def multivariate_gaussian(x_train, y_train, x_test, y_test, outdir):
# Score with Multivariate Gaussian
# Transform data using boxcox transform, and fit multivariate gaussians.
x_train_boxcox, x_test_boxcox = mv.transform_features(x_train, x_test)
rv_pos, rv_neg = mv.fit_gaussians(x_train_boxcox, y_train)
# Compute melodiness scores on train and test set
m_train, m_test = mv.compute_all_melodiness(x_train_boxcox, x_test_boxcox,
rv_pos, rv_neg)
# Compute various metrics based on melodiness scores.
melodiness_scores = mv.melodiness_metrics(m_train, m_test, y_train, y_test)
best_thresh, max_fscore, thresh_plot_data = \
eu.get_best_threshold(y_test, m_test) # THIS SHOULD PROBABLY BE VALIDATION NUMBERS...
# thresh_plot_data = pd.DataFrame(np.array(thresh_plot_data).transpose(),
# columns=['recall', 'precision',
# 'thresh', 'f1'])
# fpath = os.path.join(outdir, 'thresh_plot_data.csv')
# thresh_plot_data.to_csv(fpath)
melodiness_scores = pd.DataFrame.from_dict(melodiness_scores)
fpath = os.path.join(outdir, 'melodiness_scores.csv')
melodiness_scores.to_csv(fpath)
print "Melodiness best thresh = %s" % best_thresh
print "Melodiness max f1 score = %s" % max_fscore
print "overall melodiness scores:"
print melodiness_scores
def multivariate_gaussian(x_train, y_train, x_test, y_test, outdir):
# Score with Multivariate Gaussian
# Transform data using boxcox transform, and fit multivariate gaussians.
x_train_boxcox, x_test_boxcox = mv.transform_features(x_train, x_test)
rv_pos, rv_neg = mv.fit_gaussians(x_train_boxcox, y_train)
# Compute melodiness scores on train and test set
m_train, m_test = mv.compute_all_melodiness(x_train_boxcox, x_test_boxcox,
rv_pos, rv_neg)
# Compute various metrics based on melodiness scores.
melodiness_scores = mv.melodiness_metrics(m_train, m_test, y_train, y_test)
best_thresh, max_fscore, thresh_plot_data = \
eu.get_best_threshold(y_test, m_test) # THIS SHOULD PROBABLY BE VALIDATION NUMBERS...
# thresh_plot_data = pd.DataFrame(np.array(thresh_plot_data).transpose(),
# columns=['recall', 'precision',
# 'thresh', 'f1'])
# fpath = os.path.join(outdir, 'thresh_plot_data.csv')
# thresh_plot_data.to_csv(fpath)
melodiness_scores = pd.DataFrame.from_dict(melodiness_scores)
fpath = os.path.join(outdir, 'melodiness_scores.csv')
melodiness_scores.to_csv(fpath)
print "Melodiness best thresh = %s" % best_thresh
print "Melodiness max f1 score = %s" % max_fscore
print "overall melodiness scores:"
print melodiness_scores
def classifier(x_train, y_train, x_valid, y_valid, x_test, y_test, outdir):
""" Train Classifier
"""
# Cross Validation
best_depth, _, cv_plot_data = cu.cross_val_sweep(x_train, y_train)
print "Classifier best depth = %s" % best_depth
cv_plot_data = pd.DataFrame(np.array(cv_plot_data).transpose(),
columns=['max depth', 'accuracy', 'std'])
fpath = os.path.join(outdir, 'cv_plot_data.csv')
cv_plot_data.to_csv(fpath)
# Training
clf = cu.train_clf(x_train, y_train, best_depth)
# Predict and Score
p_train, p_valid, p_test = cu.clf_predictions(x_train, x_valid, x_test,
clf)
clf_scores = cu.clf_metrics(p_train, p_test, y_train, y_test)
print "Classifier scores:"
print clf_scores
# Get threshold that maximizes F1 score
best_thresh, max_fscore, thresh_plot_data = \
eu.get_best_threshold(y_valid, p_valid)
# thresh_plot_data = pd.DataFrame(np.array(thresh_plot_data).transpose(),
# columns=['recall', 'precision',
# 'thresh', 'f1'])
# fpath = os.path.join(outdir, 'thresh_plot_data.csv')
# thresh_plot_data.to_csv(fpath)
clf_scores = pd.DataFrame.from_dict(clf_scores)
fpath = os.path.join(outdir, 'classifier_scores.csv')
clf_scores.to_csv(fpath)
clf_outdir = os.path.join(outdir, 'classifier')
if not os.path.exists(clf_outdir):
os.mkdir(clf_outdir)
clf_fpath = os.path.join(clf_outdir, 'rf_clf.pkl')
joblib.dump(clf, clf_fpath)
print "Classifier best threshold = %s" % best_thresh
print "Classifier maximum f1 score = %s" % max_fscore
return clf, best_thresh
def classifier(x_train, y_train, x_valid, y_valid, x_test, y_test, outdir):
""" Train Classifier
"""
# Cross Validation
best_depth, _, cv_plot_data = cu.cross_val_sweep(x_train, y_train)
print "Classifier best depth = %s" % best_depth
cv_plot_data = pd.DataFrame(np.array(cv_plot_data).transpose(),
columns=['max depth', 'accuracy', 'std'])
fpath = os.path.join(outdir, 'cv_plot_data.csv')
cv_plot_data.to_csv(fpath)
# Training
clf = cu.train_clf(x_train, y_train, best_depth)
# Predict and Score
p_train, p_valid, p_test = cu.clf_predictions(x_train, x_valid, x_test, clf)
clf_scores = cu.clf_metrics(p_train, p_test, y_train, y_test)
print "Classifier scores:"
print clf_scores
# Get threshold that maximizes F1 score
best_thresh, max_fscore, thresh_plot_data = \
eu.get_best_threshold(y_valid, p_valid)
# thresh_plot_data = pd.DataFrame(np.array(thresh_plot_data).transpose(),
# columns=['recall', 'precision',
# 'thresh', 'f1'])
# fpath = os.path.join(outdir, 'thresh_plot_data.csv')
# thresh_plot_data.to_csv(fpath)
clf_scores = pd.DataFrame.from_dict(clf_scores)
fpath = os.path.join(outdir, 'classifier_scores.csv')
clf_scores.to_csv(fpath)
clf_outdir = os.path.join(outdir, 'classifier')
if not os.path.exists(clf_outdir):
os.mkdir(clf_outdir)
clf_fpath = os.path.join(clf_outdir, 'rf_clf.pkl')
joblib.dump(clf, clf_fpath)
print "Classifier best threshold = %s" % best_thresh
print "Classifier maximum f1 score = %s" % max_fscore
return clf, best_thresh
