In [1]:
%matplotlib inline
import os, sys
%run prelims
import opc_python
import numpy as np
import matplotlib.pyplot as plt
import pandas
from opc_python.utils import loading, scoring
from opc_python.gerkin import dream,fit1,fit2,params
In [2]:
perceptual_headers, perceptual_obs_data = loading.load_perceptual_data('training')
all_CIDs = sorted(loading.get_CIDs('training')+loading.get_CIDs('leaderboard')+loading.get_CIDs('testset'))
mdx = dream.get_molecular_data(['dragon','episuite','morgan','nspdk','gramian'],all_CIDs)
Episuite has 62 features for 476 molecules.
Morgan has 2437 features for 476 molecules.
NSPDK has 6163 features for 476 molecules.
NSPDK Gramian has 2437 features for 476 molecules.
There are now 15969 total features.
In [3]:
# Create the feature matrices from the feature dicts.
X_training,good1,good2,means,stds,imputer = dream.make_X(mdx,"training")
X_leaderboard_other,good1,good2,means,stds,imputer = dream.make_X(mdx,"leaderboard",target_dilution='high',good1=good1,good2=good2,means=means,stds=stds)
X_leaderboard_int,good1,good2,means,stds,imputer = dream.make_X(mdx,"leaderboard",target_dilution=-3,good1=good1,good2=good2,means=means,stds=stds)
X_testset_other,good1,good2,means,stds,imputer = dream.make_X(mdx,"testset",target_dilution='high',good1=good1,good2=good2,means=means,stds=stds)
X_testset_int,good1,good2,means,stds,imputer = dream.make_X(mdx,"testset",target_dilution=-3,good1=good1,good2=good2,means=means,stds=stds)
X_all,good1,good2,means,stds,imputer = dream.make_X(mdx,['training','leaderboard'],good1=good1,good2=good2,means=means,stds=stds)
The X matrix now has shape (676x13914) molecules by non-NaN good molecular descriptors
The X matrix now has shape (69x13914) molecules by non-NaN good molecular descriptors
The X matrix now has shape (69x13914) molecules by non-NaN good molecular descriptors
The X matrix now has shape (69x13914) molecules by non-NaN good molecular descriptors
The X matrix now has shape (69x13914) molecules by non-NaN good molecular descriptors
The X matrix now has shape (814x13914) molecules by non-NaN good molecular descriptors
In [12]:
# Create descriptor matrices for the training set.
# One is done with median imputation, and the other by masking missing values.
Y_training_imp,imputer = dream.make_Y_obs('training',target_dilution=None,imputer='median')
Y_training_mask,imputer = dream.make_Y_obs('training',target_dilution=None,imputer='mask')
The Y['mean_std'] matrix now has shape (676x42) molecules by 2 x perceptual descriptors
The Y['subject'] dict now has 49 matrices of shape (676x21) molecules by perceptual descriptors, one for each subject
The Y['mean_std'] matrix now has shape (676x42) molecules by 2 x perceptual descriptors
The Y['subject'] dict now has 49 matrices of shape (676x21) molecules by perceptual descriptors, one for each subject
In [13]:
# Create descriptor matrices for the leaderboard set.
# One is done with median imputation, and the other with no imputation
Y_leaderboard,imputer = dream.make_Y_obs('leaderboard',target_dilution='gold',imputer='mask')
Y_leaderboard_noimpute,_ = dream.make_Y_obs('leaderboard',target_dilution='gold',imputer=None)
The Y['mean_std'] matrix now has shape (69x42) molecules by 2 x perceptual descriptors
The Y['subject'] dict now has 49 matrices of shape (69x21) molecules by perceptual descriptors, one for each subject
The Y['mean_std'] matrix now has shape (69x42) molecules by 2 x perceptual descriptors
The Y['subject'] dict now has 49 matrices of shape (69x21) molecules by perceptual descriptors, one for each subject
In [14]:
