after_merge stringlengths 28 79.6k | before_merge stringlengths 20 79.6k | url stringlengths 38 71 | full_traceback stringlengths 43 922k | traceback_type stringclasses 555
values |
|---|---|---|---|---|
def get_prediction(
self, start=None, end=None, dynamic=False, index=None, exog=None, **kwargs
):
r"""
In-sample prediction and out-of-sample forecasting
Parameters
----------
start : int, str, or datetime, optional
Zero-indexed observation number at which to start forecasting, ie.,
... | def get_prediction(
self, start=None, end=None, dynamic=False, index=None, exog=None, **kwargs
):
"""
In-sample prediction and out-of-sample forecasting
Parameters
----------
start : int, str, or datetime, optional
Zero-indexed observation number at which to start forecasting, ie.,
... | https://github.com/statsmodels/statsmodels/issues/6244 | c:\git\statsmodels\statsmodels\tsa\statespace\sarimax.py:914: RuntimeWarning: overflow encountered in square
params_variance = (residuals[k_params_ma:]**2).mean()
c:\git\statsmodels\statsmodels\tsa\statespace\sarimax.py:976: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting p... | IndexError |
def summary(self, alpha=0.05, start=None):
# Create the model name
# See if we have an ARIMA component
order = ""
if self.model.k_ar + self.model.k_diff + self.model.k_ma > 0:
if self.model.k_ar == self.model.k_ar_params:
order_ar = self.model.k_ar
else:
order_ar... | def summary(self, alpha=0.05, start=None):
# Create the model name
# See if we have an ARIMA component
order = ""
if self.model.k_ar + self.model.k_diff + self.model.k_ma > 0:
if self.model.k_ar == self.model.k_ar_params:
order_ar = self.model.k_ar
else:
order_ar... | https://github.com/statsmodels/statsmodels/issues/6244 | c:\git\statsmodels\statsmodels\tsa\statespace\sarimax.py:914: RuntimeWarning: overflow encountered in square
params_variance = (residuals[k_params_ma:]**2).mean()
c:\git\statsmodels\statsmodels\tsa\statespace\sarimax.py:976: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting p... | IndexError |
def start_params(self):
params = np.zeros(self.k_params, dtype=np.float64)
endog = self.endog.copy()
mask = ~np.any(np.isnan(endog), axis=1)
endog = endog[mask]
# 1. Factor loadings (estimated via PCA)
if self.k_factors > 0:
# Use principal components + OLS as starting values
r... | def start_params(self):
params = np.zeros(self.k_params, dtype=np.float64)
endog = self.endog.copy()
# 1. Factor loadings (estimated via PCA)
if self.k_factors > 0:
# Use principal components + OLS as starting values
res_pca = PCA(endog, ncomp=self.k_factors)
mod_ols = OLS(endo... | https://github.com/statsmodels/statsmodels/issues/6230 | [ 624.6127141 27.2029389 3732.25387731 6.90035198 0.77157448]
---------------------------------------------------------------------------
MissingDataError Traceback (most recent call last)
<ipython-input-15-f70718facc54> in <module>
9 endog2.iloc[4, :] = np.nan
10 mod = sm.tsa.Dynami... | MissingDataError |
def start_params(self):
params = np.zeros(self.k_params, dtype=np.float64)
# A. Run a multivariate regression to get beta estimates
endog = pd.DataFrame(self.endog.copy())
endog = endog.interpolate()
endog = endog.fillna(method="backfill").values
exog = None
if self.k_trend > 0 and self.k_e... | def start_params(self):
params = np.zeros(self.k_params, dtype=np.float64)
# A. Run a multivariate regression to get beta estimates
endog = pd.DataFrame(self.endog.copy())
endog = endog.interpolate()
endog = endog.fillna(method="backfill").values
exog = None
if self.k_trend > 0 and self.k_e... | https://github.com/statsmodels/statsmodels/issues/6127 | AttributeError Traceback (most recent call last)
<ipython-input-150-40d241e2864d> in <module>
2 model = VARMAX(train,exog=exog_train, order=order, trend=trend, enforce_stationarity=False, enforce_invertibility=False)
3 # fit model
----> 4 model.start_params
/anaconda3/lib/python3.7/site... | AttributeError |
def fit(
self,
maxlag=None,
method="cmle",
ic=None,
trend="c",
transparams=True,
start_params=None,
solver="lbfgs",
maxiter=35,
full_output=1,
disp=1,
callback=None,
**kwargs,
):
"""
Fit the unconditional maximum likelihood of an AR(p) process.
Parameters... | def fit(
self,
maxlag=None,
method="cmle",
ic=None,
trend="c",
transparams=True,
start_params=None,
solver="lbfgs",
maxiter=35,
full_output=1,
disp=1,
callback=None,
**kwargs,
):
"""
Fit the unconditional maximum likelihood of an AR(p) process.
Parameters... | https://github.com/statsmodels/statsmodels/issues/947 | res.t_test(np.eye(len(res.params)))
Traceback (most recent call last):
File "<pyshell#0>", line 1, in <module>
res.t_test(np.eye(len(res.params)))
File "e:\josef\eclipsegworkspace\statsmodels-git\statsmodels-all-new2\statsmodels\statsmodels\base\model.py", line 1137, in t_test
raise ValueError('Need covariance of param... | ValueError |
def fit(
self,
start_params=None,
trend="c",
method="css-mle",
transparams=True,
solver="lbfgs",
maxiter=500,
full_output=1,
disp=5,
callback=None,
start_ar_lags=None,
**kwargs,
):
"""
Fits ARMA(p,q) model using exact maximum likelihood via Kalman filter.
Par... | def fit(
self,
start_params=None,
trend="c",
method="css-mle",
transparams=True,
solver="lbfgs",
maxiter=500,
full_output=1,
disp=5,
callback=None,
start_ar_lags=None,
**kwargs,
):
"""
Fits ARMA(p,q) model using exact maximum likelihood via Kalman filter.
Par... | https://github.com/statsmodels/statsmodels/issues/947 | res.t_test(np.eye(len(res.params)))
Traceback (most recent call last):
File "<pyshell#0>", line 1, in <module>
res.t_test(np.eye(len(res.params)))
File "e:\josef\eclipsegworkspace\statsmodels-git\statsmodels-all-new2\statsmodels\statsmodels\base\model.py", line 1137, in t_test
raise ValueError('Need covariance of param... | ValueError |
def attach_cov(self, result):
return DataFrame(result, index=self.cov_names, columns=self.cov_names)
| def attach_cov(self, result):
return DataFrame(result, index=self.param_names, columns=self.param_names)
| https://github.com/statsmodels/statsmodels/issues/2270 | In [34]: results._results.cov_params.shape
Out[34]: (36, 36)
In [37]: results.cov_params
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-37-14357748fa96> in <module>()
<snip>
ValueError: Shape of pa... | ValueError |
def fit(self, maxlags=None, method="ols", ic=None, trend="c", verbose=False):
# todo: this code is only supporting deterministic terms as exog.
# This means that all exog-variables have lag 0. If dealing with
# different exogs is necessary, a `lags_exog`-parameter might make
# sense (e.g. a sequence of ... | def fit(self, maxlags=None, method="ols", ic=None, trend="c", verbose=False):
# todo: this code is only supporting deterministic terms as exog.
# This means that all exog-variables have lag 0. If dealing with
# different exogs is necessary, a `lags_exog`-parameter might make
# sense (e.g. a sequence of ... | https://github.com/statsmodels/statsmodels/issues/2270 | In [34]: results._results.cov_params.shape
Out[34]: (36, 36)
In [37]: results.cov_params
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-37-14357748fa96> in <module>()
<snip>
ValueError: Shape of pa... | ValueError |
def cov_params(self):
"""Estimated variance-covariance of model coefficients
Notes
-----
Covariance of vec(B), where B is the matrix
[params_for_deterministic_terms, A_1, ..., A_p] with the shape
(K x (Kp + number_of_deterministic_terms))
Adjusted to be an unbiased estimator
Ref: Lütkep... | def cov_params(self):
"""Estimated variance-covariance of model coefficients
Notes
-----
Covariance of vec(B), where B is the matrix
[params_for_deterministic_terms, A_1, ..., A_p] with the shape
(K x (Kp + number_of_deterministic_terms))
Adjusted to be an unbiased estimator
Ref: Lütkep... | https://github.com/statsmodels/statsmodels/issues/2270 | In [34]: results._results.cov_params.shape
Out[34]: (36, 36)
In [37]: results.cov_params
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-37-14357748fa96> in <module>()
<snip>
ValueError: Shape of pa... | ValueError |
def _cov_alpha(self):
"""
Estimated covariance matrix of model coefficients w/o exog
"""
# drop exog
kn = self.k_exog * self.neqs
return self.cov_params()[kn:, kn:]
| def _cov_alpha(self):
"""
Estimated covariance matrix of model coefficients w/o exog
"""
# drop exog
return self._cov_params()[self.k_exog * self.neqs :, self.k_exog * self.neqs :]
| https://github.com/statsmodels/statsmodels/issues/2270 | In [34]: results._results.cov_params.shape
Out[34]: (36, 36)
In [37]: results.cov_params
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-37-14357748fa96> in <module>()
<snip>
ValueError: Shape of pa... | ValueError |
def stderr(self):
"""Standard errors of coefficients, reshaped to match in size"""
stderr = np.sqrt(np.diag(self.cov_params()))
return stderr.reshape((self.df_model, self.neqs), order="C")
| def stderr(self):
"""Standard errors of coefficients, reshaped to match in size"""
stderr = np.sqrt(np.diag(self._cov_params()))
return stderr.reshape((self.df_model, self.neqs), order="C")
| https://github.com/statsmodels/statsmodels/issues/2270 | In [34]: results._results.cov_params.shape
Out[34]: (36, 36)
In [37]: results.cov_params
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-37-14357748fa96> in <module>()
<snip>
ValueError: Shape of pa... | ValueError |
def ksstat(x, cdf, alternative="two_sided", args=()):
"""
Calculate statistic for the Kolmogorov-Smirnov test for goodness of fit
This calculates the test statistic for a test of the distribution G(x) of
an observed variable against a given distribution F(x). Under the null
hypothesis the two distr... | def ksstat(x, cdf, alternative="two_sided", args=()):
"""
Calculate statistic for the Kolmogorov-Smirnov test for goodness of fit
This calculates the test statistic for a test of the distribution G(x) of an observed
variable against a given distribution F(x). Under the null
hypothesis the two distr... | https://github.com/statsmodels/statsmodels/issues/5333 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/testone/lib64/python3.6/site-packages/statsmodels/stats/_lilliefors.py", line 344, in kstest_fit
pval = lilliefors_table.prob(d_ks, nobs)
File "/home/testone/lib64/python3.6/site-packages/statsmodels/stats/tabledist.py", line 120, in pro... | ValueError |
def get_lilliefors_table(dist="norm"):
"""
Generates tables for significance levels of Lilliefors test statistics
Tables for available normal and exponential distribution testing,
as specified in Lilliefors references above
Parameters
----------
dist : string.
distribution being te... | def get_lilliefors_table(dist="norm"):
"""
Generates tables for significance levels of Lilliefors test statistics
Tables for available normal and exponential distribution testing,
as specified in Lilliefors references above
Parameters
----------
dist : string.
distribution being te... | https://github.com/statsmodels/statsmodels/issues/5333 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/testone/lib64/python3.6/site-packages/statsmodels/stats/_lilliefors.py", line 344, in kstest_fit
pval = lilliefors_table.prob(d_ks, nobs)
File "/home/testone/lib64/python3.6/site-packages/statsmodels/stats/tabledist.py", line 120, in pro... | ValueError |
def pval_lf(d_max, n):
"""
Approximate pvalues for Lilliefors test
This is only valid for pvalues smaller than 0.1 which is not checked in
this function.
