In [1]:

    

import seaborn as sns
iris = sns.load_dataset("iris")
iris.head()


    

    
Out[1]:







  

    

      

      
sepal_length
      
sepal_width
      
petal_length
      
petal_width
      
species
    
  
  

    

      
0
      
5.1
      
3.5
      
1.4
      
0.2
      
setosa
    
    

      
1
      
4.9
      
3.0
      
1.4
      
0.2
      
setosa
    
    

      
2
      
4.7
      
3.2
      
1.3
      
0.2
      
setosa
    
    

      
3
      
4.6
      
3.1
      
1.5
      
0.2
      
setosa
    
    

      
4
      
5.0
      
3.6
      
1.4
      
0.2
      
setosa
    
  

In [2]:

    

%matplotlib inline
import seaborn as sns; sns.set()
sns.pairplot(iris, hue="species", size = 1.5);


    

In [3]:

    

X_iris = iris.drop("species", axis=1)
X_iris.shape


    

    
Out[3]:





(150, 4)

In [4]:

    

y_iris = iris['species']
y_iris.shape


    

    
Out[4]:





(150,)

In [5]:

    

import matplotlib.pyplot as plt
import numpy as np

rng = np.random.RandomState(42)
x = 10*rng.rand(50)
y = 2*x-1+rng.randn(50)
plt.scatter(x,y)


    

    
Out[5]:





<matplotlib.collections.PathCollection at 0x7f6dbc5320d0>






    

In [7]:

    

from sklearn.linear_model import LinearRegression
model = LinearRegression(fit_intercept=True)
model


    

    
Out[7]:





LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

In [15]:

    

X = x[:, np.newaxis]
X.shape


    

    
Out[15]:





(50, 1)

In [16]:

    

model.fit(X,y)


    

    
Out[16]:





LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

In [17]:

    

model.coef_


    

    
Out[17]:





array([ 1.9776566])

In [19]:

    

model.intercept_


    

    
Out[19]:





-0.90331072553111458

In [25]:

    

xfit = np.linspace(-1,11)
Xfit = xfit[:, np.newaxis]
yfit = model.predict(Xfit)


    

In [26]:

    

plt.scatter(x,y)
plt.plot(xfit, yfit)


    

    
Out[26]:





[<matplotlib.lines.Line2D at 0x7f6db3646610>]






    

In [27]:

    

from sklearn.cross_validation import train_test_split
Xtrain, Xtest, ytrain, ytest = train_test_split(X_iris, y_iris,
                                                random_state=1)


    

    




/home/morio/workspace/grusData/venv/local/lib/python2.7/site-packages/sklearn/cross_validation.py:41: 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 [30]:

    

from sklearn.naive_bayes import GaussianNB # 1. choose model class
model = GaussianNB()                       # 2. instantiate model
model.fit(Xtrain, ytrain)                  # 3. fit model to data
y_model = model.predict(Xtest)             # 4. predict on new data

from sklearn.metrics import accuracy_score
accuracy_score(ytest, y_model)


    

    
Out[30]:





0.97368421052631582

In [32]:

    

from sklearn.decomposition import PCA  # 1. Choose the model class
model = PCA(n_components=2)            # 2. Instantiate the model with hyperparameters
model.fit(X_iris)                      # 3. Fit to data. Notice y is not specified!
X_2D = model.transform(X_iris)         # 4. Transform the data to two dimensions

X_2D


    

    
Out[32]:





array([[-2.68412563,  0.31939725],
       [-2.71414169, -0.17700123],
       [-2.88899057, -0.14494943],
       [-2.74534286, -0.31829898],
       [-2.72871654,  0.32675451],
       [-2.28085963,  0.74133045],
       [-2.82053775, -0.08946138],
       [-2.62614497,  0.16338496],
       [-2.88638273, -0.57831175],
       [-2.6727558 , -0.11377425],
       [-2.50694709,  0.6450689 ],
       [-2.61275523,  0.01472994],
       [-2.78610927, -0.235112  ],
       [-3.22380374, -0.51139459],
       [-2.64475039,  1.17876464],
       [-2.38603903,  1.33806233],
       [-2.62352788,  0.81067951],
       [-2.64829671,  0.31184914],
       [-2.19982032,  0.87283904],
       [-2.5879864 ,  0.51356031],
       [-2.31025622,  0.39134594],
       [-2.54370523,  0.43299606],
       [-3.21593942,  0.13346807],
       [-2.30273318,  0.09870885],
       [-2.35575405, -0.03728186],
       [-2.50666891, -0.14601688],
       [-2.46882007,  0.13095149],
       [-2.56231991,  0.36771886],
       [-2.63953472,  0.31203998],
       [-2.63198939, -0.19696122],
       [-2.58739848, -0.20431849],
       [-2.4099325 ,  0.41092426],
       [-2.64886233,  0.81336382],
       [-2.59873675,  1.09314576],
       [-2.63692688, -0.12132235],
       [-2.86624165,  0.06936447],
       [-2.62523805,  0.59937002],
       [-2.80068412,  0.26864374],
       [-2.98050204, -0.48795834],
       [-2.59000631,  0.22904384],
       [-2.77010243,  0.26352753],
       [-2.84936871, -0.94096057],
       [-2.99740655, -0.34192606],
       [-2.40561449,  0.18887143],
       [-2.20948924,  0.43666314],
       [-2.71445143, -0.2502082 ],
       [-2.53814826,  0.50377114],
       [-2.83946217, -0.22794557],
       [-2.54308575,  0.57941002],
       [-2.70335978,  0.10770608],
       [ 1.28482569,  0.68516047],
       [ 0.93248853,  0.31833364],
       [ 1.46430232,  0.50426282],
       [ 0.18331772, -0.82795901],
       [ 1.08810326,  0.07459068],
       [ 0.64166908, -0.41824687],
       [ 1.09506066,  0.28346827],
       [-0.74912267, -1.00489096],
       [ 1.04413183,  0.2283619 ],
       [-0.0087454 , -0.72308191],
       [-0.50784088, -1.26597119],
       [ 0.51169856, -0.10398124],
       [ 0.26497651, -0.55003646],
       [ 0.98493451, -0.12481785],
       [-0.17392537, -0.25485421],
       [ 0.92786078,  0.46717949],
       [ 0.66028376, -0.35296967],
       [ 0.23610499, -0.33361077],
       [ 0.94473373, -0.54314555],
       [ 0.04522698, -0.58383438],
       [ 1.11628318, -0.08461685],
       [ 0.35788842, -0.06892503],
       [ 1.29818388, -0.32778731],
       [ 0.92172892, -0.18273779],
       [ 0.71485333,  0.14905594],
       [ 0.90017437,  0.32850447],
       [ 1.33202444,  0.24444088],
       [ 1.55780216,  0.26749545],
       [ 0.81329065, -0.1633503 ],
       [-0.30558378, -0.36826219],
       [-0.06812649, -0.70517213],
       [-0.18962247, -0.68028676],
       [ 0.13642871, -0.31403244],
       [ 1.38002644, -0.42095429],
       [ 0.58800644, -0.48428742],
       [ 0.80685831,  0.19418231],
       [ 1.22069088,  0.40761959],
       [ 0.81509524, -0.37203706],
       [ 0.24595768, -0.2685244 ],
       [ 0.16641322, -0.68192672],
       [ 0.46480029, -0.67071154],
       [ 0.8908152 , -0.03446444],
       [ 0.23054802, -0.40438585],
       [-0.70453176, -1.01224823],
       [ 0.35698149, -0.50491009],
       [ 0.33193448, -0.21265468],
       [ 0.37621565, -0.29321893],
       [ 0.64257601,  0.01773819],
       [-0.90646986, -0.75609337],
       [ 0.29900084, -0.34889781],
       [ 2.53119273, -0.00984911],
       [ 1.41523588, -0.57491635],
       [ 2.61667602,  0.34390315],
       [ 1.97153105, -0.1797279 ],
       [ 2.35000592, -0.04026095],
       [ 3.39703874,  0.55083667],
       [ 0.52123224, -1.19275873],
       [ 2.93258707,  0.3555    ],
       [ 2.32122882, -0.2438315 ],
       [ 2.91675097,  0.78279195],
       [ 1.66177415,  0.24222841],
       [ 1.80340195, -0.21563762],
       [ 2.1655918 ,  0.21627559],
       [ 1.34616358, -0.77681835],
       [ 1.58592822, -0.53964071],
       [ 1.90445637,  0.11925069],
       [ 1.94968906,  0.04194326],
       [ 3.48705536,  1.17573933],
       [ 3.79564542,  0.25732297],
       [ 1.30079171, -0.76114964],
       [ 2.42781791,  0.37819601],
       [ 1.19900111, -0.60609153],
       [ 3.49992004,  0.4606741 ],
       [ 1.38876613, -0.20439933],
       [ 2.2754305 ,  0.33499061],
       [ 2.61409047,  0.56090136],
       [ 1.25850816, -0.17970479],
       [ 1.29113206, -0.11666865],
       [ 2.12360872, -0.20972948],
       [ 2.38800302,  0.4646398 ],
       [ 2.84167278,  0.37526917],
       [ 3.23067366,  1.37416509],
       [ 2.15943764, -0.21727758],
       [ 1.44416124, -0.14341341],
       [ 1.78129481, -0.49990168],
       [ 3.07649993,  0.68808568],
       [ 2.14424331,  0.1400642 ],
       [ 1.90509815,  0.04930053],
       [ 1.16932634, -0.16499026],
       [ 2.10761114,  0.37228787],
       [ 2.31415471,  0.18365128],
       [ 1.9222678 ,  0.40920347],
       [ 1.41523588, -0.57491635],
       [ 2.56301338,  0.2778626 ],
       [ 2.41874618,  0.3047982 ],
       [ 1.94410979,  0.1875323 ],
       [ 1.52716661, -0.37531698],
       [ 1.76434572,  0.07885885],
       [ 1.90094161,  0.11662796],
       [ 1.39018886, -0.28266094]])

