def confusion_matrix_scores(gold_labs, pred_labs, scores=True):
'''Draw a confusion matrix and calculate accuracy, precision and recall scores'''
# Draw a confusion matrix
if not gold_labs or not pred_labs:
raise RuntimeError("One of the prediction lists is empty")
if len(gold_labs) != len(pred_labs):
raise RuntimeError(
"The number of predictions != the number of gold labels")
cm = ConfusionMatrix(gold_labs, pred_labs)
print(cm.pretty_format(show_percents=False))
# calculate accuracy, precision and recall for a SICK part (not for individual problems)
try:
pre = (cm[('E', 'E')] + cm[('C', 'C')]) / float(
sum([cm[(i, j)] for i in 'NEC' for j in 'EC']))
except:
pre = 0
try:
rec = (cm[('E', 'E')] + cm[('C', 'C')]) / float(
sum([cm[(i, j)] for i in 'EC' for j in 'NEC']))
except:
rec = 0
try:
acc = (cm[('E', 'E')] + cm[('C', 'C')] + cm[('N', 'N')]) / float(
cm._total)
except:
acc = 0
# print accuracy, precision and recall for a SICK part (not for individual problems)
if scores:
print("Accuracy: {:.2f}%\nPrecision: {:.2f}%\nRecall: {:.2f}%".format(
acc * 100, pre * 100, rec * 100))
return (acc, pre, rec), cm
def show_results(self,gold,test):
from nltk import ConfusionMatrix
correct = 0
for index,result in enumerate(gold):
if result == test[index]:
correct +=1
print 'Accuracy: {:.2%}'.format(float(correct) / float(len(gold)))
cm = ConfusionMatrix(gold, test)
print cm.pp()
def my_classify_results(true_values,classified_values):
cm = ConfusionMatrix(true_values, classified_values)
tp,tn = cm.__getitem__(("pos","pos")),cm.__getitem__(("neg","neg"))
fp,fn = cm.__getitem__(("neg","pos")),cm.__getitem__(("pos","neg"))
print tp,tn,fp,fn
allres = tp+fp+tn+fn
precision = tp / (tp+fp)
recall = tp / (tp+fn)
return "Accuracy : %s\nPrecision : %s\nRecall : %s\nF-value : %s" % ((tp+tn)/allres, precision, recall, 2*precision*recall/(precision+recall))
def confusion_matrix(gold,guess):
correct = 0
total = len(gold)
for i in range(len(gold)):
if guess[i] == gold[i]:
correct += 1
accuracy = float(correct) / float(total)
print('Accuracy: {:.2%}'.format(accuracy))
# Confusion Matrix
cm = ConfusionMatrix(gold, guess)
print (cm.pp())
def confusion_matrix(gold, guess):
correct = 0
total = len(gold)
for i in range(len(gold)):
if guess[i] == gold[i]:
correct += 1
accuracy = float(correct) / float(total)
print('Accuracy: {:.2%}'.format(accuracy))
# Confusion Matrix
cm = ConfusionMatrix(gold, guess)
print(cm.pp())
def performance(classifier):
test = []
gold = []
for i in range(len(test_features)):
test.append(classifier.classify(test_features[i][0]))
gold.append(test_features[i][1])
matrix = ConfusionMatrix(gold, test)
labels = {"female", "male"}
tp = matrix["f", "f"]
fn = matrix["m", "f"]
fp = matrix["f", "m"]
tn = matrix["m", "m"]
precision_female = tp / (tp + fp)
precision_male = tn / (tn + fn)
recall_female = tp / (tp + fn
) # actual female/ actual female + missed female
recall_male = tn / (tn + fp)
fscore_female = 2 * precision_female * recall_female / precision_female + recall_female
fscore_male = 2 * precision_male * recall_male / precision_male + recall_male
print(f"Precision for female names: {round(precision_female,2)}")
print(f"Precision for male names: {round(precision_male,2)}")
print(f"Recall for female names: {round(recall_female,2)}")
print(f"Recall for male names: {round(recall_male,2)}")
print(f"F-score for female names: {round(fscore_female,2)}")
print(f"F-score for male names: {round(fscore_male,2)}")
print("\n")
def __init__(self,
X_test,
conv_layers,
flow,
mode,
path_saver,
Y_test=None):
"""
The CNN_infer class is initialized with its arguments when its
instance is created.