# Create descriptor matrices for the combined training and leaderboard sets.
# One is done with median imputation, and the other by masking missing values.
Y_all_imp,imputer = dream.make_Y_obs(['training','leaderboard'],target_dilution=None,imputer='median')
Y_all_mask,imputer = dream.make_Y_obs(['training','leaderboard'],target_dilution=None,imputer='mask')
The Y['mean_std'] matrix now has shape (814x42) molecules by 2 x perceptual descriptors
The Y['subject'] dict now has 49 matrices of shape (814x21) molecules by perceptual descriptors, one for each subject
The Y['mean_std'] matrix now has shape (814x42) molecules by 2 x perceptual descriptors
The Y['subject'] dict now has 49 matrices of shape (814x21) molecules by perceptual descriptors, one for each subject
In [15]:
# Plot stdev vs mean for each descriptor, and fit to a theoretically-motivated function.
# These fit parameters will be used in the final model fit.
def f_transformation(x, k0=1.0, k1=1.0):
return 100*(k0*(x/100)**(k1*0.5) - k0*(x/100)**(k1*2))
def sse(x, mean, stdev):
predicted_stdev = f_transformation(mean, k0=x[0], k1=x[1])
sse = np.sum((predicted_stdev - stdev)**2)
return sse
fig,axes = plt.subplots(3,7,sharex=True,sharey=True,figsize=(12,6))
ax = axes.flat
trans_params = {col:None for col in range(21)}
from scipy.optimize import minimize
for col in range(len(ax)):
Y_mean = Y_all_mask['mean_std'][:,col]
Y_stdev = Y_all_mask['mean_std'][:,col+21]
x = [1.0,1.0]
res = minimize(sse, x, args=(Y_mean,Y_stdev), method='L-BFGS-B')
trans_params[col] = res.x # We will use these for our transformations.
ax[col].scatter(Y_mean,Y_stdev,s=0.1)
x_ = np.linspace(0,100,100)
#ax[col].plot(x_,f_transformation(x_, k0=res.x[0], k1=res.x[1]))
ax[col].set_title(perceptual_headers[col+6].split('/')[1 if col==1 else 0])
ax[col].set_xlim(0,100)
ax[col].set_ylim(0,50)
if col == 17:
ax[col].set_xlabel('Mean')
if col == 7:
ax[col].set_ylabel('StDev')
plt.tight_layout()
In [16]:
# Load optimal parameters (obtained from extensive cross-validation).
cols = range(42)
def get_params(i):
return {col:params.best[col][i] for col in cols}
use_et = get_params(0)
max_features = get_params(1)
max_depth = get_params(2)
min_samples_leaf = get_params(3)
trans_weight = get_params(4)
regularize = get_params(4)
use_mask = get_params(5)
for col in range(21):
trans_weight[col] = trans_weight[col+21]
In [17]:
X_all_other,good1,good2,means,stds,imputer = dream.make_X(mdx,['training','leaderboard'],target_dilution='high',good1=good1,good2=good2,means=means,stds=stds)
X_all_int,good1,good2,means,stds,imputer = dream.make_X(mdx,['training','leaderboard'],target_dilution=-3,good1=good1,good2=good2,means=means,stds=stds)
The X matrix now has shape (407x13914) molecules by non-NaN good molecular descriptors
The X matrix now has shape (349x13914) molecules by non-NaN good molecular descriptors
In [26]:
from sklearn.cross_validation import ShuffleSplit
n_obs = int(len(Y_all_mask['mean_std'][:,col])/2)
n_splits = 15
shuffle_split = ShuffleSplit(n_obs,n_splits,test_size=0.2,random_state=0)
rs = np.zeros((42,n_splits))
X = X_all
Y = Y_all_mask['mean_std']
for k,(train,test) in enumerate(shuffle_split):
print(k, flush=True)
train = np.concatenate((2*train,2*train+1))
test = np.concatenate((2*test,2*test+1))
