[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
/opt/hostedtoolcache/Python/3.11.17/x64/lib/python3.11/site-packages/torch/jit/_script.py:1491: FutureWarning: `torch.jit.script` is deprecated. Please switch to `torch.compile` or `torch.export`.
warnings.warn(
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 3.11 && 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-5r0k5ubl
Running command git clone --filter=blob:none --quiet https://github.com/colinbousige/OPTIMEO.git /tmp/pip-req-build-5r0k5ubl
Resolved https://github.com/colinbousige/OPTIMEO.git to commit 887b7506eb7456df2307bba86f1a2d339ae1b364
Installing build dependencies ... - \ done
Getting requirements to build wheel ... - done
Preparing metadata (pyproject.toml) ... - done
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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: | 0.932 |
|---|---|---|---|
| Model: | OLS | Adj. R-squared: | 0.926 |
| Method: | Least Squares | F-statistic: | 154.5 |
| Date: | Thu, 08 Oct 2026 | Prob (F-statistic): | 1.13e-25 |
| Time: | 09:14:12 | Log-Likelihood: | -133.06 |
| No. Observations: | 50 | AIC: | 276.1 |
| Df Residuals: | 45 | BIC: | 285.7 |
| Df Model: | 4 | ||
| Covariance Type: | nonrobust |
| coef | std err | t | P>|t| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| Intercept | -12.7778 | 2.192 | -5.829 | 0.000 | -17.193 | -8.363 |
| temp | 0.4601 | 0.020 | 22.683 | 0.000 | 0.419 | 0.501 |
| cA | 26.8548 | 1.994 | 13.468 | 0.000 | 22.839 | 30.871 |
| cB | 1.0501 | 1.886 | 0.557 | 0.580 | -2.748 | 4.848 |
| cC | -0.6542 | 1.989 | -0.329 | 0.744 | -4.660 | 3.351 |
| Omnibus: | 3.037 | Durbin-Watson: | 1.942 |
|---|---|---|---|
| Prob(Omnibus): | 0.219 | Jarque-Bera (JB): | 2.313 |
| Skew: | -0.521 | Prob(JB): | 0.315 |
| Kurtosis: | 3.161 | Cond. No. | 323. |
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: | 7.165e+04 |
| Date: | Thu, 08 Oct 2026 | Prob (F-statistic): | 7.75e-85 |
| Time: | 09:14:12 | Log-Likelihood: | 24.802 |
| No. Observations: | 50 | AIC: | -37.60 |
| Df Residuals: | 44 | BIC: | -26.13 |
| Df Model: | 5 | ||
| Covariance Type: | nonrobust |
| coef | std err | t | P>|t| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| Intercept | 2.6172 | 0.137 | 19.158 | 0.000 | 2.342 | 2.893 |
| temp | 0.1996 | 0.002 | 105.826 | 0.000 | 0.196 | 0.203 |
| temp:cA | 0.4995 | 0.003 | 155.776 | 0.000 | 0.493 | 0.506 |
| cA | 0.1901 | 0.191 | 0.993 | 0.326 | -0.196 | 0.576 |
| cB | -0.5696 | 0.082 | -6.964 | 0.000 | -0.734 | -0.405 |
| cC | -0.8114 | 0.086 | -9.482 | 0.000 | -0.984 | -0.639 |
| Omnibus: | 0.735 | Durbin-Watson: | 1.902 |
|---|---|---|---|
| Prob(Omnibus): | 0.693 | Jarque-Bera (JB): | 0.816 |
| Skew: | 0.256 | Prob(JB): | 0.665 |
| Kurtosis: | 2.641 | Cond. No. | 625. |
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 | 19.597514 | ElasticNetCV |
| 1 | 20.715602 | Linear Model |