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225,600 | dswah/pyGAM | pygam/links.py | LogitLink.gradient | def gradient(self, mu, dist):
"""
derivative of the link function wrt mu
Parameters
----------
mu : array-like of legth n
dist : Distribution instance
Returns
-------
grad : np.array of length n
"""
return dist.levels/(mu*(dist.le... | python | def gradient(self, mu, dist):
"""
derivative of the link function wrt mu
Parameters
----------
mu : array-like of legth n
dist : Distribution instance
Returns
-------
grad : np.array of length n
"""
return dist.levels/(mu*(dist.le... | [
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Parameters
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mu : array-like of legth n
dist : Distribution instance
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grad : np.array of length n | [
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225,601 | dswah/pyGAM | pygam/penalties.py | derivative | def derivative(n, coef, derivative=2, periodic=False):
"""
Builds a penalty matrix for P-Splines with continuous features.
Penalizes the squared differences between basis coefficients.
Parameters
----------
n : int
number of splines
coef : unused
for compatibility with cons... | python | def derivative(n, coef, derivative=2, periodic=False):
"""
Builds a penalty matrix for P-Splines with continuous features.
Penalizes the squared differences between basis coefficients.
Parameters
----------
n : int
number of splines
coef : unused
for compatibility with cons... | [
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Penalizes the squared differences between basis coefficients.
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225,602 | dswah/pyGAM | pygam/penalties.py | monotonicity_ | def monotonicity_(n, coef, increasing=True):
"""
Builds a penalty matrix for P-Splines with continuous features.
Penalizes violation of monotonicity in the feature function.
Parameters
----------
n : int
number of splines
coef : array-like
coefficients of the feature functio... | python | def monotonicity_(n, coef, increasing=True):
"""
Builds a penalty matrix for P-Splines with continuous features.
Penalizes violation of monotonicity in the feature function.
Parameters
----------
n : int
number of splines
coef : array-like
coefficients of the feature functio... | [
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Penalizes violation of monotonicity in the feature function.
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225,603 | dswah/pyGAM | pygam/penalties.py | none | def none(n, coef):
"""
Build a matrix of zeros for features that should go unpenalized
Parameters
----------
n : int
number of splines
coef : unused
for compatibility with constraints
Returns
-------
penalty matrix : sparse csc matrix of shape (n,n)
"""
retu... | python | def none(n, coef):
"""
Build a matrix of zeros for features that should go unpenalized
Parameters
----------
n : int
number of splines
coef : unused
for compatibility with constraints
Returns
-------
penalty matrix : sparse csc matrix of shape (n,n)
"""
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225,604 | dswah/pyGAM | pygam/penalties.py | wrap_penalty | def wrap_penalty(p, fit_linear, linear_penalty=0.):
"""
tool to account for unity penalty on the linear term of any feature.
example:
p = wrap_penalty(derivative, fit_linear=True)(n, coef)
Parameters
----------
p : callable.
penalty-matrix-generating function.
fit_linear : ... | python | def wrap_penalty(p, fit_linear, linear_penalty=0.):
"""
tool to account for unity penalty on the linear term of any feature.
example:
p = wrap_penalty(derivative, fit_linear=True)(n, coef)
Parameters
----------
p : callable.
penalty-matrix-generating function.
fit_linear : ... | [
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225,605 | dswah/pyGAM | pygam/penalties.py | sparse_diff | def sparse_diff(array, n=1, axis=-1):
"""
A ported sparse version of np.diff.
Uses recursion to compute higher order differences
Parameters
----------
array : sparse array
n : int, default: 1
differencing order
axis : int, default: -1
axis along which differences are com... | python | def sparse_diff(array, n=1, axis=-1):
"""
A ported sparse version of np.diff.
Uses recursion to compute higher order differences
Parameters
----------
array : sparse array
n : int, default: 1
differencing order
axis : int, default: -1
axis along which differences are com... | [
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Uses recursion to compute higher order differences
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225,606 | dswah/pyGAM | pygam/pygam.py | GAM._linear_predictor | def _linear_predictor(self, X=None, modelmat=None, b=None, term=-1):
"""linear predictor
compute the linear predictor portion of the model
ie multiply the model matrix by the spline basis coefficients
Parameters
---------
at least 1 of (X, modelmat)
and
... | python | def _linear_predictor(self, X=None, modelmat=None, b=None, term=-1):
"""linear predictor
compute the linear predictor portion of the model
ie multiply the model matrix by the spline basis coefficients
Parameters
---------
at least 1 of (X, modelmat)
and
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225,607 | dswah/pyGAM | pygam/pygam.py | GAM.predict_mu | def predict_mu(self, X):
"""
preduct expected value of target given model and input X
Parameters
---------
X : array-like of shape (n_samples, m_features),
containing the input dataset
Returns
-------
y : np.array of shape (n_samples,)
... | python | def predict_mu(self, X):
"""
preduct expected value of target given model and input X
Parameters
---------
X : array-like of shape (n_samples, m_features),
containing the input dataset
Returns
-------
y : np.array of shape (n_samples,)
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225,608 | dswah/pyGAM | pygam/pygam.py | GAM._modelmat | def _modelmat(self, X, term=-1):
"""
Builds a model matrix, B, out of the spline basis for each feature
B = [B_0, B_1, ..., B_p]
Parameters
---------
X : array-like of shape (n_samples, m_features)
containing the input dataset
term : int, optional
... | python | def _modelmat(self, X, term=-1):
"""
Builds a model matrix, B, out of the spline basis for each feature
B = [B_0, B_1, ..., B_p]
Parameters
---------
X : array-like of shape (n_samples, m_features)
containing the input dataset
term : int, optional
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225,609 | dswah/pyGAM | pygam/pygam.py | GAM._cholesky | def _cholesky(self, A, **kwargs):
"""
method to handle potential problems with the cholesky decomposition.