"""
_, n_H0, n_W0, n_C0 = X_test.shape
if mode == 'compare':
_, n_y = Y_test.shape
self.X, self.Y = self._create_placeholder(n_H0, n_W0, n_C0, n_y)
elif mode == "predict":
self.X = tf.placeholder(dtype=tf.float32,
shape=[None, n_H0, n_W0, n_C0])
self.parameters = self._initialize_parameters(conv_layers)
self.Z_n = self._forward_propagation(flow)
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, path_saver)
self.logits = sess.run(self.Z_n, feed_dict={self.X: X_test})
if mode == 'compare':
correct_prediction = tf.equal(tf.argmax(self.Z_n, axis=1),
tf.argmax(self.Y, axis=1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print("Test Accuracy : ",
accuracy.eval({
self.X: X_test,
self.Y: Y_test
}))
logits_argmax = np.argmax(self.logits, axis=1)
logits_argmax = [i for i in logits_argmax]
y_argmax = np.argmax(Y_test, axis=1)
y_argmax = [i for i in y_argmax]
print(ConfusionMatrix(y_argmax, logits_argmax))
elif mode == 'predict':
prediction = np.argmax(self.logits)
print("Your algorithm predicts Y = " + str(prediction))
def generate_report(gold, predict, labels, detailed=True):
"""
Generate the classification report
:param gold: the gold label
:param predict: the predict label
:param labels: label sets
:return: none
"""
if detailed:
print('macro averge: %f' % precision_recall_fscore_support(gold, predict, average='macro'))
print('micro average: %f' % precision_recall_fscore_support(gold, predict, average='micro'))
print('weighted average: %f' % precision_recall_fscore_support(gold, predict, average='weighted'))
else:
import warnings
warnings.filterwarnings("ignore")
print('accuracy: %f' % accuracy_score(gold, predict))
print(classification_report(gold, predict, target_names=labels))
print(ConfusionMatrix(gold, predict))
def __init__(self):
self.dataSets = os.listdir('data/')
# voor het gemak gaan we uit van twee annotators
self.choice = -1
self.annotation1 = {}
self.annotation2 = {}
self._loadAnnotations()
# in welke categorie staat het event (0,1,2,3,4,5)?
self.categoryEval = [[], []]
# is het een event (0) of niet (1)?
self.eventEval = [[], []]
self._makeAnnotationLists()
self.eventKappa = self._calculateKappa(self.eventEval)
self.categoryKappa = self._calculateKappa(self.categoryEval)
print("\neventKappa:", self.eventKappa)
print("categoryKappa:", self.categoryKappa, '\n')
print(ConfusionMatrix(self.judge1, self.judge2))
print("Accuracy", accuracy(self.judge1, self.judge2))
def main(estimators=[LightGbmWithLogReg, Stacking],
with_full_data_tfidf=True,
cutoff=None):
train_x, train_y, train_positions, train_file_names = get_data(
main_dir=TRAINING_DIR)
validation_x, validation_y, validation_positions, validation_file_names = get_data(
main_dir=VALIDATION_DIR)
if cutoff:
train_x = train_x[:cutoff]
validation_x = validation_x[:cutoff]
train_y = train_y[:cutoff]
validation_y = validation_y[:cutoff]
train_positions = train_positions[:cutoff]
clfs = [estimator() for estimator in estimators]
predictions_df = pd.DataFrame()
predictions_df['text'] = validation_x
predictions_df['actual'] = validation_y
for clf in clfs:
t_start = time.time()
if with_full_data_tfidf:
clf.fit_with_test(train_x, train_y, validation_x)
else:
clf.fit(train_x, train_y)
predictions = clf.predict(validation_x)
t_end = time.time()
m, s = divmod(t_end - t_start, 60)
print(ConfusionMatrix(validation_y, predictions))
acc = accuracy_score(validation_y, predictions)
model_name = clf.params['model_description']
print(model_name)
print("Done in %fm and %fs" % (round(m), round(s)))
print("Accuracy:" + str(acc))
predictions_df[model_name] = predictions
predictions_df.to_csv("data/output/model_predictions.csv", index=False)
def example(hmm, test_set, n1, n2):
"""Try to tag sentences between n1 and n2 (excluded) of the test set; just to show the result..."""
estimated_tags = []
gold_tags = []
for test_sentence in test_set[n1:n2]:
# the zip() function with the "*" operator can be used to unzip the list
# see: https://stackoverflow.com/questions/7558908/unpacking-a-list-tuple-of-pairs-into-two-lists-tuples
# [("this","is")] [("PP","VB")] <-- zip(*(["this","PP"],["is","VB"]))
unlabelled_test_sentence, test_sentence_tags = zip(*test_sentence)
