
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experimentalloaderdualrecall.py
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'''
Small class system to simplify the process of loading Experimental datasets
'''
import sys
import numpy as np
import scipy.io as sio
import matplotlib.pyplot as plt
# import matplotlib.patches as plt_patches
# import matplotlib.gridspec as plt_grid
import os
import os.path
import cPickle as pickle
# import bottleneck as bn
import em_circularmixture
import em_circularmixture_allitems_uniquekappa
import pandas as pd
import dataio as DataIO
import utils
from experimentalloader import ExperimentalLoader
class ExperimentalLoaderDualRecall(ExperimentalLoader):
"""docstring for ExperimentalLoaderDualRecall"""
def __init__(self, dataset_description):
super(self.__class__, self).__init__(dataset_description)
def preprocess(self, parameters):
'''
This is the dataset where both colour and orientation can be recalled.
- There are two groups of subjects, either with 6 or 3 items shown (no intermediates...). Stored in 'n_items'
- Subjects recalled either colour or orientation, per blocks. Stored in 'cond'
- Subject report their confidence, which is cool.
Things to change:
- 'item_location' really contains 'item_angle'...
- item_location and probe_location should be wrapped back into -pi:pi.
- Should compute the errors.
'''
# Make some aliases
self.dataset['item_angle'] = self.dataset['item_location']
self.dataset['probe_angle'] = self.dataset['probe_location']
self.dataset['n_items'] = self.dataset['n_items'].astype(int)
self.dataset['cond'] = self.dataset['cond'].astype(int)
self.dataset['subject'] = self.dataset['subject'].astype(int)
self.dataset['probe'] = np.zeros(self.dataset['probe_angle'].shape[0], dtype=int)
self.dataset['n_items_space'] = np.unique(self.dataset['n_items'])
self.dataset['n_items_size'] = self.dataset['n_items_space'].size
self.dataset['subject_space'] = np.unique(self.dataset['subject'])
self.dataset['subject_size'] = self.dataset['subject_space'].size
# Get shortcuts for colour and orientation trials
self.dataset['colour_trials'] = (self.dataset['cond'] == 1).flatten()
self.dataset['angle_trials'] = (self.dataset['cond'] == 2).flatten()
self.dataset['3_items_trials'] = (self.dataset['n_items'] == 3).flatten()
self.dataset['6_items_trials'] = (self.dataset['n_items'] == 6).flatten()
# Wrap everything around
multiply_factor = 2.
self.dataset['item_angle'] = utils.wrap_angles(multiply_factor*self.dataset['item_angle'], np.pi)
self.dataset['probe_angle'] = utils.wrap_angles(multiply_factor*self.dataset['probe_angle'], np.pi)
self.dataset['item_colour'] = utils.wrap_angles(multiply_factor*self.dataset['item_colour'], np.pi)
self.dataset['probe_colour'] = utils.wrap_angles(multiply_factor*self.dataset['probe_colour'], np.pi)
# Remove wrong trials
reject_ids = (self.dataset['reject'] == 1.0).flatten()
for key in self.dataset:
if type(self.dataset[key]) == np.ndarray and self.dataset[key].shape[0] == reject_ids.size and key in ('probe_colour', 'probe_angle', 'item_angle', 'item_colour'):
self.dataset[key][reject_ids] = np.nan
# Compute the errors
self.dataset['errors_angle_all'] = utils.wrap_angles(self.dataset['item_angle'] - self.dataset['probe_angle'], np.pi)
self.dataset['errors_colour_all'] = utils.wrap_angles(self.dataset['item_colour'] - self.dataset['probe_colour'], np.pi)
self.dataset['error_angle'] = self.dataset['errors_angle_all'][:, 0]
self.dataset['error_colour'] = self.dataset['errors_colour_all'][:, 0]
self.dataset['error'] = np.where(~np.isnan(self.dataset['error_angle']), self.dataset['error_angle'], self.dataset['error_colour'])
self.dataset['errors_nitems'] = np.empty(self.dataset['n_items_size'], dtype=np.object)
self.dataset['errors_all_nitems'] = np.empty(self.dataset['n_items_size'], dtype=np.object)
for n_items_i, n_items in enumerate(np.unique(self.dataset['n_items'])):
ids_filtered = self.dataset['angle_trials'] & (self.dataset['n_items'] == n_items).flatten()
self.dataset['errors_nitems'][n_items_i] = self.dataset['error_angle'][ids_filtered]
self.dataset['errors_all_nitems'][n_items_i
] = self.dataset['errors_angle_all'][ids_filtered]
### Split the data up
self.generate_data_to_fit()
### Fit the mixture model
if parameters['fit_mixture_model']:
self.fit_mixture_model_cached(caching_save_filename=parameters.get('mixture_model_cache', None), saved_keys=['em_fits', 'em_fits_angle_nitems_subjects', 'em_fits_angle_nitems', 'em_fits_colour_nitems_subjects', 'em_fits_colour_nitems', 'em_fits_angle_nitems_arrays', 'em_fits_colour_nitems_arrays'])
# Try with Pandas for some advanced plotting
dataset_filtered = dict((k, self.dataset[k].flatten()) for k in ('n_items', 'trial', 'subject', 'reject', 'rating', 'probe_colour', 'probe_angle', 'cond', 'error', 'error_angle', 'error_colour', 'response', 'target'))
if parameters['fit_mixture_model']:
dataset_filtered.update(self.dataset['em_fits'])
self.dataset['panda'] = pd.DataFrame(dataset_filtered)
def fit_mixture_model(self):
'''
Special fitting for this dual recall dataset
'''
self.dataset['em_fits'] = dict(
kappa=np.empty(self.dataset['probe_angle'].size),
mixt_target=np.empty(self.dataset['probe_angle'].size),
mixt_nontargets=np.empty(self.dataset['probe_angle'].size),
mixt_random=np.empty(self.dataset['probe_angle'].size),
resp_target=np.empty(self.dataset['probe_angle'].size),
resp_nontarget=np.empty(self.dataset['probe_angle'].size),
resp_random=np.empty(self.dataset['probe_angle'].size),
train_LL=np.empty(self.dataset['probe_angle'].size),
test_LL=np.empty(self.dataset['probe_angle'].size))
for key in self.dataset['em_fits']:
self.dataset['em_fits'][key].fill(np.nan)
self.dataset['em_fits_angle_nitems_subjects'] = dict()
self.dataset['em_fits_angle_nitems'] = dict(mean=dict(), std=dict(), values=dict())
self.dataset['em_fits_colour_nitems_subjects'] = dict()
self.dataset['em_fits_colour_nitems'] = dict(mean=dict(), std=dict(), values=dict())
