Plots to compare between quantile regression and diffusion
This commit is contained in:
Victor Mylle
@@ -10,7 +10,9 @@ import plotly.graph_objects as go
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.interpolate import CubicSpline
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import matplotlib.pyplot as plt
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import seaborn as sns
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import matplotlib.patches as mpatches
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def sample_from_dist(quantiles, preds):
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if isinstance(preds, torch.Tensor):
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@@ -261,35 +263,35 @@ class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
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name="test_CRPS_from_samples_transformed", value=np.mean(crps_from_samples_metric)
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)
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def get_plot_error(
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self,
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next_day,
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predictions,
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):
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metric = PinballLoss(quantiles=self.quantiles)
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fig = go.Figure()
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# def get_plot_error(
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# self,
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# next_day,
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# predictions,
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# ):
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# metric = PinballLoss(quantiles=self.quantiles)
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# fig = go.Figure()
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next_day_np = next_day.view(-1).cpu().numpy()
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predictions_np = predictions.cpu().numpy()
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# next_day_np = next_day.view(-1).cpu().numpy()
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# predictions_np = predictions.cpu().numpy()
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if True:
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next_day_np = self.data_processor.inverse_transform(next_day_np)
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predictions_np = self.data_processor.inverse_transform(predictions_np)
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# if True:
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# next_day_np = self.data_processor.inverse_transform(next_day_np)
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# predictions_np = self.data_processor.inverse_transform(predictions_np)
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# for each time step, calculate the error using the metric
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errors = []
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for i in range(96):
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# # for each time step, calculate the error using the metric
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# errors = []
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# for i in range(96):
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target_tensor = torch.tensor(next_day_np[i]).unsqueeze(0)
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prediction_tensor = torch.tensor(predictions_np[i]).unsqueeze(0)
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# target_tensor = torch.tensor(next_day_np[i]).unsqueeze(0)
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# prediction_tensor = torch.tensor(predictions_np[i]).unsqueeze(0)
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errors.append(metric(prediction_tensor, target_tensor))
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# errors.append(metric(prediction_tensor, target_tensor))
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# plot the error
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fig.add_trace(go.Scatter(x=np.arange(96), y=errors, name=metric.__class__.__name__))
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fig.update_layout(title=f"Error of {metric.__class__.__name__} for each time step")
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# # plot the error
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# fig.add_trace(go.Scatter(x=np.arange(96), y=errors, name=metric.__class__.__name__))
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# fig.update_layout(title=f"Error of {metric.__class__.__name__} for each time step")
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return fig
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# return fig
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def get_plot(
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@@ -312,26 +314,59 @@ class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
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next_day_np = self.data_processor.inverse_transform(next_day_np)
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predictions_np = self.data_processor.inverse_transform(predictions_np)
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ci_99_upper = np.quantile(predictions_np, 0.995, axis=0)
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ci_99_lower = np.quantile(predictions_np, 0.005, axis=0)
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ci_95_upper = np.quantile(predictions_np, 0.975, axis=0)
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ci_95_lower = np.quantile(predictions_np, 0.025, axis=0)
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ci_90_upper = np.quantile(predictions_np, 0.95, axis=0)
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ci_90_lower = np.quantile(predictions_np, 0.05, axis=0)
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ci_50_lower = np.quantile(predictions_np, 0.25, axis=0)
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ci_50_upper = np.quantile(predictions_np, 0.75, axis=0)
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# Add traces for current and next day
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fig.add_trace(go.Scatter(x=np.arange(96), y=current_day_np, name="Current Day"))
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fig.add_trace(go.Scatter(x=96 + np.arange(96), y=next_day_np, name="Next Day"))
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# fig.add_trace(go.Scatter(x=np.arange(96), y=current_day_np, name="Current Day"))
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# fig.add_trace(go.Scatter(x=96 + np.arange(96), y=next_day_np, name="Next Day"))
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for i, q in enumerate(self.quantiles):
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fig.add_trace(
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go.Scatter(
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x=96 + np.arange(96),
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y=predictions_np[:, i],
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name=f"Prediction (Q={q})",
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line=dict(dash="dash"),
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)
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)
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# for i, q in enumerate(self.quantiles):
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# fig.add_trace(
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# go.Scatter(
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# x=96 + np.arange(96),
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# y=predictions_np[:, i],
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# name=f"Prediction (Q={q})",
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# line=dict(dash="dash"),
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# )
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# )
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# Update the layout
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fig.update_layout(
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title="Predictions and Quantiles of the Linear Model",
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showlegend=show_legend,
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)
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# # Update the layout
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# fig.update_layout(
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# title="Predictions and Quantiles of the Linear Model",
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# showlegend=show_legend,
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# )
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sns.set_theme()
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time_steps = np.arange(0, 96)
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fig, ax = plt.subplots(figsize=(20, 10))
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ax.plot(time_steps, predictions_np.mean(axis=0), label="Mean of NRV samples", linewidth=3)
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# ax.fill_between(time_steps, ci_lower, ci_upper, color='b', alpha=0.2, label='Full Interval')
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ax.fill_between(time_steps, ci_99_lower, ci_99_upper, color='b', alpha=0.2, label='99% Interval')
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ax.fill_between(time_steps, ci_95_lower, ci_95_upper, color='b', alpha=0.2, label='95% Interval')
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ax.fill_between(time_steps, ci_90_lower, ci_90_upper, color='b', alpha=0.2, label='90% Interval')
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ax.fill_between(time_steps, ci_50_lower, ci_50_upper, color='b', alpha=0.2, label='50% Interval')
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ax.plot(next_day_np, label="Real NRV", linewidth=3)
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# full_interval_patch = mpatches.Patch(color='b', alpha=0.2, label='Full Interval')
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ci_99_patch = mpatches.Patch(color='b', alpha=0.3, label='99% Interval')
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ci_95_patch = mpatches.Patch(color='b', alpha=0.4, label='95% Interval')
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ci_90_patch = mpatches.Patch(color='b', alpha=0.5, label='90% Interval')
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ci_50_patch = mpatches.Patch(color='b', alpha=0.6, label='50% Interval')
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ax.legend(handles=[ci_99_patch, ci_95_patch, ci_90_patch, ci_50_patch, ax.lines[0], ax.lines[1]])
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return fig
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def auto_regressive(self, dataset, idx_batch, sequence_length: int = 96):
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