Parameters
----------
d_max : array_like
two-sided Kolmogorov-Smirnov test statistic
n : int or float
sample size
... | def pval_lf(Dmax, n):
"""approximate pvalues for Lilliefors test
This is only valid for pvalues smaller than 0.1 which is not checked in
this function.
Parameters
----------
Dmax : array_like
two-sided Kolmogorov-Smirnov test statistic
n : int or float
sample size
Retu... | https://github.com/statsmodels/statsmodels/issues/5333 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/testone/lib64/python3.6/site-packages/statsmodels/stats/_lilliefors.py", line 344, in kstest_fit
pval = lilliefors_table.prob(d_ks, nobs)
File "/home/testone/lib64/python3.6/site-packages/statsmodels/stats/tabledist.py", line 120, in pro... | ValueError |
def f(n):
poly = np.array([1, np.log(n), np.log(n) ** 2])
return np.exp(poly.dot(params.T))
| def f(n):
return np.array([0.86, 0.91, 0.96, 1.06, 1.25]) / np.sqrt(n)
| https://github.com/statsmodels/statsmodels/issues/5333 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/testone/lib64/python3.6/site-packages/statsmodels/stats/_lilliefors.py", line 344, in kstest_fit
pval = lilliefors_table.prob(d_ks, nobs)
File "/home/testone/lib64/python3.6/site-packages/statsmodels/stats/tabledist.py", line 120, in pro... | ValueError |
def __init__(
self, alpha, size, crit_table, asymptotic=None, min_nobs=None, max_nobs=None
):
self.alpha = np.asarray(alpha)
if self.alpha.ndim != 1:
raise ValueError("alpha is not 1d")
elif (np.diff(self.alpha) <= 0).any():
raise ValueError("alpha is not sorted")
self.size = np.asar... | def __init__(self, alpha, size, crit_table):
self.alpha = np.asarray(alpha)
self.size = np.asarray(size)
self.crit_table = np.asarray(crit_table)
self.n_alpha = len(alpha)
self.signcrit = np.sign(np.diff(self.crit_table, 1).mean())
if self.signcrit > 0: # increasing
self.critv_bounds =... | https://github.com/statsmodels/statsmodels/issues/5333 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/testone/lib64/python3.6/site-packages/statsmodels/stats/_lilliefors.py", line 344, in kstest_fit
pval = lilliefors_table.prob(d_ks, nobs)
File "/home/testone/lib64/python3.6/site-packages/statsmodels/stats/tabledist.py", line 120, in pro... | ValueError |
def _critvals(self, n):
"""
Rows of the table, linearly interpolated for given sample size
Parameters
----------
n : float
sample size, second parameter of the table
Returns
-------
critv : ndarray, 1d
critical values (ppf) corresponding to a row of the table
Notes... | def _critvals(self, n):
"""rows of the table, linearly interpolated for given sample size
Parameters
----------
n : float
sample size, second parameter of the table
Returns
-------
critv : ndarray, 1d
critical values (ppf) corresponding to a row of the table
Notes
... | https://github.com/statsmodels/statsmodels/issues/5333 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/testone/lib64/python3.6/site-packages/statsmodels/stats/_lilliefors.py", line 344, in kstest_fit
pval = lilliefors_table.prob(d_ks, nobs)
File "/home/testone/lib64/python3.6/site-packages/statsmodels/stats/tabledist.py", line 120, in pro... | ValueError |
def prob(self, x, n):
"""
Find pvalues by interpolation, either cdf(x)
Returns extreme probabilities, 0.001 and 0.2, for out of range
Parameters
----------
x : array_like
observed value, assumed to follow the distribution in the table
n : float
sample size, second parameter... | def prob(self, x, n):
"""find pvalues by interpolation, eiter cdf(x) or sf(x)
returns extrem probabilities, 0.001 and 0.2, for out of range
Parameters
----------
x : array_like
observed value, assumed to follow the distribution in the table
n : float
sample size, second paramet... | https://github.com/statsmodels/statsmodels/issues/5333 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/testone/lib64/python3.6/site-packages/statsmodels/stats/_lilliefors.py", line 344, in kstest_fit
pval = lilliefors_table.prob(d_ks, nobs)
File "/home/testone/lib64/python3.6/site-packages/statsmodels/stats/tabledist.py", line 120, in pro... | ValueError |
def crit(self, prob, n):
"""
Returns interpolated quantiles, similar to ppf or isf
use two sequential 1d interpolation, first by n then by prob
Parameters
----------
prob : array_like
probabilities corresponding to the definition of table columns
n : int or float
sample siz... | def crit(self, prob, n):
"""returns interpolated quantiles, similar to ppf or isf
use two sequential 1d interpolation, first by n then by prob
Parameters
----------
prob : array_like
probabilities corresponding to the definition of table columns
n : int or float
sample size, se... | https://github.com/statsmodels/statsmodels/issues/5333 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/testone/lib64/python3.6/site-packages/statsmodels/stats/_lilliefors.py", line 344, in kstest_fit
pval = lilliefors_table.prob(d_ks, nobs)
File "/home/testone/lib64/python3.6/site-packages/statsmodels/stats/tabledist.py", line 120, in pro... | ValueError |
def crit3(self, prob, n):
"""
Returns interpolated quantiles, similar to ppf or isf
uses Rbf to interpolate critical values as function of `prob` and `n`
Parameters
----------
prob : array_like
probabilities corresponding to the definition of table columns
n : int or float
... | def crit3(self, prob, n):
"""returns interpolated quantiles, similar to ppf or isf
uses Rbf to interpolate critical values as function of `prob` and `n`
Parameters
----------
prob : array_like
probabilities corresponding to the definition of table columns
n : int or float
sampl... | https://github.com/statsmodels/statsmodels/issues/5333 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/testone/lib64/python3.6/site-packages/statsmodels/stats/_lilliefors.py", line 344, in kstest_fit
pval = lilliefors_table.prob(d_ks, nobs)
File "/home/testone/lib64/python3.6/site-packages/statsmodels/stats/tabledist.py", line 120, in pro... | ValueError |
def _normalize_dataframe(dataframe, index):
"""Take a pandas DataFrame and count the element present in the
given columns, return a hierarchical index on those columns
"""
# groupby the given keys, extract the same columns and count the element
# then collapse them with a mean
data = dataframe[i... | def _normalize_dataframe(dataframe, index):
"""Take a pandas DataFrame and count the element present in the
given columns, return a hierarchical index on those columns
"""
# groupby the given keys, extract the same columns and count the element
# then collapse them with a mean
data = dataframe[i... | https://github.com/statsmodels/statsmodels/issues/5639 | /home/nbuser/anaconda3_501/lib/python3.6/site-packages/statsmodels/graphics/mosaicplot.py:40: RuntimeWarning: invalid value encountered in less
if np.any(proportion < 0):
posx and posy should be finite values
posx and posy should be finite values
posx and posy should be finite values
posx and posy should be finite valu... | ValueError |
def _make_arma_exog(endog, exog, trend):
k_trend = 1 # overwritten if no constant
if exog is None and trend == "c": # constant only
exog = np.ones((len(endog), 1))
elif exog is not None and trend == "c": # constant plus exogenous
exog = add_trend(exog, trend="c", prepend=True, has_constan... | def _make_arma_exog(endog, exog, trend):
k_trend = 1 # overwritten if no constant
if exog is None and trend == "c": # constant only
exog = np.ones((len(endog), 1))
elif exog is not None and trend == "c": # constant plus exogenous
exog = add_trend(exog, trend="c", prepend=True)
elif ex... | https://github.com/statsmodels/statsmodels/issues/3343 | import pandas as pd
from statsmodels.tsa.arima_model import ARIMA
X = [0.5,0.5,0.5,0.5,0.5]
Y = [-0.011866,0.003380,0.015357,0.004451,-0.020889]
T = ['2000-01-01', '2000-01-02', '2000-01-03', '2000-01-04', '2000-01-05']
TDT = pd.to_datetime(T)
df = pd.DataFrame({'x': X, 'y': Y})
df.index = TDT
df
x y
2000-01-01... | ValueError |
def _maybe_convert_ynames_int(self, ynames):
# see if they're integers
issue_warning = False
msg = (
"endog contains values are that not int-like. Uses string "
"representation of value. Use integer-valued endog to "
"suppress this warning."
)
for i in ynames:
try:
... | def _maybe_convert_ynames_int(self, ynames):
# see if they're integers
try:
for i in ynames:
if ynames[i] % 1 == 0:
ynames[i] = str(int(ynames[i]))
except TypeError:
pass
return ynames
| https://github.com/statsmodels/statsmodels/issues/3960 | result.summary()
ynames = ['='.join([yname, name]) for name in ynames]
TypeError: sequence item 1: expected str instance, numpy.float64 found
ynames = ['='.join([yname, name]) for name in ynames]
File "m:\...\statsmodels\discrete\discrete_model.py", line 3916, in <listcomp>
File "m:\...\statsmodels\discrete\discrete_mo... | TypeError |
def _make_dictnames(tmp_arr, offset=0):
"""
Helper function to create a dictionary mapping a column number
to the name in tmp_arr.
"""
col_map = {}
for i, col_name in enumerate(tmp_arr):
col_map[i + offset] = col_name
return col_map
| def _make_dictnames(tmp_arr, offset=0):
"""
Helper function to create a dictionary mapping a column number
to the name in tmp_arr.
"""
col_map = {}
for i, col_name in enumerate(tmp_arr):
col_map.update({i + offset: col_name})
return col_map
| https://github.com/statsmodels/statsmodels/issues/1342 | In [9]: sm.categorical(pd.DataFrame({'a':[1,2,12], 'b':['a','b','a']}), col='a')
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-9-5966a3ee6951> in <module>()
----> 1 sm.categorical(pd.DataFrame({'a':[... | AttributeError |
def categorical(data, col=None, dictnames=False, drop=False):
"""
Returns a dummy matrix given an array of categorical variables.
Parameters
----------
data : array
A structured array, recarray, array, Series or DataFrame. This can be
either a 1d vector of the categorical variable ... | def categorical(
data,
col=None,
dictnames=False,
drop=False,
):
"""
Returns a dummy matrix given an array of categorical variables.