will try to increase L2 regularization of the penalty matrix to
do away with non-positive-definite errors
Parameters
----------
A : np.array
Retur... | python | def _cholesky(self, A, **kwargs):
"""
method to handle potential problems with the cholesky decomposition.
will try to increase L2 regularization of the penalty matrix to
do away with non-positive-definite errors
Parameters
----------
A : np.array
Retur... | [
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225,610 | dswah/pyGAM | pygam/pygam.py | GAM._pseudo_data | def _pseudo_data(self, y, lp, mu):
"""
compute the pseudo data for a PIRLS iterations
Parameters
---------
y : array-like of shape (n,)
containing target data
lp : array-like of shape (n,)
containing linear predictions by the model
mu : ar... | python | def _pseudo_data(self, y, lp, mu):
"""
compute the pseudo data for a PIRLS iterations
Parameters
---------
y : array-like of shape (n,)
containing target data
lp : array-like of shape (n,)
containing linear predictions by the model
mu : ar... | [
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lp : array-like of shape (n,)
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mu : array-like of shape (n_samples,)
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225,611 | dswah/pyGAM | pygam/pygam.py | GAM._initial_estimate | def _initial_estimate(self, y, modelmat):
"""
Makes an inital estimate for the model coefficients.
For a LinearGAM we simply initialize to small coefficients.
For other GAMs we transform the problem to the linear space
and solve an unpenalized version.
Parameters
... | python | def _initial_estimate(self, y, modelmat):
"""
Makes an inital estimate for the model coefficients.
For a LinearGAM we simply initialize to small coefficients.
For other GAMs we transform the problem to the linear space
and solve an unpenalized version.
Parameters
... | [
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225,612 | dswah/pyGAM | pygam/pygam.py | GAM._on_loop_start | def _on_loop_start(self, variables):
"""
performs on-loop-start actions like callbacks
variables contains local namespace variables.
Parameters
---------
variables : dict of available variables
Returns
-------
None
"""
for callba... | python | def _on_loop_start(self, variables):
"""
performs on-loop-start actions like callbacks
variables contains local namespace variables.
Parameters
---------
variables : dict of available variables
Returns
-------
None
"""
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variables : dict of available variables
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225,613 | dswah/pyGAM | pygam/pygam.py | GAM._on_loop_end | def _on_loop_end(self, variables):
"""
performs on-loop-end actions like callbacks
variables contains local namespace variables.
Parameters
---------
variables : dict of available variables
Returns
-------
None
"""
for callback i... | python | def _on_loop_end(self, variables):
"""
performs on-loop-end actions like callbacks
variables contains local namespace variables.
Parameters
---------
variables : dict of available variables
Returns
-------
None
"""
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225,614 | dswah/pyGAM | pygam/pygam.py | GAM.deviance_residuals | def deviance_residuals(self, X, y, weights=None, scaled=False):
"""
method to compute the deviance residuals of the model
these are analogous to the residuals of an OLS.
Parameters
----------
X : array-like
Input data array of shape (n_saples, m_features)
... | python | def deviance_residuals(self, X, y, weights=None, scaled=False):
"""
method to compute the deviance residuals of the model
these are analogous to the residuals of an OLS.
Parameters
----------
X : array-like
Input data array of shape (n_saples, m_features)
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225,615 | dswah/pyGAM | pygam/pygam.py | GAM._estimate_model_statistics | def _estimate_model_statistics(self, y, modelmat, inner=None, BW=None,
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"""
method to compute all of the model statistics
results are stored in the 'statistics_' attribute of the model, as a
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... | python | def _estimate_model_statistics(self, y, modelmat, inner=None, BW=None,
B=None, weights=None, U1=None):
"""
method to compute all of the model statistics
results are stored in the 'statistics_' attribute of the model, as a
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225,616 | dswah/pyGAM | pygam/pygam.py | GAM._estimate_AIC | def _estimate_AIC(self, y, mu, weights=None):
"""
estimate the Akaike Information Criterion
Parameters
----------
y : array-like of shape (n_samples,)
output data vector
mu : array-like of shape (n_samples,),
expected value of the targets given th... | python | def _estimate_AIC(self, y, mu, weights=None):
"""
estimate the Akaike Information Criterion
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----------
y : array-like of shape (n_samples,)
output data vector
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225,617 | dswah/pyGAM | pygam/pygam.py | GAM._estimate_AICc | def _estimate_AICc(self, y, mu, weights=None):
"""
estimate the corrected Akaike Information Criterion
relies on the estimated degrees of freedom, which must be computed
before.
