def ppf(self, pits, parameters=None):
self._check_constraints(parameters)
scalar = np.isscalar(pits)
if scalar:
pits = np.array([pits])
eta, lam = parameters
a = self.__const_a(parameters)
b = self.__const_b(parameters)
cond = pits < (1 - lam) / 2
# slight speed up for really large problems
icdf1 = t._ppf(pits[cond] / (1 - lam), eta)
icdf2 = t._ppf(.5 + (pits[~cond] - (1 - lam) / 2) / (1 + lam), eta)
icdf = -999.99 * np.ones_like(pits)
icdf[cond] = icdf1
icdf[~cond] = icdf2
icdf = (icdf * (1 + np.sign(pits - (1 - lam) / 2) * lam) *
(1 - 2 / eta)**.5 - a)
icdf = icdf / b
if scalar:
icdf = icdf[0]
return icdf
def mean_confidence_interval(data, confidence=0.95):
a = 100.0 * np.array(data)
n = len(a)
m, se = np.mean(a), scipy.stats.sem(a)
h = se * t._ppf((1+confidence)/2., n-1)
m = np.round(m, 3)
h = np.round(h, 3)
return m, h
def confidenceinterval(x: List[float], conf_level=0.95):
"""
获得置信区间
"""
a = 1.0 * np.array(x)
n = len(a)
m, se = np.mean(a), sem(a)
h = se * t._ppf((1 + conf_level) / 2., n - 1)
return m - h, m + h
def mean_confidence_interval(data, confidence=0.95):
"""
Given a list or vector of data, this returns the mean, lower, and upper
confidence intervals to the level of confidence specified (default = 95%
confidence interval).
"""
a = 1.0*np.array(data)
n = len(a)
m, se = np.mean(a), sem(a)
h = se * t._ppf((1+confidence)/2., n-1)
return m, m-h, m+h
def mean_error(config, id_algo, id_inst, seeds): x=[] for kk in range(seeds): if shared_times[id_algo,id_inst+kk*len(config.instances)] >=0.0: x.append(shared_times[id_algo,id_inst+kk*len(config.instances)]) n=len(x) if n>=2: se=stats.sem(x) h = se*t._ppf((1+0.95)/2., n-1) return (np.mean(x),h,n) else: return (np.mean(x),-1,n)
def mean_error_boxes(config, id_algo, id_inst, seeds): x=[] for kk in range(seeds): if shared_boxes[id_algo,id_inst+kk*len(config.instances)] >=0.0: x.append(shared_boxes[id_algo,id_inst+kk*len(config.instances)]) n=len(x) if n>=2: se=stats.sem(x) h = se*t._ppf((1+0.95)/2., n-1) return (np.mean(x),h,n) else: return (np.mean(x),-1,n)
def jackknife_bias_correct(pairs,confidence=None,return_all=False,
nan_remove=True,return_raw=False):
'''
Return jackknife-bias-corrected estimate from estimate-nsamples pairs
Pairs can be either a list of tuples, or a 2 x nestimates array.
If 'confidence' is between 0 and 1, return the mean with lower and upper
bounds at -/+ the confidence interval.
If 'confidence' is None, return the mean and standard error.
If 'return_all' is True, return the mean, standard error, number of points,
and confidence interval size.
'''
data = asarray(pairs)
if nan_remove:
data = data[isfinite(data)[:,0],:]
y = data[:,0]
x = 1./data[:,1]
n = len(x)
# Compute linear regression and standard error of intercept
(slope,intercept,r,p,slope_se) = linregress(x,y)
intercept_se = slope_se * sqrt(ss(x)/n)
# Return mean and SE if no value is specified:
if confidence is None:
if return_all:
if return_raw:
np = data[:,1]
max_n = max(np)
raw_mean = mean(data[np==max_n,0])
return intercept, intercept_se, n, raw_mean
else:
return intercept, intercept_se, n
else:
return intercept, intercept_se
# Otherwise return intercept with confidence
else:
t_int = t._ppf((1+confidence)/2,n-2)
intercept_int = t_int * intercept_se
if return_all:
if return_raw:
np = data[:,1]
max_n = max(np)
raw_mean = mean(data[np==max_n,0])
return intercept, intercept_se, n, intercept_int, raw_mean
else:
return intercept, intercept_se, n, intercept_int
else:
return intercept, intercept - intercept_int, intercept + intercept_int
def lowess(x, y, f=1./3., iter=3, confidence=0.95):
"""
Performs Lowess smoothing
Code adapted from: https://gist.github.com/agramfort/850437
lowess(x, y, f=2./3., iter=3) -> yest
Lowess smoother: Robust locally weighted regression.
