_id stringlengths 2 7 | title stringlengths 1 88 | partition stringclasses 3
values | text stringlengths 75 19.8k | language stringclasses 1
value | meta_information dict |
|---|---|---|---|---|---|
q7000 | Logger.subscribe | train | def subscribe(self, queue=None, *levels):
"""
Subscribe to the aggregated log stream. On subscribe a ledis queue will be fed with all running processes
logs. Always use the returned queue name from this method, even if u specified the queue name to use
Note: it is legal to subscribe to ... | python | {
"resource": ""
} |
q7001 | AggregatorManager.query | train | def query(self, key=None, **tags):
"""
Query zero-os aggregator for current state object of monitored metrics.
Note: ID is returned as part of the key (if set) to avoid conflict with similar metrics that
has same key. For example, a cpu core nr can be the id associated with 'machine.CPU... | python | {
"resource": ""
} |
q7002 | CGroupManager.ensure | train | def ensure(self, subsystem, name):
"""
Creates a cgroup if it doesn't exist under the specified subsystem
and the given name
:param subsystem: the cgroup subsystem (currently support 'memory', and 'cpuset')
:param name: name of the cgroup to delete
"""
args = {
... | python | {
"resource": ""
} |
q7003 | dlmk | train | def dlmk(l,m,k,theta1):
"""
returns value of d^l_mk as defined in allen, ottewill 97.
Called by Dlmk
"""
if m >= k:
factor = sqrt(factorial(l-k)*factorial(l+m)/factorial(l+k)/factorial(l-m))
part2 = (cos(theta1/2))**(2*l+k-m)*(-sin(theta1/2))**(m-k)/factorial(m-k)
... | python | {
"resource": ""
} |
q7004 | Dlmk | train | def Dlmk(l,m,k,phi1,phi2,theta1,theta2):
"""
returns value of D^l_mk as defined in allen, ottewill 97.
"""
return exp(complex(0.,-m*phi1)) * dlmk(l,m,k,theta1) * \
exp(complex(0.,-k*gamma(phi1,phi2,theta1,theta2))) | python | {
"resource": ""
} |
q7005 | gamma | train | def gamma(phi1,phi2,theta1,theta2):
"""
calculate third rotation angle
inputs are angles from 2 pulsars
returns the angle.
"""
if phi1 == phi2 and theta1 == theta2:
gamma = 0
else:
gamma = atan( sin(theta2)*sin(phi2-phi1) / \
(cos(theta1)*sin(t... | python | {
"resource": ""
} |
q7006 | rotated_Gamma_ml | train | def rotated_Gamma_ml(m,l,phi1,phi2,theta1,theta2,gamma_ml):
"""
This function takes any gamma in the computational frame and rotates it to the
cosmic frame.
"""
rotated_gamma = 0
for ii in range(2*l+1):
rotated_gamma += Dlmk(l,m,ii-l,phi1,phi2,theta1,theta2).conjugate()*g... | python | {
"resource": ""
} |
q7007 | real_rotated_Gammas | train | def real_rotated_Gammas(m,l,phi1,phi2,theta1,theta2,gamma_ml):
"""
This function returns the real-valued form of the Overlap Reduction Functions,
see Eqs 47 in Mingarelli et al, 2013.
"""
if m>0:
ans=(1./sqrt(2))*(rotated_Gamma_ml(m,l,phi1,phi2,theta1,theta2,gamma_ml) + \
... | python | {
"resource": ""
} |
q7008 | chisq | train | def chisq(psr,formbats=False):
"""Return the total chisq for the current timing solution,
removing noise-averaged mean residual, and ignoring deleted points."""
if formbats:
psr.formbats()
res, err = psr.residuals(removemean=False)[psr.deleted == 0], psr.toaerrs[psr.deleted == 0]
... | python | {
"resource": ""
} |
q7009 | dchisq | train | def dchisq(psr,formbats=False,renormalize=True):
"""Return gradient of total chisq for the current timing solution,
after removing noise-averaged mean residual, and ignoring deleted points."""
if formbats:
psr.formbats()
res, err = psr.residuals(removemean=False)[psr.deleted == 0], psr.toa... | python | {
"resource": ""
} |
q7010 | create_fourier_design_matrix | train | def create_fourier_design_matrix(t, nmodes, freq=False, Tspan=None,
logf=False, fmin=None, fmax=None):
"""
Construct fourier design matrix from eq 11 of Lentati et al, 2013
:param t: vector of time series in seconds
:param nmodes: number of fourier coefficients to use
... | python | {
"resource": ""
} |
q7011 | powerlaw | train | def powerlaw(f, log10_A=-16, gamma=5):
"""Power-law PSD.
