def main():
# simulate data
data = simulate_data(50000)
train, test, params = data['train'], data['test'], data['params']
# sneak peak
print("This is what the data looks like.")
print(train.head())
print()
# Look at obvious correlations
print("This is how the true parameters differ among categories")
print(train.groupby('category').mean().drop('id', axis=1))
print()
print("This is how the true parameters differ among counts")
print(train.groupby('counts').mean().drop('id', axis=1))
print()
print('There is some correlation with the numerical variable too.')
print(train[['numerical', 'alpha_true', 'beta_true', 'age', 'alive']].corr())
print()
# START MODELING
# Create the sbs object using all features. Lets keep gamma small and let
# the model "overfit" if necessary. We have enough data.
sbs = SBGSurvival(age='age',
alive='alive',
features=['category', 'counts', 'numerical'],
gamma=1e-6,
verbose=True)
# Train model
sbs.fit(train)
# Summary of results
print(sbs.summary())
print()
# Make some predictions
pred = pd.concat([test,
sbs.predict_params(test)], axis=1)
print("Mean Absolute Error for Alpha: "
"{}".format((pred['alpha_true'] -
pred['alpha']).abs().mean()))
print("Mean Absolute Error for Beta: "
"{}".format((pred['beta_true'] -
pred['beta']).abs().mean()))
print()
# correlation between true and predicitons
print("Predictons better be correlated with true values.")
print(pred[['alpha_true', 'alpha']].corr())
print(pred[['beta_true', 'beta']].corr())
# Done
print("Not bad.")
def add_random():
data = make_raw_article_data()
# Create the sbs object using all features. Lets keep gamma small and let
# the model "overfit" if necessary. We have enough data.
sbs = SBGSurvival(age='age',
alive='alive',
features=['is_high_end', 'random'],
gamma=1e-1,
verbose=True)
sbs.fit(data)
print(sbs.summary())
pred = pd.concat([data, sbs.predict_params(data)], axis=1)
print(pred.head())
print(pred.groupby('is_high_end').mean().drop('id', axis=1))
def add_random():
data = make_raw_article_data()
# Create the sbs object using all features. Lets keep gamma small and let
# the model "overfit" if necessary. We have enough data.
sbs = SBGSurvival(age='age',
alive='alive',
features=['is_high_end', 'random'],
gamma=1e-1,
verbose=True)
sbs.fit(data)
print(sbs.summary())
pred = pd.concat([data, sbs.predict_params(data)], axis=1)
print(pred.head())
print(pred.groupby('is_high_end').mean().drop('id', axis=1))