Add machine learning scripts.

from sklearn import tree

from sklearn.externals import joblib

import graphviz # doctest: +SKIP

import json

import numpy as np

#load data

train_file = open('../train0-9.json', 'r')

dev_file = open('../dev.json')

train = json.load(train_file)

dev = json.load(dev_file)

X,Y = [],[]

for verse in train:

    for reading in verse[2]:

        X.append(reading[0])

        Y.append(reading[1])

#build model

clf = tree.DecisionTreeClassifier(max_depth=3, criterion='entropy', splitter='best')

#fit

clf.fit(X, Y)

correct = 0

total = 0

for verse in dev:

    vectors = [reading[0] for reading in verse[2]]

    probs = clf.predict_proba(vectors)

    sort_probs = sorted([(probs[i], verse[2][i][1]) for i in range(len(probs))], key=lambda x: x[0][0])

    gold = sort_probs[0][1]

    if gold == 1:

        correct += 1

    total += 1

print("Recall: {}/{} ({})".format(correct, total, correct/total))

#precision = tp/(tp+fp)

#recall = tp/(tp+fn)

#accuracy = (tp+tn)/(tp+tn+fp+fn)

#f1 = 2*((precision*recall)/(precision+recall))

#print('Precision: {}\tRecall:{}'.format(precision,recall))

#print('Accuracy: {}\tF1-Measure:{}\n'.format(accuracy, f1)) 

joblib.dump(clf, 'tree_classifier.joblib')

dot_data = tree.export_graphviz(clf, out_file=None) # doctest: +SKIP

graph = graphviz.Source(dot_data) # doctest: +SKIP

graph.render("latin_tree") # doctest: +SKIP

from sklearn.ensemble import RandomForestClassifier

from sklearn.externals import joblib

import graphviz # doctest: +SKIP

import json

import numpy as np

#load data

train_file = open('../train0-9.json', 'r')

dev_file = open('../dev.json')

train = json.load(train_file)

dev = json.load(dev_file)

X,Y = [],[]

for verse in train:

    for reading in verse[2]:

        X.append(reading[0])

        Y.append(reading[1])

#build model

clf = RandomForestClassifier()

#fit

clf.fit(X, Y)

correct = 0

total = 0

for verse in dev:

    vectors = [reading[0] for reading in verse[2]]

    probs = clf.predict_proba(vectors)

    sort_probs = sorted([(probs[i], verse[2][i][1]) for i in range(len(probs))], key=lambda x: x[0][0])

    gold = sort_probs[0][1]

    if gold == 1:

        correct += 1

    total += 1

print("Recall: {}/{} ({})".format(correct, total, correct/total))

#precision = tp/(tp+fp)

#recall = tp/(tp+fn)

#accuracy = (tp+tn)/(tp+tn+fp+fn)

#f1 = 2*((precision*recall)/(precision+recall))

#print('Precision: {}\tRecall:{}'.format(precision,recall))

#print('Accuracy: {}\tF1-Measure:{}\n'.format(accuracy, f1)) 

joblib.dump(clf, 'forest_classifier.joblib')

#dot_data = tree.export_graphviz(clf, out_file=None) # doctest: +SKIP

#graph = graphviz.Source(dot_data) # doctest: +SKIP

#graph.render("latin_tree") # doctest: +SKIP

from sklearn import svm

from sklearn.externals import joblib

from sklearn.ensemble import BaggingClassifier

from sklearn.calibration import CalibratedClassifierCV

import graphviz # doctest: +SKIP

import json

import numpy as np

#load data

train_file = open('../train0-9.json', 'r')

dev_file = open('../dev.json')

train = json.load(train_file)

dev = json.load(dev_file)

X,Y = [],[]

for verse in train:

    for reading in verse[2]:

        X.append(reading[0])

        Y.append(reading[1])

#build model

n_estimators = 10

svm = BaggingClassifier(svm.LinearSVC(), max_samples=1.0/n_estimators, n_estimators=n_estimators)

clf = CalibratedClassifierCV(svm)

#fit

clf.fit(X, Y)

correct = 0

total = 0

for verse in dev:

    vectors = [reading[0] for reading in verse[2]]

    probs = clf.predict_proba(vectors)

    sort_probs = sorted([(probs[i], verse[2][i][1]) for i in range(len(probs))], key=lambda x: x[0][0])

    gold = sort_probs[0][1]

    if gold == 1:

        correct += 1

    total += 1

print("Recall: {}/{} ({})".format(correct, total, correct/total))

joblib.dump(clf, 'svm_classifier.joblib')

#dot_data = tree.export_graphviz(clf, out_file=None) # doctest: +SKIP

#graph = graphviz.Source(dot_data) # doctest: +SKIP

#graph.render("male_female") # doctest: +SKIP