# decoding...
test_sentence_estimated_tags = hmm.best_path(unlabelled_test_sentence)
# [("this","PP"),("is","VB")] --> "this/PP is/VB"
print("Test: %s" %
' '.join([word + "/" + tag for (word, tag) in test_sentence]))
# e.g.: zip(["this","is"],["PP","VB"]) ---> [("this","PP"),("is","VB")]
print("HMM : %s" % ' '.join([
word + "/" + tag for (word, tag) in zip(
unlabelled_test_sentence, test_sentence_estimated_tags)
]))
# e.g.: zip(['PP', 'NN', 'VB'],['PP', 'NN', 'NN']) --> [('PP','PP'),('NN','NN'),('VB','NN')]
comparation_list = [
1 if tag1 == tag2 else 0
for (tag1,
tag2) in zip(test_sentence_tags, test_sentence_estimated_tags)
] # e.g.: --> [1, 1, 0]
print("Comparation:", comparation_list)
print("Accuracy : %.2f\n" %
(sum(comparation_list) / len(test_sentence) *
100)) # --> sum([1, 1, 0]) / 3 = 2/3
estimated_tags += test_sentence_estimated_tags # collects estimated tags, for further use
gold_tags += test_sentence_tags # collects correct tags, for further use
# prints confusion matrix
print(ConfusionMatrix(gold_tags, estimated_tags))
def train(train_data,
valid_data,
estimator=StackingSimple,
cv_split=10,
with_cross_validation=False,
with_validation=False,
with_full_data_tfidf=False,
train_with_validation=True,
path_to_save_model=""):
train_x, train_y, train_positions = split_data(train_data)
if train_with_validation:
validation_x, validation_y, validation_positions = split_data(
valid_data)
train_x.extend(validation_x)
train_y.extend(validation_y)
clf, cv, val, gs = None, None, None, None
if estimator:
clf = estimator()
if with_cross_validation:
if with_full_data_tfidf:
skf = StratifiedKFold(n_splits=cv_split,
random_state=42,
shuffle=True)
all_acc = []
X = np.array(train_x)
y = np.array(train_y)
for train_index, test_index in skf.split(X, y):
y_train, y_test = y[train_index], y[test_index]
X_train, X_test = X[train_index], X[test_index]
print(X_train.shape)
clf.fit_with_test(X_train.tolist(), y_train, train_positions,
X_test.tolist())
predictions = clf.predict(X_test.tolist())
all_acc.append(accuracy_score(y_test, predictions))
print("Accuracies:", all_acc)
print("Mean:", np.mean(all_acc))
print("Stdev:", np.std(all_acc))
else:
cv = cross_validate(
estimator=clf,
X=train_x,
y=train_y,
fit_params={'train_positions': train_positions},
cv=cv_split,
scoring="accuracy",
n_jobs=get_n_jobs(),
return_train_score=True)
if with_validation:
t_start = time.time()
if with_full_data_tfidf:
clf.fit_with_test(train_x, train_y, train_positions, validation_x)
else:
clf.fit(train_x, train_y, train_positions)
predictions = clf.predict(validation_x)
t_end = time.time()
print(ConfusionMatrix(validation_y, predictions))
val = {
'accuracy': accuracy_score(validation_y, predictions),
'time': t_end - t_start
}
print(val['accuracy'])
else:
clf.fit(train_x, train_y)
clf.save_model(path_to_save_model)
logreg_accy = round(accuracy_score(Target_pred, Target_test), 3)
print(logreg_accy) ## 79.8% Accuracy
##cross validation
from sklearn import metrics, cross_validation
predicted_cv = cross_validation.cross_val_predict(logreg, X, Target, cv=10)
metrics.accuracy_score(Target, predicted_cv) ## 86.05 %
from sklearn.cross_validation import cross_val_score
accuracy_cv = cross_val_score(logreg, X, Target, cv=10, scoring='accuracy')
print(accuracy_cv)
print(cross_val_score(logreg, X, Target, cv=10, scoring='accuracy').mean())
from nltk import ConfusionMatrix
print(ConfusionMatrix(list(Target), list(predicted_cv)))
## Decision tree ##
##Cross Validation
from sklearn.tree import DecisionTreeClassifier
dectree = DecisionTreeClassifier(max_depth=3,
class_weight='balanced',
min_weight_fraction_leaf=0.01)
depth = []
for i in range(3, 20):
clf = DecisionTreeClassifier(max_depth=i)
# Perform 7-fold cross validation
scores = cross_val_score(estimator=clf, X=X, y=Target, cv=7, n_jobs=4)
