Example Mini Batches¶
import pandas as pd
from river import datasets
from deep_river import regression
from torch import nn
from river import compose
from river import preprocessing
from itertools import islice
from pprint import pprint
from sklearn import metrics
class MyModule(nn.Module):
def __init__(self, n_features):
super(MyModule, self).__init__()
self.dense0 = nn.Linear(n_features, 5)
self.nonlin = nn.ReLU()
self.dense1 = nn.Linear(5, 1)
self.softmax = nn.Softmax(dim=-1)
def forward(self, X, **kwargs):
X = self.nonlin(self.dense0(X))
X = self.nonlin(self.dense1(X))
X = self.softmax(X)
return X
def batcher(iterable, batch_size):
iterator = iter(iterable)
while batch := list(islice(iterator, batch_size)):
yield batch
dataset = datasets.Bikes()
for x, y in dataset:
pprint(x)
print(f"Number of available bikes: {y}")
break
{'clouds': 75,
'description': 'light rain',
'humidity': 81,
'moment': datetime.datetime(2016, 4, 1, 0, 0, 7),
'pressure': 1017.0,
'station': 'metro-canal-du-midi',
'temperature': 6.54,
'wind': 9.3}
Number of available bikes: 1
dataset = datasets.Bikes()
model_pipeline = compose.Select(
"clouds", "humidity", "pressure", "temperature", "wind"
)
model_pipeline |= regression.Regressor(
module=MyModule(5), loss_fn="mse", optimizer_fn="sgd"
)
model_pipeline
['clouds', [...]
Select (
clouds
humidity
pressure
temperature
wind
)
Regressor
Regressor (
module=MyModule(
(dense0): Linear(in_features=5, out_features=5, bias=True)
(nonlin): ReLU()
(dense1): Linear(in_features=5, out_features=1, bias=True)
(softmax): Softmax(dim=-1)
)
loss_fn="mse"
optimizer_fn="sgd"
lr=0.001
is_feature_incremental=False
device="cpu"
seed=42
)
y_trues = []
y_preds = []
for batch in batcher(dataset.take(5000), 5):
x, y = zip(*batch)
x = pd.DataFrame(x)
y_trues.extend(y)
y_preds.extend(model_pipeline.predict_many(X=x).values)
model_pipeline.learn_many(X=x, y=pd.Series(y))
102.4412