def test_pipeline_init():
# Test the various init parameters of the pipeline.
assert_raises(TypeError, Pipeline)
# Check that we can't instantiate pipelines with objects without fit
# method
pipe = assert_raises(TypeError, Pipeline, [('svc', IncorrectT)])
# Smoke test with only an estimator
clf = T()
pipe = Pipeline([('svc', clf)])
assert_equal(
pipe.get_params(deep=True),
dict(
svc__a=None, svc__b=None, svc=clf, **pipe.get_params(deep=False)))
# Check that params are set
pipe.set_params(svc__a=0.1)
assert_equal(clf.a, 0.1)
assert_equal(clf.b, None)
# Smoke test the repr:
repr(pipe)
# Test with two objects
clf = SVC()
filter1 = SelectKBest(f_classif)
pipe = Pipeline([('anova', filter1), ('svc', clf)])
# Check that we can't use the same stage name twice
assert_raises(ValueError, Pipeline, [('svc', SVC()), ('svc', SVC())])
# Check that params are set
pipe.set_params(svc__C=0.1)
assert_equal(clf.C, 0.1)
# Smoke test the repr:
repr(pipe)
# Check that params are not set when naming them wrong
assert_raises(ValueError, pipe.set_params, anova__C=0.1)
# Test clone
pipe2 = clone(pipe)
assert_false(pipe.named_steps['svc'] is pipe2.named_steps['svc'])
# Check that apart from estimators, the parameters are the same
params = pipe.get_params(deep=True)
params2 = pipe2.get_params(deep=True)
for x in pipe.get_params(deep=False):
params.pop(x)
for x in pipe2.get_params(deep=False):
params2.pop(x)
# Remove estimators that where copied
params.pop('svc')
params.pop('anova')
params2.pop('svc')
params2.pop('anova')
assert_equal(params, params2)
def test_pipeline_init_tuple():
# Pipeline accepts steps as tuple
X = np.array([[1, 2]])
pipe = Pipeline((("transf", Transf()), ("clf", FitParamT())))
pipe.fit(X, y=None)
pipe.score(X)
pipe.set_params(transf="passthrough")
pipe.fit(X, y=None)
pipe.score(X)
def test_pipeline_raise_set_params_error():
# Test pipeline raises set params error message for nested models.
pipe = Pipeline([("cls", LinearRegression())])
with raises(ValueError, match="Invalid parameter"):
pipe.set_params(fake="nope")
# nested model check
with raises(ValueError, match="Invalid parameter"):
pipe.set_params(fake__estimator="nope")
def test_pipeline_init():
# Test the various init parameters of the pipeline.
assert_raises(TypeError, Pipeline)
# Check that we can't instantiate pipelines with objects without fit
# method
pipe = assert_raises(TypeError, Pipeline, [('svc', IncorrectT)])
# Smoke test with only an estimator
clf = T()
pipe = Pipeline([('svc', clf)])
assert_equal(
pipe.get_params(deep=True),
dict(svc__a=None, svc__b=None, svc=clf, **pipe.get_params(deep=False)))
# Check that params are set
pipe.set_params(svc__a=0.1)
assert_equal(clf.a, 0.1)
assert_equal(clf.b, None)
# Smoke test the repr:
repr(pipe)
# Test with two objects
clf = SVC()
filter1 = SelectKBest(f_classif)
pipe = Pipeline([('anova', filter1), ('svc', clf)])
# Check that we can't use the same stage name twice
assert_raises(ValueError, Pipeline, [('svc', SVC()), ('svc', SVC())])
# Check that params are set
pipe.set_params(svc__C=0.1)
assert_equal(clf.C, 0.1)
# Smoke test the repr:
repr(pipe)
# Check that params are not set when naming them wrong
assert_raises(ValueError, pipe.set_params, anova__C=0.1)
# Test clone
pipe2 = clone(pipe)
assert_false(pipe.named_steps['svc'] is pipe2.named_steps['svc'])
# Check that apart from estimators, the parameters are the same
params = pipe.get_params(deep=True)
params2 = pipe2.get_params(deep=True)
for x in pipe.get_params(deep=False):
params.pop(x)
for x in pipe2.get_params(deep=False):
params2.pop(x)
# Remove estimators that where copied
params.pop('svc')
params.pop('anova')
params2.pop('svc')
params2.pop('anova')
assert_equal(params, params2)
def test_pipeline_raise_set_params_error():
# Test pipeline raises set params error message for nested models.
pipe = Pipeline([('cls', LinearRegression())])
with raises(ValueError, match="Invalid parameter"):
pipe.set_params(fake='nope')
# nested model check
with raises(ValueError, match="Invalid parameter"):
pipe.set_params(fake__estimator='nope')
def train_imbalance(
descr_series: Series,
classes_codes: Series,
TFIDF_,
IMB_,
FS_,
req_percentage: int,
CLF_,
model_name: str,
) -> tuple:
"""Trains models using handled setting and saves them as .sav objects.
Parameters:
----------
instance:
Instance of User model.
descr_series:
description series.
classes_codes:
series with classes' codes.
TFIDF_:
vectorizer.
IMB_:
SMOTE instance.
FS_:
ranking terms method.
req_percentage:
percentage to be taken from the ranked list.
CLF_:
classifier.
model_name:
models name.
Returns:
----------
Trained model in byte representation associated to its model name.