test_tweets = BuildFeatureVector(all_tweet_array[training_size:]) print len(test_tweets) training_set = nltk.classify.apply_features(extract_features, train_tweets) test_set = nltk.classify.apply_features(extract_features, test_tweets) NBClassifier = nltk.NaiveBayesClassifier.train(training_set) NBClassifier.show_most_informative_features(20) TestSet(all_tweet_array[training_size:]) print '' print 'TRAINING accuracy:', nltk.classify.accuracy(NBClassifier, training_set) print 'TEST accuracy:', nltk.classify.accuracy(NBClassifier, test_set) print '' print 'NEU precision:', precision(refSet['NEU'], testSet['NEU']) print 'NEU recall:', recall(refSet['NEU'], testSet['NEU']) print 'NEU F-measure:', f_measure(refSet['NEU'], testSet['NEU']) print '' print 'POS precision:', precision(refSet['POZ'], testSet['POZ']) print 'POS recall:', recall(refSet['POZ'], testSet['POZ']) print 'POS F-measure:', f_measure(refSet['POZ'], testSet['POZ']) print '' print 'NEG precision:', precision(refSet['NEG'], testSet['NEG']) print 'NEG recall:', recall(refSet['NEG'], testSet['NEG']) print 'NEG F-measure:', f_measure(refSet['NEG'], testSet['NEG']) print '' print ConfusionMatrix(refSetF, testSetF)
## Part 2 X = data[cols[1:-1]] y = data.binary X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=123) ## Part 3 from sklearn.linear_model import LogisticRegression # import the estimator we want logreg = LogisticRegression() # instantiate estimator logreg.fit(X_train, y_train) # fit with training data preds = logreg.predict(X_test) # predict for test data probs = logreg.predict_proba(X_test) # predict probabilities for AUC print(ConfusionMatrix(list(y_test), list(preds))) # see how we did print(metrics.accuracy_score(y_test, preds)) # check accuracy (88.89%) print(metrics.roc_auc_score(y_test, probs[:,1])) # check AUC (93.98%) ## Part 4 # train first model (logistic regression): mod1 = LogisticRegression() scores1 = cross_val_score(mod1, X, y, cv=5, scoring='roc_auc') # train second model (KNN, K=1): from sklearn.neighbors import KNeighborsClassifier mod2 = KNeighborsClassifier(n_neighbors=1) scores2 = cross_val_score(mod2, X, y, cv=5, scoring='roc_auc')
#print(features)
return features
random.shuffle(tweets)
v_train = tweets[:2000]
v_test = tweets[:2000]
print("Training...")
training_set = apply_features(extract_features, v_train)
classifier = NaiveBayesClassifier.train(training_set)
tweet = "this movie is sweet and astonishing"
print(classifier.classify(extract_features(tweet)))
#print(classifier.show_most_informative_features(32))
print("Test...")
test_set = apply_features(extract_features, v_test)
print('\nAccuracy %f\n' % accuracy(classifier, test_set))
# build confusion matrix over test set
test_truth = [s for (t, s) in v_test]
test_predict = [classifier.classify(t) for (t, s) in test_set]
print('Confusion Matrix')
print(ConfusionMatrix(test_truth, test_predict))
print("FIN")
def exercise4(dataset, runs=5, test_portion=0.50):
LOGGER.info('Building datasets...')
# Build Test and Training Review Sets
test_reviews = None
train_reviews = None
predetermined = None
overall_classifications = []
accuracies = []
rmses = []
for n in range(runs):
if dataset.test and dataset.train:
test_reviews = dataset.test
train_reviews = dataset.train
predetermined = True
else:
test_reviews, train_reviews = dataset.make_author_test_train(
test_portion)
predetermined = False
if not predetermined:
LOGGER.info('Run %d of %d', n + 1, runs)
LOGGER.info('Building features...')
# Build Features
test_features = [(extract_features4(r), r.author)
for r in test_reviews]
train_features = [(extract_features4(r), r.author)
for r in train_reviews]
LOGGER.info('Building classifier...')
# Build Classifier
LOGGER.info('Training Examples: %d', len(train_reviews))
LOGGER.info('Training Features: %d', len(train_features))
classifier = nltk.NaiveBayesClassifier.train(train_features)
#classifier = nltk.DecisionTreeClassifier.train(train_features)
LOGGER.info('Checking accuracy...')
# Perform Classification
classifications = []
for t in test_features:
classifications.append((t[1], classifier.classify(t[0])))
LOGGER.info('Printing results...')
