[1]:
# docs-warning-suppress
import logging
import warnings
warnings.filterwarnings('ignore', category=DeprecationWarning, module=r'torch\.jit\._script')
warnings.filterwarnings('ignore', category=DeprecationWarning, module=r'matplotlib\._fontconfig_pattern')
warnings.filterwarnings('ignore', message=r".*oneOf.*deprecated.*one_of.*", module=r'matplotlib\._fontconfig_pattern')
warnings.filterwarnings('ignore', message=r".*parseString.*deprecated.*parse_string.*", module=r'matplotlib\._fontconfig_pattern')
warnings.filterwarnings('ignore', message=r".*resetCache.*deprecated.*reset_cache.*", module=r'matplotlib\._fontconfig_pattern')
try:
from ax.exceptions.core import AxParameterWarning
warnings.filterwarnings('ignore', category=AxParameterWarning)
except Exception:
pass
try:
from pyparsing import PyparsingDeprecationWarning
warnings.filterwarnings('ignore', category=PyparsingDeprecationWarning)
except Exception:
pass
logging.getLogger('ax').setLevel(logging.WARNING)
logging.getLogger('ax.service.utils.instantiation').setLevel(logging.ERROR)
logging.getLogger('ax.generation_strategy.dispatch_utils').setLevel(logging.WARNING)
try:
import plotly.graph_objects as go
_orig_show = go.Figure.show
def _responsive_show(fig, *args, **kwargs):
fig.update_layout(autosize=True)
config = kwargs.get('config') or {}
config.setdefault('responsive', True)
kwargs['config'] = config
return _orig_show(fig, *args, **kwargs)
go.Figure.show = _responsive_show
except Exception:
pass
Data Analysis and Modeling#
Installation#
Create a virtual environment named .venv, activate it, then install the package:
python -m venv .venv
source .venv/bin/activate # Linux / macOS
python -m pip install --upgrade pip
python -m pip install optimeo
After that, launch the app with:
optimeo
If you are using the repo directly, you can also install from GitHub:
python -m pip install "git+https://github.com/colinbousige/OPTIMEO.git"
This notebook shows how to analyze and model experimental data once the package is installed.
[2]:
# For Google Colab
!pip install git+https://github.com/colinbousige/OPTIMEO.git
# For local development, prefer:
# uv venv .venv --python 3.10 && source .venv/bin/activate && uv sync
Collecting git+https://github.com/colinbousige/OPTIMEO.git
Cloning https://github.com/colinbousige/OPTIMEO.git to /tmp/pip-req-build-wrw70ygx
Running command git clone --filter=blob:none --quiet https://github.com/colinbousige/OPTIMEO.git /tmp/pip-req-build-wrw70ygx
Resolved https://github.com/colinbousige/OPTIMEO.git to commit f6d79cb9ae212f0cc9d2e01c204209ab9fcd4834
Installing build dependencies ... - \ | / - \ | done
Getting requirements to build wheel ... - done
Preparing metadata (pyproject.toml) ... - done
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[notice] A new release of pip is available: 26.1.1 -> 26.1.2
[notice] To update, run: pip install --upgrade pip
Let’s create an experimental_data(temp, conc) function that simulates the yield of a chemical reaction based on temperature, concentration A, concentration B and concentration C.
[3]:
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import plotly.io as pio
# pio.renderers.default = "notebook"
pio.renderers.default = "notebook_connected"
def experimental_data(temp, cA, cB, cC):
"""
This function simulates experimental data based on temperature and concentrations.
The function is not based on any real experimental data and is purely for demonstration purposes.
"""
out = .2*temp + .5*temp*cA + (cA)/3 + (1 - cB)**2/2 + (3 - cC)/1.5 + np.random.normal(0, 0.2, len(temp))
return out
def generate_data(N=100):
temp = np.random.uniform(0, 100, N)
cA = np.random.uniform(0, 1, N)
cB = np.random.uniform(0, 1, N)
cC = np.random.uniform(0, 1, N)
exp_response = experimental_data(temp, cA, cB, cC)
# Create a DataFrame with the generated data
df = pd.DataFrame({'temp': temp,
'cA': cA,
'cB': cB,
'cC': cC,
'response': exp_response})
return df
df = generate_data(50)
df.to_csv('dataML.csv', index=False)
df.head()
pio.renderers['notebook_connected'].config = {'responsive': True}
Now, we will use the OPTIMEO package to analyse the data.
[4]:
from optimeo.analysis import DataAnalysis
data = pd.read_csv('dataML.csv')
factors = data.columns[:-1].tolist()
response = data.columns[-1]
analysis = DataAnalysis(data, factors, response)
analysis
[4]:
DataAnalysis(data=(50, 5), factors=['temp', 'cA', 'cB', 'cC'], response=response, model_type=None, split_size=0.2, encoders={})
First, let’s take a look at the correlation between the variables.
