Mini Batches¶

Iterate over a data stream in mini batches

In [18]:

import pandas as pd
from river import datasets
from deep_river import classification
from torch import nn
from river import compose
from river import preprocessing
from itertools import islice
from sklearn import metrics

import pandas as pd from river import datasets from deep_river import classification from torch import nn from river import compose from river import preprocessing from itertools import islice from sklearn import metrics

In [19]:

dataset = datasets.Phishing()

dataset = datasets.Phishing()

In [20]:

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, 2)
        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

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, 2) 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

In [21]:

model = compose.Pipeline(
    preprocessing.StandardScaler(),
    classification.Classifier(
        module=MyModule, loss_fn="binary_cross_entropy", optimizer_fn="sgd"
    ),
)
model

model = compose.Pipeline( preprocessing.StandardScaler(), classification.Classifier( module=MyModule, loss_fn="binary_cross_entropy", optimizer_fn="sgd" ), ) model

Out[21]:

StandardScaler

( with_std=True )

Classifier

( module=None loss_fn="binary_cross_entropy" optimizer_fn=<class 'torch.optim.sgd.SGD'> lr=0.001 output_is_logit=True is_class_incremental=False device="cpu" seed=42 )

In [22]:

y_trues = []
y_preds = []
for batch in batcher(dataset, 5):
    x, y = zip(*batch)
    x = pd.DataFrame(x)
    y_trues.extend(y)
    y = pd.Series(y)
    y_preds.extend(model.predict_many(X=x))
    model = model.learn_many(x, y)  # make the model learn

y_trues = [] y_preds = [] for batch in batcher(dataset, 5): x, y = zip(*batch) x = pd.DataFrame(x) y_trues.extend(y) y = pd.Series(y) y_preds.extend(model.predict_many(X=x)) model = model.learn_many(x, y) # make the model learn

In [23]:

metrics.accuracy_score(
    y_pred=[str(i) for i in y_preds], y_true=[str(i) for i in y_trues]
)

metrics.accuracy_score( y_pred=[str(i) for i in y_preds], y_true=[str(i) for i in y_trues] )

Out[23]:

0.4192