classifications.sort()
accuracy = nltk.classify.accuracy(classifier, test_features)
rmse = math.sqrt(
sum(1
for a, c in classifications if a != c) / len(classifications))
confusion = ConfusionMatrix([ref for ref, test in classifications],
[test for ref, test in classifications])
overall_classifications.extend(classifications)
if not predetermined:
HEADER = ('ACTUAL', 'CLASSIFIED')
col_width = max(len(a) for a, c in (classifications + [HEADER]))
for a, c in ([HEADER] + classifications):
print("Exercise 4: %s %s" % (a.ljust(col_width), c))
print("Exercise 4: %.3f" % (accuracy, ))
print("Exercise 4: Average RMSE Error: %.3f" % (rmse, ))
if predetermined:
return accuracy
print('Exercise 4: Confusion Matrix:\n%s' % (confusion.pretty_format(
show_percents=False, values_in_chart=True), ))
accuracies.append(accuracy)
rmses.append(rmse)
overall_confusion = ConfusionMatrix(
[ref for ref, test in overall_classifications],
[test for ref, test in overall_classifications])
print('Exercise 4: Overall Confusion Matrix:\n%s' %
(overall_confusion.pretty_format(show_percents=False,
values_in_chart=True), ))
print("Exercise 4: Runs: %d Average : %.3f Max: %.3f Min: %.3f" %
(runs, sum(accuracies) / len(accuracies), max(accuracies),
min(accuracies)))
print("Exercise 4: Runs: %d Average RMSE: %.3f Max: %.3f Min: %.3f" %
(runs, sum(rmses) / len(rmses), max(rmses), min(rmses)))
return accuracies
files = ["Clov","Hamm","Monologue","Nagg","Nell"]
ref = ""
tagged = ""
index = 0
for file in listdir("C:\Users\Cassie\Google Drive\MSiA\Fall 2014\MSiA 490\hw\characters"):
path = 'C:\Users\Cassie\Google Drive\MSiA\Fall 2014\MSiA 490\hw\characters'
filepath = path + '\\' + file
with open (filepath, "r") as myfile:
index +=1
for line in myfile:
ref = ref + ',' + files[index-1]
tag = classi.classify(word_feats(line))
tagged = tagged + ',' + tag
print ConfusionMatrix(ref.split(',')[1:], tagged.split(',')[1:])
'''
soup = BeautifulSoup(open("wiki.html"))
tables = soup.findAll("table", { "class" : "wikitable sortable" })
ref = ""
tagged = ""
continents = ["Africa","Asia","Europe","North America","South America","Oceania","Antrctica"]
index = 0
for table in tables:
index +=1
for row in table.findAll('tr'):
cells = row.findAll("td")
if len(cells) == 5:
if cells[2].find('a',text=True) is not None:
b = cells[2].find('a',text=True)
def score(change):
if change <-.02:
return "down"
elif change <.02:
return "flat"
else:
return "up"
#Generate Features & Results
training_data = [(FreqDist(d["tokens"]), score(d["2-2dayPriceChange"])) for d in training]
test_features = [FreqDist(d["tokens"]) for d in testing]
test_results = [score(d["2-2dayPriceChange"]) for d in testing]
#Train Model
model = nltk.NaiveBayesClassifier.train(training_data)
#Generate Predictions
preds = model.classify_many(test_features)
#Print Results
amounts = [ (direction, len([ t for t in test_results if t ==direction])) for direction in ["down", "flat", "up"]]
print(amounts)
print("Majority Baseline: %.2f" % (max([b for a,b in amounts]) / len(test_results)))
print("Accuracy: %.2f" % (nltk.accuracy(preds, test_results)))
print(ConfusionMatrix(preds, test_results))
print(model.show_most_informative_features(10))
def test(self, lstm_pos_tagger, test_file_path):
plot_confusion_matrix = False
test = Conllu_Manager('test',
embedding='mine',
dataSetPath=test_file_path)
posTaggedSentenceFilePath = os.path.join(os.getcwd(), 'output',
'pos_tagged_sentences.txt')
resultFilePath = os.path.join(os.getcwd(), 'output', 'result.txt')
fp_post_tagged = open(posTaggedSentenceFilePath, 'w')
fp_result = open(resultFilePath, 'w')
x, y = test.getSentences()
y_true = []
for i in range(len(y)):
y_true.extend(y[i].ravel())
y_pred = []
t = tqdm(range(len(x)))
for i in t:
y_sentence_pred = lstm_pos_tagger.predict(x[i])
y_pred.extend(y_sentence_pred)
sentence_to_write = ''
tag_predicted_to_write = ''
for w in x[i]:
sentence_to_write += '{} '.format(w)
for t in y_sentence_pred:
tag_predicted_to_write += '{} '.format(t)
fp_post_tagged.write('{}\n'.format(sentence_to_write))
fp_post_tagged.write('{}\n'.format(tag_predicted_to_write))
P_, R_, F1_, S_ = precision_recall_fscore_support(y[i],
y_sentence_pred,
average='micro')
fp_result.write(
'Precision: {} , Recall {} , F1 {} , Coverage {} \n'.format(
P_, R_, F1_, 1))
fp_post_tagged.close()
