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01_Data_Preprocessing.py
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01_Data_Preprocessing.py
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# %%
# hide
# default_exp preprocessing_setup
from nbdev.showdoc import *
# %% [markdown]
# # Data Pre-Processing
# %% [markdown]
# Missing and/or incorrect parameters initialized in `run_importance_sampler` are reset in `preprocessing_setup`.
# `preprocessing_setup` also preprocesses data based on the specified parameter configuration. From the parameter
# settings in Table 2 of the P-CIT Toolbox Manual, we see that the predictor variable can be z-scored and outliers can
# be dropped. We also can generate bootstrap data, scramble the dependent variable, scale the predictor variable between
# 0 and 1 (this is a mandatory step), and we can perform the analysis on one or more categories while leaving out data
# from irrelevant categories. Trials where the predictor variable is set to NaN are filtered out (rows removed) for
# purposes of z-scoring, dropping outliers and scaling. These filtered rows are appended to the data matrix following
# those pre-processing steps. For,
#
# **Simple data** analysis (includes both think/no-think and simulated data) the order of pre-processing is:
# 1. Filter out irrelevant category data entries (rows) from the data matrix
# 2. Drop outliers in the predictor variable, if drop outliers > 0
# 3. Z-score predictor-variable data within subjects, if zscore within subjects = TRUE
# 4. Scale predictor variable between 0 and 1
#
# **Bootstrap data** analysis the order of pre-processing is:
# 1. Generate bootstrap data from the original data matrix (see the "Nonparametric statistical tests" section of the
# main paper, and Section 4.8 of the Manual).
# 2. Filter out irrelevant category data entries (rows) from the data matrix
# 3. Drop outliers in the predictor variable, if drop outliers > 0
# 4. Z-score predictor-variable data within subjects, if zscore within subjects = TRUE
# 5. Scale predictor variable between 0 and 1
#
# **Scramble data** analysis the order of pre-processing is:
# 1. Filter out irrelevant category data entries (rows) from the data matrix
# 2. Drop outliers in the predictor variable, if drop outliers > 0
# 3. Z-score predictor-variable data within subjects, if zscore within subjects = TRUE
# 4. Scale predictor variable between 0 and 1
# 5. Scramble the dependent variable depending on the scrambling technique (see the "Nonparametric statistical tests"
# section of the main paper, and Section 4.8 of the Manual).
# %%
# export
# hide
# helper functions from pcitpy
from pcitpy.family_of_curves import family_of_curves
from pcitpy.helpers import scale_data
# other dependencies
import numpy as np
from scipy import stats
import datetime
import random
import os
def preprocessing_setup(data, analysis_settings):
"""
Performs sanity checks on the input data and the algorithm parameter struct. Massages the data (i.e. drop outliers,
zscore data, etc).
**Arguments**:
- data: Input data matrix (total number of trials x 6 columns)
- analysis_settings: Struct with algorithm parameters
**Returns**:
- data: Input data matrix (if applicable, outlier free, zscored, category specific data only, etc)
- analysis_settings: Struct with algorithm parameters; some additional parameters are added to this struct as well
"""
print('********** START OF MESSAGES **********')
# Checks if the data matrix has 6 columns
number_of_columns = np.shape(data)[1]
if number_of_columns != 6:
raise ValueError('Incorrect number of columns ({}) in the input matrix!'.format(number_of_columns))
# Registering which column in the data matrix is carrying which piece of information
if (not ('data_matrix_columns' in analysis_settings)) or (not analysis_settings['data_matrix_columns']):
# Setting it to the default
analysis_settings['data_matrix_columns'] = {}
analysis_settings['data_matrix_columns']['subject_id'] = 0
analysis_settings['data_matrix_columns']['trials'] = 1
analysis_settings['data_matrix_columns']['category'] = 2
analysis_settings['data_matrix_columns']['predictor_var'] = 3
analysis_settings['data_matrix_columns']['dependent_var'] = 4
analysis_settings['data_matrix_columns']['net_effect_clusters'] = 5
subject_id_column = analysis_settings['data_matrix_columns']['subject_id']
trials_column = analysis_settings['data_matrix_columns']['trials']
