def compare_change(data):
"""
Run multiple models: fit on training data, predict for tournament data.
Then change the data, rerun and compare performance with and without the
change.
"""
print('\nStandard dataset:\n')
# we'll look at 5 models
prediction = nx.production(nx.logistic(), data, verbosity=1)
prediction += nx.production(nx.extratrees(), data, verbosity=1)
prediction += nx.production(nx.randomforest(), data, verbosity=1)
prediction += nx.production(nx.mlpc(), data, verbosity=1)
prediction += nx.production(nx.logisticPCA(), data, verbosity=1)
# let's now make a change, could be anything; as an example let's add
# the square of each feature to the dataset
x = np.hstack((data.x, data.x * data.x))
data2 = data.xnew(x)
print('\nDataset expanded with squared features:\n')
# rerun all models with the new expanded data
prediction2 = nx.production(nx.logistic(), data2, verbosity=1)
prediction2 += nx.production(nx.extratrees(), data2, verbosity=1)
prediction2 += nx.production(nx.randomforest(), data2, verbosity=1)
prediction2 += nx.production(nx.mlpc(), data2, verbosity=1)
prediction2 += nx.production(nx.logisticPCA(), data2, verbosity=1)
# compare performance
print('\nCompare (1 is regular dataset; 2 expanded dataset):\n')
print(prediction.compare(data['validation'], prediction2))
def improve_model(data, tournament='kazutsugi'):
"""
Run multiple models: fit on training data, predict for tournament data.
Then change the data, rerun and compare performance with and without the
change.
"""
# we'll look at 5 models
models = [nx.linear(), nx.extratrees(), nx.randomforest(), nx.mlpc(),
nx.linearPCA()]
print('\nStandard dataset:\n')
# first run the base case
prediction = nx.production(models, data, tournament, verbosity=1)
# let's now make a change, could be anything; as an example let's add
# the square of each feature to the dataset
x = np.hstack((data.x, data.x * data.x))
data2 = data.xnew(x)
print('\nDataset expanded with squared features:\n')
# rerun all models with the new expanded data
prediction2 = nx.production(models, data2, tournament, verbosity=1)
# compare performance
print('\nCompare (1 is regular dataset; 2 expanded dataset):\n')
print(prediction.compare(data['validation'], prediction2, tournament))
def concordance(data, tournament='bernie'):
"""
Example showing how to calculate concordance.
Concordance must be less than 0.12 to pass numerai's check.
For an accurate concordance calculation `data` must be the full dataset.
"""
models = [nx.logistic(), nx.extratrees(), nx.mlpc()]
p = nx.production(models, data, tournament)
print("\nA concordance less than 0.12 is passing")
print(p.concordance(data))
def concordance_example(data):
"""
Example showing how to calculate concordance.
Concordance must be less than 0.12 to pass numerai's check.
For an accurate concordance calculation `data` must be the full dataset.
"""
prediction = nx.production(nx.logistic(), data)
prediction += nx.production(nx.extratrees(), data)
prediction += nx.production(nx.mlpc(), data)
print("\nA concordance less than 0.12 is passing")
print(prediction.concordance(data))
def get_models():
models = [
nx.logistic(),
nx.extratrees(),
nx.randomforest(),
nx.mlpc(),
nx.logisticPCA(),
nx.example_predictions(),
fifty()
]
return models
def get_models():
models = [nx.linear(),
nx.ridge_mean(),
nx.extratrees(),
nx.randomforest(),
nx.mlpc(),
nx.linearPCA(),
nx.example_predictions(),
nx.fifty()]
return models
def runner_example():
data = nx.play_data()
splitter = nx.CVSplitter(data)
# let's run 3 models
m1 = {'model': nx.logistic(), 'prediction_file': None, 'csv_file': None}
m2 = {'model': nx.logistic(1e-4)}
m3 = {'model': nx.extratrees()}
run_list = [m1, m2, m3]
# we won't save anything, just display the results
runner = nx.Runner(run_list, splitter, verbosity=1)
runner.run()
def compare_models(data):
"""
Run multiple models: fit on training data, predict for tournament data.
Then compare performance of the models
"""
# we'll look at 5 models
prediction = nx.production(nx.logistic(), data, verbosity=1)
prediction += nx.production(nx.extratrees(), data, verbosity=1)
prediction += nx.production(nx.randomforest(), data, verbosity=1)
prediction += nx.production(nx.mlpc(), data, verbosity=1)
prediction += nx.production(nx.logisticPCA(), data, verbosity=1)
# correlation of models with logistic regression
print('\nCorrelation:\n')
prediction.correlation('logistic')
# compare performance of models
print('\nPerformance comparison:\n')
print(prediction.performance(data['validation'], sort_by='logloss'))
# dominance of models
print('\nModel dominance:\n')
print(prediction.dominance(data['validation'], sort_by='logloss'))
# dominace between two models
print('\nModel dominance between two models:\n')
df = prediction[['logistic', 'logisticPCA']].dominance(data['validation'])
print(df)
# originality given that logistic model has already been submitted
print('\nModel originality (versus logistic):\n')
print(prediction.originality(['logistic']))
# concordance
print('\nConcordance:\n')
print(prediction.concordance(data))
def get_models():
models = [nx.logistic(), nx.extratrees(), nx.randomforest()]
if HAS_XGBOOST:
models.append(nx.xgboost())
return models
