def compare_manual_vs_model():
with open(DATA_FOLDER + "labels_int.p", "r") as f:
y_dict = pickle.load(f)
print "Loading test data"
X_test, y_test, filenames_test = dataset.load_test()
y_pred = joblib.load("../models/pred_ml_improved.pkl")
relevant = []
for pred, correct, filename in zip(y_pred, y_test, filenames_test):
if filename in FILES:
relevant.append((pred, correct, filename, CLASSIFICATIONS[filename]))
model_predictions, correct, filename, manual_predictions = zip(*relevant)
manual_predictions = learn.multilabel_binary_y(manual_predictions)
model_predictions = np.array(model_predictions)
correct = learn.multilabel_binary_y(correct)
rules = infer_topology.infer_topology_rules()
improved_manual = infer_topology.apply_topology_rules(rules, manual_predictions)
prediction_names = ["MODEL", "MANUAL", "IMPROVED_MANUAL"]
predictions = [model_predictions, manual_predictions, improved_manual]
for name, pred in zip(prediction_names, predictions):
print "\n{}\n--".format(name)
print "Zero-one classification loss", zero_one_loss(correct, pred)
print "Hamming loss", hamming_loss(correct, pred)
print "Precision:", precision_score(correct, pred, average="weighted", labels=label_list)
print "Recall :", recall_score(correct, pred, average="weighted", labels=label_list)
print "F1 score :", f1_score(correct, pred, average="weighted", labels=label_list)
def compare_manual_vs_model():
with open(DATA_FOLDER + 'labels_int.p', 'r') as f:
y_dict = pickle.load(f)
print "Loading test data"
X_test, y_test, filenames_test = dataset.load_test()
y_pred = joblib.load('../models/pred_ml_improved.pkl')
relevant = []
for pred, correct, filename in zip(y_pred, y_test, filenames_test):
if filename in FILES:
relevant.append(
(pred, correct, filename, CLASSIFICATIONS[filename]))
model_predictions, correct, filename, manual_predictions = zip(*relevant)
manual_predictions = learn.multilabel_binary_y(manual_predictions)
model_predictions = np.array(model_predictions)
correct = learn.multilabel_binary_y(correct)
rules = infer_topology.infer_topology_rules()
improved_manual = infer_topology.apply_topology_rules(
rules, manual_predictions)
prediction_names = ["MODEL", "MANUAL", "IMPROVED_MANUAL"]
predictions = [model_predictions, manual_predictions, improved_manual]
for name, pred in zip(prediction_names, predictions):
print "\n{}\n--".format(name)
print "Zero-one classification loss", zero_one_loss(correct, pred)
print "Hamming loss", hamming_loss(correct, pred)
print "Precision:", precision_score(correct,
pred,
average='weighted',
labels=label_list)
print "Recall :", recall_score(correct,
pred,
average='weighted',
labels=label_list)
print "F1 score :", f1_score(correct,
pred,
average='weighted',
labels=label_list)
def improve_predictions(
probability_predictions_file='../models/pred_ml_proba.pkl',
out_file='../models/pred_ml_improved.pkl',
use_infer_topology=True):
print "> IMPROVING PREDICTIONS\n--- Forcing at least one label (most likely)"
print "Loading probability predictions"
y_pred_proba = joblib.load(probability_predictions_file)
#Because we use a one-versus-rest classifier, there may be documents without any labels
#We deal with this by adding the most likely labels
y_pred_improved = np.zeros(y_pred_proba.shape, dtype=np.int_)
print "Converting to binary predictions"
y_pred = np.where(y_pred_proba >= 0.5, 1, 0)
for i, (prediction,
prediction_proba) in enumerate(tqdm(zip(y_pred, y_pred_proba))):
if sum(prediction) == 0:
most_likely_label_index = np.argmax(prediction_proba)
y_pred_improved[i, most_likely_label_index] = 1
y_pred_improved[i] += prediction
print np.sum(np.subtract(y_pred_improved, y_pred)), "labels added"
if use_infer_topology:
print "> IMPROVING PREDICTIONS\n--- Topology rules"
print "Loading train set y-values"
y_train, filenames_train = dataset.load_train_y()
rules = infer_topology.infer_topology_rules(y_train)
y_pred_improved = infer_topology.apply_topology_rules(
rules, y_pred_improved)
print "Saving to file"
joblib.dump(y_pred_improved, out_file)
print "Done!\n---"
def improve_predictions(
probability_predictions_file="../models/pred_ml_proba.pkl",
out_file="../models/pred_ml_improved.pkl",
use_infer_topology=True,
):
print "> IMPROVING PREDICTIONS\n--- Forcing at least one label (most likely)"
print "Loading probability predictions"
y_pred_proba = joblib.load(probability_predictions_file)
# Because we use a one-versus-rest classifier, there may be documents without any labels
# We deal with this by adding the most likely labels
y_pred_improved = np.zeros(y_pred_proba.shape, dtype=np.int_)
print "Converting to binary predictions"
y_pred = np.where(y_pred_proba >= 0.5, 1, 0)
for i, (prediction, prediction_proba) in enumerate(tqdm(zip(y_pred, y_pred_proba))):
if sum(prediction) == 0:
most_likely_label_index = np.argmax(prediction_proba)
y_pred_improved[i, most_likely_label_index] = 1
y_pred_improved[i] += prediction
print np.sum(np.subtract(y_pred_improved, y_pred)), "labels added"
if use_infer_topology:
print "> IMPROVING PREDICTIONS\n--- Topology rules"
print "Loading train set y-values"
y_train, filenames_train = dataset.load_train_y()
rules = infer_topology.infer_topology_rules(y_train)
y_pred_improved = infer_topology.apply_topology_rules(rules, y_pred_improved)
print "Saving to file"
joblib.dump(y_pred_improved, out_file)
print "Done!\n---"
