def test_fit_transform_show_and_draw_calls(self):
"""
Test calling fit, transform, and show on the pipeline
"""
pipeline = VisualPipeline([
("a", mock.MagicMock(MockTransformer())),
("b", mock.MagicMock(MockVisualTransformer())),
("c", mock.MagicMock(MockTransformer())),
("d", mock.MagicMock(MockVisualTransformer())),
("e", mock.MagicMock(MockEstimator())),
])
X = [[1, 1, 1, 1, 1], [2, 2, 2, 2, 2], [3, 3, 3, 3, 3]]
y = [1, 2, 3, 4, 5]
pipeline.fit(X, y)
for name, step in pipeline.named_steps.items():
step.fit.assert_called_once_with(X, y)
pipeline.transform(X)
for name, step in pipeline.named_steps.items():
if name == "e":
continue
step.transform.assert_called_once_with(X)
pipeline.show()
for name, step in pipeline.named_steps.items():
if name in {"a", "c", "e"}:
continue
step.show.assert_called_once_with(outpath=None)
def test_fit_transform_poof_and_draw_calls(self):
"""
Test calling fit, transform, and poof on the pipeline
"""
pipeline = VisualPipeline([
('a', mock.MagicMock(MockTransformer())),
('b', mock.MagicMock(MockVisualTransformer())),
('c', mock.MagicMock(MockTransformer())),
('d', mock.MagicMock(MockVisualTransformer())),
('e', mock.MagicMock(MockEstimator()),)
])
X = [[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 3]]
y = [1, 2, 3, 4, 5]
pipeline.fit(X, y)
for name, step in pipeline.named_steps.items():
step.fit.assert_called_once_with(X, y)
pipeline.transform(X)
for name, step in pipeline.named_steps.items():
if name == 'e': continue
step.transform.assert_called_once_with(X)
pipeline.poof()
for name, step in pipeline.named_steps.items():
if name in {'a', 'c', 'e'}: continue
step.poof.assert_called_once_with(outpath=None)
def test_fit_transform_poof_and_draw_calls(self):
"""
Test calling fit, transform, and poof on the pipeline
"""
pipeline = VisualPipeline([
('a', mock.MagicMock(MockTransformer())),
('b', mock.MagicMock(MockVisualTransformer())),
('c', mock.MagicMock(MockTransformer())),
('d', mock.MagicMock(MockVisualTransformer())),
('e', mock.MagicMock(MockEstimator()),)
])
X = [[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 3]]
y = [1, 2, 3, 4, 5]
pipeline.fit(X, y)
for name, step in pipeline.named_steps.items():
step.fit.assert_called_once_with(X, y)
pipeline.transform(X)
for name, step in pipeline.named_steps.items():
if name == 'e': continue
step.transform.assert_called_once_with(X)
pipeline.poof()
for name, step in pipeline.named_steps.items():
if name in {'a', 'c', 'e'}: continue
step.poof.assert_called_once_with(outpath=None)
def selectDiscr():
data_path = "labeled_data.csv"
data = pd.read_csv(data_path)
# We create the preprocessing pipelines for both numeric and categorical data.
numeric_features = ['count_reviews', 'rating']
numeric_transformer = Pipeline(
steps=[('imputer',
SimpleImputer(strategy='median')), ('scaler',
StandardScaler())])
categorical_features = ['product_category']
categorical_transformer = Pipeline(
steps=[('imputer',
SimpleImputer(strategy='constant', fill_value='missing')
), ('onehot', OneHotEncoder(handle_unknown='ignore'))])
preprocessor = ColumnTransformer(
transformers=[('num', numeric_transformer, numeric_features
), ('cat', categorical_transformer,
categorical_features)])
viz = DiscriminationThreshold(LogisticRegression())
clf = VisualPipeline(steps=[
('preprocessor', preprocessor),
#('classifier', LogisticRegression(solver='lbfgs')),
('viz', viz)
])
X = data.drop('label', axis=1)
y = data['label']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = clf.fit(X_train, y_train)
model.poof()
def modelSelection():
data_path = "labeled_data.csv"
data = pd.read_csv(data_path)
# Preprocessing pipelines for both numeric and categorical data.
numeric_features = ['count_reviews', 'rating']
numeric_transformer = Pipeline(
steps=[('imputer',
SimpleImputer(strategy='median')), ('scaler',
StandardScaler())])
categorical_features = ['product_category']
categorical_transformer = Pipeline(
steps=[('imputer',
SimpleImputer(strategy='constant', fill_value='missing')
), ('onehot', OneHotEncoder(handle_unknown='ignore'))])
preprocessor = ColumnTransformer(
transformers=[('num', numeric_transformer, numeric_features
), ('cat', categorical_transformer,
categorical_features)])
X = data.drop('label', axis=1)
y = data['label']
fig = plt.figure()
ax = fig.add_subplot()
#viz_ridge = ClassificationReport(RidgeClassifier(), classes = ['not recommended', 'recommended'], support=True)
viz_logistic = ClassificationReport(
LogisticRegression(),
#SGDClassifier(),
#RidgeClassifier(),
classes=['not recommended', 'recommended'],
support=True)
#clf_ridge = VisualPipeline(steps=[('preprocessor', preprocessor),
# #('classifier', LogisticRegression(solver='lbfgs')),
# ('viz', viz_ridge)])
#Visual Pipeline is used to visualize the report
clf_logistic = VisualPipeline(steps=[
('preprocessor', preprocessor),
#('classifier', LogisticRegression(solver='lbfgs')),
('viz', viz_logistic)
])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
#model_ridge = clf_ridge.fit(X_train, y_train)
model_logistic = clf_logistic.fit(X_train, y_train)
#preds_ridge = clf_ridge.predict(X_test)
preds_logistic = clf_logistic.predict(X_test)
#print("RidgeClassifier model score: %.3f" % clf_ridge.score(X_test, y_test))
print("LogisticRegression model score: %.3f" %
clf_logistic.score(X_test, y_test))
#clf_ridge.poof()
clf_logistic.poof()
# Evaluate accuracy
#print("RidgeClassifier accuracy: ", accuracy_score(y_test, preds_ridge))
print("LogisticRegression accuracy: ",
accuracy_score(y_test, preds_logistic))
final_predictions = X_test
final_predictions['target'] = y_test
final_predictions['prediction'] = preds_logistic
#print(final_predictions)
filename = 'model_products.sav'
joblib.dump(model_logistic, filename)