207 lines
7.0 KiB
Python
207 lines
7.0 KiB
Python
import numpy as np
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import dash
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from dash import dcc, html
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import plotly.graph_objs as go
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import threading
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def display_actual_vs_predicted(y_test, test_preds, timestamps, n_plot=200):
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import plotly.offline as pyo
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n_plot = min(n_plot, len(y_test))
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plot_indices = timestamps[:n_plot]
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actual = y_test[:n_plot]
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predicted = test_preds[:n_plot]
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trace_actual = go.Scatter(x=plot_indices, y=actual, mode='lines', name='Actual')
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trace_predicted = go.Scatter(x=plot_indices, y=predicted, mode='lines', name='Predicted')
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data = [trace_actual, trace_predicted]
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layout = go.Layout(
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title='Actual vs. Predicted BTC Close Prices (Test Set)',
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xaxis={'title': 'Timestamp'},
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yaxis={'title': 'BTC Close Price'},
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legend={'x': 0, 'y': 1},
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margin={'l': 40, 'b': 40, 't': 40, 'r': 10},
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hovermode='closest'
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)
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fig = go.Figure(data=data, layout=layout)
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pyo.plot(fig)
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def plot_target_distribution(y_train, y_test):
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import plotly.offline as pyo
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trace_train = go.Histogram(
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x=y_train,
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nbinsx=100,
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opacity=0.5,
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name='Train',
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marker=dict(color='blue')
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)
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trace_test = go.Histogram(
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x=y_test,
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nbinsx=100,
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opacity=0.5,
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name='Test',
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marker=dict(color='orange')
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)
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data = [trace_train, trace_test]
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layout = go.Layout(
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title='Distribution of Target Variable (Close Price)',
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xaxis=dict(title='BTC Close Price'),
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yaxis=dict(title='Frequency'),
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barmode='overlay'
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)
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fig = go.Figure(data=data, layout=layout)
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pyo.plot(fig)
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def plot_predicted_vs_actual_log_returns(y_test, test_preds, timestamps=None, n_plot=200):
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import plotly.offline as pyo
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import plotly.graph_objs as go
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n_plot = min(n_plot, len(y_test))
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actual = y_test[:n_plot]
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predicted = test_preds[:n_plot]
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if timestamps is not None:
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x_axis = timestamps[:n_plot]
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x_label = 'Timestamp'
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else:
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x_axis = list(range(n_plot))
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x_label = 'Index'
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# Line plot: Actual vs Predicted over time
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trace_actual = go.Scatter(x=x_axis, y=actual, mode='lines', name='Actual')
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trace_predicted = go.Scatter(x=x_axis, y=predicted, mode='lines', name='Predicted')
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data_line = [trace_actual, trace_predicted]
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layout_line = go.Layout(
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title='Actual vs. Predicted Log Returns (Test Set)',
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xaxis={'title': x_label},
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yaxis={'title': 'Log Return'},
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legend={'x': 0, 'y': 1},
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margin={'l': 40, 'b': 40, 't': 40, 'r': 10},
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hovermode='closest'
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)
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fig_line = go.Figure(data=data_line, layout=layout_line)
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pyo.plot(fig_line, filename='log_return_line_plot.html')
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# Scatter plot: Predicted vs Actual
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trace_scatter = go.Scatter(
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x=actual,
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y=predicted,
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mode='markers',
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name='Predicted vs Actual',
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opacity=0.5
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)
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# Diagonal reference line
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min_val = min(np.min(actual), np.min(predicted))
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max_val = max(np.max(actual), np.max(predicted))
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trace_diag = go.Scatter(
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x=[min_val, max_val],
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y=[min_val, max_val],
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mode='lines',
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name='Ideal',
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line=dict(dash='dash', color='red')
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)
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data_scatter = [trace_scatter, trace_diag]
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layout_scatter = go.Layout(
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title='Predicted vs Actual Log Returns (Scatter)',
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xaxis={'title': 'Actual Log Return'},
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yaxis={'title': 'Predicted Log Return'},
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showlegend=True,
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margin={'l': 40, 'b': 40, 't': 40, 'r': 10},
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hovermode='closest'
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)
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fig_scatter = go.Figure(data=data_scatter, layout=layout_scatter)
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pyo.plot(fig_scatter, filename='log_return_scatter_plot.html')
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def plot_predicted_vs_actual_prices(actual_prices, predicted_prices, timestamps=None, n_plot=200):
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import plotly.offline as pyo
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import plotly.graph_objs as go
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n_plot = min(n_plot, len(actual_prices))
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actual = actual_prices[:n_plot]
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predicted = predicted_prices[:n_plot]
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if timestamps is not None:
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x_axis = timestamps[:n_plot]
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x_label = 'Timestamp'
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else:
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x_axis = list(range(n_plot))
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x_label = 'Index'
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# Line plot: Actual vs Predicted over time
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trace_actual = go.Scatter(x=x_axis, y=actual, mode='lines', name='Actual Price')
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trace_predicted = go.Scatter(x=x_axis, y=predicted, mode='lines', name='Predicted Price')
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data_line = [trace_actual, trace_predicted]
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layout_line = go.Layout(
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title='Actual vs. Predicted BTC Prices (Test Set)',
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xaxis={'title': x_label},
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yaxis={'title': 'BTC Price'},
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legend={'x': 0, 'y': 1},
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margin={'l': 40, 'b': 40, 't': 40, 'r': 10},
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hovermode='closest'
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)
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fig_line = go.Figure(data=data_line, layout=layout_line)
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pyo.plot(fig_line, filename='price_line_plot.html')
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# Scatter plot: Predicted vs Actual
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trace_scatter = go.Scatter(
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x=actual,
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y=predicted,
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mode='markers',
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name='Predicted vs Actual',
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opacity=0.5
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)
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# Diagonal reference line
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min_val = min(np.min(actual), np.min(predicted))
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max_val = max(np.max(actual), np.max(predicted))
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trace_diag = go.Scatter(
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x=[min_val, max_val],
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y=[min_val, max_val],
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mode='lines',
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name='Ideal',
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line=dict(dash='dash', color='red')
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)
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data_scatter = [trace_scatter, trace_diag]
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layout_scatter = go.Layout(
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title='Predicted vs Actual Prices (Scatter)',
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xaxis={'title': 'Actual Price'},
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yaxis={'title': 'Predicted Price'},
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showlegend=True,
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margin={'l': 40, 'b': 40, 't': 40, 'r': 10},
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hovermode='closest'
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)
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fig_scatter = go.Figure(data=data_scatter, layout=layout_scatter)
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pyo.plot(fig_scatter, filename='price_scatter_plot.html')
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def plot_prediction_error_distribution(predicted_prices, actual_prices, nbins=100):
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"""
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Plots the distribution of signed prediction errors between predicted and actual prices,
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coloring negative errors (under-prediction) and positive errors (over-prediction) differently.
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"""
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import plotly.offline as pyo
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import plotly.graph_objs as go
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errors = np.array(predicted_prices) - np.array(actual_prices)
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# Separate negative and positive errors
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neg_errors = errors[errors < 0]
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pos_errors = errors[errors >= 0]
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trace_neg = go.Histogram(
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x=neg_errors,
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nbinsx=nbins,
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opacity=0.75,
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marker=dict(color='blue'),
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name='Negative Error (Under-prediction)'
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)
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trace_pos = go.Histogram(
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x=pos_errors,
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nbinsx=nbins,
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opacity=0.75,
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marker=dict(color='orange'),
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name='Positive Error (Over-prediction)'
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)
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layout = go.Layout(
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title='Distribution of Prediction Errors (Signed)',
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xaxis=dict(title='Prediction Error (Predicted - Actual)'),
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yaxis=dict(title='Frequency'),
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barmode='overlay',
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bargap=0.05
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)
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fig = go.Figure(data=[trace_neg, trace_pos], layout=layout)
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pyo.plot(fig, filename='prediction_error_distribution.html')
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