The lowess function fits a nonparametric regression curve to a scatterplot.
The arrays x and y contain an equal number of elements; each pair
(x[i], y[i]) defines a data point in the scatterplot. The function returns
the estimated (smooth) values of y.
The smoothing span is given by f. A larger value for f will result in a
smoother curve. The number of robustifying iterations is given by iter. The
function will run faster with a smaller number of iterations.
.. todo:: double check that the confidence bounds are correct
"""
n = len(x)
r = int(np.ceil(f*n))
h = [np.sort(np.abs(x - x[i]))[r] for i in range(n)]
w = np.clip(np.abs((x[:, None] - x[None, :]) / h), 0.0, 1.0)
w = (1 - w**3)**3
yest = np.zeros(n)
delta = np.ones(n)
for iteration in range(iter):
for i in range(n):
weights = delta * w[:, i]
b = np.array([np.sum(weights*y), np.sum(weights*y*x)])
A = np.array([[np.sum(weights), np.sum(weights*x)],
[np.sum(weights*x), np.sum(weights*x*x)]])
beta = linalg.solve(A, b)
yest[i] = beta[0] + beta[1]*x[i]
residuals = y - yest
s = np.median(np.abs(residuals))
delta = np.clip(residuals / (6.0 * s), -1, 1)
delta = (1 - delta**2)**2
h = np.zeros(n)
for x_idx, x_val in enumerate(x):
r2 = np.array([v*v for i, v in enumerate(residuals) if x[i] == x_val])
n = len(r2)
se = sqrt(mean(r2)) / sqrt(len(r2))
h[x_idx] = se * t._ppf((1+confidence)/2., n-1)
return yest, yest-h, yest+h
def mean_confidence_interval(a, confidence=0.95):
"""
Helper function for calculating a mean confidence interval
Athena uses a random forest from Scikit-Learn to select the best parameters. Use this constructor to create a Selection class.
Parameters
----------
a : ?
Number of estimators (decision trees) the Random Forest will use.
confidence : float
Number of threads the Random Forest will utilize.
"""
from numpy import mean
from scipy.stats import sem, t
n = len(a)
m, se = mean(a), sem(a)
h = se * t._ppf((1 + confidence) / 2., n - 1)
return m, m - h, m + h
def crossvalidateGTR(data,
labels,
n_neighbors=1,
representation="modes",
niter=200,
k=0,
m=0,
doPCA=False,
n_components=-1,
missing=False,
missing_strategy='most_frequent',
random_state=1234,
l=-1,
s=-1,
n_folds=5,
n_repetitions=10):
print("")
print("k = sqrt(grid size), m = sqrt(radial basis function grid size), "
"l = regularization, s = RBF width factor")
print("")
if k == 0:
k = int(math.sqrt(5 * math.sqrt(data.shape[0]))) + 2
if m == 0:
m = int(math.sqrt(k))
if n_components == -1 and doPCA is True:
pca = PCA(random_state=random_state)
pca.fit(data)
n_components = np.searchsorted(pca.explained_variance_ratio_.cumsum(),
0.8) + 1
print(
"Used number of components explaining 80%% of the variance = %s\n"
% n_components)
if l < 0.0:
lvec = [0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100]
else:
lvec = [l]
if s < 0.0:
svec = [0.25, 0.5, 1.0, 1.50, 2.0]
else:
svec = [s]
savemean = 999999999
saveh = 0.0
modelvec = ""
savemeanr2 = 0.0
savehr2 = 0.0
print("k:m:s:l\tRMSE with CI\tR2 with CI\t")
for s in svec:
for l in lvec:
modelstring = str(s) + ":" + str(l)
rmsevec = []
r2vec = []
for j in range(n_repetitions):
ss = KFold(n_splits=n_folds, shuffle=True, random_state=j)
y_true = []
y_pred = []
for train_index, test_index in ss.split(data):
