The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.
Example 1
def plot_power_rose(wind_directions,power,num_wd_bins):
"""Plot a power rose. Kind of a hacked wind rose.
Arguments:
wind_directions -- a np array of wind directions filtered for icing
power -- a np array of percent power production corresponding to wind_directions
num_wd_bins -- the number of wind direction bins to include on the rose.
"""
dir_bins = np.array(np.linspace(0.0,360.0 - 360.0 / num_wd_bins,num_wd_bins))
#Find the total amount of power produced in each sector.
dir_power = np.array([np.nansum(filter_obstacles(power,wind_directions,(wd + 180.0) % 360.0, 360 - 360/float(num_wd_bins))) for wd in dir_bins])
dir_power = np.round(dir_power * 100.0 / np.nansum(dir_power), decimals=0) #Normalize it and round to nearest int.
proportional_wd = np.array([])
for i in range(len(dir_power)):
for n in range(int(dir_power[i])): #Loop as many times as the percent of power produced in this sector.
proportional_wd = np.append(proportional_wd,dir_bins[i]) #i.e., if 50% of power comes from the south, append 50 instances of 180.0 degrees.
ones = np.ones(len(proportional_wd))
ax = new_axes()
ax.bar(proportional_wd, ones,normed=False, opening=0.8, edgecolor='white', bins = [0.0,100.], cmap=cm.RdGy)
set_legend(ax) Example 2
def ests_ll_quad(self, params):
"""
Calculate the loglikelihood given model parameters `params`.
This method uses Gaussian quadrature, and thus returns an *approximate*
integral.
"""
mu0, gamma0, err0 = np.split(params, 3)
x = np.tile(self.z, (self.cfg.QCOUNT, 1, 1)) # (QCOUNTXnhospXnmeas)
loc = mu0 + np.outer(QC1, gamma0)
loc = np.tile(loc, (self.n, 1, 1))
loc = np.transpose(loc, (1, 0, 2))
scale = np.tile(err0, (self.cfg.QCOUNT, self.n, 1))
zs = lpdf_3d(x=x, loc=loc, scale=scale)
w2 = np.tile(self.w, (self.cfg.QCOUNT, 1, 1))
wted = np.nansum(w2 * zs, axis=2).T # (nhosp X QCOUNT)
qh = np.tile(QC1, (self.n, 1)) # (nhosp X QCOUNT)
combined = wted + norm.logpdf(qh) # (nhosp X QCOUNT)
return logsumexp(np.nan_to_num(combined), b=QC2, axis=1) # (nhosp) Example 3
def chi2(b, dataset, model1='phoebe1model', model2='phoebe2model'):
ds = b.get_dataset(dataset) - b.get_dataset(dataset, method='*dep')
if ds.method=='lc':
depvar = 'fluxes'
elif ds.method=='rv':
depvar = 'rvs'
else:
raise NotImplementedError("chi2 doesn't support dataset method: '{}'".format(ds.method))
chi2 = 0.0
for comp in ds.components if len(ds.components) else [None]:
if comp=='_default':
continue
# phoebe gives nans for RVs when a star is completely eclipsed, whereas
# phoebe1 will give a value. So let's use nansum to just ignore those
# regions of the RV curve
print "***", depvar, dataset, model1, model2, comp
chi2 += np.nansum((b.get_value(qualifier=depvar, dataset=dataset, model=model1, component=comp, context='model')\
-b.get_value(qualifier=depvar, dataset=dataset, model=model2, component=comp, context='model'))**2)
return chi2 Example 4
def weighted_average(weights, pep_abd, group_ix):
'''
Calculate weighted geometric means for sample groups
Inputs:
weights: weights of peptides after filtering by loading threshold
pep_abd: peptide abundances after filtering by loading threshold
group_ix: array indexes of sample groups
'''
global nGroups
abd_w = pep_abd * weights[..., None]
one_w = abd_w / abd_w * weights[..., None]
a_sums = np.nansum(abd_w, axis=0)
w_sums = np.nansum(one_w, axis=0)
expr = np.empty(nGroups)
for i in range(expr.shape[0]):
expr[i] = a_sums[group_ix[i]].sum() / w_sums[group_ix[i]].sum()
return expr Example 5
def pwdist_canberra(self, seq1idx, seq2idx):
"""Compute the Canberra distance between two vectors.
References:
1. http://scipy.org/
Notes:
When `u[i]` and `v[i]` are 0 for given i, then
the fraction 0/0 = 0 is used in the calculation.