category_column = analysis_settings['data_matrix_columns']['category']
predictor_var_column = analysis_settings['data_matrix_columns']['predictor_var']
dependent_var_column = analysis_settings['data_matrix_columns']['dependent_var']
net_effect_clusters_column = analysis_settings['data_matrix_columns']['net_effect_clusters']
# Checks if the em iterations is specified; if not specified then it is set to a default of 20
if (not ('em_iterations' in analysis_settings)) or (analysis_settings['em_iterations'] <= 0):
analysis_settings['em_iterations'] = 20
print('Missing number of iterations! It is set to a default of {}'.format(analysis_settings['em_iterations']))
# Checks if the no. of particles is specified; if not specified then it is set to a default of 1000
if (not ('particles' in analysis_settings)) or (analysis_settings['particles'] <= 0):
analysis_settings['particles'] = 100000
print('Missing number of particles! It is set to a default of {}'.format(analysis_settings['particles']))
# Checks if the family of curves is specified; if not then set to 'horz_indpnt' (Refer to family of curves)
if (not ('curve_type' in analysis_settings)) or (not analysis_settings['curve_type']):
analysis_settings['curve_type'] = 'horz_indpnt';
print('Missing family of curves! It is set to a default of {}'.format(analysis_settings['curve_type']))
# Checks if the family of curves exist by fetching the number of curve parameters. This is just a sanity check
if not isinstance(family_of_curves(analysis_settings['curve_type'], 'get_nParams'), int):
raise ValueError('{} - Does not exist! Check family_of_curves.m script'.format(analysis_settings['curve_type']))
# Checks if the distribution is specified;
# If not specified and if the dependent variable is binary it's set to 'bernoulli'; otherwise set to to 'normal'
if (not ('distribution' in analysis_settings)) or (not analysis_settings['distribution']):
if len(np.unique(data[:, dependent_var_column])) == 2:
analysis_settings['distribution'] = 'bernoulli'
else:
analysis_settings['distribution'] = 'normal'
print('Missing distribution! based on the dependent variable it is set to {}'.format(
analysis_settings['distribution']))
# Checks if the distribution specific parameters exist
if (not ('dist_specific_params' in analysis_settings)) or (not analysis_settings['dist_specific_params']):
if analysis_settings['distribution'] == 'bernoulli':
# For a Bernoulli dist there are no parameters so it is empty. We still need the struct to exist
analysis_settings['dist_specific_params'] = {}
elif analysis_settings['distribution'] == 'normal':
# For normal distribution the additional parameter is sigma. We pass in sigma here.
analysis_settings['dist_specific_params'] = {}
analysis_settings['dist_specific_params']['sigma'] = 1 # Default is 1
print('Missing sigma for normal distribution! It is set to {}'.format(
analysis_settings['dist_specific_params']['sigma']))
# Checks if normal distribution specific parameter is valid i.e. sigma > 0
if (analysis_settings['distribution'] == 'normal') and (analysis_settings['dist_specific_params']['sigma'] <= 0):
raise ValueError('Normal distribution sigma will need to > 0! sigma = {}'.format(
analysis_settings['dist_specific_params']['sigma']))
# Checks if beta_0 is specified; if not specified then it is set to a default of 0
if not ('beta_0' in analysis_settings):
analysis_settings['beta_0'] = 0
print('Missing initial setting for beta_0! It is set to a default of {}'.format(analysis_settings['beta_0']))
# Checks if beta_1 is specified; if not specified then it is set to a default of 1
if not ('beta_1' in analysis_settings):
analysis_settings['beta_1'] = 1
print('Missing initial setting for beta_1! It is set to a default of {}'.format(analysis_settings['beta_1']))
# Checks if tau is specified; if not specified then it is set to a default of 0.05
if not ('tau' in analysis_settings):
analysis_settings['tau'] = 0.05
print('Missing initial setting for tau! It is set to a default of {}'.format(analysis_settings['tau']))
# Checks if this is a bootstrap run; if not specified then it is set to a default of false
if not ('bootstrap' in analysis_settings):
analysis_settings['bootstrap'] = False
print('Missing initial setting for beta_1! It is set to a default of {}'.format(analysis_settings['bootstrap']))
# Checks if bootstrap flag is boolean
if not (type(analysis_settings['bootstrap']) == bool):
raise ValueError('analysis_settings.bootstrap field will need to be boolean!')