rfcs_cv,_,_ = fit2.rfc_final(X[train],Y_all_imp['mean_std'][train],
Y[train],max_features,
min_samples_leaf,max_depth,use_et,use_mask,
trans_weight,trans_params,n_estimators=50,
quiet=True)
Y_cv = loading.make_prediction_files(rfcs_cv,X[test],X[test],
'all',2,Y_test=None,
write=False,trans_weight=trans_weight,
trans_params=trans_params)
for col in range(42):
rs[col,k] = np.ma.corrcoef(Y_cv['mean_std'][:,col],
Y[test,col])[0,1]
print(rs.mean(axis=1))
0
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97.62% [------------------------------------------------- ][ 0.70429993 0.51384299 0.52884893 0.58728364 0.60348913 0.37946705
0.5004598 0.35897501 0.31655091 0.40376677 0.42534259 0.49363106
0.29386113 0.31200202 0.35080621 0.37549074 0.4347722 0.25379221
0.35591619 0.40667879 0.6169039 0.29508031 0.09931859 0.35694575
0.45563306 0.52731457 0.32835556 0.4263107 0.29301951 0.287575
0.3756583 0.3631568 0.43923735 0.22726946 0.31959926 0.32832815
0.36475936 0.33911589 0.24603573 0.2765765 0.35876195 0.49759955]
In [32]:
best = np.array([.70429993, 0.51384299, 0.52884893, 0.58728364, 0.60348913, 0.37946705,
0.5004598, 0.35897501, 0.31655091, 0.40376677, 0.42534259, 0.49363106,
0.29386113, 0.31200202, 0.35080621, 0.37549074, 0.4347722, 0.25379221,
0.35591619, 0.40667879, 0.6169039, 0.29508031, 0.09931859, 0.35694575,
0.45563306, 0.52731457, 0.32835556, 0.4263107, 0.29301951, 0.287575,
0.3756583, 0.3631568, 0.43923735, 0.22726946, 0.31959926, 0.32832815,
0.36475936, 0.33911589, 0.24603573, 0.2765765, 0.35876195, 0.49759955])
best[0] = 0.75
best[1] = 0.70
best[2:21] *= 0.56/np.mean(best[2:21])
best[21] = 0.38
best[22] = 0.40
best[23:42] *= 0.50/np.mean(best[23:42])
best
Out[32]:
array([ 0.75 , 0.7 , 0.7035416 , 0.78127882, 0.80283741,
0.50481496, 0.66577479, 0.47755387, 0.42111597, 0.53714152,
0.5658444 , 0.65669034, 0.39093117, 0.41506447, 0.4666867 ,
0.49952518, 0.57838886, 0.33762643, 0.47348464, 0.54101546,
0.82068343, 0.38 , 0.4 , 0.49785038, 0.63549459,
0.73547243, 0.45797419, 0.5945972 , 0.4086892 , 0.40109547,
0.52394973, 0.50651325, 0.61262666, 0.31698427, 0.44576133,
0.45793596, 0.50874842, 0.47298217, 0.34315854, 0.3857553 ,
0.50038353, 0.69402738])
In [25]:
from sklearn.cross_validation import ShuffleSplit
n_obs = int(len(Y_all_mask['subject'][1][:,col])/2)
n_splits = 3
shuffle_split = ShuffleSplit(n_obs,n_splits,test_size=0.2,random_state=0)
rs = np.zeros((21,49,n_splits))
X = X_all
Y = Y_all_mask['subject']
for k,(train,test) in enumerate(shuffle_split):
print(k, flush=True)
train = np.concatenate((2*train,2*train+1))
test = np.concatenate((2*test,2*test+1))
Y_train = {i:Y[i][train] for i in range(1,50)}
Y_test = {i:Y[i][test] for i in range(1,50)}
rfcs_cv,_,_ = fit1.rfc_final(X[train],Y_train,
max_features,min_samples_leaf,max_depth,use_et,
regularize=regularize,n_estimators=2)
Y_cv = loading.make_prediction_files(rfcs_cv,X[test],X[test],
'all',1,Y_test=None,
write=False,regularize=regularize)
for col in range(21):
for subject in range(1,50):
rs[col,subject-1,k] = np.ma.corrcoef(Y_cv['subject'][subject][:,col],
Y_test[subject][:,col])[0,1]
print(rs.mean(axis=1))
0
4.00% [-- ]
/Users/rgerkin/Dropbox/python3/lib/python3.4/site-packages/sklearn/ensemble/forest.py:687: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.