"""
u = self[seq1idx]
v = self[seq2idx]
olderr = np.seterr(invalid='ignore')
try:
d = np.nansum(abs(u - v) / (abs(u) + abs(v)))
finally:
np.seterr(**olderr)
return d Example 6
def normalize(self, to=1.0):
"""
This function ...
:param to:
:return:
"""
# Calculate the sum of all the pixels
sum = np.nansum(self)
# Calculate the conversion factor
factor = to / sum
# Multiply the frame with the conversion factor
self.__imul__(factor)
# ----------------------------------------------------------------- Example 7
def normalize(self, to=1.0):
"""
This function ...
:param to:
:return:
"""
# Calculate the sum of all the pixels
sum = np.nansum(self)
# Calculate the conversion factor
factor = to / sum
# Multiply the frame with the conversion factor
self.__imul__(factor)
# ----------------------------------------------------------------- Example 8
def calculate_optimizer_time(trials):
optimizer_time = []
time_idx = 0
optimizer_time.append(trials.cv_starttime[0] - trials.starttime[time_idx])
for i in range(len(trials.cv_starttime[1:])):
if trials.cv_starttime[i + 1] > trials.endtime[time_idx]:
optimizer_time.append(trials.endtime[time_idx] -
trials.cv_endtime[i])
time_idx += 1
optimizer_time.append(trials.cv_starttime[i + 1] -
trials.starttime[time_idx])
else:
optimizer_time.append(trials.cv_starttime[i + 1] -
trials.cv_endtime[i])
optimizer_time.append(trials.endtime[time_idx] - trials.cv_endtime[-1])
trials.optimizer_time = optimizer_time
# We need to import numpy again
import numpy as np
return np.nansum(optimizer_time) Example 9
def lnlike(self, pars):
# Pull theta out of pars
theta = pars[:self.Nbins]
# Generate the inner summation
gamma = np.ones_like(self.bin_idx) * np.nan
good = (self.bin_idx < self.Nbins) & (self.bin_idx >= 0) # nans in q get put in nonexistent bins
gamma[good] = self.Nobs * self.censoring_fcn(self.mcmc_samples[good]) * theta[self.bin_idx[good]]
summation = np.nanmean(gamma, axis=1)
# Calculate the integral
I = self._integral_fcn(theta)
# Generate the log-likelihood
ll = -I + np.nansum(np.log(summation))
return ll Example 10
def test_sum_inf(self):
import pandas.core.nanops as nanops
s = Series(np.random.randn(10))
s2 = s.copy()
s[5:8] = np.inf
s2[5:8] = np.nan
self.assertTrue(np.isinf(s.sum()))
arr = np.random.randn(100, 100).astype('f4')
arr[:, 2] = np.inf
with cf.option_context("mode.use_inf_as_null", True):
assert_almost_equal(s.sum(), s2.sum())
res = nanops.nansum(arr, axis=1)
self.assertTrue(np.isinf(res).all()) Example 11
def r(self):
"""
Pearson correlation of the fitted Variogram
:return:
"""
# get the experimental and theoretical variogram and cacluate means
experimental, model = self.__model_deviations()
mx = np.nanmean(experimental)
my = np.nanmean(model)
# claculate the single pearson correlation terms
term1 = np.nansum(np.fromiter(map(lambda x, y: (x-mx) * (y-my), experimental, model), np.float))
t2x = np.nansum(np.fromiter(map(lambda x: (x-mx)**2, experimental), np.float))
t2y = np.nansum(np.fromiter(map(lambda y: (y-my)**2, model), np.float))
return term1 / (np.sqrt(t2x * t2y)) Example 12
def entropy(v, axis=0):
"""
Optimized implementation of entropy. This version is faster than that in
scipy.stats.distributions, particularly over long vectors.
"""
v = numpy.array(v, dtype='float')
s = numpy.sum(v, axis=axis)
with numpy.errstate(divide='ignore', invalid='ignore'):
