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mahmoud/orange_junk.py

Created October 21, 2012 21:46
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orange junk
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orange_junk.py
Python 2.7.3 (default, Aug 1 2012, 05:14:39)
[GCC 4.6.3] on linux2
Type "help", "copyright", "credits" or "license" for more information.
(PythonConsole)
>>> in_classifiers
[<EarthClassifier instance at 0xa6637570>]
>>>
Running script:
Traceback (most recent call last):
File "<console>", line 1, in <module>
File "<string>", line 96
"""
Meta attributes (title, etc.)
Original class
Guessed class
Guessed class score (threshold)
Score
Absolute error
Other stuff:
Mean squared error
Root mean squared error (absolute?)
Mean absolute error
Relative squared error
Root relative squared error
Relative absolute error
R-Squared
"""
^
SyntaxError: invalid syntax
>>>
Running script:
>>> in_classifiers[0].name
''
>>> in_classifiers[0].name
'd_Earth'
>>>
Running script:
>>> len(in_classifiers)
4
>>>
Running script:
>>> len(in_classifiers)
4
>>> from stats import dist_stats
>>> dist_stats([])
{'skewness': 0.0, 'mean_trimmed': 0.0, 'kurtosis_trimmed': 0.0, 'std_dev': 0.0, 'rel_std_dev': 0.0, 'kurtosis': 0.0, 'count': 0, 'skewness_trimmed': 0.0, 'median': 0, 'median_abs_dev': 0, 'std_dev_trimmed': 0.0, 'variance': 0.0, 'mean': 0.0}
>>> de_scores = [x['d_Earth_score'].value for x in data_scores]
>>> de_score[:10]
Traceback (most recent call last):
File "<console>", line 1, in <module>
NameError: name 'de_score' is not defined
>>> de_scores[:10]
[0.7449862360954285, 0.5704416036605835, 0.51088947057724, 0.7936760783195496, 0.8030709624290466, 0.6073479652404785, 0.720836877822876, 0.7581908106803894, 0.8060908913612366, 0.864799976348877]
>>> dist_stats(de_scores)
{'skewness': -0.08515198381542667, 'mean_trimmed': 0.703119730029036, 'kurtosis_trimmed': 2.3685906277776207, 'std_dev': 0.12235132373099095, 'rel_std_dev': 0.17500327836053256, 'kurtosis': 2.4382645593606687, 'count': 4894, 'skewness_trimmed': -0.07987235202856677, 'median': 0.706883579492569, 'median_abs_dev': 0.08900436758995056, 'std_dev_trimmed': 0.125753378442586, 'variance': 0.014969846418725747, 'mean': 0.6991373240387484}
>>> de_scores = [x['d_Earth_score'].value for x in data_scores if x['R_ah_current'].value == 'GA']
>>> ga_de_scores = [x['d_Earth_score'].value for x in data_scores if x['R_ah_current'].value == 'GA']
>>> dist_stats(ga_de_scores)
{'skewness': 0.3340588259850356, 'mean_trimmed': 0.6285709475407809, 'kurtosis_trimmed': 2.656262504696904, 'std_dev': 0.10707602647912888, 'rel_std_dev': 0.17047039038477402, 'kurtosis': 2.713361355962532, 'count': 2421, 'skewness_trimmed': 0.3256936211359308, 'median': 0.6231045722961426, 'median_abs_dev': 0.0786864161491394, 'std_dev_trimmed': 0.10465708515380635, 'variance': 0.01146527544655911, 'mean': 0.6281209671512117}
>>> ga_de_mean = dist_stats(ga_de_scores)
>>> ga_de_dist_stats = dist_stats(ga_de_scores)
>>> ga_de_mean = ga_de_dist_stats['mean']
>>> ga_de_variance = ga_de_dist_stats['variance']
>>> mu = ga_de_mean
>>> var = ga_de_variance
>>> -(mu * (var + mu**2 - mu))/var
12.168749719603749
>>> alpha = -(mu * (var + mu**2 - mu))/var
>>> beta = (var + mu**2 - mu)*(mu-1)/var
>>> beta
7.204508547500533
