Criminal/plot_precision.py

import sys
from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import average_precision_score
from sklearn.metrics import precision_recall_curve
import matplotlib.pyplot as plt
import numpy as np

def set_trace():
    """A Poor mans break point"""
    # without this in iPython debugger can generate strange characters.
    from IPython.core.debugger import Pdb
    Pdb().set_trace(sys._getframe().f_back)


def precision_recall(X,y):
    random_state = np.random.RandomState(0)
    n_samples, n_features = X.shape
    X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]

# Limit to the two first classes, and split into training and test
    X_train, X_test, y_train, y_test = train_test_split(X[y < 2], y[y < 2],
                                                    test_size=.5,
                                                    random_state=random_state)

# Create a simple classifier
    classifier = svm.LinearSVC(random_state=random_state)
    classifier.fit(X_train, y_train)
    y_score = classifier.decision_function(X_test)
    average_precision = average_precision_score(y_test, y_score)
    print('Average precision-recall score: {0:0.2f}'.format(average_precision))
    
    
'''def plot_precision_curve(y_test, y_score):
    precision, recall, _ = precision_recall_curve(y_test, y_score)
    plt.step(recall, precision, color='b', alpha=0.2, where='post')
    plt.fill_between(recall, precision, step='post', alpha=0.2, color='b')
    plt.xlabel('Recall')
    plt.ylabel('Precision')
    plt.ylim([0.0, 1.05])
    plt.xlim([0.0, 1.0])
    plt.title( '2-class Precision-Recall curve: AP={0:0.2f}'.format(average_precision))
    set_trace()'''