warn("Some inputs do not have OOB scores. "
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-25-2af030e5da48> in <module>()
16 rfcs_cv,_,_ = fit1.rfc_final(X[train],Y_train,
17 max_features,min_samples_leaf,max_depth,use_et,
---> 18 regularize=regularize,n_estimators=2)
19 Y_cv = loading.make_prediction_files(rfcs_cv,X[test],X[test],
20 'all',1,Y_test=None,
/Users/rgerkin/Dropbox/science/olfaction-prediction/opc_python/gerkin/fit1.py in rfc_final(X, Y, max_features, min_samples_leaf, max_depth, use_et, regularize, Y_test, n_estimators, seed)
39 from time import gmtime, strftime
40 for col in range(21):
---> 41 rfcs[col][subject].fit(X,Y[subject][:,col])
42
43 predicted0 = rfcs[0][1].predict(X)
/Users/rgerkin/Dropbox/python3/lib/python3.4/site-packages/sklearn/ensemble/forest.py in fit(self, X, y, sample_weight)
294
295 if self.oob_score:
--> 296 self._set_oob_score(X, y)
297
298 # Decapsulate classes_ attributes
/Users/rgerkin/Dropbox/python3/lib/python3.4/site-packages/sklearn/ensemble/forest.py in _set_oob_score(self, X, y)
701 for k in range(self.n_outputs_):
702 self.oob_score_ += r2_score(y[:, k],
--> 703 predictions[:, k])
704
705 self.oob_score_ /= self.n_outputs_
/Users/rgerkin/Dropbox/python3/lib/python3.4/site-packages/sklearn/metrics/regression.py in r2_score(y_true, y_pred, sample_weight, multioutput)
442 """
443 y_type, y_true, y_pred, multioutput = _check_reg_targets(
--> 444 y_true, y_pred, multioutput)
445
446 if sample_weight is not None:
/Users/rgerkin/Dropbox/python3/lib/python3.4/site-packages/sklearn/metrics/regression.py in _check_reg_targets(y_true, y_pred, multioutput)
73 """
74 check_consistent_length(y_true, y_pred)
---> 75 y_true = check_array(y_true, ensure_2d=False)
76 y_pred = check_array(y_pred, ensure_2d=False)
77
/Users/rgerkin/Dropbox/python3/lib/python3.4/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
396 % (array.ndim, estimator_name))
397 if force_all_finite:
--> 398 _assert_all_finite(array)
399
400 shape_repr = _shape_repr(array.shape)
/Users/rgerkin/Dropbox/python3/lib/python3.4/site-packages/sklearn/utils/validation.py in _assert_all_finite(X)
52 and not np.isfinite(X).all()):
53 raise ValueError("Input contains NaN, infinity"
---> 54 " or a value too large for %r." % X.dtype)
55
56
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
In [34]:
Y_all_mask,imputer = dream.make_Y_obs(['training','leaderboard'],
target_dilution=None,imputer='mask')
y = np.ma.dstack([Y_all_mask['subject'][i] for i in range(1,50)])
The Y['mean_std'] matrix now has shape (814x42) molecules by 2 x perceptual descriptors
The Y['subject'] dict now has 49 matrices of shape (814x21) molecules by perceptual descriptors, one for each subject
In [35]:
from sklearn.cross_validation import ShuffleSplit
n_splits = 100
rm = np.zeros((n_splits,21))
rs = np.zeros((n_splits,21))
shuff = ShuffleSplit(49,n_iter=n_splits,test_size=(24/49),random_state=0)
for col in range(21):
for i,(a,b) in enumerate(shuff):
ma = np.ma.mean(y[:,col,a],axis=1)
mb = np.ma.mean(y[:,col,b],axis=1)
sa = np.ma.std(y[:,col,a],axis=1)
sb = np.ma.std(y[:,col,b],axis=1)
rm[i,col] = np.corrcoef(ma,mb)[0,1]
rs[i,col] = np.corrcoef(sa,sb)[0,1]
plt.plot(range(1,22),rm.mean(axis=0))
plt.plot(range(1,22),rs.mean(axis=0))
plt.plot(range(1,22),best[0:21],c='k')
Out[35]:
[<matplotlib.lines.Line2D at 0x12ea989e8>]
In [37]:
best[21:]
Out[37]:
array([ 0.38 , 0.4 , 0.49785038, 0.63549459, 0.73547243,
0.45797419, 0.5945972 , 0.4086892 , 0.40109547, 0.52394973,
0.50651325, 0.61262666, 0.31698427, 0.44576133, 0.45793596,
0.50874842, 0.47298217, 0.34315854, 0.3857553 , 0.50038353,
0.69402738])
In [36]:
# Fit training data.
# Ignoring warning that arises if too few trees are used.
# Ignore intensity score which is based on within-sample validation,
# due to use of ExtraTreesClassifier.
n_estimators = 1000
rfcs_leaderboard,score,rs = fit2.rfc_final(X_training,Y_training_imp['mean_std'],
Y_training_mask['mean_std'],max_features,
min_samples_leaf,max_depth,use_et,use_mask,
trans_weight,trans_params,
n_estimators=n_estimators)
97.62% [------------------------------------------------- ]For subchallenge 2:
Score = 9.71
int_mean = 1.000
int_sigma = 0.936
ple_mean = 0.698
ple_sigma = 0.240
dec_mean = 0.516
dec_sigma = 0.421
In [25]:
# Make challenge 2 leaderboard prediction files from the models.
Y_pred = loading.make_prediction_files(rfcs_leaderboard,X_leaderboard_int,X_leaderboard_other,
'leaderboard',2,Y_test=Y_leaderboard_noimpute,
write=False,trans_weight=trans_weight,trans_params=trans_params)
Score: 8.961307; rs = 0.641,0.573,0.569,0.431,0.117,0.499
Wrote to file with suffix "1446422354"
Out[25]:
{'mean_std': array([[ 71.70842857, 41.52902041, 2.94663288, ..., 8.73820076,
13.5852291 , 27.35685525],
[ 59.89612245, 45.12281633, 1.75245157, ..., 9.3389807 ,
15.71728531, 29.63782626],
[ 20.93204082, 37.60470408, 1.84919165, ..., 7.97196496,
10.26965993, 24.51386267],
...,
[ 34.5317551 , 43.47964286, 1.83324128, ..., 15.72799914,
16.31699961, 23.1197447 ],
[ 40.23808163, 45.13152041, 2.66772469, ..., 10.2265469 ,
20.67600147, 23.96670738],
[ 32.96961224, 55.65241837, 2.02220511, ..., 25.55053778,
18.88106105, 24.39162737]]), 'subject': {}}
In [26]:
# Fit all available data.
# Ignoring warning that arises if too few trees are used.
# Ignore intensity score which is based on within-sample validation,
# due to use of ExtraTreesClassifier.
rfcs,score,rs = fit2.rfc_final(X_all,Y_all_imp['mean_std'],Y_all_mask['mean_std'],
max_features,min_samples_leaf,max_depth,use_et,use_mask,
trans_weight,trans_params,n_estimators=n_estimators)
97.62% [------------------------------------------------- ]For subchallenge 2:
Score = 9.75
int_mean = 1.000
int_sigma = 0.938
ple_mean = 0.700
ple_sigma = 0.209
dec_mean = 0.522
dec_sigma = 0.427
In [27]:
# Make challenge 2 testset prediction files from the models.
loading.make_prediction_files(rfcs,X_testset_int,X_testset_other,'testset',2,write=True,
trans_weight=trans_weight,trans_params=trans_params)
Wrote to file with suffix "1446435104"
Out[27]:
{'mean_std': array([[ 21.89636735, 39.71637755, 1.90954771, ..., 9.98683842,
10.47795684, 31.54727994],
[ 27.75355102, 44.74494898, 2.07851814, ..., 8.11799799,
10.28496659, 20.71858811],
[ 59.63055102, 30.67554082, 1.8075775 , ..., 8.12056301,
10.21969347, 25.82426989],
...,
[ 53.15091837, 51.24417347, 2.27342287, ..., 33.55969447,
18.75528304, 20.69043221],
[ 33.36522449, 49.04045918, 1.97972904, ..., 14.68861211,
17.35520645, 23.22568437],
[ 23.50810204, 49.45904082, 1.90788221, ..., 22.12489258,
22.48449568, 33.0394573 ]]), 'subject': {}}
In [28]:
# Fit training data for subchallenge 1.
# Ignoring warning that arises if too few trees are used.
# Ignore intensity score which is based on within-sample validation,
# due to use of ExtraTreesClassifier.
n_estimators = 50
rfcs_leaderboard,score,rs = fit1.rfc_final(X_training,Y_training_imp['subject'],max_features,
min_samples_leaf,max_depth,use_et,
Y_test=Y_leaderboard_noimpute['subject'],
regularize=regularize,
n_estimators=n_estimators)
98.00% [------------------------------------------------- ]For subchallenge 1:
Score = 37.12
int = 0.843
ple = 0.402
dec = 0.182
/anaconda/lib/python3.4/site-packages/numpy/ma/core.py:3900: UserWarning: Warning: converting a masked element to nan.
warnings.warn("Warning: converting a masked element to nan.")