# Checks if this is a scramble run; if not specified then it is set to a default of false
if not ('scramble' in analysis_settings):
analysis_settings['scramble'] = False
# Checks if scramble flag is boolean
if not (type(analysis_settings['scramble']) == bool):
raise ValueError('analysis_settings.scramble field will need to be boolean!')
# Errors if both bootstrap and scramble flags exist
if analysis_settings['scramble'] and analysis_settings['bootstrap']:
raise ValueError(
'Cannot run both scramble AND bootstrap analyses at the same time! Set any one flag to be false')
# Builds a bootstrap data matrix from the original data matrix
if analysis_settings['bootstrap'] and not (analysis_settings['scramble']):
# We need a bootstrap sample number
if (not ('bootstrap_run' in analysis_settings)) or (not analysis_settings['bootstrap_run']):
raise ValueError(
'Missing bootstrap sample number! set analysis_settings.bootstrap_run to a valid sample number')
bootstrap_data = []
new_cluster_count = 1
new_subject_count = 1
# Get the number of subjects from the data matrix
number_of_subjects = len(np.unique(data[:, subject_id_column]))
# Randomly sample with replacement the number of subjects thus generating our bootstrap sample
subj_num_with_replacement = random.choices(np.arange(number_of_subjects), k=number_of_subjects)
# For each subject in our bootstrap sample gather all relevant information
for i in range(len(subj_num_with_replacement)):
subj_idx = np.where(data[:, subject_id_column] == subj_num_with_replacement[i])
# Recreate a new net effect cluster since this will need to be unique in the data matrix
# (by repeatedly sampling subjects we could be repeating the net effect clusters)
cluster_vector = data[subj_idx, net_effect_clusters_column]
cluster_numbers = np.unique[cluster_vector]
for j in range(len(cluster_numbers)):
target_idx = np.where(data[subj_idx, net_effect_clusters_column] == cluster_numbers[j])
cluster_vector[target_idx] = new_cluster_count
new_cluster_count += 1
# Recreate a new subject id
# (by repeatedly sampling subjects we could be repeating the subject id's)
# Gather all information into a bootstrap_data matrix
bootstrap_data.append(np.concatenate(np.repmat(new_subject_count, len(subj_idx), 1),
data[subj_idx, trials_column:dependent_var_column], cluster_vector))
new_subject_count += 1
# Perform some sanity checks to ensure that the bootstrap_data matrix is similar to the actual data matrix
if not np.all(np.shape(bootstrap_data) == np.shape(data)):
raise ValueError('Size of bootstrap dataset NOT the same as original data!')
if not (len(np.unique(data[:, net_effect_clusters_column])) == len(
np.unique(bootstrap_data[:, net_effect_clusters_column]))):
raise ValueError('The number of clusters are not the same in the original and bootstrap sample!')
if not np.array_equal(data[:, subject_id_column], bootstrap_data[:, subject_id_column]):
raise ValueError('The ordering of subjects are not the same in the original and bootstrap sample!')