Parameters
----------
y : array-like of shape (n_samples,)
output data v... | python | def _estimate_AICc(self, y, mu, weights=None):
"""
estimate the corrected Akaike Information Criterion
relies on the estimated degrees of freedom, which must be computed
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----------
y : array-like of shape (n_samples,)
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225,618 | dswah/pyGAM | pygam/pygam.py | GAM._estimate_r2 | def _estimate_r2(self, X=None, y=None, mu=None, weights=None):
"""
estimate some pseudo R^2 values
currently only computes explained deviance.
results are stored
Parameters
----------
y : array-like of shape (n_samples,)
output data vector
mu... | python | def _estimate_r2(self, X=None, y=None, mu=None, weights=None):
"""
estimate some pseudo R^2 values
currently only computes explained deviance.
results are stored
Parameters
----------
y : array-like of shape (n_samples,)
output data vector
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225,619 | dswah/pyGAM | pygam/pygam.py | GAM._estimate_GCV_UBRE | def _estimate_GCV_UBRE(self, X=None, y=None, modelmat=None, gamma=1.4,
add_scale=True, weights=None):
"""
Generalized Cross Validation and Un-Biased Risk Estimator.
UBRE is used when the scale parameter is known,
like Poisson and Binomial families.
Pa... | python | def _estimate_GCV_UBRE(self, X=None, y=None, modelmat=None, gamma=1.4,
add_scale=True, weights=None):
"""
Generalized Cross Validation and Un-Biased Risk Estimator.
UBRE is used when the scale parameter is known,
like Poisson and Binomial families.
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225,620 | dswah/pyGAM | pygam/pygam.py | GAM._estimate_p_values | def _estimate_p_values(self):
"""estimate the p-values for all features
"""
if not self._is_fitted:
raise AttributeError('GAM has not been fitted. Call fit first.')
p_values = []
for term_i in range(len(self.terms)):
p_values.append(self._compute_p_value(... | python | def _estimate_p_values(self):
"""estimate the p-values for all features
"""
if not self._is_fitted:
raise AttributeError('GAM has not been fitted. Call fit first.')
p_values = []
for term_i in range(len(self.terms)):
p_values.append(self._compute_p_value(... | [
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225,621 | dswah/pyGAM | pygam/pygam.py | GAM._compute_p_value | def _compute_p_value(self, term_i):
"""compute the p-value of the desired feature
Arguments
---------
term_i : int
term to select from the data
Returns
-------
p_value : float
Notes
-----
Wood 2006, section 4.8.5:
... | python | def _compute_p_value(self, term_i):
"""compute the p-value of the desired feature
Arguments
---------
term_i : int
term to select from the data
Returns
-------
p_value : float
Notes
-----
Wood 2006, section 4.8.5:
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225,622 | dswah/pyGAM | pygam/pygam.py | GAM.confidence_intervals | def confidence_intervals(self, X, width=.95, quantiles=None):
"""estimate confidence intervals for the model.
Parameters
----------
X : array-like of shape (n_samples, m_features)
Input data matrix
width : float on [0,1], optional
quantiles : array-like of fl... | python | def confidence_intervals(self, X, width=.95, quantiles=None):
"""estimate confidence intervals for the model.
Parameters
----------
X : array-like of shape (n_samples, m_features)
Input data matrix
width : float on [0,1], optional
quantiles : array-like of fl... | [
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225,623 | dswah/pyGAM | pygam/pygam.py | GAM._get_quantiles | def _get_quantiles(self, X, width, quantiles, modelmat=None, lp=None,
prediction=False, xform=True, term=-1):
"""
estimate prediction intervals for LinearGAM
Parameters
----------
X : array
input data of shape (n_samples, m_features)
wi... | python | def _get_quantiles(self, X, width, quantiles, modelmat=None, lp=None,
prediction=False, xform=True, term=-1):
"""
estimate prediction intervals for LinearGAM
Parameters
----------
X : array
input data of shape (n_samples, m_features)
wi... | [
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225,624 | dswah/pyGAM | pygam/pygam.py | GAM._flatten_mesh | def _flatten_mesh(self, Xs, term):
"""flatten the mesh and distribute into a feature matrix"""
n = Xs[0].size
if self.terms[term].istensor:
terms = self.terms[term]
else:
terms = [self.terms[term]]
X = np.zeros((n, self.statistics_['m_features']))
... | python | def _flatten_mesh(self, Xs, term):
"""flatten the mesh and distribute into a feature matrix"""
n = Xs[0].size
if self.terms[term].istensor:
terms = self.terms[term]
else:
terms = [self.terms[term]]
X = np.zeros((n, self.statistics_['m_features']))
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225,625 | dswah/pyGAM | pygam/pygam.py | GAM.generate_X_grid | def generate_X_grid(self, term, n=100, meshgrid=False):
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if term is >= 0, we generate n samples per feature,
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225,626 | dswah/pyGAM | pygam/pygam.py | GAM.partial_dependence | def partial_dependence(self, term, X=None, width=None, quantiles=None,
meshgrid=False):
"""
Computes the term functions for the GAM
and possibly their confidence intervals.
if both width=None and quantiles=None,
then no confidence intervals are compute... | python | def partial_dependence(self, term, X=None, width=None, quantiles=None,
meshgrid=False):
"""
Computes the term functions for the GAM
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225,627 | dswah/pyGAM | pygam/pygam.py | GAM.sample | def sample(self, X, y, quantity='y', sample_at_X=None,
weights=None, n_draws=100, n_bootstraps=5, objective='auto'):
"""Simulate from the posterior of the coefficients and smoothing params.
Samples are drawn from the posterior of the coefficients and smoothing
parameters given th... | python | def sample(self, X, y, quantity='y', sample_at_X=None,
weights=None, n_draws=100, n_bootstraps=5, objective='auto'):
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225,628 | dswah/pyGAM | pygam/pygam.py | GAM._sample_coef | def _sample_coef(self, X, y, weights=None, n_draws=100, n_bootstraps=1,
objective='auto'):
"""Simulate from the posterior of the coefficients.