rhs = numpy.nansum(v * numpy.log(v), axis=axis) / s
r = numpy.log(s) - rhs
# Where dealing with binarized events, it is possible that an event always
# occurs and thus has 0 information. In this case, the negative class
# will have frequency 0, resulting in log(0) being computed as nan.
# We replace these nans with 0
nan_index = numpy.isnan(rhs)
if nan_index.any():
r[nan_index] = 0
return r Example 13
def __call__(self, y_true_proba, y_proba):
"""
See Murphy (1973) A vector partition of the probability score
"""
np.seterr(divide="ignore")
pos_obs_freq = np.histogram(
y_proba[y_true_proba == 1], bins=self.bins)[0]
fore_freq = np.histogram(y_proba, bins=self.bins)[0]
climo = y_true_proba.mean()
unc = climo * (1 - climo)
pos_obs_rel_freq = np.zeros(pos_obs_freq.size)
for p in range(pos_obs_rel_freq.size):
if fore_freq[p] > 0:
pos_obs_rel_freq[p] = pos_obs_freq[p] / fore_freq[p]
else:
pos_obs_rel_freq[p] = np.nan
score = np.nansum(fore_freq * (pos_obs_rel_freq - climo) ** 2)
score /= float(y_proba.size)
return score / unc Example 14
def cluster_f_measure(ytrue, pred):
# higher is better
assert len(ytrue) == len(pred), 'inputs length must be equal.'
label2ix = {label: i for i, label in enumerate(np.unique(ytrue))}
_ytrue = np.array([label2ix[v] for v in ytrue])
nSize = len(_ytrue)
nClassTrue = len(np.unique(ytrue))
nClassPred = len(np.unique(pred))
f = np.zeros((nClassTrue, nClassPred)).astype(dtype=np.float64)
for i in xrange(nClassTrue):
freq_i = len(_ytrue[_ytrue == i])
for j in xrange(nClassPred):
freq_j = len(pred[pred == j])
freq_i_j = float(len(filter(lambda x: x == j, pred[_ytrue == i])))
precision = freq_i_j / freq_j if freq_j != 0 else 0
recall = freq_i_j / freq_i if freq_i != 0 else 0
if precision == 0 or recall == 0:
f[i, j] = 0.
else:
f[i, j] = 2. * (precision * recall) / (precision + recall)
return np.nansum([f[i][j] * len(_ytrue[_ytrue == i]) for i in xrange(nClassTrue) for j in xrange(nClassPred)]) / nSize Example 15
def ponderateByConcentration():
print 'Loading feature concentration..........'
sdFile = open('varStandarDevs.txt','rb')
standevs=pickle.load(sdFile)
sdFile.close()
totDevs={}
for feature in standevs:
totDevs[feature]=sum([abs(standevs[feature][si]) for si in range(len(standevs[feature]))])/len(standevs[feature])
localF=['turningAngle','turningAngleDifference','Coord','LP']
globalF=['accAngle','coG','relStrokeLength','liS','quadraticError']
totalF=['turningAngle','turningAngleDifference','Coord','LP','Style','accAngle','coG','relStrokeLength','liS','quadraticError']
print 'Ponderating features..........'
weights={}
norm=np.nansum([1/float(math.sqrt(totDevs[feature])) for feature in totalF])
for feature in totalF:
weights[feature]=(1/float(math.sqrt(totDevs[feature])))/float(norm)
print 'Features weighted as'
print weights
return weights Example 16
def nandot(a, b): # TODO: speed up, avoid copying data
"A numpy.dot() replacement which treats (0*-Inf)==0 and works around BLAS NaN bugs in matrices."
# important note: a contains zeros and b contains inf/-inf/nan, not the other way around
# workaround for zero*-inf=nan in dot product (must be 0 according to 0^0=1 with probabilities)
# 1) calculate dot product
# 2) select nan entries
# 3) re-calculate matrix entries where 0*inf = 0 using np.nansum()
tmp = np.dot(a, b)
indices = np.where(np.isnan(tmp))
ri, ci = indices
with np.errstate(invalid='ignore'):
values = np.nansum(a[ri, :] * b[:, ci].T, axis=1)
values[np.isnan(values)] = 0.0
tmp[indices] = values
return tmp Example 17
def argnanmedoid(x, axis=1):
"""
Return the indices of the medoid
:param x: input array
:param axis: axis to medoid along
:return: indices of the medoid
"""
if axis == 0:
x = x.T
invalid = anynan(x, axis=0)
band, time = x.shape
diff = x.reshape(band, time, 1) - x.reshape(band, 1, time)
dist = np.sqrt(np.sum(diff * diff, axis=0)) # dist = np.linalg.norm(diff, axis=0) is slower somehow...
dist_sum = nansum(dist, axis=0)
dist_sum[invalid] = np.inf
i = np.argmin(dist_sum)
return i Example 18
def medoid_indices(arr, invalid=None):
"""
The indices of the medoid.