>>> alpha2 = mu*((mu*(1-mu))/var - 1)
>>> alpha2
12.168749719603749
>>> var < mu*(1-mu)
True
>>> var
0.01146527544655911
>>> mu * (1-mu)
0.23358501777623814
>>> beta2 = (1-mu) * (mu*(1-mu)/var - 1)
>>> beta
7.204508547500533
>>> beta2
7.2045085475005335
>>> ga_de_dist_stats
{'skewness': 0.3340588259850356, 'mean_trimmed': 0.6285709475407809, 'kurtosis_trimmed': 2.656262504696904, 'std_dev': 0.10707602647912888, 'rel_std_dev': 0.17047039038477402, 'kurtosis': 2.713361355962532, 'count': 2421, 'skewness_trimmed': 0.3256936211359308, 'median': 0.6231045722961426, 'median_abs_dev': 0.0786864161491394, 'std_dev_trimmed': 0.10465708515380635, 'variance': 0.01146527544655911, 'mean': 0.6281209671512117}
>>> from pprint import pprint
>>> pprint(ga_de_dist_stats)
{'count': 2421,
'kurtosis': 2.713361355962532,
'kurtosis_trimmed': 2.656262504696904,
'mean': 0.6281209671512117,
'mean_trimmed': 0.6285709475407809,
'median': 0.6231045722961426,
'median_abs_dev': 0.0786864161491394,
'rel_std_dev': 0.17047039038477402,
'skewness': 0.3340588259850356,
'skewness_trimmed': 0.3256936211359308,
'std_dev': 0.10707602647912888,
'std_dev_trimmed': 0.10465708515380635,
'variance': 0.01146527544655911}
>>> max(ga_de_scores)
1.0511188507080078
>>> min(ga_de_scores)
0.3523802161216736
>>> (mu - 0.3523)/(1.0511 - 0.3523)
0.3947065929467827
>>> mu
0.6281209671512117
>>> old_mu = mu
>>> mu = (mu - min(ga_de_scores))/(max(ga_de_scores) - min(ga_de_scores))
>>> mu
0.39462645598920054
>>> old_var = var
>>> var = (1/len(ga_de_scores)-1) * sum([ (x-mu)**2 for x in ga_de_scores])
>>> var
-159.7495934604055
>>> var = ((1/len(ga_de_scores))-1) * sum([ (x-mu)**2 for x in ga_de_scores])
>>> var
-159.7495934604055
>>> var = (1/(len(ga_de_scores)-1)) * sum([ (x-mu)**2 for x in ga_de_scores])
>>> var
0.0
>>> ga_de_scores[0]
0.7449862360954285
>>> var = (1/(len(ga_de_scores)-1)) * sum([ (x-mu)**2 for x in ga_de_scores], 0.0)
>>> var
0.0
>>> sum([ (x-mu)**2 for x in ga_de_scores], 0.0)
159.7495934604055
>>> (1/(len(ga_de_scores)-1))
0
>>> (1.0/(len(ga_de_scores)-1))
0.00041322314049586776
>>> var = (1.0/(len(ga_de_scores)-1)) * sum([ (x-mu)**2 for x in ga_de_scores], 0.0)
>>> var
0.06601222870264689
>>> alpha3 = mu*((mu*(1-mu))/var - 1)
>>> alpha3
1.0335156918408586
>>> beta3 = (1-mu) * (mu*(1-mu)/var - 1)
>>> beta3
1.585456442833108
>>> ga_de_dist_stats['skewness']
0.3340588259850356
>>> ga_de_dist_stats['mean']
0.6281209671512117
>>> mu = mu/max(ga_de_scores)
>>> var = (1.0/(len(ga_de_scores)-1)) * sum([ (x-mu)**2 for x in ga_de_scores], 0.0)
>>> mu = old_mu/max(ga_de_scores)
>>> var = (1.0/(len(ga_de_scores)-1)) * sum([ (x-mu)**2 for x in ga_de_scores], 0.0)
>>> var
0.012403535078447747
>>> old_var
0.01146527544655911
>>> alpha4 = mu*((mu*(1-mu))/var - 1)
>>> beta4 = (1-mu) * (mu*(1-mu)/var - 1)
>>> alpha4, beta4
(10.988167492848556, 7.3998032810829395)
>>> mu
0.5975736870555836
>>> skew = ga_de_dist_stats['skewness']
>>> kurt = ga_de_dist_stats['kurtosis']
>>> skew**2 - 2
-1.8884047007814997
>>> kurt
2.713361355962532
>>> 3/2(skew**2)
Traceback (most recent call last):
File "<console>", line 1, in <module>
TypeError: 'int' object is not callable
>>> 1.5*(skew**2)
0.16739294882775047
>>> skew**2
0.11159529921850031
>>> fa_de_dist_stats = dist_stats(fa_de_scores)