:param f: Sampling frequencies
:param log10_A: log10 of red noise Amplitude [GW units]
:param gamma: Spectral index of red noise process
"""
fyr = 1 / 3.16e7
return (10**log10_A)**2 / 12.0 / np.pi**2 * fyr**(gamma-3) * f**(-gamma) | python | {
"resource": ""
} |
q7012 | add_gwb | train | def add_gwb(psr, dist=1, ngw=1000, seed=None, flow=1e-8, fhigh=1e-5,
gwAmp=1e-20, alpha=-0.66, logspacing=True):
"""Add a stochastic background from inspiraling binaries, using the tempo2
code that underlies the GWbkgrd plugin.
Here 'dist' is the pulsar distance [in kpc]; 'ngw' is the number of... | python | {
"resource": ""
} |
q7013 | add_dipole_gwb | train | def add_dipole_gwb(psr, dist=1, ngw=1000, seed=None, flow=1e-8,
fhigh=1e-5, gwAmp=1e-20, alpha=-0.66,
logspacing=True, dipoleamps=None,
dipoledir=None, dipolemag=None):
"""Add a stochastic background from inspiraling binaries distributed
accord... | python | {
"resource": ""
} |
q7014 | add_efac | train | def add_efac(psr, efac=1.0, flagid=None, flags=None, seed=None):
"""Add nominal TOA errors, multiplied by `efac` factor.
Optionally take a pseudorandom-number-generator seed."""
if seed is not None:
N.random.seed(seed)
# default efacvec
efacvec = N.ones(psr.nobs)
# check that efac... | python | {
"resource": ""
} |
q7015 | extrap1d | train | def extrap1d(interpolator):
"""
Function to extend an interpolation function to an
extrapolation function.
:param interpolator: scipy interp1d object
:returns ufunclike: extension of function to extrapolation
"""
xs = interpolator.x
ys = interpolator.y
def pointwise(x):
... | python | {
"resource": ""
} |
q7016 | computeORFMatrix | train | def computeORFMatrix(psr):
"""
Compute ORF matrix.
:param psr: List of pulsar object instances
:returns: Matrix that has the ORF values for every pulsar
pair with 2 on the diagonals to account for the
pulsar term.
"""
# begin loop over all pulsar pairs and calculat... | python | {
"resource": ""
} |
q7017 | plotres | train | def plotres(psr,deleted=False,group=None,**kwargs):
"""Plot residuals, compute unweighted rms residual."""
res, t, errs = psr.residuals(), psr.toas(), psr.toaerrs
if (not deleted) and N.any(psr.deleted != 0):
res, t, errs = res[psr.deleted == 0], t[psr.deleted == 0], errs[psr.deleted == 0]
... | python | {
"resource": ""
} |
q7018 | plotgwsrc | train | def plotgwsrc(gwb):
"""
Plot a GWB source population as a mollweide projection.
"""
theta, phi, omega, polarization = gwb.gw_dist()
rho = phi-N.pi
eta = 0.5*N.pi - theta
# I don't know how to get rid of the RuntimeWarning -- RvH, Oct 10, 2014:
# /Users/vhaaster/env/dev/lib/python2.... | python | {
"resource": ""
} |
q7019 | merge | train | def merge(data,skip=50,fraction=1.0):
"""Merge one every 'skip' clouds into a single emcee population,
using the later 'fraction' of the run."""
w,s,d = data.chains.shape
start = int((1.0 - fraction) * s)
total = int((s - start) / skip)
return data.chains[:,start::skip,:].reshape(... | python | {
"resource": ""
} |
q7020 | cull | train | def cull(data,index,min=None,max=None):
"""Sieve an emcee clouds by excluding walkers with search variable 'index'
smaller than 'min' or larger than 'max'."""
ret = data
if min is not None:
ret = ret[ret[:,index] > min,:]
if max is not None:
ret = ret[ret[:,index] < max,:]
... | python | {
"resource": ""
} |
q7021 | make_ecc_interpolant | train | def make_ecc_interpolant():
"""
Make interpolation function from eccentricity file to
determine number of harmonics to use for a given
eccentricity.
:returns: interpolant
"""
pth = resource_filename(Requirement.parse('libstempo'),
'libstempo/ecc_vs_nharm.txt')
... | python | {
"resource": ""
} |
q7022 | best_kmers | train | def best_kmers(dt, response, sequence, k=6, consider_shift=True, n_cores=1,
seq_align="start", trim_seq_len=None):
"""
Find best k-mers for CONCISE initialization.
