# %% [markdown]
# # 📃 Solution for Exercise M4.05
# In the previous notebook, we presented a non-penalized logistic regression
# classifier. This classifier accepts a parameter `penalty` to add a
# regularization. The regularization strength is set using the parameter `C`.
#
# In this exercise, we ask you to train a l2-penalized logistic regression
# classifier and to find by yourself the effect of the parameter `C`.
#
# We will start by loading the dataset and create the helper function to show
# the decision separation as in the previous code.

# %% [markdown]
# ```{note}
# If you want a deeper overview regarding this dataset, you can refer to the
# Appendix - Datasets description section at the end of this MOOC.
# ```

# %%
import pandas as pd

penguins = pd.read_csv("../datasets/penguins_classification.csv")
# only keep the Adelie and Chinstrap classes
penguins = penguins.set_index("Species").loc[
    ["Adelie", "Chinstrap"]].reset_index()

culmen_columns = ["Culmen Length (mm)", "Culmen Depth (mm)"]
target_column = "Species"

# %%
from sklearn.model_selection import train_test_split

penguins_train, penguins_test = train_test_split(penguins, random_state=0)

data_train = penguins_train[culmen_columns]
data_test = penguins_test[culmen_columns]

target_train = penguins_train[target_column]
target_test = penguins_test[target_column]

# %% [markdown]
# First, let's create our predictive model.

# %%
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

logistic_regression = make_pipeline(
    StandardScaler(), LogisticRegression(penalty="l2"))

# %% [markdown]
# Given the following candidates for the `C` parameter, find out the impact of
# `C` on the classifier decision boundary. You can import the helper class with
# `from helpers.plotting import DecisionBoundaryDisplay` to plot the decision
# function boundary. Use the method `from_estimator` from this class.

# %%
Cs = [0.01, 0.1, 1, 10]

# solution
import matplotlib.pyplot as plt
import seaborn as sns
from helpers.plotting import DecisionBoundaryDisplay

for C in Cs:
    logistic_regression.set_params(logisticregression__C=C)
    logistic_regression.fit(data_train, target_train)
    accuracy = logistic_regression.score(data_test, target_test)

    DecisionBoundaryDisplay.from_estimator(
        logistic_regression,
        data_test,
        response_method="predict",
        cmap="RdBu_r",
        alpha=0.5,
    )
    sns.scatterplot(
        data=penguins_test, x=culmen_columns[0], y=culmen_columns[1],
        hue=target_column, palette=["tab:red", "tab:blue"])
    plt.legend(bbox_to_anchor=(1.05, 0.8), loc="upper left")
    plt.title(f"C: {C} \n Accuracy on the test set: {accuracy:.2f}")

# %% [markdown]
# Look at the impact of the `C` hyperparameter on the magnitude of the weights.

# %%
# solution
weights_ridge = []
for C in Cs:
    logistic_regression.set_params(logisticregression__C=C)
    logistic_regression.fit(data_train, target_train)
    coefs = logistic_regression[-1].coef_[0]
    weights_ridge.append(pd.Series(coefs, index=culmen_columns))

# %% tags=["solution"]
weights_ridge = pd.concat(
    weights_ridge, axis=1, keys=[f"C: {C}" for C in Cs])
weights_ridge.plot.barh()
_ = plt.title("LogisticRegression weights depending of C")

# %% [markdown] tags=["solution"]
# We see that a small `C` will shrink the weights values toward zero. It means
# that a small `C` provides a more regularized model. Thus, `C` is the inverse
# of the `alpha` coefficient in the `Ridge` model.
#
# Besides, with a strong penalty (i.e. small `C` value), the weight of the
# feature "Culmen Depth (mm)" is almost zero. It explains why the decision
# separation in the plot is almost perpendicular to the "Culmen Length (mm)"
# feature.