NOTE: A `gridsearch` is done `n_bootstraps` many times, so keep
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225,629 | dswah/pyGAM | pygam/pygam.py | GAM._bootstrap_samples_of_smoothing | def _bootstrap_samples_of_smoothing(self, X, y, weights=None,
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"""Sample the smoothing parameters using simulated response data.
For now, the grid of `lam` values is 11 random points in M-dimensional
space, where M = the... | python | def _bootstrap_samples_of_smoothing(self, X, y, weights=None,
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225,630 | dswah/pyGAM | pygam/pygam.py | GAM._simulate_coef_from_bootstraps | def _simulate_coef_from_bootstraps(
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"""Simulate coefficients using bootstrap samples."""
# Sample indices uniformly from {0, ..., n_bootstraps - 1}
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... | python | def _simulate_coef_from_bootstraps(
self, n_draws, coef_bootstraps, cov_bootstraps):
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# Sample indices uniformly from {0, ..., n_bootstraps - 1}
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225,631 | dswah/pyGAM | pygam/pygam.py | LogisticGAM.accuracy | def accuracy(self, X=None, y=None, mu=None):
"""
computes the accuracy of the LogisticGAM
Parameters
----------
note: X or mu must be defined. defaults to mu
X : array-like of shape (n_samples, m_features), optional (default=None)
containing input data
... | python | def accuracy(self, X=None, y=None, mu=None):
"""
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Parameters
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X : array-like of shape (n_samples, m_features), optional (default=None)
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225,632 | dswah/pyGAM | pygam/pygam.py | PoissonGAM._exposure_to_weights | def _exposure_to_weights(self, y, exposure=None, weights=None):
"""simple tool to create a common API
Parameters
----------
y : array-like, shape (n_samples,)
Target values (integers in classification, real numbers in
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y : array-like, shape (n_samples,)
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225,633 | dswah/pyGAM | pygam/pygam.py | PoissonGAM.predict | def predict(self, X, exposure=None):
"""
preduct expected value of target given model and input X
often this is done via expected value of GAM given input X
Parameters
---------
X : array-like of shape (n_samples, m_features), default: None
containing the inp... | python | def predict(self, X, exposure=None):
"""
preduct expected value of target given model and input X
often this is done via expected value of GAM given input X
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225,634 | dswah/pyGAM | pygam/pygam.py | PoissonGAM.gridsearch | def gridsearch(self, X, y, exposure=None, weights=None,
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**param_grids):
"""
performs a grid search over a space of parameters for a given objective
NOTE:
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225,635 | dswah/pyGAM | pygam/pygam.py | ExpectileGAM._get_quantile_ratio | def _get_quantile_ratio(self, X, y):
"""find the expirical quantile of the model
Parameters
----------
X : array-like, shape (n_samples, m_features)
Training vectors, where n_samples is the number of samples
and m_features is the number of features.
y : a... | python | def _get_quantile_ratio(self, X, y):
"""find the expirical quantile of the model
Parameters
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X : array-like, shape (n_samples, m_features)
Training vectors, where n_samples is the number of samples
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225,636 | dswah/pyGAM | pygam/pygam.py | ExpectileGAM.fit_quantile | def fit_quantile(self, X, y, quantile, max_iter=20, tol=0.01, weights=None):
"""fit ExpectileGAM to a desired quantile via binary search
Parameters
----------
X : array-like, shape (n_samples, m_features)
Training vectors, where n_samples is the number of samples
... | python | def fit_quantile(self, X, y, quantile, max_iter=20, tol=0.01, weights=None):
"""fit ExpectileGAM to a desired quantile via binary search
Parameters
----------
X : array-like, shape (n_samples, m_features)
Training vectors, where n_samples is the number of samples
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225,637 | dswah/pyGAM | pygam/core.py | nice_repr | def nice_repr(name, param_kvs, line_width=30, line_offset=5, decimals=3, args=None, flatten_attrs=True):
"""
tool to do a nice repr of a class.
Parameters
----------
name : str
class name
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225,638 | dswah/pyGAM | pygam/core.py | Core.get_params | def get_params(self, deep=False):
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deep : boolean, default: False
when True, also gets non-user-facing paramters
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-------
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deep : boolean, default: False
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225,639 | dswah/pyGAM | pygam/core.py | Core.set_params | def set_params(self, deep=False, force=False, **parameters):
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225,640 | dswah/pyGAM | pygam/terms.py | Term.build_from_info | def build_from_info(cls, info):
"""build a Term instance from a dict
Parameters
----------
cls : class
info : dict
contains all information needed to build the term
Return
------
Term instance
"""
info = deepcopy(info)
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"""build a Term instance from a dict
Parameters
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cls : class
info : dict
contains all information needed to build the term
Return
------
Term instance
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info = deepcopy(info)
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225,641 | dswah/pyGAM | pygam/terms.py | MetaTermMixin._has_terms | def _has_terms(self):
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225,642 | dswah/pyGAM | pygam/terms.py | TensorTerm.build_from_info | def build_from_info(cls, info):
"""build a TensorTerm instance from a dict
Parameters
----------
cls : class
info : dict
contains all information needed to build the term
Return
------
TensorTerm instance
"""
terms = []
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225,643 | dswah/pyGAM | pygam/terms.py | TensorTerm.hasconstraint | def hasconstraint(self):
"""bool, whether the term has any constraints
"""
constrained = False
for term in self._terms:
constrained = constrained or term.hasconstraint
return constrained | python | def hasconstraint(self):
"""bool, whether the term has any constraints
"""
constrained = False
for term in self._terms:
constrained = constrained or term.hasconstraint
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225,645 | dswah/pyGAM | pygam/terms.py | TensorTerm._iterate_marginal_coef_slices | def _iterate_marginal_coef_slices(self, i):
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225,646 | dswah/pyGAM | pygam/terms.py | TermList.info | def info(self):
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Parameters
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Returns
-------
dict containing information to duplicate the term list
"""
info = {'term_type': 'term_list', 'verbose': self.verbose}
info.update({'terms':[... | python | def info(self):
"""get information about the terms in the term list
Parameters
----------
Returns
-------
dict containing information to duplicate the term list
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225,647 | dswah/pyGAM | pygam/terms.py | TermList.build_from_info | def build_from_info(cls, info):
"""build a TermList instance from a dict
Parameters
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cls : class
info : dict
contains all information needed to build the term
Return
------
TermList instance
"""
info = deepcopy(info... | python | def build_from_info(cls, info):
"""build a TermList instance from a dict
Parameters
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info : dict
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225,648 | dswah/pyGAM | pygam/terms.py | TermList.pop | def pop(self, i=None):
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by default the last term is popped.