# This dataset is a bit special with regards to subjects, it's a conditional design:
# 8 Subjects (1 - 8) only did 6 items, both angle/colour trials
# 6 Subjects (9 - 14) did 3 items, both angle/colour trials.
# We have 160 trials per (subject, n_item, condition).
# Angles trials
for n_items_i, n_items in enumerate(self.dataset['n_items_space']):
for subject_i, subject in enumerate(self.dataset['subject_space']):
ids_filtered = ((self.dataset['subject']==subject) & (self.dataset['n_items'] == n_items) & (self.dataset.get('masked', False) == False)).flatten()
ids_filtered = self.dataset['angle_trials'] & ids_filtered
if ids_filtered.sum() > 0:
print 'Angle trials, %d items, subject %d, %d datapoints' % (n_items, subject, self.dataset['probe_angle'][ids_filtered, 0].size)
# params_fit = em_circularmixture.fit(self.dataset['probe_angle'][ids_filtered, 0], self.dataset['item_angle'][ids_filtered, 0], self.dataset['item_angle'][ids_filtered, 1:])
cross_valid_outputs = em_circularmixture.cross_validation_kfold(self.dataset['probe_angle'][ids_filtered, 0], self.dataset['item_angle'][ids_filtered, 0], self.dataset['item_angle'][ids_filtered, 1:], K=10, shuffle=True, debug=False)
params_fit = cross_valid_outputs['best_fit']
resp = em_circularmixture.compute_responsibilities(self.dataset['probe_angle'][ids_filtered, 0], self.dataset['item_angle'][ids_filtered, 0], self.dataset['item_angle'][ids_filtered, 1:], params_fit)
self.dataset['em_fits']['kappa'][ids_filtered] = params_fit['kappa']
self.dataset['em_fits']['mixt_target'][ids_filtered] = params_fit['mixt_target']
self.dataset['em_fits']['mixt_nontargets'][ids_filtered] = params_fit['mixt_nontargets']
self.dataset['em_fits']['mixt_random'][ids_filtered] = params_fit['mixt_random']
self.dataset['em_fits']['resp_target'][ids_filtered] = resp['target']
self.dataset['em_fits']['resp_nontarget'][ids_filtered] = np.sum(resp['nontargets'], axis=1)
self.dataset['em_fits']['resp_random'][ids_filtered] = resp['random']
self.dataset['em_fits']['train_LL'][ids_filtered] = params_fit['train_LL']
self.dataset['em_fits']['test_LL'][ids_filtered] = cross_valid_outputs['best_test_LL']
self.dataset['em_fits_angle_nitems_subjects'].setdefault(n_items, dict())[subject] = params_fit
## Now compute mean/std em_fits per n_items
self.dataset['em_fits_angle_nitems']['mean'][n_items] = dict()
self.dataset['em_fits_angle_nitems']['std'][n_items] = dict()
self.dataset['em_fits_angle_nitems']['values'][n_items] = dict()
# Need to extract the values for a subject/nitems pair, for all keys of em_fits. Annoying dictionary indexing needed
emfits_keys = params_fit.keys()
for key in emfits_keys:
values_allsubjects = [self.dataset['em_fits_angle_nitems_subjects'][n_items][subject][key] for subject in self.dataset['em_fits_angle_nitems_subjects'][n_items]]
self.dataset['em_fits_angle_nitems']['mean'][n_items][key] = np.mean(values_allsubjects)
self.dataset['em_fits_angle_nitems']['std'][n_items][key] = np.std(values_allsubjects)
self.dataset['em_fits_angle_nitems']['values'][n_items][key] = values_allsubjects
# Colour trials
for n_items_i, n_items in enumerate(self.dataset['n_items_space']):
for subject_i, subject in enumerate(self.dataset['subject_space']):
ids_filtered = ((self.dataset['subject']==subject) & (self.dataset['n_items'] == n_items) & (self.dataset.get('masked', False) == False)).flatten()
ids_filtered = self.dataset['colour_trials'] & ids_filtered
if ids_filtered.sum() > 0:
print 'Colour trials, %d items, subject %d, %d datapoints' % (n_items, subject, self.dataset['probe_angle'][ids_filtered, 0].size)
