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Fixes from Monday #17

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Oct 24, 2023
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Fixes from Monday #17

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22 changes: 7 additions & 15 deletions _episodes/02-regression.md
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Original file line number Diff line number Diff line change
Expand Up @@ -124,7 +124,7 @@ Then we'll define a function to make predictions using our trained model, and ca
import math
from sklearn.metrics import mean_squared_error

def predict_linear_model(lin_regress, x_data):
def predict_linear_model(lin_regress, x_data, y_data):
# predict some values using our trained estimator/model
# (in this case we predict our input data!)
linear_data = lin_regress.predict(x_data)
Expand Down Expand Up @@ -157,7 +157,7 @@ We will be training a few Linear Regression models in this episode, so let's def
def fit_predict_plot_linear(x, y):
x_data, y_data = pre_process_linear(x, y)
lin_regress = fit_a_linear_model(x_data, y_data)
linear_data = predict_linear_model(lin_regress, x_data)
linear_data = predict_linear_model(lin_regress, x_data, y_data)
plot_linear_model(x_data, y_data, linear_data)

return lin_regress
Expand Down Expand Up @@ -255,12 +255,12 @@ def fit_poly_model(x_poly, y_data):
return poly_regress


def predict_poly_model(poly_regress, x_poly):
def predict_poly_model(poly_regress, x_poly, y_data):
# predict some values using our trained estimator/model
# (in this case - our input data)
poly_data = poly_regress.predict(x_poly)

poly_error = math.sqrt(mean_squared_error(y_data,poly_data))
poly_error = math.sqrt(mean_squared_error(y_data, poly_data))
print("poly error=", poly_error)

return poly_data
Expand All @@ -276,7 +276,7 @@ def fit_predict_plot_poly(x, y):
# Combine all of the steps
x_poly, x_data, y_data = pre_process_poly(x, y)
poly_regress = fit_poly_model(x_poly, y_data)
poly_data = predict_poly_model(poly_regress, x_poly)
poly_data = predict_poly_model(poly_regress, x_poly, y_data)
plot_poly_model(x_data, poly_data)

return poly_regress
Expand Down Expand Up @@ -376,8 +376,6 @@ dataset_1 = dataset[:146]
x_data = dataset_1["body_mass_g"]
y_data = dataset_1["bill_depth_mm"]

import matplotlib.pyplot as plt

trained_model = fit_predict_plot_linear(x_data, y_data)

plt.xlabel("mass g")
Expand All @@ -393,15 +391,9 @@ Congratulations! We've taken our linear regression function and quickly created
Let's provide the model with all of the penguin samples and visually inspect how the linear regression model performs.

~~~
x_data_all = dataset["body_mass_g"]
y_data_all = dataset["bill_depth_mm"]

x_data_all = np.array(x_data_all).reshape(-1,1)

y_predictions = trained_model.predict(x_data_all)
x_data_all, y_data_all = pre_process_linear(dataset["body_mass_g"], dataset["bill_depth_mm"])

error = math.sqrt(mean_squared_error(y_data_all, y_predictions))
print("penguin error=",error)
y_predictions = predict_linear_model(trained_model, x_data_all, y_data_all)

plt.scatter(x_data_all, y_data_all, label="all data")
plt.scatter(x_data, y_data, label="training data")
Expand Down
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