Traceback (most recent call last):
File "<console>", line 1, in <module>
NameError: name 'fa_de_scores' is not defined
>>> fa_de_scores = [x['d_Earth_score'].value for x in data_scores if x['R_ah_current'].value == 'FA']
>>> fa_de_dist_stats = dist_stats(fa_de_scores)
>>> fadds = fa_de_dist_stats
>>> pprint(fadds)
{'count': 2473,
'kurtosis': 3.1807615937628353,
'kurtosis_trimmed': 2.99223840540891,
'mean': 0.7686604134138904,
'mean_trimmed': 0.7696183630074814,
'median': 0.7718024253845215,
'median_abs_dev': 0.06037336587905884,
'rel_std_dev': 0.12085667180312783,
'skewness': -0.1297249732233207,
'skewness_trimmed': -0.2011861839493487,
'std_dev': 0.0928977393120191,
'std_dev_trimmed': 0.09179196815627351,
'variance': 0.008629989969283857}
>>> fadds['mean']/fadds['std_dev']
8.27426392834127
>>> fadds['std_dev']/fadds['mean']
0.12085667180312783
>>> pprint(ga_de_dist_stats)
{'count': 2421,
'kurtosis': 2.713361355962532,
'kurtosis_trimmed': 2.656262504696904,
'mean': 0.6281209671512117,
'mean_trimmed': 0.6285709475407809,
'median': 0.6231045722961426,
'median_abs_dev': 0.0786864161491394,
'rel_std_dev': 0.17047039038477402,
'skewness': 0.3340588259850356,
'skewness_trimmed': 0.3256936211359308,
'std_dev': 0.10707602647912888,
'std_dev_trimmed': 0.10465708515380635,
'variance': 0.01146527544655911}
>>> old_var
0.01146527544655911
>>> gadds = ga_de_dist_stats
>>> var = gadds['rel_std_dev']**2
>>> var
0.029060153997937255
>>> alpha5 = mu*((mu*(1-mu))/var - 1)
>>> beta5 = (1-mu) * (mu*(1-mu)/var - 1)
>>> alpha5, beta5
(4.347484317281145, 2.927742841234558)
>>> from scipy import stats
>>> stats.beta.fit(ga_de_scores)
/usr/lib/python2.7/dist-packages/scipy/optimize/optimize.py:280: RuntimeWarning: invalid value encountered in subtract
and max(abs(fsim[0]-fsim[1:])) <= ftol):
(3.722220714664807, 6.8317652435943241, 0.35946730951350719, 0.76174067786268274)
>>> pprint(gadds)
{'count': 2421,
'kurtosis': 2.713361355962532,
'kurtosis_trimmed': 2.656262504696904,
'mean': 0.6281209671512117,
'mean_trimmed': 0.6285709475407809,
'median': 0.6231045722961426,
'median_abs_dev': 0.0786864161491394,
'rel_std_dev': 0.17047039038477402,
'skewness': 0.3340588259850356,
'skewness_trimmed': 0.3256936211359308,
'std_dev': 0.10707602647912888,
'std_dev_trimmed': 0.10465708515380635,
'variance': 0.01146527544655911}
>>> stats.lognormal.fit(ga_de_scores)
Traceback (most recent call last):
File "<console>", line 1, in <module>
AttributeError: 'module' object has no attribute 'lognormal'
>>> stats.lognorm.fit(ga_de_scores)
(1.0, 0.54643545284336015, 0.049544768882106953)
>>> stats.lognorm.fit(fa_de_scores)
(1.0, 0.69779114544364007, 0.042984383855347294)
>>> stats.weibull.fit(fa_de_scores)
Traceback (most recent call last):
File "<console>", line 1, in <module>
AttributeError: 'module' object has no attribute 'weibull'
>>> stats.rayleigh.fit(fa_de_scores)
(0.59094161471479212, 0.14179908564644281)
>>> stats.rayleigh.fit(ga_de_scores)
(0.42327827736918389, 0.16344081957041084)
>>> pprint(gadds)
{'count': 2421,
'kurtosis': 2.713361355962532,
'kurtosis_trimmed': 2.656262504696904,
'mean': 0.6281209671512117,
'mean_trimmed': 0.6285709475407809,
'median': 0.6231045722961426,
'median_abs_dev': 0.0786864161491394,
'rel_std_dev': 0.17047039038477402,
'skewness': 0.3340588259850356,
