def test_list_of_funcs():
def f(x):
return {"f": 1}
def g(x):
return {"g": 2}
def times_2(x):
return {k: v * 2 for k, v in x.items()}
expected = {"f": 2, "g": 4}
assert compose.Pipeline([f, g], times_2).transform_one(None) == expected
assert ([f, g]
| compose.FuncTransformer(times_2)).transform_one(None) == expected
def test_pipeline_funcs():
def a(x):
pass
def b(x):
pass
pipelines = [
compose.FuncTransformer(a) | b,
compose.FuncTransformer(a) | ('b', b),
compose.FuncTransformer(a) | ('b', compose.FuncTransformer(b)),
a | compose.FuncTransformer(b), ('a', a) | compose.FuncTransformer(b),
('a', compose.FuncTransformer(a)) | compose.FuncTransformer(b)
]
for pipeline in pipelines:
assert str(pipeline) == 'a | b'
def test_pipeline_funcs():
def a(x):
pass
def b(x):
pass
pipelines = [
compose.FuncTransformer(a) | b,
compose.FuncTransformer(a) | ("b", b),
compose.FuncTransformer(a) | ("b", compose.FuncTransformer(b)),
a | compose.FuncTransformer(b),
("a", a) | compose.FuncTransformer(b),
("a", compose.FuncTransformer(a)) | compose.FuncTransformer(b),
]
for pipeline in pipelines:
assert str(pipeline) == "a | b"
def test_union_funcs():
def a(x):
pass
def b(x):
pass
pipelines = [
compose.FuncTransformer(a) + b,
compose.FuncTransformer(a) + ('b', b),
compose.FuncTransformer(a) + ('b', compose.FuncTransformer(b)),
a + compose.FuncTransformer(b), ('a', a) + compose.FuncTransformer(b),
('a', compose.FuncTransformer(a)) + compose.FuncTransformer(b)
]
for i, pipeline in enumerate(pipelines):
print(i, str(pipeline))
assert str(pipeline) == 'a + b'
task=datasets.base.REG,
n_features=1,
n_samples=1440)
def __iter__(self):
return stream.iter_csv(self.path,
target='interval_qps',
converters={'interval_qps': int})
def get_ordinal_date(x):
return {'ordinal_date': int(x['secs_elapsed'])}
model = compose.Pipeline(
('ordinal_date', compose.FuncTransformer(get_ordinal_date)),
('scale', preprocessing.MinMaxScaler()),
('lin_reg', linear_model.LinearRegression()))
from river import metrics
import matplotlib.pyplot as plt
# target_data = "../log_traces/Mixgraph/1000_0.0000073_45000/report.csv"
target_data = "../log_traces/StorageMaterial.NVMeSSD/12CPU/64MB/report.csv_1180"
import os
target_data = os.path.abspath(target_data)
def evaluate_model(model):
metric = metrics.Rolling(metrics.MAE(), 12)
def get_ordinal_data(x):
return {'ordinal_data': x['month'].toordinal()}
def get_month(x):
return {
calendar.month_name[month]: month == x['month'].month
for month in range(1, 13)
}
# To monthly trend by one-hot encoding the month name
model = compose.Pipeline(
('features',
compose.TransformerUnion(
('ordinal_date', compose.FuncTransformer(get_ordinal_data)),
('month', compose.FuncTransformer(get_month)),
)), ('scale', preprocessing.StandardScaler),
('lin_reg',
linear_model.LinearRegression(intercept_lr=0, optimizer=optim.SGD(0.05))))
model = time_series.Detrender(regressor=model, window_size=12)
dates = []
dates_pred = []
y_trues = []
y_preds = []
images = []
