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https://github.com/donnemartin/data-science-ipython-notebooks.git
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Remove *.py~
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from .sgd_separator import plot_sgd_separator
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from .linear_regression import plot_linear_regression
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from .ML_flow_chart import plot_supervised_chart, plot_unsupervised_chart
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from .helpers import plot_iris_knn
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"""
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Small helpers for code that is not shown in the notebooks
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"""
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from sklearn import neighbors, datasets, linear_model
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import pylab as pl
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import numpy as np
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from matplotlib.colors import ListedColormap
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# Create color maps for 3-class classification problem, as with iris
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cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
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cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])
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def plot_iris_knn():
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iris = datasets.load_iris()
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X = iris.data[:, :2] # we only take the first two features. We could
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# avoid this ugly slicing by using a two-dim dataset
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y = iris.target
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knn = neighbors.KNeighborsClassifier(n_neighbors=3)
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knn.fit(X, y)
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x_min, x_max = X[:, 0].min() - .1, X[:, 0].max() + .1
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y_min, y_max = X[:, 1].min() - .1, X[:, 1].max() + .1
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xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),
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np.linspace(y_min, y_max, 100))
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Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])
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# Put the result into a color plot
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Z = Z.reshape(xx.shape)
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pl.figure()
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pl.pcolormesh(xx, yy, Z, cmap=cmap_light)
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# Plot also the training points
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pl.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
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pl.xlabel('sepal length (cm)')
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pl.ylabel('sepal width (cm)')
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pl.axis('tight')
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def plot_polynomial_regression():
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rng = np.random.RandomState(0)
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x = 2*rng.rand(100) - 1
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f = lambda t: 1.2 * t**2 + .1 * t**3 - .4 * t **5 - .5 * t ** 9
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y = f(x) + .4 * rng.normal(size=100)
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x_test = np.linspace(-1, 1, 100)
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pl.figure()
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pl.scatter(x, y, s=4)
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X = np.array([x**i for i in range(5)]).T
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X_test = np.array([x_test**i for i in range(5)]).T
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regr = linear_model.LinearRegression()
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regr.fit(X, y)
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pl.plot(x_test, regr.predict(X_test), label='4th order')
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X = np.array([x**i for i in range(10)]).T
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X_test = np.array([x_test**i for i in range(10)]).T
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regr = linear_model.LinearRegression()
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regr.fit(X, y)
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pl.plot(x_test, regr.predict(X_test), label='9th order')
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pl.legend(loc='best')
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pl.axis('tight')
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pl.title('Fitting a 4th and a 9th order polynomial')
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pl.figure()
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pl.scatter(x, y, s=4)
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pl.plot(x_test, f(x_test), label="truth")
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pl.axis('tight')
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pl.title('Ground truth (9th order polynomial)')
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@ -1,331 +0,0 @@
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"""
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==========
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Libsvm GUI
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==========
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A simple graphical frontend for Libsvm mainly intended for didactic
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purposes. You can create data points by point and click and visualize
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the decision region induced by different kernels and parameter settings.
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To create positive examples click the left mouse button; to create
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negative examples click the right button.
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If all examples are from the same class, it uses a one-class SVM.
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"""
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from __future__ import division, print_function
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print(__doc__)
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# Author: Peter Prettenhoer <peter.prettenhofer@gmail.com>
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#
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# License: BSD 3 clause
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import matplotlib
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matplotlib.use('TkAgg')
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from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
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from matplotlib.backends.backend_tkagg import NavigationToolbar2TkAgg
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from matplotlib.figure import Figure
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from matplotlib.contour import ContourSet
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import Tkinter as Tk
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import sys
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import numpy as np
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from sklearn import svm
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from sklearn.datasets import dump_svmlight_file
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from sklearn.externals.six.moves import xrange
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y_min, y_max = -50, 50
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x_min, x_max = -50, 50
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class Model(object):
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"""The Model which hold the data. It implements the
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observable in the observer pattern and notifies the
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registered observers on change event.
