def train_logreg(trX,
trY,
vaX=None,
vaY=None,
teX=None,
teY=None,
penalty='l1',
max_iter=100,
C=2**np.arange(-8, 1).astype(np.float),
seed=42,
model=None,
eval_test=True,
neurons=None,
drop_neurons=False,
report_metric='acc',
automatic_thresholding=False,
threshold_metric='acc',
micro=False):
# if only integer is provided for C make it iterable so we can loop over
if not isinstance(C, collections.Iterable):
C = list([C])
# extract features for given neuron indices
if neurons is not None:
if drop_neurons:
all_neurons = set(list(range(trX.shape[-1])))
neurons = set(list(neurons))
neurons = list(all_neurons - neurons)
trX = trX[:, neurons]
if vaX is not None:
vaX = vaX[:, neurons]
if teX is not None:
teX = teX[:, neurons]
# Cross validation over C
n_classes = 1
if len(trY.shape) > 1:
n_classes = trY.shape[-1]
scores = []
if model is None:
for i, c in enumerate(C):
if n_classes <= 1:
model = LogisticRegression(C=c,
penalty=penalty,
max_iter=max_iter,
random_state=seed)
model.fit(trX, trY)
blank_info_dict = {
'fp': 0,
'tp': 0,
'fn': 0,
'tn': 0,
'std': 0.,
'metric': threshold_metric,
'micro': micro
}
if vaX is not None:
info_dict = update_info_dict(
blank_info_dict.copy(), vaY,
model.predict_proba(vaX)[:, -1])
else:
info_dict = update_info_dict(
blank_info_dict.copy(), trY,
model.predict_proba(trX)[:, -1])
scores.append(get_metric(info_dict))
print(scores[-1])
del model
else:
info_dicts = []
model = []
for cls in range(n_classes):
_model = LogisticRegression(C=c,
penalty=penalty,
max_iter=max_iter,
random_state=seed)
_model.fit(trX, trY[:, cls])
blank_info_dict = {
'fp': 0,
'tp': 0,
'fn': 0,
'tn': 0,
'std': 0.,
'metric': threshold_metric,
'micro': micro
}
if vaX is not None:
info_dict = update_info_dict(
blank_info_dict.copy(), vaY[:, cls],
_model.predict_proba(vaX)[:, -1])
else:
info_dict = update_info_dict(
blank_info_dict.copy(), trY[:, cls],
_model.predict_proba(trX)[:, -1])
info_dicts.append(info_dict)
model.append(_model)
scores.append(get_metric(info_dicts))
print(scores[-1])
del model
c = C[np.argmax(scores)]
if n_classes <= 1:
model = LogisticRegression(C=c,
penalty=penalty,
max_iter=max_iter,
random_state=seed)
model.fit(trX, trY)
else:
model = []
for cls in range(n_classes):
_model = LogisticRegression(C=c,
penalty=penalty,
max_iter=max_iter,
random_state=seed)
_model.fit(trX, trY[:, cls])
model.append(_model)
else:
c = model.C
# predict probabilities and get accuracy of regression model on train, val, test as appropriate
# also get number of regression weights that are not zero. (number of features used for modeling)
scores = []
if n_classes == 1:
nnotzero = np.sum(model.coef_ != 0)
preds = model.predict_proba(trX)[:, -1]
train_score = get_metric(
update_info_dict(blank_info_dict.copy(), trY, preds),
report_metric)
else:
nnotzero = 0
preds = []
info_dicts = []
for cls in range(n_classes):
nnotzero += np.sum(model[cls].coef_ != 0)
_preds = model[cls].predict_proba(trX)[:, -1]
info_dicts.append(
update_info_dict(blank_info_dict.copy(), trY[:, cls], _preds))
preds.append(_preds)
nnotzero /= n_classes
train_score = get_metric(info_dicts, report_metric)
preds = np.concatenate([p.reshape((-1, 1)) for p in preds], axis=1)
scores.append(train_score * 100)
if vaX is None:
eval_data = trX
eval_labels = trY
val_score = train_score
else:
eval_data = vaX
eval_labels = vaY
if n_classes == 1:
preds = model.predict_proba(vaX)[:, -1]
val_score = get_metric(
update_info_dict(blank_info_dict.copy(), vaY, preds),
report_metric)
else:
preds = []
info_dicts = []
for cls in range(n_classes):
_preds = model[cls].predict_proba(vaX)[:, -1]
info_dicts.append(
update_info_dict(blank_info_dict.copy(), vaY[:, cls],
_preds))
preds.append(_preds)
val_score = get_metric(info_dicts, report_metric)
preds = np.concatenate([p.reshape((-1, 1)) for p in preds], axis=1)
val_preds = preds
val_labels = eval_labels
scores.append(val_score * 100)
eval_score = val_score
threshold = np.array([.5] * n_classes)
if automatic_thresholding:
_, threshold, _, _ = _binary_threshold(
preds.reshape(-1, n_classes), eval_labels.reshape(-1, n_classes),
threshold_metric, micro)
threshold = float(threshold.squeeze())
if teX is not None and teY is not None and eval_test:
eval_data = teX
eval_labels = teY
if n_classes == 1:
preds = model.predict_proba(eval_data)[:, -1]
else:
preds = []
for cls in range(n_classes):
_preds = model[cls].predict_proba(eval_data)[:, -1]
preds.append(_preds)
preds = np.concatenate([p.reshape((-1, 1)) for p in preds], axis=1)
if n_classes == 1:
threshold = float(threshold.squeeze())
eval_score = get_metric(
update_info_dict(blank_info_dict.copy(),
eval_labels,
preds,
threshold=threshold), report_metric)
else:
info_dicts = []
for cls in range(n_classes):
info_dicts.append(
update_info_dict(blank_info_dict.copy(),
eval_labels[:, cls],
preds[:, cls],
threshold=threshold[cls]))
eval_score = get_metric(info_dicts, report_metric)
scores.append(eval_score * 100)
return model, scores, preds, c, nnotzero
#dense_features = ngramTrain.toarray()
#dense_test = test_features.toarray()
TfidfAccuracy = []
TfidfModel = []
for classifier in randmClassifiers:
for x in range(0, 10):
random.seed(x)
fit = classifier.fit(TfidfTrain, train['Label'])
pred = fit.predict(TfidfTest)
prob = fit.predict_proba(TfidfTest)[:, 1]
accuracy = metrics.accuracy_score(pred, test['Label'])
TfidfAccuracy.append(accuracy)
TfidfModel.append(classifier.__class__.__name__)
print('Accuracy of ' + classifier.__class__.__name__ + ' is ' +
str(accuracy))
rndmModelPerf = pd.DataFrame({'Model': TfidfModel, 'Acc': TfidfAccuracy})
# hashing vectorizer - tokenizes by createing a correlational matrix
hashingVectorizer = HashingVectorizer(n_features=50)
hashingTrain = hashingVectorizer.fit_transform(trainHeadlines)
hashingModel = LogisticRegression(solver='lbfgs')
hashingModel = hashingModel.fit(hashingTrain, train["Label"])
hashingTest = hashingVectorizer.transform(testHeadlines)
hashingPreds = hashingModel.predict(hashingTest)