Returns
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term : Term
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225,649 | dswah/pyGAM | pygam/terms.py | TermList.get_coef_indices | def get_coef_indices(self, i=-1):
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225,650 | thoughtworksarts/EmoPy | EmoPy/src/csv_data_loader.py | CSVDataLoader.load_data | def load_data(self):
"""
Loads image and label data from specified csv file path.
:return: Dataset object containing image and label data.
"""
print('Extracting training data from csv...')
images = list()
labels = list()
emotion_index_map = dict()
... | python | def load_data(self):
"""
Loads image and label data from specified csv file path.
:return: Dataset object containing image and label data.
"""
print('Extracting training data from csv...')
images = list()
labels = list()
emotion_index_map = dict()
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225,651 | thoughtworksarts/EmoPy | EmoPy/src/data_loader.py | _DataLoader._load_dataset | def _load_dataset(self, images, labels, emotion_index_map):
"""
Loads Dataset object with images, labels, and other data.
:param images: numpy array of image data
:param labels: numpy array of one-hot vector labels
:param emotion_index_map: map linking string/integer emotion cla... | python | def _load_dataset(self, images, labels, emotion_index_map):
"""
Loads Dataset object with images, labels, and other data.
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:param labels: numpy array of one-hot vector labels
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225,652 | thoughtworksarts/EmoPy | EmoPy/src/directory_data_loader.py | DirectoryDataLoader.load_data | def load_data(self):
"""
Loads image and label data from specified directory path.
:return: Dataset object containing image and label data.
"""
images = list()
labels = list()
emotion_index_map = dict()
label_directories = [dir for dir in os.listdir(self.... | python | def load_data(self):
"""
Loads image and label data from specified directory path.
:return: Dataset object containing image and label data.
"""
images = list()
labels = list()
emotion_index_map = dict()
label_directories = [dir for dir in os.listdir(self.... | [
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225,653 | thoughtworksarts/EmoPy | EmoPy/src/directory_data_loader.py | DirectoryDataLoader._check_directory_arguments | def _check_directory_arguments(self):
"""
Validates arguments for loading from directories, including static image and time series directories.
"""
if not os.path.isdir(self.datapath):
raise (NotADirectoryError('Directory does not exist: %s' % self.datapath))
if self.... | python | def _check_directory_arguments(self):
"""
Validates arguments for loading from directories, including static image and time series directories.
"""
if not os.path.isdir(self.datapath):
raise (NotADirectoryError('Directory does not exist: %s' % self.datapath))
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225,654 | thoughtworksarts/EmoPy | EmoPy/src/fermodel.py | FERModel.predict | def predict(self, image_file):
"""
Predicts discrete emotion for given image.
:param images: image file (jpg or png format)
"""
image = misc.imread(image_file)
gray_image = image
if len(image.shape) > 2:
gray_image = cv2.cvtColor(image, code=cv2.COLOR... | python | def predict(self, image_file):
"""
Predicts discrete emotion for given image.
:param images: image file (jpg or png format)
"""
image = misc.imread(image_file)
gray_image = image
if len(image.shape) > 2:
gray_image = cv2.cvtColor(image, code=cv2.COLOR... | [
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225,655 | thoughtworksarts/EmoPy | EmoPy/src/fermodel.py | FERModel._check_emotion_set_is_supported | def _check_emotion_set_is_supported(self):
"""
Validates set of user-supplied target emotions.
"""
supported_emotion_subsets = [
set(['anger', 'fear', 'surprise', 'calm']),
set(['happiness', 'disgust', 'surprise']),
set(['anger', 'fear', 'surprise']),
... | python | def _check_emotion_set_is_supported(self):
"""
Validates set of user-supplied target emotions.
"""
supported_emotion_subsets = [
set(['anger', 'fear', 'surprise', 'calm']),
set(['happiness', 'disgust', 'surprise']),
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225,656 | thoughtworksarts/EmoPy | EmoPy/src/fermodel.py | FERModel._choose_model_from_target_emotions | def _choose_model_from_target_emotions(self):
"""
Initializes pre-trained deep learning model for the set of target emotions supplied by user.
"""
model_indices = [self.emotion_index_map[emotion] for emotion in self.target_emotions]
sorted_indices = [str(idx) for idx in sorted(mo... | python | def _choose_model_from_target_emotions(self):
"""
Initializes pre-trained deep learning model for the set of target emotions supplied by user.