'skewness_trimmed': 0.3256936211359308,
'std_dev': 0.10707602647912888,
'std_dev_trimmed': 0.10465708515380635,
'variance': 0.01146527544655911}
>>> ga_de_scores
[0.7449862360954285, 0.5704416036605835, 0.51088947057724, 0.7936760783195496, 0.8030709624290466, 0.6073479652404785, 0.720836877822876, 0.7581908106803894, 0.8060908913612366, 0.864799976348877, 0.529483437538147, 0.6505356431007385, 0.7326479554176331, 0.49169740080833435, 0.5287458300590515, 0.6089293360710144, 0.6868720650672913, 0.5088049173355103, 0.6119913458824158, 0.7613304853439331, 0.5555850267410278, 0.4329432249069214, 0.7747267484664917, 0.779338002204895, 0.42675647139549255, 0.7782115340232849, 0.5150765180587769, 0.5146966576576233, 0.7201509475708008, 0.6596883535385132, 0.44713354110717773, 0.6778051257133484, 0.5357926487922668, 0.6690653562545776, 0.8279620409011841, 0.5968922972679138, 0.5871190428733826, 0.554702877998352, 0.8094127774238586, 0.6675294041633606, 0.5028626322746277, 0.6641635298728943, 0.5207423567771912, 0.5524502992630005, 0.8461431264877319, 0.5166105031967163, 0.6239079236984253, 0.5275160074234009, 0.8458815813064575, 0.7588294148445129, 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>>> pprint(gadds)
{'count': 2421,
'kurtosis': 2.713361355962532,
'kurtosis_trimmed': 2.656262504696904,
'mean': 0.6281209671512117,
'mean_trimmed': 0.6285709475407809,
'median': 0.6231045722961426,
'median_abs_dev': 0.0786864161491394,
'rel_std_dev': 0.17047039038477402,
'skewness': 0.3340588259850356,
'skewness_trimmed': 0.3256936211359308,
'std_dev': 0.10707602647912888,
'std_dev_trimmed': 0.10465708515380635,
'variance': 0.01146527544655911}
>>> stats.norm
<scipy.stats.distributions.norm_gen object at 0x209b690>
>>> stats.norm()
<scipy.stats.distributions.rv_frozen object at 0x321b4190>
>>> stats.norm.stats(moments='mskv')
(array(0.0), array(1.0), array(0.0), array(0.0))
>>> stats.norm.stats(moments='mksv')
(array(0.0), array(1.0), array(0.0), array(0.0))
>>>
Running script:
>>> get_accuracy_rating(ga_de_scores, 0.5, 0.1)
Traceback (most recent call last):
File "<console>", line 1, in <module>
File "<string>", line 88, in get_accuracy_rating
TypeError: unsupported operand type(s) for -: 'float' and 'tuple'
>>>
Running script:
>>> get_accuracy_rating(ga_de_scores, 0.5, 0.1)
0.0
>>> get_accuracy_rating(ga_de_scores, 0.6, 0.1)
0.0
>>> get_accuracy_rating(ga_de_scores, 0.6, 0.1)
Running script:
>>> get_accuracy_rating(ga_de_scores, 0.6, 0.1)
0.9333119272784823
>>> get_accuracy_rating(ga_de_scores, 0.5, 0.1)
0.8286250994199471
>>> get_accuracy_rating(fa_de_scores, 0.9, 0.1)
0.7320474475372512
>>> x = {}
>>> x[in_classifiers[0]] = 1
>>> in_classifiers[0].predict(in_data[0][0])
masked_array(data = [ 0.71908697],
mask = False,
fill_value = 1e+20)
>>> in_classifiers[0].predict(in_data[0][1])
masked_array(data = [ 0.62770941],
mask = False,
fill_value = 1e+20)
>>> in_classifiers[0].predict(in_data[0][2])
masked_array(data = [ 0.57243786],
mask = False,
fill_value = 1e+20)
>>> in_classifiers[0].predict(in_data[0][3])
masked_array(data = [ 0.72272953],
mask = False,
fill_value = 1e+20)
>>> int(in_classifiers[0].predict(in_data[0][3]))
0
>>> float((in_classifiers[0].predict(in_data[0][3]))
... )
0.7227295252754061
>>>
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