def train(config, model_data, data, record):
model_class_name, percentile = model_data
model = instantiate_from_class_string(model_class_name)
try:
model.n_jobs = config['n_jobs']
except:
log.info('Cannot set n_jobs for this model...')
record['model'] = model_name(model)
record['parameters'] = model.get_params()
record['feats_percentile'] = percentile
train_x = data['train_x']
train_y = data['train_y']
test_x = data['test_x']
# estimate accuracy using cross-validation
model = make_pipeline(SelectPercentile(f_classif, percentile),
StandardScaler(), model)
scores = cross_validation.cross_val_score(model, train_x,
train_y, cv=5,
scoring='accuracy')
record['mean_acc'] = scores.mean()
# predict on the test set
fn = SelectPercentile(f_classif, percentile).fit(train_x, train_y)
train_x = fn.transform(train_x)
test_x = fn.transform(test_x)
scaler = StandardScaler().fit(train_x)
train_x = scaler.transform(train_x)
test_x = scaler.transform(test_x)
model.fit(train_x, train_y)
ids = data['test_ids']
preds = model.predict(test_x)
record['test_preds'] = [(id_, pred) for id_, pred in zip(ids, preds)]
from sklearn.datasets import load_iris
from sklearn.metrics import precision_score
from sklearn.cross_validation import train_test_split
classes = ["sklearn.ensemble.RandomForestClassifier"]
models = grid_generator.grid_from_classes(classes)
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.30)
# create a new experiment
ex = Experiment(main["logger"])
for m in models:
# create a new record
rec = ex.record()
m.fit(X_train, y_train)
preds = m.predict(X_test)
rec["precision"] = precision_score(y_test, preds)
rec["parameters"] = m.get_params()
rec["model"] = model_name(m)
# select top_k
ex.records = top_k(ex.records, "precision", 2)
# store records in the database
ex.save()