Args:
dt (pd.DataFrame): Table containing response variable and sequence.
response (str): Name of the column use... | python | {
"resource": ""
} |
q7023 | kmer_count | train | def kmer_count(seq_list, k):
"""
Generate k-mer counts from a set of sequences
Args:
seq_list (iterable): List of DNA sequences (with letters from {A, C, G, T})
k (int): K in k-mer.
Returns:
pandas.DataFrame: Count matrix for seach sequence in seq_list
Example:
>>> kmer... | python | {
"resource": ""
} |
q7024 | generate_all_kmers | train | def generate_all_kmers(k):
"""
Generate all possible k-mers
Example:
>>> generate_all_kmers(2)
['AA', 'AC', 'AG', 'AT', 'CA', 'CC', 'CG', 'CT', 'GA', 'GC', 'GG', 'GT', 'TA', 'TC', 'TG', 'TT']
"""
bases = ['A', 'C', 'G', 'T']
return [''.join(p) for p in itertools.product(bases, repeat=k)... | python | {
"resource": ""
} |
q7025 | dict_to_numpy_dict | train | def dict_to_numpy_dict(obj_dict):
"""
Convert a dictionary of lists into a dictionary of numpy arrays
"""
return {key: np.asarray(value) if value is not None else None for key, value in obj_dict.items()} | python | {
"resource": ""
} |
q7026 | rec_dict_to_numpy_dict | train | def rec_dict_to_numpy_dict(obj_dict):
"""
Same as dict_to_numpy_dict, but recursive
"""
if type(obj_dict) == dict:
return {key: rec_dict_to_numpy_dict(value) if value is not None else None for key, value in obj_dict.items()}
elif obj_dict is None:
return None
else:
return... | python | {
"resource": ""
} |
q7027 | compare_numpy_dict | train | def compare_numpy_dict(a, b, exact=True):
"""
Compare two recursive numpy dictionaries
"""
if type(a) != type(b) and type(a) != np.ndarray and type(b) != np.ndarray:
return False
# go through a dictionary
if type(a) == dict and type(b) == dict:
if not a.keys() == b.keys():
... | python | {
"resource": ""
} |
q7028 | BSpline.getS | train | def getS(self, add_intercept=False):
"""Get the penalty matrix S
Returns
np.array, of shape (n_bases + add_intercept, n_bases + add_intercept)
"""
S = self.S
if add_intercept is True:
# S <- cbind(0, rbind(0, S)) # in R
zeros = np.zeros_like(S... | python | {
"resource": ""
} |
q7029 | get_pwm_list | train | def get_pwm_list(motif_name_list, pseudocountProb=0.0001):
"""Get a list of ENCODE PWM's.
# Arguments
pwm_id_list: List of id's from the `PWM_id` column in `get_metadata()` table
pseudocountProb: Added pseudocount probabilities to the PWM
# Returns
List of `concise.utils.pwm.PWM` i... | python | {
"resource": ""
} |
q7030 | auc | train | def auc(y_true, y_pred, round=True):
"""Area under the ROC curve
"""
y_true, y_pred = _mask_value_nan(y_true, y_pred)
if round:
y_true = y_true.round()
if len(y_true) == 0 or len(np.unique(y_true)) < 2:
return np.nan
return skm.roc_auc_score(y_true, y_pred) | python | {
"resource": ""
} |
q7031 | recall_at_precision | train | def recall_at_precision(y_true, y_pred, precision):
"""Recall at a certain precision threshold
Args:
y_true: true labels
y_pred: predicted labels
precision: resired precision level at which where to compute the recall
"""
y_true, y_pred = _mask_value_nan(y_true, y_pred)
precision,... | python | {
"resource": ""
} |
q7032 | cor | train | def cor(y_true, y_pred):
"""Compute Pearson correlation coefficient.
"""
y_true, y_pred = _mask_nan(y_true, y_pred)
return np.corrcoef(y_true, y_pred)[0, 1] | python | {
"resource": ""
} |
q7033 | kendall | train | def kendall(y_true, y_pred, nb_sample=100000):
"""Kendall's tau coefficient, Kendall rank correlation coefficient
"""
y_true, y_pred = _mask_nan(y_true, y_pred)
if len(y_true) > nb_sample:
idx = np.arange(len(y_true))
np.random.shuffle(idx)
idx = idx[:nb_sample]
y_true = ... | python | {
"resource": ""
} |
q7034 | mad | train | def mad(y_true, y_pred):
"""Median absolute deviation
"""
y_true, y_pred = _mask_nan(y_true, y_pred)
return np.mean(np.abs(y_true - y_pred)) | python | {
"resource": ""
} |
q7035 | mse | train | def mse(y_true, y_pred):
"""Mean squared error
"""
y_true, y_pred = _mask_nan(y_true, y_pred)
return ((y_true - y_pred) ** 2).mean(axis=None) | python | {
"resource": ""
} |
q7036 | sample_params | train | def sample_params(params):
"""Randomly sample hyper-parameters stored in a dictionary on a predefined range and scale.