Parameters
----------
data : array
A structured array, recarray, or array. This can be either
a 1d vector of the categorical variable... | https://github.com/statsmodels/statsmodels/issues/1342 | In [9]: sm.categorical(pd.DataFrame({'a':[1,2,12], 'b':['a','b','a']}), col='a')
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-9-5966a3ee6951> in <module>()
----> 1 sm.categorical(pd.DataFrame({'a':[... | AttributeError |
def _get_names(self, arr):
if isinstance(arr, DataFrame):
if isinstance(arr.columns, MultiIndex):
# Flatten MultiIndexes into "simple" column names
return [".".join((level for level in c if level)) for c in arr.columns]
else:
return list(arr.columns)
elif isin... | def _get_names(self, arr):
if isinstance(arr, DataFrame):
return list(arr.columns)
elif isinstance(arr, Series):
if arr.name:
return [arr.name]
else:
return
else:
try:
return arr.dtype.names
except AttributeError:
pass
... | https://github.com/statsmodels/statsmodels/issues/5414 | Traceback (most recent call last):
File "xxx/__init__.py", line 451, in do_algorithm_usecase
if af.compute():
File "xxx/algorithms/es.py", line 153, in compute
preds = self.fitted_model.predict(predict_from, predict_until)
File "xxx/myenv/lib/python3.7/site-packages/statsmodels/base/wrapper.py", line 95, in wrapper
obj... | ValueError |
def __init__(
self,
data,
ncomp=None,
standardize=True,
demean=True,
normalize=True,
gls=False,
weights=None,
method="svd",
missing=None,
tol=5e-8,
max_iter=1000,
tol_em=5e-8,
max_em_iter=100,
):
self._index = None
self._columns = []
if isinstance(data... | def __init__(
self,
data,
ncomp=None,
standardize=True,
demean=True,
normalize=True,
gls=False,
weights=None,
method="svd",
missing=None,
tol=5e-8,
max_iter=1000,
tol_em=5e-8,
max_em_iter=100,
):
self._index = None
self._columns = []
if isinstance(data... | https://github.com/statsmodels/statsmodels/issues/4772 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\[...]\lib\site-packages\statsmodels\graphics\functional.py", line 32, in _pickle_method
if m.im_self is None:
AttributeError: 'function' object has no attribute 'im_self' | AttributeError |
def _compute_bw(self, bw):
"""
Computes the bandwidth of the data.
Parameters
----------
bw: array_like or str
If array_like: user-specified bandwidth.
If a string, should be one of:
- cv_ml: cross validation maximum likelihood
- normal_reference: normal ref... | def _compute_bw(self, bw):
"""
Computes the bandwidth of the data.
Parameters
----------
bw: array_like or str
If array_like: user-specified bandwidth.
If a string, should be one of:
- cv_ml: cross validation maximum likelihood
- normal_reference: normal ref... | https://github.com/statsmodels/statsmodels/issues/4772 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\[...]\lib\site-packages\statsmodels\graphics\functional.py", line 32, in _pickle_method
if m.im_self is None:
AttributeError: 'function' object has no attribute 'im_self' | AttributeError |
def __init__(self, endog, exog, var_type, reg_type="ll", bw="cv_ls", defaults=None):
self.var_type = var_type
self.data_type = var_type
self.reg_type = reg_type
self.k_vars = len(self.var_type)
self.endog = _adjust_shape(endog, 1)
self.exog = _adjust_shape(exog, self.k_vars)
self.data = np.c... | def __init__(self, endog, exog, var_type, reg_type="ll", bw="cv_ls", defaults=None):
self.var_type = var_type
self.data_type = var_type
self.reg_type = reg_type
self.k_vars = len(self.var_type)
self.endog = _adjust_shape(endog, 1)
self.exog = _adjust_shape(exog, self.k_vars)
self.data = np.c... | https://github.com/statsmodels/statsmodels/issues/4772 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\[...]\lib\site-packages\statsmodels\graphics\functional.py", line 32, in _pickle_method
if m.im_self is None:
AttributeError: 'function' object has no attribute 'im_self' | AttributeError |
def _compute_reg_bw(self, bw):
if not isinstance(bw, string_types):
self._bw_method = "user-specified"
return np.asarray(bw)
else:
# The user specified a bandwidth selection method e.g. 'cv_ls'
self._bw_method = bw
# Workaround to avoid instance methods in __dict__
... | def _compute_reg_bw(self, bw):
if not isinstance(bw, string_types):
self._bw_method = "user-specified"
return np.asarray(bw)
else:
# The user specified a bandwidth selection method e.g. 'cv_ls'
self._bw_method = bw
res = self.bw_func[bw]
X = np.std(self.exog, axis... | https://github.com/statsmodels/statsmodels/issues/4772 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\[...]\lib\site-packages\statsmodels\graphics\functional.py", line 32, in _pickle_method
if m.im_self is None:
AttributeError: 'function' object has no attribute 'im_self' | AttributeError |
def __init__(
self, endog, exog, var_type, reg_type, bw="cv_ls", censor_val=0, defaults=None
):
self.var_type = var_type
self.data_type = var_type
self.reg_type = reg_type
self.k_vars = len(self.var_type)
self.endog = _adjust_shape(endog, 1)
self.exog = _adjust_shape(exog, self.k_vars)
s... | def __init__(
self, endog, exog, var_type, reg_type, bw="cv_ls", censor_val=0, defaults=None
):
self.var_type = var_type
self.data_type = var_type
self.reg_type = reg_type
self.k_vars = len(self.var_type)
self.endog = _adjust_shape(endog, 1)
self.exog = _adjust_shape(exog, self.k_vars)
s... | https://github.com/statsmodels/statsmodels/issues/4772 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\[...]\lib\site-packages\statsmodels\graphics\functional.py", line 32, in _pickle_method
if m.im_self is None:
AttributeError: 'function' object has no attribute 'im_self' | AttributeError |
def __init__(
self,
t=None,
F=None,
sd=None,
effect=None,
df_denom=None,
df_num=None,
alpha=0.05,
**kwds,
):
self.effect = effect # Let it be None for F
if F is not None:
self.distribution = "F"
self.fvalue = F
self.statistic = self.fvalue
sel... | def __init__(
self,
t=None,
F=None,
sd=None,
effect=None,
df_denom=None,
df_num=None,
alpha=0.05,
**kwds,
):
self.effect = effect # Let it be None for F
if F is not None:
self.distribution = "F"
self.fvalue = F
self.statistic = self.fvalue
sel... | https://github.com/statsmodels/statsmodels/issues/4588 | resols2r.wald_test(np.eye(len(resols2r.params)))
---------------------------------------------------------------------------
LinAlgError Traceback (most recent call last)
<ipython-input-18-702444fbfca8> in <module>()
----> 1 resols2r.wald_test(np.eye(len(resols2r.params)))
...\statsmodels... | LinAlgError |
def summary(self, xname=None, alpha=0.05, title=None):
"""Summarize the Results of the hypothesis test
Parameters
-----------
xname : list of strings, optional
Default is `c_##` for ## in p the number of regressors
alpha : float
significance level for the confidence intervals. Defa... | def summary(self, xname=None, alpha=0.05, title=None):
"""Summarize the Results of the hypothesis test
Parameters
-----------
xname : list of strings, optional
Default is `c_##` for ## in p the number of regressors
alpha : float
significance level for the confidence intervals. Defa... | https://github.com/statsmodels/statsmodels/issues/4588 | resols2r.wald_test(np.eye(len(resols2r.params)))
---------------------------------------------------------------------------
LinAlgError Traceback (most recent call last)
<ipython-input-18-702444fbfca8> in <module>()
----> 1 resols2r.wald_test(np.eye(len(resols2r.params)))
...\statsmodels... | LinAlgError |
def _fit_start_params_hr(self, order, start_ar_lags=None):
"""
Get starting parameters for fit.
Parameters
----------
order : iterable
(p,q,k) - AR lags, MA lags, and number of exogenous variables
including the constant.
start_ar_lags : int, optional
If start_ar_lags is ... | def _fit_start_params_hr(self, order, start_ar_lags=None):
"""
Get starting parameters for fit.
Parameters
----------
order : iterable
(p,q,k) - AR lags, MA lags, and number of exogenous variables
including the constant.
start_ar_lags : int, optional
If start_ar_lags is ... | https://github.com/statsmodels/statsmodels/issues/3504 | ---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-51-964a74d33f5a> in <module>()
5
6 model = sm.tsa.arima_model.ARIMA(ts, order=(0, 2, 0))
----> 7 fitted = model.fit(disp=-1)
8
9
/usr/local/lib/python3.... | TypeError |
def plot_simultaneous(
self, comparison_name=None, ax=None, figsize=(10, 6), xlabel=None, ylabel=None
):
"""Plot a universal confidence interval of each group mean
Visiualize significant differences in a plot with one confidence
interval per group instead of all pairwise confidence intervals.
Para... | def plot_simultaneous(
self, comparison_name=None, ax=None, figsize=(10, 6), xlabel=None, ylabel=None
):
"""Plot a universal confidence interval of each group mean
Visiualize significant differences in a plot with one confidence
interval per group instead of all pairwise confidence intervals.
Para... | https://github.com/statsmodels/statsmodels/issues/3584 | ---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-1-35117e389add> in <module>()
35 alpha=0.05) # Significance level
36
---> 37 tukey.plot_simultaneous(comparison_name =... | TypeError |
def medcouple(y, axis=0):
"""
Calculates the medcouple robust measure of skew.
Parameters
----------
y : array-like
axis : int or None, optional
Axis along which the medcouple statistic is computed. If `None`, the
entire array is used.
Returns
-------
mc : ndarray
... | def medcouple(y, axis=0):
"""
Calculates the medcouple robust measure of skew.
Parameters
----------
y : array-like
axis : int or None, optional
Axis along which the medcouple statistic is computed. If `None`, the
entire array is used.
Returns
-------
mc : ndarray
... | https://github.com/statsmodels/statsmodels/issues/4243 | from statsmodels.stats.stattools import medcouple
medcouple(np.arange(9))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "...\statsmodels\stats\stattools.py", line 451, in medcouple
return np.apply_along_axis(_medcouple_1d, axis, y)
File "C:\...\python-3.4.4.amd64\lib\site-packages\numpy\li... | OverflowError |
def summary2(self, alpha=0.05, float_format="%.4f"):
"""Experimental function to summarize regression results
Parameters
-----------
alpha : float
significance level for the confidence intervals
float_format: string
print format for floats in parameters summary
Returns
----... | def summary2(self, alpha=0.05, float_format="%.4f"):
"""Experimental function to summarize regression results
Parameters
-----------
alpha : float
significance level for the confidence intervals
float_format: string
print format for floats in parameters summary
Returns
----... | https://github.com/statsmodels/statsmodels/issues/3651 | ======================================================================
ERROR: statsmodels.discrete.tests.test_discrete.test_mnlogit_factor
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/miniconda2/envs/statsmodels-test/lib/python2.7/site-pack... | ValueError |
def summary_params(
results,
yname=None,
xname=None,
alpha=0.05,
use_t=True,
skip_header=False,
float_format="%.4f",
):
"""create a summary table of parameters from results instance
Parameters
----------
res : results instance
some required information is directly ta... | def summary_params(
results,
yname=None,
xname=None,
alpha=0.05,
use_t=True,
skip_header=False,
float_format="%.4f",
):
"""create a summary table of parameters from results instance
Parameters
----------
res : results instance
some required information is directly ta... | https://github.com/statsmodels/statsmodels/issues/3651 | ======================================================================
ERROR: statsmodels.discrete.tests.test_discrete.test_mnlogit_factor
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/miniconda2/envs/statsmodels-test/lib/python2.7/site-pack... | ValueError |
def acorr_ljungbox(x, lags=None, boxpierce=False):
"""
Ljung-Box test for no autocorrelation
Parameters
----------
x : array_like, 1d
data series, regression residuals when used as diagnostic test
lags : None, int or array_like
If lags is an integer then this is taken to be the ... | def acorr_ljungbox(x, lags=None, boxpierce=False):
"""Ljung-Box test for no autocorrelation
Parameters
----------
x : array_like, 1d
data series, regression residuals when used as diagnostic test
lags : None, int or array_like
If lags is an integer then this is taken to be the large... | https://github.com/statsmodels/statsmodels/issues/3229 | from statsmodels.stats import diagnostic as diag
diag.acorr_ljungbox(np.random.random(50))[0].shape
(40,)
diag.acorr_ljungbox(np.random.random(20), lags=5)
(array([ 0.36718151, 1.02009595, 1.23734092, 3.75338034, 4.35387236]), array([ 0.54454461, 0.60046677, 0.74406305, 0.44040973, 0.49966951]))
diag.acorr_ljun... | ValueError |
def acorr_ljungbox(x, lags=None, boxpierce=False):
"""
Ljung-Box test for no autocorrelation
Parameters
----------
x : array_like, 1d
data series, regression residuals when used as diagnostic test
lags : None, int or array_like
If lags is an integer then this is taken to be the ... | def acorr_ljungbox(x, lags=None, boxpierce=False):
"""Ljung-Box test for no autocorrelation
Parameters
----------
x : array_like, 1d
data series, regression residuals when used as diagnostic test
lags : None, int or array_like
If lags is an integer then this is taken to be the large... | https://github.com/statsmodels/statsmodels/issues/3229 | from statsmodels.stats import diagnostic as diag
diag.acorr_ljungbox(np.random.random(50))[0].shape
(40,)
diag.acorr_ljungbox(np.random.random(20), lags=5)
(array([ 0.36718151, 1.02009595, 1.23734092, 3.75338034, 4.35387236]), array([ 0.54454461, 0.60046677, 0.74406305, 0.44040973, 0.49966951]))
diag.acorr_ljun... | ValueError |
def __getstate__(self):
# remove unpicklable methods
mle_settings = getattr(self, "mle_settings", None)
if mle_settings is not None:
if "callback" in mle_settings:
mle_settings["callback"] = None
if "cov_params_func" in mle_settings:
mle_settings["cov_params_func"] = ... | def __getstate__(self):
try:
# remove unpicklable callback
self.mle_settings["callback"] = None
except (AttributeError, KeyError):
pass
return self.__dict__
| https://github.com/statsmodels/statsmodels/issues/2685 | Traceback (most recent call last):
File "statsmodels/base/tests/test_shrink_pickle.py", line 290, in <module>
tt.test_remove_data_pickle()
File "statsmodels/base/tests/test_shrink_pickle.py", line 68, in test_remove_data_pickle
res, l = check_pickle(results._results)
File "statsmodels/base/tests/test_shrink_pickle.py",... | cPickle.PicklingError |
def impute_pmm(self, vname):
"""
Use predictive mean matching to impute missing values.
