def test_stateful_transform():
data_train = patsy.demo_data("x1", "x2", "y")
data_train['x1'][:] = 1
# mean of x1 is 1
data_test = patsy.demo_data("x1", "x2", "y")
data_test['x1'][:] = 0
# center x1
est = PatsyTransformer("center(x1) + x2")
est.fit(data_train)
data_trans = est.transform(data_test)
# make sure that mean of training, not test data was removed
assert_array_equal(data_trans[:, 0], -1)
def test_stateful_transform():
data_train = patsy.demo_data("x1", "x2", "y")
data_train['x1'][:] = 1
# mean of x1 is 1
data_test = patsy.demo_data("x1", "x2", "y")
data_test['x1'][:] = 0
# center x1
est = PatsyTransformer("center(x1) + x2")
est.fit(data_train)
data_trans = est.transform(data_test)
# make sure that mean of training, not test data was removed
assert_array_equal(data_trans[:, 0], -1)
def test_error_on_y_transform():
data = patsy.demo_data("x1", "x2", "x3", "y")
est = PatsyTransformer("y ~ x1 + x2")
msg = ("encountered outcome variables for a model"
" that does not expect them")
assert_raise_message(patsy.PatsyError, msg, est.fit, data)
assert_raise_message(patsy.PatsyError, msg, est.fit_transform, data)
def test_stateful_transform_dataframe():
data_train = pd.DataFrame(patsy.demo_data("x1", "x2", "y"))
data_train['x1'][:] = 1
# mean of x1 is 1
data_test = pd.DataFrame(patsy.demo_data("x1", "x2", "y"))
data_test['x1'][:] = 0
# center x1
est = PatsyTransformer("center(x1) + x2", return_type='dataframe')
est.fit(data_train)
data_trans = est.transform(data_test)
# make sure result is pandas dataframe
assert type(data_trans) is pd.DataFrame
# make sure that mean of training, not test data was removed
assert_array_equal(data_trans['center(x1)'][:],-1)
def test_stateful_transform_dataframe():
data_train = pd.DataFrame(patsy.demo_data("x1", "x2", "y"))
data_train['x1'][:] = 1
# mean of x1 is 1
data_test = pd.DataFrame(patsy.demo_data("x1", "x2", "y"))
data_test['x1'][:] = 0
# center x1
est = PatsyTransformer("center(x1) + x2", return_type='dataframe')
est.fit(data_train)
data_trans = est.transform(data_test)
# make sure result is pandas dataframe
assert type(data_trans) is pd.DataFrame
# make sure that mean of training, not test data was removed
assert_array_equal(data_trans['center(x1)'][:], -1)
def test_intercept_transformer():
data = patsy.demo_data("x1", "x2", "x3", "y")
# check wether X contains only the two features, no intercept
est = PatsyTransformer("x1 + x2")
est.fit(data)
assert_equal(est.transform(data).shape[1], 2)
# check wether X does contain intercept
est = PatsyTransformer("x1 + x2", add_intercept=True)
est.fit(data)
data_transformed = est.transform(data)
assert_array_equal(data_transformed[:, 0], 1)
assert_equal(est.transform(data).shape[1], 3)
def test_scope_transformer():
data = patsy.demo_data("x1", "x2", "x3", "y")
def myfunc(x):
tmp = np.ones_like(x)
tmp.fill(42)
return tmp
est = PatsyTransformer("x1 + myfunc(x2)")
est.fit(data)
data_trans = est.transform(data)
assert_array_equal(data_trans[:, 1], 42)
est = PatsyTransformer("x1 + myfunc(x2)")
data_trans = est.fit_transform(data)
assert_array_equal(data_trans[:, 1], 42)
# test feature names
assert_equal(est.feature_names_, ["x1", "myfunc(x2)"])
def test_coxph_model():
data = lifelines.datasets.load_dd()
# create sklearn pipeline
coxph_surv_ppl = make_pipeline(
PatsyTransformer('un_continent_name + regime + start_year -1',
return_type='dataframe'),
CoxPHFitterModel(duration_column='duration', event_col='observed'))
#split data to train and test
data_train, data_test = train_test_split(data)
#fit CoxPH model
coxph_surv_ppl.fit(data_train, y=data_train)
