def test_linear_regression_validation(self, get_regression_data):
X, y = get_regression_data
with pytest.raises(ValueError):
est = LinearRegression(metric='accuracy')
est.fit(X, y)
with pytest.raises(ValueError):
est = LinearRegression(cost='binary_cross_entropy')
est.fit(X, y)
def predict_y():
X, y = datasets.load_boston(return_X_y=True)
scaler = StandardScaler()
X = scaler.fit_transform(X)
gd = LinearRegression(epochs=5)
gd.fit(X, y)
y_pred = gd.predict(X)
return y, y_pred
def fit_multiple_models(get_regression_data):
X, y = get_regression_data
X = X[:,5]
X = np.reshape(X, (-1,1))
models = {}
bgd = LinearRegression(epochs=200, seed=50)
sgd = LinearRegression(epochs=200, seed=50, batch_size=1)
mgd = LinearRegression(epochs=200, seed=50, batch_size=32)
models= {'Batch Gradient Descent': bgd.fit(X,y),
'Stochastic Gradient Descent': sgd.fit(X,y),
'Mini-batch Gradient Descent': mgd.fit(X,y)}
return models
# Linear Regression Scatterplot
data = go.Scatter(x=X[:, 0],
y=y,
mode='markers',
marker=dict(color='steelblue'))
layout = go.Layout(title='Boston Housing Prices by Rooms',
height=400,
width=800,
showlegend=False,
xaxis_title="Average No. Rooms",
yaxis_title="Median Price ($000)",
margin=dict(l=10, r=10, t=40, b=10),
template='plotly_white')
fig = go.Figure(data=data, layout=layout)
fig.show()
po.plot(fig, filename="./content/figures/boston.html", auto_open=False)
# ---------------------------------------------------------------------------- #
# LINEAR REGRESSION #
# ---------------------------------------------------------------------------- #
#%%
# Linear Regression
lr = LinearRegression(epochs=50, learning_rate=0.05)
lr.fit(X_scaled, y)
plot = SingleModelSearch3D()
plot.search(lr, directory=directory, filename="linear_regression_search.gif")
plot = SingleModelFit2D()
plot.fit(lr, directory=directory, filename="linear_regression_fit.gif")
#%%
# %%
def test_linear_regression_name(self, get_regression_data):
X, y = get_regression_data
est = LinearRegression()
est.fit(X, y)
assert est.name == "Linear Regression with Batch Gradient Descent", "incorrect name"
# LINEAR REGRESSION #
# ---------------------------------------------------------------------------- #
#%%
# Linear Regression
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
lr = LinearRegression(epochs=1000,
learning_rate=0.01,
val_size=0.2,
patience=40,
early_stop=True,
metric='r2',
verbose=True,
checkpoint=100)
lr.fit(X_train, y_train)
print(lr.intercept_)
print(lr.coef_.shape)
# ---------------------------------------------------------------------------- #
# ANIMATIONS #
# ---------------------------------------------------------------------------- #
#%%
# Animations
plot = SingleModelSearch3D()
plot.search(lr,
directory=directory,
filename="linear_regression_search_test.gif")
plot = SingleModelFit2D()
plot.fit(lr, directory=directory, filename="linear_regression_fit_test.gif")
#%%
# ---------------------------------------------------------------------------- #