def check_pickle(obj):
fh = BytesIO()
cPickle.dump(obj, fh, protocol=cPickle.HIGHEST_PROTOCOL)
plen = fh.tell()
fh.seek(0, 0)
res = cPickle.load(fh)
fh.close()
return res, plen
def check_pickle(obj):
fh =StringIO()
cPickle.dump(obj, fh)
plen = fh.pos
fh.seek(0,0)
res = cPickle.load(fh)
fh.close()
return res, plen
def check_pickle(obj):
fh = BytesIO()
cPickle.dump(obj, fh, protocol=cPickle.HIGHEST_PROTOCOL)
plen = fh.tell()
fh.seek(0, 0)
res = cPickle.load(fh)
fh.close()
return res, plen
def load_pickle(fname):
"""
Load a previously saved object from file
Parameters
----------
fname : str
Filename to unpickle
Notes
-----
This method can be used to load *both* models and results.
"""
with get_file_obj(fname, 'rb') as fin:
return cPickle.load(fin)
def load_pickle(fname):
"""
Load a previously saved object from file
Parameters
----------
fname : str
Filename to unpickle
Notes
-----
This method can be used to load *both* models and results.
"""
with get_file_obj(fname, 'rb') as fin:
return cPickle.load(fin)
import numpy as np
import matplotlib.finance as fin
import matplotlib.pyplot as plt
import datetime as dt
import pandas as pa
from statsmodels.compat.python import cPickle
import statsmodels.api as sm
import statsmodels.sandbox as sb
import statsmodels.sandbox.tools as sbtools
from statsmodels.graphics.correlation import plot_corr, plot_corr_grid
try:
rrdm = cPickle.load(file('dj30rr','rb'))
except Exception: #blanket for any unpickling error
print("Error with unpickling, a new pickle file can be created with findow_1")
raise
ticksym = rrdm.columns.tolist()
rr = rrdm.values[1:400]
rrcorr = np.corrcoef(rr, rowvar=0)
plot_corr(rrcorr, xnames=ticksym)
nvars = rrcorr.shape[0]
plt.figure()
plt.hist(rrcorr[np.triu_indices(nvars,1)])
plt.title('Correlation Coefficients')
#print results.predict(xf)
print(results.model.predict(results.params, xf))
results.summary()
shrinkit = 1
if shrinkit:
results.remove_data()
from statsmodels.compat.python import cPickle
fname = 'try_shrink%d_ols.pickle' % shrinkit
fh = open(fname, 'w')
cPickle.dump(results._results, fh) #pickling wrapper doesn't work
fh.close()
fh = open(fname, 'r')
results2 = cPickle.load(fh)
fh.close()
print(results2.predict(xf))
print(results2.model.predict(results.params, xf))
y_count = np.random.poisson(np.exp(x.sum(1)-x.mean()))
model = sm.Poisson(y_count, x)#, exposure=np.ones(nobs), offset=np.zeros(nobs)) #bug with default
results = model.fit(method='bfgs')
results.summary()
print(results.model.predict(results.params, xf, exposure=1, offset=0))
if shrinkit:
results.remove_data()
Author: josef-pktd
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from statsmodels.compat.python import cPickle
import statsmodels.sandbox.tools as sbtools
from statsmodels.graphics.correlation import plot_corr, plot_corr_grid
try:
with open('dj30rr', 'rb') as fd:
rrdm = cPickle.load(fd)
except Exception: #blanket for any unpickling error
print("Error with unpickling, a new pickle file can be created with findow_1")
raise
ticksym = rrdm.columns.tolist()
rr = rrdm.values[1:400]
rrcorr = np.corrcoef(rr, rowvar=0)
plot_corr(rrcorr, xnames=ticksym)
nvars = rrcorr.shape[0]
plt.figure()
plt.hist(rrcorr[np.triu_indices(nvars,1)])
plt.title('Correlation Coefficients')
Created on Sat Jan 30 16:30:18 2010
Author: josef-pktd
"""
import numpy as np
import matplotlib.pyplot as plt
from statsmodels.compat.python import cPickle
import statsmodels.sandbox.tools as sbtools
from statsmodels.graphics.correlation import plot_corr, plot_corr_grid
try:
with open('dj30rr', 'rb') as fd:
rrdm = cPickle.load(fd)
except Exception: #blanket for any unpickling error
print("Error with unpickling, a new pickle file can be created with findow_1")
raise
ticksym = rrdm.columns.tolist()
rr = rrdm.values[1:400]
rrcorr = np.corrcoef(rr, rowvar=0)
plot_corr(rrcorr, xnames=ticksym)
nvars = rrcorr.shape[0]
plt.figure()
plt.hist(rrcorr[np.triu_indices(nvars,1)])
plt.title('Correlation Coefficients')
import numpy as np
import matplotlib.finance as fin
import matplotlib.pyplot as plt
import datetime as dt
import pandas as pa
from statsmodels.compat.python import cPickle
import statsmodels.api as sm
import statsmodels.sandbox as sb
import statsmodels.sandbox.tools as sbtools
from statsmodels.graphics.correlation import plot_corr, plot_corr_grid
try:
rrdm = cPickle.load(file('dj30rr', 'rb'))
except Exception: #blanket for any unpickling error
print(
"Error with unpickling, a new pickle file can be created with findow_1"
)
raise
ticksym = rrdm.columns.tolist()
rr = rrdm.values[1:400]
rrcorr = np.corrcoef(rr, rowvar=0)
plot_corr(rrcorr, xnames=ticksym)
nvars = rrcorr.shape[0]
plt.figure()
plt.hist(rrcorr[np.triu_indices(nvars, 1)])