#use pipeline to predict expected lifetime
exp_lifetime = coxph_surv_ppl.predict(data_test[0:1])
assert (exp_lifetime > 4)
def test_scope_transformer():
data = patsy.demo_data("x1", "x2", "x3", "y")
def myfunc(x):
tmp = np.ones_like(x)
tmp.fill(42)
return tmp
est = PatsyTransformer("x1 + myfunc(x2)")
est.fit(data)
data_trans = est.transform(data)
assert_array_equal(data_trans[:, 1], 42)
est = PatsyTransformer("x1 + myfunc(x2)")
data_trans = est.fit_transform(data)
assert_array_equal(data_trans[:, 1], 42)
feature_extraction_model.load_weights(cfg.VGG16_pretrained_model_path,
by_name=True)
sampled_cell_Indexs = random_sample_index(
cells_lists, 2000) #random sample 2000 cell features
feature_lists = []
for index in sampled_cell_Indexs:
a_cell_feature = feature_extraction_model.predict(cells_lists[index])
feature_lists.append(a_cell_feature)
feature_lists = np.array(feature_lists)
C = llc(feature_lists) # the codes
f = np.sum(C, axis=1) # the single vector of the patient.
#f=np.max(C,axis=1)#the single vector of the patient.
data = lifelines.datasets.load_dd()
# create sklearn pipeline
coxph_surv_ppl = make_pipeline(PatsyTransformer('un_continent_name + regime + start_year -1', \
return_type='dataframe'),
CoxPHFitterModel(duration_column='duration', event_col='observed'))
# split data to train and test
data_train, data_test = train_test_split(data)
# fit CoxPH model
coxph_surv_ppl.fit(data_train, y=data_train)
# use pipeline to predict expected lifetime
exp_lifetime = coxph_surv_ppl.predict(data_test[0:1])
print('expected lifetime: ' + str(exp_lifetime))
# or you can extract the model from the pipeline to access more methods
coxmodel = coxph_surv_ppl.named_steps['coxphfittermodel'].estimator
coxmodel.print_summary()
def test_intercept_transformer():
data = patsy.demo_data("x1", "x2", "x3", "y")
# check wether X contains only the two features, no intercept
est = PatsyTransformer("x1 + x2")
est.fit(data)
assert_equal(est.transform(data).shape[1], 2)
# check wether X does contain intercept
est = PatsyTransformer("x1 + x2", add_intercept=True)
est.fit(data)
data_transformed = est.transform(data)
assert_array_equal(data_transformed[:, 0], 1)
assert_equal(est.transform(data).shape[1], 3)
# Schedule delivery time during the entire working hours i.e. between 8 am to 6 pm
data["Is_delivery_working_hours"] = (data["time_slot_from"].str[:2].astype(
int) >= 9) & (data["time_slot_to"].str[:2].astype(int) <= 18)
# Whether committed delivery day is friday (weekend) or not
data["Is_committed_delivery_friday"] = (
data["commitment_date_weekday"] == "Friday").astype(int)
# dataframe of predictor variables
feature_df = data[[
"Schedule_loc_type", "schedule_channel", "Is_committed_delivery_friday",
"Is_delivery_working_hours"
]]
y_df = data["accurate"]
interaction_transformer = PatsyTransformer(
"C(Schedule_loc_type) + C(schedule_channel)+ Is_committed_delivery_friday + Is_delivery_working_hours + C(Schedule_loc_type):C(schedule_channel)"
)
non_interaction_transformer = PatsyTransformer(
"C(Schedule_loc_type) + C(schedule_channel)+ Is_committed_delivery_friday + Is_delivery_working_hours"
)
naive_bayes_clf = Pipeline(
[("encoding", non_interaction_transformer), ("nb_clf", GaussianNB())]
) # Because we assume conditional independence among diffrent predictor variables in Naive Bayes, interaction terms are not necessary
logistic_clf = Pipeline([('int_feature', interaction_transformer),
('log_clf',
LogisticRegression(C=10.0,
fit_intercept=True,
random_state=100))])
supportvector_clf = Pipeline([('int_feature', interaction_transformer),
('svm_clf',