fp_result.close()
print(ConfusionMatrix(y_true, y_pred))
P, R, F1, S = precision_recall_fscore_support(y_true,
y_pred,
average='micro')
if plot_confusion_matrix:
labels_name = []
for key in test.t2i:
labels_name.append(key)
cnf_matrix = confusion_matrix(y_true, y_pred)
cnf_matrix = cnf_matrix.astype('float64') / cnf_matrix.sum(
axis=1)[:, np.newaxis]
for x in range(0, cnf_matrix.shape[0]):
for y in range(0, cnf_matrix.shape[1]):
if cnf_matrix[x][y] != int(cnf_matrix[x][y]):
cnf_matrix[x][y] = round(cnf_matrix[x][y], 3)
# Plot
plt.figure(figsize=(15, 15))
df_cm = pd.DataFrame(cnf_matrix,
index=labels_name,
columns=labels_name)
sn.set(font_scale=0.85)
sn.heatmap(df_cm,
annot=True,
annot_kws={"size": 10},
cmap=plt.cm.Blues,
square=True,
linewidths=.1)
figurePath = os.path.join(os.getcwd(), 'latec',
'confusionMatrix.png')
plt.savefig(figurePath, dpi=300)
plt.close()
return dict(precision=P, recall=R, coverage=S, f1=F1)
train['60fretbinary'] = y_train_simple
test = pd.DataFrame(data=X_test_simple, columns=['ZYX5minSentiment'])
test['60fretbinary'] = y_test_simple
#Run a logistic regression
testfret_simple = LogisticRegression()
testfret_simple.fit(train[['ZYX5minSentiment']], y_train_simple)
B1 = testfret_simple.coef_[0][0]
B0 = testfret_simple.intercept_[0]
np.exp(B1)
testfret_simple.score(X_test_simple, y_test_simple)
preds_simple = testfret_simple.predict(X_test_simple)
print metrics.accuracy_score(y_test_simple, preds_simple)
print ConfusionMatrix(list(y_test_simple), list(preds_simple))
#Now look at more variables
X = data[[
'ZYX10minSentiment', 'ZYX20minSentiment', 'ZYX30minSentiment',
'ZYX60minSentiment', 'ZYX10minTweets', 'ZYX20minTweets', 'ZYX30minTweets',
'ZYX60minTweets', 'ZYX10minPriceChange', 'ZYX20minPriceChange',
'ZYX30minPriceChange', 'ZYX60minPriceChange'
]]
y = data['60fretbinary']
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.3,
random_state=1)
X = d[[
'ZYX10minSentiment', 'ZYX20minSentiment', 'ZYX30minSentiment',
def test(hmms, test_set, index_features):
total = 0
correct = 0
correct_cat = []
predicted_cat = []
for cat in test_set.keys():
for sentence in test_set[cat]:
'''test_sentence = [(tuple(word),'') for word in sentence]'''
'''feature subset selection'''
new_sentence = []
for word in sentence:
new_word = []
for feature in index_features:
new_word.append(word[feature])
new_sentence.append(new_word)
test_sentence = [(tuple(word), '') for word in new_sentence]
if verbose:
pass
max_prob = -1
sentence_cat = random.choice(
test_set.keys()) #assign a random category just to initialize
# test the probabilities that the sentence is of a type of emotion
# the higher probability is the winner
#if verbose:
# print "Probabilities of each hmm : "
for c in hmms.keys():
sentence_prob = hmms[c].probability(test_sentence)
#if verbose:
# print c," : ",sentence_prob
if sentence_prob > max_prob:
sentence_cat = c
max_prob = sentence_prob
#if verbose:
# print ""
#print ""
correct_cat.append(
cat
) # we save the correct category in a list that we'll use to build ConfusionMatrix
predicted_cat.append(
sentence_cat
) # we save the category predicted in a list that we'll use to build ConfusionMatrix
if (cat == sentence_cat):
correct += 1
total += 1
try:
accuracy = ((correct / total) * 100)
except ZeroDivisionError:
accuracy = 0 # error
# the confusionMatrix function needs the list of the correct label and the list of the predicted
matrix = ConfusionMatrix(correct_cat, predicted_cat)
print "correct:", correct
print "total:", total
print "the accuracy is: %.2f%%" % accuracy
print matrix
return accuracy, matrix
def main(estimator=MLP,
cv_split=5,
with_cross_validation=True,
with_validation=False,
with_test=False,
with_external_data=False,
validate_on_external=False,
with_grid_search=False,
with_full_data_tfidf=False,
train_with_validation=False,
predict_breaches=True,
train_on_breach=True,
cutoff=None):
if validate_on_external:
train_x, validation_x, train_y, validation_y = get_external_data(
TRAINING_EXTERNAL_FILE, 3000, 1500)
train_positions = []
elif train_on_breach:
train_x, train_y, train_positions, train_file_names = get_data(
main_dir=BREACH_DIR, external_file=None, breach=True)
else:
train_x, train_y, train_positions, train_file_names = get_data(
main_dir=TRAINING_DIR,
external_file=TRAINING_EXTERNAL_FILE
