def test_fit_predict_ensemble(teardown):
mask = iris.target != 2 # Reduce to binary problem to avoid ConvergenceWarning
x_data = iris.data
y_t_data = iris.target
random_state = 123
# baikal way
x = Input()
y_t = Input()
y1 = LogisticRegression(random_state=random_state)(x, y_t)
y2 = RandomForestClassifier(random_state=random_state)(x, y_t)
features = Stack(axis=1)([y1, y2])
y = LogisticRegression(random_state=random_state)(features, y_t)
model = Model(x, y, y_t)
model.fit(x_data, y_t_data)
y_pred_baikal = model.predict(x_data)
# traditional way
logreg = sklearn.linear_model.LogisticRegression(random_state=random_state)
logreg.fit(x_data, y_t_data)
logreg_pred = logreg.predict(x_data)
random_forest = sklearn.ensemble.RandomForestClassifier(random_state=random_state)
random_forest.fit(x_data, y_t_data)
random_forest_pred = random_forest.predict(x_data)
features = np.stack([logreg_pred, random_forest_pred], axis=1)
ensemble = sklearn.linear_model.LogisticRegression(random_state=random_state)
ensemble.fit(features, y_t_data)
y_pred_traditional = ensemble.predict(features)
assert_array_equal(y_pred_baikal, y_pred_traditional)
def train_ensemble(allele_table_path,
amr_path,
antibiotic,
gene_path=None,
core_cutoff=10,
bootstrap_instances=0.8,
bootstrap_features=0.5,
num_models=500,
log_iter=50):
'''
Trains a random subspace ensemble of SVMs to predict AMR from the allele
and/or gene content of a strain for a given antibiotic.
Parameters
----------
allele_table_path : str
Path to binary table containing allelic content of strains as CSV, where
rows = alleles, columns = strains. Will interpret blanks as 0s.
amr_path : str
Path to table containing AMR profiles of strains as CSV, where rows = strains,
columns = drugs. Will convert "Resistant" and "Intermediate" to 1s,
"Susceptible" to 0s, and ignore blanks.
antibiotic : str
Drug to train model for, must be in columns of amr_path
gene_path : str
Similar to allele_table_path, but for gene content. If provided, will
select model non-core alleles as defined by core_cutoff at the gene level,
rather than each allele individually (default None)
bootstrap_instances : float
Fraction of total strains sampled for training, must be <1.0 (default 0.8)
bootstrap_features : float
Fraction of total genetic features sampled for training, uses all if 1.0 (default 0.5)
num_models : int
Number of individual models to train for ensemble (default 500)
log_iter : int
Print a message after this many models have been trained (default 50)
Returns
-------
ensemble : RSE
Trained RSE object
df_features : DataFrame
DataFrame of binary matrix encoding genetic features of each strain
df_amr : DataFrame
DataFrame of binary vector encoding AMR phenotypes of each strain
'''
''' Reduce data to strains with AMR data for selected antibiotic '''
df_features, df_amr = __prepare_amr_data__(allele_table_path,
amr_path,
antibiotic,
gene_path=gene_path,
core_cutoff=core_cutoff)
''' Train and return ensemble '''
ensemble = RSE(num_models=num_models,
bootstrap_instances=bootstrap_instances,
bootstrap_features=bootstrap_features)
ensemble.fit(df_features.values, df_amr.values)
return ensemble, df_features, df_amr
def test_fit_predict_ensemble_with_proba_features(teardown):
mask = iris.target != 2 # Reduce to binary problem to avoid ConvergenceWarning
x_data = iris.data[mask]
y_t_data = iris.target[mask]
random_state = 123
n_estimators = 5
# baikal way
x = Input()
y_t = Input()
y1 = LogisticRegression(random_state=random_state, function="predict_proba")(x, y_t)
y2 = RandomForestClassifier(
n_estimators=n_estimators, random_state=random_state, function="apply"
)(x, y_t)
features = Concatenate(axis=1)([y1, y2])
y = LogisticRegression(random_state=random_state)(features, y_t)
model = Model(x, y, y_t)
model.fit(x_data, y_t_data)
y_pred_baikal = model.predict(x_data)
# traditional way
logreg = sklearn.linear_model.LogisticRegression(random_state=random_state)
logreg.fit(x_data, y_t_data)
logreg_proba = logreg.predict_proba(x_data)
random_forest = sklearn.ensemble.RandomForestClassifier(
n_estimators=n_estimators, random_state=random_state
)
