Sklearn

In [1]:

    

from sklearn import cross_validation, datasets, linear_model, metrics 
from matplotlib.colors import ListedColormap


    

    




/home/andrey/anaconda2/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  "This module will be removed in 0.20.", DeprecationWarning)

In [2]:

    

%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [3]:

    

clf_data, clf_target = datasets.make_classification(n_features = 2, n_informative = 2, n_classes = 2, 
                                                    n_redundant = 0, n_clusters_per_class = 1, 
                                                    random_state = 7)


    

In [4]:

    

reg_data, reg_target = datasets.make_regression(n_features = 2, n_informative = 1, n_targets = 1, 
                                                noise = 5., random_state = 7)


    

In [5]:

    

colors = ListedColormap(['red', 'blue'])
pylab.scatter(map(lambda x: x[0], clf_data), map(lambda x: x[1], clf_data), c = clf_target, cmap = colors)


    

    
Out[5]:





<matplotlib.collections.PathCollection at 0x7f12eaf33810>






    

In [6]:

    

pylab.scatter(map(lambda x:x[1], reg_data), reg_target, color = 'r')
pylab.scatter(map(lambda x:x[0], reg_data), reg_target, color = 'b')


    

    
Out[6]:





<matplotlib.collections.PathCollection at 0x7f12eadbc890>






    

In [7]:

    

clf_train_data, clf_test_data, clf_train_labels, clf_test_labels = cross_validation.train_test_split(clf_data, clf_target,
                                                                                     test_size = 0.3, random_state = 1)


    

In [8]:

    

reg_train_data, reg_test_data, reg_train_labels, reg_test_labels = cross_validation.train_test_split(reg_data, reg_target,
                                                                                     test_size = 0.3, random_state = 1)


    

In [9]:

    

classifier = linear_model.SGDClassifier(loss = 'log', random_state = 1)


    

In [10]:

    

classifier.fit(clf_train_data, clf_train_labels)


    

    
Out[10]:





SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,
       eta0=0.0, fit_intercept=True, l1_ratio=0.15,
       learning_rate='optimal', loss='log', n_iter=5, n_jobs=1,
       penalty='l2', power_t=0.5, random_state=1, shuffle=True, verbose=0,
       warm_start=False)

In [11]:

    

predictions = classifier.predict(clf_test_data)


    

In [12]:

    

probability_predictions = classifier.predict_proba(clf_test_data)


    

In [13]:

    

print clf_test_labels


    

    




[1 0 0 1 0 1 1 0 1 0 0 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 0]

In [14]:

    

print predictions


    

    




[1 0 0 1 0 1 1 0 1 0 0 1 1 1 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 0]

In [15]:

    

print probability_predictions


    

    




[[  0.00000000e+00   1.00000000e+00]
 [  9.99999993e-01   6.61556341e-09]
 [  9.99988143e-01   1.18567026e-05]
 [  0.00000000e+00   1.00000000e+00]
 [  1.00000000e+00   4.62314295e-15]
 [  3.03803294e-09   9.99999997e-01]
 [  0.00000000e+00   1.00000000e+00]
 [  1.00000000e+00   5.05766731e-13]
 [  0.00000000e+00   1.00000000e+00]
 [  9.99999999e-01   5.18878464e-10]
 [  9.99999241e-01   7.58783839e-07]
 [  2.70446065e-04   9.99729554e-01]
 [  0.00000000e+00   1.00000000e+00]
 [  4.89202012e-11   1.00000000e+00]
 [  1.00000000e+00   8.62325525e-11]
 [  9.97365075e-01   2.63492515e-03]
 [  0.00000000e+00   1.00000000e+00]
 [  1.00000000e+00   1.30597714e-15]
 [  1.00000000e+00   3.28425090e-15]
 [  1.83630888e-13   1.00000000e+00]
 [  9.99999999e-01   5.19307103e-10]
 [  9.99999639e-01   3.60586359e-07]
 [  8.85166610e-01   1.14833390e-01]
 [  1.00000000e+00   7.21139609e-14]
 [  9.99999415e-01   5.85346707e-07]
 [  9.86697401e-01   1.33025990e-02]
 [  4.76887024e-02   9.52311298e-01]
 [  1.22124533e-14   1.00000000e+00]
 [  1.29348512e-03   9.98706515e-01]
 [  1.00000000e+00   1.37248174e-13]]

In [16]:

    

sum([1. if pair[0] == pair[1] else 0. for pair in zip(clf_test_labels, predictions)])/len(clf_test_labels)


    

    
Out[16]:





0.96666666666666667

In [17]:

    

metrics.accuracy_score(clf_test_labels, predictions)


    

    
Out[17]:





0.96666666666666667

In [18]:

    

matrix = metrics.confusion_matrix(clf_test_labels, predictions)
print matrix


    

    




[[17  1]
 [ 0 12]]