Notes
-----
The `perturb_params` method must be called first to define the
model.
"""
k_pmm = self.k_pmm
endog_obs, exog_obs, exog_miss, predict_obs_kwds, predict_miss_kwds = (
self.get... | def impute_pmm(self, vname):
"""
Use predictive mean matching to impute missing values.
Notes
-----
The `perturb_params` method must be called first to define the
model.
"""
k_pmm = self.k_pmm
endog_obs, exog_obs, exog_miss, predict_obs_kwds, predict_miss_kwds = (
self.get... | https://github.com/statsmodels/statsmodels/issues/3754 | ======================================================================
ERROR: statsmodels.imputation.tests.test_mice.TestMICE.test_MICE
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/miniconda2/envs/statsmodels-test/lib/python3.6/site-package... | ValueError |
def plot_distribution(self, ax=None, exog_values=None):
"""
Plot the fitted probabilities of endog in an nominal model,
for specifed values of the predictors.
Parameters
----------
ax : Matplotlib axes instance
An axes on which to draw the graph. If None, new
figure and axes ob... | def plot_distribution(self, ax=None, exog_values=None):
"""
Plot the fitted probabilities of endog in an nominal model,
for specifed values of the predictors.
Parameters
----------
ax : Matplotlib axes instance
An axes on which to draw the graph. If None, new
figure and axes ob... | https://github.com/statsmodels/statsmodels/issues/3332 | ======================================================================
ERROR: statsmodels.genmod.tests.test_gee.TestGEE.test_nominal_plot
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/nose/case.py", line 197, in runTest
s... | TypeError |
def predict(self, exog=None, transform=True, *args, **kwargs):
"""
Call self.model.predict with self.params as the first argument.
Parameters
----------
exog : array-like, optional
The values for which you want to predict.
transform : bool, optional
If the model was fit via a fo... | def predict(self, exog=None, transform=True, *args, **kwargs):
"""
Call self.model.predict with self.params as the first argument.
Parameters
----------
exog : array-like, optional
The values for which you want to predict.
transform : bool, optional
If the model was fit via a fo... | https://github.com/statsmodels/statsmodels/issues/3182 | res.predict({'temp': x_p})
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "m:\josef_new\eclipse_ws\statsmodels\statsmodels_py34_pr\statsmodels\base\model.py", line 774, in predict
if len(exog) < len(exog_index):
TypeError: object of type 'NoneType' has no len() | TypeError |
def predict(self, exog=None, transform=True, *args, **kwargs):
"""
Call self.model.predict with self.params as the first argument.
Parameters
----------
exog : array-like, optional
The values for which you want to predict.
transform : bool, optional
If the model was fit via a fo... | def predict(self, exog=None, transform=True, *args, **kwargs):
"""
Call self.model.predict with self.params as the first argument.
Parameters
----------
exog : array-like, optional
The values for which you want to predict.
transform : bool, optional
If the model was fit via a fo... | https://github.com/statsmodels/statsmodels/issues/3182 | res.predict({'temp': x_p})
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "m:\josef_new\eclipse_ws\statsmodels\statsmodels_py34_pr\statsmodels\base\model.py", line 774, in predict
if len(exog) < len(exog_index):
TypeError: object of type 'NoneType' has no len() | TypeError |
def _get_index_loc(self, key, base_index=None):
"""
Get the location of a specific key in an index
Parameters
----------
key : label
The key for which to find the location
base_index : pd.Index, optional
Optionally the base index to search. If None, the model's index is
... | def _get_index_loc(self, key, base_index=None):
"""
Get the location of a specific key in an index
Parameters
----------
key : label
The key for which to find the location
base_index : pd.Index, optional
Optionally the base index to search. If None, the model's index is
... | https://github.com/statsmodels/statsmodels/issues/3349 | ======================================================================
ERROR: statsmodels.tsa.statespace.tests.test_sarimax.test_misc_exog
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Py\lib\site-packages\nose\case.py", line 197, in runTest
self.test... | ValueError |
def fit(
self,
start_params=None,
method="newton",
maxiter=100,
full_output=True,
disp=True,
fargs=(),
callback=None,
retall=False,
skip_hessian=False,
**kwargs,
):
"""
Fit method for likelihood based models
Parameters
----------
start_params : array-like... | def fit(
self,
start_params=None,
method="newton",
maxiter=100,
full_output=True,
disp=True,
fargs=(),
callback=None,
retall=False,
skip_hessian=False,
**kwargs,
):
"""
Fit method for likelihood based models
Parameters
----------
start_params : array-like... | https://github.com/statsmodels/statsmodels/issues/3098 | Traceback (most recent call last):
File "C:\git\statsmodels\statsmodels\base\model.py", line 447, in fit
H = -1 * self.hessian(xopt)
File "C:\git\statsmodels\statsmodels\tsa\arima_model.py", line 593, in hessian
return approx_hess_cs(params, self.loglike, args=(False,))
File "C:\git\statsmodels\statsmodels\tools\numdif... | ValueError |
def initialize(self):
if not self.score: # right now score is not optional
self.score = approx_fprime
if not self.hessian:
pass
else: # can use approx_hess_p if we have a gradient
if not self.hessian:
pass
# Initialize is called by
# statsmodels.model.Li... | def initialize(self):
if not self.score: # right now score is not optional
self.score = approx_fprime
if not self.hessian:
pass
else: # can use approx_hess_p if we have a gradient
if not self.hessian:
pass
# Initialize is called by
# statsmodels.model.Li... | https://github.com/statsmodels/statsmodels/issues/3098 | Traceback (most recent call last):
File "C:\git\statsmodels\statsmodels\base\model.py", line 447, in fit
H = -1 * self.hessian(xopt)
File "C:\git\statsmodels\statsmodels\tsa\arima_model.py", line 593, in hessian
return approx_hess_cs(params, self.loglike, args=(False,))
File "C:\git\statsmodels\statsmodels\tools\numdif... | ValueError |
def predict(self, exog=None, transform=True, *args, **kwargs):
"""
Call self.model.predict with self.params as the first argument.
Parameters
----------
exog : array-like, optional
The values for which you want to predict.
transform : bool, optional
If the model was fit via a fo... | def predict(self, exog=None, transform=True, *args, **kwargs):
"""
Call self.model.predict with self.params as the first argument.
Parameters
----------
exog : array-like, optional
The values for which you want to predict.
transform : bool, optional
If the model was fit via a fo... | https://github.com/statsmodels/statsmodels/issues/3087 | Traceback (most recent call last):
File "E:\josef_new_notebook\scripts\bug_missing_formula.py", line 10, in <modu
le>
test3 = result3.predict(df3) # Fails
File "E:\josef_new_notebook\git\statsmodels_py34_pr\statsmodels\base\model.py"
, line 767, in predict
return pd.Series(predict_results, index=exog_index)
File "C... | ValueError |
def plot_corr(
dcorr,
xnames=None,
ynames=None,
title=None,
normcolor=False,
ax=None,
cmap="RdYlBu_r",
):
"""Plot correlation of many variables in a tight color grid.
Parameters
----------
dcorr : ndarray
Correlation matrix, square 2-D array.
xnames : list of str... | def plot_corr(
dcorr,
xnames=None,
ynames=None,
title=None,
normcolor=False,
ax=None,
cmap="RdYlBu_r",
):
"""Plot correlation of many variables in a tight color grid.
Parameters
----------
dcorr : ndarray
Correlation matrix, square 2-D array.
xnames : list of str... | https://github.com/statsmodels/statsmodels/issues/2510 | ---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-24-f81f43c530b6> in <module>()
1 corr_columns = df_business[...]
