plot_outlier_fraction = None
# extra fraction of plot to show (not used if plot_outlier_fraction is None)
plot_f_edge = 0.05
# compute and save features, or laod previously computed features
if os.path.isfile(feature_file):
X = np.loadtxt(feature_file)
data_files = None
else:
print 'Computing features...'
X, data_files = features.compute_feature_matrix(data_dir,
feature_functions, feature_labels,
save_file=feature_file,
verbose=True)
X, outlier_indices = features.remove_outliers(X, n_sigma=2, verbose=True)
#X = features.scale_features(X)
# plot features for each segment
fig, axs = plt.subplots(n_features-1, n_features-1, figsize=(9,9),
sharex='col', sharey='row')
plt.subplots_adjust(hspace=0.001, wspace=0.001)
for i in range(n_features-1):
for j in range(i+1, n_features-1):
fig.delaxes(axs[i,j])
for seg_type, color, marker in zip([-1, 0, 1],
['b', 'y', 'r'],
['o', 'x', '+']):
# select rows matching a given segment type and remove (hour, type) cols.
def train_model(
features_files,
feature_columns,
classifier,
model_args,
outlier_sigma=None,
scale_features=True,
submission_file=None,
save_settings=False,
plot=False,
normalize_probs=None,
n_cv=10,
f_cv=0.3,
verbose=False,
):
"""
Fit a classification model (classifier, using arguments in model_args)
to the features in columns feature_columns in the file(s) in
features_files. Use CV with n_cv random training-CV sample splittings,
each containing a fraction f_cv in the CV subsample, to estimate AUC
for the fit.
"""
settings = locals()
hour_column = 0
type_column = 1
# read in feature matrix from file(s)
X = features.load_features(features_files)
# remove outliers
if outlier_sigma is not None:
X, retained_indices = features.remove_outliers(X, n_sigma=outlier_sigma)
# scale features
if scale_features:
X = features.scale_features(X)
# set up model
model = classifier(**model_args)
# set up plot
if plot:
fig = plt.figure(figsize=(8, 4))
fig.set_tight_layout(True)
ax0 = plt.subplot(121)
ax1 = plt.subplot(122)
# initialize plot arrays
n_learn = np.zeros(10)
learn_cv_avg = np.zeros(len(n_learn))
learn_train_avg = np.zeros(len(n_learn))
fp_rate_avg = np.linspace(0, 1, num=100)
tp_rate_avg = np.zeros(len(fp_rate_avg))
# loop over training-CV sample splittings
auc_values = []
for i_cv in range(n_cv):
cv_indices = cv.cv_split_by_hour(X, n_pre_hrs=f_cv)
if verbose:
print "\nCV iteration", i_cv + 1
print len(cv_indices["train"]), "training instances"
print len(cv_indices["cv"]), "CV instances"
# get feature matrices and class arrays for training and CV samples
train_features_all, cv_features_all = [X[cv_indices[k], :] for k in ["train", "cv"]]
train_features, cv_features = [y[:, np.array(feature_columns)] for y in [train_features_all, cv_features_all]]
train_class = train_features_all[:, type_column]
cv_class = cv_features_all[:, type_column]
# compute learning curve
if plot:
learn_mask, n_train, learn_train, learn_cv = learning_curve(
model,
(train_features, train_class),
(cv_features, cv_class),
n=len(n_learn),
normalize_probs=normalize_probs,
)
if len(learn_mask) > 0:
n_learn[learn_mask] += 1
learn_train_avg[learn_mask] += learn_train
learn_cv_avg[learn_mask] += learn_cv
ax0.plot(n_train, learn_train, linestyle="-", color=(1, 0.6, 0.6))
ax0.plot(n_train, learn_cv, linestyle="-", color=(0.7, 0.7, 0.7))
# predict probabilities
train_prob, cv_prob = predict_probs(model, train_class, train_features, cv_features, normalize_probs)
check_for_nan(train_prob)
check_for_nan(cv_prob)
if verbose:
try:
model_coef = model.coef_
print "Feature coefficients:", model_coef
except:
pass
# compute AUC
auc = roc_auc_score(cv_class, cv_prob)
auc_values.append(auc)
if verbose:
print "training AUC =", roc_auc_score(train_class, train_prob)
print "CV AUC =", auc
# plot ROC curve
if plot:
fp_rate, tp_rate, thresholds = roc_curve(cv_class, cv_prob)
tp_rate_avg += np.interp(fp_rate_avg, fp_rate, tp_rate)
ax1.plot(fp_rate, tp_rate, linestyle="-", color=(0.7, 0.7, 0.7))
# compute mean and std. dev. of AUC over CV iterations
auc_mean = np.mean(auc_values)
auc_std = np.std(auc_values)
if verbose:
print "\nAverage AUC:", auc_mean, "+/-", auc_std
# update submission CSV file
if submission_file is not None:
train_features_all = X[(X[:, type_column] == 0) | (X[:, type_column] == 1), :]
train_features = train_features_all[:, np.array(feature_columns)]
train_class = train_features_all[:, type_column]
test_features_all = X[X[:, type_column] == -1, :]
test_features = test_features_all[:, np.array(feature_columns)]
train_prob, test_prob = predict_probs(model, train_class, train_features, test_features, normalize_probs)
check_for_nan(train_prob, message="Replacing NaN probabilities with 0.")
check_for_nan(test_prob, message="Replacing NaN probabilities with 0.")
for i, ff in enumerate(features_files):
data_list_file = ".".join(ff.split(".")[:-1]) + "_data_files.txt"
with open(data_list_file, "r") as df:
if i == 0:
data_files = np.array(df.readlines())
else:
data_files = np.concatenate((data_files, df.readlines()), axis=0)
if outlier_sigma is not None:
data_files = data_files[retained_indices]
test_files = []
for f in data_files:
if "test" in f:
test_files.append(f.strip())
submission.update_submission(dict(zip(test_files, test_prob)), submission_file)
# save settings
if save_settings:
if submission_file is not None:
settings_file = ".".join(submission_file.split(".")[:-1]) + "_settings.txt"
open_mode = "a"
else:
settings_file = "train_model_settings.txt"
open_mode = "w"
with open(settings_file, open_mode) as sf:
for s in [
"features_files",
"feature_columns",
"classifier",
"model_args",
"outlier_sigma",
"scale_features",
"submission_file",
"normalize_probs",
]:
if s in settings:
sf.write(s + ": " + str(settings[s]) + "\n")
sf.write("AUC = {0:.2f}+/-{1:.2f}\n\n".format(auc_mean, auc_std))
# plot average learning curves and ROC curve
if plot:
n_train_array = len(cv_indices["train"]) / float(len(n_learn)) * np.array(range(1, len(n_learn) + 1))
ax0.plot(n_train_array, learn_train_avg / (n_learn + 1.0e-3), "r-", linewidth=3)
ax0.plot(n_train_array, learn_cv_avg / (n_learn + 1.0e-3), "k-", linewidth=3)
tp_rate_avg /= float(n_cv)
ax1.plot(fp_rate_avg, tp_rate_avg, "k-", linewidth=3)
# display plot
ax0.set_ylim((0.5, 1))
ax0.set_xlabel("number of training instances")
ax0.set_ylabel("AUC")
ax1.plot(np.linspace(0, 1), np.linspace(0, 1), "k:", linewidth=2)
ax1.set_xlabel("false positive rate")
ax1.set_ylabel("true positive rate")
plt.show(block=False)
return (model, auc_mean, auc_std)