train = np.copy(data[train_index])
test = np.copy(data[test_index])
prediction = ugtm_predictions.GTR(
train=train,
labels=labels[train_index],
test=test,
k=k,
m=m,
s=s,
l=l,
n_neighbors=n_neighbors,
niter=niter,
representation=representation,
doPCA=doPCA,
n_components=n_components,
random_state=random_state,
missing=missing,
missing_strategy=missing_strategy)
y_pred = np.append(y_pred, prediction)
y_true = np.append(y_true, labels[test_index])
rmse = math.sqrt(mean_squared_error(y_true, y_pred))
r2 = r2_score(y_true, y_pred)
rmsevec = np.append(rmsevec, rmse)
r2vec = np.append(r2vec, r2)
mean, se = np.mean(rmsevec), st.sem(rmsevec)
h = se * t._ppf((1.0 + 0.95) / 2., len(rmsevec) - 1)
meanr2, ser2 = np.mean(r2vec), st.sem(r2vec)
hr2 = ser2 * t._ppf((1.0 + 0.95) / 2., len(r2vec) - 1)
if (mean < savemean):
savemean = mean
saveh = h
modelvec = modelstring
savemeanr2, saveser2 = np.mean(r2vec), st.sem(r2vec)
savehr2 = saveser2 * t._ppf((1 + 0.95) / 2., len(r2vec) - 1)
print("%s\t%.4f +/- %.4f\t%.4f +/- %.4f" %
(str(k) + ':' + str(m) + ':' + modelstring, mean, h, meanr2,
hr2))
print('')
print("########best GTR model##########")
print("%s\t%.4f +/- %.4f\t%.4f +/- %.4f" %
(str(k) + ':' + str(m) + ':' + modelvec, savemean, saveh, savemeanr2,
savehr2))
print("")
def mean_confidence_interval(data, confidence=0.95):
a = 1.0 * numpy.array(data)
mean, se = numpy.mean(a), stats.sem(a)
h = se * t._ppf((1 + confidence) / 2., len(a) - 1)
return mean, mean - h, mean + h
def mean_confidence_interval(x, confidence=0.95):
a = np.array(x) * 1.0
mu, se = np.mean(a), scipy.stats.sem(a)
me = se * t._ppf((1 + confidence) / 2., len(a) - 1)
return mu, mu - me, mu + me
def crossvalidatePCAC(data,
labels,
doPCA=False,
n_components=-1,
missing=False,
missing_strategy='most_frequent',
random_state=1234,
n_neighbors=1,
n_folds=5,
n_repetitions=10,
maxneighbours=11):
if n_components == -1 and doPCA is True:
pca = PCA(random_state=random_state)
pca.fit(data)
n_components = np.searchsorted(pca.explained_variance_ratio_.cumsum(),
0.8) + 1
print("Used number of components "
"explaining 80%% of the variance = %s\n" % n_components)
uniqClasses, labels = np.unique(labels, return_inverse=True)
nClasses = len(uniqClasses)
print("Classes: ", uniqClasses)
print("nClasses: ", nClasses)
print("")
print("model\tparameters=k_for_kNN\trecall with CI\t"
"precision with CI\tF1-score with CI")
print("")
if n_neighbors <= 0:
Kvec = np.arange(start=1, stop=maxneighbours, step=1, dtype=np.int32)
else:
Kvec = [n_neighbors]
savemean = -9999
nummodel = 0
savemodel = ""
for c in Kvec:
nummodel += 1
modelstring = str(c)
recallvec = []
precisionvec = []
f1vec = []
recallclassvec = np.array([])
precisionclassvec = np.array([])
f1classvec = np.array([])
meanclass = np.zeros(nClasses)
meanprecisionclass = np.zeros(nClasses)
meanf1class = np.zeros(nClasses)
seclass = np.zeros(nClasses)
seprecisionclass = np.zeros(nClasses)
sef1class = np.zeros(nClasses)
hclass = np.zeros(nClasses)
hprecisionclass = np.zeros(nClasses)
hf1class = np.zeros(nClasses)
for j in range(n_repetitions):
ss = KFold(n_splits=n_folds, shuffle=True, random_state=j)
y_true = []
y_pred = []
for train_index, test_index in ss.split(data):