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"""
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def __init__(self):
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self.observers = []
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self.surface = None
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self.data = []
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self.cls = None
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self.surface_type = 0
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def changed(self, event):
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"""Notify the observers. """
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for observer in self.observers:
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observer.update(event, self)
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def add_observer(self, observer):
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"""Register an observer. """
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self.observers.append(observer)
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def set_surface(self, surface):
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self.surface = surface
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def dump_svmlight_file(self, file):
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data = np.array(self.data)
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X = data[:, 0:2]
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y = data[:, 2]
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dump_svmlight_file(X, y, file)
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class Controller(object):
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def __init__(self, model):
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self.model = model
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self.kernel = Tk.IntVar()
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self.surface_type = Tk.IntVar()
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# Whether or not a model has been fitted
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self.fitted = False
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def fit(self):
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print("fit the model")
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train = np.array(self.model.data)
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X = train[:, 0:2]
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y = train[:, 2]
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C = float(self.complexity.get())
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gamma = float(self.gamma.get())
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coef0 = float(self.coef0.get())
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degree = int(self.degree.get())
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kernel_map = {0: "linear", 1: "rbf", 2: "poly"}
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if len(np.unique(y)) == 1:
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clf = svm.OneClassSVM(kernel=kernel_map[self.kernel.get()],
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gamma=gamma, coef0=coef0, degree=degree)
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clf.fit(X)
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else:
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clf = svm.SVC(kernel=kernel_map[self.kernel.get()], C=C,
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gamma=gamma, coef0=coef0, degree=degree)
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clf.fit(X, y)
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if hasattr(clf, 'score'):
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print("Accuracy:", clf.score(X, y) * 100)
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X1, X2, Z = self.decision_surface(clf)
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self.model.clf = clf
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self.model.set_surface((X1, X2, Z))
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self.model.surface_type = self.surface_type.get()
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self.fitted = True
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self.model.changed("surface")
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def decision_surface(self, cls):
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delta = 1
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x = np.arange(x_min, x_max + delta, delta)
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y = np.arange(y_min, y_max + delta, delta)
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X1, X2 = np.meshgrid(x, y)
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Z = cls.decision_function(np.c_[X1.ravel(), X2.ravel()])
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Z = Z.reshape(X1.shape)
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return X1, X2, Z
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def clear_data(self):
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self.model.data = []
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self.fitted = False
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self.model.changed("clear")
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def add_example(self, x, y, label):
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self.model.data.append((x, y, label))
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self.model.changed("example_added")
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# update decision surface if already fitted.
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self.refit()
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def refit(self):
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"""Refit the model if already fitted. """
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if self.fitted:
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self.fit()
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class View(object):
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"""Test docstring. """
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def __init__(self, root, controller):
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f = Figure()
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ax = f.add_subplot(111)
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ax.set_xticks([])
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ax.set_yticks([])
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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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canvas = FigureCanvasTkAgg(f, master=root)
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canvas.show()
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canvas.get_tk_widget().pack(side=Tk.TOP, fill=Tk.BOTH, expand=1)
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canvas._tkcanvas.pack(side=Tk.TOP, fill=Tk.BOTH, expand=1)
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canvas.mpl_connect('button_press_event', self.onclick)
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toolbar = NavigationToolbar2TkAgg(canvas, root)
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toolbar.update()
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self.controllbar = ControllBar(root, controller)
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self.f = f
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self.ax = ax
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self.canvas = canvas
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self.controller = controller
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self.contours = []
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self.c_labels = None
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self.plot_kernels()
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def plot_kernels(self):
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self.ax.text(-50, -60, "Linear: $u^T v$")
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self.ax.text(-20, -60, "RBF: $\exp (-\gamma \| u-v \|^2)$")
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self.ax.text(10, -60, "Poly: $(\gamma \, u^T v + r)^d$")
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def onclick(self, event):
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print(event.button)
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if event.xdata and event.ydata:
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if event.button == 1:
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self.controller.add_example(event.xdata, event.ydata, 1)
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elif event.button == 3:
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self.controller.add_example(event.xdata, event.ydata, -1)
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def update_example(self, model, idx):
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x, y, l = model.data[idx]
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if l == 1:
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color = 'w'
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elif l == -1:
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color = 'k'
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self.ax.plot([x], [y], "%so" % color, scalex=0.0, scaley=0.0)
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def update(self, event, model):
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if event == "examples_loaded":
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for i in xrange(len(model.data)):
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self.update_example(model, i)
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if event == "example_added":
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self.update_example(model, -1)
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if event == "clear":
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self.ax.clear()
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self.ax.set_xticks([])
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self.ax.set_yticks([])
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self.contours = []
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self.c_labels = None
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self.plot_kernels()
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if event == "surface":
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self.remove_surface()
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self.plot_support_vectors(model.clf.support_vectors_)
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self.plot_decision_surface(model.surface, model.surface_type)
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self.canvas.draw()
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def remove_surface(self):
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"""Remove old decision surface."""
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if len(self.contours) > 0:
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for contour in self.contours:
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if isinstance(contour, ContourSet):
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for lineset in contour.collections:
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lineset.remove()
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else:
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contour.remove()
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self.contours = []
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def plot_support_vectors(self, support_vectors):
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"""Plot the support vectors by placing circles over the
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corresponding data points and adds the circle collection
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to the contours list."""
