From 155df7bc5770d57d5057656f9a31bbf5eb045675 Mon Sep 17 00:00:00 2001 From: Marian Tietz Date: Fri, 13 Oct 2017 12:45:58 +0200 Subject: [PATCH] xi -> Xi --- skorch/dataset.py | 4 ++-- skorch/net.py | 40 ++++++++++++++++++++-------------------- 2 files changed, 22 insertions(+), 22 deletions(-) diff --git a/skorch/dataset.py b/skorch/dataset.py index e186e62e5..39390cf3f 100644 --- a/skorch/dataset.py +++ b/skorch/dataset.py @@ -203,9 +203,9 @@ def __getitem__(self, i): if is_pandas_ndframe(X): X = {k: X[k].values.reshape(-1, 1) for k in X} - xi = multi_indexing(X, i) + Xi = multi_indexing(X, i) yi = y if y is None else multi_indexing(y, i) - return self.transform(xi, yi) + return self.transform(Xi, yi) class CVSplit(object): diff --git a/skorch/net.py b/skorch/net.py index d364b5959..c06d58212 100644 --- a/skorch/net.py +++ b/skorch/net.py @@ -410,7 +410,7 @@ def initialize(self): def check_data(self, X, y=None): pass - def validation_step(self, xi, yi): + def validation_step(self, Xi, yi): """Perform a forward step using batched data and return the resulting loss. @@ -419,10 +419,10 @@ def validation_step(self, xi, yi): """ self.module_.eval() - y_pred = self.infer(xi) - return self.get_loss(y_pred, yi, X=xi, train=False) + y_pred = self.infer(Xi) + return self.get_loss(y_pred, yi, X=Xi, train=False) - def train_step(self, xi, yi, optimizer): + def train_step(self, Xi, yi, optimizer): """Perform a forward step using batched data, update module parameters, and return the loss. @@ -432,8 +432,8 @@ def train_step(self, xi, yi, optimizer): """ self.module_.train() optimizer.zero_grad() - y_pred = self.infer(xi) - loss = self.get_loss(y_pred, yi, X=xi, train=True) + y_pred = self.infer(Xi) + loss = self.get_loss(y_pred, yi, X=Xi, train=True) loss.backward() if self.gradient_clip_value is not None: @@ -445,7 +445,7 @@ def train_step(self, xi, yi, optimizer): optimizer.step() return loss - def evaluation_step(self, xi, training=False): + def evaluation_step(self, Xi, training=False): """Perform a forward step to produce the output used for prediction and scoring. @@ -455,7 +455,7 @@ def evaluation_step(self, xi, training=False): """ self.module_.train(training) - return self.infer(xi) + return self.infer(Xi) def fit_loop(self, X, y=None, epochs=None): """The proper fit loop. @@ -491,23 +491,23 @@ def fit_loop(self, X, y=None, epochs=None): for _ in range(epochs): self.notify('on_epoch_begin', X=X, y=y) - for xi, yi in self.get_iterator(dataset_train, train=True): - self.notify('on_batch_begin', X=xi, y=yi, train=True) - loss = self.train_step(xi, yi, self.optimizer_) + for Xi, yi in self.get_iterator(dataset_train, train=True): + self.notify('on_batch_begin', X=Xi, y=yi, train=True) + loss = self.train_step(Xi, yi, self.optimizer_) self.history.record_batch('train_loss', loss.data[0]) - self.history.record_batch('train_batch_size', len(xi)) - self.notify('on_batch_end', X=xi, y=yi, train=True) + self.history.record_batch('train_batch_size', len(Xi)) + self.notify('on_batch_end', X=Xi, y=yi, train=True) if X_valid is None: self.notify('on_epoch_end', X=X, y=y) continue - for xi, yi in self.get_iterator(dataset_valid, train=False): - self.notify('on_batch_begin', X=xi, y=yi, train=False) - loss = self.validation_step(xi, yi) + for Xi, yi in self.get_iterator(dataset_valid, train=False): + self.notify('on_batch_begin', X=Xi, y=yi, train=False) + loss = self.validation_step(Xi, yi) self.history.record_batch('valid_loss', loss.data[0]) - self.history.record_batch('valid_batch_size', len(xi)) - self.notify('on_batch_end', X=xi, y=yi, train=False) + self.history.record_batch('valid_batch_size', len(Xi)) + self.notify('on_batch_end', X=Xi, y=yi, train=False) self.notify('on_epoch_end', X=X, y=y) return self @@ -586,9 +586,9 @@ def forward(self, X, training=False): dataset = self.dataset(X, use_cuda=self.use_cuda) iterator = self.get_iterator(dataset, train=training) y_infer = [] - for xi, _ in iterator: + for Xi, _ in iterator: y_infer.append( - self.evaluation_step(xi, training=training)) + self.evaluation_step(Xi, training=training)) return torch.cat(y_infer, dim=0) def infer(self, x):