train = np.copy(data[train_index])
test = np.copy(data[test_index])
processed = ugtm_preprocess.processTrainTest(
train, test, doPCA, n_components, missing,
missing_strategy)
y_pred = np.append(
y_pred,
ugtm_predictions.predictNNSimple(processed.train,
processed.test,
labels[train_index], c,
"classification"))
y_true = np.append(y_true, labels[test_index])
recall = recall_score(y_true, y_pred, average='weighted')
precision = precision_score(y_true, y_pred, average='weighted')
f1 = f1_score(y_true, y_pred, average='weighted')
recallvec = np.append(recallvec, recall)
precisionvec = np.append(precisionvec, precision)
f1vec = np.append(f1vec, f1)
recallclass = recall_score(y_true, y_pred, average=None)
precisionclass = precision_score(y_true, y_pred, average=None)
f1class = f1_score(y_true, y_pred, average=None)
if (j == 0):
recallclassvec = recallclass
precisionclassvec = precisionclass
f1classvec = f1class
else:
recallclassvec = np.vstack([recallclassvec, recallclass])
precisionclassvec = np.vstack(
[precisionclassvec, precisionclass])
f1classvec = np.vstack([f1classvec, f1class])
mean, se = np.mean(recallvec), st.sem(recallvec)
meanprecision, seprecision = np.mean(precisionvec), st.sem(
precisionvec)
meanf1, sef1 = np.mean(f1vec), st.sem(f1vec)
h = se * t._ppf((1 + 0.95) / 2., len(recallvec) - 1)
hprecision = seprecision * t._ppf((1 + 0.95) / 2.,
len(precisionvec) - 1)
hf1 = sef1 * t._ppf((1 + 0.95) / 2., len(f1vec) - 1)
if (meanf1 > savemean):
savemean = meanf1
savemodel = "Model " + str(nummodel)
for i in range(0, nClasses):
meanclass[i] = np.mean(recallclassvec[:, i])
seclass[i] = st.sem(recallclassvec[:, i])
meanf1class[i] = np.mean(f1classvec[:, i])
sef1class[i] = st.sem(f1classvec[:, i])
meanprecisionclass[i] = np.mean(precisionclassvec[:, i])
seprecisionclass[i] = st.sem(precisionclassvec[:, i])
hclass[i] = seclass[i] * \
t._ppf((1+0.95)/2., len(recallclassvec[:, i])-1)
hprecisionclass[i] = seprecisionclass[i] \
* t._ppf((1+0.95)/2.,
len(precisionclassvec[:, i])-1)
hf1class[i] = sef1class[i] * \
t._ppf((1+0.95)/2., len(f1classvec[:, i])-1)
print("Model %s\t%s\t%.4f +/- %.4f\t%.4f +/- %.4f\t%.4f +/- %.4f" %
(nummodel, modelstring, mean, h, meanprecision, hprecision,
meanf1, hf1))
for i in range(nClasses):
print("Class=%s\t%s\t%.4f +/- %.4f\t%.4f +/- %.4f\t%.4f +/- %.4f" %
(uniqClasses[i], modelstring, meanclass[i], hclass[i],
meanprecisionclass[i], hprecisionclass[i], meanf1class[i],
hf1class[i]))
print('')
print('')
print("########best nearest neighbors model##########")
print(savemodel)
print("")
def crossvalidateGTC(data,
labels,
n_neighbors=1,
representation="modes",
niter=200,
k=0,
m=0,
doPCA=False,
n_components=-1,
missing=False,
missing_strategy='most_frequent',
random_state=1234,
predict_mode="bayes",
prior="equiprobable",
l=-1.0,
s=-1.0,
n_folds=5,
n_repetitions=10):
print("")
print("k = sqrt(grid size), m = sqrt(radial basis function grid size), "
"l = regularization, s = RBF width factor")
print("")
uniqClasses, labels = np.unique(labels, return_inverse=True)
nClasses = len(uniqClasses)
print("Classes: ", uniqClasses)
print("nClasses: %s" % (nClasses))
print("")
print("model\tparameters=k:m:s:l\t"
