def test_logistic_regression_predict(self, get_binary_classification_data):
X, y = get_binary_classification_data
clf = LogisticRegression(epochs=100, learning_rate=0.01, checkpoint=10)
clf.fit(X,y)
y_pred = clf._predict(X)
assert y_pred.shape == (y.shape[0],), "y_pred has wrong shape for binary problem"
y_pred = clf.predict(X)
score = clf.score(X,y)
assert y_pred.shape == (y.shape[0],), "y_pred has wrong shape for binary problem"
assert score > 0.3, "Accuracy below 0.3"
assert score < 1, "Accuracy is greater than or equal to 1"
costs = history.epoch_log['train_cost']
# ---------------------------------------------------------------------------- #
# LEARNING CURVE #
# ---------------------------------------------------------------------------- #
#%%
# Learning Curve
data = go.Scatter(x=np.linspace(0, len(costs), len(costs)),
y=costs,
mode='lines',
line=dict(color='steelblue'))
layout = go.Layout(title='Wisconsin Breast Cancer Dataset Learning Curve',
xaxis_title="Epochs",
yaxis_title='Cross-Entropy Cost',
height=400,
width=800,
showlegend=False,
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/breast_cancer.html", auto_open=False)
# ---------------------------------------------------------------------------- #
# EVALUATE #
# ---------------------------------------------------------------------------- #
#%%
# Evaluate
intercept = clf.intercept
coef = clf.coef
score = clf.score(X_test, y_test)
print(score)
#%%