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Commit99d2a64

Browse files
author
Jesal Patel
committed
added plotting functions
1 parent8439c90 commit99d2a64

27 files changed

+2059
-0
lines changed

‎plotting_functions/make_blobs.py‎

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importnumbers
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importnumpyasnp
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fromsklearn.utilsimportcheck_array,check_random_state
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fromsklearn.utilsimportshuffleasshuffle_
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fromsklearn.utils.deprecationimportdeprecated
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@deprecated("Please import make_blobs directly from scikit-learn")
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defmake_blobs(n_samples=100,n_features=2,centers=2,cluster_std=1.0,
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center_box=(-10.0,10.0),shuffle=True,random_state=None):
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"""Generate isotropic Gaussian blobs for clustering.
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Read more in the :ref:`User Guide <sample_generators>`.
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Parameters
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----------
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n_samples : int, or tuple, optional (default=100)
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The total number of points equally divided among clusters.
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n_features : int, optional (default=2)
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The number of features for each sample.
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centers : int or array of shape [n_centers, n_features], optional
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(default=3)
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The number of centers to generate, or the fixed center locations.
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cluster_std: float or sequence of floats, optional (default=1.0)
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The standard deviation of the clusters.
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center_box: pair of floats (min, max), optional (default=(-10.0, 10.0))
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The bounding box for each cluster center when centers are
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generated at random.
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shuffle : boolean, optional (default=True)
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Shuffle the samples.
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random_state : int, RandomState instance or None, optional (default=None)
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`.
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Returns
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-------
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X : array of shape [n_samples, n_features]
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The generated samples.
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y : array of shape [n_samples]
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The integer labels for cluster membership of each sample.
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Examples
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--------
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>>> from sklearn.datasets.samples_generator import make_blobs
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>>> X, y = make_blobs(n_samples=10, centers=3, n_features=2,
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... random_state=0)
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>>> print(X.shape)
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(10, 2)
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>>> y
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array([0, 0, 1, 0, 2, 2, 2, 1, 1, 0])
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See also
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--------
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make_classification: a more intricate variant
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"""
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generator=check_random_state(random_state)
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ifisinstance(centers,numbers.Integral):
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centers=generator.uniform(center_box[0],center_box[1],
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size=(centers,n_features))
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else:
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centers=check_array(centers)
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n_features=centers.shape[1]
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ifisinstance(cluster_std,numbers.Real):
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cluster_std=np.ones(len(centers))*cluster_std
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X= []
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y= []
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n_centers=centers.shape[0]
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ifisinstance(n_samples,numbers.Integral):
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n_samples_per_center= [int(n_samples//n_centers)]*n_centers
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foriinrange(n_samples%n_centers):
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n_samples_per_center[i]+=1
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else:
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n_samples_per_center=n_samples
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fori, (n,std)inenumerate(zip(n_samples_per_center,cluster_std)):
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X.append(centers[i]+generator.normal(scale=std,
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size=(n,n_features)))
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y+= [i]*n
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X=np.concatenate(X)
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y=np.array(y)
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ifshuffle:
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X,y=shuffle_(X,y,random_state=generator)
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returnX,y
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importnumpyasnp
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importmatplotlib.pyplotasplt
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from .plot_helpersimportcm2,cm3,discrete_scatter
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defplot_2d_classification(classifier,X,fill=False,ax=None,eps=None,
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alpha=1,cm=cm3):
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# multiclass
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ifepsisNone:
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eps=X.std()/2.
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ifaxisNone:
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ax=plt.gca()
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x_min,x_max=X[:,0].min()-eps,X[:,0].max()+eps
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y_min,y_max=X[:,1].min()-eps,X[:,1].max()+eps
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xx=np.linspace(x_min,x_max,1000)