Useful for hyper-parameter random search.
Args:
params (dict): hyper-parameters to sample. Dictionary value-type parsing:
- :python:`[1e3, 1e7]` - uniformly ... | python | {
"resource": ""
} |
q7037 | cat_acc | train | def cat_acc(y, z):
"""Classification accuracy for multi-categorical case
"""
weights = _cat_sample_weights(y)
_acc = K.cast(K.equal(K.argmax(y, axis=-1),
K.argmax(z, axis=-1)),
K.floatx())
_acc = K.sum(_acc * weights) / K.sum(weights)
return _acc | python | {
"resource": ""
} |
q7038 | split_KFold_idx | train | def split_KFold_idx(train, cv_n_folds=5, stratified=False, random_state=None):
"""Get k-fold indices generator
"""
test_len(train)
y = train[1]
n_rows = y.shape[0]
if stratified:
if len(y.shape) > 1:
if y.shape[1] > 1:
raise ValueError("Can't use stratified K-... | python | {
"resource": ""
} |
q7039 | prepare_data | train | def prepare_data(dt, features, response, sequence, id_column=None, seq_align="end", trim_seq_len=None):
"""
Prepare data for Concise.train or ConciseCV.train.
Args:
dt: A pandas DataFrame containing all the required data.
features (List of strings): Column names of `dt` used to produce the ... | python | {
"resource": ""
} |
q7040 | EncodeSplines.fit | train | def fit(self, x):
"""Calculate the knot placement from the values ranges.
# Arguments
x: numpy array, either N x D or N x L x D dimensional.
"""
assert x.ndim > 1
self.data_min_ = np.min(x, axis=tuple(range(x.ndim - 1)))
self.data_max_ = np.max(x, axis=tuple(... | python | {
"resource": ""
} |
q7041 | EncodeSplines.transform | train | def transform(self, x, warn=True):
"""Obtain the transformed values
"""
# 1. split across last dimension
# 2. re-use ranges
# 3. Merge
array_list = [encodeSplines(x[..., i].reshape((-1, 1)),
n_bases=self.n_bases,
... | python | {
"resource": ""
} |
q7042 | InputCodon | train | def InputCodon(seq_length, ignore_stop_codons=True, name=None, **kwargs):
"""Input placeholder for array returned by `encodeCodon`
Note: The seq_length is divided by 3
Wrapper for: `keras.layers.Input((seq_length / 3, 61 or 61), name=name, **kwargs)`
"""
if ignore_stop_codons:
vocab = CODO... | python | {
"resource": ""
} |
q7043 | InputAA | train | def InputAA(seq_length, name=None, **kwargs):
"""Input placeholder for array returned by `encodeAA`
Wrapper for: `keras.layers.Input((seq_length, 22), name=name, **kwargs)`
"""
return Input((seq_length, len(AMINO_ACIDS)), name=name, **kwargs) | python | {
"resource": ""
} |
q7044 | InputRNAStructure | train | def InputRNAStructure(seq_length, name=None, **kwargs):
"""Input placeholder for array returned by `encodeRNAStructure`
Wrapper for: `keras.layers.Input((seq_length, 5), name=name, **kwargs)`
"""
return Input((seq_length, len(RNAplfold_PROFILES)), name=name, **kwargs) | python | {
"resource": ""
} |
q7045 | ConvSequence._plot_weights_heatmap | train | def _plot_weights_heatmap(self, index=None, figsize=None, **kwargs):
"""Plot weights as a heatmap
index = can be a particular index or a list of indicies
**kwargs - additional arguments to concise.utils.plot.heatmap
"""
W = self.get_weights()[0]
if index is None:
... | python | {
"resource": ""
} |
q7046 | ConvSequence._plot_weights_motif | train | def _plot_weights_motif(self, index, plot_type="motif_raw",
background_probs=DEFAULT_BASE_BACKGROUND,
ncol=1,
figsize=None):
"""Index can only be a single int
"""
w_all = self.get_weights()
if len(w_all... | python | {
"resource": ""
} |
q7047 | ConvSequence.plot_weights | train | def plot_weights(self, index=None, plot_type="motif_raw", figsize=None, ncol=1, **kwargs):
"""Plot filters as heatmap or motifs
index = can be a particular index or a list of indicies
**kwargs - additional arguments to concise.utils.plot.heatmap
"""
if "heatmap" in self.AVAILAB... | python | {
"resource": ""
} |
q7048 | _check_pwm_list | train | def _check_pwm_list(pwm_list):
"""Check the input validity
"""
for pwm in pwm_list:
if not isinstance(pwm, PWM):
raise TypeError("element {0} of pwm_list is not of type PWM".format(pwm))
return True | python | {
"resource": ""
} |
q7049 | heatmap | train | def heatmap(w, vmin=None, vmax=None, diverge_color=False,
ncol=1,
plot_name=None, vocab=["A", "C", "G", "T"], figsize=(6, 2)):
"""Plot a heatmap from weight matrix w
vmin, vmax = z axis range
diverge_color = Should we use diverging colors?
plot_name = plot_title
vocab = voca... | python | {
"resource": ""
} |
q7050 | add_letter_to_axis | train | def add_letter_to_axis(ax, let, col, x, y, height):
"""Add 'let' with position x,y and height height to matplotlib axis 'ax'.
"""
if len(let) == 2:
colors = [col, "white"]
elif len(let) == 1:
colors = [col]
else:
raise ValueError("3 or more Polygons are not supported")
f... | python | {
"resource": ""
} |
q7051 | seqlogo | train | def seqlogo(letter_heights, vocab="DNA", ax=None):
"""Make a logo plot
# Arguments
letter_heights: "motif length" x "vocabulary size" numpy array
Can also contain negative values.
vocab: str, Vocabulary name. Can be: DNA, RNA, AA, RNAStruct.
ax: matplotlib axis
"""
ax = ax o... | python | {
"resource": ""
} |
q7052 | get_cv_accuracy | train | def get_cv_accuracy(res):
"""
Extract the cv accuracy from the model
"""
ac_list = [(accuracy["train_acc_final"],
accuracy["test_acc_final"]
)
for accuracy, weights in res]
ac = np.array(ac_list)
perf = {
"mean_train_acc": np.mean(ac[:, 0])... | python | {
"resource": ""
} |
q7053 | one_hot2string | train | def one_hot2string(arr, vocab):
"""Convert a one-hot encoded array back to string
"""
tokens = one_hot2token(arr)
indexToLetter = _get_index_dict(vocab)
return [''.join([indexToLetter[x] for x in row]) for row in tokens] | python | {
"resource": ""
} |
q7054 | tokenize | train | def tokenize(seq, vocab, neutral_vocab=[]):
"""Convert sequence to integers
# Arguments
seq: Sequence to encode
vocab: Vocabulary to use
neutral_vocab: Neutral vocabulary -> assign those values to -1
# Returns
List of length `len(seq)` with integers from `-1` to `len(vocab) - 1... | python | {
"resource": ""
} |
q7055 | encodeSequence | train | def encodeSequence(seq_vec, vocab, neutral_vocab, maxlen=None,
seq_align="start", pad_value="N", encode_type="one_hot"):
"""Convert a list of genetic sequences into one-hot-encoded array.
# Arguments
seq_vec: list of strings (genetic sequences)
vocab: list of chars: List of "wo... | python | {
"resource": ""
} |
q7056 | encodeDNA | train | def encodeDNA(seq_vec, maxlen=None, seq_align="start"):
"""Convert the DNA sequence into 1-hot-encoding numpy array
# Arguments
seq_vec: list of chars
List of sequences that can have different lengths
maxlen: int or None,
Should we trim (subset) the resulting sequence. ... | python | {
"resource": ""
} |
q7057 | encodeRNA | train | def encodeRNA(seq_vec, maxlen=None, seq_align="start"):
"""Convert the RNA sequence into 1-hot-encoding numpy array as for encodeDNA
"""
return encodeSequence(seq_vec,
vocab=RNA,
neutral_vocab="N",
maxlen=maxlen,
... | python | {
"resource": ""
} |
q7058 | encodeCodon | train | def encodeCodon(seq_vec, ignore_stop_codons=True, maxlen=None, seq_align="start", encode_type="one_hot"):
"""Convert the Codon sequence into 1-hot-encoding numpy array
# Arguments
seq_vec: List of strings/DNA sequences
ignore_stop_codons: boolean; if True, STOP_CODONS are omitted from one-hot e... | python | {
"resource": ""
} |
q7059 | encodeAA | train | def encodeAA(seq_vec, maxlen=None, seq_align="start", encode_type="one_hot"):
"""Convert the Amino-acid sequence into 1-hot-encoding numpy array
# Arguments
seq_vec: List of strings/amino-acid sequences
maxlen: Maximum sequence length. See `pad_sequences` for more detail
seq_align: How ... | python | {
"resource": ""
} |
q7060 | _validate_pos | train | def _validate_pos(df):
"""Validates the returned positional object
"""
assert isinstance(df, pd.DataFrame)
assert ["seqname", "position", "strand"] == df.columns.tolist()
assert df.position.dtype == np.dtype("int64")
assert df.strand.dtype == np.dtype("O")
assert df.seqname.dtype == np.dtype... | python | {
"resource": ""
} |
q7061 | get_pwm_list | train | def get_pwm_list(pwm_id_list, pseudocountProb=0.0001):
"""Get a list of Attract PWM's.
# Arguments
pwm_id_list: List of id's from the `PWM_id` column in `get_metadata()` table
pseudocountProb: Added pseudocount probabilities to the PWM
# Returns
List of `concise.utils.pwm.PWM` inst... | python | {
"resource": ""
} |
q7062 | mask_loss | train | def mask_loss(loss, mask_value=MASK_VALUE):
"""Generates a new loss function that ignores values where `y_true == mask_value`.
# Arguments
loss: str; name of the keras loss function from `keras.losses`
mask_value: int; which values should be masked
# Returns
function; Masked versio... | python | {
"resource": ""
} |
q7063 | get_pwm_list | train | def get_pwm_list(pwm_id_list, pseudocountProb=0.0001):
"""Get a list of HOCOMOCO PWM's.
# Arguments
pwm_id_list: List of id's from the `PWM_id` column in `get_metadata()` table
pseudocountProb: Added pseudocount probabilities to the PWM
# Returns
List of `concise.utils.pwm.PWM` ins... | python | {
"resource": ""
} |
q7064 | Concise._var_res_to_weights | train | def _var_res_to_weights(self, var_res):
"""
Get model weights
"""
# transform the weights into our form
motif_base_weights_raw = var_res["motif_base_weights"][0]
motif_base_weights = np.swapaxes(motif_base_weights_raw, 0, 2)
# get weights
motif_weights = ... | python | {
"resource": ""
} |
q7065 | Concise._get_var_res | train | def _get_var_res(self, graph, var, other_var):
"""
Get the weights from our graph
"""
with tf.Session(graph=graph) as sess:
sess.run(other_var["init"])
# all_vars = tf.all_variables()
# print("All variable names")
# print([var.name for var... | python | {
"resource": ""
} |
q7066 | Concise._convert_to_var | train | def _convert_to_var(self, graph, var_res):
"""
Create tf.Variables from a list of numpy arrays
var_res: dictionary of numpy arrays with the key names corresponding to var
"""
with graph.as_default():
var = {}
for key, value in var_res.items():
... | python | {
"resource": ""
} |
q7067 | Concise.train | train | def train(self, X_feat, X_seq, y,
X_feat_valid=None, X_seq_valid=None, y_valid=None,
n_cores=3):
"""Train the CONCISE model
:py:attr:`X_feat`, :py:attr:`X_seq`, py:attr:`y` are preferrably returned by the :py:func:`concise.prepare_data` function.
Args:
X... | python | {
"resource": ""
} |
q7068 | Concise._accuracy_in_session | train | def _accuracy_in_session(self, sess, other_var, X_feat, X_seq, y):
"""
Compute the accuracy from inside the tf session
"""
y_pred = self._predict_in_session(sess, other_var, X_feat, X_seq)
return ce.mse(y_pred, y) | python | {
"resource": ""
} |
q7069 | Concise._set_var_res | train | def _set_var_res(self, weights):
"""
Transform the weights to var_res
"""
if weights is None:
return
# layer 1
motif_base_weights_raw = np.swapaxes(weights["motif_base_weights"], 2, 0)
motif_base_weights = motif_base_weights_raw[np.newaxis]
mo... | python | {
"resource": ""
} |
q7070 | ConciseCV._get_folds | train | def _get_folds(n_rows, n_folds, use_stored):
"""
Get the used CV folds
"""
# n_folds = self._n_folds
# use_stored = self._use_stored_folds
# n_rows = self._n_rows
if use_stored is not None:
# path = '~/concise/data-offline/lw-pombe/cv_folds_5.json'
... | python | {
"resource": ""
} |
q7071 | ConciseCV.train | train | def train(self, X_feat, X_seq, y, id_vec=None, n_folds=10, use_stored_folds=None, n_cores=1,
train_global_model=False):
"""Train the Concise model in cross-validation.
Args:
X_feat: See :py:func:`concise.Concise.train`
X_seq: See :py:func:`concise.Concise.train`
... | python | {
"resource": ""
} |
q7072 | ConciseCV._from_dict | train | def _from_dict(self, obj_dict):
"""
Initialize a model from the dictionary
"""
self._n_folds = obj_dict["param"]["n_folds"]
self._n_rows = obj_dict["param"]["n_rows"]
self._use_stored_folds = obj_dict["param"]["use_stored_folds"]
self._concise_model = Concise.fro... | python | {
"resource": ""
} |
q7073 | pwm_array2pssm_array | train | def pwm_array2pssm_array(arr, background_probs=DEFAULT_BASE_BACKGROUND):
"""Convert pwm array to pssm array
"""
b = background_probs2array(background_probs)
b = b.reshape([1, 4, 1])
return np.log(arr / b).astype(arr.dtype) | python | {
"resource": ""
} |
q7074 | pssm_array2pwm_array | train | def pssm_array2pwm_array(arr, background_probs=DEFAULT_BASE_BACKGROUND):
"""Convert pssm array to pwm array
"""
b = background_probs2array(background_probs)
b = b.reshape([1, 4, 1])
return (np.exp(arr) * b).astype(arr.dtype) | python | {
"resource": ""
} |
q7075 | load_motif_db | train | def load_motif_db(filename, skipn_matrix=0):
"""Read the motif file in the following format
```
>motif_name
<skip n>0.1<delim>0.2<delim>0.5<delim>0.6
...
>motif_name2
....
```
Delim can be anything supported by np.loadtxt
# Arguments
filename: str, file path
sk... | python | {
"resource": ""
} |
q7076 | iter_fasta | train | def iter_fasta(file_path):
"""Returns an iterator over the fasta file
Given a fasta file. yield tuples of header, sequence
Code modified from Brent Pedersen's:
"Correct Way To Parse A Fasta File In Python"
# Example
```python
fasta = fasta_iter("hg19.fa")
for hea... | python | {
"resource": ""
} |
q7077 | write_fasta | train | def write_fasta(file_path, seq_list, name_list=None):
"""Write a fasta file
# Arguments
file_path: file path
seq_list: List of strings
name_list: List of names corresponding to the sequences.
If not None, it should have the same length as `seq_list`
"""
if name_list is None:
... | python | {
"resource": ""
} |
q7078 | read_RNAplfold | train | def read_RNAplfold(tmpdir, maxlen=None, seq_align="start", pad_with="E"):
"""
pad_with = with which 2ndary structure should we pad the sequence?
"""
assert pad_with in {"P", "H", "I", "M", "E"}
def read_profile(tmpdir, P):
return [values.strip().split("\t")
for seq_name, val... | python | {
"resource": ""
} |
q7079 | ism | train | def ism(model, ref, ref_rc, alt, alt_rc, mutation_positions, out_annotation_all_outputs,
output_filter_mask=None, out_annotation=None, diff_type="log_odds", rc_handling="maximum"):
"""In-silico mutagenesis
Using ISM in with diff_type 'log_odds' and rc_handling 'maximum' will produce predictions as used... | python | {
"resource": ""
} |
q7080 | _train_and_eval_single | train | def _train_and_eval_single(train, valid, model,
batch_size=32, epochs=300, use_weight=False,
callbacks=[], eval_best=False, add_eval_metrics={}):
"""Fit and evaluate a keras model
eval_best: if True, load the checkpointed model for evaluation
"""
de... | python | {
"resource": ""
} |
q7081 | eval_model | train | def eval_model(model, test, add_eval_metrics={}):
"""Evaluate model's performance on the test-set.
# Arguments
model: Keras model
test: test-dataset. Tuple of inputs `x` and target `y` - `(x, y)`.