[5]:
analysis.plot_corr()
[6]:
analysis.plot_pairplot_plotly()
First, let’s look at a simple linear model:
[7]:
analysis.compute_linear_model()
analysis.linear_model.summary()
[7]:
| Dep. Variable: | response | R-squared: | 0.952 |
|---|---|---|---|
| Model: | OLS | Adj. R-squared: | 0.947 |
| Method: | Least Squares | F-statistic: | 221.3 |
| Date: | Mon, 08 Jun 2026 | Prob (F-statistic): | 5.71e-29 |
| Time: | 10:18:04 | Log-Likelihood: | -132.44 |
| No. Observations: | 50 | AIC: | 274.9 |
| Df Residuals: | 45 | BIC: | 284.4 |
| Df Model: | 4 | ||
| Covariance Type: | nonrobust |
| coef | std err | t | P>|t| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| Intercept | -12.0026 | 2.005 | -5.987 | 0.000 | -16.041 | -7.965 |
| temp | 0.4364 | 0.019 | 23.000 | 0.000 | 0.398 | 0.475 |
| cA | 30.2790 | 1.829 | 16.556 | 0.000 | 26.595 | 33.963 |
| cB | -1.9838 | 2.263 | -0.877 | 0.385 | -6.542 | 2.575 |
| cC | 1.3955 | 1.883 | 0.741 | 0.463 | -2.397 | 5.188 |
| Omnibus: | 7.805 | Durbin-Watson: | 1.532 |
|---|---|---|---|
| Prob(Omnibus): | 0.020 | Jarque-Bera (JB): | 6.935 |
| Skew: | -0.867 | Prob(JB): | 0.0312 |
| Kurtosis: | 3.565 | Cond. No. | 360. |
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[8]:
figs = analysis.plot_linear_model()
for fig in figs:
fig.show(config={'responsive': True})
The equation used for the fit is this one, you can change it if you want, e.g to add interaction terms or other polynomial terms:
[9]:
analysis.write_equation()
[9]:
'response ~ temp + cA + cB + cC '
[10]:
analysis.equation = 'response ~ temp+ temp:cA + cA + cB + cC'
analysis.compute_linear_model()
analysis.linear_model.summary()
[10]:
| Dep. Variable: | response | R-squared: | 1.000 |
|---|---|---|---|
| Model: | OLS | Adj. R-squared: | 1.000 |
| Method: | Least Squares | F-statistic: | 7.398e+04 |
| Date: | Mon, 08 Jun 2026 | Prob (F-statistic): | 3.82e-85 |
| Time: | 10:18:04 | Log-Likelihood: | 17.767 |
| No. Observations: | 50 | AIC: | -23.53 |
| Df Residuals: | 44 | BIC: | -12.06 |
| Df Model: | 5 | ||
| Covariance Type: | nonrobust |
| coef | std err | t | P>|t| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| Intercept | 2.2578 | 0.147 | 15.401 | 0.000 | 1.962 | 2.553 |
| temp | 0.2012 | 0.002 | 100.517 | 0.000 | 0.197 | 0.205 |
| temp:cA | 0.4971 | 0.004 | 133.629 | 0.000 | 0.490 | 0.505 |
| cA | 0.5773 | 0.240 | 2.401 | 0.021 | 0.093 | 1.062 |
| cB | -0.4749 | 0.114 | -4.165 | 0.000 | -0.705 | -0.245 |
| cC | -0.6009 | 0.096 | -6.286 | 0.000 | -0.794 | -0.408 |
| Omnibus: | 0.572 | Durbin-Watson: | 1.976 |
|---|---|---|---|
| Prob(Omnibus): | 0.751 | Jarque-Bera (JB): | 0.664 |
| Skew: | 0.050 | Prob(JB): | 0.717 |
| Kurtosis: | 2.444 | Cond. No. | 737. |
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[11]:
figs = analysis.plot_linear_model()
for fig in figs:
fig.show(config={'responsive': True})
Now let’s make a ML model to predict the yield based on the temperature and concentrations of A, B and C.
[12]:
analysis.model_type = "ElasticNetCV"
# analysis.model_type = "RidgeCV"
# analysis.model_type = "LinearRegression"
# analysis.model_type = "RandomForest"
# analysis.model_type = "GaussianProcess"
# analysis.model_type = "GradientBoosting"
MLmodel = analysis.compute_ML_model()
figs = analysis.plot_ML_model()
for fig in figs:
fig.show(config={'responsive': True})
And if we want to make a prediction:
[13]:
new_value = pd.DataFrame({'temp': [50],
'cA': [0.35],
'cB': [0.5],
'cC': [0.5]})
analysis.predict(new_value)
[13]:
| prediction | model | |
|---|---|---|
| 0 | 21.150542 | ElasticNetCV |
| 1 | 20.679480 | Linear Model |