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 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)])