random_forest.fit(x_data, y_t_data)
random_forest_leafidx = random_forest.apply(x_data)
features = np.concatenate([logreg_proba, random_forest_leafidx], axis=1)
ensemble = sklearn.linear_model.LogisticRegression(random_state=random_state)
ensemble.fit(features, y_t_data)
y_pred_traditional = ensemble.predict(features)
assert_array_equal(y_pred_baikal, y_pred_traditional)
#knn = KNeighborsClassifier(n_neighbors=5)
#knn.fit(train_data, train_targets)
#print('KNN Score: ', knn.score(test_data, test_targets))
ext_forest = ExtraTreesClassifier(n_estimators=200, n_jobs=-1)
ext_forest.fit(train_data, train_targets)
print('ExtraTree Score: ', ext_forest.score(test_data, test_targets))
#ext_forest_predicted = ext_forest.predict(test_data)
#print_metrics(test_targets, ext_forest_predicted)
randFor_class = ensemble.RandomForestClassifier(n_estimators=200,
random_state=1)
randFor_class.fit(train_data, train_targets)
score = randFor_class.score(test_data, test_targets)
print('RandomForest Score: ', randFor_class.score(test_data, test_targets))
#randFor_pred = ext_forest.predict(test_data)
#print_metrics(test_targets, randFor_pred)
ensemble = VotingClassifier(estimators=[('RandomForest', randFor_class),
('ExtraTrees', ext_forest)],
voting='soft')
ensemble.fit(train_data, train_targets)
print('Ensamble Score: ', ensemble.score(test_data, test_targets))
#ensemble_predicted = ensemble.predict(test_data)
#print_metrics(test_targets, ensemble_predicted)
save_classifier(ext_forest, file_name='best_classifier')
breast_cancer = datasets.load_breast_cancer()
offset = int(0.6*len(breast_cancer.data))
X_train = breast_cancer.data[:offset]
Y_train = breast_cancer.target[:offset]
X_test = breast_cancer.data[offset:]
Y_test = breast_cancer.target[offset:]
# Setup a Decision Tree Classifier so that it learns a tree with depth d
classifier = DecisionTreeClassifier(max_depth=10, min_samples_split=2, max_leaf_nodes=15, criterion='entropy', min_samples_leaf=1)
ensemble = ensemble.AdaBoostClassifier(classifier, n_estimators=15, learning_rate=.5)
start = time()
# Fit the learner to the training data
ensemble.fit(X_train, Y_train)
end = time()
print(("\nLearner took {:.4f} ").format(end - start))
# Find the MSE on the training set
start = time()
train_err = 1 - ensemble.score(X_train, Y_train)
end = time()
print(("\nTraining took {:.4f} ").format(end - start))
start = time()
test_err = 1 - ensemble.score(X_test, Y_test)
end = time()
print(("\nTesting took {:.4f} ").format(end - start))
X_train = preprocessing.normalize(dft.ix[:offset, 1:])
Y_train = dft.ix[:offset, 0]
X_test = preprocessing.normalize(dft.ix[offset:, 1:])
Y_test = dft.ix[offset:, 0]
# Setup a Decision Tree Classifier so that it learns a tree with depth d
classifier = tree.DecisionTreeClassifier(criterion='entropy',
max_depth=20,
min_samples_leaf=1)
ensemble = ensemble.AdaBoostClassifier(classifier,
n_estimators=1,
learning_rate=1.)
start = time()
# Fit the learner to the training data
ensemble.fit(X_train, Y_train)
end = time()
print(("\nLearner took {:.4f} ").format(end - start))
# Find the MSE on the training set
start = time()
train_err = 1 - ensemble.score(X_train, Y_train)
end = time()
print(("\nTraining took {:.4f} ").format(end - start))
start = time()
test_err = 1 - ensemble.score(X_test, Y_test)
end = time()
print(("\nTesting took {:.4f} ").format(end - start))
print "Train err: {:.4f}", train_err
# feature_worth(models['GradientBoostingRegressor'], train)
# # > Example: Compare validation error between models
# for model_key in models:
# print "> "+model_key
# model = models[model_key]
# if "NeuralNetwork" in model_key:
# error = cv(train_, 5, model)
# else:
# error = cv(train, 5, model)
# print model_key, error
del models['NeuralNetwork']
del models['NeuralNetwork_w_Momentum']
ensemble = Ensemble(
models=models,
combiner=ensemble.GradientBoostingRegressor(
n_estimators=100,
random_state=0
)
)
ensemble.fit(train)
d = dict(
zip(
models['GradientBoostingRegressor'].variables['casual'],
ensemble.feature_importances_
)
)
prediction = ensemble.predict(test)
output('12.0th.csv', prediction, test)