"""
transformer = feature_selection.SelectPercentile(FS_)
clf_model = Pipeline([("tfidf", TFIDF_), ("imba", IMB_),
("fs", transformer), ("clf", CLF_)])
best_params = get_best_params(clf_model, descr_series, classes_codes)
print(f"{model_name}:{best_params}")
clf_model.set_params(
fs__percentile=req_percentage,
clf__C=best_params["clf__C"],
clf__gamma=best_params["clf__gamma"],
).fit(descr_series, classes_codes)
return {model_name: clf_model}, {model_name: best_params}
def get_text_transformer(method: str, pca: bool, ngram_range):
from imblearn.pipeline import Pipeline
from sklearn.decomposition import TruncatedSVD
steps = [('vect', CountVectorizer(tokenizer=TextTokenizer().preprocess)),
('tfidf', TfidfTransformer())]
if method != "none":
steps.append(("os", get_balancing_step(method)))
if pca:
steps.append(('pca', TruncatedSVD()))
pipeline = Pipeline(steps)
pipeline.set_params(vect__ngram_range=ngram_range, tfidf__use_idf=True)
return pipeline
def train_imbalance(
descr_series: pd.Series,
classes_codes: pd.Series,
TFIDF_,
IMB_,
FS_,
req_percentage: int,
CLF_,
model_name: str,
) -> dict:
""" Trains models using handled setting and saves them as .sav objects.
Parameters:
----------
instance:
Instance of User model;
descr_series:
description series;
classes_codes:
series with classes' codes;
TFIDF_:
vectorizer;
IMB_:
SMOTE instance;
FS_:
ranking terms method;
req_percentage:
percentage to be taken from the ranked list;
CLF_:
classifier;
model_name:
models name.
Returns:
----------
Trained model in byte representation associated to its model name.
"""
transformer = feature_selection.SelectPercentile(FS_)
clf_model = Pipeline([("tfidf", TFIDF_), ("imba", IMB_),
("fs", transformer), ("clf", CLF_)])
clf_model.set_params(fs__percentile=req_percentage).fit(
descr_series, classes_codes)
return {model_name: clf_model}
def training_imbalance(descr_series, classes_codes, TFIDF_, IMB_, FS_,
req_percentage, CLF_, model_path):
""" Trains models using handled setting and saves them as .sav objects.
Parameters:
descr_series(Series): description series;
classes_codes(Series): series with classes' codes;
TFIDF_: vectorizer;
IMB_: SMOTE method;
FS_: ranking terms method;
req_percentage(int): percentage to be taken from the ranked list;
CLF_: classifier;
model_path(str): the path to the model.
"""
transformer = feature_selection.SelectPercentile(FS_)
clf_model = Pipeline([('tfidf', TFIDF_), ('imba', IMB_),
('fs', transformer), ('clf', CLF_)])
clf_model.set_params(fs__percentile=req_percentage).fit(
descr_series, classes_codes)
dump(clf_model, open(model_path + '.sav', 'wb'))
def test_set_pipeline_steps():
transf1 = Transf()
transf2 = Transf()
pipeline = Pipeline([('mock', transf1)])
assert pipeline.named_steps['mock'] is transf1
# Directly setting attr
pipeline.steps = [('mock2', transf2)]
assert 'mock' not in pipeline.named_steps
assert pipeline.named_steps['mock2'] is transf2
assert [('mock2', transf2)] == pipeline.steps
# Using set_params
pipeline.set_params(steps=[('mock', transf1)])
assert [('mock', transf1)] == pipeline.steps
# Using set_params to replace single step
pipeline.set_params(mock=transf2)
assert [('mock', transf2)] == pipeline.steps
# With invalid data
pipeline.set_params(steps=[('junk', ())])
with raises(TypeError):
pipeline.fit([[1]], [1])
with raises(TypeError):
pipeline.fit_transform([[1]], [1])
def test_set_pipeline_steps():
transf1 = Transf()
transf2 = Transf()
pipeline = Pipeline([("mock", transf1)])
assert pipeline.named_steps["mock"] is transf1
# Directly setting attr
pipeline.steps = [("mock2", transf2)]
assert "mock" not in pipeline.named_steps
assert pipeline.named_steps["mock2"] is transf2
assert [("mock2", transf2)] == pipeline.steps
# Using set_params
pipeline.set_params(steps=[("mock", transf1)])
assert [("mock", transf1)] == pipeline.steps
# Using set_params to replace single step
pipeline.set_params(mock=transf2)
assert [("mock", transf2)] == pipeline.steps
# With invalid data
pipeline.set_params(steps=[("junk", ())])
with raises(TypeError):
pipeline.fit([[1]], [1])
with raises(TypeError):
pipeline.fit_transform([[1]], [1])
def test_set_pipeline_steps():
transf1 = Transf()
transf2 = Transf()
pipeline = Pipeline([('mock', transf1)])
assert pipeline.named_steps['mock'] is transf1
# Directly setting attr
pipeline.steps = [('mock2', transf2)]
assert 'mock' not in pipeline.named_steps
assert pipeline.named_steps['mock2'] is transf2
assert [('mock2', transf2)] == pipeline.steps
# Using set_params
pipeline.set_params(steps=[('mock', transf1)])
