Logistic regression on One-hot encoding

Question

I have a Dataframe (data) for which the head looks like the following:

          status      datetime    country    amount    city  
601766  received  1.453916e+09    France       4.5     Paris
669244  received  1.454109e+09    Italy        6.9     Naples

I would like to predict the status given datetime, country, amount and city

Since status, country, city are string, I one-hot-encoded them:

one_hot = pd.get_dummies(data['country'])
data = data.drop(item, axis=1) # Drop the column as it is now one_hot_encoded
data = data.join(one_hot)

I then create a simple LinearRegression model and fit my data:

y_data = data['status']
classifier = LinearRegression(n_jobs = -1)
X_train, X_test, y_train, y_test = train_test_split(data, y_data, test_size=0.2)
columns = X_train.columns.tolist()
classifier.fit(X_train[columns], y_train)

But I got the following error:

could not convert string to float: 'received'

I have the feeling I miss something here and I would like to have some inputs on how to proceed. Thank you for having read so far!

Answer 1

Alternative (because you should really avoid using LabelEncoder on features).

ColumnTransformer and OneHotEncoder can one-hot encode features in a dataframe:

ct = ColumnTransformer(
    transformers=[
        ("ohe", OneHotEncoder(sparse_output=False), ["country", "city"]),
    ],
    remainder="passthrough",
).set_output(transform="pandas")

print(ct.fit_transform(X))

   ohe__country_France  ohe__country_Italy  ohe__city_Naples  ohe__city_Paris  remainder__datetime  remainder__amount
0                  1.0                 0.0               0.0              1.0               1.4539                4.5
1                  0.0                 1.0               1.0              0.0               1.4541                6.9
2                  1.0                 0.0               0.0              1.0               1.4561                5.0

---

Full pipeline with LogisticRegression:

import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression

raw_data = pd.DataFrame([["received", 1.4539, "France", 4.5, "Paris"], ["received", 1.4541, "Italy", 6.9, "Naples"], ["not-received", 1.4561, "France", 5.0, "Paris"]], columns=["status", "datetime", "country", "amount", "city"])

# X features include all variables except 'status', y label is 'status':
X = raw_data.drop(["status"], axis=1)
y = raw_data["status"]

# Create a pipeline with OHE for "country" and "city", then fits Logistic Regression:
pipe = make_pipeline(
    ColumnTransformer(
        transformers=[
            ("one-hot-encode", OneHotEncoder(), ["country", "city"]),
        ],
        remainder="passthrough",
    ),
    LogisticRegression(),
)

pipe.fit(X, y)

Answer 2

To do a one-hot encoding in a scikit-learn project, you may find it cleaner to use the scikit-learn-contrib project category_encoders: https://github.com/scikit-learn-contrib/categorical-encoding, which includes many common categorical variable encoding methods including one-hot.

Answer 3

Consider the following approach:

first let's one-hot-encode all non-numeric columns:

In [220]: from sklearn.preprocessing import LabelEncoder

In [221]: x = df.select_dtypes(exclude=['number']) \
                .apply(LabelEncoder().fit_transform) \
                .join(df.select_dtypes(include=['number']))

In [228]: x
Out[228]:
        status  country  city      datetime  amount
601766       0        0     1  1.453916e+09     4.5
669244       0        1     0  1.454109e+09     6.9

now we can use LinearRegression classifier:

In [230]: classifier.fit(x.drop('status',1), x['status'])
Out[230]: LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)