:arg arr: input array
:arg invalid: mask for invalid data containing NaNs
"""
# vectorized version of `argnanmedoid`
bands, times, ys, xs = arr.shape
diff = (arr.reshape(bands, times, 1, ys, xs) -
arr.reshape(bands, 1, times, ys, xs))
dist = np.linalg.norm(diff, axis=0)
dist_sum = nansum(dist, axis=0)
if invalid is None:
# compute it in case it's not already available
invalid = anynan(arr, axis=0)
dist_sum[invalid] = np.inf
return np.argmin(dist_sum, axis=0) Example 19
def frame_to_series(self, field, frame, columns=None):
"""
Convert a frame with a DatetimeIndex and sid columns into a series with
a sid index, using the aggregator defined by the given field.
"""
if isinstance(frame, pd.DataFrame):
columns = frame.columns
frame = frame.values
if not len(frame):
return pd.Series(
data=(0 if field == 'volume' else np.nan),
index=columns,
).values
if field in ['price', 'close']:
# shortcircuit for full last row
vals = frame[-1]
if np.all(~np.isnan(vals)):
return vals
return ffill(frame)[-1]
elif field == 'open':
return bfill(frame)[0]
elif field == 'volume':
return np.nansum(frame, axis=0)
elif field == 'high':
return np.nanmax(frame, axis=0)
elif field == 'low':
return np.nanmin(frame, axis=0)
else:
raise ValueError("Unknown field {}".format(field)) Example 20
def makedists(pdata,binl):
##### This is called from within makeraindist.
##### Caclulate distributions
pds=pdata.shape; nlat=pds[1]; nlon=pds[0]; nd=pds[2]
bins=np.append(0,binl)
n=np.empty((nlon,nlat,len(binl)))
binno=np.empty(pdata.shape)
for ilon in range(nlon):
for ilat in range(nlat):
# this is the histogram - we'll get frequency from this
thisn,thisbin=np.histogram(pdata[ilon,ilat,:],bins)
n[ilon,ilat,:]=thisn
# these are the bin locations. we'll use these for the amount dist
binno[ilon,ilat,:]=np.digitize(pdata[ilon,ilat,:],bins)
#### Calculate the number of days with non-missing data, for normalization
ndmat=np.tile(np.expand_dims(np.nansum(n,axis=2),axis=2),(1,1,len(bins)-1))
thisppdfmap=n/ndmat
#### Iterate back over the bins and add up all the precip - this will be the rain amount distribution
testpamtmap=np.empty(thisppdfmap.shape)
for ibin in range(len(bins)-1):
testpamtmap[:,:,ibin]=(pdata*(ibin==binno)).sum(axis=2)
thispamtmap=testpamtmap/ndmat
return thisppdfmap,thispamtmap Example 21
def makedists(pdata,binl):
##### This is called from within makeraindist.
##### Caclulate distributions
pds=pdata.shape; nlat=pds[1]; nlon=pds[0]; nd=pds[2]
bins=np.append(0,binl)
n=np.empty((nlon,nlat,len(binl)))
binno=np.empty(pdata.shape)
for ilon in range(nlon):
for ilat in range(nlat):
# this is the histogram - we'll get frequency from this
thisn,thisbin=np.histogram(pdata[ilon,ilat,:],bins)
n[ilon,ilat,:]=thisn
# these are the bin locations. we'll use these for the amount dist
binno[ilon,ilat,:]=np.digitize(pdata[ilon,ilat,:],bins)
#### Calculate the number of days with non-missing data, for normalization
ndmat=np.tile(np.expand_dims(np.nansum(n,axis=2),axis=2),(1,1,len(bins)-1))
thisppdfmap=n/ndmat
#### Iterate back over the bins and add up all the precip - this will be the rain amount distribution
testpamtmap=np.empty(thisppdfmap.shape)
for ibin in range(len(bins)-1):
testpamtmap[:,:,ibin]=(pdata*(ibin==binno)).sum(axis=2)
thispamtmap=testpamtmap/ndmat
return thisppdfmap,thispamtmap Example 22
def getJointNumFramesVisible(self, jointID):
"""
Get number of frames in which joint is visible
:param jointID: joint ID
:return: number of frames
"""
return numpy.nansum(self.gt[:, jointID, :]) / self.gt.shape[2] # 3D Example 23
def est_pmf_from_mpps(self, other, samples, eps=1e-10):
"""Estimate probability mass function from MPPovmList samples
:param MPPovmList other: An :class:`MPPovmList` instance
:param samples: Iterable of samples (e.g. from
:func:`MPPovmList.samples()`)
:returns: `(p_est, n_samples_used)`, both are shape
`self.nsoutdims` ndarrays. `p_est` provides estimated
probabilities and `n_samples_used` provides the effective
number of samples used for each probability.