In [33]:

    

iris['PCA1'] = X_2D[:, 0]
iris['PCA2'] = X_2D[:, 1]
sns.lmplot("PCA1", "PCA2", hue='species', data=iris, fit_reg=False);


    

In [34]:

    

from sklearn.mixture import GMM      # 1. Choose the model class
model = GMM(n_components=3,
            covariance_type='full')  # 2. Instantiate the model with hyperparameters
model.fit(X_iris)                    # 3. Fit to data. Notice y is not specified!
y_gmm = model.predict(X_iris)        # 4. Determine cluster labels


    

    




/home/morio/workspace/grusData/venv/local/lib/python2.7/site-packages/sklearn/utils/deprecation.py:58: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.
  warnings.warn(msg, category=DeprecationWarning)
/home/morio/workspace/grusData/venv/local/lib/python2.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function distribute_covar_matrix_to_match_covariance_type is deprecated; The function distribute_covar_matrix_to_match_covariance_typeis deprecated in 0.18 and will be removed in 0.20.
  warnings.warn(msg, category=DeprecationWarning)
/home/morio/workspace/grusData/venv/local/lib/python2.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.
  warnings.warn(msg, category=DeprecationWarning)
/home/morio/workspace/grusData/venv/local/lib/python2.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.
  warnings.warn(msg, category=DeprecationWarning)
/home/morio/workspace/grusData/venv/local/lib/python2.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.
  warnings.warn(msg, category=DeprecationWarning)
/home/morio/workspace/grusData/venv/local/lib/python2.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.
  warnings.warn(msg, category=DeprecationWarning)
/home/morio/workspace/grusData/venv/local/lib/python2.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.
  warnings.warn(msg, category=DeprecationWarning)
/home/morio/workspace/grusData/venv/local/lib/python2.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.
  warnings.warn(msg, category=DeprecationWarning)
/home/morio/workspace/grusData/venv/local/lib/python2.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.
  warnings.warn(msg, category=DeprecationWarning)
/home/morio/workspace/grusData/venv/local/lib/python2.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.
  warnings.warn(msg, category=DeprecationWarning)
/home/morio/workspace/grusData/venv/local/lib/python2.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.
  warnings.warn(msg, category=DeprecationWarning)
/home/morio/workspace/grusData/venv/local/lib/python2.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.
  warnings.warn(msg, category=DeprecationWarning)
/home/morio/workspace/grusData/venv/local/lib/python2.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.
  warnings.warn(msg, category=DeprecationWarning)
/home/morio/workspace/grusData/venv/local/lib/python2.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.
  warnings.warn(msg, category=DeprecationWarning)
/home/morio/workspace/grusData/venv/local/lib/python2.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.
  warnings.warn(msg, category=DeprecationWarning)
/home/morio/workspace/grusData/venv/local/lib/python2.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.
  warnings.warn(msg, category=DeprecationWarning)
/home/morio/workspace/grusData/venv/local/lib/python2.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.
  warnings.warn(msg, category=DeprecationWarning)
/home/morio/workspace/grusData/venv/local/lib/python2.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.
  warnings.warn(msg, category=DeprecationWarning)
/home/morio/workspace/grusData/venv/local/lib/python2.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.
  warnings.warn(msg, category=DeprecationWarning)
/home/morio/workspace/grusData/venv/local/lib/python2.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.
  warnings.warn(msg, category=DeprecationWarning)
/home/morio/workspace/grusData/venv/local/lib/python2.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.
  warnings.warn(msg, category=DeprecationWarning)
/home/morio/workspace/grusData/venv/local/lib/python2.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.
  warnings.warn(msg, category=DeprecationWarning)

