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feat(highcharts): implement logistic-regression (#3570)
## Implementation: `logistic-regression` - highcharts Implements the **highcharts** version of `logistic-regression`. **File:** `plots/logistic-regression/implementations/highcharts.py` **Parent Issue:** #3550 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20866601331)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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‎plots/logistic-regression/implementations/highcharts.py‎

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""" pyplots.ai
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logistic-regression: Logistic Regression Curve Plot
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Library: highcharts unknown | Python 3.13.11
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Quality: 91/100 | Created: 2026-01-09
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"""
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import tempfile
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import time
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import urllib.request
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from pathlib import Path
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import numpy as np
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from highcharts_core.chart import Chart
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from highcharts_core.options import HighchartsOptions
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from highcharts_core.options.series.area import AreaRangeSeries
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from highcharts_core.options.series.scatter import ScatterSeries
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from highcharts_core.options.series.spline import SplineSeries
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from selenium import webdriver
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from selenium.webdriver.chrome.options import Options
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from sklearn.linear_model import LogisticRegression
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# Data - Generate binary classification data
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np.random.seed(42)
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n_points = 150
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# Predictor: Study hours (0-10)
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x = np.random.uniform(0, 10, n_points)
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# Binary outcome: Pass/Fail (1/0) with logistic relationship
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true_prob = 1 / (1 + np.exp(-1.5 * (x - 5)))
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y = (np.random.random(n_points) < true_prob).astype(int)
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# Fit logistic regression model
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model = LogisticRegression()
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model.fit(x.reshape(-1, 1), y)
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# Generate smooth curve for plotting
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x_curve = np.linspace(0, 10, 200)
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y_prob = model.predict_proba(x_curve.reshape(-1, 1))[:, 1]
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# Calculate confidence intervals using bootstrap
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n_bootstrap = 100
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bootstrap_probs = np.zeros((n_bootstrap, len(x_curve)))
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for i in range(n_bootstrap):
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indices = np.random.choice(n_points, n_points, replace=True)
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x_boot, y_boot = x[indices], y[indices]
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model_boot = LogisticRegression()
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model_boot.fit(x_boot.reshape(-1, 1), y_boot)
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bootstrap_probs[i] = model_boot.predict_proba(x_curve.reshape(-1, 1))[:, 1]
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ci_lower = np.percentile(bootstrap_probs, 2.5, axis=0)
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ci_upper = np.percentile(bootstrap_probs, 97.5, axis=0)
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# Jitter y values for visibility
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jitter = np.random.uniform(-0.03, 0.03, n_points)
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y_jittered = y + jitter
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# Separate points by class
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x_class0 = x[y == 0].tolist()
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y_class0 = y_jittered[y == 0].tolist()
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x_class1 = x[y == 1].tolist()
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y_class1 = y_jittered[y == 1].tolist()
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# Chart setup
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chart = Chart(container="container")
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chart.options = HighchartsOptions()
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# Chart configuration
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chart.options.chart = {
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"width": 4800,
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"height": 2700,
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"backgroundColor": "#ffffff",
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"style": {"fontFamily": "Arial, sans-serif"},
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"spacingBottom": 100,
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"spacingLeft": 50,
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"spacingTop": 50,
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"spacingRight": 50,
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}
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# Title
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chart.options.title = {
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"text": "logistic-regression · highcharts · pyplots.ai",
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"style": {"fontSize": "48px", "fontWeight": "bold"},
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}
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chart.options.subtitle = {"text": "Exam Pass Probability vs Study Hours", "style": {"fontSize": "32px"}}
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# X-axis
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chart.options.x_axis = {
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"title": {"text": "Study Hours", "style": {"fontSize": "36px"}},
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"labels": {"style": {"fontSize": "28px"}},
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"min": 0,
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"max": 10,
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"gridLineWidth": 1,
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"gridLineColor": "rgba(0, 0, 0, 0.1)",
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}
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# Y-axis
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chart.options.y_axis = {
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"title": {"text": "Probability", "style": {"fontSize": "36px"}},
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"labels": {"style": {"fontSize": "28px"}},
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"min": -0.05,
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"max": 1.05,
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"gridLineWidth": 1,
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"gridLineColor": "rgba(0, 0, 0, 0.1)",
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"plotLines": [
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{
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"value": 0.5,
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"color": "#888888",
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"width": 3,
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"dashStyle": "Dash",
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"label": {
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"text": "Decision Threshold (0.5)",
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"align": "right",
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"style": {"fontSize": "24px", "color": "#888888"},
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"x": -10,
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"y": -10,
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},
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"zIndex": 4,
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}
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],
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}
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# Legend
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chart.options.legend = {
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"enabled": True,
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"itemStyle": {"fontSize": "28px"},
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"symbolRadius": 6,
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"symbolHeight": 20,
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"symbolWidth": 20,
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}
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# Plot options
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chart.options.plot_options = {
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"scatter": {"marker": {"radius": 14, "symbol": "circle"}},
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"spline": {"lineWidth": 6, "marker": {"enabled": False}},
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"arearange": {"fillOpacity": 0.25, "lineWidth": 0, "marker": {"enabled": False}},
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}
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# Confidence interval (arearange series)
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ci_data = [[float(x_curve[i]), float(ci_lower[i]), float(ci_upper[i])] for i in range(len(x_curve))]
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ci_series = AreaRangeSeries()
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ci_series.data = ci_data
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ci_series.name = "95% CI"
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ci_series.color = "rgba(48, 105, 152, 0.3)"
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ci_series.fill_opacity = 0.3
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chart.add_series(ci_series)
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# Logistic curve
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curve_data = [[float(x_curve[i]), float(y_prob[i])] for i in range(len(x_curve))]
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curve_series = SplineSeries()
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curve_series.data = curve_data
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curve_series.name = "Logistic Curve"
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curve_series.color = "#306998"
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chart.add_series(curve_series)
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# Class 0 points (Fail)
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scatter_class0 = ScatterSeries()
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scatter_class0.data = [[x_class0[i], y_class0[i]] for i in range(len(x_class0))]
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scatter_class0.name = "Fail (0)"
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scatter_class0.color = "rgba(48, 105, 152, 0.6)"
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scatter_class0.marker = {"radius": 14, "symbol": "circle"}
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chart.add_series(scatter_class0)
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# Class 1 points (Pass)
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scatter_class1 = ScatterSeries()
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scatter_class1.data = [[x_class1[i], y_class1[i]] for i in range(len(x_class1))]
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scatter_class1.name = "Pass (1)"
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scatter_class1.color = "rgba(255, 212, 59, 0.8)"
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scatter_class1.marker = {"radius": 14, "symbol": "circle"}
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chart.add_series(scatter_class1)
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# Add model accuracy annotation
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accuracy = model.score(x.reshape(-1, 1), y)
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chart.options.annotations = [
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{
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"labels": [
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{
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"point": {"x": 8.5, "y": 0.15, "xAxis": 0, "yAxis": 0},
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"text": f"Accuracy: {accuracy:.1%}",
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"style": {"fontSize": "28px"},
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"backgroundColor": "rgba(255, 255, 255, 0.8)",
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"borderColor": "#306998",
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"borderWidth": 2,
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"padding": 15,
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}
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],
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"labelOptions": {"shape": "rect"},
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}
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]
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# Credits
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chart.options.credits = {"enabled": False}
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# Download Highcharts JS
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highcharts_url = "https://code.highcharts.com/highcharts.js"
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with urllib.request.urlopen(highcharts_url, timeout=30) as response:
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highcharts_js = response.read().decode("utf-8")
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# Download highcharts-more for arearange
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highcharts_more_url = "https://code.highcharts.com/highcharts-more.js"
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with urllib.request.urlopen(highcharts_more_url, timeout=30) as response:
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highcharts_more_js = response.read().decode("utf-8")
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# Download annotations module
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annotations_url = "https://code.highcharts.com/modules/annotations.js"
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with urllib.request.urlopen(annotations_url, timeout=30) as response:
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annotations_js = response.read().decode("utf-8")
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# Generate HTML with inline scripts
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html_str = chart.to_js_literal()
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html_content = f"""<!DOCTYPE html>
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<html>
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<head>
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<meta charset="utf-8">
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<script>{highcharts_js}</script>
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<script>{highcharts_more_js}</script>
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<script>{annotations_js}</script>
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</head>
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<body style="margin:0;">
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<div id="container" style="width: 4800px; height: 2700px;"></div>
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<script>{html_str}</script>
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</body>
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</html>"""
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# Write temp HTML and take screenshot
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with tempfile.NamedTemporaryFile(mode="w", suffix=".html", delete=False, encoding="utf-8") as f:
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f.write(html_content)
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temp_path = f.name
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chrome_options = Options()
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chrome_options.add_argument("--headless")
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chrome_options.add_argument("--no-sandbox")
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chrome_options.add_argument("--disable-dev-shm-usage")
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chrome_options.add_argument("--disable-gpu")
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chrome_options.add_argument("--window-size=4900,2800")
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driver = webdriver.Chrome(options=chrome_options)
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driver.get(f"file://{temp_path}")
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time.sleep(5)
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driver.save_screenshot("plot.png")
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driver.quit()
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Path(temp_path).unlink()
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# Save interactive HTML (using CDN scripts for standalone viewing)
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html_export = f"""<!DOCTYPE html>
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<html>
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<head>
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<meta charset="utf-8">
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<title>Logistic Regression - Highcharts</title>
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<script src="https://code.highcharts.com/highcharts.js"></script>
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<script src="https://code.highcharts.com/highcharts-more.js"></script>
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<script src="https://code.highcharts.com/modules/annotations.js"></script>
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</head>
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<body style="margin:0;">
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<div id="container" style="width: 100%; height: 100vh;"></div>
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<script>{html_str}</script>
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</body>
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</html>"""
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with open("plot.html", "w", encoding="utf-8") as f:
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f.write(html_export)

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