add_eval_metrics: Additional evaluation metrics to use. Can be a dictionary or a list of f... | python | {
"resource": ""
} |
q7082 | get_model | train | def get_model(model_fn, train_data, param):
"""Feed model_fn with train_data and param
"""
model_param = merge_dicts({"train_data": train_data}, param["model"], param.get("shared", {}))
return model_fn(**model_param) | python | {
"resource": ""
} |
q7083 | _delete_keys | train | def _delete_keys(dct, keys):
"""Returns a copy of dct without `keys` keys
"""
c = deepcopy(dct)
assert isinstance(keys, list)
for k in keys:
c.pop(k)
return c | python | {
"resource": ""
} |
q7084 | _mean_dict | train | def _mean_dict(dict_list):
"""Compute the mean value across a list of dictionaries
"""
return {k: np.array([d[k] for d in dict_list]).mean()
for k in dict_list[0].keys()} | python | {
"resource": ""
} |
q7085 | CMongoTrials.get_trial | train | def get_trial(self, tid):
"""Retrieve trial by tid
"""
lid = np.where(np.array(self.tids) == tid)[0][0]
return self.trials[lid] | python | {
"resource": ""
} |
q7086 | CMongoTrials.delete_running | train | def delete_running(self, timeout_last_refresh=0, dry_run=False):
"""Delete jobs stalled in the running state for too long
timeout_last_refresh, int: number of seconds
"""
running_all = self.handle.jobs_running()
running_timeout = [job for job in running_all
... | python | {
"resource": ""
} |
q7087 | CMongoTrials.train_history | train | def train_history(self, tid=None):
"""Get train history as pd.DataFrame
"""
def result2history(result):
if isinstance(result["history"], list):
return pd.concat([pd.DataFrame(hist["loss"]).assign(fold=i)
for i, hist in enumerate(resu... | python | {
"resource": ""
} |
q7088 | CMongoTrials.as_df | train | def as_df(self, ignore_vals=["history"], separator=".", verbose=True):
"""Return a pd.DataFrame view of the whole experiment
"""
def add_eval(res):
if "eval" not in res:
if isinstance(res["history"], list):
# take the average across all folds
... | python | {
"resource": ""
} |
q7089 | effect_from_model | train | def effect_from_model(model, ref, ref_rc, alt, alt_rc, methods, mutation_positions, out_annotation_all_outputs,
extra_args=None, **argv):
"""Convenience function to execute multiple effect predictions in one call
# Arguments
model: Keras model
ref: Input sequence with the ... | python | {
"resource": ""
} |
q7090 | trades | train | def trades(ctx, market, limit, start, stop):
""" List trades in a market
"""
market = Market(market, bitshares_instance=ctx.bitshares)
t = [["time", "quote", "base", "price"]]
for trade in market.trades(limit, start=start, stop=stop):
t.append(
[
str(trade["time"]... | python | {
"resource": ""
} |
q7091 | ticker | train | def ticker(ctx, market):
""" Show ticker of a market
"""
market = Market(market, bitshares_instance=ctx.bitshares)
ticker = market.ticker()
t = [["key", "value"]]
for key in ticker:
t.append([key, str(ticker[key])])
print_table(t) | python | {
"resource": ""
} |
q7092 | cancel | train | def cancel(ctx, orders, account):
""" Cancel one or multiple orders
"""
print_tx(ctx.bitshares.cancel(orders, account=account)) | python | {
"resource": ""
} |
q7093 | orderbook | train | def orderbook(ctx, market):
""" Show the orderbook of a particular market
"""
market = Market(market, bitshares_instance=ctx.bitshares)
orderbook = market.orderbook()
ta = {}
ta["bids"] = [["quote", "sum quote", "base", "sum base", "price"]]
cumsumquote = Amount(0, market["quote"])
cumsu... | python | {
"resource": ""
} |
q7094 | buy | train | def buy(ctx, buy_amount, buy_asset, price, sell_asset, order_expiration, account):
""" Buy a specific asset at a certain rate against a base asset
"""
amount = Amount(buy_amount, buy_asset)
price = Price(
price, base=sell_asset, quote=buy_asset, bitshares_instance=ctx.bitshares
)
print_t... | python | {
"resource": ""
} |
q7095 | openorders | train | def openorders(ctx, account):
""" List open orders of an account
"""
account = Account(
account or config["default_account"], bitshares_instance=ctx.bitshares
)
t = [["Price", "Quote", "Base", "ID"]]
for o in account.openorders:
t.append(
[
"{:f} {}/{}... | python | {
"resource": ""
} |
q7096 | cancelall | train | def cancelall(ctx, market, account):
""" Cancel all orders of an account in a market
"""
market = Market(market)
ctx.bitshares.bundle = True
market.cancel([x["id"] for x in market.accountopenorders(account)], account=account)
print_tx(ctx.bitshares.txbuffer.broadcast()) | python | {
"resource": ""
} |
q7097 | spread | train | def spread(ctx, market, side, min, max, num, total, order_expiration, account):
""" Place multiple orders
\b
:param str market: Market pair quote:base (e.g. USD:BTS)
:param str side: ``buy`` or ``sell`` quote
:param float min: minimum price to place order at
:param float max... | python | {
"resource": ""
} |
q7098 | updateratio | train | def updateratio(ctx, symbol, ratio, account):
""" Update the collateral ratio of a call positions
"""
from bitshares.dex import Dex
dex = Dex(bitshares_instance=ctx.bitshares)
print_tx(dex.adjust_collateral_ratio(symbol, ratio, account=account)) | python | {
"resource": ""
} |
q7099 | bidcollateral | train | def bidcollateral(
ctx, collateral_symbol, collateral_amount, debt_symbol, debt_amount, account
):
""" Bid for collateral in the settlement fund
"""
print_tx(
ctx.bitshares.bid_collateral(
Amount(collateral_amount, collateral_symbol),
Amount(debt_amount, debt_symbol),
... | python | {
"resource": ""
} |
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