2 corr_matrix = corr_columns.corr()
----> 3 smg.plot_corr(corr_matrix, xnames=corr_colum... | ValueError |
def fit(self, maxlags=None, method="ols", ic=None, trend="c", verbose=False):
"""
Fit the VAR model
Parameters
----------
maxlags : int
Maximum number of lags to check for order selection, defaults to
12 * (nobs/100.)**(1./4), see select_order function
method : {'ols'}
E... | def fit(self, maxlags=None, method="ols", ic=None, trend="c", verbose=False):
"""
Fit the VAR model
Parameters
----------
maxlags : int
Maximum number of lags to check for order selection, defaults to
12 * (nobs/100.)**(1./4), see select_order function
method : {'ols'}
E... | https://github.com/statsmodels/statsmodels/issues/2271 | model_t = VAR(data_t)
results = model.fit(4, trend = 't')
---------------------------------------------------------------------------
UnboundLocalError Traceback (most recent call last)
<ipython-input-98-f7328095d596> in <module>()
1 model_t = VAR(data_t)
----> 2 results = model.fit(4, trend = ... | UnboundLocalError |
def _cov_alpha(self):
"""
Estimated covariance matrix of model coefficients ex intercept
"""
# drop intercept and trend
return self.cov_params[self.k_trend * self.neqs :, self.k_trend * self.neqs :]
| def _cov_alpha(self):
"""
Estimated covariance matrix of model coefficients ex intercept
"""
# drop intercept
return self.cov_params[self.neqs :, self.neqs :]
| https://github.com/statsmodels/statsmodels/issues/1636 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python2.7/dist-packages/statsmodels/tsa/vector_ar/irf.py", line 138, in plot
stderr = self.cov(orth=orth)
File "/usr/lib/python2.7/dist-packages/statsmodels/tsa/vector_ar/irf.py", line 264, in cov
covs[i] = chain_dot(Gi, self.cov_a, G... | ValueError |
def __init__(self, endog, exog=None, missing="none", hasconst=None, **kwargs):
if "design_info" in kwargs:
self.design_info = kwargs.pop("design_info")
if missing != "none":
arrays, nan_idx = self.handle_missing(endog, exog, missing, **kwargs)
self.missing_row_idx = nan_idx
self.... | def __init__(self, endog, exog=None, missing="none", hasconst=None, **kwargs):
if missing != "none":
arrays, nan_idx = self.handle_missing(endog, exog, missing, **kwargs)
self.missing_row_idx = nan_idx
self.__dict__.update(arrays) # attach all the data arrays
self.orig_endog = self.... | https://github.com/statsmodels/statsmodels/issues/2171 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python2.7/dist-packages/spyderlib/widgets/externalshell/sitecustomize.py", line 540, in runfile
execfile(filename, namespace)
File "/home/adrian/Desktop/ToDo/statsmodels_debugging/OLS.py", line 34, in <module>
fit.predict( exog=data[:... | AttributeError |
def _handle_data(self, endog, exog, missing, hasconst, **kwargs):
data = handle_data(endog, exog, missing, hasconst, **kwargs)
# kwargs arrays could have changed, easier to just attach here
for key in kwargs:
if key == "design_info": # leave this attached to data
continue
# pop ... | def _handle_data(self, endog, exog, missing, hasconst, **kwargs):
data = handle_data(endog, exog, missing, hasconst, **kwargs)
# kwargs arrays could have changed, easier to just attach here
for key in kwargs:
# pop so we don't start keeping all these twice or references
try:
seta... | https://github.com/statsmodels/statsmodels/issues/2171 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python2.7/dist-packages/spyderlib/widgets/externalshell/sitecustomize.py", line 540, in runfile
execfile(filename, namespace)
File "/home/adrian/Desktop/ToDo/statsmodels_debugging/OLS.py", line 34, in <module>
fit.predict( exog=data[:... | AttributeError |
def from_formula(cls, formula, data, subset=None, *args, **kwargs):
"""
Create a Model from a formula and dataframe.
Parameters
----------
formula : str or generic Formula object
The formula specifying the model
data : array-like
The data for the model. See Notes.
subset : a... | def from_formula(cls, formula, data, subset=None, *args, **kwargs):
"""
Create a Model from a formula and dataframe.
Parameters
----------
formula : str or generic Formula object
The formula specifying the model
data : array-like
The data for the model. See Notes.
subset : a... | https://github.com/statsmodels/statsmodels/issues/2171 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python2.7/dist-packages/spyderlib/widgets/externalshell/sitecustomize.py", line 540, in runfile
execfile(filename, namespace)
File "/home/adrian/Desktop/ToDo/statsmodels_debugging/OLS.py", line 34, in <module>
fit.predict( exog=data[:... | AttributeError |
def predict(self, exog=None, transform=True, *args, **kwargs):
"""
Call self.model.predict with self.params as the first argument.
Parameters
----------
exog : array-like, optional
The values for which you want to predict.
transform : bool, optional
If the model was fit via a fo... | def predict(self, exog=None, transform=True, *args, **kwargs):
"""
Call self.model.predict with self.params as the first argument.
Parameters
----------
exog : array-like, optional
The values for which you want to predict.
transform : bool, optional
If the model was fit via a fo... | https://github.com/statsmodels/statsmodels/issues/2171 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python2.7/dist-packages/spyderlib/widgets/externalshell/sitecustomize.py", line 540, in runfile
execfile(filename, namespace)
File "/home/adrian/Desktop/ToDo/statsmodels_debugging/OLS.py", line 34, in <module>
fit.predict( exog=data[:... | AttributeError |
def handle_formula_data(Y, X, formula, depth=0, missing="drop"):
"""
Returns endog, exog, and the model specification from arrays and formula
Parameters
----------
Y : array-like
Either endog (the LHS) of a model specification or all of the data.
Y must define __getitem__ for now.
... | def handle_formula_data(Y, X, formula, depth=0, missing="drop"):
"""
Returns endog, exog, and the model specification from arrays and formula
Parameters
----------
Y : array-like
Either endog (the LHS) of a model specification or all of the data.
Y must define __getitem__ for now.
... | https://github.com/statsmodels/statsmodels/issues/2171 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python2.7/dist-packages/spyderlib/widgets/externalshell/sitecustomize.py", line 540, in runfile
execfile(filename, namespace)
File "/home/adrian/Desktop/ToDo/statsmodels_debugging/OLS.py", line 34, in <module>
fit.predict( exog=data[:... | AttributeError |
def anova_single(model, **kwargs):
"""
ANOVA table for one fitted linear model.
Parameters
----------
model : fitted linear model results instance
A fitted linear model
typ : int or str {1,2,3} or {"I","II","III"}
Type of sum of squares to use.
**kwargs**
scale : float... | def anova_single(model, **kwargs):
"""
ANOVA table for one fitted linear model.
Parameters
----------
model : fitted linear model results instance
A fitted linear model
typ : int or str {1,2,3} or {"I","II","III"}
Type of sum of squares to use.
**kwargs**
scale : float... | https://github.com/statsmodels/statsmodels/issues/2171 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python2.7/dist-packages/spyderlib/widgets/externalshell/sitecustomize.py", line 540, in runfile
execfile(filename, namespace)
File "/home/adrian/Desktop/ToDo/statsmodels_debugging/OLS.py", line 34, in <module>
fit.predict( exog=data[:... | AttributeError |
def simultaneous_ci(q_crit, var, groupnobs, pairindices=None):
"""Compute simultaneous confidence intervals for comparison of means.
q_crit value is generated from tukey hsd test. Variance is considered
across all groups. Returned halfwidths can be thought of as uncertainty
intervals around each group ... | def simultaneous_ci(q_crit, var, groupnobs, pairindices=None):
"""Compute simultaneous confidence intervals for comparison of means.
q_crit value is generated from tukey hsd test. Variance is considered
across all groups. Returned halfwidths can be thought of as uncertainty
intervals around each group ... | https://github.com/statsmodels/statsmodels/issues/2065 | ---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-30-234737c8d0af> in <module>()
----> 1 tuk.plot_simultaneous()
/home/thauck/.virtualenvs/zues/local/lib/python2.7/site-packages/statsmodels/sandbox/stat... | NameError |
def __init__(
self,
endog,
exog,
groups,
time=None,
family=None,
cov_struct=None,
missing="none",
offset=None,
dep_data=None,
constraint=None,
update_dep=True,
):
self.missing = missing
self.dep_data = dep_data
self.constraint = constraint
self.update_dep ... | def __init__(
self,
endog,
exog,
groups,
time=None,
family=None,
cov_struct=None,
missing="none",
offset=None,
dep_data=None,
constraint=None,
update_dep=True,
):
self.missing = missing
self.dep_data = dep_data
self.constraint = constraint
self.update_dep ... | https://github.com/statsmodels/statsmodels/issues/1877 | OK!!
Traceback (most recent call last):
File "t.py", line 59, in <module>
cov_struct=Independence(), family=sm.families.Binomial())
File "/usr/local/lib/python2.7/dist-packages/statsmodels-0.6.0-py2.7-linux-x86_64.egg/statsmodels/genmod/generalized_estimating_equations.py", line 261, in __init__
constraint=constraint)
... | IndexError |
def setup_nominal(self, endog, exog, groups, time, offset):
"""
Restructure nominal data as binary indicators so that they can
be analysed using Generalized Estimating Equations.
"""
self.endog_orig = endog.copy()
self.exog_orig = exog.copy()
self.groups_orig = groups.copy()
if offset i... | def setup_nominal(self, endog, exog, groups, time, offset):
"""
Restructure nominal data as binary indicators so that they can
be analysed using Generalized Estimating Equations.
"""
self.endog_orig = endog.copy()
self.exog_orig = exog.copy()
self.groups_orig = groups.copy()
if offset i... | https://github.com/statsmodels/statsmodels/issues/1931 | ======================================================================
ERROR: statsmodels.genmod.tests.test_gee.TestGEEMultinomialCovType.test_wrapper
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\programs\python27\lib\site-packages\nose-1.0.0-py2.7.e... | ValueError |
def __init__(
self, kls, func, funcinvplus, funcinvminus, derivplus, derivminus, *args, **kwargs
):
# print args
# print kwargs
self.func = func
self.funcinvplus = funcinvplus
self.funcinvminus = funcinvminus
self.derivplus = derivplus
self.derivminus = derivminus
# explicit for sel... | def __init__(
self, kls, func, funcinvplus, funcinvminus, derivplus, derivminus, *args, **kwargs
):
# print args
# print kwargs
self.func = func
self.funcinvplus = funcinvplus
self.funcinvminus = funcinvminus
self.derivplus = derivplus
self.derivminus = derivminus
# explicit for sel... | https://github.com/statsmodels/statsmodels/issues/1864 | ======================================================================
ERROR: Failure: TypeError (__init__() takes at least 7 arguments (1 given))
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/miniconda/envs/statsmodels-test/lib/python2.7/si... | TypeError |
def _infer_freq(dates):
maybe_freqstr = getattr(dates, "freqstr", None)
if maybe_freqstr is not None:
return maybe_freqstr
try:
from pandas.tseries.api import infer_freq
freq = infer_freq(dates)
return freq
except ImportError:
pass
timedelta = datetime.timed... | def _infer_freq(dates):
if hasattr(dates, "freqstr"):
return dates.freqstr
try:
from pandas.tseries.api import infer_freq
freq = infer_freq(dates)
return freq
except ImportError:
pass
timedelta = datetime.timedelta
nobs = min(len(dates), 6)
if nobs == 1:... | https://github.com/statsmodels/statsmodels/issues/1822 | ======================================================================
ERROR: statsmodels.graphics.tests.test_tsaplots.test_plot_month
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/nose/case.py", line 197, in runTest
self... | AttributeError |
def lowess(
endog,
exog,
frac=2.0 / 3.0,
it=3,
delta=0.0,
is_sorted=False,
missing="drop",
return_sorted=True,
):
"""LOWESS (Locally Weighted Scatterplot Smoothing)
A lowess function that outs smoothed estimates of endog
at the given exog values from points (exog, endog)
... | def lowess(
endog,
exog,
frac=2.0 / 3.0,
it=3,
delta=0.0,
is_sorted=False,
missing="drop",
return_sorted=True,
):
"""LOWESS (Locally Weighted Scatterplot Smoothing)
A lowess function that outs smoothed estimates of endog
at the given exog values from points (exog, endog)
... | https://github.com/statsmodels/statsmodels/issues/967 | ======================================================================
ERROR: statsmodels.nonparametric.tests.test_lowess.TestLowess.test_options
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/nose/case.py", line 197, in r... | ValueError |
def fit(
self,
maxlag=None,
method="cmle",
ic=None,
trend="c",
transparams=True,
start_params=None,
solver=None,
maxiter=35,
full_output=1,
disp=1,
callback=None,
**kwargs,
):
"""
Fit the unconditional maximum likelihood of an AR(p) process.
Parameters
... | def fit(
self,
maxlag=None,
method="cmle",
ic=None,
trend="c",
transparams=True,
start_params=None,
solver=None,
maxiter=35,
full_output=1,
disp=1,
callback=None,
**kwargs,
):
"""
Fit the unconditional maximum likelihood of an AR(p) process.
