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# %% [markdown]
# # ðŸ“ƒ Solution for Exercise M4.01
#
# The aim of this exercise is two-fold:
#
# * understand the parametrization of a linear model;
# * quantify the fitting accuracy of a set of such models.
#
# We will reuse part of the code of the course to:
#
# * load data;
# * create the function representing a linear model.
#
# ## Prerequisites
#
# ### Data loading
# %% [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_regression.csv")
feature_name = "Flipper Length (mm)"
target_name = "Body Mass (g)"
data, target = penguins[[feature_name]], penguins[target_name]
# %% [markdown]
# ### Model definition
# %%
def linear_model_flipper_mass(
flipper_length, weight_flipper_length, intercept_body_mass
):
"""Linear model of the form y = a * x + b"""
body_mass = weight_flipper_length * flipper_length + intercept_body_mass
return body_mass
# %% [markdown]
# ## Main exercise
#
# Define a vector `weights = [...]` and a vector `intercepts = [...]` of the
# same length. Each pair of entries `(weights[i], intercepts[i])` tags a
# different model. Use these vectors along with the vector
# `flipper_length_range` to plot several linear models that could possibly fit
# our data. Use the above helper function to visualize both the models and the
# real samples.
# %%
import numpy as np
flipper_length_range = np.linspace(data.min(), data.max(), num=300)
# %%
# solution
import matplotlib.pyplot as plt
import seaborn as sns
weights = [-40, 45, 90]
intercepts = [15000, -5000, -14000]
ax = sns.scatterplot(
data=penguins, x=feature_name, y=target_name, color="black", alpha=0.5
)
label = "{0:.2f} (g / mm) * flipper length + {1:.2f} (g)"
for weight, intercept in zip(weights, intercepts):
predicted_body_mass = linear_model_flipper_mass(
flipper_length_range, weight, intercept
)
ax.plot(
flipper_length_range,
predicted_body_mass,
label=label.format(weight, intercept),
)
_ = ax.legend(loc="center left", bbox_to_anchor=(-0.25, 1.25), ncol=1)
# %% [markdown]
# In the previous question, you were asked to create several linear models. The
# visualization allowed you to qualitatively assess if a model was better than
# another.
#
# Now, you should come up with a quantitative measure which indicates the
# goodness of fit of each linear model and allows you to select the best model.
# Define a function `goodness_fit_measure(true_values, predictions)` that takes
# as inputs the true target values and the predictions and returns a single
# scalar as output.
# %%
# solution
def goodness_fit_measure(true_values, predictions):
# we compute the error between the true values and the predictions of our
# model
errors = np.ravel(true_values) - np.ravel(predictions)
# We have several possible strategies to reduce all errors to a single value.
# Computing the mean error (sum divided by the number of element) might seem
# like a good solution. However, we have negative errors that will misleadingly
# reduce the mean error. Therefore, we can either square each
# error or take the absolute value: these metrics are known as mean
# squared error (MSE) and mean absolute error (MAE). Let's use the MAE here
# as an example.
return np.mean(np.abs(errors))
# %% [markdown]
# You can now copy and paste the code below to show the goodness of fit for each
# model.
#
# ```python
# for model_idx, (weight, intercept) in enumerate(zip(weights, intercepts)):
# target_predicted = linear_model_flipper_mass(data, weight, intercept)
# print(f"Model #{model_idx}:")
# print(f"{weight:.2f} (g / mm) * flipper length + {intercept:.2f} (g)")
# print(f"Error: {goodness_fit_measure(target, target_predicted):.3f}\n")
# ```
# %%
# solution
for model_idx, (weight, intercept) in enumerate(zip(weights, intercepts)):
target_predicted = linear_model_flipper_mass(data, weight, intercept)
print(f"Model #{model_idx}:")
print(f"{weight:.2f} (g / mm) * flipper length + {intercept:.2f} (g)")
print(f"Error: {goodness_fit_measure(target, target_predicted):.3f}\n")