assert [('mock', transf1)] == pipeline.steps
# Using set_params to replace single step
pipeline.set_params(mock=transf2)
assert [('mock', transf2)] == pipeline.steps
# With invalid data
pipeline.set_params(steps=[('junk', ())])
assert_raises(TypeError, pipeline.fit, [[1]], [1])
assert_raises(TypeError, pipeline.fit_transform, [[1]], [1])
def resampling(X, Y, r):
# print(sorted(Counter(Y).items()))
smote_enn = TomekLinks()
X_resampled, y_resampled = smote_enn.fit_resample(X, Y)
#print(sorted(Counter(y_resampled).items()))
return X_resampled, y_resampled
# pipeline
pipeline = Pipeline([
('und', RandomUnderSampler()),
#('power', preprocessing.PowerTransformer()),
('standardize', preprocessing.StandardScaler()),
('normalizer', preprocessing.Normalizer()),
('lda', LinearDiscriminantAnalysis()),
#('logistic', sk.linear_model.SGDClassifier(loss="hinge", eta0=1, learning_rate="constant", penalty='l2'))
('svm', LinearSVC(verbose=0, max_iter=3000, class_weight='balanced')),
])
com_values = [1e-5, 1e-4, 1e-3, 1e-2, 0.1, 1, 10]
for c in com_values:
pipeline.set_params(svm__C=c, und__random_state=42).fit(X_train, Y_train)
# clf = CalibratedClassifierCV(base_estimator=pipeline, cv=10).fit(X,Y)
y_p = pipeline.decision_function(X_dev)
y_pred = pipeline.predict(X_dev)
print("With:", c)
print("Confusion matrix:\n", sk.metrics.confusion_matrix(Y_dev, y_pred))
one = sk.metrics.recall_score(Y_dev, y_pred, pos_label=0)
two = sk.metrics.recall_score(Y_dev, y_pred, pos_label=1)
print("UAR:", (one + two) / 2, "\n")
class Model:
def training_model(self,
model_path,
data_path,
colums,
resolution,
sw=text.ENGLISH_STOP_WORDS,
log=0):
try:
self.creater = ConfigCreator()
smt = SMOTE(ratio='minority',
random_state=0,
kind='borderline1',
n_jobs=4)
svm_imb = SVC(gamma=2,
C=1,
probability=True,
class_weight='balanced')
tfidf_imb = StemmedTfidfVectorizer(norm='l2',
sublinear_tf=True,
stop_words=sw,
analyzer='word',
max_df=0.5,
max_features=500)
anova = feature_selection.f_classif
chi2 = feature_selection.chi2
if not os.path.exists(model_path):
os.makedirs(model_path)
self.data = pandas.read_pickle(data_path)
binary_logger = BynaryTrainLogger()
multiple_logger = MultipleTrainLogger()
filter = Filter()
self.creater.create_config(
'single_mod.ini', section_name='single_mod.ini'.split('.')[0])
# models training
self.creater.update_setting('single_mod.ini',
'single_mod.ini'.split('.')[0],
'binary_col_class',
','.join(['0', '1']))
# areas
# to exclude columns with quite low percent of 1
clear_columns = [
column for column in colums
if self.data[column][self.data[column] == 1].size /
self.data[column].size > 0.005
]
self.creater.update_setting('single_mod.ini',
'single_mod.ini'.split('.')[0],
'columns', ','.join(clear_columns))
for col in clear_columns:
self.data['Description_tr'] = self.data['Description_tr']
Model.training_imbalance_kf(self, self.data['Description_tr'],
self.data[col], tfidf_imb, smt,
chi2, 50, svm_imb,
secure_filename(col), model_path)
if log == 1:
binary_logger.log('{}.log'.format(session.sid),
self.data['Description_tr'],
self.data[col], col, model_path)
# priority
# to exclude rows with quite low percent of any categorical value
self.valid_set = [
el for el in self.data['Priority'].unique().tolist()
if self.data['Priority'][self.data['Priority'] == el].size /
self.data['Priority'].size > 0.005
]
self.data_after_filter = self.data[self.data['Priority'].isin(
self.valid_set)]
if self.data.shape[0] > self.data_after_filter.shape[0]:
self.data_after_filter = filter.reindex_data(
self.data_after_filter)
self.data_after_filter['Priority_ord'] = self.data_after_filter[
'Priority'].astype("category")
self.data_after_filter['Priority_ord_codes'] = pandas.Categorical(
self.data_after_filter['Priority_ord']).codes
# train
Model.training_imbalance_kf(
self, self.data_after_filter['Description_tr'],
self.data_after_filter['Priority_ord_codes'], tfidf_imb, smt,
chi2, 50, svm_imb, 'priority', model_path)
self.creater.update_setting(
'single_mod.ini', 'single_mod.ini'.split('.')[0],
'prior_col_class',
','.join(self.data_after_filter['Priority'].unique().tolist()))
# log
if log == 1:
# print('priority')
# start = time.clock()
multiple_logger.log(
'{}.log'.format(session.sid),
self.data_after_filter['Description_tr'],
self.data_after_filter['Priority_ord_codes'], 'priority',
model_path)
# print(time.clock()-start)
bins = 4
self.ldis = [i for i in range(1, bins + 1)]
# ttr
# to exclude rows with quite low percent of any categorical value
self.data['temp_ttr_class'] = pandas.qcut(self.data['ttr_tr'],
bins,
labels=self.ldis,
duplicates='drop')
self.valid_set = [
el for el in self.data['temp_ttr_class'].unique().tolist()
if self.data['temp_ttr_class'][self.data['temp_ttr_class'] ==
el].size /
self.data['temp_ttr_class'].size > 0.005
]
self.data_after_filter = self.data[
self.data['temp_ttr_class'].isin(self.valid_set)]
if self.data.shape[0] > self.data_after_filter.shape[0]:
self.data_after_filter = filter.reindex_data(
self.data_after_filter)
# train
Model.training_imbalance_kf(
self, self.data_after_filter['Description_tr'],
self.data_after_filter['temp_ttr_class'], tfidf_imb, smt, chi2,
50, svm_imb, 'ttr', model_path)
self.ttr_col_classTemp = pandas.qcut(
self.data_after_filter['ttr_tr'], 4,
duplicates='drop').unique()
self.creater.update_setting(
'single_mod.ini', 'single_mod.ini'.split('.')[0],
'ttr_col_class', ','.join([
str(Model.ifZero(self, self.ttr_col_classTemp[el].left)) +
'-' +
str(Model.ifZero(self, self.ttr_col_classTemp[el].right))
for el in range(3)
] + [
'>' + str(
Model.ifZero(
self, self.ttr_col_classTemp[range(3)[-1]].right))
]))
# log
if log == 1:
multiple_logger.log('{}.log'.format(session.sid),
self.data_after_filter['Description_tr'],
self.data_after_filter['temp_ttr_class'],
'ttr', model_path)
# resolution
# resolution may have values wich can't be correctly processed by the system like: "Won't Fix"
# therefore we have to convert them to model name via secure_filename() function
self.bin_data = pandas.get_dummies(self.data,
prefix=list(resolution.keys()),
columns=list(resolution.keys()))
# to exclude columns with quite low percent of 1
clear_columns = {}
for key in resolution:
if isinstance(resolution[key], list):
resolutions = []
for rez in resolution[key]:
if self.bin_data[key + '_' + rez][self.bin_data[
key + '_' + rez] == 1].size / self.bin_data[
key + '_' + rez].size > 0.005:
resolutions.append(rez)
if len(resolutions) == 1:
clear_columns[key] = resolutions[0]
if len(resolutions) > 1:
clear_columns[key] = resolutions
else:
if self.bin_data[key + '_' + resolution[key]][
self.bin_data[key + '_' + resolution[key]] ==
1].size / self.bin_data[
key + '_' + resolution[key]].size > 0.005:
clear_columns[key] = resolution[key]
for key in clear_columns:
if isinstance(clear_columns[key], list):
for res in clear_columns[key]:
Model.training_imbalance_kf(
self, self.bin_data['Description_tr'],
self.bin_data[key + '_' + res],
tfidf_imb, smt, chi2, 50, svm_imb,
secure_filename(res), model_path)
self.creater.update_setting(
'single_mod.ini', 'single_mod.ini'.split('.')[0],
res + '_col_class', ','.join(['not ' + res, res]))
if log == 1:
binary_logger.log('{}.log'.format(session.sid),
self.bin_data['Description_tr'],
self.bin_data[key + '_' + res],
secure_filename(res), model_path)
else:
Model.training_imbalance_kf(
self, self.bin_data['Description_tr'],
self.bin_data[key + '_' + clear_columns[key]],
tfidf_imb, smt, chi2, 50, svm_imb,
secure_filename(clear_columns[key]), model_path)
self.creater.update_setting(
'single_mod.ini', 'single_mod.ini'.split('.')[0],
clear_columns[key] + '_col_class', ','.join(
['not ' + clear_columns[key], clear_columns[key]]))
if log == 1:
binary_logger.log(
'{}.log'.format(session.sid),
self.bin_data['Description_tr'],
self.bin_data[key + '_' + clear_columns[key]],
secure_filename(clear_columns[key]), model_path)
self.data.to_pickle(data_path)
except FileNotFoundError:
raise
def training_imbalance_kf(self, X_, Y_, TFIDF_, IMB_, FS_, pers_, CLF_,
name_, model_path):
self.transform = feature_selection.SelectPercentile(FS_)
self.clf_model = Pipeline([('tfidf', TFIDF_), ('imba', IMB_),
('fs', self.transform), ('clf', CLF_)])
kf = KFold(n_splits=10)
kf.get_n_splits(X_)
#X_train, X_test, y_train, y_test = cross_validation.train_test_split(X_,Y_,train_size=.8, stratify=Y_)
for train_index, test_index in kf.split(X_):
self.X_train, self.X_test = X_[train_index], X_[test_index]
self.y_train, self.y_test = Y_[train_index], Y_[test_index]
self.clf_model.set_params(fs__percentile=pers_).fit(
self.X_train, self.y_train)
pickle.dump(self.clf_model, open(model_path + name_ + '.sav', 'wb'))
#y_pred = clf_model.predict(X_test)
#print(classification_report(y_test, y_pred))
def ifZero(self, val):
if val < 0:
return 0
else:
return val
def proc_text(self, text, col_class, name_model, model_path):
self.test_pro = text
try:
with open('regularExpression.csv') as csv_data:
for i in [
re.compile(el1) for el in csv.reader(
csv_data, delimiter=',', quotechar='"')
for el1 in el if el1
]:
self.test_pro = re.sub(i, ' ', self.test_pro)
self.proba = {}
sys.path.append("..")
