def test_extract_trial_features_real():
csvs = [
"./data/fh_pca_space_merge_Insula_rt_fast.csv",
"./data/fh_pca_space_merge_Insula_rt_slow.csv"
]
# load_dimreduce_data_fromcl(csvs, feature_cols, label_col, cv_col, trial_tr_col)
feature_col = range(0,3)
label_col = 5
cv_col = 6
trial_tr_col = 7
Xs, ys, indices, cvcodes = load_dimreduce_data_fromcl(
csvs, feature_col, label_col, cv_col, trial_tr_col
)
X = np.concatenate(Xs)
y = create_y(np.concatenate(ys))
trials = np.concatenate(indices)
windowstr = '0:10'
window = range(*[int(i) for i in windowstr.split(':')])
Xfea, indexfea, otherfea = extract_trial_features(
X, trials, window, [trials], None
)
yea = otherfea[0]
# Xmax, Xmin, Xdiff, Xmean, Xslope
print("Xmax\n{0}".format(Xfea[:,0:3]))
print("Xmin\n{0}".format(Xfea[:,3:6]))
print("Xdiff\n{0}".format(Xfea[:,6:9]))
print("Xmean\n{0}".format(Xfea[:,9:12]))
print("Xslope\n{0}".format(Xfea[:,12:15]))
def simple(Xtrain, Xtest, labels_train, labels_test, clf, verbose):
"""Run a very simple classification exp.
Parameters
----------
X - a 2d array, column oriented
labels - a list of class labels, one for each row in X
clf - a sklearn classifier (that implements .fit() and
.predict())
verbose - Print out useful debugging/status info (True). If False
this function is silent.
Returns
-------
truths - a list of correct classes for each test set
predictions - a list of the predicted classes for each test set.
"""
Xtest = np.array(Xtest)
Xtrain = np.array(Xtrain)
labels_train = np.array(labels_train)
labels_test = np.array(labels_test)
ytrain = create_y(labels_train)
ytest = create_y(labels_test)
## Labels as ints
if verbose:
print("\tTrain labels:")
print_label_counts(labels_train)
print("\tTest labels:")
print_label_counts(labels_test)
print("\tShapes of Xtrain and ytrain: {0}, {1}".format(
Xtrain.shape, ytrain.shape))
print("\tNumber of Xtest and ytest: {0}, {1}".format(
Xtest.shape, ytest.shape))
clf.fit(scale(Xtrain), ytrain)
predictions = clf.predict(scale(Xtest))
truths = ytest
return truths, predictions
def fit_transform(self, X, y, trial_index, window, tr):
"""Converts X into time-avearage trials and decomposes that
matrix, possibly several times depending on y.
Parameters
----------
X : 2D array-like (n_sample, n_feature)
The data to decompose
y : 1D array, None by default
Sample labels for the data
trial_index : 1D array (n_sample, )
Each unique entry should match a trial.
window : int
Trial length
norm : True
A dummy argument
Return
------
Xcs : a list of 2D arrays (n_sample, n_components)
The components for each unique y.
csnames : 1D array
The names of the components matrices
"""
selector = fs.SelectPercentile(percentile=20)
Xsel = selector.fit_transform(X, create_y(y))
Xtrials = []
Xcs = []
csnames = []
Xtrial, feature_names = self.avgfn(Xsel, y, trial_index, window, tr)
unique_fn = sort_nanfirst(unique_nan(feature_names))
# Split up by feature_names
for yi in unique_fn:
Xtrials.append(Xtrial[:, feature_names == yi])
# and decompose.
if self.mode == 'decompose':
Xcs = [self._ft(Xt) for Xt in Xtrials]
elif self.mode == 'cluster':
Xcs = [self._fp(Xt) for Xt in Xtrials]
else:
raise ValueError("mode not understood.")
ti_cs = [np.arange(xc.shape[0]) for xc in Xcs]
## In this case, Xcs[i] is only 1 trial long.
return Xcs, unique_fn, ti_cs
def simpleCV(X, labels, cv, clf, verbose=True):
"""Run a simple CV based classification exp.
Parameters
----------
X - a 2d array, column oriented
labels - a list of class labels, one for each row in X
cv - a sklearn cross-validation object
clf - a sklearn classifier (that implements .fit() and
.predict())
verbose - Print out useful debugging/status info (True). If False
this function is silent.
Returns
-------
truths - a list of correct classes for each test set
predictions - a list of the predicted classes for each test set.