In [19]:

    

sum([1 if pair[0] == pair[1] else 0 for pair in zip(clf_test_labels, predictions)])


    

    
Out[19]:





29

In [20]:

    

matrix.diagonal().sum()


    

    
Out[20]:





29

In [21]:

    

metrics.precision_score(clf_test_labels, predictions, pos_label = 0)


    

    
Out[21]:





1.0

In [22]:

    

metrics.precision_score(clf_test_labels, predictions)


    

    
Out[22]:





0.92307692307692313

In [23]:

    

metrics.recall_score(clf_test_labels, predictions, pos_label = 0)


    

    
Out[23]:





0.94444444444444442

In [24]:

    

metrics.recall_score(clf_test_labels, predictions)


    

    
Out[24]:





1.0

In [25]:

    

metrics.f1_score(clf_test_labels, predictions, pos_label = 0)


    

    
Out[25]:





0.97142857142857142

In [26]:

    

metrics.f1_score(clf_test_labels, predictions)


    

    
Out[26]:





0.96000000000000008

In [27]:

    

print metrics.classification_report(clf_test_labels, predictions)


    

    




             precision    recall  f1-score   support

          0       1.00      0.94      0.97        18
          1       0.92      1.00      0.96        12

avg / total       0.97      0.97      0.97        30

In [28]:

    

fpr, tpr, _ = metrics.roc_curve(clf_test_labels, probability_predictions[:,1])


    

In [29]:

    

pylab.plot(fpr, tpr, label = 'linear model')
pylab.plot([0, 1], [0, 1], '--', color = 'grey', label = 'random')
pylab.xlim([-0.05, 1.05])
pylab.ylim([-0.05, 1.05])
pylab.xlabel('False Positive Rate')
pylab.ylabel('True Positive Rate')
pylab.title('ROC curve')
pylab.legend(loc = "lower right")


    

    
Out[29]:





<matplotlib.legend.Legend at 0x7f12ead0dcd0>






    

In [30]:

    

metrics.roc_auc_score(clf_test_labels, predictions)


    

    
Out[30]:





0.97222222222222221

In [31]:

    

metrics.roc_auc_score(clf_test_labels, probability_predictions[:,1])


    

    
Out[31]:





0.9907407407407407

In [32]:

    

metrics.average_precision_score(clf_test_labels, predictions)


    

    
Out[32]:





0.96153846153846156

In [33]:

    

metrics.log_loss(clf_test_labels, probability_predictions[:,1])


    

    
Out[33]:





0.28012062447668079

In [34]:

    

regressor = linear_model.SGDRegressor(random_state = 1, n_iter = 20)


    

In [35]:

    

regressor.fit(reg_train_data, reg_train_labels)


    

    
Out[35]:





SGDRegressor(alpha=0.0001, average=False, epsilon=0.1, eta0=0.01,
       fit_intercept=True, l1_ratio=0.15, learning_rate='invscaling',
       loss='squared_loss', n_iter=20, penalty='l2', power_t=0.25,
       random_state=1, shuffle=True, verbose=0, warm_start=False)

In [36]:

    

reg_predictions = regressor.predict(reg_test_data)


    

In [37]:

    

print reg_test_labels


    

    




[   2.67799047    7.06525927  -56.43389936   10.08001896  -22.46817716
  -19.27471232   59.44372825  -21.60494574   32.54682713  -41.89798772
  -18.16390935   32.75688783   31.04095773    2.39589626   -5.04783924
  -70.20925097   86.69034305   18.50402992   32.31573461 -101.81138022
   15.14628858   29.49813932   97.282674     25.88034991  -41.63332253
  -92.11198201   86.7177122     2.13250832  -20.24967575  -27.32511755]

In [38]:

    

print reg_predictions


    

    




[ -1.46503565   5.75776789 -50.13234306   5.05646094 -24.09370893
  -8.34831546  61.77254998 -21.98350565  30.65112022 -39.25972497
 -17.19337022  30.94178225  26.98820076  -6.08321732  -3.46551    -78.9843398
  84.80190097  14.80638314  22.91302375 -89.63572717  14.5954632
  31.64431951  95.81031534  21.5037679  -43.1101736  -95.06972123
  86.70086546   0.47837761 -16.44594704 -22.72581879]

In [39]:

    

metrics.mean_absolute_error(reg_test_labels, reg_predictions)


    

    
Out[39]:





3.748761311885298

In [40]:

    

metrics.mean_squared_error(reg_test_labels, reg_predictions)


    

    
Out[40]:





24.114925597460914

In [41]:

    

sqrt(metrics.mean_squared_error(reg_test_labels, reg_predictions))


    

    
Out[41]:





4.9106950218335603

In [42]:

    

metrics.r2_score(reg_test_labels, reg_predictions)


    

    
Out[42]:





0.98931761505469495