In [29]:
# Make challenge 1 leaderboard prediction files from the models.
loading.make_prediction_files(rfcs_leaderboard,X_leaderboard_int,X_leaderboard_other,
'leaderboard',1,Y_test=Y_leaderboard_noimpute,
write=True,regularize=regularize)
Score: 27.753160; rs = 0.429,0.317,0.178
Wrote to file with suffix "1446474725"
Out[29]:
{'subject': {1: array([[ 63.42269388, 43.78285714, 2.09603111, ..., 1.13950434,
2.5056629 , 13.40066528],
[ 59.30073469, 45.47671429, 1.22167694, ..., 2.25695301,
3.08044954, 14.100113 ],
[ 23.14522449, 41.08514286, 1.45208672, ..., 1.35192001,
1.79131942, 6.01025857],
...,
[ 32.31722449, 43.89757143, 1.51561664, ..., 3.6759995 ,
4.28113598, 7.1933659 ],
[ 39.77044898, 43.55385714, 4.83840181, ..., 2.96249798,
7.16240494, 17.33127877],
[ 42.22220408, 50.43414286, 2.24900751, ..., 8.99311304,
5.69282902, 10.37460239]]),
2: array([[ 65.46269388, 49.94485714, 2.65306529, ..., 1.0458901 ,
2.51593386, 12.89772164],
[ 62.24073469, 46.44571429, 1.28782724, ..., 2.26505963,
3.12775306, 14.64866416],
[ 28.37722449, 40.44614286, 1.63452993, ..., 1.35202984,
1.8017015 , 6.469755 ],
...,
[ 33.29722449, 46.51957143, 1.58176694, ..., 3.8762171 ,
4.20577276, 6.96001067],
[ 38.84244898, 49.74285714, 5.10544677, ..., 2.97860781,
7.45053802, 15.26246149],
[ 43.01420408, 56.94114286, 2.31515781, ..., 10.73251381,
5.29367888, 10.08977967]]),
3: array([[ 61.81469388, 44.39485714, 2.5286753 , ..., 1.04643408,
4.00960435, 12.34334894],
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5.421148 , 9.87160239]]),
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6.16516642, 9.35260239]]),
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2.78768191, 12.80156545],
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5.48326641, 13.47466528],
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3.16945767, 15.58266528],
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3.42578042, 17.242113 ],
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43: array([[ 64.73069388, 39.41185714, 2.30880873, ..., 1.1476387 ,
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2.21988782, 6.66326552],
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3.44930783, 12.27866528],
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4.994625 , 14.196113 ],
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8.60921407, 9.30260239]]),
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2.81484373, 12.61786194],
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5.58350831, 6.72042409],
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2.68740989, 12.00796312],
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3.30599636, 14.02459451],
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2.58904979, 12.62188654],
[ 59.46473469, 46.70071429, 1.21864779, ..., 2.25695301,
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1.85960348, 5.95580314],
...,
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4.49169633, 6.66591748],
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5.65489579, 9.27994002]])}}
In [30]:
# Fit all available data for subchallenge 1.
# Ignoring warning that arises if too few trees are used.
# Ignore intensity score which is based on within-sample validation,
# due to use of ExtraTreesClassifier.
rfcs1,score1,rs1 = fit1.rfc_final(X_all,Y_all_imp['subject'],max_features,
min_samples_leaf,max_depth,use_et,
regularize=regularize,
n_estimators=n_estimators)
98.00% [------------------------------------------------- ]For subchallenge 1:
Score = 37.33
int = 0.841
ple = 0.406
dec = 0.185
In [31]:
# Make challenge 1 testset prediction files from the models.
loading.make_prediction_files(rfcs1,X_testset_int,X_testset_other,
'testset',1,write=True,regularize=regularize)
Wrote to file with suffix "1446510971"
Out[31]:
{'subject': {1: array([[ 26.31755102, 40.149 , 1.9511612 , ..., 2.10466287,
2.68027593, 17.9639846 ],
[ 22.36620408, 46.73742857, 1.46409153, ..., 1.03138155,
1.93359848, 5.80899911],
[ 53.568 , 29.02685714, 1.27619873, ..., 1.14423211,
2.04727742, 9.94377144],
...,
[ 52.10897959, 48.18242857, 3.53946303, ..., 11.14731045,
5.74367517, 7.81414332],
[ 24.75934694, 50.25014286, 1.38609663, ..., 4.98177721,
4.33694397, 13.03485266],
[ 29.72277551, 45.16628571, 1.9831435 , ..., 9.46573398,
8.36780493, 23.16176282]]),
2: array([[ 27.20155102, 40.206 , 2.6977642 , ..., 2.10503145,
2.50035918, 16.61862563],
[ 21.21820408, 45.36342857, 1.59048489, ..., 1.03175554,
1.90976702, 6.40190191],
[ 57.84 , 32.22785714, 1.33925015, ..., 1.14460611,
2.06324724, 10.72999819],
...,
[ 55.69297959, 54.26642857, 3.37345641, ..., 12.46950464,
5.64337678, 7.67008843],
[ 25.79934694, 52.65914286, 1.52560821, ..., 5.14636019,
4.35291379, 12.02422601],
[ 28.40277551, 51.66128571, 2.04619493, ..., 9.4880493 ,
8.38312596, 23.28386729]]),
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3.61468545, 18.06102918],
[ 19.24220408, 42.93942857, 1.50347561, ..., 1.03634195,
2.9464519 , 6.46890224],
[ 49.34 , 35.23985714, 1.3155828 , ..., 1.22287517,
3.0107986 , 11.57409685],
...,
[ 47.56897959, 53.69342857, 3.37701044, ..., 11.15227085,
12.99154237, 6.94067053],
[ 24.38334694, 51.32714286, 1.64389971, ..., 4.98673761,
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[ 27.66677551, 51.83828571, 2.10211361, ..., 9.51661536,
12.28598021, 23.95795074]]),
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11.85322431, 16.37242706],
[ 28.99820408, 48.65142857, 1.48353052, ..., 1.05148836,
6.98034084, 9.33579143],
[ 54.084 , 34.46285714, 1.29563771, ..., 1.16433892,
8.48371636, 11.9991547 ],
...,
[ 55.82497959, 63.42842857, 3.32984398, ..., 11.16741726,
15.91419016, 7.26407883],
[ 33.03134694, 60.41414286, 1.40553562, ..., 5.16020155,
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[ 33.23077551, 52.50428571, 2.00258249, ..., 10.67633595,
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[ 17.13820408, 43.88142857, 1.45697218, ..., 1.03138155,
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[ 46.088 , 33.41585714, 1.26907937, ..., 1.14423211,
2.08704575, 9.75458188],
...,
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[ 21.00220408, 40.34142857, 1.50800964, ..., 1.03246835,
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[ 60.492 , 29.37485714, 1.2747835 , ..., 1.53583614,
2.08012774, 10.00339368],
...,
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[ 24.39820408, 41.80542857, 1.51720877, ..., 1.03138155,
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...,
[ 55.92097959, 51.10442857, 3.36352223, ..., 12.54731045,
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[ 31.12334694, 49.22714286, 1.43921387, ..., 5.18177721,
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[ 29.15477551, 49.51028571, 2.03626074, ..., 9.47373398,
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9: array([[ 26.26555102, 39.618 , 1.96149467, ..., 2.48031176,
3.54734587, 18.80928387],
[ 22.91820408, 43.68342857, 1.47442501, ..., 1.13376347,
2.68755877, 5.55221593],
[ 49.26 , 35.50985714, 1.2865322 , ..., 1.34622291,
3.22275254, 10.89124169],
...,
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[ 17.46620408, 44.50542857, 1.45722655, ..., 1.03154877,
1.88232713, 5.47852286],
[ 51.744 , 26.87285714, 1.26933375, ..., 1.29839933,
2.03580735, 9.90605148],
...,
[ 44.40097959, 50.00042857, 3.30354001, ..., 11.14747767,
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[ 24.23477551, 47.60828571, 1.97960183, ..., 9.54186983,
8.90033486, 22.4019452 ]]),
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2.62363997, 15.66227536],
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...,
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[ 26.04677551, 46.31528571, 2.02157831, ..., 9.93338226,
11.79014153, 21.65466981]]),
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2.63770587, 18.03776706],
[ 19.99420408, 42.18642857, 2.04065429, ..., 1.0369014 ,
2.0471137 , 6.07320322],
[ 51.94 , 34.36985714, 1.45815933, ..., 1.14975196,
2.20059393, 9.721278 ],
...,
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6.46522047, 7.2156535 ],
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3.24086217, 16.0036571 ],
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2.4043788 , 6.08949577],
[ 54.716 , 30.17285714, 1.27941431, ..., 1.19204428,
2.55785902, 11.70640996],
...,
[ 54.24897959, 47.51942857, 3.31387659, ..., 11.28125665,
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[ 33.93534694, 49.09514286, 1.38956824, ..., 5.01653026,
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[ 28.10277551, 46.85228571, 1.98661511, ..., 9.77521545,
10.1604949 , 22.25328765]]),
44: array([[ 26.72155102, 40.824 , 1.94613696, ..., 3.01052481,
2.94784985, 16.34815918],
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2.36926728, 6.18581474],
[ 51.084 , 33.97085714, 1.27117449, ..., 1.27311021,
2.58758345, 9.8776182 ],
...,
[ 52.69297959, 50.10542857, 3.30538075, ..., 13.4494221 ,
9.18237596, 7.09223359],
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[ 28.46677551, 51.72428571, 1.97811927, ..., 12.02195043,
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2.18704506, 5.65066124],
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...,
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2.74659463, 15.58356068],
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5.96979394, 7.38593513],
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4.55025047, 11.71372455],
[ 28.47077551, 48.69428571, 2.07884943, ..., 9.74424506,
8.63661798, 21.11369265]]),
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2.66420709, 15.5617307 ],
[ 20.21820408, 46.99242857, 1.4588169 , ..., 1.04238625,
2.08421159, 5.46544227],
[ 51.448 , 32.93885714, 1.27092409, ..., 1.30316286,
2.23427944, 9.70906177],
...,
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[ 28.91934694, 51.59714286, 1.380822 , ..., 5.01989566,
4.98788825, 11.48420423],
[ 24.59077551, 50.67428571, 2.17075232, ..., 9.82715532,
11.23973146, 22.06196122]]),
48: array([[ 24.72955102, 39.438 , 2.00405728, ..., 2.18731624,
3.65971085, 15.68042056],
[ 21.13420408, 48.66642857, 1.51698761, ..., 1.05857303,
2.30292949, 5.55651004],
[ 52.508 , 32.51585714, 1.3290948 , ..., 1.33637734,
2.45640971, 9.94715295],
...,
[ 52.00897959, 51.00542857, 3.36330107, ..., 12.06997204,
5.96354663, 7.17053059],
[ 35.40334694, 51.71414286, 1.5018982 , ..., 5.36642561,
4.74607626, 11.7053699 ],
[ 33.60677551, 48.16628571, 2.85832815, ..., 11.38138394,
9.3406053 , 22.24922727]]),
49: array([[ 22.97355102, 40.962 , 1.95382525, ..., 2.12872886,
2.57125686, 16.22846571],
[ 21.44220408, 43.74042857, 1.45677584, ..., 1.0314761 ,
1.9806647 , 5.49865119],
[ 54.8 , 31.26485714, 1.26888303, ..., 1.14432937,
2.13414492, 9.98132411],
...,
[ 51.83697959, 51.80642857, 3.30308929, ..., 13.19333157,
5.67281174, 8.58526714],
[ 29.47534694, 48.51614286, 1.37878094, ..., 5.26582218,
4.45754976, 11.70030719],
[ 28.08277551, 45.96428571, 1.97582781, ..., 9.78914356,
9.05491618, 22.44546336]])}}
Content source: dream-olfaction/olfaction-prediction
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