"""
X = np.array(X)
labels = np.array(labels)
y = create_y(labels)
## Labels as ints
truths = []
predictions = []
for train_index, test_index in cv:
# ----
# Partition the data
Xtrain = X[train_index,:]
Xtest = X[test_index,:]
ytrain = y[train_index]
ytest = y[test_index]
if verbose:
print("Next fold:")
print("\tShapes of Xtrain and Xtest: {0}, {1}".format(
Xtrain.shape, Xtest.shape))
print("\tNumber of ytrain and ytest: {0}, {1}".format(
ytrain.shape, ytest.shape))
# ----
# Class!
clf.fit(scale(Xtrain), ytrain)
truths.append(ytest)
predictions.append(clf.predict(scale(Xtest)))
return truths, predictions
def _data(csvs, feature_index, window, label_col, trial_tr_col):
"""Data loading and feature selection helper."""
Xs, ys, indices, cvcodes = load_dimreduce_data_fromcl(
csvs, feature_index, label_col,
label_col, trial_tr_col
)
X = np.concatenate(Xs)
y = create_y(np.concatenate(ys))
index = np.concatenate(indices)
cvcode = np.concatenate(cvcodes)
X, index, othermeta = extract_trial_features(X, index,
window, [y, cvcode], None
)
y, _ = othermeta ## toss cvcode, not applicable
return X, y
def fit_transform(self, X, y, trial_index, window, tr):
selector = fs.SelectPercentile(percentile=25)
Xsel = selector.fit_transform(X, create_y(y))
import ipdb; ipdb.set_trace()
if self.mode == 'decompose':
Xc = self._ft(Xsel)
elif self.mode == 'cluster':
Xc = self._fp(Xsel)
else:
raise ValueError("mode not understood.")
unique_y = sort_nanfirst(unique_nan(y))
Xcs = [Xc[y == uy,:] for uy in unique_y]
ti_cs = [trial_index[y == uy] for uy in unique_y]
return Xcs, unique_y, ti_cs
args = parser.parse_args()
prng = np.random.RandomState(42)
# ----
# Load and preprocess data
feature_index = range(*[int(i) for i in args.data.split(':')])
csvs = args.t
# load_dimreduce_data_fromcl(csvs, feature_cols, label_col, cv_col, trial_tr_col)
Xs, ys, indices, cvcodes = load_dimreduce_data_fromcl(
csvs, feature_index, args.labels,
args.cv, args.trial_tr
)
X = np.concatenate(Xs)
y = create_y(np.concatenate(ys))
index = np.concatenate(indices)
cvcode = np.concatenate(cvcodes)
window = range(*[int(i) for i in args.window.split(':')])
X, index, othermeta = extract_trial_features(
X, index, window, [y, cvcode], None
)
y, cvcode = othermeta
assert X.shape[0] == y.shape[0], "X and y length mismatch"
# ----
# Setup CV:
#
# Kfold splitting by label or by custom label
if (X is None) and (y is None):
X = np.asarray(dftmp.ix[:,feature_index])
y = np.asarray(dftmp.ix[:,args.labels], dtype=np.str)
index = np.asarray(dftmp.ix[:,args.index])
## Otherwise stack
else:
X = np.vstack([X, np.asarray(dftmp.ix[:,feature_index])])
y = np.concatenate([y, np.asarray(dftmp.ix[:,args.labels],
dtype=np.str)])
index = np.concatenate([index, np.asarray(dftmp.ix[:,args.index])])
# ----
# Preproces
# ----
# Convert y to integer codes
y = create_y(y)
# Sane so far?
assert checkX(X)
assert X.shape[0] == y.shape[0], "X and y length mismatch"
# Use trial chunks of labels to CV?
if args.labels == args.index:
cv = KFold(y.shape[0], n_folds=5, indices=True)
else:
# ----
# To split by trial
# Convert the trial index
# to trial/chunk counter
#chunks = []
#cnt = -1
def fir(X, y, trial_index, window, tr):
""" Average trials for each feature in X, using Burock's
(2000) method.
Parameters
----------
X : 2D array-like (n_sample, n_feature)
The data to decompose
y : 1D array, None by default
Sample labels for the data. In y, np.nan and 'nan' values
are treated as baseline labels.
trial_index : 1D array (n_sample, )
Each unique entry should match a trial.
window : int
Trial length
Return
------
Xfir : a 2D arrays (n_feature*unique_y, window)
The average trials
feature_names : 1D array
"""
# Norm then pad.
scaler = MinMaxScaler(feature_range=(0, 1))
X = scaler.fit_transform(X.astype(np.float))
X = np.vstack([X, np.ones((window, X.shape[1]), dtype=np.float)])
# Save the org y names
ynames = sorted(np.unique(y))
ynames = unique_sorted_with_nan(ynames)
# y becomes integers
y = create_y(y)
# Make the design matrix.
dm = _create_dm(y, window)
# dm DEBUG
#import time
#np.savetxt("dm-{0}".format(time.strftime("%m_%d_%Y_%H_%s_%m")), dm, fmt="%1.0f")
dm = np.matrix(dm)
# FIR!
fir_names = []
firs = []
for j in range(X.shape[1]):
x = np.matrix(X[:, j])
fir = np.array(np.linalg.pinv(dm.T * dm) * dm.T * x.T)[0:-1]
## Drop dummy
fir = fir.reshape(len(ynames) - 1, window)
firs.append(fir)
fir_names.extend(ynames[1:]) ## Drop nan/baseline
Xfir = np.vstack(firs).transpose()
fir_names = np.asarray(fir_names)
assert checkX(Xfir)
assert Xfir.shape[0] == window, ("After FIR rows not equal to window")
assert Xfir.shape[1] == (len(ynames[1:]) *
X.shape[1]), ("After"
"FIR wrong number of features")
assert fir_names.shape[0] == Xfir.shape[1], ("fir_names and Xfir"
"don't match")
return Xfir, fir_names
def make_bold(cond,
index,
wheelerdata,
cond_to_rt,
filtfile=None,
TR=2,
trname="TR",
n_features=10,
n_univariate=None,
n_accumulator=None,
n_decision=None,
n_noise=None,
drift_noise=False,
step_noise=False,
z_noise=False,
drift_noise_param=None,
step_noise_param=None,
z_noise_param=None,
noise_f=white,
hrf_f=None,
hrf_params=None,
prng=None):
"""Make BOLD timecourse features based on Wheelerdata
Parameters
---------
cond : str
A condition name found in the wheelerdata objects metadata
index : str
A name of a trial index found in the wheelerdata object metadata
wheelerdata : object, instance of Wheelerdata
A Wheelerdata object
cond_to_rt: dict
A map of cond (key) to reaction time (item, (int, float))
filtfile : str, None
A name of json file designed for reprocessing Wheelerdata metadata
TR : float, int
The repitition time of the experiement
trname : str
The name of the index of TRs in the metadata
n_features : int
The number of features in total (other n_* arguements
must sum to this value
n_univariate : int
The number of univariate (boxcar) features
n_accumulator : int
The number of accumulator features
n_decision : int
The number of decision features
n_noise : int
The number of noise features
drift_noise : boolean, optional
Add noise to the drift rate of the accumulator features
step_noise : boolean, optional
Add Noise to each step accumulator features
z_noise : boolean, optional
Add noise to the start value of accumulator features
drift_noise_param : None or dict, optional
Parameters for drift_noise which is drawn from a
Gaussian distribution. None defaults to:
`{"loc": 0, "scale" : 0.5}`
step_noise_param : None or dict, optional
Parameters for step_noise which is drawn from a
Gaussian distribution. None defaults to:
`{"loc" : 0, "scale" : 0.2, "size" : 1}`
z_noise_param : None or dict, optional
Parameters for z_noise which is drawn from the uniform
distribution. None defaults to:
`{"low" : 0.01, "high" : 0.5, "size" : 1}`
noise_f : function, optional
Produces noise, must have signatures like `noise, prng = f(N, prng)`
hrf_f : function, optional
Returns a haemodynamic response, signature hrf_f(**hrf_params)
hrf_params : dict
Keyword parameters for hrf_f
prng : None or RandomState object
Allows for independent random draws, used for all
random sampling
"""
# ----
# Feature composition
if n_noise == None:
n_noise = 0
if n_accumulator == None:
n_accumulator = 0
if n_decision == None:
n_decision = 0
if n_univariate == None:
n_univariate = (n_features - n_noise - n_accumulator - n_decision)
if (n_features - n_univariate - n_accumulator - n_noise - n_decision) != 0:
raise ValueError("The number of features don't add up.")
# Load wheelerdata
metas = wheelerdata.get_RT_metadata_paths()
# Get to work simulating
Xs, ys, yindices = [], [], []
for meta in metas:
# Get data, preprocess too,
data = csv_to_targets(meta)
data = tr_pad_targets(data, trname, data[trname].shape[0], pad=np.nan)
if filtfile is not None:
data = reprocess_targets(filtfile, data, np.nan,
("TR", "trialcount"))
# Check cond_to_rt
for c in unique_nan(data[cond]):
try:
cond_to_rt[c]
except KeyError:
raise KeyError("{0} not present in cond_to_rt".format(c))
# use cond to create y
y = create_y(data[cond])
yindex = data[index]
# make accumulator and decision traces
if n_accumulator > 0:
data["accumulator"] = _make_accumulator_array(y,
yindex,
cond_to_rt,
drift_noise,
step_noise,
z_noise,
drift_noise_param,
step_noise_param,
z_noise_param,
prng=prng)
if n_decision > 0:
data["decision"] = _make_decision_array(y, yindex, cond_to_rt)
# Populate Xmeta
boldsim = Reproduce(y,
data,
noise_f=noise_f,
hrf_f=hrf_f,
hrf_params=hrf_params,
TR=TR,
prng=prng)
boldsim.create_dm_from_y(convolve=False)
n_sample_feature = boldsim.dm.shape[0]
Xmeta = np.zeros((n_sample_feature, n_features))
# 1. univariate features
start = 0
stop = n_univariate
for j in range(start, stop):
boldsim.create_bold(np.sum(boldsim.dm[:, 1:], axis=1),
convolve=True)
Xmeta[:, j] = boldsim.bold
# 2. accumulator features
start = stop
stop = start + n_accumulator
for j in range(start, stop):
boldsim.create_bold(data["accumulator"], convolve=True)
Xmeta[:, j] = boldsim.bold
# 3. decision features
start = stop
stop = start + n_decision
for j in range(start, stop):
boldsim.create_bold(data["decision"], convolve=True)
Xmeta[:, j] = boldsim.bold
# 4. noise features:
start = stop
stop = start + n_noise
for j in range(start, stop):
# Drop baseline from noise
randbold = rand(boldsim.dm.shape[0])
randbold[boldsim.y == 0] = 0.0
boldsim.create_bold(randbold, convolve=True)
Xmeta[:, j] = boldsim.bold
Xs.append(Xmeta)
ys.append(y)
yindices.append(yindex)
return Xs, ys, yindices
def fir(X, y, trial_index, window, tr):
""" Average trials for each feature in X, using Burock's
(2000) method.
Parameters
----------
X : 2D array-like (n_sample, n_feature)
The data to decompose
y : 1D array, None by default
Sample labels for the data. In y, np.nan and 'nan' values
are treated as baseline labels.
trial_index : 1D array (n_sample, )
Each unique entry should match a trial.
window : int
Trial length
Return
------
Xfir : a 2D arrays (n_feature*unique_y, window)
The average trials
feature_names : 1D array
"""
# Norm then pad.
scaler = MinMaxScaler(feature_range=(0, 1))
X = scaler.fit_transform(X.astype(np.float))
X = np.vstack([X, np.ones((window, X.shape[1]), dtype=np.float)])
# Save the org y names
ynames = sorted(np.unique(y))
ynames = unique_sorted_with_nan(ynames)
# y becomes integers
y = create_y(y)
# Make the design matrix.
dm = _create_dm(y, window)
# dm DEBUG
#import time
#np.savetxt("dm-{0}".format(time.strftime("%m_%d_%Y_%H_%s_%m")), dm, fmt="%1.0f")
dm = np.matrix(dm)
# FIR!
fir_names = []
firs = []
for j in range(X.shape[1]):
x = np.matrix(X[:,j])
fir = np.array(np.linalg.pinv(dm.T * dm) * dm.T * x.T)[0:-1]
## Drop dummy
fir = fir.reshape(len(ynames)-1, window)
firs.append(fir)
fir_names.extend(ynames[1:]) ## Drop nan/baseline
Xfir = np.vstack(firs).transpose()
fir_names = np.asarray(fir_names)
assert checkX(Xfir)
assert Xfir.shape[0] == window, ("After FIR rows not equal to window")
assert Xfir.shape[1] == (len(ynames[1:]) * X.shape[1]), ("After"
"FIR wrong number of features")
assert fir_names.shape[0] == Xfir.shape[1], ("fir_names and Xfir"
"don't match")
return Xfir, fir_names