Parameters
... | https://github.com/statsmodels/statsmodels/issues/236 | res1a = AR(data.endog).fit(maxlag=9, start_params=0.1*np.ones(9.),method="mle", disp=-1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "e:\josef\eclipsegworkspace\statsmodels-git\statsmodels-josef_new\statsmodels\tsa\ar_model.py", line 550, in fit
if not start_params:
ValueError: The truth... | ValueError |
def _compute_efficient(self, bw):
"""
Computes the bandwidth by estimating the scaling factor (c)
in n_res resamples of size ``n_sub`` (in `randomize` case), or by
dividing ``nobs`` into as many ``n_sub`` blocks as needed (if
`randomize` is False).
References
----------
See p.9 in socse... | def _compute_efficient(self, bw):
"""
Computes the bandwidth by estimating the scaling factor (c)
in n_res resamples of size ``n_sub`` (in `randomize` case), or by
dividing ``nobs`` into as many ``n_sub`` blocks as needed (if
`randomize` is False).
References
----------
See p.9 in socse... | https://github.com/statsmodels/statsmodels/issues/673 | ======================================================================
ERROR: statsmodels.nonparametric.tests.test_kernel_density.TestKDEMultivariate.test_continuous_cvls_efficient
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-pac... | TypeError |
def attach_columns(self, result):
if result.squeeze().ndim <= 1 and len(result) > 1:
return Series(result, index=self.xnames)
else: # for e.g., confidence intervals
return DataFrame(result, index=self.xnames)
| def attach_columns(self, result):
if result.squeeze().ndim <= 1:
return Series(result, index=self.xnames)
else: # for e.g., confidence intervals
return DataFrame(result, index=self.xnames)
| https://github.com/statsmodels/statsmodels/issues/706 | model = ols('LREO_recovery ~ 0 + HREO_recovery', df_product)
results = model.fit()
print results.summary()
OLS Regression Results
==============================================================================
Dep. Variable: LREO_recovery R-squared: 0.999
Model: ... | Exception |
def _isdummy(X):
"""
Given an array X, returns the column indices for the dummy variables.
Parameters
----------
X : array-like
A 1d or 2d array of numbers
Examples
--------
>>> X = np.random.randint(0, 2, size=(15,5)).astype(float)
>>> X[:,1:3] = np.random.randn(15,2)
... | def _isdummy(X):
"""
Given an array X, returns the column indices for the dummy variables.
Parameters
----------
X : array-like
A 1d or 2d array of numbers
Examples
--------
>>> X = np.random.randint(0, 2, size=(15,5)).astype(float)
>>> X[:,1:3] = np.random.randn(15,2)
... | https://github.com/statsmodels/statsmodels/issues/399 | ======================================================================
ERROR: statsmodels.discrete.tests.test_discrete.TestLogitNewton.test_dummy_dydxmean
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/2.6/lib... | TypeError |
def _get_dummy_effects(effects, exog, dummy_ind, method, model, params):
for i in dummy_ind:
exog0 = exog.copy() # only copy once, can we avoid a copy?
exog0[:, i] = 0
effect0 = model.predict(params, exog0)
# fittedvalues0 = np.dot(exog0,params)
exog0[:, i] = 1
effec... | def _get_dummy_effects(effects, exog, dummy_ind, method, model, params):
for i, tf in enumerate(dummy_ind):
if tf == True:
exog0 = exog.copy() # only copy once, can we avoid a copy?
exog0[:, i] = 0
effect0 = model.predict(params, exog0)
# fittedvalues0 = np.d... | https://github.com/statsmodels/statsmodels/issues/399 | ======================================================================
ERROR: statsmodels.discrete.tests.test_discrete.TestLogitNewton.test_dummy_dydxmean
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/2.6/lib... | TypeError |
def summary_params_2dflat(
result,
endog_names=None,
exog_names=None,
alpha=0.95,
use_t=True,
keep_headers=True,
endog_cols=False,
):
# skip_headers2=True):
"""summary table for parameters that are 2d, e.g. multi-equation models
Parameter
---------
result : result instan... | def summary_params_2dflat(
result,
endog_names=None,
exog_names=None,
alpha=0.95,
use_t=True,
keep_headers=True,
endog_cols=False,
):
# skip_headers2=True):
"""summary table for parameters that are 2d, e.g. multi-equation models
Parameter
---------
result : result instan... | https://github.com/statsmodels/statsmodels/issues/339 | Traceback (most recent call last):
File "fitting.py", line 83, in <module>
print mlogit_res.summary()
File "/usr/local/lib/python2.7/dist-packages/statsmodels-0.4.1-py2.7-linux-x86_64.egg/statsmodels/discrete/discrete_model.py", line 1728, in summary
use_t=False)
File "/usr/local/lib/python2.7/dist-packages/statsmodels... | ValueError |
def add_constant(data, prepend=False):
"""
This appends a column of ones to an array if prepend==False.
For ndarrays and pandas.DataFrames, checks to make sure a constant is not
already included. If there is at least one column of ones then the
original object is returned. Does not check for a con... | def add_constant(data, prepend=False):
"""
This appends a column of ones to an array if prepend==False.
For ndarrays and pandas.DataFrames, checks to make sure a constant is not
already included. If there is at least one column of ones then the
original object is returned. Does not check for a con... | https://github.com/statsmodels/statsmodels/issues/260 | $ dmesg | grep -e"Linux version"
[ 0.000000] Linux version 2.6.32-308-ec2 (buildd@crested) (gcc version 4.4.3 (Ubuntu 4.4.3-4ubuntu5) ) #15-Ubuntu SMP Thu Aug 19 04:03:34 UTC 2010 (Ubuntu 2.6.32-308.15-ec2 2.6.32.15+drm33.5)
$ python
Python 2.6.5 (r265:79063, Apr 16 2010, 13:57:41)
[GCC 4.4.3] on linux2
Type "help",... | TypeError |
def predict(self, params, exog=None, exposure=None, offset=None, linear=False):
"""
Predict response variable of a count model given exogenous variables.
Notes
-----
If exposure is specified, then it will be logged by the method.
The user does not need to log it first.
"""
# TODO: add o... | def predict(self, params, exog=None, exposure=None, offset=None, linear=False):
"""
Predict response variable of a count model given exogenous variables.
Notes
-----
If exposure is specified, then it will be logged by the method.
The user does not need to log it first.
"""
# TODO: add o... | https://github.com/statsmodels/statsmodels/issues/175 | results3 = model.fit(start_value=-np.ones(4), method='bfgs')
Warning: Desired error not necessarily achieveddue to precision loss
Current function value: 17470.629507
Iterations: 17
Function evaluations: 32
Gradient evaluations: 31
results3.predict(xf)
Traceback (most recent call last):
File "<stdin>", line 1, in <mod... | TypeError |
def search_novel(self, query):
response = self.submit_form(search_url, {"searchword": query})
data = response.json()
results = []
for novel in data:
titleSoup = BeautifulSoup(novel["name"], "lxml")
results.append(
{
"title": titleSoup.body.text.title(),
... | def search_novel(self, query):
response = self.submit_form(search_url, {"searchword": query})
data = response.json()
results = []
for novel in data:
titleSoup = BeautifulSoup(novel["name"], "lxml")
results.append(
{
"title": titleSoup.body.text.title(),
... | https://github.com/dipu-bd/lightnovel-crawler/issues/476 | 2020-06-08 18:50:45,291 [DEBUG] (urllib3.connectionpool)
https://www.mtlnovel.com:443 "GET /wp-admin/admin-ajax.php?action=autosuggest&q=strongest%20sword%20god HTTP/1.1" 200 None
2020-06-08 18:50:45,297 [DEBUG] (SEARCH_NOVEL)
Traceback (most recent call last):
File "./src/core/novel_search.py", line 22, in get_sea... | KeyError |
def search_novel(self, query):
query = query.lower().replace(" ", "%20")
# soup = self.get_soup(search_url % query)
list_url = search_url % query
data = self.get_json(list_url)["items"][0]["results"]
results = []
for item in data:
url = item["permalink"]
results.append(
... | def search_novel(self, query):
query = query.lower().replace(" ", "%20")
# soup = self.get_soup(search_url % query)
list_url = search_url % query
data = self.get_json(list_url)["items"][0]["results"]
results = []
for item in data:
url = self.absolute_url("https://es.mtlnovel.com/?p=%s"... | https://github.com/dipu-bd/lightnovel-crawler/issues/476 | 2020-06-08 18:50:45,291 [DEBUG] (urllib3.connectionpool)
https://www.mtlnovel.com:443 "GET /wp-admin/admin-ajax.php?action=autosuggest&q=strongest%20sword%20god HTTP/1.1" 200 None
2020-06-08 18:50:45,297 [DEBUG] (SEARCH_NOVEL)
Traceback (most recent call last):
File "./src/core/novel_search.py", line 22, in get_sea... | KeyError |
def search_novel(self, query):
query = query.lower().replace(" ", "%20")
# soup = self.get_soup(search_url % query)
list_url = search_url % query
data = self.get_json(list_url)["items"][0]["results"]
results = []
for item in data:
url = item["permalink"]
results.append(
... | def search_novel(self, query):
query = query.lower().replace(" ", "%20")
# soup = self.get_soup(search_url % query)
list_url = search_url % query
data = self.get_json(list_url)["items"][0]["results"]
results = []
for item in data:
url = self.absolute_url("https://fr.mtlnovel.com/?p=%s"... | https://github.com/dipu-bd/lightnovel-crawler/issues/476 | 2020-06-08 18:50:45,291 [DEBUG] (urllib3.connectionpool)
https://www.mtlnovel.com:443 "GET /wp-admin/admin-ajax.php?action=autosuggest&q=strongest%20sword%20god HTTP/1.1" 200 None
2020-06-08 18:50:45,297 [DEBUG] (SEARCH_NOVEL)
Traceback (most recent call last):
File "./src/core/novel_search.py", line 22, in get_sea... | KeyError |
def search_novel(self, query):
query = query.lower().replace(" ", "%20")
# soup = self.get_soup(search_url % query)
list_url = search_url % query
data = self.get_json(list_url)["items"][0]["results"]
results = []
for item in data:
url = item["permalink"]
results.append(
... | def search_novel(self, query):
query = query.lower().replace(" ", "%20")
# soup = self.get_soup(search_url % query)
list_url = search_url % query
data = self.get_json(list_url)["items"][0]["results"]
results = []
for item in data:
url = self.absolute_url("https://id.mtlnovel.com/?p=%s"... | https://github.com/dipu-bd/lightnovel-crawler/issues/476 | 2020-06-08 18:50:45,291 [DEBUG] (urllib3.connectionpool)
https://www.mtlnovel.com:443 "GET /wp-admin/admin-ajax.php?action=autosuggest&q=strongest%20sword%20god HTTP/1.1" 200 None
2020-06-08 18:50:45,297 [DEBUG] (SEARCH_NOVEL)
Traceback (most recent call last):
File "./src/core/novel_search.py", line 22, in get_sea... | KeyError |
def search_novel(self, query):
query = query.lower().replace(" ", "+")
soup = self.get_soup(search_url % query)
results = []
if soup.get_text(strip=True) == "Sorry! No novel founded!":
return results
# end if
for tr in soup.select("tr"):
a = tr.select("td a")
results.app... | def search_novel(self, query):
query = query.lower().replace(" ", "+")
soup = self.get_soup(search_url % query)
results = []
for tr in soup.select("tr"):
a = tr.select("td a")
results.append(
{
"title": a[0].text.strip(),
"url": self.absolute_... | https://github.com/dipu-bd/lightnovel-crawler/issues/476 | 2020-06-08 18:50:45,291 [DEBUG] (urllib3.connectionpool)
https://www.mtlnovel.com:443 "GET /wp-admin/admin-ajax.php?action=autosuggest&q=strongest%20sword%20god HTTP/1.1" 200 None
2020-06-08 18:50:45,297 [DEBUG] (SEARCH_NOVEL)
Traceback (most recent call last):