"""
model_indices = [self.emotion_index_map[emotion] for emotion in self.target_emotions]
sorted_indices = [str(idx) for idx in sorted(mo... | [
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225,657 | thoughtworksarts/EmoPy | EmoPy/library/image.py | apply_transform | def apply_transform(sample,
transform_matrix,
channel_axis=0,
fill_mode='nearest',
cval=0.):
"""Apply the image transformation specified by a matrix.
# Arguments
sample: 2D numpy array, single sample.
transform_matr... | python | def apply_transform(sample,
transform_matrix,
channel_axis=0,
fill_mode='nearest',
cval=0.):
"""Apply the image transformation specified by a matrix.
# Arguments
sample: 2D numpy array, single sample.
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225,658 | thoughtworksarts/EmoPy | EmoPy/library/image.py | ImageDataGenerator.standardize | def standardize(self, x):
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# Arguments
x: batch of inputs to be normalized.
# Returns
The inputs, normalized.
"""
if self.preprocessing_function:
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x: batch of inputs to be normalized.
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The inputs, normalized.
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index_array = next(self.index_generator)
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225,661 | thoughtworksarts/EmoPy | EmoPy/src/neuralnets.py | ConvolutionalLstmNN._init_model | def _init_model(self):
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Composes all layers of CNN.
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model = Sequential()
model.add(ConvLSTM2D(filters=self.filters, kernel_size=self.kernel_size, activation=self.activation,
input_shape=[self.time_delay] + list(self.image_size) + [self.channels... | python | def _init_model(self):
"""
Composes all layers of CNN.
"""
model = Sequential()
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225,665 | sosreport/sos | sos/policies/__init__.py | InitSystem.is_enabled | def is_enabled(self, name):
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225,666 | sosreport/sos | sos/policies/__init__.py | InitSystem.is_disabled | def is_disabled(self, name):
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225,667 | sosreport/sos | sos/policies/__init__.py | InitSystem._query_service | def _query_service(self, name):
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225,669 | sosreport/sos | sos/policies/__init__.py | PackageManager.all_pkgs_by_name_regex | def all_pkgs_by_name_regex(self, regex_name, flags=0):
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225,670 | sosreport/sos | sos/policies/__init__.py | PackageManager.pkg_by_name | def pkg_by_name(self, name):
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225,672 | sosreport/sos | sos/policies/__init__.py | PackageManager.all_files | def all_files(self):
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225,673 | sosreport/sos | sos/policies/__init__.py | PresetDefaults.write | def write(self, presets_path):
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225,674 | sosreport/sos | sos/policies/__init__.py | Policy.get_archive_name | def get_archive_name(self):
"""
This function should return the filename of the archive without the
extension.
This uses the policy's name_pattern attribute to determine the name.
There are two pre-defined naming patterns - 'legacy' and 'friendly'
that give names like th... | python | def get_archive_name(self):
"""
This function should return the filename of the archive without the
extension.
This uses the policy's name_pattern attribute to determine the name.
There are two pre-defined naming patterns - 'legacy' and 'friendly'
that give names like th... | [
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225,675 | sosreport/sos | sos/policies/__init__.py | Policy.validate_plugin | def validate_plugin(self, plugin_class, experimental=False):
"""
Verifies that the plugin_class should execute under this policy
"""
valid_subclasses = [IndependentPlugin] + self.valid_subclasses
if experimental:
valid_subclasses += [ExperimentalPlugin]
return... | python | def validate_plugin(self, plugin_class, experimental=False):
"""
Verifies that the plugin_class should execute under this policy
"""
valid_subclasses = [IndependentPlugin] + self.valid_subclasses
if experimental:
valid_subclasses += [ExperimentalPlugin]
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225,676 | sosreport/sos | sos/policies/__init__.py | Policy._print | def _print(self, msg=None, always=False):
"""A wrapper around print that only prints if we are not running in
quiet mode"""
if always or not self.commons['cmdlineopts'].quiet:
if msg:
print_(msg)
else:
print_() | python | def _print(self, msg=None, always=False):
"""A wrapper around print that only prints if we are not running in
quiet mode"""
if always or not self.commons['cmdlineopts'].quiet:
if msg:
print_(msg)
else:
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225,677 | sosreport/sos | sos/policies/__init__.py | Policy.get_msg | def get_msg(self):
"""This method is used to prepare the preamble text to display to
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width = 72
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"""This method is used to prepare the preamble text to display to
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225,678 | sosreport/sos | sos/policies/__init__.py | Policy.register_presets | def register_presets(self, presets, replace=False):
"""Add new presets to this policy object.
Merges the presets dictionary ``presets`` into this ``Policy``
object, or replaces the current presets if ``replace`` is
``True``.
``presets`` should be a dictionary ma... | python | def register_presets(self, presets, replace=False):
"""Add new presets to this policy object.
Merges the presets dictionary ``presets`` into this ``Policy``
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225,679 | sosreport/sos | sos/policies/__init__.py | Policy.find_preset | def find_preset(self, preset):
"""Find a preset profile matching the specified preset string.
:param preset: a string containing a preset profile name.
:returns: a matching PresetProfile.
"""
# FIXME: allow fuzzy matching?
for match in self.presets.keys():
... | python | def find_preset(self, preset):
"""Find a preset profile matching the specified preset string.
:param preset: a string containing a preset profile name.
:returns: a matching PresetProfile.
