Iris CBR demo¶
Load the Iris dataset to build the example
[1]:
import pandas as pd
from sklearn import datasets
iris = datasets.load_iris()
df=pd.DataFrame(iris["data"], columns=iris["feature_names"])
df["species"]=iris["target"]
df["species"]=df["species"].apply(lambda x:iris["target_names"][x])
df
[1]:
| sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) | species | |
|---|---|---|---|---|---|
| 0 | 5.1 | 3.5 | 1.4 | 0.2 | setosa |
| 1 | 4.9 | 3.0 | 1.4 | 0.2 | setosa |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 | setosa |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 | setosa |
| 4 | 5.0 | 3.6 | 1.4 | 0.2 | setosa |
| ... | ... | ... | ... | ... | ... |
| 145 | 6.7 | 3.0 | 5.2 | 2.3 | virginica |
| 146 | 6.3 | 2.5 | 5.0 | 1.9 | virginica |
| 147 | 6.5 | 3.0 | 5.2 | 2.0 | virginica |
| 148 | 6.2 | 3.4 | 5.4 | 2.3 | virginica |
| 149 | 5.9 | 3.0 | 5.1 | 1.8 | virginica |
150 rows × 5 columns
[2]:
# Store it in a temporal file
import tempfile
f=tempfile.NamedTemporaryFile(suffix=".csv")
df.to_csv(f.name, index=False)
f.name
[2]:
'/tmp/tmp6expcq6j.csv'
Build a CBR
[3]:
import pycbr
# Define a case base from the csv file
case_base=pycbr.casebase.SimpleCSVCaseBase(f.name)
# Define the set of similarity functions
recovery=pycbr.recovery.Recovery([(x, pycbr.models.QuantileLinearAttribute()) for x in iris["feature_names"]])
# Define the aggregation method
aggregation = pycbr.aggregate.MajorityAggregate("species")
# Create a CBR instance
cbr=pycbr.CBR(case_base, recovery, aggregation)
Unable to load a logging configuration file. Using the default settings.
/usr/lib/python3.8/site-packages/sklearn/preprocessing/_data.py:2344: UserWarning: n_quantiles (1000) is greater than the total number of samples (150). n_quantiles is set to n_samples.
warnings.warn("n_quantiles (%s) is greater than the total number "
/usr/lib/python3.8/site-packages/sklearn/preprocessing/_data.py:2344: UserWarning: n_quantiles (1000) is greater than the total number of samples (150). n_quantiles is set to n_samples.
warnings.warn("n_quantiles (%s) is greater than the total number "
/usr/lib/python3.8/site-packages/sklearn/preprocessing/_data.py:2344: UserWarning: n_quantiles (1000) is greater than the total number of samples (150). n_quantiles is set to n_samples.
warnings.warn("n_quantiles (%s) is greater than the total number "
/usr/lib/python3.8/site-packages/sklearn/preprocessing/_data.py:2344: UserWarning: n_quantiles (1000) is greater than the total number of samples (150). n_quantiles is set to n_samples.
warnings.warn("n_quantiles (%s) is greater than the total number "
The Flask WSGI application is available as the app parameter.
[4]:
# Start the development server
cbr.app.run()
* Serving Flask app "pycbr" (lazy loading)
* Environment: production
WARNING: This is a development server. Do not use it in a production deployment.
Use a production WSGI server instead.
* Debug mode: off
2020-03-05 01:38:07 ophelia werkzeug[17052] INFO * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)
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