if with_external_data else None)
# return print_splits(train_x[:3000], train_positions[:3000])
if train_with_validation:
validation_x, validation_y, validation_positions, validation_file_names = get_data(
main_dir=VALIDATION_DIR)
train_x.extend(validation_x)
train_y.extend(validation_y)
if cutoff:
train_x = train_x[:cutoff]
train_y = train_y[:cutoff]
train_positions = train_positions[:cutoff]
print("Training on {0} examples".format(len(train_x)))
clf, cv, val, gs = None, None, None, None
if estimator:
clf = estimator()
if with_cross_validation:
if with_full_data_tfidf:
skf = StratifiedKFold(n_splits=cv_split,
random_state=42,
shuffle=True)
all_acc = []
X = np.array(train_x)
y = np.array(train_y)
for train_index, test_index in skf.split(X, y):
y_train, y_test = y[train_index], y[test_index]
X_train, X_test = X[train_index], X[test_index]
print(X_train.shape)
clf.fit_with_test(X_train.tolist(), y_train, train_positions,
X_test.tolist())
predictions = clf.predict(X_test.tolist())
all_acc.append(accuracy_score(y_test, predictions))
print("Accuracies:", all_acc)
print("Mean:", np.mean(all_acc))
print("Stdev:", np.std(all_acc))
elif train_on_breach:
skf = StratifiedKFold(n_splits=cv_split,
random_state=42,
shuffle=True)
f_scores = []
diff = []
r = []
p = []
all_acc = []
X = np.array(train_x)
y = np.array(train_y)
pos = np.array(train_positions)
for train_index, test_index in skf.split(X, y):
y_train, y_test = y[train_index], y[test_index]
X_train, X_test = X[train_index], X[test_index]
pos_train, pos_test = pos[train_index], pos[test_index]
print(X_train.shape)
clf.fit_with_test(X_train.tolist(), y_train, train_positions,
X_test.tolist())
change_predictions = clf.predict(X_test.tolist())
tn, fp, fn, tp = confusion_matrix(y_test,
change_predictions).ravel()
print('tn: {}, fp: {}, fn: {}, tp: {}'.format(tn, fp, fn, tp))
all_acc.append(accuracy_score(y_test, change_predictions))
predictions = get_breach_predictions(clf, X_test.tolist(),
change_predictions)
totalWinDiff, totalWinR, totalWinP, totalWinF, outStr = evaluate(
X_test, pos_test, predictions)
print("%s" % outStr)
diff.append(totalWinDiff)
r.append(totalWinR)
p.append(totalWinP)
f_scores.append(totalWinF)
print("Mean diff:", np.mean(diff))
print("Mean r:", np.mean(r))
print("Mean p:", np.mean(p))
print("Mean f:", np.mean(f_scores))
print("Accuracies:", all_acc)
print("Mean:", np.mean(all_acc))
print("Stdev:", np.std(all_acc))
# for m in all_measures:
# print("%s" % m)
# print('----------------------------------')
else:
cv = cross_validate(
estimator=clf,
X=train_x,
y=train_y,
fit_params={'train_positions': train_positions},
cv=cv_split,
scoring="accuracy",
n_jobs=get_n_jobs(),
return_train_score=True)
if with_grid_search:
clf, best_score = __grid_search(clf, clf.get_grid_params(), train_x,
train_y)
gs = {'accuracy': best_score}
if with_validation:
if not validate_on_external:
validation_x, validation_y, validation_positions, validation_file_names = get_data(
main_dir=VALIDATION_DIR)
if cutoff:
validation_x = validation_x[:cutoff]
validation_y = validation_y[:cutoff]
t_start = time.time()
if with_full_data_tfidf:
clf.fit_with_test(train_x, train_y, train_positions, validation_x)
else:
clf.fit(train_x, train_y, train_positions)
if predict_breaches:
predictions = get_breach_predictions(clf, validation_x,
validation_y)
else:
predictions = clf.predict(validation_x)
t_end = time.time()
if predict_breaches:
persist_output(OUTPUT_DIR,
predictions,
validation_file_names,
breach=predict_breaches)
print("%s" %
evaluate(validation_x, validation_positions, predictions))
else:
print(ConfusionMatrix(validation_y, predictions))
val = {
'accuracy': accuracy_score(validation_y, predictions),
'time': t_end - t_start
}
if with_test:
test(clf, train_x, train_y, train_positions, with_full_data_tfidf)
results = get_results(len(train_x),
clf_params=clf.params,
cv=cv,
val=val,
gs=gs)
print(results)
if config_local().get('persist_results', False):
write_results_to_file(results)