File "./src/core/novel_search.py", line 22, in get_sea... | KeyError |
def read_novel_info(self):
"""Get novel title, autor, cover etc"""
logger.debug("Visiting %s", self.novel_url)
soup = self.get_soup(self.novel_url)
self.novel_id = urlparse(self.novel_url).path.split("/")[1]
logger.info("Novel Id: %s", self.novel_id)
self.novel_title = soup.select_one(".series... | def read_novel_info(self):
"""Get novel title, autor, cover etc"""
logger.debug("Visiting %s", self.novel_url)
soup = self.get_soup(self.novel_url)
self.novel_id = urlparse(self.novel_url).path.split("/")[1]
logger.info("Novel Id: %s", self.novel_id)
self.novel_title = soup.select_one(".series... | https://github.com/dipu-bd/lightnovel-crawler/issues/476 | 2020-06-08 18:50:45,291 [DEBUG] (urllib3.connectionpool)
https://www.mtlnovel.com:443 "GET /wp-admin/admin-ajax.php?action=autosuggest&q=strongest%20sword%20god HTTP/1.1" 200 None
2020-06-08 18:50:45,297 [DEBUG] (SEARCH_NOVEL)
Traceback (most recent call last):
File "./src/core/novel_search.py", line 22, in get_sea... | KeyError |
def search_novel(self, query):
url = search_url % quote(query.lower())
logger.debug("Visiting: %s", url)
soup = self.get_soup(url)
results = []
for li in soup.select(".book-list-info > ul > li"):
results.append(
{
"title": li.select_one("a h4 b").text.strip(),
... | def search_novel(self, query):
url = search_url % quote(query.lower())
logger.debug("Visiting: %s", url)
soup = self.get_soup(url)
results = []
for li in soup.select(".book-list-info li"):
results.append(
{
"title": li.select_one("a h4 b").text.strip(),
... | https://github.com/dipu-bd/lightnovel-crawler/issues/476 | 2020-06-08 18:50:45,291 [DEBUG] (urllib3.connectionpool)
https://www.mtlnovel.com:443 "GET /wp-admin/admin-ajax.php?action=autosuggest&q=strongest%20sword%20god HTTP/1.1" 200 None
2020-06-08 18:50:45,297 [DEBUG] (SEARCH_NOVEL)
Traceback (most recent call last):
File "./src/core/novel_search.py", line 22, in get_sea... | KeyError |
def search_novel(self, query):
query = query.lower().replace(" ", "%20")
# soup = self.get_soup(search_url % query)
list_url = search_url % query
data = self.get_json(list_url)["items"][0]["results"]
results = []
for item in data:
url = item["permalink"]
results.append(
... | def search_novel(self, query):
query = query.lower().replace(" ", "%20")
# soup = self.get_soup(search_url % query)
list_url = search_url % query
data = self.get_json(list_url)["items"][0]["results"]
results = []
for item in data:
url = self.absolute_url("https://www.mtlnovel.com/?p=%s... | https://github.com/dipu-bd/lightnovel-crawler/issues/476 | 2020-06-08 18:50:45,291 [DEBUG] (urllib3.connectionpool)
https://www.mtlnovel.com:443 "GET /wp-admin/admin-ajax.php?action=autosuggest&q=strongest%20sword%20god HTTP/1.1" 200 None
2020-06-08 18:50:45,297 [DEBUG] (SEARCH_NOVEL)
Traceback (most recent call last):
File "./src/core/novel_search.py", line 22, in get_sea... | KeyError |
def read_novel_info(self):
# to get cookies and session info
self.parse_content_css(self.home_url)
# Determine cannonical novel name
path_fragments = urlparse(self.novel_url).path.split("/")
if path_fragments[1] == "books":
self.novel_hash = path_fragments[2]
else:
self.novel_ha... | def read_novel_info(self):
# to get cookies and session info
self.parse_content_css(self.home_url)
# Determine cannonical novel name
path_fragments = urlparse(self.novel_url).path.split("/")
if path_fragments[1] == "books":
self.novel_hash = path_fragments[2]
else:
self.novel_ha... | https://github.com/dipu-bd/lightnovel-crawler/issues/465 | Fail to get bad selectors
Traceback (most recent call last):
File "c:\python38\lib\site-packages\lncrawl\sources\babelnovel.py", line 125, in parse_content_css
data = json.loads(unquote(content[0]))
IndexError: list index out of range
! Error: 'chapterCount' | IndexError |
def predict(
self, x: np.ndarray, batch_size: int = 128, **kwargs
) -> Union[Tuple[np.ndarray, np.ndarray], np.ndarray]:
"""
Perform prediction for a batch of inputs.
:param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
could have dif... | def predict(
self, x: np.ndarray, batch_size: int = 128, **kwargs
) -> Union[Tuple[np.ndarray, np.ndarray], np.ndarray]:
"""
Perform prediction for a batch of inputs.
:param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
could have dif... | https://github.com/Trusted-AI/adversarial-robustness-toolbox/issues/688 | ---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-30-c849f56466d3> in <module>
3
4 # Generate attack
----> 5 x_adv = attack_pgd.generate(np.array([x2, x3]), y=None, batch_size=2)
/opt/conda/lib/python3.... | TypeError |
def loss_gradient(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
"""
Compute the gradient of the loss function w.r.t. `x`.
:param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
could have different lengths. A possible example... | def loss_gradient(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
"""
Compute the gradient of the loss function w.r.t. `x`.
:param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
could have different lengths. A possible example... | https://github.com/Trusted-AI/adversarial-robustness-toolbox/issues/688 | ---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-30-c849f56466d3> in <module>
3
4 # Generate attack
----> 5 x_adv = attack_pgd.generate(np.array([x2, x3]), y=None, batch_size=2)
/opt/conda/lib/python3.... | TypeError |
def loss_gradient(self, x, y, **kwargs):
"""
Compute the gradient of the loss function w.r.t. `x`.
:param x: Sample input with shape as expected by the model.
:type x: `np.ndarray`
:param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
... | def loss_gradient(self, x, y, **kwargs):
"""
Compute the gradient of the loss function w.r.t. `x`.
:param x: Sample input with shape as expected by the model.
:type x: `np.ndarray`
:param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
... | https://github.com/Trusted-AI/adversarial-robustness-toolbox/issues/355 | MXNetError Traceback (most recent call last)
<timed exec> in <module>
~/.pyenv/versions/3.7.6/envs/adversarialvideo/lib/python3.7/site-packages/art/attacks/attack.py in replacement_function(self, *args, **kwargs)
68 if len(args) > 0:
69 args = tuple(ls... | MXNetError |
def predict(self, x, batch_size=128, raw=False, **kwargs):
"""
Perform prediction for a batch of inputs. Predictions from classifiers should only be aggregated if they all
have the same type of output (e.g., probabilities). Otherwise, use `raw=True` to get predictions from all
models without aggregation... | def predict(self, x, batch_size=128, **kwargs):
"""
Perform prediction for a batch of inputs. Predictions from classifiers should only be aggregated if they all
have the same type of output (e.g., probabilities). Otherwise, use `raw=True` to get predictions from all
models without aggregation. The same ... | https://github.com/Trusted-AI/adversarial-robustness-toolbox/issues/214 | import torch
from torchvision import models
from art.classifiers import PyTorchClassifier, EnsembleClassifier
from art.attacks import ProjectedGradientDescent, HopSkipJump
# load and preprocess imagenet images
images = load_preprocess_images(...)
resnet18 = models.resnet18(pretrained=True)
alexnet = models.alexnet(p... | ValueError |
def class_gradient(self, x, label=None, raw=False, **kwargs):
"""
Compute per-class derivatives w.r.t. `x`.
:param x: Sample input with shape as expected by the model.
:type x: `np.ndarray`
:param label: Index of a specific per-class derivative. If `None`, then gradients for all
c... | def class_gradient(self, x, label=None, **kwargs):
"""
Compute per-class derivatives w.r.t. `x`.
:param x: Sample input with shape as expected by the model.
:type x: `np.ndarray`
:param label: Index of a specific per-class derivative. If `None`, then gradients for all
classes will... | https://github.com/Trusted-AI/adversarial-robustness-toolbox/issues/214 | import torch
from torchvision import models
from art.classifiers import PyTorchClassifier, EnsembleClassifier
from art.attacks import ProjectedGradientDescent, HopSkipJump
# load and preprocess imagenet images
images = load_preprocess_images(...)
resnet18 = models.resnet18(pretrained=True)
alexnet = models.alexnet(p... | ValueError |
def loss_gradient(self, x, y, raw=False, **kwargs):
"""
Compute the gradient of the loss function w.r.t. `x`.
:param x: Sample input with shape as expected by the model.
:type x: `np.ndarray`
:param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shap... | def loss_gradient(self, x, y, **kwargs):
"""
Compute the gradient of the loss function w.r.t. `x`.
:param x: Sample input with shape as expected by the model.
:type x: `np.ndarray`
:param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
... | https://github.com/Trusted-AI/adversarial-robustness-toolbox/issues/214 | import torch
from torchvision import models
from art.classifiers import PyTorchClassifier, EnsembleClassifier
from art.attacks import ProjectedGradientDescent, HopSkipJump
# load and preprocess imagenet images
images = load_preprocess_images(...)
resnet18 = models.resnet18(pretrained=True)
alexnet = models.alexnet(p... | ValueError |
def generate(self, x, **kwargs):
"""
Generate adversarial samples and return them in an array.
:param x: An array with the original inputs to be attacked.
:type x: `np.ndarray`
:param y: If `self.targeted` is true, then `y_val` represents the target labels. Otherwise, the targets are
th... | def generate(self, x, **kwargs):
"""
Generate adversarial samples and return them in an array.
:param x: An array with the original inputs to be attacked.
:type x: `np.ndarray`
:param y: If `self.targeted` is true, then `y_val` represents the target labels. Otherwise, the targets are
th... | https://github.com/Trusted-AI/adversarial-robustness-toolbox/issues/29 | Traceback (most recent call last):
File "cw_pytorch.py", line 172, in <module>
x_test_adv = cl2m.generate(inputs, **params)
File "/home/weitian/anaconda3/envs/xnor/lib/python3.6/site-packages/art/attacks/carlini.py", line 380, in generate
x_adv_batch_tanh[active_and_update_adv] = x_adv_batch_tanh[update_adv] + \
IndexE... | IndexError |
def __init__(self, classifier, expectation=None):
"""
:param classifier: A trained model.
:type classifier: :class:`Classifier`
:param expectation: An expectation over transformations to be applied when computing
classifier gradients and predictions.
:type expectation: :class... | def __init__(self, classifier):
"""
:param classifier: A trained model.
:type classifier: :class:`Classifier`
"""
self.classifier = classifier
| https://github.com/Trusted-AI/adversarial-robustness-toolbox/issues/29 | Traceback (most recent call last):
File "cw_pytorch.py", line 172, in <module>
x_test_adv = cl2m.generate(inputs, **params)
File "/home/weitian/anaconda3/envs/xnor/lib/python3.6/site-packages/art/attacks/carlini.py", line 380, in generate
x_adv_batch_tanh[active_and_update_adv] = x_adv_batch_tanh[update_adv] + \
IndexE... | IndexError |
def __init__(
self,
classifier,
confidence=0.0,
targeted=True,
learning_rate=0.01,
binary_search_steps=10,
max_iter=10,
initial_const=0.01,
max_halving=5,
max_doubling=5,
batch_size=128,
expectation=None,
):
"""
Create a Carlini L_2 attack instance.
