Cycles/xgboost/plot_results.py

import numpy as np
import dash
from dash import dcc, html
import plotly.graph_objs as go
import threading


def display_actual_vs_predicted(y_test, test_preds, timestamps, n_plot=200):
    import plotly.offline as pyo
    n_plot = min(n_plot, len(y_test))
    plot_indices = timestamps[:n_plot]
    actual = y_test[:n_plot]
    predicted = test_preds[:n_plot]

    trace_actual = go.Scatter(x=plot_indices, y=actual, mode='lines', name='Actual')
    trace_predicted = go.Scatter(x=plot_indices, y=predicted, mode='lines', name='Predicted')
    data = [trace_actual, trace_predicted]
    layout = go.Layout(
        title='Actual vs. Predicted BTC Close Prices (Test Set)',
        xaxis={'title': 'Timestamp'},
        yaxis={'title': 'BTC Close Price'},
        legend={'x': 0, 'y': 1},
        margin={'l': 40, 'b': 40, 't': 40, 'r': 10},
        hovermode='closest'
    )
    fig = go.Figure(data=data, layout=layout)
    pyo.plot(fig)

def plot_target_distribution(y_train, y_test):
    import plotly.offline as pyo
    trace_train = go.Histogram(
        x=y_train,
        nbinsx=100,
        opacity=0.5,
        name='Train',
        marker=dict(color='blue')
    )
    trace_test = go.Histogram(
        x=y_test,
        nbinsx=100,
        opacity=0.5,
        name='Test',
        marker=dict(color='orange')
    )
    data = [trace_train, trace_test]
    layout = go.Layout(
        title='Distribution of Target Variable (Close Price)',
        xaxis=dict(title='BTC Close Price'),
        yaxis=dict(title='Frequency'),
        barmode='overlay'
    )
    fig = go.Figure(data=data, layout=layout)
    pyo.plot(fig)

def plot_predicted_vs_actual_log_returns(y_test, test_preds, timestamps=None, n_plot=200):
    import plotly.offline as pyo
    import plotly.graph_objs as go
    n_plot = min(n_plot, len(y_test))
    actual = y_test[:n_plot]
    predicted = test_preds[:n_plot]
    if timestamps is not None:
        x_axis = timestamps[:n_plot]
        x_label = 'Timestamp'
    else:
        x_axis = list(range(n_plot))
        x_label = 'Index'

    # Line plot: Actual vs Predicted over time
    trace_actual = go.Scatter(x=x_axis, y=actual, mode='lines', name='Actual')
    trace_predicted = go.Scatter(x=x_axis, y=predicted, mode='lines', name='Predicted')
    data_line = [trace_actual, trace_predicted]
    layout_line = go.Layout(
        title='Actual vs. Predicted Log Returns (Test Set)',
        xaxis={'title': x_label},
        yaxis={'title': 'Log Return'},
        legend={'x': 0, 'y': 1},
        margin={'l': 40, 'b': 40, 't': 40, 'r': 10},
        hovermode='closest'
    )
    fig_line = go.Figure(data=data_line, layout=layout_line)
    pyo.plot(fig_line, filename='log_return_line_plot.html')

    # Scatter plot: Predicted vs Actual
    trace_scatter = go.Scatter(
        x=actual,
        y=predicted,
        mode='markers',
        name='Predicted vs Actual',
        opacity=0.5
    )
    # Diagonal reference line
    min_val = min(np.min(actual), np.min(predicted))
    max_val = max(np.max(actual), np.max(predicted))
    trace_diag = go.Scatter(
        x=[min_val, max_val],
        y=[min_val, max_val],
        mode='lines',
        name='Ideal',
        line=dict(dash='dash', color='red')
    )
    data_scatter = [trace_scatter, trace_diag]
    layout_scatter = go.Layout(
        title='Predicted vs Actual Log Returns (Scatter)',
        xaxis={'title': 'Actual Log Return'},
        yaxis={'title': 'Predicted Log Return'},
        showlegend=True,
        margin={'l': 40, 'b': 40, 't': 40, 'r': 10},
        hovermode='closest'
    )
    fig_scatter = go.Figure(data=data_scatter, layout=layout_scatter)
    pyo.plot(fig_scatter, filename='log_return_scatter_plot.html')

def plot_predicted_vs_actual_prices(actual_prices, predicted_prices, timestamps=None, n_plot=200):
    import plotly.offline as pyo
    import plotly.graph_objs as go
    n_plot = min(n_plot, len(actual_prices))
    actual = actual_prices[:n_plot]
    predicted = predicted_prices[:n_plot]
    if timestamps is not None:
        x_axis = timestamps[:n_plot]
        x_label = 'Timestamp'
    else:
        x_axis = list(range(n_plot))
        x_label = 'Index'

    # Line plot: Actual vs Predicted over time
    trace_actual = go.Scatter(x=x_axis, y=actual, mode='lines', name='Actual Price')
    trace_predicted = go.Scatter(x=x_axis, y=predicted, mode='lines', name='Predicted Price')
    data_line = [trace_actual, trace_predicted]
    layout_line = go.Layout(
        title='Actual vs. Predicted BTC Prices (Test Set)',
        xaxis={'title': x_label},
        yaxis={'title': 'BTC Price'},
        legend={'x': 0, 'y': 1},
        margin={'l': 40, 'b': 40, 't': 40, 'r': 10},
        hovermode='closest'
    )
    fig_line = go.Figure(data=data_line, layout=layout_line)
    pyo.plot(fig_line, filename='price_line_plot.html')

    # Scatter plot: Predicted vs Actual
    trace_scatter = go.Scatter(
        x=actual,
        y=predicted,
        mode='markers',
        name='Predicted vs Actual',
        opacity=0.5
    )
    # Diagonal reference line
    min_val = min(np.min(actual), np.min(predicted))
    max_val = max(np.max(actual), np.max(predicted))
    trace_diag = go.Scatter(
        x=[min_val, max_val],
        y=[min_val, max_val],
        mode='lines',
        name='Ideal',
        line=dict(dash='dash', color='red')
    )
    data_scatter = [trace_scatter, trace_diag]
    layout_scatter = go.Layout(
        title='Predicted vs Actual Prices (Scatter)',
        xaxis={'title': 'Actual Price'},
        yaxis={'title': 'Predicted Price'},
        showlegend=True,
        margin={'l': 40, 'b': 40, 't': 40, 'r': 10},
        hovermode='closest'
    )
    fig_scatter = go.Figure(data=data_scatter, layout=layout_scatter)
    pyo.plot(fig_scatter, filename='price_scatter_plot.html')