# Store away the bootstrap sample subject information for future reference
analysis_settings['bootstrap_run_subj_id'] = subj_num_with_replacement
data = bootstrap_data
# Checks if analysis will be performed for a specific category; if not then set to [] i.e. NOT category specific
if not ('category' in analysis_settings):
analysis_settings.category = []
print(
'Missing category specific analyses information! We are going to ignore the category dimension i.e. all '
'trials from all categories will be analysed')
# If this analysis is to be performed for a specific category then filters out data from other irrelevant categories
if len(analysis_settings['category']) > 0:
target_cat_idx = []
data_cat = np.unique(data[:, category_column])
for c in range(len(analysis_settings['category'])):
cat_exist = np.where(data_cat == analysis_settings['category'][c])[0]
if cat_exist.size == 0:
raise ValueError('Category does not exist! You have set analysis_settings.category[{}]={}'.format(
c, analysis_settings['category'][c]))
target_cat_idx = np.concatenate(target_cat_idx,
np.where(data[:, category_column] == analysis_settings['category'][c])[0])
data = data[target_cat_idx, :]
# Checks if outliers (i.e. data trials) will need to dropped; if not specified then set to 'DO NOT DROP OUTLIERS'
if not ('drop_outliers' in analysis_settings):
analysis_settings['drop_outliers'] = 3
print(
'Missing drop_outliers specific information! We are dropping outliers that are {} standard deviations away from the group mean'.format(
analysis_settings['drop_outliers']))
# If this analysis requires the outliers dropped, then drops the data trials within std devs from the GROUP MEAN
if analysis_settings['drop_outliers'] > 0:
# NaN's do not qualify as outliers so we filter them out and add them at the end of this step
nan_free_idx = np.logical_not(np.isnan(data[:, predictor_var_column]))
# NaN free data
nan_free_data = data[nan_free_idx, :]
std_dev_predictor_var = np.std(nan_free_data[:, predictor_var_column], ddof=1) * analysis_settings[
'drop_outliers']
mean_predictor_var = np.mean(nan_free_data[:, predictor_var_column])
predictor_var_idx = (nan_free_data[:, predictor_var_column] > (mean_predictor_var - std_dev_predictor_var)) & (
nan_free_data[:, predictor_var_column] < (mean_predictor_var + std_dev_predictor_var))
print('{} trials are dropped since they are regarded as outliers'.format(
np.shape(nan_free_data)[subject_id_column] - np.sum(predictor_var_idx)))
nan_free_data_outlier_dropped = nan_free_data[predictor_var_idx, :]
# NaN's trials
nan_data = data[np.logical_not(nan_free_idx), :]
# Combine the NaN data with the outlier free data
data = np.concatenate(nan_free_data_outlier_dropped, nan_data) if np.shape(nan_data)[
0] > 0 else nan_free_data_outlier_dropped
# Following the 'filter by category' and 'drop outliers', if applicable, we check if the data matrix is empty
number_of_trials = np.shape(data)[subject_id_column]
if number_of_trials <= 0:
raise ValueError('No input data!')
# Checks if we need to zscore predictor var within subjects, if not specified then it is set to default of FALSE
if not ('zscore_within_subjects' in analysis_settings):
analysis_settings['zscore_within_subjects'] = 0
print('Missing zscore_within_subjects information! We are NOT zscoring within subjects')
# Verifies if zscore within subjects is boolean
if not (type(analysis_settings['zscore_within_subjects']) == bool):
raise ValueError('zscore_within_subjects field will need to be boolean!')
# Zscore the predictor variable within each subject
if analysis_settings['zscore_within_subjects']:
# NaN's do not qualify to be zscored
nan_free_idx = np.logical_not(np.isnan(data[:, predictor_var_column]))
# NaN free data
nan_free_data = data[nan_free_idx, :]
# Get the list of subject id's (we use this cell array in zscoring the data within each subject, if applicable)
subject_id_list = np.unique(nan_free_data[:, subject_id_column])
# We get the number of subjects
number_of_subjects = len(subject_id_list)
if number_of_subjects <= 0:
raise ValueError('Not valid number of subjects!')
for s in range(number_of_subjects):
subject_idx = np.where(nan_free_data[:, subject_id_column] == subject_id_list[s])[0]
nan_free_data[subject_idx, predictor_var_column] = stats.zscore(
nan_free_data[subject_idx, predictor_var_column], ddof=1)
print('Predictor variables within each subject are zscored!')
# NaN's trials
nan_data = data[np.logical_not(nan_free_idx), :]
# Combine the NaN data with the outlier free data
data = np.concatenate(nan_free_data, nan_data) if np.shape(nan_data)[0] > 0 else nan_free_data
# Checks if resolution is specified, if not specified then set to default of 4. This translates to 1e-4 = 0.0001
if (not ('resolution' in analysis_settings)) or (analysis_settings['resolution'] <= 0):
analysis_settings['resolution'] = 4
print('Missing resolution! It is set to a default of %d'.format(analysis_settings['resolution']))
# if we have normally distributed data, we want to z-score the dependent variable
if analysis_settings['distribution'] == 'normal':
data[:, dependent_var_column] = stats.zscore(data[:, dependent_var_column], ddof=1)
# We scale the predictor var to be between 0 and 1 and round it to 4 digits
nan_free_idx = np.logical_not(np.isnan(data[:, predictor_var_column]))
nan_free_data = data[nan_free_idx, :]
nan_free_data[:, predictor_var_column] = np.round(scale_data(nan_free_data[:, predictor_var_column], 0, 1),
analysis_settings['resolution'])
nan_data = data[np.logical_not(nan_free_idx), :]
data = np.concatenate(nan_free_data, nan_data) if np.shape(nan_data)[0] > 0 else nan_free_data
# Scrambling the data matrix
if analysis_settings['scramble']:
if (not ('scramble_run' in analysis_settings)) or (not analysis_settings['scramble_run']):
raise ValueError(
'Missing scramble sample number! set analysis_settings.scramble_run to a valid sample number')
if (not ('scramble_style' in analysis_settings)) or (not analysis_settings['scramble_style']):
analysis_settings[
'scramble_style'] = 'within_subjects_within_categories' # most conservative of all scramble techniques
print('Missing scramble style! It is set a default of {}'.format(analysis_settings['scramble_style']))
# We get the list of subject id's
subject_id_list = np.unique(data[:, subject_id_column])
# We get the number of subjects in this analysis
number_of_subjects = len(subject_id_list)
if number_of_subjects <= 0:
raise ValueError('Not valid number of subjects!')
if analysis_settings['scramble_style'] == 'within_subjects_within_categories':
# Here scramble all DVs WHILE respecting the net effect boundaries, subject groupings and category groupings
categories = np.unique(data[:, category_column])
for s in range(number_of_subjects):
for c in range(len(categories)):
subject_category_idx = np.where((data[:, subject_id_column] == subject_id_list[s]) & (
data[:, category_column] == categories[c]))[0]
if len(subject_category_idx) > 1:
data[subject_category_idx, dependent_var_column] = scramble_dependent_variable(
data[subject_category_idx, dependent_var_column],
data[subject_category_idx, net_effect_clusters_column])
elif analysis_settings['scramble_style'] == 'within_subjects_across_categories':
# Here we scramble all dependent variables WHILE respecting the net effect boundaries and subject groupings
for s in range(number_of_subjects):
subject_idx = np.where(data[:, subject_id_column] == subject_id_list[s])[0]
if len(subject_idx) > 1:
data[subject_idx, dependent_var_column] = scramble_dependent_variable(
data[subject_idx, dependent_var_column], data[subject_idx, net_effect_clusters_column])
elif analysis_settings['scramble_style'] == 'across_subjects_across_categories':
# Here we scramble all dependent variables WHILE respecting the net effect boundaries
all_idx = np.arange(np.shape(data)[0])
if len(all_idx) > 1:
data[all_idx, dependent_var_column] = scramble_dependent_variable(
data[all_idx, dependent_var_column], data[all_idx, net_effect_clusters_column])
else:
raise ValueError('Invalid analysis_settings.scramble_style={}'.format(analysis_settings['scramble_style']))
# Our data matrix looks like data = [subject id, item, category, predictor var, dependent var, net effect cluster]
# We verify if the subject id and dependent var columns are unique for the net effect clusters
# Below is a example of a valid data matrix (note dependent variable is unique within net effect cluster 111)
# data(1, :) = [24, 1, 1, 0.3333, 0, 111]
# data(2, :) = [24, 2, 2, 0.2222, 0, 111]
# data(3, :) = [24, 3, 1, 0.4444, 0, 111]
# Below is a example of an invalid data matrix (note dependent variable is not unique within net effect cluster 111)
# data(1, :) = [24, 1, 1, 0.3333, 0, 111]
# data(2, :) = [24, 2, 2, 0.2222, 1, 111]
# data(3, :) = [24, 3, 1, 0.4444, 0, 111]
# Fetching the net effect clusters
net_effect_clusters = np.unique(data[:, net_effect_clusters_column])
analysis_settings['net_effect_clusters'] = net_effect_clusters
# If net effect clusters exist verify if the Subject Id and dependent variable are unique for those clusters
if len(net_effect_clusters) != np.shape(data)[0]:
for i in range(len(net_effect_clusters)):
cluster_idx = np.where(data[:, net_effect_clusters_column] == net_effect_clusters[i])[0]
if len(np.shape(np.unique(data[cluster_idx, [subject_id_column, dependent_var_column]], axis=0))) != 1:
raise ValueError('Subject Id and/or dependent variable not unique for net effect cluster {}! Check '
'the data matrix'.format(net_effect_clusters[i]))
else:
# If net effect clusters DO NOT exist then we treat each row as a net effect cluster by itself
print('Each row will be treated separately. We will NOT be computing the net effect of any rows')
# We create an analysis id unique to this analysis
if (not ('analysis_id' in analysis_settings)) or (not analysis_settings['analysis_id']):
time = datetime.datetime.now()
analysis_settings['analysis_id'] = '{}-{}-{}-{}-{}'.format(time.month, time.day, time.hour, time.minute,
time.second)
# We create a results directory if no specific target directory is mentioned
if (not ('target_dir' in analysis_settings)) or (not analysis_settings['target_dir']):
results_dir = os.path.join(os.getcwd(), 'results')
if not os.path.isdir(results_dir):
os.mkdir(results_dir)
analysis_settings['target_dir'] = results_dir
# target_directory = 'results/analysis_id'
analysis_settings['target_dir'] = os.path.join(analysis_settings['target_dir'], analysis_settings['analysis_id'])
if not os.path.isdir(analysis_settings['target_dir']):
os.mkdir(analysis_settings['target_dir'])
# Due to memory constraints we perform two chunking tricks
# Chunking trick I
# In the curve fitting algorithm we need to compute the p(current iteration curves | previous
# iteration curves). This matrix is huge when the number of particles (curves) is large, say 100,000. Even with a
# 8 Gb RAM, dedicated to Matlab, we still get a out of memory errors. To avoid this problem we chunk the matrix
# into smaller, more manageable matrices. Setting the chunk size to be particles x 0.05 -> 100,000 x 0.05 = 5000,
# translates to p(current iteration curves(5000 curves at a time) | previous iteration curves).
analysis_settings['wgt_chunks'] = analysis_settings['particles'] * 0.05
# If the chunk size is less then 5000 we set it be the number of particles itself
if analysis_settings['wgt_chunks'] < 5000:
analysis_settings['wgt_chunks'] = analysis_settings['particles']
# Chunking trick II
if not ('particle_chunks' in analysis_settings):
analysis_settings['particle_chunks'] = 2
print('Missing particle chunks! It is set to a default of {}'.format(analysis_settings['particle_chunks']))
# Depending on the number of particle chunks we get start, end points and the number of particles within each chunk.
# For instance 1000 particles divided into 4 chunks will look like,
# | 0 | 250 | 250
# | 250 | 500 | 250
# | 500 | 750 | 250
# | 750 | 1000| 250
dummy = np.arange(0, analysis_settings['particles'],
analysis_settings['particles'] / analysis_settings['particle_chunks'])
analysis_settings['ptl_chunk_idx'] = np.stack(
(dummy, dummy + analysis_settings['particles'] / analysis_settings['particle_chunks'],
np.full(np.shape(dummy), analysis_settings['particles'] / analysis_settings['particle_chunks'])),
axis=1)
# Storing analysis relevant information into the analysis_settings struct
# We get the list of subject id's
subject_id_list = np.unique(data[:, subject_id_column])
# We get the number of subjects in this analysis
analysis_settings['nSubjs'] = len(subject_id_list)
if analysis_settings['nSubjs'] <= 0:
raise ValueError('Not valid number of subjects!')
print('********** END OF MESSAGES **********')
return data, analysis_settings
# %%
show_doc(preprocessing_setup, title_level=2)
# %% [markdown]
# The function can be run in isolation from the toolbox pipeline very concisely. For example:
# %%
from pcitpy.run_importance_sampler import run_importance_sampler
preprocessing_setup(run_importance_sampler(run_sampler=False))
# %% [markdown]
# However, this isn't a typical use case for the function. The `importance_sampler` module calls the function directly
# at start of execution, making manual execution of `preprocessing_setup` redundant.
# %%
# export
# hide
def scramble_dependent_variable(target_dependent_variables,
target_net_effect_clusters, testing=False):
"""
Takes dependent variable vector and scramble it such that the net effect cluster groupings are NOT violated.
**Arguments**:
- target_dependent_variables: The vector you would like scrambled
- target_net_effect_clusters: The groupings that you would like to NOT violate. Follow the example below
**Returns** a scrambled vector
"""
if np.logical_not(np.shape(target_dependent_variables) == np.shape(target_net_effect_clusters)):
raise ValueError('Size of input vectors must be the same!')
# Detailed example
# example data matrix: target_dependent_variables = [1, 0, 1, 0, 0, 0, 1]
# and target_net_effect_clusters = [3, 5, 3, 7, 7, 5, 8]
# Fetch the sorted list of net effect clusters and their respective locations
# e.g. for [3, 5, 3, 7, 7, 5, 8] will return [3, 3, 5, 5, 7, 7, 8] and [1, 3, 2, 6, 4, 5, 7]
sorted_net_effect_clusters = np.sort(target_net_effect_clusters)
sorted_net_effect_clusters_indices = np.argsort(target_net_effect_clusters)
just_ones = np.ones(np.shape(sorted_net_effect_clusters)) # Populate a vector full of ones
# compute the length of each net effect cluster
# e.g. for [3, 5, 3, 7, 7, 5, 8] will return [2, 2, 2, 1] i.e. 3 is repeated twice and so on
length_of_each_net_effect_cluster = np.transpose(np.bincount(sorted_net_effect_clusters))
length_of_each_net_effect_cluster = length_of_each_net_effect_cluster[
length_of_each_net_effect_cluster.astype(bool)]
# Get the unique list of clusters (i.e. excluding repetitions if any) e.g. [3, 5, 7, 8]
unique_net_effect_clusters, unique_indices = np.unique(target_net_effect_clusters, return_index=True)
# Get the associated dependent variables (one per cluster; recall it is unique within a cluster) e.g. [1, 0, 0, 1]
associated_dependent_variables = np.array(target_dependent_variables)[unique_indices.astype(int)]
# scramble the dependent variables e.g. [0, 0, 1, 1]
scrambled_indices = np.random.permutation(len(associated_dependent_variables))
scrambled_dependent_variables = associated_dependent_variables[scrambled_indices]
if testing:
scrambled_dependent_variables = np.array([0, 0, 1, 1])
# Now we will need to repeat each scrambled dependent variable for the length of that net effect cluster. The
# next three lines will result in [0, 0, 0, 0, 1, 1, 1] corresponding to [3, 3, 5, 5, 7, 7, 8] since the
# scrambled dependent variable looks like [0, 0, 1, 1] for [3, 5, 7, 8]
cumsum_clusters = np.cumsum(length_of_each_net_effect_cluster)
indicator_vector = np.zeros((cumsum_clusters[-1]))
indicator_vector[np.append(np.array([0]), [cumsum_clusters[:-1]])] = 1
# Store the scrambled dependent variable in the respective cluster locations
# The original vector looked like [3, 5, 3, 7, 7, 5, 8] so the scrambled vector will look like [0, 0, 0, 1, 1, 0, 1]
scrambled_vector = np.full(np.shape(sorted_net_effect_clusters_indices), np.nan)
scrambled_vector[np.array(sorted_net_effect_clusters_indices)] = scrambled_dependent_variables[
np.cumsum(indicator_vector.astype(int)) - 1]
if np.any(np.isnan(scrambled_vector)):
raise ValueError('Nan''s in scrambled dependent variable vector!')
return scrambled_vector
# %%
show_doc(scramble_dependent_variable, title_level=2)
# %% [markdown]
# While `preprocessing_setup` is hard to demonstrate in isolation, its helper function `scramble_dependent_variable` is
# straightforward to illustrate:
# %%
scramble_dependent_variable([1, 0, 1, 0, 0, 0, 1], [3, 5, 3, 7, 7, 5, 8])
# %% [markdown]
# `array([1., 1., 1., 0., 0., 1., 0.])`