"recall with CI\tprecision with CI\tF1-score with CI")
print("")
if k == 0:
k = int(math.sqrt(5 * math.sqrt(data.shape[0]))) + 2
if m == 0:
m = int(math.sqrt(k))
if n_components == -1 and doPCA:
pca = PCA(random_state=random_state)
pca.fit(data)
n_components = np.searchsorted(pca.explained_variance_ratio_.cumsum(),
0.8) + 1
print("Used number of components explaining 80%% of "
"the variance in whole data set = %s\n" % n_components)
if l < 0.0:
lvec = [0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100]
else:
lvec = [l]
if s < 0.0:
svec = [0.25, 0.5, 1.0, 1.50, 2.0]
else:
svec = [s]
savemean = -9999
nummodel = 0
savemodel = ""
for s in svec:
for l in lvec:
modelstring = str(k) + ':' + str(m) + ":" + str(s) + ":" + str(l)
nummodel += 1
recallvec = []
precisionvec = []
f1vec = []
recallclassvec = np.array([])
precisionclassvec = np.array([])
f1classvec = np.array([])
meanclass = np.zeros(nClasses)
meanprecisionclass = np.zeros(nClasses)
meanf1class = np.zeros(nClasses)
seclass = np.zeros(nClasses)
seprecisionclass = np.zeros(nClasses)
sef1class = np.zeros(nClasses)
hclass = np.zeros(nClasses)
hprecisionclass = np.zeros(nClasses)
hf1class = np.zeros(nClasses)
for j in range(n_repetitions):
ss = KFold(n_splits=n_folds, shuffle=True, random_state=j)
y_true = []
y_pred = []
for train_index, test_index in ss.split(data):
train = np.copy(data[train_index])
test = np.copy(data[test_index])
prediction = ugtm_predictions.GTC(
train=train,
labels=labels[train_index],
test=test,
k=k,
m=m,
s=s,
l=l,
n_neighbors=n_neighbors,
niter=niter,
representation=representation,
doPCA=doPCA,
n_components=n_components,
random_state=random_state,
missing=missing,
missing_strategy=missing_strategy,
predict_mode=predict_mode,
prior=prior)
y_true = np.append(y_true, labels[test_index])
y_pred = np.append(y_pred, prediction)
recall = recall_score(y_true, y_pred, average='weighted')
precision = precision_score(y_true, y_pred, average='weighted')
f1 = f1_score(y_true, y_pred, average='weighted')
recallvec = np.append(recallvec, recall)
precisionvec = np.append(precisionvec, precision)
f1vec = np.append(f1vec, f1)
recallclass = recall_score(y_true, y_pred, average=None)
precisionclass = precision_score(y_true, y_pred, average=None)
f1class = f1_score(y_true, y_pred, average=None)
if (j == 0):
recallclassvec = recallclass
precisionclassvec = precisionclass
f1classvec = f1class
else:
recallclassvec = np.vstack([recallclassvec, recallclass])
precisionclassvec = np.vstack(
[precisionclassvec, precisionclass])
f1classvec = np.vstack([f1classvec, f1class])
mean, se = np.mean(recallvec), st.sem(recallvec)
meanprecision, seprecision = np.mean(precisionvec), st.sem(
precisionvec)
meanf1, sef1 = np.mean(f1vec), st.sem(f1vec)
h = se * t._ppf((1 + 0.95) / 2., len(recallvec) - 1)
hprecision = seprecision * \
t._ppf((1+0.95)/2., len(precisionvec)-1)
hf1 = sef1 * t._ppf((1 + 0.95) / 2., len(f1vec) - 1)
if (meanf1 > savemean):
savemean = meanf1
savemodel = "Model " + str(nummodel)
for i in range(0, nClasses):
meanclass[i] = np.mean(recallclassvec[:, i])
seclass[i] = st.sem(recallclassvec[:, i])
meanf1class[i] = np.mean(f1classvec[:, i])
sef1class[i] = st.sem(f1classvec[:, i])