"""
assert len(other.mpps) == len(samples)
pmf_ests = np.zeros((len(other.mpps),) + self.nsoutdims, float)
n_samples = np.zeros(len(other.mpps), int)
for pos, other_mpp, other_samples in zip(it.count(), other.mpps, samples):
pmf_ests[pos, ...], n_samples[pos] = self.est_pmf_from(
other_mpp, other_samples, eps)
n_out = np.prod(self.nsoutdims)
pmf_ests = pmf_ests.reshape((len(other.mpps), n_out))
given = ~np.isnan(pmf_ests)
n_samples_used = (given * n_samples[:, None]).sum(0)
# Weighted average over available estimates according to the
# number of samples underlying each estimate. Probabilities
# without any estimates produce 0.0 / 0 = nan in `pmf_est`.
pmf_est = np.nansum(pmf_ests * n_samples[:, None], 0) / n_samples_used
return (pmf_est.reshape(self.nsoutdims),
n_samples_used.reshape(self.nsoutdims)) Example 24
def test_nansum_with_boolean(self):
# gh-2978
a = np.zeros(2, dtype=np.bool)
try:
np.nansum(a)
except:
raise AssertionError() Example 25
def test_nansum(self):
tgt = np.sum(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nansum(mat), tgt) Example 26
def test_allnans(self):
# Check for FutureWarning
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
res = np.nansum([np.nan]*3, axis=None)
assert_(res == 0, 'result is not 0')
assert_(len(w) == 0, 'warning raised')
# Check scalar
res = np.nansum(np.nan)
assert_(res == 0, 'result is not 0')
assert_(len(w) == 0, 'warning raised')
# Check there is no warning for not all-nan
np.nansum([0]*3, axis=None)
assert_(len(w) == 0, 'unwanted warning raised') Example 27
def test_empty(self):
for f, tgt_value in zip([np.nansum, np.nanprod], [0, 1]):
mat = np.zeros((0, 3))
tgt = [tgt_value]*3
res = f(mat, axis=0)
assert_equal(res, tgt)
tgt = []
res = f(mat, axis=1)
assert_equal(res, tgt)
tgt = tgt_value
res = f(mat, axis=None)
assert_equal(res, tgt) Example 28
def compact_logit(x, eps=.00001):
import warnings
with warnings.catch_warnings():
warnings.filterwarnings("ignore", message="divide by zero encountered in true_divide")
warnings.filterwarnings("ignore", message="divide by zero encountered in log")
warnings.filterwarnings("ignore", message="invalid value encountered in multiply")
return np.nansum(((x<=eps)*x, (x>=(1-eps))*x, ((x>eps)&(x<(1-eps)))*((1-2*eps)*(np.log(x/(1-x)))/(2*np.log((1-eps)/eps))+.5)),axis=0) Example 29
def _get_weights(self, data, kpi, variant):
if kpi not in self.reference_kpis:
return 1.0
reference_kpi = self.reference_kpis[kpi]
x = self.get_kpi_by_name_and_variant(data, reference_kpi, variant)
zeros_and_nans = sum(x == 0) + np.isnan(x).sum()
non_zeros = len(x) - zeros_and_nans
return non_zeros/np.nansum(x) * x Example 30
def KL(a, b):
"""Calculate the Kullback Leibler divergence between a and b """
D_KL = np.nansum(np.multiply(a, np.log(np.divide(a, b+np.spacing(1)))), axis=1)
return D_KL Example 31
def calc_information(probTgivenXs, PYgivenTs, PXs, PYs):
"""Calculate the MI - I(X;T) and I(Y;T)"""
PTs = np.nansum(probTgivenXs*PXs, axis=1)
Ht = np.nansum(-np.dot(PTs, np.log2(PTs)))
Htx = - np.nansum((np.dot(np.multiply(probTgivenXs, np.log2(probTgivenXs)), PXs)))
Hyt = - np.nansum(np.dot(PYgivenTs*np.log2(PYgivenTs+np.spacing(1)), PTs))
Hy = np.nansum(-PYs * np.log2(PYs+np.spacing(1)))
IYT = Hy - Hyt
ITX = Ht - Htx
return ITX, IYT Example 32
def calc_information_1(probTgivenXs, PYgivenTs, PXs, PYs, PTs):
"""Calculate the MI - I(X;T) and I(Y;T)"""
#PTs = np.nansum(probTgivenXs*PXs, axis=1)
Ht = np.nansum(-np.dot(PTs, np.log2(PTs+np.spacing(1))))
Htx = - np.nansum((np.dot(np.multiply(probTgivenXs, np.log2(probTgivenXs+np.spacing(1))), PXs)))
Hyt = - np.nansum(np.dot(PYgivenTs*np.log2(PYgivenTs+np.spacing(1)), PTs))
Hy = np.nansum(-PYs * np.log2(PYs+np.spacing(1)))
IYT = Hy - Hyt
ITX = Ht - Htx
return ITX, IYT Example 33
def calc_information(probTgivenXs, PYgivenTs, PXs, PYs, PTs):
"""Calculate the MI - I(X;T) and I(Y;T)"""
#PTs = np.nansum(probTgivenXs*PXs, axis=1)
t_indeces = np.nonzero(PTs)
Ht = np.nansum(-np.dot(PTs, np.log2(PTs+np.spacing(1))))
Htx = - np.nansum((np.dot(np.multiply(probTgivenXs, np.log2(probTgivenXs)), PXs)))
Hyt = - np.nansum(np.dot(PYgivenTs*np.log2(PYgivenTs+np.spacing(1)), PTs))
Hy = np.nansum(-PYs * np.log2(PYs+np.spacing(1)))
IYT = Hy - Hyt
ITX = Ht - Htx
return ITX, IYT Example 34
def t_calc_information(p_x_given_t, PYgivenTs, PXs, PYs):
"""Calculate the MI - I(X;T) and I(Y;T)"""
Hx = np.nansum(-np.dot(PXs, np.log2(PXs)))
Hxt = - np.nansum((np.dot(np.multiply(p_x_given_t, np.log2(p_x_given_t)), PXs)))
Hyt = - np.nansum(np.dot(PYgivenTs*np.log2(PYgivenTs+np.spacing(1)), PTs))
Hy = np.nansum(-PYs * np.log2(PYs+np.spacing(1)))
IYT = Hy - Hyt
ITX = Hx - Hxt
return ITX, IYT Example 35
def _fit_cdd_only(df, weighted=False):
bps = [i[4:] for i in df.columns if i[:3] == 'CDD']
best_bp, best_rsquared, best_mod, best_res = None, -9e9, None, None
best_formula, cdd_qualified = None, False
try: # TODO: fix big try block anti-pattern
for bp in bps:
candidate_cdd_formula = 'upd ~ CDD_' + bp
if (np.nansum(df['CDD_' + bp] > 0) < 10) or \
(np.nansum(df['CDD_' + bp]) < 20):
continue
if weighted:
candidate_cdd_mod = smf.wls(formula=candidate_cdd_formula, data=df,
weights=df['ndays'])
else:
candidate_cdd_mod = smf.ols(formula=candidate_cdd_formula, data=df)
candidate_cdd_res = candidate_cdd_mod.fit()
candidate_cdd_rsquared = candidate_cdd_res.rsquared_adj
if (candidate_cdd_rsquared > best_rsquared and
candidate_cdd_res.params['Intercept'] >= 0 and
candidate_cdd_res.params['CDD_' + bp] >= 0 and
candidate_cdd_res.pvalues['CDD_' + bp] < 0.1):
best_bp, best_rsquared = int(bp), candidate_cdd_rsquared
best_mod, best_res = candidate_cdd_mod, candidate_cdd_res
cdd_qualified = True
best_formula = 'upd ~ CDD_' + bp
except: # TODO: catch specific error
best_rsquared, cdd_qualified = 0, False
best_formula, best_mod, best_res = None, None, None
best_bp = None
return best_formula, best_mod, best_res, best_rsquared, cdd_qualified, best_bp Example 36
def _fit_hdd_only(df, weighted=False):
bps = [i[4:] for i in df.columns if i[:3] == 'HDD']
best_bp, best_rsquared, best_mod, best_res = None, -9e9, None, None
best_formula, hdd_qualified = None, False
try: # TODO: fix big try block anti-pattern
for bp in bps:
candidate_hdd_formula = 'upd ~ HDD_' + bp
if (np.nansum(df['HDD_' + bp] > 0) < 10) or \
(np.nansum(df['HDD_' + bp]) < 20):
continue
if weighted:
candidate_hdd_mod = smf.wls(formula=candidate_hdd_formula, data=df,
weights=df['ndays'])
else:
candidate_hdd_mod = smf.ols(formula=candidate_hdd_formula, data=df)
candidate_hdd_res = candidate_hdd_mod.fit()
candidate_hdd_rsquared = candidate_hdd_res.rsquared_adj
if (candidate_hdd_rsquared > best_rsquared and
candidate_hdd_res.params['Intercept'] >= 0 and
candidate_hdd_res.params['HDD_' + bp] >= 0 and
candidate_hdd_res.pvalues['HDD_' + bp] < 0.1):
best_bp, best_rsquared = int(bp), candidate_hdd_rsquared
best_mod, best_res = candidate_hdd_mod, candidate_hdd_res
hdd_qualified = True
best_formula = 'upd ~ HDD_' + bp
except: # TODO: catch specific error
best_rsquared, hdd_qualified = 0, False
best_formula, best_mod, best_res = None, None, None
best_bp = None
return best_formula, best_mod, best_res, best_rsquared, hdd_qualified, best_bp Example 37
def calc_gross(self):
return np.nansum(self.input_data.energy) Example 38
def get_relevance_scores(matched_predictions, positive_feedback, not_rated_penalty):
users_num = matched_predictions.shape[0]
reldata = get_relevance_data(matched_predictions, positive_feedback, not_rated_penalty)
true_pos, false_pos = reldata.tp, reldata.fp
true_neg, false_neg = reldata.tn, reldata.fn
with np.errstate(invalid='ignore'):
# true positive rate
precision = true_pos / (true_pos + false_pos)
# sensitivity
recall = true_pos / (true_pos + false_neg)
# false positive rate
fallout = false_pos / (false_pos + true_neg)
# true negative rate
specifity = true_neg / (false_pos + true_neg)
# false negative rate
miss_rate = false_neg / (false_neg + true_pos)
#average over all users
precision = unmask(np.nansum(precision) / users_num)
recall = unmask(np.nansum(recall) / users_num)
fallout = unmask(np.nansum(fallout) / users_num)
specifity = unmask(np.nansum(specifity) / users_num)
miss_rate = unmask(np.nansum(miss_rate) / users_num)
scores = namedtuple('Relevance', ['precision', 'recall', 'fallout', 'specifity', 'miss_rate'])
scores = scores._make([precision, recall, fallout, specifity, miss_rate])
return scores Example 39
def get_ranking_scores(matched_predictions, feedback_data, switch_positive, alternative=True):
users_num, topk, holdout = matched_predictions.shape
ideal_scores_idx = np.argsort(feedback_data, axis=1)[:, ::-1] #returns column index only
ideal_scores_idx = np.ravel_multi_index((np.arange(feedback_data.shape[0])[:, None], ideal_scores_idx), dims=feedback_data.shape)
where = np.ma.where if np.ma.is_masked(feedback_data) else np.where
is_positive = feedback_data >= switch_positive
positive_feedback = where(is_positive, feedback_data, 0)
negative_feedback = where(~is_positive, -feedback_data, 0)
relevance_scores_pos = (matched_predictions * positive_feedback[:, None, :]).sum(axis=2)
relevance_scores_neg = (matched_predictions * negative_feedback[:, None, :]).sum(axis=2)
ideal_scores_pos = positive_feedback.ravel()[ideal_scores_idx]
ideal_scores_neg = negative_feedback.ravel()[ideal_scores_idx]
discount_num = max(holdout, topk)
if alternative:
discount = np.log2(np.arange(2, discount_num+2))
relevance_scores_pos = 2**relevance_scores_pos - 1
relevance_scores_neg = 2**relevance_scores_neg - 1
ideal_scores_pos = 2**ideal_scores_pos - 1
ideal_scores_neg = 2**ideal_scores_neg - 1
else:
discount = np.hstack([1, np.log(np.arange(2, discount_num+1))])
dcg = (relevance_scores_pos / discount[:topk]).sum(axis=1)
dcl = (relevance_scores_neg / -discount[:topk]).sum(axis=1)
idcg = (ideal_scores_pos / discount[:holdout]).sum(axis=1)
idcl = (ideal_scores_neg / -discount[:holdout]).sum(axis=1)
with np.errstate(invalid='ignore'):
ndcg = unmask(np.nansum(dcg / idcg) / users_num)
ndcl = unmask(np.nansum(dcl / idcl) / users_num)
ranking_score = namedtuple('Ranking', ['nDCG', 'nDCL'])._make([ndcg, ndcl])
return ranking_score Example 40
def vwap(df):
"""
Volume-weighted average price (VWAP) is a ratio generally used by
institutional investors and mutual funds to make buys and sells so as not
to disturb the market prices with large orders. It is the average share
price of a stock weighted against its trading volume within a particular
time frame, generally one day.