:param cl... | def __init__(
self,
classifier,
confidence=0.0,
targeted=True,
learning_rate=0.01,
binary_search_steps=10,
max_iter=10,
initial_const=0.01,
max_halving=5,
max_doubling=5,
batch_size=128,
):
"""
Create a Carlini L_2 attack instance.
:param classifier: A trained mo... | https://github.com/Trusted-AI/adversarial-robustness-toolbox/issues/29 | Traceback (most recent call last):
File "cw_pytorch.py", line 172, in <module>
x_test_adv = cl2m.generate(inputs, **params)
File "/home/weitian/anaconda3/envs/xnor/lib/python3.6/site-packages/art/attacks/carlini.py", line 380, in generate
x_adv_batch_tanh[active_and_update_adv] = x_adv_batch_tanh[update_adv] + \
IndexE... | IndexError |
def _loss(self, x, x_adv, target, c):
"""
Compute the objective function value.
:param x: An array with the original input.
:type x: `np.ndarray`
:param x_adv: An array with the adversarial input.
:type x_adv: `np.ndarray`
:param target: An array with the target class (one-hot encoded).
... | def _loss(self, x, x_adv, target, c):
"""
Compute the objective function value.
:param x: An array with the original input.
:type x: `np.ndarray`
:param x_adv: An array with the adversarial input.
:type x_adv: `np.ndarray`
:param target: An array with the target class (one-hot encoded).
... | https://github.com/Trusted-AI/adversarial-robustness-toolbox/issues/29 | Traceback (most recent call last):
File "cw_pytorch.py", line 172, in <module>
x_test_adv = cl2m.generate(inputs, **params)
File "/home/weitian/anaconda3/envs/xnor/lib/python3.6/site-packages/art/attacks/carlini.py", line 380, in generate
x_adv_batch_tanh[active_and_update_adv] = x_adv_batch_tanh[update_adv] + \
IndexE... | IndexError |
def _loss_gradient(self, z, target, x, x_adv, x_adv_tanh, c, clip_min, clip_max):
"""
Compute the gradient of the loss function.
:param z: An array with the current logits.
:type z: `np.ndarray`
:param target: An array with the target class (one-hot encoded).
:type target: `np.ndarray`
:par... | def _loss_gradient(self, z, target, x, x_adv, x_adv_tanh, c, clip_min, clip_max):
"""
Compute the gradient of the loss function.
:param z: An array with the current logits.
:type z: `np.ndarray`
:param target: An array with the target class (one-hot encoded).
:type target: `np.ndarray`
:par... | https://github.com/Trusted-AI/adversarial-robustness-toolbox/issues/29 | Traceback (most recent call last):
File "cw_pytorch.py", line 172, in <module>
x_test_adv = cl2m.generate(inputs, **params)
File "/home/weitian/anaconda3/envs/xnor/lib/python3.6/site-packages/art/attacks/carlini.py", line 380, in generate
x_adv_batch_tanh[active_and_update_adv] = x_adv_batch_tanh[update_adv] + \
IndexE... | IndexError |
def generate(self, x, **kwargs):
"""
Generate adversarial samples and return them in an array.
:param x: An array with the original inputs to be attacked.
:type x: `np.ndarray`
:param y: If `self.targeted` is true, then `y_val` represents the target labels. Otherwise, the targets are
th... | def generate(self, x, **kwargs):
"""
Generate adversarial samples and return them in an array.
:param x: An array with the original inputs to be attacked.
:type x: `np.ndarray`
:param y: If `self.targeted` is true, then `y_val` represents the target labels. Otherwise, the targets are
th... | https://github.com/Trusted-AI/adversarial-robustness-toolbox/issues/29 | Traceback (most recent call last):
File "cw_pytorch.py", line 172, in <module>
x_test_adv = cl2m.generate(inputs, **params)
File "/home/weitian/anaconda3/envs/xnor/lib/python3.6/site-packages/art/attacks/carlini.py", line 380, in generate
x_adv_batch_tanh[active_and_update_adv] = x_adv_batch_tanh[update_adv] + \
IndexE... | IndexError |
def __init__(
self,
classifier,
confidence=0.0,
targeted=True,
learning_rate=0.01,
max_iter=10,
max_halving=5,
max_doubling=5,
eps=0.3,
batch_size=128,
expectation=None,
):
"""
Create a Carlini L_Inf attack instance.
:param classifier: A trained model.
:type ... | def __init__(
self,
classifier,
confidence=0.0,
targeted=True,
learning_rate=0.01,
max_iter=10,
max_halving=5,
max_doubling=5,
eps=0.3,
batch_size=128,
):
"""
Create a Carlini L_Inf attack instance.
:param classifier: A trained model.
:type classifier: :class:`Cl... | https://github.com/Trusted-AI/adversarial-robustness-toolbox/issues/29 | Traceback (most recent call last):
File "cw_pytorch.py", line 172, in <module>
x_test_adv = cl2m.generate(inputs, **params)
File "/home/weitian/anaconda3/envs/xnor/lib/python3.6/site-packages/art/attacks/carlini.py", line 380, in generate
x_adv_batch_tanh[active_and_update_adv] = x_adv_batch_tanh[update_adv] + \
IndexE... | IndexError |
def _loss(self, x_adv, target):
"""
Compute the objective function value.
:param x_adv: An array with the adversarial input.
:type x_adv: `np.ndarray`
:param target: An array with the target class (one-hot encoded).
:type target: `np.ndarray`
:return: A tuple holding the current logits and ... | def _loss(self, x_adv, target):
"""
Compute the objective function value.
:param x_adv: An array with the adversarial input.
:type x_adv: `np.ndarray`
:param target: An array with the target class (one-hot encoded).
:type target: `np.ndarray`
:return: A tuple holding the current logits and ... | https://github.com/Trusted-AI/adversarial-robustness-toolbox/issues/29 | Traceback (most recent call last):
File "cw_pytorch.py", line 172, in <module>
x_test_adv = cl2m.generate(inputs, **params)
File "/home/weitian/anaconda3/envs/xnor/lib/python3.6/site-packages/art/attacks/carlini.py", line 380, in generate
x_adv_batch_tanh[active_and_update_adv] = x_adv_batch_tanh[update_adv] + \
IndexE... | IndexError |
def _loss_gradient(self, z, target, x_adv, x_adv_tanh, clip_min, clip_max):
"""
Compute the gradient of the loss function.
:param z: An array with the current logits.
:type z: `np.ndarray`
:param target: An array with the target class (one-hot encoded).
:type target: `np.ndarray`
:param x_a... | def _loss_gradient(self, z, target, x_adv, x_adv_tanh, clip_min, clip_max):
"""
Compute the gradient of the loss function.
:param z: An array with the current logits.
:type z: `np.ndarray`
:param target: An array with the target class (one-hot encoded).
:type target: `np.ndarray`
:param x_a... | https://github.com/Trusted-AI/adversarial-robustness-toolbox/issues/29 | Traceback (most recent call last):
File "cw_pytorch.py", line 172, in <module>
x_test_adv = cl2m.generate(inputs, **params)
File "/home/weitian/anaconda3/envs/xnor/lib/python3.6/site-packages/art/attacks/carlini.py", line 380, in generate
x_adv_batch_tanh[active_and_update_adv] = x_adv_batch_tanh[update_adv] + \
IndexE... | IndexError |
def generate(self, x, **kwargs):
"""
Generate adversarial samples and return them in an array.
:param x: An array with the original inputs to be attacked.
:type x: `np.ndarray`
:param y: If `self.targeted` is true, then `y_val` represents the target labels. Otherwise, the targets are
... | def generate(self, x, **kwargs):
"""
Generate adversarial samples and return them in an array.
:param x: An array with the original inputs to be attacked.
:type x: `np.ndarray`
:param y: If `self.targeted` is true, then `y_val` represents the target labels. Otherwise, the targets are
... | https://github.com/Trusted-AI/adversarial-robustness-toolbox/issues/29 | Traceback (most recent call last):
File "cw_pytorch.py", line 172, in <module>
x_test_adv = cl2m.generate(inputs, **params)
File "/home/weitian/anaconda3/envs/xnor/lib/python3.6/site-packages/art/attacks/carlini.py", line 380, in generate
x_adv_batch_tanh[active_and_update_adv] = x_adv_batch_tanh[update_adv] + \
IndexE... | IndexError |
def __init__(
self,
classifier,
max_iter=100,
epsilon=1e-6,
nb_grads=10,
batch_size=128,
expectation=None,
):
"""
Create a DeepFool attack instance.
:param classifier: A trained model.
:type classifier: :class:`Classifier`
:param max_iter: The maximum number of iteration... | def __init__(self, classifier, max_iter=100, epsilon=1e-6):
"""
Create a DeepFool attack instance.
:param classifier: A trained model.
:type classifier: :class:`Classifier`
:param max_iter: The maximum number of iterations.
:type max_iter: `int`
:param epsilon: Overshoot parameter.
:typ... | https://github.com/Trusted-AI/adversarial-robustness-toolbox/issues/29 | Traceback (most recent call last):
File "cw_pytorch.py", line 172, in <module>
x_test_adv = cl2m.generate(inputs, **params)
File "/home/weitian/anaconda3/envs/xnor/lib/python3.6/site-packages/art/attacks/carlini.py", line 380, in generate
x_adv_batch_tanh[active_and_update_adv] = x_adv_batch_tanh[update_adv] + \
IndexE... | IndexError |
def generate(self, x, **kwargs):
"""
Generate adversarial samples and return them in an array.
:param x: An array with the original inputs to be attacked.
:type x: `np.ndarray`
:param max_iter: The maximum number of iterations.
:type max_iter: `int`
:param epsilon: Overshoot parameter.
... | def generate(self, x, **kwargs):
"""
Generate adversarial samples and return them in an array.
:param x: An array with the original inputs to be attacked.
:type x: `np.ndarray`
:param max_iter: The maximum number of iterations.
:type max_iter: `int`
:param epsilon: Overshoot parameter.
... | https://github.com/Trusted-AI/adversarial-robustness-toolbox/issues/29 | Traceback (most recent call last):
File "cw_pytorch.py", line 172, in <module>
x_test_adv = cl2m.generate(inputs, **params)
File "/home/weitian/anaconda3/envs/xnor/lib/python3.6/site-packages/art/attacks/carlini.py", line 380, in generate
x_adv_batch_tanh[active_and_update_adv] = x_adv_batch_tanh[update_adv] + \
IndexE... | IndexError |
def set_params(self, **kwargs):
"""
Take in a dictionary of parameters and applies attack-specific checks before saving them as attributes.
:param max_iter: The maximum number of iterations.
:type max_iter: `int`
:param epsilon: Overshoot parameter.
:type epsilon: `float`
:param nb_grads: T... | def set_params(self, **kwargs):
"""Take in a dictionary of parameters and applies attack-specific checks before saving them as attributes.
:param max_iter: The maximum number of iterations.
:type max_iter: `int`
"""
# Save attack-specific parameters
super(DeepFool, self).set_params(**kwargs)
... | https://github.com/Trusted-AI/adversarial-robustness-toolbox/issues/29 | Traceback (most recent call last):
File "cw_pytorch.py", line 172, in <module>
x_test_adv = cl2m.generate(inputs, **params)
File "/home/weitian/anaconda3/envs/xnor/lib/python3.6/site-packages/art/attacks/carlini.py", line 380, in generate
x_adv_batch_tanh[active_and_update_adv] = x_adv_batch_tanh[update_adv] + \
IndexE... | IndexError |
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