"""
# FIXME: allow fuzzy matching?
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225,680 | sosreport/sos | sos/policies/__init__.py | Policy.load_presets | def load_presets(self, presets_path=None):
"""Load presets from disk.
Read JSON formatted preset data from the specified path,
or the default location at ``/var/lib/sos/presets``.
:param presets_path: a directory containing JSON presets.
"""
presets_path = p... | python | def load_presets(self, presets_path=None):
"""Load presets from disk.
Read JSON formatted preset data from the specified path,
or the default location at ``/var/lib/sos/presets``.
:param presets_path: a directory containing JSON presets.
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225,681 | sosreport/sos | sos/policies/__init__.py | Policy.add_preset | def add_preset(self, name=None, desc=None, note=None, opts=SoSOptions()):
"""Add a new on-disk preset and write it to the configured
presets path.
:param preset: the new PresetDefaults to add
"""
presets_path = self.presets_path
if not name:
raise Va... | python | def add_preset(self, name=None, desc=None, note=None, opts=SoSOptions()):
"""Add a new on-disk preset and write it to the configured
presets path.
:param preset: the new PresetDefaults to add
"""
presets_path = self.presets_path
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225,682 | sosreport/sos | sos/policies/__init__.py | LinuxPolicy.lsmod | def lsmod(self):
"""Return a list of kernel module names as strings.
"""
lines = shell_out("lsmod", timeout=0).splitlines()
return [line.split()[0].strip() for line in lines] | python | def lsmod(self):
"""Return a list of kernel module names as strings.
"""
lines = shell_out("lsmod", timeout=0).splitlines()
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225,683 | sosreport/sos | sos/plugins/lvm2.py | Lvm2.do_lvmdump | def do_lvmdump(self, metadata=False):
"""Collects an lvmdump in standard format with optional metadata
archives for each physical volume present.
"""
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lvmdump_opts = "... | python | def do_lvmdump(self, metadata=False):
"""Collects an lvmdump in standard format with optional metadata
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225,684 | sosreport/sos | sos/__init__.py | SoSOptions.__str | def __str(self, quote=False, sep=" ", prefix="", suffix=""):
"""Format a SoSOptions object as a human or machine readable string.
:param quote: quote option values
:param sep: list separator string
:param prefix: arbitrary prefix string
:param suffix: arbitrary s... | python | def __str(self, quote=False, sep=" ", prefix="", suffix=""):
"""Format a SoSOptions object as a human or machine readable string.
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225,685 | sosreport/sos | sos/__init__.py | SoSOptions.from_args | def from_args(cls, args):
"""Initialise a new SoSOptions object from a ``Namespace``
obtained by parsing command line arguments.
:param args: parsed command line arguments
:returns: an initialised SoSOptions object
:returntype: SoSOptions
"""
opts... | python | def from_args(cls, args):
"""Initialise a new SoSOptions object from a ``Namespace``
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:param args: parsed command line arguments
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225,686 | sosreport/sos | sos/__init__.py | SoSOptions.merge | def merge(self, src, skip_default=True):
"""Merge another set of ``SoSOptions`` into this object.
Merge two ``SoSOptions`` objects by setting unset or default
values to their value in the ``src`` object.
:param src: the ``SoSOptions`` object to copy from
:param ... | python | def merge(self, src, skip_default=True):
"""Merge another set of ``SoSOptions`` into this object.
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225,687 | sosreport/sos | sos/__init__.py | SoSOptions.dict | def dict(self):
"""Return this ``SoSOptions`` option values as a dictionary of
argument name to value mappings.
:returns: a name:value dictionary of option values.
"""
odict = {}
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# Do not a... | python | def dict(self):
"""Return this ``SoSOptions`` option values as a dictionary of
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225,688 | sosreport/sos | sos/__init__.py | SoSOptions.to_args | def to_args(self):
"""Return command arguments for this object.
Return a list of the non-default options of this ``SoSOptions``
object in ``sosreport`` command line argument notation:
``["--all-logs", "-vvv"]``
"""
def has_value(name, value):
... | python | def to_args(self):
"""Return command arguments for this object.
Return a list of the non-default options of this ``SoSOptions``
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``["--all-logs", "-vvv"]`` | [
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225,689 | sosreport/sos | sos/plugins/autofs.py | Autofs.checkdebug | def checkdebug(self):
""" testing if autofs debug has been enabled anywhere
"""
# Global debugging
opt = self.file_grep(r"^(DEFAULT_LOGGING|DAEMONOPTIONS)=(.*)",
*self.files)
for opt1 in opt:
for opt2 in opt1.split(" "):
if... | python | def checkdebug(self):
""" testing if autofs debug has been enabled anywhere
"""
# Global debugging
opt = self.file_grep(r"^(DEFAULT_LOGGING|DAEMONOPTIONS)=(.*)",
*self.files)
for opt1 in opt:
for opt2 in opt1.split(" "):
if... | [
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225,690 | sosreport/sos | sos/plugins/autofs.py | Autofs.getdaemondebug | def getdaemondebug(self):
""" capture daemon debug output
"""
debugout = self.file_grep(r"^(daemon.*)\s+(\/var\/log\/.*)",
*self.files)
for i in debugout:
return i[1] | python | def getdaemondebug(self):
""" capture daemon debug output
"""
debugout = self.file_grep(r"^(daemon.*)\s+(\/var\/log\/.*)",
*self.files)
for i in debugout:
return i[1] | [
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225,691 | sosreport/sos | sos/plugins/jars.py | Jars.is_jar | def is_jar(path):
"""Check whether given file is a JAR file.