meanprecisionclass[i] = np.mean(precisionclassvec[:, i])
seprecisionclass[i] = st.sem(precisionclassvec[:, i])
hclass[i] = seclass[i] * \
t._ppf((1+0.95)/2., len(recallclassvec[:, i])-1)
hprecisionclass[i] = seprecisionclass[i] \
* t._ppf((1+0.95)/2., len(precisionclassvec[:, i])-1)
hf1class[i] = sef1class[i] * \
t._ppf((1+0.95)/2., len(f1classvec[:, i])-1)
print("Model %s\t%s\t%.4f +/- %.4f\t%.4f +/- %.4f\t%.4f +/- %.4f" %
(nummodel, modelstring, mean, h, meanprecision, hprecision,
meanf1, hf1))
for i in range(nClasses):
print(
"Class=%s\t%s\t%.4f +/- %.4f\t%.4f +/- %.4f\t%.4f +/- %.4f"
% (uniqClasses[i], modelstring, meanclass[i], hclass[i],
meanprecisionclass[i], hprecisionclass[i],
meanf1class[i], hf1class[i]))
print('')
print('')
print("########best GTC model##########")
print(savemodel)
print("")
def mean_confidence_interval(data, confidence=0.95):
a = 1.0 * np.array(data)
n = len(a)
m, se = np.mean(a), sp.stats.sem(a)
h = se * t._ppf((1 + confidence) / 2., n - 1)
return m, h
def crossvalidateSVCrbf(data,
labels,
doPCA=False,
n_components=-1,
missing=False,
missing_strategy='most_frequent',
random_state=1234,
C=1,
gamma=1,
n_folds=5,
n_repetitions=10):
if C < 0.0:
Cvec = np.power(2, np.arange(start=-5, stop=15, step=1,
dtype=np.float))
else:
Cvec = [C]
if gamma < 0.0:
gvec = np.power(2.0,
np.arange(start=-15, stop=3, step=1, dtype=np.float))
else:
gvec = [gamma]
modelvec = ""
savemean = -9999.0
saveh = 0.0
nummodel = 0
if n_components == -1 and doPCA is True:
pca = PCA(random_state=random_state)
pca.fit(data)
n_components = np.searchsorted(pca.explained_variance_ratio_.cumsum(),
0.8) + 1
print("Used number of components explaining 80%% "
"of the variance = %s\n" % n_components)
uniqClasses, labels = np.unique(labels, return_inverse=True)
nClasses = len(uniqClasses)
print("Classes: ", uniqClasses)
print("nClasses: ", nClasses)
print("")
print("model\tparameters=C:gamma\trecall with CI\t"
"precision with CI\tF1-score with CI")
print("")
for C in Cvec:
for g in gvec:
modelstring = str(C) + "-" + str(g)
nummodel += 1
recallvec = []
precisionvec = []
f1vec = []
recallclassvec = np.array([])
precisionclassvec = np.array([])
f1classvec = np.array([])
meanclass = np.zeros(nClasses)
meanprecisionclass = np.zeros(nClasses)
meanf1class = np.zeros(nClasses)
seclass = np.zeros(nClasses)
seprecisionclass = np.zeros(nClasses)
sef1class = np.zeros(nClasses)
hclass = np.zeros(nClasses)
hprecisionclass = np.zeros(nClasses)
hf1class = np.zeros(nClasses)
for j in range(n_repetitions):
ss = KFold(n_splits=n_folds, shuffle=True, random_state=j)
y_true = []
y_pred = []
for train_index, test_index in ss.split(data):
train = np.copy(data[train_index])
test = np.copy(data[test_index])
processed = ugtm_preprocess.processTrainTest(
train, test, doPCA, n_components, missing,
missing_strategy)
clf = SVC(kernel='rbf', C=C, gamma=g)
clf.fit(processed.train, labels[train_index])
y_pred = np.append(y_pred, clf.predict(processed.test))
y_true = np.append(y_true, labels[test_index])
recall = recall_score(y_true, y_pred, average='weighted')
precision = precision_score(y_true, y_pred, average='weighted')
f1 = f1_score(y_true, y_pred, average='weighted')
recallvec = np.append(recallvec, recall)