self.load_model_test = pickle.load(
open(model_path + name_model + '.sav', 'rb'))
self.proba_ = list(
numpy.array(numpy.around(
self.load_model_test.predict_proba([self.test_pro])[0], 3),
dtype=float).flatten())
self.proba_dic = dict(zip(col_class, self.proba_))
return self.proba_dic
except Exception:
raise
def create_top_terms_file(self, frame, resolution):
checker = Checker()
chi2 = feature_selection.chi2
SW = text.ENGLISH_STOP_WORDS
config_reader = SettingProvider('single_mod.ini')
resol_all = []
for el in checker.get_resolutions(resolution):
resol_all += config_reader.get_setting(
section='single_mod',
setting="{el}_col_class".format(el=el),
evaluate=False).split(',')
resol = [el for el in resol_all if 'not' not in el]
prio = config_reader.get_setting(section='single_mod',
setting='prior_col_class',
evaluate=False).split(',')
areas = config_reader.get_setting(section='single_mod',
setting='columns',
evaluate=False).split(',')
all_terms = prio + resol + areas
all_mass = []
bin_data = pandas.get_dummies(
frame,
prefix=list(resolution.keys()) + ['Priority'],
columns=list(resolution.keys()) + ['Priority'])
with open('top_terms.csv', 'w', newline='\n') as csvfile:
csvwriter = csv.writer(csvfile)
csvwriter.writerow(all_terms)
for el in all_terms:
if el in prio:
prior = self.top_terms(bin_data, 'Priority_' + el, chi2,
SW)
all_mass.append(prior)
if el in resol:
key = None
for key1 in resolution:
if isinstance(resolution[key1], list):
if el in resolution[key1]:
key = key1
else:
if el == resolution[key1]:
key = key1
resol = self.top_terms(bin_data, key + '_' + el, chi2, SW)
all_mass.append(resol)
if el in areas:
area = self.top_terms(bin_data, el, chi2, SW)
all_mass.append(area)
rows = zip_longest(*all_mass)
for row in rows:
csvwriter.writerow(row)
def top_terms(self, data, field, func, SW):
tfidf = StemmedTfidfVectorizer(norm='l2',
sublinear_tf=True,
min_df=1,
stop_words=SW,
analyzer='word',
max_features=1000)
# bidata = pandas.get_dummies(data, prefix=field+'_', columns=field)
multithreaded = Multithreaded()
clear_data = ClearData()
parall_data = multithreaded.parallelize(data['Description_tr'],
clear_data.clean_descr)
tfs = tfidf.fit_transform(parall_data)
y = data[field]
selector = SelectKBest(score_func=func, k='all')
selector.fit_transform(tfs, y)
X_new = dict(zip(tfidf.get_feature_names(), selector.scores_))
temp_dict = sorted(X_new.items(), key=lambda x: x[1], reverse=True)
rez = []
mean = []
for el in temp_dict[:]:
if el[1] > 1:
rez.append(el)
mean.append(el[1])
import numpy
return [el[0] for el in rez if el[1] > numpy.mean(mean)]
def get_feature_pipeline(params: SingleBaseParams):
from imblearn.pipeline import Pipeline
from sklearn.decomposition import TruncatedSVD
feature_name = params.get_feature_name()
if feature_name == publication_year_key:
from features.text_processor import PublicationYearTransformer
steps = [('t', PublicationYearTransformer())]
else:
steps = [
('vect', CountVectorizer(tokenizer=TextTokenizer().preprocess)),
('tfidf', TfidfTransformer()),
]
if params.method != "none":
steps.append(("os", get_balancing_step(params.method)))
if params.pca:
steps.append(('pca', TruncatedSVD()))
from classifier.models import get
parameters = {}
if params.classifier.startswith('SVC'):
parameters['probability'] = True
if params.get_balanced():
parameters['class_weight'] = 'balanced'
steps.append(('clf', get(params.classifier, params=parameters)))
pipeline = Pipeline(steps)
if feature_name == publication_year_key:
pass
elif feature_name in [publication_type_key, mesh_headings_key]:
pipeline.set_params(vect__ngram_range=(1, 1))
pipeline.set_params(tfidf__use_idf=True)
elif feature_name in [
title_key, title_most_replaced_key,
title_disease_or_syndrome_replaced_key, journal_title_key
]:
pipeline.set_params(vect__ngram_range=(1, 2))
pipeline.set_params(tfidf__use_idf=True)
else:
pipeline.set_params(vect__ngram_range=(1, 4))
pipeline.set_params(tfidf__use_idf=True)
return pipeline
def test_pipeline_init():
# Test the various init parameters of the pipeline.
with raises(TypeError):
Pipeline()
# Check that we can't instantiate pipelines with objects without fit
# method
error_regex = ("Last step of Pipeline should implement fit or be the "
"string 'passthrough'")
with raises(TypeError, match=error_regex):
Pipeline([("clf", NoFit())])
# Smoke test with only an estimator
clf = NoTrans()
pipe = Pipeline([("svc", clf)])
expected = dict(svc__a=None,
svc__b=None,
svc=clf,
**pipe.get_params(deep=False))
assert pipe.get_params(deep=True) == expected
# Check that params are set
pipe.set_params(svc__a=0.1)
assert clf.a == 0.1
assert clf.b is None
# Smoke test the repr:
repr(pipe)
# Test with two objects
clf = SVC(gamma="scale")
filter1 = SelectKBest(f_classif)
pipe = Pipeline([("anova", filter1), ("svc", clf)])
# Check that we can't instantiate with non-transformers on the way
# Note that NoTrans implements fit, but not transform
error_regex = "implement fit and transform or fit_resample"
with raises(TypeError, match=error_regex):
Pipeline([("t", NoTrans()), ("svc", clf)])
# Check that params are set
pipe.set_params(svc__C=0.1)
assert clf.C == 0.1
# Smoke test the repr:
repr(pipe)
# Check that params are not set when naming them wrong
with raises(ValueError):
pipe.set_params(anova__C=0.1)
# Test clone
pipe2 = clone(pipe)
assert not pipe.named_steps["svc"] is pipe2.named_steps["svc"]
# Check that apart from estimators, the parameters are the same
params = pipe.get_params(deep=True)
params2 = pipe2.get_params(deep=True)
for x in pipe.get_params(deep=False):
params.pop(x)
for x in pipe2.get_params(deep=False):
params2.pop(x)
# Remove estimators that where copied
params.pop("svc")
params.pop("anova")
params2.pop("svc")
params2.pop("anova")
assert params == params2
def optimize(self, samp, clf, tag):
"""
单次实验流程
"""
# 打印头部信息
os.system('cls||clear')
self.tag = tag
tqdm.write("""{div}\n Stack Expert Model Optimization
{indent}--Seed: {seed}
{indent}--Sampling Method: {samp}
{indent}--Classification Method: {clf}
{indent}--Tag: {tag}\n{div}
""".format(
**{
'div': '*' * 50,
'seed': self.seed,
'indent': '\b' * 6,
'tag': self.tag,
'samp': type(samp).__name__,
'clf': type(clf).__name__
}))
# 加载数据
data, target, ratio = load_data(self.tag)
tqdm.write(
"[INFO] Data loads complete. Expert ratio:{:.2f}%\t{:s}".format(
100 * ratio, cur()))
# 设置随机数种子
self.seed = int(time())
self.validator.random_state = self.seed
self._check(type(clf).__name__)
samp.set_params(**{"random_state": self.seed})
if 'random_state' in clf.get_params().keys():
clf.set_params(**{"random_state": self.seed})
# 建立过采样和分类器的流水线模型
pipeline = Pipeline([(type(samp).__name__, samp),
(type(clf).__name__, clf)])
tqdm.write(
"[INFO] Model load completed. Start grid search...\t{:s}".format(
cur()))
# 开始进行网格搜索
for ind, (key, value) in enumerate(
tqdm(self._get_grid(pipeline, ratio).items())):
tqdm.write('-' * 15 + 'Epoch {:d}'.format(ind) + '-' * 15)
# 设置默认参数
pipeline.set_params(**self._get_params(pipeline, ratio))
# 建立网格搜索对象
grid_opti = GridSearchCV(estimator=pipeline,
param_grid={key: value},
cv=self.validator,
**self.params['GridSearchCV'])
tqdm.write("[EP{:d}] Search Paramator: {:}\t{:s}".format(
ind, key, cur()))
tqdm.write("[EP{:d}] Search Grid: {:}\t{:s}".format(
ind,
str(value) + " Fitting...", cur()))
# 拟合模型
grid_opti.fit(data.to_numpy(), target)
# 输出最佳参数及对应实验指标
df_res = pd.DataFrame(grid_opti.cv_results_)
df_res = df_res.loc[df_res['mean_test_{:s}'.format(
self.scoring)].idxmax()]
tqdm.write(
"[EP{:d}] Fit complete. Current Score: {:}\t{:s}".format(
ind, df_res['mean_test_{:s}'.format(self.scoring)], cur()))
tqdm.write("\r[EP{:d}] Best: {:}\t{:s}".format(
ind, df_res['params'], cur()))
if '{}__sampling_strategy'.format(
type(samp).__name__) in df_res['params']:
df_res['params']['{}__sampling_strategy'.format(
type(samp).__name__)] /= ratio
# 更新参数
self._set_params(df_res['params'])
# 存储实验结果
self._rec(
type(clf).__name__,
type(samp).__name__, df_res.filter(regex=r'^mean_test',
axis=0))
# 完成网格搜索，一次实验结束
tqdm.write("{:s}\n[INFO] Grid search complete.{:}\t{:s}\n".format(
'=' * 50, "", cur()))
del data, target
def train_models(self, X_train, y_train, tuning='hyperopt'):
"""Hyperparameter tuning.
Iterate over each model and find best parameter combination using 'tuning' method and cross validation
Also finally fits a voting classifier with all the optimized models
"""
valid_tuning = ['random', 'grid', 'hyperopt']
if tuning not in valid_tuning:
raise ValueError(
'train_models: tuning must be one of {}'.format(valid_tuning))
start = datetime.now()
self.best_estimators = {}
self.best_f1_scores = {}
self.scores_all = []
for model, estimator in self.models_selector(tuning):
self.best = 0
model_pipeline = Pipeline([('imputer', self.imputer),
('scaling', self.scaler),
('rus', self.rus),
(model, estimator['est'])])
if tuning == 'random':
search = RandomizedSearchCV(
model_pipeline,
param_distributions=estimator['params'],
scoring='f1',
n_iter=100,
cv=10,
verbose=False,
n_jobs=-1,
iid=False)
self.models_fit(model, search, X_train, y_train, tuning)
elif tuning == 'grid':
search = GridSearchCV(model_pipeline,
param_grid=param_dist,
scoring='f1',
cv=10,
verbose=True,
n_jobs=-1)
self.models_fit(model, search, X_train, y_train, tuning)
elif tuning == 'hyperopt':
hyperopt_objective = partial(self.raw_hyperopt_objective,
model_pipeline, X_train, y_train)
trials = Trials()
best_params = fmin(fn=hyperopt_objective,
space=estimator['params'],
algo=tpe.suggest,
max_evals=20,
trials=trials)
best_params_actual = space_eval(estimator['params'],
trials.argmin)
model_pipeline.set_params(**best_params_actual)
self.models_fit(model, model_pipeline, X_train, y_train,
tuning, trials)
# Training a Voting classifier with all above estimators
self.vot_model = VotingClassifier(estimators=list(
self.best_estimators.items()),
voting='hard')
scores = cross_val_score(self.vot_model,
X_train,
y_train,
scoring='f1',
cv=10)
self.vot_model.fit(X_train, y_train)
self.best_estimators['voting'] = self.vot_model
print('Voting Classifier F1 score mean: {:.4f}, stddev: {:.4f}'.format(
scores.mean(), scores.std()))
end = datetime.now()
print('Train time: {}'.format(end - start))
def manual_partial_model_selection(main_path, dataset_type, dataset_sub_type,
sampling, sampling_timing, fs_step_name,
classifier_step_name, chromosome):
print("##### Experiment Info #####")
print("Chromosome:", chromosome)
print("Dataset type:", dataset_type)
print("Dataset subtype:", dataset_sub_type)
print("Sampling:", sampling)
print("Sampling timing:", sampling_timing)
print("Filter FS:", fs_step_name)
print("Classifier:", classifier_step_name)
print()
print("Loading variable names...")
print()
with open(
main_path + dataset_type + '/' + dataset_sub_type + '/' +
chromosome + '/train_train.csv', 'r') as csvfile:
reader = csv.reader(csvfile, delimiter=',')
for row in reader:
variable_names = np.array(list(row))
break
variable_names = variable_names[1:]
print("Variable names size:", len(variable_names))
print()
sampling_seeds = [123, 456, 789]
print("Loading training data...")
print()
train_data = load_dataset(main_path + dataset_type + '/' +
dataset_sub_type + '/' + chromosome +
'/train_train.csv')
X_train = train_data[:, 1:]
print("X_train shape:", X_train.shape)
print()
y_train = train_data[:, 0]
print("y_train shape:", y_train.shape)
print()
experiment_results = dict()
print("Creating pipeline...")
print()
pipe = Pipeline([("imputer", Imputer(missing_values=-1)),
("variance", VarianceThreshold()),
("scaler", StandardScaler())])
if fs_step_name == "anova":
filter = SelectPercentile(f_classif, percentile=2)
if sampling_timing == "sampling_before_fs":
if sampling == "down_sample":
pipe.steps.append(
(sampling, RandomUnderSampler(random_state=sampling_seeds[0])))
elif sampling == "up_sample":
pipe.steps.append(
(sampling, RandomOverSampler(random_state=sampling_seeds[1])))
elif sampling == "smote_sample":
pipe.steps.append(
(sampling, SMOTE(n_jobs=-1, random_state=sampling_seeds[2])))
pipe.steps.append((fs_step_name, filter))
elif sampling_timing == "sampling_after_fs":
pipe.steps.append((fs_step_name, filter))
if sampling == "down_sample":
pipe.steps.append(
(sampling, RandomUnderSampler(random_state=sampling_seeds[0])))
elif sampling == "up_sample":
pipe.steps.append(
(sampling, RandomOverSampler(random_state=sampling_seeds[1])))
elif sampling == "smote_sample":
pipe.steps.append(
(sampling, SMOTE(n_jobs=-1, random_state=sampling_seeds[2])))
classifier = SVC(kernel='linear',
random_state=123456,
probability=True,
class_weight='balanced')
pipe.steps.append((classifier_step_name, classifier))
print("Performing manual gridsearch...")
print()
C_OPTIONS = [0.001, 0.01, 0.1, 1, 10, 100, 1000]
cv = StratifiedKFold(n_splits=5, random_state=123456)
f1_cv = []
mean_test_score = []
std_test_score = []
for C in C_OPTIONS:
pipe.set_params(linear_svm__C=C)
for train_indexes, validation_indexes in cv.split(X_train, y_train):
pipe.fit(X_train[train_indexes, :], y_train[train_indexes])
y_pred = pipe.predict(X_train[validation_indexes, :])
f1 = f1_score(y_train[validation_indexes],
y_pred,
average='weighted')
f1_cv.append(f1)
mean_test_score.append(np.mean(f1_cv))
std_test_score.append(np.std(f1_cv))
cv_results = dict()
cv_results['mean_test_score'] = mean_test_score
cv_results['std_test_score'] = std_test_score
cv_results['params'] = C_OPTIONS
experiment_results['cv_results'] = cv_results
print("Manual gridsearch results:")
print()
print(cv_results['mean_test_score'])
print()
print(cv_results['std_test_score'])
print()
print("Best parameters set found on development set:")
print()
print(C_OPTIONS[np.argmax(mean_test_score)])
print()
params_space = {
'undersampler__n_neighbors':
quniform_int('n_neighbors', 2, 10, 1),
'xgb__max_depth':
quniform_int('max_depth', 10, 30, 1),
'xgb__min_child_weight':
hp.quniform('min_child_weight', 1, 20, 1),
'xgb__subsample':
hp.uniform('subsample', 0.8, 1),
'xgb__n_estimators':
quniform_int('n_estimators', 1000, 10000, 50),
'xgb__learning_rate':
hp.loguniform('learning_rate', np.log(0.0001), np.log(0.5)) - 0.0001,
'xgb__gamma':
hp.loguniform('gamma', np.log(0.0001), np.log(5)) - 0.0001,
'xgb__colsample_bytree':
hp.quniform('colsample_bytree', 0.5, 1, 0.05)
}
model.set_params({
'xgb__colsample_bytree': 0.55,
'xgb__n_estimators': 1000,
'xgb__subsample': 0.81758885827,
'xgb__min_child_weight': 2.0,
'xgb__learning_rate': 0.0091861014503,
'xgb__gamma': 1.19618674618,
'undersampler__n_neighbors': 7,
'xgb__max_depth': 21
})
def test_pipeline_init():
# Test the various init parameters of the pipeline.
assert_raises(TypeError, Pipeline)
# Check that we can't instantiate pipelines with objects without fit
# method
assert_raises_regex(
TypeError, 'Last step of Pipeline should implement fit. '
'.*NoFit.*', Pipeline, [('clf', NoFit())])
# Smoke test with only an estimator
clf = NoTrans()
pipe = Pipeline([('svc', clf)])
expected = dict(svc__a=None,
svc__b=None,
svc=clf,
**pipe.get_params(deep=False))
assert pipe.get_params(deep=True) == expected
# Check that params are set
pipe.set_params(svc__a=0.1)
assert clf.a == 0.1
assert clf.b is None
# Smoke test the repr:
repr(pipe)
# Test with two objects
clf = SVC()
filter1 = SelectKBest(f_classif)
pipe = Pipeline([('anova', filter1), ('svc', clf)])
# Check that we can't instantiate with non-transformers on the way
# Note that NoTrans implements fit, but not transform
assert_raises_regex(TypeError, 'implement fit and transform or sample',
Pipeline, [('t', NoTrans()), ('svc', clf)])
# Check that params are set
pipe.set_params(svc__C=0.1)
assert clf.C == 0.1
# Smoke test the repr:
repr(pipe)
# Check that params are not set when naming them wrong
assert_raises(ValueError, pipe.set_params, anova__C=0.1)
# Test clone
pipe2 = clone(pipe)
assert not pipe.named_steps['svc'] is pipe2.named_steps['svc']
# Check that apart from estimators, the parameters are the same
params = pipe.get_params(deep=True)
params2 = pipe2.get_params(deep=True)
for x in pipe.get_params(deep=False):
params.pop(x)
for x in pipe2.get_params(deep=False):
params2.pop(x)
# Remove estimators that where copied
params.pop('svc')
params.pop('anova')
params2.pop('svc')
params2.pop('anova')
assert params == params2
filts = [
('RandomForestClassifier', RandomForestClassifier(n_estimators=25, random_state=random_state)),
('RandomForestClassifier', RandomForestClassifier(n_estimators=10, random_state=random_state)),
('DecisionTreeClassifier', DecisionTreeClassifier(random_state=random_state)),
('LogisticRegression', LogisticRegression(solver='lbfgs', random_state=random_state, multi_class='auto', max_iter=750)),
('MLPClassifier', MLPClassifier(random_state=random_state, max_iter=2000))
]
# set up model
znorm = StandardScaler()
ownmethod = MBKMeansFilter_reversed()
rfc = RandomForestClassifier(n_estimators=500, random_state=random_state)
clf = Pipeline([('ZNorm', znorm), ('OwnMethod2',ownmethod), ('RFC', rfc)])
clf.set_params(**params)
## model
clf.fit(X, y, **{'OwnMethod2__filters': filts})
pickle.dump(clf, open(MODELS_PATH+'near_final_clf_.pkl','wb'))
## model2
znorm = StandardScaler()
rfc = RandomForestClassifier(n_estimators=250, random_state=random_state, n_jobs=-1)
clf = Pipeline([('ZNorm', znorm), ('RFC', rfc)])
clf.fit(X, y)
pickle.dump(clf, open(MODELS_PATH+'final_RFC.pkl','wb'))
# ---------------------------------------------------------------------------- #
# Cross Spatial Validation
# ---------------------------------------------------------------------------- #
def test_pipeline_init():
# Test the various init parameters of the pipeline.
with raises(TypeError):
Pipeline()
# Check that we can't instantiate pipelines with objects without fit
# method
error_regex = 'Last step of Pipeline should implement fit. .*NoFit.*'
with raises(TypeError, match=error_regex):
Pipeline([('clf', NoFit())])
# Smoke test with only an estimator
clf = NoTrans()
pipe = Pipeline([('svc', clf)])
expected = dict(svc__a=None, svc__b=None, svc=clf,
**pipe.get_params(deep=False))
assert pipe.get_params(deep=True) == expected
# Check that params are set
pipe.set_params(svc__a=0.1)
assert clf.a == 0.1
assert clf.b is None
# Smoke test the repr:
repr(pipe)
# Test with two objects
clf = SVC()
filter1 = SelectKBest(f_classif)
pipe = Pipeline([('anova', filter1), ('svc', clf)])
# Check that we can't instantiate with non-transformers on the way
# Note that NoTrans implements fit, but not transform
error_regex = 'implement fit and transform or sample'
with raises(TypeError, match=error_regex):
Pipeline([('t', NoTrans()), ('svc', clf)])
# Check that params are set
pipe.set_params(svc__C=0.1)
assert clf.C == 0.1
# Smoke test the repr:
repr(pipe)
# Check that params are not set when naming them wrong
with raises(ValueError):
pipe.set_params(anova__C=0.1)
# Test clone
pipe2 = clone(pipe)
assert not pipe.named_steps['svc'] is pipe2.named_steps['svc']
# Check that apart from estimators, the parameters are the same
params = pipe.get_params(deep=True)
params2 = pipe2.get_params(deep=True)
for x in pipe.get_params(deep=False):
params.pop(x)
for x in pipe2.get_params(deep=False):
params2.pop(x)
# Remove estimators that where copied
params.pop('svc')
params.pop('anova')
params2.pop('svc')
params2.pop('anova')
assert params == params2