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cs = self.ax.scatter(support_vectors[:, 0], support_vectors[:, 1],
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s=80, edgecolors="k", facecolors="none")
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self.contours.append(cs)
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def plot_decision_surface(self, surface, type):
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X1, X2, Z = surface
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if type == 0:
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levels = [-1.0, 0.0, 1.0]
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linestyles = ['dashed', 'solid', 'dashed']
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colors = 'k'
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self.contours.append(self.ax.contour(X1, X2, Z, levels,
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colors=colors,
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linestyles=linestyles))
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elif type == 1:
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self.contours.append(self.ax.contourf(X1, X2, Z, 10,
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cmap=matplotlib.cm.bone,
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origin='lower', alpha=0.85))
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self.contours.append(self.ax.contour(X1, X2, Z, [0.0], colors='k',
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linestyles=['solid']))
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else:
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raise ValueError("surface type unknown")
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class ControllBar(object):
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def __init__(self, root, controller):
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fm = Tk.Frame(root)
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kernel_group = Tk.Frame(fm)
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Tk.Radiobutton(kernel_group, text="Linear", variable=controller.kernel,
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value=0, command=controller.refit).pack(anchor=Tk.W)
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Tk.Radiobutton(kernel_group, text="RBF", variable=controller.kernel,
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value=1, command=controller.refit).pack(anchor=Tk.W)
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Tk.Radiobutton(kernel_group, text="Poly", variable=controller.kernel,
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value=2, command=controller.refit).pack(anchor=Tk.W)
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kernel_group.pack(side=Tk.LEFT)
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valbox = Tk.Frame(fm)
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controller.complexity = Tk.StringVar()
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controller.complexity.set("1.0")
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c = Tk.Frame(valbox)
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Tk.Label(c, text="C:", anchor="e", width=7).pack(side=Tk.LEFT)
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Tk.Entry(c, width=6, textvariable=controller.complexity).pack(
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side=Tk.LEFT)
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c.pack()
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controller.gamma = Tk.StringVar()
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controller.gamma.set("0.01")
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g = Tk.Frame(valbox)
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Tk.Label(g, text="gamma:", anchor="e", width=7).pack(side=Tk.LEFT)
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Tk.Entry(g, width=6, textvariable=controller.gamma).pack(side=Tk.LEFT)
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g.pack()
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controller.degree = Tk.StringVar()
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controller.degree.set("3")
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d = Tk.Frame(valbox)
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Tk.Label(d, text="degree:", anchor="e", width=7).pack(side=Tk.LEFT)
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Tk.Entry(d, width=6, textvariable=controller.degree).pack(side=Tk.LEFT)
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d.pack()
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controller.coef0 = Tk.StringVar()
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controller.coef0.set("0")
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r = Tk.Frame(valbox)
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Tk.Label(r, text="coef0:", anchor="e", width=7).pack(side=Tk.LEFT)
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Tk.Entry(r, width=6, textvariable=controller.coef0).pack(side=Tk.LEFT)
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r.pack()
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valbox.pack(side=Tk.LEFT)
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cmap_group = Tk.Frame(fm)
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Tk.Radiobutton(cmap_group, text="Hyperplanes",
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variable=controller.surface_type, value=0,
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command=controller.refit).pack(anchor=Tk.W)
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Tk.Radiobutton(cmap_group, text="Surface",
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variable=controller.surface_type, value=1,
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command=controller.refit).pack(anchor=Tk.W)
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cmap_group.pack(side=Tk.LEFT)
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train_button = Tk.Button(fm, text='Fit', width=5,
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command=controller.fit)
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train_button.pack()
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fm.pack(side=Tk.LEFT)
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Tk.Button(fm, text='Clear', width=5,
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command=controller.clear_data).pack(side=Tk.LEFT)
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def get_parser():
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from optparse import OptionParser
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op = OptionParser()
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op.add_option("--output",
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action="store", type="str", dest="output",
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help="Path where to dump data.")
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return op
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def main(argv):
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op = get_parser()
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opts, args = op.parse_args(argv[1:])
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root = Tk.Tk()
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model = Model()
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controller = Controller(model)
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root.wm_title("Scikit-learn Libsvm GUI")
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view = View(root, controller)
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model.add_observer(view)
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Tk.mainloop()
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if opts.output:
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model.dump_svmlight_file(opts.output)
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if __name__ == "__main__":
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main(sys.argv)
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