precisionvec = np.append(precisionvec, precision)
f1vec = np.append(f1vec, f1)
recallclass = recall_score(y_true, y_pred, average=None)
precisionclass = precision_score(y_true, y_pred, average=None)
f1class = f1_score(y_true, y_pred, average=None)
if (j == 0):
recallclassvec = recallclass
precisionclassvec = precisionclass
f1classvec = f1class
else:
recallclassvec = np.vstack([recallclassvec, recallclass])
precisionclassvec = np.vstack(
[precisionclassvec, precisionclass])
f1classvec = np.vstack([f1classvec, f1class])
mean, se = np.mean(recallvec), st.sem(recallvec)
meanprecision, seprecision = np.mean(precisionvec), st.sem(
precisionvec)
meanf1, sef1 = np.mean(f1vec), st.sem(f1vec)
h = se * t._ppf((1 + 0.95) / 2., len(recallvec) - 1)
hprecision = seprecision * \
t._ppf((1+0.95)/2., len(precisionvec)-1)
hf1 = sef1 * t._ppf((1 + 0.95) / 2., len(f1vec) - 1)
if (meanf1 > savemean):
savemean = meanf1
saveh = hf1
modelvec = modelstring
savemodel = "Model " + str(nummodel)
for i in range(0, nClasses):
meanclass[i], seclass[i] = np.mean(recallclassvec[:, i]), \
st.sem(recallclassvec[:, i])
meanf1class[i], sef1class[i] = np.mean(f1classvec[:, i]), \
st.sem(f1classvec[:, i])
meanprecisionclass[i] = np.mean(precisionclassvec[:, i])
seprecisionclass[i] = st.sem(precisionclassvec[:, i])
hclass[i] = seclass[i] * \
t._ppf((1+0.95)/2., len(recallclassvec[:, i])-1)
hprecisionclass[i] = seprecisionclass[i] * \
t._ppf((1+0.95)/2., len(precisionclassvec[:, i])-1)
hf1class[i] = sef1class[i] * \
t._ppf((1+0.95)/2., len(f1classvec[:, i])-1)
print("Model %s\t%s\t%.4f +/- %.4f\t%.4f +/- %.4f\t%.4f +/- %.4f" %
(nummodel, modelstring, mean, h, meanprecision, hprecision,
meanf1, hf1))
for i in range(nClasses):
print(
"Class=%s\t%s\t%.4f +/- %.4f\t%.4f +/- %.4f\t%.4f +/- %.4f"
% (uniqClasses[i], modelstring, meanclass[i], hclass[i],
meanprecisionclass[i], hprecisionclass[i],
meanf1class[i], hf1class[i]))
print("")
print("")
print("########best RBF SVM model##########")
print(savemodel)
print("")
def crossvalidateSVR(data,
labels,
doPCA=False,
n_components=-1,
missing=False,
missing_strategy='most_frequent',
random_state=1234,
C=-1,
epsilon=-1,
n_folds=5,
n_repetitions=10):
uniqClasses, labels = np.unique(labels, return_inverse=True)
if C < 0.0:
Cvec = np.power(2, np.arange(start=-5, stop=15, step=1,
dtype=np.float))
else:
Cvec = [C]
if epsilon < 0.0:
EpsVec = [0, 0.01, 0.1, 0.5, 1, 2, 4]
else:
EpsVec = [epsilon]
modelvec = ""
savemean = 99999
saveh = 0.0
savemeanr2 = 0.0
savehr2 = 0.0
if n_components == -1 and doPCA is True:
pca = PCA(random_state=random_state)
pca.fit(data)
n_components = np.searchsorted(pca.explained_variance_ratio_.cumsum(),
0.8) + 1
print("Used number of components explaining 80%%"
"of the variance = %s\n" % n_components)
print("C:epsilon\tRMSE with CI\tR2 with CI\t")
for C in Cvec:
for eps in EpsVec:
modelstring = str(C) + ":" + str(eps)
rmsevec = []
r2vec = []
for j in range(n_repetitions):
ss = KFold(n_splits=n_folds, shuffle=True, random_state=j)
y_true = []
y_pred = []
for train_index, test_index in ss.split(data):
train = np.copy(data[train_index])
test = np.copy(data[test_index])
processed = ugtm_preprocess.processTrainTest(
