---
url: >-
  https://jer-k.github.io/effect-prophet/benchmarks/logistic/uncertainty-logistic-explicit-floor-minmax-full-samples-128.md
---

# Samples with an explicit floor

 [← All logistic growth benchmarks](/benchmarks/logistic)

128 raw simulated values per date for the same bounded model.

## Input

[`benchmark/inputs/v1/logistic-explicit-floor.json`](https://github.com/jer-k/effect-prophet/blob/main/benchmark/inputs/v1/logistic-explicit-floor.json): 96 history rows and 24 forecast rows.

## Output

Largest difference: **1.1e-11** (< 0.0001% of the input's range).

Every forecast row from both libraries.

| Date | Effect Prophet | Python Prophet | Difference |
| --- | ---: | ---: | ---: |
| 2020-04-06 | 65.8327 | 65.8327 | 1.1e-11 |
| 2020-04-07 | 66.2481 | 66.2481 | 1.1e-11 |
| 2020-04-08 | 67.2854 | 67.2854 | 1.1e-11 |
| 2020-04-09 | 67.5699 | 67.5699 | 1.1e-11 |
| 2020-04-10 | 67.9570 | 67.9570 | 1.1e-11 |
| 2020-04-11 | 68.0033 | 68.0033 | 1.0e-11 |
| 2020-04-12 | 67.8977 | 67.8977 | 1.0e-11 |
| 2020-04-13 | 67.9429 | 67.9429 | 9.9e-12 |
| 2020-04-14 | 68.6956 | 68.6956 | 9.4e-12 |
| 2020-04-15 | 68.9348 | 68.9348 | 9.5e-12 |
| 2020-04-16 | 69.5493 | 69.5493 | 9.3e-12 |
| 2020-04-17 | 69.8952 | 69.8952 | 8.9e-12 |
| 2020-04-18 | 69.9016 | 69.9016 | 8.7e-12 |
| 2020-04-19 | 69.7572 | 69.7572 | 8.5e-12 |
| 2020-04-20 | 70.1556 | 70.1556 | 7.9e-12 |
| 2020-04-21 | 70.0990 | 70.0990 | 8.1e-12 |
| 2020-04-22 | 70.6851 | 70.6851 | 7.9e-12 |
| 2020-04-23 | 71.2654 | 71.2654 | 7.7e-12 |
| 2020-04-24 | 71.5783 | 71.5783 | 7.4e-12 |
| 2020-04-25 | 71.5528 | 71.5528 | 7.2e-12 |
| 2020-04-26 | 71.7759 | 71.7759 | 6.6e-12 |
| 2020-04-27 | 71.3554 | 71.3554 | 6.8e-12 |
| 2020-04-28 | 71.6607 | 71.6607 | 6.7e-12 |
| 2020-04-29 | 72.2190 | 72.2190 | 6.5e-12 |

## Timing

Median of all measured repetitions.

| Step | Effect Prophet | Python Prophet | Effect Prophet is |
| --- | ---: | ---: | --- |
| Convert input rows | 71 µs | 509 µs | 7.2× faster |
| Save model to JSON | 28 µs | 469 µs | 17× faster |
| Load model from JSON | 152 µs | 2.48 ms | 16× faster |
| Simulate uncertainty | 733 µs | 24.1 ms | Not compared: different work |
| First simulation in a new process | 191 ms | 1.47 s | Not compared: different work |
| Load and simulate in a new process | 162 ms | 1.45 s | Not compared: different work |

Some steps aren't compared, because the two libraries' public calls produce different outputs. Their times show what each call costs, not which library is faster.

* **Simulate uncertainty, First simulation in a new process, Load and simulate in a new process:** Python's `predictive_samples(vectorized=False)` and Effect Prophet's `predictUncertainty` are different public calls: Python's timing includes resetting NumPy's random generator and returns NumPy matrices, and the two don't draw the same random numbers.

## Settings

The case declaration both libraries ran.

```json
{
  "kind": "map",
  "comparison": {
    "kind": "equivalent-objective",
    "evidenceId": "logistic-map-v1"
  },
  "configuration": {
    "growth": "logistic",
    "scaling": "minmax",
    "seasonalityMode": "additive",
    "holidaysMode": "additive",
    "changepoints": {
      "mode": "explicit",
      "timestamps": [
        "2020-02-10T00:00:00.000Z"
      ]
    },
    "changepointPriorScale": 0.2,
    "seasonalities": [
      {
        "name": "weekly-custom",
        "periodDays": 7,
        "fourierOrder": 2,
        "priorScale": 10
      }
    ],
    "events": [],
    "regressors": [
      {
        "name": "promotion",
        "priorScale": 10,
        "standardization": "never",
        "mode": "multiplicative"
      }
    ]
  },
  "pythonOptimizer": {
    "algorithm": "Auto",
    "maxIterations": 10000,
    "newtonFallback": true,
    "sigFigs": 12
  },
  "uncertainty": {
    "seed": 19,
    "samples": 128,
    "intervalWidth": 0.8,
    "output": "samples",
    "algorithm": "scalar-continuous-time",
    "evidence": "docs/validation/uncertainty.md#evidence-classes",
    "samplerMemoryLimitBytes": 67108864
  }
}
```