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yy=np.linspace(y_min,y_max,1000)
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X1,X2=np.meshgrid(xx,yy)
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X_grid=np.c_[X1.ravel(),X2.ravel()]
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decision_values=classifier.predict(X_grid)
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ax.imshow(decision_values.reshape(X1.shape),extent=(x_min,x_max,
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y_min,y_max),
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aspect='auto',origin='lower',alpha=alpha,cmap=cm)
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ax.set_xlim(x_min,x_max)
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ax.set_ylim(y_min,y_max)
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ax.set_xticks(())
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ax.set_yticks(())
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defplot_2d_scores(classifier,X,ax=None,eps=None,alpha=1,cm="viridis",
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function=None):
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# binary with fill
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ifepsisNone:
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eps=X.std()/2.
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ifaxisNone:
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ax=plt.gca()
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x_min,x_max=X[:,0].min()-eps,X[:,0].max()+eps
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y_min,y_max=X[:,1].min()-eps,X[:,1].max()+eps
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xx=np.linspace(x_min,x_max,100)
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yy=np.linspace(y_min,y_max,100)
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X1,X2=np.meshgrid(xx,yy)
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X_grid=np.c_[X1.ravel(),X2.ravel()]
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iffunctionisNone:
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function=getattr(classifier,"decision_function",
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getattr(classifier,"predict_proba"))
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else:
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function=getattr(classifier,function)
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decision_values=function(X_grid)
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ifdecision_values.ndim>1anddecision_values.shape[1]>1:
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# predict_proba
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decision_values=decision_values[:,1]
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grr=ax.imshow(decision_values.reshape(X1.shape),
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extent=(x_min,x_max,y_min,y_max),aspect='auto',
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origin='lower',alpha=alpha,cmap=cm)
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ax.set_xlim(x_min,x_max)
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ax.set_ylim(y_min,y_max)
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ax.set_xticks(())
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ax.set_yticks(())
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returngrr
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defplot_2d_separator(classifier,X,fill=False,ax=None,eps=None,alpha=1,
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cm=cm2,linewidth=None,threshold=None,
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linestyle="solid"):
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# binary?
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ifepsisNone:
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eps=X.std()/2.
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ifaxisNone:
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ax=plt.gca()
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x_min,x_max=X[:,0].min()-eps,X[:,0].max()+eps
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y_min,y_max=X[:,1].min()-eps,X[:,1].max()+eps
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xx=np.linspace(x_min,x_max,1000)
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yy=np.linspace(y_min,y_max,1000)
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X1,X2=np.meshgrid(xx,yy)
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X_grid=np.c_[X1.ravel(),X2.ravel()]
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try:
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decision_values=classifier.decision_function(X_grid)
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levels= [0]ifthresholdisNoneelse [threshold]
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fill_levels= [decision_values.min()]+levels+ [
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decision_values.max()]
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exceptAttributeError:
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# no decision_function
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decision_values=classifier.predict_proba(X_grid)[:,1]
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levels= [.5]ifthresholdisNoneelse [threshold]
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fill_levels= [0]+levels+ [1]
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iffill:
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ax.contourf(X1,X2,decision_values.reshape(X1.shape),
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levels=fill_levels,alpha=alpha,cmap=cm)
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else:
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ax.contour(X1,X2,decision_values.reshape(X1.shape),levels=levels,
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colors="black",alpha=alpha,linewidths=linewidth,
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linestyles=linestyle,zorder=5)
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ax.set_xlim(x_min,x_max)
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ax.set_ylim(y_min,y_max)
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ax.set_xticks(())
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ax.set_yticks(())
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if__name__=='__main__':
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fromsklearn.datasetsimportmake_blobs
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fromsklearn.linear_modelimportLogisticRegression
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X,y=make_blobs(centers=2,random_state=42)
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clf=LogisticRegression().fit(X,y)
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plot_2d_separator(clf,X,fill=True)
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discrete_scatter(X[:,0],X[:,1],y)
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plt.show()
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importmatplotlib.pyplotasplt
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importnumpyasnp
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fromsklearn.datasetsimportmake_blobs
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fromsklearn.clusterimportAgglomerativeClustering
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fromsklearn.neighborsimportKernelDensity
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defplot_agglomerative_algorithm():
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# generate synthetic two-dimensional data
