SkunkFx - Effects and Aromas Prediction Model

The api/stats/effects endpoint is used to predict effects and aromas that may be reported for a given product given its lab results. For technical details of the model, please see the AI documentation. In general, there are 3 main actions:

1. You can use the model to predict potentially reported effects and aromas for any cannabis flower for which you have lab results. Simply post your lab results to the api/stats/effects endpoint, specifying your model if you desire, and you will receive effect and aroma predictions.

2. You can get the model statistics by making a GET request to api/stats/effects. Currently, the model statistics include: false_positive_rate, false_negative_rate, true_positive_rate, true_negative_rate, accuracy, informedness.

3. Finally, you can post the actual effects and aromas that you may observe with the api/stats/effects/actual endpoint.

You can substitute training data, for strain reviews or lab results, as you see fit.

Parameters

Below are the parameters that you can pass and examples of how you can format a request body.

Parameter	Options	Example
model	full (default), cannabinoid_only, terpene_only, totals, simple	?model=simple

Examples

Below are a handful of examples, written in Python, that can be generalized to your favorite programming language.

import requests
import pandas as pd

BASE = 'https://cannlytics.com/api'

# Get statistics for the `full` model.
url = f'{BASE}/stats/effects'
params = {'model': 'full'}
response = requests.get(url, params=params)
model_stats = response.json()['data']

# Post lab results to get potential effects and aromas.
json = {
    'model': 'simple',
    'samples': [
        {'total_cbd': 1.0, 'total_thc': 18.0},
        {'total_cbd': 1.0, 'total_thc': 20.0},
        {'total_cbd': 1.0, 'total_thc': 30.0},
        {'total_cbd': 7.0, 'total_thc': 7.0},
    ]
}
url = f'{BASE}/stats/effects'
response = requests.post(url, json=json)
data = response.json()['data']

# Post actual aromas and effects, rating the usefulness of the prediction.
json = {
    'samples': [
        {
            'prediction_id': '01g4g8apexj1r426rbkcnfdjt9',
            'strain_name': 'Test Strain',
            'effects': ['happy', 'focused'],
            'aromas': ['citrus', 'pine'],
            'rating': 10,
        },
    ]
}
url = f'{BASE}/stats/effects/actual'
response = requests.post(url, json=json)
message = response.json()['message']

A typical response includes a list of samples with predicted_aromas and predicted_effects. There are also model_stats, summary statistics for each aroma and effect, and a prediction_id for each sample that can be used for reporting actual effects and aromas.

[{
  "model": "full",
  "model_stats": {},
  "samples" [
    {
      "lab_results": {},
      "predicted_aromas": [],
      "predicted_effects": [],
      "predicted_at": "2022-06-03T04:20:00",
      "prediction_id": "01g4g8apexj1r426rbkcnfdjt9",
      "strain_name": "",
    }
  ]
}]

At this time, the official Cannlytics effects model has only been trained on flower data and the model would not make sense for concentrates, edibles, etc..

Variables

The variates for the models are:

{
  "full": [
    "cbc",
    "cbd",
    "cbda",
    "cbg",
    "cbga",
    "cbn",
    "delta_8_thc",
    "delta_9_thc",
    "thca",
    "thcv",
    "alpha_bisabolol",
    "alpha_pinene",
    "alpha_terpinene",
    "beta_caryophyllene",
    "beta_myrcene",
    "beta_pinene",
    "camphene",
    "carene",
    "caryophyllene_oxide",
    "d_limonene",
    "eucalyptol",
    "gamma_terpinene",
    "geraniol",
    "guaiol",
    "humulene",
    "isopulegol",
    "linalool",
    "nerolidol",
    "ocimene",
    "p_cymene",
    "terpinene",
    "terpinolene"
  ],
  "terpene_only": [
    "alpha_bisabolol",
    "alpha_pinene",
    "alpha_terpinene",
    "beta_caryophyllene",
    "beta_myrcene",
    "beta_pinene",
    "camphene",
    "carene",
    "caryophyllene_oxide",
    "d_limonene",
    "eucalyptol",
    "gamma_terpinene",
    "geraniol",
    "guaiol",
    "humulene",
    "isopulegol",
    "linalool",
    "nerolidol",
    "ocimene",
    "p_cymene",
    "terpinene",
    "terpinolene"
  ],
  "cannabinoid_only": [
    "cbc",
    "cbd",
    "cbda",
    "cbg",
    "cbga",
    "cbn",
    "delta_8_thc",
    "delta_9_thc",
    "thca",
    "thcv"
  ],
  "totals": ["total_cbd", "total_thc", "total_terpenes"],
  "simple": ["total_cbd", "total_thc"]
}

The possible effects and aromas are listed below. In general, the key is used for programmatic use and the name is the expected input from the user. E.g. a value of effect_happy may be returned from the API, but you can simply post 'Happy' when reporting actual effects.

Effects

Name	Key
Anxiety	effect_anxiety
Anxious	effect_anxious
Aroused	effect_aroused
Arthritis	effect_arthritis
Creative	effect_creative
Depression	effect_depression
Dizzy	effect_dizzy
Dry Eyes	effect_dry_eyes
Dry Mouth	effect_dry_mouth
Energetic	effect_energetic
Epilepsy	effect_epilepsy
Euphoric	effect_euphoric
Eye Pressure	effect_eye_pressure
Fatigue	effect_fatigue
Focused	effect_focused
Giggly	effect_giggly
Happy	effect_happy
Headache	effect_headache
Hungry	effect_hungry
Migraines	effect_migraines
Pain	effect_pain
Paranoid	effect_paranoid
Relaxed	effect_relaxed
Seizures	effect_seizures
Sleepy	effect_sleepy
Spasticity	effect_spasticity
Stress	effect_stress
Talkative	effect_talkative
Tingly	effect_tingly
Uplifted	effect_uplifted

Aromas

Name	Key
Ammonia	aroma_ammonia
Apple	aroma_apple
Apricot	aroma_apricot
Berry	aroma_berry
Blue Cheese	aroma_blue_cheese
Blueberry	aroma_blueberry
Butter	aroma_butter
Cheese	aroma_cheese
Chemical	aroma_chemical
Chestnut	aroma_chestnut
Citrus	aroma_citrus
Coffee	aroma_coffee
Diesel	aroma_diesel
Earthy	aroma_earthy
Flowery	aroma_flowery
Fruit	aroma_fruit
Grape	aroma_grape
Grapefruit	aroma_grapefruit
Honey	aroma_honey
Lavender	aroma_lavender
Lemon	aroma_lemon
Lime	aroma_lime
Mango	aroma_mango
Menthol	aroma_menthol
Mint	aroma_mint
Nutty	aroma_nutty
Orange	aroma_orange
Peach	aroma_peach
Pear	aroma_pear
Pepper	aroma_pepper
Pine	aroma_pine
Pineapple	aroma_pineapple
Plum	aroma_plum
Pungent	aroma_pungent
Rose	aroma_rose
Sage	aroma_sage
Skunk	aroma_skunk
Spicy to herbal	aroma_spicytoherbal
Strawberry	aroma_strawberry
Sweet	aroma_sweet
Tar	aroma_tar
Tea	aroma_tea
Tobacco	aroma_tobacco
Tree	aroma_tree
Tropical	aroma_tropical
Vanilla	aroma_vanilla
Violet	aroma_violet
Woody	aroma_woody

Disclaimer

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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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SOFTWARE.