In [35]:

    

iris['cluster'] = y_gmm
sns.lmplot("PCA1", "PCA2", data=iris, hue='species',
           col='cluster', fit_reg=False);


    

In [36]:

    

from sklearn.datasets import load_digits
digits = load_digits()
digits.images.shape


    

    
Out[36]:





(1797, 8, 8)

In [37]:

    

import matplotlib.pyplot as plt

fig, axes = plt.subplots(10, 10, figsize=(8, 8),
                         subplot_kw={'xticks':[], 'yticks':[]},
                         gridspec_kw=dict(hspace=0.1, wspace=0.1))

for i, ax in enumerate(axes.flat):
    ax.imshow(digits.images[i], cmap='binary', interpolation='nearest')
    ax.text(0.05, 0.05, str(digits.target[i]),
            transform=ax.transAxes, color='green')


    

In [38]:

    

X = digits.data
X.shape


    

    
Out[38]:





(1797, 64)

In [39]:

    

y = digits.target
y.shape


    

    
Out[39]:





(1797,)

In [40]:

    

y = digits.target
y.shape


    

    
Out[40]:





(1797,)

In [41]:

    

from sklearn.manifold import Isomap
iso = Isomap(n_components=2)
iso.fit(digits.data)
data_projected = iso.transform(digits.data)
data_projected.shape


    

    
Out[41]:





(1797, 2)

In [42]:

    

plt.scatter(data_projected[:, 0], data_projected[:, 1], c=digits.target,
            edgecolor='none', alpha=0.5,
            cmap=plt.cm.get_cmap('spectral', 10))
plt.colorbar(label='digit label', ticks=range(10))
plt.clim(-0.5, 9.5);


    

    




/home/morio/workspace/grusData/venv/local/lib/python2.7/site-packages/matplotlib/cbook/deprecation.py:106: MatplotlibDeprecationWarning: The spectral and spectral_r colormap was deprecated in version 2.0. Use nipy_spectral and nipy_spectral_r instead.
  warnings.warn(message, mplDeprecation, stacklevel=1)






    

In [43]:

    

Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, random_state=0)

from sklearn.naive_bayes import GaussianNB
model = GaussianNB()
model.fit(Xtrain, ytrain)
y_model = model.predict(Xtest)


    

In [45]:

    

from sklearn.naive_bayes import GaussianNB
model = GaussianNB()
model.fit(Xtrain, ytrain)
y_model = model.predict(Xtest)


    

In [46]:

    

from sklearn.metrics import accuracy_score
accuracy_score(ytest, y_model)


    

    
Out[46]:





0.83333333333333337

In [48]:

    

from sklearn.metrics import confusion_matrix

mat = confusion_matrix(ytest, y_model)

sns.heatmap(mat, square=True, annot=True, cbar=True)
plt.xlabel('predicted value')
plt.ylabel('true value');


    

In [53]:

    

fig, axes = plt.subplots(10, 10, figsize=(8, 8),
                         subplot_kw={'xticks':[], 'yticks':[]},
                         gridspec_kw=dict(hspace=0.5, wspace=0.5))

test_images = Xtest.reshape(-1, 8, 8)

for i, ax in enumerate(axes.flat):
    ax.imshow(test_images[i], cmap='binary', interpolation='nearest')
    ax.text(0.05, 0.05, str(y_model[i]),
            transform=ax.transAxes,
            color='green' if (ytest[i] == y_model[i]) else 'red')r


    

In [ ]: