def test_pipeline_ducktyping():
pipeline = make_pipeline(Mult(5))
pipeline.predict
pipeline.transform
pipeline.inverse_transform
pipeline = make_pipeline(Transf())
assert not hasattr(pipeline, 'predict')
pipeline.transform
pipeline.inverse_transform
pipeline = make_pipeline(None)
assert not hasattr(pipeline, 'predict')
pipeline.transform
pipeline.inverse_transform
pipeline = make_pipeline(Transf(), NoInvTransf())
assert not hasattr(pipeline, 'predict')
pipeline.transform
assert not hasattr(pipeline, 'inverse_transform')
pipeline = make_pipeline(NoInvTransf(), Transf())
assert not hasattr(pipeline, 'predict')
pipeline.transform
assert not hasattr(pipeline, 'inverse_transform')
def test_make_pipeline_memory():
cachedir = mkdtemp()
try:
memory = Memory(cachedir=cachedir, verbose=10)
pipeline = make_pipeline(DummyTransf(), SVC(), memory=memory)
assert pipeline.memory is memory
pipeline = make_pipeline(DummyTransf(), SVC())
assert pipeline.memory is None
finally:
shutil.rmtree(cachedir)
def test_make_pipeline_memory():
cachedir = mkdtemp()
try:
memory = Memory(cachedir=cachedir, verbose=10)
pipeline = make_pipeline(DummyTransf(), SVC(), memory=memory)
assert pipeline.memory is memory
pipeline = make_pipeline(DummyTransf(), SVC())
assert pipeline.memory is None
finally:
shutil.rmtree(cachedir)
def svm_benchmark(isoform_list):
start = time.time()
columns_ = [
'ACC', 'BA', 'ROC-AUC', 'PR-AUC', 'MCC', 'SN', 'SP', 'PR', 'F1', 'CK'
]
df = pd.DataFrame(columns_)
isoform_list_ = isoform_list
for isoform_ in isoform_list_:
#--------------------
X_train = np.load("./data/{}/train_data.npy".format(isoform_))
X_val = np.load("./data/{}/val_data.npy".format(isoform_))
X_test = np.load("./data/{}/test_data.npy".format(isoform_))
y_train = np.load("./data/{}/train_label.npy".format(isoform_))
y_val = np.load("./data/{}/val_label.npy".format(isoform_))
y_test = np.load("./data/{}/test_label.npy".format(isoform_))
#--------------------
# Set Up Parameter
my_C = [0.001, 0.01, 0.1, 1, 10, 100]
my_gamma = [0.001, 0.01, 0.1, 1, 10, 100]
pred_val_list = []
para_list = []
for p1 in my_C:
for p2 in my_gamma:
para_list.append((p1, p2))
my_classifier = make_pipeline(
VarianceThreshold(threshold),
SVC(C=p1, gamma=p2, probability=True))
pred_val = my_classifier.fit(X_train,
y_train).predict_proba(X_val)[::,
1]
pred_val_list.append(list(pred_val))
#--------------------
auc_val_list = []
for pred in pred_val_list:
auc = roc_auc_score(y_val, pred)
auc_val_list.append(auc)
i = np.argmax(auc_val_list)
#--------------------
best_C = para_list[i][0]
best_gamma = para_list[i][1]
tuned_classifier = make_pipeline(
VarianceThreshold(threshold),
SVC(C=best_C, gamma=best_gamma, probability=True))
pred_test = tuned_classifier.fit(X_train,
y_train).predict_proba(X_test)[::, 1]
#--------------------
metric = printPerformance(y_test, pred_test)
df1 = pd.DataFrame(metric)
df = pd.concat([df, df1], axis=1)
df.columns = ["Metrics"] + isoform_list_
df.to_csv("svm_benchmark.csv", index=None)
end = time.time()
processing_time = (end - start)
print("Processing time: {}".format(processing_time))
def __build_preprocessor(self, useSelector):
"""
:return:
"""
extractor = self.__build_extractor()
if (useSelector):
selector = self.__build_selector()
return make_pipeline(extractor, selector)
else:
return make_pipeline(extractor)
def print_metrics(model, X, y, scoring='f1', oversample=False):
if oversample == True:
pipeline = make_pipeline(StandardScaler(),
RandomOverSampler(random_state=11), model)
else:
pipeline = make_pipeline(StandardScaler(), model)
score = cross_val_score(pipeline, X, y, scoring=scoring)
fitted_model = model.fit(X, y)
cm = confusion_matrix(y, fitted_model.predict(X))
print(score)
print(cm)
def rf_benchmark(isoform_list):
start = time.time()
columns_ = [
'ACC', 'BA', 'ROC-AUC', 'PR-AUC', 'MCC', 'SN', 'SP', 'PR', 'F1', 'CK'
]
df = pd.DataFrame(columns_)
isoform_list_ = isoform_list
for isoform_ in isoform_list_:
#--------------------
X_train = np.load("./data/{}/train_data.npy".format(isoform_))
X_val = np.load("./data/{}/val_data.npy".format(isoform_))
X_test = np.load("./data/{}/test_data.npy".format(isoform_))
y_train = np.load("./data/{}/train_label.npy".format(isoform_))
y_val = np.load("./data/{}/val_label.npy".format(isoform_))
y_test = np.load("./data/{}/test_label.npy".format(isoform_))
#--------------------
# Set Up Parameter
my_n_estimators = np.arange(25, 201, 25)
pred_val_list = []
para_list = []
for p in my_n_estimators:
para_list.append(p)
my_classifier = make_pipeline(
VarianceThreshold(threshold),
RandomForestClassifier(random_state=42, n_estimators=p))
pred_val = my_classifier.fit(X_train,
y_train).predict_proba(X_val)[::, 1]
pred_val_list.append(list(pred_val))
#--------------------
auc_val_list = []
for pred in pred_val_list:
auc = roc_auc_score(y_val, pred)
auc_val_list.append(auc)
i = np.argmax(auc_val_list)
#--------------------
best_n_estimators = para_list[i]
tuned_classifier = make_pipeline(
VarianceThreshold(threshold),
RandomForestClassifier(random_state=42,
n_estimators=best_n_estimators))
pred_test = tuned_classifier.fit(X_train,
y_train).predict_proba(X_test)[::, 1]
#--------------------
metric = printPerformance(y_test, pred_test)
df1 = pd.DataFrame(metric)
df = pd.concat([df, df1], axis=1)
df.columns = ["Metrics"] + isoform_list_
df.to_csv("rf_benchmark.csv", index=None)
end = time.time()
processing_time = (end - start)
print("Processing time: {}".format(processing_time))
def test_make_pipeline():
t1 = Transf()
t2 = Transf()
pipe = make_pipeline(t1, t2)
assert isinstance(pipe, Pipeline)
assert pipe.steps[0][0] == "transf-1"
assert pipe.steps[1][0] == "transf-2"
pipe = make_pipeline(t1, t2, FitParamT())
assert isinstance(pipe, Pipeline)
assert pipe.steps[0][0] == "transf-1"
assert pipe.steps[1][0] == "transf-2"
assert pipe.steps[2][0] == "fitparamt"
def test_classes_property():
iris = load_iris()
X = iris.data
y = iris.target
reg = make_pipeline(SelectKBest(k=1), LinearRegression())
reg.fit(X, y)
assert_raises(AttributeError, getattr, reg, "classes_")
clf = make_pipeline(SelectKBest(k=1), LogisticRegression(random_state=0))
assert_raises(AttributeError, getattr, clf, "classes_")
clf.fit(X, y)
assert_array_equal(clf.classes_, np.unique(y))
def test_make_pipeline():
t1 = Transf()
t2 = Transf()
pipe = make_pipeline(t1, t2)
assert_true(isinstance(pipe, Pipeline))
assert_equal(pipe.steps[0][0], "transf-1")
assert_equal(pipe.steps[1][0], "transf-2")
pipe = make_pipeline(t1, t2, FitParamT())
assert_true(isinstance(pipe, Pipeline))
assert_equal(pipe.steps[0][0], "transf-1")
assert_equal(pipe.steps[1][0], "transf-2")
assert_equal(pipe.steps[2][0], "fitparamt")
def test_classes_property():
iris = load_iris()
X = iris.data
y = iris.target
reg = make_pipeline(SelectKBest(k=1), LinearRegression())
reg.fit(X, y)
assert_raises(AttributeError, getattr, reg, "classes_")
clf = make_pipeline(SelectKBest(k=1), LogisticRegression(random_state=0))
assert_raises(AttributeError, getattr, clf, "classes_")
clf.fit(X, y)
assert_array_equal(clf.classes_, np.unique(y))
def test_make_pipeline():
t1 = Transf()
t2 = Transf()
pipe = make_pipeline(t1, t2)
assert isinstance(pipe, Pipeline)
assert pipe.steps[0][0] == "transf-1"
assert pipe.steps[1][0] == "transf-2"
pipe = make_pipeline(t1, t2, FitParamT())
assert isinstance(pipe, Pipeline)
assert pipe.steps[0][0] == "transf-1"
assert pipe.steps[1][0] == "transf-2"
assert pipe.steps[2][0] == "fitparamt"
def test_make_pipeline():
t1 = TransfT()
t2 = TransfT()
pipe = make_pipeline(t1, t2)
assert_true(isinstance(pipe, Pipeline))
assert_equal(pipe.steps[0][0], "transft-1")
assert_equal(pipe.steps[1][0], "transft-2")
pipe = make_pipeline(t1, t2, FitParamT())
assert_true(isinstance(pipe, Pipeline))
assert_equal(pipe.steps[0][0], "transft-1")
assert_equal(pipe.steps[1][0], "transft-2")
assert_equal(pipe.steps[2][0], "fitparamt")
def test_pipeline_fit_then_sample_of_three_samplers_with_sampler_last_estimator():
X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],
n_informative=3, n_redundant=1, flip_y=0,
n_features=20, n_clusters_per_class=1,
n_samples=50000, random_state=0)
rus = RandomUnderSampler(random_state=42)
enn = ENN()
pipeline = make_pipeline(rus, enn, rus)
X_fit_sample_resampled, y_fit_sample_resampled = pipeline.fit_sample(X,y)
pipeline = make_pipeline(rus, enn, rus)
pipeline.fit(X,y)
X_fit_then_sample_resampled, y_fit_then_sample_resampled = pipeline.sample(X,y)
assert_array_equal(X_fit_sample_resampled, X_fit_then_sample_resampled)
assert_array_equal(y_fit_sample_resampled, y_fit_then_sample_resampled)
def data_sampling(X, Y, k, oversampling=True, undersampling=True, class_weight='balanced'):
over = SMOTE(sampling_strategy=0.55, k_neighbors=k) # environ 55% du jeu de données
under = RandomUnderSampler(sampling_strategy=1.) # les effectifs de la classe minoritaire sont 50% de ceux de la classe majoritaire
if (oversampling and undersampling):
pipe=make_pipeline(over, under)
X1, Y1 = pipe.fit_resample(X, Y)
elif oversampling:
pipe=make_pipeline(over)
X1, Y1 = pipe.fit_resample(X, Y)
elif undersampling:
pipe=make_pipeline(under)
X1, Y1 = pipe.fit_resample(X, Y)
elif (class_weight=='balanced' or class_weight==None):
(X1, Y1)=(X, Y)
return X1, Y1
def sample(ngram_range=(1, 2), n_features=85000, methods=[]):
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import f1_score
from imblearn.pipeline import make_pipeline
methodsResults = pandas.DataFrame()
counter = -2
for osm in methods:
counter += 1
sss = StratifiedShuffleSplit(n_splits=10,
test_size=0.2,
random_state=3000)
# confusion_sum = [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
result = []
for train_index, test_index in sss.split(x, y):
cvec = CountVectorizer()
cvec.set_params(max_features=n_features, ngram_range=ngram_range)
clf = MultinomialNB()
if (osm == 0):
pipeline = make_pipeline(cvec, clf)
else:
pipeline = make_pipeline(cvec, osm, clf)
# X = cvec.fit_transform(x)
x_train, x_test = x[train_index], x[test_index]
# X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
sentiment_fit = pipeline.fit(x_train, y_train)
y_pred = sentiment_fit.predict(x_test)
# conmat = np.array(confusion_matrix(y_test, y_pred, labels=[2.0, 3.0, 4.0]))
# print(conmat)
f1 = f1_score(y_test,
y_pred,
labels=[2.0, 3.0, 4.0],
average="micro")
result.append(f1)
# print(result)
if (osm == 0):
methodsResults["Base Case"] = result
else:
methodsResults[type(osm).__name__] = result
# print(confusion_sum)
return methodsResults
def naive_bayse_cross(train_x, train_y, validation, test, test_data):
print("training data...")
clf_pipe = make_pipeline(CountVectorizer(ngram_range=(1, 2)),
RandomUnderSampler(), MultinomialNB(alpha=0.01))
scores = cross_val_score(clf_pipe, train_x, train_y, cv=10)
print("Model is fitted!")
if validation:
print("scores: ", scores)
print("std of score: ", np.std(scores))
print("Accuracy: %0.2f (+/- %0.2f)" %
(scores.mean(), scores.std() * 2))
y_pred = cross_val_predict(clf_pipe, train_x, train_y, cv=5)
# Evaluation
# classification report
print("classification reports:",
classification_report(train_y, y_pred))
# confusion matrix
conf_mat = confusion_matrix(train_y, y_pred)
print(conf_mat)
plot_conf(conf_mat)
if test:
naive_bayes(test_data)
def create_pipeline(model, sampling_strategy, y):
"""Wraps a model in a pipeline to resample training data.
Args:
model (sklearn Model): The model to wrap.
sampling_strategy (SamplingStrategy): The sampling strategy for the pipeline.
y (pandas Dataframe): A dataframe containing targets.
Returns:
sklearn pipeline: A pipeline wrapping the model.
"""
balancer = 'passthrough'
if sampling_strategy == SamplingStrategy.UNDERSAMPLING:
# We want to use a random undersampler if we use
# undersampling is the resample strategy
databalancing_stats(y, sampling_strategy)
balancer = RandomUnderSampler(random_state=SEED)
elif sampling_strategy == SamplingStrategy.OVERSAMPLING:
# We want to use a SMOTE, the most common oversampler,
# if we use oversampling is the resample strategy
databalancing_stats(y, sampling_strategy)
balancer = SMOTE(random_state=SEED, n_jobs=-1)
return make_pipeline(balancer, model)
def svm (X_train, X_test, y_train, y_test):
svm_kernel = 'poly'
param_grid = {'svr__C': [10],
'svr__gamma': [0.01], }
regr = make_pipeline(StandardScaler(), SVR(kernel=svm_kernel))
grid = GridSearchCV(regr, param_grid,
n_jobs=-1,
return_train_score=True)
grid = grid.fit(X_train, y_train)
y_pred_test = grid.predict(X_test)
y_pred_train = grid.predict(X_train)
MSE_test = mean_squared_error(y_true=y_test, y_pred=y_pred_test)
MSE_train = mean_squared_error(y_true=y_train, y_pred=y_pred_train)
print(grid)
# print('cv_results_: ', grid.cv_results_)
# print('Best score: ', grid.best_score_)
# print('Best parameter: ', grid.best_estimator_)
# print('Best parameters: ', grid.best_params_)
print('MSE train: %.2f , MSE test: %.2f'
% (MSE_train, MSE_test))
test_scores = grid.cv_results_['mean_test_score']
train_scores = grid.cv_results_['mean_train_score']
print('test_scores:', test_scores)
print('train_scores:', train_scores)
return test_scores, train_scores, MSE_train, MSE_test
def svm(X_train, X_test, y_train, y_test, param_grid):
regr = make_pipeline(StandardScaler(), SVR())
grid = GridSearchCV(regr, param_grid, n_jobs=-1, return_train_score=True)
grid = grid.fit(X_train, y_train)
# re-predict using best parameter:
params = {}
params['svr__kernel'] = [grid.best_params_['svr__kernel']]
grid = GridSearchCV(regr, params, n_jobs=-1, return_train_score=True)
grid = grid.fit(X_train, y_train)
y_pred_test = grid.predict(X_test)
y_pred_train = grid.predict(X_train)
MSE_test = mean_squared_error(y_true=y_test, y_pred=y_pred_test)
MSE_train = mean_squared_error(y_true=y_train, y_pred=y_pred_train)
# print(grid)
# print('cv_results_: ', grid.cv_results_)
# print('Best score: ', grid.best_score_)
# print('Best parameter: ', grid.best_estimator_)
# print('Best parameters: ', grid.best_params_)
# print('MSE train: %.2f , MSE test: %.2f'
# % (MSE_train, MSE_test))
test_scores = grid.cv_results_['mean_test_score']
train_scores = grid.cv_results_['mean_train_score']
slope, intercept, r_value, p_value, std_err = stats.linregress(
y_test, y_pred_test)
return test_scores, train_scores, MSE_train, MSE_test, r_value, p_value, std_err
def test_single_estimator():
# Check singleton ensembles.
X, y = make_imbalance(
iris.data,
iris.target,
sampling_strategy={
0: 20,
1: 25,
2: 50
},
random_state=0,
)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
clf1 = BalancedBaggingClassifier(
base_estimator=KNeighborsClassifier(),
n_estimators=1,
bootstrap=False,
bootstrap_features=False,
random_state=0,
).fit(X_train, y_train)
clf2 = make_pipeline(
RandomUnderSampler(
random_state=clf1.estimators_[0].steps[0][1].random_state),
KNeighborsClassifier(),
).fit(X_train, y_train)
assert_array_equal(clf1.predict(X_test), clf2.predict(X_test))
def sm_col_clf_piper(X_train, y_train, X_test, X_label, parameters, clf, scoring= 'f1'):
# parameters: dict of parameter need to tune
# clf: classifier
# n_features: number of features want to find, default is half of all features
# scoring: type of score using to tune, default is f1 score
# SMOTE training set to deal with imbalance
sm = SMOTE()
X_train, X_label = sm.fit_sample(X_train, X_label)
pipe = make_pipeline(
(SFS(clf,"best",forward=False,scoring=scoring,cv=5)),
(clf)
)
# tune model with different parameters
grid = GridSearchCV(estimator = pipe, param_grid = parameters, cv = 5, n_jobs = -1, verbose = 50, scoring = scoring)
grid.fit(X_train, y_train)
# get the selected feature index
best_pipe = grid.best_estimator_
feature_idx = (best_pipe.named_steps['sequentialfeatureselector'].transform(np.arange(len(X_train.columns)).reshape(1, -1)))[0]
# use best parameter to predict test label
pred = grid.predict(X_test)
# calculate different score based on prediction
conf = confusion_matrix(X_label,pred)
test_score = {
"accuracy":accuracy_score(X_label,pred),
"precision":precision_score(X_label,pred,"binary"),
"recall":recall_score(X_label,pred,"binary"),
"f1_score":f1_score(X_label,pred,"binary"),
"roc_auc":roc_auc_score(X_label,pred)
}
return grid.cv_results_['mean_test_score'], grid.best_params_, conf , test_score, feature_idx
def train_mnb(X, y, **kwargs):
"""
This function transforms the text corpus with a TfidfVectorizer
and trains a Naive Bayes model.
Parameter
---------
corpus : array_like
List of song lyrics as strings.
artists : array_like
List of labels/artists as strings.
**kwargs : Arbitrary keyword arguments passes as hyperparameters for MultinominalNB.
Returns
-------
A pipeline with Text-Preprocesser and the trained sklearn.naives_bayes.MultinominalNB classification model.
"""
tf = TfidfVectorizer()
ros = RandomOverSampler(random_state=20)
sm = SMOTE(random_state=20)
m = MultinomialNB(**kwargs)
pipeline = make_pipeline(tf, sm, m)
pipeline.fit(X, y)
print(f"\ntraining accuracy: {round(pipeline.score(X, y),3)}")
#print('\nConfusion matrix:')
#print(f'Classes: {pipeline.classes_}')
#print(confusion_matrix(y, pipeline.predict(X), labels=pipeline.classes_))
cross_val = cross_val_score(pipeline, X, y, cv=5)
print(f'\ncross-validation accuracy: {cross_val.round(3)}')
return pipeline
def pipeline(estimator):
'''
Model pipeline
'''
return make_pipeline(StandardScaler(),
RandomOverSampler(random_state=42, ratio='minority'),
estimator)
def adjust(dimension=yV, dimName="Valence"):
FOLDS = 10
sss = StratifiedShuffleSplit(n_splits=FOLDS,
test_size=0.2,
random_state=3000)
result = []
x = emoBank.drop(columns=["id", "sentence", dimName], inplace=False).values
for train_index, test_index in sss.split(x, dimension):
clf = MultinomialNB()
pipeline = make_pipeline(clf)
x_train, x_test = x[train_index], x[test_index]
y_train, y_test = dimension[train_index], dimension[test_index]
sentiment_fit = pipeline.fit(x_train, y_train)
y_pred = sentiment_fit.predict(x_test)
f1 = f1_score(y_test, y_pred, labels=[2.0, 3.0, 4.0], average="micro")
result.append(f1)
avgScore = 0
for score in result:
avgScore += score
elapsedTime = time.time() - start_time
print("elapsed time: " + str(elapsedTime))
print("F1 score for " + str(dimName) + ": " + str(avgScore / FOLDS))
return sentiment_fit
def run(X, y, learning_curve=False, validation_curve=False):
RANDOM_STATE = 0
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=RANDOM_STATE)
pipeline = make_pipeline(
SelectKBest(score_func=f_classif, k=10),
QuantileTransformer(),
RandomUnderSampler(random_state=RANDOM_STATE),
GradientBoostingClassifier(random_state=RANDOM_STATE),
)
if learning_curve:
ax = plot_learning_curve(pipeline,
X_train,
y_train,
cv=5,
scoring=make_scorer(fbeta_score, beta=2))
plt.show()
if validation_curve:
ax = plot_validation_curve(
pipeline,
X_train,
y_train,
cv=5,
scoring=make_scorer(fbeta_score, beta=2),
param_name="selectkbest__k",
param_range=[10, 20, 30, 40, 50],
)
plt.show()
def decisssionTreeSimpleVal(X, Y):
X_train, X_val, Y_train, Y_val = train_test_split(X, Y, train_size=0.8)
#ica = FastICA(n_components=K, whiten=True).fit(X_train, Y)
#X_red_train = ica.transform(X_train)
#X_red_val = ica.transform(X_val)
# Normalization
scalar = StandardScaler()
X_train_n = scalar.fit_transform(X_train)
X_val_n = scalar.fit_transform(X_val)
undersample = SMOTE()
tree = DecisionTreeClassifier(criterion="gini",
splitter="best",
max_depth=5,
random_state=RANDOM_STATE,
presort=True)
classifier = make_pipeline(undersample, tree)
# Prediction and evaluation
classifier.fit(X_train_n, Y_train)
prediction = classifier.predict(X_val_n)
print("\n", classification_report(prediction, Y_val))
print("N ones:", len(np.where(prediction == 1)[0]) / len(prediction))
return classifier, scalar
def create_pipelines(self):
self.model_pipelines = []
for estimator in self.estimators:
for sampler in self.samplers:
for scaler in self.scalers:
pipeline = make_pipeline(scaler, sampler, estimator)
self.model_pipelines.append(pipeline)
def OverSampling_SMOTE(df):
df.replace([np.inf, -np.inf], np.nan, inplace=True)
train_df = df[df['TARGET'].notnull()]
test_df = df[df['TARGET'].isnull()]
train_df_X = train_df.drop('TARGET', axis=1)
train_df_y = train_df.TARGET
# SMOTE
print('Creating Smote Data...')
smote = SMOTE(k_neighbors=5, n_jobs=-1)
smote_enn = make_pipeline(SimpleImputer(), SMOTEENN(smote=smote))
X_res, y_res = smote_enn.fit_resample(train_df_X, train_df_y)
X_res_df = pd.DataFrame(X_res, columns=train_df_X.columns)
train_df_new = X_res_df.join(y_res.to_frame())
df = train_df_new.append(test_df)
# Save data to csv file
df.to_csv('data/df_prepared_to_model.csv')
# Save data to pickle file
df.to_pickle("data/df_prepared_to_model.pkl")
return df
def test_pipeline_param_error():
clf = make_pipeline(LogisticRegression())
with pytest.raises(
ValueError,
match="Pipeline.fit does not accept the sample_weight parameter",
):
clf.fit([[0], [0]], [0, 1], sample_weight=[1, 1])
def svm_text_classification(vec_params,
svm_params,
train_feat,
train_label,
test_feat,
test_label,
random_state=42):
'''
A function to classify text data using count vectorization, random under-sampling,
and a random forest.
train_features = an array of training features
test_features = an array of testing features
labels = an array of training labels
vec_params = parameters for the CountVectorizer
rf_params = parameters for the RandomForestClassifier
'''
pipe = make_pipeline(CountVectorizer(**vec_params),
RandomUnderSampler(random_state=random_state),
SVC(**svm_params))
pipe_fit = pipe.fit(train_feat, train_label)
y_pred = pipe_fit.predict(test_feat)
cnf_matrix = confusion_matrix(test_label, y_pred)
return pipe, pipe_fit, y_pred, cnf_matrix
def mlpSimpleDiv(X, Y):
X_train, X_val, Y_train, Y_val = train_test_split(X, Y, train_size=0.8)
#X_red_train = FastICA(n_components=K, whiten=True).fit_transform(X_train, Y)
# X_red_val = FastICA(n_components=K, whiten=True).fit_transform(X_val, Y)
# Normalization
scalar = StandardScaler()
X_train_n = scalar.fit_transform(X_train)
X_val_n = scalar.fit_transform(X_val)
# MLP creation + trainning
scalar = StandardScaler()
mlp = MLPClassifier(activation="relu",
verbose=False,
solver="adam",
max_iter=150,
hidden_layer_sizes=(3, 200),
early_stopping=True,
tol=1e-12,
validation_fraction=0.2,
alpha=1e-4,
learning_rate_init=0.1,
beta_1=0.3,
warm_start=True,
random_state=RANDOM_STATE)
undersample = SMOTE()
classifier = make_pipeline(undersample, mlp)
# Prediction and evaluation
classifier.fit(X_train_n, Y_train)
prediction = classifier.predict(X_val_n)
print("\n", classification_report(prediction, Y_val))
print("N ones:", len(np.where(prediction == 1)[0]) / len(prediction))
return classifier, scalar
def set_pipe(clf, features, filename = 'Untitled'):
piped_clf = make_pipeline(
(ColumnSelector(cols = features)),
(SMOTE()),
(clf)
)
piped_clf.fit(X_train,y_train)
y_pred = piped_clf.predict(X_test)
con_mat = confusion_matrix(y_test, y_pred)
avg_f1 = (model_selection.cross_val_score(piped_clf, X_train, y_train, cv = 5, scoring = 'f1')).mean()
print("Cross Val acc score: ", (model_selection.cross_val_score(piped_clf, X_train, y_train, cv = 5,)).mean())
print("Cross Val f1 score: ", avg_f1)
print()
print("Overall Acc score: ", accuracy_score(y_true=y_test, y_pred=y_pred))
print("Recall score (Tru Pos Rate): ", recall_score(y_true=y_test, y_pred=y_pred))
print("Precision score: ", precision_score(y_true=y_test, y_pred=y_pred))
print("Neg Predictive Val: ", con_mat[0][0] / (con_mat[0][1] + con_mat[0][0]))
print("Tru Neg Rate(Specifi): ", con_mat[0][0] / (con_mat[1][0] + con_mat[0][0]))
print("F1 score: ", f1_score(y_true=y_test, y_pred=y_pred))
print("Auc score: ", roc_auc_score(y_true=y_test, y_score=y_pred))
print(con_mat)
print()
(pd.DataFrame(y_pred)).to_csv(filename + 'y_pred_filt_avg.csv')
return piped_clf, avg_f1
def svmSimpleVal(X, Y):
X_train, X_val, Y_train, Y_val = train_test_split(X, Y, train_size=0.8)
#ica = FastICA(n_components=K, whiten=True).fit(X_train, Y)
#X_red_train = ica.transform(X_train)
#X_red_val = ica.transform(X_val)
# Normalization
scalar = StandardScaler()
X_train_n = scalar.fit_transform(X_train)
X_val_n = scalar.fit_transform(X_val)
undersample = SMOTE()
svm = SVC(
verbose=True,
kernel="poly",
decision_function_shape="ovr",
random_state=RANDOM_STATE,
C=0.03,
degree=3,
) #class_weight="balanced"
classifier = make_pipeline(undersample, svm)
# Prediction and evaluation
classifier.fit(X_train_n, Y_train)
prediction = classifier.predict(X_val_n)
print("\n", classification_report(prediction, Y_val))
print("N ones:", len(np.where(prediction == 1)[0]) / len(prediction))
return classifier, scalar
def one_cv_for_one_algo(algorithm, X_train, y_train):
clf = make_pipeline(SMOTE(random_state=0), algorithm)
clf.fit(X_train, y_train)
del X_train, y_train
# For Testing
# X_test
fn = 'mem_file_X_test_' + str(cv_idx) + '.dat'
mem_file_name = make_path(fn, directory='')
X_test = read_memmap(mem_file_name)
print('X_test loaded')
# y_test
fn = 'mem_file_y_test_' + str(cv_idx) + '.dat'
mem_file_name = make_path(fn, directory='')
y_test = read_memmap(mem_file_name)
print('y_test loaded')
y_pred = clf.predict(X_test)
del X_test
s1 = accuracy_score(y_true=y_test, y_pred=y_pred)
s2 = precision_score(y_true=y_test, y_pred=y_pred)
s3 = recall_score(y_true=y_test, y_pred=y_pred)
s4 = f1_score(y_true=y_test, y_pred=y_pred)
del y_test
print('accuracy:', s1)
print('precision:', s2)
print('recall:', s3)
print('f1:', s4)
return [s1, s2, s3, s4]
def test_classes_property():
iris = load_iris()
X = iris.data
y = iris.target
reg = make_pipeline(SelectKBest(k=1), LinearRegression())
reg.fit(X, y)
with raises(AttributeError):
getattr(reg, "classes_")
clf = make_pipeline(SelectKBest(k=1),
LogisticRegression(solver='lbfgs', multi_class='auto',
random_state=0))
with raises(AttributeError):
getattr(clf, "classes_")
clf.fit(X, y)
assert_array_equal(clf.classes_, np.unique(y))
def test_bagging_with_pipeline():
X, y = make_imbalance(iris.data, iris.target, ratio={0: 20, 1: 25, 2: 50},
random_state=0)
estimator = BalancedBaggingClassifier(
make_pipeline(SelectKBest(k=1),
DecisionTreeClassifier()),
max_features=2)
estimator.fit(X, y).predict(X)
def test_bagging_with_pipeline():
X, y = make_imbalance(iris.data, iris.target,
sampling_strategy={0: 20, 1: 25, 2: 50},
random_state=0)
estimator = EasyEnsembleClassifier(
n_estimators=2,
base_estimator=make_pipeline(SelectKBest(k=1), AdaBoostClassifier()))
estimator.fit(X, y).predict(X)
def test_easy_ensemble_classifier_single_estimator():
X, y = make_imbalance(iris.data, iris.target,
sampling_strategy={0: 20, 1: 25, 2: 50},
random_state=0)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
clf1 = EasyEnsembleClassifier(n_estimators=1, random_state=0).fit(
X_train, y_train)
clf2 = make_pipeline(RandomUnderSampler(random_state=0),
AdaBoostClassifier(random_state=0)).fit(
X_train, y_train)
assert_array_equal(clf1.predict(X_test), clf2.predict(X_test))
def test_resampler_last_stage_passthrough():
X, y = make_classification(
n_classes=2,
class_sep=2,
weights=[0.1, 0.9],
n_informative=3,
n_redundant=1,
flip_y=0,
n_features=20,
n_clusters_per_class=1,
n_samples=50000,
random_state=0)
rus = RandomUnderSampler(random_state=42)
pipe = make_pipeline(rus, None)
pipe.fit_resample(X, y)
def test_pipeline_none_sampler_sample():
# Test pipeline using None step and a sampler
X, y = make_classification(
n_classes=2,
class_sep=2,
weights=[0.1, 0.9],
n_informative=3,
n_redundant=1,
flip_y=0,
n_features=20,
n_clusters_per_class=1,
n_samples=5000,
random_state=0)
rus = RandomUnderSampler(random_state=0)
pipe = make_pipeline(None, rus)
pipe.fit_resample(X, y)
def test_single_estimator():
# Check singleton ensembles.
X, y = make_imbalance(iris.data, iris.target, ratio={0: 20, 1: 25, 2: 50},
random_state=0)
X_train, X_test, y_train, y_test = train_test_split(X, y,
random_state=0)
clf1 = BalancedBaggingClassifier(
base_estimator=KNeighborsClassifier(),
n_estimators=1,
bootstrap=False,
bootstrap_features=False,
random_state=0).fit(X_train, y_train)
clf2 = make_pipeline(RandomUnderSampler(
random_state=clf1.estimators_[0].steps[0][1].random_state),
KNeighborsClassifier()).fit(X_train, y_train)
assert_array_equal(clf1.predict(X_test), clf2.predict(X_test))
def test_pipeline_none_classifier():
# Test pipeline using None as preprocessing step and a classifier
X, y = make_classification(
n_classes=2,
class_sep=2,
weights=[0.1, 0.9],
n_informative=3,
n_redundant=1,
flip_y=0,
n_features=20,
n_clusters_per_class=1,
n_samples=5000,
random_state=0)
clf = LogisticRegression(random_state=0)
pipe = make_pipeline(None, clf)
pipe.fit(X, y)
pipe.predict(X)
pipe.predict_proba(X)
pipe.decision_function(X)
pipe.score(X, y)
def test_pipeline_none_transformer():
# Test pipeline using None and a transformer that implements transform and
# inverse_transform
X, y = make_classification(
n_classes=2,
class_sep=2,
weights=[0.1, 0.9],
n_informative=3,
n_redundant=1,
flip_y=0,
n_features=20,
n_clusters_per_class=1,
n_samples=5000,
random_state=0)
pca = PCA(whiten=True)
pipe = make_pipeline(None, pca)
pipe.fit(X, y)
X_trans = pipe.transform(X)
X_inversed = pipe.inverse_transform(X_trans)
assert_array_almost_equal(X, X_inversed)
from imblearn import over_sampling as os
from imblearn import pipeline as pl
from imblearn.metrics import classification_report_imbalanced
print(__doc__)
RANDOM_STATE = 42
# Generate a dataset
X, y = datasets.make_classification(n_classes=2, class_sep=2,
weights=[0.1, 0.9], n_informative=10,
n_redundant=1, flip_y=0, n_features=20,
n_clusters_per_class=4, n_samples=5000,
random_state=RANDOM_STATE)
pipeline = pl.make_pipeline(os.SMOTE(random_state=RANDOM_STATE),
LinearSVC(random_state=RANDOM_STATE))
# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y,
random_state=RANDOM_STATE)
# Train the classifier with balancing
pipeline.fit(X_train, y_train)
# Test the classifier and get the prediction
y_pred_bal = pipeline.predict(X_test)
# Show the classification report
print(classification_report_imbalanced(y_test, y_pred_bal))
random_state=rng,
behaviour='new')
model.fit(X)
y_pred = model.predict(X)
return X[y_pred == 1], y[y_pred == 1]
reject_sampler = FunctionSampler(func=outlier_rejection)
X_inliers, y_inliers = reject_sampler.fit_resample(X_train, y_train)
plot_scatter(X_inliers, y_inliers, 'Training data without outliers')
##############################################################################
# Integrate it within a pipeline
##############################################################################
##############################################################################
# By elimnating outliers before the training, the classifier will be less
# affected during the prediction.
pipe = make_pipeline(FunctionSampler(func=outlier_rejection),
LogisticRegression(solver='lbfgs', multi_class='auto',
random_state=rng))
y_pred = pipe.fit(X_train, y_train).predict(X_test)
print(classification_report(y_test, y_pred))
clf = LogisticRegression(solver='lbfgs', multi_class='auto', random_state=rng)
y_pred = clf.fit(X_train, y_train).predict(X_test)
print(classification_report(y_test, y_pred))
plt.show()
print(__doc__)
RANDOM_STATE = 42
scorer = metrics.make_scorer(metrics.cohen_kappa_score)
# Generate the dataset
X, y = datasets.make_classification(n_classes=2, class_sep=2,
weights=[0.1, 0.9], n_informative=10,
n_redundant=1, flip_y=0, n_features=20,
n_clusters_per_class=4, n_samples=5000,
random_state=RANDOM_STATE)
smote = os.SMOTE(random_state=RANDOM_STATE)
cart = tree.DecisionTreeClassifier(random_state=RANDOM_STATE)
pipeline = pl.make_pipeline(smote, cart)
param_range = range(1, 11)
train_scores, test_scores = ms.validation_curve(
pipeline, X, y, param_name="smote__k_neighbors", param_range=param_range,
cv=3, scoring=scorer, n_jobs=1)
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
plt.plot(param_range, test_scores_mean, label='SMOTE')
ax.fill_between(param_range, test_scores_mean + test_scores_std,
def test_X1d_inverse_transform():
transformer = TransfT()
pipeline = make_pipeline(transformer)
X = np.ones(10)
msg = "1d X will not be reshaped in pipeline.inverse_transform"
assert_warns_message(FutureWarning, msg, pipeline.inverse_transform, X)
RepeatedEditedNearestNeighbours)
print(__doc__)
# Generate the dataset
X, y = make_classification(n_classes=2, class_sep=1.25, weights=[0.3, 0.7],
n_informative=3, n_redundant=1, flip_y=0,
n_features=5, n_clusters_per_class=1,
n_samples=5000, random_state=10)
# Instanciate a PCA object for the sake of easy visualisation
pca = PCA(n_components=2)
# Create the samplers
enn = EditedNearestNeighbours()
renn = RepeatedEditedNearestNeighbours()
# Create the classifier
knn = KNN(1)
# Make the splits
X_train, X_test, y_train, y_test = tts(X, y, random_state=42)
# Add one transformers and two samplers in the pipeline object
pipeline = make_pipeline(pca, enn, renn, knn)
pipeline.fit(X_train, y_train)
y_hat = pipeline.predict(X_test)
print(classification_report(y_test, y_hat))
y = data.target[idxs]
y[y == majority_person] = 0
y[y == minority_person] = 1
classifier = ['3NN', neighbors.KNeighborsClassifier(3)]
samplers = [
['Standard', DummySampler()],
['ADASYN', ADASYN(random_state=RANDOM_STATE)],
['ROS', RandomOverSampler(random_state=RANDOM_STATE)],
['SMOTE', SMOTE(random_state=RANDOM_STATE)],
]
pipelines = [
['{}-{}'.format(sampler[0], classifier[0]),
make_pipeline(sampler[1], classifier[1])]
for sampler in samplers
]
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
for name, pipeline in pipelines:
mean_tpr = 0.0
mean_fpr = np.linspace(0, 1, 100)
for train, test in cv.split(X, y):
probas_ = pipeline.fit(X[train], y[train]).predict_proba(X[test])
fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])
mean_tpr += interp(mean_fpr, fpr, tpr)
mean_tpr[0] = 0.0
roc_auc = auc(fpr, tpr)
X_test = np.vstack([moons, blobs])
y_test = np.hstack([np.ones(moons.shape[0], dtype=np.int8),
np.zeros(blobs.shape[0], dtype=np.int8)])
plot_scatter(X_test, y_test, 'Testing dataset')
def outlier_rejection(X, y):
model = IsolationForest(max_samples=100,
contamination=0.4,
random_state=rng)
model.fit(X)
y_pred = model.predict(X)
return X[y_pred == 1], y[y_pred == 1]
reject_sampler = FunctionSampler(func=outlier_rejection)
X_inliers, y_inliers = reject_sampler.fit_sample(X_train, y_train)
plot_scatter(X_inliers, y_inliers, 'Training data without outliers')
pipe = make_pipeline(FunctionSampler(func=outlier_rejection),
LogisticRegression(random_state=rng))
y_pred = pipe.fit(X_train, y_train).predict(X_test)
print(classification_report(y_test, y_pred))
clf = LogisticRegression(random_state=rng)
y_pred = clf.fit(X_train, y_train).predict(X_test)
print(classification_report(y_test, y_pred))
plt.show()
# does not have any knowledge regarding the underlying distribution. Therefore,
# some noisy samples can be generated, e.g. when the different classes cannot
# be well separated. Hence, it can be beneficial to apply an under-sampling
# algorithm to clean the noisy samples. Two methods are usually used in the
# literature: (i) Tomek's link and (ii) edited nearest neighbours cleaning
# methods. Imbalanced-learn provides two ready-to-use samplers ``SMOTETomek``
# and ``SMOTEENN``. In general, ``SMOTEENN`` cleans more noisy data than
# ``SMOTETomek``.
fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(3, 2,
figsize=(15, 25))
X, y = create_dataset(n_samples=1000, weights=(0.1, 0.2, 0.7))
ax_arr = ((ax1, ax2), (ax3, ax4), (ax5, ax6))
for ax, sampler in zip(ax_arr, (
SMOTE(random_state=0),
SMOTEENN(random_state=0),
SMOTETomek(random_state=0))):
clf = make_pipeline(sampler, LinearSVC())
clf.fit(X, y)
plot_decision_function(X, y, clf, ax[0])
ax[0].set_title('Decision function for {}'.format(
sampler.__class__.__name__))
plot_resampling(X, y, sampler, ax[1])
ax[1].set_title('Resampling using {}'.format(
sampler.__class__.__name__))
fig.tight_layout()
plt.show()
###############################################################################
# Random over-sampling to balance the data set
###############################################################################
###############################################################################
# Random over-sampling can be used to repeat some samples and balance the
# number of samples between the dataset. It can be seen that with this trivial
# approach the boundary decision is already less biaised toward the majority
# class.
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 7))
X, y = create_dataset(n_samples=10000, weights=(0.01, 0.05, 0.94))
clf = LinearSVC().fit(X, y)
plot_decision_function(X, y, clf, ax1)
ax1.set_title('Linear SVC with y={}'.format(Counter(y)))
pipe = make_pipeline(RandomOverSampler(random_state=0), LinearSVC())
pipe.fit(X, y)
plot_decision_function(X, y, pipe, ax2)
ax2.set_title('Decision function for RandomOverSampler')
fig.tight_layout()
###############################################################################
# More advanced over-sampling using ADASYN and SMOTE
###############################################################################
###############################################################################
# Instead of repeating the same samples when over-sampling, we can use some
# specific heuristic instead. ADASYN and SMOTE can be used in this case.
# Make an identity sampler
from sklearn.svm import LinearSVC
from sklearn.model_selection import train_test_split
from imblearn.datasets import make_imbalance
from imblearn.under_sampling import NearMiss
from imblearn.pipeline import make_pipeline
from imblearn.metrics import classification_report_imbalanced
print(__doc__)
RANDOM_STATE = 42
# Create a folder to fetch the dataset
iris = load_iris()
X, y = make_imbalance(iris.data, iris.target, ratio={0: 25, 1: 50, 2: 50},
random_state=0)
X_train, X_test, y_train, y_test = train_test_split(
X, y, random_state=RANDOM_STATE)
print('Training target statistics: {}'.format(Counter(y_train)))
print('Testing target statistics: {}'.format(Counter(y_test)))
# Create a pipeline
pipeline = make_pipeline(NearMiss(version=2, random_state=RANDOM_STATE),
LinearSVC(random_state=RANDOM_STATE))
pipeline.fit(X_train, y_train)
# Classify and report the results
print(classification_report_imbalanced(y_test, pipeline.predict(X_test)))