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X,y=make_blobs(random_state=0,n_samples=12)
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agg=AgglomerativeClustering(n_clusters=X.shape[0],compute_full_tree=True).fit(X)
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fig,axes=plt.subplots(X.shape[0]//5,5,subplot_kw={'xticks': (),
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'yticks': ()},
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figsize=(20,8))
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eps=X.std()/2
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x_min,x_max=X[:,0].min()-eps,X[:,0].max()+eps
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y_min,y_max=X[:,1].min()-eps,X[:,1].max()+eps
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xx,yy=np.meshgrid(np.linspace(x_min,x_max,100),np.linspace(y_min,y_max,100))
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gridpoints=np.c_[xx.ravel().reshape(-1,1),yy.ravel().reshape(-1,1)]
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fori,axinenumerate(axes.ravel()):
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ax.set_xlim(x_min,x_max)
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ax.set_ylim(y_min,y_max)
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agg.n_clusters=X.shape[0]-i
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agg.fit(X)
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ax.set_title("Step %d"%i)
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ax.scatter(X[:,0],X[:,1],s=60,c='grey')
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bins=np.bincount(agg.labels_)
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forclusterinrange(agg.n_clusters):
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ifbins[cluster]>1:
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points=X[agg.labels_==cluster]
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other_points=X[agg.labels_!=cluster]
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kde=KernelDensity(bandwidth=.5).fit(points)
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scores=kde.score_samples(gridpoints)
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score_inside=np.min(kde.score_samples(points))
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score_outside=np.max(kde.score_samples(other_points))
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levels=.8*score_inside+.2*score_outside
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ax.contour(xx,yy,scores.reshape(100,100),levels=[levels],
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colors='k',linestyles='solid',linewidths=2)
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axes[0,0].set_title("Initialization")
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defplot_agglomerative():
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X,y=make_blobs(random_state=0,n_samples=12)
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agg=AgglomerativeClustering(n_clusters=3)
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eps=X.std()/2.
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x_min,x_max=X[:,0].min()-eps,X[:,0].max()+eps
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y_min,y_max=X[:,1].min()-eps,X[:,1].max()+eps
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xx,yy=np.meshgrid(np.linspace(x_min,x_max,100),np.linspace(y_min,y_max,100))
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gridpoints=np.c_[xx.ravel().reshape(-1,1),yy.ravel().reshape(-1,1)]
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ax=plt.gca()
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fori,xinenumerate(X):
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ax.text(x[0]+.1,x[1],"%d"%i,horizontalalignment='left',verticalalignment='center')
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ax.scatter(X[:,0],X[:,1],s=60,c='grey')
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ax.set_xticks(())
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ax.set_yticks(())
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foriinrange(11):
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agg.n_clusters=X.shape[0]-i
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agg.fit(X)
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bins=np.bincount(agg.labels_)
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forclusterinrange(agg.n_clusters):
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ifbins[cluster]>1:
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points=X[agg.labels_==cluster]
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other_points=X[agg.labels_!=cluster]
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kde=KernelDensity(bandwidth=.5).fit(points)
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scores=kde.score_samples(gridpoints)
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score_inside=np.min(kde.score_samples(points))
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score_outside=np.max(kde.score_samples(other_points))
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levels=.8*score_inside+.2*score_outside
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ax.contour(xx,yy,scores.reshape(100,100),levels=[levels],
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colors='k',linestyles='solid',linewidths=1)
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ax.set_xlim(x_min,x_max)
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ax.set_ylim(y_min,y_max)
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fromimageioimportimread
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importmatplotlib.pyplotasplt
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defplot_animal_tree(ax=None):
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importgraphviz
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ifaxisNone:
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ax=plt.gca()
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mygraph=graphviz.Digraph(node_attr={'shape':'box'},
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edge_attr={'labeldistance':"10.5"},
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format="png")
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mygraph.node("0","Has feathers?")
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mygraph.node("1","Can fly?")
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mygraph.node("2","Has fins?")
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mygraph.node("3","Hawk")
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mygraph.node("4","Penguin")
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mygraph.node("5","Dolphin")
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mygraph.node("6","Bear")
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mygraph.edge("0","1",label="True")
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mygraph.edge("0","2",label="False")
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mygraph.edge("1","3",label="True")
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mygraph.edge("1","4",label="False")
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mygraph.edge("2","5",label="True")
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mygraph.edge("2","6",label="False")
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mygraph.render("tmp")
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ax.imshow(imread("tmp.png"))
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ax.set_axis_off()

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