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MappingV36.py
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MappingV36.py
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# import h2o4gpu as sklearn
import scipy.stats as ss
from sklearn.cross_decomposition import PLSRegression
from sklearn.linear_model import RidgeCV
from sklearn.linear_model import OrthogonalMatchingPursuitCV
import random
import numpy as np
import scipy.io
import pickle
import pandas as pd
import time
class MappingV36:
def __init__(self):
print('')
def split_objV36(self,DF_img, test_size_of_80objs=16):
objects = np.unique(DF_img['obj']).tolist()
test_size = test_size_of_80objs
test_inds = []
train_inds = []
for obj in objects:
inds_obj = np.where(DF_img['obj'] == obj)[0]
inds_objV36 = [i for i in inds_obj if DF_img.iloc[i]['var'] in ['V3', 'V6']]
for i in inds_objV36:
if DF_img.iloc[i]['var'] == 'V0':
print('Buzzzz')
# print(len(inds_obj),len(inds_objV36))
random.shuffle(inds_objV36)
test_inds_obj = inds_objV36[0:test_size]
train_inds_obj = inds_objV36[test_size:]
test_inds.extend(test_inds_obj)
train_inds.extend(train_inds_obj)
return train_inds, test_inds
def train_test_split_objV36(self,DF_img, test_size=0.2):
test_size_of_80objs = int(80 * test_size)
train_inds, test_inds = self.split_objV36(DF_img, test_size_of_80objs)
train_indsV36 = []
for i in train_inds:
train_indsV36.extend([i - 640])
test_indsV36 = []
for i in test_inds:
test_indsV36.extend([i - 640])
train_inds, test_inds = train_indsV36, test_indsV36
return train_inds, test_inds
def trial_split(self, pseed):
self.pseed = pseed
nsplits = 2
perm_trindx = np.random.RandomState(seed=pseed).permutation(46)
idx = int(46 / nsplits)
inds1, inds2 = perm_trindx[:idx], perm_trindx[idx:]
if list(set(inds1).intersection(inds2)):
print('Error! Shared trial in both halves!')
return inds1, inds2
def mean_trial_split(self,inds1, inds2, IT_trial_V36):
if list(set(inds1).intersection(inds2)):
print('Error! Shared trial in both halves!')
IT_trial_V36_dum = np.swapaxes(IT_trial_V36, 0, 2)
np.random.shuffle(IT_trial_V36_dum)
IT_trial_V36 = np.swapaxes(IT_trial_V36_dum, 0, 2)
half1 = IT_trial_V36[:, :, inds1].mean(2).T
half2 = IT_trial_V36[:, :, inds2].mean(2).T
return half1, half2
def mapping_half_V36(self, features1, features2_half, train_inds, test_inds, reg_method,
reg_params=[], zscored_observations=True, return_pred=False, return_fitted_reg=False):
X = features1
y = features2_half
if X.shape[0] != y.shape[0]:
print('Error! The number of images should match.')
print(X.shape, y.shape)
if len(X.shape) == 2 and len(y.shape) == 2:
X_train, X_test,y_train, y_test = \
X[train_inds,:],X[test_inds,:],y[train_inds,:],y[test_inds,:]
elif len(X.shape) == 2 and len(y.shape) == 1:
X_train, X_test,y_train, y_test = X[train_inds],X[test_inds],y[train_inds],y[test_inds]
if zscored_observations:
y_train = self.zscored_over_images(y_train)
y_test = self.zscored_over_images(y_test)
if reg_method == 'ridge':
n_alphas, alpha0, alpha1 = reg_params[0],reg_params[1],reg_params[2]
#n_alphas = 10
alphas = np.logspace(alpha0, alpha1, n_alphas)
reg = RidgeCV(alphas=alphas, cv=10)
reg.fit(X_train, y_train)
reg_param = reg.alpha_
if reg_param in [alpha0, alpha1]:
print(r,'alpha for ridge', reg.alpha_)
elif reg_method == 'PLS':
n_components_PLS = reg_params
reg = PLSRegression(n_components=n_components_PLS)
reg.fit(X_train, y_train)
reg_param = [reg.n_components, reg.n_iter_]
elif reg_method == 'OMP':
reg = OrthogonalMatchingPursuitCV()
# print(y_train)
# # y_train = np.argmax(y_train)
# print(y_train)
# print(reg, X_train.shape, y_train.shape)
reg.fit(X_train, y_train)
reg_param = [reg.n_iter_]
y_pred = reg.predict(X_test)
if zscored_observations:
y_pred = self.zscored_over_images(y_pred)
r_train = self.r_corr(y_train, reg.predict(X_train))
r_test = self.r_corr(y_test, y_pred)
if len(y_test.shape) == 2:
r_test_sites = self.r_corr_persite(y_test, y_pred)
elif len(y_test.shape) == 1:
r_test_sites = [r_test]
if (return_fitted_reg is False) and (return_pred is False):
return r_test, reg_param, r_test_sites
elif (return_fitted_reg is False) and (return_pred is True):
return r_test, reg_param, r_test_sites, y_pred
elif (return_fitted_reg is True) and (return_pred is False):
return r_test, reg_param, r_test_sites, reg
elif (return_fitted_reg is True) and (return_pred is True):
return r_test, reg_param, r_test_sites, y_pred, reg
def demean(self,X):
if len(X.shape) ==2:
return X-np.tile(X.mean(0),(X.shape[0],1))
elif len(X.shape) ==1:
return X-X.mean(0)
def r_corr(self,X,Y):
return ss.pearsonr(np.ravel(X),np.ravel(Y))[0]
def r_corr_persite(self, X, Y):
r_sites = np.zeros((X.shape[1]))
for c in range(X.shape[1]):
r_sites[c] = ss.pearsonr(X[:, c], Y[:, c])[0]
return r_sites
def zscored_over_images(self,features):
if len(features.shape) == 2:
features = ss.zscore(features, axis=0)
return features
def r_corrected_consis(self, numerator, RHS, LHS):
return numerator/(np.sqrt(LHS*RHS))
def spearman_brown_correction(self, r):
return 2*r/(1+r)
def Numerator(self, train_inds, test_inds, model_features_X, half1, reg_method, reg_params,
zscored_observations, return_fitted_reg):
return_pred = False
if return_fitted_reg is False:
r_test, reg_param, r_test_sites = self.mapping_half_V36(model_features_X, half1, train_inds, test_inds,
reg_method, reg_params, zscored_observations,
return_pred, return_fitted_reg)
return r_test, r_test_sites
elif return_fitted_reg:
r_test, reg_param, r_test_sites, reg = self.mapping_half_V36(model_features_X, half1, train_inds, test_inds,
reg_method, reg_params, zscored_observations,
return_pred, return_fitted_reg)
return r_test, r_test_sites, reg
def Denom_LHS(self, train_inds, test_inds, model_features_X, half1, half2, reg_method, reg_params,
zscored_observations, return_fitted_reg):
return_pred = True
if return_fitted_reg == False:
_, _, _, y1_pred = self.mapping_half_V36(model_features_X, half1,
train_inds, test_inds, reg_method, reg_params,
zscored_observations, return_pred, return_fitted_reg)
_, _, _, y2_pred = self.mapping_half_V36(model_features_X, half2,
train_inds, test_inds, reg_method, reg_params,
zscored_observations, return_pred, return_fitted_reg)
if (len(half1.shape) == 2) and (len(half2.shape) == 2):
r_test = self.r_corr(y1_pred, y2_pred)
r_test_sites = self.r_corr_persite(y1_pred, y2_pred)
else:
r_test = self.r_corr(y1_pred, y2_pred)
r_test_sites = r_test
return r_test, r_test_sites
elif return_fitted_reg:
_, _, _, y1_pred, reg1 = self.mapping_half_V36(model_features_X, half1,
train_inds, test_inds, reg_method, reg_params,
zscored_observations, return_pred, return_fitted_reg)
_, _, _, y2_pred, reg2 = self.mapping_half_V36(model_features_X, half2,
train_inds, test_inds, reg_method, reg_params,
zscored_observations, return_pred, return_fitted_reg)
if (len(half1.shape) == 2) and (len(half2.shape) == 2):
r_test = self.r_corr(y1_pred, y2_pred)
r_test_sites = self.r_corr_persite(y1_pred, y2_pred)
else:
r_test = self.r_corr(y1_pred, y2_pred)
r_test_sites = r_test
return r_test, r_test_sites, reg1, reg2
def Denom_RHS(self, test_inds, half1, half2):
if (len(half1.shape) == 2) and (len(half2.shape) == 2):
r_test = self.r_corr(half1[test_inds, :], half2[test_inds, :])
r_test_sites = self.r_corr_persite(half1[test_inds, :], half2[test_inds, :])
else:
r_test = self.r_corr(half1[test_inds], half2[test_inds])
r_test_sites = r_test
return r_test, r_test_sites
def Numerator_fixedmap(self, train_inds, test_inds, model_features_X, half2, reg_fitted, zscored_observations):
X = model_features_X
y = half2
if (X.shape[0] != y.shape[0]):
print('Error! The number of images should match.')
if (len(X.shape) == 2) and (len(y.shape) == 2):
X_train, X_test, y_train, y_test = \
X[train_inds, :], X[test_inds, :], y[train_inds, :], y[test_inds, :]
elif (len(X.shape) == 2) and (len(y.shape) == 1):
X_train, X_test, y_train, y_test = \
X[train_inds], X[test_inds], y[train_inds], y[test_inds]
if zscored_observations:
y_train = self.zscored_over_images(y_train)
y_test = self.zscored_over_images(y_test)
y_pred = reg_fitted.predict(X_test)
if zscored_observations:
y_pred = self.zscored_over_images(y_pred)
r_train = self.r_corr(y_train, reg_fitted.predict(X_train))
r_test = self.r_corr(y_test, y_pred)
if len(y_test.shape) == 2:
r_test_sites = self.r_corr_persite(y_test, y_pred)
elif len(y_test.shape) == 1:
r_test_sites = [r_test]
return r_test, r_test_sites
def Denom_LHS_fixedmap(self,train_inds, test_inds, model_features_X, half1, half2, reg1_fitted, reg2_fitted, \
zscored_observations):
X = model_features_X
y1 = half1
y2 = half2
if (X.shape[0] != y1.shape[0]):
print('Error! The number of images should match.')
if (len(X.shape) == 2) and (len(y1.shape) == 2):
X_train, X_test, y1_train, y1_test, y2_train, y2_test = X[train_inds, :], X[test_inds, :], \
y1[train_inds, :], y1[test_inds, :],\
y2[train_inds, :], y2[test_inds,:]
elif (len(X.shape) == 2) and(len(y2.shape) == 1):
X_train, X_test, y1_train, y1_test, y2_train, y2_test = \
X[train_inds], X[test_inds], y1[train_inds], y1[test_inds], y2[train_inds], y2[test_inds]
if zscored_observations:
y1_test = self.zscored_over_images(y1_test)
y2_test = self.zscored_over_images(y2_test)
y1_pred = reg1_fitted.predict(X_test)
y2_pred = reg2_fitted.predict(X_test)
if zscored_observations:
y1_pred = self.zscored_over_images(y1_pred)
y2_pred = self.zscored_over_images(y2_pred)
if (len(y1.shape) == 2) and (len(y2.shape) == 2):
r_test = self.r_corr(y1_pred, y2_pred)
r_test_sites = self.r_corr_persite(y1_pred, y2_pred)
else:
r_test = self.r_corr(y1_pred, y2_pred)
r_test_sites = r_test
return r_test, r_test_sites
def get_Neu_trial_V36(self, Neu_trial, time_interval, times):
# gives the 46 trials of V3&6 in given time interval
Neu_trial_V3, Neu_trial_V6 = Neu_trial[0], Neu_trial[1]
t0, t1 = time_interval
it0 = times.index(t0)
it1 = times.index(t1)
# For the sake of consistency we only use 46 first trials of both V3(51) and V6 (47)
Neu_features = np.concatenate(
(Neu_trial_V3[:, it0:it1, :, 0:46].mean(1), Neu_trial_V6[:, it0:it1, :, 0:46].mean(1)),
axis=1)
return Neu_features
def get_consistency(self, reg_method, reg_params, model_layer, inds1, inds2, Neu_trial, train_inds, test_inds, time_interval_fixed, flexible_times, times):
return_fitted_reg = True
zscored_observations = True
n_imsplits = 1
n_trsplits = 1
half1, half2 = self.get_Neu_trial_V36(Neu_trial, time_interval_fixed, times)
start = time.time()
_, _, reg_fitted = self.Numerator(train_inds, test_inds, model_layer, half1, reg_method,
reg_params, zscored_observations, return_fitted_reg)
_, _, reg1_fitted, reg2_fitted = self.Denom_LHS(train_inds, test_inds, model_layer, half1, half2,
reg_method, reg_params, zscored_observations, return_fitted_reg)
print((time.time() - start) / 60, 'minutes')
n_neurons = half1.shape[1]
r_pop_fixed = np.zeros((4, len(flexible_times)))
r_sites_fixed = np.zeros((4, n_neurons, len(flexible_times)))
r_pop_flexible = np.zeros((4, len(flexible_times)))
r_sites_flexible = np.zeros((4, n_neurons, len(flexible_times)))
print((time.time() - start) / 60, 'minutes')
coef_fixed = reg_fitted.coef_
coef_flexible = []
for indt, t in enumerate(flexible_times):
print(t, (time.time() - start) / 60, 'minutes')
time_interval = [t, t + 10]
half1, half2 = self.get_Neu_trial_V36(Neu_trial, time_interval, times)
# fixed map
r_Nom, r_Nom_sites = self.Numerator_fixedmap(train_inds, test_inds, model_layer, half1, reg_fitted,
zscored_observations)
r_LHS, r_LHS_sites = self.Denom_LHS_fixedmap(train_inds, test_inds, model_layer, half1, half2,
reg1_fitted, reg2_fitted, zscored_observations)
r_RHS, r_RHS_sites = self.Denom_RHS(test_inds, half1, half2)
r_corrected_sites = [self.r_corrected_consis(r_Nom_sites[s], r_RHS_sites[s], r_LHS_sites[s])
for s in range(n_neurons)]
r_corrected = self.r_corrected_consis(r_Nom, r_RHS, r_LHS)
r_pop_fixed[:, indt] = [r_Nom, r_RHS, r_LHS, r_corrected]
r_sites_fixed[0, :, indt] = r_Nom_sites
r_sites_fixed[1, :, indt] = r_RHS_sites
r_sites_fixed[2, :, indt] = r_LHS_sites
r_sites_fixed[3, :, indt] = r_corrected_sites
# flexible map
return_fitted_reg = True
r_Nom_fl, r_Nom_sites_fl, reg_flixable = self.Numerator(train_inds, test_inds, model_layer, half1, reg_method,
reg_params, zscored_observations, return_fitted_reg)
return_fitted_reg = False
r_LHS_fl, r_LHS_sites_fl = self.Denom_LHS(train_inds, test_inds, model_layer, half1, half2, reg_method,
reg_params, zscored_observations, return_fitted_reg)
r_corrected_fl = self.r_corrected_consis(r_Nom_fl, r_RHS, r_LHS_fl)
r_corrected_sites_fl = [self.r_corrected_consis(r_Nom_sites_fl[s], r_RHS_sites[s], r_LHS_sites_fl[s])
for s in range(n_neurons)]
r_pop_flexible[:, indt] = [r_Nom_fl, r_RHS, r_LHS_fl, r_corrected_fl]
r_sites_flexible[0, :, indt] = r_Nom_sites_fl
r_sites_flexible[1, :, indt] = r_RHS_sites
r_sites_flexible[2, :, indt] = r_LHS_sites_fl
r_sites_flexible[3, :, indt] = r_corrected_sites_fl
coef_flexible.append([reg_flixable.coef_])
neurons_negative_denom = set(np.where(np.isnan(r_sites_fixed[3]))[0]).union(
set(np.where(np.isnan(r_sites_flexible[3]))[0]))
list_acceptable_neurons = list(range(n_neurons))
[list_acceptable_neurons.remove(n) for n in neurons_negative_denom]
return r_sites_fixed, r_sites_flexible, list_acceptable_neurons, coef_fixed, coef_flexible
# if __name__ == "__main__":
# MappingV36()