d_2014['probs2014'] = logReg.predict_proba(d_2014[col_c])[:, 1] df =d_2014[['player','team','pred2014','probs2014']] null = 1 - sum(d.team) / float(len(d.team)) #Actuately predicting 8 award winners with a probablity 0f .5 or greater #Create predictions using the model on the test set test['pred_class'] = logReg.predict(test[col_c ]) # nicer confusion matrix from nltk import ConfusionMatrix print ConfusionMatrix(list(y_test), list(preds)) #229 possible yes; 57% ## ROC CURVE and AUC #Do I want it on X_test or 2014 data? probs = logReg.predict_proba(X_test)[:, 1] from sklearn import metrics # plot ROC curve fpr, tpr, thresholds = metrics.roc_curve(y_test, probs) plt.plot(fpr, tpr) plt.xlim([0.0, 1.0])
def evaluate(fragments,
sumfunc,
condition,
normalization,
verbose=True,
perbook=False,
topfragments=False,
breakdown=True,
conftable=False):
green = "\033[32m"
red = "\033[31m"
gray = "\033[0m" # ANSI codes
names = set(map(getauthor, fragments.values()[0]))
results = {}
# heading
if verbose and not perbook:
print "\n &", 21 * " ",
print "&".join(a.rjust(16) for a in sorted(names)),
print "&\tguess &\t\t\tconfidence\\\\"
prev = "foo.bar"
# loop over texts to be classified
for text in sorted(fragments):
if perbook and getauthor(text) != getauthor(prev):
print "\n &", 21 * " ",
print " &".join(
"\\rotatebox{45}{%s}" %
a.split(" - ")[-1].split(".")[0].replace("&", "\\&")
for a in sorted(fragments[text])), "\\\\"
if verbose:
print text.split(" - ")[-1].split(".")[0][:25].replace(
"&", "\\&").ljust(25),
inter = {}
# loop over possible authors
for author in sorted(fragments[text]):
inter[author] = sum(
map(sumfunc, filter(condition, fragments[text][author].items())
)) / normalization(text, author)
if verbose:
for author in sorted(inter):
if inter[author] == max(inter.values()):
l, r = "\\textbf{", "}"
else:
l, r = "".ljust(8), " "
if isinstance(inter[author], float):
print("& %s%.2f%s" % (l, inter[author], r)).rjust(16),
elif isinstance(inter[author], int):
print("& %s%d%s" % (l, inter[author], r)).rjust(16),
else:
print "& %s%s" % (l, repr(inter[author]).rjust(8), r),
actualauthor = getauthor(text)
guess = max(inter, key=inter.get)
results.setdefault(actualauthor, []).append(guess)
if verbose and not perbook:
print "&",
print green + "correct:" if getauthor(
guess) == actualauthor else red + "wrong: ",
print getauthor(guess).ljust(10), gray,
try:
confidence = (
100 * (max(inter.values()) - sorted(inter.values())[-2]) /
float(max(inter.values())))
except ZeroDivisionError:
confidence = 0.0
except IndexError:
confidence = 0.0
print "& %s%5.2f%s " % (
(red if confidence < 50 else green), confidence, gray)
elif verbose:
print "\\\\"
prev = text
if verbose: print
if topfragments: print "top fragments"
for name in sorted(names) if topfragments else ():
for text in sorted(fragments):
if not getauthor(text) == name: continue
print text
for label in ("(ROOT", "(S ", "(NP ", "(VP ", "(PP "):
guess = max(fragments[text],
key=lambda x: sum(
sumfunc(a) for a in fragments[text][x].items()
if condition(a)) / norm(x))
try:
frag = max((a[0]
for a in fragments[text][guess].iteritems()
if condition(a) and a[0].startswith(label)),
key=lambda x: (sumfunc((x, fragments[text][
guess][x])), fragments[text][guess][x]))
except ValueError:
pass
else:
f1 = Tree(frag)
f2 = Tree(frag)
print "%2d" % fragments[text][guess][frag], " ".join(
a.replace(" ", "_")[:-1]
for a in re.findall(r" \)|[^ )]+\)", frag)),
try:
f2.un_chomsky_normal_form()
except:
print f1.pprint(margin=9999, parens=("[", " ]"))
else:
print f2.pprint(margin=9999, parens=("[", " ]"))
print
if perbook: return
if topfragments: print
if conftable:
print "Confusion matrix"
ref = [a for a in results for b in results[a]]
test = [getauthor(b) for a in results for b in results[a]]
cf = ConfusionMatrix(ref, test)
print '\t\t&%s\\\\' % "\t& ".join(sorted(set(test)))
for a in sorted(set(ref)):
print a.ljust(15),
for b in sorted(set(test)):
c = "& "
if a == b: c = ("& \\textbf{%d}" % cf[a, b])
elif cf[a, b]: c = ("& %d" % cf[a, b])
print c.rjust(10),
print r"\\"
print
avg = sum(1 for a in results
for b in results[a] if a == getauthor(b)) / float(
sum(map(len, results.values())))
if breakdown:
print "Accuracy"
z = []
for a in sorted(results):
acc = sum(1 for b in results[a] if a == getauthor(b)) / float(
len(results[a]))
print getauthor(a).ljust(16), "& ",
print "%.2f \\%% \\\\" % (100 * acc)
z.append(acc)
print "macro average:".ljust(
16), "& %6.2f \\%% \\\\" % (100 * sum(z) / float(len(z)))
print "micro average:".ljust(16), "& %6.2f \\%% \\\\" % (100 * avg)
else:
print "average:".ljust(16), "& %6.2f \\%% \\\\" % (100 * avg)