Read more: Volume Weighted Average Price - VWAP
https://www.investopedia.com/terms/v/vwap.asp#ixzz4xt922daE
Parameters
----------
df: pd.DataFrame
Returns
-------
"""
if 'close' not in df.columns or 'volume' not in df.columns:
raise ValueError('price data must include `volume` and `close`')
vol_sum = np.nansum(df['volume'].values)
try:
ret = np.nansum(df['close'].values * df['volume'].values) / vol_sum
except ZeroDivisionError:
ret = np.nan
return ret Example 41
def _calculate(self, X, y, categorical, metafeatures, helpers):
res = np.nansum(helpers.get_value("NumSymbols"))
return res if np.isfinite(res) else 0
################################################################################
# Statistical meta features
# Only use third and fourth statistical moment because it is common to
# standardize for the other two
# see Engels & Theusinger, 1998 - Using a Data Metric for Preprocessing Advice for Data Mining Applications. Example 42
def trajectory_score_array(posterior, slope=None, intercept=None, w=None, weights=None, normalize=False):
"""Docstring goes here
This is the score that Davidson et al. maximizes, in order to get a linear trajectory,
but here we kind of assume that that we have the trajectory already, and then just score it.
w is the number of bin rows to include in score, in each direction. That is, w=0 is only the modes,
and w=1 is a band of width=3, namely the modes, and 1 bin above, and 1 bin below the mode.
The score is NOT averaged!"""
rows, cols = posterior.shape
if w is None:
w = 0
if not float(w).is_integer:
raise ValueError("w has to be an integer!")
if slope is None or intercept is None:
slope, intercept, _ = linregress_array(posterior=posterior)
x = np.arange(cols)
line_y = np.round((slope*x + intercept)) # in position bin #s
# idea: cycle each column so that the top w rows are the band surrounding the regression line
if np.isnan(slope): # this will happen if we have 0 or only 1 decoded bins
return np.nan
else:
temp = column_cycle_array(posterior, -line_y+w)
if normalize:
num_non_nan_bins = round(np.nansum(posterior))
else:
num_non_nan_bins = 1
return np.nansum(temp[:2*w+1,:])/num_non_nan_bins Example 43
def test_nsum(x):
assume(np.max(x[np.isfinite(x)]) < 1e4)
assume(np.min(x[np.isfinite(x)]) > -1e4)
aae(nsum(x), np.nansum(x)) Example 44
def test_nsum_row(x):
assume(np.max(x[np.isfinite(x)]) < 1e4)
assume(np.min(x[np.isfinite(x)]) > -1e4)
aae(nsum_row(x), np.nansum(x, axis=1)) Example 45
def test_preds_ll(alpha, mu, gamma, err, num, w):
current_impl = Lvm.preds_ll(alpha, mu, gamma, err, num, w)
simple_impl = np.nansum(w * norm.logpdf(num, mu+gamma*alpha, err))
simple_impl += np.sum(norm.logpdf(alpha))
assert_approx_equal(current_impl, simple_impl) Example 46
def ests_obj(self, params):
"""The objective function to minimize for the model parameters."""
# return -nsum(self.ests_ll(params))
return -np.nansum(self.ests_ll(params)) Example 47
def nsum_row(a):
return nansum(a, axis=1) Example 48
def getJointNumFramesVisible(self, jointID):
"""
Get number of frames in which joint is visible
:param jointID: joint ID
:return: number of frames
"""
return numpy.nansum(self.gt[:, jointID, :]) / self.gt.shape[2] # 3D Example 49
def test_basic_stats(x):
s = SummaryStats()
s.update(x)
assert s.count() == np.count_nonzero(~np.isnan(x))
np.testing.assert_allclose(s.sum(), np.nansum(x), rtol=RTOL, atol=ATOL)
np.testing.assert_equal(s.min(), np.nanmin(x) if len(x) else np.nan)
np.testing.assert_equal(s.max(), np.nanmax(x) if len(x) else np.nan)
np.testing.assert_allclose(s.mean(), np.nanmean(x) if len(x) else np.nan,
rtol=RTOL, atol=ATOL)
np.testing.assert_allclose(s.var(), np.nanvar(x) if len(x) else np.nan,
rtol=RTOL, atol=ATOL)
np.testing.assert_allclose(s.std(), np.nanstd(x) if len(x) else np.nan,
rtol=RTOL, atol=ATOL) Example 50
def log_likelihood(y, yhat):
'''Helper function to compute the log likelihood.'''
eps = np.spacing(1)
return np.nansum(y * np.log(eps + yhat) - yhat) 