JARs are ZIP files which usually include a manifest
at the canonical location 'META-INF/MANIFEST.MF'.
"""
if os.path.isfile(path) and zipfile.is_zipfile(path):
try:
with zipfile.ZipFile(path... | python | def is_jar(path):
"""Check whether given file is a JAR file.
JARs are ZIP files which usually include a manifest
at the canonical location 'META-INF/MANIFEST.MF'.
"""
if os.path.isfile(path) and zipfile.is_zipfile(path):
try:
with zipfile.ZipFile(path... | [
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225,692 | sosreport/sos | sos/plugins/jars.py | Jars.get_maven_id | def get_maven_id(jar_path):
"""Extract Maven coordinates from a given JAR file, if possible.
JARs build by Maven (most popular Java build system) contain
'pom.properties' file. We can extract Maven coordinates
from there.
"""
props = {}
try:
with zip... | python | def get_maven_id(jar_path):
"""Extract Maven coordinates from a given JAR file, if possible.
JARs build by Maven (most popular Java build system) contain
'pom.properties' file. We can extract Maven coordinates
from there.
"""
props = {}
try:
with zip... | [
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225,693 | sosreport/sos | sos/plugins/jars.py | Jars.get_jar_id | def get_jar_id(jar_path):
"""Compute JAR id.
Returns sha1 hash of a given JAR file.
"""
jar_id = ""
try:
with open(jar_path, mode="rb") as f:
m = hashlib.sha1()
for buf in iter(partial(f.read, 4096), b''):
m.update(... | python | def get_jar_id(jar_path):
"""Compute JAR id.
Returns sha1 hash of a given JAR file.
"""
jar_id = ""
try:
with open(jar_path, mode="rb") as f:
m = hashlib.sha1()
for buf in iter(partial(f.read, 4096), b''):
m.update(... | [
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225,694 | sosreport/sos | sos/plugins/qpid.py | Qpid.setup | def setup(self):
""" performs data collection for qpid broker """
options = ""
amqps_prefix = "" # set amqps:// when SSL is used
if self.get_option("ssl"):
amqps_prefix = "amqps://"
# for either present option, add --option=value to 'options' variable
for opt... | python | def setup(self):
""" performs data collection for qpid broker """
options = ""
amqps_prefix = "" # set amqps:// when SSL is used
if self.get_option("ssl"):
amqps_prefix = "amqps://"
# for either present option, add --option=value to 'options' variable
for opt... | [
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225,695 | sosreport/sos | sos/sosreport.py | SoSReport._exception | def _exception(etype, eval_, etrace):
""" Wrap exception in debugger if not in tty """
if hasattr(sys, 'ps1') or not sys.stderr.isatty():
# we are in interactive mode or we don't have a tty-like
# device, so we call the default hook
sys.__excepthook__(etype, eval_, et... | python | def _exception(etype, eval_, etrace):
""" Wrap exception in debugger if not in tty """
if hasattr(sys, 'ps1') or not sys.stderr.isatty():
# we are in interactive mode or we don't have a tty-like
# device, so we call the default hook
sys.__excepthook__(etype, eval_, et... | [
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225,696 | sosreport/sos | sos/sosreport.py | SoSReport.add_preset | def add_preset(self, name, desc="", note=""):
"""Add a new command line preset for the current options with the
specified name.
:param name: the name of the new preset
:returns: True on success or False otherwise
"""
policy = self.policy
if policy.fin... | python | def add_preset(self, name, desc="", note=""):
"""Add a new command line preset for the current options with the
specified name.
:param name: the name of the new preset
:returns: True on success or False otherwise
"""
policy = self.policy
if policy.fin... | [
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225,697 | sosreport/sos | sos/sosreport.py | SoSReport.del_preset | def del_preset(self, name):
"""Delete a named command line preset.
:param name: the name of the preset to delete
:returns: True on success or False otherwise
"""
policy = self.policy
if not policy.find_preset(name):
self.ui_log.error("Preset '%s' not ... | python | def del_preset(self, name):
"""Delete a named command line preset.
:param name: the name of the preset to delete
:returns: True on success or False otherwise
"""
policy = self.policy
if not policy.find_preset(name):
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225,698 | sosreport/sos | sos/sosreport.py | SoSReport.version | def version(self):
"""Fetch version information from all plugins and store in the report
version file"""
versions = []
versions.append("sosreport: %s" % __version__)
for plugname, plug in self.loaded_plugins:
versions.append("%s: %s" % (plugname, plug.version))
... | python | def version(self):
"""Fetch version information from all plugins and store in the report
version file"""
versions = []
versions.append("sosreport: %s" % __version__)
for plugname, plug in self.loaded_plugins:
versions.append("%s: %s" % (plugname, plug.version))
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225,699 | sosreport/sos | sos/plugins/navicli.py | Navicli.get_navicli_SP_info | def get_navicli_SP_info(self, SP_address):
""" EMC Navisphere Host Agent NAVICLI specific
information - CLARiiON - commands
"""
self.add_cmd_output([
"navicli -h %s getall" % SP_address,
"navicli -h %s getsptime -spa" % SP_address,
"navicli -h %s getsp... | python | def get_navicli_SP_info(self, SP_address):
""" EMC Navisphere Host Agent NAVICLI specific
information - CLARiiON - commands
"""
self.add_cmd_output([
"navicli -h %s getall" % SP_address,
"navicli -h %s getsptime -spa" % SP_address,
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