cross_valid_outputs = em_circularmixture.cross_validation_kfold(self.dataset['probe_colour'][ids_filtered, 0], self.dataset['item_colour'][ids_filtered, 0], self.dataset['item_colour'][ids_filtered, 1:], K=10, shuffle=True, debug=False)
params_fit = cross_valid_outputs['best_fit']
resp = em_circularmixture.compute_responsibilities(self.dataset['probe_colour'][ids_filtered, 0], self.dataset['item_colour'][ids_filtered, 0], self.dataset['item_colour'][ids_filtered, 1:], params_fit)
self.dataset['em_fits']['kappa'][ids_filtered] = params_fit['kappa']
self.dataset['em_fits']['mixt_target'][ids_filtered] = params_fit['mixt_target']
self.dataset['em_fits']['mixt_nontargets'][ids_filtered] = params_fit['mixt_nontargets']
self.dataset['em_fits']['mixt_random'][ids_filtered] = params_fit['mixt_random']
self.dataset['em_fits']['resp_target'][ids_filtered] = resp['target']
self.dataset['em_fits']['resp_nontarget'][ids_filtered] = np.sum(resp['nontargets'], axis=1)
self.dataset['em_fits']['resp_random'][ids_filtered] = resp['random']
self.dataset['em_fits']['train_LL'][ids_filtered] = params_fit['train_LL']
self.dataset['em_fits']['test_LL'][ids_filtered] = cross_valid_outputs['best_test_LL']
self.dataset['em_fits_colour_nitems_subjects'].setdefault(n_items, dict())[subject] = params_fit
## Now compute mean/std em_fits per n_items
self.dataset['em_fits_colour_nitems']['mean'][n_items] = dict()
self.dataset['em_fits_colour_nitems']['std'][n_items] = dict()
self.dataset['em_fits_colour_nitems']['values'][n_items] = dict()
# Need to extract the values for a subject/nitems pair, for all keys of em_fits. Annoying dictionary indexing needed
emfits_keys = params_fit.keys()
for key in emfits_keys:
values_allsubjects = [self.dataset['em_fits_colour_nitems_subjects'][n_items][subject][key] for subject in self.dataset['em_fits_colour_nitems_subjects'][n_items]]
self.dataset['em_fits_colour_nitems']['mean'][n_items][key] = np.mean(values_allsubjects)
self.dataset['em_fits_colour_nitems']['std'][n_items][key] = np.std(values_allsubjects)
self.dataset['em_fits_colour_nitems']['values'][n_items][key] = values_allsubjects
## Construct array versions of the em_fits_nitems mixture proportions, for convenience
self.construct_arrays_em_fits()
def construct_arrays_em_fits(self):
if 'em_fits_angle_nitems_arrays' not in self.dataset:
self.dataset['em_fits_angle_nitems_arrays'] = dict()
self.dataset['em_fits_angle_nitems_arrays']['mean'] = np.array([[self.dataset['em_fits_angle_nitems']['mean'][item][em_key] for item in np.unique(self.dataset['n_items'])] for em_key in ['kappa', 'mixt_target', 'mixt_nontargets', 'mixt_random']])
self.dataset['em_fits_angle_nitems_arrays']['std'] = np.array([[self.dataset['em_fits_angle_nitems']['std'][item][em_key] for item in np.unique(self.dataset['n_items'])] for em_key in ['kappa', 'mixt_target', 'mixt_nontargets', 'mixt_random']])
if 'sem' not in self.dataset['em_fits_angle_nitems_arrays']:
self.dataset['em_fits_angle_nitems_arrays']['sem'] = self.dataset['em_fits_angle_nitems_arrays']['std']/np.sqrt(self.dataset['subject_size'])
if 'em_fits_colour_nitems_arrays' not in self.dataset:
self.dataset['em_fits_colour_nitems_arrays'] = dict()
self.dataset['em_fits_colour_nitems_arrays']['mean'] = np.array([[self.dataset['em_fits_colour_nitems']['mean'][item][em_key] for item in np.unique(self.dataset['n_items'])] for em_key in ['kappa', 'mixt_target', 'mixt_nontargets', 'mixt_random']])
self.dataset['em_fits_colour_nitems_arrays']['std'] = np.array([[self.dataset['em_fits_colour_nitems']['std'][item][em_key] for item in np.unique(self.dataset['n_items'])] for em_key in ['kappa', 'mixt_target', 'mixt_nontargets', 'mixt_random']])
if 'sem' not in self.dataset['em_fits_colour_nitems_arrays']:
self.dataset['em_fits_colour_nitems_arrays']['sem'] = self.dataset['em_fits_colour_nitems_arrays']['std']/np.sqrt(self.dataset['subject_size'])
def generate_data_to_fit(self):
'''
Split the data up nicely, used in FitExperiment as well
'''
self.dataset['response'] = np.nan*np.empty((self.dataset['probe_angle'].size, 1))
self.dataset['target'] = np.nan*np.empty(self.dataset['probe_angle'].size)
self.dataset['nontargets'] = np.nan*np.empty((self.dataset['probe_angle'].size, self.dataset['n_items_space'][-1] - 1))
self.dataset['data_split_nitems_subjects'] = {
'angle_trials': dict(),
'colour_trials': dict(),
'n_items': self.dataset['n_items_space']
}
for n_items_i, n_items in enumerate(self.dataset['n_items_space']):
for subject_i, subject in enumerate(self.dataset['subject_space']):
ids_filtered = ((self.dataset['subject']==subject) & (self.dataset['n_items'] == n_items) & (self.dataset.get('masked', False) == False)).flatten()
# Angle trial
ids_filtered_angle = self.dataset['angle_trials'] & ids_filtered
if ids_filtered_angle.sum() > 0:
self.dataset['target'][ids_filtered_angle] = self.dataset['item_angle'][ids_filtered_angle, 0]
self.dataset['nontargets'][ids_filtered_angle] = self.dataset['item_angle'][ids_filtered_angle, 1:]
self.dataset['response'][ids_filtered_angle] = self.dataset['probe_angle'][ids_filtered_angle]
self.dataset['data_split_nitems_subjects']['angle_trials'].setdefault(n_items, dict())[subject] = dict(
target=self.dataset['target'][ids_filtered_angle],
nontargets=self.dataset['nontargets'][ids_filtered_angle],
response=self.dataset['response'][ids_filtered_angle],
item_features=self.dataset['item_angle'][ids_filtered_angle],
probe=self.dataset['probe'][ids_filtered_angle],
N=np.sum(ids_filtered_angle)
)
# Colour trial
ids_filtered_colour = self.dataset['colour_trials'] & ids_filtered
if ids_filtered_colour.sum() > 0:
self.dataset['target'][ids_filtered_colour] = self.dataset['item_colour'][ids_filtered_colour, 0]
self.dataset['nontargets'][ids_filtered_colour] = self.dataset['item_colour'][ids_filtered_colour, 1:]
self.dataset['response'][ids_filtered_colour] = self.dataset['probe_colour'][ids_filtered_colour]
self.dataset['data_split_nitems_subjects']['colour_trials'].setdefault(n_items, dict())[subject] = dict(
target=self.dataset['target'][ids_filtered_colour],
nontargets=self.dataset['nontargets'][ids_filtered_colour],
response=self.dataset['response'][ids_filtered_colour],
item_features=self.dataset['item_colour'][ids_filtered_colour],
probe=self.dataset['probe'][ids_filtered_colour],
N=np.sum(ids_filtered_colour)
)
# Also store a version collating subjects across
self.dataset['data_split_nitems'] = dict(colour_trials=dict(), angle_trials=dict())
ids_filtered = ((self.dataset['n_items'] == n_items) & (self.dataset.get('masked', False) == False)).flatten()
ids_filtered_angle = self.dataset['angle_trials'] & ids_filtered
self.dataset['data_split_nitems']['angle_trials'][n_items] = dict(
target=self.dataset['target'][ids_filtered_angle],
nontargets=self.dataset['nontargets'][ids_filtered_angle],
response=self.dataset['response'][ids_filtered_angle],
item_features=self.dataset['item_angle'][ids_filtered_angle],
probe=self.dataset['probe'][ids_filtered_angle],
N=np.sum(ids_filtered_angle)
)
ids_filtered_colour = self.dataset['colour_trials'] & ids_filtered
self.dataset['data_split_nitems']['colour_trials'][n_items] = dict(
target=self.dataset['target'][ids_filtered_colour],
nontargets=self.dataset['nontargets'][ids_filtered_colour],
response=self.dataset['response'][ids_filtered_colour],
item_features=self.dataset['item_colour'][ids_filtered_colour],
probe=self.dataset['probe'][ids_filtered_colour],
N=np.sum(ids_filtered_colour)
)