train, test, doPCA, n_components, missing,
missing_strategy)
clf = SVR(kernel='linear', C=C, epsilon=eps)
clf.fit(processed.train, labels[train_index])
y_pred = np.append(y_pred, clf.predict(processed.test))
y_true = np.append(y_true, labels[test_index])
rmse = math.sqrt(mean_squared_error(y_true, y_pred))
r2 = r2_score(y_true, y_pred)
rmsevec = np.append(rmsevec, rmse)
r2vec = np.append(r2vec, r2)
mean, se = np.mean(rmsevec), st.sem(rmsevec)
h = se * t._ppf((1 + 0.95) / 2., len(rmsevec) - 1)
meanr2, ser2 = np.mean(r2vec), st.sem(r2vec)
hr2 = ser2 * t._ppf((1 + 0.95) / 2., len(r2vec) - 1)
if (mean < savemean):
savemean = mean
saveh = h
modelvec = modelstring
savemeanr2, saveser2 = np.mean(r2vec), st.sem(r2vec)
savehr2 = saveser2 * t._ppf((1 + 0.95) / 2., len(r2vec) - 1)
print("%s\t%.4f +/- %.4f\t%.4f +/- %.4f" %
(modelstring, mean, h, meanr2, hr2))
print('')
print("########best linear SVM model##########")
print("%s\t%.4f +/- %.4f\t%.4f +/- %.4f" %
(modelvec, savemean, saveh, savemeanr2, savehr2))
print("")
def crossvalidatePCAR(data,
labels,
doPCA=False,
n_components=-1,
missing=False,
missing_strategy='most_frequent',
random_state=1234,
n_neighbors=1,
n_folds=5,
n_repetitions=10,
maxneighbours=11):
if n_components == -1 and doPCA is True:
pca = PCA(random_state=random_state)
pca.fit(data)
n_components = np.searchsorted(pca.explained_variance_ratio_.cumsum(),
0.8) + 1
print(
"Used number of components explaining 80%% of the variance = %s\n"
% n_components)
print("")
uniqClasses, labels = np.unique(labels, return_inverse=True)
if n_neighbors <= 0:
Kvec = np.arange(start=1, stop=maxneighbours, step=1, dtype=np.int32)
else:
Kvec = [n_neighbors]
modelvec = ""
savemean = 99999
saveh = 0.0
savemeanr2 = 0.0
savehr2 = 0.0
nummodel = 0
print("k = number of nearest neighbours\tRMSE with CI\tR2 with CI\t")
for c in Kvec:
nummodel += 1
modelstring = str(c)
rmsevec = []
r2vec = []
for j in range(n_repetitions):
ss = KFold(n_splits=n_folds, shuffle=True, random_state=j)
y_true = []
y_pred = []
for train_index, test_index in ss.split(data):
train = np.copy(data[train_index])
test = np.copy(data[test_index])
processed = ugtm_preprocess.processTrainTest(
train, test, doPCA, n_components, missing,
missing_strategy)
y_pred = np.append(
y_pred,
ugtm_predictions.predictNNSimple(processed.train,
processed.test,
labels[train_index], c,
"regression"))
y_true = np.append(y_true, labels[test_index])
rmse = math.sqrt(mean_squared_error(y_true, y_pred))
r2 = r2_score(y_true, y_pred)
rmsevec = np.append(rmsevec, rmse)
r2vec = np.append(r2vec, r2)
mean, se = np.mean(rmsevec), st.sem(rmsevec)
h = se * t._ppf((1 + 0.95) / 2., len(rmsevec) - 1)
meanr2, ser2 = np.mean(r2vec), st.sem(r2vec)
hr2 = ser2 * t._ppf((1 + 0.95) / 2., len(r2vec) - 1)
if (mean < savemean):
savemean = mean
saveh = h
modelvec = modelstring
savemeanr2, saveser2 = np.mean(r2vec), st.sem(r2vec)
savehr2 = saveser2 * t._ppf((1 + 0.95) / 2., len(r2vec) - 1)
print("%s\t%.4f +/- %.4f\t%.4f +/- %.4f" %
(modelstring, mean, h, meanr2, hr2))
print('')
print("########best nearest neighbors model##########")
print("%s\t%.4f +/- %.4f\t%.4f +/- %.4f" %
(modelvec, savemean, saveh, savemeanr2, savehr2))
print("")
