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Update setting for model training.
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@@ -46,7 +46,7 @@ class GRU(Model):
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dropout=0.0,
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n_epochs=200,
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lr=0.001,
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metric="IC",
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metric="",
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batch_size=2000,
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early_stop=20,
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loss="mse",
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@@ -140,21 +140,17 @@ class GRU(Model):
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def metric_fn(self, pred, label):
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mask = torch.isfinite(label)
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if self.metric == "IC":
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return self.cal_ic(pred[mask], label[mask])
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if self.metric == "" or self.metric == "loss": # use loss
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return -self.loss_fn(pred[mask], label[mask])
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raise ValueError("unknown metric `%s`" % self.metric)
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def cal_ic(self, pred, label):
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return torch.mean(pred * label)
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def train_epoch(self, x_train, y_train):
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x_train_values = x_train.values
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y_train_values = np.squeeze(y_train.values) * 100
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y_train_values = np.squeeze(y_train.values)
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self.gru_model.train()
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@@ -193,7 +189,6 @@ class GRU(Model):
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losses = []
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indices = np.arange(len(x_values))
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np.random.shuffle(indices)
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for i in range(len(indices))[:: self.batch_size]:
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